"""
Main simulation entry point.

Simulation flow:
1. Create SimPy environment
2. Create wireless channel
3. Randomly deploy nodes
4. Designate sink node
5. Start node processes
6. Run simulation
7. Output metrics
"""

import random
import json
import simpy

from sim.node.node import Node
from sim.radio.channel import Channel
from sim.core.metrics import MetricsCollector
from sim import config


def deploy_nodes(
    env: simpy.Environment,
    channel: Channel,
    num_nodes: int = None,
    area_size: float = None,
    metrics_collector: MetricsCollector = None,
) -> list:
    """
    Deploy nodes randomly in the area.

    Args:
        env: SimPy environment
        channel: Wireless channel
        num_nodes: Number of nodes (default from config)
        area_size: Area size (default from config)
        metrics_collector: Metrics collector for observability

    Returns:
        List of Node objects
    """
    if num_nodes is None:
        num_nodes = config.NODE_COUNT
    if area_size is None:
        area_size = config.AREA_SIZE

    nodes = []

    # Deploy sink node at center
    sink_x = area_size / 2
    sink_y = area_size / 2

    sink = Node(
        env=env,
        node_id=config.SINK_NODE_ID,
        x=sink_x,
        y=sink_y,
        channel=channel,
        is_sink=True,
        metrics_collector=metrics_collector,
    )
    nodes.append(sink)

    # Deploy other nodes randomly
    for i in range(1, num_nodes):
        x = random.uniform(0, area_size)
        y = random.uniform(0, area_size)

        node = Node(
            env=env,
            node_id=i,
            x=x,
            y=y,
            channel=channel,
            metrics_collector=metrics_collector,
        )
        nodes.append(node)

    return nodes


def setup_receive_callback(nodes: list, channel: Channel):
    """
    Setup receive callback from channel to nodes.

    Args:
        nodes: List of nodes
        channel: Wireless channel
    """

    def receive_dispatcher(node_id: int, received):
        """Dispatch received packet to appropriate node."""
        for node in nodes:
            if node.node_id == node_id:
                node.on_receive(received)
                break

    channel.receive_callback = receive_dispatcher


def run_simulation(
    num_nodes: int = None,
    area_size: float = None,
    sim_time: float = None,
    seed: int = None,
) -> dict:
    """
    Run the LoRa network simulation.

    Args:
        num_nodes: Number of nodes
        area_size: Area size in meters
        sim_time: Simulation time in seconds
        seed: Random seed for reproducibility

    Returns:
        Simulation results including metrics
    """
    # Set random seed
    if seed is not None:
        random.seed(seed)

    # Create environment
    env = simpy.Environment()

    # Create channel
    channel = Channel(env)

    # Create metrics collector first (before deploying nodes)
    metrics = MetricsCollector()
    metrics.set_start_time(0.0)

    # Deploy nodes with metrics collector
    if num_nodes is None:
        num_nodes = config.NODE_COUNT
    if area_size is None:
        area_size = config.AREA_SIZE
    if sim_time is None:
        sim_time = config.SIM_TIME

    nodes = deploy_nodes(env, channel, num_nodes, area_size, metrics)

    # Setup receive callbacks
    setup_receive_callback(nodes, channel)

    # Add collision callback
    initial_collisions = channel.collision_count

    # Start all nodes
    for node in nodes:
        node.start()

    # Run simulation
    env.run(until=sim_time)

    # Collect metrics
    convergence_time = config.HELLO_PERIOD * 3  # Estimate convergence
    metrics.set_convergence_time(convergence_time)

    # First add stats for non-sink nodes
    for node in nodes:
        if node.is_sink:
            continue
        stats = node.get_stats()
        metrics.add_node_stats(node.node_id, stats)

    # Then add sink stats
    for node in nodes:
        if node.is_sink:
            stats = node.get_stats()
            metrics.add_sink_stats(node.node_id, stats)
            break

    metrics.add_collision(channel.collision_count - initial_collisions)

    # Get results
    results = {
        "config": {
            "num_nodes": num_nodes,
            "area_size": area_size,
            "sim_time": sim_time,
            "seed": seed,
        },
        "metrics": metrics.get_metrics().get_summary(),
        "topology": [],
    }

    # Add topology info
    for node in nodes:
        results["topology"].append(
            {
                "node_id": node.node_id,
                "is_sink": node.is_sink,
                "x": node.x,
                "y": node.y,
                "cost": node.routing.cost if node.routing.cost != float("inf") else -1,
                "parent": node.routing.parent,
            }
        )

    return results


def main():
    """Main entry point."""
    print("=" * 60)
    print("LoRa Multi-Hop Network Simulation")
    print("=" * 60)

    # Run simulation
    results = run_simulation()

    # Print results
    print("\n--- Configuration ---")
    print(f"Nodes: {results['config']['num_nodes']}")
    print(
        f"Area: {results['config']['area_size']}m x {results['config']['area_size']}m"
    )
    print(f"Simulation time: {results['config']['sim_time']}s")

    print("\n--- Metrics ---")
    metrics = results["metrics"]
    print(f"Total sent: {metrics['total_sent']}")
    print(f"Total received: {metrics['total_received']}")
    print(f"Packet Delivery Ratio: {metrics['pdr']}%")
    print(f"Average hops: {metrics['avg_hop']}")
    print(f"Average retries: {metrics['avg_retries']}")
    print(f"Convergence time: {metrics['convergence_time']}s")
    print(f"Collisions: {metrics['collisions']}")

    print("\n--- Topology ---")
    for node_info in results["topology"]:
        parent_str = (
            f"-> {node_info['parent']}" if node_info["parent"] is not None else ""
        )
        print(
            f"Node {node_info['node_id']:2d}: cost={node_info['cost']:3d} {parent_str}"
        )

    # Save results
    with open("simulation_results.json", "w") as f:
        json.dump(results, f, indent=2)

    print("\nResults saved to simulation_results.json")


if __name__ == "__main__":
    main()