import json
import os
from datetime import datetime, timedelta, timezone

DATA_FILE = 'monitoring_data.json'

def load_data():
    if os.path.exists(DATA_FILE):
        with open(DATA_FILE, 'r') as f:
            return json.load(f)
    return []

def store_data(new_data):
    data = load_data()
    data.append(new_data)
    with open(DATA_FILE, 'w') as f:
        json.dump(data, f, indent=4)

def calculate_baselines():
    data = load_data()
    if not data:
        return {}

    # For simplicity, we'll average the last 24 hours of data
    # More complex logic can be added here
    recent_data = [d for d in data if 'timestamp' in d and datetime.fromisoformat(d['timestamp'].replace('Z', '')).replace(tzinfo=timezone.utc) > datetime.now(timezone.utc) - timedelta(hours=24)]

    if not recent_data:
        return {}

    baseline_metrics = {
        'avg_rtt': sum(d['network_metrics']['rtt_avg'] for d in recent_data if 'rtt_avg' in d['network_metrics']) / len(recent_data),
        'packet_loss': sum(d['network_metrics']['packet_loss_rate'] for d in recent_data if 'packet_loss_rate' in d['network_metrics']) / len(recent_data),
        'avg_cpu_temp': sum(d['cpu_temperature']['cpu_temperature'] for d in recent_data if d['cpu_temperature']['cpu_temperature'] != "N/A") / len(recent_data),
        'avg_gpu_temp': sum(d['gpu_temperature']['gpu_temperature'] for d in recent_data if d['gpu_temperature']['gpu_temperature'] != "N/A") / len(recent_data),
    }

    return baseline_metrics