def test_isComplex(self):
"""Rates isComplex should return True if complex elements"""
r = Rates([0,0,0.1j,0,0,0,0,0,0], self.abc_pairs)
assert r.isComplex()
r = Rates([0,0,0.1,0,0,0,0,0,0], self.abc_pairs)
assert not r.isComplex()
def test_fixNegsConstrainedOpt(self):
"""Rates fixNegsConstrainedOpt should fix negatives w/ constrained opt"""
q = Rates(array([[-0.28936029, 0.14543346, -0.02648614, 0.17041297],
[ 0.00949624, -0.31186005, 0.17313171, 0.1292321 ],
[ 0.10443209, 0.16134479, -0.30480186, 0.03902498],
[ 0.01611264, 0.12999161, 0.15558259, -0.30168684]]), DnaPairs)
r = q.fixNegsFmin()
assert not q.isValid()
assert r.isValid()
def test_fixNegsEven(self):
"""Rates fixNegsEven should fix negatives by adding evenly to others"""
q = Rates(
[[-6, 2, 2, 2], [-3, -2, 3, 2], [-2, -2, -6, 2], [4, 4, -6, -2]],
RnaPairs)
m = q.fixNegsEven()._data
self.assertEqual(
m,
array([[-6, 2, 2, 2], [0, -3, 2, 1], [0, 0, -0, 0], [2, 2, 0,
-4]]))
def test_normalize(self):
"""Rates normalize should return normalized copy of self where trace=-1"""
r = Rates([-2, 1, 1, 0, -1, 1, 2, 0, -1], self.abc_pairs)
n = r.normalize()
self.assertEqual(n._data, \
array([[-0.5,.25,.25],[0.,-.25,.25],[.5,0.,-.25]]))
#check that we didn't change the original
assert n._data is not r._data
self.assertEqual(r._data, \
array([[-2,1,1,],[0,-1,1,],[2,0,-1]]))
def test_normalize(self):
"""Rates normalize should return normalized copy of self where trace=-1"""
r = Rates([-2,1,1,0,-1,1,2,0,-1], self.abc_pairs)
n = r.normalize()
self.assertEqual(n._data, \
array([[-0.5,.25,.25],[0.,-.25,.25],[.5,0.,-.25]]))
#check that we didn't change the original
assert n._data is not r._data
self.assertEqual(r._data, \
array([[-2,1,1,],[0,-1,1,],[2,0,-1]]))
def test_fixNegsDiag(self):
"""Rates fixNegsDiag should fix negatives by adding to diagonal"""
q = Rates(
[[-6, 2, 2, 2], [-6, -2, 4, 4], [2, 2, -6, 2], [4, 4, -2, -6]],
RnaPairs)
m = q.fixNegsDiag()._data
self.assertEqual(
m,
array([[-6, 2, 2, 2], [0, -8, 4, 4], [2, 2, -6, 2], [4, 4, 0,
-8]]))
def test_fixNegsConstrainedOpt(self):
"""Rates fixNegsConstrainedOpt should fix negatives w/ constrained opt"""
q = Rates(
array([[-0.28936029, 0.14543346, -0.02648614, 0.17041297],
[0.00949624, -0.31186005, 0.17313171, 0.1292321],
[0.10443209, 0.16134479, -0.30480186, 0.03902498],
[0.01611264, 0.12999161, 0.15558259, -0.30168684]]),
DnaPairs)
r = q.fixNegsFmin()
assert not q.isValid()
assert r.isValid()
def test_isSignificantlyComplex(self):
"""Rates isSignificantlyComplex should be true if large imag component"""
r = Rates([0, 0, 0.2j, 0, 0, 0, 0, 0, 0], self.abc_pairs)
assert r.isSignificantlyComplex()
assert r.isSignificantlyComplex(0.01)
assert not r.isSignificantlyComplex(0.2)
assert not r.isSignificantlyComplex(0.3)
r = Rates([0, 0, 0.1, 0, 0, 0, 0, 0, 0], self.abc_pairs)
assert not r.isSignificantlyComplex()
assert not r.isSignificantlyComplex(1e-30)
assert not r.isSignificantlyComplex(1e3)
def test_init(self):
"""Rates init should take additional parameter to normalize"""
r = Rates([-2, 1, 1, 0, -1, 1, 0, 0, 0], self.abc_pairs)
self.assertEqual(r._data, array([[-2, 1, 1], [0, -1, 1], [0, 0, 0]]))
r = Rates([-2.5, 1, 1, 0, -1, 1, 0, 0, 0], self.abc_pairs)
self.assertEqual(r._data,
array([[-2.5, 1., 1.], [0., -1., 1.], [0., 0., 0.]]))
