Dynamic color objects compatible with Matplotlib
Project description
Highlights
colorobjects are r, g, b, a tuples that support easy conversion to other color types (e.g. HSV, HLS, hexadecimal, or Matplotlib color names) and convenient transformation (e.g. adjust color lightness, hue, saturation, or alpha). Conversions are as convenient as c = somecolor.hsv, and transformations as easy as somecolor.lightness = c! And because a colorobject is just an extension of the list type, they can be passed directly to any Matplotlib plotting command.
colorlists are lists of colorobjects that allow you to easily modify or construct colormaps (see module colormaps) or cycle through lists of maximally distinct colors (see module discretecolors). Changing the brightness of a Matplotlib colormap can be as simple as cmap_adjust(mycolormap, ‘lighten’, 0.5) and you can even construct a colormap directly from any valid Matplotlib color name with:
colorlist(color('Aqua', 'name'), color('Gold', 'name')).LinearSegmentedColormap()
Some examples:
off-the-shelf Matplotlib colormap (‘BuPu’)
cmap_lighten()
cmap_darken()
cmap_adjust() used to rotate the hue
cmap_adjust() used to change the saturation
constructing an entirely new colormap using the Matplotlib color names ‘Aqua’ and ‘Gold’ as endpoints
Looking for a list of easily distinguishable colors? You could rely on one of the lists contained in discretecolors:
cartercarter_6: 6 high contrast colors from Carter and Carter (1982)
greenarmytage_26: 26 colors intended for use against a white background from Green-Armytage (2010)
tatarize_269: 269 colors from the author of the godsnotwheregodsnot blog
Installation
colorobject has only been tested in Python 2.7
Install through pip:
$ pip install colorobject
Requires the following non-standard libraries:
matplotlib
Because preferred installation of matplotlib can vary depending on the operating system, matplotlib will not automatically be installed as a dependency. Instead, installation will raise an exception if matplotlib cannot be found in the pythonpath. In this case, please install matplotlib via your preferred method, most of which are explained by matplotlib
Changelog
0.8.0 (DECEMBER/01/2014)
First release
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