`scipy.signal._peak_finding:argrelextrema`

source: /scipy/signal/_peak_finding.py :198

Signature

   def     argrelextrema (    data    ,    comparator    ,    axis    =  0   ,    order    =  1   ,    mode    =  clip     )

Summary

Calculate the relative extrema of data.

Parameters

data : ndarray: Array in which to find the relative extrema.
comparator : callable: Function to use to compare two data points. Should take two arrays as arguments.
axis : int, optional: Axis over which to select from data. Default is 0.
order : int, optional: How many points on each side to use for the comparison to consider comparator(n, n+x) to be True.
mode : str, optional: How the edges of the vector are treated. 'wrap' (wrap around) or 'clip' (treat overflow as the same as the last (or first) element). Default is 'clip'. See numpy.take.

Returns

extrema : tuple 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.

Notes

Array API Standard Support

argrelextrema 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 dev-arrayapi for more information.

Examples

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

✓

Aliases

scipy.signal.argrelextrema