Ripser.py
Ripser.py: A Lean Persistent Homology Library for Python - Published in JOSS (2018)
Science Score: 95.0%
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Found 17 DOI reference(s) in README and JOSS metadata -
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3 of 23 committers (13.0%) from academic institutions -
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Published in Journal of Open Source Software
Keywords
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Scientific Fields
Repository
A Lean Persistent Homology Library for Python
Basic Info
- Host: GitHub
- Owner: scikit-tda
- License: other
- Language: C++
- Default Branch: master
- Homepage: http://ripser.scikit-tda.org
- Size: 20.2 MB
Statistics
- Stars: 306
- Watchers: 16
- Forks: 62
- Open Issues: 20
- Releases: 20
Topics
Metadata Files
README.md
Ripser.py
Ripser.py is a lean persistent homology package for Python. Building on the blazing fast C++ Ripser package as the core computational engine, Ripser.py provides an intuitive interface for
- computing persistence cohomology of sparse and dense data sets,
- visualizing persistence diagrams,
- computing lowerstar filtrations on images, and
- computing representative cochains.
Additionally, through extensive testing and continuous integration, Ripser.py is easy to install on Mac, Linux, and Windows platforms.
To aid your use of the package, we've put together a large set of notebooks that demonstrate many of the features available. Complete documentation about the package can be found at ripser.scikit-tda.org.
Related Projects
If you're looking for the original C++ library, you can find it at Ripser/ripser.
If you're looking for a GPU-accelerated version of Ripser, you can find it at Ripser++
Setup
Ripser.py is available on pypi with wheels for all major platforms. To install, type the following command into your environment:
bash
pip install ripser
Local build
If the above command fails or if you want to develop and contribute to
ripser.py, you can build ripser.py locally. To do so, clone this
repository. From within the cloned repository, execute pip install . to build
locally, or pip install -e . for a local,
editable
build. Either of the above two commands will install all required dependencies.
Explicitly, the dependencies of ripser.py are
- Cython,
- numpy,
- scipy,
- scikit-learn,
- persim,
and their required dependencies.
Windows users: If you are using a Windows machine, you may also need to install MinGW on your system.
Mac users: Updating your Xcode and Xcode command line tools will probably fix any issues you have with installation.
Optional dependency
Ripser.py when compiled from source can have a steroid1 shot by replacing the standard unordered_map from the STL by one of the fastest implementation available: robin_hood. Benchmarking of Ripser.py using the robin_hood implementation showed speed-ups up to 30%.
To be able to use robin_hood instead of STL, you only need to clone the repository containing the implementation:
```
Run this command at the root of the project
git clone https://github.com/martinus/robin-hood-hashing robinhood ```
After cloning robinhood with the above command, install ripser.py with
pip install -v .
This will install a local version of ripser.py with verbose output. In the verbose output,
you will see confirmation that robinhood was found or not.
1 The Python package is already compiled with robin_hood by default.
If you are having trouble installing, please let us know!
Usage
The interface is as simple as can be:
``` 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) ```
We also supply a Scikit-learn transformer style object if you would prefer to use that:
``` import numpy as np from ripser import Rips
rips = Rips() data = np.random.random((100,2)) diagrams = rips.fit_transform(data) rips.plot(diagrams) ```
Contributions
We welcome all kinds of contributions! Please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don't feel you can't contribute. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.
If you found a bug, have questions, or are just having trouble with the library, please open an issue in our issue tracker and we'll try to help resolve the concern.
License
Ripser.py is available under an MIT license! The core C++ code is derived from Ripser, which is also available under an MIT license and copyright to Ulrich Bauer. The modifications, Python code, and documentation is copyright to Christopher Tralie and Nathaniel Saul.
Citing
If you use this package, please site the JoSS paper found here and the JACT paper about Ripser found here 
.
You can use the following bibtex entries:
``` @article{ctralie2018ripser, doi = {10.21105/joss.00925}, url = {https://doi.org/10.21105/joss.00925}, year = {2018}, month = {Sep}, publisher = {The Open Journal}, volume = {3}, number = {29}, pages = {925}, author = {Christopher Tralie and Nathaniel Saul and Rann Bar-On}, title = {{Ripser.py}: A Lean Persistent Homology Library for Python}, journal = {The Journal of Open Source Software} }
@article{Bauer2021Ripser, AUTHOR = {Bauer, Ulrich}, TITLE = {Ripser: efficient computation of {V}ietoris-{R}ips persistence barcodes}, JOURNAL = {J. Appl. Comput. Topol.}, FJOURNAL = {Journal of Applied and Computational Topology}, VOLUME = {5}, YEAR = {2021}, NUMBER = {3}, PAGES = {391--423}, ISSN = {2367-1726}, MRCLASS = {55N31 (55-04)}, MRNUMBER = {4298669}, DOI = {10.1007/s41468-021-00071-5}, URL = {https://doi.org/10.1007/s41468-021-00071-5}, } ```
Owner
- Name: Scikit-TDA
- Login: scikit-tda
- Kind: organization
- Website: scikit-tda.org
- Repositories: 11
- Profile: https://github.com/scikit-tda
Topological Data Analysis for the Python ecosystem.
