Normalizing colors in matplotlib

Question

I am trying to plot a surface using matplotlib using the code below:

from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pylab as p

vima=0.5

fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, 16.67, vima)
Y = np.arange(0, 12.5, vima)
X, Y = np.meshgrid(X, Y)

Z = np.sqrt(((1.2*Y+0.6*X)**2+(0.2*Y+1.6*X)**2)/(0.64*Y**2+0.36*X**2))

surf = ax.plot_surface(X, Y, Z,rstride=1, cstride=1, alpha=1,cmap=cm.jet,  linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)

plt.show()

If you run it you will see a blue surface, but I want to use the whole color range of jet... I know there is a class "matplotlib.colors.Normalize", but I don't know how to use it. Could you please add the necessary code in order to do it?

Answer 1

I realise that the poster's issue has already been resolved, but the question of normalizing the colors was never dealt with. Since I've figured out how I thought I'd just drop this here for anyone else who might need it.

First you create a norm and pass that to the plotting function, I've tried to add this to the OP's code.

from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import axes3d, Axes3D
import pylab as p
import matplotlib

vima=0.5

fig = plt.figure()
ax = fig.gca(projection='3d')
X = np.arange(0, 16.67, vima)
Y = np.arange(0, 12.5, vima)
X, Y = np.meshgrid(X, Y)

Z = np.sqrt(((1.2*Y+0.6*X)**2+(0.2*Y+1.6*X)**2)/(0.64*Y**2+0.36*X**2))
Z = np.nan_to_num(Z)

# Make the norm
norm = matplotlib.colors.Normalize(vmin = np.min(Z), vmax = np.max(Z), clip = False)

# Plot with the norm
surf = ax.plot_surface(X, Y, Z,rstride=1, cstride=1, norm=norm, alpha=1,cmap=cm.jet,     linewidth=0)
fig.colorbar(surf, shrink=0.5, aspect=5)

plt.show()

The norm works the same way for the "imshow" command.