national_travels_emissions__cleaned.py

import os
import time
import requests
import subprocess
import pandas as pd
from utils import get_osrm_distance



#####################
#                   #   
# SETUP OSRM SERVER #
#                   #   
#####################

# Path to the OSRM server
osm_data_folder = os.path.join(os.path.dirname(__file__), 'osm_denmark')

# Function to check if OSRM server is running
def is_osrm_running():
    try:
        response = requests.get("http://localhost:5000/health")
        return response.status_code == 200
    except requests.ConnectionError:
        return False

# Function to start the OSRM server 
def start_osrm_server():
    # Ensure the OSRM data folder exists
    if not os.path.exists(osm_data_folder):
        print(f"OSRM data folder not found: '{osm_data_folder}'")
        return

    # Command to start OSRM server using Docker
    command = [
        "docker", "run", "-t", "-i", "-p", "5000:5000",
        "-v", f"{osm_data_folder}:/data", # Mount the data folder to /data in the container
        "osrm/osrm-backend",
        "osrm-routed", "/data/denmark-latest.osrm" # Update the path to match the new folder structure
        ]

    # Start the OSRM server in the background
    try:
        print("Starting OSRM server...")
        subprocess.Popen(command) # Starts the server as a background process
        print("OSRM server started successfully.")
    except Exception as e:
        print(f"Error starting OSRM server: {e}")



# Use this function to alert if OSRM is not running
if not is_osrm_running():
    print("OSRM server is not running. Starting the server...")
    start_osrm_server()
    # Add a delay to allow the server to start
    time.sleep(10)
else:
    # Proceed with your distance calculations
    print("OSRM server is running. Ready to calculate distances.")





###################################
#                                 #
# SETUP EMISSION FACTORS AND DATA # 
#                                 #
###################################

# !! BASED ON COWI (https://www.dsb.dk/globalassets/om-dsb/baeredygtighed/miljo/sammenligning-af-emmisionsfaktorer_cowi-rapport-til-dsb_okt-2023.pdf)
# !! Need to verify the emission factors (talk with local AU ENVS Emission group)

# # Car (average per km of diesel, petrol, hybrid and electric cars)
# emission_factor_car_per_km = 0.118
# # Train
# emission_factor_train_per_km = 0.041
# # Bus 
# emission_factor_bus_per_km = 0.020

# Average distance between two locations in Denmark (Kbh<->Aalborg and Kbh<->Aarhus)
average_distance_plane = (238 + 156) / 2
# Average distance between two locations in Denmark (Odden<-->Aarhus, Odden<-->Ebeltoft)
average_distance_ferry = (62.3 + 69) / 2

# Ferry (Odden<-->Ebeltoft), ~70 km (one way), 0.915 kg CO2e per person km (with car)
emission_factor_ferry_one_way = 0.915 * average_distance_ferry
# Plane (per passenger per one-way trip), average of Kbh<->Aalborg and Kbh<->Aarhus
emission_factor_plane_one_way = (39.3 + 28.5) / 2


####################
#                  #
# EMISSION FACTORS #
#                  #
####################

train_el_dk_part = 0.366 # 36.6% of the rail network is electrified in DK
train_diesel_dk_part = 0.634 # 63.4% of the rail network is diesel in DK

train_el_emission_factor = 0.041 # kg CO2e per km
train_diesel_emission_factor = 0.09 # kg CO2e per km

train_dk_average = train_el_emission_factor * train_el_dk_part + train_diesel_emission_factor * train_diesel_dk_part

petrol_car_emission_factor = 0.1779 # kg CO2e per km
electric_car_emission_factor = 0.0188 # kg CO2e per km
domestic_flight_emission_factor = 0.246 # kg CO2e per km
short_haul_flight_emission_factor = 0.151 # kg CO2e per km
medium_haul_flight_emission_factor = 0.1495 # kg CO2e per km
long_haul_flight_emission_factor = 0.148 # kg CO2e per km
bus_long_distance_dk_emission_factor = 0.020 # kg CO2e per km
bus_long_distance_eu_emission_factor = 0.031 # kg CO2e per km
ferry_emission_factor = 0.915 # kg CO2e per kmß




emission_factors = {
    'bus': bus_long_distance_dk_emission_factor,
    'car_electric': electric_car_emission_factor,
    'car_petrol': petrol_car_emission_factor,
    'train': train_dk_average,
    'ferry': emission_factor_ferry_one_way,
    'domestic_plane': emission_factor_plane_one_way
}

average_distances = {
    'plane': (238 + 156) / 2,
    'ferry': (62.3 + 69) / 2
}

