def map_coordinates(input, coordinates, output_type = None, output = None,
order = 3, mode = 'constant', cval = 0.0, prefilter = True):
"""Apply an arbritrary coordinate transformation.
The array of coordinates is used to find for each point in the output
the corresponding coordinates in the input. The value of the input at
that coordinates is determined by spline interpolation of the
requested order. Points outside the boundaries of the input are filled
according to the given mode. The parameter prefilter determines if the
input is pre-filtered before interpolation, if False it is assumed
that the input is already filtered.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
coordinates = numarray.asarray(coordinates)
if isinstance(coordinates.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
output_shape = coordinates.shape[1:]
if input.rank < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
if coordinates.shape[0] != input.rank:
raise RuntimeError, 'invalid shape for coordinate array'
mode = _ni_support._extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numarray.Float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
output_type, shape = output_shape)
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
output, order, mode, cval, None, None)
return return_value
def map_coordinates(input, coordinates, output_type = None, output = None,
order = 3, mode = 'constant', cval = 0.0, prefilter = True):
"""Apply an arbritrary coordinate transformation.
The array of coordinates is used to find, for each point in the output,
the corresponding coordinates in the input. The value of the input at
that coordinates is determined by spline interpolation of the
requested order.
The shape of the output is derived from that of the coordinate
array by dropping the first axis. The values of the array along
the first axis are the coordinates in the input array at which the
output value is found. For example, if the input has dimensions
(100,200,3), then the shape of coordinates will be (3,100,200,3),
where coordinates[:,1,2,3] specify the input coordinate at which
output[1,2,3] is found.
Points outside the boundaries of the input are filled according to
the given mode ('constant', 'nearest', 'reflect' or 'wrap'). The
parameter prefilter determines if the input is pre-filtered before
interpolation (necessary for spline interpolation of order >
1). If False it is assumed that the input is already filtered.
Example usage:
>>> a = arange(12.).reshape((4,3))
>>> print a
[[ 0. 1. 2.]
[ 3. 4. 5.]
[ 6. 7. 8.]
[ 9. 10. 11.]]
>>> output = map_coordinates(a,[[0.5, 2], [0.5, 1]],order=1)
>>> print output
[ 2. 7.]
Here, the interpolated value of a[0.5,0.5] gives output[0], while
a[2,1] is output[1].
"""
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'
coordinates = numpy.asarray(coordinates)
if numpy.iscomplexobj(coordinates):
raise TypeError, 'Complex type not supported'
output_shape = coordinates.shape[1:]
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
if coordinates.shape[0] != input.ndim:
raise RuntimeError, 'invalid shape for coordinate array'
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
output_type, shape = output_shape)
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
output, order, mode, cval, None, None)
return return_value
def affine_transform(input, matrix, offset = 0.0, output_shape = None,
output_type = None, output = None, order = 3,
mode = 'constant', cval = 0.0, prefilter = True):
"""Apply an affine transformation.
The given matrix and offset are used to find for each point in the
output the corresponding coordinates in the input by an affine
transformation. The value of the input at those coordinates is
determined by spline interpolation of the requested order. Points
outside the boundaries of the input are filled according to the given
mode. The output shape can optionally be given. If not given it is
equal to the input shape. The parameter prefilter determines if the
input is pre-filtered before interpolation, if False it is assumed
that the input is already filtered.
The matrix must be two-dimensional or can also be given as a
one-dimensional sequence or array. In the latter case, it is
assumed that the matrix is diagonal. A more efficient algorithms
is then applied that exploits the separability of the problem.
