非矩形域上的Python 3D图 [英] Python 3D Plots over non-rectangular domain
问题描述
我有一些要绘制的z=f(x,y)
数据.问题是(x,y)
不是漂亮的"矩形的一部分,而是任意的平行四边形,如所附的图像所示(这个特殊的矩形也是矩形,但是您可以想到更一般的情况).因此,在这种情况下,我很难弄清楚如何使用plot_surface
,因为这通常会将x和y当作2d数组,这里的x和y值为1d.谢谢.
I have some z=f(x,y)
data which i would like to plot. The issue is that (x,y)
are not part of a "nice" rectangle, but rather arbitrary parallelograms, as shown in the attached image (this particular one is also a rectangle, but you could think of more general cases). So I am having a hard time figuring out how I can use plot_surface
in this case, as this usually will take x and y as 2d arrays, and here my x-and y-values are 1d. Thanks.
推荐答案
缩写点可以作为一维数组提供给 matplotlib.Axes3D.plot_trisurf
.它们是否遵循特定的结构无关紧要.
Abritrary points can be supplied as 1D arrays to matplotlib.Axes3D.plot_trisurf
. It doesn't matter whether they follow a specific structure.
取决于数据结构的其他方法将是
Other methods which would depend on the structure of the data would be
- 将点插入到常规矩形网格上.可以使用
scipy.interpolate.griddata
完成.请参见示例此处 - 调整输入数组的形状,使它们驻留在常规数组上,然后使用
plot_surface()
.根据提供点的顺序,这对于具有平行四边形"形状的网格来说可能是一个非常简单的解决方案.
从球形示例可以看出,plot_surface()
在以下情况下也适用不规则的网格形状,只要它以规则的方式构造即可.
- Interpolate the points on a regular rectangular grid. This can be accomplished using
scipy.interpolate.griddata
. See example here - Reshape the input arrays such that they live on a regular and then use
plot_surface()
. Depending on the order by which the points are supplied, this could be a very easy solution for a grid with "parallelogramic" shape.
As can be seen from the sphere example,plot_surface()
also works in cases of very unequal grid shapes, as long as it's structured in a regular way.
以下是一些示例:
为完整起见,请在此处找到产生以上图像的代码:
For completeness, find here the code that produces the above image:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
f = lambda x,y: np.sin(x+0.4*y)*0.23+1
fig = plt.figure(figsize=(5,6))
plt.subplots_adjust(left=0.1, top=0.95,wspace=0.01)
ax0 = fig.add_subplot(322, projection="3d")
ma = 6*(np.random.rand(100)-0.5)
mb = 6*(np.random.rand(100)-0.5)
phi = np.pi/4
x = 1.7*ma*np.cos(phi) + 1.7*mb*np.sin(phi)
y = -1.2*ma*np.sin(phi) +1.2* mb*np.cos(phi)
z = f(x,y)
ax0.plot_trisurf(x,y,z)
ax1 = fig.add_subplot(321)
ax0.set_title("random plot_trisurf()")
ax1.set_aspect("equal")
ax1.scatter(x,y, marker="+", alpha=0.4)
for i in range(len(x)):
ax1.text(x[i],y[i], i , ha="center", va="center", fontsize=6)
n = 10
a = np.linspace(-3, 3, n)
ma, mb = np.meshgrid(a,a)
phi = np.pi/4
xm = 1.7*ma*np.cos(phi) + 1.7*mb*np.sin(phi)
ym = -1.2*ma*np.sin(phi) +1.2* mb*np.cos(phi)
shuf = np.c_[xm.flatten(), ym.flatten()]
np.random.shuffle(shuf)
x = shuf[:,0]
y = shuf[:,1]
z = f(x,y)
ax2 = fig.add_subplot(324, projection="3d")
ax2.plot_trisurf(x,y,z)
ax3 = fig.add_subplot(323)
ax2.set_title("unstructured plot_trisurf()")
ax3.set_aspect("equal")
ax3.scatter(x,y, marker="+", alpha=0.4)
for i in range(len(x)):
ax3.text(x[i],y[i], i , ha="center", va="center", fontsize=6)
x = xm.flatten()
y = ym.flatten()
z = f(x,y)
X = x.reshape(10,10)
Y = y.reshape(10,10)
Z = z.reshape(10,10)
ax4 = fig.add_subplot(326, projection="3d")
ax4.plot_surface(X,Y,Z)
ax5 = fig.add_subplot(325)
ax4.set_title("regular plot_surf()")
ax5.set_aspect("equal")
ax5.scatter(x,y, marker="+", alpha=0.4)
for i in range(len(x)):
ax5.text(x[i],y[i], i , ha="center", va="center", fontsize=6)
for axes in [ax0, ax2,ax4]:
axes.set_xlim([-3.5,3.5])
axes.set_ylim([-3.5,3.5])
axes.set_zlim([0.9,2.0])
axes.axis("off")
plt.savefig(__file__+".png")
plt.show()
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