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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 + ["Ballari"] * 14
# Simulate coordinates and zones for each location
base_hyderabad = (17.329181, 78.610091) # Vacant land space in Hyderabad
coords = {
"Hyderabad": [
(
base_hyderabad[0] + np.random.uniform(-0.00015, 0.00015),
base_hyderabad[1] + np.random.uniform(-0.00015, 0.00015),
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)
],
"Ballari": [
(15.139 + np.random.uniform(-0.04, 0.04), 76.921 + np.random.uniform(-0.04, 0.04), f"Zone_{np.random.choice(['Downtown', 'Industrial', 'Residential'])}")
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) |