Delete date_col="timestamp" statements

Author: alexcoda

_template_python/delphi_NAME/run.py

@@ -47,10 +47,7 @@ def run_module(params):
     ## aggregate & smooth
     ## TODO: add num/prop variations if needed
     for sensor, smoother, geo in product(SIGNALS, SMOOTHERS, GEOS):
-        df = mapper.replace_geocode(
-            all_data, "zip", geo,
-            new_col="geo_id",
-            date_col="timestamp")
+        df = mapper.replace_geocode(all_data, "zip", geo, new_col="geo_id")
         ## TODO: recompute sample_size, se here if not NA
         df["val"] = df[["geo_id", "val"]].groupby("geo_id")["val"].transform(
             smoother[0].smooth

combo_cases_and_deaths/delphi_combo_cases_and_deaths/run.py

@@ -72,10 +72,9 @@ def compute_special_geo_dfs(df, signal, geo):
     df = GMPR.replace_geocode(df,
                               from_col="geo_id",
                               from_code="fips",
-                              new_code="state_code",
-                              date_col="timestamp")
+                              new_code="state_code")
     df = GMPR.add_population_column(df, "state_code")  # use total state population
-    df = GMPR.replace_geocode(df, from_code="state_code", new_code=geo, date_col="timestamp")
+    df = GMPR.replace_geocode(df, from_code="state_code", new_code=geo)
     if signal.endswith("_prop"):
         df["val"] = df["val"]/df["population"] * 100000
     df.drop("population", axis=1, inplace=True)

covid_act_now/delphi_covid_act_now/geo.py

@@ -92,8 +92,7 @@ def geo_map(df: pd.DataFrame, geo_res: str) -> pd.DataFrame:
 
         df = (df
             .loc[:, ["fips", "timestamp", "pcr_tests_positive", "pcr_tests_total"]]
-            .pipe(gmpr.replace_geocode, "fips", geo_res, new_col="geo_id",
-                date_col="timestamp")
+            .pipe(gmpr.replace_geocode, "fips", geo_res, new_col="geo_id")
             .rename(columns={"pcr_tests_total": "sample_size"})
             .assign(val=positivity_rate, se=std_err)
             .reset_index()

google_symptoms/delphi_google_symptoms/run.py

@@ -90,8 +90,7 @@ def run_module(params):
         if geo_res == "state":
             df_pull = dfs["state"]
         elif geo_res in ["hhs", "nation"]:
-            df_pull = gmpr.replace_geocode(dfs["county"], "fips", geo_res, from_col="geo_id",
-                                           date_col="timestamp")
+            df_pull = gmpr.replace_geocode(dfs["county"], "fips", geo_res, from_col="geo_id")
             df_pull.rename(columns={geo_res: "geo_id"}, inplace=True)
         else:
             df_pull = geo_map(dfs["county"], geo_res)

hhs_hosp/delphi_hhs/run.py

@@ -162,10 +162,7 @@ def make_geo(state, geo, geo_mapper):
     if geo == "state":
         exported = state.rename(columns={"state": "geo_id"})
     else:
-        exported = geo_mapper.replace_geocode(
-            state, "state_code", geo,
-            new_col="geo_id",
-            date_col="timestamp")
+        exported = geo_mapper.replace_geocode(state, "state_code", geo, new_col="geo_id")
     exported["se"] = np.nan
     exported["sample_size"] = np.nan
     return exported

jhu/delphi_jhu/geo.py

@@ -44,9 +44,9 @@ def geo_map(df: pd.DataFrame, geo_res: str, sensor: str):
     elif geo_res in ("state", "hhs", "nation"):
         geo = "state_id" if geo_res == "state" else geo_res
         df = df.append(unassigned_counties) if not unassigned_counties.empty else df
-        df = gmpr.replace_geocode(df, "fips", geo, new_col="geo_id", date_col="timestamp")
+        df = gmpr.replace_geocode(df, "fips", geo, new_col="geo_id")
     else:
-        df = gmpr.replace_geocode(df, "fips", geo_res, new_col="geo_id", date_col="timestamp")
+        df = gmpr.replace_geocode(df, "fips", geo_res, new_col="geo_id")
     df["incidence"] = df["new_counts"] / df["population"] * INCIDENCE_BASE
     df["cumulative_prop"] = df["cumulative_counts"] / df["population"] * INCIDENCE_BASE
     return df

jhu/delphi_jhu/pull.py

@@ -99,9 +99,7 @@ def pull_jhu_data(base_url: str, metric: str, gmpr: GeoMapper) -> pd.DataFrame:
     df = download_data(base_url, metric)
 
     gmpr = GeoMapper()
-    df = gmpr.replace_geocode(
-        df, "jhu_uid", "fips", from_col="UID", date_col="timestamp"
-    )
+    df = gmpr.replace_geocode(df, "jhu_uid", "fips", from_col="UID")
     df = create_diffs_column(df)
     # Final sanity checks
     sanity_check_data(df)

jhu/tests/test_geo.py

@@ -107,7 +107,7 @@ def test_msa_hrr(self, jhu_confirmed_test_data):
             new_df = geo_map(test_df, geo, "cumulative_prop")
             gmpr = GeoMapper()
             test_df = gmpr.add_population_column(test_df, "fips")
-            test_df = gmpr.replace_geocode(test_df, "fips", geo, date_col="timestamp")
+            test_df = gmpr.replace_geocode(test_df, "fips", geo)
 
             new_df = new_df.set_index(["geo_id", "timestamp"]).sort_index()
             test_df = test_df.set_index([geo, "timestamp"]).sort_index()

quidel_covidtest/delphi_quidel_covidtest/geo_maps.py

@@ -1,7 +1,6 @@
 """Contains geographic mapping tools."""
 from delphi_utils import GeoMapper
 
-DATE_COL = "timestamp"
 DATA_COLS = ['totalTest', 'numUniqueDevices', 'positiveTest', "population"]
 GMPR = GeoMapper()  # Use geo utils
 GEO_KEY_DICT = {
@@ -21,8 +20,7 @@ def geo_map(geo_res, df):
     # Add population for each zipcode
     data = GMPR.add_population_column(data, "zip")
     # zip -> geo_res
-    data = GMPR.replace_geocode(data, "zip", geo_key,
-                                date_col=DATE_COL, data_cols=DATA_COLS)
+    data = GMPR.replace_geocode(data, "zip", geo_key, data_cols=DATA_COLS)
     if geo_res in ["state", "hhs", "nation"]:
         return data, geo_key
     # Add parent state

safegraph_patterns/delphi_safegraph_patterns/process.py

@@ -114,11 +114,7 @@ def aggregate(df, metric, geo_res):
     gmpr = GeoMapper()
     geo_key = GEO_KEY_DICT[geo_res]
     df = gmpr.add_population_column(df, "zip")
-    df = gmpr.replace_geocode(df,
-                              "zip",
-                              geo_key,
-                              date_col="timestamp",
-                              data_cols=[metric_count_name, "population"])
+    df = gmpr.replace_geocode(df, "zip", geo_key, data_cols=[metric_count_name, "population"])
 
     df[metric_prop_name] = df[metric_count_name] / df["population"] \
                             * INCIDENCE_BASE