Add numpdoc validation to .pre-commit-config.yaml

.gitignore

@@ -148,3 +148,6 @@ fabric.properties
 # Repo specific items
 tests/gin_test_config.json
 .pre-commit-config.yaml
+
+# Aider coding assistant
+.aider*

.pre-commit-config.yaml

@@ -17,3 +17,7 @@ repos:
         args:
         - --convention=numpy
         - --add-ignore=D1
+- repo: https://github.com/numpy/numpydoc
+  rev: 881c0abf0a75c1b517c23a819f9365fbfc9cded7
+  hooks:
+    - id: numpydoc-validation

pyproject.toml

@@ -19,3 +19,21 @@ extend-exclude = '''
   | dist
 )/
 '''
+
+[tool.numpydoc_validation]
+checks = [
+    "all",   # report on all checks, except the below
+    "GL08", # Missing docstring
+    "EX01", # Missing Example section
+    "SA01", # Missing See Also section
+    "ES01", # Missing Extended Summary section
+    "GL01", # Missing newline at beginning of docstring
+    "RT02", # Return should only specify type NOT name
+]
+exclude = [  # don't report on objects that match any of these regex
+  'test_',
+  'check_',
+  'assert_',
+]
+override_SS05 = [  # override SS05 to allow docstrings starting with these words
+]

src/roiextractors/testing.py

@@ -191,15 +191,19 @@ def generate_dummy_segmentation_extractor(
 
 
 def _assert_iterable_shape(iterable, shape):
-    """Assert that the iterable has the given shape. If the iterable is a numpy array, the shape is checked directly."""
+    """Assert that the iterable has the given shape.
+
+    If the iterable is a numpy array, the shape is checked directly.
+    """
     ar = iterable if isinstance(iterable, np.ndarray) else np.array(iterable)
     for ar_shape, given_shape in zip(ar.shape, shape):
         if isinstance(given_shape, int):
             assert ar_shape == given_shape, f"Expected {given_shape}, received {ar_shape}!"
 
 
 def _assert_iterable_shape_max(iterable, shape_max):
-    """Assert that the iterable has a shape less than or equal to the given maximum shape."""
+    """Assert that the iterable has a shape less than or equal to the given
+    maximum shape."""
     ar = iterable if isinstance(iterable, np.ndarray) else np.array(iterable)
     for ar_shape, given_shape in zip(ar.shape, shape_max):
         if isinstance(given_shape, int):
@@ -216,7 +220,8 @@ def _assert_iterable_element_dtypes(iterable, dtypes):
 
 
 def _assert_iterable_complete(iterable, dtypes=None, element_dtypes=None, shape=None, shape_max=None):
-    """Assert that the iterable is complete, i.e. it is not None and has the given dtypes, element_dtypes, shape and shape_max."""
+    """Assert that the iterable is complete, i.e. it is not None and has the
+    given dtypes, element_dtypes, shape and shape_max."""
     assert isinstance(iterable, dtypes), f"iterable {type(iterable)} is none of the types {dtypes}"
     if not isinstance(iterable, NoneType):
         if shape is not None:
@@ -270,7 +275,8 @@ def check_segmentations_images(
     segmentation_extractor1: SegmentationExtractor,
     segmentation_extractor2: SegmentationExtractor,
 ):
-    """Check that the segmentation images are equal for the given segmentation extractors."""
+    """Check that the segmentation images are equal for the given segmentation
+    extractors."""
     images_in_extractor1 = segmentation_extractor1.get_images_dict()
     images_in_extractor2 = segmentation_extractor2.get_images_dict()
 
@@ -287,7 +293,8 @@ def check_segmentations_images(
 
 
 def check_segmentation_return_types(seg: SegmentationExtractor):
-    """Check that the return types of the segmentation extractor are correct."""
+    """Check that the return types of the segmentation extractor are
+    correct."""
     assert isinstance(seg.get_num_rois(), int)
     assert isinstance(seg.get_num_frames(), int)
     assert isinstance(seg.get_num_channels(), int)
@@ -378,9 +385,11 @@ def check_imaging_equal(
 
 
 def assert_get_frames_return_shape(imaging_extractor: ImagingExtractor):
-    """Check whether an ImagingExtractor get_frames function behaves as expected.
+    """Check whether an ImagingExtractor get_frames function behaves as
+    expected.
 
-    We aim for the function to behave as numpy slicing and indexing as much as possible.
+    We aim for the function to behave as numpy slicing and indexing as
+    much as possible.
     """
     image_size = imaging_extractor.get_image_size()