def tred(counter, Namelist, finlist, Non_CleanList_Done):
original = []
noise = []
'''Returns the symmetric difference between each noise and original alignment and the original tree.'''
with open(
'fasta_orig',
"w") as fo: #Creates a file with each alignment as a Fasta-format
for n in range(len(Namelist)):
fo.write('\n' + '>' + Namelist[n] + '\n' + Non_CleanList_Done[n])
with open('fasta_noise', 'w') as fn:
for n in range(len(Namelist)):
fn.write('\n' + '>' + Namelist[n] + '\n' + finlist[n])
with open('fastprot_orig',
'w') as fpo: # Runs the command "fastprot" on file "fa_sekvens".
fpo.write(check_output(["fastprot", 'fasta_orig']))
with open('fastprot_noise', 'w') as fpn:
fpn.write(check_output(["fastprot", 'fasta_noise']))
tempo = tempfile.TemporaryFile(
mode='w+t'
) # Creates a temporary file "temp" and makes it readable as text. Writes output of fnj to it.
tempo.write(check_output(["fnj", "-O", "newick", "fastprot_orig"]))
tempo.seek(0)
tempn = tempfile.TemporaryFile(
mode='w+t'
) # Creates a temporary file "temp" and makes it readable as text. Writes output of fnj to it.
tempn.write(check_output(["fnj", "-O", "newick", "fastprot_noise"]))
tempn.seek(0)
tns = dendropy.TaxonNamespace()
t1 = dendropy.Tree.get(file=open(sys.argv[2], 'r'),
schema="newick",
tree_offset=0,
taxon_namespace=tns)
t2 = dendropy.Tree.get(file=tempo,
schema="newick",
tree_offset=0,
taxon_namespace=tns)
t3 = dendropy.Tree.get(file=tempn,
schema="newick",
tree_offset=0,
taxon_namespace=tns)
t1.encode_bipartitions()
t2.encode_bipartitions()
t3.encode_bipartitions()
original = treecompare.symmetric_difference(
t1,
t2) #Compares the symmetric difference between the two trees t1 and t2
noise = treecompare.symmetric_difference(t1, t3)
tempo.close()
tempn.close()
return original, noise
def perform_comparsions(treelist, comp_tree):
df = pd.DataFrame(columns=['RF'])
total_diffs = 2*len(comp_tree.nodes())
for et in treelist:
et.migrate_taxon_namespace(comp_tree.taxon_namespace)
df.loc[len(df)] = treecompare.symmetric_difference(et,comp_tree)/total_diffs
print(treecompare.symmetric_difference(et,comp_tree)/total_diffs)
return(df)
def runTest(self):
taxon_namespace = dendropy.TaxonNamespace([str(i+1) for i in range(5)])
tree_list = dendropy.TreeList.get_from_stream(
StringIO("""
(5,((4,3),2),1);
(5,(4,3,2),1);
(5,((4,3),2),1);
(5,(4,3),2,1);
(5,((4,3),2),1);
(5,4,3,2,1);
"""),
schema="newick",
taxon_namespace=taxon_namespace)
tree = tree_list[0]
expected_tree = tree_list[1]
tree.encode_bipartitions()
tree_leafset_bitmask = tree.seed_node.edge.bipartition._leafset_bitmask
bipartition_to_target = dendropy.Bipartition(
bitmask=0xA,
tree_leafset_bitmask=tree_leafset_bitmask,
compule_bitmasks=True)
assert bipartition_to_target._lowest_relevant_bit is not None
tree.seed_node.collapse_conflicting(bipartition_to_target)
tree.encode_bipartitions()
expected_tree.encode_bipartitions()
self.assertEqual(treecompare.symmetric_difference(tree, expected_tree), 0)
tree = tree_list[2]
expected_tree = tree_list[3]
tree.encode_bipartitions()
tree_leafset_bitmask = tree.seed_node.edge.bipartition._leafset_bitmask
bipartition_to_target = dendropy.Bipartition(bitmask=0x3,
tree_leafset_bitmask=tree_leafset_bitmask,
compile_bipartition=True)
tree.seed_node.collapse_conflicting(bipartition_to_target)
tree.encode_bipartitions()
expected_tree.encode_bipartitions()
self.assertEqual(treecompare.symmetric_difference(tree, expected_tree), 0)
tree = tree_list[4]
expected_tree = tree_list[5]
tree.encode_bipartitions()
tree_leafset_bitmask = tree.seed_node.edge.bipartition._leafset_bitmask
bipartition_to_target = dendropy.Bipartition(bitmask=0x5,
tree_leafset_bitmask=tree_leafset_bitmask,
compile_bipartition=True)
tree.seed_node.collapse_conflicting(bipartition_to_target)
tree.encode_bipartitions()
expected_tree.encode_bipartitions()
self.assertEqual(treecompare.symmetric_difference(tree, expected_tree), 0)
def tree_comparison(collapsed_tree, hogtree, phyml_tree, gene_tree_congruence, report):
print("Calculating gene tree congruence metric...")
tns = dendropy.TaxonNamespace()
tree1 = dendropy.Tree.get(file=open(collapsed_tree, 'r'), schema='newick', taxon_namespace=tns)
tree2 = dendropy.Tree.get(file=open(hogtree, 'r'), schema='newick', taxon_namespace=tns)
tree3 = dendropy.Tree.get(file=open(phyml_tree, 'r'), schema='newick', taxon_namespace=tns)
diff = treecompare.symmetric_difference(tree1,tree2, is_bipartitions_updated=False) #same as unweighted RF distance
tree1_node_num = (len(tree1.internal_nodes())) #gets number of splits (nodes) in tree
tree2_node_num = (len(tree2.internal_nodes()))
tree3_node_num = (len(tree3.internal_nodes()))
hog_metric = 1 - diff/(tree1_node_num + tree2_node_num)
num_nodes_collapsed = tree3_node_num - tree1_node_num
with open(gene_tree_congruence, 'w') as outfile1:
outfile1.write(str(hog_metric) + "\n")
with open(report, 'w') as outfile2:
with open(collapsed_tree, 'r') as collapsedtreefile:
with open(hogtree, 'r') as hogtreefile:
with open(phyml_tree, 'r') as phymltreefile:
phyml_genetree = phymltreefile.read()
hogtree = hogtreefile.read()
collapsed_genetree = collapsedtreefile.read()
collapsedplot = tree1.as_ascii_plot()
hogplot = tree2.as_ascii_plot()
outfile2.write("HOG tree:\n" + str(hogtree) + "\n"+ str(hogplot)+ "\n\nUncollapsed Gene tree:\n" + str(phyml_genetree) + "\n\nCollapsed Gene tree:\n" + str(collapsed_genetree) + "\n"+ str(collapsedplot)+ "\n\n# nodes collapsed: "+str(num_nodes_collapsed)+ "\n\nSymmetric distance: "+str(diff)+ "\n\n# Nodes (Hogtree): "+str(tree2_node_num) + "\n\n# Nodes (Uncollaped Gene tree): "+ str(tree3_node_num)+ "\n\n# Nodes (Collapsed Gene tree): " + str(tree1_node_num)+"\n\nHOG Tree Congruence:" + str(hog_metric)+ "\n\n")
def check(self, title, src_prefix):
tns = dendropy.TaxonNamespace()
input_ds = dendropy.DataSet.get_from_path(
src=pathmap.tree_source_path(src_prefix + ".dendropy-pruned.nex"),
schema='nexus',
attached_taxon_namespace=tns)
input_taxa = input_ds.taxon_namespaces[0]
output_ds = dendropy.DataSet.get_from_path(
src=pathmap.tree_source_path(src_prefix + ".paup-pruned.nex"),
schema='nexus',
taxon_namespace=input_taxa)
for set_idx, src_trees in enumerate(input_ds.tree_lists):
src_trees = input_ds.tree_lists[set_idx]
ref_trees = output_ds.tree_lists[set_idx]
for tree_idx, src_tree in enumerate(src_trees):
_LOG.debug("%s Set %d/%d, Tree %d/%d" %
(title, set_idx + 1, len(input_ds.tree_lists),
tree_idx + 1, len(src_trees)))
ref_tree = ref_trees[tree_idx]
# tree_dist = paup.symmetric_difference(src_tree, ref_tree)
# d = src_tree.symmetric_difference(ref_tree)
# if d > 0:
# print d
self.assertEqual(
treecompare.symmetric_difference(src_tree, ref_tree), 0)
def compare_trees(tree1_str, tree2_str):
try:
tns = dendropy.TaxonNamespace()
tree1 = dendropy.Tree.get(data=tree1_str, schema="newick",taxon_namespace=tns)
tree2 = dendropy.Tree.get(data=tree2_str, schema="newick",taxon_namespace=tns)
tree1.encode_bipartitions()
tree2.encode_bipartitions()
#-----------------------------------------------------------
#This method returns the symmetric distance between two trees.
