More colors for Matplotlib

 

The default Matplotlib color scheme is not good. While searching for something better for my thesis, I stumbled upon Bridget Riley's painting "Gather" in the RISD museum. Is uses five colors: white, black, ochre, blue, turquoise, and a rust-colored red. But, from a distance they combine to create something far more vibrant. The blue and the turquoise hues are similar, so perhaps not the best to combine in a plot, but the others form an attractive and distinct pallet:

White	#f1f0e9
Black	#44525c
Rust	#eb7a59
Ochre	#eea300
Azure	#5aa0df
Turquoise	#00bac9

Surprisingly, when I ran this through a color-blindness simulator , some colors become even more distinct, so it is fairly color-blind friendly. I added colors to expand the pallet, to handle gradients and plots with extra lines.

Yellow	#efcd2b
Moss	#77ae64
Mauve	#b56ab6
Magenta	#cc79a7
Violet	#8d5ccd
Indigo	#606ec3
Viridian	#11be8d
Chartreuse	#b59f1a

These are less distinct, and not all combinations are color-blind friendly. Here's a bit of Python to define and tests these colors:

from pylab import *
from matplotlib import patches
WHITE      = mpl.colors.to_rgb('#f1f0e9')
RUST       = mpl.colors.to_rgb('#eb7a59')
OCHRE      = mpl.colors.to_rgb('#eea300')
AZURE      = mpl.colors.to_rgb('#5aa0df')
TURQUOISE  = mpl.colors.to_rgb('#00bac9')
BLACK      = mpl.colors.to_rgb('#44525c')
YELLOW     = mpl.colors.to_rgb('#efcd2b')
INDIGO     = mpl.colors.to_rgb('#606ec3')
VIOLET     = mpl.colors.to_rgb('#8d5ccd')
MAUVE      = mpl.colors.to_rgb('#b56ab6')
MAGENTA    = mpl.colors.to_rgb('#cc79a7')
CHARTREUSE = mpl.colors.to_rgb('#b59f1a')
MOSS       = mpl.colors.to_rgb('#77ae64')
VIRIDIAN   = mpl.colors.to_rgb('#11be8d')
GATHER  = [WHITE,RUST,OCHRE,AZURE,TURQUOISE,BLACK]
COLORS  = [BLACK,WHITE,YELLOW,OCHRE,CHARTREUSE,MOSS,VIRIDIAN,TURQUOISE,AZURE,INDIGO,VIOLET,MAUVE,MAGENTA,RUST]
CYCLE   = [BLACK,RUST,AZURE,OCHRE,TURQUOISE,MAUVE,YELLOW,INDIGO]
mpl.rcParams['axes.prop_cycle'] = mpl.cycler(color=CYCLE)
def test_panel(COLORS):
    ax = gca()
    NCOLORS = len(COLORS)
    axis('off')
    xlim(0,NCOLORS)
    ylim(NCOLORS,0)
    for j in range(NCOLORS):
        for i in range(NCOLORS):
            ax.add_patch(patches.Rectangle((i,j),1,1,linewidth=1,
                edgecolor='none',facecolor=COLORS[i]))
            ax.add_patch(patches.Rectangle((i+.25,j+.25),.5,.5,linewidth=1,
                edgecolor='none',facecolor=COLORS[j]))
    axis('equal')
figure(figsize=(6,3),dpi=200)
subplots_adjust(0,0,1,1,0.1,0)
subplot(111)
test_panel(COLORS)
savefig('colorblind_test_panel.png',bbox_inches='tight',pad_inches=0)
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Two squences form decent color maps. They aren't perceptually uniform, but pass the color blindness simulator tests. Adding violet, mauve, and moss, to the original Gather pallet creates a hue wheel. This one flunks color-blindness test (see Crameri et al. ). Here they are in python:

riley2 = matplotlib.colors.LinearSegmentedColormap.from_list('riley2',
    [INDIGO,VIOLET,MAUVE,MAGENTA,RUST,OCHRE])
riley3 = matplotlib.colors.LinearSegmentedColormap.from_list('riley3',
   [OCHRE,CHARTREUSE,MOSS,VIRIDIAN,TURQUOISE,AZURE])
# Smoothed out mauve, violet, rust, ochre, moss, turquoise, azure, in a loop
hues = ['#8c62cc', '#9560c8', '#9e62c3', '#a765be', '#b068b6', '#bb6caa', '#c66f98',
        '#d27384', '#dd7670', '#e67c5c', '#ea8348', '#ec8b34', '#ec9421', '#e99c13',
        '#dea212', '#c9a620', '#b0a934', '#96ab4a', '#7cae60', '#62b076', '#48b38c',
        '#2eb5a2', '#1ab7b6', '#12b6c5', '#1cb2ce', '#2dadd4', '#41a7d8', '#529fdb',
        '#6194db', '#6d86d8', '#7977d4', '#836ad0']
riley = matplotlib.colors.LinearSegmentedColormap.from_list('riley',hues)
# Make new maps behave like native Matplotlib maps
plt.register_cmap(name='riley2',cmap=riley2)
plt.register_cmap(name='riley3',cmap=riley3)
plt.register_cmap(name='riley' ,cmap=riley )
# Show as figure
figure(figsize=(5,1),dpi=300)
subplot(311)
imshow([linspace(0,1,256)],cmap='riley2',aspect='auto')
axis('off'); tight_layout()
subplot(312)
imshow([linspace(0,1,256)],cmap='riley3',aspect='auto')
axis('off'); tight_layout()
subplot(313)
imshow([linspace(0,1,256)],cmap='riley',aspect='auto')
axis('off'); tight_layout()
subplots_adjust(hspace=0.3)
savefig('moremaps.png')
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