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