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test: adopt native-validated 24-target probe layout

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Horizontal y=56 and vertical x=38 through box 1, normal coordinates
[12.5..26.125] (root128 physical). Nearest-ACTIVE mapping with
deterministic tie rule (distance, finer level, smaller j, smaller i),
duplicate-cell rejection, stored centers and displacements. Replaces my
earlier offsets, which mapped two target pairs to the same U128 cell.
U512 spacing 128/512 and root step r -> U512 step 4r unchanged.
Tests: 24 unique cells per case, nested 3 L0 + 5 L1 + 4 L2 per transect,
U128 max displacement 0.625.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
ClaudeandClaude Opus 5.5 committed 2026-10-19 03:13:43 +00:00
1 parent 1e188e7419
commit caa573f978
4 files changed
+63 -42
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tests/validation/taylor_green/shear_probes.py
+38 -28
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@@ -1,12 +1,14 @@
"""Signed interface-probe traces for the nested/twin shear presets (mechanism diagnostic).
No pass gates. 24 fixed physical points on two interface-normal transects, away
from corners, crossing L0->L1 and (nested only) L1->L2:
V: y = 64.1, across the vertical faces x = 16 (L0/L1) and x = 22 (L1/L2) of box 1;
H: x = 90.1, across the horizontal faces y = 24 (L0/L1) and y = 30 (L1/L2) of box 2.
Each point is sampled at the ACTIVE leaf cell containing it, so cases with
different grids (U128, U512, nested, twin) give different cell centers: the
actual center and level are stored and analytic fields are evaluated there.
No pass gates. Native-validated 24-target layout through box 1, in root-128
physical coordinates, crossing L0->L1 and (nested only) L1->L2 away from corners:
horizontal line y = 56 across the vertical faces x = 16 and x = 22;
vertical line x = 38 across the horizontal faces y = 16 and y = 22;
normal coordinates NORMAL_COORDS on both (nested: 3 L0 + 5 L1 + 4 L2 each).
Each target maps to the nearest ACTIVE leaf center (tie rule: distance, then
finer level, then smaller j, then smaller i). Grids differ between cases
(U128, U512, nested, twin), so actual centers, levels and displacements are
stored and analytic fields are evaluated at the actual centers.
Samples are POST-collision G at root synchronization only (root-step units);
no substep content is observed, and grid-scale alternation can alias.
"""
@@ -19,9 +21,10 @@ import shear_wave as sw
from oracle import CX, CY, U
ROOT_N = 128
OFFSETS = (-3.9, -1.9, -0.4, 0.1, 0.6, 2.1, 4.1, 5.6, 6.1, 6.6, 8.1, 10.1) # from outer face
TRANSECTS = {"V": dict(axis=0, face=16.0, other=64.1), # x varies, y fixed
"H": dict(axis=1, face=24.0, other=90.1)} # y varies, x fixed
NORMAL_COORDS = (12.5, 14.5, 15.5, 16.25, 17.25, 19.25, 20.75, 21.75,
22.125, 23.125, 24.125, 26.125)
TRANSECTS = {"horizontal_y56": dict(axis=0, other=56.0), # x varies, y fixed
"vertical_x38": dict(axis=1, other=38.0)} # y varies, x fixed
STEPS = tuple(range(0, 129)) + tuple(range(1920, 2049)) # root steps, both inclusive
TRACE_MAX_BYTES = 600_000
@@ -29,10 +32,9 @@ TRACE_MAX_BYTES = 600_000
def points():
out = []
for name, t in TRANSECTS.items():
for offset in OFFSETS:
along = t["face"] + offset
for along in NORMAL_COORDS:
xy = (along, t["other"]) if t["axis"] == 0 else (t["other"], along)
out.append((name, offset, xy))
out.append((name, along, xy))
return out
@@ -42,25 +44,33 @@ def case_steps(n):
def select(masks, n=ROOT_N):
"""Leaf cell containing each point: finest level whose ACTIVE mask covers it.
"""Nearest ACTIVE leaf center for each target (deterministic tie rule).
Points and stored centers are in root-physical units (root cell = 1); a case
with root N = n has cells of size ROOT_N / (n * 2**level) there.
Targets and stored centers are in root-physical units (root cell = 1); a case
with root N = n has cell size h = ROOT_N / (n * 2**level) there (U512: 0.25).
"""
if n % ROOT_N:
raise ValueError("case root N must be a multiple of 128")
probes = []
for name, offset, (x, y) in points():
for level in reversed(range(len(masks))):
scale = (n // ROOT_N) * 2 ** level
i, j = int(math.floor(x * scale)), int(math.floor(y * scale))
if masks[level][j, i]:
h = 1.0 / scale
probes.append(dict(transect=name, offset=offset, point=[x, y], level=level,
i=i, j=j, center=[h * (i + 0.5), h * (j + 0.5)]))
break
else:
raise ValueError(f"point {(x, y)} is not covered by an ACTIVE leaf")
for name, along, (x, y) in points():
best = None
for level, mask in enumerate(masks):
h = ROOT_N / (n * 2 ** level)
i0, j0 = int(math.floor(x / h)), int(math.floor(y / h))
for j in range(j0 - 2, j0 + 3):
for i in range(i0 - 2, i0 + 3):
if 0 <= j < mask.shape[0] and 0 <= i < mask.shape[1] and mask[j, i]:
cx, cy = h * (i + 0.5), h * (j + 0.5)
key = (math.hypot(cx - x, cy - y), -level, j, i)
if best is None or key < best[0]:
best = (key, level, i, j, cx, cy)
if best is None:
raise ValueError(f"no ACTIVE leaf near target {(x, y)}")
_, level, i, j, cx, cy = best
probes.append(dict(transect=name, normal=along, point=[x, y], level=level, i=i, j=j,
center=[cx, cy], displacement=[cx - x, cy - y]))
if len({(p["level"], p["i"], p["j"]) for p in probes}) != len(probes):
raise ValueError("probe targets map to duplicate cells")
return probes
@@ -102,7 +112,7 @@ def validate(trace):
import json
if trace.get("steps") != list(STEPS) or len(trace["values"]) != len(STEPS):
raise ValueError("trace steps differ from the fixed schedule")
if len(trace["probes"]) != len(OFFSETS) * len(TRANSECTS):
if len(trace["probes"]) != len(NORMAL_COORDS) * len(TRANSECTS):
raise ValueError("unexpected probe count")
width = 3 * len(trace["probes"])
if any(len(r) != width or not all(math.isfinite(v) for v in r) for r in trace["values"]):