Sample from a multivariate t distribution python

I am wondering if there is a function for sampling from a multivariate student t-distribution in Python. I have the mean vector with 14 elements, the 14x14 covariance matrix and the degrees of freedom and I want to sample a vector from this t-distribution. For the one dimensional case I used stats.t.rvs(df,loc,scale) and I was wondering if there is something similar for the multivariate case. Any help would be very much appreciated.

Thanks

You can find this function into sandbox directory of statsmodels GitHub repo. Link to the function: https://github.com/statsmodels/statsmodels/blob/master/statsmodels/sandbox/distributions/multivariate.py#L90

Source code of the function:

#written by Enzo Michelangeli, style changes by josef-pktd
# Student's T random variable
def multivariate_t_rvs(m, S, df=np.inf, n=1):
    '''generate random variables of multivariate t distribution
    Parameters
    ----------
    m : array_like
        mean of random variable, length determines dimension of random variable
    S : array_like
        square array of covariance  matrix
    df : int or float
        degrees of freedom
    n : int
        number of observations, return random array will be (n, len(m))
    Returns
    -------
    rvs : ndarray, (n, len(m))
        each row is an independent draw of a multivariate t distributed
        random variable
    '''
    m = np.asarray(m)
    d = len(m)
    if df == np.inf:
        x = np.ones(n)
    else:
        x = np.random.chisquare(df, n) / df
    z = np.random.multivariate_normal(np.zeros(d), S, (n,))
    return m + z/np.sqrt(x)[:,None]   # same output format as random.multivariate_normal