diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..5e5bfa2
Binary files /dev/null and b/.DS_Store differ
diff --git a/model/Study_CO2_concentration_no_window_opening.py b/model/Study_CO2_concentration_no_window_opening.py
new file mode 100644
index 0000000..0abee56
--- /dev/null
+++ b/model/Study_CO2_concentration_no_window_opening.py
@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 26 16:19:28 2026
+
+@author: alisonlecointre
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import glob
+import os
+
+# %%=============================
+# File loading
+# ===============================
+
+def load_file(file_name):   
+    sheet= pd.read_excel(file_name, sheet_name=None) #loading file, sheat_name=None : return dictionary containing df for each sheet
+    
+    for sheet_name, df in sheet.items(): #loading all sheets from the file
+        df = df.dropna(axis=1, how="all") #remove empty column "all" : all elements missing
+        df.columns = df.columns.str.strip() #remove leading character
+        sheet[sheet_name]=df
+    return sheet
+
+def load_all_files(folder): #load all .xlsx files from the folder
+    files = glob.glob(f"{folder}/*.xlsx")
+    all_data={} #create dictionnary of all dictionnary of each file
+    
+    for file in files:
+        name = os.path.basename(file)
+        all_data[name] = load_file(file)
+    return all_data
+
+folder = os.getcwd()
+data = load_all_files(folder)
+
+# %%=============================
+# Parameters Provence classrooms 
+# ===============================
+
+df_Provence_rooms = pd.DataFrame({
+    "classrooms": ["A2", "A3","A4","A5","A6","A7"],
+    "volume": [308, 480, 326,274,323,272],
+    "n_student_max": [40, 78, 48,32,58,36]
+})
+
+print (df_Provence_rooms)
+
+# %%=============================
+# Calculation
+# ===============================
+
+# ===============================
+# Air volume per person
+# ===============================
+
+def calculation_air_volum_per_pers (df) :
+    air_volume_per_pers = df["Mean room volume (m3)"]/ df["Mean number of students (-)"]
+    return air_volume_per_pers
+    
+
+calculation = {}   #new dictionnary to store the air volum per person
+
+for file_name, sheet in data.items() : #for each file including sheets from this file and all included in data
+    calculation [file_name] = {}
+    for sheet_name, df in sheet.items() : #df in all sheets
+        calculation[file_name][sheet_name] = calculation_air_volum_per_pers(df)
+        
+# ================================
+# New data frame for simulation model of CO2 concentration increase over time
+# ================================
+
+def model_no_windows_opening (CO2_t0=None, N=None, V=None, df=None) : 
+    CO2_prod_th = 0.0155/60 #m3/min <=> ajusted at 15.5 l/h after crossing data with simaria model [20 l/h CO2 production per person during normal activity (cf. article 16 822.113 Ordonnance 3 du 18 août 1993 relative à la loi sur le travail (OLT 3) (Protection de la santé))
+    t = np.arange(0, 181, 1)    
+    if df is not None :
+        CO2_t0 = df["CO2 rate (ppm)"].iloc[0]
+        N = df["Mean number of students (-)"].iloc[0]
+        V = df["Mean room volume (m3)"].iloc[0]
+    CO2_t_model = CO2_t0 + ((N * CO2_prod_th * t *1e6) / V)
+    return t, CO2_t_model
+
+model = {}
+
+for file_name, sheet in data.items() : #for each file including sheets from this file and all included in data
+    model [file_name] = {}
+    for sheet_name, df in sheet.items() : #df in all sheets
+        t, CO2_t_model = model_no_windows_opening(df=df)
+        model [file_name][sheet_name] = {"t": t, "CO2_t_model": CO2_t_model}
+
+# ================================
+# Find threshold time
+# ================================
+
+threshold = 1400
+
+def find_threshold_time(t, CO2_t_model, threshold):
+    indices = np.where(CO2_t_model >= threshold)[0]
+    if len(indices) == 0:
+        return None  # never reached
+    return t[indices[0]]
+
+threshold_time = {}
+
+for file_name, sheet in model.items():
