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Fonction maximum - module scipy.ndimage

Signature de la fonction maximum

def maximum(input, labels=None, index=None) 

Description

maximum.__doc__

    Calculate the maximum of the values of an array over labeled regions.

    Parameters
    ----------
    input : array_like
        Array_like of values. For each region specified by `labels`, the
        maximal values of `input` over the region is computed.
    labels : array_like, optional
        An array of integers marking different regions over which the
        maximum value of `input` is to be computed. `labels` must have the
        same shape as `input`. If `labels` is not specified, the maximum
        over the whole array is returned.
    index : array_like, optional
        A list of region labels that are taken into account for computing the
        maxima. If index is None, the maximum over all elements where `labels`
        is non-zero is returned.

    Returns
    -------
    output : float or list of floats
        List of maxima of `input` over the regions determined by `labels` and
        whose index is in `index`. If `index` or `labels` are not specified, a
        float is returned: the maximal value of `input` if `labels` is None,
        and the maximal value of elements where `labels` is greater than zero
        if `index` is None.

    See also
    --------
    label, minimum, median, maximum_position, extrema, sum, mean, variance,
    standard_deviation

    Notes
    -----
    The function returns a Python list and not a NumPy array, use
    `np.array` to convert the list to an array.

    Examples
    --------
    >>> a = np.arange(16).reshape((4,4))
    >>> a
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11],
           [12, 13, 14, 15]])
    >>> labels = np.zeros_like(a)
    >>> labels[:2,:2] = 1
    >>> labels[2:, 1:3] = 2
    >>> labels
    array([[1, 1, 0, 0],
           [1, 1, 0, 0],
           [0, 2, 2, 0],
           [0, 2, 2, 0]])
    >>> from scipy import ndimage
    >>> ndimage.maximum(a)
    15.0
    >>> ndimage.maximum(a, labels=labels, index=[1,2])
    [5.0, 14.0]
    >>> ndimage.maximum(a, labels=labels)
    14.0

    >>> b = np.array([[1, 2, 0, 0],
    ...               [5, 3, 0, 4],
    ...               [0, 0, 0, 7],
    ...               [9, 3, 0, 0]])
    >>> labels, labels_nb = ndimage.label(b)
    >>> labels
    array([[1, 1, 0, 0],
           [1, 1, 0, 2],
           [0, 0, 0, 2],
           [3, 3, 0, 0]])
    >>> ndimage.maximum(b, labels=labels, index=np.arange(1, labels_nb + 1))
    [5.0, 7.0, 9.0]