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bundles / scipy 1.17.1 / scipy / ndimage / _interpolation / map_coordinates

function

scipy.ndimage._interpolation:map_coordinates

source: /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_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 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.

Notes

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

Array API Standard Support

map_coordinates has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

====================  ====================  ====================
Library               CPU                   GPU
====================  ====================  ====================
NumPy                 ✅                     n/a                 
CuPy                  n/a                   ✅                   
PyTorch               ✅                     ⛔                   
JAX                   ✅                     ✅                   
Dask                  ⚠️ computes graph     n/a                 
====================  ====================  ====================

See dev-arrayapi for more information.

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)
✗
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)
✓
ndimage.map_coordinates(a, inds, order=1, mode='nearest')
ndimage.map_coordinates(a, inds, order=1, cval=0, output=bool)
✗

See also

geometric_transform
scipy.interpolate
spline_filter

Aliases

  • scipy.ndimage.map_coordinates

Referenced by

This package

Other packages