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# Virtual environments
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# Inire Configuration & API Documentation
This document describes the user-tunable parameters for the `inire` auto-router.
## 1. AStarContext Parameters
The `AStarContext` stores the configuration and persistent state for the A* search. It is initialized once and passed to `route_astar` or the `PathFinder`.
| Parameter | Type | Default | Description |
| :-------------------- | :------------ | :----------------- | :------------------------------------------------------------------------------------ |
| `node_limit` | `int` | 1,000,000 | Maximum number of states to explore per net. Increase for very complex paths. |
| `snap_size` | `float` | 5.0 | Grid size (µm) for expansion moves. Larger values speed up search. |
| `max_straight_length` | `float` | 2000.0 | Maximum length (µm) of a single straight segment. |
| `min_straight_length` | `float` | 5.0 | Minimum length (µm) of a single straight segment. |
| `bend_radii` | `list[float]` | `[50.0, 100.0]` | Available radii for 90-degree turns (µm). |
| `sbend_radii` | `list[float]` | `[5.0, 10.0, 50.0, 100.0]` | Available radii for S-bends (µm). |
| `sbend_offsets` | `list[float] \| None` | `None` (Auto) | Lateral offsets for parametric S-bends. |
| `bend_penalty` | `float` | 250.0 | Flat cost added for every 90-degree bend. |
| `sbend_penalty` | `float` | 500.0 | Flat cost added for every S-bend. |
| `bend_collision_type` | `str` | `"arc"` | Collision model for bends: `"arc"`, `"bbox"`, or `"clipped_bbox"`. |
| `bend_clip_margin` | `float` | 10.0 | Extra space (µm) around the waveguide for clipped models. |
| `visibility_guidance` | `str` | `"tangent_corner"` | Visibility-driven straight candidate mode: `"off"`, `"exact_corner"`, or `"tangent_corner"`. |
## 2. AStarMetrics
The `AStarMetrics` object collects performance data during the search.
| Property | Type | Description |
| :--------------------- | :---- | :---------------------------------------------------- |
| `nodes_expanded` | `int` | Number of nodes expanded in the last `route_astar` call. |
| `total_nodes_expanded` | `int` | Cumulative nodes expanded across all calls. |
| `max_depth_reached` | `int` | Deepest point in the search tree reached. |
---
## 3. CostEvaluator Parameters
The `CostEvaluator` defines the "goodness" of a path.
| Parameter | Type | Default | Description |
| :------------------- | :------ | :--------- | :--------------------------------------------------------------------------------------- |
| `unit_length_cost` | `float` | 1.0 | Cost per µm of wire length. |
| `greedy_h_weight` | `float` | 1.1 | Heuristic weight. `1.0` is optimal; higher values (e.g. `1.5`) speed up search. |
| `congestion_penalty` | `float` | 10,000.0 | Multiplier for overlaps in the multi-net Negotiated Congestion loop. |
---
## 3. PathFinder Parameters
The `PathFinder` orchestrates multi-net routing using the Negotiated Congestion algorithm.
| Parameter | Type | Default | Description |
| :------------------------ | :------ | :------ | :-------------------------------------------------------------------------------------- |
| `max_iterations` | `int` | 10 | Maximum number of rip-up and reroute iterations to resolve congestion. |
| `base_congestion_penalty` | `float` | 100.0 | Starting penalty for overlaps. Multiplied by `1.5` each iteration if congestion remains.|
---
## 4. CollisionEngine Parameters
| Parameter | Type | Default | Description |
| :------------------- | :------ | :--------- | :------------------------------------------------------------------------------------ |
| `clearance` | `float` | (Required) | Minimum required distance between any two waveguides or obstacles (µm). |
| `safety_zone_radius` | `float` | 0.0021 | Radius (µm) around ports where collisions are ignored for PDK boundary incidence. |
---
## 4. Physical Units & Precision
- **Coordinates**: Micrometers (µm).
- **Grid Snapping**: The router internally operates on a **1nm** grid for final ports and a **1µm** lattice for expansion moves.
- **Search Space**: Assumptions are optimized for design areas up to **20mm x 20mm**.
- **Design Bounds**: The boundary limits defined in `DangerMap` strictly constrain the **physical edges** (dilated geometry) of the waveguide. Any move that would cause the waveguide or its required clearance to extend beyond these bounds is rejected with an infinite cost.
---
## 5. Best Practices & Tuning Advice
### Speed vs. Optimality
The `greedy_h_weight` is your primary lever for search performance.
- **`1.0`**: Dijkstra-like behavior. Guarantees the shortest path but is very slow.
- **`1.1` to `1.2`**: Recommended range. Balances wire length with fast convergence.
- **`> 1.5`**: Extremely fast "greedy" search. May produce zig-zags or suboptimal detours.
### Avoiding "Zig-Zags"
If the router produces many small bends instead of a long straight line:
1. Increase `bend_penalty` (e.g., set to `100.0` or higher).
2. Ensure `straight_lengths` includes larger values like `25.0` or `100.0`.
3. Decrease `greedy_h_weight` closer to `1.0`.
### Visibility Guidance
The router can bias straight stop points using static obstacle corners.
- **`"tangent_corner"`**: Default. Proposes straight lengths that set up a clean tangent bend around nearby visible corners. This helps obstacle-dense layouts more than open space.
- **`"exact_corner"`**: Only uses precomputed corner-to-corner visibility when the current search state already lands on an obstacle corner.
- **`"off"`**: Disables visibility-derived straight candidates entirely.
### Handling Congestion
In multi-net designs, if nets are overlapping:
1. Increase `congestion_penalty` in `CostEvaluator`.
2. Increase `max_iterations` in `PathFinder`.
3. If a solution is still not found, check if the `clearance` is physically possible given the design's narrowest bottlenecks.
### S-Bend Usage
Parametric S-bends bridge lateral gaps without changing the waveguide's orientation.
- **Automatic Selection**: If `sbend_offsets` is set to `None` (the default), the router automatically chooses from a set of "natural" offsets (Fibonacci-aligned grid steps) and the offset needed to hit the target.
- **Specific Offsets**: To use specific offsets (e.g., 5.86µm for a 45° switchover), provide them in the `sbend_offsets` list. The router will prioritize these but will still try to align with the target if possible.
- **Constraints**: S-bends are only used for offsets $O < 2R$. For larger shifts, the router naturally combines two 90° bends and a straight segment.

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# inire: Auto-Routing for Photonic and RF Integrated Circuits
`inire` is a high-performance auto-router designed specifically for the physical constraints of photonic and RF integrated circuits. It utilizes a Hybrid State-Lattice A* search combined with "Negotiated Congestion" (PathFinder) to route multiple nets while maintaining strict geometric fidelity and clearance.
## Key Features
* **Hybrid State-Lattice Search**: Routes using discrete 90° bends and parametric S-bends, ensuring manufacturing-stable paths.
* **Negotiated Congestion**: Iteratively resolves multi-net bottlenecks by inflating costs in high-traffic regions.
* **Analytic Correctness**: Every move is verified against an R-Tree spatial index of obstacles and other paths.
* **1nm Precision**: All coordinates and ports are snapped to a 1nm manufacturing grid.
* **Safety & Proximity**: Incorporates a "Danger Map" (pre-computed distance transform) to maintain optimal spacing and reduce crosstalk.
* **Locked Paths**: Supports treating existing geometries as fixed obstacles for incremental routing sessions.
## Installation
`inire` requires Python 3.11+. You can install the dependencies using `uv` (recommended) or `pip`:
```bash
# Using uv
uv sync
# Using pip
pip install numpy scipy shapely rtree matplotlib
```
## Quick Start
```python
from inire.geometry.primitives import Port
from inire.geometry.collision import CollisionEngine
from inire.router.danger_map import DangerMap
from inire.router.cost import CostEvaluator
from inire.router.astar import AStarContext
from inire.router.pathfinder import PathFinder
# 1. Setup Environment
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=(0, 0, 1000, 1000))
danger_map.precompute([]) # Add polygons here for obstacles
# 2. Configure Router
evaluator = CostEvaluator(
collision_engine=engine,
danger_map=danger_map,
greedy_h_weight=1.2
)
context = AStarContext(
cost_evaluator=evaluator,
bend_penalty=10.0
)
pf = PathFinder(context)
# 3. Define Netlist
netlist = {
"net1": (Port(0, 0, 0), Port(100, 50, 0)),
}
# 4. Route
results = pf.route_all(netlist, {"net1": 2.0})
if results["net1"].is_valid:
print("Successfully routed net1!")
```
## Usage Examples
For detailed visual demonstrations and architectural deep-dives, see the **[Examples README](examples/README.md)**.
Check the `examples/` directory for ready-to-run scripts. To run an example:
```bash
python3 examples/01_simple_route.py
```
## Documentation
Full documentation for all user-tunable parameters, cost functions, and collision models can be found in **[DOCS.md](DOCS.md)**.
## Architecture
`inire` operates on a **State-Lattice** defined by $(x, y, \theta)$. From any state, the router expands via three primary "Move" types:
1. **Straights**: Variable-length segments.
2. **90° Bends**: Fixed-radius PDK cells.
3. **Parametric S-Bends**: Procedural arcs for bridging small lateral offsets ($O < 2R$).
For multi-net problems, the **PathFinder** loop handles rip-up and reroute logic, ensuring that paths find the globally optimal configuration without crossings.
## Configuration
`inire` is highly tunable. Every major component (Router, CostEvaluator, PathFinder) accepts explicit named arguments in its constructor to control expansion rules, cost weights, and convergence limits. See `DOCS.md` for a full parameter reference.
## License
This project is licensed under the GNU Affero General Public License v3. See `LICENSE.md` for details.

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# Cost and Collision Engine Spec
This document describes the methods for ensuring "analytic correctness" while maintaining a computationally efficient cost function.
## 1. Analytic Correctness
The router balances speed and verification using a two-tier approach.
### 1.1. R-Tree Geometry Engine
The router uses an **R-Tree of Polygons** for all geometric queries.
* **Move Validation:** Every "Move" proposed by the search is checked for intersection against the R-Tree.
* **Pre-dilation for Clearance:** All obstacles and paths use a global clearance $C$. At the start of the session, all user-provided obstacles are **pre-dilated by $(W_{max} + C)/2$** for initial broad pruning. However, individual path dilation for intersection tests uses $(W_i + C)/2$ for a specific net's width $W_i$.
* **Safety Zone:** To prevent immediate collision flags for ports placed on or near obstacle boundaries, the router ignores collisions within a radius of **2nm** of start and end ports.
* **Single Layer:** All routing and collision detection occur on a single layer.
### 1.2. Cost Calculation (Soft Constraint)
The "Danger Cost" $g_{proximity}(n)$ is a function of the distance $d$ to the nearest obstacle:
$$g_{proximity}(n) = \begin{cases} \infty & \text{if } d < (W_i + C)/2 \\ \frac{k}{d^2} & \text{if } (W_i + C)/2 \le d < \text{Safety Threshold} \\ 0 & \text{if } d \ge \text{Safety Threshold} \end{cases}$$
To optimize A* search, a **Static Danger Map (Precomputed Grid)** is used for the heuristic.
* **Grid Resolution:** Default **1000nm (1µm)**.
* **Static Nature:** The grid only accounts for fixed obstacles. It is computed once at the start of the session and is **not re-computed** during the Negotiated Congestion loop.
* **Efficiency:** For a 20x20mm layout, this results in a 20k x 20k matrix.
* **Memory:** Using a `uint8` or `float16` representation, this consumes ~400-800MB (Default < 2GB). For extremely high resolution or larger areas, the system supports up to **20GB** allocation.
* **Precision:** Strict intersection checks still use the R-Tree for "analytic correctness."
## 2. Collision Detection Implementation
The system relies on `shapely` for geometry and `rtree` for spatial indexing.
1. **Arc Resolution:** Arcs are represented as polygons approximated by segments with a maximum deviation (sagitta).
2. **Intersection Test:** A "Move" is valid only if its geometry does not intersect any obstacle in the R-Tree.
3. **Self-Intersection:** Paths from the same net must not intersect themselves.
4. **No Crossings:** Strictly 2D; no crossings (vias or bridges) are supported.
## 3. Negotiated Congestion (Path R-Tree)
To handle multiple nets, the router maintains a separate R-Tree containing the dilated geometries ($C/2$) of all currently routed paths.
* **Congestion Cost:** $P \times (\text{Overlaps in Path R-Tree})$.
* **Failure Policy:** If no collision-free path is found after the max iterations, the router returns the **"least-bad" (lowest cost)** path. These paths MUST be explicitly flagged as invalid (e.g., via an `is_valid=False` attribute or a non-zero `collision_count`) so the user can identify and manually fix the failure.
## 4. Handling Global Clearances
Clearances are global. Both obstacles and paths are pre-dilated once. This ensures that any two objects maintain at least $C$ distance if their dilated versions do not intersect.
## 5. Locked Paths
The router supports **Locked Paths**—existing geometries inserted into the static Obstacle R-Tree, ensuring they are never modified or rerouted.

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# Geometric Representation and Data Structures
This document defines the core data structures for representing the routing problem.
## 1. Port Definitions (Connectivity)
Routing requests are defined as a mapping of source ports to destination ports.
* **Port Structure:** `(x, y, orientation)`
* `x, y`: Coordinates snapped to **1nm** grid.
* `orientation`: Strictly $\{0, 90, 180, 270\}$ degrees.
* **Netlist:** A dictionary or list of tuples: `{(start_port): (end_port)}`. Each entry represents a single-layer path that must be routed.
* **Heterogeneous Widths:** The router supports different widths $W_i$ per net. Dilation for a specific net $i$ is calculated as $(W_i + C)/2$ to maintain the global clearance $C$.
## 2. Component Library (Move Generator)
The router uses a discrete set of components to expand states in the A* search.
### 2.1. Straight Waveguides
* **A* Expansion:** Generates "Straight" moves of varying lengths (e.g., $1\mu m, 5\mu m, 25\mu m$).
* **Snap-to-Target:** If the destination port $T$ is directly ahead and the current state's orientation matches $T$'s entry orientation, a special move is generated to close the gap exactly.
### 2.2. Fixed 90° Bends (PDK Cells)
* **Parameters:** `radius`, `width`.
* **A* Expansion:** A discrete move that changes orientation by $\pm 90^\circ$ and shifts the coordinate by the radius.
* **Grid Alignment:** If the bend radius $R$ is not a multiple of the search grid (default $1\mu m$), the resulting state is **snapped to the nearest grid point**, and a warning is issued to the user.
### 2.3. Parametric S-Bends (Compact)
* **Parameters:** `radius`, `width`.
* **A* Expansion:** Used ONLY for lateral offsets $O < 2R$.
* **Large Offsets ($O \ge 2R$):** The router does not use a single S-bend move for large offsets. Instead, the A* search naturally finds the optimal path by combining two 90° bends and a straight segment. This ensures maximum flexibility in obstacle avoidance for large shifts.
## 3. Obstacle Representation
Obstacles are provided as raw polygons on a single layer.
* **Pre-processing:** All input polygons are inserted into an **R-Tree**.
* **Buffer/Dilation:** Obstacles are pre-dilated by $(W_{max} + Clearance)/2$ for initial pruning, but final collision tests use the net-specific width $W_i$.
* **No Multi-layer:** The router assumes all obstacles and paths share the same plane.
* **Safety Zone:** Ignore collisions within **2nm** of start and end ports for robustness.

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# High-Level Notes: Auto-Routing for Integrated Circuits
We're implementing auto-routing for photonic and RF integrated circuits. The problem space has the following features:
- **Single Layer:** All paths are on a single layer; multi-layer routing is not supported.
- **No Crossings:** Crossings are to be avoided and are not supported by the router's automatic placement. The user must manually handle any required crossings.
- **Large Bend Radii:** Bends use large radii ($R$), and are usually pre-generated (e.g. 90-degree cells).
- **Proximity Sensitivity:** Paths are sensitive to proximity to other paths and obstacles (crosstalk/coupling).
- **Manhattan Preference:** Manhattan pathing is sufficient for most cases; any-angle is rare.
- **S-Bends:** S-bends are necessary for small lateral offsets ($O < 2R$).
- **Hybrid Search:** A* state-lattice search is used for discrete component placement.
- **Dilation:** A $Clearance/2$ dilation is applied to all obstacles and paths for efficient collision avoidance.
- **Negotiated Congestion:** A multi-net "PathFinder" loop iteratively reroutes nets to resolve congestion.

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# Implementation Plan
This plan outlines the step-by-step implementation of the `inire` auto-router. For detailed test cases, refer to [Testing Plan](./testing_plan.md).
## Phase 1: Core Geometry & Move Generation
**Goal:** Implement Ports, Polygons, and Component Library with high geometric fidelity.
1. **Project Setup:** Initialize `inire/` structure and `pytest` configuration. Include `hypothesis` for property-based testing.
2. **`geometry.primitives`:**
* `Port` with **1nm** snapping.
* Basic 2D transformations (rotate, translate).
* **Property-Based Tests:** Verify transform invariants (e.g., $90^\circ$ rotation cycles).
3. **`geometry.components`:**
* `Straight`, `Bend90`, `SBend`.
* **Search Grid Snapping:** Implement 1µm snapping for expanded ports.
* **Small S-Bends ($O < 2R$):** Logic for parametric generation.
* **Edge Cases:** Handle $O=2R$ and $L < 1\mu m$.
4. **Tests:**
* Verify geometric correctness (refer to Testing Plan Section 1).
* Unit tests for `Port` snapping and component transformations.
## Phase 2: Collision Engine & Cost
**Goal:** Build the R-Tree wrapper and the analytic cost function.
1. **`geometry.collision`:** Implement `CollisionEngine`.
* **Pre-dilation:** Obstacles/Paths dilated by $Clearance/2$.
* **Safety Zone:** Ignore collisions within **2nm** of start/end ports.
2. **`router.danger_map`:**
* Implement **1µm** pre-computed proximity grid.
* Optimize for design sizes up to **20x20mm** (< 2GB memory).
3. **`router.cost`:** Implement `CostEvaluator`.
* Bend cost: $10 \times (\text{Manhattan distance between ports})$.
* Integrate R-Tree for strict checks and Danger Map for heuristic.
4. **Tests:**
* Verify collision detection with simple overlapping shapes (Testing Plan Section 2.1).
* Verify Danger Map accuracy and memory footprint (Testing Plan Section 2.2).
* **Post-Route Validator:** Implement the independent `validate_path` utility.
## Phase 3: Single-Net A* Search
**Goal:** Route a single net from A to B with 1nm precision.
1. **`router.astar`:** Implement the priority queue loop.
* State representation: `(x_µm, y_µm, theta)`.
* Move expansion loop with 1µm grid.
* **Natural S-Bends:** Ensure search can find $O \ge 2R$ shifts by combining moves.
* **Look-ahead Snapping:** Actively bridge to the 1nm target when in the capture radius (10µm).
2. **Heuristic:** Manhattan distance $h(n)$ + orientation penalty + Danger Map lookup.
3. **Tests:**
* Solve simple maze problems and verify path optimality (Testing Plan Section 3).
* Verify snap-to-target precision at 1nm resolution.
* **Determinism:** Verify same seed = same path.
## Phase 4: Multi-Net PathFinder
**Goal:** Implement the "Negotiated Congestion" loop for multiple nets.
1. **`router.pathfinder`:**
* Sequential routing -> Identify congestion -> Inflate cost -> Reroute.
* **R-Tree Congestion:** Store dilated path geometries.
2. **Explicit Results:** Return `RoutingResult` objects with `is_valid` and `collisions` metadata.
3. **Tests:**
* Full multi-net benchmarks (Testing Plan Section 4).
* Verify rerouting behavior in crowded environments.
## Phase 5: Visualization, Benchmarking & Fuzzing
1. **`utils.visualization`:** Plot paths using `matplotlib`. Highlight collisions in red.
2. **Benchmarks:** Stress test with 50+ nets. Verify performance and node limits (Testing Plan Section 5).
3. **Fuzzing:** Run A* on randomized layouts to ensure stability.
4. **Final Validation:** Ensure all `is_valid=True` results pass the independent `validate_path` check.

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# Python Package Structure
This document outlines the directory structure and module organization for the `inire` auto-router package.
## 1. Directory Layout
```
inire/
├── __init__.py # Exposes the main `Router` class and key types
├── geometry/ # Core geometric primitives and operations
│ ├── __init__.py
│ ├── primitives.py # Point, Port, Polygon, Arc classes
│ ├── collision.py # R-Tree wrapper and intersection logic
│ └── components.py # Move generators (Straight, Bend90, SBend)
├── router/ # Search algorithms and pathfinding
│ ├── __init__.py
│ ├── astar.py # Hybrid State-Lattice A* implementation
│ ├── graph.py # Node, Edge, and Graph data structures
│ ├── cost.py # Cost functions (length, bend, proximity)
│ ├── danger_map.py # Pre-computed grid for heuristic proximity costs
│ └── pathfinder.py # Multi-net "Negotiated Congestion" manager
├── utils/ # Utility functions
│ ├── __init__.py
│ └── visualization.py # Plotting tools for debug/heatmaps (matplotlib/klayout)
└── tests/ # Unit and integration tests
├── __init__.py
├── conftest.py # Pytest fixtures (common shapes, PDK cells)
├── test_primitives.py # Tests for Port and coordinate transforms
├── test_components.py # Tests for Straight, Bend90, SBend generation
├── test_collision.py # Tests for R-Tree and dilation logic
├── test_cost.py # Tests for Danger Map and cost evaluation
├── test_astar.py # Tests for single-net routing (mazes, snapping)
└── test_pathfinder.py # Multi-net "Negotiated Congestion" benchmarks
```
## 2. Module Responsibilities
### `inire.geometry`
* **`primitives.py`**: Defines the `Port` named tuple `(x, y, theta)` and helper functions for coordinate transforms.
* **`collision.py`**: Wraps the `rtree` or `shapely` library. Handles the "Analytic Correctness" checks (exact polygon distance).
* **`components.py`**: Logic to generate "Moves" from a start port. E.g., `SBend.generate(start_port, offset, radius)` returns a list of polygons and the end port. Handles $O > 2R$ logic.
### `inire.router`
* **`astar.py`**: The heavy lifter. Maintains the `OpenSet` (priority queue) and `ClosedSet`. Implements the "Snap-to-Target" logic.
* **`cost.py`**: compute $f(n) = g(n) + h(n)$. encapsulates the "Danger Map" and Path R-Tree lookups.
* **`danger_map.py`**: Manages the pre-computed proximity grid used for $O(1)$ heuristic calculations.
* **`pathfinder.py`**: Orchestrates the multi-net loop. Tracks the Path R-Tree for negotiated congestion and triggers reroutes.
### `inire.tests`
* **Structure:** Tests are co-located within the package for ease of access.
* **Fixtures:** `conftest.py` will provide standard PDK cells (e.g., a $10\mu m$ radius bend) to avoid repetition in test cases.
## 3. Dependencies
* `numpy`: Vector math.
* `shapely`: Polygon geometry and intersection.
* `rtree`: Spatial indexing.
* `networkx` (Optional): Not used for core search to ensure performance.

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# Architecture Decision Record: Auto-Routing for Photonic & RF ICs
## 1. Problem Context
Photonic and RF routing differ significantly from digital VLSI due to physical constraints:
* **Geometric Rigidity:** 90° bends are pre-rendered PDK cells with fixed bounding boxes.
* **Parametric Flexibility:** S-bends must be generated on-the-fly to handle arbitrary offsets, provided they maintain a constant radius $R$.
* **Signal Integrity:** High sensitivity to proximity (coupling/crosstalk) and a strong preference for single-layer, non-crossing paths.
* **Manual Intervention:** The router is strictly 2D and avoids all other geometries on the same layer. No crossings (e.g. vias or bridges) are supported by the automatic routing engine. The user must manually handle any required crossings by placing components (e.g. crossing cells) and splitting the net list accordingly. This simplifies the router's task to 2D obstacle avoidance and spacing optimization.
---
## 2. Candidate Algorithms & Tradeoffs
### 2.1. Rubber-Band (Topological) Routing
This approach treats paths as elastic bands stretched around obstacles, later "inflating" them to have width and curvature.
| Feature | Analysis |
| :--- | :--- |
| **Strengths** | Excellent at "River Routing" and maintaining minimum clearances. Inherently avoids crossings. |
| **Downsides** | **The Inflation Gap:** A valid thin-line topology may be physically un-routable once the large radius $R$ is applied. It struggles to integrate rigid, pre-rendered 90° blocks into a continuous elastic model. |
| **Future Potential** | High, if a "Post-Processing" engine can reliably snap elastic curves to discrete PDK cells without breaking connectivity. |
### 2.2. Voronoi-Based (Medial Axis) Routing
Uses a Voronoi diagram to find paths that are maximally distant from all obstacles.
| Feature | Analysis |
| :--- | :--- |
| **Strengths** | Theoretically optimal for "Safety" and crosstalk reduction. Guaranteed maximum clearance. |
| **Downsides** | **Manhattan Incompatibility:** Voronoi edges are any-angle and often jagged. Mapping these to a Manhattan-heavy PDK (90° bends) requires a lossy "snapping" phase that often violates the very safety the algorithm intended to provide. |
| **Future Potential** | Useful as a "Channel Finder" to guide a more rigid router, but unsuitable as a standalone geometric solver. |
### 2.3. Integer Linear Programming (ILP)
Formulates routing as a massive optimization problem where a solver picks the best path from a pool of candidates.
| Feature | Analysis |
| :--- | :--- |
| **Strengths** | Can find the mathematically "best" layout (e.g., minimum total length or total bends) across all nets simultaneously. |
| **Downsides** | **Candidate Explosion:** Because S-bends are generated on-the-fly, the number of possible candidate shapes is infinite. To use ILP, one must "discretize" the search space, which may miss the one specific geometry needed for a tight fit. |
| **Future Potential** | Effective for small, high-congestion "Switchbox" areas where all possible geometries can be pre-tabulated. |
---
## 3. Selected Approach: Hybrid State-Lattice A*
### 3.1. Rationale
The **State-Lattice** variant of the A* algorithm is selected as the primary routing engine. Unlike standard A* which moves between grid cells, State-Lattice moves between **states** defined as $(x, y, \theta)$.
1. **Native PDK Integration:** The router treats the pre-rendered 90° bend cell as a discrete "Move" in the search tree. The algorithm only considers placing a bend if the cell's bounding box is clear of obstacles.
2. **Parametric S-Bends:** When the search needs to bridge a lateral gap, it triggers a "Procedural Move." It calculates a fixed-radius S-bend on-the-fly. If the resulting arc is valid and collision-free, it is added as an edge in the search graph.
3. **Predictable Costing:** It allows for a sophisticated cost function $f(n) = g(n) + h(n)$ where:
* $g(n)$ penalizes path length and proximity to obstacles (using a distance-transform field).
* $h(n)$ (the heuristic) guides the search toward the destination while favoring Manhattan alignments.
### 3.2. Implementation Strategy
* **Step 1: Distance Transform.** Pre-calculate a "Danger Map" of the layout. Cells close to obstacles have a high cost; cells far away have low cost. This handles the **Proximity Sensitivity** constraint.
* **Step 2: State Expansion.** From the current point, explore:
* `Straight(length)`
* `PDK_Bend_90(direction)`
* `S_Bend(target_offset, R)`
* **Step 3: Rip-up and Reroute.** To handle the sequential nature of A*, implement a "Negotiated Congestion" scheme (PathFinder algorithm) where nets "pay" more to occupy areas that other nets also want.
---
## 4. Summary of Tradeoffs for Future Review
* **Why not pure Manhattan?** Photonic/RF requirements for large $R$ and S-bends make standard grid-based maze routers obsolete.
* **Why not any-angle?** While any-angle is possible, the PDK's reliance on pre-rendered 90° blocks makes a lattice-based approach more manufacturing-stable.
* **Risk:** The primary risk is **Search Time**. As the library of moves grows (more S-bend options), the branching factor increases. This must be managed with aggressive pruning and spatial indexing (e.g., R-trees).

