112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
import random
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from datetime import datetime, timedelta
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from pymongo import MongoClient
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MONGO_URI = "mongodb+srv://SarayuJ:[EMAIL_ADDRESS]/testing"
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client = MongoClient(MONGO_URI)
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db = client.study_buddy_db
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collection = db.study_rooms
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UMD_LOCATIONS = [
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{ "id": 'esj', "name": 'Edward St. John (ESJ)', "lng": -76.94209511596014, "lat": 38.987133359608755 },
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{ "id": 'mckeldin', "name": 'McKeldin Library', "lng": -76.94494907523277, "lat": 38.986021017749366 },
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{ "id": 'hornbake', "name": 'Hornbake Library', "lng": -76.94161787005467, "lat": 38.988233373664826 },
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{ "id": 'stem', "name": 'STEM Library', "lng": -76.93942003731279, "lat": 38.988991437126195 },
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{ "id": 'clarice', "name": 'Clarice Library', "lng": -76.9500912552473, "lat": 38.990547823732285 },
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{ "id": 'yahentamitsi', "name": 'Yahentamitsi', "lng": -76.9448027183373, "lat": 38.99108961575231 },
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{ "id": 'iribe', "name": 'Iribe', "lng": -76.93643838603555, "lat": 38.98933701397555 },
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{ "id": 'reckord', "name": 'Reckord Armory', "lng": -76.93897470250619, "lat": 38.98609556181066 },
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{ "id": 'stamp', "name": 'Stamp Student Union', "lng": -76.94473083972326, "lat": 38.988130238874874 }
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]
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def get_db_for_time_and_location(hour, loc_id):
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"""
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Generate a dB level based on the hour of the day and the location.
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This creates a recognizable pattern for AI analysis.
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"""
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base_db = 40.0
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if loc_id in ['mckeldin', 'esj']:
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if 10 <= hour <= 16:
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base_db = 75.0
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elif 17 <= hour <= 22:
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base_db = 60.0
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else:
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base_db = 45.0
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elif loc_id in ['stem', 'iribe']:
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if 14 <= hour <= 20:
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base_db = 70.0
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elif 9 <= hour <= 13:
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base_db = 55.0
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else:
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base_db = 42.0
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elif loc_id == 'stamp':
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if 12 <= hour <= 14 or 17 <= hour <= 19:
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base_db = 85.0
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elif 10 <= hour <= 21:
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base_db = 65.0
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else:
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base_db = 50.0
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else:
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if 9 <= hour <= 18:
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base_db = 60.0
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else:
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base_db = 45.0
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noise = random.uniform(-5.0, 5.0)
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return max(30.0, min(100.0, base_db + noise))
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def generate_fake_data():
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print("Clearing existing study room data...")
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collection.delete_many({})
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now = datetime.utcnow()
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start_time = now - timedelta(hours=24)
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docs_to_insert = []
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print("Generating 24 hours of fake data with patterns...")
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current_time = start_time
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while current_time <= now:
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hour = current_time.hour
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for loc in UMD_LOCATIONS:
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db_level = get_db_for_time_and_location(hour, loc["id"])
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# Estimate people based on noise level.
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# 35dB = ~0 people. Every 1.5 dB above 35 adds ~1 person.
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base_people = max(0, (db_level - 35) * 1.5)
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people_count = int(max(0, base_people + random.uniform(-5, 10)))
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doc = {
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"room_id": loc["id"],
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"location": {
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"type": "Point",
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"coordinates": [loc["lng"], loc["lat"]]
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},
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"db": round(db_level, 2),
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"people": people_count,
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"date": current_time
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}
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docs_to_insert.append(doc)
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current_time += timedelta(minutes=15)
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print(f"Inserting {len(docs_to_insert)} records into MongoDB...")
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collection.insert_many(docs_to_insert)
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print("Done!")
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if __name__ == "__main__":
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generate_fake_data()
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