def prewitt(input, axis=-1, output=None, mode="reflect", cval=0.0):
"""Calculate a Prewitt filter.
Parameters
----------
%(input)s
%(axis)s
%(output)s
%(mode)s
%(cval)s
"""
input = numpy.asarray(input)
axis = _ni_support._check_axis(axis, input.ndim)
output, return_value = _ni_support._get_output(output, input)
correlate1d(input, [-1, 0, 1], axis, output, mode, cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(
output,
[1, 1, 1],
ii,
output,
mode,
cval,
0,
)
return return_value
def maximum_filter1d(input, size, axis = -1, output = None,
mode = "reflect", cval = 0.0, origin = 0):
"""Calculate a one-dimensional maximum filter along the given axis.
The lines of the array along the given axis are filtered with a
maximum filter of given size.
Parameters
----------
%(input)s
size : int
length along which to calculate 1D maximum
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 0)
return return_value
def correlate1d(input,
weights,
axis=-1,
output=None,
mode="reflect",
cval=0.0,
origin=0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights. The weights parameter must be a one-dimensional sequence
of numbers."""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input)
weights = numarray.asarray(weights, type=numarray.Float64)
if weights.rank != 1 or weights.shape[0] < 1:
raise RuntimeError, 'no filter weights given'
if not weights.iscontiguous():
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.rank)
if ((len(weights) // 2 + origin < 0)
or (len(weights) // 2 + origin > len(weights))):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval, origin)
return return_value
def generic_filter1d(input, function, filter_size, axis = -1,
output = None, mode = "reflect", cval = 0.0, origin = 0,
extra_arguments = (), extra_keywords = {}):
"""Calculate a one-dimensional filter along the given axis.
The function iterates over the lines of the array, calling the given
function at each line. The arguments of the line are the input line,
and the output line. The input and output lines are 1D double arrays.
The input line is extended appropiately according to the filter size
and origin. The output line must be modified in-place with the result.
The origin parameter controls the placement of the filter. The mode
parameter determines how the array borders are handled, where cval is
the value when mode is equal to 'constant'. The extra_arguments and
extra_keywords arguments can be used to pass extra arguments and
keywords that are passed to the function at each call."""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input)
if filter_size < 1:
raise RuntimeError, 'invalid filter size'
axis = _ni_support._check_axis(axis, input.ndim)
if ((filter_size // 2 + origin < 0) or
(filter_size // 2 + origin > filter_size)):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments, extra_keywords)
return return_value
def correlate1d(input, weights, axis = -1, output = None, mode = "reflect",
cval = 0.0, origin = 0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights.
Parameters
----------
%(input)s
weights : array
one-dimensional sequence of numbers
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
raise RuntimeError('no filter weights given')
if not weights.flags.contiguous:
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.ndim)
if ((len(weights) // 2 + origin < 0) or
(len(weights) // 2 + origin > len(weights))):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
return return_value
def correlate1d(input, weights, axis = -1, output = None, mode = "reflect",
cval = 0.0, origin = 0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights.
Parameters
----------
%(input)s
weights : array
one-dimensional sequence of numbers
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
raise RuntimeError, 'no filter weights given'
if not weights.flags.contiguous:
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.ndim)
if ((len(weights) // 2 + origin < 0) or
(len(weights) // 2 + origin > len(weights))):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
return return_value
def uniform_filter1d(input,
size,
axis=-1,
output=None,
mode="reflect",
cval=0.0,
origin=0):
"""Calculate a one-dimensional uniform filter along the given axis.
The lines of the array along the given axis are filtered with a
uniform filter of given size.
Parameters
----------
%(input)s
size : integer
length of uniform filter
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError, 'incorrect filter size'
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.uniform_filter1d(input, size, axis, output, mode, cval, origin)
return return_value
def sobel(input, axis = -1, output = None, mode = "reflect", cval = 0.0):
"""Calculate a Sobel filter.
"""
input = numpy.asarray(input)
axis = _ni_support._check_axis(axis, input.ndim)
output, return_value = _ni_support._get_output(output, input)
correlate1d(input, [-1, 0, 1], axis, output, mode, cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(output, [1, 2, 1], ii, output, mode, cval, 0)
return return_value
def sobel(input, axis=-1, output=None, mode="reflect", cval=0.0):
"""Calculate a Sobel filter.
