How to Find Local Maxima and Minima in a 1D Numpy Array Using SciPy?

Finding Local Maxima/Minima in a 1D Numpy Array with Numpy

Identifying local maxima and minima in a 1D numpy array is a common task in signal processing and data analysis. While a simple approach involves comparing an element with its nearest neighbors, a more robust solution is sought within the numpy/scipy libraries.

Solution Using SciPy's argrelextrema

In SciPy versions 0.11 onwards, the argrelextrema function provides an efficient way to find local extrema in a 1D array:

import numpy as np
from scipy.signal import argrelextrema

x = np.random.random(12)

# Find indices of local maxima
maxima_indices = argrelextrema(x, np.greater)

# Find indices of local minima
minima_indices = argrelextrema(x, np.less)

The function returns tuples containing indices of elements that are local maxima or minima:

>>> argrelextrema(x, np.greater)
(array([1, 5, 7]),)
>>> argrelextrema(x, np.less)
(array([4, 6, 8]),)

To obtain the actual values at these local extrema:

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

Additional Functions in SciPy

In addition to argrelextrema, SciPy provides specialized functions for finding only maxima or minima:

• argrelmax: Finds indices of local maxima
• argrelmin: Finds indices of local minima

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