findpeaks

<div> <a href="https://erdogant.github.io/findpeaks/"><img src="https://github.com/erdogant/findpeaks/blob/master/docs/figs/logo.png" width="125" align="left" /></a> 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⭐️ </div>

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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. | - | - |

Resources and Links

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

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.

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

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()