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functions.py
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Fri, Oct 18, 09:53

functions.py
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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 export_database(data, path):
data.to_excel(path, index=False)
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 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

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