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Fonction gaussian_laplace - module scipy.ndimage

Signature de la fonction gaussian_laplace

def gaussian_laplace(input, sigma, output=None, mode='reflect', cval=0.0, **kwargs) 

Description

gaussian_laplace.__doc__

Multidimensional Laplace filter using Gaussian second derivatives.

    Parameters
    ----------
    input : array_like
        The input array.
    sigma : scalar or sequence of scalars
        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.
    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.
    Extra keyword arguments will be passed to gaussian_filter().

    Examples
    --------
    >>> from scipy import ndimage, misc
    >>> import matplotlib.pyplot as plt
    >>> ascent = misc.ascent()

    >>> 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

    >>> result = ndimage.gaussian_laplace(ascent, sigma=1)
    >>> ax1.imshow(result)

    >>> result = ndimage.gaussian_laplace(ascent, sigma=3)
    >>> ax2.imshow(result)
    >>> plt.show()