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bundles / scipy latest / scipy / ndimage / _interpolation / shift

function

scipy.ndimage._interpolation:shift

source: build-install/usr/lib/python3.14/site-packages/scipy/ndimage/_interpolation.py:658

Signature

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

Summary

Shift an array.

Extended Summary

The array is shifted using spline interpolation of the requested order. Points outside the boundaries of the input are filled according to the given mode.

Parameters

input : array_like

The input array.

shift : float or sequence

The shift along the axes. If a float, shift is the same for each axis. If a sequence, shift should contain one value for each axis.

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):

'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' (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 a and d overlap, while the inner pattern b c repeats. The example demonstrates one possible output, while b c a b c | a b c d | b c d b c is another, since either a or d may occur at each 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

shift : ndarray

The shifted input.

Notes

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

Examples

Import the necessary modules and an exemplary image. 
from scipy.ndimage import shift
import matplotlib.pyplot as plt
from scipy import datasets
image = datasets.ascent()
✓
Shift the image vertically by 20 pixels. 
image_shifted_vertically = shift(image, (20, 0))
✓
Shift the image vertically by -200 pixels and horizontally by 100 pixels. 
image_shifted_both_directions = shift(image, (-200, 100))
✓
Plot the original and the shifted images. 
fig, axes = plt.subplots(3, 1, figsize=(4, 12))
plt.gray()  # show the filtered result in grayscale
top, middle, bottom = axes
for ax in axes:
    ax.set_axis_off()  # remove coordinate system
✓
top.imshow(image)
top.set_title("Original image")
middle.imshow(image_shifted_vertically)
middle.set_title("Vertically shifted image")
bottom.imshow(image_shifted_both_directions)
bottom.set_title("Image shifted in both directions")
✗
Text(0.5, 1.0, 'Image shifted in both directions')

fig.tight_layout()
✓

See also

affine_transform

Affine transformations

Aliases

  • scipy.ndimage.shift