scisynth

Synthetic scientific data and synthetic observation pipelines.

from scisynth.core import Domain
from scisynth.latent import GaussianField
from scisynth.observer import Observer
from scisynth.samplers import GridSampler
from scisynth.stages import GaussianNoise

domain = Domain.from_extents([(0, 10), (0, 10)])
latent = GaussianField(domain, length_scale=1.5).realize(seed=1)
obs = Observer(GridSampler((64, 64)), [GaussianNoise(0.3)]).run(latent, seed=42)

The names are also available directly, as from scisynth import GridSampler, but the subpackages are the preferred path.

Install

pip install scisynth                 # numpy + scipy
pip install "scisynth[sklearn]"      # optional: sklearn, polars, xarray
pip install "scisynth[viz]"          # optional: plotting (plotly)

Development

uv sync --extra dev
uv run pytest
uv run ruff check . && uv run ruff format --check .
uv run mypy
uv build

API docs are made with pdoc from the docstrings. They are generated. There is one page for each subpackage (core, latent, samplers, stages, ...), listing the names it exports:

uv run python scripts/build_docs.py
open docs/api/index.html

Add --internals to document every module instead, nested by folder.

The Docs workflow (.github/workflows/docs.yml) builds the same site on every push and pull request, and publishes it to GitHub Pages from main.

Docs live in docs/; runnable examples in examples/. The usage tour is a marimo notebook: uv run marimo edit examples/example_notebook.py.