Fix merge conflicts

Author: mkcor

.github/workflows/typing.yml

@@ -47,8 +47,8 @@ jobs:
         run: |
           echo -e "## docstub output\n\`\`\`" >> $GITHUB_STEP_SUMMARY
           (set -o pipefail && \
-           docstub run --verbose --group-errors --allow-errors=989 \
-                       --out-dir ${MYPYPATH}/skimage src/skimage/ \
+           docstub run --verbose --group-errors --allow-errors=947 \
+                       --workers -1 --out-dir ${MYPYPATH}/skimage src/skimage/ \
            2>&1 | tee -a $GITHUB_STEP_SUMMARY)
            echo -e "\`\`\`" >> $GITHUB_STEP_SUMMARY
 

src/skimage/draw/_polygon2mask.py

@@ -10,9 +10,9 @@ def polygon2mask(image_shape, polygon):
     ----------
     image_shape : tuple of size 2
         The shape of the mask.
-    polygon : (N, 2) array_like
-        The polygon coordinates of shape (N, 2) where N is
-        the number of points. The coordinates are (row, column).
+    polygon : array_like of shape (N, 2)
+        The polygon coordinates, where N is the number of points.
+        The coordinates are (row, column).
 
     Returns
     -------

src/skimage/feature/_hog.py

@@ -26,7 +26,7 @@ def _hog_channel_gradient(channel):
 
     Parameters
     ----------
-    channel : (M, N) ndarray
+    channel : ndarray of shape (M, N)
         Grayscale image or one of image channel.
 
     Returns

src/skimage/feature/corner.py

@@ -548,7 +548,7 @@ def shape_index(image, sigma=1, mode='constant', cval=0):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     sigma : float, optional
         Standard deviation used for the Gaussian kernel, which is used for
@@ -613,7 +613,7 @@ def corner_kitchen_rosenfeld(image, mode='constant', cval=0):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     mode : {'constant', 'reflect', 'wrap', 'nearest', 'mirror'}, optional
         How to handle values outside the image borders.
@@ -670,7 +670,7 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     method : {'k', 'eps'}, optional
         Method to compute the response image from the auto-correlation matrix.
@@ -746,7 +746,7 @@ def corner_shi_tomasi(image, sigma=1):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     sigma : float, optional
         Standard deviation used for the Gaussian kernel, which is used as
@@ -809,7 +809,7 @@ def corner_foerstner(image, sigma=1):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     sigma : float, optional
         Standard deviation used for the Gaussian kernel, which is used as
@@ -882,7 +882,7 @@ def corner_fast(image, n=12, threshold=0.15):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     n : int, optional
         Minimum number of consecutive pixels out of 16 pixels on the circle
@@ -954,7 +954,7 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     corners : (K, 2) ndarray
         Corner coordinates `(row, col)`.
@@ -1144,7 +1144,7 @@ def corner_peaks(
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     min_distance : int, optional
         The minimal allowed distance separating peaks.
@@ -1246,7 +1246,7 @@ def corner_moravec(image, window_size=1):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     window_size : int, optional
         Window size.

src/skimage/filters/_median.py

@@ -16,40 +16,44 @@ def median(
     image : array-like
         Input image.
     footprint : ndarray, optional
-        If ``behavior=='rank'``, ``footprint`` is a 2-D array of 1's and 0's.
-        If ``behavior=='ndimage'``, ``footprint`` is a N-D array of 1's and 0's
-        with the same number of dimension than ``image``.
-        If None, ``footprint`` will be a N-D array with 3 elements for each
-        dimension (e.g., vector, square, cube, etc.)
-    out : ndarray, (same dtype as image), optional
-        If None, a new array is allocated.
-    mode : {'reflect', 'constant', 'nearest', 'mirror','‘wrap'}, optional
+        If ``behavior='rank'``, `footprint` is a 2-D array of 1's and 0's.
+        If ``behavior='ndimage'``, `footprint` is a N-D array of 1's and 0's
+        with the same number of dimensions as `image`.
+        If None, `footprint` will be a N-D array with 3 elements for each
+        dimension (e.g., vector, square, cube, etc.).
+    out : ndarray, optional
+        If None, a new array is allocated. For ``behavior='ndimage'``,
+        dtype matches input. For ``behavior='rank'``, dtype follows
+        :func:`skimage.filters.rank.median`.
+    mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
         The mode parameter determines how the array borders are handled, where
-        ``cval`` is the value when mode is equal to 'constant'.
+        `cval` is the value when `mode` is equal to 'constant'.
         Default is 'nearest'.
 
