PAUP

The paup module provides functions to estimate a tree given a data matrix, or a substitution model given a tree and a data model.

Trees can be estimated using likelihood:

#! /usr/bin/env python
# -*- coding: utf-8 -*-

import warnings
import dendropy
from dendropy.interop import paup

warnings.warn("This example requires paup to be installed to run.")

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"))

Or neighbor-joining:

#! /usr/bin/env python
# -*- coding: utf-8 -*-

import warnings
import dendropy
from dendropy.interop import paup

warnings.warn("This example requires paup to be installed to run.")

data = dendropy.DnaCharacterMatrix.get(
    path="pythonidae.nex",
    schema="nexus")
tree = paup.estimate_tree(data,
        tree_est_criterion='nj')
print(tree.as_string(schema="newick"))

Estimating a substitution model parameters requires both a tree and a data matrix:

#! /usr/bin/env python
# -*- coding: utf-8 -*-

import warnings
import dendropy
from dendropy.interop import paup

warnings.warn("This example requires paup to be installed to run.")

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_subst=2,
        unequal_base_freqs=True,
        gamma_rates=False,
        prop_invar=False)
for k, v in est_model.items():
    print("{}: {}".format(k, v))