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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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2022
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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2022
2900.0
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7
7
- 38668.288153343608
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_316377907626377216:qk:3]
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9018.625
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38668.288153343608
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-
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-
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-
-
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+
-
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-
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-
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-
-
-
-
+
+
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-
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-
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-
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-
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-
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diff --git a/Tableau/~Bilan_hugo_2.0__4496.twbr b/Tableau/~Bilan__8848.twbr
similarity index 100%
rename from Tableau/~Bilan_hugo_2.0__4496.twbr
rename to Tableau/~Bilan__8848.twbr
diff --git a/Tableau/Bilan_hugo_2.0 - Copie.twb b/Tableau/~Bilan_hugo_2.0__22216.twbr
similarity index 50%
rename from Tableau/Bilan_hugo_2.0 - Copie.twb
rename to Tableau/~Bilan_hugo_2.0__22216.twbr
index fd0a80e..c40211f 100644
--- a/Tableau/Bilan_hugo_2.0 - Copie.twb
+++ b/Tableau/~Bilan_hugo_2.0__22216.twbr
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"Actual"
"Estimate"
<_.fcp.ObjectModelEncapsulateLegacy.true...object-graph>
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+ ]]>
+
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+
+
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+
+
+
+
+
+
<formatted-text>
<run>Bilan CO2 EPFL
</run>
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-
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+
+
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+
+
+
+
+
+
+
+
+
+
+
"Restauration"
"Alimentation"
"Boissons"
"Coffee"
"Menu veg"
"Menu viande"
"Sandwich veg"
"Sandwich viande"
"Thé/café"
+ "moto/scooter"
+ "Mobilité pendulaire"
+ "moto"
+ "transports publics"
+ "Total Domestique"
"Europcar - véhicule de location"
"Mobility diesel"
- "Mobilité pendulaire"
"Mobility essence"
- "moto/scooter"
- "Total Domestique"
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"Véhicules de service essence"
"Véhicules privés"
"voiture"
- "transports publics"
- "Avion etudiant-e-s"
"Avion staff"
+ "Total International"
+ "Avion etudiant-e-s"
"Voyages professionnels"
+ "Avion collaborateur-trice-s"
"Numérique (fabrication)"
"CAD"
"Mazout"
"Gaz"
"Electricité"
+ "Electricite"
-
+
-
+
-
-
+
+
+
+
+
+
+
+
+
+
+
-
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+
+
- Thème:
+ Category:
]]>
Æ
+ Thème:
+ ]]>
+ Æ
Année:
]]>
Æ
- Facteur d'émission:
+ Facteur d'émission:
<[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]>]]>
Æ
- CO2 (kg):
+ CO2 (kg):
]]>
+ Æ
+ ]]>
<
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
> %
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
-
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
-
-
-
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[avg:Factor (kg CO2eq):qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914207850501:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[attr:Calculation_1052997914208116742:nk]
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:2]
-
-
-
- %null%
- "Avion etudiant-e-s"
- 2022
- 1591157.0
- %null%
- "Facteurs multiples"
- 4
-
-
-
-
<formatted-text>
<run>Objectif 2030 interpolation for electricity
</run>
</formatted-text>
-
+
+
+
+
+
+
-
+
-
+
+
