def test_cudapoa_complex_batch():
random.seed(2)
read_len = 500
ref = ''.join([random.choice(['A', 'C', 'G', 'T']) for _ in range(read_len)])
num_reads = 100
mutation_rate = 0.02
reads = []
for _ in range(num_reads):
new_read = ''.join([r if random.random() > mutation_rate else random.choice(['A', 'C', 'G', 'T']) for r in ref])
reads.append(new_read)
device = cuda.cuda_get_device()
free, total = cuda.cuda_get_mem_info(device)
stream = cuda.CudaStream()
batch = CudaPoaBatch(1000, 0.9 * free, stream=stream, device_id=device)
(add_status, seq_status) = batch.add_poa_group(reads)
batch.generate_poa()
consensus, coverage, status = batch.get_consensus()
consensus = consensus[0]
assert(len(consensus) == len(ref))
match_ratio = SequenceMatcher(None, ref, consensus).ratio()
assert(match_ratio == 1.0)
def test_cudaaligner_simple_batch(query, target, cigar):
"""Test valid calculation of alignments by checking cigar strings.
"""
device = cuda.cuda_get_device()
stream = cuda.CudaStream()
batch = CudaAlignerBatch(len(query), len(target), 1, alignment_type="global", stream=stream, device_id=device)
batch.add_alignment(query, target)
batch.align_all()
alignments = batch.get_alignments()
assert(len(alignments) == 1)
assert(alignments[0].cigar == cigar)