def test_trans_single(self):
"""
Calculate transition probabilities
Only calculate a single matrix
"""
k = 4
n = 1e4
rho = 1.5e-8
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
times = arghmm.get_time_points(10)
arghmm.discretize_arg(arg, times)
print "recomb", arglib.get_recomb_pos(arg)
new_name = "n%d" % (k - 1)
arg = arghmm.remove_arg_thread(arg, new_name)
model = arghmm.ArgHmm(arg, seqs, new_name=new_name, times=times)
pos = 10
tree = arg.get_marginal_tree(pos)
mat = arghmm.calc_transition_probs(tree, model.states[pos],
model.nlineages, model.times,
model.time_steps, model.popsizes,
rho)
print model.states[pos]
pc(mat)
for row in mat:
print sum(map(exp, row))
def test_trans_single(self):
"""
Calculate transition probabilities
Only calculate a single matrix
"""
k = 4
n = 1e4
rho = 1.5e-8
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
times = arghmm.get_time_points(10)
arghmm.discretize_arg(arg, times)
print "recomb", arglib.get_recomb_pos(arg)
new_name = "n%d" % (k-1)
arg = arghmm.remove_arg_thread(arg, new_name)
model = arghmm.ArgHmm(arg, seqs, new_name=new_name, times=times)
pos = 10
tree = arg.get_marginal_tree(pos)
mat = arghmm.calc_transition_probs(
tree, model.states[pos], model.nlineages,
model.times, model.time_steps, model.popsizes, rho)
print model.states[pos]
pc(mat)
for row in mat:
print sum(map(exp, row))
def test_trans2(self):
"""
Calculate transition probabilities for k=2
Only calculate a single matrix
"""
k = 2
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = 1000
times = arghmm.get_time_points(ntimes=5, maxtime=200000)
arg = arglib.sample_arg(k, 2 * n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
arghmm.discretize_arg(arg, times)
print "recomb", arglib.get_recomb_pos(arg)
new_name = "n%d" % (k - 1)
arg = arghmm.make_trunk_arg(0, length, "n0")
model = arghmm.ArgHmm(arg,
seqs,
new_name=new_name,
popsize=n,
rho=rho,
mu=mu,
times=times)
pos = 10
tree = arg.get_marginal_tree(pos)
model.check_local_tree(pos, force=True)
mat = arghmm.calc_transition_probs(tree, model.states[pos],
model.nlineages, model.times,
model.time_steps, model.popsizes,
rho)
states = model.states[pos]
nstates = len(states)
def coal(j):
return 1.0 - exp(-model.time_steps[j] / (2.0 * n))
def recoal2(k, j):
p = coal(j)
for m in range(k, j):
p *= 1.0 - coal(m)
return p
def recoal(k, j):
if j == nstates - 1:
return exp(-sum(model.time_steps[m] / (2.0 * n)
for m in range(k, j)))
else:
return ((1.0 - exp(-model.time_steps[j] / (2.0 * n))) *
exp(-sum(model.time_steps[m] / (2.0 * n)
for m in range(k, j))))
def isrecomb(i):
return 1.0 - exp(-max(rho * 2.0 * model.times[i], rho))
def recomb(i, k):
treelen = 2 * model.times[i] + model.time_steps[i]
if k < i:
return 2.0 * model.time_steps[k] / treelen / 2.0
else:
return model.time_steps[k] / treelen / 2.0
def trans(i, j):
a = states[i][1]
b = states[j][1]
p = sum(
recoal(k, b) * recomb(a, k) for k in range(0,
min(a, b) + 1))
p += sum(
recoal(k, b) * recomb(a, k) for k in range(0,
min(a, b) + 1))
p *= isrecomb(a)
if i == j:
p += 1.0 - isrecomb(a)
return p
for i in range(len(states)):
for j in range(len(states)):
print isrecomb(states[i][1])
print states[i], states[j], mat[i][j], log(trans(i, j))
fequal(mat[i][j], log(trans(i, j)))
# recombs add up to 1
fequal(sum(recomb(i, k) for k in range(i + 1)), 0.5)
# recoal add up to 1
fequal(sum(recoal(i, j) for j in range(i, nstates)), 1.0)
# recomb * recoal add up to .5
fequal(
sum(
sum(
recoal(k, j) * recomb(i, k)
for k in range(0,
min(i, j) + 1))
for j in range(0, nstates)), 0.5)
fequal(sum(trans(i, j) for j in range(len(states))), 1.0)
def test_trans2(self):
"""
Calculate transition probabilities for k=2
Only calculate a single matrix
"""
k = 2
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = 1000
times = arghmm.get_time_points(ntimes=5, maxtime=200000)
arg = arglib.sample_arg(k, 2*n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
arghmm.discretize_arg(arg, times)
print "recomb", arglib.get_recomb_pos(arg)
new_name = "n%d" % (k-1)
arg = arghmm.make_trunk_arg(0, length, "n0")
model = arghmm.ArgHmm(arg, seqs, new_name=new_name,
popsize=n, rho=rho, mu=mu,
times=times)
pos = 10
tree = arg.get_marginal_tree(pos)
model.check_local_tree(pos, force=True)
mat = arghmm.calc_transition_probs(
tree, model.states[pos], model.nlineages,
model.times, model.time_steps, model.popsizes, rho)
states = model.states[pos]
nstates = len(states)
def coal(j):
return 1.0 - exp(-model.time_steps[j]/(2.0 * n))
def recoal2(k, j):
p = coal(j)
for m in range(k, j):
p *= 1.0 - coal(m)
return p
def recoal(k, j):
if j == nstates-1:
return exp(- sum(model.time_steps[m] / (2.0 * n)
for m in range(k, j)))
else:
return ((1.0 - exp(-model.time_steps[j]/(2.0 * n))) *
exp(- sum(model.time_steps[m] / (2.0 * n)
for m in range(k, j))))
def isrecomb(i):
return 1.0 - exp(-max(rho * 2.0 * model.times[i], rho))
def recomb(i, k):
treelen = 2*model.times[i] + model.time_steps[i]
if k < i:
return 2.0 * model.time_steps[k] / treelen / 2.0
else:
return model.time_steps[k] / treelen / 2.0
def trans(i, j):
a = states[i][1]
b = states[j][1]
p = sum(recoal(k, b) * recomb(a, k)
for k in range(0, min(a, b)+1))
p += sum(recoal(k, b) * recomb(a, k)
for k in range(0, min(a, b)+1))
p *= isrecomb(a)
if i == j:
p += 1.0 - isrecomb(a)
return p
for i in range(len(states)):
for j in range(len(states)):
print isrecomb(states[i][1])
print states[i], states[j], mat[i][j], log(trans(i, j))
fequal(mat[i][j], log(trans(i, j)))
# recombs add up to 1
fequal(sum(recomb(i, k) for k in range(i+1)), 0.5)
# recoal add up to 1
fequal(sum(recoal(i, j) for j in range(i, nstates)), 1.0)
# recomb * recoal add up to .5
fequal(sum(sum(recoal(k, j) * recomb(i, k)
for k in range(0, min(i, j)+1))
for j in range(0, nstates)), 0.5)
fequal(sum(trans(i, j) for j in range(len(states))), 1.0)
