def get_cvd(data, cvd_type=CVD_TYPE, severity=100):
'''
Converts RGB values to CVD space RGB values.
Parameters
----------
data: string or 3 x 256 array
Colormap name OR array with complete color data. Invalid
colormap names throw a ValueError. Refer to _check_cmap for
more information.
cvd_type: string
Type of CVD to be simulated. Options: deuteranomaly or
protanomaly. Default: deuteranomaly.
severity: int
Severity of CVD to be simulated. Can be any integer between 0
and 100. Default: 100
Returns
----------
cvd: 3 x 256 array
Colormap data in CVD space
'''
rgb, _ = cmu.get_rgb_jab(data, calc_jab=False)
cvd_space = {
'name': 'sRGB1+CVD',
'cvd_type': cvd_type,
'severity': severity
}
cvd = cmu.convert(rgb, cvd_space, CSPACE1)
return cvd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import cmaputil as cmu
import cmaputil.cvdutil as cvu
FLABEL = 20
FAX = 16
#%% Get iteration data
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)
# Imports import numpy as np 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
def compare_points(elements_to_plot,
element_list,
data_by_element,
combo_mask,
x,
y,
distances,
name1=None,
name2=None):
data = np.copy(data_by_element)
# Find points based on highest and lowest carbon signature
c_ind = element_list.index('C')
xx = data[c_ind, :]
xx = array2img(x, y, xx)
xx[np.where(combo_mask != 0)] = np.nan
xx[distances > 250] = np.nan
p1 = np.where(xx == np.nanmax(xx))
xx[np.where(distances != distances[p1])] = np.nan
p2 = np.where(xx == np.nanmin(xx))
# Overlay points on mask
rgb, _ = cmu.get_rgb_jab('gray')
rgb = np.fliplr(rgb)
data[np.where(data <= 0.0)] = 0
combo_mask[combo_mask > 0] = 1
img = cmu.overlay_colormap(combo_mask, rgb)
img[p1[0], p1[1]] = [255, 0, 0]
img[p2[0], p2[1]] = [0, 0, 255]
# Plot
plt.figure(figsize=(8, 8))
plt.imshow(img, interpolation='none')
plt.axis('off')
if name1 is not None:
plt.savefig(name1, dpi=FIG_DPI, bbox_inches='tight')
plt.show()
# Gen bar plot to compare
plt.figure(figsize=(8, 4))
i = 1
plt.plot([0, len(elements_to_plot) + 2], [0, 0], 'k', lw=2)
for e in elements_to_plot:
ind = element_list.index(e)
intensities = data[ind, :]
img = array2img(x, y, intensities)
ratio = img[p1[0], p1[1]] - img[p2[0], p2[1]]
# ratio = np.log2(img[p1[0], p1[1]] / img[p2[0], p2[1]])
print(img[p1[0], p1[1]] - img[p2[0], p2[1]])
plt.bar(i, ratio, align='center', color='k', edgecolor='k', bottom=0)
# plt.text(i, 0.1 * (ratio / abs(ratio)), '%.2f' % ratio, color='w', fontsize=FLAB)
i += 1
# Formatting
plt.yticks([], fontsize=FAX)
plt.xticks(range(1,
len(elements_to_plot) + 1),
elements_to_plot,
fontsize=FAX)
plt.tick_params(axis='x', which='both', bottom='off', top='off')
plt.xlabel('Element', fontsize=FLAB)
plt.ylabel('log2(Raw Int. Ratio)', fontsize=FLAB)
plt.axis([0.5, 6.5, -1.5, 3.15])
if name2 is not None:
plt.savefig(name2, bbox_inches='tight', dpi=FIG_DPI)
plt.show()