style: run black

prereise/gather/demanddata/eia/map_ba.py

@@ -40,8 +40,7 @@ def get_demand_in_loadzone(agg_demand, bus_map):
     :return: (*pandas.DataFrame*) -- data frame with demand columns according
         to load zone.
     """
-    ba_agg = bus_map[["BA", "Pd"]].groupby("BA")\
-        .sum().rename(columns={"Pd": "PdTotal"})
+    ba_agg = bus_map[["BA", "Pd"]].groupby("BA").sum().rename(columns={"Pd": "PdTotal"})
 
     ba_scaling_factor = bus_map.merge(ba_agg, left_on="BA", right_on="BA")
     ba_scaling_factor = ba_scaling_factor.assign(
@@ -55,13 +54,11 @@ def get_demand_in_loadzone(agg_demand, bus_map):
         for zone_name in zone_scaling_df.index.get_level_values(0).to_list():
             if zone_name in zone_demand.columns.to_list():
                 zone_demand[zone_name] += (
-                    zone_scaling_df.loc[zone_name, "zone_scaling"]
-                    * agg_demand[ba_name]
+                    zone_scaling_df.loc[zone_name, "zone_scaling"] * agg_demand[ba_name]
                 )
             else:
                 zone_demand[zone_name] = (
-                    zone_scaling_df.loc[zone_name, "zone_scaling"]
-                    * agg_demand[ba_name]
+                    zone_scaling_df.loc[zone_name, "zone_scaling"] * agg_demand[ba_name]
                 )
     return zone_demand
 
@@ -83,8 +80,7 @@ def map_buses_to_county(bus_county_map):
     bus_county_map.loc[:, "County"] = None
     bus_county_map.loc[:, "BA"] = None
     bus_no_county_match = []
-    for index, row in tqdm(bus_county_map.iterrows(),
-                           total=len(bus_county_map)):
+    for index, row in tqdm(bus_county_map.iterrows(), total=len(bus_county_map)):
         params = {
             "latitude": row["lat"],
             "longitude": row["lon"],

prereise/gather/demanddata/eia/tests/test_map_ba.py

@@ -2,8 +2,8 @@
 from pandas.testing import assert_series_equal
 
 from prereise.gather.demanddata.eia.map_ba import (
-    get_demand_in_loadzone,
     aggregate_ba_demand,
+    get_demand_in_loadzone,
 )
 
 

prereise/gather/helpers.py

@@ -90,8 +90,7 @@ def get_monthly_net_generation(state, eia_form_923, resource, hps=True):
     # Get monthly total net generation by summing up across plants
     # with all positive values. Note that negative ones are included in
     # actual demand.
-    eia_net_generation = list(net_generation.apply(lambda x: x[x > 0]
-                                                   .sum()).values)
+    eia_net_generation = list(net_generation.apply(lambda x: x[x > 0].sum()).values)
 
     # If there is no such generator in the state, the function will return
     # a list of 0 instead of NaN.

prereise/gather/hydrodata/eia/decompose_profile.py

@@ -69,18 +69,17 @@ def get_profile_by_plant(plant_df, total_profile):
         raise TypeError("total_profile must be a pandas.Series object")
     if not all([float(val) == val for val in total_profile]):
         raise TypeError("total_profile must be all numbers")
-    if 'Pmax' not in plant_df.columns:
+    if "Pmax" not in plant_df.columns:
         raise ValueError("Pmax must be one of the columns of plant_df")
 
-    total_hydro_capacity = plant_df['Pmax'].sum()
-    res_profile = pd.DataFrame(index=total_profile.index,
-                               columns=plant_df.index)
+    total_hydro_capacity = plant_df["Pmax"].sum()
+    res_profile = pd.DataFrame(index=total_profile.index, columns=plant_df.index)
 
     for plantid in res_profile.columns:
         if total_hydro_capacity == 0:
             factor = 0
         else:
-            factor = plant_df.loc[plantid]['Pmax']/total_hydro_capacity
-        plant_profile = [val*factor for val in total_profile]
+            factor = plant_df.loc[plantid]["Pmax"] / total_hydro_capacity
+        plant_profile = [val * factor for val in total_profile]
         res_profile[plantid] = plant_profile.copy()
     return res_profile

prereise/gather/hydrodata/eia/net_demand.py

@@ -42,8 +42,9 @@ def get_net_demand_profile(state, scenario):
     wind_in_state = wind[wind_plant_in_state].sum(axis=1)
     solar_in_state = solar[solar_plant_in_state].sum(axis=1)
 
-    loadzone_in_state = [grid.zone2id[z] for z in abv2loadzone[state] if z
-                         in grid.zone2id]
+    loadzone_in_state = [
+        grid.zone2id[z] for z in abv2loadzone[state] if z in grid.zone2id
+    ]
     demand_in_state = demand[loadzone_in_state].sum(axis=1)
     net_demand_in_state = demand_in_state - wind_in_state - solar_in_state
 

prereise/gather/hydrodata/eia/tests/test_decompose_profile.py

@@ -2,8 +2,9 @@
 import pandas as pd
 import pytest
 
-from prereise.gather.hydrodata.eia.decompose_profile import \
-    get_profile_by_state
+from prereise.gather.hydrodata.eia.decompose_profile import (
+    get_profile_by_state,
+)
 
 
 def test_get_profile_argument_type():