This python package contains a collection of nice color maps for scientific visualizations and provides a convinient and unified way to pick the color maps. Some color maps are perceptually uniform and/or color blind friendly.
You can also check out the Scicolor Color Picker, a web app for browsing and picking colors.
The package is available through pypi. You can install it using the following command:
pip install scicolorimport scicolorscicolor.get_cmap('viridis')scicolor.get_cmap() works just like plt.get_cmap().
The returned value can be instances of either matplotlib.colors.ListedColormap or matplotlib.colors.LinearSegmentedColormap.
You can access the colors of matplotlib.colors.ListedColormap instances by .colors.
List all color maps
scicolor.list_cmaps()List the color maps that are sequential, discrete, and color blind friendly
scicolor.list_cmaps(cm_class='sequential', cm_type="discrete", color_blind_friendly=True)See below for more on the classes and types of the color maps.
Plot a color map
scicolor.plot_cmaps('tab20')Plot multiple color maps
scicolor.plot_cmaps(['tab20', 'tab20b'])Plot the color maps that are sequential, discrete, and color blind friendly
scicolor.plot_cmaps(cm_class='sequential', cm_type="discrete", color_blind_friendly=True)Below is a screen shot of scicolor.plot_cmaps(), which shows all available color maps.
See the following table for different characteristics of the color maps.
You can use the corresponding paramters in scicolor.list_cmaps() and scicolor.plot_cmaps() to filter the results.
| Characteristic | Parameter | Values |
|---|---|---|
| class | cm_class |
'categorical', 'diverging', 'misc', 'multi_sequential', 'sequential' |
| type | cm_type |
'categorical', 'continuous', 'discrete' |
| perceptually uniform | perceptually_uniform |
True, None |
| color blind friendly | color_blind_friendly |
True, None |
See the guideline below for more on the class and type.
Figure below contains the guideline for choosing color maps.
The figure was borrowed from The misuse of colour in science communication.
The color maps come from different sources, see the table below for details
| Source | Color maps | Link |
|---|---|---|
| matplotlib | tab20, tab20b, tab20c, viridis, inferno, cividis | https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html |
| Scientific colour maps | batlow10, batlow25, acton10, acton25, oslo10, oslo25, turku10, turku25, vik10, vik25, roma10, roma25, broc10, broc25, oleron10, oleron25, batlowS, batlow, acton, oslo, turku, vik, roma, broc, oleron | http://www.fabiocrameri.ch/colourmaps.php |
| Wes Anderson | Royal2, Zissou1, Darjeeling1, FantasticFox1, Moonrise3, Cavalcanti1, GrandBudapest2, IsleofDogs2 | https://github.com/karthik/wesanderson |
| CET | glasbey, cwr, isolum | https://peterkovesi.com/projects/colourmaps/ |
| ocean | topo | https://matplotlib.org/cmocean/ |
| Met Brewer | Archambault, Cassatt1, Cassatt2, Demuth, Derain, Egypt, Greek, Hiroshige, Hokusai2, Ingres, Isfahan1, Isfahan2, Java, Johnson, Kandinsky, Morgenstern, OKeeffe1, OKeeffe2, Pillement, Tam, Troy, Veronese | https://github.com/BlakeRMills/MetBrewer |
The present package simply offers a wrapper for the selected color maps that are included in matplotlib.
To add more matplotlib color maps to scicolor, just add the information to color_info_list in __init__.py.
Scientific colour maps provides a series of perceptually uniform and color blind friendly color maps.
To expand the list included in scicolor:
- Download the data from zenodo
- Copy the
.txtfiles to/path/to/scicolor/scicolor/scientific_colors - Add the information to
color_info_listin__init__.py
Wes Anderson Palettes provides a series of categorical colors extract from Wes Anderson movies. Note that the color maps might not be color blind friendly, use with cautions. To expand the list:
- Copy the color data to
__init__.py - Add the information to
color_info_listin__init__.py
CET (Centre for Exploration Targeting) offers many perceptually uniform color maps. To expand the list
- Download the color data from github to
/path/to/scicolor/scicolor/cet_colors - Assgin meaniful names to the color data files. References can be found in their script
- Add the information to
color_info_listin__init__.py
CMOcean (colormaps inspired by oceanography) provides a series of perceptually uniform color maps. To expand the list
- Download the color data from github to
/path/to/scicolor/scicolor/ocean_colors - Add the information to
color_info_listin__init__.py
MetBrewer provides a series of palettes inspired by works at the Metropolitan Museum of Art in New York.
Currently the scicolor package only includes the color-blind-friendly colors from MetBrewer.
- python3
- matplotlib
- pandas
- numpy