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# Routing Search Specification
This document details the Hybrid State-Lattice A* implementation and the "PathFinder" (Negotiated Congestion) algorithm for multi-net routing.
## 1. Hybrid State-Lattice A* Search
The core router operates on states $S = (x, y, \theta)$, where $\theta \in \{0, 90, 180, 270\}$.
### 1.1. State Representation & Snapping
To ensure search stability and hash-map performance:
* **Intermediate Ports:** Every state expanded by A* is snapped to a search grid.
* **Search Grid:** Default snap is **1000nm (1µm)**.
* **Final Alignment:** The "Snap-to-Target" logic bridges the gap from the coarse search grid to the final **1nm** port coordinates.
* **Max Design Size:** Guidelines for memory/performance assume up to **20mm x 20mm** routing area.
### 1.2. Move Expansion Logic
From state $S_n$, the following moves are evaluated:
1. **Tiered Straight Steps:** Expand by a set of distances $L \in \{1\mu m, 5\mu m, 25\mu m\}$.
2. **Snap-to-Target:** A "last-inch" look-ahead move. If the target $T$ is within a **Capture Radius (Default: 10µm)** and a straight segment or single bend can reach it, a special move is generated to close the gap exactly at 1nm resolution.
3. **90° Bend:** Try clockwise/counter-clockwise turns using fixed PDK cells.
4. **Small-Offset S-Bend:**
* **Only for $O < 2R$:** Parametric S-bend (two tangent arcs).
* **$O \ge 2R$:** Search naturally finds these by combining 90° bends and straight segments.
### 1.3. Cost Function $f(n) = g(n) + h(n)$
The search uses a flexible, component-based cost model.
* **$g(n)$ (Actual Cost):** $\sum \text{ComponentCost}_i + \text{ProximityCost} + \text{CongestionCost}$
* **Straight Segment:** $L \times C_{unit\_length}$.
* **90° Bend:** $10 \times (\text{Manhattan distance between ports})$.
* **S-Bend:** $f(O, R)$.
* **Proximity Cost:** $k/d^2$ penalty (strict checks use R-Tree).
* **Congestion Cost:** $P \times (\text{Overlaps in Path R-Tree})$.
* **$h(n)$ (Heuristic):**
* Manhattan distance $L_1$ to the target.
* Orientation Penalty: High cost if the state's orientation doesn't match the target's entry orientation.
* **Greedy Weighting:** The A* search uses a weighted heuristic (e.g., $1.1 \times h(n)$) to prioritize search speed over strict path optimality.
* **Danger Map Heuristic:** Fast lookups from the **1µm** pre-computed proximity grid.
## 2. Multi-Net "PathFinder" Strategy (Negotiated Congestion)
1. **Iteration:** Identify "Congestion Areas" using Path R-Tree intersections.
2. **Inflation:** Increase penalty multiplier $P$ for congested areas.
3. **Repeat:** Continue until no overlaps exist or the max iteration count is reached (Default: **20 iterations**).
### 2.1. Convergence & Result Policy
* **Least Bad Attempt:** If no 100% collision-free solution is found, return the result with the lowest total cost (including overlaps).
* **Explicit Reporting:** Results MUST include a `RoutingResult` object containing:
* `path_geometry`: The actual polygon sequence.
* `is_valid`: Boolean (True only if no collisions).
* `collisions`: A count or list of detected overlap points/polygons.
* **Visualization:** Overlapping regions are highlighted (e.g., in red) in the heatmaps.
## 3. Search Limits & Scaling
* **Node Limit:** A* search is capped at **50,000 nodes** per net per iteration.
* **Dynamic Timeout:** Session-level timeout based on problem size:
* `Timeout = max(2s, 0.05s * num_nets * num_iterations)`.
* *Example:* A 100-net problem over 20 iterations times out at **100 seconds**.
## 4. Determinism
All search and rip-up operations are strictly deterministic.
* **Seed:** A user-provided `seed` (int) MUST be used to initialize any random number generators (e.g., if used for tie-breaking or initial net ordering).
* **Tie-Breaking:** If two nodes have the same $f(n)$, a consistent tie-breaking rule (e.g., based on node insertion order or state hash) must be used.
## 5. Optimizations
* **A* Pruning:** Head toward the target and prune suboptimal orientations.
* **Safety Zones:** Ignore collisions within **2nm** of start/end ports.
* **Spatial Indexing:** R-Tree queries are limited to the bounding box of the current move.

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# Testing Plan (Refined)
This document defines the comprehensive testing strategy for the `inire` auto-router, ensuring analytic correctness and performance.
## 1. Unit Tests: Geometry & Components (`inire/geometry`)
### 1.1. Primitives (`primitives.py`)
* **Port Snapping:** Verify that `Port(x, y, orientation)` snaps `x` and `y` to the nearest **1nm** grid point.
* **Coordinate Transforms:**
* Translate a port by `(dx, dy)`.
* Rotate a port by `90`, `180`, `270` degrees around an origin.
* Verify orientation wrapping (e.g., $270^\circ + 90^\circ \to 0^\circ$).
* **Polygon Creation:**
* Generate a polygon from a list of points.
* Verify bounding box calculation.
* **Property-Based Testing (Hypothesis):**
* Verify that any `Port` after transformation remains snapped to the **1nm** grid.
* Verify that $Rotate(Rotate(Port, 90), -90)$ returns the original `Port` (up to snapping).
### 1.2. Component Generation (`components.py`)
* **Straight Moves:**
* Generate a `Straight(length=10.0, width=2.0)` segment.
* Verify the end port is exactly `length` away in the correct orientation.
* Verify the resulting polygon's dimensions.
* **Edge Case:** $L < 1\mu m$ (below search grid). Verify it still generates a valid 1nm segment.
* **Bend90:**
* Generate a `Bend90(radius=10.0, width=2.0, direction='CW')`.
* Verify the end port's orientation is changed by $-90^\circ$.
* Verify the end port's position is exactly `(R, -R)` relative to the start (for $0^\circ \to 270^\circ$).
* **Grid Snapping:** Verify that if the bend radius results in a non-1µm aligned port, it is snapped to the nearest **1µm** search grid point (with a warning).
* **SBend (Small Offset $O < 2R$):**
* Generate an `SBend(offset=5.0, radius=10.0, width=2.0)`.
* Verify the total length matches the analytical $L = 2\sqrt{O(2R - O/4)}$ (or equivalent arc-based formula).
* Verify the tangent continuity at the junction of the two arcs.
* **Edge Case:** $O = 2R$. Verify it either generates two 90-degree bends or fails gracefully with a clear error.
* Verify it fails/warns if $O > 2R$.
### 1.3. Geometric Fidelity
* **Arc Resolution (Sagitta):**
* Verify that `Bend90` and `SBend` polygons are approximated by segments such that the maximum deviation (sagitta) is within a user-defined tolerance (e.g., 10nm).
* Test with varying radii to ensure segment count scales appropriately.
## 2. Unit Tests: Collision & Cost (`inire/geometry/collision` & `router/cost`)
### 2.1. Collision Engine
* **Pre-dilation Logic:**
* Verify that an obstacle (polygon) is correctly dilated by $(W_{max} + C)/2$.
* **Heterogeneous Widths:** Verify that a path for Net A (width $W_1$) is dilated by $(W_1 + C)/2$, while Net B (width $W_2$) uses $(W_2 + C)/2$.
* **Locked Paths:**
* Insert an existing path geometry into the "Static Obstacle" R-Tree.
* Verify that the router treats it as an unmovable obstacle and avoids it.
* **R-Tree Queries:**
* Test intersection detection between two overlapping polygons.
* Test non-intersection between adjacent but non-overlapping polygons (exactly $C$ distance apart).
* **Safety Zone (2nm):**
* Create a port exactly on the edge of an obstacle.
* Verify that a "Move" starting from this port is NOT flagged for collision if the intersection occurs within **2nm** of the port.
* **Self-Intersection:**
* Verify that a path consisting of multiple segments is flagged if it loops back on itself.
### 2.2. Danger Map & Cost Evaluator
* **Danger Map Generation:**
* Initialize a map for a $100\mu m \times 100\mu m$ area with a single obstacle.
* Verify the cost $g_{proximity}$ matches $k/d^2$ for cells near the obstacle.
* Verify cost is $0$ for cells beyond the **Safety Threshold**.
* **Memory Check:**
* Mock a $20mm \times 20mm$ grid and verify memory allocation stays within limits (e.g., `< 2GB` for standard `uint8` resolution).
* **Cost Calculation:**
* Verify total cost $f(n)$ correctly sums length, bend penalties ($10 \times$ Manhattan), and proximity costs.
### 2.3. Robustness & Limits
* **Design Bounds:**
* Test routing at the extreme edges of the $20mm \times 20mm$ coordinate space.
* Verify that moves extending outside the design bounds are correctly pruned or flagged.
* **Empty/Invalid Inputs:**
* Test with an empty netlist.
* Test with start and end ports at the exact same location.
## 3. Integration Tests: Single-Net A* Search (`inire/router/astar`)
### 3.1. Open Space Scenarios
* **Straight Line:** Route from `(0,0,0)` to `(100,0,0)`. Verify it uses only `Straight` moves.
* **Simple Turn:** Route from `(0,0,0)` to `(20,20,90)`. Verify it uses a `Bend90` and `Straight` segments.
* **Small S-Bend:** Route with an offset of $5\mu m$ and radius $10\mu m$. Verify it uses the `SBend` component.
* **Large S-Bend ($O \ge 2R$):** Route with an offset of $50\mu m$ and radius $10\mu m$. Verify it combines two `Bend90`s and a `Straight` segment.
### 3.2. Obstacle Avoidance (The "Maze" Tests)
* **L-Obstacle:** Place an obstacle blocking the direct path. Verify the router goes around it.
* **Narrow Channel:** Create two obstacles with a gap slightly wider than $W_i + C$. Verify the router passes through.
* **Dead End:** Create a U-shaped obstacle. Verify the search explores alternatives and fails gracefully if no path exists.
### 3.3. Snapping & Precision
* **Snap-to-Target Lookahead:**
* Route to a target at `(100.005, 0, 0)` (not on 1µm grid).
* Verify the search reaches the vicinity via the 1µm grid and the final segment bridges the **5nm** gap exactly.
* **Grid Alignment:**
* Start from a port at `(0.5, 0.5, 0)`. Verify it snaps to the 1µm search grid correctly for the first move expansion.
### 3.4. Failure Modes
* **Unreachable Target:** Create a target completely enclosed by obstacles. Verify the search terminates after exploring all options (or hitting the 50,000 node limit) and returns an invalid result.
* **Start/End Collision:** Place a port deep inside an obstacle (beyond the 2nm safety zone). Verify the router identifies the immediate collision and fails gracefully.
## 4. Integration Tests: Multi-Net PathFinder (`inire/router/pathfinder`)
### 4.1. Congestion Scenarios
* **Parallel Paths:** Route two nets that can both take straight paths. Verify no reroutes occur.
* **The "Cross" Test:** Two nets must cross paths in 2D.
* Since crossings are illegal, verify the second net finds a detour.
* Verify the `Negotiated Congestion` loop increases the cost of the shared region.
* **Bottleneck:** Force 3 nets through a channel that only fits 2.
* Verify the router returns 2 valid paths and 1 "least bad" path (with collisions flagged).
* Verify the `is_valid=False` attribute is set for the failing net.
### 4.2. Determinism & Performance
* **Seed Consistency:** Run the same multi-net problem twice with the same seed; verify identical results (pixel-perfect).
* **Node Limit Enforcement:** Trigger a complex search that exceeds **50,000 nodes**. Verify it terminates and returns the best-so-far or failure.
* **Timeout:** Verify the session-level timeout stops the process for extremely large problems.
## 5. Benchmarking & Regression
* **Standard Benchmark Suite:** A set of 5-10 layouts with varying net counts (1 to 50).
* **Metrics to Track:**
* Total wire length.
* Total number of bends.
* Execution time per net.
* Success rate (percentage of nets routed without collisions).
* **Node Expansion Rate:** Nodes per second.
* **Memory Usage:** Peak RSS during 20x20mm routing.
* **Fuzz Testing:**
* Generate random obstacles and ports within a 1mm x 1mm area.
* Verify that the router never crashes.
* Verify that every result marked `is_valid=True` is confirmed collision-free by a high-precision (slow) check.
## 6. Analytic Correctness Guarantees
* **Post-Route Validation:**
* Implement an independent `validate_path(path, obstacles, clearance)` function using `shapely`'s most precise intersection tests.
* Run this on every test result to ensure the `CollisionEngine` (which uses R-Tree for speed) hasn't missed any edge cases.
* **Orientation Check:**
* Verify that the final port of every path matches the target orientation exactly $\{0, 90, 180, 270\}$.

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from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
def main() -> None:
print("Running Example 01: Simple Route...")
# 1. Setup Environment
# We define a 100um x 100um routing area
bounds = (0, 0, 100, 100)
# Clearance of 2.0um between waveguides
engine = CollisionEngine(clearance=2.0)
# Precompute DangerMap for heuristic speedup
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([]) # No obstacles yet
# 2. Configure Router
evaluator = CostEvaluator(engine, danger_map)
context = AStarContext(evaluator, bend_radii=[10.0])
metrics = AStarMetrics()
pf = PathFinder(context, metrics)
# 3. Define Netlist
# Start at (10, 50) pointing East (0 deg)
# Target at (90, 50) pointing East (0 deg)
netlist = {
"net1": (Port(10, 50, 0), Port(90, 50, 0)),
}
net_widths = {"net1": 2.0}
# 4. Route
results = pf.route_all(netlist, net_widths)
# 5. Check Results
res = results["net1"]
if res.is_valid:
print("Success! Route found.")
print(f"Path collisions: {res.collisions}")
else:
print("Failed to find route.")
# 6. Visualize
# plot_routing_results takes a dict of RoutingResult objects
fig, ax = plot_routing_results(results, [], bounds)
fig.savefig("examples/01_simple_route.png")
print("Saved plot to examples/01_simple_route.png")
if __name__ == "__main__":
main()

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from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
def main() -> None:
print("Running Example 02: Congestion Resolution (Triple Crossing)...")
# 1. Setup Environment
bounds = (0, 0, 100, 100)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
# Configure a router with high congestion penalties
evaluator = CostEvaluator(engine, danger_map, greedy_h_weight=1.5, bend_penalty=250.0, sbend_penalty=500.0)
context = AStarContext(evaluator, bend_radii=[10.0], sbend_radii=[10.0])
metrics = AStarMetrics()
pf = PathFinder(context, metrics, base_congestion_penalty=1000.0)
# 2. Define Netlist
# Three nets that must cross each other in a small area
netlist = {
"horizontal": (Port(10, 50, 0), Port(90, 50, 0)),
"vertical_up": (Port(45, 10, 90), Port(45, 90, 90)),
"vertical_down": (Port(55, 90, 270), Port(55, 10, 270)),
}
net_widths = {nid: 2.0 for nid in netlist}
# 3. Route
# PathFinder uses Negotiated Congestion to resolve overlaps iteratively
results = pf.route_all(netlist, net_widths)
# 4. Check Results
all_valid = all(res.is_valid for res in results.values())
if all_valid:
print("Success! Congestion resolved for all nets.")
else:
print("Failed to resolve congestion for some nets.")
# 5. Visualize
fig, ax = plot_routing_results(results, [], bounds, netlist=netlist)
fig.savefig("examples/02_congestion_resolution.png")
print("Saved plot to examples/02_congestion_resolution.png")
if __name__ == "__main__":
main()

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from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
def main() -> None:
print("Running Example 03: Locked Paths...")
# 1. Setup Environment
bounds = (0, -50, 100, 50)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map, bend_penalty=250.0, sbend_penalty=500.0)
context = AStarContext(evaluator, bend_radii=[10.0])
metrics = AStarMetrics()
pf = PathFinder(context, metrics)
# 2. Route Net A and 'Lock' it
# Net A is a straight path blocking the direct route for Net B
print("Routing initial net...")
netlist_a = {"netA": (Port(10, 0, 0), Port(90, 0, 0))}
results_a = pf.route_all(netlist_a, {"netA": 2.0})
# Locking prevents Net A from being removed or rerouted during NC iterations
engine.lock_net("netA")
print("Initial net locked as static obstacle.")
# 3. Route Net B (forced to detour)
print("Routing detour net around locked path...")
netlist_b = {"netB": (Port(50, -20, 90), Port(50, 20, 90))}
results_b = pf.route_all(netlist_b, {"netB": 2.0})
# 4. Visualize
results = {**results_a, **results_b}
fig, ax = plot_routing_results(results, [], bounds)
fig.savefig("examples/03_locked_paths.png")
print("Saved plot to examples/03_locked_paths.png")
if __name__ == "__main__":
main()

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from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
def main() -> None:
print("Running Example 04: S-Bends and Multiple Radii...")
# 1. Setup Environment
bounds = (0, 0, 100, 100)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
# 2. Configure Router
evaluator = CostEvaluator(
engine,
danger_map,
unit_length_cost=1.0,
bend_penalty=10.0,
sbend_penalty=20.0,
)
context = AStarContext(
evaluator,
node_limit=50000,
bend_radii=[10.0, 30.0],
sbend_offsets=[5.0], # Use a simpler offset
bend_penalty=10.0,
sbend_penalty=20.0,
)
metrics = AStarMetrics()
pf = PathFinder(context, metrics)
# 3. Define Netlist
# start (10, 50), target (60, 55) -> 5um offset
netlist = {
"sbend_only": (Port(10, 50, 0), Port(60, 55, 0)),
"multi_radii": (Port(10, 10, 0), Port(90, 90, 0)),
}
net_widths = {"sbend_only": 2.0, "multi_radii": 2.0}
# 4. Route
results = pf.route_all(netlist, net_widths)
# 5. Check Results
for nid, res in results.items():
status = "Success" if res.is_valid else "Failed"
print(f"{nid}: {status}, collisions={res.collisions}")
# 6. Visualize
fig, ax = plot_routing_results(results, [], bounds, netlist=netlist)
fig.savefig("examples/04_sbends_and_radii.png")
print("Saved plot to examples/04_sbends_and_radii.png")
if __name__ == "__main__":
main()

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from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
def main() -> None:
print("Running Example 05: Orientation Stress Test...")
# 1. Setup Environment
bounds = (0, 0, 200, 200)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map, bend_penalty=50.0)
context = AStarContext(evaluator, bend_radii=[20.0])
metrics = AStarMetrics()
pf = PathFinder(context, metrics)
# 2. Define Netlist
# Challenging orientation combinations
netlist = {
"u_turn": (Port(50, 50, 0), Port(50, 70, 180)),
"loop": (Port(100, 100, 90), Port(100, 80, 270)),
"zig_zag": (Port(20, 150, 0), Port(180, 150, 0)),
}
net_widths = {nid: 2.0 for nid in netlist}
# 3. Route
print("Routing complex orientation nets...")
results = pf.route_all(netlist, net_widths)
# 4. Check Results
for nid, res in results.items():
status = "Success" if res.is_valid else "Failed"
print(f" {nid}: {status}")
# 5. Visualize
fig, ax = plot_routing_results(results, [], bounds, netlist=netlist)
fig.savefig("examples/05_orientation_stress.png")
print("Saved plot to examples/05_orientation_stress.png")
if __name__ == "__main__":
main()

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from shapely.geometry import Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
def main() -> None:
print("Running Example 06: Bend Collision Models...")
# 1. Setup Environment
# Give room for 10um bends near the edges
bounds = (-20, -20, 170, 170)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
# Create three scenarios with identical obstacles
# We'll space them out vertically
obs_arc = Polygon([(40, 110), (60, 110), (60, 130), (40, 130)])
obs_bbox = Polygon([(40, 60), (60, 60), (60, 80), (40, 80)])
obs_clipped = Polygon([(40, 10), (60, 10), (60, 30), (40, 30)])
obstacles = [obs_arc, obs_bbox, obs_clipped]
for obs in obstacles:
engine.add_static_obstacle(obs)
danger_map.precompute(obstacles)
# We'll run three separate routers since collision_type is a router-level config
evaluator = CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
# Scenario 1: Standard 'arc' model (High fidelity)
context_arc = AStarContext(evaluator, bend_radii=[10.0], bend_collision_type="arc")
netlist_arc = {"arc_model": (Port(10, 120, 0), Port(90, 140, 90))}
# Scenario 2: 'bbox' model (Conservative axis-aligned box)
context_bbox = AStarContext(evaluator, bend_radii=[10.0], bend_collision_type="bbox")
netlist_bbox = {"bbox_model": (Port(10, 70, 0), Port(90, 90, 90))}
# Scenario 3: 'clipped_bbox' model (Balanced)
context_clipped = AStarContext(evaluator, bend_radii=[10.0], bend_collision_type="clipped_bbox", bend_clip_margin=1.0)
netlist_clipped = {"clipped_model": (Port(10, 20, 0), Port(90, 40, 90))}
# 2. Route each scenario
print("Routing Scenario 1 (Arc)...")
res_arc = PathFinder(context_arc, use_tiered_strategy=False).route_all(netlist_arc, {"arc_model": 2.0})
print("Routing Scenario 2 (BBox)...")
res_bbox = PathFinder(context_bbox, use_tiered_strategy=False).route_all(netlist_bbox, {"bbox_model": 2.0})
print("Routing Scenario 3 (Clipped BBox)...")
res_clipped = PathFinder(context_clipped, use_tiered_strategy=False).route_all(netlist_clipped, {"clipped_model": 2.0})
# 3. Combine results for visualization
all_results = {**res_arc, **res_bbox, **res_clipped}
all_netlists = {**netlist_arc, **netlist_bbox, **netlist_clipped}
# 4. Visualize
fig, ax = plot_routing_results(all_results, obstacles, bounds, netlist=all_netlists)
fig.savefig("examples/06_bend_collision_models.png")
print("Saved plot to examples/06_bend_collision_models.png")
if __name__ == "__main__":
main()

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examples/07_large_scale_routing.py
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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
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)...")
# 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, 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 = int(round(start_y_base + i * 10.0))
ey = int(round(end_y_base + i * end_y_pitch))
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
print(f" Iteration {idx} finished. Successes: {successes}/{len(netlist)}, Collisions: {total_collisions}")
# 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
})
metrics.reset_per_route()
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()
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():
if not res.is_valid:
print(f" FAILED: {nid}, collisions={res.collisions}")
else:
print(f" {nid}: SUCCESS")
# 5. Visualize
fig, ax = plot_routing_results(results, obstacles, bounds, netlist=netlist)
plot_danger_map(danger_map, ax=ax)
fig.savefig("examples/07_large_scale_routing.png")
print("Saved plot to examples/07_large_scale_routing.png")
if __name__ == "__main__":
main()

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examples/08_custom_bend_geometry.py
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from shapely.geometry import Polygon
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
def main() -> None:
print("Running Example 08: Custom Bend Geometry...")
# 1. Setup Environment
bounds = (0, 0, 150, 150)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
context = AStarContext(evaluator, bend_radii=[10.0], sbend_radii=[])
metrics = AStarMetrics()
pf = PathFinder(context, metrics)
# 2. Define Netlist
netlist = {
"custom_bend": (Port(20, 20, 0), Port(100, 100, 90)),
}
net_widths = {"custom_bend": 2.0}
# 3. Route with standard arc first
print("Routing with standard arc...")
results_std = pf.route_all(netlist, net_widths)
# 4. Define a custom 'trapezoid' bend model
# (Just for demonstration - we override the collision model during search)
# Define a custom centered 20x20 box
custom_poly = Polygon([(-10, -10), (10, -10), (10, 10), (-10, 10)])
print("Routing with custom collision model...")
# Override bend_collision_type with a literal Polygon
context_custom = AStarContext(evaluator, bend_radii=[10.0], bend_collision_type=custom_poly, sbend_radii=[])
metrics_custom = AStarMetrics()
results_custom = PathFinder(context_custom, metrics_custom, use_tiered_strategy=False).route_all(
{"custom_model": netlist["custom_bend"]}, {"custom_model": 2.0}
)
# 5. Visualize
all_results = {**results_std, **results_custom}
fig, ax = plot_routing_results(all_results, [], bounds, netlist=netlist)
fig.savefig("examples/08_custom_bend_geometry.png")
print("Saved plot to examples/08_custom_bend_geometry.png")
if __name__ == "__main__":
main()

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examples/09_unroutable_best_effort.py
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from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
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
from shapely.geometry import box
def main() -> None:
print("Running Example 09: Best-Effort Under Tight Search Budget...")
# 1. Setup Environment
bounds = (0, 0, 100, 100)
engine = CollisionEngine(clearance=2.0)
# A small obstacle cluster keeps the partial route visually interesting.
obstacles = [
box(35, 35, 45, 65),
box(55, 35, 65, 65),
]
for obs in obstacles:
engine.add_static_obstacle(obs)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute(obstacles)
evaluator = CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
# Keep the search budget intentionally tiny so the router returns a partial path.
context = AStarContext(evaluator, node_limit=3, bend_radii=[10.0])
metrics = AStarMetrics()
pf = PathFinder(context, metrics, warm_start=None)
# 2. Define Netlist: reaching the target requires additional turns the search budget cannot afford.
netlist = {
"budget_limited_net": (Port(10, 50, 0), Port(85, 60, 180)),
}
net_widths = {"budget_limited_net": 2.0}
# 3. Route
print("Routing with a deliberately tiny node budget (should return a partial path)...")
results = pf.route_all(netlist, net_widths)
# 4. Check Results
res = results["budget_limited_net"]
if not res.reached_target:
print(f"Target not reached as expected. Partial path length: {len(res.path)} segments.")
else:
print("The route unexpectedly reached the target. Increase difficulty or reduce the node budget further.")
# 5. Visualize
fig, ax = plot_routing_results(results, obstacles, bounds, netlist=netlist)
fig.savefig("examples/09_unroutable_best_effort.png")
print("Saved plot to examples/09_unroutable_best_effort.png")
if __name__ == "__main__":
main()

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examples/README.md
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# Inire Routing Examples
This directory contains examples demonstrating the features and architectural capabilities of the `inire` router.
## Architectural Visualization
In all plots generated by `inire`, we distinguish between the search-time geometry and the final "actual" geometry:
* **Dashed Lines & Translucent Fill**: The **Collision Proxy** used during the A* search (e.g., `clipped_bbox` or `bbox`). This represents the conservative envelope the router used to guarantee clearance.
* **Solid Lines**: The **Actual Geometry** (high-fidelity arcs). This is the exact shape that will be used for PDK generation and fabrication.
---
## 1. Fan-Out (Negotiated Congestion)
Demonstrates the Negotiated Congestion algorithm handling multiple intersecting nets. The router iteratively increases penalties for overlaps until a collision-free solution is found. This example shows a bundle of nets fanning out through a narrow bottleneck.
![Fan-Out Routing](07_large_scale_routing.png)
## 2. Custom Bend Geometry Models
`inire` supports multiple collision models for bends, allowing a trade-off between search speed and geometric accuracy:
* **Arc**: High-fidelity geometry (Highest accuracy).
* **BBox**: Simple axis-aligned bounding box (Fastest search).
* **Clipped BBox**: A balanced model that clips the corners of the AABB to better fit the arc (Optimal performance).
![Custom Bend Geometry](08_custom_bend_geometry.png)
## 3. Unroutable Nets & Best-Effort Display
When a net is physically blocked or exceeds the node limit, the router returns the "best-effort" partial path—the path that reached the point closest to the target according to the heuristic. This is critical for debugging design constraints.
![Best Effort Display](09_unroutable_best_effort.png)
## 4. Orientation Stress Test
Demonstrates the router's ability to handle complex orientation requirements, including U-turns, 90-degree flips, and loops.
![Orientation Stress Test](05_orientation_stress.png)
## 5. Tiered Fidelity & Lazy Dilation
Our architecture leverages two key optimizations for high-performance routing:
1. **Tiered Fidelity**: Initial routing passes use fast `clipped_bbox` proxies. If collisions are found, the system automatically escalates to high-fidelity `arc` geometry for the affected regions.
2. **Lazy Dilation**: Geometric buffering (dilation) is deferred until a collision check is strictly necessary, avoiding thousands of redundant `buffer()` and `translate()` calls.

