diff --git a/.github/workflows/CDA-testing.yml b/.github/workflows/CDA-testing.yml index 897b772..418ce28 100644 --- a/.github/workflows/CDA-testing.yml +++ b/.github/workflows/CDA-testing.yml @@ -80,6 +80,13 @@ jobs: POETRY_INSTALLER_ONLY_BINARY: ':all:' run: poetry install --no-root + - name: Pin pandas 3.x for Python 3.11+ + if: matrix.python-version != '3.9' + run: | + poetry env use "${{ matrix.python-version }}" + . "$(poetry env info --path)/bin/activate" + python -m pip install --upgrade --only-binary=:all: "pandas==3.0.6" + # Run pytest and generate coverage report data. - name: Run Tests run: poetry run pytest tests/cda/ --doctest-modules --cov --cov-report=xml:out/coverage.xml diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml index a1bc7e9..384b417 100644 --- a/.github/workflows/testing.yml +++ b/.github/workflows/testing.yml @@ -19,7 +19,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ['3.9', '3.10', '3.x'] + python-version: ['3.9', '3.11', '3.x'] steps: - uses: actions/checkout@v7 @@ -47,6 +47,13 @@ jobs: POETRY_INSTALLER_ONLY_BINARY: ':all:' run: poetry install + - name: Pin pandas 3.x for Python 3.11+ + if: matrix.python-version != '3.9' + run: | + poetry env use "${{ matrix.python-version }}" + . "$(poetry env info --path)/bin/activate" + python -m pip install --upgrade --only-binary=:all: "pandas==3.0.6" + # Run pytest and generate coverage report data. - name: Run Tests run: poetry run pytest tests/mock/ --doctest-modules --cov --cov-report=xml:out/coverage.xml diff --git a/.gitignore b/.gitignore index 8bc2be7..499234d 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ scripts .pytest_cache/ .venv/ +.venv-pandas3/ # Jupyter Notebook .ipynb_checkpoints diff --git a/README.md b/README.md index 95a3586..8584dca 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,17 @@ Python 3.9+ pip install cwms-python ``` +For users on Python 3.11+ who want the pandas 3.x stack, install into a fresh virtual environment and upgrade pandas explicitly: + +```sh +python3.11 -m venv .venv +. .venv/bin/activate +python -m pip install --upgrade pip +pip install cwms-python "pandas>=3,<4" +``` + +This keeps the default package install stable while allowing a modern Python environment to use pandas 3.x. + Then import the package: ```python diff --git a/cwms/cwms_types.py b/cwms/cwms_types.py index 06b3b8c..fe34493 100644 --- a/cwms/cwms_types.py +++ b/cwms/cwms_types.py @@ -77,6 +77,7 @@ def timeseries_type(orig_json: JSON, value_json: JSON) -> DataFrame: if "date-time" in df.columns: df["date-time"] = to_datetime(df["date-time"], unit="ms", utc=True) + df["date-time"] = df["date-time"].dt.as_unit("ms") return df def reorder_measurement_cols(df: DataFrame) -> DataFrame: diff --git a/cwms/timeseries/timeseries.py b/cwms/timeseries/timeseries.py index b93170c..42acba3 100644 --- a/cwms/timeseries/timeseries.py +++ b/cwms/timeseries/timeseries.py @@ -553,10 +553,10 @@ def timeseries_df_to_json( "value is a required column when posting data when posting as a dataframe" ) - # make sure that dataTime column is in iso8601 formate. - df["date-time"] = pd.to_datetime(df["date-time"], utc=True).apply( - pd.Timestamp.isoformat - ) + # make sure that date-time column is in ISO8601 format and keep CWMS + # millisecond precision to match the API and pandas 2/3 behavior. + df["date-time"] = pd.to_datetime(df["date-time"], utc=True).dt.as_unit("ms") + df["date-time"] = df["date-time"].apply(pd.Timestamp.isoformat) df = df.reindex(columns=["date-time", "value", "quality-code"]) # Replace NaN/NA/NaT in value column with None so they serialize as JSON @@ -664,8 +664,34 @@ def store_ts_ids( ts_data_all = data.copy() if "version_date" not in ts_data_all.columns: ts_data_all = ts_data_all.assign(version_date=pd.to_datetime(pd.Series([]))) + + def version_key(value: Any) -> str: + return "NaT" if pd.isna(value) else str(value) + + def get_ts_group( + ts_id: str, version_date: str + ) -> Tuple[Optional[datetime], pd.DataFrame]: + if version_date == "NaT": + return ( + None, + ts_data_all[ + (ts_data_all["ts_id"] == ts_id) & ts_data_all["version_date"].isna() + ], + ) + + version_date_dt = pd.to_datetime(version_date) + return ( + version_date_dt, + ts_data_all[ + (ts_data_all["ts_id"] == ts_id) + & (ts_data_all["version_date"] == version_date_dt) + ], + ) + unique_tsids = ( - ts_data_all["ts_id"].astype(str) + ":" + ts_data_all["version_date"].astype(str) + ts_data_all["ts_id"].astype(str) + + ":" + + ts_data_all["version_date"].map(version_key) ).unique() errors: list[tuple[str, Exception]] = [] @@ -673,18 +699,8 @@ def store_ts_ids( futures = {} for unique_tsid in unique_tsids: ts_id, version_date = unique_tsid.split(":", 1) - if version_date != "NaT": - version_date_dt = pd.to_datetime(version_date) - ts_data = ts_data_all[ - (ts_data_all["ts_id"] == ts_id) - & (ts_data_all["version_date"] == version_date_dt) - ] - else: - version_date_dt = None - ts_data = ts_data_all[ - (ts_data_all["ts_id"] == ts_id) & ts_data_all["version_date"].isna() - ] - if not data.empty: + version_date_dt, ts_data = get_ts_group(ts_id, version_date) + if not ts_data.empty: future = executor.submit( store_ts_ids, ts_data, ts_id, office_id, version_date_dt ) diff --git a/cwms/timeseries/timeseries_group.py b/cwms/timeseries/timeseries_group.py index 06f2a78..de47659 100644 --- a/cwms/timeseries/timeseries_group.py +++ b/cwms/timeseries/timeseries_group.py @@ -138,20 +138,23 @@ def timeseries_group_df_to_json( if "attribute" not in df.columns: df["attribute"] = 0 - # Replace NaN with None for optional columns + def normalize_optional_value(value: Any) -> Any: + return None if pd.isna(value) else value + + # Replace NaN/NaT/NA with None for optional columns for column in optional_columns: if column in df.columns: - df[column] = df[column].where(pd.notnull(df[column]), None) + df[column] = df[column].map(normalize_optional_value) # Build the list of time-series entries assigned_time_series = df.apply( lambda entry: { "office-id": entry["office-id"], "timeseries-id": entry["timeseries-id"], - "alias-id": entry["alias-id"], + "alias-id": normalize_optional_value(entry["alias-id"]), "attribute": entry["attribute"], **( - {"ts-code": entry["ts-code"]} + {"ts-code": normalize_optional_value(entry["ts-code"])} if "ts-code" in entry and pd.notna(entry["ts-code"]) else {} ), diff --git a/poetry.lock b/poetry.lock index 1471d93..aacf7bd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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b/tests/cda/locations/location_operations_test.py index 713aa96..fe910a3 100644 --- a/tests/cda/locations/location_operations_test.py +++ b/tests/cda/locations/location_operations_test.py @@ -8,6 +8,8 @@ def test_get_location_operations(): """ Test the retrieval of location operations from the CWMS API. """ + import uuid + TEST_OFFICE = "SPK" TEST_LOCATION_ID = f"pytestloc{uuid4().hex[:12]}" TEST_LATITUDE = 44.0 diff --git a/tests/cda/timeseries/timeseries_CDA_test.py b/tests/cda/timeseries/timeseries_CDA_test.py index 4e8a8c9..c5257f9 100644 --- a/tests/cda/timeseries/timeseries_CDA_test.py +++ b/tests/cda/timeseries/timeseries_CDA_test.py @@ -48,7 +48,7 @@ end=END_DATE_CHUNK_MULTI, freq="15min", tz="UTC", -) +).as_unit("ms") # Create DataFrame DF_CHUNK_MULTI = pd.DataFrame( {