def main(raw_args=None):
"""
Run a full RDR import.
Assumes you are passing arguments either via command line or a
list.
"""
args = parse_rdr_args(raw_args)
pipeline_logging.configure(level=logging.INFO,
add_console_handler=args.console_log)
description = f'RDR DUMP loaded from {args.bucket} dated {args.export_date}'
export_date = args.export_date.replace('-', '')
new_dataset_name = f'rdr{export_date}'
# get credentials and create client
impersonation_creds = auth.get_impersonation_credentials(
args.run_as_email, SCOPES)
client = bq.get_client(args.curation_project_id,
credentials=impersonation_creds)
dataset_object = bq.define_dataset(client.project, new_dataset_name,
description,
{'export_date': args.export_date})
client.create_dataset(dataset_object)
create_rdr_tables(client, new_dataset_name, args.bucket)
copy_vocab_tables(client, new_dataset_name, args.vocabulary)
def main(args=None):
pipeline_logging.configure(logging.DEBUG, add_console_handler=True)
args = parse_args(args)
query_list = create_queries(args.project_id, args.ticket_number,
args.pids_project_id, args.pids_dataset_id,
args.pids_table, args.dataset_list)
client = bq.get_client(args.project_id)
run_queries(query_list, client)
LOGGER.info("Retraction complete")
def main(args=None):
pipeline_logging.configure(logging.DEBUG, add_console_handler=True)
parser = get_parser()
args = parser.parse_args(args)
client = bq.get_client(args.project_id)
dataset_ids = ru.get_datasets_list(args.project_id, args.dataset_ids)
LOGGER.info(
f"Datasets to retract deactivated participants from: {dataset_ids}")
run_deactivation(client, args.project_id, dataset_ids, args.fq_deact_table,
args.fq_pid_rid_table)
LOGGER.info(
f"Retraction of deactivated participants from {dataset_ids} complete")
def main(raw_args=None):
args = get_arguments(raw_args)
if not args.console_log:
print(f'===============================================\n'
f'Warning!! By not logging to the console you \n'
f'may miss important information!\n'
f'===============================================\n')
pipeline_logging.configure(add_console_handler=args.console_log)
run_deletion(args.project_id, args.name_substrings)
def main(first_n):
pipeline_logging.configure(logging.INFO, add_console_handler=True)
project_id = app_identity.get_application_id()
sc = StorageClient(project_id)
_check_project(sc)
buckets_to_delete = _filter_stale_buckets(sc, first_n)
for stale_bucket in buckets_to_delete:
LOGGER.info(f"Running - sc.get_bucket({stale_bucket}).delete()")
sc.get_bucket(stale_bucket).delete()
return buckets_to_delete
def main(raw_args=None):
# Parses the required arguments and keyword arguments required by cleaning rules
args, kwargs = parse_deid_args(raw_args)
# Sets logging level
pipeline_logging.configure(level=logging.DEBUG,
add_console_handler=args.console_log)
# Identify the cleaning classes being run for specified data_stage
# and validate if all the required arguments are supplied
cleaning_classes = clean_cdr.DATA_STAGE_RULES_MAPPING[
f'{args.tier}_tier_{args.deid_stage}']
clean_cdr.validate_custom_params(cleaning_classes, **kwargs)
# Runs create_tier in order to generate the {args.tier}_tier_{args.data_stage} datasets and apply cleaning rules
datasets = create_tier(args.credentials_filepath, args.project_id,
args.tier, args.idataset, args.release_tag,
args.deid_stage, args.target_principal, **kwargs)
return datasets
def main():
parser = get_arg_parser()
args = parser.parse_args()
#Set up pipeline logging
pipeline_logging.configure(level=logging.DEBUG, add_console_handler=True)
# get credentials and create client
impersonation_creds = auth.get_impersonation_credentials(
args.run_as_email, SCOPES)
client = bq.get_client(args.project_id, credentials=impersonation_creds)
# Populates the validation table for the site
identify_rdr_ehr_match(client, args.project_id, args.hpo_id, EHR_OPS)
LOGGER.info('Done.')
def main(raw_args=None):
"""
Truncate and store fitbit data.
Assumes you are passing arguments either via command line or a
list.
"""
parser = get_fitbit_parser()
args, kwargs = clean_cdr.fetch_args_kwargs(parser, raw_args)
pipeline_logging.configure(level=logging.INFO,
add_console_handler=args.console_log)
# Identify the cleaning classes being run for specified data_stage
# and validate if all the required arguments are supplied
cleaning_classes = clean_cdr.DATA_STAGE_RULES_MAPPING[consts.FITBIT]
clean_cdr.validate_custom_params(cleaning_classes, **kwargs)
# get credentials and create client
impersonation_creds = auth.get_impersonation_credentials(
args.run_as_email, SCOPES)
client = bq.get_client(args.project_id, credentials=impersonation_creds)
# create staging, sandbox, backup and clean datasets with descriptions and labels
fitbit_datasets = create_fitbit_datasets(client, args.release_tag)
copy_fitbit_tables_from_views(client,
args.fitbit_dataset,
fitbit_datasets[consts.BACKUP],
table_prefix='v_')
bq.copy_datasets(client, fitbit_datasets[consts.BACKUP],
fitbit_datasets[consts.STAGING])
common_cleaning_args = [
'-p', args.project_id, '-d', fitbit_datasets[consts.STAGING], '-b',
fitbit_datasets[consts.SANDBOX], '-s', '-a', consts.FITBIT
]
fitbit_cleaning_args = args_parser.add_kwargs_to_args(
common_cleaning_args, kwargs)
clean_cdr.main(args=fitbit_cleaning_args)
# Snapshot the staging dataset to final dataset
bq.build_and_copy_contents(client, fitbit_datasets[consts.STAGING],
fitbit_datasets[consts.CLEAN])
def main(first_n):
pipeline_logging.configure(logging.INFO, add_console_handler=True)
bq_client = bq.get_client(os.environ.get('GOOGLE_CLOUD_PROJECT'))
_check_project(bq_client)
datasets_to_delete = _filter_stale_datasets(bq_client, first_n)
for stale_dataset in datasets_to_delete:
LOGGER.info(f"Running - bq_client.delete_dataset({stale_dataset})")
try:
bq_client.delete_dataset(stale_dataset)
except exceptions.BadRequest as e:
LOGGER.warning(
f"Failed to delete {stale_dataset}. Message: {e.message}")
return datasets_to_delete
def test_configure(self, mock_open):
"""
Verify that root level and handlers are properly set after configure
:param mock_open: mock to prevent side effect of opening file
"""
# names are used to uniquely identify handlers both in standard logging module
# and in this test case
expected_hdlrs = [pl._FILE_HANDLER]
pl.configure()
# root level is set to default (i.e. INFO)
self.assertEqual(logging.root.level, pl.DEFAULT_LOG_LEVEL)
# handlers are added
actual_hdlrs = [hdlr.name for hdlr in logging.root.handlers]
self.assertEqual(expected_hdlrs, actual_hdlrs)
# no duplicate handlers after additional calls to configure
pl.configure()
self.assertEqual(len(expected_hdlrs), len(logging.root.handlers))
actual_hdlrs = [hdlr.name for hdlr in logging.root.handlers]
self.assertEqual(expected_hdlrs, actual_hdlrs)
# add console log handler to configuration
pl.configure(add_console_handler=True)
actual_hdlrs = [hdlr.name for hdlr in logging.root.handlers]
expected_hdlrs = [pl._FILE_HANDLER, pl._CONSOLE_HANDLER]
self.assertEqual(expected_hdlrs, actual_hdlrs)
def assert_sane_configure(self):
"""
verifies that a utils.pipeline_logger.configure() call executed successfully
TODO: pass in expected basename of log file? bit more flexible explicit, but may not worth the refactor.
"""
# names are used to uniquely identify handlers both in standard logging module
# and in this test case
expected_hdlrs = [pl._FILE_HANDLER]
# execute configuration
pl.configure()
# root level is set to default (i.e. INFO)
self.assertEqual(logging.root.level, pl.DEFAULT_LOG_LEVEL)
# handlers are added
actual_hdlrs = [hdlr.name for hdlr in logging.root.handlers]
self.assertEqual(expected_hdlrs, actual_hdlrs)
# no duplicate handlers after additional calls to configure
pl.configure()
self.assertEqual(len(expected_hdlrs), len(logging.root.handlers))
actual_hdlrs = [hdlr.name for hdlr in logging.root.handlers]
self.assertEqual(expected_hdlrs, actual_hdlrs)
for hdlr in logging.root.handlers:
if isinstance(hdlr, logging.FileHandler):
self.assert_correct_log_filename(hdlr, pl.get_log_filename())
# add console log handler to configuration
pl.configure(add_console_handler=True)
actual_hdlrs = [hdlr.name for hdlr in logging.root.handlers]
expected_hdlrs = [pl._FILE_HANDLER, pl._CONSOLE_HANDLER]
self.assertEqual(expected_hdlrs, actual_hdlrs)
'Identifies the target dataset where the vocabulary is to be loaded',
required=False)
return argument_parser
def get_release_date(release_date: datetime.date = None) -> str:
"""
Get the name of a vocabulary release based on date
:param release_date: date the vocabulary is released
:return: name of vocabulary release
"""
if not release_date:
release_date = datetime.date.today()
release_date_str = release_date.strftime("%Y%m%d")
return release_date_str
def get_target_dataset_id(release_tag: str) -> str:
return f'vocabulary{release_tag}'
if __name__ == '__main__':
ARGS = get_arg_parser().parse_args()
RELEASE_TAG = ARGS.release_date or get_release_date()
TARGET_DATASET_ID = ARGS.target_dataset_id or get_target_dataset_id(
RELEASE_TAG)
pipeline_logging.configure(add_console_handler=True)
main(ARGS.project_id, ARGS.bucket_name, ARGS.vocab_folder_path,
TARGET_DATASET_ID)
def main(raw_args=None):
"""
Clean an RDR import.
Assumes you are passing arguments either via command line or a
list.
"""
args, kwargs = parse_rdr_args(raw_args)
pipeline_logging.configure(level=logging.INFO,
add_console_handler=args.console_log)
# specific check on truncation_date. It should not cause a failure if it is not set.
if not args.truncation_date:
LOGGER.info('truncation_date is unset. It will default to the current '
'date in the truncation cleaning rule.')
# validate we've got all required data before continuing
cleaning_classes = clean_cdr.DATA_STAGE_RULES_MAPPING.get('rdr')
clean_cdr.validate_custom_params(cleaning_classes, **kwargs)
# get credentials and create client
impersonation_creds = auth.get_impersonation_credentials(
args.run_as_email, SCOPES)
client = bq.get_client(args.curation_project_id,
credentials=impersonation_creds)
# create staging, sandbox, and clean datasets with descriptions and labels
datasets = create_datasets(client, args.rdr_dataset, args.release_tag)
# copy raw data into staging dataset
copy_raw_rdr_tables(client, args.rdr_dataset, datasets.get('staging'))
# clean the RDR staging dataset
cleaning_args = [
'-p', args.curation_project_id, '-d',
datasets.get('staging', 'UNSET'), '-b',
datasets.get('sandbox',
'UNSET'), '--data_stage', 'rdr', '--truncation_date',
args.truncation_date, '--export_date', args.export_date
]
all_cleaning_args = add_kwargs_to_args(cleaning_args, kwargs)
clean_cdr.main(args=all_cleaning_args)
bq.build_and_copy_contents(client, datasets.get('staging', 'UNSET'),
datasets.get('clean', 'UNSET'))
# update sandbox description and labels
sandbox_dataset = client.get_dataset(datasets.get(
'sandbox', 'UNSET')) # Make an API request.
sandbox_dataset.description = (
f'Sandbox created for storing records affected by the cleaning '
f'rules applied to {datasets.get("clean")}')
sandbox_dataset.labels['phase'] = 'sandbox'
sandbox_dataset = client.update_dataset(
sandbox_dataset, ["description"]) # Make an API request.
full_dataset_id = f'{sandbox_dataset.project}.{sandbox_dataset.dataset_id}'
LOGGER.info(
f'Updated dataset `{full_dataset_id}` with description `{sandbox_dataset.description}`'
)
LOGGER.info(f'RDR snapshot and cleaning, '
f'`{client.project}.{datasets.get("clean")}`, is complete.')
def test_default_level(self, mock_open, mock_file_emit, mock_stream_emit):
pl.configure(add_console_handler=True)
self.assert_logs_handled(pl.DEFAULT_LOG_LEVEL, mock_file_emit,
mock_stream_emit)
description='Add new mappings to our primary pid/rid mapping table.',
formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument(
'-r',
'--fq_rdr_mapping',
action='store',
dest='rdr_mapping',
help=('The fully qualified rdr mapping table name. '
'The project_id will be extracted from this table name.'),
type=check_table_name,
required=True)
parser.add_argument(
'-i',
'--run_as',
action='store',
dest='run_as',
help=('The email address of the service account to impersonate.'),
type=check_email_address)
args = parser.parse_args()
store_to_primary_mapping_table(args.rdr_mapping, run_as=args.run_as)
LOGGER.info("Finished pid/rid/shift storage process.")
if __name__ == '__main__':
from utils import pipeline_logging
pipeline_logging.configure()
process_mappings()
def main(raw_args=None):
args = parse_deid_args(raw_args)
pipeline_logging.configure(level=logging.DEBUG,
add_console_handler=args.console_log)
create_tier(args.credentials_filepath, args.project_id, args.tier,
args.idataset, args.release_tag, args.deid_stage)
1333234, 1310066, 715725, 1310147, 702686, 1310054, 715726, 715724,
715714, 1310146, 1310058
]
def setup_validation(self, client, *args, **keyword_args):
pass
def validate_rule(self, client, *args, **keyword_args):
pass
if __name__ == '__main__':
import cdr_cleaner.args_parser as parser
import cdr_cleaner.clean_cdr_engine as clean_engine
ARGS = parser.default_parse_args()
pipeline_logging.configure(level=logging.DEBUG, add_console_handler=True)
if ARGS.list_queries:
clean_engine.add_console_logging()
query_list = clean_engine.get_query_list(
ARGS.project_id, ARGS.dataset_id, ARGS.sandbox_dataset_id,
[(CopeSurveyResponseSuppression, )])
for query in query_list:
LOGGER.info(query)
else:
clean_engine.add_console_logging(ARGS.console_log)
clean_engine.clean_dataset(ARGS.project_id, ARGS.dataset_id,
ARGS.sandbox_dataset_id,
[(CopeSurveyResponseSuppression, )])
def test_specific_level(self, mock_open, mock_file_emit, mock_stream_emit):
pl.configure(logging.CRITICAL, add_console_handler=True)
self.assert_logs_handled(logging.CRITICAL, mock_file_emit,
mock_stream_emit)
