From 47e2d2e832e9735e873bd2a8a3f15cd2d6bd82b0 Mon Sep 17 00:00:00 2001
From: Vincent Emonet <vincent.emonet@gmail.com>
Date: Mon, 8 Apr 2024 10:09:12 +0200
Subject: [PATCH] add admin dq as dataowner of nodes too

---
 backend/src/decentriq.py | 7 ++++++-
 backend/src/upload.py    | 1 +
 2 files changed, 7 insertions(+), 1 deletion(-)

diff --git a/backend/src/decentriq.py b/backend/src/decentriq.py
index dae8703..231dd57 100644
--- a/backend/src/decentriq.py
+++ b/backend/src/decentriq.py
@@ -63,6 +63,10 @@ def create_provision_dcr(user: Any, cohort: Cohort) -> dict[str, Any]:
         user["email"],
         data_owner_of=[data_node_id],
     )
+    builder.add_participant(
+        settings.decentriq_email,
+        data_owner_of=[data_node_id],
+    )
     # Build and publish DCR
     dcr_definition = builder.build()
     dcr = client.publish_analytics_dcr(dcr_definition)
@@ -114,7 +118,7 @@ async def create_compute_dcr(
     user: Any = Depends(get_current_user),
 ) -> dict[str, Any]:
     """Create a Data Clean Room for computing with the cohorts requested using Decentriq SDK"""
-    users = [user["email"]]
+    # users = [user["email"]]
     # TODO: cohorts_request could also be a dict of union of cohorts to merge
     # {"cohorts": {"cohort_id": ["var1", "var2"], "merged_cohort3": {"cohort1": ["weight", "sex"], "cohort2": ["gender", "patient weight"]}}}
     # We automatically merge the cohorts, figuring out which variables are the same thanks to mappings
@@ -197,6 +201,7 @@ async def create_compute_dcr(
 
     # Add users permissions
     builder.add_participant(user["email"], data_owner_of=data_nodes, analyst_of=["prepare-data", *preview_nodes])
+    builder.add_participant(settings.decentriq_email, data_owner_of=data_nodes, analyst_of=["prepare-data", *preview_nodes])
 
     # Build and publish DCR
     dcr_definition = builder.build()
diff --git a/backend/src/upload.py b/backend/src/upload.py
index cba2c4a..c767f48 100644
--- a/backend/src/upload.py
+++ b/backend/src/upload.py
@@ -197,6 +197,7 @@ def load_cohort_dict_file(dict_path: str, cohort_id: str, user_email: str) -> Da
         )
     try:
         df = pd.read_csv(dict_path)
+        # df = pd.read_csv(dict_path, encoding="utf-8")
         df = df.dropna(how="all")
         df = df.fillna("")
         df.columns = df.columns.str.strip()