update
This commit is contained in:
@@ -1,169 +0,0 @@
|
||||
"""
|
||||
=========================================================
|
||||
Multidimensional image processing (:mod:`scipy.ndimage`)
|
||||
=========================================================
|
||||
|
||||
.. currentmodule:: scipy.ndimage
|
||||
|
||||
This package contains various functions for multidimensional image
|
||||
processing.
|
||||
|
||||
|
||||
Filters
|
||||
=======
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
convolve - Multidimensional convolution
|
||||
convolve1d - 1-D convolution along the given axis
|
||||
correlate - Multidimensional correlation
|
||||
correlate1d - 1-D correlation along the given axis
|
||||
gaussian_filter
|
||||
gaussian_filter1d
|
||||
gaussian_gradient_magnitude
|
||||
gaussian_laplace
|
||||
generic_filter - Multidimensional filter using a given function
|
||||
generic_filter1d - 1-D generic filter along the given axis
|
||||
generic_gradient_magnitude
|
||||
generic_laplace
|
||||
laplace - N-D Laplace filter based on approximate second derivatives
|
||||
maximum_filter
|
||||
maximum_filter1d
|
||||
median_filter - Calculates a multidimensional median filter
|
||||
minimum_filter
|
||||
minimum_filter1d
|
||||
percentile_filter - Calculates a multidimensional percentile filter
|
||||
prewitt
|
||||
rank_filter - Calculates a multidimensional rank filter
|
||||
sobel
|
||||
uniform_filter - Multidimensional uniform filter
|
||||
uniform_filter1d - 1-D uniform filter along the given axis
|
||||
|
||||
Fourier filters
|
||||
===============
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
fourier_ellipsoid
|
||||
fourier_gaussian
|
||||
fourier_shift
|
||||
fourier_uniform
|
||||
|
||||
Interpolation
|
||||
=============
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
affine_transform - Apply an affine transformation
|
||||
geometric_transform - Apply an arbritrary geometric transform
|
||||
map_coordinates - Map input array to new coordinates by interpolation
|
||||
rotate - Rotate an array
|
||||
shift - Shift an array
|
||||
spline_filter
|
||||
spline_filter1d
|
||||
zoom - Zoom an array
|
||||
|
||||
Measurements
|
||||
============
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
center_of_mass - The center of mass of the values of an array at labels
|
||||
extrema - Min's and max's of an array at labels, with their positions
|
||||
find_objects - Find objects in a labeled array
|
||||
histogram - Histogram of the values of an array, optionally at labels
|
||||
label - Label features in an array
|
||||
labeled_comprehension
|
||||
maximum
|
||||
maximum_position
|
||||
mean - Mean of the values of an array at labels
|
||||
median
|
||||
minimum
|
||||
minimum_position
|
||||
standard_deviation - Standard deviation of an N-D image array
|
||||
sum_labels - Sum of the values of the array
|
||||
value_indices - Find indices of each distinct value in given array
|
||||
variance - Variance of the values of an N-D image array
|
||||
watershed_ift
|
||||
|
||||
Morphology
|
||||
==========
|
||||
|
||||
.. autosummary::
|
||||
:toctree: generated/
|
||||
|
||||
binary_closing
|
||||
binary_dilation
|
||||
binary_erosion
|
||||
binary_fill_holes
|
||||
binary_hit_or_miss
|
||||
binary_opening
|
||||
binary_propagation
|
||||
black_tophat
|
||||
distance_transform_bf
|
||||
distance_transform_cdt
|
||||
distance_transform_edt
|
||||
generate_binary_structure
|
||||
grey_closing
|
||||
grey_dilation
|
||||
grey_erosion
|
||||
grey_opening
|
||||
iterate_structure
|
||||
morphological_gradient
|
||||
morphological_laplace
|
||||
white_tophat
|
||||
|
||||
"""
|
||||
|
||||
# Copyright (C) 2003-2005 Peter J. Verveer
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions
|
||||
# are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above
|
||||
# copyright notice, this list of conditions and the following
|
||||
# disclaimer in the documentation and/or other materials provided
|
||||
# with the distribution.
|
||||
#
|
||||
# 3. The name of the author may not be used to endorse or promote
|
||||
# products derived from this software without specific prior
|
||||
# written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
|
||||
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
||||
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
|
||||
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
|
||||
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
|
||||
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
from ._filters import * # noqa: F401 F403
|
||||
from ._fourier import * # noqa: F401 F403
|
||||
from ._interpolation import * # noqa: F401 F403
|
||||
from ._measurements import * # noqa: F401 F403
|
||||
from ._morphology import * # noqa: F401 F403
|
||||
|
||||
# Deprecated namespaces, to be removed in v2.0.0
|
||||
from . import filters # noqa: F401
|
||||
from . import fourier # noqa: F401
|
||||
from . import interpolation # noqa: F401
|
||||
from . import measurements # noqa: F401
|
||||
from . import morphology # noqa: F401
|
||||
|
||||
__all__ = [s for s in dir() if not s.startswith('_')]
|
||||
|
||||
from scipy._lib._testutils import PytestTester
|
||||
test = PytestTester(__name__)
|
||||
del PytestTester
|
||||
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.
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.
