def logpdf(self, x):

        x = np.asarray(x)

        df = self.df
        nvars = self.nvars

        x_whitened = self.whiten(x - self.mean) #should be float

        llf = - nvars * np_log(df * np_pi)
        llf -= self.logdetsigma
        llf -= (df + nvars) * np_log(1 + np.sum(x_whitened**2,-1) / df)
        llf *= 0.5
        llf += sps_gamln((df + nvars) / 2.) - sps_gamln(df / 2.)

        return llf
logarithm of probability density function
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/sandbox/distributions/mv_normal.py/MVT.logpdf