def nsharehelper(x):
if x % 1000 == 0:
print 'Random draw %s' % x
if args.matchkinship:
ns = match_kinship_sample(affinds, fullinds, threshold=0.001)
else:
ns = random.sample(fullinds, naff)
return shares(ns, shared, nmark)
def sbool(affs, shared, nmark):
return shares(affs, shared, nmark) / float(npairs(affs))
def spairs(affs, shared, nmark):
return shares(affs, shared, nmark)
with open(args.kinship) as kinshf:
for line in kinshf:
fam, ida, idb, phi = line.strip().split()
ida = tuple(fam, ida)
idb = tuple(fam, idb)
if not {ida, idb} <= fullinds:
continue
else:
kinship[frozenset({ida, idb})] = float(phi)
for a,b in combinations(fullinds, 2):
pair = frozenset({a,b})
if pair not in kinship:
kinship[pair] = 0
print 'Calculating sharing from affecteds'
affshare = shares(affinds, shared, nmark)
print 'Model: %s' % args.model
print 'Calculating emperical p-values from %s draws of %s individuals' % (args.nrep, naff)
print 'Using %s processes' % args.njobs
def nsharehelper(x):
if x % 1000 == 0:
print 'Random draw %s' % x
if args.matchkinship:
ns = match_kinship_sample(affinds, fullinds, threshold=0.001)
else:
ns = random.sample(fullinds, naff)
return shares(ns, shared, nmark)
if args.njobs > 1:
