asd

venv/lib/python3.12/site-packages/scipy/ndimage/__init__.py

@@ -0,0 +1,169 @@
+"""
+=========================================================
+Multidimensional image processing (:mod:`scipy.ndimage`)
+=========================================================
+
+.. currentmodule:: scipy.ndimage
+
+This package contains various functions for multidimensional image
+processing.
+
+
+Filters
+=======
+
+.. autosummary::
+   :toctree: generated/
+
+   convolve - Multidimensional convolution
+   convolve1d - 1-D convolution along the given axis
+   correlate - Multidimensional correlation
+   correlate1d - 1-D correlation along the given axis
+   gaussian_filter
+   gaussian_filter1d
+   gaussian_gradient_magnitude
+   gaussian_laplace
+   generic_filter - Multidimensional filter using a given function
+   generic_filter1d - 1-D generic filter along the given axis
+   generic_gradient_magnitude
+   generic_laplace
+   laplace - N-D Laplace filter based on approximate second derivatives
+   maximum_filter
+   maximum_filter1d
+   median_filter - Calculates a multidimensional median filter
+   minimum_filter
+   minimum_filter1d
+   percentile_filter - Calculates a multidimensional percentile filter
+   prewitt
+   rank_filter - Calculates a multidimensional rank filter
+   sobel
+   uniform_filter - Multidimensional uniform filter
+   uniform_filter1d - 1-D uniform filter along the given axis
+
+Fourier filters
+===============
+
+.. autosummary::
+   :toctree: generated/
+
+   fourier_ellipsoid
+   fourier_gaussian
+   fourier_shift
+   fourier_uniform
+
+Interpolation
+=============
+
+.. autosummary::
+   :toctree: generated/
+
+   affine_transform - Apply an affine transformation
+   geometric_transform - Apply an arbitrary geometric transform
+   map_coordinates - Map input array to new coordinates by interpolation
+   rotate - Rotate an array
+   shift - Shift an array
+   spline_filter
+   spline_filter1d
+   zoom - Zoom an array
+
+Measurements
+============
+
+.. autosummary::
+   :toctree: generated/
+
+   center_of_mass - The center of mass of the values of an array at labels
+   extrema - Min's and max's of an array at labels, with their positions
+   find_objects - Find objects in a labeled array
+   histogram - Histogram of the values of an array, optionally at labels
+   label - Label features in an array
+   labeled_comprehension
+   maximum
+   maximum_position
+   mean - Mean of the values of an array at labels
+   median
+   minimum
+   minimum_position
+   standard_deviation - Standard deviation of an N-D image array
+   sum_labels - Sum of the values of the array
+   value_indices - Find indices of each distinct value in given array
+   variance - Variance of the values of an N-D image array
+   watershed_ift
+
+Morphology
+==========
+
+.. autosummary::
+   :toctree: generated/
+
+   binary_closing
+   binary_dilation
+   binary_erosion
+   binary_fill_holes
+   binary_hit_or_miss
+   binary_opening
+   binary_propagation
+   black_tophat
+   distance_transform_bf
+   distance_transform_cdt
+   distance_transform_edt
+   generate_binary_structure
+   grey_closing
+   grey_dilation
+   grey_erosion
+   grey_opening
+   iterate_structure
+   morphological_gradient
+   morphological_laplace
+   white_tophat
+
+"""
+
+# Copyright (C) 2003-2005 Peter J. Verveer
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#
+# 3. The name of the author may not be used to endorse or promote
+#    products derived from this software without specific prior
+#    written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from ._filters import *
+from ._fourier import *
+from ._interpolation import *
+from ._measurements import *
+from ._morphology import *
+
+# Deprecated namespaces, to be removed in v2.0.0
+from . import filters
+from . import fourier
+from . import interpolation
+from . import measurements
+from . import morphology
+
+__all__ = [s for s in dir() if not s.startswith('_')]
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester

