Replace poetry, black, and flake8 by uv and ruff

.github/workflows/python-package.yml

@@ -19,7 +19,7 @@ jobs:
         python-version: ["3.10", "3.11", "3.12", "3.13"]
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
         with:
           submodules: 'recursive'
 
@@ -30,46 +30,21 @@ jobs:
 
       - run: python --version
 
-      # Cache the installation of Poetry itself, e.g. the next step. This prevents the workflow
-      # from installing Poetry every time, which can be slow. Note the use of the Poetry version
-      # number in the cache key, and the "-0" suffix: this allows you to invalidate the cache
-      # manually if/when you want to upgrade Poetry, or if something goes wrong.
-      - name: Cache poetry
-        uses: actions/cache@v4.2.0
+      - name: Install uv
+        uses: astral-sh/setup-uv@v6.1.0
         with:
-          path: ~/.local
-          key: poetry-2.0.1-0
+          version: 0.7.8
+          checksum: "285981409c746508c1fd125f66a1ea654e487bf1e4d9f45371a062338f788adb"
+          enable-cache: true
 
-      - name: Install poetry
-        uses: snok/install-poetry@v1
-        with:
-          version: 2.0.1
-          virtualenvs-create: true
-          virtualenvs-in-project: true
-
-      - run: poetry --version
-      - run: poetry run python --version
-
-        # Cache your dependencies (i.e. all the stuff in your `pyproject.toml`). Note the cache
-        # key: if you're using multiple Python versions, or multiple OSes, you'd need to include
-        # them in the cache key. I'm not, so it can be simple and just depend on the poetry.lock.
-      - name: Cache dependencies
-        id: cache-deps
-        uses: actions/cache@v4.2.0
-        with:
-          path: .venv
-          key: pydeps-${{ matrix.python-version }}-${{ hashFiles('**/poetry.lock') }}
-
-      - name: Install dependencies
-        # The `if` statement ensures this only runs on a cache miss.
-        run: poetry install --no-interaction
-        if: steps.cache-deps.outputs.cache-hit != 'true'
+      - run: uv --version
+      - run: uv run python --version
 
-      - name: Check code formatting with black
-        run: poetry run black . --check
+      - name: Check code linting
+        run: uv run --extra dev ruff check --no-fix
 
-      - name: Lint with flake8
-        run: poetry run flake8 . --count --show-source
+      - name: Check code formatting
+        run: uv run --extra dev ruff format --check
 
-      - name: Test with pytest
-        run: poetry run python${{ matrix.python-version }} -m pytest
+      - name: Run unit tests
+        run: uv run --extra dev python${{ matrix.python-version }} -m pytest

.gitignore

@@ -1,6 +1,6 @@
 __pycache__/
 .illustris_api_key.txt
 .venv/
-*.lock
+*.egg-info/
 /data/
 /output/

devel/show_parquet.ipynb

@@ -148,8 +148,6 @@
     }
    ],
    "source": [
-    "import pandas as pd\n",
-    "\n",
     "df = dataset.to_table().to_pandas()\n",
     "df"
    ]

devel/test_chunks.ipynb

@@ -706,7 +706,7 @@
     }
    ],
    "source": [
-    "pq.read_table('part-0.parquet').to_pandas()"
+    "pq.read_table(\"part-0.parquet\").to_pandas()"
    ]
   }
  ],

pyproject.toml

@@ -21,16 +21,21 @@ dependencies = [
     "requests>=2.32",
     "scikit-image>=0.25",
     "scipy>=1.15",
-    "pytest>=8.3",
 ]
 
 [project.optional-dependencies]
 dev = [
-    "black>=25.1",
-    "flake8>=7.0",
     "ipykernel>=6.29",
+    "pytest>=8.3",
+    "ruff>=0.11.11",
 ]
 
 [build-system]
 requires = ["setuptools >= 61.0"]
 build-backend = "setuptools.build_meta"
+
+[tool.ruff]
+line-length = 120
+
+[tool.ruff.lint.mccabe]
+max-complexity = 10

src/pest/fits_converter.py

@@ -83,9 +83,7 @@ def convert_all(
                             "data": [data.flatten()],
                             "simulation": splits[-5],
                             "snapshot": np.int32(splits[-3].split("_")[1].lstrip("0")),
-                            "subhalo_id": np.int32(
-                                splits[-1].split("_")[1].lstrip("0")
-                            ),
+                            "subhalo_id": np.int32(splits[-1].split("_")[1].lstrip("0")),
                         }
                     )
 
@@ -102,9 +100,7 @@ def convert_all(
                     table = pa.Table.from_pandas(df, schema=schema)
 
                     # Add shape metadata to the schema
-                    table = table.replace_schema_metadata(
-                        metadata={"data_shape": str(data.shape)}
-                    )
+                    table = table.replace_schema_metadata(metadata={"data_shape": str(data.shape)})
 
                     if writer is None:
                         writer = pq.ParquetWriter(

src/pest/gaia_converter.py

@@ -67,33 +67,25 @@ def convert(
                 lambda x: np.fromstring(x[1:-1], dtype=np.float32, sep=",")
             )
 
-        calibrated_data, _ = calibrate(
-            continuous_data, sampling=self.sampling, save_file=False
-        )
+        calibrated_data, _ = calibrate(continuous_data, sampling=self.sampling, save_file=False)
 
         if self.with_flux_error:
             # Convert 'flux' column to float32
-            calibrated_data["flux_error"] = calibrated_data["flux_error"].apply(
-                lambda x: np.array(x, dtype=np.float32)
-            )
+            calibrated_data["flux_error"] = calibrated_data["flux_error"].apply(lambda x: np.array(x, dtype=np.float32))
         else:
             # Remove the 'flux_error' column from the calibrated data
             if "flux_error" in calibrated_data.columns:
                 calibrated_data.drop(columns=["flux_error"], inplace=True)
 
         # Convert 'flux' column to float32
-        calibrated_data["flux"] = calibrated_data["flux"].apply(
-            lambda x: np.array(x, dtype=np.float32)
-        )
+        calibrated_data["flux"] = calibrated_data["flux"].apply(lambda x: np.array(x, dtype=np.float32))
 
         # Use pyarrow to write the data to a parquet file
         table = pa.Table.from_pandas(calibrated_data)
 
         # Add shape metadata to the schema
         data_shape = f"(1, {len(calibrated_data['flux'][0])})"
-        table = table.replace_schema_metadata(
-            metadata={"flux_shape": data_shape, "flux_error_shape": data_shape}
-        )
+        table = table.replace_schema_metadata(metadata={"flux_shape": data_shape, "flux_error_shape": data_shape})
 
         parquet.write_table(
             table,

src/pest/preprocessing.py

@@ -42,10 +42,7 @@ def __call__(self, images) -> np.ndarray:
 
         mean = np.mean(resulting_image, axis=0)
         resulting_image = (
-            resulting_image
-            * np.asinh(self.stretch * self.range * (mean - self.lower_limit))
-            / self.range
-            / mean
+            resulting_image * np.asinh(self.stretch * self.range * (mean - self.lower_limit)) / self.range / mean
         )
 
         resulting_image = np.nan_to_num(resulting_image, nan=0, posinf=0, neginf=0)