+    threshold_time[file_name] = {}
+    for sheet_name, values in sheet.items():
+        t = values["t"]
+        CO2_t_model = values["CO2_t_model"]
+        threshold_time[file_name][sheet_name] = find_threshold_time(t, CO2_t_model, threshold)
+
+# ================================
+# Air quality thresholds for graphs
+# ================================
+thresholds = {
+    "Good air quality":(0, 1400,"green"),
+    "Bad air quality": (1400, 2000,"orange"),
+    "Really bad air quality": (2000, np.inf,"red"),
+}
+
+def plot_air_quality_zones (ax, thresholds):
+    xmin, xmax = ax.get_xlim()
+    ymin, ymax = ax.get_ylim()
+    
+    for label, (y_min, y_max, color) in thresholds.items():
+        if y_max != np.inf:
+            ax.axhline(y=y_max, linestyle="--", color=color)
+        y_top = min(y_max, ymax)
+        y_bottom = max(y_min, ymin)
+        y_mid = (y_bottom + y_top) / 2
+        ax.axhspan(y_bottom, y_top, color=color, alpha=0.2)
+        ax.text(
+            (xmin + xmax) / 2,
+            y_mid,
+            label,
+            ha="center",
+            va="center",
+            color="black",
+            bbox=dict(facecolor="white", alpha=0.5)
+        )
+
+# %% =============================
+# Visualisation
+# ================================
+
+# ================================
+# Graph time evolution of CO2 concentration open data
+# ================================
+
+for file_name, sheet in data.items():
+    fig0, ax = plt.subplots(figsize=(11, 7))
+    
+    for sheet_name, df in sheet.items():
+        t = model[file_name][sheet_name]["t"]
+        CO2_t_model = model[file_name][sheet_name]["CO2_t_model"]
+        t_threshold = threshold_time[file_name][sheet_name]   
+        ax.plot(df["time (min)"], df["CO2 rate (ppm)"], label=f"{file_name} - {sheet_name}")
+        ax.plot(t,CO2_t_model, label = f"{file_name} - {sheet_name} simulated", linestyle="--")
+         
+ #       if t_threshold is not None :
+ #           ax.axvline(x = t_threshold, label = "time reaching threshold", linestyle=":", color="red")       
+        ax.legend(
+        loc="upper center",
+        bbox_to_anchor=(0.45, -0.08),
+        ncol=3 if len(ax.get_legend_handles_labels()[1]) > 1 else 1
+        )
+
+        fig0.tight_layout()
+
+    ax.set_ylim(400, 4000)   
+    plot_air_quality_zones(ax, thresholds)
+    ax.grid(True, alpha=0.7)
+    ax.set_xlabel("time (min)")
+    ax.set_ylabel("CO2 concentration (ppm)")
+    ax.set_title("CO2 concentration over time")
+
+
+# %%==========================================================================================
+# User
+# ==========================================================================================
+
+print("\n")
+volume_room = float(input("Total volume (m3): "))
+n_student = float(input("Nomber of students : "))
+initial_CO2_level = float(input("Initial CO2 level (ppm): "))
+while initial_CO2_level < 400:
+    print("The value must be >= 400 ppm, which correspond to the normal CO2 concentration in air. Try again.")
+    initial_CO2_level = float(input("Initial CO2 level (ppm): "))
+
+    
+#Create CO2 concentration evolution over time using simulation model and give time reaching threshold
+
+t_user, CO2_t_model_user = model_no_windows_opening(CO2_t0=initial_CO2_level, N=n_student, V=volume_room, df=None)
+t_threshold = find_threshold_time(t_user, CO2_t_model_user, threshold)
+
+print(f"Time reaching threshold of {threshold} ppm =", round(t_threshold),"minutes")
+
+#Display CO2 concentration evolution over time
+reponse = input("Display CO2 evolution over time? (yes/no) : ").strip().lower()
+if reponse == "yes":
+    
+    fig10, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(t_user, CO2_t_model_user , label="CO2 concentration over time (simulated)")
+
+    if t_threshold is not None :
+        ax.axvline(x = t_threshold, label = "time reaching threshold", linestyle=":", color="red")       
+        ax.legend(
+        loc="upper center",
+        bbox_to_anchor=(0.45, -0.07),
