Using GMT you can assign colors to ranges of Z values, so that (for example), Z values between 0 and 50 are given a color interpolated between (0,0,255) (blue) and (255,0,0) (red). There have been various versions of a function called gmtColorMap() posted to this list. I did some experiments today, and as far as I can tell, this function only reads the _color_ part of a GMT .cpt file, ignoring the Z values to which those color ranges are assigned. This isn't a problem as long as you have a linear color scale. However, if you have (as in my case) a color scale assigned to non-linear ranges of values, it becomes a problem.
Is there a way with imshow() to assign ranges of colors to specific ranges of Z values?
As a test, I created the attached python script, which reads in the attached .cpt file.
For those of you not aware of the GMT color palette file format, in each row you define two end points for your Z values, and two corresponding RGB triplets. In my simplified example, I have defined 8 ranges, and assigned a single color to each range. For example, the first two lines in my color palette file looks like this:
00000 255 255 255 00005 255 255 255 00005 255 255 000 00050 255 255 000which can be interpreted to mean that any values between 0 and 5 (filled in with zeros to make the columns line up), should be colored white. Similarly, any values between 5 and 50 should be colored yellow.
popcpt.cpt
Description: Binary data
#!/usr/bin/python
from matplotlib.colors import LinearSegmentedColormap,Normalize
from pylab import *
def gmtColormap(fileName):
import colorsys
import numpy as N
try:
f = open(fileName)
except:
print "file ",fileName, "not found"
return None
lines = f.readlines()
f.close()
x = []
r = []
g = []
b = []
colorModel = "RGB"
for l in lines:
ls = l.split()
if l[0] == "#":
if ls[-1] == "HSV":
colorModel = "HSV"
continue
else:
continue
if ls[0] == "B" or ls[0] == "F" or ls[0] == "N":
pass
else:
x.append(float(ls[0]))
r.append(float(ls[1]))
g.append(float(ls[2]))
b.append(float(ls[3]))
xtemp = float(ls[4])
rtemp = float(ls[5])
gtemp = float(ls[6])
btemp = float(ls[7])
x.append(xtemp)
r.append(rtemp)
g.append(gtemp)
b.append(btemp)
nTable = len(r)
x = N.array( x , N.float32)
r = N.array( r , N.float32)
g = N.array( g , N.float32)
b = N.array( b , N.float32)
if colorModel == "HSV":
for i in range(r.shape[0]):
rr,gg,bb = colorsys.hsv_to_rgb(r[i]/360.,g[i],b[i])
r[i] = rr ; g[i] = gg ; b[i] = bb
if colorModel == "HSV":
for i in range(r.shape[0]):
rr,gg,bb = colorsys.hsv_to_rgb(r[i]/360.,g[i],b[i])
r[i] = rr ; g[i] = gg ; b[i] = bb
if colorModel == "RGB":
r = r/255.
g = g/255.
b = b/255.
xNorm = (x - x[0])/(x[-1] - x[0])
red = []
blue = []
green = []
for i in range(len(x)):
red.append([xNorm[i],r[i],r[i]])
green.append([xNorm[i],g[i],g[i]])
blue.append([xNorm[i],b[i],b[i]])
colorDict = {"red":red, "green":green, "blue":blue}
return (colorDict)
colormap = 'popcpt.cpt'
cdict = gmtColormap(colormap)
palette = LinearSegmentedColormap('my_colormap',cdict)
f = figure()
data = array([[2,2,2,2,2],
[25,25,25,25,25],
[75,75,75,75,75],
[250,250,250,250,250],
[750,750,750,750,750],
[2500,2500,2500,2500,2500],
[7500,7500,7500,7500,7500],
[25000,25000,25000,25000,25000]])
imshow(data,cmap=palette)
#draw grid lines around all the cells
nrows,ncols = data.shape
for row in range(0,nrows):
plot([0,ncols],[row,row],'k')
for col in range(0,ncols):
plot([col,col],[0,nrows],'k')
axis([0,ncols-1,0,nrows])
colorbar()
savefig('output.png')
close('all')
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