treetime/treetime.py
def _poly(self, clade, merge_compressed):

        """
        Function to resolve polytomies for a given parent node. If the
        number of the direct decendants is less than three (not a polytomy), does
        nothing. Otherwise, for each pair of nodes, assess the possible LH increase
        which could be gained by merging the two nodes. The increase in the LH is
        basically the tradeoff between the gain of the LH due to the changing the
        branch lenghts towards the optimal values and the decrease due to the
        introduction of the new branch with zero optimal length.
        """

        from .branch_len_interpolator import BranchLenInterpolator

        zero_branch_slope = self.gtr.mu*self.seq_len

        def _c_gain(t, n1, n2, parent):
            """
            cost gain if nodes n1, n2 are joined and their parent is placed at time t
            cost gain = (LH loss now) - (LH loss when placed at time t)
            """
            cg2 = n2.branch_length_interpolator(parent.time_before_present - n2.time_before_present) - n2.branch_length_interpolator(t - n2.time_before_present)
            cg1 = n1.branch_length_interpolator(parent.time_before_present - n1.time_before_present) - n1.branch_length_interpolator(t - n1.time_before_present)
            cg_new = - zero_branch_slope * (parent.time_before_present - t) # loss in LH due to the new branch
            return -(cg2+cg1+cg_new)

        def cost_gain(n1, n2, parent):
            """
            cost gained if the two nodes would have been connected.
            """
            try:
                cg = sciopt.minimize_scalar(_c_gain,
                    bounds=[max(n1.time_before_present,n2.time_before_present), parent.time_before_present],
                    method='Bounded',args=(n1,n2, parent))
                return cg['x'], - cg['fun']
            except:
                self.logger("TreeTime._poly.cost_gain: optimization of gain failed", 3, warn=True)
                return parent.time_before_present, 0.0


        def merge_nodes(source_arr, isall=False):
            mergers = np.array([[cost_gain(n1,n2, clade) if i1<i2 else (0.0,-1.0)
                                    for i1,n1 in enumerate(source_arr)]
                                for i2, n2 in enumerate(source_arr)])
            LH = 0
            while len(source_arr) > 1 + int(isall):
                # max possible gains of the cost when connecting the nodes:
                # this is only a rough approximation because it assumes the new node positions
                # to be optimal
                new_positions = mergers[:,:,0]
                cost_gains = mergers[:,:,1]
                # set zero to large negative value and find optimal pair
                np.fill_diagonal(cost_gains, -1e11)
                idxs = np.unravel_index(cost_gains.argmax(),cost_gains.shape)
                if (idxs[0] == idxs[1]) or cost_gains.max()<0:
                    self.logger("TreeTime._poly.merge_nodes: node is not fully resolved "+clade.name,4)
                    return LH

                n1, n2 = source_arr[idxs[0]], source_arr[idxs[1]]
                LH += cost_gains[idxs]

                new_node = Phylo.BaseTree.Clade()

                # fix positions and branch lengths
                new_node.time_before_present = new_positions[idxs]
                new_node.branch_length = clade.time_before_present - new_node.time_before_present
                new_node.clades = [n1,n2]
                n1.branch_length = new_node.time_before_present - n1.time_before_present
                n2.branch_length = new_node.time_before_present - n2.time_before_present

                # set parameters for the new node
                new_node.up = clade
                n1.up = new_node
                n2.up = new_node
                if hasattr(clade, "cseq"):
                    new_node.cseq = clade.cseq
                    self._store_compressed_sequence_to_node(new_node)

                new_node.mutations = []
                new_node.mutation_length = 0.0
                new_node.branch_length_interpolator = BranchLenInterpolator(new_node, self.gtr, one_mutation=self.one_mutation,
                                                                            branch_length_mode = self.branch_length_mode)
                clade.clades.remove(n1)
                clade.clades.remove(n2)
                clade.clades.append(new_node)
                self.logger('TreeTime._poly.merge_nodes: creating new node as child of '+clade.name,3)
                self.logger("TreeTime._poly.merge_nodes: Delta-LH = " + str(cost_gains[idxs].round(3)), 3)

