Fixed database typo and removed unnecessary class identifier.
This commit is contained in:
parent
00ad49a143
commit
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5098 changed files with 952558 additions and 85 deletions
163
venv/Lib/site-packages/scipy/ndimage/__init__.py
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venv/Lib/site-packages/scipy/ndimage/__init__.py
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"""
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=========================================================
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Multidimensional image processing (:mod:`scipy.ndimage`)
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=========================================================
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.. currentmodule:: scipy.ndimage
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This package contains various functions for multidimensional image
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processing.
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Filters
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=======
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.. autosummary::
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:toctree: generated/
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convolve - Multidimensional convolution
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convolve1d - 1-D convolution along the given axis
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correlate - Multidimensional correlation
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correlate1d - 1-D correlation along the given axis
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gaussian_filter
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gaussian_filter1d
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gaussian_gradient_magnitude
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gaussian_laplace
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generic_filter - Multidimensional filter using a given function
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generic_filter1d - 1-D generic filter along the given axis
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generic_gradient_magnitude
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generic_laplace
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laplace - N-D Laplace filter based on approximate second derivatives
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maximum_filter
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maximum_filter1d
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median_filter - Calculates a multidimensional median filter
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minimum_filter
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minimum_filter1d
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percentile_filter - Calculates a multidimensional percentile filter
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prewitt
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rank_filter - Calculates a multidimensional rank filter
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sobel
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uniform_filter - Multidimensional uniform filter
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uniform_filter1d - 1-D uniform filter along the given axis
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Fourier filters
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===============
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.. autosummary::
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:toctree: generated/
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fourier_ellipsoid
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fourier_gaussian
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fourier_shift
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fourier_uniform
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Interpolation
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=============
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.. autosummary::
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:toctree: generated/
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affine_transform - Apply an affine transformation
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geometric_transform - Apply an arbritrary geometric transform
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map_coordinates - Map input array to new coordinates by interpolation
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rotate - Rotate an array
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shift - Shift an array
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spline_filter
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spline_filter1d
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zoom - Zoom an array
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Measurements
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============
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.. autosummary::
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:toctree: generated/
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center_of_mass - The center of mass of the values of an array at labels
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extrema - Min's and max's of an array at labels, with their positions
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find_objects - Find objects in a labeled array
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histogram - Histogram of the values of an array, optionally at labels
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label - Label features in an array
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labeled_comprehension
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maximum
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maximum_position
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mean - Mean of the values of an array at labels
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median
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minimum
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minimum_position
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standard_deviation - Standard deviation of an N-D image array
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sum - Sum of the values of the array
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variance - Variance of the values of an N-D image array
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watershed_ift
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Morphology
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==========
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.. autosummary::
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:toctree: generated/
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binary_closing
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binary_dilation
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binary_erosion
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binary_fill_holes
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binary_hit_or_miss
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binary_opening
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binary_propagation
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black_tophat
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distance_transform_bf
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distance_transform_cdt
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distance_transform_edt
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generate_binary_structure
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grey_closing
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grey_dilation
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grey_erosion
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grey_opening
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iterate_structure
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morphological_gradient
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morphological_laplace
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white_tophat
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"""
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# Copyright (C) 2003-2005 Peter J. Verveer
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions
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# are met:
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#
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# 1. Redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above
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# copyright notice, this list of conditions and the following
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# disclaimer in the documentation and/or other materials provided
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# with the distribution.
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#
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# 3. The name of the author may not be used to endorse or promote
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# products derived from this software without specific prior
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# written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
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# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
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# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
|
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
|
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# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from .filters import *
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from .fourier import *
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from .interpolation import *
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from .measurements import *
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from .morphology import *
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__version__ = '2.0'
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__all__ = [s for s in dir() if not s.startswith('_')]
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from scipy._lib._testutils import PytestTester
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test = PytestTester(__name__)
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del PytestTester
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venv/Lib/site-packages/scipy/ndimage/_ctest.cp36-win32.pyd
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venv/Lib/site-packages/scipy/ndimage/_nd_image.cp36-win32.pyd
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venv/Lib/site-packages/scipy/ndimage/_ni_docstrings.py
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venv/Lib/site-packages/scipy/ndimage/_ni_docstrings.py
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"""Docstring components common to several ndimage functions."""
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from scipy._lib import doccer
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__all__ = ['docfiller']
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_input_doc = (
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"""input : array_like
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The input array.""")
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_axis_doc = (
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"""axis : int, optional
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The axis of `input` along which to calculate. Default is -1.""")
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_output_doc = (
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"""output : array or dtype, optional
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The array in which to place the output, or the dtype of the
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returned array. By default an array of the same dtype as input
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will be created.""")
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_size_foot_doc = (
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"""size : scalar or tuple, optional
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See footprint, below. Ignored if footprint is given.
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footprint : array, optional
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Either `size` or `footprint` must be defined. `size` gives
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the shape that is taken from the input array, at every element
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position, to define the input to the filter function.
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`footprint` is a boolean array that specifies (implicitly) a
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shape, but also which of the elements within this shape will get
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passed to the filter function. Thus ``size=(n,m)`` is equivalent
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to ``footprint=np.ones((n,m))``. We adjust `size` to the number
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of dimensions of the input array, so that, if the input array is
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shape (10,10,10), and `size` is 2, then the actual size used is
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(2,2,2). When `footprint` is given, `size` is ignored.""")
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_mode_doc = (
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"""mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
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The `mode` parameter determines how the input array is extended
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beyond its boundaries. Default is 'reflect'. Behavior for each valid
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value is as follows:
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'reflect' (`d c b a | a b c d | d c b a`)
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The input is extended by reflecting about the edge of the last
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pixel.
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'constant' (`k k k k | a b c d | k k k k`)
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The input is extended by filling all values beyond the edge with
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the same constant value, defined by the `cval` parameter.
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'nearest' (`a a a a | a b c d | d d d d`)
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The input is extended by replicating the last pixel.
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'mirror' (`d c b | a b c d | c b a`)
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The input is extended by reflecting about the center of the last
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pixel.
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'wrap' (`a b c d | a b c d | a b c d`)
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The input is extended by wrapping around to the opposite edge.""")
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_mode_multiple_doc = (
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"""mode : str or sequence, optional
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The `mode` parameter determines how the input array is extended
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when the filter overlaps a border. By passing a sequence of modes
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with length equal to the number of dimensions of the input array,
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different modes can be specified along each axis. Default value is
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'reflect'. The valid values and their behavior is as follows:
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'reflect' (`d c b a | a b c d | d c b a`)
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The input is extended by reflecting about the edge of the last
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pixel.
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'constant' (`k k k k | a b c d | k k k k`)
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The input is extended by filling all values beyond the edge with
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the same constant value, defined by the `cval` parameter.
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'nearest' (`a a a a | a b c d | d d d d`)
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The input is extended by replicating the last pixel.
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'mirror' (`d c b | a b c d | c b a`)
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The input is extended by reflecting about the center of the last
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pixel.
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'wrap' (`a b c d | a b c d | a b c d`)
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The input is extended by wrapping around to the opposite edge.""")
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_cval_doc = (
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"""cval : scalar, optional
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Value to fill past edges of input if `mode` is 'constant'. Default
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is 0.0.""")
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_origin_doc = (
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"""origin : int, optional
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Controls the placement of the filter on the input array's pixels.
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A value of 0 (the default) centers the filter over the pixel, with
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positive values shifting the filter to the left, and negative ones
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to the right.""")
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_origin_multiple_doc = (
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"""origin : int or sequence, optional
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Controls the placement of the filter on the input array's pixels.
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A value of 0 (the default) centers the filter over the pixel, with
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positive values shifting the filter to the left, and negative ones
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to the right. By passing a sequence of origins with length equal to
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the number of dimensions of the input array, different shifts can
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be specified along each axis.""")
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_extra_arguments_doc = (
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"""extra_arguments : sequence, optional
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Sequence of extra positional arguments to pass to passed function.""")
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_extra_keywords_doc = (
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"""extra_keywords : dict, optional
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dict of extra keyword arguments to pass to passed function.""")
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_prefilter_doc = (
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"""prefilter : bool, optional
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Determines if the input array is prefiltered with `spline_filter`
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before interpolation. The default is True, which will create a
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temporary `float64` array of filtered values if `order > 1`. If
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setting this to False, the output will be slightly blurred if
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`order > 1`, unless the input is prefiltered, i.e. it is the result
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of calling `spline_filter` on the original input.""")
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docdict = {
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'input': _input_doc,
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'axis': _axis_doc,
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'output': _output_doc,
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'size_foot': _size_foot_doc,
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'mode': _mode_doc,
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'mode_multiple': _mode_multiple_doc,
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'cval': _cval_doc,
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'origin': _origin_doc,
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'origin_multiple': _origin_multiple_doc,
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'extra_arguments': _extra_arguments_doc,
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'extra_keywords': _extra_keywords_doc,
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'prefilter': _prefilter_doc
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}
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docfiller = doccer.filldoc(docdict)
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venv/Lib/site-packages/scipy/ndimage/_ni_label.cp36-win32.pyd
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venv/Lib/site-packages/scipy/ndimage/_ni_label.cp36-win32.pyd
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venv/Lib/site-packages/scipy/ndimage/_ni_support.py
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venv/Lib/site-packages/scipy/ndimage/_ni_support.py
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# Copyright (C) 2003-2005 Peter J. Verveer
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#
|
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# 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.
|
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|
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from collections.abc import Iterable
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import numpy
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def _extend_mode_to_code(mode):
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"""Convert an extension mode to the corresponding integer code.
