def plot_color_gradients(cmap_category, 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 CIE LAB. We want the L here.
lab = color.rgb2lab(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()
def posterize(data, colors):
# convert data to Lab color space
data_lab = colorconv.rgb2lab(data)
# Now take the norm of each vector in each matrix
distances = [color_distance(data_lab, c) for c in colors]
# Find the shortest norm to find the closest color
return numpy.argmin(distances, axis=0)
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 CIE LAB. We want the L here.
lab = color.rgb2lab(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()
def posterize(data, colors) :
# convert data to Lab color space
data_lab = colorconv.rgb2lab(data)
# Now take the norm of each vector in each matrix
distances = [color_distance(data_lab, c) for c in colors]
# Find the shortest norm to find the closest color
return numpy.argmin(distances, axis=0)
fig = plt.figure(figsize=(11.5,4*nsubplots))
for i, subplot in enumerate(xrange(nsubplots)):
locs = [] # locations for text labels
ax = fig.add_subplot(nsubplots, 1, i+1)
# pdb.set_trace()
for j, cmap in enumerate(cmap_list[i*dsub:(i+1)*dsub]):
# Get rgb values for colormap
rgb = cm.get_cmap(cmap)(x)[np.newaxis,:,:3]
# Get colormap in CIE LAB. We want the L here.
lab = color.rgb2lab(rgb)
# Plot colormap L values
# Do separately for each category so each plot can be pretty
# to make scatter markers change color along plot: http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable
if cmap_category=='Sequential':
dc = 0.6 # spacing between colormaps
ax.scatter(x+j*dc, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.)
if i==2:
ax.axis([-0.1,4.1,0,100])
else:
ax.axis([-0.1,4.7,0,100])
locs.append(x[-1]+j*dc) # store locations for colormap labels
elif cmap_category=='Sequential2':
dc = 1.15
mpl.rcParams['mathtext.tt'] = 'monospace'
mpl.rcParams['mathtext.it'] = 'sans:italic'
mpl.rcParams['mathtext.bf'] = 'sans:bold'
mpl.rcParams['mathtext.sf'] = 'sans'
mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
### Red, original Albers plot
nrows = 5
# Start with red
red = np.array([np.hstack([np.ones((nrows,1)), np.zeros((nrows,2))])])
# Get basic red in LAB
lab_add = color.rgb2lab(red)
lab_geometric = lab_add.copy()
# Alter successive rows with more black
k = 1
for i in range(red.shape[1]):
# more blackness is closer to 0 than one, and in first column of LAB
lab_add[0,i,0] = lab_add[0,i,0] - 10*i
print(i,k)
if i != 0:
lab_geometric[0,i,0] = lab_geometric[0,i,0] - 10*k
k *= 2
# Change LAB back to RGB for plotting
rgb_add = red.copy() # only change red values
temp = color.lab2rgb(lab_add)
mpl.rcParams['mathtext.rm'] = 'sans'
mpl.rcParams['mathtext.tt'] = 'monospace'
mpl.rcParams['mathtext.it'] = 'sans:italic'
mpl.rcParams['mathtext.bf'] = 'sans:bold'
mpl.rcParams['mathtext.sf'] = 'sans'
mpl.rcParams['mathtext.fallback_to_cm'] = 'True'
### Red, original Albers plot
nrows = 5
# Start with red
red = np.array([np.hstack([np.ones((nrows, 1)), np.zeros((nrows, 2))])])
# Get basic red in LAB
lab_add = color.rgb2lab(red)
lab_geometric = lab_add.copy()
# Alter successive rows with more black
k = 1
for i in range(red.shape[1]):
# more blackness is closer to 0 than one, and in first column of LAB
lab_add[0, i, 0] = lab_add[0, i, 0] - 10 * i
if i != 0:
lab_geometric[0, i, 0] = lab_geometric[0, i, 0] - 10 * k
k *= 2
# Change LAB back to RGB for plotting
rgb_add = red.copy() # only change red values
temp = color.lab2rgb(lab_add)
rgb_add[0, :, 0] = temp[0, :, 0]
fig = plt.figure(figsize=(7, 2.6 * nsubplots))
for i, subplot in enumerate(range(nsubplots)):
locs = [] # locations for text labels
ax = fig.add_subplot(nsubplots, 1, i + 1)
for j, cmap in enumerate(cmap_list[i * dsub:(i + 1) * dsub]):
# Get rgb values for colormap
rgb = cm.get_cmap(cmap)(x)[np.newaxis, :, :3]
# Get colormap in CIE LAB. We want the L here.
lab = color.rgb2lab(rgb)
# Plot colormap L values
# Do separately for each category so each plot can be pretty
# to make scatter markers change color along plot:
# http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable
if cmap_category == 'Perceptually Uniform Sequential':
dc = 1.15 # spacing between colormaps
ax.scatter(x + j * dc,
lab[0, ::-1, 0],
c=x,
cmap=cmap,
s=300,
linewidths=0.)
if i == 2:
ax.axis([-0.1, 4.1, 0, 100])
def color_to_lab(c):
l = colorconv.rgb2lab(
numpy.reshape(numpy.array(c, dtype=numpy.uint8), (1, 1, 3)))
return numpy.reshape(l, (3))
def color_to_lab(c) :
l = colorconv.rgb2lab(numpy.reshape(numpy.array(c, dtype=numpy.uint8),(1,1,3)))
return numpy.reshape(l,(3))