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 Superseded by reCTBN.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