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"""
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if mode == 'nearest':
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return 0
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elif mode == 'wrap':
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return 1
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elif mode == 'reflect':
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return 2
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elif mode == 'mirror':
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return 3
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elif mode == 'constant':
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return 4
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else:
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raise RuntimeError('boundary mode not supported')
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|
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|
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def _normalize_sequence(input, rank):
|
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"""If input is a scalar, create a sequence of length equal to the
|
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rank by duplicating the input. If input is a sequence,
|
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check if its length is equal to the length of array.
|
||||
"""
|
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is_str = isinstance(input, str)
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if not is_str and isinstance(input, Iterable):
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normalized = list(input)
|
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if len(normalized) != rank:
|
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err = "sequence argument must have length equal to input rank"
|
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raise RuntimeError(err)
|
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else:
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normalized = [input] * rank
|
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return normalized
|
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|
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|
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def _get_output(output, input, shape=None):
|
||||
if shape is None:
|
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shape = input.shape
|
||||
if output is None:
|
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output = numpy.zeros(shape, dtype=input.dtype.name)
|
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elif isinstance(output, (type, numpy.dtype)):
|
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# Classes (like `np.float32`) and dtypes are interpreted as dtype
|
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output = numpy.zeros(shape, dtype=output)
|
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elif isinstance(output, str):
|
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output = numpy.typeDict[output]
|
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output = numpy.zeros(shape, dtype=output)
|
||||
elif output.shape != shape:
|
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raise RuntimeError("output shape not correct")
|
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return output
|
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venv/Lib/site-packages/scipy/ndimage/filters.py
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venv/Lib/site-packages/scipy/ndimage/filters.py
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305
venv/Lib/site-packages/scipy/ndimage/fourier.py
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venv/Lib/site-packages/scipy/ndimage/fourier.py
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# 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.
|
||||
|
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import numpy
|
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from numpy.core.multiarray import normalize_axis_index
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from . import _ni_support
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from . import _nd_image
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__all__ = ['fourier_gaussian', 'fourier_uniform', 'fourier_ellipsoid',
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'fourier_shift']
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|
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def _get_output_fourier(output, input):
|
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if output is None:
|
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if input.dtype.type in [numpy.complex64, numpy.complex128,
|
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numpy.float32]:
|
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output = numpy.zeros(input.shape, dtype=input.dtype)
|
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else:
|
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output = numpy.zeros(input.shape, dtype=numpy.float64)
|
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elif type(output) is type:
|
||||
if output not in [numpy.complex64, numpy.complex128,
|
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numpy.float32, numpy.float64]:
|
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raise RuntimeError("output type not supported")
|
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output = numpy.zeros(input.shape, dtype=output)
|
||||
elif output.shape != input.shape:
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raise RuntimeError("output shape not correct")
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return output
|
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|
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def _get_output_fourier_complex(output, input):
|
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if output is None:
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if input.dtype.type in [numpy.complex64, numpy.complex128]:
|
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output = numpy.zeros(input.shape, dtype=input.dtype)
|
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else:
|
||||
output = numpy.zeros(input.shape, dtype=numpy.complex128)
|
||||
elif type(output) is type:
|
||||
if output not in [numpy.complex64, numpy.complex128]:
|
||||
raise RuntimeError("output type not supported")
|
||||
output = numpy.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.
|
||||
None is returned in this case.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_gaussian : ndarray
|
||||
The filtered input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, misc
|
||||
>>> import numpy.fft
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = misc.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 = numpy.asarray(input)
|
||||
output = _get_output_fourier(output, input)
|
||||
axis = normalize_axis_index(axis, input.ndim)
|
||||
sigmas = _ni_support._normalize_sequence(sigma, input.ndim)
|
||||
sigmas = numpy.asarray(sigmas, dtype=numpy.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.
|
||||
None is returned in this case.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_uniform : ndarray
|
||||
The filtered input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, misc
|
||||
>>> import numpy.fft
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = misc.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 = numpy.asarray(input)
|
||||
output = _get_output_fourier(output, input)
|
||||
axis = normalize_axis_index(axis, input.ndim)
|
||||
sizes = _ni_support._normalize_sequence(size, input.ndim)
|
||||
sizes = numpy.asarray(sizes, dtype=numpy.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 a 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.
|
||||
None is returned in this case.
|
||||
|
||||
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, misc
|
||||
>>> import numpy.fft
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = misc.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 = numpy.asarray(input)
|
||||
output = _get_output_fourier(output, input)
|
||||
axis = normalize_axis_index(axis, input.ndim)
|
||||
sizes = _ni_support._normalize_sequence(size, input.ndim)
|
||||
sizes = numpy.asarray(sizes, dtype=numpy.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.
|
||||
None is returned in this case.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_shift : ndarray
|
||||
The shifted input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, misc
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> import numpy.fft
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = misc.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 = numpy.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 = numpy.asarray(shifts, dtype=numpy.float64)
|
||||
if not shifts.flags.contiguous:
|
||||
shifts = shifts.copy()
|
||||
_nd_image.fourier_shift(input, shifts, n, axis, output)
|
||||
return output
|
||||
787
venv/Lib/site-packages/scipy/ndimage/interpolation.py
Normal file
787
venv/Lib/site-packages/scipy/ndimage/interpolation.py
Normal file
|
|
@ -0,0 +1,787 @@
|
|||
# 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 itertools
|
||||
import warnings
|
||||
|
||||
import numpy
|
||||
from numpy.core.multiarray import normalize_axis_index
|
||||
|
||||
from . import _ni_support
|
||||
from . import _nd_image
|
||||
from ._ni_docstrings import docdict
|
||||
from scipy._lib import doccer
|
||||
|
||||
# Change the default 'reflect' to 'constant' via modifying a copy of docdict
|
||||
docdict_copy = docdict.copy()
|
||||
del docdict
|
||||
docdict_copy['mode'] = docdict_copy['mode'].replace("Default is 'reflect'",
|
||||
"Default is 'constant'")
|
||||
|
||||
docfiller = doccer.filldoc(docdict_copy)
|
||||
|
||||
__all__ = ['spline_filter1d', 'spline_filter', 'geometric_transform',
|
||||
'map_coordinates', 'affine_transform', 'shift', 'zoom', 'rotate']
|
||||
|
||||
|
||||
@docfiller
|
||||
def spline_filter1d(input, order=3, axis=-1, output=numpy.float64,
|
||||
mode='mirror'):
|
||||
"""
|
||||
Calculate a 1-D spline filter along the given axis.
|
||||
|
||||
The lines of the array along the given axis are filtered by a
|
||||
spline filter. The order of the spline must be >= 2 and <= 5.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
order : int, optional
|
||||
The order of the spline, default is 3.
|
||||
axis : int, optional
|
||||
The axis along which the spline filter is applied. Default is the last
|
||||
axis.
|
||||
output : ndarray or dtype, optional
|
||||
The array in which to place the output, or the dtype of the returned
|
||||
array. Default is ``numpy.float64``.
|
||||
%(mode)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
spline_filter1d : ndarray
|
||||
The filtered input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
All functions in `ndimage.interpolation` do spline interpolation of
|
||||
the input image. If using B-splines of `order > 1`, the input image
|
||||
values have to be converted to B-spline coefficients first, which is
|
||||
done by applying this 1-D filter sequentially along all
|
||||
axes of the input. All functions that require B-spline coefficients
|
||||
will automatically filter their inputs, a behavior controllable with
|
||||
the `prefilter` keyword argument. For functions that accept a `mode`
|
||||
parameter, the result will only be correct if it matches the `mode`
|
||||
used when filtering.
|
||||
|
||||
See Also
|
||||
--------
|
||||
spline_filter : Multidimensional spline filter.
|
||||
|
||||
Examples
|
||||
--------
|
||||
We can filter an image using 1-D spline along the given axis:
|
||||
|
||||
>>> from scipy.ndimage import spline_filter1d
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> orig_img = np.eye(20) # create an image
|
||||
>>> orig_img[10, :] = 1.0
|
||||
>>> sp_filter_axis_0 = spline_filter1d(orig_img, axis=0)
|
||||
>>> sp_filter_axis_1 = spline_filter1d(orig_img, axis=1)
|
||||
>>> f, ax = plt.subplots(1, 3, sharex=True)
|
||||
>>> for ind, data in enumerate([[orig_img, "original image"],
|
||||
... [sp_filter_axis_0, "spline filter (axis=0)"],
|
||||
... [sp_filter_axis_1, "spline filter (axis=1)"]]):
|
||||
... ax[ind].imshow(data[0], cmap='gray_r')
|
||||
... ax[ind].set_title(data[1])
|
||||
>>> plt.tight_layout()
|
||||
>>> plt.show()
|
||||
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
output = _ni_support._get_output(output, input)
|
||||
if order in [0, 1]:
|
||||
output[...] = numpy.array(input)
|
||||
else:
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
axis = normalize_axis_index(axis, input.ndim)
|
||||
_nd_image.spline_filter1d(input, order, axis, output, mode)
|
||||
return output
|
||||
|
||||
|
||||
def spline_filter(input, order=3, output=numpy.float64, mode='mirror'):
|
||||
"""
|
||||
Multidimensional spline filter.
|
||||
|
||||
For more details, see `spline_filter1d`.
|
||||
|
||||
See Also
|
||||
--------
|
||||
spline_filter1d : Calculate a 1-D spline filter along the given axis.
|
||||
|
||||
Notes
|
||||
-----
|
||||
The multidimensional filter is implemented as a sequence of
|
||||
1-D spline filters. The intermediate arrays are stored
|
||||
in the same data type as the output. Therefore, for output types
|
||||
with a limited precision, the results may be imprecise because
|
||||
intermediate results may be stored with insufficient precision.
|
||||
|
||||
Examples
|
||||
--------
|
||||
We can filter an image using multidimentional splines:
|
||||
|
||||
>>> from scipy.ndimage import spline_filter
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> orig_img = np.eye(20) # create an image
|
||||
>>> orig_img[10, :] = 1.0
|
||||
>>> sp_filter = spline_filter(orig_img, order=3)
|
||||
>>> f, ax = plt.subplots(1, 2, sharex=True)
|
||||
>>> for ind, data in enumerate([[orig_img, "original image"],
|
||||
... [sp_filter, "spline filter"]]):
|
||||
... ax[ind].imshow(data[0], cmap='gray_r')
|
||||
... ax[ind].set_title(data[1])
|
||||
>>> plt.tight_layout()
|
||||
>>> plt.show()
|
||||
|
||||
"""
|
||||
if order < 2 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
output = _ni_support._get_output(output, input)
|
||||
if order not in [0, 1] and input.ndim > 0:
|
||||
for axis in range(input.ndim):
|
||||
spline_filter1d(input, order, axis, output=output, mode=mode)
|
||||
input = output
|
||||
else:
|
||||
output[...] = input[...]