r = Rates([-2, 1, 1, 0, -1, 1, 2, 0, -1],
self.abc_pairs,
normalize=True)
self.assertEqual(r._data, \
array([[-0.5,.25,.25],[0.,-.25,.25],[.5,0.,-.25]]))
def test_toSimilarProbs(self):
"""Rates toSimilarProbs should match individual steps"""
a = self.abc_pairs
p = Probs([0.75, 0.1, 0.15, 0.2, 0.7, 0.1, 0.05, 0.15, 0.8], a)
q = p.toRates()
self.assertEqual(q.toSimilarProbs(0.5), \
q.toProbs(q.timeForSimilarity(0.5)))
#test a case that didn't work for DNA
q = Rates(array(
[[-0.64098451, 0.0217681 , 0.35576469, 0.26345171],
[ 0.31144238, -0.90915091, 0.25825858, 0.33944995],
[ 0.01578521, 0.43162879, -0.99257581, 0.54516182],
[ 0.13229986, 0.04027147, 0.05817791, -0.23074925]]),
DnaPairs)
p = q.toSimilarProbs(0.66)
self.assertFloatEqual(average(diagonal(p._data), axis=0), 0.66)
def test_toProbs(self):
"""Rates toProbs should return correct probability matrix"""
a = self.abc_pairs
p = Probs([0.75, 0.1, 0.15, 0.2, 0.7, 0.1, 0.05, 0.1, 0.85], a)
q = p.toRates()
self.assertEqual(q._data, logm(p._data))
p2 = q.toProbs()
self.assertFloatEqual(p2._data, p._data)
#test a case that didn't work for DNA
q = Rates(array(
[[-0.64098451, 0.0217681 , 0.35576469, 0.26345171],
[ 0.31144238, -0.90915091, 0.25825858, 0.33944995],
[ 0.01578521, 0.43162879, -0.99257581, 0.54516182],
[ 0.13229986, 0.04027147, 0.05817791, -0.23074925]]),
DnaPairs)
self.assertFloatEqual(q.toProbs(0.5)._data, expm(q._data)(t=0.5))
def test_random_q_matrix_diag_vector(self):
"""Rates random should init with vector as diagonal"""
diag = [1, -1, 2, -2]
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs, diag)._data
for i, d, row in zip(range(4), diag, q):
self.assertFloatEqual(sum(row, axis=0), 0.0)
self.assertEqual(row[i], diag[i])
def test_toProbs(self):
"""Rates toProbs should return correct probability matrix"""
a = self.abc_pairs
p = Probs([0.75, 0.1, 0.15, 0.2, 0.7, 0.1, 0.05, 0.1, 0.85], a)
q = p.toRates()
self.assertEqual(q._data, logm(p._data))
p2 = q.toProbs()
self.assertFloatEqual(p2._data, p._data)
#test a case that didn't work for DNA
q = Rates(
array([[-0.64098451, 0.0217681, 0.35576469, 0.26345171],
[0.31144238, -0.90915091, 0.25825858, 0.33944995],
[0.01578521, 0.43162879, -0.99257581, 0.54516182],
[0.13229986, 0.04027147, 0.05817791, -0.23074925]]),
DnaPairs)
self.assertFloatEqual(q.toProbs(0.5)._data, expm(q._data)(t=0.5))
def test_toSimilarProbs(self):
"""Rates toSimilarProbs should match individual steps"""
a = self.abc_pairs
p = Probs([0.75, 0.1, 0.15, 0.2, 0.7, 0.1, 0.05, 0.15, 0.8], a)
q = p.toRates()
self.assertEqual(q.toSimilarProbs(0.5), \
q.toProbs(q.timeForSimilarity(0.5)))
#test a case that didn't work for DNA
q = Rates(