venv/lib/python3.12/site-packages/scipy/ndimage/_fourier.py

@@ -0,0 +1,306 @@
+# Copyright (C) 2003-2005 Peter J. Verveer
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#
+# 3. The name of the author may not be used to endorse or promote
+#    products derived from this software without specific prior
+#    written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+from scipy._lib._util import normalize_axis_index
+from . import _ni_support
+from . import _nd_image
+
+__all__ = ['fourier_gaussian', 'fourier_uniform', 'fourier_ellipsoid',
+           'fourier_shift']
+
+
+def _get_output_fourier(output, input):
+    if output is None:
+        if input.dtype.type in [np.complex64, np.complex128, np.float32]:
+            output = np.zeros(input.shape, dtype=input.dtype)
+        else:
+            output = np.zeros(input.shape, dtype=np.float64)
+    elif type(output) is type:
+        if output not in [np.complex64, np.complex128,
+                          np.float32, np.float64]:
+            raise RuntimeError("output type not supported")
+        output = np.zeros(input.shape, dtype=output)
+    elif output.shape != input.shape:
+        raise RuntimeError("output shape not correct")
+    return output
+
+
+def _get_output_fourier_complex(output, input):
+    if output is None:
+        if input.dtype.type in [np.complex64, np.complex128]:
+            output = np.zeros(input.shape, dtype=input.dtype)
+        else:
+            output = np.zeros(input.shape, dtype=np.complex128)
+    elif type(output) is type:
+        if output not in [np.complex64, np.complex128]:
+            raise RuntimeError("output type not supported")
+        output = np.zeros(input.shape, dtype=output)
+    elif output.shape != input.shape:
+        raise RuntimeError("output shape not correct")
+    return output
+
+
+def fourier_gaussian(input, sigma, n=-1, axis=-1, output=None):
+    """
+    Multidimensional Gaussian fourier filter.
+
+    The array is multiplied with the fourier transform of a Gaussian
+    kernel.
+
+    Parameters
+    ----------
+    input : array_like
+        The input array.
+    sigma : float or sequence
+        The sigma of the Gaussian kernel. If a float, `sigma` is the same for
+        all axes. If a sequence, `sigma` has to contain one value for each
+        axis.
+    n : int, optional
+        If `n` is negative (default), then the input is assumed to be the
+        result of a complex fft.
+        If `n` is larger than or equal to zero, the input is assumed to be the
+        result of a real fft, and `n` gives the length of the array before
+        transformation along the real transform direction.
+    axis : int, optional
+        The axis of the real transform.
+    output : ndarray, optional
+        If given, the result of filtering the input is placed in this array.
+
+    Returns
+    -------
+    fourier_gaussian : ndarray
+        The filtered input.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import numpy.fft
+    >>> import matplotlib.pyplot as plt
+    >>> fig, (ax1, ax2) = plt.subplots(1, 2)
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ascent = datasets.ascent()
+    >>> input_ = numpy.fft.fft2(ascent)
+    >>> result = ndimage.fourier_gaussian(input_, sigma=4)
+    >>> result = numpy.fft.ifft2(result)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result.real)  # the imaginary part is an artifact
+    >>> plt.show()
+    """
+    input = np.asarray(input)
+    output = _get_output_fourier(output, input)
+    axis = normalize_axis_index(axis, input.ndim)
+    sigmas = _ni_support._normalize_sequence(sigma, input.ndim)
+    sigmas = np.asarray(sigmas, dtype=np.float64)
+    if not sigmas.flags.contiguous:
+        sigmas = sigmas.copy()
+
+    _nd_image.fourier_filter(input, sigmas, n, axis, output, 0)
+    return output
+
+
+def fourier_uniform(input, size, n=-1, axis=-1, output=None):
+    """
+    Multidimensional uniform fourier filter.
+
+    The array is multiplied with the Fourier transform of a box of given
+    size.
+
+    Parameters
+    ----------
+    input : array_like
+        The input array.
+    size : float or sequence
+        The size of the box used for filtering.
+        If a float, `size` is the same for all axes. If a sequence, `size` has
+        to contain one value for each axis.
+    n : int, optional
+        If `n` is negative (default), then the input is assumed to be the
+        result of a complex fft.
+        If `n` is larger than or equal to zero, the input is assumed to be the
+        result of a real fft, and `n` gives the length of the array before
+        transformation along the real transform direction.
+    axis : int, optional
+        The axis of the real transform.
+    output : ndarray, optional
+        If given, the result of filtering the input is placed in this array.
+
+    Returns
+    -------
+    fourier_uniform : ndarray
+        The filtered input.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import numpy.fft
+    >>> import matplotlib.pyplot as plt
+    >>> fig, (ax1, ax2) = plt.subplots(1, 2)
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ascent = datasets.ascent()
+    >>> input_ = numpy.fft.fft2(ascent)
+    >>> result = ndimage.fourier_uniform(input_, size=20)
+    >>> result = numpy.fft.ifft2(result)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result.real)  # the imaginary part is an artifact
+    >>> plt.show()
+    """
+    input = np.asarray(input)
+    output = _get_output_fourier(output, input)
+    axis = normalize_axis_index(axis, input.ndim)
+    sizes = _ni_support._normalize_sequence(size, input.ndim)
+    sizes = np.asarray(sizes, dtype=np.float64)
+    if not sizes.flags.contiguous:
+        sizes = sizes.copy()
+    _nd_image.fourier_filter(input, sizes, n, axis, output, 1)
+    return output
+
+
+def fourier_ellipsoid(input, size, n=-1, axis=-1, output=None):
+    """
+    Multidimensional ellipsoid Fourier filter.
+
+    The array is multiplied with the fourier transform of an ellipsoid of
+    given sizes.
+
+    Parameters
+    ----------
+    input : array_like
+        The input array.
+    size : float or sequence
+        The size of the box used for filtering.
+        If a float, `size` is the same for all axes. If a sequence, `size` has
+        to contain one value for each axis.
+    n : int, optional
+        If `n` is negative (default), then the input is assumed to be the
+        result of a complex fft.
+        If `n` is larger than or equal to zero, the input is assumed to be the
+        result of a real fft, and `n` gives the length of the array before
+        transformation along the real transform direction.
+    axis : int, optional
+        The axis of the real transform.
+    output : ndarray, optional
+        If given, the result of filtering the input is placed in this array.
+
+    Returns
+    -------
+    fourier_ellipsoid : ndarray
+        The filtered input.
+
+    Notes
+    -----
+    This function is implemented for arrays of rank 1, 2, or 3.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import numpy.fft
+    >>> import matplotlib.pyplot as plt
+    >>> fig, (ax1, ax2) = plt.subplots(1, 2)
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ascent = datasets.ascent()
+    >>> input_ = numpy.fft.fft2(ascent)
+    >>> result = ndimage.fourier_ellipsoid(input_, size=20)
+    >>> result = numpy.fft.ifft2(result)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result.real)  # the imaginary part is an artifact
+    >>> plt.show()
+    """
+    input = np.asarray(input)
+    if input.ndim > 3:
+        raise NotImplementedError("Only 1d, 2d and 3d inputs are supported")
+    output = _get_output_fourier(output, input)
+    if output.size == 0:
+        # The C code has a bug that can result in a segfault with arrays
+        # that have size 0 (gh-17270), so check here.
+        return output
+    axis = normalize_axis_index(axis, input.ndim)
+    sizes = _ni_support._normalize_sequence(size, input.ndim)
+    sizes = np.asarray(sizes, dtype=np.float64)
+    if not sizes.flags.contiguous:
+        sizes = sizes.copy()
+    _nd_image.fourier_filter(input, sizes, n, axis, output, 2)
+    return output
+
+
+def fourier_shift(input, shift, n=-1, axis=-1, output=None):
+    """
+    Multidimensional Fourier shift filter.
+
+    The array is multiplied with the Fourier transform of a shift operation.
+
+    Parameters
+    ----------
+    input : array_like
+        The input array.
+    shift : float or sequence
+        The size of the box used for filtering.
+        If a float, `shift` is the same for all axes. If a sequence, `shift`
+        has to contain one value for each axis.
+    n : int, optional
+        If `n` is negative (default), then the input is assumed to be the
+        result of a complex fft.
+        If `n` is larger than or equal to zero, the input is assumed to be the
+        result of a real fft, and `n` gives the length of the array before
+        transformation along the real transform direction.
+    axis : int, optional
+        The axis of the real transform.
+    output : ndarray, optional
+        If given, the result of shifting the input is placed in this array.
+
+    Returns
+    -------
+    fourier_shift : ndarray
+        The shifted input.
+
+    Examples
+    --------
+    >>> from scipy import ndimage, datasets
+    >>> import matplotlib.pyplot as plt
+    >>> import numpy.fft
+    >>> fig, (ax1, ax2) = plt.subplots(1, 2)
+    >>> plt.gray()  # show the filtered result in grayscale
+    >>> ascent = datasets.ascent()
+    >>> input_ = numpy.fft.fft2(ascent)
+    >>> result = ndimage.fourier_shift(input_, shift=200)
+    >>> result = numpy.fft.ifft2(result)
+    >>> ax1.imshow(ascent)
+    >>> ax2.imshow(result.real)  # the imaginary part is an artifact
+    >>> plt.show()
+    """
+    input = np.asarray(input)
+    output = _get_output_fourier_complex(output, input)
+    axis = normalize_axis_index(axis, input.ndim)
+    shifts = _ni_support._normalize_sequence(shift, input.ndim)
+    shifts = np.asarray(shifts, dtype=np.float64)
+    if not shifts.flags.contiguous:
+        shifts = shifts.copy()
+    _nd_image.fourier_shift(input, shifts, n, axis, output)
+    return output