         .. versionadded:: 0.15
-           ``mode`` is used when ``behavior='ndimage'``.
+           `mode` is used when ``behavior='ndimage'``.
     cval : scalar, optional
-        Value to fill past edges of input if mode is 'constant'. Default is 0.0
+        Value to fill past edges of input `image` if `mode` is 'constant'.
+        Default is 0.0.
 
         .. versionadded:: 0.15
-           ``cval`` was added in 0.15 is used when ``behavior='ndimage'``.
+           `cval` was added in 0.15 and is used when ``behavior='ndimage'``.
     behavior : {'ndimage', 'rank'}, optional
-        Either to use the old behavior (i.e., < 0.15) or the new behavior.
-        The old behavior will call the :func:`skimage.filters.rank.median`.
-        The new behavior will call the :func:`scipy.ndimage.median_filter`.
+        Whether to use the old (i.e., scikit-image < 0.15) or the new behavior.
+        The old behavior will call :func:`skimage.filters.rank.median`.
+        The new behavior will call :func:`scipy.ndimage.median_filter`.
         Default is 'ndimage'.
 
         .. versionadded:: 0.15
-           ``behavior`` is introduced in 0.15
+           `behavior` is introduced in v0.15.
         .. versionchanged:: 0.16
-           Default ``behavior`` has been changed from 'rank' to 'ndimage'
+           Default `behavior` has been changed from 'rank' to 'ndimage'.
 
     Returns
     -------
-    out : 2-D array, same dtype as input `image`
-        Output image.
+    out : ndarray
+        Output image. For ``behavior='ndimage'``, dtype matches input. For
+        ``behavior='rank'``, dtype follows :func:`skimage.filters.rank.median`.
 
     See also
     --------
@@ -59,11 +63,9 @@ def median(
 
     Examples
     --------
-    >>> from skimage import data
-    >>> from skimage.morphology import disk
-    >>> from skimage.filters import median
-    >>> img = data.camera()
-    >>> med = median(img, disk(5))
+    >>> import skimage as ski
+    >>> img = ski.data.camera()
+    >>> med = ski.filters.median(img, ski.morphology.disk(5))
 
     """
     if behavior == 'rank':

src/skimage/filters/lpi_filter.py

@@ -120,7 +120,7 @@ def __call__(self, data):
 
         Parameters
         ----------
-        data : (M, N) ndarray
+        data : ndarray of shape (M, N)
 
         """
         check_nD(data, 2, 'data')
@@ -137,7 +137,7 @@ def filter_forward(
 
     Parameters
     ----------
-    data : (M, N) ndarray
+    data : ndarray of shape (M, N)
         Input data.
     impulse_response : callable `f(r, c, **filter_params)`
         Impulse response of the filter.  See LPIFilter2D.__init__.
@@ -177,7 +177,7 @@ def filter_inverse(
 
     Parameters
     ----------
-    data : (M, N) ndarray
+    data : ndarray of shape (M, N)
         Input data.
     impulse_response : callable `f(r, c, **filter_params)`
         Impulse response of the filter.  See :class:`~.LPIFilter2D`. This is a required
@@ -222,7 +222,7 @@ def wiener(
 
     Parameters
     ----------
-    data : (M, N) ndarray
+    data : ndarray of shape (M, N)
         Input data.
     K : float or (M, N) ndarray
         Ratio between power spectrum of noise and undegraded

src/skimage/filters/rank/_percentile.py

@@ -116,17 +116,17 @@ def autolevel_percentile(
 def gradient_percentile(
     image, footprint, out=None, mask=None, shift_x=0, shift_y=0, p0=0, p1=1
 ):
-    """Return local gradient of an image (i.e. local maximum - local minimum).
+    """Return local gradient of an image (i.e., local maximum - local minimum).
 
     Only grayvalues between percentiles [p0, p1] are considered in the filter.
 
     Parameters
     ----------
-    image : 2-D array (uint8, uint16)
+    image : ndarray of shape (M, N) and dtype (uint8 or uint16)
         Input image.
     footprint : ndarray
         The neighborhood expressed as a 2-D array of 1's and 0's.
-    out : 2-D array, same dtype as input `image`
+    out : ndarray of shape (M, N) and dtype int
         If None, a new array is allocated.
     mask : ndarray
         Mask array that defines (>0) area of the image included in the local
@@ -140,7 +140,7 @@ def gradient_percentile(
 
     Returns
     -------
-    out : 2-D array, same dtype as input `image`
+    out : ndarray of shape (M, N) and dtype int
         Output image.
 
