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Module « scipy.ndimage »

Fonction maximum - module scipy.ndimage

Signature de la fonction maximum

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

Description

help(scipy.ndimage.maximum)

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
--------
>>> import numpy as np
>>> 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
>>> ndimage.maximum(a, labels=labels, index=[1,2])
[5, 14]
>>> ndimage.maximum(a, labels=labels)
14

>>> 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]], dtype=int32)
>>> ndimage.maximum(b, labels=labels, index=np.arange(1, labels_nb + 1))
[5, 7, 9]

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