def test_random_p_matrix_diag_vector(self):
"""Probs random should work with a vector diagonal"""
for i in range(NUM_TESTS):
diag = [0, 0.2, 0.6, 1.0]
p = Probs.random(RnaPairs, diag)._data
for i, d, row in zip(range(4), diag, p):
self.assertFloatEqual(sum(row), 1.0)
self.assertEqual(row[i], diag[i])
def test_random_p_matrix_diag_vector(self):
"""Probs random should work with a vector diagonal"""
for i in range(NUM_TESTS):
diag = [0, 0.2, 0.6, 1.0]
p = Probs.random(RnaPairs, diag)._data
for i, d, row in zip(range(4), diag, p):
self.assertFloatEqual(sum(row), 1.0)
self.assertEqual(row[i], diag[i])
def test_random_p_matrix(self):
"""Probs random should return random Probsrows that sum to 1"""
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs)._data
for i in p:
self.assertFloatEqual(sum(i), 1.0)
#length should be 4 by default
self.assertEqual(len(p), 4)
self.assertEqual(len(p[0]), 4)
def test_random_p_matrix(self):
"""Probs random should return random Probsrows that sum to 1"""
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs)._data
for i in p:
self.assertFloatEqual(sum(i), 1.0)
#length should be 4 by default
self.assertEqual(len(p), 4)
self.assertEqual(len(p[0]), 4)
def test_random_p_matrix_diag(self):
"""Probs random should work with a scalar diagonal"""
#if diagonal is 1, off-diagonal elements should be 0
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs, 1)._data
self.assertEqual(p, identity(4, 'd'))
#if diagonal is between 0 and 1, rows should sum to 1
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs, 0.5)._data
for i in range(4):
self.assertFloatEqual(sum(p[i]), 1.0)
self.assertEqual(p[i][i], 0.5)
assert min(p[i]) >= 0
assert max(p[i]) <= 1
#if diagonal > 1, rows should still sum to 1
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs, 2)._data
for i in range(4):
self.assertEqual(p[i][i], 2.0)
self.assertFloatEqual(sum(p[i]), 1.0)
assert min(p[i]) < 0
def test_random_p_matrix_diag(self):
"""Probs random should work with a scalar diagonal"""
#if diagonal is 1, off-diagonal elements should be 0
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs, 1)._data
self.assertEqual(p, identity(4, 'd'))
#if diagonal is between 0 and 1, rows should sum to 1
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs, 0.5)._data
for i in range(4):
self.assertFloatEqual(sum(p[i]), 1.0)
self.assertEqual(p[i][i], 0.5)
assert min(p[i]) >= 0
assert max(p[i]) <= 1
#if diagonal > 1, rows should still sum to 1
for i in range(NUM_TESTS):
p = Probs.random(RnaPairs, 2)._data
for i in range(4):
self.assertEqual(p[i][i], 2.0)
self.assertFloatEqual(sum(p[i]), 1.0)
assert min(p[i]) < 0
def test_probs_to_rates(self):
"""probs_to_rates converts probs to rates, omitting problem cases"""
probs = dict([(i, Probs.random(DnaPairs)) for i in range(100)])
rates = probs_to_rates(probs)
#check we got at most the same number of items as in probs
assert len(rates) <= len(probs)
#check that we didn't get anything bad
vals = rates.values()
for v in vals:
assert not v.isSignificantlyComplex()
#check that we didn't miss anything good
for key, val in probs.items():
if key not in rates:
try:
r = val.toRates()
print r.isValid()
assert r.isSignificantlyComplex() or (not r.isValid())
except (ZeroDivisionError, OverflowError, ValueError):
pass
