def expQt(self, t):
        '''
        Parameters
        ----------

         t : float
            Time to propagate

        Returns
        --------

         expQt : numpy.array
            Matrix exponential of exo(Qt)
        '''
        eLambdaT = np.diag(self._exp_lt(t)) # vector length = a
        Qs = self.v.dot(eLambdaT.dot(self.v_inv))   # This is P(nuc1 | given nuc_2)
        return np.maximum(0,Qs)
def expQt(self, t):
        '''
        Parameters
        ----------

         t : float
            Time to propagate

        Returns
        --------

         expQt : numpy.array
            Matrix exponential of exo(Qt)
        '''
        eLambdaT = np.diag(self._exp_lt(t)) # vector length = a
        Qs = self.v.dot(eLambdaT.dot(self.v_inv))   # This is P(nuc1 | given nuc_2)
        return np.maximum(0,Qs)
def expQt ( self , t ) : eLambdaT = np . diag ( self . _exp_lt ( t ) ) # vector length = a Qs = self . v . dot ( eLambdaT . dot ( self . v_inv ) ) # This is P(nuc1 | given nuc_2) return np . maximum ( 0 , Qs )
Parameters
        ----------

         t : float
            Time to propagate

        Returns
        --------

         expQt : numpy.array
            Matrix exponential of exo(Qt)
Parameters ----------