bundles / scipy 2.0.0.dev0+git20260907.41eeb59 / scipy / ndimage / _interpolation / map_coordinates
function
scipy.ndimage._interpolation:map_coordinates
source: build-install/usr/lib/python3.14/site-packages/scipy/ndimage/_interpolation.py:374
Signature
def map_coordinates(input, coordinates, output = None, order = 3, mode = constant, cval = 0.0, prefilter = True)Summary
Map the input array to new coordinates by interpolation.
Extended Summary
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_likeThe input array.
coordinates: array_likeThe coordinates at which
inputis evaluated.output: array or dtype, optionalThe 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, optionalThe 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'}, optionalThe
modeparameter 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 boundary 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
cvalparameter. 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
cvalparameter. 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' (
b c d b c | a b c d | b c a b c)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.
For the example illustrated above, the edge values
aanddoverlap, while the inner patternb crepeats. The example demonstrates one possible output, whileb c a b c | a b c d | b c d b cis another, since eitheraordmay occur at each point of overlap.
cval: scalar, optionalValue to fill past edges of input if
modeis 'constant'. Default is 0.0.prefilter: bool, optionalDetermines if the input array is prefiltered with spline_filter before interpolation. The default is True, which will create a temporary
float64array of filtered values iforder > 1. If setting this to False, the output will be slightly blurred iforder > 1, unless the input is prefiltered, i.e. it is the result of calling spline_filter on the original input.
Returns
map_coordinates: ndarrayThe result of transforming the input. The shape of the output is derived from that of
coordinatesby dropping the first axis.
Notes
For complex-valued input, this function maps the real and imaginary components independently.
Examples
from scipy import ndimage import numpy as np a = np.arange(12.).reshape((4, 3))✓
a 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') ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)✗
array([ True, False])
See also
Aliases
scipy.ndimage.map_coordinates