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#!/usr/bin/env python
import warnings
warnings.filterwarnings('ignore', 'Not using MPI as mpi4py not found')
import numpy
# hides the warning from taking log of -ve determinant
numpy.seterr(invalid='ignore')
from cogent.util.unit_test import TestCase, main
from cogent import LoadSeqs, DNA, RNA, PROTEIN
from cogent.evolve.pairwise_distance import get_moltype_index_array, \
seq_to_indices, _fill_diversity_matrix, \
_jc69_from_matrix, JC69Pair, _tn93_from_matrix, TN93Pair, LogDetPair, \
ParalinearPair
from cogent.evolve._pairwise_distance import \
_fill_diversity_matrix as pyx_fill_diversity_matrix
__author__ = "Gavin Huttley, Yicheng Zhu and Ben Kaehler"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Gavin Huttley", "Yicheng Zhu", "Ben Kaehler"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Gavin Huttley"
__email__ = "Gavin.Huttley@anu.edu.au"
__status__ = "Production"
class TestPair(TestCase):
dna_char_indices = get_moltype_index_array(DNA)
rna_char_indices = get_moltype_index_array(RNA)
alignment = LoadSeqs(data=[('s1', 'ACGTACGTAC'),
('s2', 'GTGTACGTAC')], moltype=DNA)
ambig_alignment = LoadSeqs(data=[('s1', 'RACGTACGTACN'),
('s2', 'AGTGTACGTACA')], moltype=DNA)
diff_alignment = LoadSeqs(data=[('s1', 'ACGTACGTTT'),
('s2', 'GTGTACGTAC')], moltype=DNA)
def test_char_to_index(self):
"""should correctly recode a DNA & RNA seqs into indices"""
seq = 'TCAGRNY?-'
expected = [0, 1, 2, 3, -9, -9, -9, -9, -9]
indices = seq_to_indices(seq, self.dna_char_indices)
self.assertEquals(indices, expected)
seq = 'UCAGRNY?-'
indices = seq_to_indices(seq, self.rna_char_indices)
self.assertEquals(indices, expected)
def test_fill_diversity_matrix_all(self):
"""make correct diversity matrix when all chars valid"""
s1 = seq_to_indices('ACGTACGTAC', self.dna_char_indices)
s2 = seq_to_indices('GTGTACGTAC', self.dna_char_indices)
matrix = numpy.zeros((4,4), float)
# self-self should just be an identity matrix
_fill_diversity_matrix(matrix, s1, s1)
self.assertEquals(matrix.sum(), len(s1))
self.assertEquals(matrix,
numpy.array([[2,0,0,0],
[0,3,0,0],
[0,0,3,0],
[0,0,0,2]], float))
# small diffs
matrix.fill(0)
_fill_diversity_matrix(matrix, s1, s2)
self.assertEquals(matrix,
numpy.array([[2,0,0,0],
[1,2,0,0],
[0,0,2,1],
[0,0,0,2]], float))
def test_fill_diversity_matrix_some(self):
"""make correct diversity matrix when not all chars valid"""
s1 = seq_to_indices('RACGTACGTACN', self.dna_char_indices)
s2 = seq_to_indices('AGTGTACGTACA', self.dna_char_indices)
matrix = numpy.zeros((4,4), float)
# small diffs
matrix.fill(0)
_fill_diversity_matrix(matrix, s1, s2)
self.assertEquals(matrix,
numpy.array([[2,0,0,0],
[1,2,0,0],
[0,0,2,1],
[0,0,0,2]], float))
def test_python_vs_cython_fill_matrix(self):
"""python & cython fill_diversity_matrix give same answer"""
s1 = seq_to_indices('RACGTACGTACN', self.dna_char_indices)
s2 = seq_to_indices('AGTGTACGTACA', self.dna_char_indices)
matrix1 = numpy.zeros((4,4), float)
_fill_diversity_matrix(matrix1, s1, s2)
matrix2 = numpy.zeros((4,4), float)
pyx_fill_diversity_matrix(matrix2, s1, s2)
self.assertFloatEqual(matrix1, matrix2)
def test_jc69_from_matrix(self):
"""compute JC69 from diversity matrix"""
s1 = seq_to_indices('ACGTACGTAC', self.dna_char_indices)
s2 = seq_to_indices('GTGTACGTAC', self.dna_char_indices)
matrix = numpy.zeros((4,4), float)
_fill_diversity_matrix(matrix, s1, s2)
total, p, dist, var = _jc69_from_matrix(matrix)
self.assertEquals(total, 10.0)
self.assertEquals(p, 0.2)
def test_jc69_from_alignment(self):
"""compute JC69 dists from an alignment"""
calc = JC69Pair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
self.assertEquals(calc.Lengths['s1', 's2'], 10)
self.assertEquals(calc.Proportions['s1', 's2'], 0.2)
# value from OSX MEGA 5
self.assertFloatEqual(calc.Dists['s1', 's2'], 0.2326161962)
# value**2 from OSX MEGA 5
self.assertFloatEqual(calc.Variances['s1', 's2'],
0.029752066125078681)
# value from OSX MEGA 5
self.assertFloatEqual(calc.StdErr['s1', 's2'], 0.1724878724)
# same answer when using ambiguous alignment
calc.run(self.ambig_alignment, show_progress=False)
