def main(argv=()):
with errors.clean_commandline_error_exit():
if len(argv) > 1:
errors.log_and_raise(
'Command line parsing failure: call_variants does not accept '
'positional arguments but some are present on the command line: '
'"{}".'.format(str(argv)), errors.CommandLineError)
del argv # Unused.
proto_utils.uses_fast_cpp_protos_or_die()
logging_level.set_from_flag()
model = modeling.get_model(FLAGS.model_name)
call_variants(
examples_filename=FLAGS.examples,
checkpoint_path=FLAGS.checkpoint,
model=model,
execution_hardware=FLAGS.execution_hardware,
output_file=FLAGS.outfile,
max_batches=FLAGS.max_batches,
batch_size=FLAGS.batch_size)
if __name__ == '__main__':
flags.mark_flags_as_required([
'examples',
'outfile',
'checkpoint',
])
tf.app.run()
'truth_variants is required when in training mode.',
errors.CommandLineError)
if not options.confident_regions_filename:
errors.log_and_raise(
'confident_regions is required when in training mode.',
errors.CommandLineError)
if options.gvcf_filename:
errors.log_and_raise('gvcf is not allowed in training mode.',
errors.CommandLineError)
else:
# Check for argument issues specific to calling mode.
if options.variant_caller_options.sample_name == _UNKNOWN_SAMPLE:
errors.log_and_raise('sample_name must be specified in calling mode.',
errors.CommandLineError)
if options.variant_caller_options.gq_resolution < 1:
errors.log_and_raise('gq_resolution must be a non-negative integer.',
errors.CommandLineError)
# Run!
make_examples_runner(options)
if __name__ == '__main__':
flags.mark_flags_as_required([
'examples',
'mode',
'reads',
'ref',
])
tf.app.run()
def main(argv=()):
with errors.clean_commandline_error_exit():
if len(argv) > 1:
errors.log_and_raise(
'Command line parsing failure: call_variants does not accept '
'positional arguments but some are present on the command line: '
'"{}".'.format(str(argv)), errors.CommandLineError)
del argv # Unused.
proto_utils.uses_fast_cpp_protos_or_die()
logging_level.set_from_flag()
model = modeling.get_model(FLAGS.model_name)
call_variants(
examples_filename=FLAGS.examples,
checkpoint_path=FLAGS.checkpoint,
model=model,
execution_hardware=FLAGS.execution_hardware,
output_file=FLAGS.outfile,
max_batches=FLAGS.max_batches,
batch_size=FLAGS.batch_size)
if __name__ == '__main__':
flags.mark_flags_as_required([
'examples',
'outfile',
'checkpoint',
])
tf.app.run()
sample_name=sample_name)
variant_generator = haplotypes.maybe_resolve_conflicting_variants(
independent_variants)
write_variants_to_vcf(
variant_generator=variant_generator,
output_vcf_path=FLAGS.outfile,
header=header)
# Also write out the gVCF file if it was provided.
if FLAGS.nonvariant_site_tfrecord_path:
nonvariant_generator = io_utils.read_shard_sorted_tfrecords(
FLAGS.nonvariant_site_tfrecord_path,
key=_get_contig_based_variant_sort_keyfn(contigs),
proto=variants_pb2.Variant)
with vcf.VcfReader(FLAGS.outfile, use_index=False) as variant_reader:
lessthanfn = _get_contig_based_lessthan(contigs)
gvcf_variants = (
_transform_to_gvcf_record(variant)
for variant in variant_reader.iterate())
merged_variants = merge_variants_and_nonvariants(
gvcf_variants, nonvariant_generator, lessthanfn, fasta_reader)
write_variants_to_vcf(
variant_generator=merged_variants,
output_vcf_path=FLAGS.gvcf_outfile,
header=header)
if __name__ == '__main__':
flags.mark_flags_as_required(['infile', 'outfile', 'ref'])
tf.app.run()
multi_allelic_qual_filter=FLAGS.multi_allelic_qual_filter,
sample_name=sample_name)
variant_generator = haplotypes.maybe_resolve_conflicting_variants(
independent_variants)
write_variants_to_vcf(variant_generator=variant_generator,
output_vcf_path=FLAGS.outfile,
header=header)
# Also write out the gVCF file if it was provided.
if FLAGS.nonvariant_site_tfrecord_path:
nonvariant_generator = io_utils.read_shard_sorted_tfrecords(
FLAGS.nonvariant_site_tfrecord_path,
key=_get_contig_based_variant_sort_keyfn(contigs),
proto=variants_pb2.Variant)
with vcf.VcfReader(FLAGS.outfile,
use_index=False) as variant_reader:
lessthanfn = _get_contig_based_lessthan(contigs)
gvcf_variants = (_transform_to_gvcf_record(variant)
for variant in variant_reader.iterate())
merged_variants = merge_variants_and_nonvariants(
gvcf_variants, nonvariant_generator, lessthanfn,
fasta_reader)
write_variants_to_vcf(variant_generator=merged_variants,
output_vcf_path=FLAGS.gvcf_outfile,
header=header)
if __name__ == '__main__':
flags.mark_flags_as_required(['infile', 'outfile', 'ref'])
tf.app.run()
examples_out.write(example)
# Do a bit of convenient logging. This is very verbose if we convert a
# lot of reads...
logging.info((
'Added an example for read %s (span=%s) with cigar %s [%d added '
'of %d total reads]'), read.fragment_name,
ranges.to_literal(read_range),
cigar.format_cigar_units(read.alignment.cigar),
n_examples, i)
if max_reads is not None and n_examples >= max_reads:
return
def main(argv):
del argv # Unused.
make_ngs_error_examples(
ref_path=FLAGS.ref,
vcf_path=FLAGS.vcf,
bam_path=FLAGS.bam,
examples_out_path=FLAGS.examples_out,
max_reads=FLAGS.max_reads,
)
if __name__ == '__main__':
flags.mark_flags_as_required(['ref', 'vcf', 'bam', 'examples_out'])
app.run(main)
def main(argv):
del argv
contigs = fasta.RefFastaReader(FLAGS.ref).header.contigs
max_records = FLAGS.max_records if FLAGS.max_records >= 0 else None
variants_iter = examples_to_variants(FLAGS.examples, max_records=max_records)
if not FLAGS.sample_name:
sample_name, variants_iter = peek_sample_name(variants_iter)
else:
sample_name = FLAGS.sample_name
header = dv_vcf_constants.deepvariant_header(
contigs=contigs, sample_names=[sample_name])
with vcf.VcfWriter(FLAGS.output_vcf, header=header) as writer:
for variant in variants_iter:
variant.calls[0].call_set_name = sample_name
logging.log_every_n(logging.INFO, 'Converted %s', FLAGS.log_every,
variant_utils.variant_key(variant))
writer.write(variant)
if __name__ == '__main__':
flags.mark_flags_as_required([
'examples',
'ref',
'output_vcf',
])
app.run(main)