Description: A simple set of functions to load and use a collection of my favorite colormaps
pip install -r requirements.txt
Option 1: add path to list of directories for python interpreter to search
import sys
sys.path.append('/PATHNAME/Additional_Colormaps/')
from Additional_Colormaps import load_colormap, load_all_cmaps, plot_cmap_example
Option 2: Install python package in "development mode"
Step 1: In terminal, activate conda environment, then execute following command:
conda develop PATHNAME/Additional_Colormaps/
Step 2: import functions without needing to add path to sys.path as below:
from Additional_Colormaps import make_colormap, load_all_cmaps, plot_cmap_example
load_all_cmaps() : returns a dictionary with the key containing the name of each colormap and the value containing a matplotlib.colors.ListedColormap object
Example Call:
new_cmaps = load_all_cmaps()
plot_cmap_example() : accepts a cmap (matplotlib.colors.ListedColormap object) as the only argument and returns a quick visualization of the colormap. Function plots a grid of random numbers using matplotlib pcolormesh() method
Example Call:
new_cmaps = load_all_cmaps()
plot_cmap_example(new_cmaps['rafaj_AQ'])
Output:
get_hex_color() : accepts a list of rgb values (3) and returns the corresponding hex code (string)
Example Call:
test_blue = get_hex_color(rgb_code=[40,65,122])
plt.plot([0,1], [0,1], c=test_blue, lw=20);
Output:
make_hex_list() : accepts a list of rgb values (list of lists) and returns list of hex values (list of strings)
The above function is useful for preparing the input to make_custom_cmap_from_hex_list() which accepts the output of make_hex_list() in addition to an integer number of colors and a name (string) and creates a matplotlib LinearSegmentedColormap object
Example Call:
rgb_list = [[175,162,116], [30,72,105]]
test_hex_list = make_hex_list(rgb_list)
make_custom_cmap_from_hex_list(hex_list=test_hex_list, n_colors=256, cmap_name='test')
Output:
Finally, you can check the accessibility of colormaps using:
display_accessibility_comparison(new_cmaps['rafaj_AQ'])
Output:
color lists:
- https://xkcd.com/color/rgb/ (gallery of named colors from xkcd)
color tools:
general info:
- http://tsitsul.in/blog/coloropt/
- https://personal.sron.nl/~pault/#sec:colour_blindness
- https://waterdata.usgs.gov/blog/tolcolors/
- for (non-ordinal) categorical data, use a qualitative/categorical palette. Distinguishability is the main criteria.
- to draw contrast among ordinal or sequential data, use a diverging palette. Diverging palettes are also useful when you want to demonstrate differences in sign (+ or -)
- sequential palettes can be simple and nice when you have continuous numeric data which share the same sign