I've created matplotlib colormaps from all the color tables at http://colorbrewer.org, and http://geography.uoregon.edu/datagraphics/color_scales.htm. You can grab them at http://www.cdc.noaa.gov/people/jeffrey.s.whitaker/python/extracmaps.py.gz. Also included are the Generic Mapping Tools colormaps (at least the ones that are different from the built in mpl colormaps), for a total of 60. You can just paste the contents of that file into cm.py just above the "def get_cmap" line (although this will not make them available via pylab calls like 'jet()' and 'hot()'). To get a quick view of what the colormaps look like, I use this little script:
import matplotlib.cm as cm
import pylab as p
names = cm.datad.keys()
print len(names),'total color maps'
def plot_colorbar(cmapname,nsteps=None):
colormap = cm.__dict__[cmapname]
fig=p.figure(figsize=(8,1))
ax = fig.add_axes([0.05,0.05,0.9,0.70])
if nsteps == None:
nsteps = colormap.N+1
clevs = p.linspace(0,1,nsteps+1)
C = p.array([clevs,clevs])
X,Y = p.meshgrid(clevs,[0,1])
levs,coll = ax.contourf(C, Y, X, nsteps-1, cmap=colormap)
ax.set_xticks([])
ax.set_yticks([])
p.title(cmapname)
p.show()
if __name__ == "__main__":
cmapname = raw_input('color map name:')
if cmapname not in names:
raise KeyError, 'invalid colormap name, valid names are '+repr(names)
plot_colorbar(cmapname)
Enjoy!
-Jeff
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