+        ncol=3 if len(ax.get_legend_handles_labels()[1]) > 1 else 1
+        )
+        fig10.tight_layout()
+       
+    plot_air_quality_zones(ax, thresholds)
+    
+    ax.set_title("Time evolution of CO2 concentration - Simulated")
+    ax.set_xlabel("Time (min)")
+    ax.set_ylabel("CO2 concentration (ppm)")
+    ax.set_ylim(500, 5000)  
+    ax.grid(True)
+
+else:
+    print("Answer only with yes or no")
+
diff --git a/model/Study_CO2_concentration_window_opening.py b/model/Study_CO2_concentration_window_opening.py
new file mode 100644
index 0000000..c65457a
--- /dev/null
+++ b/model/Study_CO2_concentration_window_opening.py
@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 26 16:19:28 2026
+
+@author: alisonlecointre
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.pyplot as ticker
+import numpy as np
+import glob
+import os
+
+# %%=============================
+# File loading
+# ===============================
+
+def load_file(file_name):   
+    sheet= pd.read_excel(file_name, sheet_name=None) #loading file, sheat_name=None : return dictionary containing df for each sheet
+    
+    for sheet_name, df in sheet.items(): #loading all sheets from the file
+        df = df.dropna(axis=1, how="all") #remove empty column "all" : all elements missing
+        df.columns = df.columns.str.strip() #remove leading character
+        sheet[sheet_name]=df
+    return sheet
+
+def load_all_files(folder): #load all .xlsx files from the folder
+    files = glob.glob(f"{folder}/*.xlsx")
+    all_data={} #create dictionnary of all dictionnary of each file
+    
+    for file in files:
+        name = os.path.basename(file)
+        all_data[name] = load_file(file)
+    return all_data
+
+folder = os.getcwd()
+data = load_all_files(folder)
+
+# %%=============================
+# Parameters Provence classrooms 
+# ===============================
+
+df_Provence_rooms = pd.DataFrame({
+    "classrooms": ["A2","A3","A4","A5","A6","A7"],
+    "volume": [308, 480, 326,274,323,272],
+    "n_student_max": [40, 78, 48,32,58,36],
+    "windows_surface": [3.23, 22.12, 3.23, 22.12, 3.23, 5.53] 
+})
+
+print (df_Provence_rooms)
+
+# %%=============================
+# Calculation
+# ===============================
+
+# ===============================
+# Air volume per person
+# ===============================
+
+def calculation_air_volum_per_pers (df) :
+    air_volume_per_pers = df["Mean room volume (m3)"]/ df["Mean number of students (-)"]
+    return air_volume_per_pers
+    
+
+calculation = {}   #new dictionnary to store the air volum per person
+
+for file_name, sheet in data.items() : #for each file including sheets from this file and all included in data
+    calculation [file_name] = {}
+    for sheet_name, df in sheet.items() : #df in all sheets
+        calculation[file_name][sheet_name] = calculation_air_volum_per_pers(df)
+        
+# ================================
+# New data frame for simulation model of CO2 concentration increase over time
+# ================================
+CO2_prod_th = 0.0155/60 #m3/min <=> ajusted at 15.5 l/h after crossing data with simaria model [20 l/h CO2 production per person during normal activity (cf. article 16 822.113 Ordonnance 3 du 18 août 1993 relative à la loi sur le travail (OLT 3) (Protection de la santé))
+
+def model_no_windows_opening (CO2_t0=None, N=None, V=None, df=None) : 
+    t = np.arange(0, 181, 1)    
+    if df is not None :
+        CO2_t0 = df["CO2 rate (ppm)"].iloc[0]
+        N = df["Mean number of students (-)"].iloc[0]
+        V = df["Mean room volume (m3)"].iloc[0]
+    CO2_t_model = CO2_t0 + ((N * CO2_prod_th * t *1e6) / V)
+    return t, CO2_t_model
+
+model = {}
+
+for file_name, sheet in data.items() : #for each file including sheets from this file and all included in data
+    model [file_name] = {}
+    for sheet_name, df in sheet.items() : #df in all sheets
+        t, CO2_t_model = model_no_windows_opening(df=df)
+        model [file_name][sheet_name] = {"t": t, "CO2_t_model": CO2_t_model}