File diff suppressed because it is too large
Load Diff
@@ -1,307 +0,0 @@
|
||||
# Copyright (C) 2003-2005 Peter J. Verveer
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions
|
||||
# are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above
|
||||
# copyright notice, this list of conditions and the following
|
||||
# disclaimer in the documentation and/or other materials provided
|
||||
# with the distribution.
|
||||
#
|
||||
# 3. The name of the author may not be used to endorse or promote
|
||||
# products derived from this software without specific prior
|
||||
# written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
|
||||
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
||||
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
|
||||
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
|
||||
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
|
||||
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
import numpy
|
||||
from numpy.core.multiarray import normalize_axis_index
|
||||
from . import _ni_support
|
||||
from . import _nd_image
|
||||
|
||||
__all__ = ['fourier_gaussian', 'fourier_uniform', 'fourier_ellipsoid',
|
||||
'fourier_shift']
|
||||
|
||||
|
||||
def _get_output_fourier(output, input):
|
||||
if output is None:
|
||||
if input.dtype.type in [numpy.complex64, numpy.complex128,
|
||||
numpy.float32]:
|
||||
output = numpy.zeros(input.shape, dtype=input.dtype)
|
||||
else:
|
||||
output = numpy.zeros(input.shape, dtype=numpy.float64)
|
||||
elif type(output) is type:
|
||||
if output not in [numpy.complex64, numpy.complex128,
|
||||
numpy.float32, numpy.float64]:
|
||||
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 _get_output_fourier_complex(output, input):
|
||||
if output is None:
|
||||
if input.dtype.type in [numpy.complex64, numpy.complex128]:
|
||||
output = numpy.zeros(input.shape, dtype=input.dtype)
|
||||
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.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_gaussian : ndarray
|
||||
The filtered input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, datasets
|
||||
>>> import numpy.fft
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = datasets.ascent()
|
||||
>>> input_ = numpy.fft.fft2(ascent)
|
||||
>>> result = ndimage.fourier_gaussian(input_, sigma=4)
|
||||
>>> result = numpy.fft.ifft2(result)
|
||||
>>> ax1.imshow(ascent)
|
||||
>>> ax2.imshow(result.real) # the imaginary part is an artifact
|
||||
>>> plt.show()
|
||||
"""
|
||||
input = 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.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_uniform : ndarray
|
||||
The filtered input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, datasets
|
||||
>>> import numpy.fft
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = datasets.ascent()
|
||||
>>> input_ = numpy.fft.fft2(ascent)
|
||||
>>> result = ndimage.fourier_uniform(input_, size=20)
|
||||
>>> result = numpy.fft.ifft2(result)
|
||||
>>> ax1.imshow(ascent)
|
||||
>>> ax2.imshow(result.real) # the imaginary part is an artifact
|
||||
>>> plt.show()
|
||||
"""
|
||||
input = 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 an ellipsoid of
|
||||
given sizes.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
input : array_like
|
||||
The input array.
|
||||
size : float or sequence
|
||||
The size of the box used for filtering.
|
||||
If a float, `size` is the same for all axes. If a sequence, `size` has
|
||||
to contain one value for each axis.
|
||||
n : int, optional
|
||||
If `n` is negative (default), then the input is assumed to be the
|
||||
result of a complex fft.
|
||||
If `n` is larger than or equal to zero, the input is assumed to be the
|
||||
result of a real fft, and `n` gives the length of the array before
|
||||
transformation along the real transform direction.
|
||||
axis : int, optional
|
||||
The axis of the real transform.
|
||||
output : ndarray, optional
|
||||
If given, the result of filtering the input is placed in this array.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_ellipsoid : ndarray
|
||||
The filtered input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This function is implemented for arrays of rank 1, 2, or 3.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, datasets
|
||||
>>> import numpy.fft
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = datasets.ascent()
|
||||
>>> input_ = numpy.fft.fft2(ascent)
|
||||
>>> result = ndimage.fourier_ellipsoid(input_, size=20)
|
||||
>>> result = numpy.fft.ifft2(result)
|
||||
>>> ax1.imshow(ascent)
|
||||
>>> ax2.imshow(result.real) # the imaginary part is an artifact
|
||||
>>> plt.show()
|
||||
"""
|
||||
input = numpy.asarray(input)
|
||||
if input.ndim > 3:
|
||||
raise NotImplementedError("Only 1d, 2d and 3d inputs are supported")
|
||||
output = _get_output_fourier(output, input)
|
||||
if output.size == 0:
|
||||
# The C code has a bug that can result in a segfault with arrays
|
||||
# that have size 0 (gh-17270), so check here.
|
||||
return output
|
||||
axis = normalize_axis_index(axis, input.ndim)
|
||||
sizes = _ni_support._normalize_sequence(size, input.ndim)
|
||||
sizes = 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.
|
||||
|
||||
Returns
|
||||
-------
|
||||
fourier_shift : ndarray
|
||||
The shifted input.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, datasets
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> import numpy.fft
|
||||
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
>>> plt.gray() # show the filtered result in grayscale
|
||||
>>> ascent = datasets.ascent()
|
||||
>>> input_ = numpy.fft.fft2(ascent)
|
||||
>>> result = ndimage.fourier_shift(input_, shift=200)
|
||||
>>> result = numpy.fft.ifft2(result)
|
||||
>>> ax1.imshow(ascent)
|
||||
>>> ax2.imshow(result.real) # the imaginary part is an artifact
|
||||
>>> plt.show()
|
||||
"""
|
||||
input = 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
|
||||
@@ -1,960 +0,0 @@
|
||||
# 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 scipy import special
|
||||
from . import _ni_support
|
||||
from . import _nd_image
|
||||
from ._ni_docstrings import docfiller
|
||||
|
||||
|
||||
__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_interp_mirror)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
spline_filter1d : ndarray
|
||||
The filtered input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
All of the interpolation functions in `ndimage` 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.
|
||||
|
||||
For complex-valued `input`, this function processes the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
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 numpy as np
|
||||
>>> 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)
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
spline_filter1d(input.real, order, axis, output.real, mode)
|
||||
spline_filter1d(input.imag, order, axis, output.imag, mode)
|
||||
return output
|
||||
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.
|
||||
|
||||
For complex-valued `input`, this function processes the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
Examples
|
||||
--------
|
||||
We can filter an image using multidimentional splines:
|
||||
|
||||
>>> from scipy.ndimage import spline_filter
|
||||
>>> import numpy as np
|
||||
>>> 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)
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
spline_filter(input.real, order, output.real, mode)
|
||||
spline_filter(input.imag, order, output.imag, mode)
|
||||
return output
|
||||
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
|
||||
|
||||
|
||||
def _prepad_for_spline_filter(input, mode, cval):
|
||||
if mode in ['nearest', 'grid-constant']:
|
||||
npad = 12
|
||||
if mode == 'grid-constant':
|
||||
padded = numpy.pad(input, npad, mode='constant',
|
||||
constant_values=cval)
|
||||
elif mode == 'nearest':
|
||||
padded = numpy.pad(input, npad, mode='edge')
|
||||
else:
|
||||
# other modes have exact boundary conditions implemented so
|
||||
# no prepadding is needed
|
||||
npad = 0
|
||||
padded = input
|
||||
return padded, npad
|
||||
|
||||
|
||||
@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_interp_constant)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.
|
||||
|
||||
For complex-valued `input`, this function transforms the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
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 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')
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input, shape=output_shape,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
kwargs = dict(order=order, mode=mode, prefilter=prefilter,
|
||||
output_shape=output_shape,
|
||||
extra_arguments=extra_arguments,
|
||||
extra_keywords=extra_keywords)
|
||||
geometric_transform(input.real, mapping, output=output.real,
|
||||
cval=numpy.real(cval), **kwargs)
|
||||
geometric_transform(input.imag, mapping, output=output.imag,
|
||||
cval=numpy.imag(cval), **kwargs)
|
||||
return output
|
||||
|
||||
if prefilter and order > 1:
|
||||
padded, npad = _prepad_for_spline_filter(input, mode, cval)
|
||||
filtered = spline_filter(padded, order, output=numpy.float64,
|
||||
mode=mode)
|
||||
else:
|
||||
npad = 0
|
||||
filtered = input
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
_nd_image.geometric_transform(filtered, mapping, None, None, None, output,
|
||||
order, mode, cval, npad, 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_interp_constant)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
|
||||
|
||||
Notes
|
||||
-----
|
||||
For complex-valued `input`, this function maps the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage
|
||||
>>> import numpy as np
|
||||
>>> 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)
|
||||
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')
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input, shape=output_shape,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
kwargs = dict(order=order, mode=mode, prefilter=prefilter)
|
||||
map_coordinates(input.real, coordinates, output=output.real,
|
||||
cval=numpy.real(cval), **kwargs)
|
||||
map_coordinates(input.imag, coordinates, output=output.imag,
|
||||
cval=numpy.imag(cval), **kwargs)
|
||||
return output
|
||||
if prefilter and order > 1:
|
||||
padded, npad = _prepad_for_spline_filter(input, mode, cval)
|
||||
filtered = spline_filter(padded, order, output=numpy.float64,
|
||||
mode=mode)
|
||||
else:
|
||||
npad = 0
|
||||
filtered = input
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
|
||||
output, order, mode, cval, npad, 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_interp_constant)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)``.
