def generic_filter(input,
function,
size=None,
footprint=None,
output=None,
mode="reflect",
cval=0.0,
origin=0,
extra_arguments=(),
extra_keywords=None):
"""Calculates a multi-dimensional filter using the given function.
At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1D array of double values.
Parameters
----------
%(input)s
function : callable
function to apply at each element
%(size_foot)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'
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
raise RuntimeError, "no footprint or filter size provided"
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint)
footprint = footprint.astype(bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError, 'filter footprint array has incorrect shape.'
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
raise ValueError, 'invalid origin'
if not footprint.flags.contiguous:
footprint = footprint.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter(input, function, footprint, output, mode, cval,
origins, extra_arguments, extra_keywords)
return return_value
def generic_filter(input, function, size = None, footprint = None,
output = None, mode = "reflect", cval = 0.0, origin = 0,
extra_arguments = (), extra_keywords = None):
"""Calculates a multi-dimensional filter using the given function.
At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1D array of double values.
Parameters
----------
%(input)s
function : callable
function to apply at each element
%(size_foot)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')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint)
footprint = footprint.astype(bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter(input, function, footprint, output, mode,
cval, origins, extra_arguments, extra_keywords)
return return_value
def generic_filter(input,
function,
size=None,
footprint=None,
output=None,
mode="reflect",
cval=0.0,
origin=0,
extra_arguments=(),
extra_keywords={}):
"""Calculates a multi-dimensional filter using the given function.
At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1D array of double values.
Either a size or a footprint with the filter must be provided. An
output array can optionally be provided. 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'
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint == None:
if size == None:
raise RuntimeError, "no footprint or filter size provided"
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(size, dtype=bool)
else:
footprint = numpy.asarray(footprint)
footprint = footprint.astype(bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError, 'filter footprint array has incorrect shape.'
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
raise ValueError, 'invalid origin'
if not footprint.flags.contiguous:
footprint = footprint.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter(input, function, footprint, output, mode, cval,
origins, extra_arguments, extra_keywords)
return return_value
def generic_filter(
input,
function,
size=None,
footprint=None,
output=None,
mode="reflect",
cval=0.0,
origin=0,
extra_arguments=(),
extra_keywords={},
):
"""Calculates a multi-dimensional filter using the given function.
At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1D array of double values.
Either a size or a footprint with the filter must be provided. An
output array can optionally be provided. 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"
origins = _ni_support._normalize_sequence(origin, input.rank)
if footprint == None:
if size == None:
raise RuntimeError, "no footprint or filter size provided"
sizes = _ni_support._normalize_sequence(size, input.rank)
footprint = numarray.ones(size, type=numarray.Bool)
else:
footprint = numarray.asarray(footprint, type=numarray.Bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.rank:
raise RuntimeError, "filter footprint array has incorrect shape."
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin > lenf):
raise ValueError, "invalid origin"
if not footprint.iscontiguous():
footprint = footprint.copy()
output, return_value = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter(input, function, footprint, output, mode, cval, origins, extra_arguments, extra_keywords)
return return_value