self.assertFloatEqual(calc.Dists['s1', 's2'], 0.2326161962)
# but different answer if subsequent alignment is different
calc.run(self.diff_alignment, show_progress=False)
self.assertTrue(calc.Dists['s1', 's2'] != 0.2326161962)
def test_tn93_from_matrix(self):
"""compute TN93 distances"""
calc = TN93Pair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
self.assertEquals(calc.Lengths['s1', 's2'], 10)
self.assertEquals(calc.Proportions['s1', 's2'], 0.2)
# value from OSX MEGA 5
self.assertFloatEqual(calc.Dists['s1', 's2'], 0.2554128119)
# value**2 from OSX MEGA 5
self.assertFloatEqual(calc.Variances['s1', 's2'], 0.04444444445376601)
# value from OSX MEGA 5
self.assertFloatEqual(calc.StdErr['s1', 's2'], 0.2108185107)
# same answer when using ambiguous alignment
calc.run(self.ambig_alignment, show_progress=False)
self.assertFloatEqual(calc.Dists['s1', 's2'], 0.2554128119)
# but different answer if subsequent alignment is different
calc.run(self.diff_alignment, show_progress=False)
self.assertTrue(calc.Dists['s1', 's2'] != 0.2554128119)
def test_distance_pair(self):
"""get distances dict"""
calc = TN93Pair(DNA, alignment=self.alignment)
calc.run(show_progress=False)
dists = calc.getPairwiseDistances()
dist = 0.2554128119
expect = {('s1', 's2'): dist, ('s2', 's1'): dist}
self.assertEquals(dists.keys(), expect.keys())
self.assertFloatEqual(dists.values(), expect.values())
def test_logdet_pair_dna(self):
"""logdet should produce distances that match MEGA"""
aln = LoadSeqs('data/brca1_5.paml', moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
all_expected = {('Human', 'NineBande'): 0.075336929999999996,
('NineBande', 'DogFaced'): 0.0898575452,
('DogFaced', 'Human'): 0.1061747919,
('HowlerMon', 'DogFaced'): 0.0934480008,
('Mouse', 'HowlerMon'): 0.26422862920000001,
('NineBande', 'Human'): 0.075336929999999996,
('HowlerMon', 'NineBande'): 0.062202897899999998,
('DogFaced', 'NineBande'): 0.0898575452,
('DogFaced', 'HowlerMon'): 0.0934480008,
('Human', 'DogFaced'): 0.1061747919,
('Mouse', 'Human'): 0.26539976700000001,
('NineBande', 'HowlerMon'): 0.062202897899999998,
('HowlerMon', 'Human'): 0.036571181899999999,
('DogFaced', 'Mouse'): 0.2652555144,
('HowlerMon', 'Mouse'): 0.26422862920000001,
('Mouse', 'DogFaced'): 0.2652555144,
('NineBande', 'Mouse'): 0.22754789210000001,
('Mouse', 'NineBande'): 0.22754789210000001,
('Human', 'Mouse'): 0.26539976700000001,
('Human', 'HowlerMon'): 0.036571181899999999}
for pair in dists:
got = dists[pair]
expected = all_expected[pair]
self.assertFloatEqual(got, expected)
def test_logdet_tk_adjustment(self):
"""logdet using tamura kumar differs from classic"""
aln = LoadSeqs('data/brca1_5.paml', moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
tk = logdet_calc.getPairwiseDistances()
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
not_tk = logdet_calc.getPairwiseDistances()
self.assertNotEqual(tk, not_tk)
def test_logdet_pair_aa(self):
"""logdet shouldn't fail to produce distances for aa seqs"""
aln = LoadSeqs('data/brca1_5.paml', moltype=DNA)
aln = aln.getTranslation()
logdet_calc = LogDetPair(moltype=PROTEIN, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
def test_logdet_missing_states(self):
"""should calculate logdet measurement with missing states"""
data = [('seq1', "GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT"),
('seq2', "TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTNTTTTTTTTTTTTCCCCCCCCCCCCCCCCC")]
aln = LoadSeqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
self.assertTrue(dists.values()[0] is not None)
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
self.assertTrue(dists.values()[0] is not None)
def test_logdet_variance(self):
"""calculate logdet variance consistent with hand calculation"""
data = [('seq1', "GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT"),
('seq2', "TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC")]
aln = LoadSeqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
self.assertEqual(logdet_calc.Variances[1,1], None)
index = dict(zip('ACGT', range(4)))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
M = numpy.linalg.inv(J)
var = 0.