|
||||
|
||||
For complex-valued `input`, this function transforms the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
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 output_shape is None:
|
||||
if isinstance(output, numpy.ndarray):
|
||||
output_shape = output.shape
|
||||
else:
|
||||
output_shape = input.shape
|
||||
if input.ndim < 1 or len(output_shape) < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input, shape=output_shape,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
kwargs = dict(offset=offset, output_shape=output_shape, order=order,
|
||||
mode=mode, prefilter=prefilter)
|
||||
affine_transform(input.real, matrix, output=output.real,
|
||||
cval=numpy.real(cval), **kwargs)
|
||||
affine_transform(input.imag, matrix, output=output.imag,
|
||||
cval=numpy.imag(cval), **kwargs)
|
||||
return output
|
||||
if prefilter and order > 1:
|
||||
padded, npad = _prepad_for_spline_filter(input, mode, cval)
|
||||
filtered = spline_filter(padded, order, output=numpy.float64,
|
||||
mode=mode)
|
||||
else:
|
||||
npad = 0
|
||||
filtered = input
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
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, npad, False)
|
||||
else:
|
||||
_nd_image.geometric_transform(filtered, None, None, matrix, offset,
|
||||
output, order, mode, cval, npad, 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_interp_constant)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
shift : ndarray
|
||||
The shifted input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
For complex-valued `input`, this function shifts the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
"""
|
||||
if order < 0 or order > 5:
|
||||
raise RuntimeError('spline order not supported')
|
||||
input = numpy.asarray(input)
|
||||
if input.ndim < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
# import under different name to avoid confusion with shift parameter
|
||||
from scipy.ndimage._interpolation import shift as _shift
|
||||
|
||||
kwargs = dict(order=order, mode=mode, prefilter=prefilter)
|
||||
_shift(input.real, shift, output=output.real, cval=numpy.real(cval),
|
||||
**kwargs)
|
||||
_shift(input.imag, shift, output=output.imag, cval=numpy.imag(cval),
|
||||
**kwargs)
|
||||
return output
|
||||
if prefilter and order > 1:
|
||||
padded, npad = _prepad_for_spline_filter(input, mode, cval)
|
||||
filtered = spline_filter(padded, order, output=numpy.float64,
|
||||
mode=mode)
|
||||
else:
|
||||
npad = 0
|
||||
filtered = input
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
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,
|
||||
npad, False)
|
||||
return output
|
||||
|
||||
|
||||
@docfiller
|
||||
def zoom(input, zoom, output=None, order=3, mode='constant', cval=0.0,
|
||||
prefilter=True, *, grid_mode=False):
|
||||
"""
|
||||
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_interp_constant)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
grid_mode : bool, optional
|
||||
If False, the distance from the pixel centers is zoomed. Otherwise, the
|
||||
distance including the full pixel extent is used. For example, a 1d
|
||||
signal of length 5 is considered to have length 4 when `grid_mode` is
|
||||
False, but length 5 when `grid_mode` is True. See the following
|
||||
visual illustration:
|
||||
|
||||
.. code-block:: text
|
||||
|
||||
| pixel 1 | pixel 2 | pixel 3 | pixel 4 | pixel 5 |
|
||||
|<-------------------------------------->|
|
||||
vs.
|
||||
|<----------------------------------------------->|
|
||||
|
||||
The starting point of the arrow in the diagram above corresponds to
|
||||
coordinate location 0 in each mode.
|
||||
|
||||
Returns
|
||||
-------
|
||||
zoom : ndarray
|
||||
The zoomed input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
For complex-valued `input`, this function zooms the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, datasets
|
||||
>>> import matplotlib.pyplot as plt
|
||||
|
||||
>>> fig = plt.figure()
|
||||
>>> ax1 = fig.add_subplot(121) # left side
|
||||
>>> ax2 = fig.add_subplot(122) # right side
|
||||
>>> ascent = datasets.ascent()
|
||||
>>> result = ndimage.zoom(ascent, 3.0)
|
||||
>>> ax1.imshow(ascent, vmin=0, vmax=255)
|
||||
>>> ax2.imshow(result, vmin=0, vmax=255)
|
||||
>>> 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 input.ndim < 1:
|
||||
raise RuntimeError('input and output rank must be > 0')
|
||||
zoom = _ni_support._normalize_sequence(zoom, input.ndim)
|
||||
output_shape = tuple(
|
||||
[int(round(ii * jj)) for ii, jj in zip(input.shape, zoom)])
|
||||
complex_output = numpy.iscomplexobj(input)
|
||||
output = _ni_support._get_output(output, input, shape=output_shape,
|
||||
complex_output=complex_output)
|
||||
if complex_output:
|
||||
# import under different name to avoid confusion with zoom parameter
|
||||
from scipy.ndimage._interpolation import zoom as _zoom
|
||||
|
||||
kwargs = dict(order=order, mode=mode, prefilter=prefilter)
|
||||
_zoom(input.real, zoom, output=output.real, cval=numpy.real(cval),
|
||||
**kwargs)
|
||||
_zoom(input.imag, zoom, output=output.imag, cval=numpy.imag(cval),
|
||||
**kwargs)
|
||||
return output
|
||||
if prefilter and order > 1:
|
||||
padded, npad = _prepad_for_spline_filter(input, mode, cval)
|
||||
filtered = spline_filter(padded, order, output=numpy.float64,
|
||||
mode=mode)
|
||||
else:
|
||||
npad = 0
|
||||
filtered = input
|
||||
if grid_mode:
|
||||
# warn about modes that may have surprising behavior
|
||||
suggest_mode = None
|
||||
if mode == 'constant':
|
||||
suggest_mode = 'grid-constant'
|
||||
elif mode == 'wrap':
|
||||
suggest_mode = 'grid-wrap'
|
||||
if suggest_mode is not None:
|
||||
warnings.warn(
|
||||
("It is recommended to use mode = {} instead of {} when "
|
||||
"grid_mode is True.").format(suggest_mode, mode)
|
||||
)
|
||||
mode = _ni_support._extend_mode_to_code(mode)
|
||||
|
||||
zoom_div = numpy.array(output_shape)
|
||||
zoom_nominator = numpy.array(input.shape)
|
||||
if not grid_mode:
|
||||
zoom_div -= 1
|
||||
zoom_nominator -= 1
|
||||
|
||||
# Zooming to infinite values is unpredictable, so just choose
|
||||
# zoom factor 1 instead
|
||||
zoom = numpy.divide(zoom_nominator, zoom_div,
|
||||
out=numpy.ones_like(input.shape, dtype=numpy.float64),
|
||||
where=zoom_div != 0)
|
||||
zoom = numpy.ascontiguousarray(zoom)
|
||||
_nd_image.zoom_shift(filtered, zoom, None, output, order, mode, cval, npad,
|
||||
grid_mode)
|
||||
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_interp_constant)s
|
||||
%(cval)s
|
||||
%(prefilter)s
|
||||
|
||||
Returns
|
||||
-------
|
||||
rotate : ndarray
|
||||
The rotated input.
