prod deploy

.travis.yml

@@ -20,9 +20,9 @@ env:
     - FUNCTION=send_email TOPIC=emails TIMEOUT=60 MEMORY=256
     - FUNCTION=store_auth0_logs TOPIC=daily_cron TIMEOUT=120 MEMORY=256
     - FUNCTION=disable_inactive_users TOPIC=daily_cron TIMEOUT=120 MEMORY=256
-    - FUNCTION=vis_preprocessing TOPIC=artifact_upload TIMEOUT=120 MEMORY=512
+    - FUNCTION=vis_preprocessing TOPIC=artifact_upload TIMEOUT=540 MEMORY=1024
     - FUNCTION=derive_files_from_manifest_upload TOPIC=patient_sample_update TIMEOUT=60 MEMORY=256
-    - FUNCTION=derive_files_from_assay_or_analysis_upload TOPIC=assay_or_analysis_upload_complete TIMEOUT=60 MEMORY=1024
+    - FUNCTION=derive_files_from_assay_or_analysis_upload TOPIC=assay_or_analysis_upload_complete TIMEOUT=540 MEMORY=1024
 before_install:
   # Install and configure the gcloud SDK. Values for these environment
   # variables must be set in the Travis CI console for this to work:

functions/visualizations.py

@@ -238,6 +238,12 @@ def _metadata_to_categories(metadata_df: pd.DataFrame) -> list:
     columns = sorted(columns, key=lambda c: len(metadata_df[c].unique()))
     metadata_df = metadata_df[columns]
 
+    if "Disease progression" in columns:
+        columns[columns.index("Disease progression")] = "Disease prog"
+    if "RECIST clinical benefit status" in columns:
+        columns[columns.index("RECIST clinical benefit status")] = "Clin benefit"
+    metadata_df.columns = columns
+
     # build the output str in ClusterGrammer compatible format
     categories = []
     for idx, row in metadata_df.iterrows():

requirements.txt

@@ -10,4 +10,4 @@ scikit-learn==0.21.3 # clustergrammer dependency
 six~=1.13.0
 
 # The cidc_api_modules package
-cidc-api-modules~=0.20.6
+cidc-api-modules~=0.22.0

tests/functions/test_visualizations.py

@@ -34,10 +34,7 @@ def metadata_df():
                 "Arm_A",
                 "Arm_A",
             ],  # kept no matter cardinality; -> CG 'Cohort'
-            "collection_event_name": [
-                "Event1",
-                "Event2",
-            ],  # -> CG 'RECIST clinical benefit status'
+            "collection_event_name": ["Event1", "Event2"],  # -> CG 'Clin benefit'
         }
     )
     metadata_df.set_index("cimac_id", inplace=True)
@@ -293,31 +290,31 @@ def test_metadata_to_categories():
             "Treatment: False",
             "Collection Event: c",
             "Cohort: b",
-            "RECIST clinical benefit status: z",
+            "Clin benefit: z",
             "Participant Id: a",
         ),
         (
             "CIMAC Id: CT2",
             "Treatment: True",
             "Collection Event: f",
             "Cohort: e",
-            "RECIST clinical benefit status: y",
+            "Clin benefit: y",
             "Participant Id: d",
         ),
         (
             "CIMAC Id: CT3",
             "Treatment: True",
             "Collection Event: i",
             "Cohort: h",
-            "RECIST clinical benefit status: x",
+            "Clin benefit: x",
             "Participant Id: g",
         ),
         (
             "CIMAC Id: CT4",
             "Treatment: False",
             "Collection Event: c",
             "Cohort: e",
-            "RECIST clinical benefit status: x",
+            "Clin benefit: x",
             "Participant Id: j",
         ),
     ]