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Preserve bounded validation evidence and historical failures

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Frank14f
2026-10-15 10:13:51 +08:00
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research/fine_surface_20261014/baseline.json
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}
research/fine_surface_20261014/cpu_device_guard_cmd.txt
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PYTHONPATH=/home/frank14f/CelerisLab/src CUDA_VISIBLE_DEVICES='' /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python -m pytest tests/validation/multigrid/test_fine_surface_gpu.py::test_missing_surface_device_buffer_rejects_before_launch -q
research/fine_surface_20261014/cpu_device_guard_exit.txt
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0
research/fine_surface_20261014/cpu_device_guard_stdout.txt
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.. [100%]
2 passed in 0.09s
research/fine_surface_20261014/cpu_existing_runs.json
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[
{
"name": "cpu_maps",
"command": [
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-m",
"pytest",
"-q",
"-p",
"no:cacheprovider",
"tests/unit/test_refinement.py"
],
"exit_code": 0,
"seconds": 1.8799934879643843
},
{
"name": "cpu_shared_kernel",
"command": [
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-m",
"pytest",
"-q",
"-p",
"no:cacheprovider",
"tests/unit/test_numerical_operators.py::test_actual_semantic_and_auxiliary_kernels[0-0-0]"
],
"exit_code": 0,
"seconds": 2.1572919980389997
}
]
research/fine_surface_20261014/cpu_final_run.json
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{
"command": [
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-m",
"pytest",
"-q",
"-p",
"no:cacheprovider",
"tests/unit/test_fine_surface.py"
],
"exit_code": 0,
"seconds": 1.947228622972034,
"note": "Final CPU recheck after preflight aperture inputs were aligned with packed FP32 lever arms. No kernel/math or uploaded-array changes."
}
research/fine_surface_20261014/cpu_final_stdout.txt
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............... [100%]
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{
"command": [
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-m",
"pytest",
"-q",
"-p",
"no:cacheprovider",
"tests/validation/multigrid/test_refinement_followup.py::FaultGuardsCPU",
"tests/validation/multigrid/test_refinement_gpu.py::CoupledGPU::test_cleanup_attempts_every_allocation"
],
"exit_code": 0,
"seconds": 1.620646678027697
}
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{
"command": [
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-m",
"pytest",
"-q",
"-p",
"no:cacheprovider",
"tests/validation/multigrid/test_fine_surface_gpu.py::test_cross_interface_donor_rejects_before_cuda"
],
"exit_code": 0,
"seconds": 1.665132363094017,
"CUDA_VISIBLE_DEVICES": ""
}
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research/fine_surface_20261014/cpu_surface_command.txt
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PYTHONPATH=src /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python -m pytest tests/unit/test_fine_surface.py -q
research/fine_surface_20261014/cpu_surface_exit.txt
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PYTHONPATH=src /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python -m pytest tests/unit/test_fine_surface.py -q -k 'projection_uses_cell_center or transfer_dependencies'
research/fine_surface_20261014/cpu_surface_projection_exit.txt
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3 passed, 12 deselected in 0.40s
research/fine_surface_20261014/cpu_surface_stdout.txt
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15 passed in 0.59s
research/fine_surface_20261014/gpu_preparation_run1.py
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"""CPU preparation for one fixed D2Q9 2:1 patch and optional interior circles.
Cell indices use y*nx+x; population slots use q*(nx*ny)+cell, matching
core/layout.cuh. Bounds are half-open (x0, y0, x1, y1) in coarse cells.
The four fine halo cells implement the pinned CPUREF volumetric geometry.
Base topology is independent of boundaries; prepare_fine_boundary checks them.
"""
from dataclasses import dataclass, replace
from numbers import Integral
import numpy as np
from ..config import LBMConfig
from .descriptors import D2Q9_EX, D2Q9_EY, FLUID, SOLID
ACTIVE, INNER_GHOST, OUTER_GHOST = 0, 1, 2
HALO = 4
def _readonly(values, dtype):
array = np.ascontiguousarray(values, dtype=dtype)
array.setflags(write=False)
return array
@dataclass(frozen=True, eq=False)
class FixedPatch2D:
"""Layer metadata and compact exchange tables, constructed by from_config.
Shapes are (ny, nx), including the fine halo. ``fine_origin`` is its lower
cell edge in coarse physical units, with coarse dx=dt=1 and fine dx=dt=.5.
``ghost_fine`` and ``ghost_coarse`` pair fine cells with coarse donors.
``coalesce_coarse`` contains incoming population slots; each corresponding
row of ``coalesce_fine`` contains four fine slots in (dy, dx) row order.
Weights are all 1/4. Closed corner links are included even when the coarse
donor is uncovered. These tables contain no DDF data and allocate no GPU.
This is preparation, not permission to advance these levels with OneStep:
its channel boundaries and fused collision do not implement patch coupling.
"""
coarse_shape: tuple[int, int]
bounds: tuple[int, int, int, int]
tau_coarse: float
tau_fine: float
fine_kind: np.ndarray
ghost_fine: np.ndarray
ghost_coarse: np.ndarray
coalesce_coarse: np.ndarray
coalesce_fine: np.ndarray
@classmethod
def from_config(cls, cfg: LBMConfig, bounds) -> "FixedPatch2D":
"""Prepare maps without changing cfg or allocating coarse-sized tables.
The initial backend contract is D2Q9/SRT/FP32/unshifted/double_buffer,
without LES. Two coarse cells of margin keep all halo donors in bounds;
this does NOT certify that those donors are fluid or periodic.
"""
checked = replace(cfg)
checked.validate()
if (not checked.is_d2q9 or checked.collision != "SRT"
or checked.streaming != "double_buffer"
or checked.store_precision != "FP32"
or checked.ddf_shifting or checked.les_enabled):
raise ValueError("FixedPatch2D requires D2Q9/SRT/FP32/unshifted/double_buffer without LES")
bounds = tuple(bounds)
if len(bounds) != 4 or any(isinstance(v, (bool, np.bool_))
or not isinstance(v, Integral) for v in bounds):
raise ValueError("bounds must be four integers: (x0, y0, x1, y1)")
x0, y0, x1, y1 = bounds = tuple(map(int, bounds))
nx, ny = checked.nx, checked.ny
if not (2 <= x0 < x1 <= nx - 2 and 2 <= y0 < y1 <= ny - 2):
raise ValueError("patch must be nonempty with a two-coarse-cell margin")
fx, fy = 2 * (x1 - x0) + 2 * HALO, 2 * (y1 - y0) + 2 * HALO
if max(nx * ny, fx * fy) > np.iinfo(np.uint32).max:
raise ValueError("layer cell counts exceed uint32 indexing")
tau_c = 3.0 * checked.viscosity + 0.5
tau_f = 6.0 * checked.viscosity + 0.5
if not checked.omega_min <= 1.0 / tau_f <= checked.omega_max:
raise ValueError("fine relaxation rate lies outside configured omega guards")
kind = np.full((fy, fx), OUTER_GHOST, dtype=np.uint8)
kind[2:-2, 2:-2] = INNER_GHOST
kind[HALO:-HALO, HALO:-HALO] = ACTIVE
gy, gx = np.nonzero(kind != ACTIVE)
ghost_fine = gy * fx + gx
ghost_coarse = (y0 + (gy - HALO) // 2) * nx + x0 + (gx - HALO) // 2
targets, sources = [], []
# Only the outside ring can receive fine-to-coarse populations.
# Integer doubled midpoints include all eight diagonal corner passes.
for y in range(y0 - 1, y1 + 1):
xs = range(x0 - 1, x1 + 1) if y in (y0 - 1, y1) else (x0 - 1, x1)
for x in xs:
for q in range(1, 9):
mx, my = 2 * x + 1 - D2Q9_EX[q], 2 * y + 1 - D2Q9_EY[q]
if 2 * x0 <= mx <= 2 * x1 and 2 * y0 <= my <= 2 * y1:
targets.append(q * nx * ny + y * nx + x)
a, b = HALO + 2 * (x - x0), HALO + 2 * (y - y0)
sources.append([q * fx * fy + (b + dy) * fx + a + dx
for dy in (0, 1) for dx in (0, 1)])
return cls((ny, nx), bounds, tau_c, tau_f,
_readonly(kind, np.uint8),
_readonly(ghost_fine, np.uint32),
_readonly(ghost_coarse, np.uint32),
_readonly(targets, np.uint64),
_readonly(sources, np.uint64).reshape(-1, 4))
@property
def fine_shape(self) -> tuple[int, int]:
return self.fine_kind.shape
@property
def fine_origin(self) -> tuple[float, float]:
return self.bounds[0] - HALO / 2, self.bounds[1] - HALO / 2
@property
def table_bytes(self) -> int:
return sum(a.nbytes for a in (self.fine_kind, self.ghost_fine,
self.ghost_coarse, self.coalesce_coarse,
self.coalesce_fine))
def check_synchronization(self, coarse_steps: int, fine_steps: int, *, phase: str) -> None:
"""Check a proposed backend handoff; does not convert or collide data.
Production storage is g=C(f), while CPUREF returns arrival_f. A future
g-to-g coarse step must collide coarse cells once and fine cells twice;
fine halo ghosts never collide. Coalescence must precede coarse collision.
Converting reference f to g for comparison is an observation operation,
not an additional production collision. Counts here are completed steps.
"""
if phase != "postcollision_g":
raise ValueError("patch backend handoff requires postcollision_g")
for value in (coarse_steps, fine_steps):
if isinstance(value, (bool, np.bool_)) or not isinstance(value, Integral) or value < 0:
raise ValueError("step counts must be nonnegative integers")
if fine_steps != 2 * coarse_steps:
raise ValueError("levels are not synchronized: fine_steps must equal 2*coarse_steps")
@dataclass(frozen=True, eq=False)
class FineBoundary2D:
"""Fixed local projection plus auditable, finite transfer dependency sets.
transfer_reads rows are (hop, direction, cell), traced from coalescence at
hop 2 back to exploded G at hop 0. No previous coarse interval is traversed.
Cell footprints also include the whole explosion write/donor tables.
"""
objects: tuple
flags: np.ndarray
curved_host: tuple
boundary_cells: np.ndarray
transfer_fine_cells: np.ndarray
transfer_coarse_cells: np.ndarray
transfer_reads: np.ndarray
def _transfer_footprint(patch):
ny, nx = patch.fine_shape
cells = nx * ny
pending = [(2, *divmod(int(slot), cells)) for slot in patch.coalesce_fine.ravel()]
reads = set()
while pending:
hop, q, k = pending.pop()
if (hop, q, k) in reads:
continue
reads.add((hop, q, k))
if hop == 0:
continue
y, x = divmod(k, nx)
kind = patch.fine_kind[y, x]
if kind == OUTER_GHOST:
raise ValueError("Transfer reads an invalid post-hop outer ghost")
# A collided output depends on all nine arrivals; ghosts only stream.
for direction in (range(9) if kind == ACTIVE else (q,)):
sx, sy = x - D2Q9_EX[direction], y - D2Q9_EY[direction]
if not (0 <= sx < nx and 0 <= sy < ny):
raise ValueError("Transfer dependency leaves the fine allocation")
pending.append((hop - 1, direction, sy * nx + sx))
fine = set(map(int, patch.ghost_fine)) | {k for _, _, k in reads}
coarse = set(map(int, patch.ghost_coarse)) | set(map(
int, patch.coalesce_coarse % np.prod(patch.coarse_shape)))
return fine, coarse, _readonly(sorted(reads), np.int64)
def prepare_fine_boundary(cfg, patch, objects, *, wall_aware=False):
"""Project fixed circles and reject boundary/transfer overlap before CUDA.
Input centers/radii are in coarse physical coordinates (dx=dt=1).
SurfaceActuation retains its lattice-velocity meaning: input coarse units
are converted into independent fine-local objects with dx=dt=.5.
Original objects are not retained or mutated. No runtime geometry updates.
"""
if not objects:
return None
checked = replace(cfg)
checked.validate()
if (patch.coarse_shape != (cfg.ny, cfg.nx) or not checked.is_d2q9
or checked.collision != "SRT" or checked.streaming != "double_buffer"
or checked.store_precision != "FP32" or checked.ddf_shifting or checked.les_enabled
or patch.tau_coarse != 3*checked.viscosity+.5):
raise ValueError("Fine boundary requires the matching fixed-patch configuration")
from ..body.geometry.circle import CircleGeometry
from ..body.objects import SimObject
from ..body.coupling.soa_packer import pack_cut_links_to_soa
ny, nx = patch.fine_shape
fine_cfg = replace(cfg, nx=nx, ny=ny, viscosity=2 * cfg.viscosity)
flags = np.full(nx * ny, FLUID, np.uint16)
active = patch.fine_kind.ravel() == ACTIVE
local, links = [], []
x0, y0, x1, y1 = patch.bounds
dx = dt = .5
for bid, obj in enumerate(objects):
if (type(obj.geometry) is not CircleGeometry or obj.is_sensor or obj.is_force_region
or any(getattr(obj.state, name) != 0 for name in ("vx", "vy", "omega"))
or any(vars(obj.control).values())):
raise ValueError("Fine boundaries require stationary circles without other roles")
cx, cy = obj.geometry.center
radius = obj.geometry._radius
if (not np.isfinite([cx, cy, radius]).all() or radius <= 0
or cx-radius < x0 or cx+radius > x1 or cy-radius < y0 or cy+radius > y1):
raise ValueError("Entire fine circle must lie inside the physical patch")
center = tuple((v-o)/dx-.5 for v, o in zip((cx, cy), patch.fine_origin))
spec = obj.surface_actuation
if spec is not None:
spec = replace(spec, normal_velocity=spec.normal_velocity * dt / dx)
item = SimObject(bid, geometry=CircleGeometry(*center, radius/dx),
center=center, radius=radius/dx, surface_actuation=spec)
item.validate_surface_actuation(fine_cfg)
mask = item.get_flag_mask(nx, ny)
solid = (mask & SOLID) != 0
if not solid.any() or np.any(solid & ~active) or np.any(flags[solid] & SOLID):
raise ValueError("Fine solids must be resolved, disjoint and entirely active")
flags[solid] = mask[solid]
local.append(item)
for item in local:
cut = item.geometry.build_cut_links(nx, ny, domain_flags=flags)
if not cut:
raise ValueError("Fine circle has no resolved cut links")
if item.surface_actuation is not None:
_, aperture = item.surface_actuation.project(
np.array([link.rx for link in cut]), np.array([link.ry for link in cut]))
if not aperture.any():
raise ValueError("Surface aperture hits no resolved cut links")
for link in cut:
link.body_id = item.obj_id
links.extend(cut)
boundary, writers, receivers = set(), set(), set()
for link in links:
r, q = link.fluid_idx, link.dir
y, x = divmod(r, nx)
sx, sy = x + D2Q9_EX[q], y + D2Q9_EY[q]
s = sy * nx + sx
if not (active[r] and flags[r] & FLUID and active[s] and flags[s] & SOLID):
raise ValueError("Cut-link receiver/source must be active fluid/solid")
writer = (s, q+1 if q % 2 else q-1)
if writer in writers:
raise ValueError("Duplicate fine boundary writer")
writers.add(writer)
receivers.add((r, q))
boundary.update((r, s))
if link.q < .5:
dx_, dy_ = x - D2Q9_EX[q], y - D2Q9_EY[q]
if not (0 <= dx_ < nx and 0 <= dy_ < ny and active[dy_*nx+dx_]):
raise ValueError("q<.5 donor crosses the fine active interface; fallback cannot hide it")
boundary.add(dy_*nx+dx_)
# Reverse coverage: every fluid pull across a solid neighbor needs a writer.
required = set()
for r in np.flatnonzero(active & ((flags & FLUID) != 0)):
for q in range(1, 9):
if flags[r + D2Q9_EX[q] + nx*D2Q9_EY[q]] & SOLID:
required.add((int(r), q))
if required != receivers:
raise ValueError("Fine solid pull coverage does not match the cut links")
fine, coarse, reads = _transfer_footprint(patch)
if fine & (boundary | set(map(int, np.flatnonzero(flags & SOLID)))):
raise ValueError("Boundary stencil overlaps the two-hop transfer footprint")
for k in coarse:
y, x = divmod(k, cfg.nx)
if x0 <= x < x1 and y0 <= y < y1:
raise ValueError("Transfer coarse donor/target is not an uncovered bulk cell")
if wall_aware and (y <= 2 or y >= cfg.ny-3):
raise ValueError("Transfer footprint touches the coarse wall reconstruction stencil")
return FineBoundary2D(tuple(local), _readonly(flags, np.uint16),
tuple(_readonly(a, a.dtype) for a in pack_cut_links_to_soa(links)),
_readonly(sorted(boundary), np.uint32),
_readonly(sorted(fine), np.uint32),
_readonly(sorted(coarse), np.uint32), reads)
research/fine_surface_20261014/gpu_run.json
+48
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@@ -0,0 +1,48 @@
{
"start_utc": "2026-10-14T05:08:55.958692+00:00",
"preflight_gpu": "0, GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c, Tesla V100-SXM2-16GB, 5, 16140, 0\n1, GPU-f1c9fd5a-e917-56e0-21fb-24035ba44a36, Tesla V100-SXM2-16GB, 5, 16140, 0\n2, GPU-b8c11c01-c91f-aa0b-abf6-dad35085cc76, Tesla V100-SXM2-16GB, 5, 16140, 0\n3, GPU-8b71b032-1b75-2891-935c-ce106a53034a, Tesla V100-SXM2-16GB, 5, 16140, 0",
"preflight_apps": "",
"preflight_ram_bytes": 64091750400,
"physical_gpu_uuid": "GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c",
"command": [
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-c",
"import CelerisLab,pytest; print('SOURCE', CelerisLab.__file__, flush=True); assert CelerisLab.__file__.startswith('/home/frank14f/CelerisLab/src/'); raise SystemExit(pytest.main(['-q', '-s', '-p', 'no:cacheprovider', 'tests/validation/multigrid/test_fine_surface_gpu.py', 'tests/validation/multigrid/test_refinement_followup.py::NormalBuildGPU::test_offcenter_rectangle_margin_two_one_step', 'tests/validation/multigrid/test_refinement_followup.py::NormalBuildGPU::test_slot_reservoir_eight_steps']))"
],
"cwd": "/home/frank14f/CelerisLab",
"env": {
"PYTHONPATH": "/home/frank14f/CelerisLab/src",
"PYTHONNOUSERSITE": "1",
"PYTHONDONTWRITEBYTECODE": "1",
"OPENBLAS_NUM_THREADS": "1",
"CUDA_VISIBLE_DEVICES": "GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c",
"CUDA_CACHE_DISABLE": "1",
"TMPDIR": "/tmp/celeris-fine-surface-gpu-e9g3hz7z"
},
"preflight_temp_bytes": 1637890,
"limits": {
"wall_seconds": 120,
"temp_bytes": 2000000000,
"ram_reserve_bytes": 6409175040,
"vram_reserve_MiB": 1614
},
"note": "Sampled own process group; no fixed RSS/address-space cap.",
"exit_code": 1,
"elapsed_seconds": 25.39498820400331,
"peak_group_rss_bytes": 386883584,
"peak_temp_bytes": 3585985,
"min_ram_bytes": 63638339584,
"min_vram_free_MiB": 15826,
"termination_reason": null,
"scratch_removed": true,
"source_hashes_captured_during_run": {
"src/CelerisLab/lbm/kernels/refinement.cu": "129f3d91da93b4324f88a0da73a4da1e5390f72c14cce574ee63306712da9895",
"src/CelerisLab/lbm/kernels/step/aux_kernels.cu": "578a5b20a157241ee678547059abb1372dd5a589ad06bb2c15b987eb01ca1013",
"src/CelerisLab/lbm/kernels/step/curved_double.cu": "bfa7a1df1e939020314d49755045f7a85fe4a19cc1c68dbe62653c66c5e7c2b3",
"src/CelerisLab/lbm/refinement.py": "b3fe9e58118369adb1bdfc618fbfbd917d508a563beaf2555f656b59aeff4425",
"src/CelerisLab/lbm/refinement_stepper.py": "9178224089ee6584420b79f10cf1d91441645b668e8b6898641d19eac757687b",
"src/CelerisLab/lbm/stepper.py": "0dcb7cd7ae87e33e3dc67a38b6aa43042fbe673e37d9ad165342ee1d695933ce",
"tests/unit/test_fine_surface.py": "b232b55e75eef52480f06a35c401d72b6145892bb8b79d1d1b3cfed965b3506c",
"tests/validation/multigrid/test_fine_surface_gpu.py": "3dd0658f63a4e7a1c41eecf433842c872f8fecb391ea4b44d0b48947643d5189"
}
}
research/fine_surface_20261014/gpu_stdout.txt
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@@ -0,0 +1,79 @@
SOURCE /home/frank14f/CelerisLab/src/CelerisLab/__init__.py
speed=0.0 audit=True BFL=7.4505806e-09 ledger=[1.604948192834854e-05, -5.038455128669739e-07, -6.919726729393005e-07] budget=0.00221051117 boundary=[4.0978193283081055e-08, 0.0, 0.0] transfer=[0.0, 0.0, 0.0] drive_mass=+4.09781933e-08; counts=PASS
speed=None audit=False BFL=7.4505806e-09 ledger=[1.604948192834854e-05, -5.038455128669739e-07, -6.919726729393005e-07] budget=0.00221051117 boundary=[4.0978193283081055e-08, 0.0, 0.0] transfer=[0.0, 0.0, 0.0] drive_mass=+4.09781933e-08; counts=normal build
.speed=0.002 audit=True BFL=9.73073444e-09 ledger=[1.597730442881584e-05, -5.154870450496674e-07, -7.622875273227692e-07] budget=0.00221052543 boundary=[0.0071802823804318905, 0.003110995050519705, 0.0026390845887362957] transfer=[0.0, 0.0, 0.0] drive_mass=+0.00620033871; counts=PASS
.speed=-0.002 audit=True BFL=7.4505806e-09 ledger=[1.66366808116436e-05, -5.881302058696747e-07, -7.147900760173798e-07] budget=0.00221049693 boundary=[-0.007164992857724428, -0.0030983122996985912, -0.00262861093506217] transfer=[0.0, 0.0, 0.0] drive_mass=-0.00618575338; counts=PASS
.FF.normal rectangle 17x13 bounds=(2, 2, 6, 5) fine=16x14 steps=1: max_population=1.14050047e-07 device_arrays=35288; halo/stride PASS
host_peak_rss=279.887 MiB
.slot/reservoir steps=8 max_population=1.78241457e-07 max_BC_error=2.21654773e-07 discrete_leaf_mass_change=+0.0309633738 final_sum_rho_v_bottom=0.00426894613 top=0.000960694626; raw halos/corners/fine BC isolation PASS
host_peak_rss=279.887 MiB
.
=================================== FAILURES ===================================
_ test_missing_surface_device_buffer_rejects_before_launch[surface_velocity_gpu] _
missing = 'surface_velocity_gpu'
@pytest.mark.parametrize("missing", ["surface_velocity_gpu", "link_exchange_gpu"])
def test_missing_surface_device_buffer_rejects_before_launch(missing):
backend = implementation.FixedPatchStepper.__new__(implementation.FixedPatchStepper)
backend.fine_boundary = SimpleNamespace(objects=[object_spec(.002)])
backend._fine_curved = SimpleNamespace(surface_velocity_gpu=object(), link_exchange_gpu=object())
setattr(backend._fine_curved, missing, None)
backend._curved = Mock()
with pytest.raises(RuntimeError, match="Surface boundary device buffers are missing"):
> backend._apply_fine_boundary(object())
tests/validation/multigrid/test_fine_surface_gpu.py:289:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <CelerisLab.lbm.refinement_stepper.FixedPatchStepper object at 0x761fb9325480>
buffer = <object object at 0x761fb937c570>, kw = {}
cl = namespace(surface_velocity_gpu=None, link_exchange_gpu=<object object at 0x761fb937c630>)
def _apply_fine_boundary(self, buffer, **kw):
"""Stage solid source slots; exchange buffer holds this fine substep only."""
cl = self._fine_curved
if cl is None:
if self.fine_boundary is not None:
raise RuntimeError("Fine boundary device buffers are missing")
return
> if self._curved is None or cl.fluid_idx_gpu is None:
E AttributeError: 'types.SimpleNamespace' object has no attribute 'fluid_idx_gpu'
src/CelerisLab/lbm/refinement_stepper.py:195: AttributeError
_ test_missing_surface_device_buffer_rejects_before_launch[link_exchange_gpu] __
missing = 'link_exchange_gpu'
@pytest.mark.parametrize("missing", ["surface_velocity_gpu", "link_exchange_gpu"])
def test_missing_surface_device_buffer_rejects_before_launch(missing):
backend = implementation.FixedPatchStepper.__new__(implementation.FixedPatchStepper)
backend.fine_boundary = SimpleNamespace(objects=[object_spec(.002)])
backend._fine_curved = SimpleNamespace(surface_velocity_gpu=object(), link_exchange_gpu=object())
setattr(backend._fine_curved, missing, None)
backend._curved = Mock()
with pytest.raises(RuntimeError, match="Surface boundary device buffers are missing"):
> backend._apply_fine_boundary(object())
tests/validation/multigrid/test_fine_surface_gpu.py:289:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <CelerisLab.lbm.refinement_stepper.FixedPatchStepper object at 0x761fb92299c0>
buffer = <object object at 0x761fb937f700>, kw = {}
cl = namespace(surface_velocity_gpu=<object object at 0x761fb937f710>, link_exchange_gpu=None)
def _apply_fine_boundary(self, buffer, **kw):
"""Stage solid source slots; exchange buffer holds this fine substep only."""
cl = self._fine_curved
if cl is None:
if self.fine_boundary is not None:
raise RuntimeError("Fine boundary device buffers are missing")
return
> if self._curved is None or cl.fluid_idx_gpu is None:
E AttributeError: 'types.SimpleNamespace' object has no attribute 'fluid_idx_gpu'
src/CelerisLab/lbm/refinement_stepper.py:195: AttributeError
=========================== short test summary info ============================
FAILED tests/validation/multigrid/test_fine_surface_gpu.py::test_missing_surface_device_buffer_rejects_before_launch[surface_velocity_gpu]
FAILED tests/validation/multigrid/test_fine_surface_gpu.py::test_missing_surface_device_buffer_rejects_before_launch[link_exchange_gpu]
2 failed, 6 passed in 23.73s
research/fine_surface_20261014/gpu_test_run1.py
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"""Two coarse/four fine steps: isolated fine circle, no flux-accuracy claim.
