sync mock data wfm <=> mm

lib/workload/stateless/stacks/metadata-manager/Makefile

@@ -72,7 +72,7 @@ makemigrations:
 migrate:
 	@python manage.py migrate
 
-insert-data:
+mock: reset-db migrate
 	@python manage.py insert_mock_data
 
 suite:

lib/workload/stateless/stacks/metadata-manager/app/management/commands/clean_db.py

@@ -0,0 +1,13 @@
+from django.core.management import BaseCommand
+
+from app.tests.utils import clear_all_data
+
+
+# https://docs.djangoproject.com/en/5.0/howto/custom-management-commands/
+class Command(BaseCommand):
+    help = "Delete all DB data"
+
+    def handle(self, *args, **options):
+        clear_all_data()
+
+        print("Done")

lib/workload/stateless/stacks/metadata-manager/app/management/commands/insert_mock_data.py

@@ -1,22 +1,166 @@
 import json
 import pandas as pd
+from django.core.exceptions import ObjectDoesNotExist
 from django.core.management import BaseCommand
 
+from app.models import Subject, Library, Sample, Individual, Project, Contact
+from app.tests.utils import clear_all_data
 from proc.service.tracking_sheet_srv import sanitize_lab_metadata_df, persist_lab_metadata
 from proc.tests.test_tracking_sheet_srv import RECORD_1, RECORD_2, RECORD_3, SHEET_YEAR
 
 
 class Command(BaseCommand):
+    """
+    python manage.py insert_mock_data
+    """
     help = "Generate mock Metadata into database for local development and testing"
 
     def handle(self, *args, **options):
-        print("insert data from proc service test")
+        clear_all_data()
+        load_mock_from_wfm()
 
-        mock_sheet_data = [RECORD_1, RECORD_2, RECORD_3]
 
-        metadata_pd = pd.json_normalize(mock_sheet_data)
-        metadata_pd = sanitize_lab_metadata_df(metadata_pd)
-        result = persist_lab_metadata(metadata_pd, SHEET_YEAR, is_emit_eb_events=False)
+def load_mock_from_proc():
+    """Not in use for now, as loading data from wfm is preferred to sync data"""
+    mock_sheet_data = [RECORD_1, RECORD_2, RECORD_3]
 
-        print(json.dumps(result, indent=4))
-        print("insert mock data completed")
+    metadata_pd = pd.json_normalize(mock_sheet_data)
+    metadata_pd = sanitize_lab_metadata_df(metadata_pd)
+    result = persist_lab_metadata(metadata_pd, SHEET_YEAR, is_emit_eb_events=False)
+
+    print(json.dumps(result, indent=4))
+    print("insert mock data completed")
+
+
+def load_mock_from_wfm():
+    # The libraries are taken from WFM as of 16/10/2024
+    # Will sync this so test data sync across MM <=> WFM
+    libraries = [
+        {
+            "orcabus_id": "01J5M2JFE1JPYV62RYQEG99CP1",
+            "phenotype": "tumor",
+            "library_id": "L000001",
+            "assay": "TsqNano",
+            "type": "WGS",
+            "subject": "SBJ00001",
+            "workflow": "clinical"
+        },
+        {
+            "orcabus_id": "02J5M2JFE1JPYV62RYQEG99CP2",
+            "phenotype": "normal",
+            "library_id": "L000002",
+            "assay": "TsqNano",
+            "type": "WGS",
+            "subject": "SBJ00001",
+            "workflow": "clinical"
+        },
+        {
+            "orcabus_id": "03J5M2JFE1JPYV62RYQEG99CP3",
+            "phenotype": "tumor",
+            "library_id": "L000003",
+            "assay": "TsqNano",
+            "type": "WGS",
+            "subject": "SBJ00002",
+            "workflow": "research"
+        },
+        {
+            "orcabus_id": "04J5M2JFE1JPYV62RYQEG99CP4",
+            "phenotype": "normal",
+            "library_id": "L000004",
+            "assay": "TsqNano",
+            "type": "WGS",
+            "subject": "SBJ00002",
+            "workflow": "research"
+        },
+        {
+            "orcabus_id": "05J5M2JFE1JPYV62RYQEG99CP5",
+            "phenotype": "tumor",
+            "library_id": "L000005",
+            "assay": "ctTSOv2",
+            "type": "ctDNA",
+            "subject": "SBJ00003",
+            "workflow": "clinical"
+        },
+        {
+            "orcabus_id": "06J5M2JFE1JPYV62RYQEG99CP6",
+            "phenotype": "tumor",
+            "library_id": "L000006",
+            "assay": "ctTSOv2",
+            "type": "ctDNA",
+            "subject": "SBJ00003",
+            "workflow": "research"
+        },
+    ]
+
+    for lib in libraries:
+        idv, is_idv_created, is_idv_updated = Individual.objects.update_or_create_if_needed(
+            search_key={
+                "individual_id": 'IDV0001',
+                "source": "lab"
+            },
+            data={
+                "individual_id": 'IDV0001',
+                "source": "lab"
+            }
+        )
+
+        subject, is_sub_created, is_sub_updated = Subject.objects.update_or_create_if_needed(
+            search_key={"subject_id": lib["subject"]},
+            data={
+                "subject_id": lib["subject"],
+            }
+        )
+
+        try:
+            subject.individual_set.get(orcabus_id=idv.orcabus_id)
+        except ObjectDoesNotExist:
+            subject.individual_set.add(idv)
+
+        sample, is_smp_created, is_smp_updated = Sample.objects.update_or_create_if_needed(
+            search_key={"sample_id": f"""smp-{lib["library_id"]}"""},
+            data={
+                "sample_id": f"""smp-{lib["library_id"]}""",
+                "external_sample_id": f"""ext-smp-{lib["library_id"]}""",
+                "source": "blood",
+            }
+        )
+
+        contact, is_ctc_created, is_ctc_updated = Contact.objects.update_or_create_if_needed(
+            search_key={"contact_id": 'ctc-1'},
+            data={
+                "contact_id": 'ctc-1',
+            }
+        )
+
+        project, is_prj_created, is_prj_updated = Project.objects.update_or_create_if_needed(
+            search_key={"project_id": 'prj-1'},
+            data={
+                "project_id": 'prj-1',
+            }
+        )
+
+        try:
+            project.contact_set.get(orcabus_id=contact.orcabus_id)
+        except ObjectDoesNotExist:
+            project.contact_set.add(contact)
+
+        library, is_lib_created, is_lib_updated = Library.objects.update_or_create_if_needed(
+            search_key={'library_id': lib["library_id"]},
+            data={
+                "orcabus_id": lib["orcabus_id"],
+                "library_id": lib["library_id"],
+                "phenotype": lib["phenotype"],
+                "assay": lib["assay"],
+                "type": lib["type"],
+                "workflow": lib["workflow"],
+
+                "subject_id": subject.orcabus_id,
+                "sample_id": sample.orcabus_id,
+            }
+
+        )
+
+        try:
+            library.project_set.get(orcabus_id=project.orcabus_id)
+        except ObjectDoesNotExist:
+            library.project_set.add(project)