Python將大數據數組轉換爲matplotlib的網格

Question

我想使用定義的函數將輸入數據轉換爲matplotlib可以使用的網格數組。有關更大數據的示例，請查看我的griddata.zip文件。 這些數組看起來像例如：x = [0,1.0,2.0], y = [0.0,10.0,20.0], z = [0.0, 20.0, 50.0]這意味着它們具有相同的長度，z應該繪製在x-y網格上。：基本上，之前有一些努力來回答這個問題。可能的解決方案是由M4rtini給出：

from numpy import linspace, meshgrid 
import numpy as np 
from pprint import pprint 
from scipy.interpolate import griddata 

def grid(x, y, z, resX=100, resY=100): 
    "Convert 3 column data to matplotlib grid" 
    grid_x, grid_y = np.mgrid[min(x): max(x):1j * resX, min(y): max(y):1j * resY] 
    Z = griddata(np.array(zip(x, y)), np.array(z), (grid_x, grid_y), method='nearest') 
    return grid_x, grid_y, Z 

x =[0,1.0,2.0] 
y = [0.0,10.0,20.0] 
z = [0.0, 20.0, 50.0] 

X,Y,Z = grid(x,y,z) 

from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 

fig = plt.figure() 
ax = fig.gca(projection='3d') 

ax.plot_surface(X,Y,Z) 
plt.show() 

情節輸出應該是這樣的大的數據文件：Image of Plot output

Answer 1

from numpy import linspace, meshgrid 
import numpy as np 
from pprint import pprint 
from scipy.interpolate import griddata 

def grid(x, y, z, resX=100, resY=100): 
    "Convert 3 column data to matplotlib grid" 
    grid_x, grid_y = np.mgrid[min(x): max(x):1j * resX, min(y): max(y):1j * resY] 
    Z = griddata(np.array(zip(x, y)), np.array(z), (grid_x, grid_y), method='nearest') 
    return grid_x, grid_y, Z 


x =[0,1.0,2.0] 
y = [0.0,10.0,20.0] 
z = [0.0, 20.0, 50.0] 

X,Y,Z = grid(x,y,z) 

from mpl_toolkits.mplot3d import Axes3D 
import matplotlib.pyplot as plt 

fig = plt.figure() 
ax = fig.gca(projection='3d') 

ax.plot_surface(X,Y,Z) 
plt.show() 

任何其他插值方法失敗，此測試數據。