adapting workflow for other countries

data/demand/efficiency_gains.csv

@@ -1,2 +1,4 @@
 ,total residential space,total residential water,electricity residential,total services space,total services water,total rail,electricity rail,total domestic aviation,total international aviation,total domestic navigation,total international navigation,services electricity,agriculture electricity,agriculture oil,residential oil,residential biomass,residential gas,agriculture biomass,services oil,services biomass,services gas,total road ev,total road fcev,total road ice,total navigation oil,total navigation hydrogen,residential heat biomass,residential heat oil,residential heat gas
 MA,0.91,0.82,0.95,0.88,0.71,0.91,0.96,1.0,1.0,0.71,100,0.95,0.95,0.88,1.0,0.91,0.82,0.79,1.0,0.9,1.0, 0.3, 0.6, 1.0, 0.98, 0.85, 0.95, 0.95, 1.0
+NG,0.91,0.82,0.95,0.88,0.71,0.91,0.96,1.0,1.0,0.71,100,0.95,0.95,0.88,1.0,0.91,0.82,0.79,1.0,0.9,1.0, 0.3, 0.6, 1.0, 0.98, 0.85, 0.95, 0.95, 1.0
+BJ,0.91,0.82,0.95,0.88,0.71,0.91,0.96,1.0,1.0,0.71,100,0.95,0.95,0.88,1.0,0.91,0.82,0.79,1.0,0.9,1.0, 0.3, 0.6, 1.0, 0.98, 0.85, 0.95, 0.95, 1.0

data/demand/fuel_shares.csv

@@ -1,2 +1,5 @@
 country,oil residential heat share,biomass residential heat share,biomass to elec heat share,oil to elec heat share,biomass to elec share,oil to elec share,space to water heat share,gas to elec share,gas to elec heat share,gas residential heat share
 MA, 0.6667, 0.75,0.0, 0.5, 0.0, 0.5,0.6,0,0,0.75
+NG, 0.6667, 0.75,0.0, 0.5, 0.0, 0.5,0.6,0,0,0.75
+BJ, 0.6667, 0.75,0.0, 0.5, 0.0, 0.5,0.6,0,0,0.75
+

data/demand/growth_factors.csv

@@ -1,2 +1,4 @@
 ,total residential space,total residential water,electricity residential,total services space,total services water,total road,total rail,electricity rail,total domestic aviation,total international aviation,total domestic navigation,total international navigation,services electricity,agriculture electricity,agriculture oil,residential oil,residential biomass,residential gas,agriculture biomass,services oil,services biomass,services gas,residential heat oil,residential heat biomass,residential heat gas
 MA,1.5,1.6,2.8,1.4,1.5,1.3,2.5,2.1,2.0,1.5,1.13,1.6,2.0,1.5,0.7,1.0,1.0,1.23,1.32,0.5,1.0,0.89, 1.0, 1.0, 1.0
+NG,1.5,1.6,2.8,1.4,1.5,1.3,2.5,2.1,2.0,1.5,1.13,1.6,2.0,1.5,0.7,1.0,1.0,1.23,1.32,0.5,1.0,0.89, 1.0, 1.0, 1.0
+BJ,1.5,1.6,2.8,1.4,1.5,1.3,2.5,2.1,2.0,1.5,1.13,1.6,2.0,1.5,0.7,1.0,1.0,1.23,1.32,0.5,1.0,0.89, 1.0, 1.0, 1.0

scripts/build_base_energy_totals.py

@@ -338,6 +338,7 @@ def calc_sector(sector):
     # Fetch country list and demand base year from the config file
     year = snakemake.config["demand_data"]["base_year"]
     countries = snakemake.config["countries"]
+    #countries = ["NG", "BJ"]
 
     # Filter for the year and country
     df_yr = df[df.Year == year]

scripts/prepare_energy_totals.py

@@ -34,21 +34,30 @@ def get(item, investment_year=None):
             "prepare_energy_totals",
             simpl="",
             clusters=10,
-            demand="XX",
-            planning_horizons=2038,
+            demand="AB",
+            planning_horizons=2030,
         )
         sets_path_to_root("pypsa-earth-sec")
 
+    countries = snakemake.config["countries"]
+    #countries = ["NG", "BJ"]
+    investment_year = int(snakemake.wildcards.planning_horizons)
+    demand_sc = snakemake.wildcards.demand  # loading the demand scenrario wildcard
+
     base_energy_totals = pd.read_csv("data/energy_totals_base.csv", index_col=0)
     growth_factors = pd.read_csv("data/demand/growth_factors.csv", index_col=0)
     efficiency_gains = pd.read_csv("data/demand/efficiency_gains.csv", index_col=0)
     fuel_shares = pd.read_csv("data/demand/fuel_shares.csv", index_col=0)
     district_heating = pd.read_csv("data/demand/district_heating.csv", index_col=0)
 
+    growth_factors = growth_factors[growth_factors.index.isin(countries)]
+    efficiency_gains = efficiency_gains[efficiency_gains.index.isin(countries)]
+    fuel_shares = fuel_shares[fuel_shares.index.isin(countries)]
+    district_heating = district_heating[district_heating.index.isin(countries)]
+
     options = snakemake.config["sector"]
 
-    investment_year = int(snakemake.wildcards.planning_horizons)
-    demand_sc = snakemake.wildcards.demand  # loading the demand scenrario wildcard
+
 
     fuel_cell_share = get(
         options["land_transport_fuel_cell_share"],