'cumulative distribution function
Parameters
x : array_like
can be 1d or 2d, if 2d, then each row is taken as independent
multivariate random vector
kwds : dict
contains options for the numerical calculation of the cdf
Returns
cdf : float or array
probability density value of each random vector'

def cdf(self, x, **kwds):

    lower = ((- np.inf) * np.ones_like(x))
    upper = ((x - self.mean) / self.std_sigma)
    return mvstdtprob(lower, upper, self.corr, self.df, **kwds)