#The symmetric distance between two trees is the sum of the number of splits found in one of the trees but not the other.
#It is common to see this statistic called the Robinson-Foulds distance
areSame = True if treecompare.symmetric_difference(tree1, tree2) == 0 else False
status = 200
message = "Success"
except Error as e:
message = str(e)
status = 500
response = {'status': status, 'message': message, 'are_same_tree': areSame}
return response
def calcDistance(self):
if self.path1 != '' and self.path2 != '':
self.fileEx1 = (os.path.splitext(self.path1)[1])[1:]
self.fileEx2 = (os.path.splitext(self.path2)[1])[1:]
tns = dendropy.TaxonNamespace()
self.tree1 = dendropy.Tree.get_from_path(self.path1, self.fileEx1, taxon_namespace=tns)
self.tree2 = dendropy.Tree.get_from_path(self.path2, self.fileEx2, taxon_namespace=tns)
self.tree1.encode_bipartitions()
self.tree2.encode_bipartitions()
print(treecompare.false_positives_and_negatives(self.tree1, self.tree2))
# self.tree1 = dendropy.Tree.get_from_string('((A, B), (C, D))', 'newick')
# self.tree2 = dendropy.Tree.get_from_string('((A, B), (C, D))', 'newick')
# self.tree1.encode_bipartitions()
# self.tree2.encode_bipartitions()
# oblicz dystans
# self.symDist = self.tree1.symmetric_difference(self.tree2)
self.symDist = treecompare.symmetric_difference(self.tree1, self.tree2)
self.fpnDist = treecompare.false_positives_and_negatives(self.tree1, self.tree2)
self.eucDist = treecompare.euclidean_distance(self.tree1, self.tree2)
self.rfDist = treecompare.robinson_foulds_distance(self.tree1, self.tree2)
# pokaz wyniki
self.res1.setText(str(self.eucDist)) #eucDist
self.res2.setText(str(self.rfDist)) #rfDist
def compare_trees(expected, estimated):
# assert(estimated.euclidean_distance(expected)<= branch lengths are hard to test. TODO figure out how.
#taxon_namespace = dendropy.TaxonSet()
exp_tree = dendropy.Tree.get_from_path(expected, "newick")
est_tree = dendropy.Tree.get_from_path(
estimated, "nexus", taxon_namespace=exp_tree.taxon_namespace)
return (treecompare.symmetric_difference(est_tree, exp_tree))
def symmetric_difference(tree1, tree2):
deprecate.dendropy_deprecation_warning(
preamble="Deprecated since DendroPy 4: The 'dendropy.treecalc.symmetric_difference()' function has moved to 'dendropy.calculate.treecompare.symmetric_difference()'.",
old_construct="from dendropy import treecalc\nd = treecalc.symmetric_difference(...)",
new_construct="from dendropy.calculate import treecompare\nd = treecompare.symmetric_difference(...)",
)
return treecompare.symmetric_difference(tree1=tree1, tree2=tree2)
def testTrees(self):
tree_files = [
("dendropy-test-trees-n33-unrooted-x100a.nexus", "force-unrooted",
False),
("dendropy-test-trees-multifurcating-unrooted.nexus",
"force-unrooted", False),
("pythonidae.beast.summary.tre", "force-rooted", True),
("primates.beast.mcct.medianh.tre", "force-rooted", True),
]
for tree_file, rooting, is_rooted in tree_files:
ref_tree = dendropy.Tree.get_from_path(
pathmap.tree_source_path(tree_file), "nexus", rooting=rooting)
bipartition_encoding = ref_tree.encode_bipartitions()
t_tree = dendropy.Tree.from_bipartition_encoding(
bipartition_encoding,
taxon_namespace=ref_tree.taxon_namespace,
is_rooted=ref_tree.is_rooted)
# t_tree.encode_bipartitions()
_LOG.debug("--\n File: {} ({})".format(
tree_file, ref_tree.is_rooted))
_LOG.debug(" Original: {}".format(
ref_tree.as_string("newick")))
_LOG.debug("Reconstructed: {}".format(t_tree.as_string("newick")))
self.assertEqual(
treecompare.symmetric_difference(ref_tree, t_tree), 0)
def rf_distance_dualbros_orig(dualbros_file, original_file):
"""
:param dualbros_file: output file from dualbros rooting, contains output newick string rooted
:param original_file: original file with correct rooting, must have same taxon names as dualbros output. newick.
:return: rf distance between the two trees. 0 signifies correct root, >1 means incorrect root. >2 means something
went pretty wrong. usually is 1 or 2, as i've seen.
"""
with open(dualbros_file, "r") as fp:
file_contents = fp.read()
fp.close()
info, data = file_contents.split("------------------------")
# read in the original gene tree.
o_tree = dp.Tree.get_from_path(original_file, schema="newick")
o_tree.is_rooted = True
# make tree from output
c_tree = dp.Tree.get_from_string(data, schema="newick")
c_tree.is_rooted = True
# calculate rf distance. taxon_namespaces have to be the same between the two trees.
c_tree.migrate_taxon_namespace(
o_tree.taxon_namespace) # hopefully will not throw error
return tc.symmetric_difference(o_tree, c_tree)
