Show matplotlib colormaps with luminance

readme.md

This is what I used to make all these.

show_colormaps.py

# This example comes from the Cookbook on www.scipy.org.  According to the
# history, Andrew Straw did the conversion from an old page, but it is
# unclear who the original author is.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm


# normal progression
a = np.linspace(0, 1, 256).reshape(1, -1)

# cyclic endpoints
#a = concatenate((np.linspace(0.8, 1, 128,endpoint = False), np.linspace(0, 0.2, 128))).reshape(1,-1)

a = np.vstack((a, a))


#Luminance (standard, objective): (0.2126*R) + (0.7152*G) + (0.0722*B)
#Luminance (perceived option 1): (0.299*R + 0.587*G + 0.114*B)
#Luminance (perceived option 2, slower to calculate): sqrt( 0.241*R^2 + 0.691*G^2 + 0.068*B^2 )

def relative_luminance(RGB):
    R, G, B = RGB
    Y = 0.2126 * R + 0.7152 * G + 0.0722 * B
    return Y


# Get a list of the colormaps in matplotlib.  Ignore the ones that end with
# '_r' because these are simply reversed versions of ones that don't end
# with '_r'
#maps = [m for m in plt.colormaps() if not m.endswith("_r")]

# sort case insensitive so they're easy to locate
#maps = sorted(maps, str.lower)

# A bunch of dumb attempts at sorting

# Sort by first red value
#maps = sorted(maps, key=lambda a: relative_luminance(cm.cmap_d[a](128)[0:3]))
#maps = sorted(maps, key=lambda a: cm.cmap_d[a](64)[1])
#maps = sorted(maps, key=lambda a: cm.cmap_d[a](64)[2])

#maps = sorted(maps, key = lambda name: sum(diff(relative_luminance(cm.cmap_d[name](arange(256)),1))))

#maps = sorted(maps, key = lambda name: sum(diff([relative_luminance(x) for x in cm.cmap_d[name](arange(256))[:,0:3]])))
#maps = sorted(maps, key = lambda name: rms_flat(sum(cm.cmap_d[name](arange(256)),1)))

# blue
#maps = sorted(maps, key=lambda a: mean(cm.cmap_d[a](128)[1:]))

#Sorted by hand

maps = [
## Qualitative:
#
# 'Accent',
# 'Paired',
# 'Set1',
# 'Dark2',
# 'Set3',
# 'Pastel1',
# 'Pastel2',
# 'Set2',
#
#
## Maps:
#
# 'ocean',
# 'gist_earth',
# 'terrain',
# 'gist_stern',
# 'brg',
#
## Decorative:
#
# 'spring',
# 'autumn',
# 'summer',
# 'winter',
# 'cool_r',
#
#
## Repetitive:
#
# 'flag',
# 'prism',
#
#
## Spectral:
#
# 'gist_ncar',       # erratic non-monotonic luminance
# 'spectral',        # erratic non-monotonic luminance
# 'jet',             # unimodal but jumpy (discontinouus?) luminance
# 'gist_rainbow_r',    # erratic non-monotonic luminance
# 'hsv_r',             # erratic non-monotonic luminance, cyclic hue
# 'rainbow',         # diverging, smooth unimodal luminance curve
#
#
#
#
## Diverging:
# 'coolwarm',
# 'RdBu',
# 'seismic',
# 'bwr',
# 'RdYlBu',
# 'Spectral',
#                      'rainbow',
# 'BrBG',
# 'PiYG',
# 'PRGn',
# 'PuOr',
# 'RdGy',
# 'RdYlGn',
#
#
#
#
## Sequential:
#
# 'hot',            # jagged luminance, not enough change at extremes,
# 'afmhot',
# 'gist_heat',
# 'copper',
# 'gnuplot',
# 'gnuplot2',
# 'cubehelix',
# 'bone',
# 'gray',
## 'gist_yarg_r', # redundant
## 'gist_gray', # redundant
## 'binary_r', # redundant
 'Greys',
 'pink_r',
 'BuPu',
 'Reds',
 'PuRd',
 'OrRd',
 'YlOrRd',
 'YlOrBr',
 'Oranges',
 'BuGn',
 'Greens',
 'YlGn',
 'PuBuGn',
 'RdPu',
 'Purples',
 'YlGnBu',
 'Blues',
 'PuBu',
 'GnBu',
]

#maps = sorted(maps, key=lambda a: relative_luminance(cm.cmap_d[a](3)[0:3]))
#maps = sorted(maps, key=lambda a: argmax(cm.cmap_d[a](3)[0:3]))

nmaps = len(maps)  # + 1
fig = plt.figure(figsize=(5, 10))
fig.subplots_adjust(top=0.99, bottom=0.01, left=0.2, right=0.99)
for i, m in enumerate(maps):
    ax = plt.subplot(nmaps, 1, i+1)

# Show a colorbar for each:
#    plt.axis("off")
    plt.imshow(a, aspect='auto', cmap=plt.get_cmap(m), origin='lower')

# Plot the luminance of each:
    plt.setp(ax.get_xticklabels(), visible=False)
    plt.setp(ax.get_yticklabels(), visible=False)
    plt.setp(ax.get_xticklines(), visible=False)
    plt.setp(ax.get_yticklines(), visible=False)
    plt.xlim(0, 255)
    plt.ylim(0, 1)
    plt.plot([relative_luminance(rgb) for rgb in
              list(cm.get_cmap(m)(np.arange(256))[:, :3])], color='w')

    pos = list(ax.get_position().bounds)
    fig.text(pos[0] - 0.01, pos[1], m, fontsize=10,
             horizontalalignment='right')

plt.show()