Sampling from von Mises-Fisher distribution in Python? I am looking for a simple way to sample from a multivariate von Mises-Fisher distribution in Python. I have looked in the stats module in scipy and the numpy module but only found the univariate von Mises distribution. Is there any code available? I have not found yet.
Apparently, Wood (1994) has designed an algorithm for sampling from the vMF distribution according to this link, but I can't find the paper.
-- edit
For precision, I am interested by the algorithm which is hard to find in the literature (most of the papers focus on $S^2$). The seminal article (Wood, 1994) cannot be found for free, to my knowledge.
 A: (I apologize for the formatting here, I created an account just to reply to this question, since I was also trying to figure this out recently).
The answer of mic isn't quite right, the vector $v$ needs to come from $S^{p-2}$ in the tangent space to $\mu$, that is, $v$ should be a unit vector orthogonal to $\mu$. Otherwise, the vector $v\sqrt{1-w^2}+w\mu$ will not have norm one.  You can see this in the example provided by mic.  To fix this, use something like:
import scipy.linalg as la
def sample_tangent_unit(mu):
    mat = np.matrix(mu)

    if mat.shape[1]>mat.shape[0]:
        mat = mat.T

    U,_,_ = la.svd(mat)
    nu = np.matrix(np.random.randn(mat.shape[0])).T
    x = np.dot(U[:,1:],nu[1:,:])
    return x/la.norm(x)

and replace
v = np.random.randn(dim)
v = v / np.linalg.norm(v)

in mic's example with a call to
v = sample_tangent_unit(mu)

A: Finally, I got it. Here is my answer.
I finally put my hands on Directional Statistics (Mardia and Jupp, 1999) and on the Ulrich-Wood's algorithm for sampling. I post here what I understood from it, i.e. my code (in Python).
The rejection sampling scheme:
def rW(n, kappa, m):
    dim = m-1
    b = dim / (np.sqrt(4*kappa*kappa + dim*dim) + 2*kappa)
    x = (1-b) / (1+b)
    c = kappa*x + dim*np.log(1-x*x)

    y = []
    for i in range(0,n):
        done = False
        while not done:
            z = sc.stats.beta.rvs(dim/2,dim/2)
            w = (1 - (1+b)*z) / (1 - (1-b)*z)
            u = sc.stats.uniform.rvs()
            if kappa*w + dim*np.log(1-x*w) - c >= np.log(u):
                done = True
        y.append(w)
    return y

Then, the desired sampling is $v \sqrt{1-w^2} + w  \mu$, where $w$ is the result from the rejection sampling scheme, and $v$ is uniformly sampled over the hypersphere.
def rvMF(n,theta):
    dim = len(theta)
    kappa = np.linalg.norm(theta)
    mu = theta / kappa

    result = []
    for sample in range(0,n):
        w = rW(n, kappa, dim)
        v = np.random.randn(dim)
        v = v / np.linalg.norm(v)

        result.append(np.sqrt(1-w**2)*v + w*mu)

    return result

And, for effectively sampling with this code, here is an example:
import numpy as np
import scipy as sc
import scipy.stats

n = 10
kappa = 100000
direction = np.array([1,-1,1])
direction = direction / np.linalg.norm(direction)

res_sampling = rvMF(n, kappa * direction)