array([[-0.64098451, 0.0217681, 0.35576469, 0.26345171],
[0.31144238, -0.90915091, 0.25825858, 0.33944995],
[0.01578521, 0.43162879, -0.99257581, 0.54516182],
[0.13229986, 0.04027147, 0.05817791, -0.23074925]]),
DnaPairs)
p = q.toSimilarProbs(0.66)
self.assertFloatEqual(average(diagonal(p._data), axis=0), 0.66)
def test_random_q_matrix_diag_vector(self):
"""Rates random should init with vector as diagonal"""
diag = [1, -1, 2, -2]
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs, diag)._data
for i, d, row in zip(range(4), diag, q):
self.assertFloatEqual(sum(row, axis=0), 0.0)
self.assertEqual(row[i], diag[i])
def test_isSignificantlyComplex(self):
"""Rates isSignificantlyComplex should be true if large imag component"""
r = Rates([0,0,0.2j,0,0,0,0,0,0], self.abc_pairs)
assert r.isSignificantlyComplex()
assert r.isSignificantlyComplex(0.01)
assert not r.isSignificantlyComplex(0.2)
assert not r.isSignificantlyComplex(0.3)
r = Rates([0,0,0.1,0,0,0,0,0,0], self.abc_pairs)
assert not r.isSignificantlyComplex()
assert not r.isSignificantlyComplex(1e-30)
assert not r.isSignificantlyComplex(1e3)
def test_isValid(self):
"""Rates isValid should check row sums and neg off-diags"""
r = Rates([-2,1,1,0,-1,1,0,0,0], self.abc_pairs)
assert r.isValid()
r = Rates([0,0,0,0,0,0,0,0,0], self.abc_pairs)
assert r.isValid()
#not valid if negative off-diagonal
r = Rates([-2,-1,3,1,-1,0,2,2,-4], self.abc_pairs)
assert not r.isValid()
#not valid if rows don't all sum to 0
r = Rates([0,0.0001,0,0,0,0,0,0,0], self.abc_pairs)
assert not r.isValid()
def test_evolve(self):
"""RangeNode evolve should work on a starting vector"""
t = self.t1
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignLength(0.1)
t.assignP()
start = array([1,0,2,1,0,0,2,1,2,0,1,2,1,0,2,0,0,3,0,2,1,0,3,1,0,2,0,0,0,0,0,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,3])
t.evolve(start)
for i in t.traverse():
self.assertEqual(len(i.Sequence), len(start))
self.assertNotEqual(i.Sequence, start)
def test_isComplex(self):
"""Rates isComplex should return True if complex elements"""
r = Rates([0, 0, 0.1j, 0, 0, 0, 0, 0, 0], self.abc_pairs)
assert r.isComplex()
r = Rates([0, 0, 0.1, 0, 0, 0, 0, 0, 0], self.abc_pairs)
assert not r.isComplex()
def test_assignP(self):
"""RangeNode assignP should work when Qs set."""
t = self.t1
for i in t.traverse(self_before=True):
i.Length = random() * 0.5 #range 0 to 0.5
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignP()
t.assignIds()
for node in t.traverse(self_after=True):
if node.Parent is not None:
self.assertFloatEqual(average(1-diag(node.P._data), axis=0), \
node.Length)
def test_assignP(self):
"""RangeNode assignP should work when Qs set."""
t = self.t1
for i in t.traverse(self_before=True):
i.Length = random() * 0.5 #range 0 to 0.5
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignP()
t.assignIds()
for node in t.traverse(self_after=True):
if node.Parent is not None:
self.assertFloatEqual(average(1-diag(node.P._data), axis=0), \
node.Length)
def test_random_q_matrix(self):
"""Rates random should return matrix of correct size"""
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs)._data
self.assertEqual(len(q), 4)
self.assertEqual(len(q[0]), 4)
for row in q:
self.assertFloatEqual(sum(row), 0.0)
assert min(row) < 0
assert max(row) > 0
l = list(row)
l.sort()
assert min(l[1:]) >= 0
assert max(l[1:]) <= 1
def test_random_q_matrix(self):
"""Rates random should return matrix of correct size"""
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs)._data
self.assertEqual(len(q), 4)
self.assertEqual(len(q[0]), 4)
for row in q:
self.assertFloatEqual(sum(row), 0.0)
assert min(row) < 0
assert max(row) > 0
l = list(row)
l.sort()
assert min(l[1:]) >= 0
assert max(l[1:]) <= 1
def test_random_q_matrix_diag(self):
"""Rates random should set diagonal correctly from scalar"""
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs, -1)._data
self.assertEqual(len(q), 4)
for i, row in enumerate(q):
self.assertFloatEqual(sum(row), 0)
self.assertEqual(row[i], -1)
assert max(row) <= 1
l = list(row)
l.sort()
assert min(l[1:]) >= 0
assert max(l[1:]) <= 1
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs, -5)._data
self.assertEqual(len(q), 4)
for i, row in enumerate(q):
self.assertFloatEqual(sum(row), 0)
self.assertEqual(row[i], -5)
assert max(row) <= 5
l = list(row)
l.sort()
assert min(l[1:]) >= 0
assert max(l[1:]) <= 5
def test_random_q_matrix_diag(self):
"""Rates random should set diagonal correctly from scalar"""
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs, -1)._data
self.assertEqual(len(q), 4)
for i, row in enumerate(q):
self.assertFloatEqual(sum(row), 0)
self.assertEqual(row[i], -1)
assert max(row) <= 1
l = list(row)
l.sort()
assert min(l[1:]) >= 0
assert max(l[1:]) <= 1
for i in range(NUM_TESTS):
q = Rates.random(RnaPairs, -5)._data
self.assertEqual(len(q), 4)
for i, row in enumerate(q):
self.assertFloatEqual(sum(row), 0)
self.assertEqual(row[i], -5)
assert max(row) <= 5
l = list(row)
l.sort()
assert min(l[1:]) >= 0
assert max(l[1:]) <= 5
def test_tree_twoway_rates(self):
"""tree_twoway_rates should give plausible results on rand trees"""
t = self.t1
t.assignLength(0.05)
t.Q = Rates.random(DnaPairs).normalize()
t.assignQ()
t.assignP()
t.evolve(randint(0,4,100))
t.makeIdIndex()
result = tree_twoway_rates(t)
self.assertEqual(result.shape, (5,5,16))
#check that row sums are 0
for x in [(i,j) for i in range(5) for j in range(5)]:
self.assertFloatEqual(sum(result[x]), 0)
#need to make sure we didn't just get an empty array
self.assertGreaterThan((abs(result)).sum(), 0)
#check that it works without_diag
result = tree_twoway_rates(t, without_diag=True)
self.assertEqual(result.shape, (5,5,12))
#check that it works with/without normalize
#default: no normalization, so row sums shouldn't be 1 after omitting
#diagonal
result = tree_twoway_rates(t, without_diag=True)
self.assertEqual(result.shape, (5,5,12))
#check that the row sums are not 1 before normalization (note that they
#can be zero, though)
sums_before = []
for x in [(i,j) for i in range(5) for j in range(5)]:
curr_sum = sum(result[x])
sums_before.append(curr_sum)
#...but if we tell it to normalize, row sums should be nearly 1
#after omitting diagonal
result = tree_twoway_rates(t, without_diag=True, \
normalize=True)
self.assertEqual(result.shape, (5,5,12))