                # and modify source_arr array for the next loop
                if len(source_arr)>2: # if more than 3 nodes in polytomy, replace row/column
                    for ii in np.sort(idxs)[::-1]:
                        tmp_ind = np.arange(mergers.shape[0])!=ii
                        mergers = mergers[tmp_ind].swapaxes(0,1)
                        mergers = mergers[tmp_ind].swapaxes(0,1)

                    source_arr.remove(n1)
                    source_arr.remove(n2)
                    new_gains = np.array([[cost_gain(n1,new_node, clade) for n1 in source_arr]])
                    mergers = np.vstack((mergers, new_gains)).swapaxes(0,1)

                    source_arr.append(new_node)
                    new_gains = np.array([[cost_gain(n1,new_node, clade) for n1 in source_arr]])
                    mergers = np.vstack((mergers, new_gains)).swapaxes(0,1)
                else: # otherwise just recalculate matrix
                    source_arr.remove(n1)
                    source_arr.remove(n2)
                    source_arr.append(new_node)
                    mergers = np.array([[cost_gain(n1,n2, clade) for n1 in source_arr]
                                       for n2 in source_arr])

            return LH

        stretched = [c for c  in clade.clades if c.mutation_length < c.clock_length]
        compressed = [c for c in clade.clades if c not in stretched]

        if len(stretched)==1 and merge_compressed is False:
            return 0.0

        LH = merge_nodes(stretched, isall=len(stretched)==len(clade.clades))
        if merge_compressed and len(compressed)>1:
            LH += merge_nodes(compressed, isall=len(compressed)==len(clade.clades))

        return LH
def _poly(self, clade, merge_compressed):

        """
        Function to resolve polytomies for a given parent node. If the
        number of the direct decendants is less than three (not a polytomy), does
        nothing. Otherwise, for each pair of nodes, assess the possible LH increase
        which could be gained by merging the two nodes. The increase in the LH is
        basically the tradeoff between the gain of the LH due to the changing the
        branch lenghts towards the optimal values and the decrease due to the
        introduction of the new branch with zero optimal length.
        """

        from .branch_len_interpolator import BranchLenInterpolator

        zero_branch_slope = self.gtr.mu*self.seq_len

        def _c_gain(t, n1, n2, parent):
            """
            cost gain if nodes n1, n2 are joined and their parent is placed at time t
            cost gain = (LH loss now) - (LH loss when placed at time t)
            """
            cg2 = n2.branch_length_interpolator(parent.time_before_present - n2.time_before_present) - n2.branch_length_interpolator(t - n2.time_before_present)
            cg1 = n1.branch_length_interpolator(parent.time_before_present - n1.time_before_present) - n1.branch_length_interpolator(t - n1.time_before_present)
            cg_new = - zero_branch_slope * (parent.time_before_present - t) # loss in LH due to the new branch
            return -(cg2+cg1+cg_new)

        def cost_gain(n1, n2, parent):
            """
            cost gained if the two nodes would have been connected.
            """
            try:
                cg = sciopt.minimize_scalar(_c_gain,
                    bounds=[max(n1.time_before_present,n2.time_before_present), parent.time_before_present],
                    method='Bounded',args=(n1,n2, parent))
                return cg['x'], - cg['fun']
            except:
                self.logger("TreeTime._poly.cost_gain: optimization of gain failed", 3, warn=True)
                return parent.time_before_present, 0.0


        def merge_nodes(source_arr, isall=False):
            mergers = np.array([[cost_gain(n1,n2, clade) if i1<i2 else (0.0,-1.0)
                                    for i1,n1 in enumerate(source_arr)]
                                for i2, n2 in enumerate(source_arr)])
            LH = 0
            while len(source_arr) > 1 + int(isall):
                # max possible gains of the cost when connecting the nodes:
                # this is only a rough approximation because it assumes the new node positions
                # to be optimal
                new_positions = mergers[:,:,0]
                cost_gains = mergers[:,:,1]
                # set zero to large negative value and find optimal pair
                np.fill_diagonal(cost_gains, -1e11)
                idxs = np.unravel_index(cost_gains.argmax(),cost_gains.shape)
                if (idxs[0] == idxs[1]) or cost_gains.max()<0:
                    self.logger("TreeTime._poly.merge_nodes: node is not fully resolved "+clade.name,4)
                    return LH

                n1, n2 = source_arr[idxs[0]], source_arr[idxs[1]]
                LH += cost_gains[idxs]

                new_node = Phylo.BaseTree.Clade()

                # fix positions and branch lengths
                new_node.time_before_present = new_positions[idxs]
                new_node.branch_length = clade.time_before_present - new_node.time_before_present
                new_node.clades = [n1,n2]
                n1.branch_length = new_node.time_before_present - n1.time_before_present
                n2.branch_length = new_node.time_before_present - n2.time_before_present

