add hhs geo level (#2036)

Author: minhkhul

_delphi_utils_python/delphi_utils/geomap.py

@@ -443,7 +443,7 @@ def add_population_column(
         ---------
         data: pd.DataFrame
             The dataframe with a FIPS code column.
-        geocode_type: {"fips", "zip"}
+        geocode_type:
             The type of the geocode contained in geocode_col.
         geocode_col: str, default None
             The name of the column containing the geocodes. If None, uses the geocode_type
@@ -671,8 +671,10 @@ def aggregate_by_weighted_sum(
             to a from_geo, e.g. "wastewater collection site").
         to_geo: str
             The column name of the geocode to aggregate to.
-        sensor: str
+        sensor_col: str
             The column name of the sensor to aggregate.
+        time_col: str
+            The column name of the timestamp to aggregate over.
         population_column: str
             The column name of the population to weight the sensor by.
 

nssp/delphi_nssp/constants.py

@@ -6,6 +6,7 @@
     "nation",
     "state",
     "county",
+    "hhs",
 ]
 
 SIGNALS_MAP = {

nssp/delphi_nssp/run.py

@@ -111,6 +111,14 @@ def run_module(params):
                 df = geo_mapper.add_geocode(df, "fips", "msa", from_col="fips", new_col="geo_id")
                 df = geo_mapper.aggregate_by_weighted_sum(df, "geo_id", "val", "timestamp", "population")
                 df = df.rename(columns={"weighted_val": "val"})
+            elif geo == "hhs":
+                df = df[(df["county"] == "All") & (df["geography"] != "United States")]
+                df = df[["geography", "val", "timestamp"]]
+                df = geo_mapper.add_population_column(df, geocode_type="state_name", geocode_col="geography")
+                df = geo_mapper.add_geocode(df, "state_name", "state_code", from_col="state_name")
+                df = geo_mapper.add_geocode(df, "state_code", "hhs", from_col="state_code", new_col="geo_id")
+                df = geo_mapper.aggregate_by_weighted_sum(df, "geo_id", "val", "timestamp", "population")
+                df = df.rename(columns={"weighted_val": "val"})
             else:
                 df = df[df["county"] != "All"]
                 df["geo_id"] = df["fips"]