可视化 2 个参数及其结果

问题描述

我想为以下两个参数尝试不同的值:

There's 2 parameters where I want to try different values for:

a = [0.0, 0.5, 0.6]  # len == 3
b = [0.0, 0.02 , 0.05, 0.1]  # len == 4

对于 a 的每个值，尝试 b 的每个值.这带有 3 * 4 = 12 个不同的结果.

For each value of a, try each value of b.This comes with 3 * 4 = 12 different results.

我的数据格式为

res = [(0.0, 0.0, res1), (0.0, 0.02, res2), ...]

有什么方法可以让我巧妙地想象这个吗?我正在考虑等高线/热图或 3d 平面，但遗憾的是我无法让它发挥作用.

Is there any way I can neatly visualise this? I was thinking of a contour/heat map or 3d plane but sadly I cannot get that to work.

推荐答案

有许多不同的选项.在任何情况下，第一步都需要将您的 res 列表转换为一个 numpy 数组.

There are many different options. The first step in any case needs to be to convert your res list into a numpy array.

对于许多像 imshow、pcolor(mesh) 或 contourf 这样的图，你需要有三个二维数组，你可以通过重塑您的输入数组(假设它已正确排序).

For many plots like imshow, pcolor(mesh) or contourf, you need to have three 2D arrays, which you can obtain via reshaping of your input array (given that it is ordered correctly).

以下显示了您可以选择的一些选项:

The following shows some options you have:

res = [(0.0, 0.0, 0.5), (0.0, 0.02, 0.7), (0.0, 0.05, 0.6), (0.0, 0.10, 0.8),
       (0.5, 0.0, 0.4), (0.5, 0.02, 0.6), (0.5, 0.05, 0.5), (0.5, 0.10, 0.7),
       (0.6, 0.0, 0.3), (0.6, 0.02, 0.5), (0.6, 0.05, 0.4), (0.6, 0.10, 0.6)]

import matplotlib.pyplot as plt
import numpy as np

res = np.array(res)
A = res[:,0].reshape(3,4) #A in y direction
B = res[:,1].reshape(3,4)
Z = res[:,2].reshape(3,4)

fig, ((ax, ax2), (ax3, ax4)) = plt.subplots(2,2)
#imshow
im = ax.imshow(Z, origin="lower")
ax.set_xticks(range(len(Z[0,:])))
ax.set_yticks(range(len(Z[:,0])))
ax.set_xticklabels(B[0,:])
ax.set_yticklabels(A[:,0])

#pcolormesh, first need to extend the grid
bp = np.append(B[0,:], [0.15])
ap = np.append(A[:,0], [0.7])
Bp, Ap = np.meshgrid(bp, ap)
ax2.pcolormesh(Bp, Ap, Z)

#contour
ax3.contourf(B, A, Z, levels=np.linspace(Z.min(), Z.max(),5))
#scatter
ax4.scatter(res[:,1], res[:,0], c=res[:,2], s=121)

ax.set_title("imshow")
ax2.set_title("pcolormesh")
ax3.set_title("contourf")
ax4.set_title("scatter")
plt.tight_layout()
fig.colorbar(im, ax=fig.axes, pad=0.05)
plt.show()

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