Run only through the bounded GPU runner after a fresh resource check. The test
hooks read actual kernel inputs/outputs; they never change the stepping order.
"""
import importlib.util
from contextlib import nullcontext
from pathlib import Path
from threading import RLock
from types import SimpleNamespace
from unittest.mock import Mock, patch
import numpy as np
import pycuda.driver as cuda
import pytest
import CelerisLab
from CelerisLab.body.geometry.circle import CircleGeometry
from CelerisLab.body.objects import SimObject
from CelerisLab.config import LBMConfig
from CelerisLab.cuda import compiler_v2 as compiler
from CelerisLab.cuda.context import CudaContext
from CelerisLab.lbm import refinement_stepper as implementation
from CelerisLab.lbm.descriptors import FLUID, SOLID
ROOT = Path(__file__).resolve().parents[3]
CX = np.array([0, 1, -1, 0, 0, 1, -1, 1, -1])
CY = np.array([0, 0, 0, 1, -1, 1, -1, -1, 1])
OPP = np.array([0, 2, 1, 4, 3, 6, 5, 8, 7])
W = np.array([4/9]+[1/9]*4+[1/36]*4)
MOMENTS = np.stack((np.ones(9), CX, CY))
GEOMETRY = ("fluid_idx", "dir", "q", "body_id", "rx", "ry", "rz", "fallback_class")
BOUNDS = (4, 4, 20, 20)
def read(pointer, shape, dtype=np.float32):
values = np.empty(shape, dtype)
cuda.memcpy_dtoh(values, pointer)
return values
def rest(shape):
return np.broadcast_to(W[:, None, None], (9, *shape)).astype(np.float32).copy()
def surface(speed):
return None if speed is None else dict(angle=.7, half_width=.85, normal_velocity=speed)
def object_spec(speed):
return SimObject(0, CircleGeometry(12.25, 12.25, 2.15), center=(12.25, 12.25),
radius=2.15, surface_actuation=surface(speed))
def audit_compiler(original):
def compile_private(*args, **kwargs):
entry = Path(kwargs["build_dir"])/"fine_surface_audit.cu"
entry.write_text('#define CELERIS_REFINEMENT_AUDIT\n#include "refinement.cu"\n')
kwargs["source"] = entry.name
return original(*args, **kwargs)
return compile_private
def reflection_oracle(before, cl, speed, *, verify_projection=True):
"""FP64 BFL from frozen spatial populations, independent arc projection."""
flat = before.astype(float).reshape(9, -1)
radius = np.hypot(cl.rx.astype(float), cl.ry.astype(float))
delta = (np.arctan2(cl.ry.astype(float), cl.rx.astype(float))-.7+np.pi) % (2*np.pi)-np.pi
amplitude = 0 if speed is None else speed
strength = amplitude*np.maximum(0, 1-(delta/.85)**2)**2
velocity = strength[:, None]*np.column_stack((cl.rx, cl.ry))/radius[:, None]
if verify_projection and cl.surface_velocity is not None:
np.testing.assert_allclose(cl.surface_velocity, velocity, atol=3e-9, rtol=0)
incoming = []
for i, (k, d, q, fallback) in enumerate(zip(cl.fluid_idx, cl.dir, cl.q, cl.fallback_class)):
k, d, q = int(k), int(d), float(q)
rho = flat[:, k].sum()
dot = CX[d]*velocity[i, 0]+CY[d]*velocity[i, 1]
if fallback:
value = flat[d, k]-6*W[d]*rho*dot
elif q < .5:
value = 2*q*flat[d, k]+(1-2*q)*flat[d, k-CX[d]-40*CY[d]]-6*W[d]*rho*dot
else:
value = flat[d, k]/(2*q)+(1-1/(2*q))*flat[OPP[d], k]-3*W[d]*rho*dot/q
incoming.append(value)
return np.array(incoming)
def fine_flux(before, staged, active, solid):
"""Population ledger across the fine leaf boundary: solid versus halo.
Internal fluid-to-fluid streaming cancels pairwise; solid and halo packets
are counted in their actual direction, before any collision.
"""
boundary, transfer = np.zeros(9), np.zeros(9)
magnitude = 0.0
for d, (cx, cy) in enumerate(zip(CX, CY)):
incoming = active & ~np.roll(active, (cy, cx), (0, 1))
outgoing = active & ~np.roll(active, (-cy, -cx), (0, 1))
from_solid = np.roll(solid, (cy, cx), (0, 1))
into_solid = np.roll(solid, (-cy, -cx), (0, 1))
received = np.roll(staged[d], (cy, cx), (0, 1)).astype(float)
sent = before[d].astype(float)
for vector, inc, out in ((boundary, incoming & from_solid, outgoing & into_solid),
(transfer, incoming & ~from_solid, outgoing & ~into_solid)):
assert np.isfinite(received[inc]).all() and np.isfinite(sent[out]).all()
vector[d] = received[inc].sum()-sent[out].sum()
magnitude += np.abs(received[inc]).sum()+np.abs(sent[out]).sum()
return boundary, transfer, magnitude
def coarse_flux(before, fine, active):
"""Derive every replacement from physical midpoints, not production tables."""
x0, y0, x1, y1 = BOUNDS
incoming = np.stack([np.roll(before[d], (CY[d], CX[d]), (0, 1)) for d in range(9)]).astype(float)
magnitude = 0.0
for y, x in np.argwhere(active):
for d in range(1, 9):
if x0 <= x+.5-CX[d]/2 <= x1 and y0 <= y+.5-CY[d]/2 <= y1:
a, b = 4+2*(x-x0), 4+2*(y-y0)
children = fine[d, b:b+2, a:a+2].astype(float)
assert children.shape == (2, 2) and np.isfinite(children).all()
incoming[d, y, x] = children.sum()/4
magnitude += np.abs(children).sum()/4
assert np.isfinite(incoming[:, active]).all()
# This retains the eight grazing-corner replacements of uncovered donors.
flux = (incoming[:, active]-before[:, active].astype(float)).sum(axis=1)
magnitude += np.abs(incoming[:, active]).sum()+np.abs(before[:, active]).sum(dtype=float)
return flux, magnitude
def run_patch(speed, *, audit=True):
cfg = LBMConfig(nx=24, ny=24, viscosity=.05, velocity=0, rho=1)
coarse_active = np.ones((24, 24), bool)
coarse_active[4:20, 4:20] = False
source = object_spec(speed)
state = vars(source.state).copy()
with CudaContext(0) as context:
build = (patch.object(compiler, "compile_kernel", audit_compiler(compiler.compile_kernel))
if audit else nullcontext())
with build:
backend = implementation.FixedPatchStepper(cfg, BOUNDS, context=context, fine_objects=[source])
with backend, context.activate():
c, f = backend._levels
cl = backend._fine_curved
geometry = {name: getattr(cl, name).copy() for name in GEOMETRY}
flags = backend.fine_boundary.flags.reshape(40, 40)
solid = (flags & SOLID) != 0
active = np.zeros((40, 40), bool)
active[4:-4, 4:-4] = True
active &= (flags & FLUID) != 0
active &= ~solid
local = backend.fine_boundary.objects[0]
assert local.center == (20., 20.) and local.radius == 4.3
assert source.center == (12.25, 12.25) and source.radius == 2.15
yy, xx = np.mgrid[:40, :40]
roi = active & ((xx-20)**2+(yy-20)**2 <= 9**2)
backend.initialize(rest((24, 24)), rest((40, 40)))
if audit:
for level in backend._levels:
for name in ("collision_counts", "explosion_counts", "incoming_counts"):
pointer, size = level.module.get_global(name)
cuda.memset_d8(pointer, 0, size)
initial = backend.download()
initial_pop = initial[0][:, coarse_active].sum(axis=1, dtype=float)
initial_pop += .25*initial[1][:, active].sum(axis=1, dtype=float)
boundary, fine_transfer, coarse_transfer = np.zeros(9), np.zeros(9), np.zeros(9)
budget_sum, reflection_error, drive_mass = 0., 0., 0.
traces = []
original_curved, original_fine, original_coarse = backend._curved, f.kernel, c.kernel
def curved(*args, **kwargs):
nonlocal boundary, fine_transfer, budget_sum, reflection_error, drive_mass
backend._stream.synchronize()
before = read(args[0], f.shape)
expected = reflection_oracle(before, cl, speed)
original_curved(*args, **kwargs)
backend._stream.synchronize()
staged = read(args[0], f.shape)
d, k = cl.dir.astype(int), cl.fluid_idx.astype(int)
values = staged.reshape(9, -1)[OPP[d], k+CX[d]+40*CY[d]]
passive = reflection_oracle(before, cl, None, verify_projection=False)
drive_mass += .25*float(np.sum(values-passive))
error = float(np.max(np.abs(values-expected)))
reflection_error = max(reflection_error, error)
assert error <= 1e-7
np.testing.assert_array_equal(staged[:, active], before[:, active])
b, transfer, magnitude = fine_flux(before, staged, active, solid)
boundary += .25*b
fine_transfer += .25*transfer
budget_sum += .25*magnitude
def fine(*args, **kwargs):
nonlocal budget_sum
backend._stream.synchronize()
staged = read(args[0], f.shape)
original_fine(*args, **kwargs)
backend._stream.synchronize()
advanced = read(args[1], f.shape)
assert np.isfinite(advanced[:, active]).all()
assert np.isnan(advanced[:, solid]).all()
# Include actual collision operands in the FP32 rounding budget.
budget_sum += .25*np.abs(staged[:, active]).sum(dtype=float)
traces.append(advanced[:, roi].copy())
def coarse(*args, **kwargs):
nonlocal coarse_transfer, budget_sum
backend._stream.synchronize()
before, fine_values = read(args[0], c.shape), read(args[2], f.shape)
transfer, magnitude = coarse_flux(before, fine_values, coarse_active)
coarse_transfer += transfer
budget_sum += magnitude
return original_coarse(*args, **kwargs)
with patch.object(backend, "_curved", curved), patch.object(f, "kernel", fine), \
patch.object(c, "kernel", coarse):
backend.step(2)
actual = backend.download()
population = actual[0][:, coarse_active].sum(axis=1, dtype=float)
population += .25*actual[1][:, active].sum(axis=1, dtype=float)
ledger = boundary+fine_transfer+coarse_transfer
error = MOMENTS@(population-initial_pop-ledger)
budget = 8*np.finfo(np.float32).eps*budget_sum
assert np.isfinite(budget_sum) and budget_sum > 0
assert np.max(np.abs(error)) <= budget
if speed:
assert speed*drive_mass > 0
assert len(traces) == 4 and backend.step_count == 2
if audit:
for level, mask, count in ((c, coarse_active, 2), (f, active, 4)):
pointer, size = level.module.get_global("collision_counts")
counts = read(pointer, mask.shape, np.uint32)
assert size == counts.nbytes
np.testing.assert_array_equal(counts, count*mask)
for name, values in geometry.items():
np.testing.assert_array_equal(getattr(cl, name), values)
assert vars(source.state) == state and source.geometry.center == source.center
print(f"speed={speed} audit={audit} BFL={reflection_error:.9g} ledger={error.tolist()} "
f"budget={budget:.9g} boundary={list(MOMENTS@boundary)} "
f"transfer={list(MOMENTS@(fine_transfer+coarse_transfer))} "
f"drive_mass={drive_mass:+.9g}; counts={'PASS' if audit else 'normal build'}", flush=True)
return traces, roi, actual
def uniform_reference(speed, roi):
# Reuse the existing explicit config helper without importing unrelated tests.
spec = importlib.util.spec_from_file_location("fine_surface_cases", ROOT/"tests/integration/_cases.py")
cases = importlib.util.module_from_spec(spec)
spec.loader.exec_module(cases)
with cases.make_sim(nx=48, ny=48, viscosity=.1, velocity=0, rho=1) as sim:
sim.add_body("circle", center=(24, 24), radius=4.3, surface_actuation=surface(speed))
sim.initialize()
# Uniform 48^2 has the same dx=dt=.5 representation. Its distant channel
# boundaries cannot influence the circle ROI within these four hops.
traces = []
for _ in range(4):
sim.run(1)
traces.append(sim.get_ddf().reshape(9, 48, 48)[:, 4:44, 4:44][:, roi].copy())
return traces
@pytest.mark.parametrize("speed", [0., .002, -.002])
def test_fine_surface_four_hops(speed):
assert Path(CelerisLab.__file__).resolve() == ROOT/"src/CelerisLab/__init__.py"
assert Path(compiler._KERNEL_DIR).resolve() == ROOT/"src/CelerisLab/lbm/kernels"
assert Path(implementation.__file__).resolve() == ROOT/"src/CelerisLab/lbm/refinement_stepper.py"
actual, roi, final = run_patch(speed)
reference = uniform_reference(speed, roi)
for got, wanted in zip(actual, reference):
np.testing.assert_allclose(got, wanted, atol=3e-6, rtol=0)
np.testing.assert_allclose(MOMENTS@got.astype(float), MOMENTS@wanted.astype(float),
atol=2e-5, rtol=0)
if speed == 0:
passive, passive_roi, passive_final = run_patch(None, audit=False)
np.testing.assert_array_equal(passive_roi, roi)
for got, wanted in zip(actual+list(final), passive+list(passive_final)):
np.testing.assert_array_equal(got, wanted)
@pytest.mark.parametrize("missing", ["surface_velocity_gpu", "link_exchange_gpu"])
def test_missing_surface_device_buffer_rejects_before_launch(missing):
backend = implementation.FixedPatchStepper.__new__(implementation.FixedPatchStepper)
backend.fine_boundary = SimpleNamespace(objects=[object_spec(.002)])
backend._fine_curved = SimpleNamespace(surface_velocity_gpu=object(), link_exchange_gpu=object())
setattr(backend._fine_curved, missing, None)
backend._curved = Mock()
with pytest.raises(RuntimeError, match="Surface boundary device buffers are missing"):
backend._apply_fine_boundary(object())
backend._curved.assert_not_called()
def test_close_frees_fine_boundary_after_stream_failure_once():
backend = implementation.FixedPatchStepper.__new__(implementation.FixedPatchStepper)
backend.ctx = SimpleNamespace(activate=nullcontext)
backend._lock = RLock()
backend._status = "ready"
stream, soa, first, second = Mock(), Mock(), Mock(), Mock()
stream.synchronize.side_effect = RuntimeError("stream synchronization failed")
backend._stream = stream
backend._fine_curved = soa
backend._fine_flags = first
backend._curved = Mock()
backend._levels = [object(), object()]
backend._allocations = [first, second]
with pytest.raises(RuntimeError, match="stream synchronization failed"):
backend.close()
assert backend._status == "closed"
assert backend._levels == [] and backend._allocations == []
assert backend._fine_curved is None and backend._fine_flags is None
assert backend._curved is None and backend._stream is None
backend.close()
stream.synchronize.assert_called_once_with()
soa.free.assert_called_once_with()
first.free.assert_called_once_with()
second.free.assert_called_once_with()
research/fine_surface_20261014/incremental.diff
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research/fine_surface_20261014/report.txt
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2026-10-14 — 固定加密区内静止圆表面吹吸：受限整合交付
状态：当前代码与验证完成，停在独立审查点。A 的单层静止圆功能保持；本次只
把完整边界 stencil 位于 fine active 的圆接入 FixedPatchStepper。未增加
checkpoint、forcing、运动刚体、喷嘴内部、墙穿接口、blockforest 或临时 ledger。
所有 B 反例与既有证据保留。
入口与单位
FixedPatchStepper(cfg, bounds, context=context, fine_objects=[obj])。
obj 是现有 SimObject/CircleGeometry；中心/半径用 coarse 物理坐标，速度沿用
coarse lattice 单位（当前 dx=dt=1）。构造独立 fine-local 副本，不修改调用方对象。
C_local=(C_phys-origin)/dx-.5，R_local=R_phys/dx，U_local=U_phys*dt/dx。
真实 hit 反变换为 origin+dx*(hit_local+.5)，lever arm 乘 dx，角度/q 不变。
维持 D2Q9/SRT/FP32/unshifted/double_buffer、无 LES/forcing 的原 backend 范围。
生产改动：5 个现有文件 + 1 个共享文件，+300/−71，净 +229 行。
- refinement.py：FineBoundary2D、prepare_fine_boundary；纯 CPU 建图和拒绝检查。
- refinement_stepper.py：固定 fine 对象、flags/SoA 管理、两个 BFL 子步、solid
叶掩码、初始化与释放；不接完整 Simulation/ObjectManager。
- aux_kernels.cu → step/curved_double.cu：原 action_omega/CurvedBoundaryKernel
代码块逐字相同，只抽取共享位置并增加 include guard，已比对确认。
- refinement.cu：包含共享 BFL，PatchFineStep 接 flags；solid 不 pull/collide，
输出 NaN；所有 Python launch 传完整参数。
- 原 lbm/stepper.py：allocation 显式 is not None；驱动缓冲缺失报错。
数据/所有权
explosion → BFL(current) → fine pull+collision → BFL(spare) → fine pull+collision
→ coarse pull/coalescence+collision。active fluid 每 coarse 步碰撞两次；solid
与 ghost 不碰撞。BFL 只填当前输入的 solid source 反射槽，下一子步重新填充。
下载仅发布流体叶节点，coarse covered、fine solid/ghost 均 NaN。
flags/level/transfer arrays 归 stepper._allocations；SoA 的列及速度/交换缓冲只
归 SoA，未重复登记。initialize 清空最新交换缓存。无对象不创建这些新缓冲。
内部交换缓冲仅代表最后一个 fine 子步，未新增公共 coarse 累计诊断。
测试账本分别读取四个 fine 子步的实际 population，按面积 1/4 求和；不是用
latest 值冒称区间总量，也不是喷嘴总推力。
有限依赖规则
plan.transfer_reads 保存 (hop, direction, cell)，从实际 coalesce_fine 的 hop 2
源槽反向两次 pull；遇 active collision 展开全部 9 个输入，hop 0 停止。
transfer_fine_cells 加入全部 ghost_fine/explosion 写集；coarse 集合包含实际
ghost_coarse donors 与 coalesce_coarse targets。ghost 可以合法参与运输；
outer ghost 的各阶段有效性仍按原 kernel 判断。
逐条检查 r active fluid、s=r+c active solid、反射写槽唯一、q<.5 的 r−c
仍在 active；不可用 fallback 掩盖跨层 donor。同层真实 solid 阻挡的原 fallback
语义不改。逆查所有 active-fluid→solid 邻接，确保与 cut links 完全一致。
整个 solid 集合及 r/s/donor stencil 均不得碰到上述 transfer footprint。
coarse donors/targets 必须 uncovered bulk；slot 模式另外拒绝其真实壁面
reconstruction pull stencil。无对象旧 slot 模式保持原规则。
CPU 结果（原始 command/stdout/exit 见同目录）
- 新建图/单位/边界覆盖/拒绝检查：15 passed，最终代码复测 15 passed，exit 0。
同行补充的非对角圆心、origin=(2,2)、四子格平均等于 coarse I+.5 也通过。
- 旧 maps：9 passed、23 subtests；共享 actual CUDA-header CPU 回归：1 passed。
- 旧生命周期/边界模式：5 passed、5 subtests。
- 新缺失驱动缓冲拒绝：2 passed，exit 0；同步失败后释放且不重复释放：通过。
- 指定 donor 反例通过构造器检查，context.activate/mem_alloc 均未调用：1 passed。
真实 GPU 结果与首次批次失败说明
使用现有 pycuda_3_10、显式 PYTHONPATH=/home/frank14f/CelerisLab/src；stdout
首先打印并断言实际包来源。设备 0 UUID GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c，
开始时占用 5 MiB、util=0、无计算进程；未用其他设备。
运行批次真实 exit=1：5 项 GPU 检查和 1 项 CPU cleanup 检查通过，另 2 项 CPU
缺失缓冲夹具失败，因为夹具未提供新增前置检查所需的 fluid_idx_gpu 字段。
仅补齐夹具后，定向 CPU 重测这 2 项 exit=0；原日志/exit=1 保留，未重跑 GPU。
5 项 GPU 检查：
1–3. speed=0,+.002,−.002 的批准小案例，各 2 coarse/4 fine 步。
coarse24²，patch[4,20)²，fine40²，origin(2,2)，dx=dt=.5；物理圆心
(12.25,12.25)、R2.15 → local(20,20)、R4.3；angle.7/half_width.85；
nu_phys=.05，tau_c=.65/tau_f=.8，rho1/u0/G=feq。
三个驱动使用私有 audit build 验证 fine fluid 碰撞4次、coarse fluid2次，
solid/ghost0次。零驱动另与正常 production build 的 same-solid/no-actuation
路径逐元素相等。参考采用正常 A build 的48²、dx=dt=.5、circle local(24,24)，
对齐真实物理位置与时间（4 fine步=物理时间2），不是跨分辨率逐元素比较。
4. 旧无对象 periodic 非居中矩形 normal build：1 coarse步，最大人口误差
1.14050047e−7，通过。
5. 旧无对象 slot/reservoir normal build：8 coarse步，最大人口误差
1.78241457e−7、最大BC误差2.21654773e−7，通过。
主要数值：
speed max |BFL误差| max |累计质量/动量账本残差| 预定FP32预算
0 7.4505806e−9 1.6049482e−5 0.00221051117
+.002 9.7307344e−9 1.5977304e−5 0.00221052543
−.002 7.4505806e−9 1.6636681e−5 0.00221049693
BFL阈值1e−7；同细分辨率、未触接口的圆周ROI人口atol3e−6、局部矩2e−5均通过。
账本预算为8*eps32*实际参与加权population绝对值总和，未放宽。喷吸附加质量
分别约+0.00620033871/−0.00618575338，符号正确。该短例净transfer项为0；
仍逐项统计真实非零出入人口，但喷吸扰动尚未触达接口。
拒绝证据
- physical C=(5.05,12.25),R=.7 → fine C=(5.6,20),R=1.4：r=(4,20),c=(1,0),
hit=(4.2,20),q≈.2，donor=(3,20)为ghost，构造在任何CUDA调用前拒绝。
- physical C=(6.05,12.25),R=.7：全部直接r/s/donor都active，仍因与真实
两hop transfer footprint相交而拒绝。说明通过边界检查不能仅靠包围盒或边距。
资源与记录
GPU批次外部耗时25.395s；进程组RSS峰值386883584 bytes，RAM最小空闲
63638339584 bytes，VRAM最小空闲15826 MiB，相关临时空间采样峰值3585985 bytes。
按实际余量保护，无固定RSS/AS上限；120s与2GB本次临时量约束未触发。监测是
采样方式，不是OS硬资源隔离。编译临时目录已清理；不会清理其他任务或旧证据。
baseline.json保存382个src/tests/examples文件哈希；其中377个保持不变。
manifest.json和incremental.diff只描述本轮；共享kernel抽取相等与反向patch检查通过。
GPU执行后仅将建图时的孔口预检输入改成与实际八列完全一致的FP32 lever arm，
避免极窄孔口在分配前后得到不同覆盖判断；最终CPU15项复测通过。GPU算子、上传
数组生成方式、步序和测试数值部分未变。gpu_test_run1.py/gpu_preparation_run1.py
是按GPU运行中已记录的SHA256逐项核对后的执行版快照，保留首次运行的精确来源。
参考与边界
直接复用A已接受BFL。ref/OpenLB/src/boundary/defineU2D.hh:74–79用于真实hit
速度投影对照，offBoundaryPostProcessors2D.hh:115–127用于两分支stencil对照；
OpenLB commit fb62326f260bf0c5ec2666fc5e055c0813ff50a9，仅算法对照、未复制代码。
现有CPUREF-0.3与实际refinement kernel的ghost不碰撞、两hop/每层tau、incoming
替换后coarse碰撞保持不变，未移植任何B候选。
不声称喷流穿粗细接口精度、长时稳定性、空间收敛或性能已验证。设备free()自身
抛错时沿用既有close限制，不保证完整释放或重试；上下文激活失败可保留资源重试。
research/fine_surface_joint_20261014/baseline_00.json
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research/fine_surface_joint_20261014/blow_boundary.csv
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@@ -0,0 +1,49 @@
step,substep,time,bfl_error,drive_mass,latest_body_impulse_x,latest_body_impulse_y,latest_body_angular_impulse,boundary_m,boundary_px,boundary_py,fine_transfer_m,fine_transfer_px,fine_transfer_py
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research/fine_surface_joint_20261014/blow_collisions.csv
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step,level,substep,max_error,max_ratio,regime,drift_m,drift_px,drift_py,ideal_defect_m,ideal_defect_px,ideal_defect_py,measured_rounding_m,measured_rounding_px,measured_rounding_py
1,fine,1,6.200234548003891e-08,0.28965071213188953,True,1.6061589121818542e-05,1.7695128917694092e-08,6.51925802230835e-09,-3.41740524767431e-16,1.214306433183765e-17,-8.673617379884035e-18,1.6061589122160283e-05,1.7695128905551027e-08,6.519258030981967e-09
1,fine,2,9.592780092493314e-08,0.3574847775115332,True,-3.2549723982810974e-07,-1.3969838619232178e-09,-1.4435499906539917e-08,-5.4539706084710815e-15,1.3877787807814457e-17,3.469446951953614e-17,-3.2549723437413913e-07,-1.3969838758010056e-09,-1.4435499941234387e-08
1,coarse,1,1.1080350609260492e-08,0.10456730670501622,True,-1.5292316675186157e-06,-1.30385160446167e-08,-1.5832483768463135e-07,-4.288236432614667e-15,-5.551115123125783e-17,1.3877787807814457e-17,-1.5292316632303793e-06,-1.3038515989105548e-08,-1.5832483769850914e-07
2,fine,1,7.754918973645886e-08,0.34580397879479957,True,8.987262845039368e-07,-2.3283064365386963e-08,-1.9744038581848145e-07,-5.167741234934908e-15,-1.5612511283791264e-17,2.931682674400804e-16,8.98726289671678e-07,-2.328306434977445e-08,-1.974403861116497e-07
2,fine,2,1.155261522001183e-07,0.321440603069343,True,2.284534275531769e-06,-2.8870999813079834e-08,8.66129994392395e-08,-1.1794384913166311e-14,-7.112366251504909e-17,2.203098814490545e-16,2.2845342873261537e-06,-2.887099974195617e-08,8.661299921892962e-08
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5,fine,1,1.2583242237074188e-07,0.3554646638422362,True,9.815674275159836e-06,-2.789311110973358e-07,-3.608874976634979e-07,-1.565240992373873e-14,-1.9949319973733282e-16,-3.642919299551295e-17,9.815674290812246e-06,-2.789311108978426e-07,-3.6088749762706873e-07
5,fine,2,1.2541811456046048e-07,0.3685724336736591,True,9.207520633935928e-06,-2.5192275643348694e-07,-4.0512531995773315e-08,-1.5307199952019346e-14,3.469446951953614e-17,4.3021142204224816e-16,9.207520649243128e-06,-2.519227564681814e-07,-4.051253242598474e-08
5,coarse,1,1.2532259030528792e-07,0.38942307019337585,True,1.49458646774292e-05,9.723007678985596e-07,1.2628734111785889e-06,-1.6535384173010925e-14,1.5959455978986625e-16,6.175615574477433e-16,1.4945864693964583e-05,9.72300767738965e-07,1.2628734105610273e-06
6,fine,1,1.0399005534456052e-07,0.4260869217553865,True,5.527399480342865e-06,-8.754432201385498e-08,-1.555308699607849e-07,-1.4764231504038605e-14,4.336808689942018e-17,3.86843335142828e-16,5.5273994951070965e-06,-8.754432205722307e-08,-1.5553087034762825e-07