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def geometric_transform(input, mapping, output_shape=None,
|
||||
output=None, order=3,
|
||||
mode='constant', cval=0.0, prefilter=True,
|
||||
extra_arguments=(), extra_keywords={}):
|
||||
"""
|
||||
Apply an arbitrary geometric transform.
|
||||
|
||||
The given mapping function is used to find, for each point in the
|
||||
output, the corresponding coordinates in the input. The value of the
|
||||
input at those coordinates is determined by spline interpolation of
|
||||
the requested order.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
mapping : {callable, scipy.LowLevelCallable}
|
||||
A callable object that accepts a tuple of length equal to the output
|
||||
array rank, and returns the corresponding input coordinates as a tuple
|
||||
of length equal to the input array rank.
|
||||
output_shape : tuple of ints, optional
|
||||
Shape tuple.
|
||||
%(output)s
|
||||
order : int, optional
|
||||
The order of the spline interpolation, default is 3.
|
||||
The order has to be in the range 0-5.
|
||||
%(mode)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
extra_arguments : tuple, optional
|
||||
Extra arguments passed to `mapping`.
|
||||
extra_keywords : dict, optional
|
||||
Extra keywords passed to `mapping`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
output : ndarray
|
||||
The filtered input.
|
||||
|
||||
See Also
|
||||
--------
|
||||
map_coordinates, affine_transform, spline_filter1d
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
This function also accepts low-level callback functions with one
|
||||
the following signatures and wrapped in `scipy.LowLevelCallable`:
|
||||
|
||||
.. code:: c
|
||||
|
||||
int mapping(npy_intp *output_coordinates, double *input_coordinates,
|
||||
int output_rank, int input_rank, void *user_data)
|
||||
int mapping(intptr_t *output_coordinates, double *input_coordinates,
|
||||
int output_rank, int input_rank, void *user_data)
|
||||
|
||||
The calling function iterates over the elements of the output array,
|
||||
calling the callback function at each element. The coordinates of the
|
||||
current output element are passed through ``output_coordinates``. The
|
||||
callback function must return the coordinates at which the input must
|
||||
be interpolated in ``input_coordinates``. The rank of the input and
|
||||
output arrays are given by ``input_rank`` and ``output_rank``
|
||||
respectively. ``user_data`` is the data pointer provided
|
||||
to `scipy.LowLevelCallable` as-is.
|
||||
|
||||
The callback function must return an integer error status that is zero
|
||||
if something went wrong and one otherwise. If an error occurs, you should
|
||||
normally set the Python error status with an informative message
|
||||
before returning, otherwise a default error message is set by the
|
||||
calling function.
|
||||
|
||||
In addition, some other low-level function pointer specifications
|
||||
are accepted, but these are for backward compatibility only and should
|
||||
not be used in new code.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> import numpy as np
|
||||
>>> from scipy.ndimage import geometric_transform
|
||||
>>> a = np.arange(12.).reshape((4, 3))
|
||||
>>> def shift_func(output_coords):
|
||||
... return (output_coords[0] - 0.5, output_coords[1] - 0.5)
|
||||
...
|
||||
>>> geometric_transform(a, shift_func)
|
||||
array([[ 0. , 0. , 0. ],
|
||||
[ 0. , 1.362, 2.738],
|
||||
[ 0. , 4.812, 6.187],
|
||||
[ 0. , 8.263, 9.637]])
|
||||
|
||||
>>> b = [1, 2, 3, 4, 5]
|
||||
>>> def shift_func(output_coords):
|
||||
... return (output_coords[0] - 3,)
|
||||
...
|
||||
>>> geometric_transform(b, shift_func, mode='constant')
|
||||
array([0, 0, 0, 1, 2])
|
||||
>>> geometric_transform(b, shift_func, mode='nearest')
|
||||
array([1, 1, 1, 1, 2])
|
||||
>>> geometric_transform(b, shift_func, mode='reflect')
|
||||
array([3, 2, 1, 1, 2])
|
||||
>>> geometric_transform(b, shift_func, mode='wrap')
|
||||
array([2, 3, 4, 1, 2])
|
||||
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
if output_shape is None:
|
||||
output_shape = input.shape
|
||||
if input.ndim < 1 or len(output_shape) < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
if prefilter and order > 1:
|
||||
filtered = spline_filter(input, order, output=numpy.float64)
|
||||
else:
|
||||
filtered = input
|
||||
output = _ni_support._get_output(output, input, shape=output_shape)
|
||||
_nd_image.geometric_transform(filtered, mapping, None, None, None, output,
|
||||
order, mode, cval, extra_arguments,
|
||||
extra_keywords)
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def map_coordinates(input, coordinates, output=None, order=3,
|
||||
mode='constant', cval=0.0, prefilter=True):
|
||||
"""
|
||||
Map the input array to new coordinates by interpolation.
|
||||
|
||||
The array of coordinates is used to find, for each point in the output,
|
||||
the corresponding coordinates in the input. The value of the input at
|
||||
those coordinates is determined by spline interpolation of the
|
||||
requested order.
|
||||
|
||||
The shape of the output is derived from that of the coordinate
|
||||
array by dropping the first axis. The values of the array along
|
||||
the first axis are the coordinates in the input array at which the
|
||||
output value is found.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
coordinates : array_like
|
||||
The coordinates at which `input` is evaluated.
|
||||
%(output)s
|
||||
order : int, optional
|
||||
The order of the spline interpolation, default is 3.
|
||||
The order has to be in the range 0-5.
|
||||
%(mode)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
map_coordinates : ndarray
|
||||
The result of transforming the input. The shape of the output is
|
||||
derived from that of `coordinates` by dropping the first axis.
|
||||
|
||||
See Also
|
||||
--------
|
||||
spline_filter, geometric_transform, scipy.interpolate
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage
|
||||
>>> a = np.arange(12.).reshape((4, 3))
|
||||
>>> a
|
||||
array([[ 0., 1., 2.],
|
||||
[ 3., 4., 5.],
|
||||
[ 6., 7., 8.],
|
||||
[ 9., 10., 11.]])
|
||||
>>> ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1)
|
||||
array([ 2., 7.])
|
||||
|
||||
Above, the interpolated value of a[0.5, 0.5] gives output[0], while
|
||||
a[2, 1] is output[1].
|
||||
|
||||
>>> inds = np.array([[0.5, 2], [0.5, 4]])
|
||||
>>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3)
|
||||
array([ 2. , -33.3])
|
||||
>>> ndimage.map_coordinates(a, inds, order=1, mode='nearest')
|
||||
array([ 2., 8.])
|
||||
>>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
|
||||
array([ True, False], dtype=bool)
|
||||
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
coordinates = numpy.asarray(coordinates)
|
||||
if numpy.iscomplexobj(coordinates):
|
||||
raise TypeError('Complex type not supported')
|
||||
output_shape = coordinates.shape[1:]
|
||||
if input.ndim < 1 or len(output_shape) < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
if coordinates.shape[0] != input.ndim:
|
||||
raise RuntimeError('invalid shape for coordinate array')
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
if prefilter and order > 1:
|
||||
filtered = spline_filter(input, order, output=numpy.float64)
|
||||
else:
|
||||
filtered = input
|
||||
output = _ni_support._get_output(output, input,
|
||||
shape=output_shape)
|
||||
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
|
||||
output, order, mode, cval, None, None)
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def affine_transform(input, matrix, offset=0.0, output_shape=None,
|
||||
output=None, order=3,
|
||||
mode='constant', cval=0.0, prefilter=True):
|
||||
"""
|
||||
Apply an affine transformation.
|
||||
|
||||
Given an output image pixel index vector ``o``, the pixel value
|
||||
is determined from the input image at position
|
||||
``np.dot(matrix, o) + offset``.
|
||||
|
||||
This does 'pull' (or 'backward') resampling, transforming the output space
|
||||
to the input to locate data. Affine transformations are often described in
|
||||
the 'push' (or 'forward') direction, transforming input to output. If you
|
||||
have a matrix for the 'push' transformation, use its inverse
|
||||
(:func:`numpy.linalg.inv`) in this function.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
matrix : ndarray
|
||||
The inverse coordinate transformation matrix, mapping output
|
||||
coordinates to input coordinates. If ``ndim`` is the number of
|
||||
dimensions of ``input``, the given matrix must have one of the
|
||||
following shapes:
|
||||
|
||||
- ``(ndim, ndim)``: the linear transformation matrix for each
|
||||
output coordinate.
|
||||
- ``(ndim,)``: assume that the 2-D transformation matrix is
|
||||
diagonal, with the diagonal specified by the given value. A more
|
||||
efficient algorithm is then used that exploits the separability
|
||||
of the problem.
|
||||
- ``(ndim + 1, ndim + 1)``: assume that the transformation is
|
||||
specified using homogeneous coordinates [1]_. In this case, any
|
||||
value passed to ``offset`` is ignored.
|
||||
- ``(ndim, ndim + 1)``: as above, but the bottom row of a
|
||||
homogeneous transformation matrix is always ``[0, 0, ..., 1]``,
|
||||
and may be omitted.
|
||||
|
||||
offset : float or sequence, optional
|
||||
The offset into the array where the transform is applied. If a float,
|
||||
`offset` is the same for each axis. If a sequence, `offset` should
|
||||
contain one value for each axis.
|
||||
output_shape : tuple of ints, optional
|
||||
Shape tuple.
|
||||
%(output)s
|
||||
order : int, optional
|
||||
The order of the spline interpolation, default is 3.
|
||||
The order has to be in the range 0-5.
|
||||
%(mode)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
affine_transform : ndarray
|
||||
The transformed input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
The given matrix and offset are used to find for each point in the
|
||||
output the corresponding coordinates in the input by an affine
|
||||
transformation. The value of the input at those coordinates is
|
||||
determined by spline interpolation of the requested order. Points
|
||||
outside the boundaries of the input are filled according to the given
|
||||
mode.
|
||||
|
||||
.. versionchanged:: 0.18.0
|
||||
Previously, the exact interpretation of the affine transformation
|
||||
depended on whether the matrix was supplied as a 1-D or a
|
||||
2-D array. If a 1-D array was supplied
|
||||
to the matrix parameter, the output pixel value at index ``o``
|
||||
was determined from the input image at position
|
||||
``matrix * (o + offset)``.
