def test_uniquereads_empty_sequence(self, mpopen, msys, mpool):
mpopen.return_value.stdout = self.headers
r = subsamplebam.make_subselected_bam('foo.bam', self.uniquereads)
ecalls = []
for c in self.headers:
ecalls.append(mock.call(c))
msys.stdout.write.assert_has_calls(ecalls)
def test_uniquereads_empty_sequence(self, mpopen, msys, mpool):
mpopen.return_value.stdout = self.headers
r = subsamplebam.make_subselected_bam('foo.bam', self.uniquereads)
ecalls = []
for c in self.headers:
ecalls.append(mock.call(c))
msys.stdout.write.assert_has_calls(ecalls)
def main():
'''
Parse the sam view, initialize the DepthMatrix, and trim the sequence matrix using minimize_depths.
Then, ouptut the results (as a sam view) to stdout.
'''
#TODO: need to handle case where fasta file does not span entire reference genome; depth array should be limited in that case to the largest overlap
args = subsamplebam.parse_args(wrapper=False)
sys.stderr.write(str(args)+'\n')
# ref_length = int(args.regionstr.split(':')[-1].split('-')[-1])
# region_str = args.regionstr.split(':')[0]
matrix = DepthMatrix(args.subsample, allow_orphans=args.count_orphans, more_random=args.more_random)
matrix.make_seq_matrix(args.bamfile, args.refseq)
matrix.minimize_depths()
''' Flatten the matrix '''
sampled_seqs = flatten_and_filter_matrix(matrix.seq_matrix)
'''Print results to stdout as a sam view (.sam file)'''
subsamplebam.make_subselected_bam(args.bamfile, sampled_seqs)
def main():
'''
Parse the sam view, initialize the DepthMatrix, and trim the sequence matrix using minimize_depths.
Then, ouptut the results (as a sam view) to stdout.
'''
#TODO: need to handle case where fasta file does not span entire reference genome; depth array should be limited in that case to the largest overlap
args = subsamplebam.parse_args(wrapper=False)
sys.stderr.write(str(args) + '\n')
# ref_length = int(args.regionstr.split(':')[-1].split('-')[-1])
# region_str = args.regionstr.split(':')[0]
matrix = DepthMatrix(args.subsample,
allow_orphans=args.count_orphans,
more_random=args.more_random)
matrix.make_seq_matrix(args.bamfile, args.refseq)
matrix.minimize_depths()
''' Flatten the matrix '''
sampled_seqs = flatten_and_filter_matrix(matrix.seq_matrix)
'''Print results to stdout as a sam view (.sam file)'''
subsamplebam.make_subselected_bam(args.bamfile, sampled_seqs)
def test_contains_bam_header_and_samviewrows(self, mpopen, msys, mpool):
mpool.return_value.map = map
mpopen.return_value.stdout = self.headers
r = subsamplebam.make_subselected_bam('foo.bam', self.uniquereads)
msys.stdout.write.assert_has_calls([
mock.call(self.headers[0]),
mock.call(self.headers[1]),
mock.call(self.headers[2]),
mock.call(self.uniquereads[0])
])
def test_contains_bam_header_and_samviewrows(self, mpopen, msys, mpool):
mpool.return_value.map = map
mpopen.return_value.stdout = self.headers
r = subsamplebam.make_subselected_bam('foo.bam', self.uniquereads)
msys.stdout.write.assert_has_calls([
mock.call(self.headers[0]),
mock.call(self.headers[1]),
mock.call(self.headers[2]),
mock.call(self.uniquereads[0])
])