Speed up rasterization ~40x and large solves ~2x

- Hybrid rasterizer: PIL scanline fill for the bulk, with cells in a
  ~2 px band around each ring edge re-tested exactly against the
  polygon - cell-for-cell identical to the old center-in-polygon pass
  (equivalence test added) but O(vertices + cells) instead of
  O(vertices x cells). Measured 4.5 s -> 0.11 s at 1.45M cells with
  8.8k polygon vertices.
- AMG-preconditioned CG (pyamg, new requirement) above 500k unknowns:
  measured 7.0 s vs 15.3 s spsolve at 1.4M unknowns at a fraction of
  the memory, R identical to 1e-6; the old Jacobi-CG (kept as fallback
  when pyamg is missing) needed tens of minutes there. spsolve stays
  the default below 500k where it is exact and fastest.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -114,9 +114,10 @@ SWIG API. Requires KiCad **10.0.1+**.
   Rule of thumb for 70 µm foil: skin is negligible below ~300 kHz
   (δ = 173 µm at 142 kHz), ~+11 % at 1 MHz.
 - 5-point FDM per layer on an auto-sized shared grid (~2 M cells total
-  across layers by default). Direct sparse solve up to 2.5 M unknowns,
-  Jacobi-CG above. Discretization error typically ≲ 2 % at defaults —
-  halve the cell size and compare to judge convergence.
+  across layers by default). Direct sparse solve up to 500 k unknowns,
+  AMG-preconditioned CG (pyamg) above — Jacobi-CG if pyamg is missing.
+  Discretization error typically ≲ 2 % at defaults — halve the cell size
+  and compare to judge convergence.
 
 ## Offline / development
 
@@ -133,7 +134,7 @@ Linux/macOS use `.venv/bin/python`):
 
 ```powershell
 uv venv --python 3.11 .venv
-uv pip install --python .venv\Scripts\python.exe kicad-python numpy scipy matplotlib pytest
+uv pip install --python .venv\Scripts\python.exe kicad-python numpy scipy pyamg matplotlib pytest
 .venv\Scripts\python.exe -m pytest tests -q          # incl. exact analytic cases
 .venv\Scripts\python.exe tools\api_probe.py          # IPC API probe vs live KiCad
 .venv\Scripts\python.exe -m fill_resistance.board_io dump.json [NET]  # extract only

fill_resistance/config.py

@@ -5,10 +5,9 @@ A future version may read overrides from <project>/fill_res_config.json.
 
 # --- Grid sizing ---
 # Benchmarked on the VOUT+ plane (147x59 mm): R changes < 0.3% from
-# 150 um cells down to 50 um; 1.7M unknowns direct-solve in ~17 s
-# (raster ~20 s). Accuracy is feature-limited (slots/necks narrower than
-# one cell), not plane-limited - override CELL_UM_OVERRIDE for boards
-# with sub-cell slots.
+# 150 um cells down to 50 um. Accuracy is feature-limited (slots/necks
+# narrower than one cell), not plane-limited - override CELL_UM_OVERRIDE
+# for boards with sub-cell slots.
 TARGET_CELLS = 2_000_000        # auto cell size aims for roughly this many cells
 HARD_MAX_CELLS = 16_000_000     # abort above this (see GridSizeError message)
 MIN_CELL_UM = 25.0              # clamp for auto cell size
@@ -51,10 +50,13 @@ CONTACT_MODEL = "uniform"       # "uniform": conductor pressed on top injects
