findpeaks

findpeaks is a comprehensive Python library for robust detection and analysis of peaks and valleys in both 1D vectors and 2D arrays (images). The library provides multiple detection algorithms including topology-based persistent homology (most robust), mask-based local maximum filtering, and traditional peakdetect approaches. It can be used for time series analysis, signal processing, image analysis, and spatial data. ⭐️Star it if you like it⭐️

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Key Features

Feature	Description
Topology Detection	Mathematically grounded peak detection using persistent homology.
Peakdetect Method	Traditional peak detection algorithm for noisy signals.
Mask Detection	Local maximum filtering for 2D image analysis.
Caerus Method	Specialized algorithm for financial time series analysis.
Preprocessing	Denoising, scaling, interpolation, and image preprocessing.
Visualization	Rich plotting capabilities including persistence diagrams and 3D mesh plots.

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Resources and Links

• Example Notebooks: Examples
• Blog Posts: Medium
• Documentation: Website
• Bug Reports and Feature Requests: GitHub Issues

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Background

• Topology Method: The most robust detection method based on persistent homology from topological data analysis. It quantifies peak significance through persistence scores and provides mathematically stable results even in noisy data.

• Peakdetect Method: Traditional algorithm that excels at finding local maxima and minima in noisy signals without requiring extensive preprocessing. Uses a lookahead approach to distinguish between true peaks and noise-induced fluctuations.

• Mask Method: Local maximum filtering approach specifically designed for 2D data (images). Employs 8-connected neighborhood analysis and background removal for spatial peak detection.

• Preprocessing Pipeline: Comprehensive preprocessing capabilities including interpolation, denoising (Lee, Frost, Kuan filters), scaling, and image resizing to improve detection accuracy.

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Installation

Install findpeaks from PyPI

pip install findpeaks

Install from Github source

pip install git+https://github.com/erdogant/findpeaks

Import Library

import findpeaks
print(findpeaks.__version__)

# Import library
from findpeaks import findpeaks

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Quick Start

# Import library
from findpeaks import findpeaks

# Initialize with topology method (most robust)
fp = findpeaks(method='topology')

# Example data
X = fp.import_example('1dpeaks')

# Peak detection
results = fp.fit(X)

# Plot results
fp.plot()

# Plot persistence diagram
fp.plot_persistence()

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Examples

1D Signal Analysis

• Find peaks in low sampled dataset

• Comparison of peak detection methods

• Find peaks in high sampled dataset

2D Image Analysis

• Find peaks in an image (2D-array)

• 3D mesh visualization

Financial Time Series

• Bitcoin price analysis
• Facebook stock analysis

SAR/SONAR Image Processing

• Peak detection in SAR/SONAR images

Image Denoising

• Denoising with Lee, Kuan, Fastnl, Bilateral, Frost, Mean, Median filters

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References

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

Contributors

Special thanks to the contributors!

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• Yes! This library is entirely free but it runs on coffee! :) Feel free to support with a Coffee.