     """
@@ -167,11 +167,11 @@ def mean_percentile(
 
     Parameters
     ----------
-    image : 2-D array (uint8, uint16)
+    image : ndarray of shape (M, N) and dtype (uint8 or uint16)
         Input image.
     footprint : ndarray
         The neighborhood expressed as a 2-D array of 1's and 0's.
-    out : 2-D array, same dtype as input `image`
+    out : ndarray of shape (M, N) and dtype int
         If None, a new array is allocated.
     mask : ndarray
         Mask array that defines (>0) area of the image included in the local
@@ -185,7 +185,7 @@ def mean_percentile(
 
     Returns
     -------
-    out : 2-D array, same dtype as input `image`
+    out : ndarray of shape (M, N) and dtype int
         Output image.
 
     """

src/skimage/filters/rank/generic.py

@@ -1694,11 +1694,11 @@ def majority(
 
     Parameters
     ----------
-    image : ndarray of dtype (uint8 or uint16)
+    image : ndarray of dtype (int or float)
         Image array.
     footprint : 2-D array (integer or float)
         The neighborhood expressed as a 2-D array of 1's and 0's.
-    out : ndarray, same dtype as input `image`, optional
+    out : ndarray of dtype int, optional
         If None, a new array will be allocated.
     mask : ndarray of dtype (int or float), optional
         Mask array that defines (>0) area of the image included in the local
@@ -1709,20 +1709,18 @@ def majority(
 
     Returns
     -------
-    out : ndarray, same dtype as input `image`
+    out : ndarray of dtype int, optional
         Output image.
 
     Examples
     --------
-    >>> from skimage import data
-    >>> from skimage.filters.rank import majority
-    >>> from skimage.morphology import disk, ball
     >>> import numpy as np
-    >>> img = data.camera()
+    >>> import skimage as ski
+    >>> img = ski.data.camera()
     >>> rng = np.random.default_rng()
-    >>> volume = rng.integers(0, 255, size=(10,10,10), dtype=np.uint8)
-    >>> maj_img = majority(img, disk(5))
-    >>> maj_img_vol = majority(volume, ball(5))
+    >>> volume = rng.integers(0, 255, size=(10, 10, 10), dtype=np.uint8)
+    >>> maj_img = ski.filters.rank.majority(img, ski.morphology.disk(5))
+    >>> maj_img_vol = ski.filters.rank.majority(volume, ski.morphology.ball(5))
 
     """
 

src/skimage/filters/thresholding.py

@@ -44,7 +44,7 @@ def _try_all(image, methods=None, figsize=None, num_cols=2, verbose=True):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     methods : dict, optional
         Names and associated functions.
@@ -114,7 +114,7 @@ def try_all_threshold(image, figsize=(8, 5), verbose=True):
 
     Parameters
     ----------
-    image : (M, N) ndarray
+    image : ndarray of shape (M, N)
         Input image.
     figsize : tuple, optional
         Figure size (in inches).

src/skimage/measure/_colocalization.py

@@ -17,9 +17,9 @@ def pearson_corr_coeff(image0, image1, mask=None):
 
     Parameters
     ----------
-    image0 : (M, N) ndarray
+    image0 : ndarray of shape (M, N)
         Image of channel A.
-    image1 : (M, N) ndarray
+    image1 : ndarray of shape (M, N)
         Image of channel 2 to be correlated with channel B.
         Must have same dimensions as `image0`.
     mask : (M, N) ndarray of dtype bool, optional
@@ -102,7 +102,7 @@ def manders_coloc_coeff(image0, image1_mask, mask=None):
 
     Parameters
     ----------
-    image0 : (M, N) ndarray
+    image0 : ndarray of shape (M, N)
         Input image (first channel). All pixel values should be non-negative.
     image1_mask : (M, N) ndarray of dtype bool
         Binary image giving the regions of interest in the second channel.
@@ -185,15 +185,13 @@ def manders_overlap_coeff(image0, image1, mask=None):
 
     Parameters
     ----------
-    image0 : (M, N) ndarray
+    image0 : ndarray of shape (M, N)
         Image of channel A. All pixel values should be non-negative.
-    image1 : (M, N) ndarray
+    image1 : ndarray of shape (M, N)
         Image of channel B. All pixel values should be non-negative.
-        Must have same dimensions as `image0`
-    mask : (M, N) ndarray of dtype bool, optional
+    mask : ndarray of shape (M, N) and dtype bool, optional
         Only `image0` and `image1` pixel values within this region of interest
         mask are included in the calculation.
-        Must have ♣same dimensions as `image0`.
 
     Returns
     -------