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https://github.com/volodia99/cblind
A colorblind-friendly python module that allows optimal color choice for plotting multiple curves
https://github.com/volodia99/cblind
Last synced: 11 days ago
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A colorblind-friendly python module that allows optimal color choice for plotting multiple curves
- Host: GitHub
- URL: https://github.com/volodia99/cblind
- Owner: volodia99
- License: gpl-3.0
- Created: 2018-11-27T22:34:32.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-08-21T14:09:28.000Z (3 months ago)
- Last Synced: 2024-09-21T14:36:31.459Z (about 2 months ago)
- Language: Python
- Size: 621 KB
- Stars: 18
- Watchers: 1
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# cblind
[](https://pypi.org/project/cblind/)
A colorblind-friendly python module that allows color choice for plotting multiple curves
Works only with python $\geq$ 3.7
8 colormaps are now available to map 2D fields
Authors: Gaylor Wafflard-Fernandez, Clément Robert
Author-email: [email protected]
## Installation
Install with `pip`
```
pip install cblind
```
To import cblind:
```python
import cblind as cb
```
## Usage for plotting
10 palette functions to plot curves are available for now in the Colorplots class, with the corresponding test plotting functions.
### cblind
```python
color, linestyle = cb.Colorplots().cblind(nb_of_plots)
```
from 1 to 12 plots [DISTINCT COLORS]. For more than 12 plots, the linestyle is changed.
```python
cb.test_cblind(nb_of_plots)
```
### contrast
```python
color, linestyle = cb.Colorplots().contrast(nb_of_plots)
```
for less than 4 contrast plots [DISTINCT COLORS]. For more than 12 plots, the linestyle is changed.
```python
cb.test_contrast(nb_of_plots)
```
### huescale
```python
color, linestyle = cb.Colorplots().huescale(nb_of_plots, *option)
```
from 1 to 9 plots [SEQUENTIAL DATA]. With option "blue","bluegreen","green", "gold","brown","rose","purple" for less than 3 plots, otherwise ocherscale.
```python
cb.test_huescale(nb_of_plots, *option)
```
### rbscale
```python
color, linestyle = cb.Colorplots().rbscale(nb_of_plots)
```
from 3 to 11 plots [DIVERGING DATA].
```python
cb.test_rbscale(nb_of_plots)
```
### rainbow
```python
color, linestyle = cb.Colorplots().rainbow(nb_of_plots)
```
from 4 to 12 plots [RAINBOW SCHEME].
```python
cb.test_rainbow(nb_of_plots)
```
### extreme_rainbow
```python
color, linestyle = cb.Colorplots().extreme_rainbow(nb_of_plots)
```
from 1 to 34 plots [RAINBOW SCHEME].
```python
cb.test_extreme_rainbow(nb_of_plots)
```
### solstice
```python
color, linestyle = cb.Colorplots().solstice(nb_of_plots)
```
for less than 11 plots [DIVERGING DATA]
```python
cb.test_solstice(nb_of_plots)
```
### bird
```python
color, linestyle = cb.Colorplots().bird(nb_of_plots)
```
for less than 9 plots [DIVERGING DATA]
```python
cb.test_bird(nb_of_plots)
```
### pregunta
```python
color, linestyle = cb.Colorplots().pregunta(nb_of_plots)
```
for less than 9 plots [DIVERGING DATA]
```python
cb.test_pregunta(nb_of_plots)
```
### monocolor
```python
color, linestyle = cb.Colorplots().monocolor(nb_of_plots, *option)
```
from 1 to 13 monochromatic plots [MONOCOLOR/PRINTING] with different linestyles. With option "b&w", "blue", "red", "yellow", "green", "purple".
```python
cb.test_monocolor(nb_of_plots, *option)
```
## Usage for colormaps
8 cblind palettes are available for now : "cb.rbscale", "cb.rainbow", "cb.extreme_rainbow", "cb.huescale",
"cb.solstice", "cb.bird", "cb.pregunta", "cb.iris", but also all colormaps from matplotlib + "\_r" variants for reverse colormaps.
```python
cmap = cb.cbmap(palette, nbin)
```
The `nbin` argument is used to discretize the colormaps.
To test the colormaps, you can try:
```python
cb.test_mapping(palette, nbin)
```
### Example with a field `data2d`
```python
import numpy as np
import matplotlib.pyplot as plt
data2d = np.repeat(np.linspace(0,1,100),20).reshape(100,20).T
fig, ax = plt.subplots()
im = ax.imshow(data2d, cmap=cb.cbmap("cb.rainbow_r", nbin=10), aspect='auto')
fig.colorbar(im)
plt.show()
```
### Basic mapping function
```python
cb.mapping(fig,ax,data2d,extent,palette=palette,nbin=nbin)
```
**REFERENCE**
Paul Tol. 2012. "Colour Schemes." SRON Technical Note, SRON/EPS/TN/09-002.
https://personal.sron.nl/~pault/data/colourschemes.pdf