def _multiLCA(activities, methods):
"""Simple wrapper around brightway API"""
bw2.calculation_setups['process'] = {'inv': activities, 'ia': methods}
lca = bw2.MultiLCA('process')
cols = [
act for act_amount in activities
for act, amount in act_amount.items()
]
return pd.DataFrame(lca.results.T,
index=[method for method in methods],
columns=cols)
def get_CFs(self, indicators=(), activities=(), show=False, path=''):
'''
Get impact characterization factors.
Parameters
----------
indicators : iterable
Keys of the indicators in the `indicators` property.
Will be defaulted to all loaded indicators if not provided.
activities : iterable
Keys of the activities in the `activities` property.
Will be defaulted to all loaded indicators if not provided.
show : bool
Whether to print all characterization factors in the console.
path : str
If provided, the :class:`pandas.DataFrame` will be saved to the given file path.
Returns
-------
df: :class:`pandas.DataFrame`
Characterization factors.
Tip
---
[1] Use `show_activity` to see the functional unit of the activity.
The quantity will be 1.
[2] If you run into an "FileNotFoundError", most likely there are some
indicators that do not acutally have corresponding impact assessment methods,
try to load indicators one at a time to look for the culprit.
'''
if not self.indicators:
raise ValueError('No loaded indicators.')
elif not self.activities:
raise ValueError('No loaded activities.')
if not set(indicators).issubset(self.indicators):
raise ValueError('Provided indicator(s) not all loaded.')
inds = indicators if indicators else self.indicators
ind_units = [bw2.methods.get(i)['unit'] for i in inds] + ['-', '-']
acts = [self.activities[k] for k in activities] if activities \
else [v for v in self.activities.values()]
bw2.calculation_setups['multiLCA'] = {
'inv': [{
a: 1
} for a in acts],
'ia': inds
}
# This is to prevent bw2 from printing in the console
stdout = sys.stdout
sys.stdout = open(os.devnull, 'w')
mlca = bw2.MultiLCA('multiLCA')
sys.stdout = stdout
# pd_indices = [a['name'] for a in acts]
pd_cols = pd.MultiIndex.from_tuples(inds,
names=('method', 'category',
'indicator'))
cf_df = pd.DataFrame(data=mlca.results, columns=pd_cols)
unit_df = pd.DataFrame(
data={k: v
for k, v in zip(cf_df.columns, ind_units)},
index=[
-1,
],
dtype='object')
# cf_df = pd.DataFrame(data=mlca.results, index=pd_indices, columns=pd_cols)
cf_df[('-', '-', 'activity name')] = [a['name'] for a in acts]
cf_df[('-', '-', 'functional unit')] = [a['unit'] for a in acts]
df = pd.concat((unit_df, cf_df))
df.sort_index(axis=1, inplace=True)
df.reset_index(drop=True, inplace=True)
if show:
print(df)
export_df(df, path, show)
self._CFs = df
return df
'alumina_production_recipe', 'alumina_consumption_recipe',
'aluminium_ingot_production_recipe'
]
#Output
lca_df = pd.DataFrame()
for year in year_list:
utils_update.dbUpdate_ElecAluLiq(bw_db_name=bw_db_name, year=year)
utils_update.dbUpdate_EnerAlumina(bw_db_name=bw_db_name, year=year)
utils_update.dbUpdate_cons_mix(bw_db_name=bw_db_name,
year=year,
mineral='bauxite')
utils_update.dbUpdate_cons_mix(bw_db_name=bw_db_name,
year=year,
mineral='alumina')
for calculation_setup_name in multilca_list:
MultiLCA = bw.MultiLCA(calculation_setup_name)
temp_dt = utils_bw.get_multilca_to_dataframe(MultiLCA)
temp_dt['Year'] = year
lca_df = lca_df.append(temp_dt, ignore_index=True)
for country in country_list:
alu_cons_act = bw_db.get('market for consumption of aluminium')
alu_cons_act['location'] = country
alu_cons_act.save()
utils_update.dbUpdate_cons_mix(bw_db_name=bw_db_name,
year=year,
mineral='aluminium')
bw.calculation_setups['aluminium_consumption_recipe']['inv'] = [{
alu_cons_act:
1
}]
MultiLCA = bw.MultiLCA('aluminium_consumption_recipe')
mc_sto.switch_method(method)
CF_matr[method] = mc_sto.characterization_matrix.copy()
mc_sto_results[method] = []
from time import time
start = time()
#Calculate results for each method and n_iter iterations
for _ in range(n_iter):
for method in CF_matr:
mc_sto_results[method].append(
(CF_matr[method] * mc_sto.inventory).sum())
next(mc_sto)
print("Time elapsed: ", time() - start)
##### OTHER #####
#Montecarlo with static presample (it uses uncertainty in activities and in ecoinvent database)
mc_sta = bw.MonteCarloLCA({electricity_prod: 1},
IPCC,
presamples=[static_filepath])
n_iter = 1000 # Number of iterations
mc_sta_results = np.array([next(mc_sta) for _ in range(n_iter)])
sb.distplot(mc_sta_results)
############ TESTING #########
#MultiLCA and my_calculatio_setup for multi-method LCA
functional_unit = [{electricity_prod: 1}]
my_calculation_setup = {"inv": functional_unit, "ia": ILCD}
bw.calculation_setups["ILCD"] = my_calculation_setup
mlca = bw.MultiLCA("ILCD")
mlca.results
elec_act_exc_list = [exc for exc in act.technosphere() if 'electricity production' in exc.input['name']]
for exc in elec_act_exc_list:
exc.input.delete()
exc.delete()
[exc for exc in act.technosphere()]
'''
#Check the LCA score are equivalent for all locations
act_name = "market for electricity, high voltage, aluminium industry"
act_list = [act for act in bw_db if act_name in act['name']]
fu_list = [{act: 1} for act in act_list]
method = ('IPCC 2013', 'climate change', 'GWP 100a')
bw.calculation_setups['aluminium_elec_high_ipcc'] = {
'inv': fu_list,
'ia': [method]
}
MultiLCA = bw.MultiLCA('aluminium_elec_high_ipcc')
pd.DataFrame(index=[list(fu.keys())[0]['location'] for fu in fu_list],
columns=[method],
data=MultiLCA.results)
#2) Create 'electricity medium voltage' for GLO without any exchange.
act_name = "market for electricity, medium voltage, aluminium industry"
act_list = [act for act in bw_db if act_name in act['name']]
#Check all locations exist
diff_set = set(iai_reg_l).difference(set([act['location']
for act in act_list]))
#If GLO does not exist. Create the activity WITHOUT EXCHANGES
if 'GLO' in diff_set:
new_act = bw_db.new_activity(act_name)
new_act['name'] = act_name