def test_trans_switch_single(self):
"""
Calculate transitions probabilities for switching between blocks
Only calculate a single matrix
"""
k = 5
n = 1e4
rho = 1.5e-8 * 100
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
#arglib.write_arg("tmp/a.arg", arg)
#arg = arglib.read_arg("tmp/a.arg")
#arg.set_ancestral()
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
times = arghmm.get_time_points(5)
arghmm.discretize_arg(arg, times)
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)
# get recombs
recombs = list(x.pos for x in arghmm.iter_visible_recombs(arg))
print "recomb", recombs
pos = recombs[0] + 1
tree = arg.get_marginal_tree(pos - .5)
last_tree = arg.get_marginal_tree(pos - 1 - .5)
print "states1>>", model.states[pos - 1]
print "states2>>", model.states[pos]
treelib.draw_tree_names(last_tree.get_tree(), minlen=5, maxlen=5)
treelib.draw_tree_names(tree.get_tree(), minlen=5, maxlen=5)
print "pos>>", pos
recomb = [x for x in tree
if x.event == "recomb" and x.pos + 1 == pos][0]
mat = arghmm.calc_transition_probs_switch(tree, last_tree, recomb.name,
model.states[pos - 1],
model.states[pos],
model.nlineages, model.times,
model.time_steps,
model.popsizes, rho)
pc(mat)
def test_trans_switch_single(self):
"""
Calculate transitions probabilities for switching between blocks
Only calculate a single matrix
"""
k = 5
n = 1e4
rho = 1.5e-8 * 100
mu = 2.5e-8
length = 1000
arg = arglib.sample_arg(k, n, rho, start=0, end=length)
#arglib.write_arg("tmp/a.arg", arg)
#arg = arglib.read_arg("tmp/a.arg")
#arg.set_ancestral()
muts = arglib.sample_arg_mutations(arg, mu)
seqs = arglib.make_alignment(arg, muts)
times = arghmm.get_time_points(5)
arghmm.discretize_arg(arg, times)
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)
# get recombs
recombs = list(x.pos for x in arghmm.iter_visible_recombs(arg))
print "recomb", recombs
pos = recombs[0] + 1
tree = arg.get_marginal_tree(pos-.5)
last_tree = arg.get_marginal_tree(pos-1-.5)
print "states1>>", model.states[pos-1]
print "states2>>", model.states[pos]
treelib.draw_tree_names(last_tree.get_tree(), minlen=5, maxlen=5)
treelib.draw_tree_names(tree.get_tree(), minlen=5, maxlen=5)
print "pos>>", pos
recomb = [x for x in tree
if x.event == "recomb" and x.pos+1 == pos][0]
mat = arghmm.calc_transition_probs_switch(
tree, last_tree, recomb.name,
model.states[pos-1], model.states[pos],
model.nlineages, model.times,
model.time_steps, model.popsizes, rho)
pc(mat)
#arg2.prune()
# load model
mu = 2.5e-8
rho = 1.5e-8
new_name = "n4"
model = arghmm.ArgHmm(arg2, seqs, new_name=new_name, times=times,
rho=rho, mu=mu)
pos = 9910
tree = arg2.get_marginal_tree(pos-.5)
last_tree = arg2.get_marginal_tree(pos-1-.5)
recomb = arghmm.find_tree_next_recomb(arg2, pos - 1)
states1 = model.states[pos-1]
states2 = model.states[pos]
model.check_local_tree(pos)
mat = arghmm.calc_transition_probs_switch(
tree, last_tree, recomb.name,
states1, states2,
model.nlineages, model.times,
model.time_steps, model.popsizes, model.rho)
#pos2 = 7000
#n = model.get_num_states(pos2)
#mat2 = [[model.prob_transition(pos2-1, i, pos2, j)
# for j in range(n)] for i in range(n)]
# load model
mu = 2.5e-8
rho = 1.5e-8
new_name = "n4"
model = arghmm.ArgHmm(arg2,
seqs,
new_name=new_name,
times=times,
rho=rho,
mu=mu)
pos = 9910
tree = arg2.get_marginal_tree(pos - .5)
last_tree = arg2.get_marginal_tree(pos - 1 - .5)
recomb = arghmm.find_tree_next_recomb(arg2, pos - 1)
states1 = model.states[pos - 1]
states2 = model.states[pos]
model.check_local_tree(pos)
mat = arghmm.calc_transition_probs_switch(tree, last_tree, recomb.name,
states1, states2,
model.nlineages, model.times,
model.time_steps, model.popsizes,
model.rho)
#pos2 = 7000
#n = model.get_num_states(pos2)
#mat2 = [[model.prob_transition(pos2-1, i, pos2, j)
# for j in range(n)] for i in range(n)]