sums_after = []
for x in [(i,j) for i in range(5) for j in range(5)]:
curr_sum = sum(result[x])
sums_after.append(curr_sum)
if curr_sum != 0:
self.assertFloatEqual(curr_sum, 1)
try:
self.assertFloatEqual(sums_before, sums_after)
except AssertionError:
pass
else:
raise AssertionError, "Expected different arrays before/after norm"
def test_tree_threeway_rates(self):
"""tree_threeway_rates should give plausible results on rand trees"""
#note: the following fails occasionally, but repeating it 5 times
#and checking that one passes is fairly safe
for i in range(5):
try:
t = self.t1
t.assignLength(0.05)
t.Q = Rates.random(DnaPairs).normalize()
t.assignQ()
t.assignP()
t.evolve(randint(0,4,100))
t.makeIdIndex()
depths = t.leafLcaDepths()
result = tree_threeway_rates(t, depths)
self.assertEqual(result.shape, (5,5,5,16))
#check that row sums are 0
for x in [(i,j,k) for i in range(5) for j in range(5) \
for k in range(5)]:
self.assertFloatEqual(sum(result[x]), 0)
assert any(result)
#check that it works without_diag
result = tree_threeway_rates(t, depths, without_diag=True)
self.assertEqual(result.shape, (5,5,5,12))
#check that it works with/without normalize
#default: no normalization, so row sums shouldn't be 1 after
#omitting diagonal
result = tree_threeway_rates(t, depths, without_diag=True)
self.assertEqual(result.shape, (5,5,5,12))
for x in [(i,j,k) for i in range(5) for j in range(5) \
for k in range(5)]:
assert sum(result[x]) == 0 or abs(sum(result[x]) - 1) > 0.01
#...but if we tell it to normalize, row sums should be nearly 1
#after omitting diagonal
result = tree_threeway_rates(t, depths, without_diag=True, \
normalize=True)
self.assertEqual(result.shape, (5,5,5,12))
for x in [(i,j,k) for i in range(5) for j in range(5) \
for k in range(5)]:
s = sum(result[x])
if s != 0:
self.assertFloatEqual(s, 1)
break
except AssertionError:
pass
def test_tree_threeway_rates(self):
"""tree_threeway_rates should give plausible results on rand trees"""
#note: the following fails occasionally, but repeating it 5 times
#and checking that one passes is fairly safe
for i in range(5):
try:
t = self.t1
t.assignLength(0.05)
t.Q = Rates.random(DnaPairs).normalize()
t.assignQ()
t.assignP()
t.evolve(randint(0,4,100))
t.makeIdIndex()
depths = t.leafLcaDepths()
result = tree_threeway_rates(t, depths)
self.assertEqual(result.shape, (5,5,5,16))
#check that row sums are 0
for x in [(i,j,k) for i in range(5) for j in range(5) \
for k in range(5)]:
self.assertFloatEqual(sum(result[x]), 0)
assert any(result)
#check that it works without_diag
result = tree_threeway_rates(t, depths, without_diag=True)
self.assertEqual(result.shape, (5,5,5,12))
#check that it works with/without normalize
#default: no normalization, so row sums shouldn't be 1 after
#omitting diagonal
result = tree_threeway_rates(t, depths, without_diag=True)
self.assertEqual(result.shape, (5,5,5,12))
for x in [(i,j,k) for i in range(5) for j in range(5) \
for k in range(5)]:
assert sum(result[x]) == 0 or abs(sum(result[x]) - 1) > 0.01
#...but if we tell it to normalize, row sums should be nearly 1
#after omitting diagonal
result = tree_threeway_rates(t, depths, without_diag=True, \
normalize=True)
self.assertEqual(result.shape, (5,5,5,12))
for x in [(i,j,k) for i in range(5) for j in range(5) \
for k in range(5)]:
s = sum(result[x])
if s != 0:
self.assertFloatEqual(s, 1)
break
except AssertionError:
pass
def test_assignPs(self):
"""RangeNode assignPs should assign multiple scaled P matrices"""
t = self.t1
for i in t.traverse(self_before=True):
i.Length = random() * 0.5 #range 0 to 0.5
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignPs([1, 0.5, 0.25])