                # set parameters for the new node
                new_node.up = clade
                n1.up = new_node
                n2.up = new_node
                if hasattr(clade, "cseq"):
                    new_node.cseq = clade.cseq
                    self._store_compressed_sequence_to_node(new_node)

                new_node.mutations = []
                new_node.mutation_length = 0.0
                new_node.branch_length_interpolator = BranchLenInterpolator(new_node, self.gtr, one_mutation=self.one_mutation,
                                                                            branch_length_mode = self.branch_length_mode)
                clade.clades.remove(n1)
                clade.clades.remove(n2)
                clade.clades.append(new_node)
                self.logger('TreeTime._poly.merge_nodes: creating new node as child of '+clade.name,3)
                self.logger("TreeTime._poly.merge_nodes: Delta-LH = " + str(cost_gains[idxs].round(3)), 3)

                # and modify source_arr array for the next loop
                if len(source_arr)>2: # if more than 3 nodes in polytomy, replace row/column
                    for ii in np.sort(idxs)[::-1]:
                        tmp_ind = np.arange(mergers.shape[0])!=ii
                        mergers = mergers[tmp_ind].swapaxes(0,1)
                        mergers = mergers[tmp_ind].swapaxes(0,1)

                    source_arr.remove(n1)
                    source_arr.remove(n2)
                    new_gains = np.array([[cost_gain(n1,new_node, clade) for n1 in source_arr]])
                    mergers = np.vstack((mergers, new_gains)).swapaxes(0,1)

                    source_arr.append(new_node)
                    new_gains = np.array([[cost_gain(n1,new_node, clade) for n1 in source_arr]])
                    mergers = np.vstack((mergers, new_gains)).swapaxes(0,1)
                else: # otherwise just recalculate matrix
                    source_arr.remove(n1)
                    source_arr.remove(n2)
                    source_arr.append(new_node)
                    mergers = np.array([[cost_gain(n1,n2, clade) for n1 in source_arr]
                                       for n2 in source_arr])

            return LH

        stretched = [c for c  in clade.clades if c.mutation_length < c.clock_length]
        compressed = [c for c in clade.clades if c not in stretched]

        if len(stretched)==1 and merge_compressed is False:
            return 0.0

        LH = merge_nodes(stretched, isall=len(stretched)==len(clade.clades))
        if merge_compressed and len(compressed)>1:
            LH += merge_nodes(compressed, isall=len(compressed)==len(clade.clades))

