def estimate_tree_from_assemblage_leafsets(
taxon_namespace,
assemblage_memberships,
membership_element_type=dendropy.Taxon,
only_include_taxa=None,
):
d = collections.OrderedDict()
blank = ["0" for i in range(len(assemblage_memberships))]
exclude_from_taxa_blocks = set()
for taxon in taxon_namespace:
if not only_include_taxa or taxon in only_include_taxa:
d[taxon] = list(blank)
else:
exclude_from_taxa_blocks.add(taxon)
for idx, taxa in enumerate(assemblage_memberships):
if membership_element_type is dendropy.Taxon:
for taxon in taxa:
d[taxon][idx] = "1"
elif membership_element_type is dendropy.Node:
for node in taxa:
d[node.taxon][idx] = "1"
else:
raise TypeError(membership_element_type)
# for membership_idx, membership in enumerate(assemblage_memberships):
# for element in membership:
# if membership_element_type is dendropy.Taxon:
# taxon = element
# elif membership_element_type is dendropy.Node:
# taxon = element.taxon
# else:
# raise TypeError(membership_element_type)
char_matrix = dendropy.StandardCharacterMatrix.from_dict(
source_dict=d, taxon_namespace=taxon_namespace)
try:
tree = paup.estimate_tree(
char_matrix=char_matrix,
tree_est_criterion="parsimony",
num_states=len(assemblage_memberships),
char_matrix_writing_kwargs={
"exclude_from_taxa_blocks": exclude_from_taxa_blocks
},
timeout=60,
)
except subprocess.TimeoutExpired:
raise error.SummaryStatisticCalculationExternalProcessTimeoutExpired
return tree
def write_nexus(self):
d = dendropy.StandardCharacterMatrix(taxon_set=self.tree.taxon_set)
sa = dendropy.StateAlphabet()
sa.append(dendropy.StateAlphabetElement(symbol="0"))
sa.append(dendropy.StateAlphabetElement(symbol="1"))
col = dendropy.CharacterType(state_alphabet=sa, label="occurs")
d.state_alphabets = [sa]
d.default_state_alphabet = sa
for t in self.tree.taxon_set:
v = dendropy.CharacterDataVector(taxon=t)
for r in self.regions:
if r in t.regions:
v.append(dendropy.CharacterDataCell(value=sa.state_for_symbol(symbol="1"), character_type=col))
else:
v.append(dendropy.CharacterDataCell(value=sa.state_for_symbol(symbol="0"), character_type=col))
d[t] = v
area_tree = paup.estimate_tree(char_matrix=d, tree_est_criterion="parsimony")
area_tree.label = "areas"
trees = dendropy.TreeList([self.tree, area_tree], taxon_set=self.tree.taxon_set)
dataset = dendropy.DataSet(d, trees)
dataset.write_to_path(self.output_prefix + "." + self.run_title + ".nex", "nexus")
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import dendropy
from dendropy.interop import paup
data = dendropy.DnaCharacterMatrix.get(path="pythonidae.nex", schema="nexus")
tree = paup.estimate_tree(data,
tree_est_criterion='likelihood',
num_subst=2,
unequal_base_freqs=True,
gamma_rates=False,
prop_invar=False)
print(tree.as_string(schema="newick"))
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import dendropy
from dendropy.interop import paup
data = dendropy.DnaCharacterMatrix.get(
path="pythonidae.nex",
schema="nexus")
tree = paup.estimate_tree(data,
tree_est_criterion='likelihood',
num_states=2,
unequal_base_freqs=True,
gamma_rates=False,
prop_invar=False)
print tree.as_string(schema="newick")
#! /usr/bin/env python
import dendropy
from dendropy.interop import paup
data = dendropy.DnaCharacterMatrix.get(
path="pythonidae.nex",
schema="nexus")
tree = paup.estimate_tree(data,
tree_est_criterion='nj')
est_tree, est_model = paup.estimate_model(data,
tree,
num_states=2,
unequal_base_freqs=True,
gamma_rates=False,
prop_invar=False)
for k, v in est_model.items():
print("{}: {}".format(k, v))
#! /usr/bin/env python
import dendropy
from dendropy.interop import paup
data = dendropy.DnaCharacterMatrix.get_from_path("pythonidae.nex", "nexus")
tree = paup.estimate_tree(data, tree_est_criterion='nj')
print tree.as_string("newick")
