def colouring(data, colours, steps):
colour_list = []
for i in range(len(steps)):
colour_list.append(
(float(steps[i] - steps[0]) / (steps[-1] - steps[0]),
[x / 255.0 for x in colours[i]]))
my_cmap = LinearSegmentedColormap.from_list('my_cmap', colour_list, N=1024)
return shading.shade(data, cmap=my_cmap, intensity=shading.intensity(data))
color_list = []
for i in range(len(steps)):
color_list.append((float(steps[i]-steps[0])/(steps[-1]-steps[0]), [x/255.0 for x in colors[i]]))
my_cmap = LinearSegmentedColormap.from_list('my_cmap', color_list, N=1024)
where_are_nan =numpy.isnan(r)
r[where_are_nan] =0
where_less_than_minus_200 = (r<-200)
r[where_less_than_minus_200] = -200
where_greater_than_200 = (r>200)
r[where_greater_than_200] = 200
rgb = shading.shade(r,shading.intensity(r), cmap=my_cmap)
rgb[where_are_nan] = [128.0/255,128.0/255,128.0/255]
#masked_data = numpy.ma.masked_where(numpy.isnan(rgb),rgb)
#print masked_data
#m.imshow(masked_data,cmap=cmap,interpolation='sinc')
rgb = numpy.flipud(rgb)
m.imshow(rgb,interpolation='sinc')
#plot.show()
fig.savefig('/mnt/workspace/EMAG2/EMAG2.tiff',bbox_inches='tight',pad_inches=0,dpi=144,transparent=True,frameon=False)
