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bilan_CO2/scripts/footprint.py
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footprint.py
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import pandas as pd
import numpy as np
from functions import *
class footprint():
# Constants for column names
YEAR = "Year"
CATEGORY = "Category"
VALUE = "Value"
CO2 = 'Factor [kg CO2eq]'
def __init__(self, CO2_col, EPT_infile, input_file):
self.general_attributes = {
"author": "Hugo Cruz",
"author_mail": "hugo.cruz@epfl.ch",
}
self.CO2_col = CO2_col
self.bilan = pd.DataFrame()
self.data = pd.DataFrame()
self.category_name = {}
self.Theme_name = {}
self.ignore_column = []
#Translate this into english : Nomenclature utilisée, à gauche est le nom tel qu'il est dans le fichier d'entrée, à droite est le nom tel qu'il est écrit dans le fichier facteur emission
self.input_file = input_file
def run_population(self, EPT_infile):
#open EPT file, this EPT is not up to date, the evolution is not yet taken into account
EPT = pd.read_excel(EPT_infile)
self.EPT_year = EPT[EPT["Category"]== 'Population GRI'].groupby("Year").sum()["EPT"]
def run_energy_emission(self, elec_path):
df = pd.read_excel(elec_path)
df = df.rename(columns={"year": "Year", "campus": "Campus", "category": "Category", "value": "Value"})
df["Theme"] = df["Category"] #We understand that the theme is Energy, however it's more relevant to separate when we visualize it
df["Scope"] = 2
df["Unit"] = df["unit"]
df["Path"] = elec_path
df.drop("unit", axis=1, inplace=True)
mask_mazout = df["Category"]=="Mazout"
mask_gaz = df["Category"]=="Gaz"
df.loc[df["Category"]=="Electricite","Category"] = self.nomenclature["Electricite"]
df.loc[mask_gaz|mask_mazout, "Scope"] = 1
self.data = pd.concat([self.data, df], ignore_index=True)
def run_dechets_emission(self, dechets_path):
#Dechets is not yet taken into account
df = pd.read_excel(dechets_path)
df = df.rename(columns={"waste_type":"Category", "kg":"Value", "year": "Year"})
df["Scope"] = 3
df["Unit"] = "kg"
df["Theme"] = "Dechets"
df["ID_category"] = df["Category"]
#Strategy ojective 2025
mask = (df["disposal_method"]=="Recycling")&(df["Year"]==2022)
recycled = df[(df["disposal_method"]=="Recycling")&(df["Year"]==2022)]["Value"].sum()
incineration = df[(df["disposal_method"]=="incineration")&(df["Year"]==2022)]["Value"].sum()
total = recycled+incineration
recycled_2025 = total*0.8
incineration_2025 = total*0.2
df["Path"] = dechets_path
df.drop("disposal_method", axis=1, inplace=True)
print("In 2025, the amount of recycled waste should be {} kg based on 80% recycled in 2022".format(recycled_2025))
self.data = pd.concat([self.data, df], ignore_index=True)
def run_plane_emission(self,path):
staff_emission = pd.read_excel(path, sheet_name="Avions (staff)", skiprows=1)
staff_CO2 = staff_emission.set_index("Data*").loc["CO2 footprint (tons) / Year Données: Atmosfair"]
staff_CO2.index = staff_CO2.index.astype(int)
staff_CO2 = staff_CO2.astype(float)
Students_emission = pd.read_excel(path, sheet_name="Avions (étudiant-e-s)")
Students_group = Students_emission.groupby("Year").sum()
Students_CO2 = Students_group["CO2 RFI2,7"]
CO2_staff = staff_CO2*1000 #Convert t to kg
CO2_students = Students_CO2*1000 #Convert t to kg
df_stud = pd.DataFrame(CO2_students).reset_index()
df_staff = pd.DataFrame(CO2_staff).reset_index()
self.category_name["Avion etudiant-e-s"] = "Avion etudiant-e-s"
