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Module « scipy.ndimage »

Fonction map_coordinates - module scipy.ndimage

Signature de la fonction map_coordinates

def map_coordinates(input, coordinates, output=None, order=3, mode='constant', cval=0.0, prefilter=True) 

Description

help(scipy.ndimage.map_coordinates)

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 : array_like
    The input array.
coordinates : array_like
    The coordinates at which `input` is evaluated.
output : array or dtype, optional
    The array in which to place the output, or the dtype of the
    returned array. By default an array of the same dtype as input
    will be created.
order : int, optional
    The order of the spline interpolation, default is 3.
    The order has to be in the range 0-5.
mode : {'reflect', 'grid-mirror', 'constant', 'grid-constant', 'nearest', 'mirror', 'grid-wrap', 'wrap'}, optional
    The `mode` parameter determines how the input array is extended
    beyond its boundaries. Default is 'constant'. Behavior for each valid
    value is as follows (see additional plots and details on
    :ref:`boundary modes <ndimage-interpolation-modes>`):

    'reflect' (`d c b a | a b c d | d c b a`)
        The input is extended by reflecting about the edge of the last
        pixel. This mode is also sometimes referred to as half-sample
        symmetric.

    'grid-mirror'
        This is a synonym for 'reflect'.

    'constant' (`k k k k | a b c d | k k k k`)
        The input is extended by filling all values beyond the edge with
        the same constant value, defined by the `cval` parameter. No
        interpolation is performed beyond the edges of the input.

    'grid-constant' (`k k k k | a b c d | k k k k`)
        The input is extended by filling all values beyond the edge with
        the same constant value, defined by the `cval` parameter. Interpolation
        occurs for samples outside the input's extent  as well.

    'nearest' (`a a a a | a b c d | d d d d`)
        The input is extended by replicating the last pixel.

    'mirror' (`d c b | a b c d | c b a`)
        The input is extended by reflecting about the center of the last
        pixel. This mode is also sometimes referred to as whole-sample
        symmetric.

    'grid-wrap' (`a b c d | a b c d | a b c d`)
        The input is extended by wrapping around to the opposite edge.

    'wrap' (`d b c d | a b c d | b c a b`)
        The input is extended by wrapping around to the opposite edge, but in a
        way such that the last point and initial point exactly overlap. In this
        case it is not well defined which sample will be chosen at the point of
        overlap.
cval : scalar, optional
    Value to fill past edges of input if `mode` is 'constant'. Default
    is 0.0.
prefilter : bool, optional
    Determines if the input array is prefiltered with `spline_filter`
    before interpolation. The default is True, which will create a
    temporary `float64` array of filtered values if ``order > 1``. If
    setting this to False, the output will be slightly blurred if
    ``order > 1``, unless the input is prefiltered, i.e. it is the result
    of calling `spline_filter` on the original input.

Returns
-------
map_coordinates : 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

Notes
-----
For complex-valued `input`, this function maps the real and imaginary
components independently.

.. versionadded:: 1.6.0
    Complex-valued support added.

Examples
--------
>>> from scipy import ndimage
>>> import numpy as np
>>> 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)
array([ 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)

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