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contrib_colormaps: User-contributed colormaps

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Latest release Github release PyPI version contrib_colormaps version conda-forge version defaults version
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What is it?

contrib_colormaps is a collection of user-contributed colormaps for use with Python plotting programs such as Bokeh, Matplotlib, HoloViews, and Datashader.

Installation

contrib_colormaps supports Python 2.7, 3.5, 3.6 and 3.7 on Linux, Windows, or Mac and can be installed with conda from the pyviz channel:

conda install -c pyviz contrib_colormaps

or with pip:

pip install contrib_colormaps

Contributing

To add a colormap, open a pull request on this repository adding the following files:

  1. comma-separated file of RGB values to the contrib_colormaps/colormaps directory. This file should look like:

    0, 0.20755, 0.97632
    0, 0.22113, 0.96201
    
  2. A Jupyter notebook in examples/colormaps meeting the following criteria:

    1. a name that matches the name of the csv e.g. for a new colormap called rainforest with a csv rainforest.csv there should be a corresponding rainforest.ipynb
    2. an explanation of the colormap - what is it? and when/why would someone use it?
    3. a swatch of the colormap - we recommend using our swatch function, but it's not required
    4. at least one example plot using the colormap - it can be exclusively Bokeh, Matplotlib, or Holoviews

    The notebook should be cleared of all outputs. To use the UI, click Cell -> All Outputs -> Clear

    Clear all outputs

    OR clear them automatically on commit using the predefined git hook. From within the cloned repository, run:

    git config core.hooksPath .githooks
  3. A pytest-mpl baseline image for tests. To create this image first install pytest-mpl:

    pip install pytest-mpl

    Then generate the figure from within the tests directory run:

    pytest --mpl-generate-path=baseline

    See examples/colormaps for more details.

Sample Pull Request

You can use this sample pull request as a model: #3

About PyViz

contrib_colormaps is part of the PyViz initiative for making Python-based visualization tools work well together. See pyviz.org.

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