+
+# ================================
+# Find threshold time
+# ================================
+
+threshold = 1400
+
+def find_threshold_time(t, CO2_t_model, threshold):
+    indices = np.where(CO2_t_model >= threshold)[0]
+    if len(indices) == 0:
+        return None  # never reached
+    return t[indices[0]]
+
+threshold_time = {}
+
+for file_name, sheet in model.items():
+    threshold_time[file_name] = {}
+    for sheet_name, values in sheet.items():
+        t = values["t"]
+        CO2_t_model = values["CO2_t_model"]
+        threshold_time[file_name][sheet_name] = find_threshold_time(t, CO2_t_model, threshold)
+        
+# ================================
+# New data frame for simulation model of CO2 concentration decrease over time (using SIA 382/1 norm)
+# ================================
+v_air = 0.5 #m/s Assumption (cf. article)
+
+def model_windows_opening_CO2 (S_windows=None, volume=None, df=None): 
+    t = np.arange(0, 1, 0.01)  
+    if df is not None:    
+        for i, row in df.iterrows():
+            S_windows = df_Provence_rooms["windows_surface"].iloc[i]
+            volume = df_Provence_rooms["volume"].iloc[i]
+    q_air_change = S_windows * v_air * 3600  #m3/h
+    CO2_ext = 400 #ppm
+    CO2_int_0 = threshold #ppm
+    CO2_t_model_ventilation = ((CO2_int_0 - CO2_ext - ((0.001 * (CO2_prod_th * 1000*60))/q_air_change))* np.exp((-q_air_change * t)/volume) + CO2_ext + ((0.001 * (CO2_prod_th * 1000*60))/q_air_change) ) #ppm    CO2_t_model_ventilation = ((CO2_int_0 - CO2_ext - ((0.001 * (CO2_prod_thx))/q_air_change))* np.exp((-q_air_change * t)/volume) + CO2_ext + ((0.001 * (CO2_prod_thx))/q_air_change) ) #ppm
+    t = t*60
+    return t, CO2_t_model_ventilation
+    
+model_with_windows = {}
+for i, row in df.iterrows():
+    t, CO2_t_model_ventilation = model_windows_opening_CO2(df=row.to_frame().T)
+    model_with_windows[i] = pd.DataFrame({"CO2_t_model_ventilation" : CO2_t_model_ventilation, "t" : t})
+
+# ================================
+# Find window opening duration
+# ================================
+
+def find_windows_opening_time (t,CO2_t_model_ventilation):
+    windows_opening_duration = np.interp(405, CO2_t_model_ventilation[::-1] , t[::-1])
+    return windows_opening_duration
+
+
+# ================================
+# Air quality thresholds for graphs
+# ================================
+thresholds = {
+    "Good air quality":(0, 1400,"green"),
+    "Bad air quality": (1400, 2000,"orange"),
+    "Really bad air quality": (2000, np.inf,"red"),
+}
+
+def plot_air_quality_zones (ax, thresholds):
+    xmin, xmax = ax.get_xlim()
+    ymin, ymax = ax.get_ylim()
+    
+    for label, (y_min, y_max, color) in thresholds.items():
+        if y_max != np.inf:
+            ax.axhline(y=y_max, linestyle="--", color=color)
+        y_top = min(y_max, ymax)
+        y_bottom = max(y_min, ymin)
+        y_mid = (y_bottom + y_top) / 2
+        ax.axhspan(y_bottom, y_top, color=color, alpha=0.2)
+        ax.text(
+            (xmin + xmax) / 2,
+            y_mid,
+            label,
+            ha="center",
+            va="center",
+            color="black",
+            bbox=dict(facecolor="white", alpha=0.5)
+        )
+
+# %% =============================
+# Visualisation
+# ================================
+
+# ================================
+# Graph time evolution of CO2 concentration open data
+# ================================
+
+for file_name, sheet in data.items():
+    fig0, ax = plt.subplots(figsize=(10, 7))
+    
+    for sheet_name, df in sheet.items():
+        t = model[file_name][sheet_name]["t"]
+        CO2_t_model = model[file_name][sheet_name]["CO2_t_model"]
+        t_threshold = threshold_time[file_name][sheet_name]   
+        ax.plot(df["time (min)"], df["CO2 rate (ppm)"], label=f"{file_name} - {sheet_name}")
+        ax.plot(t,CO2_t_model, label = f"{file_name} - {sheet_name} simulated", linestyle="--")
+         
+        if t_threshold is not None :
