This is an overview of all the ready-to-use algorithms I've found to perform peak detection in Python. I've also written a blog post on the subject.

Overview

How to make your choice?

When you're selecting an algorithm, you might consider:

• The function interface. You may want the function to work natively with Numpy arrays or may search something similar to other platform algorithms, like the MatLab findpeaks.
• The dependencies. Does it require extra dependency? Does is it easy to make it run on a fresh box?
• The filtering support. Does the algorithm allows to define multiple filters? Which ones do you need?

## scipy.signal.find_peaks_cwt

import numpy as np
from vector import vector, plot_peaks
import scipy.signal
print('Detect peaks without any filters.')
indexes = scipy.signal.find_peaks_cwt(vector, np.arange(1, 4),
    max_distances=np.arange(1, 4)*2)
indexes = np.array(indexes) - 1
print('Peaks are: %s' % (indexes))

Documentation. Sample code.

The peak detection algorithm from the Scipy signal processing package. It appears like the obvious choice when you already work with Scipy, but may in fact not be as it uses a wavelet convolution approach.

Thus this function requires to understand wavelets to be well used, which is less trivial and direct than other algorithms. However this can a good choice on noisy data.

detect_peaks from Marcos Duarte

import numpy as np
from vector import vector, plot_peaks
from libs import detect_peaks
print('Detect peaks with minimum height and distance filters.')
indexes = detect_peaks.detect_peaks(vector, mph=7, mpd=2)
print('Peaks are: %s' % (indexes))

Documentation. Source. Sample code.

This algorithm comes from a notebook written by Marcos Duarte and is pretty trivial to use.

The function has an interface very similar and consistent results with the MatLab Signal Processing Toolbox findpeaks, yet with less complete filtering and tuning support.

peakutils.peak.indexes

import numpy as np
from vector import vector, plot_peaks
import peakutils.peak
print('Detect peaks with minimum height and distance filters.')
indexes = peakutils.peak.indexes(np.array(vector),
    thres=7.0/max(vector), min_dist=2)
print('Peaks are: %s' % (indexes))

Documentation. Package. Sample code.

This algorithm can be used as an equivalent of the MatLab findpeaks and will give easily give consistent results if you only need minimal distance and height filtering.

peakdetect from sixtenbe

import numpy as np
from vector import vector, plot_peaks
from libs import peakdetect
print('Detect peaks with distance filters.')
peaks = peakdetect.peakdetect(np.array(vector), lookahead=2, delta=2)
# peakdetect returns two lists, respectively positive and negative peaks,
# with for each peak a tuple of (indexes, values).
indexes = []
for posOrNegPeaks in peaks:
    for peak in posOrNegPeaks:
        indexes.append(peak[0])
print('Peaks are: %s' % (indexes))

Source and documentation. Sample code.

The algorithm was written by sixtenbe based on the previous work of endolith and Eli Billauer.

Easy to setup as it comes in a single source file, but the lookahead parameter make it hard to use on low-sampled signals or short samples. May miss filtering capacities (only minimum peak distance with the delta parameter).

Octave-Forge findpeaks

import numpy as np
from vector import vector, plot_peaks
from oct2py import octave
# Load the Octage-Forge signal package.
octave.eval("pkg load signal")
print('Detect peaks with minimum height and distance filters.')
(pks, indexes) = octave.findpeaks(np.array(vector), 'DoubleSided',
    'MinPeakHeight', 6, 'MinPeakDistance', 2, 'MinPeakWidth', 0)
# The results are in a 2D array and in floats: get back to 1D array and convert
# peak indexes to integer. Also this is MatLab-style indexation (one-based),
# so we must substract one to get back to Python indexation (zero-based).
indexes = indexes[0].astype(int) - 1
print('Peaks are: %s' % (indexes))

Documentation. oct2py package. Sample code.

Use findpeaks from the Octave-Forge signal package through the oct2py bridge. This algorithm allows to make a double sided detection, which means it will detect both local maximam and minima in a single run.

Requires a rather complicated and not very efficient setup to be called from Python code. Of course, you will need an up-to-date distribution of Octave, with the signal package installed from Octave-Forge.

Although the function have an interface close to the MatLab findpeaks, it is harder to have the exact same results that with detect_peaks or peakutils.peak.indexes.

Janko Slavic findpeaks

import numpy as np
from vector import vector, plot_peaks
from libs.findpeaks import findpeaks
indexes = findpeaks(np.array(vector), spacing=, limit=7)
print('Peaks are: %s' % (indexes))

Documentation. Source. Sample code.

Small and fast peak detection algorithm, with minimum distance and height filtering support. Comes as an handy single function, depending only on Numpy.

Contrary to the MatLab findpeaks-like distance filters, the Janko Slavic findpeaks spacing param requires that all points within the specified width to be lower than the peak. If you work on very low sampled signal the minimum distance filter may miss fine granularity tuning .

Tony Beltramelli detect_peaks

import numpy as np
from vector import vector, plot_peaks
from libs.tony_beltramelli_detect_peaks import detect_peaks
print('Detect peaks with height threshold.')
indexes = detect_peaks(vector, 1.5)
print('Peaks are: %s' % (indexes))

Source and documentation. Sample code.

Straightforward, simple and lightweight peak detection algorithm, with minimum distance filtering support.

No minimum peak height filtering support.

Contribute

Feel free to open a new ticket or submit a PR to improve this overview.

Happy processing!