76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
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from inire.geometry.collision import CollisionEngine
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from inire.geometry.primitives import Port
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from inire.router.astar import AStarRouter
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from inire.router.cost import CostEvaluator
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from inire.router.danger_map import DangerMap
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from inire.router.pathfinder import PathFinder
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from inire.utils.visualization import plot_routing_results
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def main() -> None:
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print("Running Example 03: Locked Paths (Incremental Routing)...")
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# 1. Setup Environment
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bounds = (0, 0, 100, 100)
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engine = CollisionEngine(clearance=2.0)
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danger_map = DangerMap(bounds=bounds)
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danger_map.precompute([]) # No initial obstacles
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evaluator = CostEvaluator(engine, danger_map)
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router = AStarRouter(evaluator)
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pf = PathFinder(router, evaluator)
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# 2. Phase 1: Route a "Critical" Net
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# This net gets priority and takes the best path.
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netlist_phase1 = {
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"critical_net": (Port(10, 50, 0), Port(90, 50, 0)),
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}
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print("Phase 1: Routing critical_net...")
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results1 = pf.route_all(netlist_phase1, {"critical_net": 3.0}) # Wider trace
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if not results1["critical_net"].is_valid:
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print("Error: Phase 1 failed.")
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return
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# 3. Lock the Critical Net
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# This converts the dynamic path into a static obstacle in the collision engine.
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print("Locking critical_net...")
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engine.lock_net("critical_net")
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# Update danger map to reflect the new obstacle (optional but recommended for heuristics)
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# Extract polygons from result
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path_polys = [p for comp in results1["critical_net"].path for p in comp.geometry]
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danger_map.precompute(path_polys)
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# 4. Phase 2: Route a Secondary Net
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# This net must route *around* the locked critical_net.
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# Start and end points force a crossing path if it were straight.
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netlist_phase2 = {
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"secondary_net": (Port(50, 10, 90), Port(50, 90, 90)),
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}
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print("Phase 2: Routing secondary_net around locked path...")
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results2 = pf.route_all(netlist_phase2, {"secondary_net": 2.0})
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if results2["secondary_net"].is_valid:
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print("Success! Secondary net routed around locked path.")
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else:
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print("Failed to route secondary net.")
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# 5. Visualize
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# Combine results for plotting
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all_results = {**results1, **results2}
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# Note: 'critical_net' is now in engine.static_obstacles internally,
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# but for visualization we plot it from the result object to see it clearly.
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# We pass an empty list for 'static_obstacles' to plot_routing_results
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# because we want to see the path colored, not grayed out as an obstacle.
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fig, ax = plot_routing_results(all_results, [], bounds)
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fig.savefig("examples/locked.png")
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print("Saved plot to examples/locked.png")
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if __name__ == "__main__":
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main()
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