🔥 Use ruff for formatting.

.github/workflows/ruff.yml

@@ -0,0 +1,12 @@
+name: Ruff
+
+on: [push, pull_request]
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: chartboost/ruff-action@v1
+        with:
+          python-version: "3.11"

.pre-commit-config.yaml

@@ -1,5 +1,9 @@
 repos:
-  - hooks:
-      - id: black
-    repo: https://github.com/psf/black
-    rev: 23.3.0
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.3.3
+    hooks:
+      # Run the linter
+      # - id: ruff
+      #  args: [ --fix ]
+      # Run the formatter
+      - id: ruff-format

pyproject.toml

@@ -42,7 +42,7 @@ build-backend = "hatchling.build"
 managed = true
 dev-dependencies = [
   "colorama>=0.4.6",
-  "black>=23.3.0",
+  "ruff>=0.3.3",
   "pytest>=7.3.2",
   "pytest-cov",
   "pytest-qt>=4.2.0",
@@ -100,6 +100,7 @@ lint.ignore = [
 lint.select = ["ALL"]
 target-version = "py310"
 line-length = 100
+indent-width = 4
 
 exclude = ["scripts", "docs", "conda"]
 

src/arpes/analysis/decomposition.py

@@ -181,7 +181,7 @@ def decomposition_along(
 
 @wraps(decomposition_along)
 def pca_along(
-    *args: * tuple[xr.DataArray, list[str]],
+    *args: *tuple[xr.DataArray, list[str]],
     **kwargs: Unpack[PCAParam],
 ) -> tuple[xr.DataArray, sklearn.decomposition.PCA]:
     """Specializes `decomposition_along` with `sklearn.decomposition.PCA`."""
@@ -192,7 +192,7 @@ def pca_along(
 
 @wraps(decomposition_along)
 def factor_analysis_along(
-    *args: * tuple[xr.DataArray, list[str]],
+    *args: *tuple[xr.DataArray, list[str]],
     **kwargs: Unpack[FactorAnalysisParam],
 ) -> tuple[xr.DataArray, sklearn.decomposition.FactorAnalysis]:
     """Specializes `decomposition_along` with `sklearn.decomposition.FactorAnalysis`."""
@@ -201,7 +201,7 @@ def factor_analysis_along(
 
 @wraps(decomposition_along)
 def ica_along(
-    *args: * tuple[xr.DataArray, list[str]],
+    *args: *tuple[xr.DataArray, list[str]],
     **kwargs: Unpack[FastICAParam],
 ) -> tuple[xr.DataArray, sklearn.decomposition.FastICA]:
     """Specializes `decomposition_along` with `sklearn.decomposition.FastICA`."""
@@ -210,7 +210,7 @@ def ica_along(
 
 @wraps(decomposition_along)
 def nmf_along(
-    *args: * tuple[xr.DataArray, list[str]],
+    *args: *tuple[xr.DataArray, list[str]],
     **kwargs: Unpack[NMFParam],
 ) -> tuple[xr.DataArray, sklearn.decomposition.NMF]:
     """Specializes `decomposition_along` with `sklearn.decomposition.NMF`."""

src/arpes/analysis/deconvolution.py

@@ -166,7 +166,7 @@ def make_psf(
     )
 
     if fwhm:
-        sigmas = {k: v / (2 * np.sqrt(2 * np.log(2))) for k, v, in sigmas.items()}
+        sigmas = {k: v / (2 * np.sqrt(2 * np.log(2))) for k, v in sigmas.items()}
     cov: NDArray[np.float_] = np.zeros((len(sigmas), len(sigmas)))
     for i, dim in enumerate(data.dims):
         cov[i][i] = sigmas[dim] ** 2  # sigma is deviation, but multivariate_normal uses covariant

src/arpes/corrections/fermi_edge_corrections.py

@@ -103,9 +103,7 @@ def apply_direct_fermi_edge_correction(
         correction = build_direct_fermi_edge_correction(arr, *args, **kwargs)
 
     assert isinstance(correction, xr.Dataset)
-    shift_amount = (
-        -correction / arr.G.stride(generic_dim_names=False)["eV"]
-    )  # pylint: disable=invalid-unary-operand-type
+    shift_amount = -correction / arr.G.stride(generic_dim_names=False)["eV"]  # pylint: disable=invalid-unary-operand-type
     energy_axis_index = list(arr.dims).index("eV")
 
     correction_axis = list(arr.dims).index(correction.dims[0])

src/arpes/load_pxt.py

@@ -104,8 +104,7 @@ def read_igor_binary_wave(raw_bytes: bytes) -> xr.DataArray:
 
     wave_data = np.fromstring(
         raw_bytes[
-            igor_wave_header_dtype.itemsize
-            + offset : igor_wave_header_dtype.itemsize
+            igor_wave_header_dtype.itemsize + offset : igor_wave_header_dtype.itemsize
             + n_points * point_size
             + offset
         ],

src/arpes/simulation.py

@@ -181,9 +181,9 @@ def cloud_to_arr(
         cloud_as_image[(int(np.floor(x)) + 1) % shape_x][int(np.floor(y)) % shape_y] += (
             1 - frac_low_x
         ) * frac_low_y
-        cloud_as_image[int(np.floor(x)) % shape_x][
-            (int(np.floor(y)) + 1) % shape_y
-        ] += frac_low_x * (1 - frac_low_y)
+        cloud_as_image[int(np.floor(x)) % shape_x][(int(np.floor(y)) + 1) % shape_y] += (
+            frac_low_x * (1 - frac_low_y)
+        )
         cloud_as_image[(int(np.floor(x)) + 1) % shape_x][(int(np.floor(y)) + 1) % shape_y] += (
             1 - frac_low_x
         ) * (1 - frac_low_y)