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This is a very basic but very fast window discriminator for detecting and extracting spikes in a time series. It was developed for analyzing extracellular neural recordings, but also works with intracellular data and probably many other kinds of time series. The algorithm iterates through the time series. When the signal crosses the threshold. Its indexes function allows you to detect peaks with minimum height and distance filtering. import numpy as np import peakutils cb = np.array( [-0.010223, ... ]) indexes = peakutils.indexes(cb, thres=0.02/max(cb), min_dist=100) The PeakUtils indexes function is easy to use and allows to filter on an height threshold and on a minimum distance.

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Overview. I was trying to find a function that returns peaks and valleys of a graph. I tested scipy.signal.find_peaks_cwt () but it turns out to be not suitable for my use case. It looks like it is only suitable to handle signal graph. After all, the function is under the signal package. See Chart output section below for good and bad cases.

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