Finding local maxima/minima with Numpy in a 1D numpy array

Question

Can you suggest a module function from numpy/scipy that can find local maxima/minima in a 1D numpy array? Obviously the simplest approach ever is to have a look at the nearest neighbours, but I would like to have an accepted solution that is part of the numpy distro.

Answer 1

In SciPy >= 0.11

import numpy as np from scipy.signal import argrelextrema  x = np.random.random(12)  # for local maxima argrelextrema(x, np.greater)  # for local minima argrelextrema(x, np.less) 

Produces

>>> x array([ 0.56660112,  0.76309473,  0.69597908,  0.38260156,  0.24346445,     0.56021785,  0.24109326,  0.41884061,  0.35461957,  0.54398472,     0.59572658,  0.92377974]) >>> argrelextrema(x, np.greater) (array([1, 5, 7]),) >>> argrelextrema(x, np.less) (array([4, 6, 8]),) 

Note, these are the indices of x that are local max/min. To get the values, try:

>>> x[argrelextrema(x, np.greater)[0]] 

scipy.signal also provides argrelmax and argrelmin for finding maxima and minima respectively.

Answer 2

If you are looking for all entries in the 1d array a smaller than their neighbors, you can try

numpy.r_[True, a[1:] < a[:-1]] & numpy.r_[a[:-1] < a[1:], True] 

You could also smooth your array before this step using numpy.convolve().

I don't think there is a dedicated function for this.