def _get_input_dimensions(known_args, pipeline_args):
pipeline_mode = pipeline_common.get_pipeline_mode(known_args.all_patterns)
beam_pipeline_options = pipeline_options.PipelineOptions(pipeline_args)
google_cloud_options = beam_pipeline_options.view_as(
pipeline_options.GoogleCloudOptions)
estimate_sizes_job_name = pipeline_common.generate_unique_name(
_ESTIMATE_SIZES_JOB_NAME)
if google_cloud_options.job_name:
google_cloud_options.job_name += '-' + estimate_sizes_job_name
else:
google_cloud_options.job_name = estimate_sizes_job_name
temp_directory = google_cloud_options.temp_location or tempfile.mkdtemp()
temp_estimated_input_size_file_name = '-'.join(
[google_cloud_options.job_name, _ESTIMATE_SIZES_FILE_NAME])
temp_estimated_input_size_file_path = filesystems.FileSystems.join(
temp_directory, temp_estimated_input_size_file_name)
with beam.Pipeline(options=beam_pipeline_options) as p:
estimates = pipeline_common.get_estimates(p, pipeline_mode,
known_args.all_patterns)
files_size = (estimates
| 'GetFilesSize' >> extract_input_size.GetFilesSize())
file_count = (estimates
| 'CountAllFiles' >> beam.combiners.Count.Globally())
sample_map = (estimates
|
'ExtractSampleMap' >> extract_input_size.GetSampleMap())
estimated_value_count = (sample_map
| extract_input_size.GetEstimatedValueCount())
estimated_sample_count = (sample_map
|
extract_input_size.GetEstimatedSampleCount())
estimated_variant_count = (
estimates
| 'GetEstimatedVariantCount' >>
extract_input_size.GetEstimatedVariantCount())
_ = (estimated_variant_count
| beam.ParDo(extract_input_size.print_estimates_to_file,
beam.pvalue.AsSingleton(estimated_sample_count),
beam.pvalue.AsSingleton(estimated_value_count),
beam.pvalue.AsSingleton(files_size),
beam.pvalue.AsSingleton(file_count),
temp_estimated_input_size_file_path))
with filesystems.FileSystems.open(
temp_estimated_input_size_file_path) as f:
estimates = f.readlines()
if len(estimates) != 5:
raise ValueError('Exactly 5 estimates were expected in {}.'.format(
temp_estimated_input_size_file_path))
known_args.estimated_variant_count = int(estimates[0].strip())
known_args.estimated_sample_count = int(estimates[1].strip())
known_args.estimated_value_count = int(estimates[2].strip())
known_args.files_size = int(estimates[3].strip())
known_args.file_count = int(estimates[4].strip())
def test_get_estimated_variant_count(self):
vcf_estimates = self._create_vcf_estimates()
pipeline = TestPipeline()
estimated_variant_count = (
pipeline
| transforms.Create(vcf_estimates)
| 'GetEstimatedVariantCount' >>
extract_input_size.GetEstimatedVariantCount())
assert_that(estimated_variant_count, equal_to([32]))
pipeline.run()