contrib_colormaps: User-contributed colormaps
|Latest dev release|
What is it?
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
To add a colormap, open a pull request on this repository adding the following files:
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
A Jupyter notebook in examples/colormaps meeting the following criteria:
- a name that matches the name of the csv
e.g. for a new colormap called
rainforestwith a csv rainforest.csv there should be a corresponding rainforest.ipynb
- an explanation of the colormap - what is it? and when/why would someone use it?
- a swatch of the colormap - we recommend using our swatch function, but it's not required
- 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
OR clear them automatically on commit using the predefined git hook. From within the cloned repository, run:
git config core.hooksPath .githooks
- a name that matches the name of the csv e.g. for a new colormap called
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:
See examples/colormaps for more details.
Sample Pull Request
You can use this sample pull request as a model: #3
contrib_colormaps is part of the PyViz initiative for making Python-based visualization tools work well together. See pyviz.org.
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