def test_post_c(self):
k = 3
n = 1e4
rho = 1.5e-8 * 30
mu = 2.5e-8 * 100
length = 100
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
arg.prune()
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
print arglib.get_recomb_pos(arg)
print "muts", len(muts)
print "recomb", len(arglib.get_recomb_pos(arg))
times = arghmm.get_time_points(ntimes=10)
arghmm.discretize_arg(arg, times)
tree = arg.get_marginal_tree(0)
print tree.root.age
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
# remove chrom
keep = ["n%d" % i for i in range(k - 1)]
arglib.subarg_by_leaf_names(arg, keep)
model = arghmm.ArgHmm(arg,
seqs,
new_name="n%d" % (k - 1),
times=times,
rho=rho,
mu=mu)
print "states", len(model.states[0])
util.tic("C")
probs1 = list(arghmm.get_posterior_probs(model, length, verbose=True))
util.toc()
util.tic("python")
probs2 = list(hmm.get_posterior_probs(model, length, verbose=True))
util.toc()
print "probs1"
pc(probs1)
print "probs2"
pc(probs2)
for col1, col2 in izip(probs1, probs2):
for a, b in izip(col1, col2):
fequal(a, b)
def test_post_c(self):
k = 3
n = 1e4
rho = 1.5e-8 * 30
mu = 2.5e-8 * 100
length = 100
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
arg.prune()
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
print arglib.get_recomb_pos(arg)
print "muts", len(muts)
print "recomb", len(arglib.get_recomb_pos(arg))
times = arghmm.get_time_points(ntimes=10)
arghmm.discretize_arg(arg, times)
tree = arg.get_marginal_tree(0)
print tree.root.age
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
# remove chrom
keep = ["n%d" % i for i in range(k-1)]
arglib.subarg_by_leaf_names(arg, keep)
model = arghmm.ArgHmm(arg, seqs, new_name="n%d" % (k-1), times=times,
rho=rho, mu=mu)
print "states", len(model.states[0])
util.tic("C")
probs1 = list(arghmm.get_posterior_probs(model, length, verbose=True))
util.toc()
util.tic("python")
probs2 = list(hmm.get_posterior_probs(model, length, verbose=True))
util.toc()
print "probs1"
pc(probs1)
print "probs2"
pc(probs2)
for col1, col2 in izip(probs1, probs2):
for a, b in izip(col1, col2):
fequal(a, b)
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_post(self):
k = 6
n = 1e4
rho = 1.5e-8 * 10
mu = 2.5e-8 * 10
length = 10000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
print "muts", len(muts)
print "recombs", len(arglib.get_recomb_pos(arg))
times = arghmm.get_time_points(ntimes=10)
arghmm.discretize_arg(arg, times)
tree = arg.get_marginal_tree(0)
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
# remove chrom
new_name = "n%d" % (k - 1)
keep = set(arg.leaf_names()) - set([new_name])
arglib.subarg_by_leaf_names(arg, keep)
arg = arglib.smcify_arg(arg)
model = arghmm.ArgHmm(arg, seqs, new_name=new_name,
times=times, rho=rho, mu=mu)
print "states", len(model.states[0])
probs = arghmm.get_posterior_probs(model, length, verbose=True)
for pcol in probs:
p = sum(map(exp, pcol))
print p, " ".join("%.3f" % f for f in map(exp, pcol))
fequal(p, 1.0, rel=1e-2)
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_post(self):
k = 6
n = 1e4
rho = 1.5e-8 * 10
mu = 2.5e-8 * 10
length = 10000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
print "muts", len(muts)
print "recombs", len(arglib.get_recomb_pos(arg))
times = arghmm.get_time_points(ntimes=10)
arghmm.discretize_arg(arg, times)
tree = arg.get_marginal_tree(0)
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
# remove chrom
new_name = "n%d" % (k - 1)
keep = set(arg.leaf_names()) - set([new_name])
