def print_table(self):
'''Print a table of probabilities at each SNP.'''
options = np.get_printoptions()
np.set_printoptions(precision=3, suppress=True, threshold=np.nan, linewidth=200)
print 'lambda = %s, Delta = %s, eps = %.1e' % (self.lam, repr(self.Delta)[6:-1], self.e)
print 'Viterbi path (frame SNPs): ' + ' -> '.join(map(lambda x: '%d (%d-%d)' % (x[0], x[1][0], x[1][1]),
itemutil.groupby_with_range(self.Q_star + 1)))
print 'Viterbi path (SNPs): ' + ' -> '.join(map(lambda x: '%d (%d-%d)' % (x[0], self.snps[x[1][0]], self.snps[x[1][1]]),
itemutil.groupby_with_range(self.Q_star + 1)))
print ' %-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s' % \
('t', 'SNP#', 'Obs', 'G1', 'G2', 'lam*dx', 'p',
'Gam1', 'Gam2', 'Gam3', 'Gam4', 'Gam5', 'Gam6', 'Gam7', 'Gam8', 'Gam9',
'p(IBD)', 'Viterbi', 'IBD?')
print np.concatenate((np.arange(len(self.x))[np.newaxis].transpose(),
self.snps[np.newaxis].transpose(),
self.Obs[np.newaxis].transpose(),
np.array([ProbIbdHmmCalculator._T_STATE_G[t][0] for t in self.Obs])[np.newaxis].transpose(),
np.array([ProbIbdHmmCalculator._T_STATE_G[t][1] for t in self.Obs])[np.newaxis].transpose(),
np.concatenate((self.lam_x, [0]))[np.newaxis].transpose(),
self.p[np.newaxis].transpose(),
self.Gamma.transpose(),
self.p_ibd_gamma[np.newaxis].transpose(),
(self.Q_star + 1)[np.newaxis].transpose(),
self.p_ibd_viterbi[np.newaxis].transpose()
), axis=1)
util.set_printoptions(options)
def print_table(self):
'''Print a table of probabilities at each SNP.'''
options = np.get_printoptions()
np.set_printoptions(precision=3,
suppress=True,
threshold=np.nan,
linewidth=200)
print 'lambda = %.2f, f = %.2f, eps = %.1e' % (self.lam, self.f,
self.e)
print 'Viterbi path (frame SNPs): ' + ' -> '.join(
map(lambda x: '%d (%d-%d)' % (x[0], x[1][0], x[1][1]),
itemutil.groupby_with_range(self.Q_star)))
print 'Viterbi path (SNPs): ' + ' -> '.join(
map(
lambda x: '%d (%d-%d)' %
(x[0], self.snps[x[1][0]], self.snps[x[1][1]]),
itemutil.groupby_with_range(self.Q_star)))
print ' %-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s' % \
('t', 'SNP#', 'H1', 'H2', 'x', 'lam*dx', 'p', 'Gam0', 'Gam1', 'p(IBD)', 'Viterbi', 'IBD?')
print np.concatenate(
(np.arange(len(self.x))[np.newaxis].transpose(),
self.snps[np.newaxis].transpose(),
self.Obs[np.newaxis].transpose() / 2,
self.Obs[np.newaxis].transpose() % 2,
self.x[np.newaxis].transpose(),
np.concatenate((self.lam * np.diff(self.x), [
0
]))[np.newaxis].transpose(), self.p[np.newaxis].transpose(),
self.Gamma.transpose(), self.p_ibd_gamma[np.newaxis].transpose(),
(self.Q_star)[np.newaxis].transpose(),
self.p_ibd_viterbi[np.newaxis].transpose()),
axis=1)
util.set_printoptions(options)
def print_table(self):
'''Print a table of probabilities at each SNP.'''
options = np.get_printoptions()
np.set_printoptions(precision=3,
suppress=True,
threshold=np.nan,
linewidth=200)
print 'lambda = %s, Delta = %s, eps = %.1e' % (
self.lam, repr(self.Delta)[6:-1], self.e)
print 'Viterbi path (frame SNPs): ' + ' -> '.join(
map(lambda x: '%d (%d-%d)' % (x[0], x[1][0], x[1][1]),
itemutil.groupby_with_range(self.Q_star + 1)))
print 'Viterbi path (SNPs): ' + ' -> '.join(
map(
lambda x: '%d (%d-%d)' %
(x[0], self.snps[x[1][0]], self.snps[x[1][1]]),
itemutil.groupby_with_range(self.Q_star + 1)))
print ' %-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s' % \
('t', 'SNP#', 'Obs', 'G1', 'G2', 'lam*dx', 'p',
'Gam1', 'Gam2', 'Gam3', 'Gam4', 'Gam5', 'Gam6', 'Gam7', 'Gam8', 'Gam9',
'p(IBD)', 'Viterbi', 'IBD?')
print np.concatenate(
(np.arange(len(self.x))[np.newaxis].transpose(),
self.snps[np.newaxis].transpose(),
self.Obs[np.newaxis].transpose(),
np.array(
[ProbIbdHmmCalculator._T_STATE_G[t][0]
for t in self.Obs])[np.newaxis].transpose(),
np.array([
ProbIbdHmmCalculator._T_STATE_G[t][1] for t in self.Obs
])[np.newaxis].transpose(), np.concatenate(
(self.lam_x, [0]))[np.newaxis].transpose(),
self.p[np.newaxis].transpose(), self.Gamma.transpose(),
self.p_ibd_gamma[np.newaxis].transpose(),
(self.Q_star + 1)[np.newaxis].transpose(),
self.p_ibd_viterbi[np.newaxis].transpose()),
axis=1)
util.set_printoptions(options)
def print_table(self):
'''Print a table of probabilities at each SNP.'''
options = np.get_printoptions()
np.set_printoptions(precision=3, suppress=True, threshold=np.nan, linewidth=200)
print 'lambda = %.2f, f = %.2f, eps = %.1e' % (self.lam, self.f, self.e)
print 'Viterbi path (frame SNPs): ' + ' -> '.join(map(lambda x: '%d (%d-%d)' % (x[0], x[1][0], x[1][1]),
itemutil.groupby_with_range(self.Q_star)))
print 'Viterbi path (SNPs): ' + ' -> '.join(map(lambda x: '%d (%d-%d)' % (x[0], self.snps[x[1][0]], self.snps[x[1][1]]),
itemutil.groupby_with_range(self.Q_star)))
print ' %-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s%-10s' % \
('t', 'SNP#', 'H1', 'H2', 'x', 'lam*dx', 'p', 'Gam0', 'Gam1', 'p(IBD)', 'Viterbi', 'IBD?')
print np.concatenate((np.arange(len(self.x))[np.newaxis].transpose(),
self.snps[np.newaxis].transpose(),
self.Obs[np.newaxis].transpose() / 2,
self.Obs[np.newaxis].transpose() % 2,
self.x[np.newaxis].transpose(),
np.concatenate((self.lam * np.diff(self.x), [0]))[np.newaxis].transpose(),
self.p[np.newaxis].transpose(),
self.Gamma.transpose(),
self.p_ibd_gamma[np.newaxis].transpose(),
(self.Q_star)[np.newaxis].transpose(),
self.p_ibd_viterbi[np.newaxis].transpose()
), axis=1)
util.set_printoptions(options)
# After: q, hh
p = im.io.read_npz(im.itu.FAMILY225_STAGE1)
q = im.io.read_npz(im.itu.FAMILY225_STAGE1)
phaser = family_child_comparison_phaser(debug=True)
(g, h) = p.data
(gg, hh) = q.data
f = p.families(genotyped=True)[0]
(father, mother) = f.parents
children = np.array(f.children_list)
phaser.run(q)
# Print a portion of the father and corresponding children haplotypes
snps = np.arange(2094, 2118) #np.arange(0, p.num_snps)
for parent_type in ALLELES:
print_haps(h, hh, parent_type, snps)
# Recombinations
comparator = im.ic.ChildComparator(p, f)
template = {PATERNAL: 5, MATERNAL: 3}
for parent_type in ALLELES:
(_, _, info) = comparator.child_recombinations(parent_type, template[parent_type], remove_errors=False)
print 'Recombinations, parent_type', parent_type
print info.recombination_snp
# Plot recombinations
#im.plots.plot_all_family_comparisons(q, f)
util.set_printoptions(old_printoptions)
Created on September 7, 2012
@author: Oren Livne <*****@*****.**>
============================================================
'''
import numpy as np, matplotlib.pyplot as plt, util
from impute import impute_test_util as itu
from impute.plot import plots
from impute.data import io
if __name__ == '__main__':
'''
--------------------------------------------------
Main program
--------------------------------------------------
'''
# Load nuclear family data
p = io.read_npz(itu.FAMILY2003_STAGE3)
f = p.families(genotyped=False)[0]
old_printoptions = np.get_printoptions()
np.set_printoptions(precision=2)
children = np.array([x for x in f.children_list if p.is_genotyped(x)])
h = p.haplotype.data
plots.plot_hap_corr_matrix(h, children, (0, 0))
plt.savefig('hap00.png')
plots.plot_hap_corr_matrix(h, children, (0, 1))
plt.savefig('hap01.png')
util.set_printoptions(old_printoptions)
