不规则 X Y Z 数据的等高线/imshow 图

问题描述 投票:0回答:6

我有 X、Y、Z 格式的数据,其中都是一维数组,Z 是坐标 (X,Y) 处的测量幅度。我想将此数据显示为等高线或“imshow”图,其中等高线/颜色代表 Z 值(振幅)。

用于测量以及 X 和 Y 外观的网格间隔不规则。

非常感谢,

len(X)=100

len(Y)=100

len(Z)=100

python plot contour imshow
6个回答
60
投票

plt.tricontourf(x,y,z)
满足您的要求吗?

它将绘制不规则间隔数据(非直线网格)的填充轮廓。

您可能还想了解一下

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')

tripcolor and tricontourf example


4
投票

六年后,我可能有点晚了,但是使用 Gouraud 插值对 Oliver W. 的答案进行以下扩展可能会给出“平滑”结果:

import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1234)  # fix seed for reproducibility
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, clear=True)
for axes, shading in zip(ax, ['flat', 'gouraud']):
    axes.tripcolor(x,y,z, shading=shading)
    axes.plot(x,y, 'k.')
    axes.set_title(shading)
plt.savefig('shading.png')

comparison between flat and gouraud shading


2
投票

(源代码@最后...)

这是我用它制作的一些养眼的东西。 它探讨了网格网格的线性变换仍然是网格网格这一事实。 IE。在所有图的左侧,我正在使用 2-d(输入)函数的 X 和 Y 坐标。 在右侧,我想使用 (AVG(X, Y), Y-X) 坐标来实现相同的功能。

我尝试在本机坐标中制作网格,并将它们转换为其他坐标的网格。 如果变换是线性的,则效果很好。

对于底部两张图,我使用随机抽样来直接解决您的问题。

以下是带有

setlims=False
的图像: enter image description here

setlims=True
相同: enter image description here

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()

1
投票

散点图可能适用于您的情况:

import numpy as np
import matplotlib.pyplot as plt

# Generate random data, x,y for coordinates, z for values(amplitude)
x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)

# Scatter plot
plt.scatter(x=x,y=y,c=z)

使用选项

c
可视化您的幅度。


0
投票

如果你准备从 Python 转向它的竞争对手 R,我刚刚向 CRAN 提交了一个包(应该明天或后天可用),它可以在非规则网格上进行轮廓绘制 - 可以实现以下目标几行代码:

library(contoureR)
set.seed(1)
x = runif(100)
y = runif(100)
z = sin(x) + cos(y)
df = getContourLines(x,y,z,binwidth=0.0005)
ggplot(data=df,aes(x,y,group=Group)) + 
  geom_polygon(aes(fill=z)) + 
  scale_fill_gradient(low="blue",high="red") + 
  theme_bw()

产生以下结果:

example

如果您想要更规则的网格,并且可以承受一些额外的计算时间:

x = seq(0,1,by=0.005)
y = seq(0,1,by=0.005)
d = expand.grid(x=x,y=y)
d$z = with(d,sin(x) + cos(y))
df = getContourLines(d,binwidth=0.0005)
ggplot(data=df,aes(x,y,group=Group)) + 
  geom_polygon(aes(fill=z)) + 
  scale_fill_gradient(low="blue",high="red") + 
  theme_bw()

example2

上面的模糊边缘,我知道如何解决,应该在下一版本的软件中修复....


-1
投票
xx, yy = np.meshgrid(x, y)

plt.contour(xx, yy, z)

如果它们间隔不规则也没关系,等高线图和 3D 图需要网格。

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