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Fonction minimum_filter - module scipy.ndimage

Signature de la fonction minimum_filter

def minimum_filter(input, size=None, footprint=None, output=None, mode='reflect', cval=0.0, origin=0) 

Description

minimum_filter.__doc__

Calculate a multidimensional minimum filter.

    Parameters
    ----------
    input : array_like
        The input array.
    size : scalar or tuple, optional
        See footprint, below. Ignored if footprint is given.
    footprint : array, optional
        Either `size` or `footprint` must be defined. `size` gives
        the shape that is taken from the input array, at every element
        position, to define the input to the filter function.
        `footprint` is a boolean array that specifies (implicitly) a
        shape, but also which of the elements within this shape will get
        passed to the filter function. Thus ``size=(n,m)`` is equivalent
        to ``footprint=np.ones((n,m))``.  We adjust `size` to the number
        of dimensions of the input array, so that, if the input array is
        shape (10,10,10), and `size` is 2, then the actual size used is
        (2,2,2). When `footprint` is given, `size` is ignored.
    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.
    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
    -------
    minimum_filter : ndarray
        Filtered array. Has the same shape as `input`.

    Notes
    -----
    A sequence of modes (one per axis) is only supported when the footprint is
    separable. Otherwise, a single mode string must be provided.

    Examples
    --------
    >>> from scipy import ndimage, 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 = ndimage.minimum_filter(ascent, size=20)
    >>> ax1.imshow(ascent)
    >>> ax2.imshow(result)
    >>> plt.show()