venv/lib/python3.12/site-packages/scipy/ndimage/_ni_docstrings.py

@@ -0,0 +1,208 @@
+"""Docstring components common to several ndimage functions."""
+from scipy._lib import doccer
+
+__all__ = ['docfiller']
+
+
+_input_doc = (
+"""input : array_like
+    The input array.""")
+_axis_doc = (
+"""axis : int, optional
+    The axis of `input` along which to calculate. Default is -1.""")
+_output_doc = (
+"""output : array or dtype, optional
+    The array in which to place the output, or the dtype of the
+    returned array. By default an array of the same dtype as input
+    will be created.""")
+_size_foot_doc = (
+"""size : scalar or tuple, optional
+    See footprint, below. Ignored if footprint is given.
+footprint : array, optional
+    Either `size` or `footprint` must be defined. `size` gives
+    the shape that is taken from the input array, at every element
+    position, to define the input to the filter function.
+    `footprint` is a boolean array that specifies (implicitly) a
+    shape, but also which of the elements within this shape will get
+    passed to the filter function. Thus ``size=(n,m)`` is equivalent
+    to ``footprint=np.ones((n,m))``.  We adjust `size` to the number
+    of dimensions of the input array, so that, if the input array is
+    shape (10,10,10), and `size` is 2, then the actual size used is
+    (2,2,2). When `footprint` is given, `size` is ignored.""")
+_mode_reflect_doc = (
+"""mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
+    The `mode` parameter determines how the input array is extended
+    beyond its boundaries. Default is 'reflect'. Behavior for each valid
+    value is as follows:
+
+    'reflect' (`d c b a | a b c d | d c b a`)
+        The input is extended by reflecting about the edge of the last
+        pixel. This mode is also sometimes referred to as half-sample
+        symmetric.
+
+    'constant' (`k k k k | a b c d | k k k k`)
+        The input is extended by filling all values beyond the edge with
+        the same constant value, defined by the `cval` parameter.
+
+    'nearest' (`a a a a | a b c d | d d d d`)
+        The input is extended by replicating the last pixel.
+
+    'mirror' (`d c b | a b c d | c b a`)
+        The input is extended by reflecting about the center of the last
+        pixel. This mode is also sometimes referred to as whole-sample
+        symmetric.
+
+    'wrap' (`a b c d | a b c d | a b c d`)
+        The input is extended by wrapping around to the opposite edge.
+
+    For consistency with the interpolation functions, the following mode
+    names can also be used:
+
+    'grid-mirror'
+        This is a synonym for 'reflect'.
+
+    'grid-constant'
+        This is a synonym for 'constant'.
+
+    'grid-wrap'
+        This is a synonym for 'wrap'.""")
+
+_mode_interp_constant_doc = (
+"""mode : {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', \
+'mirror', 'grid-wrap', 'wrap'}, optional
+    The `mode` parameter determines how the input array is extended
+    beyond its boundaries. Default is 'constant'. Behavior for each valid
+    value is as follows (see additional plots and details on
+    :ref:`boundary modes <ndimage-interpolation-modes>`):
+
+    'reflect' (`d c b a | a b c d | d c b a`)
+        The input is extended by reflecting about the edge of the last
+        pixel. This mode is also sometimes referred to as half-sample
+        symmetric.
+
+    'grid-mirror'
+        This is a synonym for 'reflect'.
+
+    'constant' (`k k k k | a b c d | k k k k`)
+        The input is extended by filling all values beyond the edge with
+        the same constant value, defined by the `cval` parameter. No
+        interpolation is performed beyond the edges of the input.
+
+    'grid-constant' (`k k k k | a b c d | k k k k`)
+        The input is extended by filling all values beyond the edge with
+        the same constant value, defined by the `cval` parameter. Interpolation
+        occurs for samples outside the input's extent  as well.
+
+    'nearest' (`a a a a | a b c d | d d d d`)
+        The input is extended by replicating the last pixel.
+
+    'mirror' (`d c b | a b c d | c b a`)
+        The input is extended by reflecting about the center of the last
+        pixel. This mode is also sometimes referred to as whole-sample
+        symmetric.
+
+    'grid-wrap' (`a b c d | a b c d | a b c d`)
+        The input is extended by wrapping around to the opposite edge.
+
+    'wrap' (`d b c d | a b c d | b c a b`)
+        The input is extended by wrapping around to the opposite edge, but in a
+        way such that the last point and initial point exactly overlap. In this
+        case it is not well defined which sample will be chosen at the point of
+        overlap.""")
+_mode_interp_mirror_doc = (
+    _mode_interp_constant_doc.replace("Default is 'constant'",
+                                      "Default is 'mirror'")
+)
+assert _mode_interp_mirror_doc != _mode_interp_constant_doc, \
+    'Default not replaced'
+
+_mode_multiple_doc = (
+"""mode : str or sequence, optional
+    The `mode` parameter determines how the input array is extended
+    when the filter overlaps a border. By passing a sequence of modes
+    with length equal to the number of dimensions of the input array,
+    different modes can be specified along each axis. Default value is
+    'reflect'. The valid values and their behavior is as follows:
+
+    'reflect' (`d c b a | a b c d | d c b a`)
+        The input is extended by reflecting about the edge of the last
+        pixel. This mode is also sometimes referred to as half-sample
+        symmetric.
+
+    'constant' (`k k k k | a b c d | k k k k`)
+        The input is extended by filling all values beyond the edge with
+        the same constant value, defined by the `cval` parameter.
+
+    'nearest' (`a a a a | a b c d | d d d d`)
+        The input is extended by replicating the last pixel.
+
+    'mirror' (`d c b | a b c d | c b a`)
+        The input is extended by reflecting about the center of the last
+        pixel. This mode is also sometimes referred to as whole-sample
+        symmetric.
+
+    'wrap' (`a b c d | a b c d | a b c d`)
+        The input is extended by wrapping around to the opposite edge.
+
+    For consistency with the interpolation functions, the following mode
+    names can also be used:
+
+    'grid-constant'
+        This is a synonym for 'constant'.
+
+    'grid-mirror'
+        This is a synonym for 'reflect'.
+
+    'grid-wrap'
+        This is a synonym for 'wrap'.""")
+_cval_doc = (
+"""cval : scalar, optional
+    Value to fill past edges of input if `mode` is 'constant'. Default
+    is 0.0.""")
+_origin_doc = (
+"""origin : int, optional
+    Controls the placement of the filter on the input array's pixels.
+    A value of 0 (the default) centers the filter over the pixel, with
+    positive values shifting the filter to the left, and negative ones
+    to the right.""")
+_origin_multiple_doc = (
+"""origin : int or sequence, optional
+    Controls the placement of the filter on the input array's pixels.
+    A value of 0 (the default) centers the filter over the pixel, with
+    positive values shifting the filter to the left, and negative ones
+    to the right. By passing a sequence of origins with length equal to
+    the number of dimensions of the input array, different shifts can
+    be specified along each axis.""")
+_extra_arguments_doc = (
+"""extra_arguments : sequence, optional
+    Sequence of extra positional arguments to pass to passed function.""")
+_extra_keywords_doc = (
+"""extra_keywords : dict, optional
+    dict of extra keyword arguments to pass to passed function.""")
+_prefilter_doc = (
+"""prefilter : bool, optional
+    Determines if the input array is prefiltered with `spline_filter`
+    before interpolation. The default is True, which will create a
+    temporary `float64` array of filtered values if `order > 1`. If
+    setting this to False, the output will be slightly blurred if
+    `order > 1`, unless the input is prefiltered, i.e. it is the result
+    of calling `spline_filter` on the original input.""")
+
+docdict = {
+    'input': _input_doc,
+    'axis': _axis_doc,
+    'output': _output_doc,
+    'size_foot': _size_foot_doc,
+    'mode_interp_constant': _mode_interp_constant_doc,
+    'mode_interp_mirror': _mode_interp_mirror_doc,
+    'mode_reflect': _mode_reflect_doc,
+    'mode_multiple': _mode_multiple_doc,
+    'cval': _cval_doc,
+    'origin': _origin_doc,
+    'origin_multiple': _origin_multiple_doc,
+    'extra_arguments': _extra_arguments_doc,
+    'extra_keywords': _extra_keywords_doc,
+    'prefilter': _prefilter_doc
+    }
+
+docfiller = doccer.filldoc(docdict)

