def test_unsupported_model():
test_newick = """
(a:1[&&momi:model=exponential:N=2.0:lineages=10],
b:1[&&momi:model=constant:N=1.5:lineages=8]):3[&&momi:model=constant:N=10.0];
"""
with pytest.raises(Exception):
demo = Demography.from_newick(test_newick)
def time_runs(args):
ms_path, n_taxa, lineages_per_taxon, moranOnly = args
print "# Starting job with n_per_deme=%d, n_demes=%d" % (lineages_per_taxon, n_taxa)
# Get a random phylogeny
newick_str = random_binary_tree(n_taxa)
demo = Demography.from_newick(newick_str, lineages_per_taxon)
n = n_taxa * lineages_per_taxon
results = []
snp_list = run_simulation(ms_path, demo, 100, lineages_per_taxon)
for snp,state in enumerate(snp_list):
#print(state)
state_tuple = tuple([v['derived'] for k,v in sorted(state.iteritems())])
rid = random.getrandbits(32)
method_list = [("moran",False)]
if not moranOnly:
method_list += [("chen",True)]
for name,use_chen_eqs in method_list:
with Timer() as t:
ret = demo.sfs(state, use_chen_eqs)
results.append((name,n_taxa, lineages_per_taxon, snp, t.interval, ret, rid, str(state_tuple), newick_str))
print "# Finished job with n_per_deme=%d, n_demes=%d" % (lineages_per_taxon, n_taxa)
return results
def do_job((ms_path, n_demes, n_per_pop, moranOnly, seed)):
print "# Starting job with n_per_deme=%d, n_demes=%d" % (n_per_pop, n_demes)
random.seed(seed)
# Get a random phylogeny
newick_str = random_binary_tree(n_demes)
demo = Demography.from_newick(newick_str, n_per_pop)
n = n_demes * n_per_pop
snp_list = run_simulation(ms_path, demo, loci_per_rep, n_per_pop)
snp_list = [{pop: counts['derived'] for pop,counts in snp.iteritems()}
for snp in snp_list]
# only keep unique snps
snp_list = set([snp.iteritems() for snp in snp_list])
snp_list = map(dict, snp_list)
method_list = [("momi",False)]
if not moranOnly:
method_list += [("Chen",True)]
results = {}
for name,use_chen_eqs in method_list:
with Timer() as t:
demo.compute_sfs([snp_list[0]], use_chen_eqs)
precompute_t = t.interval
with Timer() as t:
results[name] = {'sfs': demo.compute_sfs(snp_list, use_chen_eqs)}
results[name].update({'timing': {"'Per SNP'" : t.interval / float(len(snp_list)), "'Precomputation'" : precompute_t}})
print "# Finished job with n_per_deme=%d, n_demes=%d" % (n_per_pop, n_demes)
return {'n': n_demes * n_per_pop, 'n_pops' : n_demes, 'results': results, 'seed' : seed}
def test_unsupported_model():
test_newick = """
(a:1[&&momi:model=exponential:N=2.0:lineages=10],
b:1[&&momi:model=constant:N=1.5:lineages=8]):3[&&momi:model=constant:N=10.0];
"""
with pytest.raises(Exception):
demo = Demography.from_newick(test_newick)
## where ... contains:
## lineages= # alleles (if population is leaf)
## model= population history (default=constant)
## N, N_top, N_bottom= parameters for constant/exponential size history
## model_i= model for i-th epoch of piecewise history (either constant or exponential)
## N_i, N_top_i, N_bottom_i, tau_i= parameters for i-th epoch of piecewise history
newick_str = """
((
a:.25[&&momi:lineages=10:model=constant:N=10.0],
b:.3[&&momi:lineages=5:model=exponential:N_top=1.0:N_bottom=10.0]
):.1[&&momi:model=constant:N=1.5],
c:.3[&&momi:lineages=8:model=piecewise:model_0=exponential:tau_0=.2:N_top_0=.1:N_bottom_0=1.0:model_1=constant:tau_1=.1:N_1=.3]
)[&&momi:N=3.0]
"""
demo3 = Demography.from_newick(newick_str)
# construct a [list] of SNPs to compute SFS for
# each SNP is represented as a {dict} giving the derived allele counts
derived_counts_list = [{'a': 0, 'b': 0, 'c':1}, # singleton SNP in population 'c'
{'a': 2, 'b':0, 'c':0}, # doubleton SNP in population 'a'
{'a': 10,'b':5, 'c':0}, # SNP with derived allele fixed in 'a','b', but not present in 'c'
]
# compute the SFS
sfs_list = demo3.compute_sfs(derived_counts_list)
print "Printing a few SFS entries for a 3 population demography"
print "Derived_Counts","\t","SFS_Value"
def demo():
test_newick = """
(a:1[&&momi:model=constant:N=2.0:lineages=10],
b:1[&&momi:model=constant:N=1.5:lineages=8]):3[&&momi:model=constant:N=10.0];
"""
return Demography.from_newick(test_newick)
def test_requires_lineages():
with pytest.raises(Exception):
Demography.from_newick("(a:1,b:1)")
def demo():
test_newick = """
(a:1[&&momi:model=constant:N=2.0:lineages=10],
b:1[&&momi:model=constant:N=1.5:lineages=8]):3[&&momi:model=constant:N=10.0];
"""
return Demography.from_newick(test_newick)
def test_requires_lineages():
with pytest.raises(Exception):
Demography.from_newick("(a:1,b:1)")
## where ... contains:
## lineages= # alleles (if population is leaf)
## model= population history (default=constant)
## N, N_top, N_bottom= parameters for constant/exponential size history
## model_i= model for i-th epoch of piecewise history (either constant or exponential)
## N_i, N_top_i, N_bottom_i, tau_i= parameters for i-th epoch of piecewise history
newick_str = """
((
a:.25[&&momi:lineages=10:model=constant:N=10.0],
b:.3[&&momi:lineages=5:model=exponential:N_top=1.0:N_bottom=10.0]
):.1[&&momi:model=constant:N=1.5],
c:.3[&&momi:lineages=8:model=piecewise:model_0=exponential:tau_0=.2:N_top_0=.1:N_bottom_0=1.0:model_1=constant:tau_1=.1:N_1=.3]
)[&&momi:N=3.0]
"""
demo3 = Demography.from_newick(newick_str)
# construct a [list] of SNPs to compute SFS for
# each SNP is represented as a {dict} giving the derived allele counts
derived_counts_list = [
{
'a': 0,
'b': 0,
'c': 1
}, # singleton SNP in population 'c'
{
'a': 2,
'b': 0,
'c': 0
}, # doubleton SNP in population 'a'
