feat(back):add unique_dataset_id to imprt synthese

- Revision alembic to add column unique_dataset_id and src_unique_dataset_id
- Add into bib_fields and into cor_entity_fields
- change the model Synthese Geonature to add unique_dataset_id

Reviewed-by: andriacap

backend/geonature/migrations/versions/imports/6e1852ecfea2_add_column_unique_dataset_id_to_t_.py

@@ -0,0 +1,192 @@
+"""add column unique_dataset_id to t_imports_synthese and insert into bib_fields and cor_entity_field
+
+Revision ID: 6e1852ecfea2
+Revises: bfc90691737d
+Create Date: 2024-03-04 12:31:00.861460
+
+"""
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects.postgresql import UUID
+from sqlalchemy.schema import Table, MetaData
+
+# revision identifiers, used by Alembic.
+revision = "6e1852ecfea2"
+down_revision = "bfc90691737d"
+branch_labels = None
+depends_on = None
+
+
+def upgrade():
+    meta = MetaData(bind=op.get_bind())
+
+    # Add columns to t_imports_synthese table
+    with op.batch_alter_table("t_imports_synthese", schema="gn_imports") as batch_op:
+        batch_op.add_column(sa.Column("src_unique_dataset_id", sa.String))
+        batch_op.add_column(sa.Column("unique_dataset_id", UUID(as_uuid=True)))
+        batch_op.add_column(sa.Column("id_dataset", sa.Integer))
+    # Fetch id_destination for 'synthese' from bib_destinations table
+    destination = Table("bib_destinations", meta, autoload=True, schema="gn_imports")
+    id_dest_synthese = (
+        op.get_bind()
+        .execute(sa.select([destination.c.id_destination]).where(destination.c.code == "synthese"))
+        .scalar()
+    )
+
+    # Fetch id_field for 'unique_dataset_id' from bib_fields table
+    field = Table("bib_fields", meta, autoload=True, schema="gn_imports")
+    field_unique_dataset_id_info = {
+        "name_field": "unique_dataset_id",
+        "fr_label": "Identifiant JDD (UUID)",
+        "mandatory": False,
+        "autogenerated": False,
+        "display": True,
+        "mnemonique": None,
+        "source_field": "src_unique_dataset_id",
+        "dest_field": "unique_dataset_id",
+    }
+    insert_data = {"id_destination": id_dest_synthese, **field_unique_dataset_id_info}
+
+    # Insert the data into the 'bib_fields' table
+    insert_stmt = sa.insert(field).values(insert_data).returning(field.c.id_field)
+    id_field = op.get_bind().execute(insert_stmt).scalar()
+
+    # Fetch id_theme_general from bib_themes table
+    theme = Table("bib_themes", meta, autoload=True, schema="gn_imports")
+    id_theme_general = (
+        op.get_bind()
+        .execute(sa.select([theme.c.id_theme]).where(theme.c.name_theme == "general_info"))
+        .scalar()
+    )
+
+    # Fetch id_entity_observation for id_destination from bib_entities table
+    entity = Table("bib_entities", meta, autoload=True, schema="gn_imports")
+    id_entity_observation = (
+        op.get_bind()
+        .execute(sa.select([entity.c.id_entity]).where(entity.c.id_destination == id_dest_synthese))
+        .scalar()
+    )
+
+    fields_entities = {
+        "id_theme": id_theme_general,
+        "order_field": 3,
+        "comment": "Correspondance champs standard: metadonneeId ou jddMetaId",
+    }
+
+    # Insert data into cor_entity_field table
+    cor_entity_field = Table("cor_entity_field", meta, autoload=True, schema="gn_imports")
+    insert_data = {"id_entity": id_entity_observation, "id_field": id_field, **fields_entities}
+    op.execute(sa.insert(cor_entity_field).values(insert_data))
+
+    # Update model contentmapping to add unique_dataset_id
+    t_mappings = t_fieldmappings = Table("t_mappings", meta, autoload=True, schema="gn_imports")
+    t_fieldmappings = Table("t_fieldmappings", meta, autoload=True, schema="gn_imports")
+
+    id_t_mapping_synthese = (
+        op.get_bind()
+        .execute(sa.select([t_mappings.c.id]).where(t_mappings.c.label == "Synthese GeoNature"))
+        .scalar()
+    )
+
+    query = t_fieldmappings.select().where(t_fieldmappings.c.id == id_t_mapping_synthese)
+    result = op.get_bind().execute(query)
+    existing_data = result.fetchone()
+
+    json_data = existing_data["values"]
+    data_dict = json_data or {}
+
+    new_key = "unique_dataset_id"
+    new_value = "unique_dataset_id"
+    data_dict[new_key] = new_value
+
+    # Update the JSONB column in the database with the modified dictionary
+    update_query = (
+        sa.update(t_fieldmappings)
+        .where(t_fieldmappings.c.id == id_t_mapping_synthese)
+        .values(values=data_dict)
+    )
+
+    op.get_bind().execute(update_query)
+
+
+def downgrade():
+    meta = MetaData(bind=op.get_bind())
+
+    # Drop columns from t_imports_synthese table
+    with op.batch_alter_table("t_imports_synthese", schema="gn_imports") as batch_op:
+        batch_op.drop_column("unique_dataset_id")
+        batch_op.drop_column("src_unique_dataset_id")
+        batch_op.drop_column("id_dataset")
+
+    # Fetch id_destination for 'synthese' from bib_destinations table
+    destination = Table("bib_destinations", meta, autoload=True, schema="gn_imports")
+    id_dest_synthese = (
+        op.get_bind()
+        .execute(sa.select([destination.c.id_destination]).where(destination.c.code == "synthese"))
+        .scalar()
+    )
+
+    # Fetch id_entity_observation for id_destination from bib_entities table
+    entity = Table("bib_entities", meta, autoload=True, schema="gn_imports")
+    id_entity_observation = (
+        op.get_bind()
+        .execute(sa.select([entity.c.id_entity]).where(entity.c.id_destination == id_dest_synthese))
+        .scalar()
+    )
+
+    # Fetch id_field inserted into bib_fields table
+    field = Table("bib_fields", meta, autoload=True, schema="gn_imports")
+    id_field = (
+        op.get_bind()
+        .execute(
+            sa.select([field.c.id_field]).where(
+                sa.and_(
+                    field.c.name_field == "unique_dataset_id",
+                    field.c.id_destination == id_dest_synthese,
+                )
+            )
+        )
+        .scalar()
+    )
+    # Delete the inserted data from bib_fields table
+    op.execute(field.delete().where(field.c.id_field == id_field))
+
+    # Delete the inserted data from cor_entity_field table
+    cor_entity_field = Table("cor_entity_field", meta, autoload=True, schema="gn_imports")
+    op.execute(
+        cor_entity_field.delete()
+        .where(cor_entity_field.c.id_entity == id_entity_observation)
+        .where(cor_entity_field.c.id_field == id_field)
+    )
+
+    # Delete unique_dataset_id to t_fieldmappings
+
+    t_mappings = Table("t_mappings", meta, autoload=True, schema="gn_imports")
+    t_fieldmappings = Table("t_fieldmappings", meta, autoload=True, schema="gn_imports")
+
+    # Get the ID of the "Synthese GeoNature" mapping
+    id_t_mapping_synthese = (
+        op.get_bind()
+        .execute(sa.select([t_mappings.c.id]).where(t_mappings.c.label == "Synthese GeoNature"))
+        .scalar()
+    )
+
+    query = t_fieldmappings.select().where(t_fieldmappings.c.id == id_t_mapping_synthese)
+    result = op.get_bind().execute(query)
+    existing_data = result.fetchone()
+
+    json_data = existing_data["values"]
+    data_dict = json_data or {}
+
+    key_to_delete = "unique_dataset_id"
+    del data_dict[key_to_delete]
+
+    # Update the JSONB column in the database with the modified dictionary
+    update_query = (
+        sa.update(t_fieldmappings)
+        .where(t_fieldmappings.c.id == id_t_mapping_synthese)
+        .values(values=data_dict)
+    )
+
+    op.get_bind().execute(update_query)