venv/lib/python3.12/site-packages/scipy/ndimage/_ni_support.py

@@ -0,0 +1,119 @@
+# Copyright (C) 2003-2005 Peter J. Verveer
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above
+#    copyright notice, this list of conditions and the following
+#    disclaimer in the documentation and/or other materials provided
+#    with the distribution.
+#
+# 3. The name of the author may not be used to endorse or promote
+#    products derived from this software without specific prior
+#    written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
+# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
+# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
+# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from collections.abc import Iterable
+import operator
+import warnings
+import numpy as np
+
+
+def _extend_mode_to_code(mode):
+    """Convert an extension mode to the corresponding integer code.
+    """
+    if mode == 'nearest':
+        return 0
+    elif mode == 'wrap':
+        return 1
+    elif mode in ['reflect', 'grid-mirror']:
+        return 2
+    elif mode == 'mirror':
+        return 3
+    elif mode == 'constant':
+        return 4
+    elif mode == 'grid-wrap':
+        return 5
+    elif mode == 'grid-constant':
+        return 6
+    else:
+        raise RuntimeError('boundary mode not supported')
+
+
+def _normalize_sequence(input, rank):
+    """If input is a scalar, create a sequence of length equal to the
+    rank by duplicating the input. If input is a sequence,
+    check if its length is equal to the length of array.
+    """
+    is_str = isinstance(input, str)
+    if not is_str and isinstance(input, Iterable):
+        normalized = list(input)
+        if len(normalized) != rank:
+            err = "sequence argument must have length equal to input rank"
+            raise RuntimeError(err)
+    else:
+        normalized = [input] * rank
+    return normalized
+
+
+def _get_output(output, input, shape=None, complex_output=False):
+    if shape is None:
+        shape = input.shape
+    if output is None:
+        if not complex_output:
+            output = np.zeros(shape, dtype=input.dtype.name)
+        else:
+            complex_type = np.promote_types(input.dtype, np.complex64)
+            output = np.zeros(shape, dtype=complex_type)
+    elif isinstance(output, (type, np.dtype)):
+        # Classes (like `np.float32`) and dtypes are interpreted as dtype
+        if complex_output and np.dtype(output).kind != 'c':
+            warnings.warn("promoting specified output dtype to complex", stacklevel=3)
+            output = np.promote_types(output, np.complex64)
+        output = np.zeros(shape, dtype=output)
+    elif isinstance(output, str):
+        output = np.dtype(output)
+        if complex_output and output.kind != 'c':
+            raise RuntimeError("output must have complex dtype")
+        elif not issubclass(output.type, np.number):
+            raise RuntimeError("output must have numeric dtype")
+        output = np.zeros(shape, dtype=output)
+    elif output.shape != shape:
+        raise RuntimeError("output shape not correct")
+    elif complex_output and output.dtype.kind != 'c':
+        raise RuntimeError("output must have complex dtype")
+    return output
+
+
+def _check_axes(axes, ndim):
+    if axes is None:
+        return tuple(range(ndim))
+    elif np.isscalar(axes):
+        axes = (operator.index(axes),)
+    elif isinstance(axes, Iterable):
+        for ax in axes:
+            axes = tuple(operator.index(ax) for ax in axes)
+            if ax < -ndim or ax > ndim - 1:
+                raise ValueError(f"specified axis: {ax} is out of range")
+        axes = tuple(ax % ndim if ax < 0 else ax for ax in axes)
+    else:
+        message = "axes must be an integer, iterable of integers, or None"
+        raise ValueError(message)
+    if len(tuple(set(axes))) != len(axes):
+        raise ValueError("axes must be unique")
+    return axes

venv/lib/python3.12/site-packages/scipy/ndimage/filters.py

@@ -0,0 +1,27 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.ndimage` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'correlate1d', 'convolve1d', 'gaussian_filter1d',
+    'gaussian_filter', 'prewitt', 'sobel', 'generic_laplace',
+    'laplace', 'gaussian_laplace', 'generic_gradient_magnitude',
+    'gaussian_gradient_magnitude', 'correlate', 'convolve',
+    'uniform_filter1d', 'uniform_filter', 'minimum_filter1d',
+    'maximum_filter1d', 'minimum_filter', 'maximum_filter',
+    'rank_filter', 'median_filter', 'percentile_filter',
+    'generic_filter1d', 'generic_filter'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package='ndimage', module='filters',
+                                   private_modules=['_filters'], all=__all__,
+                                   attribute=name)