# Print emissions factors 
print('\n\nEmission factors:')
for key, value in emission_factors.items():
    print(f'{key}: {value:.4f} kg CO2e per km')

print('\n\nAverage distances:')
for key, value in average_distances.items():
    print(f'{key}: {value:.2f} km')

exit()

# Load data
FILE_PATH = 'csv/cleaned_BusinessTravels.xlsx'
national_travel = pd.read_excel(FILE_PATH, sheet_name='DK_BusinessTravels_clean')




#############################
#                           #
# BUS TRAVELS AND EMISSIONS #
#                           #
#############################
'''
    Bus travel emissions are estimated from the average emissions of long-distance buses in Denmark.
'''


# Take all columns with 'bus' in the header and create new df
national_bus = national_travel[[column for column in national_travel.columns if 'bus' in column.lower()]]
# Remove the first column which is a question
national_bus = national_bus.iloc[:, 1:]
# Remove all rows with only NaN values and reset the index
national_bus = national_bus.dropna(how='all', axis=0).reset_index(drop=True)
# The rows are basically a repetition of the pattern '', '_from', '_to', '_via', '_times, so each 5th row is the start of a new travel

# Number of buses
num_buses = 5

# Reshape the dataframe
rows = []
for i in range(1, num_buses + 1):
    bus = f'bus_dk_{i}'
    bus_from = f'{bus}_from'
    bus_to = f'{bus}_to'
    bus_via = f'{bus}_via'
    bus_times = f'{bus}_times'
    
    temp_df = national_bus[[bus, bus_from, bus_to, bus_via, bus_times]].copy()
    temp_df.columns = ['', 'from', 'to', 'via', 'times']
    temp_df = temp_df.dropna(subset=['', 'from', 'to'], how='all').reset_index(drop=True)

    # Because the 'times' column is roundtrips, we need to multiply the number of times by 2
    temp_df['times'] = pd.to_numeric(temp_df['times'], errors='coerce') * 2
    
    rows.append(temp_df)

# Concatenate all the reshaped dataframes
reshaped_df = pd.concat(rows, ignore_index=True)

# Remove all rows with a '-' sign anywhere in the row
reshaped_df = reshaped_df[~reshaped_df.apply(lambda x: x.str.contains('-').any(), axis=1)].reset_index(drop=True)

# Remove the first column
reshaped_df = reshaped_df.iloc[:, 1:]


# Use osrm to calculate the distance between the locations
# Loop through the dataframe, compute the distance between locations, and calculate the emissions
# The lat_lon_file should contain the latitudes and longitudes of the all locations.
lat_lon_file = 'csv/national_destinations.csv'

# Add a new column 'Distance' to the dataframe
reshaped_df['distance'] = 0
for index, row in reshaped_df.iterrows():
    distance = get_osrm_distance(row['from'], row['to'], lat_lon_file, row['via']) 
    # Get the number of times the trip was made
    distance = distance * row['times']
    reshaped_df.loc[index, 'distance'] = distance

# Add new column 'Emissions' to the dataframe
reshaped_df['emissions'] = reshaped_df['distance'] * bus_long_distance_dk_emission_factor

national_bus_clean = reshaped_df

# Display the reshaped dataframe
print('\n\nBus travels:')
print(national_bus_clean)
print(f'Total emissions from bus travels: {national_bus_clean["emissions"].sum():.2f} kg CO2e')

