def optimal_reroot(self, force_positive=True, slope=None):
        """
        determine the best root and reroot the tree to this value.
        Note that this can change the parent child relations of the tree
        and values associated with branches rather than nodes
        (e.g. confidence) might need to be re-evaluated afterwards

        Parameters
        ----------
        force_positive : bool, optional
            if True, the search for a root will only consider positive rate estimates

        slope : float, optional
            if given, it will find the optimal root given a fixed rate. If slope==0, this
            corresponds to minimal root-to-tip variance rooting (min_dev)

        Returns
        -------
        dict
            regression parameters
        """
        best_root = self.find_best_root(force_positive=force_positive, slope=slope)
        best_node = best_root["node"]

        x = best_root["split"]
        if x<1e-5:
            new_node = best_node
        elif x>1.0-1e-5:
            new_node = best_node.up
        else:
            # create new node in the branch and root the tree to it
            new_node = Phylo.BaseTree.Clade()

            # insert the new node in the middle of the branch
            # by simple re-wiring the links on the both sides of the branch
            # and fix the branch lengths
            new_node.branch_length = best_node.branch_length*(1-x)
            new_node.up = best_node.up
            new_node.clades = [best_node]
            new_node.up.clades = [k if k!=best_node else new_node
                                  for k in best_node.up.clades]

            best_node.branch_length *= x
            best_node.up = new_node

        new_node.rtt_regression = best_root
        self.tree.root_with_outgroup(new_node)

        self.tree.ladderize()
        for n in self.tree.get_nonterminals(order='postorder'):
            for c in n:
                c.up=n
        return best_root
def optimal_reroot(self, force_positive=True, slope=None):
        """
        determine the best root and reroot the tree to this value.
        Note that this can change the parent child relations of the tree
        and values associated with branches rather than nodes
        (e.g. confidence) might need to be re-evaluated afterwards

        Parameters
        ----------
        force_positive : bool, optional
            if True, the search for a root will only consider positive rate estimates

        slope : float, optional
            if given, it will find the optimal root given a fixed rate. If slope==0, this
            corresponds to minimal root-to-tip variance rooting (min_dev)

        Returns
        -------
        dict
            regression parameters
        """
        best_root = self.find_best_root(force_positive=force_positive, slope=slope)
        best_node = best_root["node"]

        x = best_root["split"]
        if x<1e-5:
            new_node = best_node
        elif x>1.0-1e-5:
            new_node = best_node.up
        else:
            # create new node in the branch and root the tree to it
            new_node = Phylo.BaseTree.Clade()

            # insert the new node in the middle of the branch
            # by simple re-wiring the links on the both sides of the branch
            # and fix the branch lengths
            new_node.branch_length = best_node.branch_length*(1-x)
            new_node.up = best_node.up
            new_node.clades = [best_node]
            new_node.up.clades = [k if k!=best_node else new_node
                                  for k in best_node.up.clades]

            best_node.branch_length *= x
            best_node.up = new_node

        new_node.rtt_regression = best_root
        self.tree.root_with_outgroup(new_node)

        self.tree.ladderize()
        for n in self.tree.get_nonterminals(order='postorder'):
            for c in n:
                c.up=n
        return best_root
def optimal_reroot ( self , force_positive = True , slope = None ) : best_root = self . find_best_root ( force_positive = force_positive , slope = slope ) best_node = best_root [ "node" ] x = best_root [ "split" ] if x < 1e-5 : new_node = best_node elif x > 1.0 - 1e-5 : new_node = best_node . up else : # create new node in the branch and root the tree to it new_node = Phylo . BaseTree . Clade ( ) # insert the new node in the middle of the branch # by simple re-wiring the links on the both sides of the branch # and fix the branch lengths new_node . branch_length = best_node . branch_length * ( 1 - x ) new_node . up = best_node . up new_node . clades = [ best_node ] new_node . up . clades = [ k if k != best_node else new_node for k in best_node . up . clades ] best_node . branch_length *= x best_node . up = new_node new_node . rtt_regression = best_root self . tree . root_with_outgroup ( new_node ) self . tree . ladderize ( ) for n in self . tree . get_nonterminals ( order = 'postorder' ) : for c in n : c . up = n return best_root
determine the best root and reroot the tree to this value.
        Note that this can change the parent child relations of the tree
        and values associated with branches rather than nodes
        (e.g. confidence) might need to be re-evaluated afterwards

        Parameters
        ----------
        force_positive : bool, optional
            if True, the search for a root will only consider positive rate estimates

        slope : float, optional
            if given, it will find the optimal root given a fixed rate. If slope==0, this
            corresponds to minimal root-to-tip variance rooting (min_dev)

        Returns
        -------
        dict
            regression parameters
determine the best root and reroot the tree to this value . Note that this can change the parent child relations of the tree and values associated with branches rather than nodes ( e . g . confidence ) might need to be re - evaluated afterwards