将径向数据转换为笛卡尔网格以进行表面绘图 [英] Translating radial data to a cartesian grid for surface plot

问题描述

我有一个数据列表，其成分与2D网格上某个径向距离r处的电势相对应.数据对应于极坐标中的数据点，并且在theta分量中是对称的

import numpy as np
import matplotlib.pyplot as plt

r = np.linspace(1.0, 5.0, 99)
#Data looks like:
V = np.array([9.0,...,0.0])

x = np.linspace(-5.0,5.0,99)
y = np.linspace(-5.0,5.0,99)

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

我想在(x，y)空间中创建数据的表面图，但是要使用matplotlib，您需要一个与每个(x，y)位置上的电势相对应的数据点网格.假设我有一组在(r，theta)空间中测量的数据，如何创建表面图?

解决方案

您将需要先将数据转换为笛卡尔坐标，并通过为每个theta重复值V来创建与网格网格形状相同的值数组

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

ax = fig.gca(projection='3d')

r = np.linspace(1.0, 5.0, 99)
V = np.sqrt(np.sinc(r-0.5)**2) # your data here (same length as r)


theta = np.linspace(0,2*np.pi,50)
R, Theta = np.meshgrid(r,theta)
X = R * np.cos(Theta)
Y = R * np.sin(Theta)
Z = np.tile(V,(len(theta),1))

norm = plt.Normalize(vmin=Z.min(), vmax=Z.max())
ax.plot_surface(X,Y,Z, facecolors=plt.cm.RdYlGn(norm(Z)))

plt.show()

I have a list of data whose components correspond to the potential at some radial distance r on a 2D grid. The data corresponds to data points in polar coordinates and is symmetric in the theta component

import numpy as np
import matplotlib.pyplot as plt

r = np.linspace(1.0, 5.0, 99)
#Data looks like:
V = np.array([9.0,...,0.0])

x = np.linspace(-5.0,5.0,99)
y = np.linspace(-5.0,5.0,99)

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

I would like to create a surface plot of the data in (x,y) space, however to use matplotlib you need a grid of data points corresponding to the potential at each (x,y) location. Given that I have a set of data measured in (r,theta) space, how can I create a surface plot?

解决方案

You will need to tranform your data to cartesian coordinates first and create a value array of the same shape as the meshgrid by repeating the value V for each theta.

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()

ax = fig.gca(projection='3d')

r = np.linspace(1.0, 5.0, 99)
V = np.sqrt(np.sinc(r-0.5)**2) # your data here (same length as r)


theta = np.linspace(0,2*np.pi,50)
R, Theta = np.meshgrid(r,theta)
X = R * np.cos(Theta)
Y = R * np.sin(Theta)
Z = np.tile(V,(len(theta),1))

norm = plt.Normalize(vmin=Z.min(), vmax=Z.max())
ax.plot_surface(X,Y,Z, facecolors=plt.cm.RdYlGn(norm(Z)))

plt.show()

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