maximum — SciPy v1.15.2 Manual (original) (raw)

scipy.ndimage.

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.

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]