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] https://en.wikipedia.org/wiki/Homogeneous_coordinates
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
if output_shape is None:
|
||||
output_shape = input.shape
|
||||
if input.ndim < 1 or len(output_shape) < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
if prefilter and order > 1:
|
||||
filtered = spline_filter(input, order, output=numpy.float64)
|
||||
else:
|
||||
filtered = input
|
||||
output = _ni_support._get_output(output, input,
|
||||
shape=output_shape)
|
||||
matrix = numpy.asarray(matrix, dtype=numpy.float64)
|
||||
if matrix.ndim not in [1, 2] or matrix.shape[0] < 1:
|
||||
raise RuntimeError('no proper affine matrix provided')
|
||||
if (matrix.ndim == 2 and matrix.shape[1] == input.ndim + 1 and
|
||||
(matrix.shape[0] in [input.ndim, input.ndim + 1])):
|
||||
if matrix.shape[0] == input.ndim + 1:
|
||||
exptd = [0] * input.ndim + [1]
|
||||
if not numpy.all(matrix[input.ndim] == exptd):
|
||||
msg = ('Expected homogeneous transformation matrix with '
|
||||
'shape %s for image shape %s, but bottom row was '
|
||||
'not equal to %s' % (matrix.shape, input.shape, exptd))
|
||||
raise ValueError(msg)
|
||||
# assume input is homogeneous coordinate transformation matrix
|
||||
offset = matrix[:input.ndim, input.ndim]
|
||||
matrix = matrix[:input.ndim, :input.ndim]
|
||||
if matrix.shape[0] != input.ndim:
|
||||
raise RuntimeError('affine matrix has wrong number of rows')
|
||||
if matrix.ndim == 2 and matrix.shape[1] != output.ndim:
|
||||
raise RuntimeError('affine matrix has wrong number of columns')
|
||||
if not matrix.flags.contiguous:
|
||||
matrix = matrix.copy()
|
||||
offset = _ni_support._normalize_sequence(offset, input.ndim)
|
||||
offset = numpy.asarray(offset, dtype=numpy.float64)
|
||||
if offset.ndim != 1 or offset.shape[0] < 1:
|
||||
raise RuntimeError('no proper offset provided')
|
||||
if not offset.flags.contiguous:
|
||||
offset = offset.copy()
|
||||
if matrix.ndim == 1:
|
||||
warnings.warn(
|
||||
"The behavior of affine_transform with a 1-D "
|
||||
"array supplied for the matrix parameter has changed in "
|
||||
"SciPy 0.18.0."
|
||||
)
|
||||
_nd_image.zoom_shift(filtered, matrix, offset/matrix, output, order,
|
||||
mode, cval)
|
||||
else:
|
||||
_nd_image.geometric_transform(filtered, None, None, matrix, offset,
|
||||
output, order, mode, cval, None, None)
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def shift(input, shift, output=None, order=3, mode='constant', cval=0.0,
|
||||
prefilter=True):
|
||||
"""
|
||||
Shift an array.
|
||||
|
||||
The array is shifted using spline interpolation of the requested order.
|
||||
Points outside the boundaries of the input are filled according to the
|
||||
given mode.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
shift : float or sequence
|
||||
The shift along the axes. If a float, `shift` is the same for each
|
||||
axis. If a sequence, `shift` should contain one value for each axis.
|
||||
%(output)s
|
||||
order : int, optional
|
||||
The order of the spline interpolation, default is 3.
|
||||
The order has to be in the range 0-5.
|
||||
%(mode)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
shift : ndarray
|
||||
The shifted input.
|
||||
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
if input.ndim < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
if prefilter and order > 1:
|
||||
filtered = spline_filter(input, order, output=numpy.float64)
|
||||
else:
|
||||
filtered = input
|
||||
output = _ni_support._get_output(output, input)
|
||||
shift = _ni_support._normalize_sequence(shift, input.ndim)
|
||||
shift = [-ii for ii in shift]
|
||||
shift = numpy.asarray(shift, dtype=numpy.float64)
|
||||
if not shift.flags.contiguous:
|
||||
shift = shift.copy()
|
||||
_nd_image.zoom_shift(filtered, None, shift, output, order, mode, cval)
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0,
|
||||
prefilter=True):
|
||||
"""
|
||||
Zoom an array.
|
||||
|
||||
The array is zoomed using spline interpolation of the requested order.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
zoom : float or sequence
|
||||
The zoom factor along the axes. If a float, `zoom` is the same for each
|
||||
axis. If a sequence, `zoom` should contain one value for each axis.
|
||||
%(output)s
|
||||
order : int, optional
|
||||
The order of the spline interpolation, default is 3.
|
||||
The order has to be in the range 0-5.
|
||||
%(mode)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
zoom : ndarray
|
||||
The zoomed input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, misc
|
||||
>>> import matplotlib.pyplot as plt
|
||||
|
||||
>>> fig = plt.figure()
|
||||
>>> ax1 = fig.add_subplot(121) # left side
|
||||
>>> ax2 = fig.add_subplot(122) # right side
|
||||
>>> ascent = misc.ascent()
|
||||
>>> result = ndimage.zoom(ascent, 3.0)
|
||||
>>> ax1.imshow(ascent)
|
||||
>>> ax2.imshow(result)
|
||||
>>> plt.show()
|
||||
|
||||
>>> print(ascent.shape)
|
||||
(512, 512)
|
||||
|
||||
>>> print(result.shape)
|
||||
(1536, 1536)
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if numpy.iscomplexobj(input):
|
||||
raise TypeError('Complex type not supported')
|
||||
if input.ndim < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
if prefilter and order > 1:
|
||||
filtered = spline_filter(input, order, output=numpy.float64)
|
||||
else:
|
||||
filtered = input
|
||||
zoom = _ni_support._normalize_sequence(zoom, input.ndim)
|
||||
output_shape = tuple(
|
||||
[int(round(ii * jj)) for ii, jj in zip(input.shape, zoom)])
|
||||
|
||||
zoom_div = numpy.array(output_shape, float) - 1
|
||||
# Zooming to infinite values is unpredictable, so just choose
|
||||
# zoom factor 1 instead
|
||||
zoom = numpy.divide(numpy.array(input.shape) - 1, zoom_div,
|
||||
out=numpy.ones_like(input.shape, dtype=numpy.float64),
|
||||
where=zoom_div != 0)
|
||||
|
||||
output = _ni_support._get_output(output, input,
|
||||
shape=output_shape)
|
||||
zoom = numpy.ascontiguousarray(zoom)
|
||||
_nd_image.zoom_shift(filtered, zoom, None, output, order, mode, cval)
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def rotate(input, angle, axes=(1, 0), reshape=True, output=None, order=3,
|
||||
mode='constant', cval=0.0, prefilter=True):
|
||||
"""
|
||||
Rotate an array.
|
||||
|
||||
The array is rotated in the plane defined by the two axes given by the
|
||||
`axes` parameter using spline interpolation of the requested order.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
%(input)s
|
||||
angle : float
|
||||
The rotation angle in degrees.
|
||||
axes : tuple of 2 ints, optional
|
||||
The two axes that define the plane of rotation. Default is the first
|
||||
two axes.
|
||||
reshape : bool, optional
|
||||
If `reshape` is true, the output shape is adapted so that the input
|
||||
array is contained completely in the output. Default is True.
|
||||
%(output)s
|
||||
order : int, optional
|
||||
The order of the spline interpolation, default is 3.
|
||||
The order has to be in the range 0-5.
