Copyright 2025 Ethan Dintzner
A collection of several hundred multicolor palettes from Color Inspirations by Darius A. Monsef IV. [1] Additionally includes a series of functions that allow you to browse color pallets, test colorblind friendliness for inbuilt or custom pallets, produce matplotlib-compatible continuous colormaps, and convert from RGB to HEX.
Below are examples of using this package:
import colorschemes as cs
ColorSchemes = cs.ColorSchemes()
The first function display_examples will display sixteen random examples of color schemes using matplotlib. It is a nice way to explore new color schemes.
ColorSchemes.display_examples()
To display a specific example color scheme in detail, such as this red-orange pallette, utilize the display_scheme function. It will plot the scheme using matplotlib and display the hex values for each color.
ColorSchemes.display_scheme('red_orange28')
For a particular color scheme, you can access several different attributes including the hexcodes for the colors (colors), the harmony type (harmony), and the actual name (name). The colors attribute gives you a list of the hex values within this particular scheme, e.g.
ColorSchemes.red_orange19.colors
['#001860', '#036B2E', '#F09000', '#F06018', '#C00000']
An example of using the name attribute is as such:
ColorSchemes.red_orange19.name
'red_orange19'
And finally for displaying the harmony type for this particular color scheme using harmony:
ColorSchemes.red_orange19.harmony
'split-complementary'
This package has an inbuilt function to convert from hex to RGB called hex_to_rgb. It returns a 3-element list, e.g.
ColorSchemes.hex_to_rgb('#001860')
[0, 24, 96]
To convert RGB to hex, there is an inverse function called rgb_to_hex that is used similarly. The input is a 3-element list and the output is a string,
ColorSchemes.rgb_to_hex([0, 24, 96])
'#001860'
To simulate colorblindness for a particular hexcode color, use the colorblindness_test function. It returns a very simple matrix-transformation based simulation of Protanopia, Deuteranopia, Tritanopia, Protanomaly and Deuteranomaly disorders based on matrices from https://gist.github.com/Lokno/df7c3bfdc9ad32558bb7?permalink_comment_id=3943065. It is used for individual colors as such:
ColorSchemes.colorblindness_test('#001860')
['#0a0a4e', '#09074a', '#01403d', '#041057', '#041155']
To view a particular scheme from this package with the five simulated colorblindness disorders from above, run the scheme_colorblindness function on a scheme name within this package. for instance 'violet_red13'. Powered by matplotlib, it will return a figure of 6 labeled panels if you run this:
ColorSchemes.scheme_colorblindness('violet_red13')
If you have a custom list of hexcodes that you want to simulate colorblind-friendliness for, then use the custom_colorblindness function. It takes in a list of hexcodes as an input and returns six panels similar to above as an output.
ColorSchemes.custom_colorblindness(['#E73155', '#C72559', '#A7195E', '#860C62', '#670067','#00154A',
'#002F59', '#6B6159', '#F9A516', '#FFBA5F'])
If you want to convert any of the schemes within this package into custom continuous matplotlib colormaps, use the scheme_to_cmap function. It takes in a scheme name as an input and returns a matplotlib-compatible colormap. It uses the matplotlib.pyplot function LinearSegmentedColormap to achieve this.
cmap = ColorSchemes.scheme_to_cmap('orange17')
Here is an example of making a plot from a colormap generating using a scheme from ColorSchemes:
# making a multivariable function to plot our new colormap
y, x = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))
z = (1 - x / 2. + x ** 5 + y ** 3) * np.exp(-x ** 2 - y ** 2)
z = z[:-1, :-1]
z_min, z_max = -np.abs(z).max(), np.abs(z).max()
# making a heatmap using the colormap
fig, ax = plt.subplots()
c = ax.pcolormesh(x, y, z, cmap=cmap)
ax.set_title('custom heatmap')
# set the limits of the plot to the limits of the data
ax.axis([x.min(), x.max(), y.min(), y.max()])
fig.colorbar(c, ax=ax)
plt.show()
You can also use this package for a custom list with the custom_scheme_to_cmap function:
cmap = ColorSchemes.custom_scheme_to_cmap(['#E73155', '#C72559', '#A7195E', '#860C62', '#670067','#00154A', '#002F59', '#6B6159', '#F9A516', '#FFBA5F'])
[1] Monsef, D. A. (2011). Color Inspirations: More Than 3,000 Innovative Palettes from the Colourlovers. Com Community. Simon and Schuster.
A huge thanks to Dr. Kiri Choi for all of his help setting up my code for packaging on PyPI, and for his valuable suggestions about making continuous colormaps from discrete points.