arglib.subarg_by_leaf_names(arg, keep)
arg = arglib.smcify_arg(arg)
model = arghmm.ArgHmm(arg,
seqs,
new_name=new_name,
times=times,
rho=rho,
mu=mu)
print "states", len(model.states[0])
probs = arghmm.get_posterior_probs(model, length, verbose=True)
for pcol in probs:
p = sum(map(exp, pcol))
print p, " ".join("%.3f" % f for f in map(exp, pcol))
fequal(p, 1.0, rel=1e-2)
def test_forward():
k = 4
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = int(100e3 / 20)
times = argweaver.get_time_points(ntimes=100)
arg = arglib.sample_arg_smc(k, 2 * n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
print "muts", len(muts)
print "recomb", len(arglib.get_recomb_pos(arg))
argweaver.discretize_arg(arg, times)
# remove chrom
new_name = "n%d" % (k - 1)
arg = argweaver.remove_arg_thread(arg, new_name)
carg = argweaverc.arg2ctrees(arg, times)
util.tic("C fast")
probs1 = argweaverc.argweaver_forward_algorithm(carg, seqs, times=times)
util.toc()
util.tic("C slow")
probs2 = argweaverc.argweaver_forward_algorithm(carg,
seqs,
times=times,
slow=True)
util.toc()
for i, (col1, col2) in enumerate(izip(probs1, probs2)):
for a, b in izip(col1, col2):
fequal(a, b, rel=.0001)
def test_recomb(self):
"""
Investigate the fact that some recombinations are not visible
"""
k = 3
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
times = arghmm.get_time_points(10)
arghmm.discretize_arg(arg, times)
arg.set_ancestral()
arg.prune()
recombs = arglib.get_recomb_pos(arg)
# find recombs by walking
recombs2 = []
i = 0
while True:
tree = arg.get_marginal_tree(i - .5)
recomb = arghmm.find_tree_next_recomb(tree, i + 1, tree=True)
if recomb:
recombs2.append(recomb.pos)
i = recomb.pos
else:
break
# these are suppose to differ because some recombination occur
# in the hole of ancestral sequence intervals
print recombs
print recombs2
arglib.write_arg("tmp/b.arg", arg)
def test_recomb(self):
"""
Investigate the fact that some recombinations are not visible
"""
k = 3
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
times = arghmm.get_time_points(10)
arghmm.discretize_arg(arg, times)
arg.set_ancestral()
arg.prune()
recombs = arglib.get_recomb_pos(arg)
# find recombs by walking
recombs2 = []
i = 0
while True:
tree = arg.get_marginal_tree(i-.5)
recomb = arghmm.find_tree_next_recomb(tree, i+1, tree=True)
if recomb:
recombs2.append(recomb.pos)
i = recomb.pos
else:
break
# these are suppose to differ because some recombination occur
# in the hole of ancestral sequence intervals
print recombs
print recombs2
arglib.write_arg("tmp/b.arg", arg)
def test_remove_thread(self):
"""
Remove a leaf from an ARG
"""
k = 3
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
times = arghmm.get_time_points(10)
arghmm.discretize_arg(arg, times)
chrom = "n%d" % (k - 1)
arg = arghmm.remove_arg_thread(arg, chrom)
recomb = arglib.get_recomb_pos(arg)
print "recomb", recomb
tree = arg.get_marginal_tree(-.5)
draw_tree_names(tree.get_tree(), minlen=5, maxlen=5)
print sorted([(x.pos, x.event) for x in tree])
def test_remove_thread(self):
"""
Remove a leaf from an ARG
"""
k = 3
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
times = arghmm.get_time_points(10)
arghmm.discretize_arg(arg, times)
chrom = "n%d" % (k-1)
arg = arghmm.remove_arg_thread(arg, chrom)
recomb = arglib.get_recomb_pos(arg)
print "recomb", recomb
tree = arg.get_marginal_tree(-.5)
draw_tree_names(tree.get_tree(), minlen=5, maxlen=5)
print sorted([(x.pos, x.event) for x in tree])
def test_forward():
k = 4
n = 1e4
rho = 1.5e-8 * 20
mu = 2.5e-8 * 20
length = int(100e3 / 20)
times = argweaver.get_time_points(ntimes=100)
arg = arglib.sample_arg_smc(k, 2*n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
print "muts", len(muts)
print "recomb", len(arglib.get_recomb_pos(arg))
argweaver.discretize_arg(arg, times)
# remove chrom
new_name = "n%d" % (k - 1)
arg = argweaver.remove_arg_thread(arg, new_name)
carg = argweaverc.arg2ctrees(arg, times)
util.tic("C fast")
probs1 = argweaverc.argweaver_forward_algorithm(carg, seqs, times=times)
util.toc()
util.tic("C slow")
probs2 = argweaverc.argweaver_forward_algorithm(carg, seqs, times=times,
slow=True)
util.toc()
for i, (col1, col2) in enumerate(izip(probs1, probs2)):
for a, b in izip(col1, col2):
fequal(a, b, rel=.0001)
def test_trans_two():
"""
Calculate transition probabilities for k=2
Only calculate a single matrix
"""
k = 2
n = 1e4
rho = 1.5e-8 * 20
length = 1000
times = argweaver.get_time_points(ntimes=5, maxtime=200000)
time_steps = [times[i] - times[i - 1] for i in range(1, len(times))]
time_steps.append(200000 * 10000.0)
popsizes = [n] * len(times)
arg = arglib.sample_arg(k, 2 * n, rho, start=0, end=length)
argweaver.discretize_arg(arg, times)
print "recomb", arglib.get_recomb_pos(arg)
arg = argweaver.make_trunk_arg(0, length, "n0")
pos = 10
tree = arg.get_marginal_tree(pos)
nlineages = argweaver.get_nlineages_recomb_coal(tree, times)
states = list(argweaver.iter_coal_states(tree, times))
mat = argweaver.calc_transition_probs(tree, states, nlineages, times,
time_steps, popsizes, rho)
nstates = len(states)
def coal(j):
return 1.0 - exp(-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(time_steps[m] / (2.0 * n) for m in range(k, j)))
else:
return ((1.0 - exp(-time_steps[j] / (2.0 * n))) *
exp(-sum(time_steps[m] / (2.0 * n) for m in range(k, j))))
def isrecomb(i):
return 1.0 - exp(-max(rho * 2.0 * times[i], rho))
def recomb(i, k):
treelen = 2 * times[i] + time_steps[i]
if k < i:
return 2.0 * time_steps[k] / treelen / 2.0
else:
return 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)
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_trans_two():
"""
Calculate transition probabilities for k=2
Only calculate a single matrix
"""
k = 2
n = 1e4
rho = 1.5e-8 * 20
length = 1000
times = argweaver.get_time_points(ntimes=5, maxtime=200000)
time_steps = [times[i] - times[i-1]
for i in range(1, len(times))]
time_steps.append(200000*10000.0)
popsizes = [n] * len(times)
arg = arglib.sample_arg(k, 2*n, rho, start=0, end=length)
argweaver.discretize_arg(arg, times)
print "recomb", arglib.get_recomb_pos(arg)
arg = argweaver.make_trunk_arg(0, length, "n0")
pos = 10
tree = arg.get_marginal_tree(pos)
nlineages = argweaver.get_nlineages_recomb_coal(tree, times)
states = list(argweaver.iter_coal_states(tree, times))
mat = argweaver.calc_transition_probs(
tree, states, nlineages,
times, time_steps, popsizes, rho)
nstates = len(states)
def coal(j):
return 1.0 - exp(-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(time_steps[m] / (2.0 * n)
for m in range(k, j)))
else:
return ((1.0 - exp(-time_steps[j]/(2.0 * n))) *
exp(- sum(time_steps[m] / (2.0 * n)
for m in range(k, j))))
def isrecomb(i):
return 1.0 - exp(-max(rho * 2.0 * times[i], rho))
def recomb(i, k):
treelen = 2*times[i] + time_steps[i]
if k < i:
return 2.0 * time_steps[k] / treelen / 2.0
else:
return 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)
