From 40fa9e02be5561d9911dc8042159a800be310af4 Mon Sep 17 00:00:00 2001
From: Rok Roskar <rokroskar@gmail.com>
Date: Thu, 9 Apr 2020 17:11:14 +0200
Subject: [PATCH] chore: apply black
---
.../covid_19_utils/converters/italy.py | 2 +-
.../covid_19_utils/converters/jhu.py | 31 ++++++++++++-------
.../covid_19_utils/converters/spain.py | 8 +++--
3 files changed, 27 insertions(+), 14 deletions(-)
diff --git a/src/covid-19/covid_19_utils/covid_19_utils/converters/italy.py b/src/covid-19/covid_19_utils/covid_19_utils/converters/italy.py
index 66b5673f5..1b45b301e 100644
--- a/src/covid-19/covid_19_utils/covid_19_utils/converters/italy.py
+++ b/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()
diff --git a/src/covid-19/covid_19_utils/covid_19_utils/converters/jhu.py b/src/covid-19/covid_19_utils/covid_19_utils/converters/jhu.py
index dfd6b7953..0be24407c 100644
--- a/src/covid-19/covid_19_utils/covid_19_utils/converters/jhu.py
+++ b/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):
diff --git a/src/covid-19/covid_19_utils/covid_19_utils/converters/spain.py b/src/covid-19/covid_19_utils/covid_19_utils/converters/spain.py
index 3fcbb2259..6fc55482b 100644
--- a/src/covid-19/covid_19_utils/covid_19_utils/converters/spain.py
+++ b/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)
--
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