venv/lib/python3.12/site-packages/scipy/ndimage/fourier.py

@@ -0,0 +1,21 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.ndimage` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'fourier_gaussian', 'fourier_uniform',
+    'fourier_ellipsoid', 'fourier_shift'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package='ndimage', module='fourier',
+                                   private_modules=['_fourier'], all=__all__,
+                                   attribute=name)

venv/lib/python3.12/site-packages/scipy/ndimage/interpolation.py

@@ -0,0 +1,22 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.ndimage` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'spline_filter1d', 'spline_filter',
+    'geometric_transform', 'map_coordinates',
+    'affine_transform', 'shift', 'zoom', 'rotate',
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package='ndimage', module='interpolation',
+                                   private_modules=['_interpolation'], all=__all__,
+                                   attribute=name)

venv/lib/python3.12/site-packages/scipy/ndimage/measurements.py

@@ -0,0 +1,24 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.ndimage` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'label', 'find_objects', 'labeled_comprehension',
+    'sum', 'mean', 'variance', 'standard_deviation',
+    'minimum', 'maximum', 'median', 'minimum_position',
+    'maximum_position', 'extrema', 'center_of_mass',
+    'histogram', 'watershed_ift', 'sum_labels'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package='ndimage', module='measurements',
+                                   private_modules=['_measurements'], all=__all__,
+                                   attribute=name)

venv/lib/python3.12/site-packages/scipy/ndimage/morphology.py

@@ -0,0 +1,27 @@
+# This file is not meant for public use and will be removed in SciPy v2.0.0.
+# Use the `scipy.ndimage` namespace for importing the functions
+# included below.
+
+from scipy._lib.deprecation import _sub_module_deprecation
+
+
+__all__ = [  # noqa: F822
+    'iterate_structure', 'generate_binary_structure',
+    'binary_erosion', 'binary_dilation', 'binary_opening',
+    'binary_closing', 'binary_hit_or_miss', 'binary_propagation',
+    'binary_fill_holes', 'grey_erosion', 'grey_dilation',
+    'grey_opening', 'grey_closing', 'morphological_gradient',
+    'morphological_laplace', 'white_tophat', 'black_tophat',
+    'distance_transform_bf', 'distance_transform_cdt',
+    'distance_transform_edt'
+]
+
+
+def __dir__():
+    return __all__
+
+
+def __getattr__(name):
+    return _sub_module_deprecation(sub_package='ndimage', module='morphology',
+                                   private_modules=['_morphology'], all=__all__,
+                                   attribute=name)

venv/lib/python3.12/site-packages/scipy/ndimage/tests/__init__.py

@@ -0,0 +1,13 @@
+from __future__ import annotations
+import numpy as np
+
+# list of numarray data types
+integer_types: list[type] = [
+    np.int8, np.uint8, np.int16, np.uint16,
+    np.int32, np.uint32, np.int64, np.uint64]
+
+float_types: list[type] = [np.float32, np.float64]
+
+complex_types: list[type] = [np.complex64, np.complex128]
+
+types: list[type] = integer_types + float_types

venv/lib/python3.12/site-packages/scipy/ndimage/tests/data/label_inputs.txt

@@ -0,0 +1,21 @@
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 0 1 1 1
+1 1 0 0 0 1 1
+1 0 1 0 1 0 1
+0 0 0 1 0 0 0
+1 0 1 0 1 0 1
+1 1 0 0 0 1 1
+1 1 1 0 1 1 1
+1 0 1 1 1 0 1
+0 0 0 1 0 0 0
+1 0 0 1 0 0 1
+1 1 1 1 1 1 1
+1 0 0 1 0 0 1
+0 0 0 1 0 0 0
+1 0 1 1 1 0 1

venv/lib/python3.12/site-packages/scipy/ndimage/tests/data/label_results.txt

@@ -0,0 +1,294 @@
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+2 2 2 2 2 2 2
+3 3 3 3 3 3 3
+4 4 4 4 4 4 4
+5 5 5 5 5 5 5
+6 6 6 6 6 6 6
+7 7 7 7 7 7 7
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 2 3 4 5 6 7
+8 9 10 11 12 13 14
+15 16 17 18 19 20 21
+22 23 24 25 26 27 28
+29 30 31 32 33 34 35
+36 37 38 39 40 41 42
+43 44 45 46 47 48 49
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 2 3 4 5 6 7
+8 1 2 3 4 5 6
+9 8 1 2 3 4 5
+10 9 8 1 2 3 4
+11 10 9 8 1 2 3
+12 11 10 9 8 1 2
+13 12 11 10 9 8 1
+1 2 3 4 5 6 7
+1 2 3 4 5 6 7
+1 2 3 4 5 6 7
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+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
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+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 2 1 2 1 2 1
+2 1 2 1 2 1 2
+1 2 1 2 1 2 1
+2 1 2 1 2 1 2
+1 2 1 2 1 2 1
+2 1 2 1 2 1 2
+1 2 1 2 1 2 1
+1 2 3 4 5 6 7
+2 3 4 5 6 7 8
+3 4 5 6 7 8 9
+4 5 6 7 8 9 10
+5 6 7 8 9 10 11
+6 7 8 9 10 11 12
+7 8 9 10 11 12 13
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 1 1 1 1
+1 1 1 0 2 2 2
+1 1 0 0 0 2 2
+1 0 3 0 2 0 4
+0 0 0 2 0 0 0
+5 0 2 0 6 0 7
+2 2 0 0 0 7 7
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+1 1 1 0 2 2 2
+1 1 0 0 0 2 2
+3 0 1 0 4 0 2
+0 0 0 1 0 0 0
+5 0 6 0 1 0 7
+5 5 0 0 0 1 1
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+1 1 1 0 2 2 2
+3 3 0 0 0 4 4
+5 0 6 0 7 0 8
+0 0 0 9 0 0 0
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+14 14 0 0 0 15 15
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+1 0 2 2 2 0 3
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+1 0 2 3 3 0 4
+0 0 0 3 0 0 0
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+5 5 3 3 3 6 6
+5 0 0 3 0 0 6
+0 0 0 3 0 0 0
+7 0 3 3 8 0 9
+1 0 2 3 4 0 5
+0 0 0 6 0 0 0
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+10 11 12 13 14 15 16
+17 0 0 18 0 0 19
+0 0 0 20 0 0 0
+21 0 22 23 24 0 25
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+2 2 2 2 2 2 2
+2 0 0 2 0 0 2
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+1 0 2 2 2 0 3
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+2 2 2 2 2 2 2
+2 0 0 2 0 0 2
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+6 0 0 7 0 0 8
+9 6 10 11 7 12 13
+14 0 0 10 0 0 12
+0 0 0 15 0 0 0
+16 0 17 18 15 0 19
+1 0 2 3 4 0 5
+0 0 0 3 0 0 0
+6 0 0 3 0 0 7
+6 8 9 3 10 11 7
+6 0 0 3 0 0 7
+0 0 0 3 0 0 0
+12 0 13 3 14 0 15
+1 0 2 2 2 0 3
+0 0 0 2 0 0 0
+2 0 0 2 0 0 2
+2 2 2 2 2 2 2
+2 0 0 2 0 0 2
+0 0 0 2 0 0 0
+4 0 2 2 2 0 5
+1 0 2 2 3 0 4
+0 0 0 2 0 0 0
+5 0 0 2 0 0 6
+5 5 2 2 2 6 6
+5 0 0 2 0 0 6
+0 0 0 2 0 0 0
+7 0 8 2 2 0 9
+1 0 2 3 2 0 4
+0 0 0 2 0 0 0
+5 0 0 6 0 0 7
+8 5 6 9 6 7 10
+5 0 0 6 0 0 7
+0 0 0 11 0 0 0
+12 0 11 13 11 0 14
+1 0 2 3 4 0 5
+0 0 0 4 0 0 0
+6 0 0 7 0 0 8
+9 10 7 11 12 8 13
+10 0 0 12 0 0 14
+0 0 0 15 0 0 0
+16 0 15 17 18 0 19
+1 0 2 2 2 0 3
+0 0 0 2 0 0 0
+2 0 0 2 0 0 2
+2 2 2 2 2 2 2
+2 0 0 2 0 0 2
+0 0 0 2 0 0 0
+4 0 2 2 2 0 5

