treetime/treetime.py
def _find_best_root(self, covariation=True, force_positive=True, slope=0, **kwarks):
        '''
        Determine the node that, when the tree is rooted on this node, results
        in the best regression of temporal constraints and root to tip distances.

        Parameters
        ----------

         infer_gtr : bool
            If True, infer new GTR model after re-root

         covariation : bool
            account for covariation structure when rerooting the tree

         force_positive : bool
            only accept positive evolutionary rate estimates when rerooting the tree

        '''
        for n in self.tree.find_clades():
            n.branch_length=n.mutation_length
        self.logger("TreeTime._find_best_root: searching for the best root position...",2)
        Treg = self.setup_TreeRegression(covariation=covariation)
        return Treg.optimal_reroot(force_positive=force_positive, slope=slope)['node']
def _find_best_root(self, covariation=True, force_positive=True, slope=0, **kwarks):
        '''
        Determine the node that, when the tree is rooted on this node, results
        in the best regression of temporal constraints and root to tip distances.

        Parameters
        ----------

         infer_gtr : bool
            If True, infer new GTR model after re-root

         covariation : bool
            account for covariation structure when rerooting the tree

         force_positive : bool
            only accept positive evolutionary rate estimates when rerooting the tree

        '''
        for n in self.tree.find_clades():
            n.branch_length=n.mutation_length
        self.logger("TreeTime._find_best_root: searching for the best root position...",2)
        Treg = self.setup_TreeRegression(covariation=covariation)
        return Treg.optimal_reroot(force_positive=force_positive, slope=slope)['node']
def _find_best_root ( self , covariation = True , force_positive = True , slope = 0 , * * kwarks ) : for n in self . tree . find_clades ( ) : n . branch_length = n . mutation_length self . logger ( "TreeTime._find_best_root: searching for the best root position..." , 2 ) Treg = self . setup_TreeRegression ( covariation = covariation ) return Treg . optimal_reroot ( force_positive = force_positive , slope = slope ) [ 'node' ]
Determine the node that, when the tree is rooted on this node, results
        in the best regression of temporal constraints and root to tip distances.

        Parameters
        ----------

         infer_gtr : bool
            If True, infer new GTR model after re-root

         covariation : bool
            account for covariation structure when rerooting the tree

         force_positive : bool
            only accept positive evolutionary rate estimates when rerooting the tree
Determine the node that when the tree is rooted on this node results in the best regression of temporal constraints and root to tip distances .