# testing out the function.
#print(rf_distance_dualbros_orig("sample_output/tree4.txt", "../final-project-src/AllSimulatedDatasets/R-025-HI-NR/formatted_for_optroot/tree4"))
def compare_rf_distance(ref,
method_types,
testset_dir,
num_testset,
size_testset,
size_phy,
out_filename=None,
silent=False):
if out_filename is None:
out_filename = 'RF_dist.csv'
# establish common taxon namespace
tns = dendropy.TaxonNamespace()
total_result_file = os.path.join(testset_dir, out_filename)
total_contents = []
for i in range(num_testset):
print('testset[{}] Calculating RF distance ...'.format(i + 1))
total_content = []
ref_trees = []
for j in range(size_testset):
ref_tree_file = os.path.join(testset_dir, str(i + 1),
'%d_%s.nwk' % (j + 1, ref))
ref_trees.append(
dendropy.Tree.get(path=ref_tree_file,
schema='newick',
taxon_namespace=tns))
result_file = os.path.join(testset_dir, str(i + 1), out_filename)
with open(result_file, 'wt') as f_write:
f_write.write('reference: {}\n'.format(ref))
for method_type in method_types:
s = 0
contents = [method_type]
for j in range(size_testset):
tree_file = os.path.join(
testset_dir, str(i + 1),
'%d_%s.nwk' % (j + 1, method_type))
tree = dendropy.Tree.get(path=tree_file,
schema='newick',
taxon_namespace=tns)
rf_dist = treecompare.symmetric_difference(
ref_trees[j], tree) / (2 * size_phy - 6)
s += rf_dist
contents.append(str(rf_dist))
f_write.write('{}\n'.format(','.join(contents)))
if not silent:
# print(', '.join(contents), 'avg: ', s / size_testset)
print('{}: {}'.format(method_type, s / size_testset))
total_content.append('{:.3f}'.format(s / size_testset))
total_contents.append(total_content)
total_contents = [list(t) for t in zip(*total_contents)]
with open(total_result_file, 'wt') as f_write:
for idx, method_type in enumerate(method_types):
f_write.write('{},{}\n'.format(method_type,
','.join(total_contents[idx])))
def symmetric_difference(tree1, tree2):
deprecate.dendropy_deprecation_warning(
preamble="Deprecated since DendroPy 4: The 'dendropy.treecalc.symmetric_difference()' function has moved to 'dendropy.calculate.treecompare.symmetric_difference()'.",
old_construct="from dendropy import treecalc\nd = treecalc.symmetric_difference(...)",
new_construct="from dendropy.calculate import treecompare\nd = treecompare.symmetric_difference(...)")
return treecompare.symmetric_difference(
tree1=tree1,
tree2=tree2)
def tree_compare(tempdir):
# CHANGE to tempdir
tns = dendropy.TaxonNamespace()
tree1 = Tree.get_from_path(tempdir + "/ref.tree",
"newick",
taxon_namespace=tns)
tree2 = Tree.get_from_path(tempdir + "/normal_tree",
"newick",
taxon_namespace=tns)
tree3 = Tree.get_from_path(tempdir + "/red_tree",
"newick",
taxon_namespace=tns)
tree1.encode_bipartitions()
tree2.encode_bipartitions()
tree3.encode_bipartitions()
distance_normal = treecompare.symmetric_difference(tree1, tree2)
distance_reduced = treecompare.symmetric_difference(tree1, tree3)
return distance_normal, distance_reduced
def main():
#Files are all stored in a files
files = glob.glob('./*.txt')
tree_combinations = list(combinations([i for i in range(len(files))], 2))
labels = []
scores = []
for tup in tree_combinations:
#Name of gene 1
x = files[tup[0]]
x = x.replace(".", "")
x = x.replace("\\", "")
x = x.replace(".txt", "")
x = x.replace("txt", "")
x = x.replace("_", " ")
x = x.replace("tree", "")
#Name of gene 2
y = files[tup[1]]
y = y.replace(".", "")
y = y.replace("\\", "")
y = y.replace(".txt", "")
y = y.replace("txt", "")
y = y.replace("_", " ")
y = y.replace("tree", "")
label = x + "vs " + y
print(label)
infile_1 = open(files[tup[0]])
infile_2 = open(files[tup[1]])
lines1 = str(infile_1.readline())
lines2 = str(infile_2.readline())
s1 = lines1
s2 = lines2
# establish common taxon namespace
tns = dendropy.TaxonNamespace()
# ensure all trees loaded use common namespace
tree1 = dendropy.Tree.get(data=s1,
schema='newick',
preserve_underscores=True,
suppress_internal_node_taxa=False,
taxon_namespace=tns)
tree2 = dendropy.Tree.get(data=s2,
schema='newick',
preserve_underscores=True,
suppress_internal_node_taxa=False,
taxon_namespace=tns)
## Unweighted Robinson-Foulds distance
score = treecompare.symmetric_difference(tree1, tree2)
print("Comparing tree ", files[tup[0]], " and ", files[tup[1]], ": ",
score)
labels.append(label)
scores.append(score)
print(scores)
print(labels)
def compare_trees(expected,estimated):
# assert(estimated.euclidean_distance(expected)<= branch lengths are hard to test. TODO figure out how.
#taxon_namespace = dendropy.TaxonSet()
exp_tree = dendropy.Tree.get_from_path(
expected,
"newick")
est_tree = dendropy.Tree.get_from_path(
estimated,
"nexus",
taxon_namespace=exp_tree.taxon_namespace)
return(treecompare.symmetric_difference(est_tree, exp_tree))
def robinson_foulds(self, tree1, tree2, taxa_list):
"""Calculate Robinson-Foulds (i.e., symmetric_difference) distance between two trees."""
tree1, tree2 = self._read_trees(tree1, tree2, taxa_list)
rf = treecompare.symmetric_difference(tree1, tree2)
num_taxa = len([t for t in tree1.leaf_node_iter()])
normalized_rf = float(rf) / (2 * (num_taxa - 3))
return rf, normalized_rf
def test_sum_of_credibilities(self):
ta = self.trees.as_tree_array(is_rooted_trees=True)
sd = self.get_trees().split_distribution(is_bipartitions_updated=False) # for independent verification
scores, max_idx = ta.calculate_sum_of_split_supports()
self.assertEqual(len(scores), len(self.trees))
for score, tree in zip(scores, self.trees):
self.assertAlmostEqual(score, sd.sum_of_split_support_on_tree(tree))
self.assertEqual(max_idx, 73)
self.assertAlmostEqual(scores[max_idx], 30.89)
t0 = self.trees[73]
t1 = ta.maximum_sum_of_split_support_tree()
self.assertEqual(treecompare.symmetric_difference(t0, t1), 0)
def all_dist_among_trees_sym(treeDict):
"""
distance matrix of Robinson Foulds difference between every pair of trees
"""
res = []
keys = treeDict.keys()
comb = combinations(keys, 2)
for treeName1, treeName2 in comb:
tree1 = deepcopy(treeDict[treeName1])
tree2 = deepcopy(treeDict[treeName2])
res.append(treecompare.symmetric_difference(tree1, tree2))
return res
def testPrunedThenEncoding(self):
inp = StringIO('''(a,b,c,(d,e));
(b,d,(c,e));''')
first, second = dendropy.TreeList.get_from_stream(inp, schema='newick')
# prune tree 1 to have the same leaf set as tree 2.
# this removes the first taxon in the taxon list "A"
retain_list = set([node.taxon for node in second.leaf_nodes()])
exclude_list = [node for node in first.leaf_nodes() if node.taxon not in retain_list]
for nd in exclude_list:
first.prune_subtree(nd)
# the trees are now (b,c,(d,e)) and (b,d,(c,e)) so the symmetric diff is 2
self.assertEqual(2, treecompare.symmetric_difference(first, second))
def testPrunedThenEncoding(self):
inp = StringIO('''(a,b,c,(d,e));
(b,d,(c,e));''')
first, second = dendropy.TreeList.get_from_stream(inp, schema='newick')