|
||||
|
||||
Notes
|
||||
-----
|
||||
For complex-valued `input`, this function rotates the real and imaginary
|
||||
components independently.
|
||||
|
||||
.. versionadded:: 1.6.0
|
||||
Complex-valued support added.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> from scipy import ndimage, datasets
|
||||
>>> import matplotlib.pyplot as plt
|
||||
>>> fig = plt.figure(figsize=(10, 3))
|
||||
>>> ax1, ax2, ax3 = fig.subplots(1, 3)
|
||||
>>> img = datasets.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_layout_engine('tight')
|
||||
>>> 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()
|
||||
|
||||
c, s = special.cosdg(angle), special.sindg(angle)
|
||||
|
||||
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)
|
||||
|
||||
complex_output = numpy.iscomplexobj(input_arr)
|
||||
output = _ni_support._get_output(output, input_arr, shape=output_shape,
|
||||
complex_output=complex_output)
|
||||
|
||||
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
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Binary file not shown.
Binary file not shown.
@@ -1,208 +0,0 @@
|
||||
"""Docstring components common to several ndimage functions."""
|
||||
from scipy._lib import doccer
|
||||
|
||||
__all__ = ['docfiller']
|
||||
|
||||
|
||||
_input_doc = (
|
||||
"""input : array_like
|
||||
The input array.""")
|
||||
_axis_doc = (
|
||||
"""axis : int, optional
|
||||
The axis of `input` along which to calculate. Default is -1.""")
|
||||
_output_doc = (
|
||||
"""output : array or dtype, optional
|
||||
The array in which to place the output, or the dtype of the
|
||||
returned array. By default an array of the same dtype as input
|
||||
will be created.""")
|
||||
_size_foot_doc = (
|
||||
"""size : scalar or tuple, optional
|
||||
See footprint, below. Ignored if footprint is given.
|
||||
footprint : array, optional
|
||||
Either `size` or `footprint` must be defined. `size` gives
|
||||
the shape that is taken from the input array, at every element
|
||||
position, to define the input to the filter function.
|
||||
`footprint` is a boolean array that specifies (implicitly) a
|
||||
shape, but also which of the elements within this shape will get
|
||||
passed to the filter function. Thus ``size=(n,m)`` is equivalent
|
||||
to ``footprint=np.ones((n,m))``. We adjust `size` to the number
|
||||
of dimensions of the input array, so that, if the input array is
|
||||
shape (10,10,10), and `size` is 2, then the actual size used is
|
||||
(2,2,2). When `footprint` is given, `size` is ignored.""")
|
||||
_mode_reflect_doc = (
|
||||
"""mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
|
||||
The `mode` parameter determines how the input array is extended
|
||||
beyond its boundaries. Default is 'reflect'. Behavior for each valid
|
||||
value is as follows:
|
||||
|
||||
'reflect' (`d c b a | a b c d | d c b a`)
|
||||
The input is extended by reflecting about the edge of the last
|
||||
pixel. This mode is also sometimes referred to as half-sample
|
||||
symmetric.
|
||||
|
||||
'constant' (`k k k k | a b c d | k k k k`)
|
||||
The input is extended by filling all values beyond the edge with
|
||||
the same constant value, defined by the `cval` parameter.
|
||||
|
||||
'nearest' (`a a a a | a b c d | d d d d`)
|
||||
The input is extended by replicating the last pixel.
|
||||
|
||||
'mirror' (`d c b | a b c d | c b a`)
|
||||
The input is extended by reflecting about the center of the last
|
||||
pixel. This mode is also sometimes referred to as whole-sample
|
||||
symmetric.
|
||||
|
||||
'wrap' (`a b c d | a b c d | a b c d`)
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
|
||||
For consistency with the interpolation functions, the following mode
|
||||
names can also be used:
|
||||
|
||||
'grid-mirror'
|
||||
This is a synonym for 'reflect'.
|
||||
|
||||
'grid-constant'
|
||||
This is a synonym for 'constant'.
|
||||
|
||||
'grid-wrap'
|
||||
This is a synonym for 'wrap'.""")
|
||||
|
||||
_mode_interp_constant_doc = (
|
||||
"""mode : {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', \
|
||||
'mirror', 'grid-wrap', 'wrap'}, optional
|
||||
The `mode` parameter determines how the input array is extended
|
||||
beyond its boundaries. Default is 'constant'. Behavior for each valid
|
||||
value is as follows (see additional plots and details on
|
||||
:ref:`boundary modes <ndimage-interpolation-modes>`):
|
||||
|
||||
'reflect' (`d c b a | a b c d | d c b a`)
|
||||
The input is extended by reflecting about the edge of the last
|
||||
pixel. This mode is also sometimes referred to as half-sample
|
||||
symmetric.
|
||||
|
||||
'grid-mirror'
|
||||
This is a synonym for 'reflect'.
|
||||
|
||||
'constant' (`k k k k | a b c d | k k k k`)
|
||||
The input is extended by filling all values beyond the edge with
|
||||
the same constant value, defined by the `cval` parameter. No
|
||||
interpolation is performed beyond the edges of the input.
|
||||
|
||||
'grid-constant' (`k k k k | a b c d | k k k k`)
|
||||
The input is extended by filling all values beyond the edge with
|
||||
the same constant value, defined by the `cval` parameter. Interpolation
|
||||
occurs for samples outside the input's extent as well.
|
||||
|
||||
'nearest' (`a a a a | a b c d | d d d d`)
|
||||
The input is extended by replicating the last pixel.