self.category_name["Avion collaborateur-trice-s"] = "Avion collaborateur-trice-s"
df_staff["Category"] = self.category_name["Avion collaborateur-trice-s"]
df_stud["Category"] = self.category_name["Avion etudiant-e-s"]
df_staff.columns = ["Year", "CO2", "Category"]
df_stud.columns = ["Year", "CO2", "Category"]
df = pd.concat([df_stud, df_staff], ignore_index=True)
df["Campus"] = "EPFL"
df["Theme"] = "Voyages professionnels"
df["Unit"] = "kg CO2e"
df["Scope"] = 3
df["Path"] = path
self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def run_train_emission(self, input_file):
#Open and format the data
df_train = pd.read_excel(input_file,skiprows=1, sheet_name="Train")
df_train = df_train.set_index("Km").stack().reset_index()
df_train.columns=["Category", "Year", "Value"]
#Select only interesting data
df = df_train[(df_train["Category"]=="Total Domestique")|(df_train["Category"]=="Total International")]
#Change df["Category"] == "Total Domestique" to self.nomenclature["Total Domestique"]
df.loc[df["Category"]=="Total Domestique", "Category"] = self.nomenclature["Total Domestique"]
df.loc[df["Category"]=="Total International", "Category"] = self.nomenclature["Total International"]
df["Year"] = df["Year"].astype(int)
df["Value"] = df["Value"].astype(float)
df["Unit"] = "km"
df["Theme"] = "Voyages professionnels"
df["Scope"] = 3
df["Path"] = input_file
self.data = pd.concat([self.data, df], ignore_index=True)
def run_cars_emission(self, input_file):
df_cars = pd.read_excel(input_file, skiprows=1, sheet_name="Voiture")
V_service = df_cars.iloc[0:5]
V_service.reset_index(drop=True, inplace=True)
V_service.iloc[3,0] = self.nomenclature["Véhicule de location"]
V_service.iloc[4,0] = self.nomenclature["Véhicules privés"]
Mobility = df_cars.iloc[9:12]
Mobility.reset_index(drop=True, inplace=True)
Mobility.iloc[0,0] = self.nomenclature["Mobility essence"]
Mobility.iloc[1,0] = self.nomenclature["Mobility diesel"]
Mobility.iloc[2,0] = self.nomenclature["Mobility electrique"]
Mobility.columns = df_cars.iloc[8]
col_int = list(Mobility.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
#Set Mobility.columns[1:] to col_int
Mobility.columns = ["Litres / kW pour électriques"] + col_int
db_Mobility = Mobility.set_index("Litres / kW pour électriques").stack().reset_index()
db_Mobility.columns=["Category", "Year", "Value"]
db_Mobility["Value"] = db_Mobility["Value"].astype(str).str.replace("-", "NaN").str.split(".").str[0].astype(float)
db_Mobility["Year"] = db_Mobility["Year"].astype(int)
db_Mobility["Unit"] = "L"
db_service = V_service.set_index("Kilomètres parcourus").stack().reset_index()
db_service.columns=["Category", "Year", "Value"]
db_service["Year"] = db_service["Year"].astype(int)
db_service["Value"] = db_service["Value"].astype(float)
db_service["Unit"] = "km"
V_service_L = df_cars.iloc[15:17]
V_service_L.reset_index(drop=True, inplace=True)
V_service_L.iloc[0,0] = self.nomenclature["Véhicules de service essence"]
V_service_L.iloc[1,0] = self.nomenclature["Véhicules de service diesel"]
V_service_L.columns = df_cars.iloc[14]
col_int = list(V_service_L.columns[1:].astype(str).str.replace(" ", "").str.split(".").str[0])
#Set Mobility.columns[1:] to col_int
V_service_L.columns = ["Litres"] + col_int
db_service_L = V_service_L.set_index("Litres").stack().reset_index()
db_service_L.columns=["Category", "Year", "Value"]
