Data

Tools

Indexes

Applications

Experiments

Open Source Science

https://github.com/dynamicsandneuralsystems/catch22

catch22: CAnonical Time-series CHaracteristics

https://github.com/dynamicsandneuralsystems/catch22

Science Score: 49.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: zenodo.org
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (13.8%) to scientific vocabulary

Keywords

feature-extraction time-series time-series-analysis
Last synced: 6 months ago · JSON representation

Repository

catch22: CAnonical Time-series CHaracteristics

Basic Info
Statistics
  • Stars: 429
  • Watchers: 11
  • Forks: 69
  • Open Issues: 13
  • Releases: 5
Topics
feature-extraction time-series time-series-analysis
Created over 7 years ago · Last pushed 6 months ago
Metadata Files
Readme License

README.md

catch22 logo

catch22: CAnonical Time-series CHaracteristics

catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Matlab, and Julia, licensed under the GNU GPL v3 license (or later). The catch22 features are a high-performing subset of the over 7000 features in hctsa.

The features were selected based on their classification performance across a collection of 93 real-world time-series classification problems, as described in our open-access paper, 📗 Lubba et al. (2019). catch22: CAnonical Time-series CHaracteristics.

📙📘📗catch22 documentation

There is comprehensive documentation for catch22, including:

  • Installation instructions (across C, python, R, Julia, and Matlab)
  • Information about the theory behind and behavior of each of the features,
  • A list of publications that have used or extended catch22
  • And more :yum:

Installation and Usage in Python, R, Matlab, Julia, and compiled C

There are also native versions of this code for other programming languages:

You can also use the C-compiled features directly, or in Matlab, following the detailed installation instructions in the documentation.

Acknowledgement :+1:

If you use this software, please read and cite this open-access article:

Performance Summary

Summary of the performance of the catch22 feature set across 93 classification problems, and a comparison to the hctsa feature set (cf. Fig. 4 from our paper):

Notes

  • When presenting results using catch22, you must identify the version used to allow clear reproduction of your results. For example, CO_f1ecac was altered from an integer-valued output to a linearly interpolated real-valued output from v0.3.
  • catch22 features only evaluate dynamical properties of time series and do not respond to basic differences in the location (e.g., mean) or spread (e.g., variance).
  • If the location and spread of the raw time-series distribution may be important for your application, you should apply the function argument catch24 = true (TRUE in R, True in Python) to your call to the catch22 function in the language of your choice. This will result in 24 features being calculated: the catch22 features in addition to mean and standard deviation.
  • Time series are z-scored internally (for features other than mean and standard deviation), which means that, e.g., constant time series will lead to NaN outputs.
  • Time-series data are taken as an ordered sequence of values (without time stamps). We assume an evenly sampled time series.
  • See language-specific usage information in the docs.
  • The computational pipeline used to generate the catch22 feature set is in the op_importance repository.

Owner

  • Name: The Dynamics and Neural Systems Group, The University of Sydney
  • Login: DynamicsAndNeuralSystems
  • Kind: organization
  • Location: Sydney, Australia

We are a group of scientists working in The School of Physics at The University of Sydney, interested in understanding neural systems and their dynamics

GitHub Events

Total
  • Issues event: 1
  • Watch event: 60
  • Push event: 287
  • Fork event: 2
Last Year
  • Issues event: 1
  • Watch event: 60
  • Push event: 287
  • Fork event: 2

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 27
  • Total pull requests: 11
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 2 months
  • Total issue authors: 23
  • Total pull request authors: 8
  • Average comments per issue: 2.44
  • Average comments per pull request: 0.91
  • Merged pull requests: 7
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 2.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • Adblu (2)
  • sanjayankur31 (2)
  • benfulcher (2)
  • musicinmybrain (2)
  • albertoazzari (1)
  • cahuja1992 (1)
  • Haozhou98 (1)
  • jhossbach (1)
  • chanshing (1)
  • brendanjohnharris (1)
  • neezi (1)
  • danlurie (1)
  • xiaoluffy (1)
  • IrenXu (1)
  • RoyiAvital (1)
Pull Request Authors
  • hendersontrent (3)
  • jmoo2880 (2)
  • olivercliff (2)
  • albertoazzari (2)
  • Olivier-tl (1)
  • 9yifanchen9 (1)
  • imraniac (1)
  • brendanjohnharris (1)
Top Labels
Issue Labels
Pull Request Labels
enhancement (1)

Packages

  • Total packages: 2
  • Total downloads: unknown
  • Total dependent packages: 0
    (may contain duplicates)
  • Total dependent repositories: 0
    (may contain duplicates)
  • Total versions: 10
proxy.golang.org: github.com/dynamicsandneuralsystems/catch22
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago
proxy.golang.org: github.com/DynamicsAndNeuralSystems/catch22
  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 5.5%
Average: 5.6%
Dependent repos count: 5.8%
Last synced: 6 months ago

Dependencies

.github/workflows/github-repo-stats.yml actions
  • jgehrcke/github-repo-stats RELEASE composite