Update modules/simulator.py

modules/simulator.py

@@ -1,43 +1,25 @@
 import pandas as pd
 import numpy as np
 import datetime
-import uuid
 
-def simulate_data(n=50, faults=True, update_time=None):
+def simulate_data(n=10, faults=True):
     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] + (i * 0.0001) + np.random.uniform(-0.00005, 0.00005),  # Approx 10-15 ft spacing
-                base_hyderabad[1] + (i * 0.0001) + np.random.uniform(-0.00005, 0.00005),
-                f"Zone_{np.random.choice(['North', 'South', 'Central'])}"
-            ) for i 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)):
+
+    # Define location coordinates for the sites
+    locations = {
+        "Hyderabad": (17.385044, 78.486671),
+        "Gadwal": (16.2333, 77.1833),
+        "Kurnool": (15.8281, 78.0469),
+        "Ballari": (15.1502, 75.9246)
+    }
+
+    # Assign poles to sites randomly
+    for pole in poles:
+        site = np.random.choice(list(locations.keys()))
+        lat, lon = locations[site]
+
         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)
@@ -46,43 +28,42 @@ def simulate_data(n=50, faults=True, update_time=None):
         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")
+                anomaly.append("Pole Tilt Risk")
             if vib > 2.0:
-                anomaly.append("Excessive Vibration")
+                anomaly.append("Vibration Alert")
             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,
+            "Pole ID": pole,
             "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
+            "Solar Gen (kWh)": solar,
+            "Wind Gen (kWh)": wind,
+            "Power Required (kWh)": required,
+            "Power Sufficient": sufficient,
+            "Camera Status": cam,
+            "Tilt (°)": tilt,
+            "Vibration (g)": vib,
+            "Anomalies": "; ".join(anomaly) if anomaly else "None",
+            "Alert Level": alert,
+            "Latitude": lat,
+            "Longitude": lon
         })
-    return pd.DataFrame(data)
+
+    return pd.DataFrame(data)