db_service_L["Year"] = db_service_L["Year"].astype(int)
db_service_L["Value"] = db_service_L["Value"].astype(float)
db_service_L["Unit"] = "L"
#Merge db_service db_Mobility and db_service_L into new dataframe
#Avoid a double count by selecting only the volume of L used in service vehicles:
db_service = db_service[~db_service["Category"].isin(["Véhicules de service essence", "Véhicules de service diesel", "Mobility carsharing"])]
db_Mobility = db_Mobility[~db_Mobility["Category"].isin(["Mobility électriques"])] #L'Electricite est achetée par l'EPFL et redistribuée, elle est donc deja comptabilisée
df = pd.concat([db_service, db_Mobility, db_service_L], ignore_index=True)
df["Theme"] = "Voyages professionnels"
df["Scope"] = 3
df.loc[df["Category"]==self.nomenclature["Véhicules de service essence"], "Scope"] = 1
df.loc[df["Category"]==self.nomenclature["Véhicules de service diesel"], "Scope"] = 1
df.loc[df["Category"]==self.nomenclature["Mobility diesel"], "Scope"] = 1
df.loc[df["Category"]==self.nomenclature["Mobility essence"], "Scope"] = 1
df["Path"] = input_file
self.data = pd.concat([self.data, df], ignore_index=True)
def run_mobilite_EPFL(self, pendulair_final):
df = pd.read_excel(pendulair_final, skiprows=2, index_col="Unnamed: 0")
#Select columns to keep in dataframe
col_dist=[]
for col in df.columns:
if "km" in col:
col_dist.append(col)
df_dist = df.iloc[:7][col_dist].T.replace("-", np.nan)
#if an index contain "jour" drop it
for idx in df_dist.index:
if "jour" in idx:
df_dist = df_dist.drop(idx)
if "Hiver" in idx:
#Replace "hiver" in index by ""
df_dist.loc[idx] = df_dist.loc[idx.replace("Hiver", "Ete")]+df_dist.loc[idx]
#Rename idx by idx.replace("Hiver", "")
df_dist = df_dist.rename(index={idx:idx.replace("Hiver", "")})
#Drop idx.replace("Hiver", "Ete")
df_dist = df_dist.drop(idx.replace("Hiver", "Ete"))
df_dist.index = df_dist.index.str.extract("(\d+)").squeeze()
#if index==np.nan drop it
df_dist = df_dist.loc[~df_dist.index.isna()]
df_dist = df_dist.reset_index().rename(columns={0:"Year"})
#Columns of df_dist are transport mode, convert df_dist to database
db = df_dist.set_index("Year").stack().reset_index()
db.rename(columns={"level_1":"Category", 0:"Value"}, inplace=True)
#Convert index to int
#Change db["Category"] == "voiture" to self.nomenclature["voiture"]
db.loc[db["Category"]=="voiture", "Category"] = self.nomenclature["voiture"]
db.loc[db["Category"]=="moto/scooter", "Category"] = self.nomenclature["moto/scooter"]
db["Year"] = db["Year"].astype(int)
db["Theme"] = "Mobilité pendulaire"
db["Scope"] = 3
db["Unit"] = "km"
#Filter Category=="autres"
db = db[db["Category"]!="autres"]
#Remove from db db["Year"] below 2018. Before 2018, the data collection has changed and is not comparable (3 order of magnitude lower)
self.raw_pendular = db.copy()
db.loc[db["Year"]<2018, "Value"] = np.nan
db["Path"] = pendulair_final
self.data = pd.concat([self.data, db], ignore_index=True)
def run_alimentation(self, alimentation_infile):
df = pd.read_excel(alimentation_infile) #Read file of computed CO2 by quantis
df["CO2"] = df["CO2"]*1000 #Convert to kg
df["Campus"] = "EPFL"
df["Theme"] = "Alimentation"
df["Scope"] = 3
df["Path"] = alimentation_infile
#Filter all values before 2020 because the methodology has changed and extrapolation of 2019 seems incorrect:
df = df[df["Year"]>=2020]
#append self.ignore_column with df["Category"].unique()
