def build_tree(treeFile):
# build the tree #
sys.stderr.write('Building tree\n')
with open(treeFile,"r") as inFH:
tree_string = inFH.read().strip()
tree = multitree.multitree()
tree.build(tree_string)
return tree
def build_tree(treeFile):
# build the tree #
sys.stderr.write('Building tree\n')
with open(treeFile, "r") as inFH:
tree_string = inFH.read().strip()
tree = multitree.multitree()
tree.build(tree_string)
return tree
def build_tree(treeFile):
"""Build tree
"""
sys.stderr.write("Building Tree\n")
tree_file = open(treeFile, "r")
tree_string = tree_file.read().strip()
tree = multitree.multitree()
tree.build(tree_string)
if tree.root != None:
msg = "Input tree is rooted. You must use unrooted tree"
sys.stderr.write(msg + "\n")
sys.exit()
tree_file.close()
return tree
def build_tree(treeFile):
"""Build tree
"""
sys.stderr.write('Building Tree\n')
tree_file = open(treeFile,'r')
tree_string = tree_file.read().strip()
tree = multitree.multitree()
tree.build(tree_string)
if tree.root != None:
msg = 'Input tree is rooted. You must use unrooted tree'
sys.stderr.write(msg + '\n')
sys.exit()
tree_file.close()
return tree
def RunAnGST(input_dict):
# unpack input variables
input_info = input_dict['input_info']
mem_str = input_dict['mem_str']
write_out = input_dict['write_out']
# build the reconciliation model #
model = reconcile.dlt_model(input_info)
# build the species tree #
print "* build species tree"
species_tree_file = open(input_info.species_tree_filename,"r")
species_tree_string = species_tree_file.read().strip()
species_tree = multitree.multitree()
species_tree.Build(species_tree_string)
species_tree_file.close()
model.species_tree = species_tree
mem_str.append(PyVM.MemoryUpdate("build species tree",'return'))
# make sure no delimiters in the species tree
for node in species_tree.node_dict:
if node.count('.') > 0 or node.count('_') > 0:
print "no delimiters in species tree please."
sys.exit(1)
# get all the pairwise distances on the species tree
species_tree.root.FindDists()
species_tree.root.GetHeights()
# get the time constraints
if input_info.ultra_tree_bool is True:
ultra_tree_file = open(input_info.species_tree_filename,"r")
ultra_tree_string = ultra_tree_file.read().strip()
ultra_tree = multitree.multitree()
ultra_tree.Build(ultra_tree_string)
ultra_tree_file.close()
mem_str.append(PyVM.MemoryUpdate("build ultra tree",'return'))
ultra_tree.model = model
ultra_tree.root.FindDists()
ultra_tree.root.GetHeights()
print "foo!"
# link the species and ultrametric tree nodess
tree_link = species_tree.TreeLink(ultra_tree)
else:
ultra_tree = None
tree_link = None
# build the gene trees
print "* build gene trees"
bootstraps = open(input_info.boots_file,'r').readlines()
boot_list = []
for boot in bootstraps:
boot_list.append(multitree.multitree())
boot_list[-1].Build(boot)
mem_str.append(PyVM.MemoryUpdate("build bootstraps",'return'))
# is there a single rooted bootstrap?
if len(boot_list) == 1 and boot_list[0].root is not None:
gene_tree = boot_list[0]
tree = gene_tree
else:
# you've got bootstraps
# check that all of the trees have the same leaves
leaf_set = boot_list[0].MakeLeafSet()
for ind in range(1,len(boot_list)):
boot_list[ind].TestLeafSet(leaf_set)
# if doing HR, make sure nodes map one-to-one between species tree
# and gene tree
if model.special == 'hr':
gene_leaves = [leaf.species for leaf in boot_list[0].leaf_dict.values()]
species_leaves = species_tree.root.leaves.keys()
for leaf in gene_leaves:
if leaf in species_leaves:
species_leaves.remove(leaf)
else:
print "leaf " + leaf + " in gene tree, but not species tree."
