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adjust make summary functions to work without industry sector

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This commit is contained in:
lisazeyen 2021-02-23 15:42:43 +01:00
parent 8cabeb5871
commit 088c5ab805
1 changed files with 10 additions and 11 deletions
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21
scripts/make_summary.py
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@ -196,25 +196,25 @@ def calculate_costs(n,label,costs):
return costs
def calculate_cumulative_cost():
def calculate_cumulative_cost():
planning_horizons = snakemake.config['scenario']['planning_horizons']
cumulative_cost = pd.DataFrame(index = df["costs"].sum().index,
columns=pd.Series(data=np.arange(0,0.1, 0.01), name='social discount rate'))
#discount cost and express them in money value of planning_horizons[0]
for r in cumulative_cost.columns:
cumulative_cost[r]=[df["costs"].sum()[index]/((1+r)**(index[-1]-planning_horizons[0])) for index in cumulative_cost.index]
#integrate cost throughout the transition path
for r in cumulative_cost.columns:
for r in cumulative_cost.columns:
for cluster in cumulative_cost.index.get_level_values(level=0).unique():
for lv in cumulative_cost.index.get_level_values(level=1).unique():
for sector_opts in cumulative_cost.index.get_level_values(level=2).unique():
cumulative_cost.loc[(cluster, lv, sector_opts,'cumulative cost'),r] = np.trapz(cumulative_cost.loc[idx[cluster, lv, sector_opts,planning_horizons],r].values, x=planning_horizons)
return cumulative_cost
return cumulative_cost
def calculate_nodal_capacities(n,label,nodal_capacities):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
@ -285,7 +285,7 @@ def calculate_supply(n,label,supply):
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map)]
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0:
continue
@ -330,7 +330,7 @@ def calculate_supply_energy(n,label,supply_energy):
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map)]
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0:
continue
@ -611,7 +611,7 @@ if __name__ == "__main__":
print(networks_dict)
Nyears = 1
costs_db = prepare_costs(snakemake.input.costs,
snakemake.config['costs']['USD2013_to_EUR2013'],
snakemake.config['costs']['discountrate'],
@ -623,10 +623,9 @@ if __name__ == "__main__":
df["metrics"].loc["total costs"] = df["costs"].sum()
to_csv(df)
if snakemake.config["foresight"]=='myopic':
cumulative_cost=calculate_cumulative_cost()
cumulative_cost.to_csv(snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/csvs/cumulative_cost.csv')