scipy.ndimage.

extrema#

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

Calculate the minimums and maximums of the values of an array at labels, along with their positions.

Parameters:

inputndarray: N-D image data to process.
labelsndarray, optional: Labels of features in input. If not None, must be same shape as input.
indexint or sequence of ints, optional: Labels to include in output. If None (default), all values where non-zero labels are used.

Returns:

minimums, maximumsint or ndarray: Values of minimums and maximums in each feature.
min_positions, max_positionstuple or list of tuples: Each tuple gives the N-D coordinates of the corresponding minimum or maximum.

See also

maximum, minimum, maximum_position, minimum_position, center_of_mass

Examples

>>> import numpy as np
>>> a = np.array([[1, 2, 0, 0],
...               [5, 3, 0, 4],
...               [0, 0, 0, 7],
...               [9, 3, 0, 0]])
>>> from scipy import ndimage
>>> ndimage.extrema(a)
(0, 9, (0, 2), (3, 0))

Features to process can be specified using labels and index:

>>> lbl, nlbl = ndimage.label(a)
>>> ndimage.extrema(a, lbl, index=np.arange(1, nlbl+1))
(array([1, 4, 3]),
 array([5, 7, 9]),
 [(0, 0), (1, 3), (3, 1)],
 [(1, 0), (2, 3), (3, 0)])

If no index is given, non-zero labels are processed:

>>> ndimage.extrema(a, lbl)
(1, 9, (0, 0), (3, 0))