ripser
A Lean Persistent Homology Library for Python
Decision gist · record as of 2026-08-14
Yes. Ripser is a specialized but well-maintained tool for topological data analysis with no security issues, permissive licensing, and broad platform support. Install friction is moderate (compilation required) but manageable via wheels. Suitable if you need persistent homology or topological feature extraction; not necessary for standard clustering or dimensionality reduction.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Windows users may need MinGW installed; macOS users should update Xcode and command line tools.
- Compilation from source requires Cython.
- Medium install friction due to Cython compilation and compiled dependencies (numpy, scipy, scikit-learn).
License · maintenance · safety
permissive license (permissive) — MIT license (permissive) allows commercial and private use with minimal restrictions. Derived from the original Ripser C++ library (also MIT), with modifications and Python code copyright to Christopher Tralie and Nathaniel Saul.
last release 2026-05-27 (79 days) · last repo commit 2026-08-08 · 340 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,680 downloads/mo, #14,208 on PyPI
Alternatives
Verify before relying
pip install ripser
import numpy as np
from ripser import ripser
from persim import plot_diagrams
data = np.random.random((100, 2))
diagrams = ripser(data)['dgms']
plot_diagrams(diagrams, show=True)- Whether the optional robin_hood hash table integration (claimed to provide up to 30% speedup) is available in precompiled wheels or only when building from source.
- Specific minimum Python version requirements (requires_python is unspecified in metadata).
- Performance characteristics and scalability limits for large point clouds.
What it is and what it does
Ripser.py is a Python wrapper around the fast C++ Ripser library for computing persistent homology, a technique from algebraic topology that tracks how topological features (connected components, holes, voids) persist across different scales in data. It takes point clouds or distance matrices as input and outputs persistence diagrams—visualizations showing which features appear and disappear as you vary a scale parameter. The package provides both a functional interface (ripser function) and a scikit-learn compatible transformer (Rips class) for integration into machine learning pipelines.
The package depends on Cython, numpy, scipy, scikit-learn, and persim for visualization. It compiles to native code, giving it speed advantages over pure-Python implementations. Installation is straightforward on major platforms through precompiled wheels, though Windows users may need MinGW and macOS users should have current Xcode tools. The library is actively maintained and carries no known security vulnerabilities.
Use it for
- Analyze the topological structure of high-dimensional datasets to discover clusters, holes, or voids without specifying cluster count in advance.
- Visualize persistence diagrams to understand which topological features are robust (signal) versus noise in your data.
- Compute lowerstar filtrations on image data to extract topological features for computer vision tasks.
- Extract representative cochains to identify which data points or features drive topological structure.
- Use as a scikit-learn transformer in unsupervised learning pipelines for topological feature engineering.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Ripser is a specialized but well-maintained tool for topological data analysis with no security issues, permissive licensing, and broad platform support. Install friction is moderate (compilation required) but manageable via wheels. Suitable if you need persistent homology or topological feature extraction; not necessary for standard clustering or dimensionality reduction.
Install
ripser on PyPI
Before you install
Medium install friction due to Cython compilation and compiled dependencies (numpy, scipy, scikit-learn). Wheels available across macOS, Linux, and Windows platforms. Active maintenance with recent release and continuous integration support.
Windows users may need MinGW installed; macOS users should update Xcode and command line tools. Compilation from source requires Cython.
License in practice
MIT license (permissive) allows commercial and private use with minimal restrictions. Derived from the original Ripser C++ library (also MIT), with modifications and Python code copyright to Christopher Tralie and Nathaniel Saul.
Quickstart
pip install ripser
import numpy as np
from ripser import ripser
from persim import plot_diagrams
data = np.random.random((100, 2))
diagrams = ripser(data)['dgms']
plot_diagrams(diagrams, show=True)Verify before relying
- Whether the optional robin_hood hash table integration (claimed to provide up to 30% speedup) is available in precompiled wheels or only when building from source.
- Specific minimum Python version requirements (requires_python is unspecified in metadata).
- Performance characteristics and scalability limits for large point clouds.
Package facts
| License | permissive license permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 5 packagesCythonnumpypersimscipyscikit-learn |
| Maintenance | Actively maintained 79 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 81,680 / month, #14,208 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Healthcare IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: PythonTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Mathematics |
Evidence: ripser-0.6.15-cp310-cp310-macosx_10_9_x86_64.whl; ripser-0.6.15-cp310-cp310-macosx_11_0_arm64.whl; ripser-0.6.15-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; ripser-0.6.15-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; ripser-0.6.15-cp310-cp310-musllinux_1_2_aarch64.whl; ripser-0.6.15-cp310-cp310-musllinux_1_2_x86_64.whl; ripser-0.6.15-cp310-cp310-win32.whl; ripser-0.6.15-cp310-cp310-win_amd64.whl; ripser-0.6.15-cp311-cp311-macosx_10_9_x86_64.whl; ripser-0.6.15-cp311-cp311-macosx_11_0_arm64.whl; ripser-0.6.15-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; ripser-0.6.15-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; ripser-0.6.15-cp311-cp311-musllinux_1_2_aarch64.whl; ripser-0.6.15-cp311-cp311-musllinux_1_2_x86_64.whl; ripser-0.6.15-cp311-cp311-win32.whl; ripser-0.6.15-cp311-cp311-win_amd64.whl; ripser-0.6.15-cp312-cp312-macosx_10_13_x86_64.whl; ripser-0.6.15-cp312-cp312-macosx_11_0_arm64.whl; ripser-0.6.15-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; ripser-0.6.15-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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