colorcet

A set of useful perceptually uniform colormaps for plotting scientific data

https://github.com/holoviz/colorcet

Science Score: 36.0%

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
  • Academic publication links
    Links to: arxiv.org
  • Committers with academic emails
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (13.5%) to scientific vocabulary

Keywords

bokeh colorcet colormaps holoviz matplotlib plotly

Keywords from Contributors

dataviz control-panels dataapp datascience holoviews hvplot panel geoparquet gtk qt
Last synced: 6 months ago · JSON representation

Repository

A set of useful perceptually uniform colormaps for plotting scientific data

Basic Info
Statistics
  • Stars: 709
  • Watchers: 16
  • Forks: 61
  • Open Issues: 2
  • Releases: 11
Topics
bokeh colorcet colormaps holoviz matplotlib plotly
Created over 9 years ago · Last pushed 9 months ago
Metadata Files
Readme License Code of conduct

README.md



Colorcet: Collection of perceptually uniform colormaps

| | | | --- | --- | | Build Status | Linux/MacOS Build Status | | Coverage | codecov || | Latest dev release | Github tag dev-site | | Latest release | Github release PyPI version colorcet version conda-forge version defaults version | | Python | Python support | | Docs | gh-pages site |

What is it?

Colorcet is a collection of perceptually uniform colormaps for use with Python plotting programs like bokeh, matplotlib, holoviews, and datashader based on the set of perceptually uniform colormaps created by Peter Kovesi at the Center for Exploration Targeting.

Installation

Colorcet supports Python 3.9 and greater on Linux, Windows, or Mac and can be installed with conda:

sh conda install colorcet

or with pip:

sh python -m pip install colorcet

To work with JupyterLab you will also need the PyViz JupyterLab extension:

sh conda install -c conda-forge jupyterlab jupyter labextension install @pyviz/jupyterlab_pyviz

Once you have installed JupyterLab and the extension launch it with:

sh jupyter-lab

If you want to try out the latest features between releases, you can get the latest dev release by installing:

sh conda install -c pyviz/label/dev colorcet

For more information take a look at Getting Started.

Learning more

You can see all the details about the methods used to create these colormaps in Peter Kovesi's 2015 arXiv paper. Other useful background is available in a 1996 paper from IBM.

The Matplotlib project also has a number of relevant resources, including an excellent 2015 SciPy talk, the viscm tool for creating maps like the four in mpl, the cmocean site collecting a set of maps created by viscm, and the discussion of how the mpl maps were created.

Samples

Some of the Colorcet colormaps that have short, memorable names (which are probably the most useful ones) are visible here:

But the complete set of 100+ is shown in the User Guide.

Owner

  • Name: HoloViz
  • Login: holoviz
  • Kind: organization

High-level tools to simplify visualization in Python

GitHub Events

Total
  • Issues event: 9
  • Watch event: 20
  • Delete event: 4
  • Issue comment event: 39
  • Push event: 32
  • Pull request review comment event: 65
  • Pull request review event: 69
  • Pull request event: 19
  • Fork event: 10
  • Create event: 4
Last Year
  • Issues event: 9
  • Watch event: 20
  • Delete event: 4
  • Issue comment event: 39
  • Push event: 32
  • Pull request review comment event: 65
  • Pull request review event: 69
  • Pull request event: 19
  • Fork event: 10
  • Create event: 4

Committers

Last synced: 9 months ago

All Time
  • Total Commits: 123
  • Total Committers: 20
  • Avg Commits per committer: 6.15
  • Development Distribution Score (DDS): 0.61
Past Year
  • Commits: 12
  • Committers: 7
  • Avg Commits per committer: 1.714
  • Development Distribution Score (DDS): 0.75
Top Committers
Name Email Commits
James A. Bednar j****r@c****o 48
Maxime Liquet 3****t 28
Julia Signell j****l@g****m 13
Kenneth Bowen k****0@g****m 6
Simon Høxbro Hansen s****n@m****m 5
Azaya 9****9 3
Chris B c****l 3
Kian-Meng Ang k****g@g****m 2
Jean-Luc Stevens j****s@g****m 2
Randy Pittman r****n@o****m 2
myd7349 m****9@g****m 2
Coderambling 1****g 1
Demetris Roumis r****d@g****m 1
Dougal J. Sutherland d****l@g****m 1
Jeremy T. Hetzel j****l@g****m 1
Kim Pevey k****y@g****m 1
Philipp Kats c****l@g****m 1
Tim Waterson 5****n 1
bersbersbers 1****s 1
Gil Ilan Haham g****6@g****m 1
Committer Domains (Top 20 + Academic)