                                 # (J ramps across the contact); "equipotential":
                                 # ideal bonded lug (Dirichlet). The two bracket
                                 # a real contact: R_equi <= R_real <= R_uniform.
-SPSOLVE_MAX_UNKNOWNS = 2_500_000  # above this, use CG (Jacobi) instead of
-                                  # direct solve (measured: direct is ~14x
-                                  # faster at 1.7M unknowns, ~3 GB peak)
-CG_TOL = 1e-8
+SPSOLVE_MAX_UNKNOWNS = 500_000  # above this, AMG-preconditioned CG (pyamg;
+                                # Jacobi-CG if pyamg is missing). Direct is
+                                # exact and fastest for small grids; measured
+                                # at 1.4M unknowns: spsolve 13 s / ~3 GB,
+                                # AMG-CG 6 s at a fraction of the memory
+AMG_TOL = 1e-10                 # relative residual of the AMG-CG solve
+CG_TOL = 1e-8                   # Jacobi-CG fallback (no pyamg)
 CG_MAXITER = 50_000             # CG iterations are cheap; large grids need many
 
 # --- Geometry ---

fill_resistance/raster.py

@@ -18,6 +18,7 @@ from dataclasses import dataclass
 
 import numpy as np
 from matplotlib.path import Path as MplPath
+from PIL import Image, ImageDraw
 from scipy import ndimage
 
 from . import config
@@ -106,7 +107,10 @@ def choose_cell_size(bbox_nm: tuple[int, int, int, int], nlayers: int) -> float:
 def _paint_ring(stack: RasterStack, ring: np.ndarray, value: bool,
                 target: np.ndarray) -> None:
     """Set target (2D) cells whose center lies inside ring to `value`,
-    testing only cells within the ring's bbox (cheap for small holes)."""
+    working only within the ring's bbox. Hybrid rasterizer: PIL scanline
+    fill for the bulk (fast, O(vertices + cells)), then the cells within
+    a ~2 px band around the ring edge are re-tested exactly against the
+    polygon, so the result is identical to a pure center-in-polygon pass."""
     ny, nx = stack.shape2d
     h = stack.h_nm
     j0 = max(0, int((ring[:, 0].min() - stack.x0_nm) / h) - 1)
@@ -115,13 +119,35 @@ def _paint_ring(stack: RasterStack, ring: np.ndarray, value: bool,
     i1 = min(ny, int((ring[:, 1].max() - stack.y0_nm) / h) + 2)
     if i0 >= i1 or j0 >= j1:
         return
-    xg, yg = stack.cell_centers(i0, i1, j0, j1)
-    pts = np.column_stack([xg.ravel(), yg.ravel()])
-    # Path(closed=True) treats the LAST vertex as the CLOSEPOLY dummy, so
-    # the first vertex must be appended or the ring loses its last corner
-    verts = np.vstack([ring, ring[:1]])
-    inside = MplPath(verts, closed=True).contains_points(pts)
-    inside = inside.reshape(i1 - i0, j1 - j0)
+    w, ht = j1 - j0, i1 - i0
+
+    # cell (i, j) center <-> pixel (j - j0, i - i0)
+    px = (ring[:, 0] - stack.x0_nm) / h - 0.5 - j0
+    py = (ring[:, 1] - stack.y0_nm) / h - 0.5 - i0
+    pts = list(zip(px.tolist(), py.tolist()))
+
+    inside = np.zeros((ht, w), dtype=bool)
+    band = np.ones((ht, w), dtype=bool)
+    if len(pts) >= 3:
+        fill_img = Image.new("1", (w, ht), 0)
+        ImageDraw.Draw(fill_img).polygon(pts, fill=1)
+        inside = np.array(fill_img, dtype=bool)
+        band_img = Image.new("1", (w, ht), 0)
+        ImageDraw.Draw(band_img).line(pts + pts[:1], fill=1, width=5,
+                                      joint="curve")
+        band = np.array(band_img, dtype=bool)
+
+    bi, bj = np.nonzero(band)
+    if len(bi):
+        xs = stack.x0_nm + (bj + j0 + 0.5) * h
+        ys = stack.y0_nm + (bi + i0 + 0.5) * h
+        # Path(closed=True) treats the LAST vertex as the CLOSEPOLY dummy,
+        # so the first vertex must be appended or the ring loses its last
+        # corner
+        verts = np.vstack([ring, ring[:1]])
+        inside[bi, bj] = MplPath(verts, closed=True).contains_points(
+            np.column_stack([xs, ys]))
+
     sub = target[i0:i1, j0:j1]
     sub[inside] = value
 

fill_resistance/solver.py

@@ -293,10 +293,40 @@ def solve_system(A: sparse.csr_matrix, b: np.ndarray) -> tuple[np.ndarray, Solve
     if n <= config.SPSOLVE_MAX_UNKNOWNS:
         x = sla.spsolve(A.tocsc(), b)
         return x, SolveInfo(method="spsolve", n_unknowns=n)
+    try:
+        return _solve_amg(A, b)
+    except ImportError:
+        print("note: pyamg not installed - falling back to Jacobi-CG "
+              "(much slower on large grids)")
+        return _solve_cg_jacobi(A, b)
 
+
+def _solve_amg(A: sparse.csr_matrix, b: np.ndarray) -> tuple[np.ndarray, SolveInfo]:
+    """CG preconditioned with smoothed-aggregation AMG: near-linear
+    scaling on these 2D Laplacians and a fraction of spsolve's memory."""
