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Module « scipy.ndimage »

Fonction gaussian_filter1d - module scipy.ndimage

Signature de la fonction gaussian_filter1d

def gaussian_filter1d(input, sigma, axis=-1, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0, *, radius=None) 

Description

help(scipy.ndimage.gaussian_filter1d)

1-D Gaussian filter.

Parameters
----------
input : array_like
    The input array.
sigma : scalar
    standard deviation for Gaussian kernel
axis : int, optional
    The axis of `input` along which to calculate. Default is -1.
order : int, optional
    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 : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional
    The `mode` parameter determines how the input array is extended
    beyond its boundaries. Default is 'reflect'. Behavior for each valid
    value 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-mirror'
        This is a synonym for 'reflect'.

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

    '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, optional
    Radius of the Gaussian kernel. If specified, the size of
    the kernel will be ``2*radius + 1``, and `truncate` is ignored.
    Default is None.

Returns
-------
gaussian_filter1d : ndarray

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

Examples
--------
>>> from scipy.ndimage import gaussian_filter1d
>>> import numpy as np
>>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 1)
array([ 1.42704095,  2.06782203,  3.        ,  3.93217797,  4.57295905])
>>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 4)
array([ 2.91948343,  2.95023502,  3.        ,  3.04976498,  3.08051657])
>>> import matplotlib.pyplot as plt
>>> rng = np.random.default_rng()
>>> x = rng.standard_normal(101).cumsum()
>>> y3 = gaussian_filter1d(x, 3)
>>> y6 = gaussian_filter1d(x, 6)
>>> plt.plot(x, 'k', label='original data')
>>> plt.plot(y3, '--', label='filtered, sigma=3')
>>> plt.plot(y6, ':', label='filtered, sigma=6')
>>> plt.legend()
>>> plt.grid()
>>> plt.show()

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