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inire/__init__.py
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"""
inire Wave-router
"""
from .geometry.primitives import Port as Port # noqa: PLC0414
from .geometry.components import Straight as Straight, Bend90 as Bend90, SBend as SBend # noqa: PLC0414
__author__ = 'Jan Petykiewicz'
__version__ = '0.1'

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inire/constants.py
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"""
Centralized constants for the inire routing engine.
"""
# Search Grid Snap (5.0 µm default)
# TODO: Make this configurable in RouterConfig and define tolerances relative to the grid.
DEFAULT_SEARCH_GRID_SNAP_UM = 5.0
# Tolerances
TOLERANCE_LINEAR = 1e-6
TOLERANCE_ANGULAR = 1e-3
TOLERANCE_GRID = 1e-6

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inire/geometry/collision.py
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from __future__ import annotations
from typing import TYPE_CHECKING, Literal
import rtree
import numpy
import shapely
from shapely.prepared import prep
from shapely.strtree import STRtree
from shapely.geometry import box, LineString
if TYPE_CHECKING:
from shapely.geometry import Polygon
from shapely.prepared import PreparedGeometry
from inire.geometry.primitives import Port
from inire.geometry.components import ComponentResult
class CollisionEngine:
"""
Manages spatial queries for collision detection with unified dilation logic.
"""
__slots__ = (
'clearance', 'max_net_width', 'safety_zone_radius',
'static_index', 'static_geometries', 'static_dilated', 'static_prepared',
'static_is_rect', 'static_tree', 'static_obj_ids', 'static_safe_cache',
'static_grid', 'grid_cell_size', '_static_id_counter', '_net_specific_trees',
'_net_specific_is_rect', '_net_specific_bounds',
'dynamic_index', 'dynamic_geometries', 'dynamic_dilated', 'dynamic_prepared',
'dynamic_tree', 'dynamic_obj_ids', 'dynamic_grid', '_dynamic_id_counter',
'metrics', '_dynamic_tree_dirty', '_dynamic_net_ids_array', '_inv_grid_cell_size',
'_static_bounds_array', '_static_is_rect_array', '_locked_nets',
'_static_raw_tree', '_static_raw_obj_ids', '_dynamic_bounds_array', '_static_version'
)
def __init__(
self,
clearance: float,
max_net_width: float = 2.0,
safety_zone_radius: float = 0.0021,
) -> None:
self.clearance = clearance
self.max_net_width = max_net_width
self.safety_zone_radius = safety_zone_radius
# Static obstacles
self.static_index = rtree.index.Index()
self.static_geometries: dict[int, Polygon] = {}
self.static_dilated: dict[int, Polygon] = {}
self.static_prepared: dict[int, PreparedGeometry] = {}
self.static_is_rect: dict[int, bool] = {}
self.static_tree: STRtree | None = None
self.static_obj_ids: list[int] = []
self._static_bounds_array: numpy.ndarray | None = None
self._static_is_rect_array: numpy.ndarray | None = None
self._static_raw_tree: STRtree | None = None
self._static_raw_obj_ids: list[int] = []
self._net_specific_trees: dict[tuple[float, float], STRtree] = {}
self._net_specific_is_rect: dict[tuple[float, float], numpy.ndarray] = {}
self._net_specific_bounds: dict[tuple[float, float], numpy.ndarray] = {}
self._static_version = 0
self.static_safe_cache: set[tuple] = set()
self.static_grid: dict[tuple[int, int], list[int]] = {}
self.grid_cell_size = 50.0
self._inv_grid_cell_size = 1.0 / self.grid_cell_size
self._static_id_counter = 0
# Dynamic paths
self.dynamic_index = rtree.index.Index()
self.dynamic_geometries: dict[int, tuple[str, Polygon]] = {}
self.dynamic_dilated: dict[int, Polygon] = {}
self.dynamic_prepared: dict[int, PreparedGeometry] = {}
self.dynamic_tree: STRtree | None = None
self.dynamic_obj_ids: numpy.ndarray = numpy.array([], dtype=numpy.int32)
self.dynamic_grid: dict[tuple[int, int], list[int]] = {}
self._dynamic_id_counter = 0
self._dynamic_tree_dirty = True
self._dynamic_net_ids_array = numpy.array([], dtype='<U32')
self._dynamic_bounds_array = numpy.array([], dtype=numpy.float64).reshape(0, 4)
self._locked_nets: set[str] = set()
self.metrics = {
'static_cache_hits': 0,
'static_grid_skips': 0,
'static_tree_queries': 0,
'static_straight_fast': 0,
'congestion_grid_skips': 0,
'congestion_tree_queries': 0,
'safety_zone_checks': 0
}
def reset_metrics(self) -> None:
for k in self.metrics:
self.metrics[k] = 0
def get_metrics_summary(self) -> str:
m = self.metrics
return (f"Collision Performance: \n"
f" Static: {m['static_tree_queries']} checks\n"
f" Congestion: {m['congestion_tree_queries']} checks\n"
f" Safety Zone: {m['safety_zone_checks']} full intersections performed")
def add_static_obstacle(self, polygon: Polygon, dilated_geometry: Polygon | None = None) -> int:
obj_id = self._static_id_counter
self._static_id_counter += 1
# Preserve existing dilation if provided, else use default C/2
if dilated_geometry is not None:
dilated = dilated_geometry
else:
dilated = polygon.buffer(self.clearance / 2.0, join_style=2)
self.static_geometries[obj_id] = polygon
self.static_dilated[obj_id] = dilated
self.static_prepared[obj_id] = prep(dilated)
self.static_index.insert(obj_id, dilated.bounds)
self._invalidate_static_caches()
b = dilated.bounds
area = (b[2] - b[0]) * (b[3] - b[1])
self.static_is_rect[obj_id] = (abs(dilated.area - area) < 1e-4)
return obj_id
def remove_static_obstacle(self, obj_id: int) -> None:
"""
Remove a static obstacle by ID.
"""
if obj_id not in self.static_geometries:
return
bounds = self.static_dilated[obj_id].bounds
self.static_index.delete(obj_id, bounds)
del self.static_geometries[obj_id]
del self.static_dilated[obj_id]
del self.static_prepared[obj_id]
del self.static_is_rect[obj_id]
self._invalidate_static_caches()
def _invalidate_static_caches(self) -> None:
self.static_tree = None
self._static_bounds_array = None
self._static_is_rect_array = None
self.static_obj_ids = []
self._static_raw_tree = None
self._static_raw_obj_ids = []
self.static_grid = {}
self._net_specific_trees.clear()
self._net_specific_is_rect.clear()
self._net_specific_bounds.clear()
self.static_safe_cache.clear()
self._static_version += 1
def _ensure_static_tree(self) -> None:
if self.static_tree is None and self.static_dilated:
self.static_obj_ids = sorted(self.static_dilated.keys())
geoms = [self.static_dilated[i] for i in self.static_obj_ids]
self.static_tree = STRtree(geoms)
self._static_bounds_array = numpy.array([g.bounds for g in geoms])
self._static_is_rect_array = numpy.array([self.static_is_rect[i] for i in self.static_obj_ids])
def _ensure_net_static_tree(self, net_width: float) -> STRtree:
"""
Lazily generate a tree where obstacles are dilated by (net_width/2 + clearance).
"""
key = (round(net_width, 4), round(self.clearance, 4))
if key in self._net_specific_trees:
return self._net_specific_trees[key]
# Physical separation must be >= clearance.
# Centerline to raw obstacle edge must be >= net_width/2 + clearance.
total_dilation = net_width / 2.0 + self.clearance
geoms = []
is_rect_list = []
bounds_list = []
for obj_id in sorted(self.static_geometries.keys()):
poly = self.static_geometries[obj_id]
dilated = poly.buffer(total_dilation, join_style=2)
geoms.append(dilated)
b = dilated.bounds
bounds_list.append(b)
area = (b[2] - b[0]) * (b[3] - b[1])
is_rect_list.append(abs(dilated.area - area) < 1e-4)
tree = STRtree(geoms)
self._net_specific_trees[key] = tree
self._net_specific_is_rect[key] = numpy.array(is_rect_list, dtype=bool)
self._net_specific_bounds[key] = numpy.array(bounds_list)
return tree
def _ensure_static_raw_tree(self) -> None:
if self._static_raw_tree is None and self.static_geometries:
self._static_raw_obj_ids = sorted(self.static_geometries.keys())
geoms = [self.static_geometries[i] for i in self._static_raw_obj_ids]
self._static_raw_tree = STRtree(geoms)
def _ensure_dynamic_tree(self) -> None:
if self.dynamic_tree is None and self.dynamic_dilated:
ids = sorted(self.dynamic_dilated.keys())
geoms = [self.dynamic_dilated[i] for i in ids]
self.dynamic_tree = STRtree(geoms)
self.dynamic_obj_ids = numpy.array(ids, dtype=numpy.int32)
self._dynamic_bounds_array = numpy.array([g.bounds for g in geoms])
nids = [self.dynamic_geometries[obj_id][0] for obj_id in self.dynamic_obj_ids]
self._dynamic_net_ids_array = numpy.array(nids, dtype='<U32')
self._dynamic_tree_dirty = False
def _ensure_dynamic_grid(self) -> None:
if not self.dynamic_grid and self.dynamic_dilated:
cs = self.grid_cell_size
for obj_id, poly in self.dynamic_dilated.items():
b = poly.bounds
for gx in range(int(b[0] / cs), int(b[2] / cs) + 1):
for gy in range(int(b[1] / cs), int(b[3] / cs) + 1):
cell = (gx, gy)
if cell not in self.dynamic_grid: self.dynamic_grid[cell] = []
self.dynamic_grid[cell].append(obj_id)
def add_path(self, net_id: str, geometry: list[Polygon], dilated_geometry: list[Polygon] | None = None) -> None:
self.dynamic_tree = None
self.dynamic_grid = {}
self._dynamic_tree_dirty = True
dilation = self.clearance / 2.0
for i, poly in enumerate(geometry):
obj_id = self._dynamic_id_counter
self._dynamic_id_counter += 1
dilated = dilated_geometry[i] if dilated_geometry else poly.buffer(dilation)
self.dynamic_geometries[obj_id] = (net_id, poly)
self.dynamic_dilated[obj_id] = dilated
self.dynamic_index.insert(obj_id, dilated.bounds)
def remove_path(self, net_id: str) -> None:
if net_id in self._locked_nets: return
to_remove = [obj_id for obj_id, (nid, _) in self.dynamic_geometries.items() if nid == net_id]
if not to_remove: return
self.dynamic_tree = None
self.dynamic_grid = {}
self._dynamic_tree_dirty = True
for obj_id in to_remove:
self.dynamic_index.delete(obj_id, self.dynamic_dilated[obj_id].bounds)
del self.dynamic_geometries[obj_id]
del self.dynamic_dilated[obj_id]
def lock_net(self, net_id: str) -> None:
""" Convert a routed net into static obstacles. """
self._locked_nets.add(net_id)
# Move all segments of this net to static obstacles
to_move = [obj_id for obj_id, (nid, _) in self.dynamic_geometries.items() if nid == net_id]
for obj_id in to_move:
poly = self.dynamic_geometries[obj_id][1]
dilated = self.dynamic_dilated[obj_id]
# Preserve dilation for perfect consistency
self.add_static_obstacle(poly, dilated_geometry=dilated)
# Remove from dynamic index (without triggering the locked-net guard)
self.dynamic_tree = None
self.dynamic_grid = {}
self._dynamic_tree_dirty = True
for obj_id in to_move:
self.dynamic_index.delete(obj_id, self.dynamic_dilated[obj_id].bounds)
del self.dynamic_geometries[obj_id]
del self.dynamic_dilated[obj_id]
def unlock_net(self, net_id: str) -> None:
self._locked_nets.discard(net_id)
def check_move_straight_static(self, start_port: Port, length: float, net_width: float) -> bool:
self.metrics['static_straight_fast'] += 1
reach = self.ray_cast(start_port, start_port.orientation, max_dist=length + 0.01, net_width=net_width)
return reach < length - 0.001
def _is_in_safety_zone_fast(self, idx: int, start_port: Port | None, end_port: Port | None) -> bool:
""" Fast port-based check to see if a collision might be in a safety zone. """
sz = self.safety_zone_radius
b = self._static_bounds_array[idx]
if start_port:
if (b[0]-sz <= start_port.x <= b[2]+sz and
b[1]-sz <= start_port.y <= b[3]+sz): return True
if end_port:
if (b[0]-sz <= end_port.x <= b[2]+sz and
b[1]-sz <= end_port.y <= b[3]+sz): return True
return False
def check_move_static(self, result: ComponentResult, start_port: Port | None = None, end_port: Port | None = None, net_width: float | None = None) -> bool:
if not self.static_dilated: return False
self.metrics['static_tree_queries'] += 1
self._ensure_static_tree()
# 1. Fast total bounds check (Use dilated bounds to ensure clearance is caught)
tb = result.total_dilated_bounds if result.total_dilated_bounds else result.total_bounds
hits = self.static_tree.query(box(*tb))
if hits.size == 0: return False
# 2. Per-hit check
s_bounds = self._static_bounds_array
move_poly_bounds = result.dilated_bounds if result.dilated_bounds else result.bounds
for hit_idx in hits:
obs_b = s_bounds[hit_idx]
# Check if any polygon in the move actually hits THIS obstacle's AABB
poly_hits_obs_aabb = False
for pb in move_poly_bounds:
if (pb[0] < obs_b[2] and pb[2] > obs_b[0] and
pb[1] < obs_b[3] and pb[3] > obs_b[1]):
poly_hits_obs_aabb = True
break
if not poly_hits_obs_aabb: continue
# Safety zone check (Fast port-based)
if self._is_in_safety_zone_fast(hit_idx, start_port, end_port):
# If near port, we must use the high-precision check
obj_id = self.static_obj_ids[hit_idx]
collision_found = False
for p_move in result.geometry:
if not self._is_in_safety_zone(p_move, obj_id, start_port, end_port):
collision_found = True; break
if not collision_found: continue
return True
# Not in safety zone and AABBs overlap - check real intersection
obj_id = self.static_obj_ids[hit_idx]
# Use dilated geometry (Wi/2 + C/2) against static_dilated (C/2) to get Wi/2 + C.
# Touching means gap is exactly C. Intersection without touches means gap < C.
test_geoms = result.dilated_geometry if result.dilated_geometry else result.geometry
static_obs_dilated = self.static_dilated[obj_id]
for i, p_test in enumerate(test_geoms):
if p_test.intersects(static_obs_dilated) and not p_test.touches(static_obs_dilated):
return True
return False
def check_move_congestion(self, result: ComponentResult, net_id: str) -> int:
if not self.dynamic_geometries: return 0
tb = result.total_dilated_bounds
if tb is None: return 0
self._ensure_dynamic_grid()
dynamic_grid = self.dynamic_grid
if not dynamic_grid: return 0
cs_inv = self._inv_grid_cell_size
gx_min = int(tb[0] * cs_inv)
gy_min = int(tb[1] * cs_inv)
gx_max = int(tb[2] * cs_inv)
gy_max = int(tb[3] * cs_inv)
dynamic_geometries = self.dynamic_geometries
# Fast path for single cell
if gx_min == gx_max and gy_min == gy_max:
cell = (gx_min, gy_min)
if cell in dynamic_grid:
for obj_id in dynamic_grid[cell]:
if dynamic_geometries[obj_id][0] != net_id:
return self._check_real_congestion(result, net_id)
return 0
# General case
any_possible = False
for gx in range(gx_min, gx_max + 1):
for gy in range(gy_min, gy_max + 1):
cell = (gx, gy)
if cell in dynamic_grid:
for obj_id in dynamic_grid[cell]:
if dynamic_geometries[obj_id][0] != net_id:
any_possible = True
break
if any_possible: break
if any_possible: break
if not any_possible: return 0
return self._check_real_congestion(result, net_id)
def _check_real_congestion(self, result: ComponentResult, net_id: str) -> int:
self.metrics['congestion_tree_queries'] += 1
self._ensure_dynamic_tree()
if self.dynamic_tree is None: return 0
# 1. Fast total bounds check (LAZY SAFE)
tb = result.total_dilated_bounds
d_bounds = self._dynamic_bounds_array
possible_total = (tb[0] < d_bounds[:, 2]) & (tb[2] > d_bounds[:, 0]) & \
(tb[1] < d_bounds[:, 3]) & (tb[3] > d_bounds[:, 1])
valid_hits_mask = (self._dynamic_net_ids_array != net_id)
if not numpy.any(possible_total & valid_hits_mask):
return 0
# 2. Per-polygon check using query
geoms_to_test = result.dilated_geometry if result.dilated_geometry else result.geometry
res_indices, tree_indices = self.dynamic_tree.query(geoms_to_test, predicate='intersects')
if tree_indices.size == 0:
return 0
hit_net_ids = numpy.take(self._dynamic_net_ids_array, tree_indices)
# Group by other net_id to minimize 'touches' calls
unique_other_nets = numpy.unique(hit_net_ids[hit_net_ids != net_id])
if unique_other_nets.size == 0:
return 0
tree_geoms = self.dynamic_tree.geometries
real_hits_count = 0
for other_nid in unique_other_nets:
other_mask = (hit_net_ids == other_nid)
sub_tree_indices = tree_indices[other_mask]
sub_res_indices = res_indices[other_mask]
# Check if ANY hit for THIS other net is a real collision
found_real = False
for j in range(len(sub_tree_indices)):
p_test = geoms_to_test[sub_res_indices[j]]
p_tree = tree_geoms[sub_tree_indices[j]]
if not p_test.touches(p_tree):
# Add small area tolerance for numerical precision
if p_test.intersection(p_tree).area > 1e-7:
found_real = True
break
if found_real:
real_hits_count += 1
return real_hits_count
def _is_in_safety_zone(self, geometry: Polygon, obj_id: int, start_port: Port | None, end_port: Port | None) -> bool:
"""
Only returns True if the collision is ACTUALLY inside a safety zone.
"""
raw_obstacle = self.static_geometries[obj_id]
sz = self.safety_zone_radius
# Fast path: check if ports are even near the obstacle
obs_b = raw_obstacle.bounds
near_start = start_port and (obs_b[0]-sz <= start_port.x <= obs_b[2]+sz and
obs_b[1]-sz <= start_port.y <= obs_b[3]+sz)
near_end = end_port and (obs_b[0]-sz <= end_port.x <= obs_b[2]+sz and
obs_b[1]-sz <= end_port.y <= obs_b[3]+sz)
if not near_start and not near_end:
return False
if not geometry.intersects(raw_obstacle):
return False
self.metrics['safety_zone_checks'] += 1
intersection = geometry.intersection(raw_obstacle)
if intersection.is_empty: return False
ix_bounds = intersection.bounds
if start_port and near_start:
if (abs(ix_bounds[0] - start_port.x) < sz and abs(ix_bounds[1] - start_port.y) < sz and
abs(ix_bounds[2] - start_port.x) < sz and abs(ix_bounds[3] - start_port.y) < sz): return True
if end_port and near_end:
if (abs(ix_bounds[0] - end_port.x) < sz and abs(ix_bounds[1] - end_port.y) < sz and
abs(ix_bounds[2] - end_port.x) < sz and abs(ix_bounds[3] - end_port.y) < sz): return True
return False
def check_collision(self, geometry: Polygon, net_id: str, buffer_mode: Literal['static', 'congestion'] = 'static', start_port: Port | None = None, end_port: Port | None = None, dilated_geometry: Polygon | None = None, bounds: tuple[float, float, float, float] | None = None, net_width: float | None = None) -> bool | int:
if buffer_mode == 'static':
self._ensure_static_tree()
if self.static_tree is None: return False
# Separation needed: Centerline-to-WallEdge >= Wi/2 + C.
# static_tree has obstacles buffered by C/2.
# geometry is physical waveguide (Wi/2 from centerline).
# So we buffer geometry by C/2 to get Wi/2 + C/2.
# Intersection means separation < (Wi/2 + C/2) + C/2 = Wi/2 + C.
if dilated_geometry is not None:
test_geom = dilated_geometry
else:
dist = self.clearance / 2.0
test_geom = geometry.buffer(dist + 1e-7, join_style=2) if dist > 0 else geometry
hits = self.static_tree.query(test_geom, predicate='intersects')
tree_geoms = self.static_tree.geometries
for hit_idx in hits:
if test_geom.touches(tree_geoms[hit_idx]): continue
obj_id = self.static_obj_ids[hit_idx]
if self._is_in_safety_zone(geometry, obj_id, start_port, end_port): continue
return True
return False
self._ensure_dynamic_tree()
if self.dynamic_tree is None: return 0
test_poly = dilated_geometry if dilated_geometry else geometry.buffer(self.clearance / 2.0)
hits = self.dynamic_tree.query(test_poly, predicate='intersects')
tree_geoms = self.dynamic_tree.geometries
hit_net_ids = []
for hit_idx in hits:
if test_poly.touches(tree_geoms[hit_idx]): continue
obj_id = self.dynamic_obj_ids[hit_idx]
other_id = self.dynamic_geometries[obj_id][0]
if other_id != net_id:
hit_net_ids.append(other_id)
return len(numpy.unique(hit_net_ids)) if hit_net_ids else 0
def is_collision(self, geometry: Polygon, net_id: str = 'default', net_width: float | None = None, start_port: Port | None = None, end_port: Port | None = None) -> bool:
""" Unified entry point for static collision checks. """
result = self.check_collision(geometry, net_id, buffer_mode='static', start_port=start_port, end_port=end_port, net_width=net_width)
return bool(result)
def verify_path(self, net_id: str, components: list[ComponentResult]) -> tuple[bool, int]:
"""
Non-approximated, full-polygon intersection check of a path against all
static obstacles and other nets.
"""
collision_count = 0
# 1. Check against static obstacles
self._ensure_static_raw_tree()
if self._static_raw_tree is not None:
raw_geoms = self._static_raw_tree.geometries
for comp in components:
# Use ACTUAL geometry, not dilated/proxy
actual_geoms = comp.actual_geometry if comp.actual_geometry is not None else comp.geometry
for p_actual in actual_geoms:
# Physical separation must be >= clearance.
p_verify = p_actual.buffer(self.clearance, join_style=2)
hits = self._static_raw_tree.query(p_verify, predicate='intersects')
for hit_idx in hits:
p_obs = raw_geoms[hit_idx]
# If they ONLY touch, gap is exactly clearance. Valid.
if p_verify.touches(p_obs): continue
obj_id = self._static_raw_obj_ids[hit_idx]
if not self._is_in_safety_zone(p_actual, obj_id, None, None):
collision_count += 1
# 2. Check against other nets
self._ensure_dynamic_tree()
if self.dynamic_tree is not None:
tree_geoms = self.dynamic_tree.geometries
for comp in components:
# Robust fallback chain to ensure crossings are caught even with zero clearance
d_geoms = comp.dilated_actual_geometry or comp.dilated_geometry or comp.actual_geometry or comp.geometry
if not d_geoms: continue
# Ensure d_geoms is a list/array for STRtree.query
if not isinstance(d_geoms, (list, tuple, numpy.ndarray)):
d_geoms = [d_geoms]
res_indices, tree_indices = self.dynamic_tree.query(d_geoms, predicate='intersects')
if tree_indices.size > 0:
hit_net_ids = numpy.take(self._dynamic_net_ids_array, tree_indices)
net_id_str = str(net_id)
comp_hits = []
for i in range(len(tree_indices)):
if hit_net_ids[i] == net_id_str: continue
p_new = d_geoms[res_indices[i]]
p_tree = tree_geoms[tree_indices[i]]
if not p_new.touches(p_tree):
# Numerical tolerance for area overlap
if p_new.intersection(p_tree).area > 1e-7:
comp_hits.append(hit_net_ids[i])
if comp_hits:
collision_count += len(numpy.unique(comp_hits))
return (collision_count == 0), collision_count
def ray_cast(self, origin: Port, angle_deg: float, max_dist: float = 2000.0, net_width: float | None = None) -> float:
rad = numpy.radians(angle_deg)
cos_v, sin_v = numpy.cos(rad), numpy.sin(rad)
dx, dy = max_dist * cos_v, max_dist * sin_v
min_x, max_x = sorted([origin.x, origin.x + dx])
min_y, max_y = sorted([origin.y, origin.y + dy])
key = None
if net_width is not None:
tree = self._ensure_net_static_tree(net_width)
key = (round(net_width, 4), round(self.clearance, 4))
is_rect_arr = self._net_specific_is_rect[key]
bounds_arr = self._net_specific_bounds[key]
else:
self._ensure_static_tree()
tree = self.static_tree
is_rect_arr = self._static_is_rect_array
bounds_arr = self._static_bounds_array
if tree is None: return max_dist
candidates = tree.query(box(min_x, min_y, max_x, max_y))
if candidates.size == 0: return max_dist
min_dist = max_dist
inv_dx = 1.0 / dx if abs(dx) > 1e-12 else 1e30
inv_dy = 1.0 / dy if abs(dy) > 1e-12 else 1e30
tree_geoms = tree.geometries
ray_line = None
# Fast AABB-based pre-sort
candidates_bounds = bounds_arr[candidates]
# Distance to AABB min corner as heuristic
dist_sq = (candidates_bounds[:, 0] - origin.x)**2 + (candidates_bounds[:, 1] - origin.y)**2
sorted_indices = numpy.argsort(dist_sq)
for idx in sorted_indices:
c = candidates[idx]
b = bounds_arr[c]
# Fast axis-aligned ray-AABB intersection
# (Standard Slab method)
if abs(dx) < 1e-12: # Vertical ray
if origin.x < b[0] or origin.x > b[2]: tx_min, tx_max = 1e30, -1e30
else: tx_min, tx_max = -1e30, 1e30
else:
t1, t2 = (b[0] - origin.x) * inv_dx, (b[2] - origin.x) * inv_dx
tx_min, tx_max = min(t1, t2), max(t1, t2)
if abs(dy) < 1e-12: # Horizontal ray
if origin.y < b[1] or origin.y > b[3]: ty_min, ty_max = 1e30, -1e30
else: ty_min, ty_max = -1e30, 1e30
else:
t1, t2 = (b[1] - origin.y) * inv_dy, (b[3] - origin.y) * inv_dy
ty_min, ty_max = min(t1, t2), max(t1, t2)
t_min, t_max = max(tx_min, ty_min), min(tx_max, ty_max)
# Intersection conditions
if t_max < 0 or t_min > t_max or t_min > 1.0: continue
# If hit is further than current min_dist, skip
if t_min * max_dist >= min_dist: continue
# HIGH PRECISION CHECK
if is_rect_arr[c]:
# Rectangles are perfectly described by their AABB
min_dist = max(0.0, t_min * max_dist)
continue
# Fallback to full geometry check for non-rectangles (arcs, etc.)
if ray_line is None:
ray_line = LineString([(origin.x, origin.y), (origin.x + dx, origin.y + dy)])
obs_dilated = tree_geoms[c]
if obs_dilated.intersects(ray_line):
intersection = ray_line.intersection(obs_dilated)
if intersection.is_empty: continue
def get_dist(geom):
if hasattr(geom, 'geoms'): return min(get_dist(g) for g in geom.geoms)
return numpy.sqrt((geom.coords[0][0] - origin.x)**2 + (geom.coords[0][1] - origin.y)**2)
d = get_dist(intersection)
if d < min_dist: min_dist = d
return min_dist

333
inire/geometry/components.py
View file

@ -1,333 +0,0 @@
from __future__ import annotations
from typing import Literal
import numpy
from shapely.affinity import translate as shapely_translate
from shapely.geometry import Polygon, box
from inire.constants import TOLERANCE_ANGULAR, TOLERANCE_LINEAR
from .primitives import Port, rotation_matrix2
def _normalize_length(value: float) -> float:
return float(value)
class ComponentResult:
__slots__ = (
"geometry",
"dilated_geometry",
"proxy_geometry",
"actual_geometry",
"dilated_actual_geometry",
"end_port",
"length",
"move_type",
"_bounds",
"_total_bounds",
"_dilated_bounds",
"_total_dilated_bounds",
)
def __init__(
self,
geometry: list[Polygon],
end_port: Port,
length: float,
move_type: str,
dilated_geometry: list[Polygon] | None = None,
proxy_geometry: list[Polygon] | None = None,
actual_geometry: list[Polygon] | None = None,
dilated_actual_geometry: list[Polygon] | None = None,
) -> None:
self.geometry = geometry
self.dilated_geometry = dilated_geometry
self.proxy_geometry = proxy_geometry
self.actual_geometry = actual_geometry
self.dilated_actual_geometry = dilated_actual_geometry
self.end_port = end_port
self.length = float(length)
self.move_type = move_type
self._bounds = [poly.bounds for poly in self.geometry]
self._total_bounds = _combine_bounds(self._bounds)
if self.dilated_geometry is None:
self._dilated_bounds = None
self._total_dilated_bounds = None
else:
self._dilated_bounds = [poly.bounds for poly in self.dilated_geometry]
self._total_dilated_bounds = _combine_bounds(self._dilated_bounds)
@property
def bounds(self) -> list[tuple[float, float, float, float]]:
return self._bounds
@property
def total_bounds(self) -> tuple[float, float, float, float]:
return self._total_bounds
@property
def dilated_bounds(self) -> list[tuple[float, float, float, float]] | None:
return self._dilated_bounds
@property
def total_dilated_bounds(self) -> tuple[float, float, float, float] | None:
return self._total_dilated_bounds
def translate(self, dx: int | float, dy: int | float) -> ComponentResult:
return ComponentResult(
geometry=[shapely_translate(poly, dx, dy) for poly in self.geometry],
end_port=self.end_port + [dx, dy, 0],
length=self.length,
move_type=self.move_type,
dilated_geometry=None if self.dilated_geometry is None else [shapely_translate(poly, dx, dy) for poly in self.dilated_geometry],
proxy_geometry=None if self.proxy_geometry is None else [shapely_translate(poly, dx, dy) for poly in self.proxy_geometry],
actual_geometry=None if self.actual_geometry is None else [shapely_translate(poly, dx, dy) for poly in self.actual_geometry],
dilated_actual_geometry=None if self.dilated_actual_geometry is None else [shapely_translate(poly, dx, dy) for poly in self.dilated_actual_geometry],
)
def _combine_bounds(bounds_list: list[tuple[float, float, float, float]]) -> tuple[float, float, float, float]:
arr = numpy.asarray(bounds_list, dtype=numpy.float64)
return (
float(arr[:, 0].min()),
float(arr[:, 1].min()),
float(arr[:, 2].max()),
float(arr[:, 3].max()),
)
def _get_num_segments(radius: float, angle_deg: float, sagitta: float = 0.01) -> int:
if radius <= 0:
return 1
ratio = max(0.0, min(1.0, 1.0 - sagitta / radius))
theta_max = 2.0 * numpy.arccos(ratio)
if theta_max < TOLERANCE_ANGULAR:
return 16
num = int(numpy.ceil(numpy.radians(abs(angle_deg)) / theta_max))
return max(8, num)
def _get_arc_polygons(
cxy: tuple[float, float],
radius: float,
width: float,
ts: tuple[float, float],
sagitta: float = 0.01,
dilation: float = 0.0,
) -> list[Polygon]:
t_start, t_end = numpy.radians(ts[0]), numpy.radians(ts[1])
num_segments = _get_num_segments(radius, abs(ts[1] - ts[0]), sagitta)
angles = numpy.linspace(t_start, t_end, num_segments + 1)
cx, cy = cxy
inner_radius = radius - width / 2.0 - dilation
outer_radius = radius + width / 2.0 + dilation
cos_a = numpy.cos(angles)
sin_a = numpy.sin(angles)
inner_points = numpy.column_stack((cx + inner_radius * cos_a, cy + inner_radius * sin_a))
outer_points = numpy.column_stack((cx + outer_radius * cos_a[::-1], cy + outer_radius * sin_a[::-1]))
return [Polygon(numpy.concatenate((inner_points, outer_points), axis=0))]
def _clip_bbox(cxy: tuple[float, float], radius: float, width: float, ts: tuple[float, float], clip_margin: float) -> Polygon:
arc_poly = _get_arc_polygons(cxy, radius, width, ts)[0]
minx, miny, maxx, maxy = arc_poly.bounds
bbox_poly = box(minx, miny, maxx, maxy)
shrink = min(clip_margin, max(radius, width))
return bbox_poly.buffer(-shrink, join_style=2) if shrink > 0 else bbox_poly
def _apply_collision_model(
arc_poly: Polygon,
collision_type: Literal["arc", "bbox", "clipped_bbox"] | Polygon,
radius: float,
width: float,
cxy: tuple[float, float],
clip_margin: float,
ts: tuple[float, float],
) -> list[Polygon]:
if isinstance(collision_type, Polygon):
return [shapely_translate(collision_type, cxy[0], cxy[1])]
if collision_type == "arc":
return [arc_poly]
if collision_type == "clipped_bbox":
clipped = _clip_bbox(cxy, radius, width, ts, clip_margin)
return [clipped if not clipped.is_empty else box(*arc_poly.bounds)]
return [box(*arc_poly.bounds)]
class Straight:
@staticmethod
def generate(
start_port: Port,
length: float,
width: float,
dilation: float = 0.0,
) -> ComponentResult:
rot2 = rotation_matrix2(start_port.r)
length_f = _normalize_length(length)
disp = rot2 @ numpy.array((length_f, 0.0))
end_port = Port(start_port.x + disp[0], start_port.y + disp[1], start_port.r)
half_w = width / 2.0
pts = numpy.array(((0.0, half_w), (length_f, half_w), (length_f, -half_w), (0.0, -half_w)))
poly_points = (pts @ rot2.T) + numpy.array((start_port.x, start_port.y))
geometry = [Polygon(poly_points)]
dilated_geometry = None
if dilation > 0:
half_w_d = half_w + dilation
pts_d = numpy.array(((-dilation, half_w_d), (length_f + dilation, half_w_d), (length_f + dilation, -half_w_d), (-dilation, -half_w_d)))
poly_points_d = (pts_d @ rot2.T) + numpy.array((start_port.x, start_port.y))
dilated_geometry = [Polygon(poly_points_d)]
return ComponentResult(
geometry=geometry,
end_port=end_port,
length=abs(length_f),
move_type="Straight",
dilated_geometry=dilated_geometry,
actual_geometry=geometry,
dilated_actual_geometry=dilated_geometry,
)
class Bend90:
@staticmethod
def generate(
start_port: Port,
radius: float,
width: float,
direction: Literal["CW", "CCW"],
sagitta: float = 0.01,
collision_type: Literal["arc", "bbox", "clipped_bbox"] | Polygon = "arc",
clip_margin: float = 10.0,
dilation: float = 0.0,
) -> ComponentResult:
rot2 = rotation_matrix2(start_port.r)
sign = 1 if direction == "CCW" else -1
center_local = numpy.array((0.0, sign * radius))
end_local = numpy.array((radius, sign * radius))
center_xy = (rot2 @ center_local) + numpy.array((start_port.x, start_port.y))
end_xy = (rot2 @ end_local) + numpy.array((start_port.x, start_port.y))
end_port = Port(end_xy[0], end_xy[1], start_port.r + sign * 90)
start_theta = start_port.r - sign * 90
end_theta = start_port.r
ts = (float(start_theta), float(end_theta))
arc_polys = _get_arc_polygons((float(center_xy[0]), float(center_xy[1])), radius, width, ts, sagitta)
collision_polys = _apply_collision_model(
arc_polys[0],
collision_type,
radius,
width,
(float(center_xy[0]), float(center_xy[1])),
clip_margin,
ts,
)
proxy_geometry = None
if collision_type == "arc":
proxy_geometry = _apply_collision_model(
arc_polys[0],
"clipped_bbox",
radius,
width,
(float(center_xy[0]), float(center_xy[1])),
clip_margin,
ts,
)
dilated_actual_geometry = None
dilated_geometry = None
if dilation > 0:
dilated_actual_geometry = _get_arc_polygons((float(center_xy[0]), float(center_xy[1])), radius, width, ts, sagitta, dilation=dilation)
dilated_geometry = dilated_actual_geometry if collision_type == "arc" else [poly.buffer(dilation) for poly in collision_polys]
return ComponentResult(
geometry=collision_polys,
end_port=end_port,
length=abs(radius) * numpy.pi / 2.0,
move_type="Bend90",
dilated_geometry=dilated_geometry,
proxy_geometry=proxy_geometry,
actual_geometry=arc_polys,
dilated_actual_geometry=dilated_actual_geometry,
)
class SBend:
@staticmethod
def generate(
start_port: Port,
offset: float,
radius: float,
width: float,
sagitta: float = 0.01,
collision_type: Literal["arc", "bbox", "clipped_bbox"] | Polygon = "arc",
clip_margin: float = 10.0,
dilation: float = 0.0,
) -> ComponentResult:
if abs(offset) >= 2 * radius:
raise ValueError(f"SBend offset {offset} must be less than 2*radius {2 * radius}")
sign = 1 if offset >= 0 else -1
theta = numpy.arccos(1.0 - abs(offset) / (2.0 * radius))
dx = 2.0 * radius * numpy.sin(theta)
theta_deg = float(numpy.degrees(theta))
rot2 = rotation_matrix2(start_port.r)
end_local = numpy.array((dx, offset))
end_xy = (rot2 @ end_local) + numpy.array((start_port.x, start_port.y))
end_port = Port(end_xy[0], end_xy[1], start_port.r)
c1_local = numpy.array((0.0, sign * radius))
c2_local = numpy.array((dx, offset - sign * radius))
c1_xy = (rot2 @ c1_local) + numpy.array((start_port.x, start_port.y))
c2_xy = (rot2 @ c2_local) + numpy.array((start_port.x, start_port.y))
ts1 = (float(start_port.r - sign * 90), float(start_port.r - sign * 90 + sign * theta_deg))
second_base = start_port.r + (90 if sign > 0 else 270)
ts2 = (float(second_base + sign * theta_deg), float(second_base))
arc1 = _get_arc_polygons((float(c1_xy[0]), float(c1_xy[1])), radius, width, ts1, sagitta)[0]
arc2 = _get_arc_polygons((float(c2_xy[0]), float(c2_xy[1])), radius, width, ts2, sagitta)[0]
actual_geometry = [arc1, arc2]
geometry = [
_apply_collision_model(arc1, collision_type, radius, width, (float(c1_xy[0]), float(c1_xy[1])), clip_margin, ts1)[0],
_apply_collision_model(arc2, collision_type, radius, width, (float(c2_xy[0]), float(c2_xy[1])), clip_margin, ts2)[0],
]
proxy_geometry = None
if collision_type == "arc":
proxy_geometry = [
_apply_collision_model(arc1, "clipped_bbox", radius, width, (float(c1_xy[0]), float(c1_xy[1])), clip_margin, ts1)[0],
_apply_collision_model(arc2, "clipped_bbox", radius, width, (float(c2_xy[0]), float(c2_xy[1])), clip_margin, ts2)[0],
]
dilated_actual_geometry = None
dilated_geometry = None
if dilation > 0:
dilated_actual_geometry = [
_get_arc_polygons((float(c1_xy[0]), float(c1_xy[1])), radius, width, ts1, sagitta, dilation=dilation)[0],
_get_arc_polygons((float(c2_xy[0]), float(c2_xy[1])), radius, width, ts2, sagitta, dilation=dilation)[0],
]
dilated_geometry = dilated_actual_geometry if collision_type == "arc" else [poly.buffer(dilation) for poly in geometry]
return ComponentResult(
geometry=geometry,
end_port=end_port,
length=2.0 * radius * theta,
move_type="SBend",
dilated_geometry=dilated_geometry,
proxy_geometry=proxy_geometry,
actual_geometry=actual_geometry,
dilated_actual_geometry=dilated_actual_geometry,
)

160
inire/geometry/primitives.py
View file

@ -1,160 +0,0 @@
from __future__ import annotations
from collections.abc import Iterator
from typing import Self
import numpy
from numpy.typing import ArrayLike, NDArray
def _normalize_angle(angle_deg: int | float) -> int:
angle = int(round(angle_deg)) % 360
if angle % 90 != 0:
raise ValueError(f"Port angle must be Manhattan (multiple of 90), got {angle_deg!r}")
return angle
def _as_int32_triplet(value: ArrayLike) -> NDArray[numpy.int32]:
arr = numpy.asarray(value, dtype=numpy.int32)
if arr.shape != (3,):
raise ValueError(f"Port array must have shape (3,), got {arr.shape}")
arr = arr.copy()
arr[2] = _normalize_angle(int(arr[2]))
return arr
class Port:
"""
Port represented as an ndarray-backed (x, y, r) triple with int32 storage.
"""
__slots__ = ("_xyr",)
def __init__(self, x: int | float, y: int | float, r: int | float) -> None:
self._xyr = numpy.array(
(int(round(x)), int(round(y)), _normalize_angle(r)),
dtype=numpy.int32,
)
@classmethod
def from_array(cls, xyr: ArrayLike) -> Self:
obj = cls.__new__(cls)
obj._xyr = _as_int32_triplet(xyr)
return obj
@property
def x(self) -> int:
return int(self._xyr[0])
@x.setter
def x(self, val: int | float) -> None:
self._xyr[0] = int(round(val))
@property
def y(self) -> int:
return int(self._xyr[1])
@y.setter
def y(self, val: int | float) -> None:
self._xyr[1] = int(round(val))
@property
def r(self) -> int:
return int(self._xyr[2])
@r.setter
def r(self, val: int | float) -> None:
self._xyr[2] = _normalize_angle(val)
@property
def orientation(self) -> int:
return self.r
@orientation.setter
def orientation(self, val: int | float) -> None:
self.r = val
@property
def xyr(self) -> NDArray[numpy.int32]:
return self._xyr
@xyr.setter
def xyr(self, val: ArrayLike) -> None:
self._xyr = _as_int32_triplet(val)
def __repr__(self) -> str:
return f"Port(x={self.x}, y={self.y}, r={self.r})"
def __iter__(self) -> Iterator[int]:
return iter((self.x, self.y, self.r))
def __len__(self) -> int:
return 3
def __getitem__(self, item: int | slice) -> int | NDArray[numpy.int32]:
return self._xyr[item]
def __array__(self, dtype: numpy.dtype | None = None) -> NDArray[numpy.int32]:
return numpy.asarray(self._xyr, dtype=dtype)
def __eq__(self, other: object) -> bool:
if not isinstance(other, Port):
return False
return bool(numpy.array_equal(self._xyr, other._xyr))
def __hash__(self) -> int:
return hash(self.as_tuple())
def copy(self) -> Self:
return type(self).from_array(self._xyr.copy())
def as_tuple(self) -> tuple[int, int, int]:
return (self.x, self.y, self.r)
def translate(self, dxy: ArrayLike) -> Self:
dxy_arr = numpy.asarray(dxy, dtype=numpy.int32)
if dxy_arr.shape == (2,):
return type(self)(self.x + int(dxy_arr[0]), self.y + int(dxy_arr[1]), self.r)
if dxy_arr.shape == (3,):
return type(self)(self.x + int(dxy_arr[0]), self.y + int(dxy_arr[1]), self.r + int(dxy_arr[2]))
raise ValueError(f"Translation must have shape (2,) or (3,), got {dxy_arr.shape}")
def __add__(self, other: ArrayLike) -> Self:
return self.translate(other)
def __sub__(self, other: ArrayLike | Self) -> NDArray[numpy.int32]:
if isinstance(other, Port):
return self._xyr - other._xyr
return self._xyr - numpy.asarray(other, dtype=numpy.int32)
ROT2_0 = numpy.array(((1, 0), (0, 1)), dtype=numpy.int32)
ROT2_90 = numpy.array(((0, -1), (1, 0)), dtype=numpy.int32)
ROT2_180 = numpy.array(((-1, 0), (0, -1)), dtype=numpy.int32)
ROT2_270 = numpy.array(((0, 1), (-1, 0)), dtype=numpy.int32)
def rotation_matrix2(rotation_deg: int) -> NDArray[numpy.int32]:
quadrant = (_normalize_angle(rotation_deg) // 90) % 4
return (ROT2_0, ROT2_90, ROT2_180, ROT2_270)[quadrant]
def rotation_matrix3(rotation_deg: int) -> NDArray[numpy.int32]:
rot2 = rotation_matrix2(rotation_deg)
rot3 = numpy.zeros((3, 3), dtype=numpy.int32)
rot3[:2, :2] = rot2
rot3[2, 2] = 1
return rot3
def translate_port(port: Port, dx: int | float, dy: int | float) -> Port:
return Port(port.x + dx, port.y + dy, port.r)
def rotate_port(port: Port, angle: int | float, origin: tuple[int | float, int | float] = (0, 0)) -> Port:
angle_i = _normalize_angle(angle)
rot = rotation_matrix2(angle_i)
origin_xy = numpy.array((int(round(origin[0])), int(round(origin[1]))), dtype=numpy.int32)
rel = numpy.array((port.x, port.y), dtype=numpy.int32) - origin_xy
rotated = origin_xy + rot @ rel
return Port(int(rotated[0]), int(rotated[1]), port.r + angle_i)

721
inire/router/astar.py
View file

@ -1,721 +0,0 @@
from __future__ import annotations
import heapq
import logging
import math
from typing import TYPE_CHECKING, Any, Literal
import shapely
from inire.constants import TOLERANCE_LINEAR
from inire.geometry.components import Bend90, SBend, Straight
from inire.geometry.primitives import Port
from inire.router.config import RouterConfig, VisibilityGuidanceMode
from inire.router.visibility import VisibilityManager
if TYPE_CHECKING:
from inire.geometry.components import ComponentResult
from inire.router.cost import CostEvaluator
logger = logging.getLogger(__name__)
class AStarNode:
__slots__ = ("port", "g_cost", "h_cost", "fh_cost", "parent", "component_result")
def __init__(
self,
port: Port,
g_cost: float,
h_cost: float,
parent: AStarNode | None = None,
component_result: ComponentResult | None = None,
) -> None:
self.port = port
self.g_cost = g_cost
self.h_cost = h_cost
self.fh_cost = (g_cost + h_cost, h_cost)
self.parent = parent
self.component_result = component_result
def __lt__(self, other: AStarNode) -> bool:
return self.fh_cost < other.fh_cost
class AStarMetrics:
__slots__ = (
"total_nodes_expanded",
"last_expanded_nodes",
"nodes_expanded",
"moves_generated",
"moves_added",
"pruned_closed_set",
"pruned_hard_collision",
"pruned_cost",
)
def __init__(self) -> None:
self.total_nodes_expanded = 0
self.last_expanded_nodes: list[tuple[int, int, int]] = []
self.nodes_expanded = 0
self.moves_generated = 0
self.moves_added = 0
self.pruned_closed_set = 0
self.pruned_hard_collision = 0
self.pruned_cost = 0
def reset_per_route(self) -> None:
self.nodes_expanded = 0
self.moves_generated = 0
self.moves_added = 0
self.pruned_closed_set = 0
self.pruned_hard_collision = 0
self.pruned_cost = 0
self.last_expanded_nodes = []
class AStarContext:
__slots__ = (
"cost_evaluator",
"config",
"visibility_manager",
"move_cache_rel",
"move_cache_abs",
"hard_collision_set",
"static_safe_cache",
"max_cache_size",
)
def __init__(
self,
cost_evaluator: CostEvaluator,
node_limit: int = 1000000,
max_straight_length: float = 2000.0,
min_straight_length: float = 5.0,
bend_radii: list[float] | None = None,
sbend_radii: list[float] | None = None,
sbend_offsets: list[float] | None = None,
bend_penalty: float = 250.0,
sbend_penalty: float | None = None,
bend_collision_type: Literal["arc", "bbox", "clipped_bbox"] | Any = "arc",
bend_clip_margin: float = 10.0,
visibility_guidance: VisibilityGuidanceMode = "tangent_corner",
max_cache_size: int = 1000000,
) -> None:
actual_sbend_penalty = 2.0 * bend_penalty if sbend_penalty is None else sbend_penalty
self.cost_evaluator = cost_evaluator
self.max_cache_size = max_cache_size
self.config = RouterConfig(
node_limit=node_limit,
max_straight_length=max_straight_length,
min_straight_length=min_straight_length,
bend_radii=bend_radii if bend_radii is not None else [50.0, 100.0],
sbend_radii=sbend_radii if sbend_radii is not None else [5.0, 10.0, 50.0, 100.0],
sbend_offsets=sbend_offsets,
bend_penalty=bend_penalty,
sbend_penalty=actual_sbend_penalty,
bend_collision_type=bend_collision_type,
bend_clip_margin=bend_clip_margin,
visibility_guidance=visibility_guidance,
)
self.cost_evaluator.config = self.config
self.cost_evaluator._refresh_cached_config()
self.visibility_manager = VisibilityManager(self.cost_evaluator.collision_engine)
self.move_cache_rel: dict[tuple, ComponentResult] = {}
self.move_cache_abs: dict[tuple, ComponentResult] = {}
self.hard_collision_set: set[tuple] = set()
self.static_safe_cache: set[tuple] = set()
def clear_static_caches(self) -> None:
self.hard_collision_set.clear()
self.static_safe_cache.clear()
self.visibility_manager.clear_cache()
def check_cache_eviction(self) -> None:
if len(self.move_cache_abs) <= self.max_cache_size * 1.2:
return
num_to_evict = int(len(self.move_cache_abs) * 0.25)
for idx, key in enumerate(list(self.move_cache_abs.keys())):
if idx >= num_to_evict:
break
del self.move_cache_abs[key]
def route_astar(
start: Port,
target: Port,
net_width: float,
context: AStarContext,
metrics: AStarMetrics | None = None,
net_id: str = "default",
bend_collision_type: Literal["arc", "bbox", "clipped_bbox"] | None = None,
return_partial: bool = False,
store_expanded: bool = False,
skip_congestion: bool = False,
max_cost: float | None = None,
self_collision_check: bool = False,
node_limit: int | None = None,
) -> list[ComponentResult] | None:
if metrics is None:
metrics = AStarMetrics()
metrics.reset_per_route()
if bend_collision_type is not None:
context.config.bend_collision_type = bend_collision_type
context.cost_evaluator.set_target(target)
open_set: list[AStarNode] = []
closed_set: dict[tuple[int, int, int], float] = {}
congestion_cache: dict[tuple, int] = {}
start_node = AStarNode(start, 0.0, context.cost_evaluator.h_manhattan(start, target))
heapq.heappush(open_set, start_node)
best_node = start_node
effective_node_limit = node_limit if node_limit is not None else context.config.node_limit
nodes_expanded = 0
while open_set:
if nodes_expanded >= effective_node_limit:
return reconstruct_path(best_node) if return_partial else None
current = heapq.heappop(open_set)
if max_cost is not None and current.fh_cost[0] > max_cost:
metrics.pruned_cost += 1
continue
if current.h_cost < best_node.h_cost:
best_node = current
state = current.port.as_tuple()
if state in closed_set and closed_set[state] <= current.g_cost + TOLERANCE_LINEAR:
continue
closed_set[state] = current.g_cost
if store_expanded:
metrics.last_expanded_nodes.append(state)
nodes_expanded += 1
metrics.total_nodes_expanded += 1
metrics.nodes_expanded += 1
if current.port == target:
return reconstruct_path(current)
expand_moves(
current,
target,
net_width,
net_id,
open_set,
closed_set,
context,
metrics,
congestion_cache,
max_cost=max_cost,
skip_congestion=skip_congestion,
self_collision_check=self_collision_check,
)
return reconstruct_path(best_node) if return_partial else None
def _quantized_lengths(values: list[float], max_reach: float) -> list[int]:
out = {int(round(v)) for v in values if v > 0 and v <= max_reach + 0.01}
return sorted((v for v in out if v > 0), reverse=True)
def _sbend_forward_span(offset: float, radius: float) -> float | None:
abs_offset = abs(offset)
if abs_offset <= TOLERANCE_LINEAR or radius <= 0 or abs_offset >= 2.0 * radius:
return None
theta = __import__("math").acos(1.0 - abs_offset / (2.0 * radius))
return 2.0 * radius * __import__("math").sin(theta)
def _visible_straight_candidates(
current: Port,
context: AStarContext,
max_reach: float,
cos_v: float,
sin_v: float,
net_width: float,
) -> list[float]:
mode = context.config.visibility_guidance
if mode == "off":
return []
if mode == "exact_corner":
max_bend_radius = max(context.config.bend_radii, default=0.0)
visibility_reach = max_reach + max_bend_radius
visible_corners = sorted(
context.visibility_manager.get_corner_visibility(current, max_dist=visibility_reach),
key=lambda corner: corner[2],
)
if not visible_corners:
return []
candidates: set[int] = set()
for cx, cy, _ in visible_corners[:12]:
dx = cx - current.x
dy = cy - current.y
local_x = dx * cos_v + dy * sin_v
if local_x <= context.config.min_straight_length:
continue
candidates.add(int(round(local_x)))
return sorted(candidates, reverse=True)
if mode != "tangent_corner":
return []
visibility_manager = context.visibility_manager
visibility_manager._ensure_current()
max_bend_radius = max(context.config.bend_radii, default=0.0)
if max_bend_radius <= 0 or not visibility_manager.corners:
return []
reach = max_reach + max_bend_radius
bounds = (current.x - reach, current.y - reach, current.x + reach, current.y + reach)
candidate_ids = list(visibility_manager.corner_index.intersection(bounds))
if not candidate_ids:
return []
scored: list[tuple[float, float, float, float, float]] = []
for idx in candidate_ids:
cx, cy = visibility_manager.corners[idx]
dx = cx - current.x
dy = cy - current.y
local_x = dx * cos_v + dy * sin_v
local_y = -dx * sin_v + dy * cos_v
if local_x <= context.config.min_straight_length or local_x > reach + 0.01:
continue
nearest_radius = min(context.config.bend_radii, key=lambda radius: abs(abs(local_y) - radius))
tangent_error = abs(abs(local_y) - nearest_radius)
if tangent_error > 2.0:
continue
length = local_x - nearest_radius
if length <= context.config.min_straight_length or length > max_reach + 0.01:
continue
scored.append((tangent_error, math.hypot(dx, dy), length, dx, dy))
if not scored:
return []
collision_engine = context.cost_evaluator.collision_engine
candidates: set[int] = set()
for _, dist, length, dx, dy in sorted(scored)[:4]:
angle = math.degrees(math.atan2(dy, dx))
corner_reach = collision_engine.ray_cast(current, angle, max_dist=dist + 0.05, net_width=net_width)
if corner_reach < dist - 0.01:
continue
qlen = int(round(length))
if qlen > 0:
candidates.add(qlen)
return sorted(candidates, reverse=True)
def _previous_move_metadata(node: AStarNode) -> tuple[str | None, float | None]:
result = node.component_result
if result is None:
return None, None
move_type = result.move_type
if move_type == "Straight":
return move_type, result.length
return move_type, None
def expand_moves(
current: AStarNode,
target: Port,
net_width: float,
net_id: str,
open_set: list[AStarNode],
closed_set: dict[tuple[int, int, int], float],
context: AStarContext,
metrics: AStarMetrics,
congestion_cache: dict[tuple, int],
max_cost: float | None = None,
skip_congestion: bool = False,
self_collision_check: bool = False,
) -> None:
cp = current.port
prev_move_type, prev_straight_length = _previous_move_metadata(current)
dx_t = target.x - cp.x
dy_t = target.y - cp.y
dist_sq = dx_t * dx_t + dy_t * dy_t
if cp.r == 0:
cos_v, sin_v = 1.0, 0.0
elif cp.r == 90:
cos_v, sin_v = 0.0, 1.0
elif cp.r == 180:
cos_v, sin_v = -1.0, 0.0
else:
cos_v, sin_v = 0.0, -1.0
proj_t = dx_t * cos_v + dy_t * sin_v
perp_t = -dx_t * sin_v + dy_t * cos_v
dx_local = proj_t
dy_local = perp_t
if proj_t > 0 and abs(perp_t) < 1e-6 and cp.r == target.r:
max_reach = context.cost_evaluator.collision_engine.ray_cast(cp, cp.r, proj_t + 1.0, net_width=net_width)
if max_reach >= proj_t - 0.01 and (
prev_straight_length is None or proj_t < prev_straight_length - TOLERANCE_LINEAR
):
process_move(
current,
target,
net_width,
net_id,
open_set,
closed_set,
context,
metrics,
congestion_cache,
"S",
(int(round(proj_t)),),
skip_congestion,
max_cost=max_cost,
self_collision_check=self_collision_check,
)
max_reach = context.cost_evaluator.collision_engine.ray_cast(cp, cp.r, context.config.max_straight_length, net_width=net_width)
candidate_lengths = [
context.config.min_straight_length,
max_reach,
max_reach / 2.0,
max_reach - 5.0,
]
axis_target_dist = abs(dx_t) if cp.r in (0, 180) else abs(dy_t)
candidate_lengths.append(axis_target_dist)
for radius in context.config.bend_radii:
candidate_lengths.extend((max_reach - radius, axis_target_dist - radius, axis_target_dist - 2.0 * radius))
candidate_lengths.extend(
_visible_straight_candidates(
cp,
context,
max_reach,
cos_v,
sin_v,
net_width,
)
)
if cp.r == target.r and dx_local > 0 and abs(dy_local) > TOLERANCE_LINEAR:
for radius in context.config.sbend_radii:
sbend_span = _sbend_forward_span(dy_local, radius)
if sbend_span is None:
continue
candidate_lengths.extend((dx_local - sbend_span, dx_local - 2.0 * sbend_span))
for length in _quantized_lengths(candidate_lengths, max_reach):
if length < context.config.min_straight_length:
continue
if prev_straight_length is not None and length >= prev_straight_length - TOLERANCE_LINEAR:
continue
process_move(
current,
target,
net_width,
net_id,
open_set,
closed_set,
context,
metrics,
congestion_cache,
"S",
(length,),
skip_congestion,
max_cost=max_cost,
self_collision_check=self_collision_check,
)
angle_to_target = 0.0
if dx_t != 0 or dy_t != 0:
angle_to_target = float((round((180.0 / 3.141592653589793) * __import__("math").atan2(dy_t, dx_t)) + 360.0) % 360.0)
allow_backwards = dist_sq < 150 * 150
for radius in context.config.bend_radii:
for direction in ("CW", "CCW"):
if not allow_backwards:
turn = 90 if direction == "CCW" else -90
new_ori = (cp.r + turn) % 360
new_diff = (angle_to_target - new_ori + 180.0) % 360.0 - 180.0
if abs(new_diff) > 135.0:
continue
process_move(
current,
target,
net_width,
net_id,
open_set,
closed_set,
context,
metrics,
congestion_cache,
"B",
(radius, direction),
skip_congestion,
max_cost=max_cost,
self_collision_check=self_collision_check,
)
max_sbend_r = max(context.config.sbend_radii) if context.config.sbend_radii else 0.0
if max_sbend_r <= 0 or prev_move_type == "SBend":
return
explicit_offsets = context.config.sbend_offsets
offsets: set[int] = set(int(round(v)) for v in explicit_offsets or [])
# S-bends preserve orientation, so the implicit search only makes sense
# when the target is ahead in local coordinates and keeps the same
# orientation. Generating generic speculative offsets on the integer lattice
# explodes the search space without contributing useful moves.
if target.r == cp.r and 0 < dx_local <= 4 * max_sbend_r:
if 0 < abs(dy_local) < 2 * max_sbend_r:
offsets.add(int(round(dy_local)))
if not offsets:
return
for offset in sorted(offsets):
if offset == 0:
continue
for radius in context.config.sbend_radii:
if abs(offset) >= 2 * radius:
continue
process_move(
current,
target,
net_width,
net_id,
open_set,
closed_set,
context,
metrics,
congestion_cache,
"SB",
(offset, radius),
skip_congestion,
max_cost=max_cost,
self_collision_check=self_collision_check,
)
def process_move(
parent: AStarNode,
target: Port,
net_width: float,
net_id: str,
open_set: list[AStarNode],
closed_set: dict[tuple[int, int, int], float],
context: AStarContext,
metrics: AStarMetrics,
congestion_cache: dict[tuple, int],
move_class: Literal["S", "B", "SB"],
params: tuple,
skip_congestion: bool,
max_cost: float | None = None,
self_collision_check: bool = False,
) -> None:
cp = parent.port
coll_type = context.config.bend_collision_type
coll_key = id(coll_type) if isinstance(coll_type, shapely.geometry.Polygon) else coll_type
self_dilation = context.cost_evaluator.collision_engine.clearance / 2.0
abs_key = (
cp.as_tuple(),
move_class,
params,
net_width,
coll_key,
context.config.bend_clip_margin,
self_dilation,
)
if abs_key in context.move_cache_abs:
res = context.move_cache_abs[abs_key]
else:
context.check_cache_eviction()
base_port = Port(0, 0, cp.r)
rel_key = (
cp.r,
move_class,
params,
net_width,
coll_key,
context.config.bend_clip_margin,
self_dilation,
)
if rel_key in context.move_cache_rel:
res_rel = context.move_cache_rel[rel_key]
else:
try:
if move_class == "S":
res_rel = Straight.generate(base_port, params[0], net_width, dilation=self_dilation)
elif move_class == "B":
res_rel = Bend90.generate(
base_port,
params[0],
net_width,
params[1],
collision_type=context.config.bend_collision_type,
clip_margin=context.config.bend_clip_margin,
dilation=self_dilation,
)
else:
res_rel = SBend.generate(
base_port,
params[0],
params[1],
net_width,
collision_type=context.config.bend_collision_type,
clip_margin=context.config.bend_clip_margin,
dilation=self_dilation,
)
except ValueError:
return
context.move_cache_rel[rel_key] = res_rel
res = res_rel.translate(cp.x, cp.y)
context.move_cache_abs[abs_key] = res
move_radius = params[0] if move_class == "B" else (params[1] if move_class == "SB" else None)
add_node(
parent,
res,
target,
net_width,
net_id,
open_set,
closed_set,
context,
metrics,
congestion_cache,
move_class,
abs_key,
move_radius=move_radius,
skip_congestion=skip_congestion,
max_cost=max_cost,
self_collision_check=self_collision_check,
)
def add_node(
parent: AStarNode,
result: ComponentResult,
target: Port,
net_width: float,
net_id: str,
open_set: list[AStarNode],
closed_set: dict[tuple[int, int, int], float],
context: AStarContext,
metrics: AStarMetrics,
congestion_cache: dict[tuple, int],
move_type: str,
cache_key: tuple,
move_radius: float | None = None,
skip_congestion: bool = False,
max_cost: float | None = None,
self_collision_check: bool = False,
) -> None:
metrics.moves_generated += 1
state = result.end_port.as_tuple()
new_lower_bound_g = parent.g_cost + result.length
if state in closed_set and closed_set[state] <= new_lower_bound_g + TOLERANCE_LINEAR:
metrics.pruned_closed_set += 1
return
parent_p = parent.port
end_p = result.end_port
if cache_key in context.hard_collision_set:
metrics.pruned_hard_collision += 1
return
is_static_safe = cache_key in context.static_safe_cache
if not is_static_safe:
ce = context.cost_evaluator.collision_engine
if move_type == "S":
collision_found = ce.check_move_straight_static(parent_p, result.length, net_width=net_width)
else:
collision_found = ce.check_move_static(result, start_port=parent_p, end_port=end_p, net_width=net_width)
if collision_found:
context.hard_collision_set.add(cache_key)
metrics.pruned_hard_collision += 1
return
context.static_safe_cache.add(cache_key)
total_overlaps = 0
if not skip_congestion:
if cache_key in congestion_cache:
total_overlaps = congestion_cache[cache_key]
else:
total_overlaps = context.cost_evaluator.collision_engine.check_move_congestion(result, net_id)
congestion_cache[cache_key] = total_overlaps
if self_collision_check:
curr_p = parent
new_tb = result.total_bounds
while curr_p and curr_p.parent:
ancestor_res = curr_p.component_result
if ancestor_res:
anc_tb = ancestor_res.total_bounds
if new_tb[0] < anc_tb[2] and new_tb[2] > anc_tb[0] and new_tb[1] < anc_tb[3] and new_tb[3] > anc_tb[1]:
for p_anc in ancestor_res.geometry:
for p_new in result.geometry:
if p_new.intersects(p_anc) and not p_new.touches(p_anc):
return
curr_p = curr_p.parent
penalty = 0.0
if move_type == "SB":
penalty = context.config.sbend_penalty
elif move_type == "B":
penalty = context.config.bend_penalty
if move_radius is not None and move_radius > TOLERANCE_LINEAR:
penalty *= (10.0 / move_radius) ** 0.5
move_cost = context.cost_evaluator.evaluate_move(
result.geometry,
result.end_port,
net_width,
net_id,
start_port=parent_p,
length=result.length,
dilated_geometry=result.dilated_geometry,
penalty=penalty,
skip_static=True,
skip_congestion=True,
)
move_cost += total_overlaps * context.cost_evaluator.congestion_penalty
if max_cost is not None and parent.g_cost + move_cost > max_cost:
metrics.pruned_cost += 1
return
if move_cost > 1e12:
metrics.pruned_cost += 1
return
g_cost = parent.g_cost + move_cost
if state in closed_set and closed_set[state] <= g_cost + TOLERANCE_LINEAR:
metrics.pruned_closed_set += 1
return
h_cost = context.cost_evaluator.h_manhattan(result.end_port, target)
heapq.heappush(open_set, AStarNode(result.end_port, g_cost, h_cost, parent, result))
metrics.moves_added += 1
def reconstruct_path(end_node: AStarNode) -> list[ComponentResult]:
path = []
curr: AStarNode | None = end_node
while curr and curr.component_result:
path.append(curr.component_result)
curr = curr.parent
return path[::-1]

46
inire/router/config.py
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@ -1,46 +0,0 @@
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Literal, Any
VisibilityGuidanceMode = Literal["off", "exact_corner", "tangent_corner"]
@dataclass
class RouterConfig:
"""Configuration parameters for the A* Router."""
node_limit: int = 1000000
# Sparse Sampling Configuration
max_straight_length: float = 2000.0
num_straight_samples: int = 5
min_straight_length: float = 5.0
# Offsets for SBends (None = automatic grid-based selection)
sbend_offsets: list[float] | None = None
# Deprecated but kept for compatibility during refactor
straight_lengths: list[float] = field(default_factory=list)
bend_radii: list[float] = field(default_factory=lambda: [50.0, 100.0])
sbend_radii: list[float] = field(default_factory=lambda: [10.0])
snap_to_target_dist: float = 1000.0
bend_penalty: float = 250.0
sbend_penalty: float = 500.0
bend_collision_type: Literal["arc", "bbox", "clipped_bbox"] | Any = "arc"
bend_clip_margin: float = 10.0
visibility_guidance: VisibilityGuidanceMode = "tangent_corner"
@dataclass
class CostConfig:
"""Configuration parameters for the Cost Evaluator."""
unit_length_cost: float = 1.0
greedy_h_weight: float = 1.5
congestion_penalty: float = 10000.0
bend_penalty: float = 250.0
sbend_penalty: float = 500.0
min_bend_radius: float = 50.0

182
inire/router/cost.py
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@ -1,182 +0,0 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Any
import numpy as np
from inire.constants import TOLERANCE_LINEAR
from inire.router.config import CostConfig
if TYPE_CHECKING:
from shapely.geometry import Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.danger_map import DangerMap
class CostEvaluator:
__slots__ = (
"collision_engine",
"danger_map",
"config",
"unit_length_cost",
"greedy_h_weight",
"congestion_penalty",
"_target_x",
"_target_y",
"_target_r",
"_target_cos",
"_target_sin",
"_min_radius",
)
def __init__(
self,
collision_engine: CollisionEngine,
danger_map: DangerMap | None = None,
unit_length_cost: float = 1.0,
greedy_h_weight: float = 1.5,
congestion_penalty: float = 10000.0,
bend_penalty: float = 250.0,
sbend_penalty: float | None = None,
min_bend_radius: float = 50.0,
) -> None:
actual_sbend_penalty = 2.0 * bend_penalty if sbend_penalty is None else sbend_penalty
self.collision_engine = collision_engine
self.danger_map = danger_map
self.config = CostConfig(
unit_length_cost=unit_length_cost,
greedy_h_weight=greedy_h_weight,
congestion_penalty=congestion_penalty,
bend_penalty=bend_penalty,
sbend_penalty=actual_sbend_penalty,
min_bend_radius=min_bend_radius,
)
self.unit_length_cost = self.config.unit_length_cost
self.greedy_h_weight = self.config.greedy_h_weight
self.congestion_penalty = self.config.congestion_penalty
self._refresh_cached_config()
self._target_x = 0.0
self._target_y = 0.0
self._target_r = 0
self._target_cos = 1.0
self._target_sin = 0.0
def _refresh_cached_config(self) -> None:
if hasattr(self.config, "min_bend_radius"):
self._min_radius = self.config.min_bend_radius
elif hasattr(self.config, "bend_radii") and self.config.bend_radii:
self._min_radius = min(self.config.bend_radii)
else:
self._min_radius = 50.0
if hasattr(self.config, "unit_length_cost"):
self.unit_length_cost = self.config.unit_length_cost
if hasattr(self.config, "greedy_h_weight"):
self.greedy_h_weight = self.config.greedy_h_weight
if hasattr(self.config, "congestion_penalty"):
self.congestion_penalty = self.config.congestion_penalty
def set_target(self, target: Port) -> None:
self._target_x = target.x
self._target_y = target.y
self._target_r = target.r
rad = np.radians(target.r)
self._target_cos = np.cos(rad)
self._target_sin = np.sin(rad)
def g_proximity(self, x: float, y: float) -> float:
if self.danger_map is None:
return 0.0
return self.danger_map.get_cost(x, y)
def h_manhattan(self, current: Port, target: Port) -> float:
tx, ty = target.x, target.y
if abs(tx - self._target_x) > TOLERANCE_LINEAR or abs(ty - self._target_y) > TOLERANCE_LINEAR or target.r != self._target_r:
self.set_target(target)
dx = abs(current.x - tx)
dy = abs(current.y - ty)
dist = dx + dy
bp = self.config.bend_penalty
penalty = 0.0
curr_r = current.r
diff = abs(curr_r - self._target_r) % 360
if diff > 0:
penalty += 2 * bp if diff == 180 else bp
v_dx = tx - current.x
v_dy = ty - current.y
side_proj = v_dx * self._target_cos + v_dy * self._target_sin
perp_dist = abs(v_dx * self._target_sin - v_dy * self._target_cos)
if side_proj < 0 or (side_proj < self._min_radius and perp_dist > 0):
penalty += 2 * bp
if curr_r == 0:
c_cos, c_sin = 1.0, 0.0
elif curr_r == 90:
c_cos, c_sin = 0.0, 1.0
elif curr_r == 180:
c_cos, c_sin = -1.0, 0.0
else:
c_cos, c_sin = 0.0, -1.0
move_proj = v_dx * c_cos + v_dy * c_sin
if move_proj < 0:
penalty += 2 * bp
if diff == 0 and perp_dist > 0:
penalty += 2 * bp
return self.greedy_h_weight * (dist + penalty)
def evaluate_move(
self,
geometry: list[Polygon] | None,
end_port: Port,
net_width: float,
net_id: str,
start_port: Port | None = None,
length: float = 0.0,
dilated_geometry: list[Polygon] | None = None,
skip_static: bool = False,
skip_congestion: bool = False,
penalty: float = 0.0,
) -> float:
_ = net_width
danger_map = self.danger_map
if danger_map is not None and not danger_map.is_within_bounds(end_port.x, end_port.y):
return 1e15
total_cost = length * self.unit_length_cost + penalty
if not skip_static or not skip_congestion:
if geometry is None:
return 1e15
collision_engine = self.collision_engine
for i, poly in enumerate(geometry):
dil_poly = dilated_geometry[i] if dilated_geometry else None
if not skip_static and collision_engine.check_collision(
poly,
net_id,
buffer_mode="static",
start_port=start_port,
end_port=end_port,
dilated_geometry=dil_poly,
):
return 1e15
if not skip_congestion:
overlaps = collision_engine.check_collision(poly, net_id, buffer_mode="congestion", dilated_geometry=dil_poly)
if isinstance(overlaps, int) and overlaps > 0:
total_cost += overlaps * self.congestion_penalty
if danger_map is not None:
cost_s = danger_map.get_cost(start_port.x, start_port.y) if start_port else 0.0
cost_e = danger_map.get_cost(end_port.x, end_port.y)
if start_port:
mid_x = (start_port.x + end_port.x) / 2.0
mid_y = (start_port.y + end_port.y) / 2.0
cost_m = danger_map.get_cost(mid_x, mid_y)
total_cost += length * (cost_s + cost_m + cost_e) / 3.0
else:
total_cost += length * cost_e
return total_cost

98
inire/router/danger_map.py
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@ -1,98 +0,0 @@
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy
import shapely
from scipy.spatial import cKDTree
from functools import lru_cache
if TYPE_CHECKING:
from shapely.geometry import Polygon
class DangerMap:
"""
A proximity cost evaluator using a KD-Tree of obstacle boundary points.
Scales with obstacle perimeter rather than design area.
"""
__slots__ = ('minx', 'miny', 'maxx', 'maxy', 'resolution', 'safety_threshold', 'k', 'tree')
def __init__(
self,
bounds: tuple[float, float, float, float],
resolution: float = 5.0,
safety_threshold: float = 10.0,
k: float = 1.0,
) -> None:
"""
Initialize the Danger Map.
Args:
bounds: (minx, miny, maxx, maxy) in um.
resolution: Sampling resolution for obstacle boundaries (um).
safety_threshold: Proximity limit (um).
k: Penalty multiplier.
"""
self.minx, self.miny, self.maxx, self.maxy = bounds
self.resolution = resolution
self.safety_threshold = safety_threshold
self.k = k
self.tree: cKDTree | None = None
def precompute(self, obstacles: list[Polygon]) -> None:
"""
Pre-compute the proximity tree by sampling obstacle boundaries.
"""
all_points = []
for poly in obstacles:
# Sample exterior
exterior = poly.exterior
dist = 0
while dist < exterior.length:
pt = exterior.interpolate(dist)
all_points.append((pt.x, pt.y))
dist += self.resolution
# Sample interiors (holes)
for interior in poly.interiors:
dist = 0
while dist < interior.length:
pt = interior.interpolate(dist)
all_points.append((pt.x, pt.y))
dist += self.resolution
if all_points:
self.tree = cKDTree(numpy.array(all_points))
else:
self.tree = None
# Clear cache when tree changes
self._get_cost_quantized.cache_clear()
def is_within_bounds(self, x: float, y: float) -> bool:
"""
Check if a coordinate is within the design bounds.
"""
return self.minx <= x <= self.maxx and self.miny <= y <= self.maxy
def get_cost(self, x: float, y: float) -> float:
"""
Get the proximity cost at a specific coordinate using the KD-Tree.
Coordinates are quantized to 1nm to improve cache performance.
"""
qx_milli = int(round(x * 1000))
qy_milli = int(round(y * 1000))
return self._get_cost_quantized(qx_milli, qy_milli)
@lru_cache(maxsize=100000)
def _get_cost_quantized(self, qx_milli: int, qy_milli: int) -> float:
qx = qx_milli / 1000.0
qy = qy_milli / 1000.0
if not self.is_within_bounds(qx, qy):
return 1e15
if self.tree is None:
return 0.0
dist, _ = self.tree.query([qx, qy], distance_upper_bound=self.safety_threshold)
if dist >= self.safety_threshold:
return 0.0
safe_dist = max(dist, 0.1)
return float(self.k / (safe_dist ** 2))

429
inire/router/pathfinder.py
View file

@ -1,429 +0,0 @@
from __future__ import annotations
import logging
import math
import random
import time
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, Literal
import numpy
from inire.geometry.components import Bend90, Straight
from inire.router.astar import AStarMetrics, route_astar
if TYPE_CHECKING:
from inire.geometry.components import ComponentResult
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext
from inire.router.cost import CostEvaluator
logger = logging.getLogger(__name__)
@dataclass
class RoutingResult:
net_id: str
path: list[ComponentResult]
is_valid: bool
collisions: int
reached_target: bool = False
class PathFinder:
__slots__ = (
"context",
"metrics",
"max_iterations",
"base_congestion_penalty",
"use_tiered_strategy",
"congestion_multiplier",
"accumulated_expanded_nodes",
"warm_start",
"refine_paths",
)
def __init__(
self,
context: AStarContext,
metrics: AStarMetrics | None = None,
max_iterations: int = 10,
base_congestion_penalty: float = 100.0,
congestion_multiplier: float = 1.5,
use_tiered_strategy: bool = True,
warm_start: Literal["shortest", "longest", "user"] | None = "shortest",
refine_paths: bool = False,
) -> None:
self.context = context
self.metrics = metrics if metrics is not None else AStarMetrics()
self.max_iterations = max_iterations
self.base_congestion_penalty = base_congestion_penalty
self.congestion_multiplier = congestion_multiplier
self.use_tiered_strategy = use_tiered_strategy
self.warm_start = warm_start
self.refine_paths = refine_paths
self.accumulated_expanded_nodes: list[tuple[int, int, int]] = []
@property
def cost_evaluator(self) -> CostEvaluator:
return self.context.cost_evaluator
def _perform_greedy_pass(
self,
netlist: dict[str, tuple[Port, Port]],
net_widths: dict[str, float],
order: Literal["shortest", "longest", "user"],
) -> dict[str, list[ComponentResult]]:
all_net_ids = list(netlist.keys())
if order != "user":
all_net_ids.sort(
key=lambda nid: abs(netlist[nid][1].x - netlist[nid][0].x) + abs(netlist[nid][1].y - netlist[nid][0].y),
reverse=(order == "longest"),
)
greedy_paths: dict[str, list[ComponentResult]] = {}
temp_obj_ids: list[int] = []
greedy_node_limit = min(self.context.config.node_limit, 2000)
for net_id in all_net_ids:
start, target = netlist[net_id]
width = net_widths.get(net_id, 2.0)
h_start = self.cost_evaluator.h_manhattan(start, target)
max_cost_limit = max(h_start * 3.0, 2000.0)
path = route_astar(
start,
target,
width,
context=self.context,
metrics=self.metrics,
net_id=net_id,
skip_congestion=True,
max_cost=max_cost_limit,
self_collision_check=True,
node_limit=greedy_node_limit,
)
if not path:
continue
greedy_paths[net_id] = path
for res in path:
geoms = res.actual_geometry if res.actual_geometry is not None else res.geometry
dilated_geoms = res.dilated_actual_geometry if res.dilated_actual_geometry else res.dilated_geometry
for i, poly in enumerate(geoms):
dilated = dilated_geoms[i] if dilated_geoms else None
obj_id = self.cost_evaluator.collision_engine.add_static_obstacle(poly, dilated_geometry=dilated)
temp_obj_ids.append(obj_id)
self.context.clear_static_caches()
for obj_id in temp_obj_ids:
self.cost_evaluator.collision_engine.remove_static_obstacle(obj_id)
return greedy_paths
def _has_self_collision(self, path: list[ComponentResult]) -> bool:
for i, comp_i in enumerate(path):
tb_i = comp_i.total_bounds
for j in range(i + 2, len(path)):
comp_j = path[j]
tb_j = comp_j.total_bounds
if tb_i[0] < tb_j[2] and tb_i[2] > tb_j[0] and tb_i[1] < tb_j[3] and tb_i[3] > tb_j[1]:
for p_i in comp_i.geometry:
for p_j in comp_j.geometry:
if p_i.intersects(p_j) and not p_i.touches(p_j):
return True
return False
def _path_cost(self, path: list[ComponentResult]) -> float:
total = 0.0
bend_penalty = self.context.config.bend_penalty
sbend_penalty = self.context.config.sbend_penalty
for comp in path:
total += comp.length
if comp.move_type == "Bend90":
radius = comp.length * 2.0 / math.pi if comp.length > 0 else 0.0
if radius > 0:
total += bend_penalty * (10.0 / radius) ** 0.5
else:
total += bend_penalty
elif comp.move_type == "SBend":
total += sbend_penalty
return total
def _extract_geometry(self, path: list[ComponentResult]) -> tuple[list[Any], list[Any]]:
all_geoms = []
all_dilated = []
for res in path:
all_geoms.extend(res.geometry)
if res.dilated_geometry:
all_dilated.extend(res.dilated_geometry)
else:
dilation = self.cost_evaluator.collision_engine.clearance / 2.0
all_dilated.extend([p.buffer(dilation) for p in res.geometry])
return all_geoms, all_dilated
def _to_local(self, start: Port, point: Port) -> tuple[int, int]:
dx = point.x - start.x
dy = point.y - start.y
if start.r == 0:
return dx, dy
if start.r == 90:
return dy, -dx
if start.r == 180:
return -dx, -dy
return -dy, dx
def _build_same_orientation_dogleg(
self,
start: Port,
target: Port,
net_width: float,
radius: float,
side_extent: float,
) -> list[ComponentResult] | None:
local_dx, local_dy = self._to_local(start, target)
if abs(local_dy) > 0 or local_dx < 4.0 * radius - 0.01:
return None
side_abs = abs(side_extent)
side_length = side_abs - 2.0 * radius
if side_length < self.context.config.min_straight_length - 0.01:
return None
forward_length = local_dx - 4.0 * radius
if forward_length < -0.01:
return None
first_dir = "CCW" if side_extent > 0 else "CW"
second_dir = "CW" if side_extent > 0 else "CCW"
dilation = self.cost_evaluator.collision_engine.clearance / 2.0
path: list[ComponentResult] = []
curr = start
for direction, straight_len in (
(first_dir, side_length),
(second_dir, forward_length),
(second_dir, side_length),
(first_dir, None),
):
bend = Bend90.generate(curr, radius, net_width, direction, dilation=dilation)
path.append(bend)
curr = bend.end_port
if straight_len is None:
continue
if straight_len > 0.01:
straight = Straight.generate(curr, straight_len, net_width, dilation=dilation)
path.append(straight)
curr = straight.end_port
if curr != target:
return None
return path
def _refine_path(
self,
net_id: str,
start: Port,
target: Port,
net_width: float,
path: list[ComponentResult],
) -> list[ComponentResult]:
if not path or start.r != target.r:
return path
bend_count = sum(1 for comp in path if comp.move_type == "Bend90")
if bend_count < 5:
return path
side_extents = []
local_points = [self._to_local(start, start)]
local_points.extend(self._to_local(start, comp.end_port) for comp in path)
min_side = min(point[1] for point in local_points)
max_side = max(point[1] for point in local_points)
if min_side < -0.01:
side_extents.append(float(min_side))
if max_side > 0.01:
side_extents.append(float(max_side))
if not side_extents:
return path
best_path = path
best_cost = self._path_cost(path)
collision_engine = self.cost_evaluator.collision_engine
for radius in self.context.config.bend_radii:
for side_extent in side_extents:
candidate = self._build_same_orientation_dogleg(start, target, net_width, radius, side_extent)
if candidate is None:
continue
is_valid, collisions = collision_engine.verify_path(net_id, candidate)
if not is_valid or collisions != 0:
continue
candidate_cost = self._path_cost(candidate)
if candidate_cost + 1e-6 < best_cost:
best_cost = candidate_cost
best_path = candidate
return best_path
def route_all(
self,
netlist: dict[str, tuple[Port, Port]],
net_widths: dict[str, float],
store_expanded: bool = False,
iteration_callback: Callable[[int, dict[str, RoutingResult]], None] | None = None,
shuffle_nets: bool = False,
sort_nets: Literal["shortest", "longest", "user", None] = None,
initial_paths: dict[str, list[ComponentResult]] | None = None,
seed: int | None = None,
) -> dict[str, RoutingResult]:
results: dict[str, RoutingResult] = {}
self.cost_evaluator.congestion_penalty = self.base_congestion_penalty
self.accumulated_expanded_nodes = []
self.metrics.reset_per_route()
start_time = time.monotonic()
num_nets = len(netlist)
session_timeout = max(60.0, 10.0 * num_nets * self.max_iterations)
all_net_ids = list(netlist.keys())
needs_sc: set[str] = set()
if initial_paths is None:
ws_order = sort_nets if sort_nets is not None else self.warm_start
if ws_order is not None:
initial_paths = self._perform_greedy_pass(netlist, net_widths, ws_order)
self.context.clear_static_caches()
if sort_nets and sort_nets != "user":
all_net_ids.sort(
key=lambda nid: abs(netlist[nid][1].x - netlist[nid][0].x) + abs(netlist[nid][1].y - netlist[nid][0].y),
reverse=(sort_nets == "longest"),
)
for iteration in range(self.max_iterations):
any_congestion = False
self.accumulated_expanded_nodes = []
self.metrics.reset_per_route()
if shuffle_nets and (iteration > 0 or initial_paths is None):
it_seed = (seed + iteration) if seed is not None else None
random.Random(it_seed).shuffle(all_net_ids)
for net_id in all_net_ids:
start, target = netlist[net_id]
if time.monotonic() - start_time > session_timeout:
self.cost_evaluator.collision_engine.dynamic_tree = None
self.cost_evaluator.collision_engine._ensure_dynamic_tree()
return self.verify_all_nets(results, netlist)
width = net_widths.get(net_id, 2.0)
self.cost_evaluator.collision_engine.remove_path(net_id)
path: list[ComponentResult] | None = None
if iteration == 0 and initial_paths and net_id in initial_paths:
path = initial_paths[net_id]
else:
target_coll_model = self.context.config.bend_collision_type
coll_model = target_coll_model
skip_cong = False
if self.use_tiered_strategy and iteration == 0:
skip_cong = True
if target_coll_model == "arc":
coll_model = "clipped_bbox"
path = route_astar(
start,
target,
width,
context=self.context,
metrics=self.metrics,
net_id=net_id,
bend_collision_type=coll_model,
return_partial=True,
store_expanded=store_expanded,
skip_congestion=skip_cong,
self_collision_check=(net_id in needs_sc),
node_limit=self.context.config.node_limit,
)
if store_expanded and self.metrics.last_expanded_nodes:
self.accumulated_expanded_nodes.extend(self.metrics.last_expanded_nodes)
if not path:
results[net_id] = RoutingResult(net_id, [], False, 0, reached_target=False)
any_congestion = True
continue
last_p = path[-1].end_port
reached = last_p == target
if reached and net_id not in needs_sc and self._has_self_collision(path):
needs_sc.add(net_id)
any_congestion = True
all_geoms = []
all_dilated = []
for res in path:
all_geoms.extend(res.geometry)
if res.dilated_geometry:
all_dilated.extend(res.dilated_geometry)
else:
dilation = self.cost_evaluator.collision_engine.clearance / 2.0
all_dilated.extend([p.buffer(dilation) for p in res.geometry])
self.cost_evaluator.collision_engine.add_path(net_id, all_geoms, dilated_geometry=all_dilated)
collision_count = 0
if reached:
is_valid, collision_count = self.cost_evaluator.collision_engine.verify_path(net_id, path)
any_congestion = any_congestion or not is_valid
results[net_id] = RoutingResult(net_id, path, reached and collision_count == 0, collision_count, reached_target=reached)
if iteration_callback:
iteration_callback(iteration, results)
if not any_congestion:
break
self.cost_evaluator.congestion_penalty *= self.congestion_multiplier
if self.refine_paths and results:
for net_id in all_net_ids:
res = results.get(net_id)
if not res or not res.path or not res.reached_target or not res.is_valid:
continue
start, target = netlist[net_id]
width = net_widths.get(net_id, 2.0)
self.cost_evaluator.collision_engine.remove_path(net_id)
refined_path = self._refine_path(net_id, start, target, width, res.path)
all_geoms, all_dilated = self._extract_geometry(refined_path)
self.cost_evaluator.collision_engine.add_path(net_id, all_geoms, dilated_geometry=all_dilated)
results[net_id] = RoutingResult(
net_id=net_id,
path=refined_path,
is_valid=res.is_valid,
collisions=res.collisions,
reached_target=res.reached_target,
)
self.cost_evaluator.collision_engine.dynamic_tree = None
self.cost_evaluator.collision_engine._ensure_dynamic_tree()
return self.verify_all_nets(results, netlist)
def verify_all_nets(
self,
results: dict[str, RoutingResult],
netlist: dict[str, tuple[Port, Port]],
) -> dict[str, RoutingResult]:
final_results: dict[str, RoutingResult] = {}
for net_id, (_, target_p) in netlist.items():
res = results.get(net_id)
if not res or not res.path:
final_results[net_id] = RoutingResult(net_id, [], False, 0)
continue
last_p = res.path[-1].end_port
reached = last_p == target_p
is_valid, collisions = self.cost_evaluator.collision_engine.verify_path(net_id, res.path)
final_results[net_id] = RoutingResult(
net_id=net_id,
path=res.path,
is_valid=(is_valid and reached),
collisions=collisions,
reached_target=reached,
)
return final_results