"""
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'
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
output_type, shape = output_shape)
matrix = numpy.asarray(matrix, dtype = numpy.float64)
if matrix.ndim not in [1, 2] or matrix.shape[0] < 1:
raise RuntimeError, 'no proper affine matrix provided'
if matrix.shape[0] != input.ndim:
raise RuntimeError, 'affine matrix has wrong number of rows'
if matrix.ndim == 2 and matrix.shape[1] != output.ndim:
raise RuntimeError, 'affine matrix has wrong number of columns'
if not matrix.flags.contiguous:
matrix = matrix.copy()
offset = _ni_support._normalize_sequence(offset, input.ndim)
offset = numpy.asarray(offset, dtype = numpy.float64)
if offset.ndim != 1 or offset.shape[0] < 1:
raise RuntimeError, 'no proper offset provided'
if not offset.flags.contiguous:
offset = offset.copy()
if matrix.ndim == 1:
_nd_image.zoom_shift(filtered, matrix, offset, output, order,
mode, cval)
else:
_nd_image.geometric_transform(filtered, None, None, matrix, offset,
output, order, mode, cval, None, None)
return return_value
def affine_transform(input,
matrix,
offset=0.0,
output_shape=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True):
if order < 0 or order > 5:
raise RuntimeError('spline order not supported')
input = np.asarray(input)
if np.iscomplexobj(input):
raise TypeError('Complex type not supported')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=np.float64)
else:
filtered = input
output = _get_output(output, input, shape=output_shape)
matrix = np.asarray(matrix, dtype=np.float64)
if matrix.ndim not in [1, 2] or matrix.shape[0] < 1:
raise RuntimeError('no proper affine matrix provided')
if (matrix.ndim == 2 and matrix.shape[1] == input.ndim + 1
and (matrix.shape[0] in [input.ndim, input.ndim + 1])):
if matrix.shape[0] == input.ndim + 1:
exptd = [0] * input.ndim + [1]
if not np.all(matrix[input.ndim] == exptd):
msg = ('Expected homogeneous transformation matrix with '
'shape %s for image shape %s, but bottom row was '
'not equal to %s' % (matrix.shape, input.shape, exptd))
raise ValueError(msg)
# assume input is homogeneous coordinate transformation matrix
offset = matrix[:input.ndim, input.ndim]
matrix = matrix[:input.ndim, :input.ndim]
if matrix.shape[0] != input.ndim:
raise RuntimeError('affine matrix has wrong number of rows')
if matrix.ndim == 2 and matrix.shape[1] != output.ndim:
raise RuntimeError('affine matrix has wrong number of columns')
if not matrix.flags.contiguous:
matrix = matrix.copy()
offset = _normalize_sequence(offset, input.ndim)
offset = np.asarray(offset, dtype=np.float64)
if offset.ndim != 1 or offset.shape[0] < 1:
raise RuntimeError('no proper offset provided')
if not offset.flags.contiguous:
offset = offset.copy()
if matrix.ndim == 1:
_nd_image.zoom_shift(filtered, matrix, offset / matrix, output, order,
mode, cval)
else:
_nd_image.geometric_transform(filtered, None, None, matrix, offset,
output, order, mode, cval, None, None)
return output
def geometric_transform(input, mapping, output_shape = None,
output_type = None, output = None, order = 3,
mode = 'constant', cval = 0.0, prefilter = True,
extra_arguments = (), extra_keywords = {}):
"""Apply an arbritrary geometric transform.
The given mapping function is used to find, for each point in the
output, the corresponding coordinates in the input. The value of the
input at those coordinates is determined by spline interpolation of
the requested order.
mapping must be a callable object that accepts a tuple of length
equal to the output array rank and returns the corresponding input
coordinates as a tuple of length equal to the input array
rank. Points outside the boundaries of the input are filled
according to the given mode ('constant', 'nearest', 'reflect' or
'wrap'). The output shape can optionally be given. If not given,
it is equal to the input shape. The parameter prefilter determines
if the input is pre-filtered before interpolation (necessary for
spline interpolation of order > 1). If False it is assumed that
the input is already filtered. The extra_arguments and
extra_keywords arguments can be used to provide extra arguments
and keywords that are passed to the mapping function at each call.
Example
-------
>>> a = arange(12.).reshape((4,3))
>>> def shift_func(output_coordinates):
... return (output_coordinates[0]-0.5, output_coordinates[1]-0.5)
...
>>> print geometric_transform(a,shift_func)
array([[ 0. , 0. , 0. ],
[ 0. , 1.3625, 2.7375],
[ 0. , 4.8125, 6.1875],
[ 0. , 8.2625, 9.6375]])
"""
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'
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
output_type, shape = output_shape)
_nd_image.geometric_transform(filtered, mapping, None, None, None,
output, order, mode, cval, extra_arguments, extra_keywords)
return return_value
def geometric_transform(input,
mapping,
output_shape=None,
output_type=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True,
extra_arguments=(),
extra_keywords={}):
"""Apply an arbritrary geometric transform.