|
||||
%(mode)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
rotate : ndarray
|
||||
The rotated input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, misc
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig = plt.figure(figsize=(10, 3))
|
||||
>>> ax1, ax2, ax3 = fig.subplots(1, 3)
|
||||
>>> img = misc.ascent()
|
||||
>>> img_45 = ndimage.rotate(img, 45, reshape=False)
|
||||
>>> full_img_45 = ndimage.rotate(img, 45, reshape=True)
|
||||
>>> ax1.imshow(img, cmap='gray')
|
||||
>>> ax1.set_axis_off()
|
||||
>>> ax2.imshow(img_45, cmap='gray')
|
||||
>>> ax2.set_axis_off()
|
||||
>>> ax3.imshow(full_img_45, cmap='gray')
|
||||
>>> ax3.set_axis_off()
|
||||
>>> fig.set_tight_layout(True)
|
||||
>>> plt.show()
|
||||
>>> print(img.shape)
|
||||
(512, 512)
|
||||
>>> print(img_45.shape)
|
||||
(512, 512)
|
||||
>>> print(full_img_45.shape)
|
||||
(724, 724)
|
||||
|
||||
"""
|
||||
input_arr = numpy.asarray(input)
|
||||
ndim = input_arr.ndim
|
||||
|
||||
if ndim < 2:
|
||||
raise ValueError('input array should be at least 2D')
|
||||
|
||||
axes = list(axes)
|
||||
|
||||
if len(axes) != 2:
|
||||
raise ValueError('axes should contain exactly two values')
|
||||
|
||||
if not all([float(ax).is_integer() for ax in axes]):
|
||||
raise ValueError('axes should contain only integer values')
|
||||
|
||||
if axes[0] < 0:
|
||||
axes[0] += ndim
|
||||
if axes[1] < 0:
|
||||
axes[1] += ndim
|
||||
if axes[0] < 0 or axes[1] < 0 or axes[0] >= ndim or axes[1] >= ndim:
|
||||
raise ValueError('invalid rotation plane specified')
|
||||
|
||||
axes.sort()
|
||||
|
||||
angle_rad = numpy.deg2rad(angle)
|
||||
c, s = numpy.cos(angle_rad), numpy.sin(angle_rad)
|
||||
|
||||
rot_matrix = numpy.array([[c, s],
|
||||
[-s, c]])
|
||||
|
||||
img_shape = numpy.asarray(input_arr.shape)
|
||||
in_plane_shape = img_shape[axes]
|
||||
if reshape:
|
||||
# Compute transformed input bounds
|
||||
iy, ix = in_plane_shape
|
||||
out_bounds = rot_matrix @ [[0, 0, iy, iy],
|
||||
[0, ix, 0, ix]]
|
||||
# Compute the shape of the transformed input plane
|
||||
out_plane_shape = (out_bounds.ptp(axis=1) + 0.5).astype(int)
|
||||
else:
|
||||
out_plane_shape = img_shape[axes]
|
||||
|
||||
out_center = rot_matrix @ ((out_plane_shape - 1) / 2)
|
||||
in_center = (in_plane_shape - 1) / 2
|
||||
offset = in_center - out_center
|
||||
|
||||
output_shape = img_shape
|
||||
output_shape[axes] = out_plane_shape
|
||||
output_shape = tuple(output_shape)
|
||||
|
||||
output = _ni_support._get_output(output, input_arr, shape=output_shape)
|
||||
|
||||
if ndim <= 2:
|
||||
affine_transform(input_arr, rot_matrix, offset, output_shape, output,
|
||||
order, mode, cval, prefilter)
|
||||
else:
|
||||
# If ndim > 2, the rotation is applied over all the planes
|
||||
# parallel to axes
|
||||
planes_coord = itertools.product(
|
||||
*[[slice(None)] if ax in axes else range(img_shape[ax])
|
||||
for ax in range(ndim)])
|
||||
|
||||
out_plane_shape = tuple(out_plane_shape)
|
||||
|
||||
for coordinates in planes_coord:
|
||||
ia = input_arr[coordinates]
|
||||
oa = output[coordinates]
|
||||
affine_transform(ia, rot_matrix, offset, out_plane_shape,
|
||||
oa, order, mode, cval, prefilter)
|
||||
|
||||
return output
|
||||
1525
venv/Lib/site-packages/scipy/ndimage/measurements.py
Normal file
1525
venv/Lib/site-packages/scipy/ndimage/measurements.py
Normal file
File diff suppressed because it is too large
Load diff
2235
venv/Lib/site-packages/scipy/ndimage/morphology.py
Normal file
2235
venv/Lib/site-packages/scipy/ndimage/morphology.py
Normal file
File diff suppressed because it is too large
Load diff
57
venv/Lib/site-packages/scipy/ndimage/setup.py
Normal file
57
venv/Lib/site-packages/scipy/ndimage/setup.py
Normal file
|
|
@ -0,0 +1,57 @@
|
|||
import os
|
||||
|
||||
from numpy.distutils.core import setup
|
||||
from numpy.distutils.misc_util import Configuration
|
||||
from numpy import get_include
|
||||
from scipy._build_utils import numpy_nodepr_api
|
||||
|
||||
|
||||
def configuration(parent_package='', top_path=None):
|
||||
|
||||
config = Configuration('ndimage', parent_package, top_path)
|
||||
|
||||
include_dirs = ['src',
|
||||
get_include(),
|
||||
os.path.join(os.path.dirname(__file__), '..', '_lib', 'src')]
|
||||
|
||||
config.add_extension("_nd_image",
|
||||
sources=["src/nd_image.c",
|
||||
"src/ni_filters.c",
|
||||
"src/ni_fourier.c",
|
||||
"src/ni_interpolation.c",
|
||||
"src/ni_measure.c",
|
||||
"src/ni_morphology.c",
|
||||
"src/ni_splines.c",
|
||||
"src/ni_support.c"],
|
||||
include_dirs=include_dirs,
|
||||
**numpy_nodepr_api)
|
||||
|
||||
# Cython wants the .c and .pyx to have the underscore.
|
||||
config.add_extension("_ni_label",
|
||||
sources=["src/_ni_label.c",],
|
||||
include_dirs=['src']+[get_include()])
|
||||
|
||||
config.add_extension("_ctest",
|
||||
sources=["src/_ctest.c"],
|
||||
include_dirs=[get_include()],
|
||||
**numpy_nodepr_api)
|
||||
|
||||
_define_macros = [("OLDAPI", 1)]
|
||||
if 'define_macros' in numpy_nodepr_api:
|
||||
_define_macros.extend(numpy_nodepr_api['define_macros'])
|
||||
|
||||
config.add_extension("_ctest_oldapi",
|
||||
sources=["src/_ctest.c"],
|
||||
include_dirs=[get_include()],
|
||||
define_macros=_define_macros)
|
||||
|
||||
config.add_extension("_cytest",
|
||||
sources=["src/_cytest.c"])
|
||||
|
||||
config.add_data_dir('tests')
|
||||
|
||||
return config
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
setup(**configuration(top_path='').todict())
|
||||
0
venv/Lib/site-packages/scipy/ndimage/tests/__init__.py
Normal file
0
venv/Lib/site-packages/scipy/ndimage/tests/__init__.py
Normal file
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,4 @@
|
|||
label_inputs.txt, label_strels.txt, and label_results.txt are test
|
||||
vectors generated using ndimage.label from scipy version 0.10.0, and
|
||||
are used to verify that the cython version behaves as expected. The
|
||||
script to generate them is in ../../utils/generate_label_testvectors.py
|
||||
|
|
@ -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
|
||||
|
|
@ -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
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 7
|
||||
1 2 3 4 5 6 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 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 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
|
||||
2 2 2 0 7 7 7
|
||||
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
|
||||
5 5 5 0 1 1 1
|
||||
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
|
||||
10 0 11 0 12 0 13
|
||||
14 14 0 0 0 15 15
|
||||
16 16 16 0 17 17 17
|
||||
1 1 1 0 2 3 3
|
||||
1 1 0 0 0 3 3
|
||||
1 0 4 0 3 0 3
|
||||
0 0 0 3 0 0 0
|
||||
3 0 3 0 5 0 6
|
||||
3 3 0 0 0 6 6
|
||||
3 3 7 0 6 6 6
|
||||
1 2 3 0 4 5 6
|
||||
7 8 0 0 0 9 10
|
||||
11 0 12 0 13 0 14
|
||||
0 0 0 15 0 0 0
|
||||
16 0 17 0 18 0 19
|
||||
20 21 0 0 0 22 23
|
||||
24 25 26 0 27 28 29
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 3 0 2 0 2
|
||||
0 0 0 2 0 0 0
|
||||
2 0 2 0 4 0 5
|
||||
2 2 0 0 0 5 5
|
||||
2 2 2 0 5 5 5
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 3 0 4 0 2
|
||||
0 0 0 5 0 0 0
|
||||
6 0 7 0 8 0 9
|
||||
6 6 0 0 0 9 9
|
||||
6 6 6 0 9 9 9
|
||||
1 2 3 0 4 5 6
|
||||
7 1 0 0 0 4 5
|
||||
8 0 1 0 9 0 4
|
||||
0 0 0 1 0 0 0
|
||||
10 0 11 0 1 0 12
|
||||
13 10 0 0 0 1 14
|
||||
15 13 10 0 16 17 1
|
||||
1 2 3 0 4 5 6
|
||||
1 2 0 0 0 5 6
|
||||
1 0 7 0 8 0 6
|
||||
0 0 0 9 0 0 0
|
||||
10 0 11 0 12 0 13
|
||||
10 14 0 0 0 15 13
|
||||
10 14 16 0 17 15 13
|
||||
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 1 2 0 3 3 3
|
||||
1 1 0 0 0 3 3
|
||||
1 0 1 0 4 0 3
|
||||
0 0 0 1 0 0 0
|
||||
5 0 6 0 1 0 1
|
||||
5 5 0 0 0 1 1
|
||||
5 5 5 0 7 1 1
|
||||
1 2 1 0 1 3 1
|
||||
2 1 0 0 0 1 3
|
||||
1 0 1 0 1 0 1
|
||||
0 0 0 1 0 0 0
|
||||
1 0 1 0 1 0 1
|
||||
4 1 0 0 0 1 5
|
||||
1 4 1 0 1 5 1
|
||||
1 2 3 0 4 5 6
|
||||
2 3 0 0 0 6 7
|
||||
3 0 8 0 6 0 9
|
||||
0 0 0 6 0 0 0
|
||||
10 0 6 0 11 0 12
|
||||
13 6 0 0 0 12 14
|
||||
6 15 16 0 12 14 17
|
||||
1 1 1 0 2 2 2
|
||||
1 1 0 0 0 2 2
|
||||
1 0 1 0 3 0 2
|
||||
0 0 0 1 0 0 0
|
||||
4 0 5 0 1 0 1
|
||||
4 4 0 0 0 1 1
|
||||
4 4 4 0 1 1 1
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
4 0 0 5 0 0 5
|
||||
5 5 5 5 5 5 5
|
||||
5 0 0 5 0 0 6
|
||||
0 0 0 7 0 0 0
|
||||
8 0 7 7 7 0 9
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 2 0 0 0
|
||||
4 0 0 4 0 0 5
|
||||
4 4 4 4 4 4 4
|
||||
6 0 0 4 0 0 4
|
||||
0 0 0 7 0 0 0
|
||||
8 0 7 7 7 0 9
|
||||
1 0 2 2 2 0 3
|
||||
0 0 0 4 0 0 0
|
||||
5 0 0 6 0 0 7
|
||||
8 8 8 8 8 8 8
|
||||
9 0 0 10 0 0 11
|
||||
0 0 0 12 0 0 0
|
||||
13 0 14 14 14 0 15
|
||||
1 0 2 3 3 0 4
|
||||
0 0 0 3 0 0 0
|
||||
5 0 0 3 0 0 6
|
||||
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
|
||||
7 0 0 8 0 0 9
|
||||
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
|
||||
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 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 3 4 0 5
|
||||
0 0 0 2 0 0 0
|
||||
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
|
||||
|
|
@ -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
|
||||
BIN
venv/Lib/site-packages/scipy/ndimage/tests/dots.png
Normal file
BIN
venv/Lib/site-packages/scipy/ndimage/tests/dots.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 2.1 KiB |
98
venv/Lib/site-packages/scipy/ndimage/tests/test_c_api.py
Normal file
98
venv/Lib/site-packages/scipy/ndimage/tests/test_c_api.py
Normal file
|
|
@ -0,0 +1,98 @@
|
|||
import numpy as np
|
||||
from numpy.testing import assert_allclose
|
||||
|
||||
from scipy import ndimage
|
||||
from scipy.ndimage import _ctest
|
||||
from scipy.ndimage import _ctest_oldapi
|
||||
from scipy.ndimage import _cytest
|
||||
from scipy._lib._ccallback import LowLevelCallable
|
||||
|
||||
FILTER1D_FUNCTIONS = [
|
||||
lambda filter_size: _ctest.filter1d(filter_size),
|
||||
lambda filter_size: _ctest_oldapi.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: _ctest_oldapi.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: _ctest_oldapi.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="#{} failed".format(j))
|
||||
|
||||
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="#{} failed".format(j))
|
||||
|
||||
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="#{} failed".format(j))
|
||||
|
||||
for j, func in enumerate(TRANSFORM_FUNCTIONS):
|
||||
check(j)
|
||||
66
venv/Lib/site-packages/scipy/ndimage/tests/test_datatypes.py
Normal file
66
venv/Lib/site-packages/scipy/ndimage/tests/test_datatypes.py
Normal file
|
|
@ -0,0 +1,66 @@
|
|||
""" Testing data types for ndimage calls
|
||||
"""
|
||||
import sys
|
||||
|
||||
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(not sys.platform == 'darwin', reason="runs only on darwin")
|
||||
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.
