diff --git a/nmdc_automation/re_iding/scripts/re_id_tool.py b/nmdc_automation/re_iding/scripts/re_id_tool.py index 9238338c..a76437c5 100755 --- a/nmdc_automation/re_iding/scripts/re_id_tool.py +++ b/nmdc_automation/re_iding/scripts/re_id_tool.py @@ -137,9 +137,9 @@ def extract_records(ctx, study_id, api_base_url): for data_object_id in omics_processing_has_outputs: data_object_record = api_client.get_data_object(data_object_id) - # If the data object is an orphan, fail the omics processing record and its data objects + # If the data object is Missing, fail the omics processing record and its data objects if not data_object_record: - logging.error(f"OmicsProcessingOrphanDataObject: {data_object_id} for {omics_id}") + logging.error(f"OmicsProcessingMissingDataObject: {data_object_id} for {omics_id}") is_failed_data = True is_omics_missing_has_output = True omics_level_failure_count += 1 @@ -549,7 +549,7 @@ def delete_old_records(ctx, old_records_file): @click.pass_context def delete_old_binning_data(ctx, mongo_uri, database_name, direct_connection, no_delete=False): """ - Delete old binning data from the MongoDB database. + Delete old binning data with non-comforming IDs from the MongoDB database. Some binning data objects can be found by their data_object_type: 'Metagenome Bins' or 'CheckM Statistics' Un-typed data objects can be found by looking for 'metabat2' in the description @@ -569,8 +569,11 @@ def delete_old_binning_data(ctx, mongo_uri, database_name, direct_connection, no logging.info(f"Connected to MongoDB server at {mongo_uri}") db_client = client[database_name] - # Find and delete old binning data with a known data object type + logging.info("Searching for old binning data records with a known data object type and non-comforming IDs") + # Find and delete old binning data with a known data object type and non-comforming IDs binning_data_query = { + # Exclude data objects with conforming IDs nmdc:dobj-* + "id": {"$not": {"$regex": "^nmdc:dobj-"}}, "data_object_type": {"$in": ["Metagenome Bins", "CheckM Statistics"]}, } binning_data = db_client["data_object_set"].find(binning_data_query) @@ -586,8 +589,12 @@ def delete_old_binning_data(ctx, mongo_uri, database_name, direct_connection, no for record in binning_data: logging.info(f"Skipping delete for record: {record['id']} {record['data_object_type']} {record['description']}") - # Find and delete old binning data with a null data object type and 'metabat2' in the description + # Find and delete old binning data with a null data object type and 'metabat2' in the description and + # non-comforming IDs + logging.info("Searching for old binning data records with a null data object type and 'metabat2' in the description") null_binning_data_query = { + # Exclude data objects with conforming IDs nmdc:dobj-* + "id": {"$not": {"$regex": "^nmdc:dobj-"}}, "data_object_type": None, "description": {"$regex": "metabat2"}, } @@ -604,22 +611,37 @@ def delete_old_binning_data(ctx, mongo_uri, database_name, direct_connection, no for record in null_binning_data: logging.info(f"Skipping delete for record: {record['id']} /{record['description']}") - # Find and delete old proteomics data objects - proteomics_data_query = { - "id": {"$regex": "emsl:output_"}, + # Find Lipidomics OmicsProcessing and their associated DataObjects and delete them + logging.info("Searching for Lipidomics OmicsProcessing records and their associated DataObjects") + lipidomics_omics_processing_query = { + "omics_type.has_raw_value": "Lipidomics", } - proteomics_data = db_client["data_object_set"].find(proteomics_data_query) - logging.info(f"Found {len(list(proteomics_data.clone()))} old proteomics data records") + lipidomics_omics_processing = db_client["omics_processing_set"].find(lipidomics_omics_processing_query) + logging.info(f"Found {len(list(lipidomics_omics_processing.clone()))} lipidomics omics processing records") + + # Go through the lipidomics omics processing records and get the data object IDs to be deleted + lipidomics_data_object_ids = set() + for record in lipidomics_omics_processing: + logging.info(f"Found lipidomics omics processing record: {record['id']}") + for data_object_id in record["has_output"]: + lipidomics_data_object_ids.add(data_object_id) + logging.info(f"Found {len(lipidomics_data_object_ids)} lipidomics data object records") + if not no_delete: - for record in proteomics_data: - logging.info(f"Deleting proteomics data record: {record['id']} {record['description']}") - logging.info(f"Deleting old proteomics data records") - delete_result = db_client["data_object_set"].delete_many(proteomics_data_query) - logging.info(f"Deleted {delete_result.deleted_count} old proteomics data records") + for data_object_id in lipidomics_data_object_ids: + logging.info(f"Deleting lipidomics data object record: {data_object_id}") + logging.info(f"Deleting lipidomics data object records") + delete_result = db_client["data_object_set"].delete_many({"id": {"$in": list(lipidomics_data_object_ids)}}) + logging.info(f"Deleted {delete_result.deleted_count} lipidomics data object records") + # delete the lipidomics omics processing records + delete_result = db_client["omics_processing_set"].delete_many(lipidomics_omics_processing_query) + logging.info(f"Deleted {delete_result.deleted_count} lipidomics omics processing records") else: logging.info("No-delete flag is set, skipping delete") - for record in proteomics_data: - logging.info(f"Skipping delete for record: {record['id']} /{record['description']}") + for data_object_id in lipidomics_data_object_ids: + logging.info(f"Skipping delete for lipidomics data object record: {data_object_id}") + for record in lipidomics_omics_processing: + logging.info(f"Skipping delete for lipidomics omics processing record: {record['id']}") logging.info(f"Elapsed time: {time.time() - start_time}") @@ -641,7 +663,7 @@ def orphan_data_objects(ctx, study_id, api_base_url, untyped_data_objects=False) Write the results to a JSON file of nmdc DataObject instances. """ start_time = time.time() - logging.info(f"Scanning for orphaned data objects for {study_id}") + logging.info(f"Scanning for missing data objects for {study_id}") api_client = NmdcApi(api_base_url) @@ -692,7 +714,7 @@ def orphan_data_objects(ctx, study_id, api_base_url, untyped_data_objects=False) with open(f"{study_id}_untyped_data_objects.json", "w") as f: f.write(json.dumps(untyped_data_objects, indent=4)) else: - logging.info(f"Found {len(orphan_data_object_ids)} orphaned data objects") + logging.info(f"Found {len(orphan_data_object_ids)} missing data objects") # get orphaned data objects from the data_objects_by_id if present orphaned_data_objects = [] for data_object_id in orphan_data_object_ids: