def getLCAresults(mydb):
bw2_db = lci_to_bw2(mydb) # Perfect.
if 'Routes' in databases: del databases['Routes']
t_db = Database("Routes")
t_db.write(bw2_db)
all_activities = []
results = []
for act in t_db:
all_activities.append(act['name'])
results.append(dolcacalc(act, 1000))
print(act['name'])
results_dict = dict(zip(all_activities, results))
#results_df = pd.DataFrame({'Route': all_activities, 'GWP': results})
#results_df = results_df.sort_values(by =['Route'])
return results_dict
'bw2_import_ecoinvent_3.4') # Find a project where there is ecoinvent
databases
# Import csv file
mydb = pd.read_csv('test_db_excel_w_ecoinvent.csv', header=0,
sep=";") # if using csv file
# clean up a bit
mydb = mydb.drop('Notes', 1) # remove the columns not needed
mydb['Exchange uncertainty type'] = mydb['Exchange uncertainty type'].fillna(
0).astype(int) # uncertainty as integer
### Note: (can't have the full column if there are mixed nan and values, so use zero as default)
mydb
# Create a dict that can be written as database
bw2_db = lci_to_bw2(mydb) # Perfect.
bw2_db
if 'testdb' in databases: del databases['testdb']
t_db = Database("testdb")
t_db.write(bw2_db)
[print(act) for act in t_db] # check more stuff
[[print(act, exc) for exc in list(act.exchanges())]
for act in t_db] # check more stuff
[[print(exc.uncertainty) for exc in list(act.exchanges())]
for act in t_db] # check more stuff
myact = Database("testdb").get('Fuel production')
list(myact.exchanges())
header=0,
sep=";",
encoding='utf-8-sig') # important to specify encoding
# CCU_data = pd.read_csv('LCI_CCU_2018_lt_final.csv', header = 0, sep = ";", encoding = 'utf-8-sig') # important to specify encoding
# clean up
CCU_data = CCU_data.drop(['OPENLCA names', 'Ecospold_code_OPENLCA'],
1) # remove the columns not needed
CCU_data['Exchange uncertainty type'] = CCU_data[
'Exchange uncertainty type'].fillna(0).astype(
int) # uncertainty as integer
### Note: (can't have the full column if there are mixed nan and values, so use zero as default)
print(CCU_data.head())
# create a dict that can be written as database
CCU_dict = lci_to_bw2(CCU_data) # perfect
print(databases)
if 'CCU' in databases: del databases['CCU']
CCU = Database("CCU")
CCU.write(CCU_dict)
[print(act) for act in CCU]
#explore all activities
for activity in Database('CCU'):
print('--------ooo--------')
print(activity['name'])
print('--------ooo--------')
for i in list(activity.exchanges()): # explore the activity
print(i['type'])
projects.set_current('HH2')
databases
if 'Routes' in databases: del databases['Routes']
if 'Ferries' in databases: del databases['Ferries']
if 'HH' in databases: del databases['HH']
# Import ferries
fer_data = pd.read_csv('Ferries_ei4.csv',
header=0,
sep=";",
encoding='utf-8-sig')
fer_data = fer_data.drop(['Simapro names', 'BW2 names'], 1)
fer_data['Exchange uncertainty type'] = fer_data[
'Exchange uncertainty type'].fillna(0).astype(int)
fer_dict = lci_to_bw2(fer_data)
if 'Ferries' in databases: del databases['Ferries']
ferries = Database("Ferries")
ferries.write(fer_dict)
[print(act) for act in ferries]
# Import routes
rou_data = pd.read_csv('Routes_ei4.csv',
header=0,
sep=";",
encoding='utf-8-sig')
rou_data_routes = rou_data.loc[:, [
'Activity code', 'Country', 'From', 'To', 'Via', 'By', 'Ferry'
]].drop_duplicates()
rou_data = rou_data.drop([
'Simapro names', 'BW2 names', 'Country', 'From', 'To', 'Via', 'By', 'Ferry'
sep=";",
encoding='utf-8-sig') # important to specify encoding
# clean up
fer_data = fer_data.drop(['Simapro names', 'BW2 names'],
1) # remove the columns not needed
fer_data['Exchange uncertainty type'] = fer_data[
'Exchange uncertainty type'].fillna(0).astype(
int) # uncertainty as integer
### Note: (can't have the full column if there are mixed nan and values, so use zero as default)
fer_data.head()
fer_data.tail()
fer_data.iloc[:, 6] # no encoding problems
# Create a dict that can be written as database
fer_dict = lci_to_bw2(fer_data) # Perfect.
fer_dict
# Write a bw2 database
databases
if 'Ferries' in databases: del databases['Ferries']
ferries = Database("Ferries")
ferries.write(fer_dict)
[print(act) for act in ferries]
# Import routes
rou_data = pd.read_csv('Routes_ei4.csv',
header=0,
sep=";",
encoding='utf-8-sig') # important to specify encoding
rou_data.head()