+    import pyamg
+
+    n = A.shape[0]
+    ml = pyamg.smoothed_aggregation_solver(A.tocsr(), max_coarse=500)
+    residuals: list[float] = []
+    x = ml.solve(b, tol=config.AMG_TOL, maxiter=300, accel="cg",
+                 residuals=residuals)
+    res = float(np.linalg.norm(b - A @ x) / max(np.linalg.norm(b), 1e-300))
+    if not np.isfinite(res) or res > 1e-6:
+        raise SolverError(
+            f"AMG-CG did not converge (residual {res:.2e}). Try a "
+            f"different grid size, or force the direct solver by raising "
+            f"SPSOLVE_MAX_UNKNOWNS in config.py."
+        )
+    return x, SolveInfo(method="amg+cg", n_unknowns=n,
+                        iterations=max(len(residuals) - 1, 0), residual=res)
+
+
+def _solve_cg_jacobi(A: sparse.csr_matrix, b: np.ndarray) -> tuple[np.ndarray, SolveInfo]:
     # The matrix is SPD, so CG is guaranteed to converge. Jacobi is the
     # only preconditioner in scipy that keeps the preconditioned operator
     # SPD without a factorization that can break down at this scale.
+    n = A.shape[0]
     d = A.diagonal()
     M = sla.LinearOperator((n, n), lambda v: v / d)
     iters = 0

fill_resistance/standalone.py

@@ -42,7 +42,8 @@ def main(argv=None) -> int:
     ap.add_argument("--extra-cu-um", type=float, default=None,
                     help="override the added copper in mask openings [um]")
     ap.add_argument("--force-iterative", action="store_true",
-                    help="use CG (Jacobi) regardless of problem size")
+                    help="use the iterative solver (AMG-CG, or Jacobi-CG "
+                         "without pyamg) regardless of problem size")
     args = ap.parse_args(argv)
 
     if args.cell_um is not None:

metadata.json

@@ -2,7 +2,7 @@
     "$schema": "https://go.kicad.org/pcm/schemas/v2",
     "name": "Fill Resistance",
     "description": "DC/AC resistance of copper zone fills between two contacts, single- or multi-layer with via coupling, with current and power density maps.",
-    "description_full": "Computes the DC or AC resistance of copper zone fills between two contacts (marker rectangles on User.1/User.2 and/or selected pads), single- or multi-layer: the chosen net's fills are solved as coupled finite-difference sheets linked by the net's via and through-hole-pad barrels.\n\nShows per-layer rasterized maps, potential, current density and power density, reports per-via currents (via ampacity) and total dissipation at a selectable test current. At a user-set frequency the exact 1D foil/barrel skin-effect correction is applied (AC results are a rigorous lower bound). PNGs, a text summary and a re-solvable geometry dump are saved per run.\n\nNote: the first load builds the plugin's Python environment (numpy, scipy, matplotlib, PySide6) and can take several minutes.",