|
||||
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)))
|
||||
443
venv/Lib/site-packages/scipy/ndimage/tests/test_filters.py
Normal file
443
venv/Lib/site-packages/scipy/ndimage/tests/test_filters.py
Normal file
|
|
@ -0,0 +1,443 @@
|
|||
''' Some tests for filters '''
|
||||
import numpy as np
|
||||
|
||||
from numpy.testing import (assert_equal, assert_allclose,
|
||||
assert_array_equal, assert_almost_equal,
|
||||
suppress_warnings)
|
||||
from pytest import raises as assert_raises
|
||||
|
||||
import scipy.ndimage as sndi
|
||||
from scipy.ndimage.filters import _gaussian_kernel1d, rank_filter
|
||||
|
||||
def test_ticket_701():
|
||||
# Test generic filter sizes
|
||||
arr = np.arange(4).reshape((2,2))
|
||||
func = lambda x: np.min(x)
|
||||
res = sndi.generic_filter(arr, func, size=(1,1))
|
||||
# The following raises an error unless ticket 701 is fixed
|
||||
res2 = sndi.generic_filter(arr, func, size=1)
|
||||
assert_equal(res, res2)
|
||||
|
||||
|
||||
def test_gh_5430():
|
||||
# At least one of these raises an error unless gh-5430 is
|
||||
# fixed. In py2k an int is implemented using a C long, so
|
||||
# which one fails depends on your system. In py3k there is only
|
||||
# one arbitrary precision integer type, so both should fail.
|
||||
sigma = np.int32(1)
|
||||
out = sndi._ni_support._normalize_sequence(sigma, 1)
|
||||
assert_equal(out, [sigma])
|
||||
sigma = np.int64(1)
|
||||
out = sndi._ni_support._normalize_sequence(sigma, 1)
|
||||
assert_equal(out, [sigma])
|
||||
# This worked before; make sure it still works
|
||||
sigma = 1
|
||||
out = sndi._ni_support._normalize_sequence(sigma, 1)
|
||||
assert_equal(out, [sigma])
|
||||
# This worked before; make sure it still works
|
||||
sigma = [1, 1]
|
||||
out = sndi._ni_support._normalize_sequence(sigma, 2)
|
||||
assert_equal(out, sigma)
|
||||
# Also include the OPs original example to make sure we fixed the issue
|
||||
x = np.random.normal(size=(256, 256))
|
||||
perlin = np.zeros_like(x)
|
||||
for i in 2**np.arange(6):
|
||||
perlin += sndi.filters.gaussian_filter(x, i, mode="wrap") * i**2
|
||||
# This also fixes gh-4106, show that the OPs example now runs.
|
||||
x = np.int64(21)
|
||||
sndi._ni_support._normalize_sequence(x, 0)
|
||||
|
||||
|
||||
def test_gaussian_kernel1d():
|
||||
radius = 10
|
||||
sigma = 2
|
||||
sigma2 = sigma * sigma
|
||||
x = np.arange(-radius, radius + 1, dtype=np.double)
|
||||
phi_x = np.exp(-0.5 * x * x / sigma2)
|
||||
phi_x /= phi_x.sum()
|
||||
assert_allclose(phi_x, _gaussian_kernel1d(sigma, 0, radius))
|
||||
assert_allclose(-phi_x * x / sigma2, _gaussian_kernel1d(sigma, 1, radius))
|
||||
assert_allclose(phi_x * (x * x / sigma2 - 1) / sigma2,
|
||||
_gaussian_kernel1d(sigma, 2, radius))
|
||||
assert_allclose(phi_x * (3 - x * x / sigma2) * x / (sigma2 * sigma2),
|
||||
_gaussian_kernel1d(sigma, 3, radius))
|
||||
|
||||
|
||||
def test_orders_gauss():
|
||||
# Check order inputs to Gaussians
|
||||
arr = np.zeros((1,))
|
||||
assert_equal(0, sndi.gaussian_filter(arr, 1, order=0))
|
||||
assert_equal(0, sndi.gaussian_filter(arr, 1, order=3))
|
||||
assert_raises(ValueError, sndi.gaussian_filter, arr, 1, -1)
|
||||
assert_equal(0, sndi.gaussian_filter1d(arr, 1, axis=-1, order=0))
|
||||
assert_equal(0, sndi.gaussian_filter1d(arr, 1, axis=-1, order=3))
|
||||
assert_raises(ValueError, sndi.gaussian_filter1d, arr, 1, -1, -1)
|
||||
|
||||
|
||||
def test_valid_origins():
|
||||
"""Regression test for #1311."""
|
||||
func = lambda x: np.mean(x)
|
||||
data = np.array([1,2,3,4,5], dtype=np.float64)
|
||||
assert_raises(ValueError, sndi.generic_filter, data, func, size=3,
|
||||
origin=2)
|
||||
assert_raises(ValueError, sndi.generic_filter1d, data, func,
|
||||
filter_size=3, origin=2)
|
||||
assert_raises(ValueError, sndi.percentile_filter, data, 0.2, size=3,
|
||||
origin=2)
|
||||
|
||||
for filter in [sndi.uniform_filter, sndi.minimum_filter,
|
||||
sndi.maximum_filter, sndi.maximum_filter1d,
|
||||
sndi.median_filter, sndi.minimum_filter1d]:
|
||||
# This should work, since for size == 3, the valid range for origin is
|
||||
# -1 to 1.
|
||||
list(filter(data, 3, origin=-1))
|
||||
list(filter(data, 3, origin=1))
|
||||
# Just check this raises an error instead of silently accepting or
|
||||
# segfaulting.
|
||||
assert_raises(ValueError, filter, data, 3, origin=2)
|
||||
|
||||
|
||||
def test_bad_convolve_and_correlate_origins():
|
||||
"""Regression test for gh-822."""
|
||||
# Before gh-822 was fixed, these would generate seg. faults or
|
||||
# other crashes on many system.
|
||||
assert_raises(ValueError, sndi.correlate1d,
|
||||
[0, 1, 2, 3, 4, 5], [1, 1, 2, 0], origin=2)
|
||||
assert_raises(ValueError, sndi.correlate,
|
||||
[0, 1, 2, 3, 4, 5], [0, 1, 2], origin=[2])
|
||||
assert_raises(ValueError, sndi.correlate,
|
||||
np.ones((3, 5)), np.ones((2, 2)), origin=[0, 1])
|
||||
|
||||
assert_raises(ValueError, sndi.convolve1d,
|
||||
np.arange(10), np.ones(3), origin=-2)
|
||||
assert_raises(ValueError, sndi.convolve,
|
||||
np.arange(10), np.ones(3), origin=[-2])
|
||||
assert_raises(ValueError, sndi.convolve,
|
||||
np.ones((3, 5)), np.ones((2, 2)), origin=[0, -2])
|
||||
|
||||
|
||||
def test_multiple_modes():
