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aadm_colorpalette.py
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aadm_colorpalette.py
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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.cm as cm
import os
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# https://mycarta.wordpress.com/color-palettes/
def creacolormap(arr):
b3=arr[:,2] # value of blue at sample n
b2=arr[:,2] # value of blue at sample n
b1=np.linspace(0,1,len(b2)) # position of sample n - ranges from 0 to 1
g3=arr[:,1]
g2=arr[:,1]
g1=np.linspace(0,1,len(g2))
r3=arr[:,0]
r2=arr[:,0]
r1=np.linspace(0,1,len(r2))
# creating list
R=zip(r1,r2,r3)
G=zip(g1,g2,g3)
B=zip(b1,b2,b3)
# transposing list
RGB=zip(R,G,B)
rgb=zip(*RGB)
# creating dictionary
k=['red', 'green', 'blue']
colori = dict(zip(k,rgb)) # makes a dictionary from 2 lists
return colori
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# http://schubert.atmos.colostate.edu/~cslocum/custom_cmap.html
def make_cmap(colors, position=None, bit=False):
'''
make_cmap takes a list of tuples which contain RGB values. The RGB
values may either be in 8-bit [0 to 255] (in which bit must be set to
True when called) or arithmetic [0 to 1] (default). make_cmap returns
a cmap with equally spaced colors.
Arrange your tuples so that the first color is the lowest value for the
colorbar and the last is the highest.
position contains values from 0 to 1 to dictate the location of each color.
'''
bit_rgb = np.linspace(0,1,256)
if position == None:
position = np.linspace(0,1,len(colors))
else:
if len(position) != len(colors):
sys.exit("position length must be the same as colors")
elif position[0] != 0 or position[-1] != 1:
sys.exit("position must start with 0 and end with 1")
if bit:
for i in range(len(colors)):
colors[i] = (bit_rgb[colors[i][0]],
bit_rgb[colors[i][1]],
bit_rgb[colors[i][2]])
cdict = {'red':[], 'green':[], 'blue':[]}
for pos, color in zip(position, colors):
cdict['red'].append((pos, color[0], color[0]))
cdict['green'].append((pos, color[1], color[1]))
cdict['blue'].append((pos, color[2], color[2]))
cmap = LinearSegmentedColormap('my_colormap',cdict,256)
return cmap
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# http://stackoverflow.com/questions/3279560/invert-colormap-in-matplotlib
def reverse_colourmap(cmap, name = 'my_cmap_r'):
"""
In:
cmap, name
Out:
my_cmap_r
Explanation:
t[0] goes from 0 to 1
row i: x y0 y1 -> t[0] t[1] t[2]
/
/
row i+1: x y0 y1 -> t[n] t[1] t[2]
so the inverse should do the same:
row i+1: x y1 y0 -> 1-t[0] t[2] t[1]
/
/
row i: x y1 y0 -> 1-t[n] t[2] t[1]
"""
reverse = []
k = []
for key in cmap._segmentdata:
k.append(key)
channel = cmap._segmentdata[key]
data = []
for t in channel:
data.append((1-t[0],t[2],t[1]))
reverse.append(sorted(data))
LinearL = dict(zip(k,reverse))
my_cmap_r = LinearSegmentedColormap(name, LinearL)
return my_cmap_r
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# adattato da http://matplotlib.org/examples/color/colormaps_reference.html
def plot_color_gradients(colormaps):
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
nrows = len(colormaps)
fig, axes = plt.subplots(nrows=nrows)
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
for ax, name in zip(axes, colormaps):
ax.imshow(gradient, aspect='auto', cmap=name)
pos = list(ax.get_position().bounds)
x_text = pos[0]
y_text = pos[1] + pos[3]/2.
fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
for ax in axes:
ax.set_axis_off()
#==== colorbars di Matteo Niccoli
cc = np.loadtxt('colormap_sawtooth.csv', delimiter=',')
cmap_jetsaw = LinearSegmentedColormap('jetsaw',creacolormap(cc))
cm.register_cmap(name='jetsaw', cmap=cmap_jetsaw)
cmap_jetsaw_r = LinearSegmentedColormap('jetsaw_r',creacolormap(np.flipud(cc)))
cm.register_cmap(name='jetsaw_r', cmap=cmap_jetsaw_r)
#==== colorbars di Peter Kovesi http://peterkovesi.com/projects/colourmaps/
path = 'CETperceptual_csv_0_1'
for ff in os.listdir(path):
pippo=ff.split(sep='_')[0:3]
cname='_'.join(pippo)
# print('{:>50s} --> {:<30s}'.format(ff, cname))
cc = np.loadtxt(os.path.join(path,ff), delimiter=',')
cm.register_cmap(name=cname, cmap=LinearSegmentedColormap(cname,creacolormap(cc)))
cm.register_cmap(name=cname+'_r', cmap=LinearSegmentedColormap(cname,creacolormap(np.flipud(cc))))
#==== colorbar di Decision Space
# black purple blue cyan green red yellow
colors =[(0,0,0), (255,0,255), (0,0,255), (0,255,255), (0,255,0), (255,0,0), (255,255,0), (255,255,255)]
cmap_landmark = make_cmap(colors, bit=True)
cm.register_cmap(name='landmark', cmap=cmap_landmark)
cmap_landmark_r = reverse_colourmap(cmap_landmark)
cm.register_cmap(name='landmark_r', cmap=cmap_landmark_r)
#==== pulizia
del cc,colors,cmap_jetsaw,cmap_jetsaw_r,cmap_landmark,cmap_landmark_r
del cm, LinearSegmentedColormap, creacolormap, make_cmap, reverse_colourmap