6,fine,2,1.295425238190262e-07,0.36331211716460343,True,5.567912012338638e-06,-2.6542693376541138e-08,-2.0908191800117493e-07,-1.4812803761365956e-14,-5.186823193170653e-16,1.734723475976807e-18,5.567912027151442e-06,-2.654269285785882e-08,-2.0908191800290965e-07
6,coarse,1,1.0239773140519048e-07,0.43028837580529966,True,1.3461336493492126e-05,5.308538675308228e-07,4.2654573917388916e-07,-2.033095913844818e-14,2.0816681711721685e-16,1.2212453270876722e-15,1.3461336513823086e-05,5.308538673226559e-07,4.2654573795264383e-07
7,fine,1,1.1958961998459472e-07,0.4029625240438387,True,5.150679498910904e-06,-5.3551048040390015e-08,-7.497146725654602e-08,-1.5931700403370996e-14,-5.204170427930421e-17,-1.700029006457271e-16,5.150679514842604e-06,-5.355104798834831e-08,-7.497146708654312e-08
7,fine,2,1.265965450247819e-07,0.3851888208747954,True,3.789551556110382e-06,0.0,3.725290298461914e-09,-1.5173626244369132e-14,7.806255641895632e-17,3.903127820947816e-16,3.7895515712840083e-06,-7.806255641895632e-17,3.725289908149132e-09
7,coarse,1,1.0902046165828239e-07,0.38161058826685446,True,1.2835487723350525e-05,8.326023817062378e-07,-2.6635825634002686e-07,-2.4501234374696423e-14,-5.48172618408671e-16,-2.5673907444456745e-16,1.283548774785176e-05,8.326023822544104e-07,-2.663582560832878e-07
8,fine,1,1.2448945463372496e-07,0.3720247984466396,True,2.2617168724536896e-06,7.869675755500793e-08,3.259629011154175e-09,-1.5289852717259578e-14,7.28583859910259e-17,-2.636779683484747e-16,2.2617168877435423e-06,7.869675748214955e-08,3.259629274832143e-09
8,fine,2,1.3473279458775522e-07,0.39946262369614577,True,2.4368055164813995e-06,7.031485438346863e-08,-1.5366822481155396e-08,-1.6287318715946242e-14,4.683753385137379e-17,-1.196959198423997e-16,2.4368055327687183e-06,7.03148543366311e-08,-1.5366822361459476e-08
8,coarse,1,1.265961931951054e-07,0.38341345468786764,True,8.130446076393127e-06,7.506459951400757e-07,-5.830079317092896e-07,-2.2391810627908626e-14,-1.6861512186494565e-15,1.0408340855860843e-16,8.130446098784938e-06,7.506459968262269e-07,-5.83007931813373e-07
9,fine,1,1.3222774730703435e-07,0.3900959817916434,True,2.078711986541748e-06,8.568167686462402e-08,6.239861249923706e-08,-1.5612511283791264e-14,-2.5326962749261384e-16,2.42861286636753e-16,2.0787120021542593e-06,8.568167711789365e-08,6.239861225637577e-08
9,fine,2,1.2824470274619415e-07,0.4272642246416919,True,8.78702849149704e-07,9.266659617424011e-08,-1.3830140233039856e-07,-1.4988010832439613e-14,-1.6653345369377348e-16,-1.2836953722228372e-16,8.787028641377148e-07,9.266659634077357e-08,-1.3830140220202902e-07
9,coarse,1,1.1335454486172125e-07,0.3876201969620664,True,5.185604095458984e-06,-2.384185791015625e-07,-6.07222318649292e-07,-2.583350200424661e-14,3.122502256758253e-16,7.632783294297951e-17,5.185604121292486e-06,-2.384185794138127e-07,-6.072223187256198e-07
10,fine,1,1.2645846059200494e-07,0.3781818251482199,True,2.073124051094055e-06,6.426125764846802e-08,1.0151416063308716e-07,-1.6764367671839864e-14,2.949029909160572e-16,-2.1163626406917047e-16,2.073124067858423e-06,6.426125735356503e-08,1.0151416084472342e-07
10,fine,2,1.3663077158554415e-07,0.43888780198316496,True,1.5008263289928436e-06,7.031485438346863e-08,1.9883736968040466e-07,-1.6273440928138427e-14,-1.4051260155412137e-16,6.071532165918825e-17,1.5008263452662846e-06,7.031485452398123e-08,1.9883736961968934e-07
10,coarse,1,1.5257769281351585e-07,0.4176679708473079,True,4.520639777183533e-06,-5.904585123062134e-07,-3.166496753692627e-08,-2.443184543565735e-14,-4.649058915617843e-16,1.0408340855860843e-16,4.520639801615378e-06,-5.904585118413075e-07,-3.166496764100968e-08
11,fine,1,1.4342478449336227e-07,0.40055388671598813,True,9.159557521343231e-07,1.4388933777809143e-07,1.103617250919342e-07,-1.6368850719317152e-14,-7.979727989493313e-17,-1.3877787807814457e-17,9.159557685031738e-07,1.438893378578887e-07,1.1036172510581199e-07
11,fine,2,1.229973988214894e-07,0.44018383771634995,True,1.3504177331924438e-06,-2.7939677238464355e-08,-3.725290298461914e-09,-1.6167622796103842e-14,-1.942890293094024e-16,1.457167719820518e-16,1.3504177493600666e-06,-2.7939677044175326e-08,-3.725290444178686e-09
11,coarse,1,1.0126133054289e-07,0.37920635013850246,True,2.175569534301758e-06,-1.341104507446289e-07,1.2665987014770508e-07,-2.38073449843057e-14,9.020562075079397e-17,5.759281940243e-16,2.175569558109103e-06,-1.3411045083483453e-07,1.2665986957177688e-07
12,fine,1,1.2071681543268653e-07,0.4311470967916149,True,1.3746321201324463e-06,-2.1420419216156006e-08,1.4528632164001465e-07,-1.6273440928138427e-14,-2.237793284010081e-16,-4.0072112295064244e-16,1.3746321364058872e-06,-2.1420418992376677e-08,1.4528632204073577e-07
12,fine,2,1.371591822341145e-07,0.44227593234711177,True,1.982320100069046e-06,9.73232090473175e-08,1.448206603527069e-07,-1.6818144099595145e-14,-2.96637714392034e-16,1.214306433183765e-17,1.98232011688719e-06,9.732320934395522e-08,1.4482066034056384e-07
12,coarse,1,1.2813768046626706e-07,0.42388935775355246,True,3.3173710107803345e-06,-4.898756742477417e-07,-1.8067657947540283e-07,-2.7193525209412428e-14,-1.1171619185290638e-15,-4.649058915617843e-16,3.3173710379738597e-06,-4.898756731305798e-07,-1.8067657901049694e-07
13,fine,1,1.3950153099351326e-07,0.4396993900025004,True,1.2670643627643585e-06,2.3050233721733093e-07,1.2153759598731995e-07,-1.662038562333379e-14,3.903127820947816e-16,9.194034422677078e-17,1.2670643793847441e-06,2.3050233682701815e-07,1.215375958953796e-07
13,fine,2,1.2581214114959494e-07,0.4721292711508529,True,1.1995434761047363e-06,3.7159770727157593e-07,1.0710209608078003e-07,-1.5909148998183298e-14,-8.153200337090993e-17,9.540979117872439e-17,1.1995434920138853e-06,3.7159770735310793e-07,1.0710209598537024e-07
13,coarse,1,1.2660152992616247e-07,0.45318197065007865,True,7.878988981246948e-07,-1.210719347000122e-07,3.334134817123413e-07,-2.346040028911034e-14,-4.2327252813834093e-16,2.706168622523819e-16,7.878989215850951e-07,-1.2107193427673968e-07,3.3341348144172445e-07
14,fine,1,1.5054562552130335e-07,0.5104225030616341,True,6.658956408500671e-07,1.778826117515564e-07,-2.60770320892334e-08,-1.6335890973273592e-14,2.96637714392034e-16,3.86843335142828e-16,6.658956571859581e-07,1.7788261145491868e-07,-2.6077032476076734e-08
14,fine,2,1.3530934717120857e-07,0.4009751179733903,True,1.2051314115524292e-06,-1.955777406692505e-08,1.6205012798309326e-07,-1.6099968580540747e-14,-1.5785983631388945e-16,8.500145032286355e-17,1.2051314276523978e-06,-1.9557773909065213e-08,1.620501278980918e-07
14,coarse,1,1.3409460186686317e-07,0.39963893479139145,True,3.9692968130111694e-06,8.754432201385498e-08,2.9616057872772217e-07,-2.5528190672474693e-14,-6.245004513516506e-17,-6.314393452555578e-16,3.96929683853936e-06,8.754432207630503e-08,2.961605793591615e-07
15,fine,1,1.2702620799665e-07,0.37981873927853255,True,1.000240445137024e-06,-3.166496753692627e-08,7.729977369308472e-08,-1.607568245187707e-14,-2.1337098754514727e-16,1.4051260155412137e-16,1.0002404612127064e-06,-3.166496732355528e-08,7.729977355257212e-08
15,fine,2,1.1264902777252672e-07,0.42131603992753336,True,8.651986718177795e-07,-4.377216100692749e-08,-1.0151416063308716e-07,-1.5992415725030185e-14,4.336808689942018e-17,3.2959746043559335e-17,8.651986878101953e-07,-4.377216105029558e-08,-1.015141606660469e-07
15,coarse,1,1.305862756817433e-07,0.5086178241462508,True,1.6335397958755493e-06,-6.146728992462158e-08,2.2165477275848389e-07,-2.533390164316529e-14,-4.85722573273506e-17,2.706168622523819e-16,1.633539821209451e-06,-6.146728987604932e-08,2.2165477248786702e-07
16,fine,1,1.3092614292720128e-07,0.40502514569399495,True,9.629875421524048e-07,-9.685754776000977e-08,8.66129994392395e-08,-1.6838960781306866e-14,-9.194034422677078e-17,8.500145032286355e-17,9.629875589913656e-07,-9.685754766806942e-08,8.661299935423805e-08
16,fine,2,1.3893401662468108e-07,0.39764517507033376,True,1.5990808606147766e-06,-8.009374141693115e-08,-5.3085386753082275e-08,-1.6518036938251157e-14,3.0531133177191805e-16,3.5735303605122226e-16,1.5990808771328135e-06,-8.009374172224248e-08,-5.308538711043531e-08
16,coarse,1,1.2888908407227007e-07,0.4083972967001409,True,5.159527063369751e-06,-4.3213367462158203e-07,1.4901161193847656e-07,-2.6131874442114622e-14,7.216449660063518e-16,-3.885780586188048e-16,5.159527089501625e-06,-4.32133675343227e-07,1.4901161232705462e-07
17,fine,1,1.249536419889452e-07,0.39288318869275274,True,1.12876296043396e-06,-1.7601996660232544e-07,-2.3283064365386963e-07,-1.7957857423311907e-14,-5.551115123125783e-16,1.3530843112619095e-16,1.1287629783918174e-06,-1.7601996604721393e-07,-2.3283064378917806e-07
17,fine,2,1.3328934989065289e-07,0.46401164838116316,True,7.129274308681488e-07,-2.1373853087425232e-07,-2.6496127247810364e-07,-1.634803403760543e-14,-2.914335439641036e-16,9.71445146547012e-17,7.129274472161828e-07,-2.1373853058281878e-07,-2.6496127257524815e-07
17,coarse,1,1.246264329513025e-07,0.4613853551985751,True,5.302950739860535e-06,-2.0675361156463623e-07,1.1362135410308838e-07,-2.8373137173076657e-14,7.7021722333370235e-16,-6.869504964868156e-16,5.302950768233672e-06,-2.0675361233485345e-07,1.1362135479003888e-07
18,fine,1,1.5840482392448507e-07,0.48249679461224454,True,8.10716301202774e-07,-1.4016404747962952e-07,-2.6402994990348816e-07,-1.6878859421254333e-14,7.632783294297951e-17,2.0816681711721685e-17,8.107163180816335e-07,-1.4016404755595735e-07,-2.6402994992430484e-07
18,fine,2,1.217794476371381e-07,0.4447119430321027,True,1.4016404747962952e-07,-2.8545036911964417e-07,-8.847564458847046e-09,-1.709223040879948e-14,-1.1275702593849246e-16,1.8214596497756474e-16,1.4016406457185993e-07,-2.8545036900688714e-07,-8.847564640993011e-09
18,coarse,1,1.3857981828735433e-07,0.4519491616828685,True,5.850568413734436e-06,-1.2479722499847412e-07,-1.471489667892456e-07,-2.688821387764051e-14,-8.534839501805891e-16,9.08995101411847e-16,5.85056844062265e-06,-1.2479722414499017e-07,-1.471489676982407e-07
19,fine,1,1.4433919059975153e-07,0.3821888438774327,True,1.9441358745098114e-06,-1.3969838619232178e-09,-2.7474015951156616e-08,-1.628905343942222e-14,-3.920475055707584e-16,2.42861286636753e-17,1.944135890798865e-06,-1.3969834698757122e-09,-2.7474015975442745e-08
19,fine,2,1.4349667698532187e-07,0.4183999473130793,True,2.1490268409252167e-06,1.7834827303886414e-07,-7.869675755500793e-08,-1.678344963007561e-14,-5.204170427930421e-18,2.949029909160572e-17,2.1490268577086663e-06,1.783482730440683e-07,-7.869675758449823e-08
19,coarse,1,1.264100825126846e-07,0.5347769553832019,True,5.165114998817444e-06,3.91155481338501e-08,1.1734664440155029e-07,-2.6804947150793623e-14,-4.2327252813834093e-16,-3.8163916471489756e-16,5.165115025622391e-06,3.9115548557122626e-08,1.1734664478318946e-07
20,fine,1,1.1464598925758906e-07,0.39800949885565845,True,1.8593855202198029e-06,7.031485438346863e-08,-2.2817403078079224e-08,-1.695171780724536e-14,1.457167719820518e-16,-7.632783294297951e-17,1.8593855371715207e-06,7.031485423775186e-08,-2.281740300175139e-08
20,fine,2,1.3166252321727967e-07,0.41581763506478076,True,2.2118911147117615e-06,9.313225746154785e-10,9.033828973770142e-08,-1.7095699855751434e-14,-3.7643499428696714e-16,2.0990154059319366e-16,2.2118911318074613e-06,9.313229510504728e-10,9.033828952779988e-08
20,coarse,1,1.3024161815700097e-07,0.47356066681074566,True,4.023313522338867e-06,5.997717380523682e-07,-8.568167686462402e-08,-2.7228219678931964e-14,3.0531133177191805e-16,3.7470027081099033e-16,4.023313549567087e-06,5.997717377470568e-07,-8.56816772393243e-08
21,fine,1,1.361732063775989e-07,0.4205892378883329,True,2.439599484205246e-06,1.4854595065116882e-07,-1.327134668827057e-07,-1.6781714906599632e-14,3.885780586188048e-16,8.673617379884035e-17,2.439599500986961e-06,1.4854595026259076e-07,-1.3271346696944186e-07
21,fine,2,1.2473161092874108e-07,0.45376916235253945,True,2.2514723241329193e-06,3.5902485251426697e-07,1.9976869225502014e-07,-1.692396223162973e-14,6.938893903907228e-17,-1.457167719820518e-16,2.2514723410568815e-06,3.5902485244487803e-07,1.9976869240073691e-07
21,coarse,1,1.5484813187249813e-07,0.561763881940829,True,3.507360816001892e-06,7.431954145431519e-07,6.202608346939087e-07,-2.6131874442114622e-14,-4.579669976578771e-16,1.5265566588595902e-16,3.5073608421337665e-06,7.431954150011189e-07,6.20260834541253e-07
22,fine,1,1.1594882032284204e-07,0.46103443091228286,True,1.2228265404701233e-06,2.300366759300232e-07,1.9185245037078857e-07,-1.5529244556944377e-14,-3.469446951953614e-18,7.28583859910259e-17,1.2228265559993678e-06,2.3003667593349264e-07,1.918524502979302e-07
22,fine,2,1.4032262196650436e-07,0.41123793671363457,True,2.0829029381275177e-06,1.317821443080902e-07,1.1594966053962708e-07,-1.6656814816329302e-14,-2.42861286636753e-17,2.7755575615628914e-17,2.0829029547843325e-06,1.3178214433237634e-07,1.159496605118715e-07
22,coarse,1,1.4061314457247676e-07,0.5696050037738337,True,5.517154932022095e-06,1.6167759895324707e-06,1.0058283805847168e-06,-2.643718577388654e-14,-4.996003610813204e-16,-4.579669976578771e-16,5.5171549584592805e-06,1.616775990032071e-06,1.0058283810426838e-06
23,fine,1,1.172129048265802e-07,0.43537551205849856,True,1.8416903913021088e-06,2.230517566204071e-07,2.1746382117271423e-07,-1.620752143605131e-14,-4.735795089416683e-16,1.9081958235744878e-17,1.8416904075096302e-06,2.2305175709398661e-07,2.1746382115363228e-07
23,fine,2,1.2559042006365928e-07,0.4297035689234779,True,1.998152583837509e-06,7.031485438346863e-08,4.98257577419281e-08,-1.62231339473351e-14,3.0184188481996443e-16,-3.7816971776294395e-16,1.998152600060643e-06,7.031485408162674e-08,4.982575812009782e-08
23,coarse,1,1.5135824793510722e-07,0.4407189177218073,True,1.2038275599479675e-05,1.3243407011032104e-06,7.692724466323853e-07,-2.7304547511874944e-14,1.4502288259166107e-15,-4.371503159461554e-16,1.2038275626784223e-05,1.3243406996529816e-06,7.692724470695356e-07
24,fine,1,1.1879041472706575e-07,0.3912946498849803,True,1.0398216545581818e-06,1.62515789270401e-07,1.3737007975578308e-07,-1.705927066275592e-14,1.6306400674181987e-16,-9.020562075079397e-17,1.0398216716174524e-06,1.62515789107337e-07,1.373700798459887e-07
24,fine,2,1.4235627243674998e-07,0.4195513876580241,True,1.6004778444766998e-06,1.3830140233039856e-07,1.7927959561347961e-07,-1.668977456237286e-14,-1.9949319973733282e-16,1.2663481374630692e-16,1.6004778611664744e-06,1.3830140252989176e-07,1.792795954868448e-07
24,coarse,1,1.5865499780609582e-07,0.5231448442629695,True,9.544193744659424e-06,4.5821070671081543e-07,2.942979335784912e-07,-2.545880173343562e-14,3.8163916471489756e-16,-1.457167719820518e-16,9.544193770118226e-06,4.5821070632917626e-07,2.94297933724208e-07
1 step level substep max_error max_ratio regime drift_m drift_px drift_py ideal_defect_m ideal_defect_px ideal_defect_py measured_rounding_m measured_rounding_px measured_rounding_py
2 1 fine 1 6.200234548003891e-08 0.28965071213188953 True 1.6061589121818542e-05 1.7695128917694092e-08 6.51925802230835e-09 -3.41740524767431e-16 1.214306433183765e-17 -8.673617379884035e-18 1.6061589122160283e-05 1.7695128905551027e-08 6.519258030981967e-09
3 1 fine 2 9.592780092493314e-08 0.3574847775115332 True -3.2549723982810974e-07 -1.3969838619232178e-09 -1.4435499906539917e-08 -5.4539706084710815e-15 1.3877787807814457e-17 3.469446951953614e-17 -3.2549723437413913e-07 -1.3969838758010056e-09 -1.4435499941234387e-08
4 1 coarse 1 1.1080350609260492e-08 0.10456730670501622 True -1.5292316675186157e-06 -1.30385160446167e-08 -1.5832483768463135e-07 -4.288236432614667e-15 -5.551115123125783e-17 1.3877787807814457e-17 -1.5292316632303793e-06 -1.3038515989105548e-08 -1.5832483769850914e-07
5 2 fine 1 7.754918973645886e-08 0.34580397879479957 True 8.987262845039368e-07 -2.3283064365386963e-08 -1.9744038581848145e-07 -5.167741234934908e-15 -1.5612511283791264e-17 2.931682674400804e-16 8.98726289671678e-07 -2.328306434977445e-08 -1.974403861116497e-07
6 2 fine 2 1.155261522001183e-07 0.321440603069343 True 2.284534275531769e-06 -2.8870999813079834e-08 8.66129994392395e-08 -1.1794384913166311e-14 -7.112366251504909e-17 2.203098814490545e-16 2.2845342873261537e-06 -2.887099974195617e-08 8.661299921892962e-08
7 2 coarse 1 1.6365805183271576e-08 0.1544470929589978 True -1.426786184310913e-06 -3.0919909477233887e-07 -3.0919909477233887e-07 -6.286637876939949e-15 -8.326672684688674e-17 -8.326672684688674e-17 -1.4267861780242752e-06 -3.0919909468907214e-07 -3.0919909468907214e-07
8 3 fine 1 9.149310453704373e-08 0.3150717610922152 True 4.654750227928162e-06 1.0896474123001099e-07 6.146728992462158e-08 -1.1813466871402056e-14 1.1449174941446927e-16 2.1163626406917047e-16 4.6547502397416285e-06 1.0896474111551924e-07 6.146728971298532e-08
9 3 fine 2 1.0172501863081607e-07 0.3828123813437159 True 7.561873644590378e-06 1.257285475730896e-08 5.168840289115906e-08 -1.7838161503469507e-14 8.673617379884035e-18 3.2786273695961654e-16 7.561873662428539e-06 1.2572854748635343e-08 5.168840256329632e-08
10 3 coarse 1 1.0010523732173482e-07 0.2854567053212911 True 9.115785360336304e-06 3.3155083656311035e-07 3.203749656677246e-07 -3.837208328860697e-15 1.5334955527634975e-15 1.2420620087993939e-15 9.115785364173512e-06 3.315508350296148e-07 3.203749644256626e-07
11 4 fine 1 1.1511500297434196e-07 0.3632811360585352 True 1.1234544217586517e-05 3.3155083656311035e-07 2.3469328880310059e-07 -2.0365653607967715e-14 2.5673907444456745e-16 2.393918396847994e-16 1.1234544237952171e-05 3.315508363063713e-07 2.3469328856370875e-07
12 4 fine 2 1.323716875001324e-07 0.420721981505359 True 1.0875053703784943e-05 7.171183824539185e-08 5.4016709327697754e-08 -1.6948248360293405e-14 -6.83481049534862e-16 -4.0939474033052647e-16 1.0875053720733191e-05 7.17118389288729e-08 5.4016709737092494e-08
13 4 coarse 1 1.2150177930436712e-07 0.37139422446265463 True 1.3064593076705933e-05 5.997717380523682e-07 5.62518835067749e-07 -1.2559397966072083e-14 -5.967448757360216e-16 -2.498001805406602e-16 1.306459308926533e-05 5.99771738649113e-07 5.625188353175492e-07
14 5 fine 1 1.2583242237074188e-07 0.3554646638422362 True 9.815674275159836e-06 -2.789311110973358e-07 -3.608874976634979e-07 -1.565240992373873e-14 -1.9949319973733282e-16 -3.642919299551295e-17 9.815674290812246e-06 -2.789311108978426e-07 -3.6088749762706873e-07
15 5 fine 2 1.2541811456046048e-07 0.3685724336736591 True 9.207520633935928e-06 -2.5192275643348694e-07 -4.0512531995773315e-08 -1.5307199952019346e-14 3.469446951953614e-17 4.3021142204224816e-16 9.207520649243128e-06 -2.519227564681814e-07 -4.051253242598474e-08
16 5 coarse 1 1.2532259030528792e-07 0.38942307019337585 True 1.49458646774292e-05 9.723007678985596e-07 1.2628734111785889e-06 -1.6535384173010925e-14 1.5959455978986625e-16 6.175615574477433e-16 1.4945864693964583e-05 9.72300767738965e-07 1.2628734105610273e-06
17 6 fine 1 1.0399005534456052e-07 0.4260869217553865 True 5.527399480342865e-06 -8.754432201385498e-08 -1.555308699607849e-07 -1.4764231504038605e-14 4.336808689942018e-17 3.86843335142828e-16 5.5273994951070965e-06 -8.754432205722307e-08 -1.5553087034762825e-07
18 6 fine 2 1.295425238190262e-07 0.36331211716460343 True 5.567912012338638e-06 -2.6542693376541138e-08 -2.0908191800117493e-07 -1.4812803761365956e-14 -5.186823193170653e-16 1.734723475976807e-18 5.567912027151442e-06 -2.654269285785882e-08 -2.0908191800290965e-07
19 6 coarse 1 1.0239773140519048e-07 0.43028837580529966 True 1.3461336493492126e-05 5.308538675308228e-07 4.2654573917388916e-07 -2.033095913844818e-14 2.0816681711721685e-16 1.2212453270876722e-15 1.3461336513823086e-05 5.308538673226559e-07 4.2654573795264383e-07
20 7 fine 1 1.1958961998459472e-07 0.4029625240438387 True 5.150679498910904e-06 -5.3551048040390015e-08 -7.497146725654602e-08 -1.5931700403370996e-14 -5.204170427930421e-17 -1.700029006457271e-16 5.150679514842604e-06 -5.355104798834831e-08 -7.497146708654312e-08
21 7 fine 2 1.265965450247819e-07 0.3851888208747954 True 3.789551556110382e-06 0.0 3.725290298461914e-09 -1.5173626244369132e-14 7.806255641895632e-17 3.903127820947816e-16 3.7895515712840083e-06 -7.806255641895632e-17 3.725289908149132e-09
22 7 coarse 1 1.0902046165828239e-07 0.38161058826685446 True 1.2835487723350525e-05 8.326023817062378e-07 -2.6635825634002686e-07 -2.4501234374696423e-14 -5.48172618408671e-16 -2.5673907444456745e-16 1.283548774785176e-05 8.326023822544104e-07 -2.663582560832878e-07
23 8 fine 1 1.2448945463372496e-07 0.3720247984466396 True 2.2617168724536896e-06 7.869675755500793e-08 3.259629011154175e-09 -1.5289852717259578e-14 7.28583859910259e-17 -2.636779683484747e-16 2.2617168877435423e-06 7.869675748214955e-08 3.259629274832143e-09
24 8 fine 2 1.3473279458775522e-07 0.39946262369614577 True 2.4368055164813995e-06 7.031485438346863e-08 -1.5366822481155396e-08 -1.6287318715946242e-14 4.683753385137379e-17 -1.196959198423997e-16 2.4368055327687183e-06 7.03148543366311e-08 -1.5366822361459476e-08
25 8 coarse 1 1.265961931951054e-07 0.38341345468786764 True 8.130446076393127e-06 7.506459951400757e-07 -5.830079317092896e-07 -2.2391810627908626e-14 -1.6861512186494565e-15 1.0408340855860843e-16 8.130446098784938e-06 7.506459968262269e-07 -5.83007931813373e-07
26 9 fine 1 1.3222774730703435e-07 0.3900959817916434 True 2.078711986541748e-06 8.568167686462402e-08 6.239861249923706e-08 -1.5612511283791264e-14 -2.5326962749261384e-16 2.42861286636753e-16 2.0787120021542593e-06 8.568167711789365e-08 6.239861225637577e-08