# prune tree 1 to have the same leaf set as tree 2.
# this removes the first taxon in the taxon list "A"
retain_list = set([node.taxon for node in second.leaf_nodes()])
exclude_list = [node for node in first.leaf_nodes() if node.taxon not in retain_list]
for nd in exclude_list:
first.prune_subtree(nd)
# the trees are now (b,c,(d,e)) and (b,d,(c,e)) so the symmetric diff is 2
self.assertEqual(2, treecompare.symmetric_difference(first, second))
def main():
treefile1 = sys.argv[1]
treefile2 = sys.argv[2]
treelist = TreeList()
treelist.read(file=open(treefile1, 'rU'), schema="nexus")
treelist.read(file=open(treefile2, 'rU'), schema="nexus")
if treecompare.symmetric_difference(treelist.__getitem__(0),
treelist.__getitem__(1)) == 0:
print "trees are identical"
else:
print "trees are NOT identical"
def verify_resolve_polytomies(self, tree_string, rng):
tree = dendropy.Tree.get_from_string(tree_string, "newick")
if "&U" in tree_string:
assert not tree.is_rooted
else:
assert tree.is_rooted
for nd in tree:
nd.edge.length = 100
tree.resolve_polytomies(rng=rng)
tree.encode_bipartitions()
tree._debug_check_tree(
check_bipartitions=True,
unique_bipartition_edge_mapping=True)
for nd in tree:
if nd is tree.seed_node and not tree.is_rooted:
self.assertEqual(len(nd._child_nodes), 3)
elif len(nd._child_nodes) > 0:
self.assertEqual(len(nd._child_nodes), 2)
tree2 = dendropy.Tree.get_from_string(tree_string, "newick", taxon_namespace=tree.taxon_namespace)
self.assertNotEqual(treecompare.symmetric_difference(tree, tree2), 0)
tree.collapse_unweighted_edges()
self.assertEqual(treecompare.symmetric_difference(tree, tree2), 0)
def dist_among_trees_sym(treeDict):
"""
distance matrix of symmetric difference between every pair of trees
"""
res = {}
for treeName1 in treeDict.keys():
tree1 = treeDict[treeName1]
res[treeName1] = {}
for treeName2 in treeDict.keys():
tree2 = treeDict[treeName2]
res[treeName1][treeName2] = treecompare.symmetric_difference(
tree1, tree2)
return res
def runTest(self):
n = '(Basichlsac,(Lamprothma,Mougeotisp),(((Haplomitr2,Petalaphy),((Angiopteri,(((Azollacaro,((Dennstasam,(Oleandrapi,Polypodapp)),Dicksonant)),Vittarifle),Botrychbit)),(Isoetesmel,((((Agathismac,Agathisova),Pseudotsu),(((Libocedrus,Juniperusc),Callitris),Athrotaxi)),((Liriodchi,Nelumbo),Sagittari))))),Thuidium));'
k = dendropy.TreeList.get_from_stream(StringIO(n), schema="newick")[0]
trees = dendropy.TreeList.get_from_stream(StringIO(n+n), schema="newick", taxon_namespace=k.taxon_namespace)
ref = trees[0]
changing = trees[1]
rng = MockRandom()
for i in range(50):
changing.randomly_reorient(rng=rng, update_bipartitions=True)
self.assertNotEqual(str(changing), n)
changing._debug_check_tree(logger_obj=_LOG, check_bipartitions=True)
d = treecompare.symmetric_difference(ref, changing, is_bipartitions_updated=False)
if d != 0:
self.fail("\n{}\n!=\n{}\nRF={}".format(str(ref), str(changing), d))
def test_sum_of_credibilities(self):
ta = self.trees.as_tree_array(is_rooted_trees=True)
sd = self.get_trees().split_distribution(
is_bipartitions_updated=False) # for independent verification
scores, max_idx = ta.calculate_sum_of_split_supports()
self.assertEqual(len(scores), len(self.trees))
for score, tree in zip(scores, self.trees):
self.assertAlmostEqual(score,
sd.sum_of_split_support_on_tree(tree))
self.assertEqual(max_idx, 73)
self.assertAlmostEqual(scores[max_idx], 30.89)
t0 = self.trees[73]
t1 = ta.maximum_sum_of_split_support_tree()
self.assertEqual(treecompare.symmetric_difference(t0, t1), 0)
def testConsensus(self):
con_tree = self.tree_list.consensus(
min_freq=0.50,
is_bipartitions_updated=False,
support_label_decimals=2)
con_tree.encode_bipartitions()
self.assertEqual(treecompare.symmetric_difference(self.mb_con_tree, con_tree), 0)
self.assertEqual(len(con_tree.bipartition_encoding), len(self.mb_con_tree.bipartition_encoding))
for bipartition in self.mb_con_tree.bipartition_encoding:
edge1 = self.mb_con_tree.bipartition_edge_map[bipartition]
edge2 = con_tree.bipartition_edge_map[bipartition]
if edge1.head_node.label and edge2.head_node.label:
s1 = float(edge1.head_node.label)
s2 = round(float(edge2.head_node.label), 2)
self.assertAlmostEqual(s1, s2, 2)
def compute_dist_matrix(self,
dendropy=False,
weighted=False,
resolve=True,
overwrite=False):
import dendropy
from dendropy.calculate import treecompare
db = tables.open_file(self.h5name, mode="a")
trees, intvals = self.grab_trees(db)
if (not db.__contains__("/" + "dist_matrix") or overwrite):
D = np.zeros((len(trees), len(trees)))
if (not dendropy):
for n in range(len(trees) - 1):
for nn in range(n + 1, len(trees)):
D[n, nn] = self.compare_trees(trees[n], trees[nn])
D[nn, n] = D[n, nn]
else:
T = dendropy.TreeList([
dendropy.Tree.get(data=t.write(), schema='newick')
for t in trees
])
for n in range(len(trees) - 1):
for nn in range(n + 1, len(trees)):
if (weighted):
w_rf = treecompare.euclidean_distance(
T[n], T[nn]
) #weighted_robinson_foulds_distance(T[n],T[nn])
else:
w_rf = treecompare.symmetric_difference(
T[n], T[nn]
) #weighted_robinson_foulds_distance(T[n],T[nn])
D[n, nn] = w_rf
D[nn, n] = w_rf
if (overwrite):
del db["/dist_matrix"]
db.create_array("/", "dist_matrix", D)
else:
D = np.array([
np.array(row)
for row in db.get_node("/dist_matrix", classname="Array")
])
db.flush()
db.close()
return D
def distance(file_path, file_format, file_path2):
taxon_namespace = dendropy.TaxonNamespace()
tree1 = dendropy.Tree.get_from_path(file_path,
file_format,
taxon_namespace=taxon_namespace)
tree2 = dendropy.Tree.get_from_path(file_path2,
file_format,
taxon_namespace=taxon_namespace)
sym_diff = treecompare.symmetric_difference(tree1, tree2)
euc_dis = treecompare.euclidean_distance(tree1, tree2)
false_pos = treecompare.false_positives_and_negatives(tree1, tree2)
robinson_dis = treecompare.robinson_foulds_distance(tree1, tree2)
print("Symetric difference: ", sym_diff)
print("Robinson Foulds distance: ", robinson_dis)
print("False positives and negatives: ", false_pos)
print("Euclidean distance: ", euc_dis)
def runTest(self):
n = '(Basichlsac,(Lamprothma,Mougeotisp),(((Haplomitr2,Petalaphy),((Angiopteri,(((Azollacaro,((Dennstasam,(Oleandrapi,Polypodapp)),Dicksonant)),Vittarifle),Botrychbit)),(Isoetesmel,((((Agathismac,Agathisova),Pseudotsu),(((Libocedrus,Juniperusc),Callitris),Athrotaxi)),((Liriodchi,Nelumbo),Sagittari))))),Thuidium));'
trees = dendropy.TreeList.get_from_stream(StringIO(n+n), schema="newick")
ref = trees[0]
changing = trees[1]
rng = MockRandom()
ref.encode_bipartitions()
changing.encode_bipartitions()
orig_root = changing.seed_node
for i in range(50):
changing.randomly_rotate(rng=rng)
self.assertNotEqual(str(changing), n)
self.assertEqual(orig_root, changing.seed_node)
changing._debug_check_tree(logger_obj=_LOG, check_bipartitions=True)
if treecompare.symmetric_difference(ref, changing) != 0:
self.fail("\n%s\n!=\n%s" % (str(ref), str(changing)))
def testConsensus(self):
con_tree = self.tree_list.consensus(min_freq=0.50,
is_bipartitions_updated=False,
support_label_decimals=2)
con_tree.encode_bipartitions()
self.assertEqual(
treecompare.symmetric_difference(self.mb_con_tree, con_tree), 0)
self.assertEqual(len(con_tree.bipartition_encoding),
len(self.mb_con_tree.bipartition_encoding))
for bipartition in self.mb_con_tree.bipartition_encoding:
edge1 = self.mb_con_tree.bipartition_edge_map[bipartition]
edge2 = con_tree.bipartition_edge_map[bipartition]
if edge1.head_node.label and edge2.head_node.label:
s1 = float(edge1.head_node.label)
s2 = round(float(edge2.head_node.label), 2)
self.assertAlmostEqual(s1, s2, 2)
def tree_comparison(collapsed_tree, hogtree, phyml_tree, gene_tree_congruence,
report):
print("Calculating gene tree congruence metric...")
tns = dendropy.TaxonNamespace()
tree1 = dendropy.Tree.get(file=open(collapsed_tree, 'r'),
schema='newick',
taxon_namespace=tns)
tree2 = dendropy.Tree.get(file=open(hogtree, 'r'),
schema='newick',
taxon_namespace=tns)
tree3 = dendropy.Tree.get(file=open(phyml_tree, 'r'),
schema='newick',
taxon_namespace=tns)
diff = treecompare.symmetric_difference(
tree1, tree2,
is_bipartitions_updated=False) #same as unweighted RF distance
tree1_node_num = (len(tree1.internal_nodes())
) #gets number of splits (nodes) in tree
tree2_node_num = (len(tree2.internal_nodes()))
tree3_node_num = (len(tree3.internal_nodes()))
hog_metric = 1 - diff / (tree1_node_num + tree2_node_num)
num_nodes_collapsed = tree3_node_num - tree1_node_num
with open(gene_tree_congruence, 'w') as outfile1:
outfile1.write(str(hog_metric) + "\n")
with open(report, 'w') as outfile2:
with open(collapsed_tree, 'r') as collapsedtreefile:
with open(hogtree, 'r') as hogtreefile:
with open(phyml_tree, 'r') as phymltreefile:
phyml_genetree = phymltreefile.read()
hogtree = hogtreefile.read()
collapsed_genetree = collapsedtreefile.read()
collapsedplot = tree1.as_ascii_plot()
hogplot = tree2.as_ascii_plot()
outfile2.write(
"HOG tree:\n" + str(hogtree) + "\n" + str(hogplot) +
"\n\nUncollapsed Gene tree:\n" + str(phyml_genetree) +
"\n\nCollapsed Gene tree:\n" +
str(collapsed_genetree) + "\n" + str(collapsedplot) +
"\n\n# nodes collapsed: " + str(num_nodes_collapsed) +
"\n\nSymmetric distance: " + str(diff) +
"\n\n# Nodes (Hogtree): " + str(tree2_node_num) +
"\n\n# Nodes (Uncollaped Gene tree): " +
str(tree3_node_num) +
"\n\n# Nodes (Collapsed Gene tree): " +
str(tree1_node_num) + "\n\nHOG Tree Congruence:" +
str(hog_metric) + "\n\n")
def main(tree_path_1, tree_path_2):
tns = dendropy.TaxonNamespace()
tree1 = read_tree(tree_path_1, tns)
tree2 = read_tree(tree_path_2, tns)
tree1.encode_bipartitions()
tree2.encode_bipartitions()
print("Number of leaves in tree 1: ", len(tree1.leaf_nodes()))
print("Number of leaves in tree 2: ", len(tree2.leaf_nodes()))
print("Unweighted Robinson-Fould distance: ",
treecompare.symmetric_difference(tree1, tree2))
print("Weighted Robinson-Fould distance: ",
treecompare.weighted_robinson_foulds_distance(tree1, tree2))
print("Euclidean distance: ",
treecompare.euclidean_distance(tree1, tree2))
def dist_tree_all(treeFiles, treeTrueFile):
treeTrue = dendropy.Tree.get_from_path(treeTrueFile, schema='newick')
treeTreeTotalLength = treeTrue.length()
treeTrueScaled = deepcopy(treeTrue)
treeTrueScaled.scale_edges(1. / treeTreeTotalLength)
distRf = []
distRfScaled = []
distSym = []
for treeFile in treeFiles:
tree = dendropy.Tree.get_from_path(treeFile, schema='newick')
distSym.append(treecompare.symmetric_difference(treeTrue, tree))
distRf.append(
treecompare.weighted_robinson_foulds_distance(treeTrue, tree))
tree.scale_edges(1. / tree.length())
distRfScaled.append(
treecompare.weighted_robinson_foulds_distance(
treeTrueScaled, tree))
return distRf, distRfScaled, distSym
def evaluate(ref, file_name):
# To store the data during the process, we create two temporary files.
tmp1 = tempfile.mkstemp()
tmp2 = tempfile.mkstemp()
# Use the commands of fastprot and fnj.
# The output of the FastPhylo programs is in file 'tmp2'.
os.system("fastprot -m -o " + tmp1[1] + " " + file_name)
os.system("fnj -O newick -m FNJ -o " + tmp2[1] + " " + tmp1[1])
#Use Dendropy to compare the trees.
