scipy.signal.

argrelmax#

scipy.signal.argrelmax(data, axis=0, order=1, mode='clip')[source]#

Calculate the relative maxima of data.

Parameters:

datandarray: Array in which to find the relative maxima.
axisint, optional: Axis over which to select from data. Default is 0.
orderint, optional: How many points on each side to use for the comparison to consider comparator(n, n+x) to be True.
modestr, optional: How the edges of the vector are treated. Available options are ‘wrap’ (wrap around) or ‘clip’ (treat overflow as the same as the last (or first) element). Default ‘clip’. See numpy.take.

Returns:

extrematuple of ndarrays: Indices of the maxima in arrays of integers. extrema[k] is the array of indices of axis k of data. Note that the return value is a tuple even when data is 1-D.

See also

argrelextrema, argrelmin, find_peaks

Notes

This function uses argrelextrema with np.greater as comparator. Therefore, it requires a strict inequality on both sides of a value to consider it a maximum. This means flat maxima (more than one sample wide) are not detected. In case of 1-D data find_peaks can be used to detect all local maxima, including flat ones.

Added in version 0.11.0.

Array API Standard Support

argrelmax has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> import numpy as np
>>> from scipy.signal import argrelmax
>>> x = np.array([2, 1, 2, 3, 2, 0, 1, 0])
>>> argrelmax(x)
(array([3, 6]),)
>>> y = np.array([[1, 2, 1, 2],
...               [2, 2, 0, 0],
...               [5, 3, 4, 4]])
...
>>> argrelmax(y, axis=1)
(array([0]), array([1]))