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Ripserer.jl

Ripserer.jl: flexible and efficient persistent homology computation in Julia - Published in JOSS (2020)

https://github.com/mtsch/ripserer.jl

Science Score: 98.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    Found .zenodo.json file
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    DOI references
    Found 1 DOI reference(s) in JOSS metadata
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    Academic publication links
    Links to: joss.theoj.org
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    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

cohomology homology julia persistent-homology ripser tda topological-data-analysis topology

Keywords from Contributors

standardization
Last synced: 4 months ago · JSON representation ·

Repository

Flexible and efficient persistent homology computation.

Basic Info
Statistics
  • Stars: 74
  • Watchers: 3
  • Forks: 8
  • Open Issues: 9
  • Releases: 48
Topics
cohomology homology julia persistent-homology ripser tda topological-data-analysis topology
Created almost 6 years ago · Last pushed 5 months ago
Metadata Files
Readme Changelog Contributing License Citation

README.md

Ripserer.jl _Flexible and efficient persistent homology computation._ [![Coverage Status](https://coveralls.io/repos/github/mtsch/Ripserer.jl/badge.svg?branch=master)](https://coveralls.io/github/mtsch/Ripserer.jl?branch=master) [![Build Status](https://github.com/mtsch/Ripserer.jl/workflows/Test/badge.svg)](https://github.com/mtsch/Ripserer.jl/actions?query=workflow%3ATest) [![Documentation](https://img.shields.io/badge/docs-latest-blue.svg)](https://mtsch.github.io/Ripserer.jl/dev) [![status](https://joss.theoj.org/papers/0c8b6abead759ba068ee178fedc998a9/status.svg)](https://joss.theoj.org/papers/0c8b6abead759ba068ee178fedc998a9)

Introduction

Ripserer is a pure Julia implementation of the Ripser algorithm for computing persistent homology. Its aims are to be easy to use, generic, and fast.

See the documentation for more information and usage examples.

If you're looking for persistence diagram-related functionality such as Wasserstein or bottleneck distances, persistence images, or persistence curves, please see PersistenceDiagrams.jl.

Quick start

This package is registered. To install it, run the following.

julia julia> using Pkg julia> Pkg.add("Ripserer")

Now, generate some data.

julia julia> data = [(rand(), rand(), rand()) for _ in 1:200]

The main exported function in this package is ripserer. By default, it computes Vietoris-Rips persistent homology on point cloud data and distance matrices.

```julia julia> ripserer(data)

2-element Vector{PersistenceDiagrams.PersistenceDiagram}:

200-element 0-dimensional PersistenceDiagram

84-element 1-dimensional PersistenceDiagram

```

Several other filtration types are supported. We tell ripserer to use them by passing them as the first argument.

```julia julia> ripserer(EdgeCollapsedRips, data)

2-element Vector{PersistenceDiagrams.PersistenceDiagram}:

200-element 0-dimensional PersistenceDiagram

84-element 1-dimensional PersistenceDiagram

```

Sometimes you may want to initialize a filtration in advance.

```julia julia> rips = EdgeCollapsedRips(data, threshold=1)

EdgeCollapsedRips{Int64, Float64}(nv=200)

julia julia> ripserer(rips, dim_max=2)

3-element Vector{PersistenceDiagrams.PersistenceDiagram}:

200-element 0-dimensional PersistenceDiagram

84-element 1-dimensional PersistenceDiagram

16-element 2-dimensional PersistenceDiagram

```

Ripserer supports plotting with Plots.jl. Experimental Makie.jl support is also available here.

Plotting persistence diagrams and barcodes is straightforward:

julia using Plots result = ripserer(data, dim_max=2) plot(plot(result), barcode(result)

julia barcode(result)

Owner

  • Login: mtsch
  • Kind: user

Matija Čufar

JOSS Publication

Ripserer.jl: flexible and efficient persistent homology computation in Julia
Published
October 19, 2020
DOI
Volume 5, Issue 54, Page 2614
Authors
Matija Čufar
Independent Researcher
Editor
Viviane Pons ORCID
Tags
Persistent Homology Topological Data Analysis
View PDF Review Thread Software Archive

Citation (CITATION.bib) 

@article{cufar2020ripserer,
  doi = {10.21105/joss.02614},
  url = {https://doi.org/10.21105/joss.02614},
  year = {2020},
  publisher = {The Open Journal},
  volume = {5},
  number = {54},
  pages = {2614},
  author = {Matija Čufar},
  title = {Ripserer.jl: flexible and efficient persistent homology computation in Julia},
  journal = {Journal of Open Source Software}
}

@misc{cufar2021fast,
      title = {Fast computation of persistent homology representatives with involuted persistent homology},
      author = {Matija Čufar and Žiga Virk},
      year = {2021},
      url = {https://arxiv.org/abs/2105.03629},
      eprint = {2105.03629},
      archivePrefix = {arXiv},
      primaryClass = {math.AT}
}

GitHub Events

Total
  • Create event: 5
  • Commit comment event: 6
  • Issues event: 1
  • Watch event: 9
  • Delete event: 7
  • Issue comment event: 38
  • Push event: 15
  • Pull request review event: 4
  • Pull request review comment event: 8
  • Pull request event: 12
  • Fork event: 2
Last Year
  • Create event: 5
  • Commit comment event: 6
  • Issues event: 1
  • Watch event: 9
  • Delete event: 7
  • Issue comment event: 38
  • Push event: 15
  • Pull request review event: 4
  • Pull request review comment event: 8
  • Pull request event: 12
  • Fork event: 2

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 205
  • Total Committers: 2
  • Avg Commits per committer: 102.5
  • Development Distribution Score (DDS): 0.034
Past Year
  • Commits: 8
  • Committers: 1
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
mtsch m****r@g****m 198
github-actions[bot] 4****] 7

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 15
  • Total pull requests: 107
  • Average time to close issues: 5 months
  • Average time to close pull requests: 3 days
  • Total issue authors: 11
  • Total pull request authors: 2
  • Average comments per issue: 5.27
  • Average comments per pull request: 3.64
  • Merged pull requests: 96
  • Bot issues: 0
  • Bot pull requests: 11
Past Year
  • Issues: 1
  • Pull requests: 8
  • Average time to close issues: N/A
  • Average time to close pull requests: 5 days
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 6.0
  • Average comments per pull request: 3.25
  • Merged pull requests: 8
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • lampretl (3)
  • RaimelMedina (2)
  • mkreisel (2)
  • chudur-budur (1)
  • FedeClaudi (1)
  • 00krishna (1)
  • wardjm (1)
  • JuliaTagBot (1)
  • vtjnash (1)
  • juliohm (1)
Pull Request Authors
  • mtsch (104)
  • github-actions[bot] (12)
Top Labels
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Packages

  • Total packages: 1
  • Total downloads:
    • julia 37 total
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 52
juliahub.com: Ripserer

Flexible and efficient persistent homology computation.

  • Versions: 52
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 37 Total
Rankings
Dependent repos count: 9.9%
Stargazers count: 14.0%
Average: 16.6%
Forks count: 19.4%
Dependent packages count: 23.0%
Last synced: 4 months ago

Dependencies

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