in_tree = Tree.get_from_stream(os.fdopen(tmp2[0]),
schema='newick',
taxon_namespace=tns)
ref_tree = Tree.get_from_path(ref, schema='newick', taxon_namespace=tns)
sym_diff = treecompare.symmetric_difference(ref_tree, in_tree)
return sym_diff
def testTrees(self):
tree_files = [
("dendropy-test-trees-n33-unrooted-x100a.nexus", "force-unrooted", False),
("dendropy-test-trees-multifurcating-unrooted.nexus", "force-unrooted", False),
("pythonidae.beast.summary.tre", "force-rooted", True),
("primates.beast.mcct.medianh.tre", "force-rooted", True),
]
for tree_file, rooting, is_rooted in tree_files:
ref_tree = dendropy.Tree.get_from_path(pathmap.tree_source_path(tree_file),
"nexus",
rooting=rooting)
bipartition_encoding = ref_tree.encode_bipartitions()
t_tree = dendropy.Tree.from_bipartition_encoding(
bipartition_encoding,
taxon_namespace=ref_tree.taxon_namespace,
is_rooted=ref_tree.is_rooted)
# t_tree.encode_bipartitions()
_LOG.debug("--\n File: {} ({})".format(tree_file, ref_tree.is_rooted))
_LOG.debug(" Original: {}".format(ref_tree.as_string("newick")))
_LOG.debug("Reconstructed: {}".format(t_tree.as_string("newick")))
self.assertEqual(treecompare.symmetric_difference(ref_tree, t_tree), 0)
def testMidpointRooting(self):
taxa = dendropy.TaxonNamespace()
test_trees = dendropy.TreeList.get_from_path(pathmap.tree_source_path('pythonidae.random.bd0301.randomly-rooted.tre'),
"nexus",
taxon_namespace=taxa,
rooting="force-rooted")
expected_trees = dendropy.TreeList.get_from_path(pathmap.tree_source_path('pythonidae.random.bd0301.midpoint-rooted.tre'),
"nexus",
taxon_namespace=taxa,
rooting="force-rooted")
for idx, test_tree in enumerate(test_trees):
expected_tree = expected_trees[idx]
test_tree.reroot_at_midpoint(update_bipartitions=True)
self.assertEqual(treecompare.symmetric_difference(test_tree, expected_tree), 0)
for bipartition in test_tree.bipartition_encoding:
if test_tree.bipartition_edge_map[bipartition].head_node is test_tree.seed_node:
continue
# self.assertAlmostEqual(bipartition.edge.length, expected_tree.split_bitmask_edge_map[bipartition.split_bitmask].length, 3)
self.assertAlmostEqual(test_tree.bipartition_edge_map[bipartition].length,
expected_tree.bipartition_edge_map[bipartition].length,
3)
def calcDistance(self):
if self.path1 != '' and self.path2 != '':
self.fileEx1 = (os.path.splitext(self.path1)[1])[1:]
self.fileEx2 = (os.path.splitext(self.path2)[1])[1:]
tns = dendropy.TaxonNamespace()
self.tree1 = dendropy.Tree.get_from_path(self.path1,
self.fileEx1,
taxon_namespace=tns)
self.tree2 = dendropy.Tree.get_from_path(self.path2,
self.fileEx2,
taxon_namespace=tns)
self.tree1.encode_bipartitions()
self.tree2.encode_bipartitions()
print(
treecompare.false_positives_and_negatives(
self.tree1, self.tree2))
# self.tree1 = dendropy.Tree.get_from_string('((A, B), (C, D))', 'newick')
# self.tree2 = dendropy.Tree.get_from_string('((A, B), (C, D))', 'newick')
# self.tree1.encode_bipartitions()
# self.tree2.encode_bipartitions()
# oblicz dystans
# self.symDist = self.tree1.symmetric_difference(self.tree2)
self.symDist = treecompare.symmetric_difference(
self.tree1, self.tree2)
self.fpnDist = treecompare.false_positives_and_negatives(
self.tree1, self.tree2)
self.eucDist = treecompare.euclidean_distance(
self.tree1, self.tree2)
self.rfDist = treecompare.robinson_foulds_distance(
self.tree1, self.tree2)
# pokaz wyniki
self.res1.setText(str(self.eucDist)) #eucDist
self.res2.setText(str(self.rfDist)) #rfDist
def check(self, title, src_prefix):
tns = dendropy.TaxonNamespace()
input_ds = dendropy.DataSet.get_from_path(
src=pathmap.tree_source_path(src_prefix + ".dendropy-pruned.nex"),
schema='nexus',
attached_taxon_namespace=tns)
input_taxa = input_ds.taxon_namespaces[0]
output_ds = dendropy.DataSet.get_from_path(
src=pathmap.tree_source_path(src_prefix + ".paup-pruned.nex"),
schema='nexus',
taxon_namespace=input_taxa)
for set_idx, src_trees in enumerate(input_ds.tree_lists):
src_trees = input_ds.tree_lists[set_idx]
ref_trees = output_ds.tree_lists[set_idx]
for tree_idx, src_tree in enumerate(src_trees):
_LOG.debug("%s Set %d/%d, Tree %d/%d" % (title, set_idx+1, len(input_ds.tree_lists), tree_idx+1, len(src_trees)))
ref_tree = ref_trees[tree_idx]
# tree_dist = paup.symmetric_difference(src_tree, ref_tree)
# d = src_tree.symmetric_difference(ref_tree)
# if d > 0:
# print d
self.assertEqual(treecompare.symmetric_difference(src_tree, ref_tree), 0)
def is_concordant(G, S, sp_to_genes):
G2 = dendropy.Tree(G)
for s in S.leaf_nodes():
genes = sp_to_genes[s.taxon.label]
mrca = G2.mrca(taxon_labels=genes)
leaf_taxa = map(lambda x: x.taxon.label, mrca.leaf_nodes())
if set(leaf_taxa) != set(genes):
return False
children = get_children(mrca)
for child in children:
mrca.remove_child(child)
mrca.taxon = G.taxon_namespace.get_taxon(label=s.taxon.label)
# maybe update bipartitions before?
S.encode_bipartitions()
G2.encode_bipartitions()
diff = treecompare.symmetric_difference(G2, S)
if diff == 0:
return True
else:
return False
def check(self,
title,
src_prefix,
to_retain=False):
input_ds = dendropy.DataSet.get_from_path(
src=pathmap.tree_source_path(src_prefix + ".pre-pruned.nex"),
schema='nexus')
tns1 = dendropy.TaxonNamespace()
input_ds.attach_taxon_namespace(tns1)
input_taxa = input_ds.taxon_namespaces[0]
output_ds = dendropy.DataSet.get_from_path(
src=pathmap.tree_source_path(src_prefix + ".paup-pruned.nex"),
schema='nexus',
taxon_namespace=input_taxa)
tns2 = dendropy.TaxonNamespace()
output_ds.attach_taxon_namespace(tns2)
if to_retain:
taxf = open(pathmap.tree_source_path(src_prefix + ".retained_taxa.txt"), "r")
else:
taxf = open(pathmap.tree_source_path(src_prefix + ".pruned_taxa.txt"), "r")
rows = taxf.readlines()
taxon_idxs_list = [ [int(i) for i in row.split()] for row in rows ]
for set_idx, src_trees in enumerate(input_ds.tree_lists):
src_trees = input_ds.tree_lists[set_idx]
ref_trees = output_ds.tree_lists[set_idx]
taxon_idxs = taxon_idxs_list[set_idx]
sub_taxa = [src_trees.taxon_namespace[i] for i in taxon_idxs]
for tree_idx, src_tree in enumerate(src_trees):
_LOG.debug("%s Set %d/%d, Tree %d/%d" % (title, set_idx+1, len(input_ds.tree_lists), tree_idx+1, len(src_trees)))
ref_tree = ref_trees[tree_idx]
if to_retain:
src_tree.retain_taxa(sub_taxa)
else:
src_tree.prune_taxa(sub_taxa)
# tree_dist = paup.symmetric_difference(src_tree, ref_tree)
self.assertEqual(treecompare.symmetric_difference(src_tree, ref_tree), 0)
taxf.close()
def compare_to_strain_tree(self, rf=True):
import dendropy
from dendropy.calculate import treecompare
from ete3 import Tree
if (rf):
with open(self.strainTree, 'r') as ref:
Ref = dendropy.Tree.get(file=ref, schema="newick")
T = dendropy.TreeList([Ref] + [
dendropy.Tree.get(data=t.write(), schema='newick')
for t in self.grab_trees()[0]
])
d = np.zeros(len(T) - 1)
for n in range(1, len(T)):
d[n - 1] = treecompare.symmetric_difference(T[0], T[n])
else:
Ref = Tree(self.strainTree, format=1)
trees = self.grab_trees()[0]
d = np.zeros(len(trees))
for n, tree in enumerate(trees):
d[n] = self.compare_trees(Ref, tree)
return d
def tred(counter, Namelist, finlist, Non_CleanList_Done):
'''Returns the symmetric difference between each noise and original alignment and the original tree.'''
