def test_post_plot(self):
k = 6
n = 1e4
rho = 1.5e-8 * 50
mu = 2.5e-8 * 50
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)
times = arghmm.get_time_points(ntimes=30)
arghmm.discretize_arg(arg, times)
pause()
# save
#arglib.write_arg("test/data/k4.arg", arg)
#fasta.write_fasta("test/data/k4.fa", seqs)
new_name = "n%d" % (k - 1)
thread = list(
arghmm.iter_chrom_thread(arg, arg[new_name], by_block=False))
p = plot(cget(thread, 1), style="lines", ymin=times[1], ylog=10)
# remove chrom
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,
rho=rho,
mu=mu)
print "states", len(model.states[0])
print "muts", len(muts)
print "recomb", len(model.recomb_pos) - 2, model.recomb_pos[1:-1]
p.plot(model.recomb_pos, [10000] * len(model.recomb_pos),
style="points")
probs = arghmm.get_posterior_probs(model, length, verbose=True)
print "done"
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.gnuplot("set linestyle 2")
p.plot(high, style="lines")
p.gnuplot("set linestyle 2")
p.plot(low, style="lines")
#write_list("test/data/post_real.txt", cget(thread, 1))
#write_list("test/data/post_high.txt", high)
#write_list("test/data/post_low.txt", low)
pause()
def test_norecomb_plot(self):
k = 50
n = 1e4
rho = 1.5e-8 * .0001
rho2 = 1.5e-8 * 10
mu = 2.5e-8 * 100
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(ntimes=20)
arghmm.discretize_arg(arg, times)
tree = arg.get_marginal_tree(0)
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
# get thread
new_name = "n%d" % (k - 1)
keep = ["n%d" % i for i in range(k - 1)]
arglib.subarg_by_leaf_names(arg, keep)
arg.set_ancestral()
arg.prune()
model = arghmm.ArgHmm(arg,
seqs,
new_name=new_name,
times=times,
rho=rho2,
mu=mu)
print "states", len(model.states[0])
print "muts", len(muts)
# simulate a new thread
states = list(islice(hmm.sample_hmm_states(model), 0, arg.end))
data = list(hmm.sample_hmm_data(model, states))
seqs[new_name] = "".join(data)
#alignlib.print_align(seqs)
thread = [
model.times[model.states[i][s][1]] for i, s in enumerate(states)
]
p = plot(thread, style="lines")
probs = arghmm.get_posterior_probs(model, length, verbose=True)
print "done"
high = list(arghmm.iter_posterior_times(model, probs, .75))
low = list(arghmm.iter_posterior_times(model, probs, .25))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_post_plot(self):
k = 6
n = 1e4
rho = 1.5e-8 * 50
mu = 2.5e-8 * 50
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)
times = arghmm.get_time_points(ntimes=30)
arghmm.discretize_arg(arg, times)
pause()
# save
#arglib.write_arg("test/data/k4.arg", arg)
#fasta.write_fasta("test/data/k4.fa", seqs)
new_name = "n%d" % (k-1)
thread = list(arghmm.iter_chrom_thread(arg, arg[new_name],
by_block=False))
p = plot(cget(thread, 1), style="lines", ymin=times[1],
ylog=10)
# remove chrom
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,
rho=rho, mu=mu)
print "states", len(model.states[0])
print "muts", len(muts)
print "recomb", len(model.recomb_pos) - 2, model.recomb_pos[1:-1]
p.plot(model.recomb_pos, [10000] * len(model.recomb_pos),
style="points")
probs = arghmm.get_posterior_probs(model, length, verbose=True)
print "done"
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.gnuplot("set linestyle 2")
p.plot(high, style="lines")
p.gnuplot("set linestyle 2")
p.plot(low, style="lines")
#write_list("test/data/post_real.txt", cget(thread, 1))
#write_list("test/data/post_high.txt", high)
#write_list("test/data/post_low.txt", low)
pause()
def test_post_real(self):
k = 3
n = 1e4
rho = 1.5e-8
mu = 2.5e-8
length = 100000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
#arg = arglib.read_arg("test/data/real.arg")
#seqs = fasta.read_fasta("test/data/real.fa")
#arglib.write_arg("test/data/real.arg", arg)
#fasta.write_fasta("test/data/real.fa", seqs)
times = arghmm.get_time_points(maxtime=50000, ntimes=20)
arghmm.discretize_arg(arg, times)
new_name = "n%d" % (k - 1)
thread = list(
arghmm.iter_chrom_thread(arg, arg[new_name], by_block=False))
tree = arg.get_marginal_tree(0)
print tree.root.age
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
p = plot(cget(thread, 1), style="lines", ymin=10, ylog=10)
#alignlib.print_align(seqs)
# remove chrom
keep = ["n%d" % i for i in range(k - 1)]
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])
#print "muts", len(muts)
print "recomb", len(model.recomb_pos) - 2, model.recomb_pos[1:-1]
probs = arghmm.get_posterior_probs(model, length, verbose=True)
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_norecomb_plot(self):
k = 50
n = 1e4
rho = 1.5e-8 * .0001
rho2 = 1.5e-8 * 10
mu = 2.5e-8 * 100
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(ntimes=20)
arghmm.discretize_arg(arg, times)
tree = arg.get_marginal_tree(0)
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
# get thread
new_name = "n%d" % (k-1)
keep = ["n%d" % i for i in range(k-1)]
arglib.subarg_by_leaf_names(arg, keep)
arg.set_ancestral()
arg.prune()
model = arghmm.ArgHmm(arg, seqs, new_name=new_name, times=times,
rho=rho2, mu=mu)
print "states", len(model.states[0])
print "muts", len(muts)
# simulate a new thread
states = list(islice(hmm.sample_hmm_states(model), 0, arg.end))
data = list(hmm.sample_hmm_data(model, states))
seqs[new_name] = "".join(data)
#alignlib.print_align(seqs)
thread = [model.times[model.states[i][s][1]]
for i, s in enumerate(states)]
p = plot(thread, style="lines")
probs = arghmm.get_posterior_probs(model, length, verbose=True)
print "done"
high = list(arghmm.iter_posterior_times(model, probs, .75))
low = list(arghmm.iter_posterior_times(model, probs, .25))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_post_real(self):
k = 3
n = 1e4
rho = 1.5e-8
mu = 2.5e-8
length = 100000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
#arg = arglib.read_arg("test/data/real.arg")
#seqs = fasta.read_fasta("test/data/real.fa")
#arglib.write_arg("test/data/real.arg", arg)
#fasta.write_fasta("test/data/real.fa", seqs)
times = arghmm.get_time_points(maxtime=50000, ntimes=20)
arghmm.discretize_arg(arg, times)
new_name = "n%d" % (k - 1)
thread = list(arghmm.iter_chrom_thread(arg, arg[new_name],
by_block=False))
tree = arg.get_marginal_tree(0)
print tree.root.age
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
p = plot(cget(thread, 1), style="lines", ymin=10, ylog=10)
#alignlib.print_align(seqs)
# remove chrom
keep = ["n%d" % i for i in range(k-1)]
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])
#print "muts", len(muts)
print "recomb", len(model.recomb_pos) - 2, model.recomb_pos[1:-1]
probs = arghmm.get_posterior_probs(model, length, verbose=True)
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_post3(self):
k = 3
n = 1e4
rho = 1.5e-8 * 3
mu = 2.5e-8 * 100
length = 10000
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)
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)
thread = list(arghmm.iter_chrom_thread(arg, arg["n2"], by_block=False))
p = plot(cget(thread, 1), style="lines", ymin=0)
# remove chrom
keep = ["n0", "n1"]
arglib.subarg_by_leaf_names(arg, keep)
arg.set_ancestral()
arg.prune()
model = arghmm.ArgHmm(arg,
seqs,
new_name="n2",
times=times,
rho=rho,
mu=mu)
print "states", len(model.states[0])
print "muts", len(muts)
print "recomb", len(model.recomb_pos) - 2, model.recomb_pos[1:-1]
p.plot(model.recomb_pos, [1000] * len(model.recomb_pos),
style="points")
probs = arghmm.get_posterior_probs(model, length, verbose=True)
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_post3(self):
k = 3
n = 1e4
rho = 1.5e-8 * 3
mu = 2.5e-8 * 100
length = 10000
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)
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)
thread = list(arghmm.iter_chrom_thread(arg, arg["n2"], by_block=False))
p = plot(cget(thread, 1), style="lines", ymin=0)
# remove chrom
keep = ["n0", "n1"]
arglib.subarg_by_leaf_names(arg, keep)
arg.set_ancestral()
arg.prune()
model = arghmm.ArgHmm(arg, seqs, new_name="n2", times=times,
rho=rho, mu=mu)
print "states", len(model.states[0])
print "muts", len(muts)
print "recomb", len(model.recomb_pos) - 2, model.recomb_pos[1:-1]
p.plot(model.recomb_pos, [1000] * len(model.recomb_pos),
style="points")
probs = arghmm.get_posterior_probs(model, length, verbose=True)
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_post2(self):
k = 2
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)
times = arghmm.get_time_points()
arghmm.discretize_arg(arg, times)
thread = list(arghmm.iter_chrom_thread(arg, arg["n1"], by_block=False))
tree = arg.get_marginal_tree(0)
print tree.root.age
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
p = plot(cget(thread, 1), style="lines", ymin=0)
#alignlib.print_align(seqs)
# remove chrom
keep = ["n0"]
arglib.subarg_by_leaf_names(arg, keep)
arg = arglib.smcify_arg(arg)
model = arghmm.ArgHmm(arg,
seqs,
new_name="n1",
times=times,
rho=rho,
mu=mu)
print "states", len(model.states[0])
probs = arghmm.get_posterior_probs(model, length, verbose=True)
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
def test_post2(self):
k = 2
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)
times = arghmm.get_time_points()
arghmm.discretize_arg(arg, times)
thread = list(arghmm.iter_chrom_thread(arg, arg["n1"], by_block=False))
tree = arg.get_marginal_tree(0)
print tree.root.age
treelib.draw_tree_names(tree.get_tree(), minlen=5, scale=4e-4)
p = plot(cget(thread, 1), style="lines", ymin=0)
#alignlib.print_align(seqs)
# remove chrom
keep = ["n0"]
arglib.subarg_by_leaf_names(arg, keep)
arg = arglib.smcify_arg(arg)
model = arghmm.ArgHmm(arg, seqs, new_name="n1", times=times,
rho=rho, mu=mu)
print "states", len(model.states[0])
probs = arghmm.get_posterior_probs(model, length, verbose=True)
high = list(arghmm.iter_posterior_times(model, probs, .95))
low = list(arghmm.iter_posterior_times(model, probs, .05))
p.plot(high, style="lines")
p.plot(low, style="lines")
pause()
