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Module « scipy.ndimage »

Fonction shift - module scipy.ndimage

Signature de la fonction shift

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

Description

help(scipy.ndimage.shift)

Shift an array.

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
    :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
-------
shift : ndarray
    The shifted input.

See Also
--------
affine_transform : Affine transformations

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

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

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")
>>> fig.tight_layout()

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