+
+
-
-
-
-
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
-
+
Æ
Year:
]]>
Æ
Data description:
]]>
Æ
CO2:
]]>
+ Æ
+ ]]>
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
+
+
+
+ <formatted-text />
+
+
-
+
+
+
+
+
+
+
+
+
-
+
+
+
+
+
+
+
-
+
+
+
-
+
+
+
+
-
+
+
+
+
-
+
-
-
+
+
+
+
+
+ 3
+ 2
+ 1
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Scope:ok]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Campus:nk]
-
+
-
+
+
+
+
+
+
+
+ Le scope
+ ]]>
+ Æ
+ représente
+ ]]>
+ t CO2e pour l'année
+ ]]>
+ Æ
+
+
+
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_316377907629977601:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
-
+
<formatted-text>
<run>Electricité Objectifs 2024 & 2030
</run>
</formatted-text>
-
+
+
+
+
+
"[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596495876:qk]"
"[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Calculation_999517661596172291:qk]"
"[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2 (copy)_999517661598375941:qk]"
-
+
+
+
+
+
+
+
-
+
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[Multiple Values]
([federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok] / [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[:Measure Names])
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Theme:nk]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Category:
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+ Æ
+ Dataset name:
+ ]]>
+ Æ
+ Unit formated:
+ ]]>
+ Æ
+ Scope:
+ ]]>
+ Æ
+ Theme:
+ ]]>
+ Æ
+ Facteur d'emission (kg CO2eq):
+ ]]>
+ Æ
+ ]]>
+
+
+
+
+
+ [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Category:nk]
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+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Achats (estimation)"
+ "Numérique (fabrication)"
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<formatted-text>
<run> Bilan CO2 EPFL</run>
</formatted-text>
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-
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+ "Achats (estimation)"
"Alimentation"
"Numérique"
+ "Numérique (fabrication)"
"Mobilité pendulaire"
"Voyages professionnels"
- "Numérique (fabrication)"
"CAD"
"Mazout"
"Gaz"
"Electricité"
+ "Electricite"
-
-
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Data description:nk]
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- Theme:
- ]]>
- Æ
- Year:
- ]]>
- Æ
- CO2 (kg):
- ]]>
- Æ
- % CO2
- :
- %]]>
- Æ
- ]]>
+ La catégorie
+ ]]>
+ est responsable d'une émission de
+ t CO2e]]>
+ en
+ .]]>
+
Cela correspond à
+ ]]>
+ % du bilan carbone de l'année.
+
]]>
%
- [federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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2022
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[usr:Calculation_1052997914215399431:qk:7]
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2022
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2022
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10
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2022
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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2022
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+ "Electricite"
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-
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-
-
- "Alimentation"
- "Numérique"
- "Mobilité pendulaire"
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- "CAD"
- "Mazout"
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:CO2:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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Year:
]]>
Æ
Value:
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[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[sum:Value:qk]
[federated.1vvrr5x1t7r13w1ei0rzz04rhce3].[none:Year:ok]
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+
-
-
-
+
+
+
-
-
+
+
+
+
+
+
+
+
+
+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
+
-
-
-
+
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+ Open source code:
+ https://c4science.ch/source/bilan_CO2/
+ Æ
+
-
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-
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+
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-
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-
-
+
+
+
+
-
-
+
+
-
-
- Open source code:
- https://c4science.ch/source/bilan_CO2/
- Æ
-
+
+
+
+
+
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+
+
+
+
+
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-
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-
+
+
+
-
+
-
-
- Open source code:
- https://c4science.ch/source/bilan_CO2/
- Æ
-
+
+
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diff --git a/data/Resultats/All_data.xlsx b/data/Resultats/All_data.xlsx
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diff --git a/scripts/__pycache__/footprint.cpython-310.pyc b/scripts/__pycache__/footprint.cpython-310.pyc
index 633e9ad..6f3b043 100644
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diff --git a/scripts/__pycache__/functions.cpython-310.pyc b/scripts/__pycache__/functions.cpython-310.pyc
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diff --git a/scripts/footprint.py b/scripts/footprint.py
index ce60495..70a7ede 100644
--- a/scripts/footprint.py
+++ b/scripts/footprint.py
@@ -1,483 +1,503 @@
import pandas as pd
import numpy as np
from functions import *
class footprint():
def __init__(self, CO2_col, EPT_infile):
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 = []
#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 read_emission_factor(self, emission_factor_path):
if "google" in emission_factor_path:
factor = pd.read_csv(emission_factor_path, encoding= 'cp1252', delimiter=';', decimal=",")
else:
factor = pd.read_excel(emission_factor_path, index_col=0)
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
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"] = "Electricite"
df.loc[mask_gaz|mask_mazout, "Scope"] = 1
+ #ADD FRIBOURG ELECTRICITE BECAUSE NOT IN GRI
+ Fribourg_electricite = (36003.81+ 206631.15)/1000 #MWh
+ #Add to df Fribourg_eletricite
+ df = df.append({"Category": "Electricite", "Year": 2022, "Campus": "Fribourg", "Value": Fribourg_electricite, "Theme": "Electricite", "Scope": 2, "Unit": "MWh", "Path": "Bilan CO2 code"}, ignore_index=True)
+
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["Campus"] = "Vaud"
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")]
df["Year"] = df["Year"].astype(int)
df["Value"] = df["Value"].astype(float)
df["Unit"] = "km"
df["Theme"] = "Voyages professionnels"
df["Scope"] = 3
df["Campus"] = "EPFL"
-
+ 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]
Mobility = df_cars.iloc[9:12]
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.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["Campus"] = "EPFL"
df["Theme"] = "Voyages professionnels"
df["Scope"] = 3
df.loc[df["Category"]=="Véhicules de service essence", "Scope"] = 1
df.loc[df["Category"]=="Véhicules de service diesel", "Scope"] = 1
df.loc[df["Category"]=="Mobility diesel", "Scope"] = 1
df.loc[df["Category"]=="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
db["Year"] = db["Year"].astype(int)
db["Theme"] = "Mobilité pendulaire"
db["Scope"] = 3
db["Unit"] = "km"
db["Campus"] = "EPFL"
#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_numeric(self):
- df = pd.DataFrame(data= {"CO2": [4829,2900], "Year":[2019, 2022]})
+ df = pd.DataFrame(data= {"CO2": [4829,2429.05], "Year":[2019, 2022]})
df["CO2"] = df["CO2"] * 1000 #Convert to kg
df["Campus"] = "EPFL"
self.numeric_name = "Numérique (fabrication)"
df["Category"] = self.numeric_name
df["Theme"] = "Numérique (fabrication)"
df["Scope"] = 3
+ df["Path"] = "Bilan CO2 code"
self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
self.data = pd.concat([self.data, df], 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
#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 run_achats(self):
df = pd.DataFrame(data= {"CO2": [42000], "Year":[2019]})