|
||||
|
||||
'mirror' (`d c b | a b c d | c b a`)
|
||||
The input is extended by reflecting about the center of the last
|
||||
pixel. This mode is also sometimes referred to as whole-sample
|
||||
symmetric.
|
||||
|
||||
'grid-wrap' (`a b c d | a b c d | a b c d`)
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
|
||||
'wrap' (`d b c d | a b c d | b c a b`)
|
||||
The input is extended by wrapping around to the opposite edge, but in a
|
||||
way such that the last point and initial point exactly overlap. In this
|
||||
case it is not well defined which sample will be chosen at the point of
|
||||
overlap.""")
|
||||
_mode_interp_mirror_doc = (
|
||||
_mode_interp_constant_doc.replace("Default is 'constant'",
|
||||
"Default is 'mirror'")
|
||||
)
|
||||
assert _mode_interp_mirror_doc != _mode_interp_constant_doc, \
|
||||
'Default not replaced'
|
||||
|
||||
_mode_multiple_doc = (
|
||||
"""mode : str or sequence, optional
|
||||
The `mode` parameter determines how the input array is extended
|
||||
when the filter overlaps a border. By passing a sequence of modes
|
||||
with length equal to the number of dimensions of the input array,
|
||||
different modes can be specified along each axis. Default value is
|
||||
'reflect'. The valid values and their behavior is as follows:
|
||||
|
||||
'reflect' (`d c b a | a b c d | d c b a`)
|
||||
The input is extended by reflecting about the edge of the last
|
||||
pixel. This mode is also sometimes referred to as half-sample
|
||||
symmetric.
|
||||
|
||||
'constant' (`k k k k | a b c d | k k k k`)
|
||||
The input is extended by filling all values beyond the edge with
|
||||
the same constant value, defined by the `cval` parameter.
|
||||
|
||||
'nearest' (`a a a a | a b c d | d d d d`)
|
||||
The input is extended by replicating the last pixel.
|
||||
|
||||
'mirror' (`d c b | a b c d | c b a`)
|
||||
The input is extended by reflecting about the center of the last
|
||||
pixel. This mode is also sometimes referred to as whole-sample
|
||||
symmetric.
|
||||
|
||||
'wrap' (`a b c d | a b c d | a b c d`)
|
||||
The input is extended by wrapping around to the opposite edge.
|
||||
|
||||
For consistency with the interpolation functions, the following mode
|
||||
names can also be used:
|
||||
|
||||
'grid-constant'
|
||||
This is a synonym for 'constant'.
|
||||
|
||||
'grid-mirror'
|
||||
This is a synonym for 'reflect'.
|
||||
|
||||
'grid-wrap'
|
||||
This is a synonym for 'wrap'.""")
|
||||
_cval_doc = (
|
||||
"""cval : scalar, optional
|
||||
Value to fill past edges of input if `mode` is 'constant'. Default
|
||||
is 0.0.""")
|
||||
_origin_doc = (
|
||||
"""origin : int, optional
|
||||
Controls the placement of the filter on the input array's pixels.
|
||||
A value of 0 (the default) centers the filter over the pixel, with
|
||||
positive values shifting the filter to the left, and negative ones
|
||||
to the right.""")
|
||||
_origin_multiple_doc = (
|
||||
"""origin : int or sequence, optional
|
||||
Controls the placement of the filter on the input array's pixels.
|
||||
A value of 0 (the default) centers the filter over the pixel, with
|
||||
positive values shifting the filter to the left, and negative ones
|
||||
to the right. By passing a sequence of origins with length equal to
|
||||
the number of dimensions of the input array, different shifts can
|
||||
be specified along each axis.""")
|
||||
_extra_arguments_doc = (
|
||||
"""extra_arguments : sequence, optional
|
||||
Sequence of extra positional arguments to pass to passed function.""")
|
||||
_extra_keywords_doc = (
|
||||
"""extra_keywords : dict, optional
|
||||
dict of extra keyword arguments to pass to passed function.""")
|
||||
_prefilter_doc = (
|
||||
"""prefilter : bool, optional
|
||||
Determines if the input array is prefiltered with `spline_filter`
|
||||
before interpolation. The default is True, which will create a
|
||||
temporary `float64` array of filtered values if `order > 1`. If
|
||||
setting this to False, the output will be slightly blurred if
|
||||
`order > 1`, unless the input is prefiltered, i.e. it is the result
|
||||
of calling `spline_filter` on the original input.""")
|
||||
|
||||
docdict = {
|
||||
'input': _input_doc,
|
||||
'axis': _axis_doc,
|
||||
'output': _output_doc,
|
||||
'size_foot': _size_foot_doc,
|
||||
'mode_interp_constant': _mode_interp_constant_doc,
|
||||
'mode_interp_mirror': _mode_interp_mirror_doc,
|
||||
'mode_reflect': _mode_reflect_doc,
|
||||
'mode_multiple': _mode_multiple_doc,
|
||||
'cval': _cval_doc,
|
||||
'origin': _origin_doc,
|
||||
'origin_multiple': _origin_multiple_doc,
|
||||
'extra_arguments': _extra_arguments_doc,
|
||||
'extra_keywords': _extra_keywords_doc,
|
||||
'prefilter': _prefilter_doc
|
||||
}
|
||||
|
||||
docfiller = doccer.filldoc(docdict)
|
||||
Binary file not shown.
Binary file not shown.
@@ -1,97 +0,0 @@
|
||||
# Copyright (C) 2003-2005 Peter J. Verveer
|
||||
#
|
||||
# Redistribution and use in source and binary forms, with or without
|
||||
# modification, are permitted provided that the following conditions
|
||||
# are met:
|
||||
#
|
||||
# 1. Redistributions of source code must retain the above copyright
|
||||
# notice, this list of conditions and the following disclaimer.
|
||||
#
|
||||
# 2. Redistributions in binary form must reproduce the above
|
||||
# copyright notice, this list of conditions and the following
|
||||
# disclaimer in the documentation and/or other materials provided
|
||||
# with the distribution.
|
||||
#
|
||||
# 3. The name of the author may not be used to endorse or promote
|
||||
# products derived from this software without specific prior
|
||||
# written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
|
||||
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
||||
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
|
||||
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
||||
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
|
||||
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
||||
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
|
||||
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
||||
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
|
||||
from collections.abc import Iterable
|
||||
import warnings
|
||||
import numpy
|
||||
|
||||
|
||||
def _extend_mode_to_code(mode):
|
||||
"""Convert an extension mode to the corresponding integer code.
