def ibd_posterior_hh(lam, Delta, m):
'''Simple case: sibs with (1,1) genotypes along the segment.'''
x = np.arange(m)
m = len(x)
return ProbIbdHmmHapCalculator(lam=lam,
f=kinship(Delta),
x=x,
p=0.2 * np.ones(m),
h1=np.zeros((m, ), dtype=np.uint),
h2=np.zeros((m, ), dtype=np.uint),
e=0.01).prob()
def ibd_posterior_hh(lam, Delta, m):
"""Simple case: sibs with (1,1) genotypes along the segment."""
x = np.arange(m)
m = len(x)
return ProbIbdHmmHapCalculator(
lam=lam,
f=kinship(Delta),
x=x,
p=0.2 * np.ones(m),
h1=np.zeros((m,), dtype=np.uint),
h2=np.zeros((m,), dtype=np.uint),
e=0.01,
).prob()
m = 1024
lam, Delta = models['sibs']
gg = ibd_posterior_gg(lam, Delta, m)
hh = ibd_posterior_hh(lam, Delta, m)
a = [gg, hh]
print m, np.max(gg), np.max(hh)
# Create a confidence vs. x plot for various segment sizes M
np.set_printoptions(precision=2, suppress=True)
n = 5
fig_num = 0
for model, (lam, Delta) in models.iteritems():
fig_num += 1
P.figure(fig_num)
P.clf()
[plot_posterior(lam, Delta, n, i, 4**i) for i in xrange(1, n + 1)]
P.subplot(n, 1, 1)
P.title(
'IBD Posterior Probability\n$\lambda = %.2f, \Delta = %s, f = %.2f$'
% (lam, repr(Delta)[6:-1], kinship(Delta)))
P.subplot(n, 1, n)
P.xlabel('Genetic Distance')
# Crappy aspect ratio but doesn't matter for now
P.show()
P.savefig('%s/ibd_%s.png' % (
out_dir,
model,
))
np.set_printoptions(precision=15, suppress=True)
m = 1024
lam, Delta = models["sibs"]
gg = ibd_posterior_gg(lam, Delta, m)
hh = ibd_posterior_hh(lam, Delta, m)
a = [gg, hh]
print m, np.max(gg), np.max(hh)
# Create a confidence vs. x plot for various segment sizes M
np.set_printoptions(precision=2, suppress=True)
n = 5
fig_num = 0
for model, (lam, Delta) in models.iteritems():
fig_num += 1
P.figure(fig_num)
P.clf()
[plot_posterior(lam, Delta, n, i, 4 ** i) for i in xrange(1, n + 1)]
P.subplot(n, 1, 1)
P.title(
"IBD Posterior Probability\n$\lambda = %.2f, \Delta = %s, f = %.2f$"
% (lam, repr(Delta)[6:-1], kinship(Delta))
)
P.subplot(n, 1, n)
P.xlabel("Genetic Distance")
# Crappy aspect ratio but doesn't matter for now
P.show()
P.savefig("%s/ibd_%s.png" % (out_dir, model))
def parse_line(line):
'''Parse a segment file line into a key (sample1,sample2) and value (segment set).'''
items = [int(x) for x in line]
return tuple(items[0:2]), im.segment.DisjointSegmentSet(zip(items[2::2], items[3::2]))
####################################################################################
if __name__ == '__main__':
'''
--------------------------------------------------
Main program
--------------------------------------------------
'''
options = parse_command_line_args()
param = im.PhaseParam()
if options.id_coef_file:
param.id_coef_file = options.id_coef_file
try:
for line in csv.reader(sys.stdin, delimiter=' ', skipinitialspace=True):
key, A = parse_line(line)
# Output statistics. Only log pairs for which samples were found
len_A = A.length
if len_A != 0:
#f = param.kinship(key[0], key[1])
_, Delta = param.id_coefs(key[0], key[1])
f = kinship(Delta)
pibd = p_ibd(Delta)
sys.stdout.write('%d %d %f %f %d\n' % (key[0], key[1], f, pibd, len_A))
except:
traceback.print_exc(file=sys.stdout)
sys.exit(util.EXIT_FAILURE)
zip(items[2::2], items[3::2]))
####################################################################################
if __name__ == '__main__':
'''
--------------------------------------------------
Main program
--------------------------------------------------
'''
options = parse_command_line_args()
param = im.PhaseParam()
if options.id_coef_file:
param.id_coef_file = options.id_coef_file
try:
for line in csv.reader(sys.stdin, delimiter=' ',
skipinitialspace=True):
key, A = parse_line(line)
# Output statistics. Only log pairs for which samples were found
len_A = A.length
if len_A != 0:
#f = param.kinship(key[0], key[1])
_, Delta = param.id_coefs(key[0], key[1])
f = kinship(Delta)
pibd = p_ibd(Delta)
sys.stdout.write('%d %d %f %f %d\n' %
(key[0], key[1], f, pibd, len_A))
except:
traceback.print_exc(file=sys.stdout)
sys.exit(util.EXIT_FAILURE)