"""
input = numarray.asarray(input)
axis = _ni_support._check_axis(axis, input.rank)
output, return_value = _ni_support._get_output(output, input)
correlate1d(input, [-1, 0, 1], axis, output, mode, cval, 0)
axes = [ii for ii in range(input.rank) if ii != axis]
for ii in axes:
correlate1d(output, [1, 2, 1], ii, output, mode, cval, 0)
return return_value
def generic_filter1d(input,
function,
filter_size,
axis=-1,
output=None,
mode="reflect",
cval=0.0,
origin=0,
extra_arguments=(),
extra_keywords=None):
"""Calculate a one-dimensional filter along the given axis.
generic_filter1d iterates over the lines of the array, calling the
given function at each line. The arguments of the line are the
input line, and the output line. The input and output lines are 1D
double arrays. The input line is extended appropriately according
to the filter size and origin. The output line must be modified
in-place with the result.
Parameters
----------
%(input)s
function : callable
function to apply along given axis
filter_size : scalar
length of the filter
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
%(extra_arguments)s
%(extra_keywords)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input)
if filter_size < 1:
raise RuntimeError, 'invalid filter size'
axis = _ni_support._check_axis(axis, input.ndim)
if ((filter_size // 2 + origin < 0)
or (filter_size // 2 + origin > filter_size)):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments,
extra_keywords)
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_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 maximum_filter1d(input, size, axis=-1, output=None, mode="reflect", cval=0.0, origin=0):
"""Calculate a one-dimensional maximum filter along the given axis.
The lines of the array along the given axis are filtered with a
maximum filter of given size."""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, "Complex type not supported"
axis = _ni_support._check_axis(axis, input.rank)
if size < 1:
raise RuntimeError, "incorrect filter size"
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
raise ValueError, "invalid origin"
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval, origin, 0)
return return_value
def spline_filter1d(input,
order=3,
axis=-1,
output=numpy.float64,
output_type=None):
"""
Calculates a one-dimensional 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 : array_like
The input array.
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`.
output_type : dtype, optional
DEPRECATED, DO NOT USE. If used, a RuntimeError is raised.
Returns
-------
return_value : ndarray or None
The filtered input. If `output` is given as a parameter, None is
returned.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input, output_type)
if order in [0, 1]:
output[...] = numpy.array(input)
else:
axis = _ni_support._check_axis(axis, input.ndim)
_nd_image.spline_filter1d(input, order, axis, output)
return return_value
def prewitt(input, axis = -1, output = None, mode = "reflect", cval = 0.0):
"""Calculate a Prewitt filter.
Parameters
----------
%(input)s
%(axis)s
%(output)s
%(mode)s
%(cval)s
"""
input = numpy.asarray(input)
axis = _ni_support._check_axis(axis, input.ndim)
output, return_value = _ni_support._get_output(output, input)
correlate1d(input, [-1, 0, 1], axis, output, mode, cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(output, [1, 1, 1], ii, output, mode, cval, 0,)
return return_value
def minimum_filter1d(input, size, axis = -1, output = None,
mode = "reflect", cval = 0.0, origin = 0):
"""Calculate a one-dimensional minimum filter along the given axis.
The lines of the array along the given axis are filtered with a
minimum filter of given size."""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError, 'incorrect filter size'
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 1)
return return_value
def generic_filter1d(input, function, filter_size, axis = -1,
output = None, mode = "reflect", cval = 0.0, origin = 0,
extra_arguments = (), extra_keywords = None):
"""Calculate a one-dimensional filter along the given axis.
generic_filter1d iterates over the lines of the array, calling the
given function at each line. The arguments of the line are the
input line, and the output line. The input and output lines are 1D
double arrays. The input line is extended appropriately according
to the filter size and origin. The output line must be modified
in-place with the result.
Parameters
----------
%(input)s
function : callable
function to apply along given axis
filter_size : scalar
length of the filter
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
%(extra_arguments)s
%(extra_keywords)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output, return_value = _ni_support._get_output(output, input)
if filter_size < 1:
raise RuntimeError('invalid filter size')
axis = _ni_support._check_axis(axis, input.ndim)
if ((filter_size // 2 + origin < 0) or
(filter_size // 2 + origin > filter_size)):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments, extra_keywords)
return return_value
def spline_filter1d(input, order = 3, axis = -1, output = numpy.float64,
output_type = None):
"""
Calculates a one-dimensional 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 : array_like
The input array.