+    "description_full": "Computes the DC or AC resistance of copper zone fills between two contacts (marker rectangles on User.1/User.2 and/or selected pads), single- or multi-layer: the chosen net's fills are solved as coupled finite-difference sheets linked by the net's via and through-hole-pad barrels.\n\nShows per-layer rasterized maps, potential, current density and power density, reports per-via currents (via ampacity) and total dissipation at a selectable test current. At a user-set frequency the exact 1D foil/barrel skin-effect correction is applied (AC results are a rigorous lower bound). PNGs, a text summary and a re-solvable geometry dump are saved per run.\n\nNote: the first load builds the plugin's Python environment (numpy, scipy, pyamg, matplotlib, PySide6) and can take several minutes.",
     "identifier": "th.co.b4l.fill-resistance",
     "type": "plugin",
     "author": {

requirements.txt

@@ -1,5 +1,6 @@
 kicad-python>=0.7.0
 numpy
 scipy
+pyamg
 matplotlib
 PySide6

tests/test_raster.py

@@ -22,6 +22,35 @@ def test_exact_cell_count_square_with_hole():
     assert int(stack.masks[0].sum()) == 100 - 16
 
 
+def test_hybrid_raster_matches_exact_point_test():
+    """The PIL-fill + exact-edge-band rasterizer must be cell-for-cell
+    identical to a pure center-in-polygon pass, including awkward
+    fractional offsets, concave lobes and a hole."""
+    from matplotlib.path import Path as MplPath
+    ang = np.linspace(0, 2 * np.pi, 257, endpoint=False)
+    r = 7.3 + 1.7 * np.sin(5 * ang) + 0.9 * np.cos(9 * ang + 0.4)
+    blob = np.stack([20.05 + r * np.cos(ang), 20.13 + r * np.sin(ang)],
+                    axis=1)
+    hole = np.stack([20.4 + 2.1 * np.cos(ang), 19.8 + 2.2 * np.sin(ang)],
+                    axis=1)
+    p = make_problem([(blob.tolist(), [hole.tolist()])],
+                     rect1_mm=(14, 19, 16, 21), rect2_mm=(24, 19, 26, 21))
+    stack = _stack(p, 0.25)
+
+    ny, nx = stack.shape2d
+    xg, yg = stack.cell_centers(0, ny, 0, nx)
+    pts = np.column_stack([xg.ravel(), yg.ravel()])
+
+    def exact(ring):
+        verts = np.vstack([ring, ring[:1]])
+        return MplPath(verts, closed=True).contains_points(pts).reshape(
+            ny, nx)
+
+    poly = p.layers[0].polygons[0]
+    ref = exact(poly.outline) & ~exact(poly.holes[0])
+    assert np.array_equal(stack.masks[0], ref)
+
+
 def test_margin_cells_are_empty():
     p = strip_problem()
     stack = _stack(p, 0.5)

tests/test_solver.py

@@ -79,10 +79,21 @@ def test_hole_increases_resistance_and_converges():
     assert abs(r_a.R_ohm - r_b.R_ohm) < 0.01 * r_b.R_ohm
 
 
-def test_cg_path_matches_direct(monkeypatch):
+def test_iterative_paths_match_direct(monkeypatch):
+    """AMG-CG (default iterative) and Jacobi-CG (pyamg-missing fallback)
+    both reproduce the direct solve."""
     p = strip_problem(length=50, width=10, e_len=5)
     r_direct, _ = _solve(p, 0.25)
     monkeypatch.setattr(config, "SPSOLVE_MAX_UNKNOWNS", 0)
+    r_amg, _ = _solve(p, 0.25)
+    assert r_amg.solve_info.method == "amg+cg"
+    assert r_amg.R_ohm == pytest.approx(r_direct.R_ohm, rel=1e-6)
+    assert r_amg.mismatch_rel < 1e-5
+
+    def no_pyamg(A, b):
+        raise ImportError("pyamg unavailable")
+
+    monkeypatch.setattr(solver, "_solve_amg", no_pyamg)
     r_cg, _ = _solve(p, 0.25)
     assert r_cg.solve_info.method == "cg+jacobi"
     assert r_cg.R_ohm == pytest.approx(r_direct.R_ohm, rel=1e-6)