JOSS Publication
Ripser.py: A Lean Persistent Homology Library for Python
Authors
Department of Mathematics, Duke University
Department of Mathematics and Statistics, Washington State University
Department of Mathematics, Duke University
Editor
Ariel Rokem
Tags
Persistent Homology Topological Data AnalysisPapers & Mentions
Total mentions: 1
A roadmap for the computation of persistent homology
- DOI: 10.1140/epjds/s13688-017-0109-5
- OpenAlex ID: https://openalex.org/W3098455240
- Published: August 2017
GitHub Events
Total
- Create event: 8
- Release event: 2
- Issues event: 8
- Watch event: 26
- Delete event: 8
- Issue comment event: 21
- Push event: 5
- Pull request event: 23
- Fork event: 1
Last Year
- Create event: 8
- Release event: 2
- Issues event: 8
- Watch event: 26
- Delete event: 8
- Issue comment event: 21
- Push event: 5
- Pull request event: 23
- Fork event: 1
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Nathaniel Saul | n****t@s****m | 225 |
| Ulrich Bauer | u****r@t****e | 217 |
| Chris Tralie | c****e@g****m | 85 |
| julian | j****z@h****h | 29 |
| Bradley Dice | b****e@b****m | 19 |
| Michael Catanzaro | c****j@p****e | 12 |
| dependabot[bot] | 4****] | 8 |
| Rann Bar-On | r****n@m****u | 7 |
| PEP8 Speaks | p****s@g****m | 7 |
| mtsch | m****r@g****m | 3 |
| Umberto Lupo | 4****o | 3 |
| Edoardo Putti | e****i@g****m | 2 |
| Ghiles Meddour | g****b@g****m | 2 |
| Nathaniel Rivera Saul | n****l@n****m | 2 |
| Brandon Meredith | 6****h | 1 |
| Arfon Smith | a****n | 1 |
| Haruki Nakajima | s****e@g****m | 1 |
| Ippei Obayashi | o****i@k****p | 1 |
| Thiago | t****t@g****m | 1 |
| Daniel Carmody | d****y@m****u | 1 |
| blasern | b****n | 1 |
| dylia | 4****a | 1 |
| joperea | j****a@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 81
- Total pull requests: 74
- Average time to close issues: 10 months
- Average time to close pull requests: about 2 months
- Total issue authors: 69
- Total pull request authors: 18
- Average comments per issue: 3.11
- Average comments per pull request: 1.55
- Merged pull requests: 47
- Bot issues: 0
- Bot pull requests: 24
Past Year
- Issues: 4
- Pull requests: 30
- Average time to close issues: 22 days
- Average time to close pull requests: 18 days
- Issue authors: 4
- Pull request authors: 4
- Average comments per issue: 2.0
- Average comments per pull request: 0.33
- Merged pull requests: 13
- Bot issues: 0
- Bot pull requests: 18
Top Authors
Issue Authors
- sauln (4)
- jonyscathe (3)
- galenseilis (3)
- catanzaromj (3)
- xgao32 (2)
- tmelorc (2)
- ulupo (2)
- christian-oreilly (1)
- sohailiqbal (1)
- DrMSAbdullahi (1)
- mm04926412 (1)
- Sandy4321 (1)
- tianboqi (1)
- anthtan (1)
- HapeFs (1)
Pull Request Authors
- dependabot[bot] (34)
- catanzaromj (18)
- ctralie (7)
- sauln (5)
- agentmarketbot (4)
- edoput (4)
- reds-heig (3)
- ulupo (3)
- bdice (3)
- minimalProviderAgentMarket (2)
- ghilesmeddour (2)
- Luftalian (2)
- ohai (1)
- refu6enik (1)
- dcarmod2 (1)
Top Labels
Issue Labels
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Packages
- Total packages: 2
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Total downloads:
- pypi 20,641 last-month
- Total docker downloads: 104
-
Total dependent packages: 10
(may contain duplicates) -
Total dependent repositories: 31
(may contain duplicates) - Total versions: 36
- Total maintainers: 3
pypi.org: ripser
A Lean Persistent Homology Library for Python
- Homepage: https://ripser.scikit-tda.org
- Documentation: https://ripser.scikit-tda.org
- License: MIT License
-
Latest release: 0.6.12
published 10 months ago
Rankings
Maintainers (3)
conda-forge.org: ripser
Ripser.py is a lean persistent homology package for Python. Building on the blazing fast C++ Ripser package as the core computational engine, Ripser.py provides an intuitive interface for computing persistence cohomology of sparse and dense data sets, visualizing persistence diagrams, computing lowerstar filtrations on images, and computing representative cochains.
- Homepage: http://github.com/scikit-tda/ripser.py
- License: MIT
-
Latest release: 0.6.4
published over 3 years ago
Rankings
Dependencies
- cython *
- Cython *
- numpy *
- persim *
- scikit-learn *
- scipy *
- actions/checkout v2 composite
- actions/setup-python v2 composite
- conda-incubator/setup-miniconda v2 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite