我试图得到10分二维轮廓图
我试图用的GridData生成我的网格,但它似乎没有工作,我只看到NAN我插的网格。
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
xi = np.linspace(0,7500.0,100)
yi = np.linspace(0,7500.0,100)
indie_coords_y=[195,695,1195,1695,2195,2695,3195,3695,4195,4695]
indie_coords_x=[87,90,92,95,97,100,103,105,107,110]
z1_final=[12,13,14,15,16,17,18,19,20,21]
zi = griddata((indie_coords_x, indie_coords_y), z1_final, (xi[None,:],
yi[:,None]), method='linear')
CS = plt.contourf(xi,yi,zi,cmap='jet', vmin=min(z1_final),
vmax=max(z1_final))
当我使用上面的代码,我看到我的字阵刚刚NAN值,而我希望看到一些轮廓
任何一个可以请帮助
我修改的输入数据(混洗indie_coords_y
)。此外,个内插有一格的所有点进行。 np.meshgrid
用于构建满格。 .flatten()
用于将网格转换成点的列表(即,形状NUMBER_OF_POINTS X number_of_dim的阵列)。内插后,reshape
用于点的列表转换回格(两个n乘n阵列)。
现在插值和图形正在努力:
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
# Data
indie_coords_y = [195, 2195, 3195, 2695, 3695, 4695, 695, 1195, 1695, 4195] # Modified!
# using np.random.shuffle(indie_coords_y)
indie_coords_x = [87,90,92,95,97,100,103,105,107,110]
z1_final = [12,13,14,15,16,17,18,19,20,21]
# Interpolation
xi = np.linspace(80, 120.0, 30) # modified range
yi = np.linspace(0, 5000.0, 30)
X_grid, Y_grid = np.meshgrid(xi, yi) # Create a grid (i.e. 100x100 arrays)
zi = griddata((indie_coords_x, indie_coords_y), z1_final,
(X_grid.flatten(), Y_grid.flatten()), method='linear')
Z_grid = zi.reshape( X_grid.shape )
# Graph
CS = plt.contourf(X_grid, Y_grid, Z_grid, cmap='jet')
plt.plot(indie_coords_x, indie_coords_y, 'ko', label='data points')
plt.plot(X_grid.flatten(), Y_grid.flatten(), 'r,', label='interpolation points')
plt.xlabel('x'); plt.ylabel('y');
plt.colorbar(); plt.legend();
图为: