if not found:
if VERBOSE >= 1:
print 'not filtered (probably no HIV reads)'
continue
frac_dist = 1.0 * n_distant / n_good
if frac_dist < 0.01:
if VERBOSE >= 1:
print '< 1% of reads are distant'
else:
if VERBOSE >= 1:
print '{:3.0%}'.format(frac_dist), 'of reads are distant'
consrec = sample.get_consensus(fragment)
bamfilename = get_mapped_filename(data_folder, adaID, fragment,
filtered=False)
(ds, edges, seqs) = fish_distant_reads(bamfilename, consrec, VERBOSE=VERBOSE,
min_mismatches=min_mismatches,
max_mismatches=max_mismatches,
maxseqs=maxseqs)
indrandom = np.arange(len(ds))
np.random.shuffle(indrandom)
ds = ds[indrandom]
edges = np.array(edges)[indrandom]
seqs = [seqs[i] for i in indrandom]
for irp, (dpair, edgepair, seqpair) in enumerate(izip(ds, edges, seqs)):
# NOTE: Take only the most distant read of a pair
