def rearr_Worker(t, veal, target_label, dest_label, target_parent_label, result_queue, sema):
t_prime = dendropy.Tree(t)
try:
t_prime, affected = Subtree.move_subtree(t_prime, target_label, dest_label)
assert len(t_prime.nodes()) == len(t.nodes())
except Exception:
print("EXCEPTION ENCOUNTERED!!!!")
result_queue.put((None, "Subtree.move_subtree Failed"))
#result_queue.close() # do not use if using Manager.Queue()
sema.release()
return
# the three local branche we need to optimize are edges of
# target, dest, and parent of target
lst = [(target_parent_label, target_label),\
(target_parent_label, dest_label),\
(t_prime.find_node_with_label(target_parent_label).parent_node.label, \
target_parent_label)]
log_l_prime, t_prime = veal(t_prime, lst)
# print log_l_prime, t_prime
if log_l_prime is not None: # if is None, means optimization failed
result_queue.put((log_l_prime, t_prime))
# result_queue.close() # do not use if using Manager.Queue()
else:
#print("OPTIMIZATION FAILED!!!")
result_queue.put((None, "Subtree.move_subtree Failed"))
# result_queue.close() # do not use if using Manager.Queue()
sema.release()
def rearr(t, log_l, rL, rU):
for target in t.postorder_node_iter():
if target.parent_node is None:
break
# first make a deep copy of the tree
t_prime = dendropy.Tree(t)
print "finding subtree rearrangement for {0}".format(target.label)
dest = cello.subtree_rearrangement(t_prime, target, rL, rU)
print "inserting subtree {0} into branch above {1}".format(target.label, dest.label)
try:
# attach the subtree of <target> to branch above <dest>
t_prime, affected = Subtree.move_subtree(t_prime, target.label, dest.label)
except Exception:
print "could not succesfully do the move, forget it! move on!"
continue
cheat_order = Tree.postorder_traverse_with_cheat(t_prime, affected)
# the three local branche we need to optimize are edges of
# target, dest, and parent of target
lst = []
for i, (k, bunch) in enumerate(cheat_order):
for j, (a, t_a) in enumerate(bunch):
if a in (target.label, dest.label, target.parent_node.label):
lst.append((i, j))
print 'list is', lst
log_l_prime = MyMat.optimize_branch_fast(lst, cheat_order, msa, single_model, paired_model, S, P)
if log_l_prime < log_l:
print "subtree rearrangement improved log like from {0}-->{1}, keep!".format(log_l, log_l_prime)
t = t_prime
log_l = log_l_prime
# put in the fastly optimized branch lengths
for i, j in lst:
t.find_node_with_label(cheat_order[i][1][j][0]).edge_length = cheat_order[i][1][j][1]
raw_input("continue?")
return
else:
print "subtree rearrangment made log likelihood worse. Move on"
raw_input("continue")
return
return t, log_l