venv/lib/python3.12/site-packages/scipy/ndimage/tests/data/label_strels.txt

@@ -0,0 +1,42 @@
+0 0 1
+1 1 1
+1 0 0
+1 0 0
+1 1 1
+0 0 1
+0 0 0
+1 1 1
+0 0 0
+0 1 1
+0 1 0
+1 1 0
+0 0 0
+0 0 0
+0 0 0
+0 1 1
+1 1 1
+1 1 0
+0 1 0
+1 1 1
+0 1 0
+1 0 0
+0 1 0
+0 0 1
+0 1 0
+0 1 0
+0 1 0
+1 1 1
+1 1 1
+1 1 1
+1 1 0
+0 1 0
+0 1 1
+1 0 1
+0 1 0
+1 0 1
+0 0 1
+0 1 0
+1 0 0
+1 1 0
+1 1 1
+0 1 1

venv/lib/python3.12/site-packages/scipy/ndimage/tests/test_c_api.py

@@ -0,0 +1,102 @@
+import numpy as np
+from numpy.testing import assert_allclose
+
+from scipy import ndimage
+from scipy.ndimage import _ctest
+from scipy.ndimage import _cytest
+from scipy._lib._ccallback import LowLevelCallable
+
+FILTER1D_FUNCTIONS = [
+    lambda filter_size: _ctest.filter1d(filter_size),
+    lambda filter_size: _cytest.filter1d(filter_size, with_signature=False),
+    lambda filter_size: LowLevelCallable(
+                            _cytest.filter1d(filter_size, with_signature=True)
+                        ),
+    lambda filter_size: LowLevelCallable.from_cython(
+                            _cytest, "_filter1d",
+                            _cytest.filter1d_capsule(filter_size),
+                        ),
+]
+
+FILTER2D_FUNCTIONS = [
+    lambda weights: _ctest.filter2d(weights),
+    lambda weights: _cytest.filter2d(weights, with_signature=False),
+    lambda weights: LowLevelCallable(_cytest.filter2d(weights, with_signature=True)),
+    lambda weights: LowLevelCallable.from_cython(_cytest,
+                                                 "_filter2d",
+                                                 _cytest.filter2d_capsule(weights),),
+]
+
+TRANSFORM_FUNCTIONS = [
+    lambda shift: _ctest.transform(shift),
+    lambda shift: _cytest.transform(shift, with_signature=False),
+    lambda shift: LowLevelCallable(_cytest.transform(shift, with_signature=True)),
+    lambda shift: LowLevelCallable.from_cython(_cytest,
+                                               "_transform",
+                                               _cytest.transform_capsule(shift),),
+]
+
+
+def test_generic_filter():
+    def filter2d(footprint_elements, weights):
+        return (weights*footprint_elements).sum()
+
+    def check(j):
+        func = FILTER2D_FUNCTIONS[j]
+
+        im = np.ones((20, 20))
+        im[:10,:10] = 0
+        footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
+        footprint_size = np.count_nonzero(footprint)
+        weights = np.ones(footprint_size)/footprint_size
+
+        res = ndimage.generic_filter(im, func(weights),
+                                     footprint=footprint)
+        std = ndimage.generic_filter(im, filter2d, footprint=footprint,
+                                     extra_arguments=(weights,))
+        assert_allclose(res, std, err_msg=f"#{j} failed")
+
+    for j, func in enumerate(FILTER2D_FUNCTIONS):
+        check(j)
+
+
+def test_generic_filter1d():
+    def filter1d(input_line, output_line, filter_size):
+        for i in range(output_line.size):
+            output_line[i] = 0
+            for j in range(filter_size):
+                output_line[i] += input_line[i+j]
+        output_line /= filter_size
+
+    def check(j):
+        func = FILTER1D_FUNCTIONS[j]
+
+        im = np.tile(np.hstack((np.zeros(10), np.ones(10))), (10, 1))
+        filter_size = 3
+
+        res = ndimage.generic_filter1d(im, func(filter_size),
+                                       filter_size)
+        std = ndimage.generic_filter1d(im, filter1d, filter_size,
+                                       extra_arguments=(filter_size,))
+        assert_allclose(res, std, err_msg=f"#{j} failed")
+
+    for j, func in enumerate(FILTER1D_FUNCTIONS):
+        check(j)
+
+
+def test_geometric_transform():
+    def transform(output_coordinates, shift):
+        return output_coordinates[0] - shift, output_coordinates[1] - shift
+
+    def check(j):
+        func = TRANSFORM_FUNCTIONS[j]
+
+        im = np.arange(12).reshape(4, 3).astype(np.float64)
+        shift = 0.5
+
+        res = ndimage.geometric_transform(im, func(shift))
+        std = ndimage.geometric_transform(im, transform, extra_arguments=(shift,))
+        assert_allclose(res, std, err_msg=f"#{j} failed")
+
+    for j, func in enumerate(TRANSFORM_FUNCTIONS):
+        check(j)