27 9 fine 2 1.2824470274619415e-07 0.4272642246416919 True 8.78702849149704e-07 9.266659617424011e-08 -1.3830140233039856e-07 -1.4988010832439613e-14 -1.6653345369377348e-16 -1.2836953722228372e-16 8.787028641377148e-07 9.266659634077357e-08 -1.3830140220202902e-07
28 9 coarse 1 1.1335454486172125e-07 0.3876201969620664 True 5.185604095458984e-06 -2.384185791015625e-07 -6.07222318649292e-07 -2.583350200424661e-14 3.122502256758253e-16 7.632783294297951e-17 5.185604121292486e-06 -2.384185794138127e-07 -6.072223187256198e-07
29 10 fine 1 1.2645846059200494e-07 0.3781818251482199 True 2.073124051094055e-06 6.426125764846802e-08 1.0151416063308716e-07 -1.6764367671839864e-14 2.949029909160572e-16 -2.1163626406917047e-16 2.073124067858423e-06 6.426125735356503e-08 1.0151416084472342e-07
30 10 fine 2 1.3663077158554415e-07 0.43888780198316496 True 1.5008263289928436e-06 7.031485438346863e-08 1.9883736968040466e-07 -1.6273440928138427e-14 -1.4051260155412137e-16 6.071532165918825e-17 1.5008263452662846e-06 7.031485452398123e-08 1.9883736961968934e-07
31 10 coarse 1 1.5257769281351585e-07 0.4176679708473079 True 4.520639777183533e-06 -5.904585123062134e-07 -3.166496753692627e-08 -2.443184543565735e-14 -4.649058915617843e-16 1.0408340855860843e-16 4.520639801615378e-06 -5.904585118413075e-07 -3.166496764100968e-08
32 11 fine 1 1.4342478449336227e-07 0.40055388671598813 True 9.159557521343231e-07 1.4388933777809143e-07 1.103617250919342e-07 -1.6368850719317152e-14 -7.979727989493313e-17 -1.3877787807814457e-17 9.159557685031738e-07 1.438893378578887e-07 1.1036172510581199e-07
33 11 fine 2 1.229973988214894e-07 0.44018383771634995 True 1.3504177331924438e-06 -2.7939677238464355e-08 -3.725290298461914e-09 -1.6167622796103842e-14 -1.942890293094024e-16 1.457167719820518e-16 1.3504177493600666e-06 -2.7939677044175326e-08 -3.725290444178686e-09
34 11 coarse 1 1.0126133054289e-07 0.37920635013850246 True 2.175569534301758e-06 -1.341104507446289e-07 1.2665987014770508e-07 -2.38073449843057e-14 9.020562075079397e-17 5.759281940243e-16 2.175569558109103e-06 -1.3411045083483453e-07 1.2665986957177688e-07
35 12 fine 1 1.2071681543268653e-07 0.4311470967916149 True 1.3746321201324463e-06 -2.1420419216156006e-08 1.4528632164001465e-07 -1.6273440928138427e-14 -2.237793284010081e-16 -4.0072112295064244e-16 1.3746321364058872e-06 -2.1420418992376677e-08 1.4528632204073577e-07
36 12 fine 2 1.371591822341145e-07 0.44227593234711177 True 1.982320100069046e-06 9.73232090473175e-08 1.448206603527069e-07 -1.6818144099595145e-14 -2.96637714392034e-16 1.214306433183765e-17 1.98232011688719e-06 9.732320934395522e-08 1.4482066034056384e-07
37 12 coarse 1 1.2813768046626706e-07 0.42388935775355246 True 3.3173710107803345e-06 -4.898756742477417e-07 -1.8067657947540283e-07 -2.7193525209412428e-14 -1.1171619185290638e-15 -4.649058915617843e-16 3.3173710379738597e-06 -4.898756731305798e-07 -1.8067657901049694e-07
38 13 fine 1 1.3950153099351326e-07 0.4396993900025004 True 1.2670643627643585e-06 2.3050233721733093e-07 1.2153759598731995e-07 -1.662038562333379e-14 3.903127820947816e-16 9.194034422677078e-17 1.2670643793847441e-06 2.3050233682701815e-07 1.215375958953796e-07
39 13 fine 2 1.2581214114959494e-07 0.4721292711508529 True 1.1995434761047363e-06 3.7159770727157593e-07 1.0710209608078003e-07 -1.5909148998183298e-14 -8.153200337090993e-17 9.540979117872439e-17 1.1995434920138853e-06 3.7159770735310793e-07 1.0710209598537024e-07
40 13 coarse 1 1.2660152992616247e-07 0.45318197065007865 True 7.878988981246948e-07 -1.210719347000122e-07 3.334134817123413e-07 -2.346040028911034e-14 -4.2327252813834093e-16 2.706168622523819e-16 7.878989215850951e-07 -1.2107193427673968e-07 3.3341348144172445e-07
41 14 fine 1 1.5054562552130335e-07 0.5104225030616341 True 6.658956408500671e-07 1.778826117515564e-07 -2.60770320892334e-08 -1.6335890973273592e-14 2.96637714392034e-16 3.86843335142828e-16 6.658956571859581e-07 1.7788261145491868e-07 -2.6077032476076734e-08
42 14 fine 2 1.3530934717120857e-07 0.4009751179733903 True 1.2051314115524292e-06 -1.955777406692505e-08 1.6205012798309326e-07 -1.6099968580540747e-14 -1.5785983631388945e-16 8.500145032286355e-17 1.2051314276523978e-06 -1.9557773909065213e-08 1.620501278980918e-07
43 14 coarse 1 1.3409460186686317e-07 0.39963893479139145 True 3.9692968130111694e-06 8.754432201385498e-08 2.9616057872772217e-07 -2.5528190672474693e-14 -6.245004513516506e-17 -6.314393452555578e-16 3.96929683853936e-06 8.754432207630503e-08 2.961605793591615e-07
44 15 fine 1 1.2702620799665e-07 0.37981873927853255 True 1.000240445137024e-06 -3.166496753692627e-08 7.729977369308472e-08 -1.607568245187707e-14 -2.1337098754514727e-16 1.4051260155412137e-16 1.0002404612127064e-06 -3.166496732355528e-08 7.729977355257212e-08
45 15 fine 2 1.1264902777252672e-07 0.42131603992753336 True 8.651986718177795e-07 -4.377216100692749e-08 -1.0151416063308716e-07 -1.5992415725030185e-14 4.336808689942018e-17 3.2959746043559335e-17 8.651986878101953e-07 -4.377216105029558e-08 -1.015141606660469e-07
46 15 coarse 1 1.305862756817433e-07 0.5086178241462508 True 1.6335397958755493e-06 -6.146728992462158e-08 2.2165477275848389e-07 -2.533390164316529e-14 -4.85722573273506e-17 2.706168622523819e-16 1.633539821209451e-06 -6.146728987604932e-08 2.2165477248786702e-07
47 16 fine 1 1.3092614292720128e-07 0.40502514569399495 True 9.629875421524048e-07 -9.685754776000977e-08 8.66129994392395e-08 -1.6838960781306866e-14 -9.194034422677078e-17 8.500145032286355e-17 9.629875589913656e-07 -9.685754766806942e-08 8.661299935423805e-08
48 16 fine 2 1.3893401662468108e-07 0.39764517507033376 True 1.5990808606147766e-06 -8.009374141693115e-08 -5.3085386753082275e-08 -1.6518036938251157e-14 3.0531133177191805e-16 3.5735303605122226e-16 1.5990808771328135e-06 -8.009374172224248e-08 -5.308538711043531e-08
49 16 coarse 1 1.2888908407227007e-07 0.4083972967001409 True 5.159527063369751e-06 -4.3213367462158203e-07 1.4901161193847656e-07 -2.6131874442114622e-14 7.216449660063518e-16 -3.885780586188048e-16 5.159527089501625e-06 -4.32133675343227e-07 1.4901161232705462e-07
50 17 fine 1 1.249536419889452e-07 0.39288318869275274 True 1.12876296043396e-06 -1.7601996660232544e-07 -2.3283064365386963e-07 -1.7957857423311907e-14 -5.551115123125783e-16 1.3530843112619095e-16 1.1287629783918174e-06 -1.7601996604721393e-07 -2.3283064378917806e-07
51 17 fine 2 1.3328934989065289e-07 0.46401164838116316 True 7.129274308681488e-07 -2.1373853087425232e-07 -2.6496127247810364e-07 -1.634803403760543e-14 -2.914335439641036e-16 9.71445146547012e-17 7.129274472161828e-07 -2.1373853058281878e-07 -2.6496127257524815e-07
52 17 coarse 1 1.246264329513025e-07 0.4613853551985751 True 5.302950739860535e-06 -2.0675361156463623e-07 1.1362135410308838e-07 -2.8373137173076657e-14 7.7021722333370235e-16 -6.869504964868156e-16 5.302950768233672e-06 -2.0675361233485345e-07 1.1362135479003888e-07
53 18 fine 1 1.5840482392448507e-07 0.48249679461224454 True 8.10716301202774e-07 -1.4016404747962952e-07 -2.6402994990348816e-07 -1.6878859421254333e-14 7.632783294297951e-17 2.0816681711721685e-17 8.107163180816335e-07 -1.4016404755595735e-07 -2.6402994992430484e-07
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71 24 fine 1 1.1879041472706575e-07 0.3912946498849803 True 1.0398216545581818e-06 1.62515789270401e-07 1.3737007975578308e-07 -1.705927066275592e-14 1.6306400674181987e-16 -9.020562075079397e-17 1.0398216716174524e-06 1.62515789107337e-07 1.373700798459887e-07
72 24 fine 2 1.4235627243674998e-07 0.4195513876580241 True 1.6004778444766998e-06 1.3830140233039856e-07 1.7927959561347961e-07 -1.668977456237286e-14 -1.9949319973733282e-16 1.2663481374630692e-16 1.6004778611664744e-06 1.3830140252989176e-07 1.792795954868448e-07
73 24 coarse 1 1.5865499780609582e-07 0.5231448442629695 True 9.544193744659424e-06 4.5821070671081543e-07 2.942979335784912e-07 -2.545880173343562e-14 3.8163916471489756e-16 -1.457167719820518e-16 9.544193770118226e-06 4.5821070632917626e-07 2.94297933724208e-07
research/fine_surface_joint_20261014/blow_ledger.csv
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step,time,average_bound_ratio,leaf_m,leaf_px,leaf_py,delta_m,delta_px,delta_py,boundary_m,boundary_px,boundary_py,fine_transfer_m,fine_transfer_px,fine_transfer_py,coarse_transfer_m,coarse_transfer_px,coarse_transfer_py,raw_m,raw_px,raw_py,collision_drift_m,collision_drift_px,collision_drift_py,average_rounding_m,average_rounding_px,average_rounding_py,closure_m,closure_px,closure_py,cumulative_raw_m,cumulative_raw_px,cumulative_raw_py,cumulative_closure_m,cumulative_closure_px,cumulative_closure_py
1,1,0.28124994289828603,560.7534663206898,0.0016594058834016323,0.0014196881093084812,0.003462142776697874,0.0016594058834016323,0.0014196881093084812,0.0034477608278393745,0.0016594044864177704,0.001420031301677227,-3.557652235031128e-07,0.0,0.0,5.308538675308228e-07,-1.862645149230957e-09,-1.7695128917694092e-07,1.4206860214471817e-05,3.259629011154175e-09,-1.6624107956886292e-07,1.4206860214471817e-05,3.259629011154175e-09,-1.6624107956886292e-07,1.7508864402770996e-07,-1.862645149230957e-09,-1.7695128917694092e-07,0.0,0.0,0.0,1.4206860214471817e-05,3.259629011154175e-09,-1.6624107956886292e-07,0.0,0.0,0.0
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13,13,0.37499972898527817,560.8006243831478,0.01216893782839179,0.010273906867951155,0.0039300331845879555,0.0003994964063167572,0.0003364281728863716,0.003926742821931839,0.00039900559931993484,0.00033587682992219925,-0.004740437027066946,-0.0014326325617730618,-0.0012463550083339214,0.004740472882986069,0.0014326423406600952,0.0012463442981243134,3.2545067369937897e-06,4.810281097888947e-07,5.620531737804413e-07,3.2545067369937897e-06,4.810281097888947e-07,5.620531737804413e-07,3.585591912269592e-08,9.778887033462524e-09,-1.0710209608078003e-08,0.0,0.0,0.0,0.00019620871171355247,3.16789373755455e-06,1.1795200407505035e-06,0.0,0.0,0.0
14,14,0.4687474688530803,560.8045558938757,0.012523647397756577,0.010573056526482105,0.003931510727852583,0.00035470956936478615,0.00029914965853095055,0.003925675991922617,0.00035448791459202766,0.00029869796708226204,-0.005264284089207649,-0.0015086885541677475,-0.0013435659930109978,0.0052642785012722015,0.0015086643397808075,0.0013435855507850647,5.840323865413666e-06,2.4586915969848633e-07,4.3213367462158203e-07,5.840323865413666e-06,2.4586915969848633e-07,4.3213367462158203e-07,-5.587935447692871e-09,-2.421438694000244e-08,1.955777406692505e-08,0.0,0.0,0.0,0.00020204903557896614,3.4137628972530365e-06,1.6116537153720856e-06,0.0,0.0,0.0
15,15,0.4682317851482336,560.8084840090014,0.012841148767620325,0.010841074865311384,0.003928115125745535,0.00031750136986374855,0.00026801833882927895,0.003924637567251921,0.00031766900792717934,0.00026782089844346046,-0.005472308956086636,-0.0014677206054329872,-0.001318071037530899,0.005472287535667419,0.001467689871788025,0.001318071037530899,3.4989789128303528e-06,-1.3690441846847534e-07,1.9744038581848145e-07,3.4989789128303528e-06,-1.3690441846847534e-07,1.9744038581848145e-07,-2.1420419216156006e-08,-3.073364496231079e-08,0.0,0.0,0.0,0.0,0.0002055480144917965,3.276858478784561e-06,1.809094101190567e-06,0.0,0.0,0.0
16,16,0.4687439328019107,560.8124153451063,0.013128423597663641,0.011083992663770914,0.0039313361048698425,0.0002872748300433159,0.00024291779845952988,0.003923660609871149,0.0002878890372812748,0.00024270685389637947,-0.005496661644428968,-0.0013386714272201061,-0.0011936011724174023,0.005496615543961525,0.0013386663049459457,0.001193629577755928,7.721595466136932e-06,-6.09084963798523e-07,1.825392246246338e-07,7.721595466136932e-06,-6.09084963798523e-07,1.825392246246338e-07,-4.6100467443466187e-08,-5.122274160385132e-09,2.8405338525772095e-08,0.0,0.0,0.0,0.00021326960995793343,2.667773514986038e-06,1.991633325815201e-06,0.0,0.0,0.0
17,17,0.46873638940438433,560.8163452413864,0.013391933869570494,0.011306562926620245,0.003929896280169487,0.0002635102719068527,0.0002225702628493309,0.003922717645764351,0.0002640876919031143,0.00022296980023384094,-0.005448666866868734,-0.0011560707353055477,-0.0010099620558321476,0.005448700860142708,0.0011560898274183273,0.0010099466890096664,7.1446411311626434e-06,-5.96512109041214e-07,-3.841705620288849e-07,7.1446411311626434e-06,-5.96512109041214e-07,-3.841705620288849e-07,3.3993273973464966e-08,1.909211277961731e-08,-1.5366822481155396e-08,0.0,0.0,0.0,0.00022041425108909607,2.0712614059448242e-06,1.607462763786316e-06,0.0,0.0,0.0
18,18,0.4687180348178792,560.8202739986591,0.013636329676955938,0.011513229925185442,0.003928757272660732,0.00024439580738544464,0.000206666998565197,0.003921979106962681,0.00024497322738170624,0.0002071000635623932,-0.00538009125739336,-0.0009485194459557533,-0.0008049774914979935,0.005380067974328995,0.0009484924376010895,0.0008049644529819489,6.80144876241684e-06,-5.504116415977478e-07,-4.200264811515808e-07,6.80144876241684e-06,-5.504116415977478e-07,-4.200264811515808e-07,-2.3283064365386963e-08,-2.7008354663848877e-08,-1.30385160446167e-08,0.0,0.0,0.0,0.0002272156998515129,1.5208497643470764e-06,1.1874362826347351e-06,0.0,0.0,0.0
19,19,0.4687494647810733,560.8242048402317,0.013865197543054819,0.011706916149705648,0.003930841572582722,0.00022886786609888077,0.00019368622452020645,0.0039215972647070885,0.00022865179926156998,0.00019369274377822876,-0.005283902399241924,-0.0007343888282775879,-0.0006027696654200554,0.005283888429403305,0.0007343888282775879,0.0006027519702911377,9.258277714252472e-06,2.1606683731079102e-07,1.1175870895385742e-08,9.258277714252472e-06,2.1606683731079102e-07,1.1175870895385742e-08,-1.3969838619232178e-08,0.0,-1.7695128917694092e-08,0.0,0.0,0.0,0.00023647397756576538,1.7369166016578674e-06,1.1986121535301208e-06,0.0,0.0,0.0
20,20,0.468749291904748,560.8281343518756,0.01407972676679492,0.011888547334820032,0.003929511643946171,0.00021452922374010086,0.0001816311851143837,0.0039214082062244415,0.00021384842693805695,0.00018165074288845062,-0.005120935384184122,-0.0005169562064111233,-0.00041141873225569725,0.005120944231748581,0.0005169659852981567,0.00041141733527183533,8.094590157270432e-06,6.710179150104523e-07,-1.816079020500183e-08,8.094590157270432e-06,6.710179150104523e-07,-1.816079020500183e-08,8.847564458847046e-09,9.778887033462524e-09,-1.3969838619232178e-09,0.0,0.0,0.0,0.0002445685677230358,2.4079345166683197e-06,1.180451363325119e-06,0.0,0.0,0.0
21,21,0.4686316241476506,560.8320638332516,0.014281701296567917,0.012059765867888927,0.003929481375962496,0.0002019745297729969,0.00017121853306889534,0.003921300172805786,0.000200699083507061,0.00017050933092832565,-0.004840303678065538,-0.00029240502044558525,-0.00022668996825814247,0.004840286448597908,0.00029242970049381256,0.00022671185433864594,8.198432624340057e-06,1.2507662177085876e-06,6.873160600662231e-07,8.198432624340057e-06,1.2507662177085876e-06,6.873160600662231e-07,-1.7229467630386353e-08,2.468004822731018e-08,2.1886080503463745e-08,0.0,0.0,0.0,0.00025276700034737587,3.6587007343769073e-06,1.8677674233913422e-06,0.0,0.0,0.0
22,22,0.46868277541627273,560.835993598681,0.014473678078502417,0.012222256977111101,0.0039297654293477535,0.00019197678193449974,0.0001624911092221737,0.003920916933566332,0.00018997164443135262,0.0001611732877790928,-0.004407043103128672,-5.385419353842735e-05,-3.726920112967491e-05,0.004407068714499474,5.3880736231803894e-05,3.727339208126068e-05,8.822884410619736e-06,1.978594809770584e-06,1.3136304914951324e-06,8.822884410619736e-06,1.978594809770584e-06,1.3136304914951324e-06,2.561137080192566e-08,2.6542693376541138e-08,4.190951585769653e-09,0.0,0.0,0.0,0.0002615898847579956,5.6372955441474915e-06,3.1813979148864746e-06,0.0,0.0,0.0
23,23,0.4687343150126283,560.8399298018776,0.014657547231763601,0.01237749820575118,0.003936203196644783,0.0001838691532611847,0.0001552412286400795,0.003920321352779865,0.000182238407433033,0.000154225155711174,-0.0038209911435842514,0.0001984424889087677,0.00016180798411369324,0.00382099486887455,-0.00019842945039272308,-0.00016182847321033478,1.5878118574619293e-05,1.6177073121070862e-06,1.0365620255470276e-06,1.5878118574619293e-05,1.6177073121070862e-06,1.0365620255470276e-06,3.725290298461914e-09,1.30385160446167e-08,-2.0489096641540527e-08,0.0,0.0,0.0,0.0002774680033326149,7.255002856254578e-06,4.217959940433502e-06,0.0,0.0,0.0
24,24,0.46876041911371336,560.8438616539352,0.014835720416158438,0.012527641374617815,0.0039318520575761795,0.00017817318439483643,0.00015014316886663437,0.003919683396816254,0.0001774495467543602,0.00014953874051570892,-0.003126208670437336,0.0004524495452642441,0.0003674151375889778,0.0031261928379535675,-0.00045248493552207947,-0.0003674216568470001,1.2184493243694305e-05,7.59027898311615e-07,6.109476089477539e-07,1.2184493243694305e-05,7.59027898311615e-07,6.109476089477539e-07,-1.5832483768463135e-08,-3.5390257835388184e-08,-6.51925802230835e-09,0.0,0.0,0.0,0.0002896524965763092,8.014030754566193e-06,4.828907549381256e-06,0.0,0.0,0.0
1 step time average_bound_ratio leaf_m leaf_px leaf_py delta_m delta_px delta_py boundary_m boundary_px boundary_py fine_transfer_m fine_transfer_px fine_transfer_py coarse_transfer_m coarse_transfer_px coarse_transfer_py raw_m raw_px raw_py collision_drift_m collision_drift_px collision_drift_py average_rounding_m average_rounding_px average_rounding_py closure_m closure_px closure_py cumulative_raw_m cumulative_raw_px cumulative_raw_py cumulative_closure_m cumulative_closure_px cumulative_closure_py
2 1 1 0.28124994289828603 560.7534663206898 0.0016594058834016323 0.0014196881093084812 0.003462142776697874 0.0016594058834016323 0.0014196881093084812 0.0034477608278393745 0.0016594044864177704 0.001420031301677227 -3.557652235031128e-07 0.0 0.0 5.308538675308228e-07 -1.862645149230957e-09 -1.7695128917694092e-07 1.4206860214471817e-05 3.259629011154175e-09 -1.6624107956886292e-07 1.4206860214471817e-05 3.259629011154175e-09 -1.6624107956886292e-07 1.7508864402770996e-07 -1.862645149230957e-09 -1.7695128917694092e-07 0.0 0.0 0.0 1.4206860214471817e-05 3.259629011154175e-09 -1.6624107956886292e-07 0.0 0.0 0.0
3 2 2 0.37499987985940486 560.7572004375979 0.003110479563474655 0.002638322301208973 0.0037341169081628323 0.0014510736800730228 0.0012186341919004917 0.003732521552592516 0.0014515905641019344 0.0012190532870590687 -2.193264663219452e-06 -4.6566128730773926e-09 -1.685693860054016e-07 2.032145857810974e-06 -1.5087425708770752e-07 1.695007085800171e-07 1.7564743757247925e-06 -3.6135315895080566e-07 -4.200264811515808e-07 1.7564743757247925e-06 -3.6135315895080566e-07 -4.200264811515808e-07 -1.6111880540847778e-07 -1.555308699607849e-07 9.313225746154785e-10 0.0 0.0 0.0 1.596333459019661e-05 -3.580935299396515e-07 -5.862675607204437e-07 0.0 0.0 0.0
4 3 3 0.2812499146093721 560.7611049492843 0.004511175211519003 0.0038183839060366154 0.00390451168641448 0.0014006956480443478 0.0011800616048276424 0.0038831778801977634 0.0014002365060150623 0.0011796201579272747 -3.6400742828845978e-06 4.698522388935089e-07 4.6798959374427795e-07 3.641471266746521e-06 -4.637986421585083e-07 -4.600733518600464e-07 2.1332409232854843e-05 4.530884325504303e-07 4.3353065848350525e-07 2.1332409232854843e-05 4.530884325504303e-07 4.3353065848350525e-07 1.3969838619232178e-09 6.05359673500061e-09 7.916241884231567e-09 0.0 0.0 0.0 3.729574382305145e-05 9.499490261077881e-08 -1.5273690223693848e-07 0.0 0.0 0.0
5 4 4 0.37499987985940486 560.7650738614611 0.005782689433544874 0.004893188830465078 0.003968912176787853 0.0012715142220258713 0.001074804924428463 0.003933848347514868 0.0012704567052423954 0.001073895487934351 -6.910879164934158e-06 3.1711533665657043e-07 3.9534643292427063e-07 6.800517439842224e-06 -2.6263296604156494e-07 -3.371387720108032e-07 3.517419099807739e-05 1.0030344128608704e-06 8.512288331985474e-07 3.517419099807739e-05 1.0030344128608704e-06 8.512288331985474e-07 -1.103617250919342e-07 5.448237061500549e-08 5.820766091346741e-08 0.0 0.0 0.0 7.246993482112885e-05 1.0980293154716492e-06 6.984919309616089e-07 0.0 0.0 0.0
6 5 5 0.3749998295680158 560.7690546456724 0.006910111755132675 0.005846448242664337 0.003980784211307764 0.001127422321587801 0.0009532594121992588 0.0039466856978833675 0.001126923132687807 0.0009523364715278149 -7.287599146366119e-06 1.0672956705093384e-06 4.637986421585083e-07 7.417052984237671e-06 -1.0095536708831787e-06 -4.023313522338867e-07 3.396905958652496e-05 4.414469003677368e-07 8.614733815193176e-07 3.396905958652496e-05 4.414469003677368e-07 8.614733815193176e-07 1.2945383787155151e-07 5.774199962615967e-08 6.146728992462158e-08 0.0 0.0 0.0 0.00010643899440765381 1.539476215839386e-06 1.5599653124809265e-06 0.0 0.0 0.0
7 6 6 0.46874973195382685 560.7730254461057 0.007904952857643366 0.006686040665954351 0.003970800433307886 0.0009948411025106907 0.0008395924232900143 0.003946303389966488 0.0009944625198841095 0.00083962082862854 -6.226822733879089e-06 2.0954757928848267e-07 -1.8533319234848022e-07 6.167218089103699e-06 -2.477318048477173e-07 9.499490261077881e-08 2.455664798617363e-05 4.1676685214042664e-07 6.193295121192932e-08 2.455664798617363e-05 4.1676685214042664e-07 6.193295121192932e-08 -5.960464477539063e-08 -3.818422555923462e-08 -9.033828973770142e-08 0.0 0.0 0.0 0.00013099564239382744 1.9562430679798126e-06 1.6218982636928558e-06 0.0 0.0 0.0