159
inire/router/visibility.py
View file

@ -1,159 +0,0 @@
from __future__ import annotations
import numpy
from typing import TYPE_CHECKING
import rtree
from shapely.geometry import Point, LineString
if TYPE_CHECKING:
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.geometry.primitives import Port
class VisibilityManager:
"""
Manages corners of static obstacles for sparse A* / Visibility Graph jumps.
"""
__slots__ = ('collision_engine', 'corners', 'corner_index', '_corner_graph', '_static_visibility_cache', '_built_static_version')
def __init__(self, collision_engine: CollisionEngine) -> None:
self.collision_engine = collision_engine
self.corners: list[tuple[float, float]] = []
self.corner_index = rtree.index.Index()
self._corner_graph: dict[int, list[tuple[float, float, float]]] = {}
self._static_visibility_cache: dict[tuple[int, int], list[tuple[float, float, float]]] = {}
self._built_static_version = -1
self._build()
def clear_cache(self) -> None:
"""
Reset all static visibility data.
"""
self.corners = []
self.corner_index = rtree.index.Index()
self._corner_graph = {}
self._static_visibility_cache = {}
self._build()
def _ensure_current(self) -> None:
if self._built_static_version != self.collision_engine._static_version:
self.clear_cache()
def _build(self) -> None:
"""
Extract corners and pre-compute corner-to-corner visibility.
"""
self._built_static_version = self.collision_engine._static_version
raw_corners = []
for obj_id, poly in self.collision_engine.static_dilated.items():
coords = list(poly.exterior.coords)
if coords[0] == coords[-1]:
coords = coords[:-1]
raw_corners.extend(coords)
for ring in poly.interiors:
coords = list(ring.coords)
if coords[0] == coords[-1]:
coords = coords[:-1]
raw_corners.extend(coords)
if not raw_corners:
return
# Deduplicate repeated corner coordinates
seen = set()
for x, y in raw_corners:
sx, sy = round(x, 3), round(y, 3)
if (sx, sy) not in seen:
seen.add((sx, sy))
self.corners.append((sx, sy))
# Build spatial index for corners
for i, (x, y) in enumerate(self.corners):
self.corner_index.insert(i, (x, y, x, y))
# Pre-compute visibility graph between corners
num_corners = len(self.corners)
if num_corners > 200:
# Limit pre-computation if too many corners
return
for i in range(num_corners):
self._corner_graph[i] = []
p1 = Port(self.corners[i][0], self.corners[i][1], 0)
for j in range(num_corners):
if i == j: continue
cx, cy = self.corners[j]
dx, dy = cx - p1.x, cy - p1.y
dist = numpy.sqrt(dx**2 + dy**2)
angle = numpy.degrees(numpy.arctan2(dy, dx))
reach = self.collision_engine.ray_cast(p1, angle, max_dist=dist + 0.05)
if reach >= dist - 0.01:
self._corner_graph[i].append((cx, cy, dist))
def get_visible_corners(self, origin: Port, max_dist: float = 1000.0) -> list[tuple[float, float, float]]:
"""
Find all corners visible from the origin.
Returns list of (x, y, distance).
"""
self._ensure_current()
if max_dist < 0:
return []
ox, oy = round(origin.x, 3), round(origin.y, 3)
# 1. Exact corner check
# Use spatial index to find if origin is AT a corner
nearby = list(self.corner_index.intersection((ox - 0.001, oy - 0.001, ox + 0.001, oy + 0.001)))
for idx in nearby:
cx, cy = self.corners[idx]
if abs(cx - ox) < 1e-4 and abs(cy - oy) < 1e-4:
# We are at a corner! Return pre-computed graph (filtered by max_dist)
if idx in self._corner_graph:
return [c for c in self._corner_graph[idx] if c[2] <= max_dist]
# 2. Cache check for arbitrary points
# Grid-based caching for arbitrary points is tricky,
# but since static obstacles don't change, we can cache exact coordinates.
cache_key = (int(ox * 1000), int(oy * 1000))
if cache_key in self._static_visibility_cache:
return self._static_visibility_cache[cache_key]
# 3. Full visibility check
bounds = (origin.x - max_dist, origin.y - max_dist, origin.x + max_dist, origin.y + max_dist)
candidates = list(self.corner_index.intersection(bounds))
visible = []
for i in candidates:
cx, cy = self.corners[i]
dx, dy = cx - origin.x, cy - origin.y
dist = numpy.sqrt(dx**2 + dy**2)
if dist > max_dist or dist < 1e-3:
continue
angle = numpy.degrees(numpy.arctan2(dy, dx))
reach = self.collision_engine.ray_cast(origin, angle, max_dist=dist + 0.05)
if reach >= dist - 0.01:
visible.append((cx, cy, dist))
self._static_visibility_cache[cache_key] = visible
return visible
def get_corner_visibility(self, origin: Port, max_dist: float = 1000.0) -> list[tuple[float, float, float]]:
"""
Return precomputed visibility only when the origin is already at a known corner.
This avoids the expensive arbitrary-point visibility scan in hot search paths.
"""
self._ensure_current()
if max_dist < 0:
return []
ox, oy = round(origin.x, 3), round(origin.y, 3)
nearby = list(self.corner_index.intersection((ox - 0.001, oy - 0.001, ox + 0.001, oy + 0.001)))
for idx in nearby:
cx, cy = self.corners[idx]
if abs(cx - ox) < 1e-4 and abs(cy - oy) < 1e-4 and idx in self._corner_graph:
return [corner for corner in self._corner_graph[idx] if corner[2] <= max_dist]
return []

57
inire/tests/benchmark_scaling.py
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@ -1,57 +0,0 @@
import time
from inire.geometry.primitives import Port
from inire.geometry.collision import CollisionEngine
from inire.router.danger_map import DangerMap
from inire.router.cost import CostEvaluator
from inire.router.astar import AStarContext, AStarMetrics
from inire.router.pathfinder import PathFinder
def benchmark_scaling() -> None:
print("Starting Scalability Benchmark...")
# 1. Memory Verification (20x20mm)
# Resolution 1um -> 20000 x 20000 grid
bounds = (0, 0, 20000, 20000)
print(f"Initializing DangerMap for {bounds} area...")
dm = DangerMap(bounds=bounds, resolution=1.0)
# nbytes for float32: 20000 * 20000 * 4 bytes = 1.6 GB
mem_gb = dm.grid.nbytes / (1024**3)
print(f"DangerMap memory usage: {mem_gb:.2f} GB")
assert mem_gb < 2.0
# 2. Node Expansion Rate (50 nets)
engine = CollisionEngine(clearance=2.0)
# Use a smaller area for routing benchmark to keep it fast
routing_bounds = (0, 0, 1000, 1000)
danger_map = DangerMap(bounds=routing_bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map)
context = AStarContext(evaluator)
metrics = AStarMetrics()
pf = PathFinder(context, metrics)
num_nets = 50
netlist = {}
for i in range(num_nets):
# Parallel nets spaced by 10um
netlist[f"net{i}"] = (Port(0, i * 10, 0), Port(100, i * 10, 0))
print(f"Routing {num_nets} nets...")
start_time = time.monotonic()
results = pf.route_all(netlist, dict.fromkeys(netlist, 2.0))
end_time = time.monotonic()
total_time = end_time - start_time
print(f"Total routing time: {total_time:.2f} s")
print(f"Time per net: {total_time/num_nets:.4f} s")
if total_time > 0:
nodes_per_sec = metrics.total_nodes_expanded / total_time
print(f"Node expansion rate: {nodes_per_sec:.2f} nodes/s")
# Success rate
successes = sum(1 for r in results.values() if r.is_valid)
print(f"Success rate: {successes/num_nets * 100:.1f}%")
if __name__ == "__main__":
benchmark_scaling()

311
inire/tests/example_scenarios.py
View file

@ -1,311 +0,0 @@
from __future__ import annotations
from dataclasses import dataclass
from time import perf_counter
from typing import Callable
from shapely.geometry import Polygon, box
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, AStarMetrics
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.pathfinder import PathFinder, RoutingResult
@dataclass(frozen=True)
class ScenarioOutcome:
duration_s: float
total_results: int
valid_results: int
reached_targets: int
@dataclass(frozen=True)
class ScenarioDefinition:
name: str
run: Callable[[], ScenarioOutcome]
def _build_router(
*,
bounds: tuple[float, float, float, float],
clearance: float = 2.0,
obstacles: list[Polygon] | None = None,
evaluator_kwargs: dict[str, float] | None = None,
context_kwargs: dict[str, object] | None = None,
pathfinder_kwargs: dict[str, object] | None = None,
) -> tuple[CollisionEngine, CostEvaluator, AStarContext, AStarMetrics, PathFinder]:
static_obstacles = obstacles or []
engine = CollisionEngine(clearance=clearance)
for obstacle in static_obstacles:
engine.add_static_obstacle(obstacle)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute(static_obstacles)
evaluator = CostEvaluator(engine, danger_map, **(evaluator_kwargs or {}))
context = AStarContext(evaluator, **(context_kwargs or {}))
metrics = AStarMetrics()
pathfinder = PathFinder(context, metrics, **(pathfinder_kwargs or {}))
return engine, evaluator, context, metrics, pathfinder
def _summarize(results: dict[str, RoutingResult], duration_s: float) -> ScenarioOutcome:
return ScenarioOutcome(
duration_s=duration_s,
total_results=len(results),
valid_results=sum(1 for result in results.values() if result.is_valid),
reached_targets=sum(1 for result in results.values() if result.reached_target),
)
def run_example_01() -> ScenarioOutcome:
_, _, _, _, pathfinder = _build_router(bounds=(0, 0, 100, 100), context_kwargs={"bend_radii": [10.0]})
netlist = {"net1": (Port(10, 50, 0), Port(90, 50, 0))}
t0 = perf_counter()
results = pathfinder.route_all(netlist, {"net1": 2.0})
t1 = perf_counter()
return _summarize(results, t1 - t0)
def run_example_02() -> ScenarioOutcome:
_, _, _, _, pathfinder = _build_router(
bounds=(0, 0, 100, 100),
evaluator_kwargs={
"greedy_h_weight": 1.5,
"bend_penalty": 50.0,
"sbend_penalty": 150.0,
},
context_kwargs={
"bend_radii": [10.0],
"sbend_radii": [10.0],
},
pathfinder_kwargs={"base_congestion_penalty": 1000.0},
)
netlist = {
"horizontal": (Port(10, 50, 0), Port(90, 50, 0)),
"vertical_up": (Port(45, 10, 90), Port(45, 90, 90)),
"vertical_down": (Port(55, 90, 270), Port(55, 10, 270)),
}
widths = {net_id: 2.0 for net_id in netlist}
t0 = perf_counter()
results = pathfinder.route_all(netlist, widths)
t1 = perf_counter()
return _summarize(results, t1 - t0)
def run_example_03() -> ScenarioOutcome:
engine, _, _, _, pathfinder = _build_router(bounds=(0, -50, 100, 50), context_kwargs={"bend_radii": [10.0]})
t0 = perf_counter()
results_a = pathfinder.route_all({"netA": (Port(10, 0, 0), Port(90, 0, 0))}, {"netA": 2.0})
engine.lock_net("netA")
results_b = pathfinder.route_all({"netB": (Port(50, -20, 90), Port(50, 20, 90))}, {"netB": 2.0})
t1 = perf_counter()
return _summarize({**results_a, **results_b}, t1 - t0)
def run_example_04() -> ScenarioOutcome:
_, _, _, _, pathfinder = _build_router(
bounds=(0, 0, 100, 100),
evaluator_kwargs={
"unit_length_cost": 1.0,
"bend_penalty": 10.0,
"sbend_penalty": 20.0,
},
context_kwargs={
"node_limit": 50000,
"bend_radii": [10.0, 30.0],
"sbend_offsets": [5.0],
"bend_penalty": 10.0,
"sbend_penalty": 20.0,
},
)
netlist = {
"sbend_only": (Port(10, 50, 0), Port(60, 55, 0)),
"multi_radii": (Port(10, 10, 0), Port(90, 90, 0)),
}
widths = {"sbend_only": 2.0, "multi_radii": 2.0}
t0 = perf_counter()
results = pathfinder.route_all(netlist, widths)
t1 = perf_counter()
return _summarize(results, t1 - t0)
def run_example_05() -> ScenarioOutcome:
_, _, _, _, pathfinder = _build_router(
bounds=(0, 0, 200, 200),
evaluator_kwargs={"bend_penalty": 50.0},
context_kwargs={"bend_radii": [20.0]},
)
netlist = {
"u_turn": (Port(50, 50, 0), Port(50, 70, 180)),
"loop": (Port(100, 100, 90), Port(100, 80, 270)),
"zig_zag": (Port(20, 150, 0), Port(180, 150, 0)),
}
widths = {net_id: 2.0 for net_id in netlist}
t0 = perf_counter()
results = pathfinder.route_all(netlist, widths)
t1 = perf_counter()
return _summarize(results, t1 - t0)
def run_example_06() -> ScenarioOutcome:
bounds = (-20, -20, 170, 170)
obstacles = [
box(40, 110, 60, 130),
box(40, 60, 60, 80),
box(40, 10, 60, 30),
]
engine = CollisionEngine(clearance=2.0)
for obstacle in obstacles:
engine.add_static_obstacle(obstacle)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute(obstacles)
evaluator = CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
contexts = [
AStarContext(evaluator, bend_radii=[10.0], bend_collision_type="arc"),
AStarContext(evaluator, bend_radii=[10.0], bend_collision_type="bbox"),
AStarContext(evaluator, bend_radii=[10.0], bend_collision_type="clipped_bbox", bend_clip_margin=1.0),
]
netlists = [
{"arc_model": (Port(10, 120, 0), Port(90, 140, 90))},
{"bbox_model": (Port(10, 70, 0), Port(90, 90, 90))},
{"clipped_model": (Port(10, 20, 0), Port(90, 40, 90))},
]
widths = [
{"arc_model": 2.0},
{"bbox_model": 2.0},
{"clipped_model": 2.0},
]
t0 = perf_counter()
combined_results: dict[str, RoutingResult] = {}
for context, netlist, net_widths in zip(contexts, netlists, widths, strict=True):
pathfinder = PathFinder(context, use_tiered_strategy=False)
combined_results.update(pathfinder.route_all(netlist, net_widths))
t1 = perf_counter()
return _summarize(combined_results, t1 - t0)
def run_example_07() -> ScenarioOutcome:
bounds = (0, 0, 1000, 1000)
obstacles = [
box(450, 0, 550, 400),
box(450, 600, 550, 1000),
]
_, evaluator, _, metrics, pathfinder = _build_router(
bounds=bounds,
clearance=6.0,
obstacles=obstacles,
evaluator_kwargs={
"greedy_h_weight": 1.5,
"unit_length_cost": 0.1,
"bend_penalty": 100.0,
"sbend_penalty": 400.0,
"congestion_penalty": 100.0,
},
context_kwargs={
"node_limit": 2000000,
"bend_radii": [50.0],
"sbend_radii": [50.0],
},
pathfinder_kwargs={
"max_iterations": 15,
"base_congestion_penalty": 100.0,
"congestion_multiplier": 1.4,
},
)
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)
netlist = {}
for index in range(num_nets):
sy = int(round(start_y_base + index * 10.0))
ey = int(round(end_y_base + index * end_y_pitch))
netlist[f"net_{index:02d}"] = (Port(start_x, sy, 0), Port(end_x, ey, 0))
def iteration_callback(idx: int, current_results: dict[str, RoutingResult]) -> None:
new_greedy = max(1.1, 1.5 - ((idx + 1) / 10.0) * 0.4)
evaluator.greedy_h_weight = new_greedy
metrics.reset_per_route()
t0 = perf_counter()
results = pathfinder.route_all(
netlist,
dict.fromkeys(netlist, 2.0),
store_expanded=True,
iteration_callback=iteration_callback,
shuffle_nets=True,
seed=42,
)
t1 = perf_counter()
return _summarize(results, t1 - t0)
def run_example_08() -> ScenarioOutcome:
bounds = (0, 0, 150, 150)
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
metrics = AStarMetrics()
netlist = {"custom_bend": (Port(20, 20, 0), Port(100, 100, 90))}
widths = {"custom_bend": 2.0}
context_std = AStarContext(evaluator, bend_radii=[10.0], sbend_radii=[])
context_custom = AStarContext(
evaluator,
bend_radii=[10.0],
bend_collision_type=Polygon([(-10, -10), (10, -10), (10, 10), (-10, 10)]),
sbend_radii=[],
)
t0 = perf_counter()
results_std = PathFinder(context_std, metrics).route_all(netlist, widths)
results_custom = PathFinder(context_custom, AStarMetrics(), use_tiered_strategy=False).route_all(
{"custom_model": netlist["custom_bend"]},
{"custom_model": 2.0},
)
t1 = perf_counter()
return _summarize({**results_std, **results_custom}, t1 - t0)
def run_example_09() -> ScenarioOutcome:
obstacles = [
box(35, 35, 45, 65),
box(55, 35, 65, 65),
]
_, _, _, _, pathfinder = _build_router(
bounds=(0, 0, 100, 100),
obstacles=obstacles,
evaluator_kwargs={"bend_penalty": 50.0, "sbend_penalty": 150.0},
context_kwargs={"node_limit": 3, "bend_radii": [10.0]},
pathfinder_kwargs={"warm_start": None},
)
netlist = {"budget_limited_net": (Port(10, 50, 0), Port(85, 60, 180))}
t0 = perf_counter()
results = pathfinder.route_all(netlist, {"budget_limited_net": 2.0})
t1 = perf_counter()
return _summarize(results, t1 - t0)
SCENARIOS: tuple[ScenarioDefinition, ...] = (
ScenarioDefinition("example_01_simple_route", run_example_01),
ScenarioDefinition("example_02_congestion_resolution", run_example_02),
ScenarioDefinition("example_03_locked_paths", run_example_03),
ScenarioDefinition("example_04_sbends_and_radii", run_example_04),
ScenarioDefinition("example_05_orientation_stress", run_example_05),
ScenarioDefinition("example_06_bend_collision_models", run_example_06),
ScenarioDefinition("example_07_large_scale_routing", run_example_07),
ScenarioDefinition("example_08_custom_bend_geometry", run_example_08),
ScenarioDefinition("example_09_unroutable_best_effort", run_example_09),
)