+            ax.axvline(x = t_threshold, label = "time reaching threshold", linestyle=":", color="red")       
+        ax.legend(
+        loc="upper center",
+        bbox_to_anchor=(0.45, -0.08),
+        ncol=3 if len(ax.get_legend_handles_labels()[1]) > 1 else 1
+        )
+
+        fig0.tight_layout()
+
+    ax.set_ylim(0, 5000)   
+    plot_air_quality_zones (ax, thresholds)
+    ax.grid(True, alpha=0.7)
+    ax.set_xlabel("time (min)")
+    ax.set_ylabel("CO2 concentration (ppm)")
+    ax.set_title("CO2 concentration over time")
+
+# ================================
+# Graph time evolution of CO2 concentration after window opening
+# ================================
+
+fig1, ax= plt.subplots(figsize=(10, 6))
+for i in model_with_windows:
+    t = model_with_windows [i]["t"]
+    CO2_t_model_ventilation = model_with_windows[i]["CO2_t_model_ventilation"]
+    ax.plot(t, CO2_t_model_ventilation, label=df_Provence_rooms.loc[i, "classrooms"])
+    ax.legend()
+ax.xaxis.set_major_locator(ticker.MultipleLocator(2))
+ax.set_xlim(0, 20)   
+ax.grid(True, alpha=0.7)
+ax.set_xlabel("time (min)")
+ax.set_ylabel("CO2 concentration (ppm)")
+ax.set_title("CO2 concentration over time after window opening - with open data")
+
+# %%==========================================================================================
+# User
+# ==========================================================================================
+
+print("\n")
+name_classroom = input("Classroom number (A2 to A7) : ")
+volume_room = df_Provence_rooms.loc[df_Provence_rooms["classrooms"] == name_classroom, "volume"].values
+S_windows_room = df_Provence_rooms.loc[df_Provence_rooms["classrooms"] == name_classroom, "windows_surface"].values
+
+n_student = float(input("Number of students : "))
+
+initial_CO2_level = float(input("Initial CO2 level (ppm): "))
+
+while initial_CO2_level < 400:
+    print("The value must be >= 400 ppm, which correspond to the normal CO2 concentration in air. Try again.")
+    initial_CO2_level = float(input("Initial CO2 level (ppm): "))
+
+    
+#Create CO2 concentration evolution over time using simulation model and give time reaching threshold
+
+t_user, CO2_t_model_user = model_no_windows_opening(CO2_t0=initial_CO2_level, N=n_student, V=volume_room, df=None)
+t_threshold = find_threshold_time(t_user, CO2_t_model_user, threshold)
+
+t_user2, CO2_t_model_ventilation_user =  model_windows_opening_CO2(S_windows =S_windows_room, volume = volume_room, df=None)
+t_user3 = t_user2 + t_threshold #start at the time reaching threshold
+
+windows_opening_duration = find_windows_opening_time (t_user2, CO2_t_model_ventilation_user)
+
+print(f"Time reaching threshold of {threshold} ppm =", round(t_threshold),"minutes")
+print(f"Open windows for {windows_opening_duration:.0f} min after reaching threshold")
+
+#Display CO2 concentration evolution over time
+reponse = input("Display CO2 evolution over time? (yes/no) : ").strip().lower()
+while reponse not in ["yes", "no"]:
+    print("Answer with yes or no")
+    reponse = input("Display CO2 evolution over time? (yes/no) : ").strip().lower()
+
+if reponse == "yes":
+    
+    fig10, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(t_user, CO2_t_model_user , label="CO2 concentration over time (simulated)")
+    ax.plot(t_user3, CO2_t_model_ventilation_user, label="with opened windows", color="brown")
+
+    if t_threshold is not None :
+        ax.axvline(x = t_threshold, label = "time reaching threshold", linestyle=":", color="red")       
+        ax.legend(
+        loc="upper center",
+        bbox_to_anchor=(0.45, -0.07),
+        ncol=3 if len(ax.get_legend_handles_labels()[1]) > 1 else 1
+        )
+        fig10.tight_layout()
+       
+    plot_air_quality_zones(ax, thresholds)
+    
+    ax.set_title("Time evolution of CO2 concentration - Simulated")
+    ax.set_xlabel("Time (min)")
+    ax.set_ylabel("CO2 concentration (ppm)")
+    ax.set_xlim(0, 150)  
+    ax.set_ylim(400, 5000)  
+    ax.grid(True)
+
+