# Save dataframe 
national_bus_clean.to_csv('csv/national_bus_travels.csv')






#######################################
#                                     #
# CAR PASSENGER TRAVELS AND EMISSIONS #
#                                     #
#######################################
'''
    Car passenger travels are divided into electric and petrol cars. The emissions are calculated based on the average emissions of electric and petrol cars in Denmark.
    If an answer is given for both electric and petrol, the distance is divided in half and the emissions are calculated for each car type.
    As this is calculations for 'passenger' travels, the emissions are divided by 2 assuming there are two passengers in the car.
'''

# Do the same for Car passenger
# Take all columns with 'car' in the header and create new df
national_car = national_travel[[column for column in national_travel.columns if 'carpassenger' in column.lower()]]
# print(national_car)
# # Remove the first column which is a question - NO WE KEEP SO WE CAN SEE IF ELECTRIC OR PETROL
# national_car = national_car.iloc[:, 1:]
# Remove all rows with only NaN values and reset the index
national_car = national_car.dropna(how='all', axis=0).reset_index(drop=True)

# The rows are basically a repetition of the pattern '', '_from', '_to', '_via', '_times, so each 5th row is the start of a new travel
# Number of cars
num_cars = 8

# Reshape the dataframe
rows = []
for i in range(1, num_cars + 1):
    car = f'carPassenger_dk_{i}'
    car_from = f'{car}_from'
    car_to = f'{car}_to'
    car_via = f'{car}_via'
    car_times = f'{car}_times'
    car_electric = f'Q_carPassenger_gas_dk'
    car_petrol = f'Q_carPassenger_electric_dk'
    
    temp_df = national_car[[car, car_from, car_to, car_via, car_times, car_electric, car_petrol]].copy()

    temp_df.columns = ['', 'from', 'to', 'via', 'times', 'electric', 'petrol']
    temp_df = temp_df.dropna(subset=['', 'from', 'to'], how='all').reset_index(drop=True)
    
    # Because the 'times' is roundtrip, but osrm calculates one way, we need to multiply the car_times by 2
    temp_df['times'] = pd.to_numeric(temp_df['times'], errors='coerce') * 2

    rows.append(temp_df)

# Concatenate all the reshaped dataframes
reshaped_df = pd.concat(rows, ignore_index=True)

# Remove all rows with a '-' sign anywhere in the row
reshaped_df = reshaped_df[~reshaped_df.apply(lambda x: x.str.contains('-').any(), axis=1)].reset_index(drop=True)
# Remove the first column
reshaped_df = reshaped_df.iloc[:, 1:]


# Use osrm to calculate the distance between the locations
# Loop through the dataframe, compute the distance between locations, and calculate the emissions

# Add a new column 'Distance' to the dataframe
reshaped_df['distance_electric'] = 0
reshaped_df['distance_petrol'] = 0
for index, row in reshaped_df.iterrows():
    # Calculate the distance between the locations
    distance = get_osrm_distance(row['from'], row['to'], lat_lon_file, row['via'])
    # Get the number of times the trip was made
    distance = distance * row['times']

    if row['electric'] == 1 and row['petrol'] == 0:
        reshaped_df.loc[index, 'distance_electric'] = distance
    elif row['petrol'] == 1 and row['electric'] == 0:
        reshaped_df.loc[index, 'distance_petrol'] = distance
    elif row['petrol'] == 1 and row['electric'] == 1:
        reshaped_df.loc[index, 'distance_electric'] = distance / 2
        reshaped_df.loc[index, 'distance_petrol'] = distance / 2
reshaped_df['distance'] = reshaped_df['distance_electric'] + reshaped_df['distance_petrol']