|
||||
# Test that the filters with multiple mode cababilities for different
|
||||
# dimensions give the same result as applying a single mode.
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
mode1 = 'reflect'
|
||||
mode2 = ['reflect', 'reflect']
|
||||
|
||||
assert_equal(sndi.gaussian_filter(arr, 1, mode=mode1),
|
||||
sndi.gaussian_filter(arr, 1, mode=mode2))
|
||||
assert_equal(sndi.prewitt(arr, mode=mode1),
|
||||
sndi.prewitt(arr, mode=mode2))
|
||||
assert_equal(sndi.sobel(arr, mode=mode1),
|
||||
sndi.sobel(arr, mode=mode2))
|
||||
assert_equal(sndi.laplace(arr, mode=mode1),
|
||||
sndi.laplace(arr, mode=mode2))
|
||||
assert_equal(sndi.gaussian_laplace(arr, 1, mode=mode1),
|
||||
sndi.gaussian_laplace(arr, 1, mode=mode2))
|
||||
assert_equal(sndi.maximum_filter(arr, size=5, mode=mode1),
|
||||
sndi.maximum_filter(arr, size=5, mode=mode2))
|
||||
assert_equal(sndi.minimum_filter(arr, size=5, mode=mode1),
|
||||
sndi.minimum_filter(arr, size=5, mode=mode2))
|
||||
assert_equal(sndi.gaussian_gradient_magnitude(arr, 1, mode=mode1),
|
||||
sndi.gaussian_gradient_magnitude(arr, 1, mode=mode2))
|
||||
assert_equal(sndi.uniform_filter(arr, 5, mode=mode1),
|
||||
sndi.uniform_filter(arr, 5, mode=mode2))
|
||||
|
||||
|
||||
def test_multiple_modes_sequentially():
|
||||
# Test that the filters with multiple mode cababilities for different
|
||||
# dimensions give the same result as applying the filters with
|
||||
# different modes sequentially
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
expected = sndi.gaussian_filter1d(arr, 1, axis=0, mode=modes[0])
|
||||
expected = sndi.gaussian_filter1d(expected, 1, axis=1, mode=modes[1])
|
||||
assert_equal(expected,
|
||||
sndi.gaussian_filter(arr, 1, mode=modes))
|
||||
|
||||
expected = sndi.uniform_filter1d(arr, 5, axis=0, mode=modes[0])
|
||||
expected = sndi.uniform_filter1d(expected, 5, axis=1, mode=modes[1])
|
||||
assert_equal(expected,
|
||||
sndi.uniform_filter(arr, 5, mode=modes))
|
||||
|
||||
expected = sndi.maximum_filter1d(arr, size=5, axis=0, mode=modes[0])
|
||||
expected = sndi.maximum_filter1d(expected, size=5, axis=1, mode=modes[1])
|
||||
assert_equal(expected,
|
||||
sndi.maximum_filter(arr, size=5, mode=modes))
|
||||
|
||||
expected = sndi.minimum_filter1d(arr, size=5, axis=0, mode=modes[0])
|
||||
expected = sndi.minimum_filter1d(expected, size=5, axis=1, mode=modes[1])
|
||||
assert_equal(expected,
|
||||
sndi.minimum_filter(arr, size=5, mode=modes))
|
||||
|
||||
|
||||
def test_multiple_modes_prewitt():
|
||||
# Test prewitt filter for multiple extrapolation modes
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
expected = np.array([[1., -3., 2.],
|
||||
[1., -2., 1.],
|
||||
[1., -1., 0.]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
assert_equal(expected,
|
||||
sndi.prewitt(arr, mode=modes))
|
||||
|
||||
|
||||
def test_multiple_modes_sobel():
|
||||
# Test sobel filter for multiple extrapolation modes
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
expected = np.array([[1., -4., 3.],
|
||||
[2., -3., 1.],
|
||||
[1., -1., 0.]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
assert_equal(expected,
|
||||
sndi.sobel(arr, mode=modes))
|
||||
|
||||
|
||||
def test_multiple_modes_laplace():
|
||||
# Test laplace filter for multiple extrapolation modes
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
expected = np.array([[-2., 2., 1.],
|
||||
[-2., -3., 2.],
|
||||
[1., 1., 0.]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
assert_equal(expected,
|
||||
sndi.laplace(arr, mode=modes))
|
||||
|
||||
|
||||
def test_multiple_modes_gaussian_laplace():
|
||||
# Test gaussian_laplace filter for multiple extrapolation modes
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
expected = np.array([[-0.28438687, 0.01559809, 0.19773499],
|
||||
[-0.36630503, -0.20069774, 0.07483620],
|
||||
[0.15849176, 0.18495566, 0.21934094]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
assert_almost_equal(expected,
|
||||
sndi.gaussian_laplace(arr, 1, mode=modes))
|
||||
|
||||
|
||||
def test_multiple_modes_gaussian_gradient_magnitude():
|
||||
# Test gaussian_gradient_magnitude filter for multiple
|
||||
# extrapolation modes
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
expected = np.array([[0.04928965, 0.09745625, 0.06405368],
|
||||
[0.23056905, 0.14025305, 0.04550846],
|
||||
[0.19894369, 0.14950060, 0.06796850]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
calculated = sndi.gaussian_gradient_magnitude(arr, 1, mode=modes)
|
||||
|
||||
assert_almost_equal(expected, calculated)
|
||||
|
||||
|
||||
def test_multiple_modes_uniform():
|
||||
# Test uniform filter for multiple extrapolation modes
|
||||
arr = np.array([[1., 0., 0.],
|
||||
[1., 1., 0.],
|
||||
[0., 0., 0.]])
|
||||
|
||||
expected = np.array([[0.32, 0.40, 0.48],
|
||||
[0.20, 0.28, 0.32],
|
||||
[0.28, 0.32, 0.40]])
|
||||
|
||||
modes = ['reflect', 'wrap']
|
||||
|
||||
assert_almost_equal(expected,
|
||||
sndi.uniform_filter(arr, 5, mode=modes))
|
||||
|
||||
|
||||
def test_gaussian_truncate():
|
||||
# Test that Gaussian filters can be truncated at different widths.
|
||||
# These tests only check that the result has the expected number
|
||||
# of nonzero elements.
|
||||
arr = np.zeros((100, 100), float)
|
||||
arr[50, 50] = 1
|
||||
num_nonzeros_2 = (sndi.gaussian_filter(arr, 5, truncate=2) > 0).sum()
|
||||
assert_equal(num_nonzeros_2, 21**2)
|
||||
num_nonzeros_5 = (sndi.gaussian_filter(arr, 5, truncate=5) > 0).sum()
|
||||
assert_equal(num_nonzeros_5, 51**2)
|
||||
|
||||
# Test truncate when sigma is a sequence.
|
||||
f = sndi.gaussian_filter(arr, [0.5, 2.5], truncate=3.5)
|
||||
fpos = f > 0
|
||||
n0 = fpos.any(axis=0).sum()
|
||||
# n0 should be 2*int(2.5*3.5 + 0.5) + 1
|
||||
assert_equal(n0, 19)
|
||||
n1 = fpos.any(axis=1).sum()
|
||||
# n1 should be 2*int(0.5*3.5 + 0.5) + 1
|
||||
assert_equal(n1, 5)
|
||||
|
||||
# Test gaussian_filter1d.
|
||||
x = np.zeros(51)
|
||||
x[25] = 1
|
||||
f = sndi.gaussian_filter1d(x, sigma=2, truncate=3.5)
|
||||
n = (f > 0).sum()
|
||||
assert_equal(n, 15)
|
||||
|
||||
# Test gaussian_laplace
|
||||
y = sndi.gaussian_laplace(x, sigma=2, truncate=3.5)
|
||||
nonzero_indices = np.nonzero(y != 0)[0]
|
||||
n = nonzero_indices.ptp() + 1
|
||||
assert_equal(n, 15)
|
||||
|
||||
# Test gaussian_gradient_magnitude
|
||||
y = sndi.gaussian_gradient_magnitude(x, sigma=2, truncate=3.5)
|
||||
nonzero_indices = np.nonzero(y != 0)[0]
|
||||
n = nonzero_indices.ptp() + 1
|
||||
assert_equal(n, 15)
|
||||
|
||||
|
||||
class TestThreading(object):
|
||||
def check_func_thread(self, n, fun, args, out):
|
||||
from threading import Thread
|
||||
thrds = [Thread(target=fun, args=args, kwargs={'output': out[x]}) for x in range(n)]
|
||||
[t.start() for t in thrds]
|
||||
[t.join() for t in thrds]
|
||||
|
||||
def check_func_serial(self, n, fun, args, out):
|
||||
for i in range(n):
|
||||
fun(*args, output=out[i])
|
||||
|
||||
def test_correlate1d(self):
|
||||
d = np.random.randn(5000)
|
||||
os = np.empty((4, d.size))
|
||||
ot = np.empty_like(os)
|
||||
self.check_func_serial(4, sndi.correlate1d, (d, np.arange(5)), os)
|
||||
self.check_func_thread(4, sndi.correlate1d, (d, np.arange(5)), ot)
|
||||
assert_array_equal(os, ot)
|
||||
|
||||
def test_correlate(self):
|
||||
d = np.random.randn(500, 500)
|
||||
k = np.random.randn(10, 10)
|
||||
os = np.empty([4] + list(d.shape))
|
||||
ot = np.empty_like(os)
|
||||
self.check_func_serial(4, sndi.correlate, (d, k), os)
|
||||
self.check_func_thread(4, sndi.correlate, (d, k), ot)
|
||||
assert_array_equal(os, ot)
|
||||
|
||||
def test_median_filter(self):
|
||||
d = np.random.randn(500, 500)
|
||||
os = np.empty([4] + list(d.shape))
|
||||
ot = np.empty_like(os)
|
||||
self.check_func_serial(4, sndi.median_filter, (d, 3), os)
|
||||
self.check_func_thread(4, sndi.median_filter, (d, 3), ot)
|
||||
assert_array_equal(os, ot)
|
||||
|
||||
def test_uniform_filter1d(self):