        return LH
def _poly ( self , clade , merge_compressed ) : from . branch_len_interpolator import BranchLenInterpolator zero_branch_slope = self . gtr . mu * self . seq_len def _c_gain ( t , n1 , n2 , parent ) : """
            cost gain if nodes n1, n2 are joined and their parent is placed at time t
            cost gain = (LH loss now) - (LH loss when placed at time t)
            """ cg2 = n2 . branch_length_interpolator ( parent . time_before_present - n2 . time_before_present ) - n2 . branch_length_interpolator ( t - n2 . time_before_present ) cg1 = n1 . branch_length_interpolator ( parent . time_before_present - n1 . time_before_present ) - n1 . branch_length_interpolator ( t - n1 . time_before_present ) cg_new = - zero_branch_slope * ( parent . time_before_present - t ) # loss in LH due to the new branch return - ( cg2 + cg1 + cg_new ) def cost_gain ( n1 , n2 , parent ) : """
            cost gained if the two nodes would have been connected.
            """ try : cg = sciopt . minimize_scalar ( _c_gain , bounds = [ max ( n1 . time_before_present , n2 . time_before_present ) , parent . time_before_present ] , method = 'Bounded' , args = ( n1 , n2 , parent ) ) return cg [ 'x' ] , - cg [ 'fun' ] except : self . logger ( "TreeTime._poly.cost_gain: optimization of gain failed" , 3 , warn = True ) return parent . time_before_present , 0.0 def merge_nodes ( source_arr , isall = False ) : mergers = np . array ( [ [ cost_gain ( n1 , n2 , clade ) if i1 < i2 else ( 0.0 , - 1.0 ) for i1 , n1 in enumerate ( source_arr ) ] for i2 , n2 in enumerate ( source_arr ) ] ) LH = 0 while len ( source_arr ) > 1 + int ( isall ) : # max possible gains of the cost when connecting the nodes: # this is only a rough approximation because it assumes the new node positions # to be optimal new_positions = mergers [ : , : , 0 ] cost_gains = mergers [ : , : , 1 ] # set zero to large negative value and find optimal pair np . fill_diagonal ( cost_gains , - 1e11 ) idxs = np . unravel_index ( cost_gains . argmax ( ) , cost_gains . shape ) if ( idxs [ 0 ] == idxs [ 1 ] ) or cost_gains . max ( ) < 0 : self . logger ( "TreeTime._poly.merge_nodes: node is not fully resolved " + clade . name , 4 ) return LH n1 , n2 = source_arr [ idxs [ 0 ] ] , source_arr [ idxs [ 1 ] ] LH += cost_gains [ idxs ] new_node = Phylo . BaseTree . Clade ( ) # fix positions and branch lengths new_node . time_before_present = new_positions [ idxs ] new_node . branch_length = clade . time_before_present - new_node . time_before_present new_node . clades = [ n1 , n2 ] n1 . branch_length = new_node . time_before_present - n1 . time_before_present n2 . branch_length = new_node . time_before_present - n2 . time_before_present # set parameters for the new node new_node . up = clade n1 . up = new_node n2 . up = new_node if hasattr ( clade , "cseq" ) : new_node . cseq = clade . cseq self . _store_compressed_sequence_to_node ( new_node ) new_node . mutations = [ ] new_node . mutation_length = 0.0 new_node . branch_length_interpolator = BranchLenInterpolator ( new_node , self . gtr , one_mutation = self . one_mutation , branch_length_mode = self . branch_length_mode ) clade . clades . remove ( n1 ) clade . clades . remove ( n2 ) clade . clades . append ( new_node ) self . logger ( 'TreeTime._poly.merge_nodes: creating new node as child of ' + clade . name , 3 ) self . logger ( "TreeTime._poly.merge_nodes: Delta-LH = " + str ( cost_gains [ idxs ] . round ( 3 ) ) , 3 ) # and modify source_arr array for the next loop if len ( source_arr ) > 2 : # if more than 3 nodes in polytomy, replace row/column for ii in np . sort ( idxs ) [ : : - 1 ] : tmp_ind = np . arange ( mergers . shape [ 0 ] ) != ii mergers = mergers [ tmp_ind ] . swapaxes ( 0 , 1 ) mergers = mergers [ tmp_ind ] . swapaxes ( 0 , 1 ) source_arr . remove ( n1 ) source_arr . remove ( n2 ) new_gains = np . array ( [ [ cost_gain ( n1 , new_node , clade ) for n1 in source_arr ] ] ) mergers = np . vstack ( ( mergers , new_gains ) ) . swapaxes ( 0 , 1 ) source_arr . append ( new_node ) new_gains = np . array ( [ [ cost_gain ( n1 , new_node , clade ) for n1 in source_arr ] ] ) mergers = np . vstack ( ( mergers , new_gains ) ) . swapaxes ( 0 , 1 ) else : # otherwise just recalculate matrix source_arr . remove ( n1 ) source_arr . remove ( n2 ) source_arr . append ( new_node ) mergers = np . array ( [ [ cost_gain ( n1 , n2 , clade ) for n1 in source_arr ] for n2 in source_arr ] ) return LH stretched = [ c for c in clade . clades if c . mutation_length < c . clock_length ] compressed = [ c for c in clade . clades if c not in stretched ] if len ( stretched ) == 1 and merge_compressed is False : return 0.0 LH = merge_nodes ( stretched , isall = len ( stretched ) == len ( clade . clades ) ) if merge_compressed and len ( compressed ) > 1 : LH += merge_nodes ( compressed , isall = len ( compressed ) == len ( clade . clades ) ) return LH
Function to resolve polytomies for a given parent node. If the
        number of the direct decendants is less than three (not a polytomy), does
        nothing. Otherwise, for each pair of nodes, assess the possible LH increase
        which could be gained by merging the two nodes. The increase in the LH is
        basically the tradeoff between the gain of the LH due to the changing the
        branch lenghts towards the optimal values and the decrease due to the
        introduction of the new branch with zero optimal length.
Function to resolve polytomies for a given parent node . If the number of the direct decendants is less than three ( not a polytomy ) does nothing . Otherwise for each pair of nodes assess the possible LH increase which could be gained by merging the two nodes . The increase in the LH is basically the tradeoff between the gain of the LH due to the changing the branch lenghts towards the optimal values and the decrease due to the introduction of the new branch with zero optimal length .