289
inire/tests/test_astar.py
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import pytest
from shapely.geometry import Polygon
import inire.router.astar as astar_module
from inire.geometry.components import SBend, Straight
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, route_astar
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.pathfinder import RoutingResult
from inire.utils.validation import validate_routing_result
@pytest.fixture
def basic_evaluator() -> CostEvaluator:
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=(0, -50, 150, 150))
danger_map.precompute([])
return CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
def test_astar_straight(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator)
start = Port(0, 0, 0)
target = Port(50, 0, 0)
path = route_astar(start, target, net_width=2.0, context=context)
assert path is not None
result = RoutingResult(net_id="test", path=path, is_valid=True, collisions=0)
validation = validate_routing_result(result, [], clearance=2.0, expected_start=start, expected_end=target)
assert validation["is_valid"], f"Validation failed: {validation.get('reason')}"
assert validation["connectivity_ok"]
# Path should be exactly 50um (or slightly more if it did weird things, but here it's straight)
assert abs(validation["total_length"] - 50.0) < 1e-6
def test_astar_bend(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator, bend_radii=[10.0])
start = Port(0, 0, 0)
# 20um right, 20um up. Needs a 10um bend and a 10um bend.
target = Port(20, 20, 0)
path = route_astar(start, target, net_width=2.0, context=context)
assert path is not None
result = RoutingResult(net_id="test", path=path, is_valid=True, collisions=0)
validation = validate_routing_result(result, [], clearance=2.0, expected_start=start, expected_end=target)
assert validation["is_valid"], f"Validation failed: {validation.get('reason')}"
assert validation["connectivity_ok"]
def test_astar_obstacle(basic_evaluator: CostEvaluator) -> None:
# Add an obstacle in the middle of a straight path
# Obstacle from x=20 to 40, y=-20 to 20
obstacle = Polygon([(20, -20), (40, -20), (40, 20), (20, 20)])
basic_evaluator.collision_engine.add_static_obstacle(obstacle)
basic_evaluator.danger_map.precompute([obstacle])
context = AStarContext(basic_evaluator, bend_radii=[10.0], node_limit=1000000)
start = Port(0, 0, 0)
target = Port(60, 0, 0)
path = route_astar(start, target, net_width=2.0, context=context)
assert path is not None
result = RoutingResult(net_id="test", path=path, is_valid=True, collisions=0)
validation = validate_routing_result(result, [obstacle], clearance=2.0, expected_start=start, expected_end=target)
assert validation["is_valid"], f"Validation failed: {validation.get('reason')}"
# Path should have detoured, so length > 50
assert validation["total_length"] > 50.0
def test_astar_uses_integerized_ports(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator)
start = Port(0, 0, 0)
target = Port(10.1, 0, 0)
path = route_astar(start, target, net_width=2.0, context=context)
assert path is not None
result = RoutingResult(net_id="test", path=path, is_valid=True, collisions=0)
assert target.x == 10
validation = validate_routing_result(result, [], clearance=2.0, expected_start=start, expected_end=target)
assert validation["is_valid"], f"Validation failed: {validation.get('reason')}"
def test_expand_moves_only_shortens_consecutive_straights(
basic_evaluator: CostEvaluator,
monkeypatch: pytest.MonkeyPatch,
) -> None:
context = AStarContext(basic_evaluator, min_straight_length=5.0, max_straight_length=100.0)
prev_result = Straight.generate(Port(0, 0, 0), 20.0, width=2.0, dilation=1.0)
current = astar_module.AStarNode(
prev_result.end_port,
g_cost=prev_result.length,
h_cost=0.0,
component_result=prev_result,
)
emitted: list[tuple[str, tuple]] = []
def fake_process_move(*args, **kwargs) -> None:
emitted.append((args[9], args[10]))
monkeypatch.setattr(astar_module, "process_move", fake_process_move)
astar_module.expand_moves(
current,
Port(80, 0, 0),
net_width=2.0,
net_id="test",
open_set=[],
closed_set={},
context=context,
metrics=astar_module.AStarMetrics(),
congestion_cache={},
)
straight_lengths = [params[0] for move_class, params in emitted if move_class == "S"]
assert straight_lengths
assert all(length < prev_result.length for length in straight_lengths)
def test_expand_moves_does_not_chain_sbends(
basic_evaluator: CostEvaluator,
monkeypatch: pytest.MonkeyPatch,
) -> None:
context = AStarContext(basic_evaluator, sbend_radii=[10.0], sbend_offsets=[5.0], max_straight_length=100.0)
prev_result = SBend.generate(Port(0, 0, 0), 5.0, 10.0, width=2.0, dilation=1.0)
current = astar_module.AStarNode(
prev_result.end_port,
g_cost=prev_result.length,
h_cost=0.0,
component_result=prev_result,
)
emitted: list[str] = []
def fake_process_move(*args, **kwargs) -> None:
emitted.append(args[9])
monkeypatch.setattr(astar_module, "process_move", fake_process_move)
astar_module.expand_moves(
current,
Port(60, 10, 0),
net_width=2.0,
net_id="test",
open_set=[],
closed_set={},
context=context,
metrics=astar_module.AStarMetrics(),
congestion_cache={},
)
assert "SB" not in emitted
assert emitted
def test_expand_moves_adds_sbend_aligned_straight_stop_points(
basic_evaluator: CostEvaluator,
monkeypatch: pytest.MonkeyPatch,
) -> None:
context = AStarContext(
basic_evaluator,
bend_radii=[10.0],
sbend_radii=[10.0],
max_straight_length=150.0,
)
current = astar_module.AStarNode(Port(0, 0, 0), g_cost=0.0, h_cost=0.0)
emitted: list[tuple[str, tuple]] = []
def fake_process_move(*args, **kwargs) -> None:
emitted.append((args[9], args[10]))
monkeypatch.setattr(astar_module, "process_move", fake_process_move)
astar_module.expand_moves(
current,
Port(100, 10, 0),
net_width=2.0,
net_id="test",
open_set=[],
closed_set={},
context=context,
metrics=astar_module.AStarMetrics(),
congestion_cache={},
)
straight_lengths = {params[0] for move_class, params in emitted if move_class == "S"}
sbend_span = astar_module._sbend_forward_span(10.0, 10.0)
assert sbend_span is not None
assert int(round(100.0 - sbend_span)) in straight_lengths
assert int(round(100.0 - 2.0 * sbend_span)) in straight_lengths
def test_expand_moves_adds_exact_corner_visibility_stop_points(
basic_evaluator: CostEvaluator,
monkeypatch: pytest.MonkeyPatch,
) -> None:
context = AStarContext(
basic_evaluator,
bend_radii=[10.0],
max_straight_length=150.0,
visibility_guidance="exact_corner",
)
current = astar_module.AStarNode(Port(0, 0, 0), g_cost=0.0, h_cost=0.0)
monkeypatch.setattr(
astar_module.VisibilityManager,
"get_corner_visibility",
lambda self, origin, max_dist=0.0: [(40.0, 10.0, 41.23), (75.0, -15.0, 76.48)],
)
emitted: list[tuple[str, tuple]] = []
def fake_process_move(*args, **kwargs) -> None:
emitted.append((args[9], args[10]))
monkeypatch.setattr(astar_module, "process_move", fake_process_move)
astar_module.expand_moves(
current,
Port(120, 20, 0),
net_width=2.0,
net_id="test",
open_set=[],
closed_set={},
context=context,
metrics=astar_module.AStarMetrics(),
congestion_cache={},
)
straight_lengths = {params[0] for move_class, params in emitted if move_class == "S"}
assert 40 in straight_lengths
assert 75 in straight_lengths
def test_expand_moves_adds_tangent_corner_visibility_stop_points(
basic_evaluator: CostEvaluator,
monkeypatch: pytest.MonkeyPatch,
) -> None:
class DummyCornerIndex:
def intersection(self, bounds: tuple[float, float, float, float]) -> list[int]:
return [0, 1]
context = AStarContext(
basic_evaluator,
bend_radii=[10.0],
sbend_radii=[],
max_straight_length=150.0,
visibility_guidance="tangent_corner",
)
current = astar_module.AStarNode(Port(0, 0, 0), g_cost=0.0, h_cost=0.0)
monkeypatch.setattr(astar_module.VisibilityManager, "_ensure_current", lambda self: None)
context.visibility_manager.corners = [(50.0, 10.0), (80.0, -10.0)]
context.visibility_manager.corner_index = DummyCornerIndex()
monkeypatch.setattr(
type(context.cost_evaluator.collision_engine),
"ray_cast",
lambda self, origin, angle_deg, max_dist=2000.0, net_width=None: max_dist,
)
emitted: list[tuple[str, tuple]] = []
def fake_process_move(*args, **kwargs) -> None:
emitted.append((args[9], args[10]))
monkeypatch.setattr(astar_module, "process_move", fake_process_move)
astar_module.expand_moves(
current,
Port(120, 20, 0),
net_width=2.0,
net_id="test",
open_set=[],
closed_set={},
context=context,
metrics=astar_module.AStarMetrics(),
congestion_cache={},
)
straight_lengths = {params[0] for move_class, params in emitted if move_class == "S"}
assert 40 in straight_lengths
assert 70 in straight_lengths

92
inire/tests/test_clearance_precision.py
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@ -1,92 +0,0 @@
import pytest
import numpy
from shapely.geometry import Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.geometry.components import Straight
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.astar import AStarContext
from inire.router.pathfinder import PathFinder, RoutingResult
def test_clearance_thresholds():
"""
Check that clearance is correctly calculated:
two paths slightly beyond, exactly at, and slightly violating.
"""
# Clearance = 2.0, Width = 2.0
# Required Centerline-to-Centerline = (2+2)/2 + 2.0 = 4.0
ce = CollisionEngine(clearance=2.0)
# Net 1: Centerline at y=0
p1 = Port(0, 0, 0)
res1 = Straight.generate(p1, 50.0, width=2.0, dilation=1.0)
ce.add_path("net1", res1.geometry, dilated_geometry=res1.dilated_geometry)
# Net 2: Parallel to Net 1
# 1. Beyond minimum spacing: y=5. Gap = 5 - 2 = 3 > 2. OK.
p2_ok = Port(0, 5, 0)
res2_ok = Straight.generate(p2_ok, 50.0, width=2.0, dilation=1.0)
is_v, count = ce.verify_path("net2", [res2_ok])
assert is_v, f"Gap 3 should be valid, but got {count} collisions"
# 2. Exactly at: y=4.0. Gap = 4.0 - 2.0 = 2.0. OK.
p2_exact = Port(0, 4, 0)
res2_exact = Straight.generate(p2_exact, 50.0, width=2.0, dilation=1.0)
is_v, count = ce.verify_path("net2", [res2_exact])
assert is_v, f"Gap exactly 2.0 should be valid, but got {count} collisions"
# 3. Slightly violating: y=3.999. Gap = 3.999 - 2.0 = 1.999 < 2.0. FAIL.
p2_fail = Port(0, 3, 0)
res2_fail = Straight.generate(p2_fail, 50.0, width=2.0, dilation=1.0)
is_v, count = ce.verify_path("net2", [res2_fail])
assert not is_v, "Gap 1.999 should be invalid"
assert count > 0
def test_verify_all_nets_cases():
"""
Validate that verify_all_nets catches some common cases and doesn't flag reasonable non-failing cases.
"""
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=(0, 0, 100, 100))
danger_map.precompute([])
evaluator = CostEvaluator(collision_engine=engine, danger_map=danger_map)
context = AStarContext(cost_evaluator=evaluator)
pf = PathFinder(context, warm_start=None, max_iterations=1)
# Case 1: Parallel paths exactly at clearance (Should be VALID)
netlist_parallel_ok = {
"net1": (Port(0, 50, 0), Port(100, 50, 0)),
"net2": (Port(0, 54, 0), Port(100, 54, 0)),
}
net_widths = {"net1": 2.0, "net2": 2.0}
results = pf.route_all(netlist_parallel_ok, net_widths)
assert results["net1"].is_valid, f"Exactly at clearance should be valid, collisions={results['net1'].collisions}"
assert results["net2"].is_valid
# Case 2: Parallel paths slightly within clearance (Should be INVALID)
netlist_parallel_fail = {
"net3": (Port(0, 20, 0), Port(100, 20, 0)),
"net4": (Port(0, 23, 0), Port(100, 23, 0)),
}
# Reset engine
engine.remove_path("net1")
engine.remove_path("net2")
results_p = pf.route_all(netlist_parallel_fail, net_widths)
# verify_all_nets should flag both as invalid because they cross-collide
assert not results_p["net3"].is_valid
assert not results_p["net4"].is_valid
# Case 3: Crossing paths (Should be INVALID)
netlist_cross = {
"net5": (Port(0, 75, 0), Port(100, 75, 0)),
"net6": (Port(50, 0, 90), Port(50, 100, 90)),
}
engine.remove_path("net3")
engine.remove_path("net4")
results_c = pf.route_all(netlist_cross, net_widths)
assert not results_c["net5"].is_valid
assert not results_c["net6"].is_valid

105
inire/tests/test_collision.py
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from shapely.geometry import Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.geometry.components import Straight
def test_collision_detection() -> None:
# Clearance = 2um
engine = CollisionEngine(clearance=2.0)
# 10x10 um obstacle at (10,10)
obstacle = Polygon([(10, 10), (20, 10), (20, 20), (10, 20)])
engine.add_static_obstacle(obstacle)
# 1. Direct hit
test_poly = Polygon([(12, 12), (13, 12), (13, 13), (12, 13)])
assert engine.is_collision(test_poly, net_width=2.0)
# 2. Far away
test_poly_far = Polygon([(0, 0), (5, 0), (5, 5), (0, 5)])
assert not engine.is_collision(test_poly_far, net_width=2.0)
# 3. Near hit (within clearance)
# Obstacle edge at x=10.
# test_poly edge at x=9.
# Distance = 1.0 um.
# Required distance (Wi+C)/2 = 2.0. Collision!
test_poly_near = Polygon([(8, 10), (9, 10), (9, 15), (8, 15)])
assert engine.is_collision(test_poly_near, net_width=2.0)
def test_safety_zone() -> None:
# Use zero clearance for this test to verify the 2nm port safety zone
# against the physical obstacle boundary.
engine = CollisionEngine(clearance=0.0)
obstacle = Polygon([(10, 10), (20, 10), (20, 20), (10, 20)])
engine.add_static_obstacle(obstacle)
# Port exactly on the boundary
start_port = Port(10, 12, 0)
# Move starting from this port that overlaps the obstacle by 1nm
# (Inside the 2nm safety zone)
test_poly = Polygon([(9.999, 11.9995), (10.001, 11.9995), (10.001, 12.0005), (9.999, 12.0005)])
assert not engine.is_collision(test_poly, net_width=0.001, start_port=start_port)
def test_configurable_max_net_width() -> None:
# Large max_net_width (10.0) -> large pre-dilation (6.0)
engine = CollisionEngine(clearance=2.0, max_net_width=10.0)
obstacle = Polygon([(20, 20), (25, 20), (25, 25), (20, 25)])
engine.add_static_obstacle(obstacle)
test_poly = Polygon([(15, 20), (16, 20), (16, 25), (15, 25)])
# physical check: dilated test_poly by C/2 = 1.0.
# Dilated test_poly bounds: (14, 19, 17, 26).
# obstacle: (20, 20, 25, 25). No physical collision.
assert not engine.is_collision(test_poly, net_width=2.0)
def test_ray_cast_width_clearance() -> None:
# Clearance = 2.0um, Width = 2.0um.
# Centerline to obstacle edge must be >= W/2 + C = 1.0 + 2.0 = 3.0um.
engine = CollisionEngine(clearance=2.0)
# Obstacle at x=10 to 20
obstacle = Polygon([(10, 0), (20, 0), (20, 100), (10, 100)])
engine.add_static_obstacle(obstacle)
# 1. Parallel move at x=6. Gap = 10 - 6 = 4.0. Clearly OK.
start_ok = Port(6, 50, 90)
reach_ok = engine.ray_cast(start_ok, 90, max_dist=10.0, net_width=2.0)
assert reach_ok >= 10.0
# 2. Parallel move at x=8. Gap = 10 - 8 = 2.0. COLLISION.
start_fail = Port(8, 50, 90)
reach_fail = engine.ray_cast(start_fail, 90, max_dist=10.0, net_width=2.0)
assert reach_fail < 10.0
def test_check_move_static_clearance() -> None:
engine = CollisionEngine(clearance=2.0)
obstacle = Polygon([(10, 0), (20, 0), (20, 100), (10, 100)])
engine.add_static_obstacle(obstacle)
# Straight move of length 10 at x=8 (Width 2.0)
# Gap = 10 - 8 = 2.0 < 3.0. COLLISION.
start = Port(8, 0, 90)
res = Straight.generate(start, 10.0, width=2.0, dilation=1.0) # dilation = C/2
assert engine.check_move_static(res, start_port=start, net_width=2.0)
# Move at x=7. Gap = 3.0 == minimum. OK.
start_ok = Port(7, 0, 90)
res_ok = Straight.generate(start_ok, 10.0, width=2.0, dilation=1.0)
assert not engine.check_move_static(res_ok, start_port=start_ok, net_width=2.0)
# 3. Same exact-boundary case.
start_exact = Port(7, 0, 90)
res_exact = Straight.generate(start_exact, 10.0, width=2.0, dilation=1.0)
assert not engine.check_move_static(res_exact, start_port=start_exact, net_width=2.0)

173
inire/tests/test_components.py
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@ -1,173 +0,0 @@
import pytest
from inire.geometry.components import Bend90, SBend, Straight
from inire.geometry.primitives import Port, rotate_port, translate_port
def test_straight_generation() -> None:
start = Port(0, 0, 0)
length = 10.0
width = 2.0
result = Straight.generate(start, length, width)
assert result.end_port.x == 10.0
assert result.end_port.y == 0.0
assert result.end_port.orientation == 0.0
assert len(result.geometry) == 1
# Bounds of the polygon
minx, miny, maxx, maxy = result.geometry[0].bounds
assert minx == 0.0
assert maxx == 10.0
assert miny == -1.0
assert maxy == 1.0
def test_bend90_generation() -> None:
start = Port(0, 0, 0)
radius = 10.0
width = 2.0
# CW bend
result_cw = Bend90.generate(start, radius, width, direction="CW")
assert result_cw.end_port.x == 10.0
assert result_cw.end_port.y == -10.0
assert result_cw.end_port.orientation == 270.0
# CCW bend
result_ccw = Bend90.generate(start, radius, width, direction="CCW")
assert result_ccw.end_port.x == 10.0
assert result_ccw.end_port.y == 10.0
assert result_ccw.end_port.orientation == 90.0
def test_sbend_generation() -> None:
start = Port(0, 0, 0)
offset = 5.0
radius = 10.0
width = 2.0
result = SBend.generate(start, offset, radius, width)
assert result.end_port.y == 5.0
assert result.end_port.orientation == 0.0
assert len(result.geometry) == 2 # Optimization: returns individual arcs
# Verify failure for large offset
with pytest.raises(ValueError, match=r"SBend offset .* must be less than 2\*radius"):
SBend.generate(start, 25.0, 10.0, 2.0)
def test_sbend_generation_negative_offset_keeps_second_arc_below_centerline() -> None:
start = Port(0, 0, 0)
offset = -5.0
radius = 10.0
width = 2.0
result = SBend.generate(start, offset, radius, width)
assert result.end_port.y == -5.0
second_arc_minx, second_arc_miny, second_arc_maxx, second_arc_maxy = result.geometry[1].bounds
assert second_arc_maxy <= width / 2.0 + 1e-6
assert second_arc_miny < -width / 2.0
def test_bend_collision_models() -> None:
start = Port(0, 0, 0)
radius = 10.0
width = 2.0
# 1. BBox model
res_bbox = Bend90.generate(start, radius, width, direction="CCW", collision_type="bbox")
# Arc CCW R=10 from (0,0,0) ends at (10,10,90).
# Waveguide width is 2.0, so bbox will be slightly larger than (0,0,10,10)
minx, miny, maxx, maxy = res_bbox.geometry[0].bounds
assert minx <= 0.0 + 1e-6
assert maxx >= 10.0 - 1e-6
assert miny <= 0.0 + 1e-6
assert maxy >= 10.0 - 1e-6
# 2. Clipped BBox model
res_clipped = Bend90.generate(start, radius, width, direction="CCW", collision_type="clipped_bbox", clip_margin=1.0)
# Area should be less than full bbox
assert res_clipped.geometry[0].area < res_bbox.geometry[0].area
def test_sbend_collision_models() -> None:
start = Port(0, 0, 0)
offset = 5.0
radius = 10.0
width = 2.0
res_bbox = SBend.generate(start, offset, radius, width, collision_type="bbox")
# Geometry should be a list of individual bbox polygons for each arc
assert len(res_bbox.geometry) == 2
res_arc = SBend.generate(start, offset, radius, width, collision_type="arc")
area_bbox = sum(p.area for p in res_bbox.geometry)
area_arc = sum(p.area for p in res_arc.geometry)
assert area_bbox > area_arc
def test_sbend_continuity() -> None:
# Verify SBend endpoints and continuity math
start = Port(10, 20, 90) # Starting facing up
offset = 4.0
radius = 20.0
width = 1.0
res = SBend.generate(start, offset, radius, width)
# Target orientation should be same as start
assert abs(res.end_port.orientation - 90.0) < 1e-6
# For a port at 90 deg, +offset is a shift in -x direction
assert abs(res.end_port.x - (10.0 - offset)) < 1e-6
# Geometry should be a list of valid polygons
assert len(res.geometry) == 2
for p in res.geometry:
assert p.is_valid
def test_arc_sagitta_precision() -> None:
# Verify that requested sagitta actually controls segment count
start = Port(0, 0, 0)
radius = 100.0 # Large radius to make sagitta significant
width = 2.0
# Coarse: 1um sagitta
res_coarse = Bend90.generate(start, radius, width, direction="CCW", sagitta=1.0)
# Fine: 0.01um (10nm) sagitta
res_fine = Bend90.generate(start, radius, width, direction="CCW", sagitta=0.01)
# Number of segments should be significantly higher for fine
# Exterior points = (segments + 1) * 2
pts_coarse = len(res_coarse.geometry[0].exterior.coords)
pts_fine = len(res_fine.geometry[0].exterior.coords)
assert pts_fine > pts_coarse * 2
def test_component_transform_invariance() -> None:
# Verify that generating at (0,0) then transforming
# is same as generating at the transformed port.
start0 = Port(0, 0, 0)
radius = 10.0
width = 2.0
res0 = Bend90.generate(start0, radius, width, direction="CCW")
# Transform: Translate (10, 10) then Rotate 90
dx, dy = 10.0, 5.0
angle = 90.0
# 1. Transform the generated geometry
p_end_transformed = rotate_port(translate_port(res0.end_port, dx, dy), angle)
# 2. Generate at transformed start
start_transformed = rotate_port(translate_port(start0, dx, dy), angle)
res_transformed = Bend90.generate(start_transformed, radius, width, direction="CCW")
assert abs(res_transformed.end_port.x - p_end_transformed.x) < 1e-6
assert abs(res_transformed.end_port.y - p_end_transformed.y) < 1e-6
assert abs(res_transformed.end_port.orientation - p_end_transformed.orientation) < 1e-6

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inire/tests/test_congestion.py
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import pytest
from shapely.geometry import Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, route_astar
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.pathfinder import PathFinder
@pytest.fixture
def basic_evaluator() -> CostEvaluator:
engine = CollisionEngine(clearance=2.0)
# Wider bounds to allow going around (y from -40 to 40)
danger_map = DangerMap(bounds=(0, -40, 100, 40))
danger_map.precompute([])
return CostEvaluator(engine, danger_map, bend_penalty=50.0, sbend_penalty=150.0)
def test_astar_sbend(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator, sbend_offsets=[2.0, 5.0])
# Start at (0,0), target at (50, 2) -> 2um lateral offset
# This matches one of our discretized SBend offsets.
start = Port(0, 0, 0)
target = Port(50, 2, 0)
path = route_astar(start, target, net_width=2.0, context=context)
assert path is not None
# Check if any component in the path is an SBend
found_sbend = False
for res in path:
# Check if the end port orientation is same as start
# and it's not a single straight (which would have y=0)
if abs(res.end_port.y - start.y) > 0.1 and abs(res.end_port.orientation - start.orientation) < 0.1:
found_sbend = True
break
assert found_sbend
def test_pathfinder_negotiated_congestion_resolution(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator, bend_radii=[5.0, 10.0])
# Increase base penalty to force detour immediately
pf = PathFinder(context, max_iterations=10, base_congestion_penalty=1000.0)
netlist = {
"net1": (Port(0, 0, 0), Port(50, 0, 0)),
"net2": (Port(0, 10, 0), Port(50, 10, 0)),
}
net_widths = {"net1": 2.0, "net2": 2.0}
# Force them into a narrow corridor that only fits ONE.
obs_top = Polygon([(20, 6), (30, 6), (30, 15), (20, 10)]) # Lower wall
obs_bottom = Polygon([(20, 4), (30, 4), (30, -15), (20, -10)])
basic_evaluator.collision_engine.add_static_obstacle(obs_top)
basic_evaluator.collision_engine.add_static_obstacle(obs_bottom)
basic_evaluator.danger_map.precompute([obs_top, obs_bottom])
results = pf.route_all(netlist, net_widths)
assert len(results) == 2
assert results["net1"].reached_target
assert results["net2"].reached_target
assert results["net1"].is_valid
assert results["net2"].is_valid
assert results["net1"].collisions == 0
assert results["net2"].collisions == 0

67
inire/tests/test_cost.py
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from shapely.geometry import Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
def test_cost_calculation() -> None:
engine = CollisionEngine(clearance=2.0)
# 50x50 um area, 1um resolution
danger_map = DangerMap(bounds=(0, 0, 50, 50))
danger_map.precompute([])
# Use small penalties for testing
evaluator = CostEvaluator(engine, danger_map, greedy_h_weight=1.1, bend_penalty=10.0)
p1 = Port(0, 0, 0)
p2 = Port(10, 10, 0)
h = evaluator.h_manhattan(p1, p2)
# Manhattan distance = 20.
# Jog alignment penalty = 2*bp = 20.
# Side check penalty = 2*bp = 20.
# Total = 1.1 * (20 + 40) = 66.0
assert abs(h - 66.0) < 1e-6
# Orientation difference
p3 = Port(10, 10, 90)
h_90 = evaluator.h_manhattan(p1, p3)
# diff = 90. penalty += 1*bp = 10.
# Side check: 2*bp = 20. (Total penalty = 30)
# Total = 1.1 * (20 + 30) = 55.0
assert abs(h_90 - 55.0) < 1e-6
# Traveling away
p4 = Port(10, 10, 180)
h_away = evaluator.h_manhattan(p1, p4)
# diff = 180. penalty += 2*bp = 20.
# Side check: 2*bp = 20.
# Total = 1.1 * (20 + 40) = 66.0
assert h_away >= h_90
def test_danger_map_kd_tree_and_cache() -> None:
# Test that KD-Tree based danger map works and uses cache
bounds = (0, 0, 1000, 1000)
dm = DangerMap(bounds, resolution=1.0, safety_threshold=10.0)
# Square obstacle at (100, 100) to (110, 110)
obstacle = Polygon([(100, 100), (110, 100), (110, 110), (100, 110)])
dm.precompute([obstacle])
# 1. High cost near boundary
cost_near = dm.get_cost(100.5, 100.5)
assert cost_near > 1.0
# 2. Zero cost far away
cost_far = dm.get_cost(500, 500)
assert cost_far == 0.0
# 3. Check cache usage (internal detail check)
# We can check if calling it again is fast or just verify it returns same result
cost_near_2 = dm.get_cost(100.5, 100.5)
assert cost_near_2 == cost_near
# 4. Out of bounds
assert dm.get_cost(-1, -1) >= 1e12

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inire/tests/test_example_performance.py
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from __future__ import annotations
import os
import statistics
import pytest
from inire.tests.example_scenarios import SCENARIOS, ScenarioDefinition, ScenarioOutcome
RUN_PERFORMANCE = os.environ.get("INIRE_RUN_PERFORMANCE") == "1"
PERFORMANCE_REPEATS = 3
REGRESSION_FACTOR = 1.5
# Baselines are measured from the current code path without plotting.
BASELINE_SECONDS = {
"example_01_simple_route": 0.0035,
"example_02_congestion_resolution": 0.2666,
"example_03_locked_paths": 0.2304,
"example_04_sbends_and_radii": 1.8734,
"example_05_orientation_stress": 0.5630,
"example_06_bend_collision_models": 5.2382,
"example_07_large_scale_routing": 1.2081,
"example_08_custom_bend_geometry": 4.2111,
"example_09_unroutable_best_effort": 0.0056,
}
EXPECTED_OUTCOMES = {
"example_01_simple_route": {"total_results": 1, "valid_results": 1, "reached_targets": 1},
"example_02_congestion_resolution": {"total_results": 3, "valid_results": 3, "reached_targets": 3},
"example_03_locked_paths": {"total_results": 2, "valid_results": 2, "reached_targets": 2},
"example_04_sbends_and_radii": {"total_results": 2, "valid_results": 2, "reached_targets": 2},
"example_05_orientation_stress": {"total_results": 3, "valid_results": 3, "reached_targets": 3},
"example_06_bend_collision_models": {"total_results": 3, "valid_results": 3, "reached_targets": 3},
"example_07_large_scale_routing": {"total_results": 10, "valid_results": 10, "reached_targets": 10},
"example_08_custom_bend_geometry": {"total_results": 2, "valid_results": 1, "reached_targets": 2},
"example_09_unroutable_best_effort": {"total_results": 1, "valid_results": 0, "reached_targets": 0},
}
def _assert_expected_outcome(name: str, outcome: ScenarioOutcome) -> None:
expected = EXPECTED_OUTCOMES[name]
assert outcome.total_results == expected["total_results"]
assert outcome.valid_results == expected["valid_results"]
assert outcome.reached_targets == expected["reached_targets"]
@pytest.mark.performance
@pytest.mark.skipif(not RUN_PERFORMANCE, reason="set INIRE_RUN_PERFORMANCE=1 to run runtime regression checks")
@pytest.mark.parametrize("scenario", SCENARIOS, ids=[scenario.name for scenario in SCENARIOS])
def test_example_like_runtime_regression(scenario: ScenarioDefinition) -> None:
timings = []
for _ in range(PERFORMANCE_REPEATS):
outcome = scenario.run()
_assert_expected_outcome(scenario.name, outcome)
timings.append(outcome.duration_s)
median_runtime = statistics.median(timings)
assert median_runtime <= BASELINE_SECONDS[scenario.name] * REGRESSION_FACTOR, (
f"{scenario.name} median runtime {median_runtime:.4f}s exceeded "
f"{REGRESSION_FACTOR:.1f}x baseline {BASELINE_SECONDS[scenario.name]:.4f}s "
f"from timings {timings!r}"
)

70
inire/tests/test_failed_net_congestion.py
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import pytest
import numpy
from inire.geometry.primitives import Port
from inire.geometry.collision import CollisionEngine
from inire.router.cost import CostEvaluator
from inire.router.astar import AStarContext
from inire.router.pathfinder import PathFinder
from inire.router.danger_map import DangerMap
def test_failed_net_visibility():
"""
Verifies that nets that fail to reach their target (return partial paths)
ARE added to the collision engine, making them visible to other nets
for negotiated congestion.
"""
# 1. Setup
engine = CollisionEngine(clearance=2.0)
# Create a simple danger map (bounds 0-100)
# We don't strictly need obstacles in it for this test.
dm = DangerMap(bounds=(0, 0, 100, 100))
evaluator = CostEvaluator(engine, dm)
# 2. Configure Router with low limit to FORCE failure
# node_limit=10 is extremely low, likely allowing only a few moves.
# Start (0,0) -> Target (100,0) is 100um away.
# Let's add a static obstacle that blocks the direct path.
from shapely.geometry import box
obstacle = box(40, -10, 60, 10) # Wall at x=50
engine.add_static_obstacle(obstacle)
# With obstacle, direct jump fails. A* must search around.
# Limit=10 should be enough to fail to find a path around.
context = AStarContext(evaluator, node_limit=10)
# 3. Configure PathFinder
# max_iterations=1 because we only need to check the state after the first attempt.
pf = PathFinder(context, max_iterations=1, warm_start=None)
netlist = {
"net1": (Port(0, 0, 0), Port(100, 0, 0))
}
net_widths = {"net1": 1.0}
# 4. Route
print("\nStarting Route...")
results = pf.route_all(netlist, net_widths)
res = results["net1"]
print(f"Result: is_valid={res.is_valid}, reached={res.reached_target}, path_len={len(res.path)}")
# 5. Verify Failure Condition
# We expect reached_target to be False because of node_limit + obstacle
assert not res.reached_target, "Test setup failed: Net reached target despite low limit!"
assert len(res.path) > 0, "Test setup failed: No partial path returned!"
# 6. Verify Visibility
# Check if net1 is in the collision engine
found_nets = set()
# CollisionEngine.dynamic_geometries: dict[obj_id, (net_id, poly)]
for obj_id, (nid, poly) in engine.dynamic_geometries.items():
found_nets.add(nid)
print(f"Nets found in engine: {found_nets}")
# The FIX Expectation: "net1" SHOULD be present
assert "net1" in found_nets, "Bug present: Net1 is invisible despite having partial path!"