# Add new column 'Emissions' to the dataframe. Calculate emissions based on the car type
reshaped_df['emissions_electric'] = 0
reshaped_df['emissions_petrol'] = 0
for index, row in reshaped_df.iterrows():
    if row['electric'] == 1 and row['petrol'] == 0:
        reshaped_df.loc[index, 'emissions_electric'] = row['distance_electric'] * electric_car_emission_factor / 2 # Divided by 2 since we assume there are two passengers
    elif row['petrol'] == 1 and row['electric'] == 0:
        reshaped_df.loc[index, 'emissions_petrol'] = row['distance_petrol'] * petrol_car_emission_factor / 2 # Divided by 2 since we assume there are two passengers
    elif row['petrol'] == 1 and row['electric'] == 1:
        reshaped_df.loc[index, 'emissions_electric'] = row['distance_electric'] * electric_car_emission_factor / 2
        reshaped_df.loc[index, 'emissions_petrol'] = row['distance_petrol'] * petrol_car_emission_factor / 2

reshaped_df['emissions'] = reshaped_df['emissions_electric'] + reshaped_df['emissions_petrol']


national_carPassenger_clean = reshaped_df

print(national_carPassenger_clean)
# Display the reshaped dataframe
print('\n\nCar passenger travels:')
print(national_carPassenger_clean)
print(f'Total emissions from electric car passenger travels: {national_carPassenger_clean["emissions_electric"].sum():.2f} kg CO2e')
print(f'Total emissions from petrol car passenger travels: {national_carPassenger_clean["emissions_petrol"].sum():.2f} kg CO2e')
print(f'\nTotal distance in electric car passenger travels: {national_carPassenger_clean["distance_electric"].sum():.2f} km')
print(f'Total distance in petrol car passenger travels: {national_carPassenger_clean["distance_petrol"].sum():.2f} km')
print(f'\nTotal distance in car passenger travels: {national_carPassenger_clean["distance"].sum():.2f} km')
print(f'Total emissions from car passenger travels: {national_carPassenger_clean["emissions"].sum():.2f} kg CO2e\n\n')


# Save dataframe 
national_carPassenger_clean.to_csv('csv/national_carPassenger_travels.csv')













####################################
#                                  #
# CAR DRIVER TRAVELS AND EMISSIONS #
#                                  #
####################################
'''
    Car driver travels are divided into electric and petrol cars. The emissions are calculated based on the average emissions of electric and petrol cars in Denmark.
    If an answer is given for both electric and petrol, the distance is divided in half and the emissions are calculated for each car type.
    As this is calculations for 'driver' travels, the emissions are calculated based on the length of 'passenger' travels and the length of 'driver' travels
    thus assuming that (1 - percentage_two_passenger_trips) of the trips are with one passenger and the rest are with two passengers.
'''

# Do the same for Car driver 
# Take all columns with 'car' in the header and create new df
national_car = national_travel[[column for column in national_travel.columns if 'cardriver' in column.lower()]]
# # Remove the first column which is a question
# national_car = national_car.iloc[:, 1:]
# Remove all rows with only NaN values and reset the index
national_car = national_car.dropna(how='all', axis=0).reset_index(drop=True)
# The rows are basically a repetition of the pattern '', '_from', '_to', '_via', '_times, so each 5th row is the start of a new travel
# Number of cars
num_cars = 8
print(national_car)
# Reshape the dataframe
rows = []
for i in range(1, num_cars + 1):
    car = f'carDriver_dk_{i}'
    car_from = f'{car}_from'
    car_to = f'{car}_to'
    car_via = f'{car}_via'
    car_times = f'{car}_times'
    car_electric = f'Q_carDriver_gas_dk'
    car_petrol = f'Q_carDriver_electric_dk'
    
    temp_df = national_car[[car, car_from, car_to, car_via, car_times, car_electric, car_petrol]].copy()
    temp_df.columns = ['', 'from', 'to', 'via', 'times', 'electric', 'petrol']
    temp_df = temp_df.dropna(subset=['', 'from', 'to'], how='all').reset_index(drop=True)
    
    # Because the 'times' is roundtrip, but osrm calculates one way, we need to multiply the car_times by 2
    temp_df['times'] = pd.to_numeric(temp_df['times'], errors='coerce') * 2
    
    rows.append(temp_df)

