findpeaks

The library findpeaks aims to detect peaks in a 1-dimensional vector and 2-dimensional arrays (images) without making any assumption on the peak shape or baseline noise. To make sure that peaks can be detected across global and local heights, and in noisy data, multiple pre-processing and denoising methods are implemented.

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Install findpeaks from PyPI

pip install findpeaks

Import findpeaks package

from findpeaks import findpeaks

Blog findpeaks

Documentation pages

On the documentation pages you can find detailed information about the working of the findpeaks with many examples.

Examples

• Example: Find peaks in 1D-vector with low number of samples

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• Example: Comparison peak detection methods

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• Example: Find peaks in 1D-vector with high number of samples

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• Example: Find peaks in an image (2D-array)

• Example: Conversion from 2d to 3d mesh plot)

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• Example: Find peaks and valleys in stockmarkets (Bitcoin)
• Example: Find peaks and valleys in stockmarkets (Facebook)

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• Example: Find peaks in SAR/SONAR images)

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• Example: Denoising images using Lee, Kuan, Fastnl, Bilateral, Frost, Mean, median)

References

• https://github.com/erdogant/findpeaks
• https://github.com/Anaxilaus/peakdetect
• https://www.sthu.org/blog/13-perstopology-peakdetection/index.html

Contribute

• All kinds of contributions are welcome!

Citation

Please cite findpeaks in your publications if this is useful for your research. See column right for citation information.

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• If you wish to buy me a Coffee for this work, it is very appreciated :)