def _add_inferred_headers(all_patterns, # type: List[str]
pipeline, # type: beam.Pipeline
known_args, # type: argparse.Namespace
merged_header, # type: pvalue.PCollection
pipeline_mode # type: int
):
# type: (...) -> pvalue.PCollection
annotation_fields_to_infer = (known_args.annotation_fields if
known_args.infer_annotation_types else [])
inferred_headers = (
_read_variants(all_patterns,
pipeline,
known_args,
pipeline_mode)
| 'FilterVariants' >> filter_variants.FilterVariants(
reference_names=known_args.reference_names)
| 'InferHeaderFields' >> infer_headers.InferHeaderFields(
pvalue.AsSingleton(merged_header),
known_args.allow_incompatible_records,
known_args.infer_headers,
annotation_fields_to_infer))
merged_header = (
(inferred_headers, merged_header)
| 'FlattenHeaders' >> beam.Flatten()
| 'MergeHeadersFromVcfAndVariants' >> merge_headers.MergeHeaders(
known_args.split_alternate_allele_info_fields,
known_args.allow_incompatible_records))
return merged_header
def expand(self, pcoll):
return (pcoll
| 'InferUndefinedHeaderFields' >> beam.ParDo(
_InferUndefinedHeaderFields(), self._defined_headers)
# TODO(nmousavi): Modify the MergeHeaders to resolve 1 vs '.'
# mistmatch for headers extracted from variants.
#
# Note: argument `split_alternate_allele_info_fileds` is not
# relevant here since no fields with `Number=A` will be extracted
# from variants, therefore we let the default value (True) for it
# be used. Should this changes, we should modify the default value.
| 'MergeHeaders' >> merge_headers.MergeHeaders(
split_alternate_allele_info_fields=True))
def _add_inferred_headers(pipeline, known_args, merged_header):
inferred_headers = (
_read_variants(pipeline, known_args)
| 'FilterVariants' >> filter_variants.FilterVariants(
reference_names=known_args.reference_names)
| ' InferUndefinedHeaderFields' >>
infer_undefined_headers.InferUndefinedHeaderFields(
beam.pvalue.AsSingleton(merged_header)))
merged_header = (
(inferred_headers, merged_header)
| beam.Flatten()
| 'MergeHeadersFromVcfAndVariants' >> merge_headers.MergeHeaders(
known_args.split_alternate_allele_info_fields))
return merged_header
def _get_inferred_headers(variants, # type: pvalue.PCollection
merged_header # type: pvalue.PCollection
):
# type: (...) -> (pvalue.PCollection, pvalue.PCollection)
inferred_headers = (variants
| 'FilterVariants' >> filter_variants.FilterVariants()
| ' InferUndefinedHeaderFields' >>
infer_undefined_headers.InferUndefinedHeaderFields(
pvalue.AsSingleton(merged_header)))
merged_header = (
(inferred_headers, merged_header)
| beam.Flatten()
| 'MergeHeadersFromVcfAndVariants' >> merge_headers.MergeHeaders(
allow_incompatible_records=True))
return inferred_headers, merged_header
def get_merged_headers(headers,
split_alternate_allele_info_fields=True,
allow_incompatible_records=True):
# type: (pvalue.PCollection, bool, bool) -> pvalue.PCollection
"""Applies the ``MergeHeaders`` PTransform on PCollection of ``VcfHeader``.
Args:
headers: The VCF headers.
split_alternate_allele_info_fields: If true, the INFO fields with `Number=A`
in BigQuery schema is not repeated. This is relevant as it changes the
header compatibility rules.
allow_incompatible_records: If true, always resolve the conflicts when
merging headers.
"""
return (headers | 'MergeHeaders' >> merge_headers.MergeHeaders(
split_alternate_allele_info_fields, allow_incompatible_records))
def _merge_headers(known_args, pipeline_args, pipeline_mode):
"""Merges VCF headers using beam based on pipeline_mode."""
if known_args.representative_header_file:
return
options = PipelineOptions(pipeline_args)
# Always run pipeline locally if data is small.
if (pipeline_mode == PipelineModes.SMALL and
not known_args.infer_undefined_headers):
options.view_as(StandardOptions).runner = 'DirectRunner'
google_cloud_options = options.view_as(GoogleCloudOptions)
if google_cloud_options.job_name:
google_cloud_options.job_name += '-' + _MERGE_HEADERS_JOB_NAME
else:
google_cloud_options.job_name = _MERGE_HEADERS_JOB_NAME
temp_directory = google_cloud_options.temp_location or tempfile.mkdtemp()
# Add a time prefix to ensure files are unique in case multiple
# pipelines are run at the same time.
temp_merged_headers_file_name = '-'.join([
datetime.datetime.now().strftime('%Y%m%d-%H%M%S'),
google_cloud_options.job_name,
_MERGE_HEADERS_FILE_NAME])
known_args.representative_header_file = FileSystems.join(
temp_directory, temp_merged_headers_file_name)
with beam.Pipeline(options=options) as p:
headers = p
if pipeline_mode == PipelineModes.LARGE:
headers |= (beam.Create([known_args.input_pattern])
| vcf_header_io.ReadAllVcfHeaders())
else:
headers |= vcf_header_io.ReadVcfHeaders(known_args.input_pattern)
merged_header = (headers
| 'MergeHeaders' >> merge_headers.MergeHeaders(
known_args.split_alternate_allele_info_fields))
if known_args.infer_undefined_headers:
merged_header = _add_inferred_headers(p, known_args, merged_header)
_ = (merged_header | 'WriteHeaders' >> vcf_header_io.WriteVcfHeaders(
known_args.representative_header_file))
def _add_inferred_headers(
pipeline, # type: beam.Pipeline
known_args, # type: argparse.Namespace
merged_header # type: pvalue.PCollection
):
# type: (...) -> pvalue.PCollection
inferred_headers = (_read_variants(pipeline, known_args)
| 'FilterVariants' >> filter_variants.FilterVariants(
reference_names=known_args.reference_names)
| ' InferUndefinedHeaderFields' >>
infer_undefined_headers.InferUndefinedHeaderFields(
pvalue.AsSingleton(merged_header)))
merged_header = (
(inferred_headers, merged_header)
| beam.Flatten()
| 'MergeHeadersFromVcfAndVariants' >> merge_headers.MergeHeaders(
known_args.split_alternate_allele_info_fields,
known_args.allow_incompatible_records))
return merged_header
def add_annotation_headers(pipeline, known_args, pipeline_mode, merged_header,
annotated_vcf_pattern):
if pipeline_mode == PipelineModes.LARGE:
annotation_headers = (
pipeline
| 'ReadAnnotatedVCF' >> beam.Create([annotated_vcf_pattern])
| 'ReadHeaders' >> vcf_header_io.ReadAllVcfHeaders())
else:
annotation_headers = (
pipeline
| 'ReadHeaders' >>
vcf_header_io.ReadVcfHeaders(annotated_vcf_pattern))
merged_header = ((merged_header, annotation_headers)
| beam.Flatten()
|
'MergeWithOriginalHeaders' >> merge_headers.MergeHeaders(
known_args.split_alternate_allele_info_fields,
known_args.allow_incompatible_records))
return merged_header
def test_combine_pipeline(self):
headers_1 = self._get_header_from_lines(FILE_1_LINES)
headers_2 = self._get_header_from_lines(FILE_2_LINES)
# TODO(nmousavi): Either use TestPipeline or combiner_fn.* everywhere.
# After moving out _HeaderMerger to its file, it makes sense to use
# TestPipeline everywhere.
header_merger = HeaderMerger(
vcf_field_conflict_resolver.FieldConflictResolver(
split_alternate_allele_info_fields=True))
expected = vcf_header_io.VcfHeader()
header_merger.merge(expected, headers_1)
header_merger.merge(expected, headers_2)
pipeline = TestPipeline()
merged_headers = (pipeline
| Create([headers_1, headers_2])
| 'MergeHeaders' >> merge_headers.MergeHeaders())
assert_that(merged_headers, equal_to([expected]))
