不规则X Y Z数据的轮廓/ imshow图

Question

我有X，Y，Z格式的数据，其中所有都是一维数组，Z是坐标（X，Y）处的测量幅度。我希望将这些数据显示为轮廓或“显示”。绘制轮廓/颜色代表值Z（振幅）的图。

测量网格和X和Y外观不规则。

非常感谢，

LEN（X）= 100

LEN（Y）= 100

LEN（Z）= 100

Answer 1

plt.tricontourf(x,y,z)是否满足您的要求？

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

（源代码@结束......）

这是我用它制作的一点点眼睛糖果。它探讨了一个事实，即网格网格的线性变换仍然是网格网格。即在我所有情节的左侧，我正在使用X和Y坐标进行2-d（输入）功能。在右边，我想使用相同功能的（AVG（X，Y），Y-X）坐标。

我在本机坐标中制作网格物体并将其转换为其他坐标的网格物体。如果变换是线性的，则可以正常工作。

对于底部两张图，我使用随机抽样直接解决您的问题。

以下是setlims=False的图片：

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()

Answer 3

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

plt.contour(xx, yy, z)

如果它们是不规则的间距并不重要，轮廓和3d图需要网格栅。

Answer 4

如果您准备将Python与其竞争对手R分开，我刚刚向CRAN提交了一个软件包（明天或第二天应该可用），它可以在非常规网格上进行轮廓加工 - 以下内容通过几行代码实现：

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()

产生以下内容：

如果你想要一个更规则的网格，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()

上面的模糊边缘，我知道如何解决，应该修复下一版本的软件....

Answer 5

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 可视化您的振幅。