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

def simulate_data(n=50, faults=True, update_time=None):
    today = datetime.date.today()
    update_time = update_time or datetime.datetime.now()  # Use provided time or current time
    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
    # Simulate coordinates and zones for each location
    coords = {
        "Hyderabad": [(17.385 + np.random.uniform(-0.05, 0.05), 78.486 + np.random.uniform(-0.05, 0.05), f"Zone_{np.random.choice(['North', 'South', 'Central'])}") for _ in range(12)],
        "Gadwal": [(16.235 + np.random.uniform(-0.03, 0.03), 77.795 + np.random.uniform(-0.03, 0.03), f"Zone_{np.random.choice(['East', 'West'])}") for _ in range(12)],
        "Kurnool": [(15.828 + np.random.uniform(-0.04, 0.04), 78.037 + np.random.uniform(-0.04, 0.04), f"Zone_{np.random.choice(['Urban', 'Rural'])}") for _ in range(12)],
        "Bangalore": [(12.971 + np.random.uniform(-0.06, 0.06), 77.594 + np.random.uniform(-0.06, 0.06), f"Zone_{np.random.choice(['Downtown', 'Suburban', 'Industrial'])}") for _ in range(14)]
    }
    location_coords = []
    for loc in locations:
        coord = coords[loc].pop(0)
        location_coords.append(coord)
    data = []
    for i, (pole, location, (lat, lon, zone)) in enumerate(zip(poles, locations, location_coords)):
        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,
            "Zone": zone,
            "Latitude": lat,
            "Longitude": lon,
            "Date": today,
            "Timestamp": update_time,
            "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)