# Concatenate all the reshaped dataframes
reshaped_df = pd.concat(rows, ignore_index=True)

# Remove all rows with a '-' sign anywhere in the row
reshaped_df = reshaped_df[~reshaped_df.apply(lambda x: x.str.contains('-').any(), axis=1)].reset_index(drop=True)

# Remove the first column
reshaped_df = reshaped_df.iloc[:, 1:]


# Use osrm to calculate the distance between the locations
# Loop through the dataframe, compute the distance between locations, and calculate the emissions

# Add a new column 'Distance' to the dataframe
reshaped_df['distance_electric'] = 0
reshaped_df['distance_petrol'] = 0
for index, row in reshaped_df.iterrows():
    distance = get_osrm_distance(row['from'], row['to'], lat_lon_file, row['via']) 
    # Get the number of times the trip was made
    distance = distance * row['times']

    if row['electric'] == 1 and row['petrol'] == 0:
        reshaped_df.loc[index, 'distance_electric'] = distance
    elif row['petrol'] == 1 and row['electric'] == 0:
        reshaped_df.loc[index, 'distance_petrol'] = distance
    elif row['petrol'] == 1 and row['electric'] == 1:
        reshaped_df.loc[index, 'distance_electric'] = distance / 2
        reshaped_df.loc[index, 'distance_petrol'] = distance / 2
    elif row['petrol'] == 0 and row['electric'] == 0:
        # If no answer is given, assume it is a petrol car and set the 'petrol' to 1
        reshaped_df.loc[index, 'distance_petrol'] = distance
        reshaped_df.loc[index, 'petrol'] = 1

reshaped_df['distance'] = reshaped_df['distance_electric'] + reshaped_df['distance_petrol']

# Add new column 'Emissions' to the dataframe. Calculate emissions based on the car type
reshaped_df['emissions_electric'] = reshaped_df['distance_electric'] * electric_car_emission_factor
reshaped_df['emissions_petrol'] = reshaped_df['distance_petrol'] * petrol_car_emission_factor

# Estimate how large a portion of the trips were with two passengers (based on length of carPassenger and carDriver trips)
percentage_two_passenger_trips = len(national_carPassenger_clean) / len(reshaped_df)
print(f'\n\nPercentage of two passenger trips (National): {percentage_two_passenger_trips:.2f}')
# Adjust the emissions for the car driver trips to account for the two passengers
reshaped_df['emissions_electric'] = reshaped_df['emissions_electric'] * (1 - percentage_two_passenger_trips) + reshaped_df['emissions_electric'] * percentage_two_passenger_trips / 2 # Half of the emissions for the two passenger trips
reshaped_df['emissions_petrol'] = reshaped_df['emissions_petrol'] * (1 - percentage_two_passenger_trips) + reshaped_df['emissions_petrol'] * percentage_two_passenger_trips / 2 # Half of the emissions for the two passenger trips
reshaped_df['emissions'] = reshaped_df['emissions_electric'] + reshaped_df['emissions_petrol']

national_carDriver_clean = reshaped_df

# Display the reshaped dataframe
print('\n\nCar driver travels:')
print(national_carDriver_clean)
print(f'\nTotal emissions from electric car driver travels: {national_carDriver_clean["emissions_electric"].sum():.2f} kg CO2e')
print(f'Total emissions from petrol car driver travels: {national_carDriver_clean["emissions_petrol"].sum():.2f} kg CO2e')
print(f'\nTotal distance in electric car driver travels: {national_carDriver_clean["distance_electric"].sum():.2f} km')
print(f'Total distance in petrol car driver travels: {national_carDriver_clean["distance_petrol"].sum():.2f} km')
print(f'\nTotal distance in car driver travels: {national_carDriver_clean["distance"].sum():.2f} km')
print(f'Total emissions from car driver travels: {national_carDriver_clean["emissions"].sum():.2f} kg CO2\n\n')

# Save dataframe 
national_carDriver_clean.to_csv('csv/national_carDriver_travels.csv')







###############################
#                             #
# TRAIN TRAVELS AND EMISSIONS #
#                             #
###############################
'''
    Train travel emissions are estimated from the average emissions of electric 36.6% and diesel 63.4% trains in Denmark.
'''