8 7 7 0.4687496533735736 560.7769900038838 0.008779809810221195 0.007423080503940582 0.003964557778090239 0.0008748569525778294 0.0007370398379862309 0.003942790906876326 0.0008741165511310101 0.0007373974658548832 -2.6883091777563095e-05 -1.3766344636678696e-05 -7.656868547201157e-06 2.6874244213104248e-05 1.3727694749832153e-05 7.636845111846924e-06 2.177571877837181e-05 7.790513336658478e-07 -3.3760443329811096e-07 2.177571877837181e-05 7.790513336658478e-07 -3.3760443329811096e-07 -8.847564458847046e-09 -3.864988684654236e-08 -2.0023435354232788e-08 0.0 0.0 0.0 0.00015277136117219925 2.7352944016456604e-06 1.2842938303947449e-06 0.0 0.0 0.0
9 8 8 0.4687496533735736 560.7809417177923 0.009547093417495489 0.00806809077039361 0.0039517139084637165 0.0007672836072742939 0.0006450102664530277 0.0039388518780469894 0.0007664291188120842 0.0006456412374973297 -0.00016948627308011055 -8.736597374081612e-05 -5.452195182442665e-05 0.0001695193350315094 8.732080459594727e-05 5.4486095905303955e-05 1.2828968465328217e-05 8.996576070785522e-07 -5.951151251792908e-07 1.2828968465328217e-05 8.996576070785522e-07 -5.951151251792908e-07 3.306195139884949e-08 -4.516914486885071e-08 -3.585591912269592e-08 0.0 0.0 0.0 0.00016560032963752747 3.6349520087242126e-06 6.891787052154541e-07 0.0 0.0 0.0
10 9 9 0.4686406250816172 560.7848850805312 0.010217653587460518 0.008631827309727669 0.0039433627389371395 0.0006705601699650288 0.0005637365393340588 0.00393525930121541 0.0006706290878355503 0.0005643940530717373 -0.000617987010627985 -0.0002765483222901821 -0.00019021378830075264 0.0006179474294185638 0.00027653947472572327 0.00019023939967155457 8.143018931150436e-06 -6.007030606269836e-08 -6.831251084804535e-07 8.143018931150436e-06 -6.007030606269836e-08 -6.831251084804535e-07 -3.958120942115784e-08 -8.847564458847046e-09 2.561137080192566e-08 0.0 0.0 0.0 0.0001737433485686779 3.5748817026615143e-06 6.05359673500061e-09 0.0 0.0 0.0
11 10 10 0.4685945715880829 560.7888254267164 0.010803885292261839 0.00912527134642005 0.00394034618511796 0.000586231704801321 0.0004934440366923809 0.0039321803487837315 0.000586662907153368 0.0004931562580168247 -0.0014882697723805904 -0.0005795764736831188 -0.00043088337406516075 0.0014883410185575485 0.0005796011537313461 0.00043090246617794037 8.094590157270432e-06 -4.5588240027427673e-07 2.6868656277656555e-07 8.094590157270432e-06 -4.5588240027427673e-07 2.6868656277656555e-07 7.12461769580841e-08 2.468004822731018e-08 1.909211277961731e-08 0.0 0.0 0.0 0.00018183793872594833 3.1189993023872375e-06 2.7474015951156616e-07 0.0 0.0 0.0
12 11 11 0.37499977927664024 560.7927596913651 0.011317913420498371 0.009557455778121948 0.003934264648705721 0.0005140281282365322 0.0004321844317018986 0.003929825499653816 0.0005140695720911026 0.0004319213330745697 -0.0026630824431777 -0.0009266296401619911 -0.0007359907031059265 0.002663079649209976 0.0009266063570976257 0.0007360205054283142 4.441943019628525e-06 -1.816079020500183e-08 2.3329630494117737e-07 4.441943019628525e-06 -1.816079020500183e-08 2.3329630494117737e-07 -2.7939677238464355e-09 -2.3283064365386963e-08 2.9802322387695312e-08 0.0 0.0 0.0 0.00018627988174557686 3.1008385121822357e-06 5.080364644527435e-07 0.0 0.0 0.0
13 12 12 0.46874663591177923 560.7966943499632 0.011769441422075033 0.009937478695064783 0.003934658598154783 0.00045152800157666206 0.00038002291694283485 0.003928040154278278 0.00045189354568719864 0.0003798864781856537 -0.0038390979170799255 -0.0012310631573200226 -0.0010304069146513939 0.0038390420377254486 0.0012311115860939026 0.0010304339230060577 6.674323230981827e-06 -4.139728844165802e-07 1.0943040251731873e-07 6.674323230981827e-06 -4.139728844165802e-07 1.0943040251731873e-07 -5.587935447692871e-08 4.842877388000488e-08 2.7008354663848877e-08 0.0 0.0 0.0 0.00019295420497655869 2.6868656277656555e-06 6.174668669700623e-07 0.0 0.0 0.0
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research/fine_surface_joint_20261014/blow_propagation.csv
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@@ -0,0 +1,97 @@
metric,time,signal,baseline,absolute,any_crossed,peak_crossed,x,y,q
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fine_band_neq,7,2.0898831959437203e-06,9.934099098884985e-09,2.0799490968448353e-06,True,True,19.75,13.25,
fine_band_neq,8,5.086951299526632e-06,9.934099098884985e-09,5.077017200427747e-06,True,True,19.75,13.75,
fine_band_neq,9,7.3187303596400355e-06,1.1589783133203468e-08,7.307140576506832e-06,True,True,19.75,14.25,
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fine_band_neq,17,3.59763095919563e-06,6.622736969941201e-09,3.598458797438031e-06,True,True,19.75,17.75,
fine_band_neq,18,3.1864885083332517e-06,1.7384672978959514e-08,3.2038731813122112e-06,True,True,19.75,18.75,
fine_band_neq,19,3.2079558324737967e-06,9.934106745546067e-09,3.2054722903795962e-06,True,True,19.75,19.75,
fine_band_neq,20,3.2478981990546885e-06,8.278412177986638e-09,3.256176611232675e-06,True,True,19.75,19.75,
fine_band_neq,21,2.8408409309954585e-06,1.1589792486832451e-08,2.83256250521724e-06,True,True,19.75,19.75,
fine_band_neq,22,2.2599308021065134e-06,1.241763983306221e-09,2.2599308009962904e-06,True,True,19.75,19.75,
fine_band_neq,23,1.7067130013148635e-06,2.0696062086322797e-09,1.7075408424993554e-06,True,True,19.75,19.75,
fine_band_neq,24,1.7691307190870198e-06,7.864502313270627e-09,1.768302879068262e-06,True,True,4.25,8.25,
transfer_neq,1,0.0,1.5004641487625925e-09,1.5004641487625925e-09,False,False,3.5,3.5,6
transfer_neq,2,0.0,3.000928290586291e-09,3.000928290586291e-09,False,False,3.5,3.5,6
transfer_neq,3,0.0,3.000928290586291e-09,3.000928290586291e-09,False,False,3.5,3.5,6
transfer_neq,4,0.0,6.881438113781613e-09,6.881438113781613e-09,False,False,3.5,3.5,6
transfer_neq,5,0.0,6.467520081987477e-09,6.467520081987477e-09,False,False,3.5,3.5,6
transfer_neq,6,4.801482146599145e-08,1.7384695072397705e-08,3.063012639359375e-08,False,False,20.5,12.5,1
transfer_neq,7,9.917490157301811e-07,1.4901174696935193e-08,9.76847841033246e-07,True,True,20.5,13.5,1
transfer_neq,8,5.3319513640959215e-06,7.450589978308386e-09,5.324500774117613e-06,True,True,20.5,13.5,1
transfer_neq,9,1.3926900000016396e-05,4.967061900673642e-09,1.3921932938115722e-05,True,True,20.5,13.5,1
transfer_neq,10,2.2683459353481372e-05,7.0366557441969846e-09,2.269049600922557e-05,True,True,20.5,13.5,1
transfer_neq,11,2.6366830318105827e-05,8.692339972804497e-09,2.6375522658078632e-05,True,True,20.5,14.5,1
transfer_neq,12,2.307753771932375e-05,1.0761948318616099e-08,2.3088299667642365e-05,True,True,20.5,14.5,1
transfer_neq,13,1.804053278801898e-05,6.6227364148296886e-09,1.804715552443381e-05,True,True,16.5,20.5,3
transfer_neq,14,1.3273871487681776e-05,3.725288785783043e-09,1.327759677646756e-05,True,True,17.5,20.5,3
transfer_neq,15,1.109810416584367e-05,1.7074248230553035e-09,1.1096396741020614e-05,True,True,20.5,20.5,5
transfer_neq,16,1.0547037354428218e-05,3.777030622292843e-09,1.0550814385050511e-05,True,True,20.5,20.5,5
transfer_neq,17,7.76073680465103e-06,1.5522043823879006e-09,7.762289009033418e-06,True,True,20.5,20.5,5
transfer_neq,18,6.477044739522375e-06,2.4835230261199825e-09,6.479528262548495e-06,True,True,16.5,3.5,4
transfer_neq,19,6.888961226400592e-06,4.553130164564045e-09,6.893514356565156e-06,True,True,16.5,3.5,4
transfer_neq,20,8.081834403372845e-06,1.6556842147297246e-09,8.080178719158115e-06,True,True,3.5,15.5,2
transfer_neq,21,9.19306995308955e-06,4.553133203799575e-09,9.19762308629335e-06,True,True,3.5,15.5,2
transfer_neq,22,9.90222802181584e-06,1.241762831449833e-09,9.90346978464729e-06,True,True,3.5,15.5,2
transfer_neq,23,9.820215213826589e-06,3.725291089495819e-09,9.823940504916084e-06,True,True,3.5,16.5,2
transfer_neq,24,9.034586770997888e-06,6.208816516473092e-09,9.028377954481415e-06,True,True,3.5,17.5,2
1 metric time signal baseline absolute any_crossed peak_crossed x y q
2 coarse_interface_u 1 0.0 0.0 0.0 False False 3.5 3.5
3 coarse_interface_u 2 0.0 2.63417801721126e-09 2.63417801721126e-09 False False 3.5 3.5
4 coarse_interface_u 3 0.0 2.63417801721126e-09 2.63417801721126e-09 False False 3.5 3.5
5 coarse_interface_u 4 0.0 6.585443742795605e-08 6.585443742795605e-08 False False 3.5 3.5
6 coarse_interface_u 5 0.0 1.2644051067663912e-07 1.2644051067663912e-07 False False 3.5 3.5
7 coarse_interface_u 6 2.123413592043705e-07 1.4528626697542904e-07 3.5762762617979955e-07 False False 20.5 12.5
8 coarse_interface_u 7 4.0664670353703905e-06 2.0116559817931723e-07 4.267265792606936e-06 True True 20.5 13.5
9 coarse_interface_u 8 2.316397368189694e-05 1.5646213891164667e-07 2.3320343327342335e-05 True True 20.5 13.5
10 coarse_interface_u 9 6.795742008554701e-05 1.5646213891164667e-07 6.81138583592495e-05 True True 20.5 13.5
11 coarse_interface_u 10 0.00013453387391202865 9.750014426779573e-08 0.00013463070833560589 True True 20.5 13.5
12 coarse_interface_u 11 0.00020315493961354394 1.1795114315203827e-07 0.00020326776624343072 True True 20.5 13.5
13 coarse_interface_u 12 0.0002578325145358002 7.455233297611785e-08 0.00025790659921174714 True True 20.5 14.5
14 coarse_interface_u 13 0.0002848355625376438 3.83541997932092e-08 0.0002848672209318115 True True 20.5 14.5
15 coarse_interface_u 14 0.00028850325159438407 3.003423681142711e-08 0.00028847339892346693 True True 20.5 15.5
16 coarse_interface_u 15 0.0002790426599299092 6.516592454722021e-08 0.00027900289954439403 True True 20.5 16.5
17 coarse_interface_u 16 0.0002656231695763881 1.006517304071618e-07 0.0002656687325338991 True True 20.5 17.5
18 coarse_interface_u 17 0.00025167224259872663 9.86322156278415e-08 0.00025171418857411254 True True 20.5 18.5
19 coarse_interface_u 18 0.00023917435962541014 1.0286787296380317e-07 0.00023916330914968713 True True 20.5 19.5
20 coarse_interface_u 19 0.0002264648202014007 7.783088195871973e-08 0.00022649832134267317 True True 20.5 20.5
21 coarse_interface_u 20 0.00021450009842274482 8.503127640307697e-08 0.0002145262119900782 True True 20.5 20.5
22 coarse_interface_u 21 0.00019565444290966713 7.893743237430962e-08 0.00019572665753668725 True True 20.5 20.5
23 coarse_interface_u 22 0.00017530342242023255 5.2749351285234925e-08 0.0001753561669734725 True True 20.5 20.5
24 coarse_interface_u 23 0.00016284198169591366 8.178685339035058e-08 0.00016288798599953632 True True 20.5 14.5
25 coarse_interface_u 24 0.00016001299284349154 6.715860617082587e-08 0.00016005458983486885 True True 20.5 14.5
26 coarse_bulk_u 1 0.0 0.0 0.0 False False 0.5 0.5
27 coarse_bulk_u 2 0.0 0.0 0.0 False False 0.5 0.5
28 coarse_bulk_u 3 0.0 0.0 0.0 False False 0.5 0.5
29 coarse_bulk_u 4 0.0 0.0 0.0 False False 0.5 0.5
30 coarse_bulk_u 5 0.0 0.0 0.0 False False 0.5 0.5
31 coarse_bulk_u 6 0.0 0.0 0.0 False False 0.5 0.5
32 coarse_bulk_u 7 9.685747920378016e-08 2.12341462371735e-07 3.0919894157551517e-07 False False 21.5 12.5
33 coarse_bulk_u 8 1.885523449607647e-06 2.458690369633922e-07 2.131116250194691e-06 True False 21.5 13.5
34 coarse_bulk_u 9 1.1801222962468464e-05 1.564621179284487e-07 1.1957591962850096e-05 True True 21.5 13.5
35 coarse_bulk_u 10 3.7763148161723895e-05 1.0327184058071679e-07 3.786536052602384e-05 True True 21.5 13.5
36 coarse_bulk_u 11 8.290997527773004e-05 4.214683516511236e-08 8.293984645156337e-05 True True 21.5 13.5
37 coarse_bulk_u 12 0.00013975592467875463 5.890198697622995e-09 0.00013975397806857782 True True 21.5 13.5
38 coarse_bulk_u 13 0.00019498935130707817 4.806920811870088e-08 0.00019495727755690072 True True 21.5 14.5
39 coarse_bulk_u 14 0.00023699059195898703 3.215422633434082e-08 0.00023698211801486242 True True 21.5 14.5
40 coarse_bulk_u 15 0.000255587117158192 3.434546877545681e-08 0.0002555629409290573 True True 21.5 14.5
41 coarse_bulk_u 16 0.00025699080829703886 0.0 0.00025699080829703886 True True 21.5 15.5
42 coarse_bulk_u 17 0.00024815159847585574 6.606481633104396e-08 0.0002482080995082901 True True 21.5 16.5
43 coarse_bulk_u 18 0.00023639704637690832 6.929437569595666e-08 0.00023645799303108044 True True 21.5 17.5
44 coarse_bulk_u 19 0.00022522644482484474 1.0390817551817351e-07 0.00022530812767909556 True True 21.5 19.5
45 coarse_bulk_u 20 0.0002151574621150763 1.042082306083734e-07 0.00021524519922552202 True True 22.5 18.5
46 coarse_bulk_u 21 0.00020618043430475382 9.442706670805767e-08 0.00020625960942046836 True True 21.5 20.5
47 coarse_bulk_u 22 0.0001977039381507293 5.556802130737047e-08 0.00019775833634301496 True True 22.5 20.5
48 coarse_bulk_u 23 0.00018973381549205 4.4703464263670685e-08 0.00018970107840731907 True True 22.5 21.5
49 coarse_bulk_u 24 0.00018031585591623997 9.995996742145265e-08 0.0001802207328329628 True True 22.5 22.5
50 fine_band_neq 1 0.0 3.000928300994632e-09 3.000928300994632e-09 False False 4.25 4.25
51 fine_band_neq 2 0.0 4.501393077727123e-09 4.501393077727123e-09 False False 4.25 4.25
52 fine_band_neq 3 0.0 2.069606169774474e-08 2.069606169774474e-08 False False 4.25 4.25
53 fine_band_neq 4 0.0 4.1392115429639276e-09 4.1392115429639276e-09 False False 4.25 4.25
54 fine_band_neq 5 0.0 4.1392115429639276e-09 4.1392115429639276e-09 False False 4.25 4.25
55 fine_band_neq 6 3.8661114470928126e-07 9.934099098884985e-09 3.766770456103963e-07 True True 19.75 13.25
56 fine_band_neq 7 2.0898831959437203e-06 9.934099098884985e-09 2.0799490968448353e-06 True True 19.75 13.25
57 fine_band_neq 8 5.086951299526632e-06 9.934099098884985e-09 5.077017200427747e-06 True True 19.75 13.75
58 fine_band_neq 9 7.3187303596400355e-06 1.1589783133203468e-08 7.307140576506832e-06 True True 19.75 14.25
59 fine_band_neq 10 7.498180437826019e-06 9.106256526614231e-09 7.489074181299404e-06 True True 19.75 15.25
60 fine_band_neq 11 6.85740365241605e-06 5.794905943989903e-09 6.86319855836004e-06 True True 19.75 17.25
61 fine_band_neq 12 6.20376639004494e-06 6.622735776451449e-09 6.200455026694751e-06 True True 19.75 18.25
62 fine_band_neq 13 5.366229072267803e-06 4.139212333997833e-09 5.369540443278531e-06 True True 19.75 19.25
63 fine_band_neq 14 5.300103189165295e-06 4.967056432825245e-09 5.30507024559812e-06 True True 19.75 19.75
64 fine_band_neq 15 4.0156351262532475e-06 4.553132745832578e-09 4.018946498263176e-06 True True 19.75 19.75
65 fine_band_neq 16 3.7773208383384826e-06 6.622738135675377e-09 3.783943576474158e-06 True True 19.75 16.75
66 fine_band_neq 17 3.59763095919563e-06 6.622736969941201e-09 3.598458797438031e-06 True True 19.75 17.75
67 fine_band_neq 18 3.1864885083332517e-06 1.7384672978959514e-08 3.2038731813122112e-06 True True 19.75 18.75
68 fine_band_neq 19 3.2079558324737967e-06 9.934106745546067e-09 3.2054722903795962e-06 True True 19.75 19.75
69 fine_band_neq 20 3.2478981990546885e-06 8.278412177986638e-09 3.256176611232675e-06 True True 19.75 19.75
70 fine_band_neq 21 2.8408409309954585e-06 1.1589792486832451e-08 2.83256250521724e-06 True True 19.75 19.75
71 fine_band_neq 22 2.2599308021065134e-06 1.241763983306221e-09 2.2599308009962904e-06 True True 19.75 19.75
72 fine_band_neq 23 1.7067130013148635e-06 2.0696062086322797e-09 1.7075408424993554e-06 True True 19.75 19.75
73 fine_band_neq 24 1.7691307190870198e-06 7.864502313270627e-09 1.768302879068262e-06 True True 4.25 8.25
74 transfer_neq 1 0.0 1.5004641487625925e-09 1.5004641487625925e-09 False False 3.5 3.5 6
75 transfer_neq 2 0.0 3.000928290586291e-09 3.000928290586291e-09 False False 3.5 3.5 6
76 transfer_neq 3 0.0 3.000928290586291e-09 3.000928290586291e-09 False False 3.5 3.5 6
77 transfer_neq 4 0.0 6.881438113781613e-09 6.881438113781613e-09 False False 3.5 3.5 6
78 transfer_neq 5 0.0 6.467520081987477e-09 6.467520081987477e-09 False False 3.5 3.5 6
79 transfer_neq 6 4.801482146599145e-08 1.7384695072397705e-08 3.063012639359375e-08 False False 20.5 12.5 1
80 transfer_neq 7 9.917490157301811e-07 1.4901174696935193e-08 9.76847841033246e-07 True True 20.5 13.5 1
81 transfer_neq 8 5.3319513640959215e-06 7.450589978308386e-09 5.324500774117613e-06 True True 20.5 13.5 1
82 transfer_neq 9 1.3926900000016396e-05 4.967061900673642e-09 1.3921932938115722e-05 True True 20.5 13.5 1
83 transfer_neq 10 2.2683459353481372e-05 7.0366557441969846e-09 2.269049600922557e-05 True True 20.5 13.5 1
84 transfer_neq 11 2.6366830318105827e-05 8.692339972804497e-09 2.6375522658078632e-05 True True 20.5 14.5 1
85 transfer_neq 12 2.307753771932375e-05 1.0761948318616099e-08 2.3088299667642365e-05 True True 20.5 14.5 1
86 transfer_neq 13 1.804053278801898e-05 6.6227364148296886e-09 1.804715552443381e-05 True True 16.5 20.5 3
87 transfer_neq 14 1.3273871487681776e-05 3.725288785783043e-09 1.327759677646756e-05 True True 17.5 20.5 3
88 transfer_neq 15 1.109810416584367e-05 1.7074248230553035e-09 1.1096396741020614e-05 True True 20.5 20.5 5
89 transfer_neq 16 1.0547037354428218e-05 3.777030622292843e-09 1.0550814385050511e-05 True True 20.5 20.5 5
90 transfer_neq 17 7.76073680465103e-06 1.5522043823879006e-09 7.762289009033418e-06 True True 20.5 20.5 5
91 transfer_neq 18 6.477044739522375e-06 2.4835230261199825e-09 6.479528262548495e-06 True True 16.5 3.5 4
92 transfer_neq 19 6.888961226400592e-06 4.553130164564045e-09 6.893514356565156e-06 True True 16.5 3.5 4
93 transfer_neq 20 8.081834403372845e-06 1.6556842147297246e-09 8.080178719158115e-06 True True 3.5 15.5 2
94 transfer_neq 21 9.19306995308955e-06 4.553133203799575e-09 9.19762308629335e-06 True True 3.5 15.5 2
95 transfer_neq 22 9.90222802181584e-06 1.241762831449833e-09 9.90346978464729e-06 True True 3.5 15.5 2
96 transfer_neq 23 9.820215213826589e-06 3.725291089495819e-09 9.823940504916084e-06 True True 3.5 16.5 2
97 transfer_neq 24 9.034586770997888e-06 6.208816516473092e-09 9.028377954481415e-06 True True 3.5 17.5 2
research/fine_surface_joint_20261014/blow_signals.npz
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research/fine_surface_joint_20261014/blow_summary.json
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{
"speed": 0.002,
"initial_leaf": [
560.7500041779131,
0.0,
0.0
],
"final_leaf": [
560.8438616539352,
0.014835720416158438,
0.012527641374617815
],
"raw_residual": [
0.0002896524965763092,
8.014030754566193e-06,
4.828907549381256e-06
],
"exact_average_residual": [
0.00028958916664123535,
7.874332368373871e-06,
4.7245994210243225e-06
],
"cumulative": {
"boundary": [
0.09356788685545325,
0.014827846083790064,
0.01252291677519679
],
"fine_transfer": [
-0.0672332551330328,
-0.01191190630197525,
-0.010114157572388649
],
"coarse_transfer": [
0.06723319180309772,
0.011911766603589058,
0.010114053264260292
],
"rounding": [
0.0002896524965763092,
8.014030754566193e-06,
4.828907549381256e-06
],
"average": [
-6.332993507385254e-08,
-1.3969838619232178e-07,
-1.043081283569336e-07
]
},
"checks": {
"validity": true,
"geometry_unchanged": true,
"boundary_count": 48,
"collision_count": 72,
"local_collision": true,
"local_regime": true,
"bfl": true,
"transfer_rounding": true,
"interval_closure": true,
"cumulative_closure": true,
"propagation": true,
"drive_sign": true
},
"max_collision_ratio": 0.5696050037738337,
"max_collision_error": 1.5865499780609582e-07,
"max_bfl_error": 1.2725840020655355e-08,
"max_interval_closure": 0.0,
"drive_mass": 0.07436401406617413,
"propagation": {
"coarse_interface_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 4.0664670353703905e-06,
"baseline": 2.0116559817931723e-07,
"absolute": 4.267265792606936e-06,
"x": 20.5,
"y": 13.5
},
"peak": {
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"time": 14,
"signal": 0.00028850325159438407,
"baseline": 3.003423681142711e-08,
"absolute": 0.00028847339892346693,
"any_crossed": true,
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"x": 20.5,
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}
},
"coarse_bulk_u": {
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"first_arrival": {
"time": 8,
"signal": 1.1069172403255332e-06,
"baseline": 1.005827957173589e-07,
"absolute": 1.2074577299858699e-06,
"x": 13.5,
"y": 21.5
},
"peak": {
"metric": "coarse_bulk_u",
"time": 16,
"signal": 0.00025699080829703886,
"baseline": 0.0,
"absolute": 0.00025699080829703886,
"any_crossed": true,
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"x": 21.5,
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}
},
"fine_band_neq": {
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"first_arrival": {
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"signal": 3.8661114470928126e-07,
"baseline": 9.934099098884985e-09,
"absolute": 3.766770456103963e-07,
"x": 19.75,
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},
"peak": {
"metric": "fine_band_neq",
"time": 10,
"signal": 7.498180437826019e-06,
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"absolute": 7.489074181299404e-06,
"any_crossed": true,
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"x": 19.75,
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}
},
"transfer_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 9.917490157301811e-07,
"baseline": 1.4901174696935193e-08,
"absolute": 9.76847841033246e-07,
"q": 1,
"x": 20.5,
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},
"peak": {
"metric": "transfer_neq",
"time": 11,
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"absolute": 2.6375522658078632e-05,
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"y": 14.5
}
}
},
"raw_minus_zero": [
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9.126961231231689e-07
]
}
research/fine_surface_joint_20261014/command.txt
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/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python /home/frank14f/CelerisLab/research/fine_surface_joint_20261014/runner.py
Child command, explicit environment, fresh resource preflight and actual exit are stored in gpu_run.json.