The given mapping function is used to find for each point in the
output the corresponding coordinates in the input. The value of the
input at those coordinates is determined by spline interpolation of
the requested order. Points outside the boundaries of the input are
filled according to the given mode. The output shape can optionally be
given. If not given, it is equal to the input shape. The parameter
prefilter determines if the input is pre-filtered before
interpolation, if False it is assumed that the input is already
filtered. The extra_arguments and extra_keywords arguments can be
used to provide extra arguments and keywords that are passed to the
mapping function at each call.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if output_shape == None:
output_shape = input.shape
if input.rank < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
mode = _ni_support._extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numarray.Float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output,
input,
output_type,
shape=output_shape)
_nd_image.geometric_transform(filtered, mapping, None, None, None, output,
order, mode, cval, extra_arguments,
extra_keywords)
return return_value
def map_coordinates(input,
coordinates,
output_type=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True):
"""Apply an arbritrary coordinate transformation.
The array of coordinates is used to find for each point in the output
the corresponding coordinates in the input. The value of the input at
that coordinates is determined by spline interpolation of the
requested order. Points outside the boundaries of the input are filled
according to the given mode. The parameter prefilter determines if the
input is pre-filtered before interpolation, if False it is assumed
that the input is already filtered.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
coordinates = numarray.asarray(coordinates)
if isinstance(coordinates.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
output_shape = coordinates.shape[1:]
if input.rank < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
if coordinates.shape[0] != input.rank:
raise RuntimeError, 'invalid shape for coordinate array'
mode = _ni_support._extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numarray.Float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output,
input,
output_type,
shape=output_shape)
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
output, order, mode, cval, None, None)
return return_value
def geometric_transform(input, mapping, output_shape = None,
output_type = None, output = None, order = 3,
mode = 'constant', cval = 0.0, prefilter = True,
extra_arguments = (), extra_keywords = {}):
"""Apply an arbritrary geometric transform.
The given mapping function is used to find for each point in the
output the corresponding coordinates in the input. The value of the
input at those coordinates is determined by spline interpolation of
the requested order. Points outside the boundaries of the input are
filled according to the given mode. The output shape can optionally be
given. If not given, it is equal to the input shape. The parameter
prefilter determines if the input is pre-filtered before
interpolation, if False it is assumed that the input is already
filtered. The extra_arguments and extra_keywords arguments can be
used to provide extra arguments and keywords that are passed to the
mapping function at each call.
"""
if order < 0 or order > 5:
raise RuntimeError, 'spline order not supported'
input = numarray.asarray(input)
if isinstance(input.type(), numarray.ComplexType):
raise TypeError, 'Complex type not supported'
if output_shape == None:
output_shape = input.shape
if input.rank < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
mode = _ni_support._extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numarray.Float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
output_type, shape = output_shape)
_nd_image.geometric_transform(filtered, mapping, None, None, None,
output, order, mode, cval, extra_arguments, extra_keywords)
return return_value
def affine_transform(input, matrix, offset=0.0, output_shape=None,
output=None, order=3,
mode='constant', cval=0.0, prefilter=True):
"""
Apply an affine transformation.
The given matrix and offset are used to find for each point in the
output the corresponding coordinates in the input by an affine
transformation. The value of the input at those coordinates is
determined by spline interpolation of the requested order. Points
outside the boundaries of the input are filled according to the given
mode.
Parameters
----------
input : ndarray
The input array.
matrix : ndarray
The matrix must be two-dimensional or can also be given as a
one-dimensional sequence or array. In the latter case, it is assumed
that the matrix is diagonal. A more efficient algorithms is then
applied that exploits the separability of the problem.
offset : float or sequence, optional
The offset into the array where the transform is applied. If a float,
`offset` is the same for each axis. If a sequence, `offset` should
contain one value for each axis.
output_shape : tuple of ints, optional
Shape tuple.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array.
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
already filtered. Default is True.
