import pandas as pd
import numpy as np
import datetime
import uuid

def simulate_data(n=50, faults=True):
    today = datetime.date.today()
    poles = [f"Pole_{i+1:03}" for i in range(n)]
    # Distribute poles across 4 locations
    locations = ["Hyderabad"] * 12 + ["Gadwal"] * 12 + ["Kurnool"] * 12 + ["Bangalore"] * 14
    np.random.shuffle(locations)  # Randomize for realism
    data = []
    for i, (pole, location) in enumerate(zip(poles, locations)):
        solar = round(np.random.uniform(3.0, 7.5), 2)
        wind = round(np.random.uniform(0.5, 2.0), 2)
        required = round(np.random.uniform(1.0, 1.5), 2)
        total = solar + wind
        cam = np.random.choice(['Online', 'Offline'], p=[0.85, 0.15]) if faults else "Online"
        tilt = round(np.random.uniform(0, 12), 1)
        vib = round(np.random.uniform(0.1, 2.5), 2)
        sufficient = "Yes" if total >= required else "No"
        rfid = str(uuid.uuid4())[:16]  # 16-digit unique RFID
        anomaly = []
        if faults:
            if solar < 4.0:
                anomaly.append("Low Solar Output")
            if wind < 0.7:
                anomaly.append("Low Wind Output")
            if tilt > 10:
                anomaly.append("High Pole Tilt Risk")
            if vib > 2.0:
                anomaly.append("Excessive Vibration")
            if cam == "Offline":
                anomaly.append("Camera Offline")
            if sufficient == "No":
                anomaly.append("Power Insufficient")
        alert = "Green"
        if len(anomaly) == 1:
            alert = "Yellow"
        elif len(anomaly) > 1:
            alert = "Red"
        data.append({
            "PoleID": pole,
            "RFID": rfid,
            "Location": location,
            "Date": today,
            "SolarGen(kWh)": solar,
            "WindGen(kWh)": wind,
            "PowerRequired(kWh)": required,
            "PowerSufficient": sufficient,
            "CameraStatus": cam,
            "Tilt(°)": tilt,
            "Vibration(g)": vib,
            "Anomalies": ";".join(anomaly) if anomaly else "None",
            "AlertLevel": alert
        })
    return pd.DataFrame(data)