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()