self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def add_manual_input(self, input_file):
inputs = pd.read_excel(input_file, sheet_name="Manual input")
self.Scope1_categories = ["Mazout", "Gaz"]
self.Scope2_categories = ["Electricite", "CAD"]
inputs.columns = ["Year", "Theme", "CO2"]
inputs["CO2"] = inputs["CO2"]*1000 #Convert to kg
inputs.loc[inputs["Theme"].isin(self.Scope1_categories), "Scope"] = 1
inputs.loc[inputs["Theme"].isin(self.Scope2_categories), "Scope"] = 2
inputs.loc[~inputs["Theme"].isin(self.Scope1_categories+self.Scope2_categories), "Scope"] = 3
inputs["Category"] = inputs["Theme"]
inputs["Path"] = input_file
self.data = pd.concat([self.data, inputs], ignore_index=True)
def export_database(self, path, Level="L1"):
if Level=="L1":
self.data.to_excel(path, index=False)
if Level=="L2":
self.data_L2.to_excel(path, index=False)
if Level=="L3A":
self.data_L3A.to_excel(path, index=False)
if Level=="L3B":
self.data_L3B.to_excel(path, index=False)
def import_database(self, path):
self.data = pd.read_excel(path)
def format_unit(self, emission_factor_df):
factor = emission_factor_df
factor.fillna(method='ffill', inplace=True)
CO2_col = self.CO2_col
#Standardize unit: MWh, m3, km
factor_df = factor.copy()
mask_kWh = factor_df["Unit"]=="kWh"
factor_df[CO2_col] = factor_df[CO2_col].where(~mask_kWh,factor_df[CO2_col]*1000)
factor_df["Unit"] = factor_df["Unit"].replace("kWh", "MWh")
mask_MJ = factor_df["Unit"]=="MJ"
factor_df[CO2_col] = factor_df[CO2_col].where(~mask_MJ,factor_df[CO2_col]*3600)
factor_df["Unit"] = factor_df["Unit"].replace("MJ", "MWh")
mask_m = factor_df["Unit"]=="m"
factor_df[CO2_col] = factor_df[CO2_col].where(~mask_m,factor_df[CO2_col]/1000)
factor_df["Unit"] = factor_df["Unit"].replace("m", "km")
self.emission_factor = factor_df
return factor_df
def calculate_emission(self):
manual_input_path = self.input_file
inputs = pd.read_excel(manual_input_path, sheet_name="Facteurs emissions")
inputs = self.format_unit(inputs)
inputs[["Theme", "Scope", "Data name mapping"]] = inputs[["Theme", "Scope", "Data name mapping"]].fillna(method='ffill')
raw_data = self.data.copy()
raw_data["Data description"] = "Valeur calculée"
NaN_values = raw_data[(raw_data["Value"].isna())&(~raw_data["CO2"].isna())]
raw_data = raw_data[~raw_data["Value"].isna()]
facteurs_emissions = inputs.copy()
raw_data["Year"] = self.data["Year"].astype(float).astype(str)
raw_data["Value"] = self.data["Value"].astype(float)
facteurs_emissions["Year"] = facteurs_emissions["Year"].astype(str)
facteurs_emissions.rename(columns={'Data name mapping': 'Category'}, inplace=True)
# Attempt to join on Category, Year, and Campus
facteurs_emissions_sub = facteurs_emissions[~facteurs_emissions["Campus"].isna()&(~(facteurs_emissions["Year"].isna()=="nan"))]
joined_data = pd.merge(raw_data, facteurs_emissions_sub, on=['Category', 'Year', 'Campus'], how='left', suffixes=('', '_drop'))
unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
# Now, replace the index of unmatched with the original indices
#Filter with not Nan Year so it's won't merge based on common NaN Year
facteurs_emissions_sub = facteurs_emissions[(facteurs_emissions["Campus"].isna())&(~(facteurs_emissions["Year"]=="nan"))]
# Join on Category and Year if previous join had no match
if not unmatched.empty:
unmatched_index=unmatched.index
joined_data_year = pd.merge(unmatched, facteurs_emissions_sub, on=['Category', 'Year'], how='left', suffixes=('', '_drop'))
