Matplotlib smoothing 3D surface data-CodePudding

I have an issue with smoothing out the mesh representation of my 3D surface with matplotlib. Below, please see my example. I am having a hard time figuring out how to make the plot look nicer/smoother if possible. Thank you for your time in advance!

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LightSource
import numpy as np

X = [1,1,1,1,1,1,50,50,50,50,50,50]

Y = [3,5,7,8,9,10,3,5,7,8,9,10]

Z = [5.23,3.11,17.54,0.93,40.11,10.15,1.47,14.32,5.46,55.93,40.8,10.2]

x = np.reshape(X, (2, 6))
y = np.reshape(Y, (2, 6))
z = np.reshape(Z, (2, 6))
X, Y = np.meshgrid(x, y)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.plot_surface(x, y, z)

ax.set_xlabel('Persistence Length')
ax.set_ylabel('Complexity')
ax.set_zlabel('Relative number of configurational states')

surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm, 
                       linewidth=0, antialiased=False)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

CodePudding user response：

To obtain smooth line/surface you can set antialiased=True on the surface plot. Note that you were plotting two identical surface: in the following example I have eliminated the first.

To obtain a smoother mesh, you probably want to interpolate between your data points. One way to do that is to use griddata from the scipy.interpolate module.

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from scipy.interpolate import griddata

X = [1,1,1,1,1,1,50,50,50,50,50,50]

Y = [3,5,7,8,9,10,3,5,7,8,9,10]

Z = [5.23,3.11,17.54,0.93,40.11,10.15,1.47,14.32,5.46,55.93,40.8,10.2]

points = np.array([X, Y]).T

# create a grid of coordinates between the minimum and
# maximum of your X and Y. 50j indicates 50 discretization
# points between the minimum and maximum.
X_grid, Y_grid = np.mgrid[1:50:50j, 3:10:50j]
# interpolate your values on the grid defined above
Z_grid = griddata(points, Z, (X_grid, Y_grid), method='cubic')

fig = plt.figure(constrained_layout=True)
ax = fig.add_subplot(111, projection='3d')

ax.set_xlabel('Persistence Length')
ax.set_ylabel('Complexity')
ax.set_zlabel('Relative number of configurational states')

surf = ax.plot_surface(X_grid, Y_grid, Z_grid, cmap=cm.coolwarm, 
                       linewidth=0, antialiased=True)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

Here is an example of antialiased=False on the left, vs antialiased=True on the right: