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Module « scipy.ndimage »

Fonction gaussian_filter - module scipy.ndimage

Signature de la fonction gaussian_filter

def gaussian_filter(input, sigma, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0, *, radius=None, axes=None) 

Description

help(scipy.ndimage.gaussian_filter)

Multidimensional Gaussian filter.

Parameters
----------
input : array_like
    The input array.
sigma : scalar or sequence of scalars
    Standard deviation for Gaussian kernel. The standard
    deviations of the Gaussian filter are given for each axis as a
    sequence, or as a single number, in which case it is equal for
    all axes.
order : int or sequence of ints, optional
    The order of the filter along each axis is given as a sequence
    of integers, or as a single number. An order of 0 corresponds
    to convolution with a Gaussian kernel. A positive order
    corresponds to convolution with that derivative of a Gaussian.
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.
mode : str or sequence, optional
    The `mode` parameter determines how the input array is extended
    when the filter overlaps a border. By passing a sequence of modes
    with length equal to the number of dimensions of the input array,
    different modes can be specified along each axis. Default value is
    'reflect'. The valid values and their behavior is as follows:

    '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.

    '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.

    '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.

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

    For consistency with the interpolation functions, the following mode
    names can also be used:

    'grid-constant'
        This is a synonym for 'constant'.

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

    'grid-wrap'
        This is a synonym for 'wrap'.
cval : scalar, optional
    Value to fill past edges of input if `mode` is 'constant'. Default
    is 0.0.
truncate : float, optional
    Truncate the filter at this many standard deviations.
    Default is 4.0.
radius : None or int or sequence of ints, optional
    Radius of the Gaussian kernel. The radius are given for each axis
    as a sequence, or as a single number, in which case it is equal
    for all axes. If specified, the size of the kernel along each axis
    will be ``2*radius + 1``, and `truncate` is ignored.
    Default is None.
axes : tuple of int or None, optional
    If None, `input` is filtered along all axes. Otherwise,
    `input` is filtered along the specified axes. When `axes` is
    specified, any tuples used for `sigma`, `order`, `mode` and/or `radius`
    must match the length of `axes`. The ith entry in any of these tuples
    corresponds to the ith entry in `axes`.

Returns
-------
gaussian_filter : ndarray
    Returned array of same shape as `input`.

Notes
-----
The multidimensional filter is implemented as a sequence of
1-D convolution filters. The intermediate arrays are
stored in the same data type as the output. Therefore, for output
types with a limited precision, the results may be imprecise
because intermediate results may be stored with insufficient
precision.

The Gaussian kernel will have size ``2*radius + 1`` along each axis. If
`radius` is None, the default ``radius = round(truncate * sigma)`` will be
used.

Examples
--------
>>> from scipy.ndimage import gaussian_filter
>>> import numpy as np
>>> a = np.arange(50, step=2).reshape((5,5))
>>> a
array([[ 0,  2,  4,  6,  8],
       [10, 12, 14, 16, 18],
       [20, 22, 24, 26, 28],
       [30, 32, 34, 36, 38],
       [40, 42, 44, 46, 48]])
>>> gaussian_filter(a, sigma=1)
array([[ 4,  6,  8,  9, 11],
       [10, 12, 14, 15, 17],
       [20, 22, 24, 25, 27],
       [29, 31, 33, 34, 36],
       [35, 37, 39, 40, 42]])

>>> from scipy import datasets
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray()  # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121)  # left side
>>> ax2 = fig.add_subplot(122)  # right side
>>> ascent = datasets.ascent()
>>> result = gaussian_filter(ascent, sigma=5)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()

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