research/fine_surface_joint_20261014/criteria.txt
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FROZEN BEFORE GPU — bounded fine-surface/interface validation, 2026-10-14
Scope: exactly three independent trajectories speed=0,+.002,-.002, each24
coarse/48fine steps, same physical time[0,24]. No other GPU tests, extensions,
parameter sweeps or time extensions. Read-only hook instrumentation of normal
production build, no audit macro and no stepping/formula changes.
Geometry: periodic24x24 coarse domain; patch edge bounds[4,20)x[4,20);
fine40x40 including4halo; origin edge(2,2); fine dx=dt=.5; coarse dx=dt=1.
Circle physical center(12.25,12.25),radius2.15 => fine integer center(20,20),R4.3.
Stationary solid; surface angle.7rad,half_width.85rad,C1 amplitude0,+/-.002.
nu_coarse=.05,tau_coarse=.65,tau_fine=.8; unforcedD2Q9SRT,FP32,unshiftedDB.
Initial rho1,u0,Q-major postcollision equilibrium, same FP32 initial populations.
All initial sources and device omega are checked. Actual package path must be
/home/frank14f/CelerisLab/src. The preserved old tests are not changed or run.
Validity at every fine/coarse hop and publication:
All valid fluid leaves finite, density(.9,1.1) and |u|<.05 in local collision
oracle validity regime. Fine solids and outermost2halo after each fine hopNaN;
inner ghost exactly stream-only including legitimate directionalNaNs.
Published fine ghost/solid and coarse coveredNaN. Geometry and flags unchanged.
Propagation definitions (strict >; horizons and masks never adjusted):
- Fine band: fine active fluid nodes with any D2Q9 nonrest neighbor outside
geometric active patch, excluding solid boundaries.
- Coarse interface: uncovered receiver cells of actual coalescence map.
- Coarse bulk: ALL uncovered coarse fluid except that interface receiver set.
- Transfer neq: actual replaced q component of the full reconstructed coarse
incoming vector minus FP64feq(rho,u) of that same incoming vector, BEFORE
coarse collision. Do not form full macros from partly valid ghost children.
- Fine band neq: postcollision g minus FP64 equilibrium from its own macros.
- Velocities: FP64 moments of published postcollision g / rho.
- Physical coordinate coarse(I+.5,J+.5);fine(2+.5*(i+.5),2+.5*(j+.5)).
Interface transfer and publication sampled at integer physical t=1..24.
Boundary substeps at t=n-.5,n; no claim of sub-integer arrival resolution.
- At each same point/q,time,phase compare driven and ZERO trajectory:
velocity norm difference>1e-6 AND >10*zero velocity norm;
neq absolute difference>1e-7 AND >10*abs(zero neq).
Fine-band criterion uses maxq at each node for BOTH signal/baseline; this
conservatively requires excess over the largest zero neq at that node.
- For BOTH signs, all4 metrics (fine-band neq, actual-transfer neq,
coarse-interface velocity, coarse-bulk velocity) must cross by24 or FAIL.
Record each first qualified sample and global peak's time,physicallocation,
baseline,absolutevalue,signal; per-step maxima and qualifying flag retained.
These are aligned signal witnesses, not necessarily the same node/direction.
This tests startup distribution/macroscopic propagation, possibly acoustic;
not established jet advection, physical accuracy or shear-only transmission.
Ledger (M maps9populations to mass,px,py; area weights coarse1,fine1/4):
L_n=M(sum coarse leaves+1/4 sum fine fluid leaves), FP64 accumulation.
Each of48fine hops individually captures pre-BFL and staged BFL populations.
B_h=1/4 M(actual solid-to-fluid arrivals minus fluid-to-solid departures).
F_h=1/4 M(actual ghost-to-fluid arrivals minus fluid-to-ghost departures).
B_n=B_h1+B_h2; F_n=F_h1+F_h2. No using latest buffer as two-hop sum.
C_n=M(actual reconstructed coarse incoming minus previous coarse leaves),
including periodic native streaming, all replacement links and8grazingcorners.
Coarse incoming constructed from independent physical midpoint map; compare
all target/source slots to actual tables. avg32=.25*((a+b)+c+d) inFP32.
RAW_n=L_n-L_(n-1)-B_n-F_n-C_n; cumulative raw uses same initial/final leaves.
Report RAW and driven-minus-zero RAW FIRST, never call them corrected to zero.
Source-based rounding decomposition and separate fixed checks:
1. Each fine/coarse collision: reconstruct actual FP32 input using pure pull
and avg32 where applicable. Independently evaluate FP64 SRT at the actual
device FP32omega, verifying omega against tau and force=0.
Every q,node must obey |GPUout-SRT64(input)| <= 8*eps32*|input_q|.
This fixed local, near-rest acceptance bound reflects short FP32 rho/macro/
feq/FMA path and fast-math compilation; it is not a general roundoff theorem.
Report max absolute error and max normalized ratio, and fail if ratio>1.
Cround = weighted sum M(GPUout-input) measured LOCALLY, independently of L
or RAW. Record FP64 ideal moment defect and measured GPUout-ideal separately.
Arbitrary collision error cannot be excused solely by global closure.
2. Transfer Tround=M(avg32-avg64exact), computed independently from actual4
children. Require each slot <=gamma3*sum|children|/4, u=eps32/2,
gamma3=3u/(1-3u); exact power-of-two scaling in this normal positive regime.
avg32 already in C_n, so DO NOT subtract Tround a second time.
Also report RAW_exact_average=RAW+Tround to expose this source explicitly.
3. BFL local oracle abs error<=1e-7, actual active cells unchanged.
4. |RAW_n-Cround_n|<=1e-10 componentwise per interval;
|sum RAW-sum Cround|<=1e-9 cumulative. These are FP64 bookkeeping bounds,
NOT a physical-conservation/accuracy test. No broad eps*whole-domain
accumulated budget used to label raw conservation as passing.
5. Both driven cases' cumulative actual-minus-passive-oracle boundary mass
has same sign as specified normal speed. BFL passive evaluated on same
actual populations; not another trajectory and not a conservation fix.
Saved data: pertrajectory24ledger rows,72local collision rows,48fine boundary
rows; small compressed diagnostic signals for reproducible zero comparisons;
no full-field populations. Per-step link_exchange_GPU also saved separately:
latest fine Fx,Fy scaled1/4; torque scaled1/8 for physical lever length. These
body momentum diagnostics differ from fluid mass exchange and are NOT thrust
or exchange rate. A fine-hop fluid exchange rate is B_h/.5; the coarse interval
rate is (B_h1+B_h2)/1, numerically equal to interval sum only because dt_c=1.
Do not equate B_h2/latest with two-hop interval sum.
No post-result tolerance/formula/window changes, retries or added steps to
chase PASS. Preserve errors with real stdout/exit. Report precise unmet checks.
Resource guards: fresh physicalGPU0UUID/apps/VRAM/RAM, onlyGPU0ifidle; no
other task interference; 180secondwalltimeout; current available RAM reserve
max(4GiB,10%),VRAM reserve max(512MiB,10%), own temporary+evidence<=2GB.
Sample own process group RSS, no fixed address-space/RSS cap; remove only owned
scratch. Every text artifact<=40000characters. Hash criteria/script/source at
freeze then verify before launch; preserve all previous dirty/evidence.
Reference trace: accepted local tests/test_fine_surface_gpu.py BFL/flux hooks;
refinement.cu actual two fine hops and coalesce order; macro.cuh and
collision_srt.cuh actual FP32 collision path; compiler_v2.py --use_fast_math.
Previously accepted OpenLB defineU2D.hh74-79/offBoundaryPostProcessors2D.hh115-127
remain BFL hit/stencil reference; no new CFD formula or borrowed code.
Production change limited to one coordinate-frame documentation sentence.
research/fine_surface_joint_20261014/frozen.json
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research/fine_surface_joint_20261014/gpu_run.json
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research/fine_surface_joint_20261014/incremental.diff
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--- a/src/CelerisLab/lbm/refinement_stepper.py
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velocities are in coarse lattice units (dx=dt=1). Independent local copies
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and disjoint from the actual two-hop transfer footprint.
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research/fine_surface_joint_20261014/report.txt
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2026-10-14 — fine surface + fixed patch：24粗步短联合验证
结果：仅运行0、+.002、-.002三条同时间轨迹，各24coarse/48fine、物理时间24；
唯一GPU批次exit0，全部预设局部算子、有效性、记账与传播检查通过，已停止。
这不是严格守恒、物理精度、成熟喷流、空间收敛或长期稳定性认证。
先报告未修正累计收支残差（质量、Px、Py；实际FP32 transfer账本）：
零驱动 +2.2911839187e-4 +5.9055164456e-6 +3.9162114263e-6
正吹 +2.8965249658e-4 +8.0140307546e-6 +4.8289075494e-6
负吸 +3.3393828198e-4 +2.1932646632e-7 +2.6496127248e-7
相对零驱动的原始残差差值（不是修正后的守恒量）：
正吹 +6.0534104705e-5 +2.1085143089e-6 +9.1269612312e-7
负吸 +1.0481989011e-4 -5.6861899793e-6 -3.6512501538e-6
零驱动本身有FP32累计漂移，不能把bitwise非零视为传播，也不能把舍入后闭合0
说成原始残差为0。正吹/负吸的原始质量残差分别约占实际边界净质量交换的
0.310%/0.357%；扣同时间零驱动基线后的差值分别约占0.0647%/0.1122%。
这里只报告比例，没有事后以这些比例新增通过判据。
传播证据（两驱动同样的首次达标时间和物理坐标；时间以整数粗步采样）：
量 首次t/位置 正吹差值 负吸差值
fine接口内侧post-G neq 6 /(19.75,13.25) 3.86611e-7 3.73349e-7
真实transfer arrival neq 7 /(20.5,13.5),q1 9.91749e-7 1.00666e-6
coarse接收圈post-G速度 7 /(20.5,13.5) 4.06647e-6 4.09198e-6
接收圈之外coarse bulk速度 8 /(13.5,21.5) 1.10692e-6 1.01799e-6
对应零驱动基线依次9.93410e-9、1.49012e-8、2.01166e-7、1.00583e-7。
预先固定速度差>1e-6，neq差>1e-7，且均>同位置/时间/阶段基线10倍；两符号均满足。
负吸bulk首次点的绝对速度9.17512e-7，差值1.01799e-6；判据本来就针对差值，
未把它改写为绝对速度超过1e-6。随后峰值处绝对速度和差值均明显非零。
峰值（相对零驱动，全部位于预设采样窗口内）：
量 峰值t/位置 正吹 负吸
fine接口内侧neq 10 /(19.75,15.25) 7.49818e-6 7.38603e-6
真实transfer neq 11 /(20.5,14.5),q1 2.63668e-5 2.62411e-5
coarse接收圈速度 14 /(20.5,15.5) 2.88503e-4 2.87819e-4
接收圈之外bulk速度 16 /(21.5,15.5) 2.56991e-4 2.56441e-4
这里证明喷吸启动扰动实际到达并通过接口，可能含压力/声学传播。不同指标的
首次点是各自固定区域的检测见证，不冒称同一个流体质点的轨迹。24粗步内不能
判断成熟射流或纯剪切扰动的跨接口精度。区域及实际传递方向保存在regions.json；
每步指标、first/peak、同点基线和绝对值均有CSV/JSON；小型NPZ供离线复核。
逐步收支与舍入：
每条轨迹24行复合叶节点质量/动量账本；48行真实fine边界交换（每区间2行、
每次面积权重1/4）；72行局部collision诊断（每区间fine2次、coarse1次）。
实际fluid boundary、fine transfer和coarse transfer分别统计，未用最新一帧
link_exchange代替两子步累计。最新body impulse另列，仅为该子步的动量交换；
fine rate=该子步交换/.5，coarse rate=两子步之和/1，不冒充总推力。
24区间累计（质量、Px、Py）：
正吹B = (+.0935678868555, +.0148278460838, +.0125229167752)
正吹F = (-.0672332551330, -.0119119063020, -.0101141575724)
正吹C = (+.0672331918031, +.0119117666036, +.0101140532643)
负吸B = (-.0934597565793, -.0147664458491, -.0124674369581)
负吸F = (+.0670307464898, +.0118954917416, +.0100988540798)
负吸C = (-.0670307874680, -.0118956044316, -.0100990422070)
其中B为solid边界净交换，F为fine叶/halo交换，C为coarse真实incoming收支。
正吹/负吸同实际population上相对passive BFL oracle的累计附加质量分别
+.0743640140662、-.0742686590511，符号正确；这不是额外轨迹或守恒修正。
每个碰撞逐q/节点独立FP64 SRT参考：使用实际读回FP32omega并核对tau/force；
固定近静止判据|GPUout-SRT64(actual_input)|<=8*eps32*|input_q|。
三轨迹最大归一化误差分别.463942、.569605、.528511，全部<1；
最大绝对误差分别1.29143e-7、1.58655e-7、1.64771e-7。
它是本例的固定局部容差，考虑实际fast-math/FMA路径，不是一般舍入定理。
每次局部M(output-input)独立测得的累计漂移与原始账本残差吻合；逐区间和累计
剩余闭合误差在FP64计账中恰为0，满足预设1e-10/1e-9。这个零是记账分解验证，
不是离散方案的精确物理守恒结论，也未以全域大容差掩盖raw残差。
coalescence的顺序FP32四子格平均与精确FP64均值之差独立由实际children计算，
所有槽通过固定gamma3局部界限；实际transfer账本已经包含它，没有重复扣除。
累计平均舍入（质量、Px、Py）：
零驱动 (-5.7183206081e-7, -1.8812716007e-7, -2.8312206268e-7)
正吹 (-6.3329935074e-8, -1.3969838619e-7, -1.0430812836e-7)
负吸 (-4.0978193283e-8, -1.1269003153e-7, -1.8812716007e-7)
采用精确平均记账的原始残差亦保存在各summary的exact_average_residual中。
BFL最大局部误差三轨迹均<=1.27259e-8，低于冻结1e-7。
未从末态差反推任何收支项，也未看到结果后改公式、阈值、窗口或步数。
有效性：每细步active流体有限、solid与最外2层halo为NaN；inner ghost按真实
逐方向stream-only（包含合法NaN）核对。每粗步发布fine全部非叶和coarse covered
均NaN；leaf finite、局部密度/速度范围达标；几何/flags/源对象不变。
coarse320叶节点，其中68接口接收节点、252外部bulk；fine963流体叶，
124接口内侧节点；实际coalescence196方向槽，包括原有8个grazing-corner槽。
执行和范围：
冻结2026-10-14 05:55:03 UTC，GPU启动05:55:16 UTC，实际耗时9.596s，exit0。
GPU0 UUID GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c，预检5MiB/util0/无计算进程。
包来源严格为/home/frank14f/CelerisLab/src；现有pycuda_3_10环境；未安装依赖。
峰值进程组RSS297836544bytes，最少可用RAM63740788736bytes，最少可用VRAM
15826MiB，本轮临时与证据采样峰值2081386bytes，远小于2GB；无保护触发。
采样保护不是OS硬隔离；独占scratch已删除，GPU退出后5MiB且无计算进程。
生产增量只有refinement_stepper.py一行文档，+1/-0，明确fine_objects采用
edge/cell-center坐标（coarse中心I+.5），普通Simulation使用integer-node坐标。
完整610文件基线比对仅这一文件改变；既有dirty、旧审查和运行证据全部保留；
100个冻结脚本/源码哈希复核一致；不改生产公式、helper、网格或推进时序；
无提交/推送、无DynamisLab修改、无额外GPU矩阵。默认旧测试未改未重跑。
证据导航：
criteria.txt + frozen.json：运行前完整判据、时间戳、SHA256；不可事后改写。
command.txt + runner.py + gpu_run.json + stdout.txt：确切命令、环境、预检、退出。
validate.py：本次独立验证脚本，复用已接受test_fine_surface_gpu.py的只读钩子。
zero/blow/suction_{ledger,collisions,boundary}.csv：必要逐步量与分解。
blow/suction_propagation.csv、*_summary.json、*_signals.npz：信号与对齐基线。
result.json：固定判据结果；regions.json：坐标/方向；integrity.json：范围完整性。
incremental.diff：唯一生产增量；baseline_*.json：保留旧文件的哈希基线。
代码/数值只读agent交叉复核见review.txt。所有最终文本每份<=40000字符。
限制：以上仅短时离散联合一致性和启动扰动跨接口证据。原始FP32漂移仍存在，
未在本轮修正；不证明物理精度、空间收敛、稳态、长时稳定、性能或通用AMR。
此步已完成并停止，没有延伸B墙穿接口候选、checkpoint或其他特性。
research/fine_surface_joint_20261014/result.json
+412
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@@ -0,0 +1,412 @@
{
"passed": true,
"runs": {
"zero": {
"speed": 0.0,
"initial_leaf": [
560.7500041779131,
0.0,
0.0
],
"final_leaf": [
560.7502328557894,
4.124827682971954e-06,
3.1795352697372437e-06
],
"raw_residual": [
0.00022911839187145233,
5.905516445636749e-06,
3.916211426258087e-06
],
"exact_average_residual": [
0.00022854655981063843,
5.717389285564423e-06,
3.6330893635749817e-06
],
"cumulative": {
"boundary": [
1.3131648302078247e-07,
-1.5925616025924683e-06,
-4.5355409383773804e-07
],
"fine_transfer": [
-9.47713851928711e-05,
3.1068921089172363e-06,
-2.084299921989441e-06
],
"coarse_transfer": [
9.419955313205719e-05,
-3.295019268989563e-06,
1.8011778593063354e-06
],
"rounding": [
0.00022911839187145233,
5.905516445636749e-06,
3.916211426258087e-06
],
"average": [
-5.718320608139038e-07,
-1.8812716007232666e-07,
-2.8312206268310547e-07
]
},
"checks": {
"validity": true,
"geometry_unchanged": true,
"boundary_count": 48,
"collision_count": 72,
"local_collision": true,
"local_regime": true,
"bfl": true,
"transfer_rounding": true,
"interval_closure": true,
"cumulative_closure": true
},
"max_collision_ratio": 0.46394191845799526,
"max_collision_error": 1.2914339875269576e-07,
"max_bfl_error": 1.0332863675799331e-08,
"max_interval_closure": 0.0,
"drive_mass": 3.733417470608985e-07
},
"blow": {
"speed": 0.002,
"initial_leaf": [
560.7500041779131,
0.0,
0.0
],
"final_leaf": [
560.8438616539352,
0.014835720416158438,
0.012527641374617815
],
"raw_residual": [
0.0002896524965763092,
8.014030754566193e-06,
4.828907549381256e-06
],
"exact_average_residual": [
0.00028958916664123535,
7.874332368373871e-06,
4.7245994210243225e-06
],
"cumulative": {
"boundary": [
0.09356788685545325,
0.014827846083790064,
0.01252291677519679
],
"fine_transfer": [
-0.0672332551330328,
-0.01191190630197525,
-0.010114157572388649
],
"coarse_transfer": [
0.06723319180309772,
0.011911766603589058,
0.010114053264260292
],
"rounding": [
0.0002896524965763092,
8.014030754566193e-06,
4.828907549381256e-06
],
"average": [
-6.332993507385254e-08,
-1.3969838619232178e-07,
-1.043081283569336e-07
]
},
"checks": {
"validity": true,
"geometry_unchanged": true,
"boundary_count": 48,
"collision_count": 72,
"local_collision": true,
"local_regime": true,
"bfl": true,
"transfer_rounding": true,
"interval_closure": true,
"cumulative_closure": true,
"propagation": true,
"drive_sign": true
},
"max_collision_ratio": 0.5696050037738337,
"max_collision_error": 1.5865499780609582e-07,
"max_bfl_error": 1.2725840020655355e-08,
"max_interval_closure": 0.0,
"drive_mass": 0.07436401406617413,
"propagation": {
"coarse_interface_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 4.0664670353703905e-06,
"baseline": 2.0116559817931723e-07,
"absolute": 4.267265792606936e-06,
"x": 20.5,
"y": 13.5
},
"peak": {
"metric": "coarse_interface_u",
"time": 14,
"signal": 0.00028850325159438407,
"baseline": 3.003423681142711e-08,
"absolute": 0.00028847339892346693,
"any_crossed": true,
"peak_crossed": true,
"x": 20.5,
"y": 15.5
}
},
"coarse_bulk_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 8,
"signal": 1.1069172403255332e-06,
"baseline": 1.005827957173589e-07,
"absolute": 1.2074577299858699e-06,
"x": 13.5,
"y": 21.5
},
"peak": {
"metric": "coarse_bulk_u",
"time": 16,
"signal": 0.00025699080829703886,
"baseline": 0.0,
"absolute": 0.00025699080829703886,
"any_crossed": true,
"peak_crossed": true,
"x": 21.5,
"y": 15.5
}
},
"fine_band_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 6,
"signal": 3.8661114470928126e-07,
"baseline": 9.934099098884985e-09,
"absolute": 3.766770456103963e-07,
"x": 19.75,
"y": 13.25
},
"peak": {
"metric": "fine_band_neq",
"time": 10,
"signal": 7.498180437826019e-06,
"baseline": 9.106256526614231e-09,
"absolute": 7.489074181299404e-06,
"any_crossed": true,
"peak_crossed": true,
"x": 19.75,
"y": 15.25
}
},
"transfer_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 9.917490157301811e-07,
"baseline": 1.4901174696935193e-08,
"absolute": 9.76847841033246e-07,
"q": 1,
"x": 20.5,
"y": 13.5
},
"peak": {
"metric": "transfer_neq",
"time": 11,
"signal": 2.6366830318105827e-05,
"baseline": 8.692339972804497e-09,
"absolute": 2.6375522658078632e-05,
"any_crossed": true,
"peak_crossed": true,
"q": 1,
"x": 20.5,
"y": 14.5
}
}
},
"raw_minus_zero": [
6.053410470485687e-05,
2.1085143089294434e-06,
9.126961231231689e-07
]
},
"suction": {
"speed": -0.002,
"initial_leaf": [
560.7500041779131,
0.0,
0.0
],
"final_leaf": [
560.6568783186376,
-0.014766339212656021,
-0.012467360123991966
],
"raw_residual": [
0.00033393828198313713,
2.193264663219452e-07,
2.6496127247810364e-07
],
"exact_average_residual": [
0.00033389730378985405,
1.0663643479347229e-07,
7.683411240577698e-08
],
"cumulative": {
"boundary": [
-0.0934597565792501,
-0.014766445849090815,
-0.012467436958104372
],
"fine_transfer": [
0.06703074648976326,
0.011895491741597652,
0.010098854079842567
],
"coarse_transfer": [
-0.06703078746795654,
-0.011895604431629181,
-0.01009904220700264
],
"rounding": [
0.00033393828198313713,
2.193264663219452e-07,
2.6496127247810364e-07
],
"average": [
-4.0978193283081055e-08,
-1.126900315284729e-07,
-1.8812716007232666e-07
]
},
"checks": {
"validity": true,
"geometry_unchanged": true,
"boundary_count": 48,
"collision_count": 72,
"local_collision": true,
"local_regime": true,
"bfl": true,
"transfer_rounding": true,
"interval_closure": true,
"cumulative_closure": true,
"propagation": true,
"drive_sign": true
},
"max_collision_ratio": 0.5285112236646456,
"max_collision_error": 1.6477090769573266e-07,
"max_bfl_error": 1.2498588997100768e-08,
"max_interval_closure": 0.0,
"drive_mass": -0.07426865905109248,
"propagation": {
"coarse_interface_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 4.091979903802207e-06,
"baseline": 2.0116559817931723e-07,
"absolute": 3.8910894220610345e-06,
"x": 20.5,
"y": 13.5
},
"peak": {
"metric": "coarse_interface_u",
"time": 14,
"signal": 0.0002878189146376881,
"baseline": 3.003423681142711e-08,
"absolute": 0.0002878487673732557,
"any_crossed": true,
"peak_crossed": true,
"x": 20.5,
"y": 15.5
}
},
"coarse_bulk_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 8,
"signal": 1.0179867624062477e-06,
"baseline": 1.005827957173589e-07,
"absolute": 9.175116276483961e-07,
"x": 13.5,
"y": 21.5
},
"peak": {
"metric": "coarse_bulk_u",
"time": 16,
"signal": 0.00025644070267233306,
"baseline": 0.0,
"absolute": 0.00025644070267233306,
"any_crossed": true,
"peak_crossed": true,
"x": 21.5,
"y": 15.5
}
},
"fine_band_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 6,
"signal": 3.733491271140643e-07,
"baseline": 9.934099098884985e-09,
"absolute": 3.832832262129493e-07,
"x": 19.75,
"y": 13.25
},
"peak": {
"metric": "fine_band_neq",
"time": 10,
"signal": 7.386030289713297e-06,
"baseline": 9.106256526614231e-09,
"absolute": 7.395136546239911e-06,
"any_crossed": true,
"peak_crossed": true,
"x": 19.75,
"y": 15.25
}
},
"transfer_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 1.0066612778220074e-06,
"baseline": 1.4901174696935193e-08,
"absolute": 1.0215624525189426e-06,
"q": 1,
"x": 20.5,
"y": 13.5
},
"peak": {
"metric": "transfer_neq",
"time": 11,
"signal": 2.6241128339946074e-05,
"baseline": 8.692339972804497e-09,
"absolute": 2.623243599997327e-05,
"any_crossed": true,
"peak_crossed": true,
"q": 1,
"x": 20.5,
"y": 14.5
}
}
},
"raw_minus_zero": [
0.0001048198901116848,
-5.686189979314804e-06,
-3.6512501537799835e-06
]
}
}
}
research/fine_surface_joint_20261014/review.txt
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Read-only agent review, 2026-10-14
/root/architecture — pre-run:
No blocking issue in hooked kernel arguments/order, masks or area weighting.
