生成分布在单位球体表面上的点的随机样本 [英] Generate a random sample of points distributed on the surface of a unit sphere
问题描述
我试图用numpy在球体表面上生成随机点。我已阅读了解释均匀分发
同样的方法也推广到在单位圆上( ndim = 2
)或高维单位超球体的表面上选取均匀分布的点。 p>
I am trying to generate random points on the surface of the sphere using numpy. I have reviewed the post that explains uniform distribution here. However, need ideas on how to generate the points only on the surface of the sphere. I have coordinates (x, y, z) and the radius of each of these spheres.
I am not very well-versed with Mathematics at this level and trying to make sense of the Monte Carlo simulation.
Any help will be much appreciated.
Thanks, Parin
Based on the last approach on this page, you can simply generate a vector consisting of independent samples from three standard normal distributions, then normalize the vector such that its magnitude is 1:
import numpy as np
def sample_spherical(npoints, ndim=3):
vec = np.random.randn(ndim, npoints)
vec /= np.linalg.norm(vec, axis=0)
return vec
For example:
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import axes3d
phi = np.linspace(0, np.pi, 20)
theta = np.linspace(0, 2 * np.pi, 40)
x = np.outer(np.sin(theta), np.cos(phi))
y = np.outer(np.sin(theta), np.sin(phi))
z = np.outer(np.cos(theta), np.ones_like(phi))
xi, yi, zi = sample_spherical(100)
fig, ax = plt.subplots(1, 1, subplot_kw={'projection':'3d', 'aspect':'equal'})
ax.plot_wireframe(x, y, z, color='k', rstride=1, cstride=1)
ax.scatter(xi, yi, zi, s=100, c='r', zorder=10)
The same method also generalizes to picking uniformly distributed points on the unit circle (ndim=2
) or on the surfaces of higher-dimensional unit hyperspheres.
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