df["CO2"] = df["CO2"] * 1000 #Convert to kg
df["Campus"] = "EPFL"
self.achats_name = "Achats (estimation)"
df["Category"] = self.achats_name
df["Theme"] = "Achats (estimation)"
df["Scope"] = 3
+ df["Path"]= "Bilan CO2 code"
self.ignore_column = np.append(self.ignore_column,df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def add_2006_emission(self, path_2006):
df = pd.read_excel(path_2006).iloc[0].reset_index().iloc[1:-1]
df.columns = ["Theme", "CO2"]
df["CO2"] = df["CO2"]*1000 #Convert to kg
df["Scope"] = [2,1,1,3,3,3,3]
df["Campus"] = "EPFL"
df["Year"]=2006
df["Category"] = df["Theme"]
#Drop Theme=="Alimentation" car il est compté dans la database Quantis
df = df[df["Theme"]!="Alimentation"]
-
+ df["Path"] = path_2006
self.ignore_column = np.append(self.ignore_column, df["Category"].unique())
self.data = pd.concat([self.data, df], ignore_index=True)
def assign_factor(self, join_factor_path):
#Assigne un facteur d'emission à chacune des lignes de la database self.data
#Join_factor_path: path to the file containing the factor to join to the database
#Aucun calcul n'est fait, juste une assignation de facteur. Les calculs sont fait dans la fonction calculate_emission
join_factor = pd.read_excel(join_factor_path)
Check_join_data_category(self.data, join_factor, ignore_column=self.ignore_column, ignore_year=2006)
self.data["Year"] = self.data["Year"].astype(int)
self.data["Value"] = self.data["Value"].astype(float)
self.data["Data description"] = "Valeur calculée"
+
+ #A SUPPRIMER QUAND LA VALEUR ALIMEN 2022 EST SÛRE
+ mask = (self.data["Year"]==2022) & (self.data["Theme"]=="Alimentation")
+ self.data.loc[mask, "Data description"] = "Valeur estimée"
+
df = self.data
df["factor name"] = np.nan
# First assign factor to Year
mask_year = ~join_factor["Year"].isna()
df_year = join_factor.loc[mask_year]
for year, campus, category, factor in df_year[["Year", "Campus", "Category", "factor name"]].values:
mask = (df["Year"]==year) & (df["Campus"]==campus) & (df["Category"]==category) & df["factor name"].isna()
df.loc[mask, "factor name"] = factor
# If no Year specified, assign factor to Campus
mask_campus = join_factor["Year"].isna() & ~join_factor["Campus"].isna()
df_campus = join_factor.loc[mask_campus].set_index(["Campus", "Category"])
for campus, category in df_campus.index.unique():
if (mask := (df["Campus"]==campus) & (df["Category"]==category) & df["factor name"].isna()).any():
factor = df_campus.loc[(campus, category)]["factor name"]
df.loc[mask, "factor name"] = factor
# If no year and no campus, assign factor to Category
mask_category = join_factor["Year"].isna() & join_factor["Campus"].isna()
df_category = join_factor.loc[mask_category].set_index("Category")["factor name"]
for category in df["Category"].unique():
if (mask := (df["Category"]==category) & df["factor name"].isna()).any():
factor = df_category.get(category)
#If factor have multiple value, assign the first one
if isinstance(factor, pd.Series):
factor = factor.iloc[0]
df.loc[mask, "factor name"] = factor
self.data = df.copy()
#Print missing factor, les facteurs non trouvés
print_missing_factor(self.data, self.emission_factor)
def calculate_emission(self):
#Calcule les emissions liées à chaque ligne de la database self.data
#Mask les valeurs n'ayant pas de facteur d'emission (ex: avions dont la donnée de base n'est pas utilisée mais on importe directement le CO2)
mask_na_factor = self.data["factor name"].isna()|~self.data["CO2"].isna()
data_L2 = self.data[~mask_na_factor].join(self.emission_factor.set_index("Data name mapping"), on="factor name", rsuffix="_CO2", how="left")
#Complete les données maskées par des données brutes de CO2
data_L2["CO2"] = data_L2["Value"]*data_L2[self.CO2_col]
mask_brute_CO2 = ~self.data["CO2"].isna()
data_L2 = pd.concat([data_L2, self.data[mask_brute_CO2]], ignore_index=True)
#Drop unecessary columns
data_L2.drop(columns =["Utilisation", "Comments", "Lien source"], inplace=True)
self.data_L2 = data_L2.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.achats_name, "CO2"] = 42000e3
