This is documentation for an old release of SciPy (version 0.9.0). Read this page in the documentation of the latest stable release (version 1.15.1).
Calculate the maximum of the values of an array over labeled regions.
Parameters : | input : array_like
labels : array_like, optional
index : array_like, optional
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Returns : | output : float or list of floats
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See also
label, minimum, 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]