|
||||
"""
|
||||
if mode == 'nearest':
|
||||
return 0
|
||||
elif mode == 'wrap':
|
||||
return 1
|
||||
elif mode in ['reflect', 'grid-mirror']:
|
||||
return 2
|
||||
elif mode == 'mirror':
|
||||
return 3
|
||||
elif mode == 'constant':
|
||||
return 4
|
||||
elif mode == 'grid-wrap':
|
||||
return 5
|
||||
elif mode == 'grid-constant':
|
||||
return 6
|
||||
else:
|
||||
raise RuntimeError('boundary mode not supported')
|
||||
|
||||
|
||||
def _normalize_sequence(input, rank):
|
||||
"""If input is a scalar, create a sequence of length equal to the
|
||||
rank by duplicating the input. If input is a sequence,
|
||||
check if its length is equal to the length of array.
|
||||
"""
|
||||
is_str = isinstance(input, str)
|
||||
if not is_str and isinstance(input, Iterable):
|
||||
normalized = list(input)
|
||||
if len(normalized) != rank:
|
||||
err = "sequence argument must have length equal to input rank"
|
||||
raise RuntimeError(err)
|
||||
else:
|
||||
normalized = [input] * rank
|
||||
return normalized
|
||||
|
||||
|
||||
def _get_output(output, input, shape=None, complex_output=False):
|
||||
if shape is None:
|
||||
shape = input.shape
|
||||
if output is None:
|
||||
if not complex_output:
|
||||
output = numpy.zeros(shape, dtype=input.dtype.name)
|
||||
else:
|
||||
complex_type = numpy.promote_types(input.dtype, numpy.complex64)
|
||||
output = numpy.zeros(shape, dtype=complex_type)
|
||||
elif isinstance(output, (type, numpy.dtype)):
|
||||
# Classes (like `np.float32`) and dtypes are interpreted as dtype
|
||||
if complex_output and numpy.dtype(output).kind != 'c':
|
||||
warnings.warn("promoting specified output dtype to complex")
|
||||
output = numpy.promote_types(output, numpy.complex64)
|
||||
output = numpy.zeros(shape, dtype=output)
|
||||
elif isinstance(output, str):
|
||||
output = numpy.sctypeDict[output]
|
||||
if complex_output and numpy.dtype(output).kind != 'c':
|
||||
raise RuntimeError("output must have complex dtype")
|
||||
output = numpy.zeros(shape, dtype=output)
|
||||
elif output.shape != shape:
|
||||
raise RuntimeError("output shape not correct")
|
||||
elif complex_output and output.dtype.kind != 'c':
|
||||
raise RuntimeError("output must have complex dtype")
|
||||
return output
|
||||
@@ -1,35 +0,0 @@
|
||||
# This file is not meant for public use and will be removed in SciPy v2.0.0.
|
||||
# Use the `scipy.ndimage` namespace for importing the functions
|
||||
# included below.
|
||||
|
||||
import warnings
|
||||
from . import _filters
|
||||
|
||||
|
||||
__all__ = [ # noqa: F822
|
||||
'correlate1d', 'convolve1d', 'gaussian_filter1d',
|
||||
'gaussian_filter', 'prewitt', 'sobel', 'generic_laplace',
|
||||
'laplace', 'gaussian_laplace', 'generic_gradient_magnitude',
|
||||
'gaussian_gradient_magnitude', 'correlate', 'convolve',
|
||||
'uniform_filter1d', 'uniform_filter', 'minimum_filter1d',
|
||||
'maximum_filter1d', 'minimum_filter', 'maximum_filter',
|
||||
'rank_filter', 'median_filter', 'percentile_filter',
|
||||
'generic_filter1d', 'generic_filter', 'normalize_axis_index'
|
||||
]
|
||||
|
||||
|
||||
def __dir__():
|
||||
return __all__
|
||||
|
||||
|
||||
def __getattr__(name):
|
||||
if name not in __all__:
|
||||
raise AttributeError(
|
||||
"scipy.ndimage.filters is deprecated and has no attribute "
|
||||
f"{name}. Try looking in scipy.ndimage instead.")
|
||||
|
||||
warnings.warn(f"Please use `{name}` from the `scipy.ndimage` namespace, "
|
||||
"the `scipy.ndimage.filters` namespace is deprecated.",
|
||||
category=DeprecationWarning, stacklevel=2)
|
||||
|
||||
return getattr(_filters, name)
|
||||
@@ -1,29 +0,0 @@
|
||||
# This file is not meant for public use and will be removed in SciPy v2.0.0.
|
||||
# Use the `scipy.ndimage` namespace for importing the functions
|
||||
# included below.
|
||||
|
||||
import warnings
|
||||
from . import _fourier
|
||||
|
||||
|
||||
__all__ = [ # noqa: F822
|
||||
'fourier_gaussian', 'fourier_uniform',
|
||||
'fourier_ellipsoid', 'fourier_shift', 'normalize_axis_index'
|
||||
]
|
||||
|
||||
|
||||
def __dir__():
|
||||
return __all__
|
||||
|
||||
|
||||
def __getattr__(name):
|
||||
if name not in __all__:
|
||||
raise AttributeError(
|
||||
"scipy.ndimage.fourier is deprecated and has no attribute "
|
||||
f"{name}. Try looking in scipy.ndimage instead.")
|
||||
|
||||
warnings.warn(f"Please use `{name}` from the `scipy.ndimage` namespace, "
|
||||
"the `scipy.ndimage.fourier` namespace is deprecated.",
|
||||
category=DeprecationWarning, stacklevel=2)
|
||||
|
||||
return getattr(_fourier, name)
|
||||
@@ -1,31 +0,0 @@
|
||||
# This file is not meant for public use and will be removed in SciPy v2.0.0.
|
||||
# Use the `scipy.ndimage` namespace for importing the functions
|
||||
# included below.
|
||||
|
||||
import warnings
|
||||
from . import _interpolation
|
||||
|
||||
|
||||
__all__ = [ # noqa: F822
|
||||
'spline_filter1d', 'spline_filter',
|
||||
'geometric_transform', 'map_coordinates',
|
||||
'affine_transform', 'shift', 'zoom', 'rotate',
|
||||
'normalize_axis_index', 'docfiller'
|
||||
]
|
||||
|
||||
|
||||
def __dir__():
|
||||
return __all__
|
||||
|
||||
|
||||
def __getattr__(name):
|
||||
if name not in __all__:
|
||||
raise AttributeError(
|
||||
"scipy.ndimage.interpolation is deprecated and has no attribute "
|
||||
f"{name}. Try looking in scipy.ndimage instead.")