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`.
output_type : dtype, optional
DEPRECATED, DO NOT USE. If used, a RuntimeError is raised.
Returns
-------
return_value : ndarray or None
The filtered input. If `output` is given as a parameter, None is
returned.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input,
output_type)
if order in [0, 1]:
output[...] = numpy.array(input)
else:
axis = _ni_support._check_axis(axis, input.ndim)
_nd_image.spline_filter1d(input, order, axis, output)
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_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 = numarray.asarray(input)
output, return_value = _get_output_fourier(output, input)
axis = _ni_support._check_axis(axis, input.rank)
sizes = _ni_support._normalize_sequence(size, input.rank)
sizes = numarray.asarray(sizes, type = numarray.Float64)
if not sizes.iscontiguous():
sizes = sizes.copy()
_nd_image.fourier_filter(input, sizes, n, axis, output, 1)
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
def spline_filter1d(input, order = 3, axis = -1, output = numpy.float64,
output_type = None):
"""Calculates a one-dimensional 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.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input,
output_type)
if order in [0, 1]:
output[...] = numpy.array(input)
else:
axis = _ni_support._check_axis(axis, input.ndim)
_nd_image.spline_filter1d(input, order, axis, output)
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 correlate1d(input, weights, axis=-1, output=None, mode="reflect", cval=0.0, origin=0):
"""Calculate a one-dimensional correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights. The weights parameter must be a one-dimensional sequence
of numbers."""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, "Complex type not supported"
output, return_value = _ni_support._get_output(output, input)
weights = numarray.asarray(weights, type=numarray.Float64)
if weights.rank != 1 or weights.shape[0] < 1:
raise RuntimeError, "no filter weights given"
if not weights.iscontiguous():
weights = weights.copy()
axis = _ni_support._check_axis(axis, input.rank)
if (len(weights) // 2 + origin < 0) or (len(weights) // 2 + origin > len(weights)):
raise ValueError, "invalid origin"
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval, origin)
return return_value
def uniform_filter1d(input,
size,
axis=-1,
output=None,
mode="reflect",
cval=0.0,
origin=0):
"""Calculate a one-dimensional uniform filter along the given axis.
The lines of the array along the given axis are filtered with a
uniform filter of given size."""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
axis = _ni_support._check_axis(axis, input.rank)
if size < 1:
raise RuntimeError, 'incorrect filter size'
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.uniform_filter1d(input, size, axis, output, mode, cval, origin)
return return_value
def generic_filter1d(input,
function,
filter_size,
axis=-1,
output=None,
mode="reflect",
cval=0.0,
origin=0,
extra_arguments=(),
extra_keywords={}):
"""Calculate a one-dimensional filter along the given axis.
The function iterates over the lines of the array, calling the given
function at each line. The arguments of the line are the input line,
and the output line. The input and output lines are 1D double arrays.
The input line is extended appropiately according to the filter size
and origin. The output line must be modified in-place with the result.
The origin parameter controls the placement of the filter. The mode
parameter determines how the array borders are handled, where cval is
the value when mode is equal to 'constant'. The extra_arguments and
extra_keywords arguments can be used to pass extra arguments and
keywords that are passed to the function at each call."""
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
output, return_value = _ni_support._get_output(output, input)
if filter_size < 1:
raise RuntimeError, 'invalid filter size'
axis = _ni_support._check_axis(axis, input.rank)
if ((filter_size // 2 + origin < 0)
or (filter_size // 2 + origin > filter_size)):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments,
extra_keywords)
return return_value
def maximum_filter1d(input,
size,
axis=-1,
output=None,
mode="reflect",
cval=0.0,
origin=0):
"""Calculate a one-dimensional maximum filter along the given axis.
The lines of the array along the given axis are filtered with a
maximum filter of given size."""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError, 'Complex type not supported'
axis = _ni_support._check_axis(axis, input.ndim)
if size < 1:
raise RuntimeError, 'incorrect filter size'
output, return_value = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin > size):
raise ValueError, 'invalid origin'
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 0)
return return_value