Caught CSV signal-location ambiguity and exchange-unit ambiguity before freeze:
fixed any_crossed versus peak_crossed; renamed latest_body_impulse_x/y and
latest_body_angular_impulse. No production behavior changes.
/root/numerical — pre-run:
Avoid rho/u at partly valid halo children; transfer neq is defined at full
coarse incoming vector, actual replaced q. No double-counting average rounding.
Require independent local SRT oracle before describing measured local drift as
rounding. Freeze pointwise baseline comparisons; startup propagation may be
acoustic, not established jet advection. Suggestions incorporated before GPU.
/root/architecture — post-run:
All frozen hashes match current sources; frozen.json digest matches gpu_run.
Freeze05:55:03 precedes launch05:55:16UTC, real exit0. Each trajectory has correct
ordered24ledger/72collision/48boundary rows; result/summary/final ledger agree.
Independently reconstructed all4metrics' per-time any_crossed/peak_crossed,
first-arrival and peaks/locations from saved signals.npz; all agree.
All text artifacts inspected within40000characters. No material gap.
/root/numerical — post-run:
Independently summed all3ledger/collision CSVs: all components of locally
measured collision drift exactly reproduce raw global residual. Each interval
fine_transfer+coarse_transfer-average_rounding is0. Largest FP64ideal moment
defect about2.94e-14. Maximum independent local oracle normalized error.569605
is below fixed1. Thus ledger closure is not the sole acceptance gate.
No blocking issue. Raw mass residuals2.29118e-4/2.89652e-4/3.33938e-4 remain;
identifying their FP32 source does not physically repair them or establish
strict conservation. First6/7/8 are integer sampling threshold witnesses,
not accurate propagation-speed or mature-jet/accuracy claims.
Agents performed read-only inspection/offline data parsing, no file edits,
no GPU launch and no old regression runs. Root is the sole editor/launcher.
research/fine_surface_joint_20261014/runner.py
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"""One launch, fresh resource guards, only owned scratch/process-group cleanup."""
import hashlib
import json
import os
from pathlib import Path
import shutil
import signal
import subprocess
import tempfile
import time
from datetime import datetime, timezone
ROOT = Path('/home/frank14f/CelerisLab')
OUT = Path(__file__).resolve().parent
PYTHON = '/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python'
UUID = 'GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c'
def command(args):
return subprocess.check_output(args, text=True, timeout=10)
def gpu():
return command(['nvidia-smi', '--query-gpu=index,uuid,name,memory.used,memory.free,utilization.gpu', '--format=csv,noheader,nounits'])
def apps():
return command(['nvidia-smi', '--query-compute-apps=gpu_uuid,pid,process_name,used_memory', '--format=csv,noheader,nounits'])
def ram():
for line in Path('/proc/meminfo').read_text().splitlines():
if line.startswith('MemAvailable:'):
return int(line.split()[1])*1024
raise RuntimeError('MemAvailable unavailable')
def disk(paths):
return sum(p.stat().st_size for root in paths for p in root.rglob('*') if p.is_file())
def group_rss(pgid):
total = 0
for proc in Path('/proc').iterdir():
if not proc.name.isdigit():
continue
try:
fields = (proc/'stat').read_text().rsplit(') ', 1)[1].split()
if int(fields[2]) == pgid:
for line in (proc/'status').read_text().splitlines():
if line.startswith('VmRSS:'):
total += int(line.split()[1])*1024
except (OSError, ValueError, IndexError):
pass
return total
def main():
assert not (OUT/'gpu_run.json').exists(), 'Do not repeat an existing launch'
data = dict(start_utc=datetime.now(timezone.utc).isoformat(), command=[PYTHON, str(OUT/'validate.py')],
cwd=str(ROOT), physical_gpu_uuid=UUID, note='Sampled guards, not an OS hard isolation; no fixed RSS/AS cap.')
scratch = None
process = None
started = time.monotonic()
code = 1
try:
frozen = json.loads((OUT/'frozen.json').read_text())
for path, digest in frozen['hashes'].items():
assert hashlib.sha256((ROOT/path).read_bytes()).hexdigest() == digest, path
data['preflight_gpu'], data['preflight_apps'], data['preflight_ram_bytes'] = gpu(), apps(), ram()
fields = next(line.split(', ') for line in data['preflight_gpu'].splitlines() if UUID in line)
used, free, util = map(int, fields[-3:])
assert fields[0] == '0' and used <= 16 and free >= 2048 and util == 0
assert UUID not in data['preflight_apps'] and data['preflight_ram_bytes'] >= 4*1024**3
limits = dict(wall_seconds=180, temp_bytes=2_000_000_000,
ram_reserve_bytes=max(4*1024**3, data['preflight_ram_bytes']//10),
vram_reserve_MiB=max(512, free//10))
data['limits'] = limits
scratch = Path(tempfile.mkdtemp(prefix='celeris-joint24-'))
env = dict(PYTHONPATH=str(ROOT/'src'), PYTHONNOUSERSITE='1', PYTHONDONTWRITEBYTECODE='1',
OPENBLAS_NUM_THREADS='1', CUDA_VISIBLE_DEVICES=UUID,
CUDA_CACHE_DISABLE='1', TMPDIR=str(scratch))
data['env'] = env
data['frozen_sha256'] = hashlib.sha256((OUT/'frozen.json').read_bytes()).hexdigest()
data['peak_group_rss_bytes'] = 0
data['peak_temp_bytes'] = disk([OUT,scratch])
data['min_ram_bytes'] = data['preflight_ram_bytes']
data['min_vram_free_MiB'] = free
data['termination_reason'] = None
with (OUT/'stdout.txt').open('w') as output:
process = subprocess.Popen(data['command'], cwd=ROOT, env={**os.environ, **env},
stdout=output, stderr=subprocess.STDOUT, start_new_session=True)
while process.poll() is None:
available, occupied = ram(), disk([OUT,scratch])
free = int(next(line.split(', ')[-2] for line in gpu().splitlines() if UUID in line))
data['peak_group_rss_bytes'] = max(data['peak_group_rss_bytes'], group_rss(process.pid))
data['peak_temp_bytes'] = max(data['peak_temp_bytes'], occupied)
data['min_ram_bytes'] = min(data['min_ram_bytes'], available)
data['min_vram_free_MiB'] = min(data['min_vram_free_MiB'], free)
if (time.monotonic()-started > limits['wall_seconds'] or occupied > limits['temp_bytes']
or available < limits['ram_reserve_bytes'] or free < limits['vram_reserve_MiB']):
data['termination_reason'] = 'Time/disk/RAM/VRAM guard triggered'
os.killpg(process.pid, signal.SIGTERM)
try:
process.wait(timeout=5)
except subprocess.TimeoutExpired:
os.killpg(process.pid, signal.SIGKILL)
break
time.sleep(.5)
code = process.wait(timeout=10)
data['postflight_gpu'], data['postflight_apps'] = gpu(), apps()
except BaseException as error:
data['runner_error'] = repr(error)
if process is not None and process.poll() is None:
os.killpg(process.pid, signal.SIGKILL)
process.wait(timeout=10)
raise
finally:
data['exit_code'] = code
data['elapsed_seconds'] = time.monotonic()-started
if scratch is not None:
shutil.rmtree(scratch)
data['scratch_removed'] = scratch is None or not scratch.exists()
(OUT/'gpu_run.json').write_text(json.dumps(data, indent=2)+'\n')
print(json.dumps(data, indent=2), flush=True)
raise SystemExit(code)
if __name__ == '__main__':
main()
research/fine_surface_joint_20261014/stdout.txt
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File diff suppressed because one or more lines are too long
research/fine_surface_joint_20261014/suction_boundary.csv
+49
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@@ -0,0 +1,49 @@
step,substep,time,bfl_error,drive_mass,latest_body_impulse_x,latest_body_impulse_y,latest_body_angular_impulse,boundary_m,boundary_px,boundary_py,fine_transfer_m,fine_transfer_px,fine_transfer_py
1,1,0.5,3.7331066016221826e-09,-0.0015482641756534576,0.0010669892653822899,0.0009111212566494942,1.329833948571224e-05,-0.0015482641756534576,-0.0010669846087694168,-0.0009111212566494942,0.0,0.0,0.0
1,2,1.0,7.450580596923828e-09,-0.001546488821844781,0.0005881106480956078,0.0005055302754044533,7.090981909652072e-06,-0.0018966365605592728,-0.000588112510740757,-0.0005055312067270279,-3.557652235031128e-07,0.0,0.0
2,1,1.5,7.450580596923828e-09,-0.0015455698188904868,0.0007474450394511223,0.000630686990916729,6.323601846380811e-06,-0.0017942930571734905,-0.0007474464364349842,-0.0006306902505457401,-8.530914783477783e-07,-7.450580596923828e-09,-1.6763806343078613e-07
2,2,2.0,7.450580596923828e-09,-0.0015454305605910922,0.0006957640871405602,0.0005812598392367363,-9.326976928458741e-06,-0.0019257990643382072,-0.0006957687437534332,-0.0005812682211399078,-1.3401731848716736e-06,2.7939677238464355e-09,-9.313225746154785e-10
3,1,2.5,7.450580596923828e-09,-0.0015454182331193405,0.0007162690162658691,0.0006032083183526993,2.0262676536653146e-06,-0.0019086366519331932,-0.0007162792608141899,-0.0006032129749655724,-1.6577541828155518e-06,1.5832483768463135e-07,1.5832483768463135e-07
3,2,3.0,8.221284986120736e-09,-0.0015455955498631406,0.000675516203045845,0.0005694478750228882,-2.283530538660372e-06,-0.0019611637108027935,-0.0006755241192877293,-0.0005694576539099216,-1.982320100069046e-06,3.1152740120887756e-07,3.096647560596466e-07
4,1,3.5,7.450580596923828e-09,-0.0015457821940822736,0.0006534429267048836,0.000552385114133358,3.1464089307320364e-06,-0.001949384342879057,-0.0006534415297210217,-0.000552391167730093,-3.2121315598487854e-06,3.5297125577926636e-07,3.762543201446533e-07
4,2,4.0,7.450580596923828e-09,-0.0015460265831878312,0.0006101150065660477,0.0005156379193067551,2.1656969761973954e-07,-0.001972564961761236,-0.000610120128840208,-0.0005156439729034901,-3.698747605085373e-06,-3.585591912269592e-08,1.909211277961731e-08
5,1,4.5,7.450580596923828e-09,-0.0015462236236817618,0.0005800491198897362,0.0004902100190520287,2.4401852956046355e-06,-0.0019627874717116356,-0.0005800472572445869,-0.0004902174696326256,-3.6563724279403687e-06,5.550682544708252e-07,2.4121254682540894e-07
5,2,5.0,8.17224322935406e-09,-0.001546429669342637,0.0005416562780737877,0.00045779068022966385,6.411905253411732e-07,-0.0019741454161703587,-0.0005416539497673512,-0.000457793939858675,-3.6312267184257507e-06,5.122274160385132e-07,2.2258609533309937e-07
6,1,5.5,7.450580596923828e-09,-0.0015465991853560995,0.0005121780559420586,0.00043242890387773514,1.926527342988038e-06,-0.001966074574738741,-0.0005121775902807713,-0.000432425644248724,-2.8940849006175995e-06,5.164183676242828e-07,3.213062882423401e-08
6,2,6.0,1.0613033563089402e-08,-0.001546759810737387,0.00047842320054769516,0.00040392857044935226,7.323118594859679e-07,-0.001972377300262451,-0.0004784250631928444,-0.00040392763912677765,-2.0721927285194397e-06,6.435438990592957e-07,9.12696123123169e-08
7,1,6.5,7.450580596923828e-09,-0.0015468885373133533,0.00045062508434057236,0.0003800736740231514,1.5839877249845813e-06,-0.0019662114791572094,-0.0004506339319050312,-0.00038006948307156563,2.334360033273697e-06,3.114808350801468e-06,1.4849938452243805e-06
7,2,7.0,8.999190503899968e-09,-0.0015470132347384351,0.00042080413550138474,0.0003548497334122658,8.012649583832854e-07,-0.0019701728597283363,-0.0004208004102110863,-0.00035485532134771347,1.5432480722665787e-05,1.1287163943052292e-05,5.935784429311752e-06
8,1,7.5,1.0738867337045654e-08,-0.0015471137993936443,0.0003952737897634506,0.00033300556242465973,1.388882160391347e-06,-0.0019654585048556328,-0.0003952831029891968,-0.0003329990431666374,5.064625293016434e-05,2.6551075279712677e-05,1.5786848962306976e-05
8,2,8.0,7.94032639639397e-09,-0.0015472117748432483,0.00036898814141750336,0.00031079165637493134,8.204820101109789e-07,-0.0019681239500641823,-0.0003689778968691826,-0.0003107842057943344,0.00010942388325929642,6.19497150182724e-05,3.8951635360717773e-05
9,1,8.5,8.976009713279609e-09,-0.0015472804270270975,0.00034602265805006027,0.0002911807969212532,1.3696224959725e-06,-0.001964463386684656,-0.0003460417501628399,-0.00029119057580828667,0.0002314508892595768,9.650783613324165e-05,6.443960592150688e-05
9,2,9.0,1.0052167986884797e-08,-0.001547337366734857,0.00032305438071489334,0.0002718055620789528,8.737507810607781e-07,-0.00196637911722064,-0.00032305577769875526,-0.0002718069590628147,0.0003751874901354313,0.00017973734065890312,0.00012484891340136528
10,1,9.5,7.4023787099974925e-09,-0.001547404346943595,0.00030282605439424515,0.0002545500174164772,1.3337265093449346e-06,-0.0019635148346424103,-0.00030282698571681976,-0.00025455933064222336,0.0006195390596985817,0.00022383593022823334,0.000162455253303051
10,2,10.0,6.968939966767174e-09,-0.001547448671976346,0.0002828845754265785,0.00023786630481481552,9.501839344228102e-07,-0.001965020317584276,-0.00028289156034588814,-0.00023785652592778206,0.0008522137068212032,0.00035300618037581444,0.0002658138982951641
11,1,10.5,1.0530962046839676e-08,-0.0015474870615136364,0.00026516150683164597,0.00022298935800790787,1.3016631345008634e-06,-0.0019626920111477375,-0.00026516104117035866,-0.00022298330441117287,0.0011819470673799515,0.0003880225121974945,0.00030118972063064575
11,2,11.0,7.354702624695619e-09,-0.0015475354224584415,0.0002481304109096527,0.00020870380103588104,8.941672690054858e-07,-0.0019638948142528534,-0.00024813972413539886,-0.00020870286971330643,0.00145752914249897,0.0005337242037057877,0.00042963773012161255
12,1,11.5,8.767222503536232e-09,-0.001547562607513273,0.00023286882787942886,0.00019576307386159897,1.1601679523742803e-06,-0.001962078735232353,-0.0002328716218471527,-0.00019576400518417358,0.0017802291549742222,0.0005506309680640697,0.00045162299647927284
12,2,12.0,9.096382980189333e-09,-0.0015476002059964218,0.00021828524768352509,0.00018361210823059082,8.859918445569548e-07,-0.0019631017930805683,-0.00021829502657055855,-0.00018362188711762428,0.002031440380960703,0.0006739604286849499,0.000572004821151495
13,1,12.5,6.5197561377461355e-09,-0.0015476129016728998,0.00020523089915513992,0.00017269793897867203,1.2139267550059785e-06,-0.0019616251811385155,-0.00020523276180028915,-0.00017269421368837357,0.0022674263454973698,0.00067532854154706,0.0005780183710157871
13,2,13.0,5.146194359539891e-09,-0.0015476392796307777,0.00019292067736387253,0.00016248133033514023,9.107929975771256e-07,-0.0019624661654233932,-0.0001929309219121933,-0.0001624850556254387,0.002446614671498537,0.0007499088533222675,0.0006604096852242947
14,1,13.5,8.330306375259333e-09,-0.0015476535128897996,0.0001819990575313568,0.00015316344797611237,1.1346972110803932e-06,-0.0019612740725278854,-0.0001819925382733345,-0.00015317555516958237,0.0025721797719597816,0.0007412796840071678,0.0006537484005093575
14,2,14.0,8.454041688921166e-09,-0.0015476762461938956,0.00017176009714603424,0.00014458410441875458,9.925571977653647e-07,-0.0019619660452008247,-0.00017175264656543732,-0.0001445794478058815,0.0026705721393227577,0.000760519877076149,0.0006827609613537788
15,1,14.5,8.024249306681774e-09,-0.0015476863880568553,0.0001627029851078987,0.00013693515211343765,1.1326488877916319e-06,-0.0019609066657721996,-0.00016270345076918602,-0.00013692351058125496,0.0027098902501165867,0.0007453667931258678,0.0006675352342426777
15,2,15.0,8.5509712643983e-09,-0.0015477023138736976,0.00015427079051733017,0.0001298980787396431,1.0080659256228586e-06,-0.0019614454358816147,-0.00015425775200128555,-0.00012989435344934464,0.0027480190619826317,0.0007168371230363846,0.0006446875631809235
16,1,15.5,1.2498588997100768e-08,-0.0015477122014419843,0.0001468295231461525,0.000123598612844944,1.1824087963185903e-06,-0.001960494089871645,-0.00014683278277516365,-0.000123604666441679,0.002740874420851469,0.0006989189423620701,0.000626422930508852
16,2,16.0,9.256705402194143e-09,-0.0015477301148594871,0.00014006812125444412,0.00011796411126852036,9.308136234009723e-07,-0.0019609369337558746,-0.00014007184654474258,-0.00011796131730079651,0.002747458405792713,0.0006359582766890526,0.0005640769377350807
17,1,16.5,9.86708736938624e-09,-0.0015477351078586202,0.00013425014913082123,0.00011301040649414062,1.1052945718681428e-06,-0.001960096415132284,-0.0001342552714049816,-0.0001130090095102787,0.002724500373005867,0.0006184643134474754,0.0005468325689435005
17,2,17.0,8.028477438415393e-09,-0.0015477600973967448,0.00012889597564935684,0.00010859500616788864,9.277017700703993e-07,-0.001960489433258772,-0.0001288936473429203,-0.00010859174653887749,0.0027201655320823193,0.0005353703163564205,0.00046224379912018776
18,1,17.5,7.699842763386755e-09,-0.0015477556081742207,0.00012428779155015945,0.00010465551167726517,1.036442421664674e-06,-0.00195977371186018,-0.0001242849975824356,-0.00010466109961271286,0.0026930058375000954,0.0005191462114453316,0.0004479791969060898
18,2,18.0,7.779478172764698e-09,-0.0015477652772308585,0.00011980719864368439,0.00010108202695846558,9.591615347526172e-07,-0.001960138790309429,-0.00011981278657913208,-0.0001010717824101448,0.0026853615418076515,0.00042929593473672867,0.0003578970208764076
19,1,18.5,1.0115823734224705e-08,-0.0015477747316547663,0.00011577177792787552,9.778235107660294e-05,1.1382919499203226e-06,-0.001959627028554678,-0.00011577224358916283,-9.777536615729332e-05,0.002648819237947464,0.0004100445657968521,0.0003434009850025177
19,2,19.0,9.594118610678493e-09,-0.0015477738185972504,0.00011185649782419205,9.45916399359703e-05,8.908682038732962e-07,-0.0019600135274231434,-0.0001118597574532032,-9.458651766180992e-05,0.0026350892148911953,0.0003254576586186886,0.00026165833696722984
20,1,19.5,8.744199350707582e-09,-0.0015477812550560438,0.00010816752910614014,9.154155850410461e-05,1.0807081907149296e-06,-0.001959587913006544,-0.0001081652007997036,-9.154481813311577e-05,0.0025749970227479935,0.0002942616119980812,0.00023942533880472183
20,2,20.0,7.241793387180451e-09,-0.0015478027222764852,0.00010451860725879669,8.863583207130432e-05,9.283607647070369e-07,-0.0019599981606006622,-0.00010452233254909515,-8.862745016813278e-05,0.002547156997025013,0.00022500474005937576,0.0001754332333803177
21,1,20.5,9.080666218963529e-09,-0.0015478099147822168,0.00010123103857040405,8.586794137954712e-05,1.0566826715852073e-06,-0.0019596004858613014,-0.00010123662650585175,-8.586607873439789e-05,0.0024467245675623417,0.0001708432100713253,0.0001354874111711979
21,2,21.0,8.309436028142159e-09,-0.001547811782321707,9.799562394618988e-05,8.317828178405762e-05,9.359370445327642e-07,-0.0019598989747464657,-9.798770770430565e-05,-8.317967876791954e-05,0.0023957067169249058,0.0001242239959537983,9.476905688643456e-05
22,1,21.5,8.807171658631319e-09,-0.0015478071837582907,9.528174996376038e-05,8.07885080575943e-05,1.0299947542646848e-06,-0.001959493849426508,-9.528035297989845e-05,-8.077593520283699e-05,0.0022453111596405506,3.82973812520504e-05,2.8362032026052475e-05
22,2,22.0,7.708700941466695e-09,-0.0015478350620986127,9.280815720558167e-05,7.856637239456177e-05,9.84289633354507e-07,-0.0019597294740378857,-9.280489757657051e-05,-7.857801392674446e-05,0.0021656760945916176,1.8442049622535706e-05,1.2994743883609772e-05
23,1,22.5,5.829010607527607e-09,-0.0015478298144738873,9.077228605747223e-05,7.675401866436005e-05,1.0561968476485717e-06,-0.0019593103788793087,-9.077740833163261e-05,-7.675448432564735e-05,0.001967994961887598,-0.00010093534365296364,-8.322251960635185e-05
23,2,23.0,1.00678847481106e-08,-0.0015478396129139588,8.917506784200668e-05,7.518846541643143e-05,9.189075473869934e-07,-0.001959436573088169,-8.916761726140976e-05,-7.51987099647522e-05,0.0018595317378640175,-9.398628026247025e-05,-7.435213774442673e-05
24,1,23.5,9.096645881001564e-09,-0.0015478476094394076,8.79606232047081e-05,7.397029548883438e-05,9.397743796490943e-07,-0.0019590607844293118,-8.79545696079731e-05,-7.396889850497246e-05,0.0016328254714608192,-0.00023874826729297638,-0.00019633769989013672
24,2,24.0,8.815424390462567e-09,-0.001547844811637395,8.694268763065338e-05,7.304549217224121e-05,9.335749210359623e-07,-0.001959147397428751,-8.693570271134377e-05,-7.305433973670006e-05,0.0015028249472379684,-0.0002091461792588234,-0.00016683153808116913
1 step substep time bfl_error drive_mass latest_body_impulse_x latest_body_impulse_y latest_body_angular_impulse boundary_m boundary_px boundary_py fine_transfer_m fine_transfer_px fine_transfer_py
2 1 1 0.5 3.7331066016221826e-09 -0.0015482641756534576 0.0010669892653822899 0.0009111212566494942 1.329833948571224e-05 -0.0015482641756534576 -0.0010669846087694168 -0.0009111212566494942 0.0 0.0 0.0
