不规则X-Y-Z-d的等高线/imshow图

Answer 1

plt.tricontourf(x,y,z)是否满足您的要求？

它将绘制不规则间距数据（非直线网格）的填充等高线。

您可能还需要查看plt.tripcolor()。

import numpy as np
import matplotlib.pyplot as plt
x = np.random.rand(100)
y = np.random.rand(100)
z = np.sin(x)+np.cos(y)
f, ax = plt.subplots(1,2, sharex=True, sharey=True)
ax[0].tripcolor(x,y,z)
ax[1].tricontourf(x,y,z, 20) # choose 20 contour levels, just to show how good its interpolation is
ax[1].plot(x,y, 'ko ')
ax[0].plot(x,y, 'ko ')
plt.savefig('test.png')

Answer 2

xx, yy = np.meshgrid(x, y)

plt.contour(xx, yy, z)

不管它们是否间隔不规则，等高线图和三维图都需要网格。

Answer 3

（源代码@结尾…）

这是我在玩这个时做的一点眼糖。它探索了网格的线性变换仍然是网格的事实。一、 在我所有绘图的左边，我正在处理二维（输入）函数的X和Y坐标。在右边，我想使用相同函数的（平均（X，Y），Y-X）坐标。

我在本机坐标系中创建网格，并将它们转换为其他坐标系的网格。如果变换是线性的，则工作正常。

对于下面两个图，我使用随机抽样直接解决您的问题。

以下是带有setlims=False的图像：

与setlims=True相同：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

def f(x, y):
    return y**2 - x**2
lim = 2
xlims = [-lim , lim]
ylims = [-lim, lim]

setlims = False

pde = 1
numpts = 50
numconts = 20

xs_even = np.linspace(*xlims, num=numpts)
ys_even = np.linspace(*ylims, num=numpts)

xs_rand = np.random.uniform(*xlims, size=numpts**2)
ys_rand = np.random.uniform(*ylims, size=numpts**2)

XS_even, YS_even = np.meshgrid(xs_even, ys_even)

levels = np.linspace(np.min(f(XS_even, YS_even)), np.max(f(XS_even, YS_even)), num=numconts)

cmap = sns.blend_palette([sns.xkcd_rgb['cerulean'], sns.xkcd_rgb['purple']], as_cmap=True)

fig, axes = plt.subplots(3, 2, figsize=(10, 15))

ax = axes[0, 0]
H = XS_even
V = YS_even
Z = f(XS_even, YS_even)
ax.contour(H, V, Z, levels, cmap=cmap)
ax.plot(H.flatten()[::pde], V.flatten()[::pde], linestyle='None', marker='.', color='.75', alpha=0.5, zorder=1, markersize=4)
if setlims:
    ax.set_xlim([-lim/2., lim/2.])
    ax.set_ylim([-lim/2., lim/2.])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Points on grid, contour')

ax = axes[1, 0]
H = H.flatten()
V = V.flatten()
Z = Z.flatten()
ax.tricontour(H, V, Z, levels, cmap=cmap)
ax.plot(H.flatten()[::pde], V.flatten()[::pde], linestyle='None', marker='.', color='.75', alpha=0.5, zorder=1, markersize=4)
if setlims:
    ax.set_xlim([-lim/2., lim/2.])
    ax.set_ylim([-lim/2., lim/2.])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Points on grid, tricontour')

ax = axes[0, 1]
H = (XS_even + YS_even) / 2.
V = YS_even - XS_even
Z = f(XS_even, YS_even)
ax.contour(H, V, Z, levels, cmap=cmap)
ax.plot(H.flatten()[::pde], V.flatten()[::pde], linestyle='None', marker='.', color='.75', alpha=0.5, zorder=1, markersize=4)
if setlims:
    ax.set_xlim([-lim/2., lim/2.])
    ax.set_ylim([-lim, lim])
ax.set_xlabel('AVG')
ax.set_ylabel('DIFF')
ax.set_title('Points on transformed grid, contour')

ax = axes[1, 1]
H = H.flatten()
V = V.flatten()
Z = Z.flatten()
ax.tricontour(H, V, Z, levels, cmap=cmap)
ax.plot(H.flatten()[::pde], V.flatten()[::pde], linestyle='None', marker='.', color='.75', alpha=0.5, zorder=1, markersize=4)
if setlims:
    ax.set_xlim([-lim/2., lim/2.])
    ax.set_ylim([-lim, lim])
ax.set_xlabel('AVG')
ax.set_ylabel('DIFF')
ax.set_title('Points on transformed grid, tricontour')

ax=axes[2, 0]
H = xs_rand
V = ys_rand
Z = f(xs_rand, ys_rand)
ax.tricontour(H, V, Z, levels, cmap=cmap)
ax.plot(H[::pde], V[::pde], linestyle='None', marker='.', color='.75', alpha=0.5, zorder=1, markersize=4)
if setlims:
    ax.set_xlim([-lim/2., lim/2.])
    ax.set_ylim([-lim/2., lim/2.])
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_title('Points random, tricontour')

ax=axes[2, 1]
H = (xs_rand + ys_rand) / 2.
V = ys_rand - xs_rand
Z = f(xs_rand, ys_rand)
ax.tricontour(H, V, Z, levels, cmap=cmap)
ax.plot(H[::pde], V[::pde], linestyle='None', marker='.', color='.75', alpha=0.5, zorder=1, markersize=4)
if setlims:
    ax.set_xlim([-lim/2., lim/2.])
    ax.set_ylim([-lim, lim])
ax.set_xlabel('AVG')
ax.set_ylabel('DIFF')
ax.set_title('Points random transformed, tricontour')

fig.tight_layout()