def test_legacy_errors(self):
defaults = pyslim.default_slim_metadata
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.decode_mutation(defaults('mutation'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.decode_population(defaults('population'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.decode_individual(defaults('individual'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.decode_node(defaults('node'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.encode_mutation(defaults('mutation'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.encode_population(defaults('population'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.encode_individual(defaults('individual'))
with self.assertRaisesRegex(ValueError, "legacy"):
pyslim.encode_node(defaults('node'))
def test_mutation_metadata(self):
for md_length in [0, 1, 5]:
md = [pyslim.MutationMetadata(
mutation_type=j, selection_coeff=0.5, population=j,
slim_time=10 + j, nucleotide=(j % 5) - 1)
for j in range(md_length)]
md_bytes = pyslim.encode_mutation(md)
new_md = pyslim.decode_mutation(md_bytes)
self.assertEqual(len(md), len(new_md))
for x, y in zip(md, new_md):
self.assertEqual(x, y)
def test_annotate_mutations(self):
for ts in self.get_slim_examples():
tables = ts.tables
new_tables = ts.tables
metadata = []
for md in tskit.unpack_bytes(tables.mutations.metadata,
tables.mutations.metadata_offset):
dm = pyslim.decode_mutation(md)
edm = pyslim.encode_mutation(dm)
self.assertEqual(md, edm)
metadata.append(dm)
pyslim.annotate_mutation_metadata(new_tables, metadata)
self.assertEqual(tables, new_tables)
import pyslim
import msprime
ts = pyslim.load("simple.trees")
tables = ts.tables
print(tables)
# mutations
mut_metadata = []
for md in msprime.unpack_bytes(tables.mutations.metadata,
tables.mutations.metadata_offset):
dm = pyslim.decode_mutation(md)
edm = pyslim.encode_mutation(dm)
assert (md == edm)
mut_metadata.append(dm)
pyslim.annotate_mutations(tables, mut_metadata)
# nodes
node_metadata = []
for md in msprime.unpack_bytes(tables.nodes.metadata,
tables.nodes.metadata_offset):
dn = pyslim.decode_node(md)
edn = pyslim.encode_node(dn)
assert (md == edn)
node_metadata.append(dn)
pyslim.annotate_nodes(tables, node_metadata)
def throw_mut_on_tree(ts):
# this function takes an unmutated tree sequence and "throws" a single mutation onto it, representing
# the standing variant in a sweep that starts immediately after burnin
global args
if not args.af:
n = args.Ne
else:
n = 2 * 14474
l = args.l
r = args.r
q = args.q
c = args.c
# find total tree length times sequence extent
tree_sizes = np.array([
t.total_branch_length *
(np.ceil(t.interval[1]) - np.ceil(t.interval[0])) for t in ts.trees()
])
tree_sizes /= sum(tree_sizes)
# pick the tree
tree_index = np.random.choice(ts.num_trees, size=1, p=tree_sizes)
t = ts.first()
for (i, t) in enumerate(ts.trees()):
if i == tree_index:
break
assert (t.index == tree_index)
# pick the branch
cpicked = -1
while cpicked < c:
treeloc = t.total_branch_length * np.random.uniform()
for mut_n in t.nodes():
if mut_n != t.root:
treeloc -= t.branch_length(mut_n)
if treeloc <= 0:
cpicked = t.num_samples(mut_n) / (n)
#print(cpicked)
break
# pick the location on the sequence
mut_base = 0.0 + np.random.randint(
low=np.ceil(t.interval[0]), high=np.ceil(t.interval[1]), size=1)
# the following assumes that there's no other mutations in the tree sequence
assert (ts.num_sites == 0)
# the mutation metadata
mut_md = pyslim.MutationMetadata(mutation_type=1,
selection_coeff=0.0,
population=1,
slim_time=1)
tables = ts.tables
site_id = tables.sites.add_row(position=mut_base, ancestral_state=b'')
tables.mutations.add_row(site=site_id,
node=mut_n,
derived_state='1',
metadata=pyslim.encode_mutation([mut_md]))
mut_ts = pyslim.load_tables(tables)
# genotypes
#out_slim_targets = open('%s.slim.targets'%(out),'w')
#for i,g in enumerate(mut_ts.genotype_matrix()[0]):
# if g == 1:
# #print(i)
# out_slim_targets.write('%d\n'%(i))
#out_slim_targets.close()
if not args.q:
print(mut_ts.genotype_matrix())
print('%d / %d' % (np.sum(mut_ts.genotype_matrix()), n))
freq = np.sum(mut_ts.genotype_matrix()) / (n)
return mut_base, freq, mut_ts
