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#! /usr/bin/env python
##############################################################################
## DendroPy Phylogenetic Computing Library.
##
## Copyright 2010-2015 Jeet Sukumaran and Mark T. Holder.
## All rights reserved.
##
## See "LICENSE.rst" for terms and conditions of usage.
##
## If you use this work or any portion thereof in published work,
## please cite it as:
##
## Sukumaran, J. and M. T. Holder. 2010. DendroPy: a Python library
## for phylogenetic computing. Bioinformatics 26: 1569-1571.
##
##############################################################################
"""
Tests of Fitch algorithm calculations.
"""
import random
import unittest
import math
import sys
if not (sys.version_info.major >= 3 and sys.version_info.minor >= 4):
from dendropy.utility.filesys import pre_py34_open as open
import dendropy
from dendropy.calculate.treescore import fitch_down_pass
from dendropy.test.support import pathmap
class FitchTest(unittest.TestCase):
def test_pscores_with_gaps_as_new_state(self):
# #NEXUS
# begin paup;
# set warnroot = no;
# exe apternodus.chars.nexus;
# gett file = apternodus.tre;
# set criterion = parsimony;
# pset gap = newstate;
# pscore;
# end;
expected_scores = [396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 396, 713, 715, 723, 733, 672, 719, 734, 709, 695, 686]
self.verify_pscores("apternodus.chars.nexus", "apternodus.tre", False, expected_scores)
def test_pscores_with_gaps_as_missing(self):
# #NEXUS
# begin paup;
# set warnroot = no;
# exe apternodus.chars.nexus;
# gett file = apternodus.tre;
# set criterion = parsimony;
# pset gap = missing;
# pscore;
# end;
expected_scores = [ 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 370, 671, 670, 678, 687, 633, 675, 689, 668, 652, 644]
self.verify_pscores("apternodus.chars.nexus", "apternodus.tre", True, expected_scores)
def verify_pscores(self, char_fname, trees_fname, gaps_as_missing, expected_scores):
dataset = dendropy.DataSet.get_from_path(
pathmap.char_source_path(char_fname),
"nexus")
dataset.read_from_path(
pathmap.tree_source_path(trees_fname),
schema='NEXUS',
taxon_namespace=dataset.taxon_namespaces[0])
char_mat = dataset.char_matrices[0]
# sa = char_mat.default_state_alphabet
# for x in sa:
# print("{}: {}".format(x, x.is_gap_state))
# for x in sa:
# print("{}\t{}\t{}\t\t\t\t{}".format(x, x._index, x.fundamental_indexes, x.fundamental_indexes_with_gaps_as_missing))
taxon_state_sets_map = char_mat.taxon_state_sets_map(gaps_as_missing=gaps_as_missing)
tree_list = dataset.tree_lists[0]
self.assertEqual(len(expected_scores), len(tree_list))
for n, tree in enumerate(tree_list):
node_list = tree.postorder_node_iter()
pscore = fitch_down_pass(node_list, taxon_state_sets_map=taxon_state_sets_map)
# print("{} vs. {}".format(expected_scores[n], pscore))
self.assertEqual(expected_scores[n], pscore)
if __name__ == "__main__":
unittest.main()
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