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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) 

Description

gaussian_filter.__doc__

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
        Truncate the filter at this many standard deviations.
        Default is 4.0.

    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.

    Examples
    --------
    >>> from scipy.ndimage import gaussian_filter
    >>> 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 misc
    >>> 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 = misc.ascent()
    >>> result = gaussian_filter(ascent, sigma=5)
    >>> ax1.imshow(ascent)
    >>> ax2.imshow(result)
    >>> plt.show()