def recalc_financial_objective(df, scenarios, obj_col):
f_cols = [col for col in df.columns if col.endswith('_financial_model')]
data = dict()
for idx, row in df.iterrows():
for i, scenario in enumerate(scenarios):
for j, col in enumerate(f_cols):
new_col = col.split('_')[0] + '_' + scenario['name']
if new_col not in data:
data[new_col] = []
d = expand_financial_model(row[col])
model = Singleowner.new() #.default(defaults[tech_prefix[j]])
model.assign(d)
for key, val in scenario['values'].items():
if key == 'cp_capacity_credit_percent':
model.value(key, calc_capacity_credit_perc(d, val))
else:
model.value(key, val)
model.execute()
data[new_col].append(model.value(obj_col))
for key, val in data.items():
df[key] = val
return df
def recalc_financials(result, assumptions):
cols = [col for col in result.keys() if col.endswith('_financial_model')]
models = []
for col in cols:
model_dict = expand_financial_model(result[col])
model = Singleowner.new() #.default(defaults[tech_prefix[j]])
model.assign(model_dict)
for key, val in assumptions.items():
if key == 'cp_capacity_credit_percent':
val = calc_capacity_credit_percent(model_dict, N=val)
model.value(key, val)
model.execute()
models.append(model)
return models