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.