Find local maximums in numpy array

There exists a bulit-in function argrelextrema that gets this task done:

import numpy as npfrom scipy.signal import argrelextrema    a = np.array([1,2,3,4,5,4,3,2,1,2,3,2,1,2,3,4,5,6,5,4,3,2,1])# determine the indices of the local maximamax_ind = argrelextrema(a, np.greater)# get the actual values using these indicesr = a[max_ind]  # array([5, 3, 6])

That gives you the desired output for r.

As of SciPy version 1.1, you can also use find_peaks. Below are two examples taken from the documentation itself.

Using the height argument, one can select all maxima above a certain threshold (in this example, all non-negative maxima; this can be very useful if one has to deal with a noisy baseline; if you want to find minima, just multiply you input by -1):

import matplotlib.pyplot as pltfrom scipy.misc import electrocardiogramfrom scipy.signal import find_peaksimport numpy as npx = electrocardiogram()[2000:4000]peaks, _ = find_peaks(x, height=0)plt.plot(x)plt.plot(peaks, x[peaks], "x")plt.plot(np.zeros_like(x), "--", color="gray")plt.show()

Another extremely helpful argument is distance, which defines the minimum distance between two peaks:

peaks, _ = find_peaks(x, distance=150)# difference between peaks is >= 150print(np.diff(peaks))# prints [186 180 177 171 177 169 167 164 158 162 172]plt.plot(x)plt.plot(peaks, x[peaks], "x")plt.show()

If your original data is noisy, then using statistical methods is preferable, as not all peaks are going to be significant. For your a array, a possible solution is to use double differentials:

peaks = a[1:-1][np.diff(np.diff(a)) < 0]# peaks = array([5, 3, 6])

>> import numpy as np>> from scipy.signal import argrelextrema>> a = np.array([1,2,3,4,5,4,3,2,1,2,3,2,1,2,3,4,5,6,5,4,3,2,1])>> argrelextrema(a, np.greater)array([ 4, 10, 17]),)>> a[argrelextrema(a, np.greater)]array([5, 3, 6])

If your input represents a noisy distribution, you can try smoothing it with NumPy convolve function.