def check_predestinations(tree, reps=10000):
dic = {}
for _ in xrange(reps):
pks = {}
ntree, _, _ = deladmix(tree, pks=pks)
unique_id = unique_identifier(ntree)
tup = (pks['remove_key'], pks['remove_branch']
) #,pks['sink_key'],pks['sink_branch'])
if tup in dic:
assert dic[tup][
0] == unique_id, 'There is not a unique mapping from sink source to unique id'
assert 1.0 / dic[tup][1] == pks[
'forward_choices'], 'There was not a unqiue number of forward choices for ' #+str(tup)+' : '+str(pks['forward_choices'])+'><'
dic[tup] = (unique_id, 1.0 / pks['forward_choices'],
dic[tup][2] + 1)
else:
dic[tup] = (unique_id, 1.0 / pks['forward_choices'], 1)
res_ret = []
total = 0
for key, element in dic.items():
print key, ': ', element
total += element[1]
res_ret.append(list(key) + list(element))
print 'TOTAL=', total
with open("results3.csv", "wb") as f:
wr = writer(f)
wr.writerows(res_ret)
return 'done'
def test_topological_prior_density(n, k, sim_length):
dictionary_of_probabilities = {}
list_of_simulated_trees = []
for _ in xrange(sim_length):
tree = generate_admix_topology(n, k)
unique_id = unique_identifier(tree)
list_of_simulated_trees.append(unique_id)
if unique_id not in dictionary_of_probabilities:
print unique_id
dictionary_of_probabilities[unique_id] = exp(
uniform_topological_prior_function(tree))
ad, ad2 = dictionary_of_probabilities, Counter(list_of_simulated_trees)
for key, val in ad.items():
print val, 'vs', float(ad2[key]) / sim_length, ': ', key
print 'Total number of simulations', sim_length
print 'Unique sampled trees', len(ad2)
print 'Simulated total probability', sum(ad.values())
n = float(sim_length)
c = float(len(ad2))
def f(m):
return m * (1 - ((m - 1) / m)**n) - c
maxval = c
while f(maxval) < 0:
maxval *= 2
ex = brentq(f, c, maxval)
print 'Expected number of unique', ex
print 'Expected total probability', c / ex
def summary_of_phylogeny(self, tree):
return unique_identifier(tree)
def __call__(self, **kwargs):
tree = kwargs['proposed_tree']
return unique_identifier(tree)
def __call__(self, **kwargs):
old_tree = kwargs['tree']
return unique_identifier(old_tree)