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Fonction convolve - module scipy.ndimage

Signature de la fonction convolve

def convolve(input, weights, output=None, mode='reflect', cval=0.0, origin=0) 

Description

convolve.__doc__

    Multidimensional convolution.

    The array is convolved with the given kernel.

    Parameters
    ----------
    input : array_like
        The input array.
    weights : array_like
        Array of weights, same number of dimensions as input
    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
    origin : int or sequence, optional
        Controls the placement of the filter on the input array's pixels.
        A value of 0 (the default) centers the filter over the pixel, with
        positive values shifting the filter to the left, and negative ones
        to the right. By passing a sequence of origins with length equal to
        the number of dimensions of the input array, different shifts can
        be specified along each axis.

    Returns
    -------
    result : ndarray
        The result of convolution of `input` with `weights`.

    See Also
    --------
    correlate : Correlate an image with a kernel.

    Notes
    -----
    Each value in result is :math:`C_i = \sum_j{I_{i+k-j} W_j}`, where
    W is the `weights` kernel,
    j is the N-D spatial index over :math:`W`,
    I is the `input` and k is the coordinate of the center of
    W, specified by `origin` in the input parameters.

    Examples
    --------
    Perhaps the simplest case to understand is ``mode='constant', cval=0.0``,
    because in this case borders (i.e., where the `weights` kernel, centered
    on any one value, extends beyond an edge of `input`) are treated as zeros.

    >>> a = np.array([[1, 2, 0, 0],
    ...               [5, 3, 0, 4],
    ...               [0, 0, 0, 7],
    ...               [9, 3, 0, 0]])
    >>> k = np.array([[1,1,1],[1,1,0],[1,0,0]])
    >>> from scipy import ndimage
    >>> ndimage.convolve(a, k, mode='constant', cval=0.0)
    array([[11, 10,  7,  4],
           [10,  3, 11, 11],
           [15, 12, 14,  7],
           [12,  3,  7,  0]])

    Setting ``cval=1.0`` is equivalent to padding the outer edge of `input`
    with 1.0's (and then extracting only the original region of the result).

    >>> ndimage.convolve(a, k, mode='constant', cval=1.0)
    array([[13, 11,  8,  7],
           [11,  3, 11, 14],
           [16, 12, 14, 10],
           [15,  6, 10,  5]])

    With ``mode='reflect'`` (the default), outer values are reflected at the
    edge of `input` to fill in missing values.

    >>> b = np.array([[2, 0, 0],
    ...               [1, 0, 0],
    ...               [0, 0, 0]])
    >>> k = np.array([[0,1,0], [0,1,0], [0,1,0]])
    >>> ndimage.convolve(b, k, mode='reflect')
    array([[5, 0, 0],
           [3, 0, 0],
           [1, 0, 0]])

    This includes diagonally at the corners.

    >>> k = np.array([[1,0,0],[0,1,0],[0,0,1]])
    >>> ndimage.convolve(b, k)
    array([[4, 2, 0],
           [3, 2, 0],
           [1, 1, 0]])

    With ``mode='nearest'``, the single nearest value in to an edge in
    `input` is repeated as many times as needed to match the overlapping
    `weights`.

    >>> c = np.array([[2, 0, 1],
    ...               [1, 0, 0],
    ...               [0, 0, 0]])
    >>> k = np.array([[0, 1, 0],
    ...               [0, 1, 0],
    ...               [0, 1, 0],
    ...               [0, 1, 0],
    ...               [0, 1, 0]])
    >>> ndimage.convolve(c, k, mode='nearest')
    array([[7, 0, 3],
           [5, 0, 2],
           [3, 0, 1]])