print "un-matched nodes; can't do HR"
sys.exit(1)
if len(species_leaves) > 0:
print "un-matched nodes; can't do HR"
sys.exit(1)
# are the bootstraps rooted or unrooted?
rooted_c = 0
for boot_tree in boot_list:
if boot_tree.root is not None:
rooted_c += 1
#endif
#endfor
if rooted_c == len(boot_list):
''' all boots rooted '''
# reconcile each of the rooted bootstraps
overall_best_score = sys.maxint
overall_best_tree = None
random.shuffle(boot_list)
for boot_tree in boot_list:
node_link = reconcile.node_link(species_tree,model,ultra_tree,tree_link)
node_link.hr_scaling = input_info.hr_scaling
node_link.learn_events = 1
rec_res = reconcile.LocalReconcile(boot_tree.root,None,node_link)
boot_tree.root.lca_lookups = rec_res
best_score = min(rec_res['scores'].values())
if best_score < overall_best_score:
overall_best_score = best_score
overall_best_tree = boot_tree
# select one that gives best reconciliation
gene_tree = overall_best_tree
elif rooted_c == 0:
''' all boots unrooted '''
# link the nodes
print "* linking nodes"
node_link = reconcile.node_link(species_tree,model,ultra_tree,tree_link)
node_link.hr_scaling = input_info.hr_scaling
for tree in boot_list:
node_link_dict = {}
tree.a_node.GetNodeLinkDict(node_link_dict)
node_link.MergeNodeLinkDicts(node_link_dict)
del node_link_dict
mem_str.append(PyVM.MemoryUpdate("link nodes",'return'))
# optimize ALL leaves
print "* initial reconciliation"
for l in node_link.link_dict.keys():
if model.special == 'hr':
node_link.learn_events = 1
model.dup_penalty = math.exp(500)
elif model.event_guide is not None:
node_link.learn_events = 1
else:
node_link.learn_events = 0
node_link.GlobalReconcile(l)
mem_str.append(PyVM.MemoryUpdate("reconcile",'return'))
# evaluate all roots
print "* evaluate roots"
for leaf_str in node_link.link_dict:
node_pair = node_link.link_dict[leaf_str][0]
# only care about one root if you're doing HR
if model.outgroup is not None:
if node_pair[0].name == model.outgroup:
node_link.TryRoot(node_pair)
if node_pair[1].name == model.outgroup:
node_link.TryRoot(node_pair)
else:
node_link.TryRoot(node_pair)
mem_str.append(PyVM.MemoryUpdate("find root",'return'))
best_score = node_link.best_res['score']
best_newick = "(" + node_link.best_res['newick'] + ":0.01337);"
gene_tree = multitree.multitree()
gene_tree.Build(best_newick)
else:
print "boot root mismatch?!"
sys.exit(1)
#endif
if write_out:
# perform a final reconciliation on best gene tree to get all data
print "* final reconciliation"
species_tree.rec_dict = {}
species_tree.los_dict = {}
mem_str.append(PyVM.MemoryUpdate("rebuild gene tree",'return'))
node_link = reconcile.node_link(species_tree,model,ultra_tree,tree_link)
node_link.hr_scaling = input_info.hr_scaling
node_link.learn_events = 2 # 2 == full learning
rec_res = reconcile.LocalReconcile(gene_tree.root,None,node_link)
gene_tree.root.lca_lookups = rec_res
mem_str.append(PyVM.MemoryUpdate("last reconciliation",'return'))
best_score = min(rec_res['scores'].values())
# establish output directories
results_dirs = output.MakeDirectories(input_info.output_dir)
# collect the data that's going to be printed out
print "* write output"
counts = {}
gene_c = None
gene_c = len(str(gene_tree.root).split('-'))
counts["gene"] = gene_c
counts["spec"] = (len(species_tree.node_dict) + 1)/2
counts["boots"] = len(bootstraps)
output_args = {}
output_args["node_link"] = node_link
output_args["results"] = results_dirs
output_args["res_dict"] = rec_res
output_args["start_time"] = input_dict['start_time']
output_args["gene_tree"] = gene_tree
output_args["model"] = model
output_args["counts"] = counts
output_args["species_file"] = input_info.species_tree_filename
output_args["boot_file"] = input_info.boots_file
output_args["mem_str"] = ''.join(mem_str)
output_args["event_guide"] = input_info.event_guide_fn
output.SaveResults(output_args)
print best_score
return best_score
def RunAnGST(input_dict):
# unpack input variables
input_info = input_dict['input_info']
mem_str = input_dict['mem_str']
write_out = input_dict['write_out']
# build the reconciliation model #
model = reconcile.dlt_model(input_info)
# build the species tree #
print "* build species tree"
species_tree_file = open(input_info.species_tree_filename, "r")
species_tree_string = species_tree_file.read().strip()
species_tree = multitree.multitree()
species_tree.Build(species_tree_string)
species_tree_file.close()
model.species_tree = species_tree
mem_str.append(PyVM.MemoryUpdate("build species tree", 'return'))
# make sure no delimiters in the species tree
for node in species_tree.node_dict:
if node.count('.') > 0 or node.count('_') > 0:
print "no delimiters in species tree please."
