From 3c099095dc50ce7d27d080686b005b49248cc216 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Fri, 6 Jan 2023 20:51:49 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 scripts/add_electricity.py     | 4 ++--
 scripts/build_hydro_profile.py | 4 +++-
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/scripts/add_electricity.py b/scripts/add_electricity.py
index e36b3d3e..0b2e2844 100755
--- a/scripts/add_electricity.py
+++ b/scripts/add_electricity.py
@@ -223,7 +223,7 @@ def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.
     regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
     opsd_load = pd.read_csv(load, index_col=0, parse_dates=True).filter(items=countries)
 
-    ua_md_gdp = pd.read_csv(ua_md_gdp, dtype={'name': 'str'}).set_index('name')
+    ua_md_gdp = pd.read_csv(ua_md_gdp, dtype={"name": "str"}).set_index("name")
 
     logger.info(f"Load data scaled with scalling factor {scaling}.")
     opsd_load *= scaling
@@ -250,7 +250,7 @@ def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.
             # relative factors 0.6 and 0.4 have been determined from a linear
             # regression on the country to continent load data
             factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
-            if cntry in ['UA', 'MD']:
+            if cntry in ["UA", "MD"]:
                 # overwrite factor because nuts3 provides no data for UA+MD
                 factors = normed(ua_md_gdp.loc[group.index, "GDP_PPP"].squeeze())
 
diff --git a/scripts/build_hydro_profile.py b/scripts/build_hydro_profile.py
index aa3dd7b4..9831521a 100644
--- a/scripts/build_hydro_profile.py
+++ b/scripts/build_hydro_profile.py
@@ -73,7 +73,9 @@ cc = coco.CountryConverter()
 
 def get_eia_annual_hydro_generation(fn, countries):
     # in billion kWh/a = TWh/a
-    df = pd.read_csv(fn, skiprows=2, index_col=1, na_values=[" ", "--"], decimal=",").iloc[1:, 1:]
+    df = pd.read_csv(
+        fn, skiprows=2, index_col=1, na_values=[" ", "--"], decimal=","
+    ).iloc[1:, 1:]
     df.index = df.index.str.strip()
 
     former_countries = {