cmap = 'viridis' # The optimized version of this colormap is cividis!
# Get original RGB/Jab
t = time()
rgb1, jab1 = cmu.get_rgb_jab(cmap)
# Get CVD RGB/Jab
rgb2, jab2 = cmu.get_rgb_jab(cvu.get_cvd(rgb1, severity=100))
# Make perceptual deltas (for a' vs. b') straight
# Need to set a and b before straightening J
jab3 = cmu.make_linear(jab2)
rgb3 = cmu.convert(jab3, cmu.CSPACE2, cmu.CSPACE1)
# Linearize J'
jab4, jab5 = cmu.correct_J(
jab3) # This will create a figure showing the J bounds
if jab4 is not None:
rgb4 = cmu.convert(jab4, cmu.CSPACE2, cmu.CSPACE1)
rgb4 = np.clip(rgb4, 0, 1)
if jab5 is not None:
rgb5 = cmu.convert(jab5, cmu.CSPACE2, cmu.CSPACE1)
rgb5 = np.clip(rgb5, 0, 1)
#%% Figure: Iterations for optimization
fig = plt.figure(figsize=(25, 12), dpi=500)
# Plot original colormap properties
ax = plt.subplot(5, 5, 1)
plt.title('Input', fontsize=FLABEL)
cmu.plot_colormap_info(fig, rgb1, sp=[5, 5, 1], show=False)
import cmaputil as cmu import cmaputil.cvdutil as cvu # Globals FLABEL = 20 FAX = 16 # Input colormap name cmap = 'viridis' # Optimize rgb1, jab1 = cmu.get_rgb_jab(cmap) # Original colormap rgb2, jab2 = cmu.get_rgb_jab(cvu.get_cvd(rgb1)) # CVD colormap jab3 = cmu.make_linear(jab2) # Uniformize hue (a' vs. b') _, jab4 = cmu.correct_J(jab3) # Linearize J' # Convert back to sRGB rgb4 = cmu.convert(jab4, cmu.CSPACE2, cmu.CSPACE1) rgb4 = np.clip(rgb4, 0, 1) # Resimulate CVD in case corrections took the map outside CVD-safe space rgb4 = cvu.get_cvd(rgb4) #%% Creat CDPS plots (shown in Fig 1 and 5 of the paper) # Import test data img_name = 'example_nanosims_image.txt' # Image used for paper img = np.loadtxt(img_name)[45:, :-45] # Make square high = 3 low = -1 # Std. Dev bounds for normalizing