Update pytest config key

continuous_integration/environment-3.10.yml

@@ -10,7 +10,7 @@ dependencies:
   - flake8==6.0.0
   - pytest==7.2.2
   - pytest-cov==4.0.0
-  - pytest-flake8==1.1.1
+  - pytest-flake8==1.3.0
   - pytest-timeout >=2.3.1
   - dask==2024.4.1
   - numpy==1.24.2

continuous_integration/environment-3.11.yml

@@ -10,7 +10,7 @@ dependencies:
   - flake8==6.0.0
   - pytest==7.2.2
   - pytest-cov==4.0.0
-  - pytest-flake8==1.1.1
+  - pytest-flake8==1.3.0
   - pytest-timeout >=2.3.1
   - dask==2024.4.1
   - numpy==1.24.2

continuous_integration/environment-3.12.yml

@@ -10,7 +10,7 @@ dependencies:
   - flake8==7.0.0
   - pytest==8.2.0
   - pytest-cov==5.0.0
-  - pytest-flake8==1.1.1
+  - pytest-flake8==1.3.0
   - pytest-timeout >=2.3.1
   - dask==2024.4.1
   - numpy==1.26.4

continuous_integration/environment-3.9.yml

@@ -10,7 +10,7 @@ dependencies:
   - flake8==7.0.0
   - pytest==8.2.0
   - pytest-cov==5.0.0
-  - pytest-flake8==1.1.1
+  - pytest-flake8==1.3.0
   - pytest-timeout >=2.3.1
   - dask==2024.4.1
   - numpy==1.26.4
@@ -21,4 +21,4 @@ dependencies:
   - pandas==2.2.2
   - twine==5.0.0
   - pip:
-    - build==1.2.1
+    - build==1.2.1

dask_image/ndfilters/_generic.py

@@ -26,7 +26,7 @@ def generic_filter(image,
     depth = _utils._get_depth(footprint.shape, origin)
     depth, boundary = _utils._get_depth_boundary(footprint.ndim, depth, "none")
 
-    if type(image._meta) == np.ndarray:
+    if type(image._meta) is np.ndarray:
         kwargs = {"extra_arguments": extra_arguments,
                   "extra_keywords": extra_keywords}
     else:  # pragma: no cover

dask_image/ndfilters/_threshold.py

@@ -1,4 +1,3 @@
-import dask.array as da
 import numpy as np
 
 from ..dispatch._dispatch_ndfilters import dispatch_threshold_local_mean

dask_image/ndfourier/_utils.py

@@ -1,7 +1,5 @@
 # -*- coding: utf-8 -*-
 
-import functools
-import operator
 import numbers
 
 import dask.array as da

dask_image/ndinterp/__init__.py

@@ -20,9 +20,6 @@
 )
 from ..ndfilters._utils import _get_depth_boundary
 
-from ..dispatch._dispatch_ndinterp import (dispatch_affine_transform,
-                                           dispatch_asarray)
-
 __all__ = [
     "affine_transform",
     "rotate",
@@ -263,9 +260,9 @@ def rotate(
     The array is rotated in the plane defined by the two axes given by the
     `axes` parameter using spline interpolation of the requested order.
 
-    Chunkwise processing is performed using `dask_image.ndinterp.affine_transform`,
-    for which further parameters supported by the ndimage functions can be
-    passed as keyword arguments.
+    Chunkwise processing is performed using
+    `dask_image.ndinterp.affine_transform`, for which further parameters
+    supported by the ndimage functions can be passed as keyword arguments.
 
     Notes
     -----
@@ -386,10 +383,10 @@ def rotate(
     matrix_nd = np.eye(ndim)
     offset_nd = np.zeros(ndim)
 
-    for o_x,idx in enumerate(axes):
+    for o_x, idx in enumerate(axes):
 
-        matrix_nd[idx,axes[0]] = rot_matrix[o_x,0]
-        matrix_nd[idx,axes[1]] = rot_matrix[o_x,1]
+        matrix_nd[idx, axes[0]] = rot_matrix[o_x, 0]
+        matrix_nd[idx, axes[1]] = rot_matrix[o_x, 1]
 
         offset_nd[idx] = offset[o_x]
 

dask_image/ndmeasure/__init__.py

@@ -14,7 +14,11 @@
 
 from . import _utils
 from ._utils import _label
-from ._utils._find_objects import _array_chunk_location, _find_bounding_boxes, _find_objects
+from ._utils._find_objects import (
+    _array_chunk_location,
+    _find_bounding_boxes,
+    _find_objects,
+)
 
