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'''
Show what matplotlib colormaps look like in grayscale.
Uses lightness L* as a proxy for grayscale value.
'''
from colormaps import cmaps
#from skimage import color
# we are using a local copy of colorconv from scikit-image to reduce dependencies.
# You should probably use the one from scikit-image in most cases.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib as mpl
from colorspacious import cspace_converter
mpl.rcParams.update({'font.size': 14})
# indices to step through colormap
x = np.linspace(0.0, 1.0, 100)
# nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
def plot_color_gradients(cmap_category, cmap_list):
nrows = len(cmap_list)
fig, axes = plt.subplots(nrows=nrows, ncols=2)
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99,
wspace=0.05)
fig.suptitle(cmap_category + ' colormaps', fontsize=14, y=1.0, x=0.6)
for ax, name in zip(axes, cmap_list):
# Get rgb values for colormap
rgb = cm.get_cmap(plt.get_cmap(name))(x)[np.newaxis,:,:3]
# Get colormap in CAM02-UCS colorspace. We want the lightness.
lab = cspace_converter("sRGB1", "CAM02-UCS")(rgb)
L = lab[0,:,0]
L = np.float32(np.vstack((L, L, L)))
ax[0].imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
ax[1].imshow(L, aspect='auto', cmap='binary_r', vmin=0., vmax=100.)
pos = list(ax[0].get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3]/2.
fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
ax[0].set_axis_off()
ax[1].set_axis_off()
plt.show()
for cmap_category, cmap_list in cmaps:
plot_color_gradients(cmap_category, cmap_list)
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