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#!/usr/bin/env python
import unittest
from cogent import LoadTree
from cogent.evolve import substitution_model
def a_c(x, y):
return (x == 'A' and y == 'C') or (x == 'C' and y == 'A')
from cogent.evolve.predicate import MotifChange, replacement
__author__ = "Peter Maxwell and Gavin Huttley"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Production"
a_c = MotifChange('A', 'C')
trans = MotifChange('A', 'G') | MotifChange('T', 'C')
TREE = LoadTree(tip_names='ab')
class ScaleRuleTests(unittest.TestCase):
def _makeModel(self, do_scaling, predicates, scale_rules=[]):
return substitution_model.Nucleotide(
do_scaling=do_scaling, equal_motif_probs=True,
model_gaps=False, predicates=predicates, scales=scale_rules)
def _getScaledLengths(self, model, params):
LF = model.makeLikelihoodFunction(TREE)
for param in params:
LF.setParamRule(param, value=params[param], is_constant=True)
result = {}
for predicate in model.scale_masks:
result[predicate] = LF.getScaledLengths(predicate)['a']
return result
def test_scaled(self):
"""Scale rule requiring matrix entries to have all pars specified"""
model = self._makeModel(True, {'k':trans}, {
'ts':trans, 'tv': ~trans})
self.assertEqual(
self._getScaledLengths(model, {'k':6.0, 'length':4.0}),
{'ts': 3.0, 'tv':1.0})
def test_binned(self):
model = self._makeModel(True, {'k':trans}, {
'ts':trans, 'tv': ~trans})
LF = model.makeLikelihoodFunction(TREE, bins=2)
LF.setParamRule('length', value=4.0, is_constant=True)
LF.setParamRule('k', value=6.0, bin='bin0', is_constant=True)
LF.setParamRule('k', value=1.0, bin='bin1', is_constant=True)
for (bin, expected) in [('bin0', 3.0), ('bin1', 4.0/3), (None, 13.0/6)]:
self.assertEqual(LF.getScaledLengths('ts', bin=bin)['a'], expected)
def test_unscaled(self):
"""Scale rule on a model which has scaling performed after calculation
rather than during it"""
model = self._makeModel(False, {'k':trans}, {
'ts':trans, 'tv': ~trans})
self.assertEqual(
self._getScaledLengths(model, {'k':6.0, 'length':2.0}),
{'ts': 3.0, 'tv':1.0})
def test_scaled_or(self):
"""Scale rule where matrix entries can have any of the pars specified"""
model = self._makeModel(True, {'k':trans, 'ac':a_c}, {
'or': (trans | a_c), 'not': ~(trans | a_c)})
self.assertEqual(
self._getScaledLengths(model, {'k':6.0,'length':6.0, 'ac': 3.0}),
{'or': 5.0, 'not': 1.0})
def test_scaling(self):
"""Testing scaling calculations using Dn and Ds as an example."""
model = substitution_model.Codon(
do_scaling=True, model_gaps=False, recode_gaps=True,
predicates = {
'k': trans,
'r': replacement},
motif_probs={
'TAT': 0.0088813702685557206, 'TGT': 0.020511736096426307,
'TCT': 0.024529498836963416, 'TTT': 0.019454430112074435,
'TGC': 0.0010573059843518714, 'TGG': 0.0042292239374074857,
'TAC': 0.002326073165574117, 'TTC': 0.0086699090716853451,
'TCG': 0.0010573059843518714, 'TTA': 0.020723197293296681,
'TTG': 0.01036159864664834, 'TCC': 0.0082469866779445976,
'TCA': 0.022414886868259674, 'GCA': 0.015648128568407697,
'GTA': 0.014590822584055826, 'GCC': 0.0095157538591668436,
'GTC': 0.0063438359061112285, 'GCG': 0.0016916895749629942,
'GTG': 0.0067667582998519769, 'CAA': 0.018185662930852189,
'GTT': 0.021569042080778176, 'GCT': 0.014167900190315077,
'ACC': 0.0042292239374074857, 'GGT': 0.014167900190315077,
'CGA': 0.0012687671812222456, 'CGC': 0.0010573059843518714,
'GAT': 0.030238951152463524, 'AAG': 0.034891097483611758,
'CGG': 0.002326073165574117, 'ACT': 0.028758722774370905,
'GGG': 0.0071896806935927262, 'GGA': 0.016282512159018821,
'GGC': 0.0090928314654260944, 'GAG': 0.031296257136815393,
'AAA': 0.05476844998942694, 'GAC': 0.011207443434129837,
'CGT': 0.0033833791499259885, 'GAA': 0.076337492070205112,
'CTT': 0.010573059843518714, 'ATG': 0.012687671812222457,
'ACA': 0.021991964474518927, 'ACG': 0.00084584478748149711,
'ATC': 0.0076126030873334746, 'AAC': 0.022837809262000422,
'ATA': 0.017762740537111441, 'AGG': 0.013533516599703954,
'CCT': 0.025586804821315288, 'AGC': 0.029393106364982026,
'AGA': 0.021991964474518927, 'CAT': 0.021357580883907802,
'AAT': 0.05772890674561218, 'ATT': 0.019031507718333687,
'CTG': 0.012899133009092831, 'CTA': 0.013744977796574329,
'CTC': 0.0078240642842038483, 'CAC': 0.0050750687248889825,
'CCG': 0.00021146119687037428, 'AGT': 0.03742863184605625,
'CAG': 0.024106576443222668, 'CCA': 0.021357580883907802,
'CCC': 0.0069782194967223515},
scales = {'dN': replacement, 'dS': ~replacement},
mprob_model = 'tuple',
)
length = 0.1115
a = self._getScaledLengths(model,
{'k': 3.6491, 'r': 0.6317, 'length': length})
b = self._getScaledLengths(model,
{'k': 3.6491, 'r': 1.0, 'length': length})
dN = length * a['dN'] / (3.0 * b['dN'])
dS = length * a['dS'] / (3.0 * b['dS'])
# following are results from PAML
self.assertEqual('%.4f' % dN, '0.0325')
self.assertEqual('%.4f' % dS ,'0.0514')
if __name__ == '__main__':
unittest.main()
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