# Take all columns with 'train' in the header and create new df
national_train = national_travel[[column for column in national_travel.columns if 'train' in column.lower()]]
# Remove the first column which is a question
national_train = national_train.iloc[:, 1:]
# Remove all rows with only NaN values and reset the index
national_train = national_train.dropna(how='all', axis=0).reset_index(drop=True)
# The rows are basically a repetition of the pattern '', '_from', '_to', '_via', '_times, so each 5th row is the start of a new travel
# Number of trains
num_trains = 5

# Reshape the dataframe
rows = []

for i in range(1, num_trains + 1):
    train = f'train_dk_{i}'
    train_from = f'{train}_from'
    train_to = f'{train}_to'
    train_times = f'{train}_times'
    temp_df = national_train[[train, train_from, train_to, train_times]].copy()
    temp_df.columns = ['', 'from', 'to', 'times']
    temp_df = temp_df.dropna(subset=['', 'from', 'to'], how='all').reset_index(drop=True)
    # Make sure the 'times' column is a number
    temp_df['times'] = pd.to_numeric(temp_df['times'], errors='coerce')

    # Because the 'times' is roundtrip, but osrm calculates one way, we need to multiply the train_times by 2
    temp_df['times'] = temp_df['times'] * 2

    rows.append(temp_df)

# Concatenate all the reshaped dataframes
reshaped_df = pd.concat(rows, ignore_index=True)

# Remove all rows with a '-' sign anywhere in the row
reshaped_df = reshaped_df[~reshaped_df.apply(lambda x: x.str.contains('-').any(), axis=1)].reset_index(drop=True)

# Remove the first column
reshaped_df = reshaped_df.iloc[:, 1:]


# Use osrm to calculate the distance between the locations
# Loop through the dataframe, compute the distance between locations, and calculate the emissions

# Add a new column 'Distance' to the dataframe
reshaped_df['distance'] = 0
for index, row in reshaped_df.iterrows():
    distance = get_osrm_distance(row['from'], row['to'], lat_lon_file)
    # Get the number of times the trip was made (make sure they are same dtype)
    distance = distance * row['times']

    reshaped_df.loc[index, 'distance'] = distance

# Add new column 'Emissions' to the dataframe
reshaped_df['emissions'] = reshaped_df['distance'] * train_dk_average

national_train_clean = reshaped_df

# Display the reshaped dataframe
print('\n\nTrain travels:')
print(national_train_clean)
print(f'Total emissions from train travels: {national_train_clean["emissions"].sum():.2f} kg CO2e')
print(f'Total distance in train travels: {national_train_clean["distance"].sum():.2f} km')

# Save dataframe 
national_train_clean.to_csv('csv/national_train_travels.csv')




###############################
#                             #
# FERRY TRAVELS AND EMISSIONS #
#                             #
###############################

# For ferry, only one column with the number of travels
for column in national_travel.columns:
    if 'ferry' in column.lower():
        national_ferry = national_travel[column]
        break

# Remove all rows with only NaN values and reset the index
national_ferry = national_ferry.dropna(how='all', axis=0).reset_index(drop=True)
# Remove all rows with 0 travels
national_ferry = national_ferry[national_ferry != 0].reset_index(drop=True)

# Create new df with national_ferry.values[1:] and 'ferry_travels' as column name
national_ferry = pd.DataFrame(national_ferry.values[1:], columns=['ferry_travels'])

# Add a new column 'distance' to the dataframe (average of Kbh<->Aalborg and Kbh<->Aarhus)
national_ferry['distance'] = average_distance_ferry * national_ferry['ferry_travels'] * 2 # Round trip

# Add a new column 'Emissions' to the dataframe 
national_ferry['emissions'] = national_ferry['distance'] * ferry_emission_factor

national_ferry_clean = national_ferry

# Display the dataframe
print('\n\nFerry travels:')
print(national_ferry_clean)
print(f'Total emissions from ferry travels: {national_ferry_clean["emissions"].sum():.2f} kg CO2e')
print(f'Total distance in ferry travels: {national_ferry_clean["distance"].sum():.2f} km')