sys.exit(1)
# get all the pairwise distances on the species tree
species_tree.root.FindDists()
species_tree.root.GetHeights()
# get the time constraints
if input_info.ultra_tree_bool is True:
ultra_tree_file = open(input_info.species_tree_filename, "r")
ultra_tree_string = ultra_tree_file.read().strip()
ultra_tree = multitree.multitree()
ultra_tree.Build(ultra_tree_string)
ultra_tree_file.close()
mem_str.append(PyVM.MemoryUpdate("build ultra tree", 'return'))
ultra_tree.model = model
ultra_tree.root.FindDists()
ultra_tree.root.GetHeights()
print "foo!"
# link the species and ultrametric tree nodess
tree_link = species_tree.TreeLink(ultra_tree)
else:
ultra_tree = None
tree_link = None
# build the gene trees
print "* build gene trees"
bootstraps = open(input_info.boots_file, 'r').readlines()
boot_list = []
for boot in bootstraps:
boot_list.append(multitree.multitree())
boot_list[-1].Build(boot)
mem_str.append(PyVM.MemoryUpdate("build bootstraps", 'return'))
# is there a single rooted bootstrap?
if len(boot_list) == 1 and boot_list[0].root is not None:
gene_tree = boot_list[0]
tree = gene_tree
else:
# you've got bootstraps
# check that all of the trees have the same leaves
leaf_set = boot_list[0].MakeLeafSet()
for ind in range(1, len(boot_list)):
boot_list[ind].TestLeafSet(leaf_set)
# if doing HR, make sure nodes map one-to-one between species tree
# and gene tree
if model.special == 'hr':
gene_leaves = [
leaf.species for leaf in boot_list[0].leaf_dict.values()
]
species_leaves = species_tree.root.leaves.keys()
for leaf in gene_leaves:
if leaf in species_leaves:
species_leaves.remove(leaf)
else:
print "leaf " + leaf + " in gene tree, but not species tree."
print "un-matched nodes; can't do HR"
sys.exit(1)
if len(species_leaves) > 0:
print "un-matched nodes; can't do HR"
sys.exit(1)
# are the bootstraps rooted or unrooted?
rooted_c = 0
for boot_tree in boot_list:
if boot_tree.root is not None:
rooted_c += 1
#endif
#endfor
if rooted_c == len(boot_list):
''' all boots rooted '''
# reconcile each of the rooted bootstraps
overall_best_score = sys.maxint
overall_best_tree = None
random.shuffle(boot_list)
for boot_tree in boot_list:
node_link = reconcile.node_link(species_tree, model,
ultra_tree, tree_link)
node_link.hr_scaling = input_info.hr_scaling
node_link.learn_events = 1
rec_res = reconcile.LocalReconcile(boot_tree.root, None,
node_link)
boot_tree.root.lca_lookups = rec_res
best_score = min(rec_res['scores'].values())
if best_score < overall_best_score:
overall_best_score = best_score
overall_best_tree = boot_tree
# select one that gives best reconciliation
gene_tree = overall_best_tree
elif rooted_c == 0:
''' all boots unrooted '''
# link the nodes
print "* linking nodes"
node_link = reconcile.node_link(species_tree, model, ultra_tree,
tree_link)
node_link.hr_scaling = input_info.hr_scaling
for tree in boot_list:
node_link_dict = {}
tree.a_node.GetNodeLinkDict(node_link_dict)
node_link.MergeNodeLinkDicts(node_link_dict)
del node_link_dict
mem_str.append(PyVM.MemoryUpdate("link nodes", 'return'))
# optimize ALL leaves
print "* initial reconciliation"
for l in node_link.link_dict.keys():
if model.special == 'hr':
node_link.learn_events = 1
model.dup_penalty = math.exp(500)
elif model.event_guide is not None:
node_link.learn_events = 1
else:
node_link.learn_events = 0
node_link.GlobalReconcile(l)
mem_str.append(PyVM.MemoryUpdate("reconcile", 'return'))
# evaluate all roots
print "* evaluate roots"
for leaf_str in node_link.link_dict:
node_pair = node_link.link_dict[leaf_str][0]
# only care about one root if you're doing HR
if model.outgroup is not None:
if node_pair[0].name == model.outgroup:
node_link.TryRoot(node_pair)
if node_pair[1].name == model.outgroup:
node_link.TryRoot(node_pair)
else:
node_link.TryRoot(node_pair)
mem_str.append(PyVM.MemoryUpdate("find root", 'return'))
best_score = node_link.best_res['score']
best_newick = "(" + node_link.best_res['newick'] + ":0.01337);"
gene_tree = multitree.multitree()
gene_tree.Build(best_newick)
else:
print "boot root mismatch?!"