Returns
-------
return_value : ndarray or None
The transformed 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')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
shape=output_shape)
matrix = numpy.asarray(matrix, dtype = numpy.float64)
if matrix.ndim not in [1, 2] or matrix.shape[0] < 1:
raise RuntimeError('no proper affine matrix provided')
if matrix.shape[0] != input.ndim:
raise RuntimeError('affine matrix has wrong number of rows')
if matrix.ndim == 2 and matrix.shape[1] != output.ndim:
raise RuntimeError('affine matrix has wrong number of columns')
if not matrix.flags.contiguous:
matrix = matrix.copy()
offset = _ni_support._normalize_sequence(offset, input.ndim)
offset = numpy.asarray(offset, dtype = numpy.float64)
if offset.ndim != 1 or offset.shape[0] < 1:
raise RuntimeError('no proper offset provided')
if not offset.flags.contiguous:
offset = offset.copy()
if matrix.ndim == 1:
_nd_image.zoom_shift(filtered, matrix, offset, output, order,
mode, cval)
else:
_nd_image.geometric_transform(filtered, None, None, matrix, offset,
output, order, mode, cval, None, None)
return return_value
def map_coordinates(input, coordinates, output=None, order=3,
mode='constant', cval=0.0, prefilter=True):
"""
Map the input array to new coordinates by interpolation.
The array of coordinates is used to find, for each point in the output,
the corresponding coordinates in the input. The value of the input at
those coordinates is determined by spline interpolation of the
requested order.
The shape of the output is derived from that of the coordinate
array by dropping the first axis. The values of the array along
the first axis are the coordinates in the input array at which the
output value is found.
Parameters
----------
input : ndarray
The input array.
coordinates : array_like
The coordinates at which `input` is evaluated.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array.
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
already filtered. Default is True.
Returns
-------
return_value : ndarray
The result of transforming the input. The shape of the output is
derived from that of `coordinates` by dropping the first axis.
See Also
--------
spline_filter, geometric_transform, scipy.interpolate
Examples
--------
>>> import scipy.ndimage
>>> a = np.arange(12.).reshape((4, 3))
>>> a
array([[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.],
[ 9., 10., 11.]])
>>> sp.ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1)
[ 2. 7.]
Above, the interpolated value of a[0.5, 0.5] gives output[0], while
a[2, 1] is output[1].
>>> inds = np.array([[0.5, 2], [0.5, 4]])
>>> sp.ndimage.map_coordinates(a, inds, order=1, cval=-33.3)
array([ 2. , -33.3])
>>> sp.ndimage.map_coordinates(a, inds, order=1, mode='nearest')
array([ 2., 8.])
>>> sp.ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
array([ True, False], dtype=bool
"""
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')
coordinates = numpy.asarray(coordinates)
if numpy.iscomplexobj(coordinates):
raise TypeError('Complex type not supported')
output_shape = coordinates.shape[1:]
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
if coordinates.shape[0] != input.ndim:
raise RuntimeError('invalid shape for coordinate array')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
shape=output_shape)
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
output, order, mode, cval, None, None)
return return_value
def geometric_transform(input, mapping, output_shape=None,
output=None, order=3,
mode='constant', cval=0.0, prefilter=True,
extra_arguments=(), extra_keywords={}):
"""
Apply an arbritrary geometric transform.
The given mapping function is used to find, for each point in the
output, the corresponding coordinates in the input. The value of the
input at those coordinates is determined by spline interpolation of
the requested order.
Parameters
----------
input : array_like
The input array.
mapping : callable
A callable object that accepts a tuple of length equal to the output
array rank, and returns the corresponding input coordinates as a tuple
of length equal to the input array rank.
output_shape : tuple of ints
Shape tuple.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array.
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
already filtered. Default is True.
extra_arguments : tuple, optional
Extra arguments passed to `mapping`.
extra_keywords : dict, optional
Extra keywords passed to `mapping`.
Returns
-------
return_value : ndarray or None
The filtered input. If `output` is given as a parameter, None is
returned.
See Also
--------
map_coordinates, affine_transform, spline_filter1d
Examples
--------
>>> a = np.arange(12.).reshape((4, 3))
>>> def shift_func(output_coords):
... return (output_coords[0] - 0.5, output_coords[1] - 0.5)
...
>>> sp.ndimage.geometric_transform(a, shift_func)
array([[ 0. , 0. , 0. ],
[ 0. , 1.362, 2.738],
[ 0. , 4.812, 6.187],
[ 0. , 8.263, 9.637]])
"""
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')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output = numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output, input,
shape=output_shape)
_nd_image.geometric_transform(filtered, mapping, None, None, None,
output, order, mode, cval, extra_arguments, extra_keywords)
return return_value
def affine_transform(input,
matrix,
offset=0.0,
output_shape=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True):
"""
Apply an affine transformation.