with open(
'fa_sekvens', "w"
) as sh: #Creates a file with each alignment as a Fasta-format, B.A.J.S= Beskrivning Av Justerad Sekvens
for n in range(len(Namelist)):
sh.write('\n' + '>' + Namelist[n] + '\n' + Non_CleanList_Done[n])
with open(
'fastprot_text', 'w'
) as ph: # Runs the command "fastprot" on file "fa_sekvens". K.I.S.S = Kalkylerar Individuell Sekvensiell Strcka av BAJS
out = check_output(["fastprot", 'fa_sekvens'])
ph.write(out)
with open(
str(counter), 'w'
) as kd: #Creates a treefile using command "fnj" for each alignment and outputs it as a file with name as a number.
tree = check_output(["fnj", "-O", "newick", "fastprot_text"])
kd.write(tree)
tns = dendropy.TaxonNamespace()
t1 = dendropy.Tree.get(
file=open('asymmetric_0.5.tree', 'r'),
schema="newick",
tree_offset=0,
taxon_namespace=tns) #Opens original tree and defines it as t1
t2 = dendropy.Tree.get(
file=open(str(counter), 'r'),
schema="newick",
tree_offset=0,
taxon_namespace=tns) #Opens each tree made from aligments as t2
t1.encode_bipartitions() # Makes sure t1 and t2 have the same bipartitions
t2.encode_bipartitions()
print(treecompare.symmetric_difference(t1, t2)
) #Compares the symmetric difference between the two trees t1 and t2
return
def calculateDistance(self):
if self.path1 != '' and self.path2 != '':
#get files extensions
self.fileExtension1 = (os.path.splitext(self.path1)[1])[1:]
self.fileExtension2 = (os.path.splitext(self.path2)[1])[1:]
#open tree files
tns = dendropy.TaxonNamespace()
self.tree1 = dendropy.Tree.get_from_path(self.path1, self.fileExtension1, taxon_namespace=tns)
self.tree2 = dendropy.Tree.get_from_path(self.path2, self.fileExtension2, taxon_namespace=tns)
self.tree1.encode_bipartitions()
self.tree2.encode_bipartitions()
print(treecompare.false_positives_and_negatives(self.tree1, self.tree2))
# self.tree1 = dendropy.Tree.get_from_string('((A, B), (C, D))', 'newick')
# self.tree2 = dendropy.Tree.get_from_string('((A, B), (C, D))', 'newick')
# self.tree1.encode_bipartitions()
#self.tree2.encode_bipartitions()
#calculate distances
#self.symDist = self.tree1.symmetric_difference(self.tree2)
self.symDist = treecompare.symmetric_difference(self.tree1, self.tree2)
self.fpnDist = treecompare.false_positives_and_negatives(self.tree1, self.tree2)
self.eucDist = treecompare.euclidean_distance(self.tree1, self.tree2)
self.rfDist = treecompare.robinson_foulds_distance(self.tree1, self.tree2)
#show distances
self.dist1Value.setText(str(self.eucDist))
self.dist2Value.setText(str(self.rfDist))
self.dist3Value.setText(str(self.symDist))
self.dist4Value.setText(str(self.fpnDist))
ilist = find_files(top=o_file, filename_filter=ext)
olist = find_files(top=i_file, filename_filter=ext)
split1 = [os.path.split(file)[1] for file in ilist]
split2 = [os.path.split(file)[1] for file in olist]
RF = []
TLdiff = []
T1L = []
T2L = []
shared_files = []
for file in ilist:
tree1 = dendropy.Tree.get_from_path(file, 'nexus')
TL1 = tree1.length()
T1L.append(TL1)
if os.path.split(file)[1] in split2:
shared_files.append(file)
tree2 = dendropy.Tree.get_from_path(file, 'nexus', taxon_namespace=tree1.taxon_namespace)
TL2= tree2.length()
T2L.append(TL2)
TLdiff.append(TL1-TL2)
RF.append(treecompare.symmetric_difference(tree1,tree2))
df = pd.DataFrame(shared_files)
df['RF'] = RF
df['TLDiff'] = TLdiff
df['TL1'] = TL1
df['TL2'] = TL2
print(df)
df.to_csv('c.csv')
path = file_path+'/'+onlyfiles[j]
fil = open('/home/4/u1we1f44/Documents/appbio15/projekt/data/'+path, 'r')
lines_list=fil.readlines()
fil.close()
test = SeqDic(lines_list) # If this dose not worke we do not have a FASTA file
##
# Makes a newick tree and checks if the referense tree is recovered. The none reducing file.
##
line = 'cat /home/4/u1we1f44/Documents/appbio15/projekt/data/'+path+' | fastprot -I fasta -O phylip | fnj -I phylip -O "newick" -o "Treeout.txt"'
os.system(line)
TreePath=file_path+'/'+RefTree
t1=Tree.get(file=open('/home/4/u1we1f44/Documents/appbio15/projekt/data/'+TreePath,'r'),schema="newick",tree_offset=0)
t2=Tree.get(file=open('/home/4/u1we1f44/Documents/appbio15/projekt/src/Treeout.txt','r'),schema="newick",tree_offset=0,taxon_namespace=t1.taxon_namespace)
t1.encode_bipartitions()
t2.encode_bipartitions()
if treecompare.symmetric_difference(t1, t2)==0:
NotFixedCount += 1
os.remove('/home/4/u1we1f44/Documents/appbio15/projekt/src/Treeout.txt')
Total += 1
else:
Total += 1
os.remove('/home/4/u1we1f44/Documents/appbio15/projekt/src/Treeout.txt')
##
# Makes a temporary file. In the temporary file with data with the nosie columns remoeved. MAkes a newick tree and checks if the refernse tree is recovered.
