inire/examples/07_large_scale_routing.py

import numpy as np
import time
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics, route_astar
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.pathfinder import PathFinder
from inire.utils.visualization import plot_routing_results, plot_danger_map, plot_expanded_nodes, plot_expansion_density
from shapely.geometry import box

def main() -> None:
    print("Running Example 07: Fan-Out (10 Nets, 50um Radius, 5um Grid)...")

    # 1. Setup Environment
    bounds = (0, 0, 1000, 1000)
    engine = CollisionEngine(clearance=6.0)

    # Bottleneck at x=500, 200um gap
    obstacles = [
        box(450, 0, 550, 400),
        box(450, 600, 550, 1000),
    ]
    for obs in obstacles:
        engine.add_static_obstacle(obs)

    danger_map = DangerMap(bounds=bounds)
    danger_map.precompute(obstacles)

    evaluator = CostEvaluator(engine, danger_map, greedy_h_weight=1.5, unit_length_cost=0.1, bend_penalty=100.0, sbend_penalty=400.0, congestion_penalty=100.0)

    context = AStarContext(evaluator, node_limit=2000000, snap_size=5.0, bend_radii=[50.0], sbend_radii=[50.0])
    metrics = AStarMetrics()
    pf = PathFinder(context, metrics, max_iterations=15, base_congestion_penalty=100.0, congestion_multiplier=1.4)

    # 2. Define Netlist
    netlist = {}
    num_nets = 10
    start_x = 50
    start_y_base = 500 - (num_nets * 10.0) / 2.0

    end_x = 950
    end_y_base = 100
    end_y_pitch = 800.0 / (num_nets - 1)

    for i in range(num_nets):
        sy = round((start_y_base + i * 10.0) / 5.0) * 5.0
        ey = round((end_y_base + i * end_y_pitch) / 5.0) * 5.0
        netlist[f"net_{i:02d}"] = (Port(start_x, sy, 0), Port(end_x, ey, 0))

    net_widths = {nid: 2.0 for nid in netlist}

    # 3. Route
    print(f"Routing {len(netlist)} nets through 200um bottleneck...")

    iteration_stats = []

    def iteration_callback(idx, current_results):
        successes = sum(1 for r in current_results.values() if r.is_valid)
        total_collisions = sum(r.collisions for r in current_results.values())
        total_nodes = metrics.nodes_expanded

        # Identify Hotspots
        hotspots = {}
        overlap_matrix = {} # (net_a, net_b) -> count

        for nid, res in current_results.items():
            if not res.path:
                continue
            for comp in res.path:
                for poly in comp.geometry:
                    # Check what it overlaps with
                    overlaps = engine.dynamic_index.intersection(poly.bounds)
                    for other_obj_id in overlaps:
                        if other_obj_id in engine.dynamic_geometries:
                            other_nid, other_poly = engine.dynamic_geometries[other_obj_id]
                            if other_nid != nid:
                                if poly.intersects(other_poly):
                                    # Record hotspot
                                    cx, cy = poly.centroid.x, poly.centroid.y
                                    grid_key = (int(cx/20)*20, int(cy/20)*20)
                                    hotspots[grid_key] = hotspots.get(grid_key, 0) + 1

                                    # Record pair
                                    pair = tuple(sorted((nid, other_nid)))
                                    overlap_matrix[pair] = overlap_matrix.get(pair, 0) + 1

        print(f"  Iteration {idx} finished. Successes: {successes}/{len(netlist)}, Collisions: {total_collisions}")
        if overlap_matrix:
            top_pairs = sorted(overlap_matrix.items(), key=lambda x: x[1], reverse=True)[:3]
            print(f"  Top Conflicts: {top_pairs}")
        if hotspots:
            top_hotspots = sorted(hotspots.items(), key=lambda x: x[1], reverse=True)[:3]
            print(f"  Top Hotspots: {top_hotspots}")

