Sampling points uniformly on parametrized manifolds

Here I'll describe how to sample points uniformly on a (parametrized) manifold, along with an actual implementation in Python. Let $M$ be a $m$-dimensional manifold embedded in $\R^n$ via $f:\R^m\to \R^n$. Moreover, assume that $f$ is Lipschitz (true if $M$ is compact), injective (true if $M$ is embedded), and is a parameterization, in the sense that there is an $m$-rectangle $A = [a_1,b_1]\times \cdots \times [a_m,b_m]$ such that $f(A) = M$ (the intervals need not be closed). Set $(\widetilde Jf)^2 = \det(Df\cdot Df^T)$ to be the $m$-dimensional Jacobian, and calculate $$c = \int_{a_m}^{b_m}\cdots \int_{a_1}^{b_1} \widetilde J f\ dx_1\cdots dx_m.$$ Recall the brief statistical background presented in a previous blog post ("Reconstructing a manifold from sample data, with noise," 2016-05-26). A uniform or constant probability density function is valued the same at every point on its domain.

Proposition: In the setting above:

• (completely separable) Let $g_1,\dots,g_m$ be probability density functions on $[a_1,b_1],\dots,[a_m,b_m]$, respectively. If $g_1\cdots g_m = \widetilde J f / c$, then the joint probability density function of $g_1,\dots,g_m$ is uniform on $M$ with respect to the metric induced from $\R^n$.
• (non-separable) Let $g$ be a probability density function on $[a_1,b_1]\times\cdots\times[a_m,b_m]$. If $g=\widetilde J f/c$, then $g$ is uniform on $M$ with respect to the metric induced from $\R^n$.

A much more abstract statement and proof are given in [2], Section 3.2.5, but assuming $f$ is injective and $M$ is in $\R^n$, we evade the worst notation. Section 2.2 of [1] gives a brief explanation of how the given statement follows, while Section 2 of [3] goes into more detail of why the above is true.

Example: Let $M = S^2$, the sphere of radius $r$, and $f:[0,2\pi)\times [0,\pi) \to \R^3$ the natural embedding given by
$$(\theta,\varphi) \mapsto (r\cos(\theta)\sin(\varphi),r\sin(\theta)\sin(\varphi), r\cos(\varphi)),$$ with $$\begin{align*} Df & = \begin{bmatrix} -r \sin(\varphi) \sin(\theta) & r \cos(\theta)\sin(\varphi) &  0 \\ r \cos(\varphi) \cos(\theta) & r \cos(\varphi) \sin(\theta) & -r \sin(\varphi) \end{bmatrix}, & \widetilde Jf & = r^2\sin(\varphi), \\ Df\cdot Df^T & = \begin{bmatrix} r^2 \sin^2(\varphi) & 0 \\ 0 & r^2 \end{bmatrix}, & c & = 4\pi r^2. \end{align*}$$
Let $g_1(\theta) = 1/2\pi$ be the uniform distribution over $[0,2\pi)$, meaning that $g_2(\varphi) = \sin(\varphi)/2$ over $[0,\pi)$. Sampling points randomly from these two distributions and applying $f$ will give uniformly sampled points on $S^2$.

Example: Let $M = T^2$, the torus of major radius $R$ and minor radius $r$, and $f:[0,2\pi)\times [0,2\pi) \to \R^3$ the natural embedding given by
$$(\theta,\varphi) \mapsto ((R+r\cos(\theta))\cos(\varphi),(R+r\cos(\theta))\sin(\varphi), r\sin(\theta)),$$ with $$\begin{align*} Df & = \begin{bmatrix} -r \cos(\varphi)\sin(\theta) & -r \sin(\varphi) \sin(\theta) & r \cos(\theta) \\ -(R + r \cos(\theta)) \sin(\varphi) & \cos(\varphi) (R + r \cos(\theta)) & 0 \end{bmatrix}, & \widetilde Jf & = r(R+r\cos(\theta)), \\ Df\cdot Df^T & = \begin{bmatrix} r^2 & 0 \\ 0 & (R + r \cos(\theta))^2 \end{bmatrix}, & c & = 4\pi^2rR. \end{align*}$$
Let $g_2(\varphi) = 1/2\pi$ be the uniform distribution over $[0,2\pi)$, meaning that $g_1(\theta) = (1+r\cos(\theta)/R)/(2\pi)$ over $[0,2\pi)$. Sampling points randomly from these two distributions and applying $f$ will give uniformly sampled points on $T^2$.

Below I give a simple implementation of how to actually sample points, in Python using the SciPy package. The functions $f,g_1,\dots,g_m$ are all assumed to be given.

import scipy.stats as st

class var_g1(st.rv_continuous):
    'Uniform variable 1'
    def _pdf(self, x):
        return g1(x)
...
class var_gm(st.rv_continuous):
    'Uniform variable m'
    def _pdf(self, x):
        return gm(x)

dist_g1 = var_g1(a=a1, b=b1, name='Uniform distribution 1')
...
dist_gm = var_gm(a=am, b=bm, name='Uniform distribution m')

def mfld_sample():
    return f(dist_g1.rvs(),...,dist_gm.rvs())

A further application for this would be to understand how to sample points uniformly on projective manifolds, with a leading example the Grassmannian, embedded via Plucker coordinates.

References:
[1] Diaconis, Holmes, and Shahshahani (Sampling from a manifold, Section 2.2)
[2] Federer (Geometric measure theory, Section 3.2.5)
[3] Rhee, Zhou, and Qiu (An iterative algorithm for sampling from manifolds, Section 2)