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.
Parameters
----------
input : array_like
The input array.
size : float or sequence
The size of the box used for filtering.
If a float, `size` is the same for all axes. If a sequence, `size` has
to contain one value for each axis.
n : int, optional
If `n` is negative (default), then the input is assumed to be the
result of a complex fft.
If `n` is larger than or equal to zero, the input is assumed to be the
result of a real fft, and `n` gives the length of the array before
transformation along the real transform direction.
axis : int, optional
The axis of the real transform.
output : ndarray, optional
If given, the result of filtering the input is placed in this array.
None is returned in this case.
Returns
-------
return_value : ndarray or None
The filtered input. If `output` is given as a parameter, None is
returned.
Notes
-----
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_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.
Parameters
----------
input : array_like
The input array.
size : float or sequence
The size of the box used for filtering.
If a float, `size` is the same for all axes. If a sequence, `size` has
to contain one value for each axis.
n : int, optional
If `n` is negative (default), then the input is assumed to be the
result of a complex fft.
If `n` is larger than or equal to zero, the input is assumed to be the
result of a real fft, and `n` gives the length of the array before
transformation along the real transform direction.
axis : int, optional
The axis of the real transform.
output : ndarray, optional
If given, the result of filtering the input is placed in this array.
None is returned in this case.
Returns
-------
return_value : ndarray or None
The filtered input. If `output` is given as a parameter, None is
returned.
Notes
-----
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_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_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_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 = numarray.asarray(input)
output, return_value = _get_output_fourier(output, input)
axis = _ni_support._check_axis(axis, input.rank)
sigmas = _ni_support._normalize_sequence(sigma, input.rank)
sigmas = numarray.asarray(sigmas, type = numarray.Float64)
if not sigmas.iscontiguous():
sigmas = sigmas.copy()
_nd_image.fourier_filter(input, sigmas, n, axis, output, 0)
return 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 = numarray.asarray(input)
output, return_value = _get_output_fourier(output, input)
axis = _ni_support._check_axis(axis, input.rank)
sigmas = _ni_support._normalize_sequence(sigma, input.rank)
sigmas = numarray.asarray(sigmas, type=numarray.Float64)
if not sigmas.iscontiguous():
sigmas = sigmas.copy()
_nd_image.fourier_filter(input, sigmas, n, axis, output, 0)
return return_value
