treetime/treeregression.py
def Cov(self):
        """
        calculate the covariance matrix of the tips assuming variance
        has accumulated along branches of the tree accoriding to the
        the provided
        Returns
        -------

         M : (np.array)
            covariance matrix with tips arranged standard transersal order.
        """
        # accumulate the covariance matrix by adding 'squares'
        M = np.zeros((self.N, self.N))
        for n in self.tree.find_clades():
            if n == self.tree.root:
                continue
            M[np.meshgrid(n._ii, n._ii)] += self.branch_variance(n)
        return M
def Cov(self):
        """
        calculate the covariance matrix of the tips assuming variance
        has accumulated along branches of the tree accoriding to the
        the provided
        Returns
        -------

         M : (np.array)
            covariance matrix with tips arranged standard transersal order.
        """
        # accumulate the covariance matrix by adding 'squares'
        M = np.zeros((self.N, self.N))
        for n in self.tree.find_clades():
            if n == self.tree.root:
                continue
            M[np.meshgrid(n._ii, n._ii)] += self.branch_variance(n)
        return M
def Cov ( self ) : # accumulate the covariance matrix by adding 'squares' M = np . zeros ( ( self . N , self . N ) ) for n in self . tree . find_clades ( ) : if n == self . tree . root : continue M [ np . meshgrid ( n . _ii , n . _ii ) ] += self . branch_variance ( n ) return M
calculate the covariance matrix of the tips assuming variance
        has accumulated along branches of the tree accoriding to the
        the provided
        Returns
        -------

         M : (np.array)
            covariance matrix with tips arranged standard transersal order.
calculate the covariance matrix of the tips assuming variance has accumulated along branches of the tree accoriding to the the provided Returns -------