The given matrix and offset are used to find for each point in the
output the corresponding coordinates in the input by an affine
transformation. The value of the input at those coordinates is
determined by spline interpolation of the requested order. Points
outside the boundaries of the input are filled according to the given
mode.
Parameters
----------
input : ndarray
The input array.
matrix : ndarray
The matrix must be two-dimensional or can also be given as a
one-dimensional sequence or array. In the latter case, it is assumed
that the matrix is diagonal. A more efficient algorithms is then
applied that exploits the separability of the problem.
offset : float or sequence, optional
The offset into the array where the transform is applied. If a float,
`offset` is the same for each axis. If a sequence, `offset` should
contain one value for each axis.
output_shape : tuple of ints, optional
Shape tuple.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array.
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
already filtered. Default is True.
Returns
-------
return_value : ndarray or None
The transformed 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')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output,
input,
shape=output_shape)
matrix = numpy.asarray(matrix, dtype=numpy.float64)
if matrix.ndim not in [1, 2] or matrix.shape[0] < 1:
raise RuntimeError('no proper affine matrix provided')
if matrix.shape[0] != input.ndim:
raise RuntimeError('affine matrix has wrong number of rows')
if matrix.ndim == 2 and matrix.shape[1] != output.ndim:
raise RuntimeError('affine matrix has wrong number of columns')
if not matrix.flags.contiguous:
matrix = matrix.copy()
offset = _ni_support._normalize_sequence(offset, input.ndim)
offset = numpy.asarray(offset, dtype=numpy.float64)
if offset.ndim != 1 or offset.shape[0] < 1:
raise RuntimeError('no proper offset provided')
if not offset.flags.contiguous:
offset = offset.copy()
if matrix.ndim == 1:
_nd_image.zoom_shift(filtered, matrix, offset, output, order, mode,
cval)
else:
_nd_image.geometric_transform(filtered, None, None, matrix, offset,
output, order, mode, cval, None, None)
return return_value
def map_coordinates(input,
coordinates,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True):
"""
Map the input array to new coordinates by interpolation.
The array of coordinates is used to find, for each point in the output,
the corresponding coordinates in the input. The value of the input at
those coordinates is determined by spline interpolation of the
requested order.
The shape of the output is derived from that of the coordinate
array by dropping the first axis. The values of the array along
the first axis are the coordinates in the input array at which the
output value is found.
Parameters
----------
input : ndarray
The input array.
coordinates : array_like
The coordinates at which `input` is evaluated.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array.
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
already filtered. Default is True.
Returns
-------
return_value : ndarray
The result of transforming the input. The shape of the output is
derived from that of `coordinates` by dropping the first axis.
See Also
--------
spline_filter, geometric_transform, scipy.interpolate
Examples
--------
>>> from scipy import ndimage
>>> a = np.arange(12.).reshape((4, 3))
>>> a
array([[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.],
[ 9., 10., 11.]])
>>> ndimage.map_coordinates(a, [[0.5, 2], [0.5, 1]], order=1)
[ 2. 7.]
Above, the interpolated value of a[0.5, 0.5] gives output[0], while
a[2, 1] is output[1].
>>> inds = np.array([[0.5, 2], [0.5, 4]])
>>> ndimage.map_coordinates(a, inds, order=1, cval=-33.3)
array([ 2. , -33.3])
>>> ndimage.map_coordinates(a, inds, order=1, mode='nearest')
array([ 2., 8.])
>>> ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
array([ True, False], dtype=bool
"""
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')
coordinates = numpy.asarray(coordinates)
if numpy.iscomplexobj(coordinates):
raise TypeError('Complex type not supported')
output_shape = coordinates.shape[1:]
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
if coordinates.shape[0] != input.ndim:
raise RuntimeError('invalid shape for coordinate array')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output,
input,
shape=output_shape)
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
output, order, mode, cval, None, None)
return return_value
def geometric_transform(input,
mapping,
output_shape=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True,
extra_arguments=(),
extra_keywords={}):
"""
Apply an arbritrary geometric transform.
The given mapping function is used to find, for each point in the
output, the corresponding coordinates in the input. The value of the
input at those coordinates is determined by spline interpolation of
the requested order.
