如何使用 Scipy 插入 2D 表面以进行 Matplotlib 渲染？

Question

Given a 2D surface with few points that is displayed using Matplotlib:

import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np

fig, ax = plt.subplots(subplot_kw={"projection": "3d"})

x = np.arange(0, 4, 1)
y = np.arange(0, 4, 1)
x, y = np.meshgrid(x, y)
z = np.array([
    [0, 1, 1, 0],
    [1, 2, 2, 1],
    [1, 3, 2, 1],
    [0, 1, 1, 0]])

surf = ax.plot_surface(x, y, z, cmap=cm.cool)
plt.show()

How can I interpolate the data to make the surface smoother? Ideally, I would want to use a spline interpolation (similar to this example ). Consequently, I tried to use interpolate.bisplrep from Scipy but got various TypeError: len(x)==len(y)==len(z) must hold errors. How can I prepare the data?

Answer 1

interpolate.bisplrep requires that the x, y edges are arranged using a meshgrid :

import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
from scipy import interpolate

fig, ax = plt.subplots(subplot_kw={"projection": "3d"})

x = np.arange(0, 4, 1)
y = np.arange(0, 4, 1)
x, y = np.meshgrid(x, y)
z = np.array([
    [0, 1, 1, 0],
    [1, 2, 2, 1],
    [1, 3, 2, 1],
    [0, 1, 1, 0]])

# new grid is 40x40
xnew = np.linspace(0, 3, num=40)
ynew = np.linspace(0, 3, num=40)
tck = interpolate.bisplrep(x, y, z, s=0)
znew = interpolate.bisplev(xnew, ynew, tck)

xnew, ynew = np.meshgrid(xnew, ynew)

surf = ax.plot_surface(xnew, ynew, znew, cmap=cm.cool)
plt.show()