Updated utility scripts to incorporate change to Tier data download.

enqueue_pipeline.sh

@@ -2,9 +2,9 @@
 
 # Enqueues COVID-19 pipelines
 
-CASES_FILE="data/Anonymised Combined Line List 20201228.csv"
-DATE_LOW="2020-10-01"
-DATE_HIGH="2020-12-25"
+CASES_FILE="data/Anonymised Combined Line List 20210104.csv"
+DATE_LOW="2020-10-09"
+DATE_HIGH="2021-01-01"
 
 TEMPLATE_CONFIG=template_config.yaml
 
@@ -14,7 +14,7 @@ switch-gpu
 
 for PILLAR in both 1
 do
-    for CASE_DATE_TYPE in specimen report
+    for CASE_DATE_TYPE in specimen
     do
 	RESULTS_DIR=$global_scratch/covid19/${DATE_HIGH}_${PILLAR}_${CASE_DATE_TYPE}
 	JOB_NAME="covid_${DATE_HIGH}_${PILLAR}_${CASE_DATE_TYPE}"	

prepare_config.py

@@ -58,7 +58,7 @@ for key in ["reported_cases", "commute_volume", "case_date_type", "pillar"]:
         config["data"][key] = val
 
 if args.inference_period is not None:
-    config["settings"]["inference_period"] = [str(x) for x in args.inference_period]
+    config["Global"]["inference_period"] = [str(x) for x in args.inference_period]
 
 for key in ["results_dir"]:
     val = getattr(args, key)

summary.py

@@ -166,15 +166,11 @@ if __name__ == "__main__":
         config = yaml.load(f, Loader=yaml.FullLoader)
 
     inference_period = [
-        np.datetime64(x) for x in config["settings"]["inference_period"]
+        np.datetime64(x) for x in config["Global"]["inference_period"]
     ]
 
     # Load covariate data
-    covar_data = model_spec.read_covariates(
-        config["data"],
-        date_low=inference_period[0],
-        date_high=inference_period[1],
-    )
+    covar_data = model_spec.read_covariates(config)
 
     # Load posterior file
     posterior_path = os.path.join(

within_between.py

@@ -65,13 +65,11 @@ with open(args.config, "r") as f:
     config = yaml.load(f, Loader=yaml.FullLoader)
 
     inference_period = [
-        np.datetime64(x) for x in config["settings"]["inference_period"]
+        np.datetime64(x) for x in config["Global"]["inference_period"]
     ]
 
 # Load covariate data
-covar_data = model_spec.read_covariates(
-    config["data"], date_low=inference_period[0], date_high=inference_period[1]
-)
+covar_data = model_spec.read_covariates(config)
 
 # Load posterior file
 posterior = h5py.File(