        # Adaptive Greediness: Decay from 1.5 to 1.1 over 10 iterations
        new_greedy = max(1.1, 1.5 - ((idx + 1) / 10.0) * 0.4)
        evaluator.greedy_h_weight = new_greedy
        print(f"  Adaptive Greedy Weight for Next Iteration: {new_greedy:.3f}")

        iteration_stats.append({
            'Iteration': idx,
            'Success': successes,
            'Congestion': total_collisions,
            'Nodes': total_nodes
        })

        # Save plots only for certain iterations to save time
#        if idx % 20 == 0 or idx == pf.max_iterations - 1:
        if True:
            # Save a plot of this iteration's result
            fig, ax = plot_routing_results(current_results, obstacles, bounds, netlist=netlist)
            plot_danger_map(danger_map, ax=ax)

            # Overlay failures: show where they stopped
            for nid, res in current_results.items():
                if not res.is_valid and res.path:
                    last_p = res.path[-1].end_port
                    target_p = netlist[nid][1]
                    dist = abs(last_p.x - target_p.x) + abs(last_p.y - target_p.y)
                    ax.scatter(last_p.x, last_p.y, color='red', marker='x', s=100)
                    ax.text(last_p.x, last_p.y, f" {nid} (rem: {dist:.0f}um)", color='red', fontsize=8)

            fig.savefig(f"examples/07_iteration_{idx:02d}.png")
            import matplotlib.pyplot as plt
            plt.close(fig)

            # Plot Expansion Density if data is available
            if pf.accumulated_expanded_nodes:
                 fig_d, ax_d = plot_expansion_density(pf.accumulated_expanded_nodes, bounds)
                 fig_d.savefig(f"examples/07_iteration_{idx:02d}_density.png")
                 plt.close(fig_d)

        metrics.reset_per_route()

    import cProfile, pstats
    profiler = cProfile.Profile()
    profiler.enable()
    t0 = time.perf_counter()
    results = pf.route_all(netlist, net_widths, store_expanded=True, iteration_callback=iteration_callback, shuffle_nets=True, seed=42)
    t1 = time.perf_counter()
    profiler.disable()

    # ... (rest of the code)
    stats = pstats.Stats(profiler).sort_stats('tottime')
    stats.print_stats(20)
    print(f"Routing took {t1-t0:.4f}s")

    # 4. Check Results
    print("\n--- Iteration Summary ---")
    print(f"{'Iter':<5} | {'Success':<8} | {'Congest':<8} | {'Nodes':<10}")
    print("-" * 40)
    for s in iteration_stats:
        print(f"{s['Iteration']:<5} | {s['Success']:<8} | {s['Congestion']:<8} | {s['Nodes']:<10}")

    success_count = sum(1 for res in results.values() if res.is_valid)
    print(f"\nFinal: Routed {success_count}/{len(netlist)} nets successfully.")

    for nid, res in results.items():
        target_p = netlist[nid][1]
        if not res.is_valid:
            last_p = res.path[-1].end_port if res.path else netlist[nid][0]
            dist = abs(last_p.x - target_p.x) + abs(last_p.y - target_p.y)
            print(f"  FAILED: {nid} (Stopped {dist:.1f}um from target)")
        else:
            types = [move.move_type for move in res.path]
            from collections import Counter
            counts = Counter(types)
            print(f"  {nid}: {len(res.path)} segments, {dict(counts)}")

    # 5. Visualize
    fig, ax = plot_routing_results(results, obstacles, bounds, netlist=netlist)

    # Overlay Danger Map
    plot_danger_map(danger_map, ax=ax)

    # Overlay Expanded Nodes from last routed net (as an example)
    if metrics.last_expanded_nodes:
        print(f"Plotting {len(metrics.last_expanded_nodes)} expanded nodes for the last net...")
        plot_expanded_nodes(metrics.last_expanded_nodes, ax=ax, color='blue', alpha=0.1)

    fig.savefig("examples/07_large_scale_routing.png")
    print("Saved plot to examples/07_large_scale_routing.png")

if __name__ == "__main__":
    main()