-
Notifications
You must be signed in to change notification settings - Fork 76
/
aadm_colorpalette.py
152 lines (133 loc) · 5.49 KB
/
aadm_colorpalette.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
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