sys.exit(1)
#endif
if write_out:
# perform a final reconciliation on best gene tree to get all data
print "* final reconciliation"
species_tree.rec_dict = {}
species_tree.los_dict = {}
mem_str.append(PyVM.MemoryUpdate("rebuild gene tree", 'return'))
node_link = reconcile.node_link(species_tree, model, ultra_tree,
tree_link)
node_link.hr_scaling = input_info.hr_scaling
node_link.learn_events = 2 # 2 == full learning
rec_res = reconcile.LocalReconcile(gene_tree.root, None, node_link)
gene_tree.root.lca_lookups = rec_res
mem_str.append(PyVM.MemoryUpdate("last reconciliation", 'return'))
best_score = min(rec_res['scores'].values())
# establish output directories
results_dirs = output.MakeDirectories(input_info.output_dir)
# collect the data that's going to be printed out
print "* write output"
counts = {}
gene_c = None
gene_c = len(str(gene_tree.root).split('-'))
counts["gene"] = gene_c
counts["spec"] = (len(species_tree.node_dict) + 1) / 2
counts["boots"] = len(bootstraps)
output_args = {}
output_args["node_link"] = node_link
output_args["results"] = results_dirs
output_args["res_dict"] = rec_res
output_args["start_time"] = input_dict['start_time']
output_args["gene_tree"] = gene_tree
output_args["model"] = model
output_args["counts"] = counts
output_args["species_file"] = input_info.species_tree_filename
output_args["boot_file"] = input_info.boots_file
output_args["mem_str"] = ''.join(mem_str)
output_args["event_guide"] = input_info.event_guide_fn
output.SaveResults(output_args)
print best_score
return best_score
bars_f.write(bar_header + "\n")
cluster_fn = write_dir + "/cluster.file"
cluster_f = open(cluster_fn,"w")
prune_fn = write_dir + "/prune.file"
prune_f = open(prune_fn,"w")
cdist_fn = write_dir + "/cdist.file"
cdist_f = open(cdist_fn,"w")
lik_fn = write_dir + "/lik.file"
lik_f = open(lik_fn,"w")
# build the tree #
print "Building tree"
tree_f = open(tree_fn,"r")
tree_string = tree_f.read().strip()
tree = multitree.multitree()
tree.build(tree_string)
tree_f.close()
# root it and remove zero branch lengths
min_len = min([b.length for b in tree.branch_list if b.length > 0.0])
for b in tree.branch_list:
if b.length <= 0.0:
b.length = min_len
names_1 = b.ends[0].name_dict[b.ends[1]]
names_2 = b.ends[1].name_dict[b.ends[0]]
if names_1 == outgroup or names_2 == outgroup:
tree.rootify(b)
# write the data files
full_fn = write_dir + "/full.file"
cluster_fn = write_dir + "/cluster.file"
cluster_f = open(cluster_fn, "w")
prune_fn = write_dir + "/prune.file"
prune_f = open(prune_fn, "w")
cdist_fn = write_dir + "/cdist.file"
cdist_f = open(cdist_fn, "w")
lik_fn = write_dir + "/lik.file"
lik_f = open(lik_fn, "w")
# build the tree #
print "Building tree"
tree_f = open(tree_fn, "r")
tree_string = tree_f.read().strip()
tree = multitree.multitree()
tree.build(tree_string)
tree_f.close()
# root it and remove zero branch lengths
min_len = min([b.length for b in tree.branch_list if b.length > 0.0])
for b in tree.branch_list:
if b.length <= 0.0:
b.length = min_len
names_1 = b.ends[0].name_dict[b.ends[1]]
names_2 = b.ends[1].name_dict[b.ends[0]]
if names_1 == outgroup or names_2 == outgroup:
tree.rootify(b)
# write the data files
full_fn = write_dir + "/full.file"