joined_data_year.index = unmatched_index
joined_data_year_not_nan = joined_data_year[~joined_data_year['Factor [kg CO2eq]'].isna()]
#Subsitute the original columns with the "_drop" columns
joined_data.update(joined_data_year_not_nan)
# Check for unmatched entries again
unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
#Filter with not Nan campus so it's won't merge based on common NaN campus
facteurs_emissions_sub = facteurs_emissions[(~facteurs_emissions["Campus"].isna())&((facteurs_emissions["Year"]=="nan"))]
# Join on Category and Campus if previous join had no match
if not unmatched.empty:
unmatched_index=unmatched.index
joined_data_campus = pd.merge(unmatched, facteurs_emissions_sub, on=['Category', 'Campus'], how='left', suffixes=('', '_drop'))
joined_data_campus.index = unmatched_index
joined_data_campus_not_nan = joined_data_campus[~joined_data_campus['Factor [kg CO2eq]'].isna()]
joined_data.update(joined_data_campus_not_nan)
# Check for unmatched entries again
unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()][raw_data.columns]
facteurs_emissions_sub = facteurs_emissions[(facteurs_emissions["Campus"].isna())&((facteurs_emissions["Year"]=="nan"))]
# Finally, join just on Category if none of the above conditions are met
if not unmatched.empty:
unmatched_index=unmatched.index
joined_data_category = pd.merge(unmatched, facteurs_emissions_sub, on='Category', how='left', suffixes=('', '_drop'))
joined_data_category.index = unmatched_index
joined_data_category_not_nan = joined_data_category[~joined_data_category['Factor [kg CO2eq]'].isna()]
joined_data.update(joined_data_category_not_nan)
# Drop duplicate columns resulted from the merges
joined_data = joined_data[[col for col in joined_data.columns if not col.endswith('_drop')]]
joined_data.drop_duplicates(subset = ['Category', 'Year', 'Campus'],inplace=True, keep="first")
unmatched = joined_data[joined_data['Factor [kg CO2eq]'].isna()]
print(unmatched["Category"].unique())
joined_data["CO2"] = joined_data["Factor [kg CO2eq]"]*joined_data["Value"]
joined_data["Year"] = joined_data["Year"].astype(float).astype(int)
#Add NaN_values to joined_data
joined_data = pd.concat([joined_data, NaN_values], ignore_index=True)
self.data_L2 = joined_data.copy()
def extrapolate(self):
#Si une valeur est manquante pour une année, l'algorithme cherche une valeur dans le future et l'adapte selon la croissance
#Si c'est une valeur dans le future n'est pas disponible, alors elle va chercher dans le passé et adpate selon croissance
data = self.data_L2.copy()
years = data["Year"].unique()
years.sort()
years = years[:-1]
EPT_year = self.EPT_year
## Student plane interpolation
Category = self.category_name["Avion etudiant-e-s"]
Stud_years = [2016,2017, 2018]
data = extrapolate_student_plane(data, EPT_year, Stud_years, Category)
for year in years:
for Theme in data["Theme"].unique():
mask = (data["Year"]==year)&(data["Theme"]==Theme)
#For each Theme where there is no data in this year, take the data from the nearest year
if data.loc[mask, "CO2"].isna().all():
#Find the nearest year where data["Theme"] is not empty
nearest_year = year
while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
nearest_year += 1
if nearest_year>2100:
nearest_year = year
while (data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "CO2"].isna().all()):
nearest_year -= 1
nearest_mask = (data["Year"]==nearest_year)&(data["Theme"]==Theme)
data_masked = data.loc[nearest_mask]
for category in data_masked["Category"].unique():
serie = pd.Series()
serie["Year"] = year
serie["Theme"] = Theme
serie["Category"] = category
if Theme == "Electricite":
print(1)
if Theme == "Mobilité pendulaire":
#Produit en croix pour estimer la valeur de la mobilité pendulaire en fonction de l'évolution de la population
data_pendular = data.loc[(data["Year"]==nearest_year)&(data["Theme"]=="Mobilité pendulaire")]
pendular_year = self.raw_pendular.loc[(self.raw_pendular["Year"] == year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
pendular_year["Part_modale"] = pendular_year["Value"]/pendular_year["Value"].sum()
pendular_nearest = self.raw_pendular.loc[(self.raw_pendular["Year"] == nearest_year)&(self.raw_pendular["Theme"]=="Mobilité pendulaire")&(self.raw_pendular["Category"]==category)]
pendular_nearest["Part_modale"] = pendular_nearest["Value"]/pendular_nearest["Value"].sum()
mask = data_pendular["Category"]==category
pendular_EPT_a = pendular_year[pendular_year["Category"]==category]["Part_modale"].values*EPT_year[year]
pendular_CO2_n = data_pendular[mask]["CO2"].values
pendular_EPT_n = pendular_nearest[pendular_nearest["Category"]==category]["Part_modale"].values*EPT_year[nearest_year]
#Produit en croix:
serie["CO2"] = (pendular_EPT_a*pendular_CO2_n/pendular_EPT_n)[0]
else:
ratio_evolution = ((EPT_year.loc[year]-EPT_year.loc[nearest_year])/(EPT_year.loc[nearest_year]))
serie["CO2"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "CO2"].sum()* (1+ratio_evolution)
serie["Value"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme)&(data["Category"]==category), "Value"].sum()*(1+ratio_evolution)
serie["Data description"] = "Estimé"
serie["Scope"] = data.loc[(data["Year"]==nearest_year)&(data["Theme"]==Theme), "Scope"].median()
serie["Campus"] = "EPFL"
serie["Category"] = Theme
data = data.append(serie, ignore_index=True)
self.data_L3A = data
def objectives(self, year_reference=2022):
data_L3A = self.data_L3A.copy()
#Objectif 2024
self.df_objectifs_2024 = objectives_2024(data_L3A, year_reference)
self.year_reference = year_reference
#Objectif 2030
df = pd.DataFrame()
df["Theme"] = data_L3A["Theme"].unique()
df["Year"] = 2030
df["Campus"] = "EPFL"
df["Scope"] = np.nan
#Definition des objectifs 2030:
mask = (data_L3A["Theme"] == "Electricite")|(data_L3A["Theme"] == "Mazout")|(data_L3A["Theme"]=="CAD")|(data_L3A["Theme"] == "Gaz")
Energy_2006 = data_L3A.loc[(data_L3A["Year"]==2006)&(mask)].sum()
Objective_2030_energy = Energy_2006["CO2"]*0.5
gaz_2030 = data_L3A.loc[(data_L3A["Year"]==2006)&(data_L3A["Theme"] == "Gaz")]["CO2"]*0.5 #Assume que le gaz en 2030 est la moitié de 2006
elec_2030 = Objective_2030_energy-gaz_2030
df.loc[df["Theme"]=="Electricite", "CO2"] = elec_2030.squeeze()
df.loc[df["Theme"]=="Electricite", "Objectif"] = "-50% energie par rapport à 2006"
df.loc[df["Theme"]=="Gaz", "CO2"] = gaz_2030.squeeze()
df.loc[df["Theme"]=="Gaz", "Objectif"] = "-50% energie par rapport à 2006"
df.loc[df["Theme"]=="Mazout", "CO2"] = 0
df.loc[df["Theme"]=="Mazout", "Objectif"] = "Remplacement par Electricite"
df.loc[df["Theme"]=="CAD", "CO2"] = 0
df.loc[df["Theme"]=="CAD", "Objectif"] = "Remplacement par Electricite"
mask_voyages_prof = (data_L3A["Theme"]=="Voyages professionnels")
trip_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(mask_voyages_prof)]["CO2"].sum()
Objective_2030_trip = trip_2019*0.7
df.loc[df["Theme"]=="Voyages professionnels", "CO2"] = Objective_2030_trip
df.loc[df["Theme"]=="Voyages professionnels", "Objectif"] = "-30% par rapport à 2019"
Pendulaire_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Mobilité pendulaire")]["CO2"].sum()
Objective_2030_pendulaire = Pendulaire_2019*0.7
df.loc[df["Theme"]=="Mobilité pendulaire", "CO2"] = Objective_2030_pendulaire
df.loc[df["Theme"]=="Mobilité pendulaire", "Objectif"] = "-30% par rapport à 2019"
Alimentation_2019 = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Alimentation")]["CO2"].sum()
Objective_2030_alimentation = Alimentation_2019*0.6
df.loc[df["Theme"]=="Alimentation", "CO2"] = Objective_2030_alimentation
df.loc[df["Theme"]=="Alimentation", "Objectif"] = "-40% par rapport à 2019"
df.loc[df["Theme"]=="Numérique (fabrication)", "CO2"] = 4829e3
df.loc[df["Theme"]=="Numérique (fabrication)", "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
df.loc[df["Theme"]==self.nomenclature["Achats"], "CO2"] = 42000e3
df.loc[df["Theme"]==self.nomenclature["Achats"], "Objectif"] = "Malgré la croissance, maintenir l'impact à 2019"
df.loc[df["Theme"]=="Dechets", "CO2"] = data_L3A.loc[(data_L3A["Year"]==2019)&(data_L3A["Theme"]=="Dechets")]["CO2"].sum()
df["Data description"] = "Objectifs 2030 (confédération+EPFL)"
df["Category"] = df["Theme"]
for Theme in df["Theme"].unique():
df.loc[df["Theme"]==Theme, "Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
self.df_2030 = df
def interpolation_objectifs(self):
data_L3A = self.data_L3A.copy()
df_2030 = self.df_2030
years = np.arange(data_L3A["Year"].max(),2030)
for Theme in df_2030["Theme"].unique():
for year in years:
df_temp = pd.Series()
df_temp["Theme"] = Theme
df_temp["Year"] = year
df_temp["Campus"] = "EPFL"
df_temp["Category"] = df_temp["Theme"]
df_temp["Data description"] = "Objectifs 2030 (confédération+EPFL)"
df_temp["Scope"] = data_L3A[data_L3A["Theme"] == Theme]["Scope"].iloc[0]
mask = (data_L3A["Year"]==data_L3A["Year"].max())&(data_L3A["Theme"]==Theme)
if mask.sum():
df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[mask, "CO2"].sum(), df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
if year != data_L3A["Year"].max():
df_2030 = df_2030.append(df_temp, ignore_index=True)
else:
#If this data is not available for a specific theme, we take the value of the last year
df_temp["CO2"] = np.interp(year, [data_L3A["Year"].max(), 2030], [data_L3A.loc[(data_L3A["Year"]==data_L3A["Year"].max()-1)&(data_L3A["Theme"]==Theme), "CO2"].iloc[0], df_2030.loc[(df_2030["Theme"]==Theme)&(df_2030["Year"]==2030), "CO2"].sum()])
df_2030 = df_2030.append(df_temp, ignore_index=True)
data_L3B = pd.concat([data_L3A, df_2030, self.df_objectifs_2024], ignore_index=True)
Elect_emission_factor = data_L3B[(data_L3B["Theme"]=="Electricite")&(data_L3B["Year"]==self.year_reference)][self.CO2_col].mean()
nan_mask = (data_L3B["Value"].isna())&(data_L3B["Category"] == "Electricite")
data_L3B.loc[nan_mask, "Value"] = data_L3B[nan_mask]["CO2"].iloc[0]/Elect_emission_factor
self.data_L3B = data_L3B.copy()
def simulation():
#Ajoute les reformes et leurs impacts sur le bilan CO2 et les predictions: Exemple: politique voyage
#Dans le cas de la simple croissance, des simulations peuvent être faites.
print(1)
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