scipy.ndimage.maximum#

scipy.ndimage.maximum(input, labels=None, index=None)[source]#

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

Parameters

inputarray_like: Array_like of values. For each region specified by labels, the maximal values of input over the region is computed.
labelsarray_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.
indexarray_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

outputfloat 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]