venv/lib/python3.12/site-packages/scipy/ndimage/tests/test_datatypes.py

@@ -0,0 +1,66 @@
+""" Testing data types for ndimage calls
+"""
+import numpy as np
+from numpy.testing import assert_array_almost_equal, assert_
+import pytest
+
+from scipy import ndimage
+
+
+def test_map_coordinates_dts():
+    # check that ndimage accepts different data types for interpolation
+    data = np.array([[4, 1, 3, 2],
+                     [7, 6, 8, 5],
+                     [3, 5, 3, 6]])
+    shifted_data = np.array([[0, 0, 0, 0],
+                             [0, 4, 1, 3],
+                             [0, 7, 6, 8]])
+    idx = np.indices(data.shape)
+    dts = (np.uint8, np.uint16, np.uint32, np.uint64,
+           np.int8, np.int16, np.int32, np.int64,
+           np.intp, np.uintp, np.float32, np.float64)
+    for order in range(0, 6):
+        for data_dt in dts:
+            these_data = data.astype(data_dt)
+            for coord_dt in dts:
+                # affine mapping
+                mat = np.eye(2, dtype=coord_dt)
+                off = np.zeros((2,), dtype=coord_dt)
+                out = ndimage.affine_transform(these_data, mat, off)
+                assert_array_almost_equal(these_data, out)
+                # map coordinates
+                coords_m1 = idx.astype(coord_dt) - 1
+                coords_p10 = idx.astype(coord_dt) + 10
+                out = ndimage.map_coordinates(these_data, coords_m1, order=order)
+                assert_array_almost_equal(out, shifted_data)
+                # check constant fill works
+                out = ndimage.map_coordinates(these_data, coords_p10, order=order)
+                assert_array_almost_equal(out, np.zeros((3,4)))
+            # check shift and zoom
+            out = ndimage.shift(these_data, 1)
+            assert_array_almost_equal(out, shifted_data)
+            out = ndimage.zoom(these_data, 1)
+            assert_array_almost_equal(these_data, out)
+
+
+@pytest.mark.xfail(True, reason="Broken on many platforms")
+def test_uint64_max():
+    # Test interpolation respects uint64 max.  Reported to fail at least on
+    # win32 (due to the 32 bit visual C compiler using signed int64 when
+    # converting between uint64 to double) and Debian on s390x.
+    # Interpolation is always done in double precision floating point, so
+    # we use the largest uint64 value for which int(float(big)) still fits
+    # in a uint64.
+    # This test was last enabled on macOS only, and there it started failing
+    # on arm64 as well (see gh-19117).
+    big = 2**64 - 1025
+    arr = np.array([big, big, big], dtype=np.uint64)
+    # Tests geometric transform (map_coordinates, affine_transform)
+    inds = np.indices(arr.shape) - 0.1
+    x = ndimage.map_coordinates(arr, inds)
+    assert_(x[1] == int(float(big)))
+    assert_(x[2] == int(float(big)))
+    # Tests zoom / shift
+    x = ndimage.shift(arr, 0.1)
+    assert_(x[1] == int(float(big)))
+    assert_(x[2] == int(float(big)))

venv/lib/python3.12/site-packages/scipy/ndimage/tests/test_fourier.py

@@ -0,0 +1,137 @@
+import numpy as np
+from numpy import fft
+from numpy.testing import (assert_almost_equal, assert_array_almost_equal,
+                           assert_equal)
+
+import pytest
+
+from scipy import ndimage
+
+
+class TestNdimageFourier:
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec', [(np.float32, 6), (np.float64, 14)])
+    def test_fourier_gaussian_real01(self, shape, dtype, dec):
+        a = np.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.rfft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a), 1, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [(np.complex64, 6), (np.complex128, 14)])
+    def test_fourier_gaussian_complex01(self, shape, dtype, dec):
+        a = np.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.fft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec', [(np.float32, 6), (np.float64, 14)])
+    def test_fourier_uniform_real01(self, shape, dtype, dec):
+        a = np.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.rfft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [(np.complex64, 6), (np.complex128, 14)])
+    def test_fourier_uniform_complex01(self, shape, dtype, dec):
+        a = np.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.fft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [(np.float32, 4), (np.float64, 11)])
+    def test_fourier_shift_real01(self, shape, dtype, dec):
+        expected = np.arange(shape[0] * shape[1], dtype=dtype)
+        expected.shape = shape
+        a = fft.rfft(expected, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1], decimal=dec)
+        assert_array_almost_equal(a.imag, np.zeros(shape), decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [(np.complex64, 4), (np.complex128, 11)])
+    def test_fourier_shift_complex01(self, shape, dtype, dec):
+        expected = np.arange(shape[0] * shape[1], dtype=dtype)
+        expected.shape = shape
+        a = fft.fft(expected, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_shift(a, [1, 1], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_array_almost_equal(a.real[1:, 1:], expected[:-1, :-1], decimal=dec)
+        assert_array_almost_equal(a.imag, np.zeros(shape), decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
+    @pytest.mark.parametrize('dtype, dec', [(np.float32, 5), (np.float64, 14)])
+    def test_fourier_ellipsoid_real01(self, shape, dtype, dec):
+        a = np.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.rfft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_ellipsoid(a, [5.0, 2.5],
+                                      shape[0], 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.irfft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
+    @pytest.mark.parametrize('dtype, dec', [(np.complex64, 5), (np.complex128, 14)])
+    def test_fourier_ellipsoid_complex01(self, shape, dtype, dec):
+        a = np.zeros(shape, dtype)
+        a[0, 0] = 1.0
+        a = fft.fft(a, shape[0], 0)
+        a = fft.fft(a, shape[1], 1)
+        a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
+        a = fft.ifft(a, shape[1], 1)
+        a = fft.ifft(a, shape[0], 0)
+        assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
+
+    def test_fourier_ellipsoid_unimplemented_ndim(self):
+        # arrays with ndim > 3 raise NotImplementedError
+        x = np.ones((4, 6, 8, 10), dtype=np.complex128)
+        with pytest.raises(NotImplementedError):
+            ndimage.fourier_ellipsoid(x, 3)
+
+    def test_fourier_ellipsoid_1d_complex(self):
+        # expected result of 1d ellipsoid is the same as for fourier_uniform
+        for shape in [(32, ), (31, )]:
+            for type_, dec in zip([np.complex64, np.complex128], [5, 14]):
+                x = np.ones(shape, dtype=type_)
+                a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
+                b = ndimage.fourier_uniform(x, 5, -1, 0)
+                assert_array_almost_equal(a, b, decimal=dec)
+
+    @pytest.mark.parametrize('shape', [(0, ), (0, 10), (10, 0)])
+    @pytest.mark.parametrize('dtype', [np.float32, np.float64,
+                                       np.complex64, np.complex128])
+    @pytest.mark.parametrize('test_func',
+                             [ndimage.fourier_ellipsoid,
+                              ndimage.fourier_gaussian,
+                              ndimage.fourier_uniform])
+    def test_fourier_zero_length_dims(self, shape, dtype, test_func):
+        a = np.ones(shape, dtype)
+        b = test_func(a, 3)
+        assert_equal(a, b)

