janik
1a28f2a593
Two display fixes for the adaptive grid, from field feedback: - Equipotential contours showed leaf-sized staircase corners on plane interiors: the potential is now expanded piecewise-LINEARLY from each leaf's reconstructed gradient instead of constant-per-leaf, and the default ADAPTIVE_MAX_CELL_UM drops 2 mm -> 1 mm (interior leaves beyond that buy almost nothing). - The raster map now overlays the adaptive mesh: boundaries of coarse leaves draw in darker copper (fine regions stay plain = fully resolved), with a legend entry. Uniform-grid runs are unchanged. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
206 lines
9.0 KiB
Python
206 lines
9.0 KiB
Python
"""Adaptive solve path (phase 2): full run_solve equivalence against the
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uniform grid across the feature set. On piecewise-linear fields (strips)
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the leaf system is EXACT, so those compare at solver precision."""
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import numpy as np
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import pytest
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from fill_resistance import config, raster, solver
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from fill_resistance.geometry import Electrode, LayerFill, Polygon, Problem, \
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TrackSeg
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from tests.test_capping import _two_layer
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from tests.util import NM, make_multilayer, make_problem, rect_mm, \
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strip_problem
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def _run(problem, h_mm, model="equipotential", adaptive=False,
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monkeypatch=None, parts=False, freq=0.0):
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if monkeypatch is not None:
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monkeypatch.setattr(config, "ADAPTIVE_CELLS", adaptive)
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stack = raster.rasterize_stack(problem, h_mm * NM)
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e1, e2 = raster.electrode_masks(stack, problem)
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kw = {}
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if parts:
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p1, p2 = raster.electrode_partition(stack, problem)
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kw = dict(parts1=p1, parts2=p2)
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return solver.run_solve(problem, stack, e1, e2, 1.0, freq,
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contact_model=model, **kw)
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def test_strip_close_both_models(monkeypatch):
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"""Uniform strip, both contact models. The raw interface flux error
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(~1.7% low here, the worst case) is removed by the default deferred-
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correction pass; the corrected currents keep the power identity."""
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for model in ("equipotential", "uniform"):
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p = strip_problem(length=50, width=10, e_len=5)
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ref = _run(p, 0.25, model, adaptive=False, monkeypatch=monkeypatch)
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p2 = strip_problem(length=50, width=10, e_len=5)
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ada = _run(p2, 0.25, model, adaptive=True, monkeypatch=monkeypatch)
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=2e-3), model
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assert ada.n_free < ref.n_free
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assert ada.power_balance_rel < 1e-9
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def test_correction_passes_remove_bias(monkeypatch):
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"""0 passes shows the raw coarse-fine bias; the default single pass
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removes it by more than an order of magnitude."""
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p = strip_problem(length=50, width=10, e_len=5)
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ref = _run(p, 0.25, adaptive=False, monkeypatch=monkeypatch)
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monkeypatch.setattr(config, "ADAPTIVE_CORRECTION_PASSES", 0)
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raw = _run(strip_problem(length=50, width=10, e_len=5), 0.25,
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adaptive=True, monkeypatch=monkeypatch)
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err_raw = abs(raw.R_ohm / ref.R_ohm - 1)
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assert err_raw > 5e-3 # bias is real without it
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monkeypatch.setattr(config, "ADAPTIVE_CORRECTION_PASSES", 1)
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fix = _run(strip_problem(length=50, width=10, e_len=5), 0.25,
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adaptive=True, monkeypatch=monkeypatch)
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err_fix = abs(fix.R_ohm / ref.R_ohm - 1)
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assert err_fix < err_raw / 10
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assert err_fix < 1e-3
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def test_plate_with_holes_close(monkeypatch):
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holes = []
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for i in range(5):
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for j in range(5):
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x, y = 8 * i + 3, 8 * j + 3
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holes.append([(x, y), (x + 1, y), (x + 1, y + 1), (x, y + 1)])
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outline = [(0, 0), (40, 0), (40, 40), (0, 40)]
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def prob():
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return make_problem([(outline, holes)], rect1_mm=(0, 15, 2, 25),
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rect2_mm=(38, 15, 40, 25))
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ref = _run(prob(), 0.1, adaptive=False, monkeypatch=monkeypatch)
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ada = _run(prob(), 0.1, adaptive=True, monkeypatch=monkeypatch)
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assert ada.n_free < 0.5 * ref.n_free
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=1e-3)
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def test_via_chain_exact(monkeypatch):
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"""1-cell strips + via: everything is keep-fine or boundary, so the
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adaptive path must reproduce the exact discrete solution."""
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STRIP = [(0, 0), (10, 0), (10, 1), (0, 1)]
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def prob():
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return make_multilayer(
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[[(STRIP, [])], [(STRIP, [])]],
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rect1_mm=(0, 0, 1, 1), rect2_mm=(9, 0, 10, 1),
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contact1="L0", contact2="L1",
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vias_mm=[(5.5, 0.5)], gap_mm=1.0)
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ref = _run(prob(), 1.0, adaptive=False, monkeypatch=monkeypatch)
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ada = _run(prob(), 1.0, adaptive=True, monkeypatch=monkeypatch)
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=1e-9)
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assert len(ada.via_reports) == 1
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assert ada.via_reports[0].current_a == pytest.approx(1.0, rel=1e-9)
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def test_1d_trace_bridge(monkeypatch):
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"""Sub-resolution trace bridging two pours: chain cells are pinned
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fine, pours coarsen; R matches the uniform grid closely."""
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pour1 = [(0, 0), (30, 0), (30, 30), (0, 30)]
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pour2 = [(50, 0), (80, 0), (80, 30), (50, 30)]
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def prob():
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seg = TrackSeg(layer_name="F.Cu",
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points=np.array([[15 * NM, 15 * NM],
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[65 * NM, 15 * NM]], dtype=np.int64),
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width_nm=int(0.2 * NM))
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return Problem(
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board_path="synthetic", net_name="TEST", rho_ohm_m=1.68e-8,
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plating_nm=18_000,
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layers=[LayerFill(
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layer_name="F.Cu", thickness_nm=70_000, z_nm=0,
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polygons=[Polygon(outline=(np.array(pour1) * NM
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).astype(np.int64)),
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Polygon(outline=(np.array(pour2) * NM
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).astype(np.int64))])],
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vias=[],
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electrodes1=[Electrode(rect=rect_mm((0, 10, 2, 20)))],
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electrodes2=[Electrode(rect=rect_mm((78, 10, 80, 20)))],
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tracks=[seg])
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ref = _run(prob(), 0.5, adaptive=False, monkeypatch=monkeypatch)
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ada = _run(prob(), 0.5, adaptive=True, monkeypatch=monkeypatch)
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# max leaf = 1 mm = 2 cells at h = 0.5, so the coarsening is modest
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assert ada.n_free < 0.7 * ref.n_free
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=2e-3)
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def test_buildup_close_on_strip(monkeypatch):
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"""Half-coverage buildup strip: buildup cells are pinned fine; the
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plain half's interface bias is removed by the correction pass."""
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from tests.test_buildup import _with_buildup
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def prob():
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return _with_buildup(strip_problem(length=50, width=10, e_len=5),
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[[(25, 0), (50, 0), (50, 10), (25, 10)]])
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ref = _run(prob(), 0.5, adaptive=False, monkeypatch=monkeypatch)
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ada = _run(prob(), 0.5, adaptive=True, monkeypatch=monkeypatch)
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=2e-3)
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def test_capped_via_close(monkeypatch):
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"""Ring + thin-cap mouth (thick_scale) under the adaptive grid."""
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ref = _run(_two_layer(drill_mm=2.0, pad_mm=2.6), 0.25,
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adaptive=False, monkeypatch=monkeypatch)
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ada = _run(_two_layer(drill_mm=2.0, pad_mm=2.6), 0.25,
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adaptive=True, monkeypatch=monkeypatch)
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=2e-3)
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def test_part_currents_and_ac(monkeypatch):
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"""Per-part currents and the AC path work on leaves."""
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p = strip_problem(length=50, width=10, e_len=5)
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ref = _run(p, 0.5, adaptive=False, monkeypatch=monkeypatch,
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parts=True, freq=2e6)
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p2 = strip_problem(length=50, width=10, e_len=5)
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ada = _run(p2, 0.5, adaptive=True, monkeypatch=monkeypatch,
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parts=True, freq=2e6)
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assert ada.R_ohm == pytest.approx(ref.R_ohm, rel=2e-3)
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assert ada.part_currents1[0][1] == pytest.approx(
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ref.part_currents1[0][1], rel=1e-9) # single part = full current
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assert ada.rs_ratios == ref.rs_ratios
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def test_auto_cell_size_finer_with_adaptive(monkeypatch):
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"""The auto sizer affords a larger fine-cell budget (finer h) when
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the adaptive grid is on."""
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from fill_resistance import raster
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p = make_problem([([(0, 0), (300, 0), (300, 200), (0, 200)], [])],
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rect1_mm=(0, 90, 2, 110), rect2_mm=(298, 90, 300, 110))
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monkeypatch.setattr(config, "ADAPTIVE_CELLS", False)
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h_uniform = raster.choose_cell_size(p.copper_bbox(), 1)
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monkeypatch.setattr(config, "ADAPTIVE_CELLS", True)
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h_adaptive = raster.choose_cell_size(p.copper_bbox(), 1)
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assert h_adaptive < h_uniform
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assert h_adaptive == pytest.approx(
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h_uniform / (config.TARGET_CELLS_ADAPTIVE
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/ config.TARGET_CELLS) ** 0.5, rel=1e-9)
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def test_max_cell_size_respected(monkeypatch):
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from fill_resistance.adaptive import _max_block
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assert _max_block(100_000.0) == 8 # 1000 um / 100 um cells
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monkeypatch.setattr(config, "ADAPTIVE_MAX_CELL_UM", 250.0)
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assert _max_block(100_000.0) == 2
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monkeypatch.setattr(config, "ADAPTIVE_MAX_CELL_UM", 50.0)
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assert _max_block(100_000.0) == 1 # never below the fine cell
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def test_potential_expansion_is_smooth(monkeypatch):
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"""The potential map is expanded piecewise-linearly from the leaf
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gradients: on a strip it must track the uniform-grid potential to a
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small fraction of the total span (constant-per-leaf expansion would
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show leaf-sized steps of ~1-2% of the span)."""
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p = strip_problem(length=50, width=10, e_len=5)
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ref = _run(p, 0.25, adaptive=False, monkeypatch=monkeypatch)
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p2 = strip_problem(length=50, width=10, e_len=5)
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ada = _run(p2, 0.25, adaptive=True, monkeypatch=monkeypatch)
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span = np.nanmax(ref.V)
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both = np.isfinite(ref.V) & np.isfinite(ada.V)
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dev = np.abs(ada.V[both] - ref.V[both]).max()
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assert dev < 0.005 * span |