changed out-of-range handling and params for run when flash not part of the json

Author: Ananya Joshi

_delphi_utils_python/delphi_utils/flash_eval/eval_day.py

@@ -274,8 +274,7 @@ def flash_eval(lag, day, input_df, signal, params, logger=None):
 
     # discuss where to do out-of-range handling
     out_range = input_df.columns[input_df.lt(int(params['flash']['support'][0])).iloc[0, :].values
-                        & input_df.gt(int(params['flash']['support'][1])).iloc[0, :].values ]
-
+                        | input_df.gt(int(params['flash']['support'][1])).iloc[0, :].values ]
 
     #only rank streams without out of range data
     input_df = input_df[filter(lambda x: x not in out_range, input_df.columns)]

_delphi_utils_python/delphi_utils/flash_eval/run.py

@@ -15,23 +15,24 @@ def run_module(params):
     The parameters dictionary must include the signals and signals.
     We are only considering lag-1 data.
     """
-    signals = params["flash"]["signals"]
-    for signal in signals:
-        export_files = read_filenames(params["common"]["export_dir"])
-        days = {}
-        for (x, _) in export_files:
-            #Concat the data from recent files at nation, state, and county resolution per day.
-            if signal in x and pd.Series([y in x for y in ['state', 'county', 'nation']]).any():
-                day = pd.to_datetime(x.split('_')[0], format="%Y%m%d", errors='raise')
-                days[day] = pd.concat([days.get(day, pd.DataFrame()),
-                                       load_csv(f"{params['common']['export_dir']}/{x}")])
+    if params.get("flash", None):
+        signals = params["flash"].get("signals", [])
+        for signal in signals:
+            export_files = read_filenames(params["common"]["export_dir"])
+            days = {}
+            for (x, _) in export_files:
+                #Concat the data from recent files at nation, state, and county resolution per day.
+                if signal in x and pd.Series([y in x for y in ['state', 'county', 'nation']]).any():
+                    day = pd.to_datetime(x.split('_')[0], format="%Y%m%d", errors='raise')
+                    days[day] = pd.concat([days.get(day, pd.DataFrame()),
+                                           load_csv(f"{params['common']['export_dir']}/{x}")])
 
-        for day, input_df in dict(sorted(days.items())).items():
-            input_df = input_df[['geo_id', 'val']].set_index('geo_id').T
-            input_df.index = [day]
-            today = date.today()
-            lag= (pd.to_datetime(today)-pd.to_datetime(day)).days
-            # inital flash implementation assume lag == 1 always
-            #if str(lag) in params["flash"]["lags"]:
-            lag=1
-            flash_eval(int(lag), day, input_df, signal, params)
+            for day, input_df in dict(sorted(days.items())).items():
+                input_df = input_df[['geo_id', 'val']].set_index('geo_id').T
+                input_df.index = [day]
+                today = date.today()
+                lag= (pd.to_datetime(today)-pd.to_datetime(day)).days
+                # inital flash implementation assume lag == 1 always
+                #if str(lag) in params["flash"]["lags"]:
+                lag=1
+                flash_eval(int(lag), day, input_df, signal, params)