import pandas as pd import numpy as np import matplotlib.pyplot as plt from matplotlib import style style.use("bmh") data = pd.read_csv("smart_room_air_quality_data_final.csv")

#C02 sensor dynamics plt.figure(figsize=(16, 6)) plt.rcParams.update({"font.size": 15}) plt.plot(data["time_min"], data["co2_ppm"], "k", label="CO2 sensor data") plt.axhline(1000, color="blue", linestyle="--", label="Warning threshold") plt.axhline(1500, color="red", linestyle="--", label="Critical threshold") # Event region plt.axvspan(20, 45, color="red", alpha=0.12, label="Poor ventilation event") # First threshold crossing co2_cross = np.where(data["co2_ppm"] > 1000)[0][0] plt.plot(data["time_min"][co2_cross], data["co2_ppm"][co2_cross], "ro", label="CO2 threshold exceeded") # First actuator activation vent_on = np.where(data["ventilation_state"] == 1)[0][0] plt.xlabel("Time [min]") plt.ylabel("CO2 [ppm]") plt.title("CO2 Dynamics with Detection and Actuation") plt.legend() plt.grid(True) plt.savefig("C02Dynamics.pdf", bbox_inches="tight", pad_inches=0.1, dpi=600) plt.show()

#PM2.5 sensor dynamics plt.figure(figsize=(16, 6)) plt.rcParams.update({"font.size": 15}) plt.plot(data["time_min"], data["pm25_ugm3"], "k", label="PM2.5 sensor data") plt.axhline(15, color="blue", linestyle="--", label="Warning threshold") plt.axhline(35, color="red", linestyle="--", label="High threshold") plt.axvspan(50, 70, color="red", alpha=0.12, label="Particle pollution event") pm_cross = np.where(data["pm25_ugm3"] > 15)[0][0] plt.plot(data["time_min"][pm_cross], data["pm25_ugm3"][pm_cross], "ro", label="PM2.5 threshold exceeded") plt.xlabel("Time [min]") plt.ylabel("PM2.5 [µg/m³]") plt.title("PM2.5 Dynamics with Pollution Event") plt.legend() plt.grid(True) plt.savefig("PM2.5 Dynamics.pdf", bbox_inches="tight", pad_inches=0.1, dpi=600) plt.show()

#Dynamics of temperature plt.figure(figsize=(16, 6)) plt.rcParams.update({"font.size": 15}) plt.plot(data["time_min"], data["temperature_c"], "k", label="Temperature sensor data") plt.axhline(27, color="red", linestyle="--", label="High threshold") plt.axhline(18, color="blue", linestyle="--", label="Low threshold") plt.axvspan(30, 60, color="red", alpha=0.12, label="Overheating event") temp_cross = np.where(data["temperature_c"] > 27)[0][0] plt.plot(data["time_min"][temp_cross], data["temperature_c"][temp_cross], "ro", label="Temperature threshold exceeded") plt.xlabel("Time [min]") plt.ylabel("Temperature [°C]") plt.title("Temperature Dynamics with Detection and Actuation") plt.legend() plt.grid(True) plt.savefig("temp_dynamics.pdf", bbox_inches="tight", pad_inches=0.1, dpi=600) plt.show()

#Humidity sensor plt.figure(figsize=(16, 6)) plt.rcParams.update({"font.size": 15}) plt.plot(data["time_min"], data["humidity_percent"], "k", label="Humidity sensor data") plt.axhline(70, color="red", linestyle="--", label="High threshold") plt.axhline(25, color="blue", linestyle="--", label="Low threshold") plt.axvspan(65, 90, color="red", alpha=0.12, label="High humidity event") hum_cross = np.where(data["humidity_percent"] > 70)[0][0] plt.plot(data["time_min"][hum_cross], data["humidity_percent"][hum_cross], "ro", label="Humidity threshold exceeded") plt.xlabel("Time [min]") plt.ylabel("Humidity [%]") plt.title("Humidity Dynamics with Detection and Actuation") plt.legend() plt.grid(True) plt.savefig("humidity_dynamics.pdf", bbox_inches="tight", pad_inches=0.1, dpi=600) plt.show()

#Dynamics of action plt.figure(figsize=(16, 6)) plt.rcParams.update({"font.size": 15}) plt.plot(data["time_min"], data["risk_level"], "k", label="Risk level") plt.plot(data["time_min"], data["ventilation_state"], "--", label="Ventilation state") plt.plot(data["time_min"], data["window_state"], "-.", label="Window state") plt.xlabel("Time [min]") plt.ylabel("State") plt.title("Risk Level ") plt.yticks([0, 1, 2, 3], ["Normal", "Warning", "High", "Critical"]) plt.legend() plt.grid(True) plt.savefig("RISKLEVEL.pdf", bbox_inches="tight", pad_inches=0.1, dpi=600) plt.show()