使用网格数据绘制轮廓时只看到 NAN

Question

我正在尝试获得十个点的二维等高线图我尝试使用 griddata 来生成我的网格，但是它似乎不起作用，我只能在我的插值网格中看到 NAN。import pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom scipy.interpolate import griddataxi = 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时，我看到我的 zi 数组只有 NAN 值，而我希望看到一些轮廓任何人都可以帮忙吗

Answer 1

我修改了输入数据（shuffle indie_coords_y）。此外，必须对网格的所有点执行插值。np.meshgrid用于构建完整的网格。.flatten()用于将网格转换为点列表（即形状为 number_of_points x number_of_dim 的数组）。插值后，reshape用于将点列表转换回网格（两个 n × n 数组）。

现在插值和图形正在工作：

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();

该图是：