|
||||
|
||||
warnings.warn(f"Please use `{name}` from the `scipy.ndimage` namespace, "
|
||||
"the `scipy.ndimage.interpolation` namespace is deprecated.",
|
||||
category=DeprecationWarning, stacklevel=2)
|
||||
|
||||
return getattr(_interpolation, name)
|
||||
@@ -1,32 +0,0 @@
|
||||
# This file is not meant for public use and will be removed in SciPy v2.0.0.
|
||||
# Use the `scipy.ndimage` namespace for importing the functions
|
||||
# included below.
|
||||
|
||||
import warnings
|
||||
from . import _measurements
|
||||
|
||||
|
||||
__all__ = [ # noqa: F822
|
||||
'label', 'find_objects', 'labeled_comprehension',
|
||||
'sum', 'mean', 'variance', 'standard_deviation',
|
||||
'minimum', 'maximum', 'median', 'minimum_position',
|
||||
'maximum_position', 'extrema', 'center_of_mass',
|
||||
'histogram', 'watershed_ift', 'sum_labels'
|
||||
]
|
||||
|
||||
|
||||
def __dir__():
|
||||
return __all__
|
||||
|
||||
|
||||
def __getattr__(name):
|
||||
if name not in __all__:
|
||||
raise AttributeError(
|
||||
"scipy.ndimage.measurements is deprecated and has no attribute "
|
||||
f"{name}. Try looking in scipy.ndimage instead.")
|
||||
|
||||
warnings.warn(f"Please use `{name}` from the `scipy.ndimage` namespace, "
|
||||
"the `scipy.ndimage.measurements` namespace is deprecated.",
|
||||
category=DeprecationWarning, stacklevel=2)
|
||||
|
||||
return getattr(_measurements, name)
|
||||
@@ -1,35 +0,0 @@
|
||||
# This file is not meant for public use and will be removed in SciPy v2.0.0.
|
||||
# Use the `scipy.ndimage` namespace for importing the functions
|
||||
# included below.
|
||||
|
||||
import warnings
|
||||
from . import _morphology
|
||||
|
||||
|
||||
__all__ = [ # noqa: F822
|
||||
'iterate_structure', 'generate_binary_structure',
|
||||
'binary_erosion', 'binary_dilation', 'binary_opening',
|
||||
'binary_closing', 'binary_hit_or_miss', 'binary_propagation',
|
||||
'binary_fill_holes', 'grey_erosion', 'grey_dilation',
|
||||
'grey_opening', 'grey_closing', 'morphological_gradient',
|
||||
'morphological_laplace', 'white_tophat', 'black_tophat',
|
||||
'distance_transform_bf', 'distance_transform_cdt',
|
||||
'distance_transform_edt'
|
||||
]
|
||||
|
||||
|
||||
def __dir__():
|
||||
return __all__
|
||||
|
||||
|
||||
def __getattr__(name):
|
||||
if name not in __all__:
|
||||
raise AttributeError(
|
||||
"scipy.ndimage.morphology is deprecated and has no attribute "
|
||||
f"{name}. Try looking in scipy.ndimage instead.")
|
||||
|
||||
warnings.warn(f"Please use `{name}` from the `scipy.ndimage` namespace, "
|
||||
"the `scipy.ndimage.morphology` namespace is deprecated.",
|
||||
category=DeprecationWarning, stacklevel=2)
|
||||
|
||||
return getattr(_morphology, name)
|
||||
@@ -1,15 +0,0 @@
|
||||
|
||||
from __future__ import annotations
|
||||
from typing import List, Type
|
||||
import numpy
|
||||
|
||||
# list of numarray data types
|
||||
integer_types: List[Type] = [
|
||||
numpy.int8, numpy.uint8, numpy.int16, numpy.uint16,
|
||||
numpy.int32, numpy.uint32, numpy.int64, numpy.uint64]
|
||||
|
||||
float_types: List[Type] = [numpy.float32, numpy.float64]
|
||||
|
||||
complex_types: List[Type] = [numpy.complex64, numpy.complex128]
|
||||
|
||||
types: List[Type] = integer_types + float_types
|
||||
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.
@@ -1,21 +0,0 @@
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
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
|
||||
@@ -1,294 +0,0 @@
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 1 1 1
|
||||
1 1 1 1 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
|
||||
@@ -1,42 +0,0 @@
|
||||
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
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 2.1 KiB |
@@ -1,94 +0,0 @@
|
||||
import numpy as np
|
||||
from numpy.testing import assert_allclose
|
||||
|
||||
from scipy import ndimage
|
||||
from scipy.ndimage import _ctest
|
||||
from scipy.ndimage import _cytest
|
||||
from scipy._lib._ccallback import LowLevelCallable
|
||||
|
||||
FILTER1D_FUNCTIONS = [
|
||||
lambda filter_size: _ctest.filter1d(filter_size),
|
||||
lambda filter_size: _cytest.filter1d(filter_size, with_signature=False),
|
||||
lambda filter_size: LowLevelCallable(_cytest.filter1d(filter_size, with_signature=True)),
|
||||
lambda filter_size: LowLevelCallable.from_cython(_cytest, "_filter1d",
|
||||
_cytest.filter1d_capsule(filter_size)),
|
||||
]
|
||||
|
||||
FILTER2D_FUNCTIONS = [
|
||||
lambda weights: _ctest.filter2d(weights),
|
||||
lambda weights: _cytest.filter2d(weights, with_signature=False),
|
||||
lambda weights: LowLevelCallable(_cytest.filter2d(weights, with_signature=True)),
|
||||
lambda weights: LowLevelCallable.from_cython(_cytest, "_filter2d", _cytest.filter2d_capsule(weights)),
|
||||
]
|
||||
|
||||
TRANSFORM_FUNCTIONS = [
|
||||
lambda shift: _ctest.transform(shift),
|
||||
lambda shift: _cytest.transform(shift, with_signature=False),
|
||||
lambda shift: LowLevelCallable(_cytest.transform(shift, with_signature=True)),
|
||||
lambda shift: LowLevelCallable.from_cython(_cytest, "_transform", _cytest.transform_capsule(shift)),
|
||||
]
|
||||
|
||||
|
||||
def test_generic_filter():
|
||||
def filter2d(footprint_elements, weights):
|
||||
return (weights*footprint_elements).sum()
|
||||
|
||||
def check(j):
|
||||
func = FILTER2D_FUNCTIONS[j]
|
||||
|
||||
im = np.ones((20, 20))
|
||||
im[:10,:10] = 0
|
||||
footprint = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
|
||||
footprint_size = np.count_nonzero(footprint)
|
||||
weights = np.ones(footprint_size)/footprint_size
|
||||
|
||||
res = ndimage.generic_filter(im, func(weights),
|
||||
footprint=footprint)
|
||||
std = ndimage.generic_filter(im, filter2d, footprint=footprint,
|
||||
extra_arguments=(weights,))
|
||||
assert_allclose(res, std, err_msg="#{} 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)
|
||||
@@ -1,66 +0,0 @@
|
||||
""" 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)))
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,151 +0,0 @@
|
||||
import numpy
|
||||
from numpy import fft
|
||||
from numpy.testing import (assert_almost_equal, assert_array_almost_equal,
|
||||
assert_equal)
|
||||
|
||||
import pytest
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
|
||||
class TestNdimageFourier:
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 6), (numpy.float64, 14)])
|
||||
def test_fourier_gaussian_real01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.rfft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_gaussian(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a), 1, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 6), (numpy.complex128, 14)])
|
||||
def test_fourier_gaussian_complex01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.fft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_gaussian(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 6), (numpy.float64, 14)])
|
||||
def test_fourier_uniform_real01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.rfft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_uniform(a, [5.0, 2.5], shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 6), (numpy.complex128, 14)])
|
||||
def test_fourier_uniform_complex01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.fft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_uniform(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 4), (numpy.float64, 11)])
|
||||
def test_fourier_shift_real01(self, shape, dtype, dec):
|
||||
expected = numpy.arange(shape[0] * shape[1], dtype=dtype)
|
||||
expected.shape = shape
|
||||
a = fft.rfft(expected, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_shift(a, [1, 1], shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_array_almost_equal(a[1:, 1:], expected[:-1, :-1],
|
||||
decimal=dec)
|
||||
assert_array_almost_equal(a.imag, numpy.zeros(shape),
|
||||
decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 6), (numpy.complex128, 11)])
|
||||
def test_fourier_shift_complex01(self, shape, dtype, dec):
|
||||
expected = numpy.arange(shape[0] * shape[1], dtype=dtype)
|
||||
expected.shape = shape
|
||||
a = fft.fft(expected, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_shift(a, [1, 1], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_array_almost_equal(a.real[1:, 1:], expected[:-1, :-1],
|
||||
decimal=dec)
|
||||
assert_array_almost_equal(a.imag, numpy.zeros(shape),
|
||||
decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15), (1, 10)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.float32, 5), (numpy.float64, 14)])
|
||||
def test_fourier_ellipsoid_real01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.rfft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5],
|
||||
shape[0], 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.irfft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a), 1.0, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(32, 16), (31, 15)])
|
||||
@pytest.mark.parametrize('dtype, dec',
|
||||
[(numpy.complex64, 5), (numpy.complex128, 14)])
|
||||
def test_fourier_ellipsoid_complex01(self, shape, dtype, dec):
|
||||
a = numpy.zeros(shape, dtype)
|
||||
a[0, 0] = 1.0
|
||||
a = fft.fft(a, shape[0], 0)
|
||||
a = fft.fft(a, shape[1], 1)
|
||||
a = ndimage.fourier_ellipsoid(a, [5.0, 2.5], -1, 0)
|
||||
a = fft.ifft(a, shape[1], 1)
|
||||
a = fft.ifft(a, shape[0], 0)
|
||||
assert_almost_equal(ndimage.sum(a.real), 1.0, decimal=dec)
|
||||
|
||||
def test_fourier_ellipsoid_unimplemented_ndim(self):
|
||||
# arrays with ndim > 3 raise NotImplementedError
|
||||
x = numpy.ones((4, 6, 8, 10), dtype=numpy.complex128)
|
||||
with pytest.raises(NotImplementedError):
|
||||
a = ndimage.fourier_ellipsoid(x, 3)
|
||||
|
||||
def test_fourier_ellipsoid_1d_complex(self):
|
||||
# expected result of 1d ellipsoid is the same as for fourier_uniform
|
||||
for shape in [(32, ), (31, )]:
|
||||
for type_, dec in zip([numpy.complex64, numpy.complex128],
|
||||
[5, 14]):
|
||||
x = numpy.ones(shape, dtype=type_)
|
||||
a = ndimage.fourier_ellipsoid(x, 5, -1, 0)
|
||||
b = ndimage.fourier_uniform(x, 5, -1, 0)
|
||||
assert_array_almost_equal(a, b, decimal=dec)
|
||||
|
||||
@pytest.mark.parametrize('shape', [(0, ), (0, 10), (10, 0)])
|
||||
@pytest.mark.parametrize('dtype',
|
||||
[numpy.float32, numpy.float64,
|
||||
numpy.complex64, numpy.complex128])
|
||||
@pytest.mark.parametrize('test_func',
|
||||
[ndimage.fourier_ellipsoid,
|
||||
ndimage.fourier_gaussian,
|
||||
ndimage.fourier_uniform])
|
||||
def test_fourier_zero_length_dims(self, shape, dtype, test_func):
|
||||
a = numpy.ones(shape, dtype)
|
||||
b = test_func(a, 3)
|
||||
assert_equal(a, b)
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,65 +0,0 @@
|
||||
"""Tests for spline filtering."""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from numpy.testing import assert_almost_equal
|
||||
|
||||
from scipy import ndimage
|
||||
|
||||
|
||||
def get_spline_knot_values(order):
|
||||
"""Knot values to the right of a B-spline's center."""
|
||||
knot_values = {0: [1],
|
||||
1: [1],
|
||||
2: [6, 1],
|
||||
3: [4, 1],
|
||||
4: [230, 76, 1],
|
||||
5: [66, 26, 1]}
|
||||
|
||||
return knot_values[order]
|
||||
|
||||
|
||||
def make_spline_knot_matrix(n, order, mode='mirror'):
|
||||
"""Matrix to invert to find the spline coefficients."""
|
||||
knot_values = get_spline_knot_values(order)
|
||||
|
||||
matrix = np.zeros((n, n))
|
||||
for diag, knot_value in enumerate(knot_values):
|
||||
indices = np.arange(diag, n)
|
||||
if diag == 0:
|
||||
matrix[indices, indices] = knot_value
|
||||
else:
|
||||
matrix[indices, indices - diag] = knot_value
|
||||
matrix[indices - diag, indices] = knot_value
|
||||
|
||||
knot_values_sum = knot_values[0] + 2 * sum(knot_values[1:])
|
||||
|
||||
if mode == 'mirror':
|
||||
start, step = 1, 1
|
||||
elif mode == 'reflect':
|
||||
start, step = 0, 1
|
||||
elif mode == 'grid-wrap':
|
||||
start, step = -1, -1
|
||||
else:
|
||||
raise ValueError('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', 'grid-wrap', 'reflect'])
|
||||
def test_spline_filter_vs_matrix_solution(order, mode):
|
||||
n = 100
|
||||
eye = np.eye(n, dtype=float)
|
||||
spline_filter_axis_0 = ndimage.spline_filter1d(eye, axis=0, order=order,
|
||||
mode=mode)
|
||||
spline_filter_axis_1 = ndimage.spline_filter1d(eye, axis=1, order=order,
|
||||
mode=mode)
|
||||
matrix = make_spline_knot_matrix(n, order, mode=mode)
|
||||
assert_almost_equal(eye, np.dot(spline_filter_axis_0, matrix))
|
||||
assert_almost_equal(eye, np.dot(spline_filter_axis_1, matrix.T))
|
||||
Reference in New Issue
Block a user