findpeaks 2.7.5

findpeaks is for the detection of peaks and valleys in a 1D vector and 2D array (image).

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	Medium	Gumroad & Podcast
Topology Detection	Mathematically grounded peak detection using persistent homology.	-	-
Peakdetect Method	Peak detection algorithm for noisy signals.	link	link
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.