venv/lib/python3.12/site-packages/scipy/ndimage/tests/test_ni_support.py

@@ -0,0 +1,77 @@
+import pytest
+
+import numpy as np
+from .._ni_support import _get_output
+
+
+@pytest.mark.parametrize(
+    'dtype',
+    [
+        # String specifiers
+        'f4', 'float32', 'complex64', 'complex128',
+        # Type and dtype specifiers
+        np.float32, float, np.dtype('f4'),
+        # Derive from input
+        None,
+    ],
+)
+def test_get_output_basic(dtype):
+    shape = (2, 3)
+
+    input_ = np.zeros(shape, 'float32')
+
+    # For None, derive dtype from input
+    expected_dtype = 'float32' if dtype is None else dtype
+
+    # Output is dtype-specifier, retrieve shape from input
+    result = _get_output(dtype, input_)
+    assert result.shape == shape
+    assert result.dtype == np.dtype(expected_dtype)
+
+    # Output is dtype specifier, with explicit shape, overriding input
+    result = _get_output(dtype, input_, shape=(3, 2))
+    assert result.shape == (3, 2)
+    assert result.dtype == np.dtype(expected_dtype)
+
+    # Output is pre-allocated array, return directly
+    output = np.zeros(shape, dtype)
+    result = _get_output(output, input_)
+    assert result is output
+
+
+def test_get_output_complex():
+    shape = (2, 3)
+
+    input_ = np.zeros(shape)
+
+    # None, promote input type to complex
+    result = _get_output(None, input_, complex_output=True)
+    assert result.shape == shape
+    assert result.dtype == np.dtype('complex128')
+
+    # Explicit type, promote type to complex
+    with pytest.warns(UserWarning, match='promoting specified output dtype to complex'):
+        result = _get_output(float, input_, complex_output=True)
+    assert result.shape == shape
+    assert result.dtype == np.dtype('complex128')
+
+    # String specifier, simply verify complex output
+    result = _get_output('complex64', input_, complex_output=True)
+    assert result.shape == shape
+    assert result.dtype == np.dtype('complex64')
+
+
+def test_get_output_error_cases():
+    input_ = np.zeros((2, 3), 'float32')
+
+    # Two separate paths can raise the same error
+    with pytest.raises(RuntimeError, match='output must have complex dtype'):
+        _get_output('float32', input_, complex_output=True)
+    with pytest.raises(RuntimeError, match='output must have complex dtype'):
+        _get_output(np.zeros((2, 3)), input_, complex_output=True)
+
+    with pytest.raises(RuntimeError, match='output must have numeric dtype'):
+        _get_output('void', input_)
+
+    with pytest.raises(RuntimeError, match='shape not correct'):
+        _get_output(np.zeros((3, 2)), input_)

venv/lib/python3.12/site-packages/scipy/ndimage/tests/test_splines.py

@@ -0,0 +1,65 @@
+"""Tests for spline filtering."""
+import numpy as np
+import pytest
+
+from numpy.testing import assert_almost_equal
+
+from scipy import ndimage
+
+
+def get_spline_knot_values(order):
+    """Knot values to the right of a B-spline's center."""
+    knot_values = {0: [1],
+                   1: [1],
+                   2: [6, 1],
+                   3: [4, 1],
+                   4: [230, 76, 1],
+                   5: [66, 26, 1]}
+
+    return knot_values[order]
+
+
+def make_spline_knot_matrix(n, order, mode='mirror'):
+    """Matrix to invert to find the spline coefficients."""
+    knot_values = get_spline_knot_values(order)
+
+    matrix = np.zeros((n, n))
+    for diag, knot_value in enumerate(knot_values):
+        indices = np.arange(diag, n)
+        if diag == 0:
+            matrix[indices, indices] = knot_value
+        else:
+            matrix[indices, indices - diag] = knot_value
+            matrix[indices - diag, indices] = knot_value
+
+    knot_values_sum = knot_values[0] + 2 * sum(knot_values[1:])
+
+    if mode == 'mirror':
+        start, step = 1, 1
+    elif mode == 'reflect':
+        start, step = 0, 1
+    elif mode == 'grid-wrap':
+        start, step = -1, -1
+    else:
+        raise ValueError(f'unsupported mode {mode}')
+
+    for row in range(len(knot_values) - 1):
+        for idx, knot_value in enumerate(knot_values[row + 1:]):
+            matrix[row, start + step*idx] += knot_value
+            matrix[-row - 1, -start - 1 - step*idx] += knot_value
+
+    return matrix / knot_values_sum
+
+
+@pytest.mark.parametrize('order', [0, 1, 2, 3, 4, 5])
+@pytest.mark.parametrize('mode', ['mirror', 'grid-wrap', 'reflect'])
+def test_spline_filter_vs_matrix_solution(order, mode):
+    n = 100
+    eye = np.eye(n, dtype=float)
+    spline_filter_axis_0 = ndimage.spline_filter1d(eye, axis=0, order=order,
+                                                   mode=mode)
+    spline_filter_axis_1 = ndimage.spline_filter1d(eye, axis=1, order=order,
+                                                   mode=mode)
+    matrix = make_spline_knot_matrix(n, order, mode=mode)
+    assert_almost_equal(eye, np.dot(spline_filter_axis_0, matrix))
+    assert_almost_equal(eye, np.dot(spline_filter_axis_1, matrix.T))