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 37
  • Total pull requests: 102
  • Average time to close issues: 4 months
  • Average time to close pull requests: 23 days
  • Total issue authors: 29
  • Total pull request authors: 25
  • Average comments per issue: 3.19
  • Average comments per pull request: 1.61
  • Merged pull requests: 85
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 4
  • Pull requests: 22
  • Average time to close issues: 8 days
  • Average time to close pull requests: 4 days
  • Issue authors: 3
  • Pull request authors: 8
  • Average comments per issue: 1.25
  • Average comments per pull request: 1.86
  • Merged pull requests: 17
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • bersbersbers (3)
  • endolith (2)
  • jbednar (2)
  • randallpittman (2)
  • maximlt (2)
  • philippjfr (2)
  • Azaya89 (2)
  • lcniel (1)
  • pnsvk (1)
  • wmayner (1)
  • jpjarnoux (1)
  • jsharpe (1)
  • ivan-marroquin (1)
  • andreaskoelsch (1)
  • QuLogic (1)
Pull Request Authors
  • maximlt (32)
  • jsignell (13)
  • jbednar (7)
  • kebowen730 (6)
  • Azaya89 (6)
  • randallpittman (5)
  • myd7349 (4)
  • ceball (4)
  • hoxbro (4)
  • Hoxbro (3)
  • kianmeng (2)
  • jlstevens (2)
  • bersbersbers (2)
  • kcpevey (2)
  • einsmein (2)
Top Labels
Issue Labels
enhancement (3) bug (2) good first issue (2) docs (2)
Pull Request Labels

Packages

  • Total packages: 4
  • Total downloads:
    • pypi 896,471 last-month
  • Total docker downloads: 19,100,942
  • Total dependent packages: 125
    (may contain duplicates)
  • Total dependent repositories: 1,078
    (may contain duplicates)
  • Total versions: 50
  • Total maintainers: 3
pypi.org: colorcet

Collection of perceptually uniform colormaps

  • Versions: 24
  • Dependent Packages: 101
  • Dependent Repositories: 876
  • Downloads: 896,471 Last month
  • Docker Downloads: 19,100,942
Rankings
Dependent packages count: 0.2%
Dependent repos count: 0.4%
Downloads: 0.6%
Docker downloads count: 0.6%
Average: 1.7%
Stargazers count: 2.5%
Forks count: 5.8%
Maintainers (3)
Last synced: 6 months ago
proxy.golang.org: github.com/holoviz/colorcet
  • Versions: 12
  • Dependent Packages: 0
  • Dependent Repositories: 0
Rankings
Dependent packages count: 6.5%
Average: 6.7%
Dependent repos count: 7.0%
Last synced: 6 months ago
conda-forge.org: colorcet

HoloViz Colorcet provides perceptually accurate, 256-color colormaps, including both continuous and categorical types, designed for use with tools like Bokeh, Matplotlib, HoloViews, and Datashader.

  • Versions: 6
  • Dependent Packages: 19
  • Dependent Repositories: 101
Rankings
Dependent packages count: 3.3%
Dependent repos count: 3.4%
Average: 12.1%
Stargazers count: 16.1%
Forks count: 25.6%
Last synced: 6 months ago
anaconda.org: colorcet

HoloViz Colorcet provides perceptually accurate, 256-color colormaps, including both continuous and categorical types, designed for use with tools like Bokeh, Matplotlib, HoloViews, and Datashader.

  • Versions: 8
  • Dependent Packages: 5
  • Dependent Repositories: 101
Rankings
Dependent packages count: 7.0%
Dependent repos count: 18.6%
Average: 23.2%
Stargazers count: 28.5%
Forks count: 38.5%
Last synced: 6 months ago

Dependencies

setup.py pypi
  • pyct *
.github/workflows/build.yaml actions
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.github/workflows/test.yaml actions
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  • conda-incubator/setup-miniconda v2 composite
.github/workflows/downstream_tests.yaml actions
pyproject.toml pypi