def estimate_params(self,
seq_len=10000,
kappa=1.0,
base_freqs=[0.25, 0.25, 0.25, 0.25],
unequal_base_freqs=True,
gamma_rates=False,
prop_invar=False):
output_ds = seqsim.generate_hky_dataset(seq_len,
tree_model=self.tree_model,
kappa=kappa,
base_freqs=base_freqs)
self.tree_model.reindex_taxa(output_ds.char_matrices[0].taxon_set)
est_tree, mle = paup.estimate_model(char_matrix=output_ds.char_matrices[0],
tree_model=self.tree_model,
num_states=2,
unequal_base_freqs=unequal_base_freqs,
gamma_rates=gamma_rates,
prop_invar=prop_invar,
tree_est_criterion="likelihood",
tree_user_brlens=True,
paup_path='paup')
return mle
#! /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(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))