|
||||
d = np.random.randn(5000)
|
||||
os = np.empty((4, d.size))
|
||||
ot = np.empty_like(os)
|
||||
self.check_func_serial(4, sndi.uniform_filter1d, (d, 5), os)
|
||||
self.check_func_thread(4, sndi.uniform_filter1d, (d, 5), ot)
|
||||
assert_array_equal(os, ot)
|
||||
|
||||
def test_minmax_filter(self):
|
||||
d = np.random.randn(500, 500)
|
||||
os = np.empty([4] + list(d.shape))
|
||||
ot = np.empty_like(os)
|
||||
self.check_func_serial(4, sndi.maximum_filter, (d, 3), os)
|
||||
self.check_func_thread(4, sndi.maximum_filter, (d, 3), ot)
|
||||
assert_array_equal(os, ot)
|
||||
self.check_func_serial(4, sndi.minimum_filter, (d, 3), os)
|
||||
self.check_func_thread(4, sndi.minimum_filter, (d, 3), ot)
|
||||
assert_array_equal(os, ot)
|
||||
|
||||
|
||||
def test_minmaximum_filter1d():
|
||||
# Regression gh-3898
|
||||
in_ = np.arange(10)
|
||||
out = sndi.minimum_filter1d(in_, 1)
|
||||
assert_equal(in_, out)
|
||||
out = sndi.maximum_filter1d(in_, 1)
|
||||
assert_equal(in_, out)
|
||||
# Test reflect
|
||||
out = sndi.minimum_filter1d(in_, 5, mode='reflect')
|
||||
assert_equal([0, 0, 0, 1, 2, 3, 4, 5, 6, 7], out)
|
||||
out = sndi.maximum_filter1d(in_, 5, mode='reflect')
|
||||
assert_equal([2, 3, 4, 5, 6, 7, 8, 9, 9, 9], out)
|
||||
#Test constant
|
||||
out = sndi.minimum_filter1d(in_, 5, mode='constant', cval=-1)
|
||||
assert_equal([-1, -1, 0, 1, 2, 3, 4, 5, -1, -1], out)
|
||||
out = sndi.maximum_filter1d(in_, 5, mode='constant', cval=10)
|
||||
assert_equal([10, 10, 4, 5, 6, 7, 8, 9, 10, 10], out)
|
||||
# Test nearest
|
||||
out = sndi.minimum_filter1d(in_, 5, mode='nearest')
|
||||
assert_equal([0, 0, 0, 1, 2, 3, 4, 5, 6, 7], out)
|
||||
out = sndi.maximum_filter1d(in_, 5, mode='nearest')
|
||||
assert_equal([2, 3, 4, 5, 6, 7, 8, 9, 9, 9], out)
|
||||
# Test wrap
|
||||
out = sndi.minimum_filter1d(in_, 5, mode='wrap')
|
||||
assert_equal([0, 0, 0, 1, 2, 3, 4, 5, 0, 0], out)
|
||||
out = sndi.maximum_filter1d(in_, 5, mode='wrap')
|
||||
assert_equal([9, 9, 4, 5, 6, 7, 8, 9, 9, 9], out)
|
||||
|
||||
|
||||
def test_uniform_filter1d_roundoff_errors():
|
||||
# gh-6930
|
||||
in_ = np.repeat([0, 1, 0], [9, 9, 9])
|
||||
for filter_size in range(3, 10):
|
||||
out = sndi.uniform_filter1d(in_, filter_size)
|
||||
assert_equal(out.sum(), 10 - filter_size)
|
||||
|
||||
|
||||
def test_footprint_all_zeros():
|
||||
# regression test for gh-6876: footprint of all zeros segfaults
|
||||
arr = np.random.randint(0, 100, (100, 100))
|
||||
kernel = np.zeros((3, 3), bool)
|
||||
with assert_raises(ValueError):
|
||||
sndi.maximum_filter(arr, footprint=kernel)
|
||||
|
||||
def test_gaussian_filter():
|
||||
# Test gaussian filter with np.float16
|
||||
# gh-8207
|
||||
data = np.array([1],dtype = np.float16)
|
||||
sigma = 1.0
|
||||
with assert_raises(RuntimeError):
|
||||
sndi.gaussian_filter(data,sigma)
|
||||
|
||||
|
||||
def test_rank_filter_noninteger_rank():
|
||||
# regression test for issue 9388: ValueError for
|
||||
# non integer rank when performing rank_filter
|
||||
arr = np.random.random((10, 20, 30))
|
||||
assert_raises(TypeError, rank_filter, arr, 0.5,
|
||||
footprint=np.ones((1, 1, 10), dtype=bool))
|
||||
|
||||
|
||||
def test_size_footprint_both_set():
|
||||
# test for input validation, expect user warning when
|
||||
# size and footprint is set
|
||||
with suppress_warnings() as sup:
|
||||
sup.filter(UserWarning,
|
||||
"ignoring size because footprint is set")
|
||||
arr = np.random.random((10, 20, 30))
|
||||
rank_filter(arr, 5, size=2, footprint=np.ones((1, 1, 10), dtype=bool))
|
||||
1086
venv/Lib/site-packages/scipy/ndimage/tests/test_measurements.py
Normal file
1086
venv/Lib/site-packages/scipy/ndimage/tests/test_measurements.py
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -0,0 +1,49 @@
|
|||
import numpy
|
||||
from pytest import raises as assert_raises
|
||||
|
||||
import scipy.ndimage as sndi
|
||||
|
||||
|
||||
def test_binary_erosion_noninteger_iterations():
|
||||
# regression test for gh-9905, gh-9909: ValueError for
|
||||
# non integer iterations
|
||||
data = numpy.ones([1])
|
||||
assert_raises(TypeError, sndi.binary_erosion, data, iterations=0.5)
|
||||
assert_raises(TypeError, sndi.binary_erosion, data, iterations=1.5)
|
||||
|
||||
|
||||
def test_binary_dilation_noninteger_iterations():
|
||||
# regression test for gh-9905, gh-9909: ValueError for
|
||||
# non integer iterations
|
||||
data = numpy.ones([1])
|
||||
assert_raises(TypeError, sndi.binary_dilation, data, iterations=0.5)
|
||||
assert_raises(TypeError, sndi.binary_dilation, data, iterations=1.5)
|
||||
|
||||
|
||||
def test_binary_opening_noninteger_iterations():
|
||||
# regression test for gh-9905, gh-9909: ValueError for
|
||||
# non integer iterations
|
||||
data = numpy.ones([1])
|
||||
assert_raises(TypeError, sndi.binary_opening, data, iterations=0.5)
|
||||
assert_raises(TypeError, sndi.binary_opening, data, iterations=1.5)
|
||||
|
||||
|
||||
def test_binary_closing_noninteger_iterations():
|
||||
# regression test for gh-9905, gh-9909: ValueError for
|
||||
# non integer iterations
|
||||
data = numpy.ones([1])
|
||||
assert_raises(TypeError, sndi.binary_closing, data, iterations=0.5)
|
||||
assert_raises(TypeError, sndi.binary_closing, data, iterations=1.5)
|
||||
|
||||
|
||||
def test_binary_closing_noninteger_brute_force_passes_when_true():
|
||||
# regression test for gh-9905, gh-9909: ValueError for
|
||||
# non integer iterations
|
||||
data = numpy.ones([1])
|
||||
|
||||
assert sndi.binary_erosion(
|
||||
data, iterations=2, brute_force=1.5
|
||||
) == sndi.binary_erosion(data, iterations=2, brute_force=bool(1.5))
|
||||
assert sndi.binary_erosion(
|
||||
data, iterations=2, brute_force=0.0
|
||||
) == sndi.binary_erosion(data, iterations=2, brute_force=bool(0.0))
|
||||
4807
venv/Lib/site-packages/scipy/ndimage/tests/test_ndimage.py
Normal file
4807
venv/Lib/site-packages/scipy/ndimage/tests/test_ndimage.py
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -0,0 +1,45 @@
|
|||
import numpy as np
|
||||
from numpy.testing import assert_array_almost_equal
|
||||
|
||||
import scipy.ndimage as ndimage
|
||||
|
||||
|
||||
def test_byte_order_median():
|
||||
"""Regression test for #413: median_filter does not handle bytes orders."""
|
||||
a = np.arange(9, dtype='<f4').reshape(3, 3)
|
||||
ref = ndimage.filters.median_filter(a,(3, 3))
|
||||
b = np.arange(9, dtype='>f4').reshape(3, 3)
|
||||
t = ndimage.filters.median_filter(b, (3, 3))
|
||||
assert_array_almost_equal(ref, t)
|
||||
|
||||
|
||||
def test_zoom_output_shape():
|
||||
"""Ticket #643"""
|
||||
x = np.arange(12).reshape((3,4))
|
||||
ndimage.zoom(x, 2, output=np.zeros((6,8)))
|
||||
|
||||
|
||||
def test_ticket_742():
|
||||
def SE(img, thresh=.7, size=4):
|
||||
mask = img > thresh
|
||||
rank = len(mask.shape)
|
||||
la, co = ndimage.label(mask,
|
||||
ndimage.generate_binary_structure(rank, rank))
|
||||
_ = ndimage.find_objects(la)
|
||||
|
||||
if np.dtype(np.intp) != np.dtype('i'):
|
||||
shape = (3,1240,1240)
|
||||
a = np.random.rand(np.prod(shape)).reshape(shape)
|
||||
# shouldn't crash
|
||||
SE(a)
|
||||
|
||||
|
||||
def test_gh_issue_3025():
|
||||
"""Github issue #3025 - improper merging of labels"""
|
||||
d = np.zeros((60,320))
|
||||
d[:,:257] = 1
|
||||
d[:,260:] = 1
|
||||
d[36,257] = 1
|
||||
d[35,258] = 1
|
||||
d[35,259] = 1
|
||||
assert ndimage.label(d, np.ones((3,3)))[1] == 1
|
||||
65
venv/Lib/site-packages/scipy/ndimage/tests/test_splines.py
Normal file
65
venv/Lib/site-packages/scipy/ndimage/tests/test_splines.py
Normal file
|
|
@ -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 == 'wrap':
|
||||
start, step = -1, -1
|
||||
else:
|
||||
raise ValueError('unsupported mode {}'.format(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', '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))
|
||||
Loading…
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Reference in a new issue