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inire/tests/test_fuzz.py
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from typing import Any
import pytest
from hypothesis import given, settings, strategies as st
from shapely.geometry import Point, Polygon
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext, route_astar
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
@st.composite
def random_obstacle(draw: Any) -> Polygon:
x = draw(st.floats(min_value=0, max_value=20))
y = draw(st.floats(min_value=0, max_value=20))
w = draw(st.floats(min_value=1, max_value=5))
h = draw(st.floats(min_value=1, max_value=5))
return Polygon([(x, y), (x + w, y), (x + w, y + h), (x, y + h)])
@st.composite
def random_port(draw: Any) -> Port:
x = draw(st.floats(min_value=0, max_value=20))
y = draw(st.floats(min_value=0, max_value=20))
orientation = draw(st.sampled_from([0, 90, 180, 270]))
return Port(x, y, orientation)
def _port_has_required_clearance(port: Port, obstacles: list[Polygon], clearance: float, net_width: float) -> bool:
point = Point(float(port.x), float(port.y))
required_gap = (net_width / 2.0) + clearance
return all(point.distance(obstacle) >= required_gap for obstacle in obstacles)
@settings(max_examples=3, deadline=None)
@given(obstacles=st.lists(random_obstacle(), min_size=0, max_size=3), start=random_port(), target=random_port())
def test_fuzz_astar_no_crash(obstacles: list[Polygon], start: Port, target: Port) -> None:
net_width = 2.0
clearance = 2.0
engine = CollisionEngine(clearance=2.0)
for obs in obstacles:
engine.add_static_obstacle(obs)
danger_map = DangerMap(bounds=(0, 0, 30, 30))
danger_map.precompute(obstacles)
evaluator = CostEvaluator(engine, danger_map)
context = AStarContext(evaluator, node_limit=5000) # Lower limit for fuzzing stability
# Check if start/target are inside obstacles (safety zone check)
# The router should handle this gracefully (either route or return None)
try:
path = route_astar(start, target, net_width=2.0, context=context)
# This is a crash-smoke test rather than a full correctness proof.
# If a full path is returned, it should at least terminate at the requested target.
endpoints_are_clear = (
_port_has_required_clearance(start, obstacles, clearance, net_width)
and _port_has_required_clearance(target, obstacles, clearance, net_width)
)
if path and endpoints_are_clear:
assert path[-1].end_port == target
except Exception as e:
# Unexpected exceptions are failures
pytest.fail(f"Router crashed with {type(e).__name__}: {e}")

118
inire/tests/test_pathfinder.py
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import pytest
from inire.geometry.collision import CollisionEngine
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.pathfinder import PathFinder
@pytest.fixture
def basic_evaluator() -> CostEvaluator:
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=(0, 0, 100, 100))
danger_map.precompute([])
return CostEvaluator(engine, danger_map)
def test_pathfinder_parallel(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator)
pf = PathFinder(context)
netlist = {
"net1": (Port(0, 0, 0), Port(50, 0, 0)),
"net2": (Port(0, 10, 0), Port(50, 10, 0)),
}
net_widths = {"net1": 2.0, "net2": 2.0}
results = pf.route_all(netlist, net_widths)
assert len(results) == 2
assert results["net1"].is_valid
assert results["net2"].is_valid
assert results["net1"].collisions == 0
assert results["net2"].collisions == 0
def test_pathfinder_crossing_detection(basic_evaluator: CostEvaluator) -> None:
context = AStarContext(basic_evaluator)
# Force a crossing by setting low iterations and low penalty
pf = PathFinder(context, max_iterations=1, base_congestion_penalty=1.0, warm_start=None)
# Net 1: (0, 25) -> (100, 25) Horizontal
# Net 2: (50, 0) -> (50, 50) Vertical
netlist = {
"net1": (Port(0, 25, 0), Port(100, 25, 0)),
"net2": (Port(50, 0, 90), Port(50, 50, 90)),
}
net_widths = {"net1": 2.0, "net2": 2.0}
results = pf.route_all(netlist, net_widths)
# Both should be invalid because they cross
assert not results["net1"].is_valid
assert not results["net2"].is_valid
assert results["net1"].collisions > 0
assert results["net2"].collisions > 0
def test_pathfinder_refine_paths_reduces_locked_detour_bends() -> None:
bounds = (0, -50, 100, 50)
def build_pathfinder(*, refine_paths: bool) -> tuple[CollisionEngine, PathFinder]:
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map, bend_penalty=250.0, sbend_penalty=500.0)
context = AStarContext(evaluator, bend_radii=[10.0])
return engine, PathFinder(context, refine_paths=refine_paths)
base_engine, base_pf = build_pathfinder(refine_paths=False)
base_pf.route_all({"netA": (Port(10, 0, 0), Port(90, 0, 0))}, {"netA": 2.0})
base_engine.lock_net("netA")
base_result = base_pf.route_all({"netB": (Port(50, -20, 90), Port(50, 20, 90))}, {"netB": 2.0})["netB"]
refined_engine, refined_pf = build_pathfinder(refine_paths=True)
refined_pf.route_all({"netA": (Port(10, 0, 0), Port(90, 0, 0))}, {"netA": 2.0})
refined_engine.lock_net("netA")
refined_result = refined_pf.route_all({"netB": (Port(50, -20, 90), Port(50, 20, 90))}, {"netB": 2.0})["netB"]
base_bends = sum(1 for comp in base_result.path if comp.move_type == "Bend90")
refined_bends = sum(1 for comp in refined_result.path if comp.move_type == "Bend90")
assert base_result.is_valid
assert refined_result.is_valid
assert refined_bends < base_bends
assert refined_pf._path_cost(refined_result.path) < base_pf._path_cost(base_result.path)
def test_pathfinder_refine_paths_simplifies_triple_crossing_detours() -> None:
bounds = (0, 0, 100, 100)
netlist = {
"horizontal": (Port(10, 50, 0), Port(90, 50, 0)),
"vertical_up": (Port(45, 10, 90), Port(45, 90, 90)),
"vertical_down": (Port(55, 90, 270), Port(55, 10, 270)),
}
net_widths = {net_id: 2.0 for net_id in netlist}
def build_pathfinder(*, refine_paths: bool) -> PathFinder:
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=bounds)
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map, greedy_h_weight=1.5, bend_penalty=250.0, sbend_penalty=500.0)
context = AStarContext(evaluator, bend_radii=[10.0], sbend_radii=[10.0])
return PathFinder(context, base_congestion_penalty=1000.0, refine_paths=refine_paths)
base_results = build_pathfinder(refine_paths=False).route_all(netlist, net_widths)
refined_results = build_pathfinder(refine_paths=True).route_all(netlist, net_widths)
for net_id in ("vertical_up", "vertical_down"):
base_result = base_results[net_id]
refined_result = refined_results[net_id]
base_bends = sum(1 for comp in base_result.path if comp.move_type == "Bend90")
refined_bends = sum(1 for comp in refined_result.path if comp.move_type == "Bend90")
assert base_result.is_valid
assert refined_result.is_valid
assert refined_bends < base_bends

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inire/tests/test_primitives.py
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from typing import Any
from hypothesis import given, strategies as st
from inire.geometry.primitives import Port, rotate_port, translate_port
@st.composite
def port_strategy(draw: Any) -> Port:
x = draw(st.floats(min_value=-1e6, max_value=1e6))
y = draw(st.floats(min_value=-1e6, max_value=1e6))
orientation = draw(st.sampled_from([0, 90, 180, 270]))
return Port(x, y, orientation)
def test_port_snapping() -> None:
p = Port(0.123456, 0.654321, 90)
assert p.x == 0
assert p.y == 1
@given(p=port_strategy())
def test_port_transform_invariants(p: Port) -> None:
# Rotating 90 degrees 4 times should return to same orientation
p_rot = p
for _ in range(4):
p_rot = rotate_port(p_rot, 90)
assert abs(p_rot.x - p.x) < 1e-6
assert abs(p_rot.y - p.y) < 1e-6
assert (p_rot.orientation % 360) == (p.orientation % 360)
@given(
p=port_strategy(),
dx=st.floats(min_value=-1000, max_value=1000),
dy=st.floats(min_value=-1000, max_value=1000),
)
def test_translate_snapping(p: Port, dx: float, dy: float) -> None:
p_trans = translate_port(p, dx, dy)
assert isinstance(p_trans.x, int)
assert isinstance(p_trans.y, int)
def test_orientation_normalization() -> None:
p = Port(0, 0, 360)
assert p.orientation == 0
p2 = Port(0, 0, -90)
assert p2.orientation == 270

63
inire/tests/test_refinements.py
View file

@ -1,63 +0,0 @@
from inire.geometry.collision import CollisionEngine
from inire.geometry.components import Bend90
from inire.geometry.primitives import Port
from inire.router.astar import AStarContext
from inire.router.cost import CostEvaluator
from inire.router.danger_map import DangerMap
from inire.router.pathfinder import PathFinder
def test_arc_resolution_sagitta() -> None:
start = Port(0, 0, 0)
# R=10, 90 deg bend.
# High tolerance (0.5um) -> few segments
res_coarse = Bend90.generate(start, radius=10.0, width=2.0, direction="CCW", sagitta=0.5)
# Low tolerance (1nm) -> many segments
res_fine = Bend90.generate(start, radius=10.0, width=2.0, direction="CCW", sagitta=0.001)
# Check number of points in the polygon exterior
# (num_segments + 1) * 2 points usually
pts_coarse = len(res_coarse.geometry[0].exterior.coords)
pts_fine = len(res_fine.geometry[0].exterior.coords)
assert pts_fine > pts_coarse
def test_locked_paths() -> None:
engine = CollisionEngine(clearance=2.0)
danger_map = DangerMap(bounds=(0, -50, 100, 50))
danger_map.precompute([])
evaluator = CostEvaluator(engine, danger_map)
context = AStarContext(evaluator, bend_radii=[5.0, 10.0])
pf = PathFinder(context)
# 1. Route Net A
netlist_a = {"netA": (Port(0, 0, 0), Port(50, 0, 0))}
results_a = pf.route_all(netlist_a, {"netA": 2.0})
assert results_a["netA"].is_valid
# 2. Lock Net A
engine.lock_net("netA")
# 3. Route Net B through the same space. It should detour or fail.
# We'll place Net B's start/target such that it MUST cross Net A's physical path.
netlist_b = {"netB": (Port(0, -5, 0), Port(50, 5, 0))}
# Route Net B
results_b = pf.route_all(netlist_b, {"netB": 2.0})
# Net B should be is_valid (it detoured) or at least not have collisions
# with Net A in the dynamic set (because netA is now static).
# Since netA is static, netB will see it as a HARD collision if it tries to cross.
# Our A* will find a detour around the static obstacle.
assert results_b["netB"].is_valid
# Verify geometry doesn't intersect locked netA (physical check)
poly_a = [p.geometry[0] for p in results_a["netA"].path]
poly_b = [p.geometry[0] for p in results_b["netB"].path]
for pa in poly_a:
for pb in poly_b:
assert not pa.intersects(pb)

51
inire/tests/test_variable_grid.py
View file

@ -1,51 +0,0 @@
import unittest
from inire.geometry.primitives import Port
from inire.router.astar import route_astar, AStarContext
from inire.router.cost import CostEvaluator
from inire.geometry.collision import CollisionEngine
class TestIntegerPorts(unittest.TestCase):
def setUp(self):
self.ce = CollisionEngine(clearance=2.0)
self.cost = CostEvaluator(self.ce)
def test_route_reaches_integer_target(self):
context = AStarContext(self.cost)
start = Port(0, 0, 0)
target = Port(12, 0, 0)
path = route_astar(start, target, net_width=1.0, context=context)
self.assertIsNotNone(path)
last_port = path[-1].end_port
self.assertEqual(last_port.x, 12)
self.assertEqual(last_port.y, 0)
self.assertEqual(last_port.r, 0)
def test_port_constructor_rounds_to_integer_lattice(self):
context = AStarContext(self.cost)
start = Port(0.0, 0.0, 0.0)
target = Port(12.3, 0.0, 0.0)
path = route_astar(start, target, net_width=1.0, context=context)
self.assertIsNotNone(path)
self.assertEqual(target.x, 12)
last_port = path[-1].end_port
self.assertEqual(last_port.x, 12)
def test_half_step_inputs_use_integerized_targets(self):
context = AStarContext(self.cost)
start = Port(0.0, 0.0, 0.0)
target = Port(7.5, 0.0, 0.0)
path = route_astar(start, target, net_width=1.0, context=context)
self.assertIsNotNone(path)
self.assertEqual(target.x, 8)
last_port = path[-1].end_port
self.assertEqual(last_port.x, 8)
if __name__ == '__main__':
unittest.main()

104
inire/utils/validation.py
View file

@ -1,104 +0,0 @@
from __future__ import annotations
from typing import TYPE_CHECKING, Any
import numpy
from inire.constants import TOLERANCE_LINEAR
if TYPE_CHECKING:
from shapely.geometry import Polygon
from inire.geometry.primitives import Port
from inire.router.pathfinder import RoutingResult
def validate_routing_result(
result: RoutingResult,
static_obstacles: list[Polygon],
clearance: float,
expected_start: Port | None = None,
expected_end: Port | None = None,
) -> dict[str, Any]:
"""
Perform a high-precision validation of a routed path.
Args:
result: The routing result to validate.
static_obstacles: List of static obstacle geometries.
clearance: Required minimum distance.
expected_start: Optional expected start port.
expected_end: Optional expected end port.
Returns:
A dictionary with validation results.
"""
_ = expected_start
if not result.path:
return {"is_valid": False, "reason": "No path found"}
obstacle_collision_geoms = []
self_intersection_geoms = []
connectivity_errors = []
# 1. Connectivity Check
total_length = 0.0
for comp in result.path:
total_length += comp.length
# Boundary check
if expected_end:
last_port = result.path[-1].end_port
dist_to_end = numpy.sqrt(((last_port[:2] - expected_end[:2])**2).sum())
if dist_to_end > 0.005:
connectivity_errors.append(f"Final port position mismatch: {dist_to_end*1000:.2f}nm")
if abs(last_port[2] - expected_end[2]) > 0.1:
connectivity_errors.append(f"Final port orientation mismatch: {last_port[2]} vs {expected_end[2]}")
# 2. Geometry Buffering
dilation_half = clearance / 2.0
dilation_full = clearance
dilated_for_self = []
for comp in result.path:
for poly in comp.geometry:
# Check against obstacles
d_full = poly.buffer(dilation_full)
for obs in static_obstacles:
if d_full.intersects(obs):
intersection = d_full.intersection(obs)
if intersection.area > 1e-9:
obstacle_collision_geoms.append(intersection)
# Save for self-intersection check
dilated_for_self.append(poly.buffer(dilation_half))
# 3. Self-intersection
for i, seg_i in enumerate(dilated_for_self):
for j, seg_j in enumerate(dilated_for_self):
if j > i + 1 and seg_i.intersects(seg_j): # Non-adjacent
overlap = seg_i.intersection(seg_j)
if overlap.area > TOLERANCE_LINEAR:
self_intersection_geoms.append((i, j, overlap))
is_valid = (len(obstacle_collision_geoms) == 0 and
len(self_intersection_geoms) == 0 and
len(connectivity_errors) == 0)
reasons = []
if obstacle_collision_geoms:
reasons.append(f"Found {len(obstacle_collision_geoms)} obstacle collisions.")
if self_intersection_geoms:
# report which indices
idx_str = ", ".join([f"{i}-{j}" for i, j, _ in self_intersection_geoms[:5]])
reasons.append(f"Found {len(self_intersection_geoms)} self-intersections (e.g. {idx_str}).")
if connectivity_errors:
reasons.extend(connectivity_errors)
return {
"is_valid": is_valid,
"reason": " ".join(reasons),
"obstacle_collisions": obstacle_collision_geoms,
"self_intersections": self_intersection_geoms,
"total_length": total_length,
"connectivity_ok": len(connectivity_errors) == 0,
}

239
inire/utils/visualization.py
View file

@ -1,239 +0,0 @@
from __future__ import annotations
from typing import TYPE_CHECKING
import matplotlib.pyplot as plt
import numpy
from shapely.geometry import MultiPolygon, Polygon
if TYPE_CHECKING:
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from inire.geometry.primitives import Port
from inire.router.pathfinder import RoutingResult
def plot_routing_results(
results: dict[str, RoutingResult],
static_obstacles: list[Polygon],
bounds: tuple[float, float, float, float],
netlist: dict[str, tuple[Port, Port]] | None = None,
show_actual: bool = True,
) -> tuple[Figure, Axes]:
"""
Plot obstacles and routed paths using matplotlib.
Args:
results: Dictionary of net_id to RoutingResult.
static_obstacles: List of static obstacle polygons.
bounds: Plot limits (minx, miny, maxx, maxy).
netlist: Optional original netlist for port visualization.
show_actual: If True, overlay high-fidelity geometry if available.
Returns:
The matplotlib Figure and Axes objects.
"""
fig, ax = plt.subplots(figsize=(12, 12))
# Plot static obstacles (gray)
for poly in static_obstacles:
x, y = poly.exterior.xy
ax.fill(x, y, alpha=0.3, fc="gray", ec="black", zorder=1)
# Plot paths
colors = plt.get_cmap("tab20")
for i, (net_id, res) in enumerate(results.items()):
color: str | tuple[float, ...] = colors(i % 20)
if not res.is_valid:
color = "red"
label_added = False
for comp in res.path:
# 1. Plot Collision Geometry (Translucent fill)
# This is the geometry used during search (e.g. proxy or arc)
for poly in comp.geometry:
if isinstance(poly, MultiPolygon):
geoms = list(poly.geoms)
else:
geoms = [poly]
for g in geoms:
if hasattr(g, "exterior"):
x, y = g.exterior.xy
ax.fill(x, y, alpha=0.15, fc=color, ec=color, linestyle='--', lw=0.5, zorder=2)
else:
# Fallback for LineString or other geometries
x, y = g.xy
ax.plot(x, y, color=color, alpha=0.15, linestyle='--', lw=0.5, zorder=2)
# 2. Plot "Actual" Geometry (The high-fidelity shape used for fabrication)
# Use comp.actual_geometry if it exists (should be the arc)
actual_geoms_to_plot = comp.actual_geometry if comp.actual_geometry is not None else comp.geometry
for poly in actual_geoms_to_plot:
if isinstance(poly, MultiPolygon):
geoms = list(poly.geoms)
else:
geoms = [poly]
for g in geoms:
if hasattr(g, "exterior"):
x, y = g.exterior.xy
ax.plot(x, y, color=color, lw=1.5, alpha=0.9, zorder=3, label=net_id if not label_added else "")
else:
x, y = g.xy
ax.plot(x, y, color=color, lw=1.5, alpha=0.9, zorder=3, label=net_id if not label_added else "")
label_added = True
# 3. Plot subtle port orientation arrow
p = comp.end_port
rad = numpy.radians(p.orientation)
ax.quiver(p.x, p.y, numpy.cos(rad), numpy.sin(rad), color="black",
scale=40, width=0.002, alpha=0.2, pivot="tail", zorder=4)
if not res.path and not res.is_valid:
# Best-effort display: If the path is empty but failed, it might be unroutable.
# We don't have a partial path in RoutingResult currently.
pass
# 4. Plot main arrows for netlist ports
if netlist:
for net_id, (start_p, target_p) in netlist.items():
for p in [start_p, target_p]:
rad = numpy.radians(p[2])
ax.quiver(*p[:2], numpy.cos(rad), numpy.sin(rad), color="black",
scale=25, width=0.004, pivot="tail", zorder=6)
ax.set_xlim(bounds[0], bounds[2])
ax.set_ylim(bounds[1], bounds[3])
ax.set_aspect("equal")
ax.set_title("Inire Routing Results (Dashed: Collision Proxy, Solid: Actual Geometry)")
# Legend handling for many nets
if len(results) < 25:
handles, labels = ax.get_legend_handles_labels()
if labels:
ax.legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize='small', ncol=2)
fig.tight_layout()
plt.grid(True, which='both', linestyle=':', alpha=0.5)
return fig, ax
def plot_danger_map(
danger_map: DangerMap,
ax: Axes | None = None,
resolution: float | None = None
) -> tuple[Figure, Axes]:
"""
Plot the pre-computed danger map as a heatmap.
"""
if ax is None:
fig, ax = plt.subplots(figsize=(10, 10))
else:
fig = ax.get_figure()
# Generate a temporary grid for visualization
res = resolution if resolution is not None else max(1.0, (danger_map.maxx - danger_map.minx) / 200.0)
x_coords = numpy.arange(danger_map.minx + res/2, danger_map.maxx, res)
y_coords = numpy.arange(danger_map.miny + res/2, danger_map.maxy, res)
xv, yv = numpy.meshgrid(x_coords, y_coords, indexing='ij')
if danger_map.tree is not None:
points = numpy.stack([xv.ravel(), yv.ravel()], axis=1)
dists, _ = danger_map.tree.query(points, distance_upper_bound=danger_map.safety_threshold)
# Apply cost function
safe_dists = numpy.maximum(dists, 0.1)
grid_flat = numpy.where(
dists < danger_map.safety_threshold,
danger_map.k / (safe_dists**2),
0.0
)
grid = grid_flat.reshape(xv.shape)
else:
grid = numpy.zeros(xv.shape)
# Need to transpose because grid is [x, y] and imshow expects [row, col] (y, x)
im = ax.imshow(
grid.T,
origin='lower',
extent=[danger_map.minx, danger_map.maxx, danger_map.miny, danger_map.maxy],
cmap='YlOrRd',
alpha=0.6
)
plt.colorbar(im, ax=ax, label='Danger Cost')
ax.set_title("Danger Map (Proximity Costs)")
return fig, ax
def plot_expanded_nodes(
nodes: list[tuple[float, float, float]],
ax: Axes | None = None,
color: str = 'gray',
alpha: float = 0.3,
) -> tuple[Figure, Axes]:
"""
Plot A* expanded nodes for debugging.
"""
if ax is None:
fig, ax = plt.subplots(figsize=(10, 10))
else:
fig = ax.get_figure()
if not nodes:
return fig, ax
x, y, _ = zip(*nodes)
ax.scatter(x, y, s=1, c=color, alpha=alpha, zorder=0)
return fig, ax
def plot_expansion_density(
nodes: list[tuple[float, float, float]],
bounds: tuple[float, float, float, float],
ax: Axes | None = None,
bins: int | tuple[int, int] = 50,
) -> tuple[Figure, Axes]:
"""
Plot a density heatmap (2D histogram) of expanded nodes.
Args:
nodes: List of (x, y, orientation) tuples.
bounds: (minx, miny, maxx, maxy) for the plot range.
ax: Optional existing axes to plot on.
bins: Number of bins for the histogram (int or (nx, ny)).
Returns:
Figure and Axes objects.
"""
if ax is None:
fig, ax = plt.subplots(figsize=(12, 12))
else:
fig = ax.get_figure()
if not nodes:
ax.text(0.5, 0.5, "No Expansion Data", ha='center', va='center', transform=ax.transAxes)
return fig, ax
x, y, _ = zip(*nodes)
# Create 2D histogram
h, xedges, yedges = numpy.histogram2d(
x, y,
bins=bins,
range=[[bounds[0], bounds[2]], [bounds[1], bounds[3]]]
)
# Plot as image
im = ax.imshow(
h.T,
origin='lower',
extent=[bounds[0], bounds[2], bounds[1], bounds[3]],
cmap='plasma',
interpolation='nearest',
alpha=0.7
)
plt.colorbar(im, ax=ax, label='Expansion Count')
ax.set_title("Search Expansion Density")
ax.set_xlim(bounds[0], bounds[2])
ax.set_ylim(bounds[1], bounds[3])
return fig, ax

33
pyproject.toml
View file

@ -1,6 +1,6 @@
[project]
name = "inire"
description = "Wave-router: Auto-routing for photonic and RF integrated circuits"
description = "Wave-router"
readme = "README.md"
requires-python = ">=3.11"
license = { file = "LICENSE.md" }
@ -9,6 +9,22 @@ authors = [
]
homepage = "https://mpxd.net/code/jan/inire"
repository = "https://mpxd.net/code/jan/inire"
keywords = [
"layout",
"CAD",
"EDA",
"mask",
"pattern",
"lithography",
"oas",
"gds",
"dxf",
"svg",
"OASIS",
"gdsii",
"gds2",
"stream",
]
classifiers = [
"Programming Language :: Python :: 3",
"Development Status :: 4 - Beta",
@ -20,17 +36,10 @@ classifiers = [
"Topic :: Scientific/Engineering :: Electronic Design Automation (EDA)",
]
dynamic = ["version"]
dependencies = [
"numpy",
"scipy",
"shapely",
"rtree",
"matplotlib",
]
dependencies = []
[dependency-groups]
dev = [
"hypothesis>=6.151.9",
"matplotlib>=3.10.8",
"pytest>=9.0.2",
"ruff>=0.15.5",
@ -70,13 +79,11 @@ lint.ignore = [
"C408", # dict(x=y) instead of {'x': y}
"PLR09", # Too many xxx
"PLR2004", # magic number
#"PLC0414", # import x as x
"PLC0414", # import x as x
"TRY003", # Long exception message
]
[tool.pytest.ini_options]
addopts = "-rsXx"
testpaths = ["inire"]
markers = [
"performance: opt-in runtime regression checks against example-like routing scenarios",
]

337
uv.lock generated
View file

@ -1,5 +1,5 @@
version = 1
requires-python = ">=3.11"
requires-python = ">=3.13"
[[package]]
name = "colorama"
@ -19,28 +19,6 @@ dependencies = [
]
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