import pandas as pd
import matplotlib.pyplot as plt

# Load the datasets (update paths as needed)
df1 = pd.read_csv("../pool2/temps_humid_pressure.csv", skiprows=1, on_bad_lines='skip')
df2 = pd.read_csv("../pool/temps_humid_pressure.csv", skiprows=1, on_bad_lines='skip')


# Combine DATE and TIME into a single datetime column
df1["DATETIME"] = pd.to_datetime(df1["DATE"] + " " + df1["TIME"])
df2["DATETIME"] = pd.to_datetime(df2["DATE"] + " " + df2["TIME"])
df1.columns = df1.columns.str.strip()
df2.columns = df2.columns.str.strip()


# Calculate average reading interval in seconds, then convert to minutes
df1["DELTA"] = df1["DATETIME"].diff().dt.total_seconds()
df2["DELTA"] = df2["DATETIME"].diff().dt.total_seconds()
avg_time_df1 = round(df1["DELTA"].mean() / 60)
avg_time_df2 = round(df2["DELTA"].mean() / 60)

print(f"Average reading interval for file 1: {avg_time_df1} minutes")
print(f"Average reading interval for file 2: {avg_time_df2} minutes")

# Plot overlay of temperatures
plt.figure(figsize=(10, 5))
plt.plot(df1["DATETIME"], df1["ATH_T"], label="ATH_T (File 1)", marker='o')
plt.plot(df1["DATETIME"], df1["BMP_T"], label="BMP_T (File 1)", marker='x')
plt.plot(df2["DATETIME"], df2["ATH_T"], label="ATH_T (File 2)", marker='o')
plt.plot(df2["DATETIME"], df2["BMP_T"], label="BMP_T (File 2)", marker='x')
plt.xlabel("Time")
plt.ylabel("Temperature (°C)")
plt.title("Overlay of ATH_T and BMP_T")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.xticks(rotation=45)
plt.show()

# Plot overlay of humidity
plt.figure(figsize=(10, 5))
plt.plot(df1["DATETIME"], df1["ATH_H"], label="ATH_H (File 1)", marker='o')
plt.plot(df2["DATETIME"], df2["ATH_H"], label="ATH_H (File 2)", marker='x')
plt.xlabel("Time")
plt.ylabel("Humidity (%)")
plt.title("Overlay of ATH_H")
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.xticks(rotation=45)
plt.show()