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Normalizing colors in matplotlib

I am trying to build a surface using matplotlib using the following 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

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 entire color row of the jet ... I know that there is a class "matplotlib.colors.Normalize", but I do not know how to use it. Could you add the code you need to do this?

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3 answers

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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()
+4

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, OP.

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()

"imshow".

+19

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# Added colors to the matplotlib import list
from matplotlib import cm, colors
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))

# MAIN IDEA: Added normalisation using nanmin and nanmax functions
norm = colors.Normalize(vmin = np.nanmin(Z), 
                        vmax = np.nanmax(Z))

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

plt.show()

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