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# Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import types
import numpy
import _ni_support
import _nd_image
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)
return_value = output
elif type(output) is types.TypeType:
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)
return_value = output
else:
if output.shape != input.shape:
raise RuntimeError, "output shape not correct"
return_value = None
return output, return_value
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.Complex64)
return_value = output
elif type(output) is types.TypeType:
if output not in [numpy.complex64, numpy.complex128]:
raise RuntimeError, "output type not supported"
output = numpy.zeros(input.shape, dtype = output)
return_value = output
else:
if output.shape != input.shape:
raise RuntimeError, "output shape not correct"
return_value = None
return output, return_value
def fourier_gaussian(input, sigma, n = -1, axis = -1, output = None):
"""Multi-dimensional Gaussian fourier filter.
The array is multiplied with the fourier transform of a Gaussian
kernel. If the parameter n is negative, then the input is assumed to be
the result of a complex fft. If n is larger or equal to zero, the input
is assumed to be the result of a real fft, and n gives the length of
the of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis
parameter.
"""
input = numpy.asarray(input)
output, return_value = _get_output_fourier(output, input)
axis = _ni_support._check_axis(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 return_value
def fourier_uniform(input, size, n = -1, axis = -1, output = None):
"""Multi-dimensional Uniform fourier filter.
The array is multiplied with the fourier transform of a box of given
sizes. If the parameter n is negative, then the input is assumed to be
the result of a complex fft. If n is larger or equal to zero, the input
is assumed to be the result of a real fft, and n gives the length of
the of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis
parameter.
"""
input = numpy.asarray(input)
output, return_value = _get_output_fourier(output, input)
axis = _ni_support._check_axis(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 return_value
def fourier_ellipsoid(input, size, n = -1, axis = -1, output = None):
"""Multi-dimensional ellipsoid fourier filter.
The array is multiplied with the fourier transform of a ellipsoid of
given sizes. If the parameter n is negative, then the input is assumed
to be the result of a complex fft. If n is larger or equal to zero, the
input is assumed to be the result of a real fft, and n gives the length
of the of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis
parameter. This function is implemented for arrays of
rank 1, 2, or 3.
"""
input = numpy.asarray(input)
output, return_value = _get_output_fourier(output, input)
axis = _ni_support._check_axis(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 return_value
def fourier_shift(input, shift, n = -1, axis = -1, output = None):
"""Multi-dimensional fourier shift filter.
The array is multiplied with the fourier transform of a shift operation
If the parameter n is negative, then the input is assumed to be the
result of a complex fft. If n is larger or equal to zero, the input is
assumed to be the result of a real fft, and n gives the length of the
of the array before transformation along the the real transform
direction. The axis of the real transform is given by the axis
parameter.
"""
input = numpy.asarray(input)
output, return_value = _get_output_fourier_complex(output, input)
axis = _ni_support._check_axis(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 return_value
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