t.assignIds()
for node in t.traverse(self_after=True):
if node.Parent is not None:
self.assertEqual(len(node.Ps), 3)
self.assertFloatEqual(average(1-diag(node.Ps[0]._data), axis=0), \
node.Length)
self.assertFloatEqual(average(1-diag(node.Ps[1]._data), axis=0), \
0.5*node.Length)
self.assertFloatEqual(average(1-diag(node.Ps[2]._data), axis=0), \
0.25*node.Length)
def test_assignPs(self):
"""RangeNode assignPs should assign multiple scaled P matrices"""
t = self.t1
for i in t.traverse(self_before=True):
i.Length = random() * 0.5 #range 0 to 0.5
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignPs([1, 0.5, 0.25])
t.assignIds()
for node in t.traverse(self_after=True):
if node.Parent is not None:
self.assertEqual(len(node.Ps), 3)
self.assertFloatEqual(average(1-diag(node.Ps[0]._data), axis=0), \
node.Length)
self.assertFloatEqual(average(1-diag(node.Ps[1]._data), axis=0), \
0.5*node.Length)
self.assertFloatEqual(average(1-diag(node.Ps[2]._data), axis=0), \
0.25*node.Length)
def test_rates_to_array(self):
"""rates_to_array should pack rates into array correctly"""
m1 = array([[-1, 1, 1, 1], [2, -2, 2, 2], [3, 3, -3, 3], [1, 2, 3,
-4]])
m2 = m1 * 2
m3 = m1 * 0.5
m4 = zeros((4, 4))
m5 = array([0, 0])
r1, r2, r3, r4, r5 = [Rates(i, DnaPairs) for i in m1, m2, m3, m4, m5]
data = {(0, 1, 0): r1, (1, 2, 0): r2, (2, 0, 0): r3, (2, 1, 1): r4}
#note that array can be, but need not be, floating point
to_fill = zeros((3, 3, 3, 16), 'float64')
result = rates_to_array(data, to_fill)
#check that the thnigs we deliberately set are OK
self.assertEqual(to_fill[0][1][0], ravel(m1))
self.assertNotEqual(to_fill[0][1][0], ravel(m2))
self.assertEqual(to_fill[1, 2, 0], ravel(m2))
self.assertEqual(to_fill[2][0][0], ravel(m3))
self.assertEqual(to_fill[2][1][1], ravel(m4))
#check that everything else is zero
nonzero = [(0, 1, 0), (1, 2, 0), (2, 0, 0)]
for x in [(i, j, k) for i in range(3) for j in range(3) \
for k in range(3)]:
if x not in nonzero:
self.assertEqual(to_fill[x], zeros(16))
#check that it works omitting the diagonal
to_fill = zeros((3, 3, 3, 12), 'float64')
result = rates_to_array(data, to_fill, without_diagonal=True)
#check that the thnigs we deliberately set are OK
m1_nodiag = array([[1, 1, 1], [2, 2, 2], [3, 3, 3], [1, 2, 3]])
self.assertEqual(to_fill[0][1][0], ravel(m1_nodiag))
self.assertNotEqual(to_fill[0][1][0], ravel(m1_nodiag * 2))
self.assertEqual(to_fill[1, 2, 0], ravel(m1_nodiag * 2))
self.assertEqual(to_fill[2][0][0], ravel(m1_nodiag * 0.5))
self.assertEqual(to_fill[2][1][1], zeros(12))
#check that everything else is zero
nonzero = [(0, 1, 0), (1, 2, 0), (2, 0, 0)]
for x in [(i, j, k) for i in range(3) for j in range(3) \
for k in range(3)]:
if x not in nonzero:
self.assertEqual(to_fill[x], zeros(12))
def test_evolveSeqs(self):
"""PhlyoNode evolveSeqs should evolve multiple sequences"""
t = self.t1
for i in t.traverse(self_before=True):
i.Length = 0.5
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignPs([1, 1, 0.1])
t.assignIds()
orig_seqs = [array(i) for i in [randint(0,4,200), randint(0,4,200), \
randint(0,4,200)]]
t.evolveSeqs(orig_seqs)
for node in t.traverse(): #only look at leaves
if node.Parent is not None:
self.assertEqual(len(node.Sequences), 3)
for orig, new in zip(orig_seqs, node.Sequences):
self.assertEqual(len(orig), len(new))
self.assertNotEqual(orig, new)
assert sum(orig_seqs[1]!=node.Sequences[1]) > \
sum(orig_seqs[2]!=node.Sequences[2])
def test_evolveSeqs(self):
"""PhlyoNode evolveSeqs should evolve multiple sequences"""
t = self.t1
for i in t.traverse(self_before=True):
i.Length = 0.5
t.Q = Rates.random(DnaPairs)
t.assignQ()
t.assignPs([1, 1, 0.1])
t.assignIds()
orig_seqs = [array(i) for i in [randint(0,4,200), randint(0,4,200), \
randint(0,4,200)]]
t.evolveSeqs(orig_seqs)
for node in t.traverse(): #only look at leaves
if node.Parent is not None:
self.assertEqual(len(node.Sequences), 3)
for orig, new in zip(orig_seqs, node.Sequences):
self.assertEqual(len(orig), len(new))
self.assertNotEqual(orig, new)
assert sum(orig_seqs[1]!=node.Sequences[1]) > \
sum(orig_seqs[2]!=node.Sequences[2])
def test_fixNegsReflect(self):
"""Rates fixNegsReflect should reflect negatives across diagonal"""
ab = Alphabet('ab')**2
#should leave matrix alone if no off-diagonal elements
q = Rates([0,0,1,-1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[0,0],[1,-1]]))
q = Rates([-2,2,1,-1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-2,2],[1,-1]]))
#should work if precisely one off-diag element in a pair is negative
q = Rates([2,-2,1,-1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[0,0],[3,-3]]))
q = Rates([-1,1,-2,2], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-3,3],[0,-0]]))
#should work if both off-diag elements in a pair are negative
q = Rates([1,-1,-2,2], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-2,2],[1,-1]]))
q = Rates([2,-2,-1,1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-1,1],[2,-2]]))
q = Rates([[ 0, 3, -2, -1],
[ 2, -1, 2, -3],
[-1, -1, 2, 0],
[-3, 2, 0, 1]], RnaPairs)
q2 = q.fixNegsReflect()
self.assertEqual(q2._data, \
array([[-7, 3, 1, 3],
[ 2, -5, 3, 0],
[ 2, 0, -2, 0],
[ 1, 5, 0, -6]]))
def test_isValid(self):
"""Rates isValid should check row sums and neg off-diags"""
r = Rates([-2, 1, 1, 0, -1, 1, 0, 0, 0], self.abc_pairs)
assert r.isValid()
r = Rates([0, 0, 0, 0, 0, 0, 0, 0, 0], self.abc_pairs)
assert r.isValid()
#not valid if negative off-diagonal
r = Rates([-2, -1, 3, 1, -1, 0, 2, 2, -4], self.abc_pairs)
assert not r.isValid()
#not valid if rows don't all sum to 0
r = Rates([0, 0.0001, 0, 0, 0, 0, 0, 0, 0], self.abc_pairs)
assert not r.isValid()
def test_fixNegsEven(self):
"""Rates fixNegsEven should fix negatives by adding evenly to others"""
q = Rates([[-6,2,2,2],[-3,-2,3,2],[-2,-2,-6,2],[4,4,-6,-2]], RnaPairs)
m = q.fixNegsEven()._data
self.assertEqual(m,array([[-6,2,2,2],[0,-3,2,1],[0,0,-0,0],[2,2,0,-4]]))
def test_fixNegsDiag(self):
"""Rates fixNegsDiag should fix negatives by adding to diagonal"""
q = Rates([[-6,2,2,2],[-6,-2,4,4],[2,2,-6,2],[4,4,-2,-6]], RnaPairs)
m = q.fixNegsDiag()._data
self.assertEqual(m,array([[-6,2,2,2],[0,-8,4,4],[2,2,-6,2],[4,4,0,-8]]))
def test_fixNegsReflect(self):
"""Rates fixNegsReflect should reflect negatives across diagonal"""
ab = Alphabet('ab')**2
#should leave matrix alone if no off-diagonal elements
q = Rates([0, 0, 1, -1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[0, 0], [1, -1]]))
q = Rates([-2, 2, 1, -1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-2, 2], [1, -1]]))
#should work if precisely one off-diag element in a pair is negative
q = Rates([2, -2, 1, -1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[0, 0], [3, -3]]))
q = Rates([-1, 1, -2, 2], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-3, 3], [0, -0]]))
#should work if both off-diag elements in a pair are negative
q = Rates([1, -1, -2, 2], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-2, 2], [1, -1]]))
q = Rates([2, -2, -1, 1], ab)
self.assertEqual(q.fixNegsReflect()._data, array([[-1, 1], [2, -2]]))
q = Rates(
[[0, 3, -2, -1], [2, -1, 2, -3], [-1, -1, 2, 0], [-3, 2, 0, 1]],
RnaPairs)
q2 = q.fixNegsReflect()
self.assertEqual(q2._data, \
array([[-7, 3, 1, 3],
[ 2, -5, 3, 0],
[ 2, 0, -2, 0],
[ 1, 5, 0, -6]]))