Parameters
----------
input : array_like
The input array.
mapping : callable
A callable object that accepts a tuple of length equal to the output
array rank, and returns the corresponding input coordinates as a tuple
of length equal to the input array rank.
output_shape : tuple of ints
Shape tuple.
output : ndarray or dtype, optional
The array in which to place the output, or the dtype of the returned
array.
order : int, optional
The order of the spline interpolation, default is 3.
The order has to be in the range 0-5.
mode : str, optional
Points outside the boundaries of the input are filled according
to the given mode ('constant', 'nearest', 'reflect' or 'wrap').
Default is 'constant'.
cval : scalar, optional
Value used for points outside the boundaries of the input if
``mode='constant'``. Default is 0.0
prefilter : bool, optional
The parameter prefilter determines if the input is pre-filtered with
`spline_filter` before interpolation (necessary for spline
interpolation of order > 1). If False, it is assumed that the input is
already filtered. Default is True.
extra_arguments : tuple, optional
Extra arguments passed to `mapping`.
extra_keywords : dict, optional
Extra keywords passed to `mapping`.
Returns
-------
return_value : ndarray or None
The filtered input. If `output` is given as a parameter, None is
returned.
See Also
--------
map_coordinates, affine_transform, spline_filter1d
Examples
--------
>>> a = np.arange(12.).reshape((4, 3))
>>> def shift_func(output_coords):
... return (output_coords[0] - 0.5, output_coords[1] - 0.5)
...
>>> sp.ndimage.geometric_transform(a, shift_func)
array([[ 0. , 0. , 0. ],
[ 0. , 1.362, 2.738],
[ 0. , 4.812, 6.187],
[ 0. , 8.263, 9.637]])
"""
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')
if output_shape is None:
output_shape = input.shape
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError('input and output rank must be > 0')
mode = _extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output,
input,
shape=output_shape)
_nd_image.geometric_transform(filtered, mapping, None, None, None, output,
order, mode, cval, extra_arguments,
extra_keywords)
return return_value
def map_coordinates(input,
coordinates,
output_type=None,
output=None,
order=3,
mode='constant',
cval=0.0,
prefilter=True):
"""Apply an arbritrary coordinate transformation.
The array of coordinates is used to find, for each point in the output,
the corresponding coordinates in the input. The value of the input at
that coordinates is determined by spline interpolation of the
requested order.
The shape of the output is derived from that of the coordinate
array by dropping the first axis. The values of the array along
the first axis are the coordinates in the input array at which the
output value is found. For example, if the input has dimensions
(100,200,3), then the shape of coordinates will be (3,100,200,3),
where coordinates[:,1,2,3] specify the input coordinate at which
output[1,2,3] is found.
Points outside the boundaries of the input are filled according to
the given mode ('constant', 'nearest', 'reflect' or 'wrap'). The
parameter prefilter determines if the input is pre-filtered before
interpolation (necessary for spline interpolation of order >
1). If False it is assumed that the input is already filtered.
Example usage:
>>> a = arange(12.).reshape((4,3))
>>> print a
[[ 0. 1. 2.]
[ 3. 4. 5.]
[ 6. 7. 8.]
[ 9. 10. 11.]]
>>> output = map_coordinates(a,[[0.5, 2], [0.5, 1]],order=1)
>>> print output
[ 2. 7.]
Here, the interpolated value of a[0.5,0.5] gives output[0], while
a[2,1] is output[1].
"""
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'
coordinates = numpy.asarray(coordinates)
if numpy.iscomplexobj(coordinates):
raise TypeError, 'Complex type not supported'
output_shape = coordinates.shape[1:]
if input.ndim < 1 or len(output_shape) < 1:
raise RuntimeError, 'input and output rank must be > 0'
if coordinates.shape[0] != input.ndim:
raise RuntimeError, 'invalid shape for coordinate array'
mode = _ni_support._extend_mode_to_code(mode)
if prefilter and order > 1:
filtered = spline_filter(input, order, output=numpy.float64)
else:
filtered = input
output, return_value = _ni_support._get_output(output,
input,
output_type,
shape=output_shape)
_nd_image.geometric_transform(filtered, None, coordinates, None, None,
output, order, mode, cval, None, None)
return return_value
