def test_emit_internal(self):
"""
Calculate emission probabilities
"""
k = 10
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = int(10e3) / 20
times = arghmm.get_time_points(ntimes=20, maxtime=200000)
arg = arghmm.sample_arg_dsmc(k,
2 * n,
rho,
start=0,
end=length,
times=times)
muts = arghmm.sample_arg_mutations(arg, mu, times)
seqs = arghmm.make_alignment(arg, muts)
trees, names = arghmm.arg2ctrees(arg, times)
seqs2, nseqs, seqlen = arghmm.seqs2cseqs(seqs, names)
assert arghmm.arghmm_assert_emit_internal(trees, len(times), times, mu,
seqs2, nseqs, seqlen)
def test_emit(self):
"""
Calculate emission probabilities
"""
k = 10
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = int(1e3) / 20
times = arghmm.get_time_points(ntimes=20, maxtime=200000)
arg = arghmm.sample_arg_dsmc(k, 2*n, rho, start=0, end=length,
times=times)
muts = arghmm.sample_arg_mutations(arg, mu, times)
seqs = arghmm.make_alignment(arg, muts)
new_name = "n%d" % (k-1)
arg = arghmm.remove_arg_thread(arg, new_name)
trees, names = arghmm.arg2ctrees(arg, times)
seqs2, nseqs, seqlen = arghmm.seqs2cseqs(seqs, names + [new_name])
assert arghmm.arghmm_assert_emit(trees, len(times), times, mu,
seqs2, nseqs, seqlen)
def test_trans_switch_internal(self):
"""
Calculate transition probabilities for k=2
Only calculate a single matrix
"""
k = 10
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = int(100e3) / 20
times = arghmm.get_time_points(ntimes=20, maxtime=200000)
popsizes = [n] * len(times)
arg = arghmm.sample_arg_dsmc(k,
2 * n,
rho,
start=0,
end=length,
times=times)
trees, names = arghmm.arg2ctrees(arg, times)
assert arghmm.assert_transition_probs_switch_internal(
trees, times, popsizes, rho)
def test_prior_tree(self):
k = 10
n = 1e4
popsizes = [n] * 20
length = 10
times = arghmm.get_time_points(ntimes=20, maxtime=1000000)
arg = arghmm.sample_arg_dsmc(k, 2*n, 1e-50, start=0, end=length,
times=times)
trees, names = arghmm.arg2ctrees(arg, times)
print arghmm.arghmm_tree_prior_prob(trees, times, len(times), popsizes)
def test_prior_tree(self):
k = 10
n = 1e4
popsizes = [n] * 20
length = 10
times = arghmm.get_time_points(ntimes=20, maxtime=1000000)
arg = arghmm.sample_arg_dsmc(k,
2 * n,
1e-50,
start=0,
end=length,
times=times)
trees, names = arghmm.arg2ctrees(arg, times)
print arghmm.arghmm_tree_prior_prob(trees, times, len(times), popsizes)
def test_arg_treelens(self):
k = 10
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = 10000
times = arghmm.get_time_points(ntimes=20, maxtime=200000)
arg = arghmm.sample_arg_dsmc(k, 2*n, rho, start=0, end=length,
times=times)
# convert to C++ and back
trees, names = arghmm.arg2ctrees(arg, times)
treelens = [0.0] * arghmm.get_local_trees_ntrees(trees)
arghmm.get_treelens(trees, times, len(times), treelens)
print treelens
def test_arg_treelens(self):
k = 10
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = 10000
times = arghmm.get_time_points(ntimes=20, maxtime=200000)
arg = arghmm.sample_arg_dsmc(k,
2 * n,
rho,
start=0,
end=length,
times=times)
# convert to C++ and back
trees, names = arghmm.arg2ctrees(arg, times)
treelens = [0.0] * arghmm.get_local_trees_ntrees(trees)
arghmm.get_treelens(trees, times, len(times), treelens)
print treelens
def test_trans_switch_internal(self):
"""
Calculate transition probabilities for k=2
Only calculate a single matrix
"""
k = 10
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = int(100e3) / 20
times = arghmm.get_time_points(ntimes=20, maxtime=200000)
popsizes = [n] * len(times)
arg = arghmm.sample_arg_dsmc(k, 2*n, rho, start=0, end=length,
times=times)
trees, names = arghmm.arg2ctrees(arg, times)
assert arghmm.assert_transition_probs_switch_internal(
trees, times, popsizes, rho)
