import cppcolormap as cm
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
cmap = cm.Reds()
m_cmap = mpl.colors.ListedColormap(cmap, name="Reds", N=cmap.shape[0])
xterm = cm.xterm()
idx = cm.match(cmap, xterm, cm.metric.perceptual)
m_xterm = mpl.colors.ListedColormap(xterm[idx, :],
name="xterm",
N=cmap.shape[0])
fig, axes = plt.subplots(figsize=(16, 8), ncols=2)
x, y = np.meshgrid(np.linspace(0, 99, 100), np.linspace(0, 99, 100))
z = (x - 50)**2.0 + (y - 50)**2.0
im = axes[0].imshow(z, cmap=m_cmap, clim=(0, 5000))
im = axes[1].imshow(z, cmap=m_xterm, clim=(0, 5000))
plt.savefig("match.pdf")
plt.close()
"Red",
"RedOrange",
"RedViolet",
"Rhodamine",
"RoyalBlue",
"RoyalPurple",
"RubineRed",
"Salmon",
"SeaGreen",
"Sepia",
"SkyBlue",
"SpringGreen",
"Tan",
"TealBlue",
"Thistle",
"Turquoise",
"Violet",
"VioletRed",
"WildStrawberry",
"Yellow",
"YellowGreen",
"YellowOrange")
dvips = np.array([list(cm.colormap(name, 1)[0, :]) for name in names])
jet = cm.jet(256)
idx = cm.match(dvips, jet, cm.metric.perceptual)
jdx = np.argsort(idx)
print('\n'.join([names[i] for i in jdx]))
import cppcolormap as cm
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
cmap = cm.Reds()
m_cmap = mpl.colors.ListedColormap(cmap, name='Reds', N=cmap.shape[0])
xterm = cm.xterm()
idx = cm.match(cmap, xterm, cm.DistanceMetric.perceptual)
m_xterm = mpl.colors.ListedColormap(xterm[idx, :],
name='xterm',
N=cmap.shape[0])
fig, axes = plt.subplots(figsize=(16, 8), ncols=2)
x, y = np.meshgrid(np.linspace(0, 99, 100), np.linspace(0, 99, 100))
z = (x - 50)**2. + (y - 50)**2.
im = axes[0].imshow(z, cmap=m_cmap, clim=(0, 5000))
im = axes[1].imshow(z, cmap=m_xterm, clim=(0, 5000))
plt.show()