如何使用Python改善插值结果？

Question

我有这个XYZ文本文件（3列），我正在尝试进行插值，因此可以创建一个不错的颜色模型。问题是返回的结果不能正确表示我的数据分布的地形...我的意思是，它的表面不像我想要的那样光滑。底色（红色）紧跟数据（黑点），但顶色（蓝色）不紧随数据。有帮助吗？

我得到的是：我的代码：

import matplotlib.pyplot as plt
import scipy.interpolate

x,y,z = np.loadtxt("vel_model.txt",usecols=(0,1,2),unpack=True)

x_grid = np.linspace(x.min(),x.max(),100)
y_grid = np.linspace(y.min(),y.max(),100)
xi,yi = np.meshgrid(x_grid,y_grid)
zi = scipy.interpolate.griddata((x, y), z, (xi, yi), method = 'cubic')
fig = plt.figure()
ax = fig.add_subplot(111)
cm = ax.contourf(xi, yi, zi, cmap='jet')
ax.scatter(x,y,color='black',s = 6, label = 'Data points')
ax.set_aspect('equal')
ax.set_xlabel('Distance (m)')
ax.set_ylabel('Elevation (m)')
plt.legend(loc = 'best')
plt.grid(color='grey', linestyle='--', linewidth=0.3)
plt.show()```

Answer 1

让它按照以下问题工作：Limit/mask matplotlib contour to data area

结果：

代码：

import numpy as np
import matplotlib.pyplot as plt
import scipy.interpolate
from matplotlib.path import Path
from matplotlib.patches import PathPatch

x,y,z = np.loadtxt("modeloVS_teste.txt",usecols=(0,1,2),unpack=True)
x_grid = np.linspace(x.min(),x.max(),100)
y_grid = np.linspace(y.min(),y.max(),100)
xi,yi = np.meshgrid(x_grid,y_grid)
zi = scipy.interpolate.griddata((x, y), z, (xi, yi), method = 'cubic')
fig = plt.figure()
ax = fig.add_subplot(111)
cm = ax.contourf(xi, yi, zi, cmap='jet')
ax.scatter(x,y,color = 'black',s = 5, label = 'Data points')
ax.set_aspect('equal')
ax.set_xlabel('Distance (m)')
ax.set_ylabel('Elevation (m)')
plt.legend(loc = 'best')
plt.grid(color='grey', linestyle='--', linewidth=0.3)
columns = {}
for i in range(len(x)):
    columns.update({x[i]:[]})
for i in range(len(x)):  
    columns[x[i]].append((y[i]))  
limits = []
for key in columns:
    limits.append((key,max(columns[key])))
limits = limits[::-1]
for key in columns:
    limits.append((key,min(columns[key])))
clippath = Path(limits)
patch = PathPatch(clippath, facecolor='none', alpha = 0)
ax.add_patch(patch)
for c in cm.collections:
    c.set_clip_path(patch)
plt.show()