# Save dataframe 
national_ferry_clean.to_csv('csv/national_ferry_travels.csv')





################################
#                              #
# FLIGHT TRAVELS AND EMISSIONS #
#                              #
################################

# For flight, only one column with the number of travels
national_flight = national_travel[[column for column in national_travel.columns if 'flight' in column.lower()]]
national_flight = national_flight[1:]

# Remove all rows with only NaN values and reset the index
national_flight = national_flight.dropna(how='all', axis=0).reset_index(drop=True)

# Remove all rows with 0 travels
national_flight = national_flight[national_flight['flight_dk'] != 0].reset_index(drop=True)

# Add a new column 'distance' to the dataframe (average of Kbh<->Aalborg and Kbh<->Aarhus)
national_flight['distance'] = average_distance_plane * national_flight['flight_dk'] * 2 # Round trip

# Add a new column 'Emissions' to the dataframe 
national_flight['emissions'] = national_flight['distance'] * domestic_flight_emission_factor 


national_flight_clean = national_flight

# Display the dataframe
print('\n\nFlight travels:')
print(national_flight_clean)
print(f'Total emissions from national flight travels: {national_flight_clean["emissions"].sum():.2f} kg CO2e')
print(f'Total distance in national flight travels: {national_flight_clean["distance"].sum():.2f} km')

# Save dataframe 
national_flight_clean.to_csv('csv/national_plane_travels.csv')






#################################
#                               #
# TOTAL DISTANCES AND EMISSIONS #
#                               #
#################################

# Make new df with total emissions from all travels (only NOT empty dataframes)
all_dfs = [national_bus_clean, national_carPassenger_clean, national_carDriver_clean, national_train_clean, national_ferry_clean, national_flight_clean]
all_df_strs = ['Bus', 'Car Passenger', 'Car Driver', 'Train', 'Ferry', 'Plane']

all_df_strs_notEmpty = []
all_df_notEmpty = []
for i, df in enumerate(all_dfs):
    if df.empty:
        pass
    else:
        all_df_strs_notEmpty.append(all_df_strs[i])
        all_df_notEmpty.append(df)

print(all_df_strs_notEmpty)
for i, df in enumerate(all_dfs):
    if all_df_strs_notEmpty[i] == 'Car Passenger' or all_df_strs_notEmpty[i] == 'Car Driver':
        print(f'{all_df_strs_notEmpty[i]}: {df["emissions_electric"].sum() + df["emissions_petrol"].sum()}')
    else:
        print(f'{all_df_strs_notEmpty[i]}: {df["emissions"].sum()}')

# Make a new dataframe with total distances and emissions
total_distances = pd.DataFrame({'Travel Type': all_df_strs,
                                'Total Distance (km)': [df['distance'].sum() for df in all_dfs]})


# Add a new column 'Emissions' to the dataframe
total_emissions = pd.DataFrame({'Travel Type': all_df_strs,
                                'Total Emissions (kg CO2e)': [df['emissions'].sum() for df in all_dfs]})

# Merge the two dataframes
total_data = pd.merge(total_distances, total_emissions, on='Travel Type')


print('\n\nTotal distances and emissions from all travels:')
print(total_data)

print('\n\n' + 50*'-')
print(f'Total emissions from all travels: {total_data["Total Emissions (kg CO2e)"].sum():.2f} kg CO2e')
print(50*'-' + '\n\n')


# Make a pie chart of the total emissions
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(6, 6))
plt.pie(total_data['Total Emissions (kg CO2e)'], labels=total_data['Travel Type'], autopct='%1.1f%%', startangle=140)
ax.axis('equal')
ax.set_title(f'Total emissions from all travels, {total_data["Total Emissions (kg CO2e)"].sum():.2f} kg CO2e')
plt.show()

# All transports other than plane are very low emitting