 __all__ = [
     "area",
@@ -242,12 +246,17 @@ def find_objects(label_image):
         arrays.append(delayed(_find_bounding_boxes)(block, array_location))
 
     bag = db.from_sequence(arrays)
-    result = bag.fold(functools.partial(_find_objects, label_image.ndim), split_every=2).to_delayed()
+    result = bag.fold(
+        functools.partial(_find_objects, label_image.ndim), split_every=2
+    ).to_delayed()
     meta = dd.utils.make_meta([(i, object) for i in range(label_image.ndim)])
-    result = delayed(compute)(result)[0]  # avoid the user having to call compute twice on result
+    # avoid the user having to call compute twice on result
+    result = delayed(compute)(result)[0]
 
     with dask_config.set({'dataframe.convert-string': False}):
-        result = dd.from_delayed(result, meta=meta, prefix="find-objects-", verify_meta=False)
+        result = dd.from_delayed(
+            result, meta=meta, prefix="find-objects-", verify_meta=False
+        )
 
     return result
 
@@ -327,7 +336,8 @@ def label(image, structure=None, wrap_axes=None):
              [0,1,0]]
 
     wrap_axes : tuple of int, optional
-        Whether labels should be wrapped across array boundaries, and if so which axes.
+        Whether labels should be wrapped across array boundaries, and if so
+        which axes.
         This feature is not present in `ndimage.label`.
         Examples:
         - (0,) only wrap across the 0th axis.
@@ -381,7 +391,6 @@ def label(image, structure=None, wrap_axes=None):
     relabeled = _label.relabel_blocks(block_labeled, new_labeling)
     n = da.max(relabeled)
 
-
     return (relabeled, n)
 
 

dask_image/ndmeasure/_utils/_find_objects.py

@@ -29,7 +29,13 @@ def _find_bounding_boxes(x, array_location):
     result = {}
     for val in unique_vals:
         positions = np.where(x == val)
-        slices = tuple(slice(np.min(pos) + array_location[i], np.max(pos) + 1 + array_location[i]) for i, pos in enumerate(positions))
+        slices = tuple(
+            slice(
+                np.min(pos) + array_location[i],
+                np.max(pos) + 1 + array_location[i]
+            )
+            for i, pos in enumerate(positions)
+        )
         result[val] = slices
     column_names = [i for i in range(x.ndim)]  # column names are: 0, 1, ... nD
     return pd.DataFrame.from_dict(result, orient='index', columns=column_names)
@@ -46,7 +52,7 @@ def _combine_slices(slices):
 
 
 def _merge_bounding_boxes(x, ndim):
-    """Merge the bounding boxes describing objects over multiple image chunks."""
+    "Merge the bounding boxes describing objects over multiple image chunks."
     x = x.dropna()
     data = {}
     # For each dimension in the array,

dask_image/ndmeasure/_utils/_label.py

@@ -243,14 +243,17 @@ def _chunk_faces(chunks, shape, structure, wrap_axes=None):
         for pos_structure_coord in np.array(np.where(structure)).T:
 
             # only consider forward neighbors
-            if min(pos_structure_coord) < 1 or \
-               max(pos_structure_coord) < 2: continue
+            if min(pos_structure_coord) < 1 or max(pos_structure_coord) < 2:
+                continue
 
-            neigh_block = [curr_block[dim] + pos_structure_coord[dim] - 1
-                           for dim in range(ndim)]
+            neigh_block = [
+                curr_block[dim] + pos_structure_coord[dim] - 1
+                for dim in range(ndim)
+            ]
 
             if max([neigh_block[dim] >= block_summary.shape[dim]
-                    for dim in range(ndim)]): continue
+                    for dim in range(ndim)]):
+                continue
 
             # get current slice index
             ind_curr_block = block_summary[tuple(curr_block)]