for i in range(4):
for j in range(4):
var += M[j, i]**2 * J[i, j] - 1
var /= 16 * len(data[0][1])
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
self.assertFloatEqual(logdet_calc.Variances[1,1], var, eps=1e-3)
def test_logdet_for_determinant_lte_zero(self):
"""returns distance of None if the determinant is <= 0"""
data = dict(seq1="AGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
seq2="TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC")
aln = LoadSeqs(data=data, moltype=DNA)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(use_tk_adjustment=True, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
self.assertTrue(dists.values()[0] is None)
logdet_calc.run(use_tk_adjustment=False, show_progress=False)
dists = logdet_calc.getPairwiseDistances()
self.assertTrue(dists.values()[0] is None)
def test_paralinear_pair_aa(self):
"""paralinear shouldn't fail to produce distances for aa seqs"""
aln = LoadSeqs('data/brca1_5.paml', moltype=DNA)
aln = aln.getTranslation()
paralinear_calc = ParalinearPair(moltype=PROTEIN, alignment=aln)
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.getPairwiseDistances()
def test_paralinear_distance(self):
"""calculate paralinear variance consistent with hand calculation"""
data = [('seq1', "GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT"),
('seq2', "TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC")]
aln = LoadSeqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
index = dict(zip('ACGT', range(4)))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
M = numpy.linalg.inv(J)
f = J.sum(1), J.sum(0)
dist = -0.25 * numpy.log( numpy.linalg.det(J) / \
numpy.sqrt(f[0].prod() * f[1].prod()) )
self.assertFloatEqual(paralinear_calc.Dists[1,1], dist, eps=1e-3)
def test_paralinear_variance(self):
"""calculate paralinear variance consistent with hand calculation"""
data = [('seq1', "GGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT"),
('seq2', "TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC")]
aln = LoadSeqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
index = dict(zip('ACGT', range(4)))
J = numpy.zeros((4, 4))
for p in zip(data[0][1], data[1][1]):
J[index[p[0]], index[p[1]]] += 1
for i in range(4):
if J[i, i] == 0:
J[i, i] += 0.5
J /= J.sum()
M = numpy.linalg.inv(J)
f = J.sum(1), J.sum(0)
var = 0.
for i in range(4):
for j in range(4):
var += M[j, i]**2 * J[i, j]
var -= 1 / numpy.sqrt(f[0][i] * f[1][i])
var /= 16 * len(data[0][1])
self.assertFloatEqual(paralinear_calc.Variances[1,1], var, eps=1e-3)
def test_paralinear_for_determinant_lte_zero(self):
"""returns distance of None if the determinant is <= 0"""
data = dict(seq1="AGGGGGGGGGGCCCCCCCCCCCCCCCCCGGGGGGGGGGGGGGGCGGTTTTTTTTTTTTTTTTTT",
seq2="TAAAAAAAAAAGGGGGGGGGGGGGGGGGGTTTTTTTTTTTTTTTTTTCCCCCCCCCCCCCCCCC")
aln = LoadSeqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.getPairwiseDistances()
self.assertTrue(dists.values()[0] is None)
paralinear_calc.run(show_progress=False)
dists = paralinear_calc.getPairwiseDistances()
self.assertTrue(dists.values()[0] is None)
def test_paralinear_pair_dna(self):
"""calculate paralinear distance consistent with logdet distance"""
data = [('seq1', 'TAATTCATTGGGACGTCGAATCCGGCAGTCCTGCCGCAAAAGCTTCCGGAATCGAATTTTGGCA'),
('seq2', 'AAAAAAAAAAAAAAAACCCCCCCCCCCCCCCCTTTTTTTTTTTTTTTTGGGGGGGGGGGGGGGG')]
aln = LoadSeqs(data=data, moltype=DNA)
paralinear_calc = ParalinearPair(moltype=DNA, alignment=aln)
paralinear_calc.run(show_progress=False)
logdet_calc = LogDetPair(moltype=DNA, alignment=aln)
logdet_calc.run(show_progress=False)
self.assertFloatEqual(logdet_calc.Dists[1,1],
paralinear_calc.Dists[1,1], eps=1e-3)
self.assertFloatEqual(paralinear_calc.Variances[1,1],
logdet_calc.Variances[1,1], eps=1e-3)
if __name__ == '__main__':
main()
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