chore: apply black

src/covid-19/covid_19_utils/covid_19_utils/converters/italy.py

@@ -104,4 +104,4 @@ def _correct_trentino(df):
 
 
 ItalyRegionalCaseConverter._register()
-ItalyNationalCaseConverter._register()
\ No newline at end of file
+ItalyNationalCaseConverter._register()

src/covid-19/covid_19_utils/covid_19_utils/converters/jhu.py

@@ -52,20 +52,29 @@ class JhuCsseGlobalCaseConverter(CaseConverterImpl):
             "Slovakia": "Slovak Republic",
             "Saint Martin": "St. Martin (French part)",
             "Syria": "Syrian Arab Republic",
-            'Taiwan*': 'Taiwan',
+            "Taiwan*": "Taiwan",
             "Venezuela": "Venezuela, RB",
             "US": "United States",
         }
         df = df.replace(region_jhu_wb_map)
 
         # add in missing data from Harvard worldmap
-        missing_countries = pd.unique(df.loc[df["region_label"].isin(pop_df["Country Name"]) == False, "region_label"])
-        worldmap_df = pd.read_csv(self.atlas_folder / "worldmap" / "country_centroids.csv")
-        worldmap_df = worldmap_df[['name', 'sov_a3', 'pop_est']]
-        worldmap_df = worldmap_df.rename({"name": "Country Name",
-                                          "sov_a3": "Country Code",
-                                          "pop_est": "2018"}, axis=1)
-        worldmap_df = worldmap_df.loc[worldmap_df["Country Name"].isin(missing_countries)]
+        missing_countries = pd.unique(
+            df.loc[
+                df["region_label"].isin(pop_df["Country Name"]) == False, "region_label"
+            ]
+        )
+        worldmap_df = pd.read_csv(
+            self.atlas_folder / "worldmap" / "country_centroids.csv"
+        )
+        worldmap_df = worldmap_df[["name", "sov_a3", "pop_est"]]
+        worldmap_df = worldmap_df.rename(
+            {"name": "Country Name", "sov_a3": "Country Code", "pop_est": "2018"},
+            axis=1,
+        )
+        worldmap_df = worldmap_df.loc[
+            worldmap_df["Country Name"].isin(missing_countries)
+        ]
         pop_df = pop_df.append(worldmap_df)
 
         pop_ser = pop_df.set_index("Country Code")["2018"]
@@ -74,12 +83,12 @@ class JhuCsseGlobalCaseConverter(CaseConverterImpl):
             for i, r in pop_df[["Country Name", "Country Code"]].iterrows()
         }
         df["country"] = df["region_label"].replace(country_code_map)
-        df['country_label'] = df['region_label']
+        df["country_label"] = df["region_label"]
 
         merged = df.loc[df["country"].isin(pop_ser.index)].copy()
         merged["population"] = merged.apply(lambda r: pop_ser.loc[r["country"]], axis=1)
-        merged['region_iso'] = merged['country']
-        merged['tested'] = np.nan
+        merged["region_iso"] = merged["country"]
+        merged["tested"] = np.nan
         return self._set_common_columns(merged)
 
     def read_ser(self, path, name):

src/covid-19/covid_19_utils/covid_19_utils/converters/spain.py

@@ -31,7 +31,11 @@ region_populations = [
     {"region_iso": "ES-IB", "region_label": "Baleares", "population": "1150839"},
     {"region_iso": "ES-CN", "region_label": "Canarias", "population": "2127685"},
     {"region_iso": "ES-CB", "region_label": "Cantabria", "population": "580229"},
-    {"region_iso": "ES-CM", "region_label": "Castilla-La Mancha", "population": "2106331"},
+    {
+        "region_iso": "ES-CM",
+        "region_label": "Castilla-La Mancha",
+        "population": "2106331",
+    },
     {"region_iso": "ES-CL", "region_label": "Castilla y León", "population": "2418694"},
     {"region_iso": "ES-CT", "region_label": "Cataluña", "population": "7619494"},
     {"region_iso": "ES-CE", "region_label": "Ceuta", "population": "84777"},
@@ -67,7 +71,7 @@ class SpainCaseConverter(CaseConverter):
 
         # calculate incidence rates
         merged = df_conv.merge(pd.DataFrame(region_populations))
-        merged['country'] = 'ESP'
+        merged["country"] = "ESP"
 
         return self._set_common_columns(merged)