# The nosie columns removed.
##
os.system("touch temp.fa")
tempf = open('temp.fa','w')
seq_dic = MoreThan2(lines_list)
for key in seq_dic:
tempf.write('>'+key+'\n'+seq_dic[key]+'\n')
#f = open("results.txt", "a")
#file1=open("/home/ubuntu/PhD_Study/Research/Simulated_Datasets/Taxa/t200","r")
file1 = open(
"/mnt/e/PhD_Study/FromLinux/PhD_Study/Research/Simulated_Datasets/Indelible/indelible_tests/height/h2.0/t10.mt",
"r")
s1 = file1.readline()
#name="RAxML_bestTree.t200BS"
#file2name="/home/ubuntu/PhD_Study/Research/Simulated_Datasets/Taxa/outputs/" + name
#file2 = open(file2name, "r")
file2 = open(
"/mnt/e/PhD_Study/FromLinux/PhD_Study/Research/Simulated_Datasets/Indelible/indelible_tests/height/h2.0/t10.fast",
"r")
s2 = file2.readline()
# establish common taxon namespace
tns = dendropy.TaxonNamespace()
# ensure all trees loaded use common namespace
try:
tree1 = dendropy.Tree.get(data=s1, schema='newick', taxon_namespace=tns)
tree2 = dendropy.Tree.get(data=s2, schema='newick', taxon_namespace=tns)
## Unweighted Robinson-Foulds distance
rf_distance = treecompare.symmetric_difference(tree1, tree2)
print(rf_distance)
#f.write(name)
#f.write("\n")
#f.write(rf_distance.__str__())
#f.write("\n")
except:
print("Input model tree problem")
import dendropy
from dendropy.calculate import treecompare
tns = dendropy.TaxonNamespace()
tree1 = dendropy.Tree.get(
path=
"/home/mys/Documents/git/AiBToS-Project1/input/clustalo-quicktree.newick",
schema='newick',
taxon_namespace=tns)
tree2 = dendropy.Tree.get(
path=
"/home/mys/Documents/git/AiBToS-Project1/input/clustalo-rapidnj.newick",
schema='newick',
taxon_namespace=tns)
tree1.encode_bipartitions()
tree2.encode_bipartitions()
print(treecompare.symmetric_difference(tree1, tree2))
#! /usr/bin/env python
import dendropy
from dendropy.calculate import treecompare
distances = []
taxa = dendropy.TaxonNamespace()
mle_tree = dendropy.Tree.get(path='pythonidae.mle.nex',
schema='nexus',
taxon_namespace=taxa)
mcmc_tree_file_paths = [
'pythonidae.mb.run1.t', 'pythonidae.mb.run2.t', 'pythonidae.mb.run3.t',
'pythonidae.mb.run4.t'
]
for mcmc_tree in dendropy.Tree.yield_from_files(files=mcmc_tree_file_paths,
schema='nexus',
taxon_namespace=taxa):
distances.append(treecompare.symmetric_difference(mle_tree, mcmc_tree))
print("Mean symmetric distance between MLE and MCMC trees: %d" %
float(sum(distances) / len(distances)))
tns = dendropy.TaxonNamespace()
try:
# ensure all trees loaded use common namespace
tree1 = dendropy.Tree.get(data=s1,
schema='newick',
taxon_namespace=tns)
tree2 = dendropy.Tree.get(data=s2,
schema='newick',
taxon_namespace=tns)
tree3 = dendropy.Tree.get(data=s3,
schema='newick',
taxon_namespace=tns)
## Unweighted Robinson-Foulds distance
rf_fast = treecompare.symmetric_difference(tree1, tree2)
rf_raxml = treecompare.symmetric_difference(tree1, tree3)
#print(rf_distance)
result_fast.write(str(rf_fast))
result_fast.write("\n")
result_raxml.write(str(rf_raxml))
result_raxml.write("\n")
except:
result_fast.write("tree error")
result_fast.write("\n")
result_raxml.write("tree error")
result_raxml.write("\n")
result_fast.write("\n\n")
result_raxml.write("\n\n")
import dendropy from dendropy.calculate import treecompare tree_str1 = "((A,B),C);" tree_list1 = dendropy.TreeList() tree_list1.read(data=tree_str1, schema="newick") tree_list2 = dendropy.TreeList(taxon_namespace=tree_list1.taxon_namespace) tree_list2.read(data=tree_str1, schema="newick") # Results in: 0 print(treecompare.symmetric_difference(tree_list1[0], tree_list2[0]))
import dendropy
from dendropy.calculate import treecompare
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('reftree')
parser.add_argument('tree')
args = parser.parse_args()
InFile = open(args.reftree)
tree = ''
for line in InFile:
tree = line
tns = dendropy.TaxonNamespace()
tree1 = dendropy.Tree.get(data=tree,schema='newick',taxon_namespace=tns) #reference tree
InFile =open(args.tree)
tree = ''
for line in InFile:
tree = line
tree2 = dendropy.Tree.get(data=tree,schema='newick',taxon_namespace=tns) #original or noise reduced tree
n = treecompare.symmetric_difference(tree1,tree2) #get a numerical value for symmetric difference..
sys.stdout.write(str(n)+'\n') #and write it to standard output
T_seq_list = dendropy.TreeList(taxon_namespace=T_H_list.taxon_namespace) T_seq_list.read(data=T_seq_string, schema="newick") T_F_list = dendropy.TreeList(taxon_namespace=T_H_list.taxon_namespace) T_F_list.read(data=T_F_string, schema="newick") # so this creation of the namespaces works, as these two lists below will print out the contents of # the original tree files, AND the statistical functions below also successfully run. #print T_H_list[0] #print T_DMC_list[0] # Calculating symmetric differences (unweighted robinson foulds). # symmetric difference is the number of splits found in one of the trees but not the other. # it is defined as the number of transformations needed to turn one tree into the other. print "Symmetric difference between T_H and T_DMC: " + str(treecompare.symmetric_difference(T_H_list[0], T_DMC_list[0])) print "Symmetric difference between T_H and T_seq: " + str(treecompare.symmetric_difference(T_H_list[0], T_seq_list[0])) print "Symmetric difference between T_H with T_F: " + str(treecompare.symmetric_difference(T_H_list[0], T_F_list[0])) print "Symmetric difference between T_DMC with T_seq: " + str(treecompare.symmetric_difference(T_DMC_list[0], T_seq_list[0])) print "Symmetric difference between T_DMC with T_F: " + str(treecompare.symmetric_difference(T_DMC_list[0], T_F_list[0])) print "Symmetric difference between T_seq with T_F: " + str(treecompare.symmetric_difference(T_seq_list[0], T_F_list[0])) # Calculating the robinson foulds distances # This is the weighted symmetric difference, which is the sum of the square of differences in branch lengths for equivalent splits between two trees. # It takes edge lengths into account, and therefore will yield a non-zero answer for trees with identical relationships, but have different branch lengths. # This explains why the unweighted distance between T_H and T_seq is 0, but is >0 for the weighted distance. print "Robinson-Foulds distance between T_H and T_DMC: " + str(treecompare.weighted_robinson_foulds_distance(T_H_list[0], T_DMC_list[0])) print "Robinson-Foulds distance between T_H and T_seq: " + str(treecompare.weighted_robinson_foulds_distance(T_H_list[0], T_seq_list[0])) print "Robinson-Foulds distance between T_H and T_F: " + str(treecompare.weighted_robinson_foulds_distance(T_H_list[0], T_F_list[0])) print "Robinson-Foulds distance between T_DMC and T_seq: " + str(treecompare.weighted_robinson_foulds_distance(T_DMC_list[0], T_seq_list[0])) print "Robinson-Foulds distance between T_DMC and T_F: " + str(treecompare.weighted_robinson_foulds_distance(T_DMC_list[0], T_F_list[0]))
import dendropy
from dendropy.calculate import treecompare
s1 = "(a,(b,(c,d)));"
s2 = "(a,(d,(b,c)));"
# establish common taxon namespace
tns = dendropy.TaxonNamespace()
# ensure all trees loaded use common namespace
tree1 = dendropy.Tree.get(
data=s1,
schema='newick',
taxon_namespace=tns)
tree2 = dendropy.Tree.get(
data=s2,
schema='newick',
taxon_namespace=tns)
## Unweighted Robinson-Foulds distance
print(treecompare.symmetric_difference(tree1, tree2))
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import dendropy
from dendropy.calculate import treecompare
distances = []
taxa = dendropy.TaxonNamespace()
mle_tree = dendropy.Tree.get(
path='pythonidae.mle.nex',
schema='nexus',
taxon_namespace=taxa)
mcmc_tree_file_paths = ['pythonidae.mb.run1.t',
'pythonidae.mb.run2.t',
'pythonidae.mb.run3.t',
'pythonidae.mb.run4.t']
for mcmc_tree in dendropy.Tree.yield_from_files(
files=mcmc_tree_file_paths,
schema='nexus',
taxon_namespace=taxa):
distances.append(treecompare.symmetric_difference(mle_tree, mcmc_tree))
print("Mean symmetric distance between MLE and MCMC trees: %d"
% float(sum(distances)/len(distances)))