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?

As JoshAdel noted in a comment (credit belongs to him), it appears that the surface plot is improperly ranging the colormap when a `NaN`

is in the Z array. A simple work-around is to simply convert the NaN's to zero or very large or very small numbers so that the colormap can be normalized to the z-axis range.

```
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))
Z = np.nan_to_num(Z) # added this line
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()
```

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.

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