df.loc[df["Theme"]==self.achats_name, "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)
\ No newline at end of file
diff --git a/scripts/functions.py b/scripts/functions.py
index 3223ca2..99ec479 100644
--- a/scripts/functions.py
+++ b/scripts/functions.py
@@ -1,124 +1,126 @@
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import datetime
import os
import shutil
def extrapolate_student_plane(data, EPT_year, Stud_years, Category):
## Student plane interpolation
for year in Stud_years:
mask = (data["Year"]==year)&(data["Category"]==Category)
nearest_year = year
while (data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category), "CO2"].isna().all()):
nearest_year += 1
serie = pd.Series()
serie["Year"] = year
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["Category"]==Category)&(data["Category"]==Category), "CO2"].sum()* (1+ratio_evolution)
serie["Value"] = data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category)&(data["Category"]==Category), "Value"].sum()
serie["Data description"] = "Estimé"
serie["Scope"] = data.loc[(data["Year"]==nearest_year)&(data["Category"]==Category), "Scope"].median()
serie["Campus"] = "EPFL"
serie["Category"] = Category
serie["Theme"] = "Voyages professionnels"
data = data.append(serie, ignore_index=True)
return data
+
def objectives_2024(data_objectives, year_reference):
mask = (data_objectives["Category"] == "Electricite")&(data_objectives["Year"]==2019)
Electricity_2019 = data_objectives.loc[mask].sum()
Objective_2024_Electricity = Electricity_2019["CO2"]*0.85 #CO2 et kWh varient lineairement, le facteur de reduction est donc le meme
Objective_2024_Electricity_value = Electricity_2019["Value"]*0.85
Electricity_ref_year = data_objectives.loc[(data_objectives["Category"] == "Electricite")&(data_objectives["Year"]==year_reference)].sum()
Obj_2024= np.interp([2023], [year_reference, 2024], [Electricity_ref_year["CO2"], Objective_2024_Electricity])
Obj_2024_value = np.interp([2023], [year_reference, 2024], [Electricity_ref_year["Value"], Objective_2024_Electricity_value])
#Append Objective 2024 to obj_2023
df_2024 = pd.DataFrame()
df_2024["Theme"] = ["Electricite","Electricite"]
df_2024["Year"] = [2023, 2024]
df_2024["Campus"] = ["EPFL", "EPFL"]
df_2024["CO2"] = np.append(Obj_2024, Objective_2024_Electricity)
df_2024["Data description"] = "Objectif EPFL 15% de diminution d'ici 2024"
df_2024["Category"] = "Electricite"
+ df_2024["Scope"] = "2"
df_2024["Unit"] = "kg CO2"
df_2024["Value"] = np.append(Obj_2024_value, Objective_2024_Electricity_value)
return df_2024
def Check_join_data_category(data, join_factor, ignore_column = ["Plane staff", "Plane students"], ignore_year = 2006):
data = data[data["Year"]!=ignore_year]
NaN_factor = join_factor[join_factor["factor name"].isna()]["Category"]
mask_data_NaN = ~data["Category"].isin(NaN_factor)
filtered_nan_data = data[mask_data_NaN]
NaN_categories = data[~mask_data_NaN]["Category"].unique()
missing_category = filtered_nan_data[~filtered_nan_data["Category"].isin(join_factor[~join_factor["factor name"].isna()]["Category"])]["Category"]
missing_category = missing_category[~missing_category.isin(ignore_column)]
#Print if filtered_nan_data["Category"] is not in join_factor["Category"] and reversed
if len(missing_category.unique())>0:
print("Missing category in join_factor")
#Substract NaN_categories from missing_category
missing_category = missing_category[~missing_category.isin(NaN_categories)]
print(missing_category.unique())
ValueError("Check coherency between join_factor and data")
if len(join_factor[~join_factor["factor name"].isna()]["Category"].unique()) != len(join_factor[~join_factor["factor name"].isna()]["Category"]):
print("Duplicate category in join_factor")
ValueError("Check coherency between join_factor and data")
def print_missing_factor(df, emission_factor):
#Remove None
df = df[~df["factor name"].isna()]
if not df["factor name"].isin(emission_factor["Data name mapping"]).all():
print(df["factor name"][~df["factor name"].isin(emission_factor["Data name mapping"])])
raise ValueError("Some factor name are not found in the emission factor dataframe")
def join_emission_factor(emission_join, factor_df, Primary_key, CO2_col="GWP [kg CO2eq]"):
emission_factor = emission_join[Primary_key]
name = factor_df[factor_df["Data name mapping"]==emission_factor][CO2_col].squeeze()
if not name:
ValueError("Could not find the data name")
return name
def sum_variable(df, meta, field):
meta_field = list(field.keys())[0]
mask = np.where(meta.loc[meta_field]==field[meta_field])[0]+1
tot = np.sum(df[mask],1)
return tot
def parse_df_from_ID(df_format, L0_ID_elec):
df_elec_L0 = df_format[L0_ID_elec]
df_elec_L0.columns = df_elec_L0.iloc[0]
df_elec_L0 = df_elec_L0[1:]
df_elec_L0=df_elec_L0.replace("-", np.nan)
return df_elec_L0
def export_history(year, emission_factor_path, factor_join, df):
date = str(datetime.now().year) + "_"+str(datetime.now().month)+"_"+str(datetime.now().day)
export_folder = "data/Level1/"+str(year)+"/"+date
if date not in os.listdir("data/Level1/"+str(year)):
os.makedirs(export_folder)
shutil.copy(factor_join, export_folder+"/factor_join.json")
#shutil.copy(emission_factor_path, export_folder+"/emission_factor_db.xlsx")
df.to_csv(export_folder+f"/bilan_{year}.csv")
def join_factor_variable(df, factor, join_df):
df_emission = pd.DataFrame(np.nan, index=df.index, columns=df.columns)
for ID in df.columns:
if "factor_name" in join_df[str(ID)]:
for elem in join_df[str(ID)]["factor_name"]:
date_range = list(elem.values())[0]
factor_name = list(elem.keys())[0]
for date in date_range:
idx_factor=np.where(factor["Data name mapping"] == factor_name)[0]
if not idx_factor:
print(f"Could not find the factor {factor_name}")
emission_factor = factor["GWP [kg CO2eq]"].iloc[idx_factor]
df_emission[ID][int(date)] = df.loc[int(date)]*emission_factor
else:
idx_factor=np.where(factor["Data name mapping"] == join_df[str(ID)])[0]
emission = factor["GWP [kg CO2eq]"].iloc[idx_factor]
df_emission[ID] = emission*df[ID]
return df_emission
\ No newline at end of file
diff --git a/scripts/input_path.yaml b/scripts/input_path.yaml
index ccc8c62..195b6c6 100644
--- a/scripts/input_path.yaml
+++ b/scripts/input_path.yaml
@@ -1,15 +1,15 @@
factor_join: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/join_factor.xlsx"
emission_factor: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/emission_factor.xlsx"
EPT_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Population/data/df_EPT.xlsx"
strategy_bilan: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/Brute/bilan_strategie.xlsx"
reporting_pendulaire: "Z:/42-DUR-MOB-E-E/42.05-Mobilite/42.05.01-Aff-en-cours/05-Reporting-inventaires/Rapport-environnemental-2022/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
reporting_voyages: "Z:/42-DUR-MOB-E-E/42.06-Voyages/42.06.01-Aff-en-cours/05-Reporting/42.06.01-Reporting-voyages-2023.xlsx"
plane_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Mobilité/Avions/data/Resultats/atmosfair/atmosfair_evolution.xlsx"
dechets_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Dechets/data/Resultats/waste.xlsx"
papier_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Achats/papier_epfl/data/Resultats/papier_db.xlsx"
energy_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/energy_db.xlsx"
eau_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Energy/data/Resultats/eau_db.xlsx"
-alimentation_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Alimentation/data/Resultats/Quantis_data.xlsx"
+alimentation_infile: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets/Alimentation/data/Resultats/Format_Alimentation_CO2.xlsx"
#Backup files:
reporting_pendulaire_backup: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/Brute/pendulaire/42.05.01-reporting-pendulaire-2021-20220412.xlsx"
reporting_voyages_backup: "Z:/40-DURABILITE/40.04-Donnees/40.04.01-Donnees-Durabilite/Projets transversaux/Bilan_CO2/data/Brute/voyages/42.06.01-Reporting-voyages-2023.xlsx"
\ No newline at end of file