3 1 2 1.0 7.450580596923828e-09 -0.001546488821844781 0.0005881106480956078 0.0005055302754044533 7.090981909652072e-06 -0.0018966365605592728 -0.000588112510740757 -0.0005055312067270279 -3.557652235031128e-07 0.0 0.0
4 2 1 1.5 7.450580596923828e-09 -0.0015455698188904868 0.0007474450394511223 0.000630686990916729 6.323601846380811e-06 -0.0017942930571734905 -0.0007474464364349842 -0.0006306902505457401 -8.530914783477783e-07 -7.450580596923828e-09 -1.6763806343078613e-07
5 2 2 2.0 7.450580596923828e-09 -0.0015454305605910922 0.0006957640871405602 0.0005812598392367363 -9.326976928458741e-06 -0.0019257990643382072 -0.0006957687437534332 -0.0005812682211399078 -1.3401731848716736e-06 2.7939677238464355e-09 -9.313225746154785e-10
6 3 1 2.5 7.450580596923828e-09 -0.0015454182331193405 0.0007162690162658691 0.0006032083183526993 2.0262676536653146e-06 -0.0019086366519331932 -0.0007162792608141899 -0.0006032129749655724 -1.6577541828155518e-06 1.5832483768463135e-07 1.5832483768463135e-07
7 3 2 3.0 8.221284986120736e-09 -0.0015455955498631406 0.000675516203045845 0.0005694478750228882 -2.283530538660372e-06 -0.0019611637108027935 -0.0006755241192877293 -0.0005694576539099216 -1.982320100069046e-06 3.1152740120887756e-07 3.096647560596466e-07
8 4 1 3.5 7.450580596923828e-09 -0.0015457821940822736 0.0006534429267048836 0.000552385114133358 3.1464089307320364e-06 -0.001949384342879057 -0.0006534415297210217 -0.000552391167730093 -3.2121315598487854e-06 3.5297125577926636e-07 3.762543201446533e-07
9 4 2 4.0 7.450580596923828e-09 -0.0015460265831878312 0.0006101150065660477 0.0005156379193067551 2.1656969761973954e-07 -0.001972564961761236 -0.000610120128840208 -0.0005156439729034901 -3.698747605085373e-06 -3.585591912269592e-08 1.909211277961731e-08
10 5 1 4.5 7.450580596923828e-09 -0.0015462236236817618 0.0005800491198897362 0.0004902100190520287 2.4401852956046355e-06 -0.0019627874717116356 -0.0005800472572445869 -0.0004902174696326256 -3.6563724279403687e-06 5.550682544708252e-07 2.4121254682540894e-07
11 5 2 5.0 8.17224322935406e-09 -0.001546429669342637 0.0005416562780737877 0.00045779068022966385 6.411905253411732e-07 -0.0019741454161703587 -0.0005416539497673512 -0.000457793939858675 -3.6312267184257507e-06 5.122274160385132e-07 2.2258609533309937e-07
12 6 1 5.5 7.450580596923828e-09 -0.0015465991853560995 0.0005121780559420586 0.00043242890387773514 1.926527342988038e-06 -0.001966074574738741 -0.0005121775902807713 -0.000432425644248724 -2.8940849006175995e-06 5.164183676242828e-07 3.213062882423401e-08
13 6 2 6.0 1.0613033563089402e-08 -0.001546759810737387 0.00047842320054769516 0.00040392857044935226 7.323118594859679e-07 -0.001972377300262451 -0.0004784250631928444 -0.00040392763912677765 -2.0721927285194397e-06 6.435438990592957e-07 9.12696123123169e-08
14 7 1 6.5 7.450580596923828e-09 -0.0015468885373133533 0.00045062508434057236 0.0003800736740231514 1.5839877249845813e-06 -0.0019662114791572094 -0.0004506339319050312 -0.00038006948307156563 2.334360033273697e-06 3.114808350801468e-06 1.4849938452243805e-06
15 7 2 7.0 8.999190503899968e-09 -0.0015470132347384351 0.00042080413550138474 0.0003548497334122658 8.012649583832854e-07 -0.0019701728597283363 -0.0004208004102110863 -0.00035485532134771347 1.5432480722665787e-05 1.1287163943052292e-05 5.935784429311752e-06
16 8 1 7.5 1.0738867337045654e-08 -0.0015471137993936443 0.0003952737897634506 0.00033300556242465973 1.388882160391347e-06 -0.0019654585048556328 -0.0003952831029891968 -0.0003329990431666374 5.064625293016434e-05 2.6551075279712677e-05 1.5786848962306976e-05
17 8 2 8.0 7.94032639639397e-09 -0.0015472117748432483 0.00036898814141750336 0.00031079165637493134 8.204820101109789e-07 -0.0019681239500641823 -0.0003689778968691826 -0.0003107842057943344 0.00010942388325929642 6.19497150182724e-05 3.8951635360717773e-05
18 9 1 8.5 8.976009713279609e-09 -0.0015472804270270975 0.00034602265805006027 0.0002911807969212532 1.3696224959725e-06 -0.001964463386684656 -0.0003460417501628399 -0.00029119057580828667 0.0002314508892595768 9.650783613324165e-05 6.443960592150688e-05
19 9 2 9.0 1.0052167986884797e-08 -0.001547337366734857 0.00032305438071489334 0.0002718055620789528 8.737507810607781e-07 -0.00196637911722064 -0.00032305577769875526 -0.0002718069590628147 0.0003751874901354313 0.00017973734065890312 0.00012484891340136528
20 10 1 9.5 7.4023787099974925e-09 -0.001547404346943595 0.00030282605439424515 0.0002545500174164772 1.3337265093449346e-06 -0.0019635148346424103 -0.00030282698571681976 -0.00025455933064222336 0.0006195390596985817 0.00022383593022823334 0.000162455253303051
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research/fine_surface_joint_20261014/suction_collisions.csv
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step,level,substep,max_error,max_ratio,regime,drift_m,drift_px,drift_py,ideal_defect_m,ideal_defect_px,ideal_defect_py,measured_rounding_m,measured_rounding_px,measured_rounding_py
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10,fine,1,1.6477090769573266e-07,0.3991506368171169,True,2.605374902486801e-06,1.2619420886039734e-07,6.007030606269836e-08,-1.5024440025435126e-14,-3.2786273695961654e-16,-2.8622937353617317e-16,2.605374917511241e-06,1.2619420918826008e-07,6.007030634892774e-08
10,fine,2,1.233670676725751e-07,0.3467327260376902,True,2.4153850972652435e-06,-1.5133991837501526e-07,-1.19674950838089e-07,-1.5607307113363333e-14,-1.9949319973733282e-16,4.631711680858075e-16,2.4153851128725506e-06,-1.5133991817552206e-07,-1.1967495130126016e-07
10,coarse,1,1.3984166169489498e-07,0.4038458162128909,True,6.22868537902832e-06,-2.942979335784912e-07,3.725290298461914e-09,-2.3592239273284576e-14,-4.163336342344337e-16,1.1657341758564144e-15,6.22868540262056e-06,-2.942979331621576e-07,3.725289132727738e-09
11,fine,1,1.1264907540109448e-07,0.37435057774799535,True,1.6032718122005463e-06,4.190951585769653e-09,2.9336661100387573e-08,-1.5607307113363333e-14,2.723515857283587e-16,-1.0234868508263162e-16,1.6032718278078534e-06,4.190951313418068e-09,2.9336661202736258e-08
11,fine,2,1.3385929209519531e-07,0.4296446436631812,True,1.1781230568885803e-06,2.3469328880310059e-07,1.126900315284729e-07,-1.665854953980528e-14,2.0296264668928643e-16,3.2959746043559335e-17,1.1781230735471299e-06,2.3469328860013794e-07,1.1269003149551315e-07
11,coarse,1,1.2484760447684096e-07,0.3737977990108256,True,1.9688159227371216e-06,-6.146728992462158e-08,-2.421438694000244e-08,-2.295386103412511e-14,-1.5265566588595902e-16,-9.575673587391975e-16,1.9688159456909826e-06,-6.146728977196592e-08,-2.4214385982435083e-08
12,fine,1,1.1342225503252479e-07,0.3929712248585018,True,2.9103830456733704e-06,1.1827796697616577e-07,1.6670674085617065e-07,-1.6981208106336965e-14,2.5153490401663703e-16,1.0234868508263162e-16,2.9103830626545785e-06,1.1827796672463087e-07,1.6670674075382197e-07
12,fine,2,1.2337913590787508e-07,0.3632480304781915,True,2.22865492105484e-06,7.450580596923828e-09,1.3690441846847534e-07,-1.7019372022808454e-14,-1.9081958235744878e-17,1.6479873021779667e-16,2.228654938074212e-06,7.450580616005786e-09,1.369044183036766e-07
12,coarse,1,1.0392955884785948e-07,0.34435036745116887,True,4.416331648826599e-06,8.009374141693115e-08,-7.264316082000732e-08,-2.665923037881157e-14,1.942890293094024e-16,3.3306690738754696e-16,4.4163316754858295e-06,8.009374122264212e-08,-7.264316115307423e-08
13,fine,1,1.120637568297056e-07,0.3794727716066827,True,1.1702068150043488e-06,-1.4016404747962952e-07,-1.0477378964424133e-07,-1.6571813366006438e-14,8.153200337090993e-17,-1.0928757898653885e-16,1.1702068315761621e-06,-1.4016404756116152e-07,-1.0477378953495375e-07
13,fine,2,1.1608141914365433e-07,0.3684207409368108,True,1.93621963262558e-06,-2.1979212760925293e-07,-7.543712854385376e-08,-1.696733031852915e-14,1.5612511283791264e-17,4.0072112295064244e-16,1.93621964959291e-06,-2.1979212762486544e-07,-7.543712894457488e-08
13,coarse,1,1.1553440504297185e-07,0.413461159221051,True,2.12155282497406e-06,9.872019290924072e-08,3.1851232051849365e-07,-2.4966140266258208e-14,1.0685896612017132e-15,3.885780586188048e-16,2.1215528499402003e-06,9.872019184065106e-08,3.185123201299156e-07
14,fine,1,1.3012184874039434e-07,0.4470057319678252,True,2.093147486448288e-06,-2.7474015951156616e-08,1.816079020500183e-08,-1.6941309466389498e-14,2.0469737016526324e-16,-2.393918396847994e-16,2.0931475033895974e-06,-2.7474016155853986e-08,1.816079044439367e-08
14,fine,2,1.161938236737825e-07,0.43455223301865037,True,2.180226147174835e-06,1.6577541828155518e-07,3.166496753692627e-08,-1.662038562333379e-14,-3.139849491518021e-16,-5.724587470723463e-17,2.180226163795221e-06,1.6577541859554012e-07,3.1664967594172144e-08
14,coarse,1,1.171716388359556e-07,0.34014613609932465,True,3.285706043243408e-06,5.21540641784668e-08,-8.940696716308594e-08,-2.5222879340702775e-14,2.0122792321330962e-16,-3.95516952522712e-16,3.2857060684662875e-06,5.2154063977238874e-08,-8.940696676756898e-08
15,fine,1,1.188765739179587e-07,0.3778719310571328,True,2.386048436164856e-06,1.0989606380462646e-07,4.190951585769653e-08,-1.6297727056802103e-14,3.0010716134398763e-16,5.0306980803327406e-17,2.386048452462583e-06,1.098960635045193e-07,4.190951580738955e-08
15,fine,2,1.2815464434101642e-07,0.3887039950605819,True,2.2151507437229156e-06,5.3551048040390015e-08,1.5599653124809265e-07,-1.6632528687665626e-14,-3.469446951953614e-17,-2.220446049250313e-16,2.2151507603554443e-06,5.3551048075084484e-08,1.5599653147013726e-07
15,coarse,1,1.132467172260121e-07,0.3798076391349909,True,4.431232810020447e-06,-6.891787052154541e-08,-4.6566128730773926e-08,-2.4917568008930857e-14,5.898059818321144e-16,7.008282842946301e-16,4.431232834938015e-06,-6.891787111135139e-08,-4.656612943160221e-08
16,fine,1,1.1449205894464853e-07,0.4232816528057199,True,1.267995685338974e-06,7.962808012962341e-08,8.89413058757782e-08,-1.7224069392973718e-14,1.9255430583342559e-16,2.3071822230491534e-16,1.2679957025630434e-06,7.962807993706911e-08,8.894130564505998e-08
16,fine,2,1.1465035798519096e-07,0.38660659294745114,True,1.61072239279747e-06,2.0070001482963562e-07,8.242204785346985e-08,-1.688579831515824e-14,2.0816681711721685e-16,-3.95516952522712e-16,1.6107224096832684e-06,2.007000146214688e-07,8.24220482489868e-08
16,coarse,1,1.026492656430733e-07,0.39551928594354874,True,4.755333065986633e-06,2.8870999813079834e-07,-1.30385160446167e-08,-2.5340840537069198e-14,8.465450562766819e-16,-1.4988010832439613e-15,4.755333091327474e-06,2.887099972842533e-07,-1.3038514545815616e-08
17,fine,1,1.4306400941688224e-07,0.3804206132029011,True,2.477318048477173e-06,2.952292561531067e-07,2.1420419216156006e-07,-1.6018436577169837e-14,1.1102230246251565e-16,-2.498001805406602e-16,2.4773180644956094e-06,2.952292560420844e-07,2.1420419241136024e-07
17,fine,2,1.206053866775747e-07,0.40931703720753354,True,2.6957131922245026e-06,3.655441105365753e-07,1.7834827303886414e-07,-1.704365815147213e-14,3.382710778154774e-16,-1.43982048506075e-16,2.6957132092681607e-06,3.6554411019830424e-07,1.7834827318284618e-07
17,coarse,1,1.3320810943184824e-07,0.4098554436464823,True,4.893168807029724e-06,2.3655593395233154e-07,3.632158041000366e-07,-2.93723378952393e-14,-4.649058915617843e-16,4.2327252813834093e-16,4.893168836402062e-06,2.3655593441723743e-07,3.632158036767641e-07
18,fine,1,1.1518374609664761e-07,0.37525164303996766,True,3.0049122869968414e-06,2.92900949716568e-07,2.4633482098579407e-07,-1.704365815147213e-14,-1.474514954580286e-16,5.724587470723463e-17,3.0049123040404996e-06,2.929009498640195e-07,2.463348209285482e-07
18,fine,2,1.5383020013359783e-07,0.4339170567027524,True,2.28220596909523e-06,3.2549723982810974e-07,2.48197466135025e-07,-1.700029006457271e-14,-1.214306433183765e-16,-3.122502256758253e-16,2.28220598609552e-06,3.254972399495404e-07,2.4819746644727525e-07
18,coarse,1,1.4574858631100085e-07,0.42004802685791165,True,4.898756742477417e-06,-1.564621925354004e-07,-2.123415470123291e-07,-2.679106936298581e-14,1.457167719820518e-16,-4.85722573273506e-17,4.898756769268486e-06,-1.5646219268111716e-07,-2.1234154696375684e-07
19,fine,1,1.1289196333308382e-07,0.4426868958150174,True,2.4051405489444733e-06,3.8230791687965393e-07,4.23286110162735e-07,-1.7246620798161416e-14,-5.447031714567174e-16,-2.42861286636753e-16,2.405140566191094e-06,3.823079174243571e-07,4.2328611040559627e-07
19,fine,2,1.3318459290978524e-07,0.4458767766498759,True,2.4051405489444733e-06,-1.3504177331924438e-08,-1.6298145055770874e-08,-1.6679366221517e-14,-1.8561541192951836e-16,4.683753385137379e-17,2.4051405656238395e-06,-1.3504177146309027e-08,-1.6298145102608408e-08
19,coarse,1,1.3627775263813646e-07,0.37598585924709055,True,6.38328492641449e-06,-2.514570951461792e-07,2.1792948246002197e-07,-2.724209746673978e-14,-7.91033905045424e-16,-1.3877787807814457e-17,6.383284953656587e-06,-2.514570943551453e-07,2.1792948247389976e-07
20,fine,1,1.2067538701643343e-07,0.397654453667931,True,3.3676624298095703e-06,-7.636845111846924e-08,8.195638656616211e-08,-1.6471199404399783e-14,1.0581813203458523e-16,7.806255641895632e-17,3.3676624462807697e-06,-7.636845122428737e-08,8.195638648809955e-08
20,fine,2,1.1349498318935503e-07,0.4276325956343959,True,3.5767443478107452e-06,-7.869675755500793e-08,6.565824151039124e-08,-1.5959455978986625e-14,9.020562075079397e-17,5.898059818321144e-17,3.5767443637702012e-06,-7.869675764521356e-08,6.565824145141064e-08
20,coarse,1,1.5850306378517587e-07,0.5285112236646456,True,3.5408884286880493e-06,-1.993030309677124e-07,-3.7066638469696045e-07,-2.525757381022231e-14,7.91033905045424e-16,-2.3592239273284576e-16,3.540888453945623e-06,-1.993030317587463e-07,-3.7066638446103806e-07
21,fine,1,1.2747028055315468e-07,0.3807671146447972,True,3.180466592311859e-06,-9.872019290924072e-08,-4.377216100692749e-08,-1.619537837171947e-14,5.204170427930421e-17,-1.4224732503009818e-16,3.1804666085072375e-06,-9.872019296128243e-08,-4.3772160864680165e-08
21,fine,2,1.1436689684174439e-07,0.41457208617159613,True,2.8158538043498993e-06,-4.516914486885071e-08,3.119930624961853e-08,-1.622833811776303e-14,-2.0296264668928643e-16,-1.7867651802561113e-16,2.8158538205782374e-06,-4.516914466588806e-08,3.119930642829505e-08
21,coarse,1,1.3219836714206679e-07,0.4111722561110483,True,5.48921525478363e-06,-9.816139936447144e-07,-5.345791578292847e-07,-2.7602919949742954e-14,4.0245584642661925e-16,2.7755575615628914e-16,5.48921528238655e-06,-9.816139940471702e-07,-5.345791581068404e-07
22,fine,1,1.247986221031283e-07,0.4025936491208882,True,2.5955960154533386e-06,-1.1734664440155029e-07,7.636845111846924e-08,-1.670712179713263e-14,2.0643209364124004e-16,-1.196959198423997e-16,2.5955960321604604e-06,-1.1734664460798239e-07,7.636845123816516e-08
22,fine,2,1.2467687349193568e-07,0.4026463456064581,True,2.169981598854065e-06,-2.0023435354232788e-07,-2.5331974029541016e-07,-1.5954251808558695e-14,4.336808689942018e-17,8.847089727481716e-17,2.1699816148083167e-06,-2.0023435358569597e-07,-2.5331974038388105e-07
22,coarse,1,1.303169513411362e-07,0.4534581358023719,True,4.8317015171051025e-06,-1.4826655387878418e-06,-8.456408977508545e-07,-2.6728619317850644e-14,-9.71445146547012e-17,-4.163336342344337e-16,4.831701543833722e-06,-1.4826655386906973e-06,-8.456408973345209e-07
23,fine,1,1.1580148451262318e-07,0.3892248238329876,True,2.83215194940567e-06,-4.284083843231201e-08,-1.3317912817001343e-07,-1.7317744460676465e-14,2.2551405187698492e-17,-3.9898639947466563e-17,2.8321519667234146e-06,-4.284083845486342e-08,-1.331791281301148e-07
23,fine,2,1.4772394407724931e-07,0.44883956143532583,True,2.432148903608322e-06,-1.0570511221885681e-07,-3.306195139884949e-08,-1.628211454551831e-14,4.85722573273506e-17,1.1796119636642288e-16,2.4321489198904367e-06,-1.0570511226742907e-07,-3.3061951516810684e-08
23,coarse,1,1.4415727089822639e-07,0.46236304392819116,True,8.864328265190125e-06,-8.624047040939331e-07,-5.19677996635437e-07,-2.839395385478838e-14,-3.7470027081099033e-16,-1.0269562977782698e-15,8.864328293584078e-06,-8.624047037192328e-07,-5.196779956084807e-07
24,fine,1,1.1816075007331506e-07,0.40334771123509106,True,2.196524292230606e-06,-5.2619725465774536e-08,-1.327134668827057e-07,-1.619537837171947e-14,3.400058012914542e-16,7.979727989493313e-17,2.1965243084259845e-06,-5.261972580578034e-08,-1.3271346696250297e-07
24,fine,2,1.2605529897680157e-07,0.4651221485857391,True,2.941582351922989e-06,-2.2584572434425354e-07,6.05359673500061e-09,-1.802204219192305e-14,-2.5153490401663703e-16,2.3418766925686896e-16,2.941582369945031e-06,-2.2584572409271864e-07,6.053596500812941e-09
24,coarse,1,1.573361411155716e-07,0.43294013951653565,True,9.939074516296387e-06,-2.905726432800293e-07,1.4156103134155273e-07,-2.768618667658984e-14,-1.6653345369377348e-16,1.1102230246251565e-15,9.939074543982573e-06,-2.9057264311349584e-07,1.415610302313297e-07
1 step level substep max_error max_ratio regime drift_m drift_px drift_py ideal_defect_m ideal_defect_px ideal_defect_py measured_rounding_m measured_rounding_px measured_rounding_py
2 1 fine 1 8.229037262719174e-08 0.3569820693406681 True 1.626182347536087e-05 -3.3527612686157227e-08 -1.7695128917694092e-08 -3.0010716134398763e-16 -1.214306433183765e-17 -1.734723475976807e-18 1.6261823475660978e-05 -3.352761267401416e-08 -1.7695128915959368e-08
3 1 fine 2 5.173531247848473e-08 0.2677766155245442 True -3.394670784473419e-07 1.3969838619232178e-09 1.7229467630386353e-08 -5.745404152435185e-15 -1.0408340855860843e-17 5.898059818321144e-17 -3.3946707270193777e-07 1.3969838723315586e-09 1.7229467571405754e-08
4 1 coarse 1 1.1080350609260492e-08 0.10456730670501622 True -1.5292316675186157e-06 -1.30385160446167e-08 -1.5832483768463135e-07 -4.288236432614667e-15 -5.551115123125783e-17 1.3877787807814457e-17 -1.5292316632303793e-06 -1.3038515989105548e-08 -1.5832483769850914e-07
5 2 fine 1 9.892327201832529e-08 0.29769478490638285 True 9.881332516670227e-07 -3.3527612686157227e-08 -1.993030309677124e-07 -5.195496810550537e-15 -2.949029909160572e-17 2.1337098754514727e-16 9.881332568625195e-07 -3.352761265666693e-08 -1.993030311810834e-07
6 2 fine 2 8.832159215099722e-08 0.3121116042355555 True 2.668239176273346e-06 -4.284083843231201e-08 1.2852251529693604e-07 -1.1539380562197721e-14 -1.457167719820518e-16 2.2898349882893854e-16 2.6682391878127265e-06 -4.284083828659524e-08 1.2852251506795254e-07
7 2 coarse 1 1.6365805183271576e-08 0.1544470929589978 True -1.426786184310913e-06 -3.0919909477233887e-07 -3.0919909477233887e-07 -6.286637876939949e-15 -8.326672684688674e-17 -8.326672684688674e-17 -1.4267861780242752e-06 -3.0919909468907214e-07 -3.0919909468907214e-07
8 3 fine 1 8.23703105723439e-08 0.31152334896616624 True 4.902016371488571e-06 1.5227124094963074e-07 1.280568540096283e-07 -1.1922754450388595e-14 9.540979117872439e-17 3.452099717193846e-16 4.902016383411326e-06 1.5227124085422095e-07 1.2805685366441832e-07
9 3 fine 2 1.0607572520360264e-07 0.3828123813437159 True 8.435919880867004e-06 7.35744833946228e-08 -3.259629011154175e-08 -1.7525911277793682e-14 9.540979117872439e-17 3.5561831257524545e-16 8.435919898392916e-06 7.357448329921301e-08 -3.259629046716006e-08
10 3 coarse 1 1.0010523732173482e-07 0.2854567053212911 True 9.115785360336304e-06 3.3155083656311035e-07 3.203749656677246e-07 -3.837208328860697e-15 1.5334955527634975e-15 1.2420620087993939e-15 9.115785364173512e-06 3.315508350296148e-07 3.203749644256626e-07
11 4 fine 1 9.437402781475868e-08 0.3632811360585352 True 1.0774470865726471e-05 2.8405338525772095e-07 3.0174851417541504e-07 -2.0582494042464816e-14 2.411265631607762e-16 2.7929047963226594e-16 1.0774470886308965e-05 2.840533850165944e-07 3.0174851389612456e-07
12 4 fine 2 1.411709008580786e-07 0.4323516759917293 True 1.1160969734191895e-05 2.169981598854065e-07 5.774199962615967e-08 -1.6453852169640015e-14 -5.533767888366015e-16 -4.2847669856627135e-16 1.1160969750645747e-05 2.1699816043878328e-07 5.7742000054636367e-08
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14 5 fine 1 1.2376571800754022e-07 0.4335945297787668 True 1.0852236300706863e-05 -3.022141754627228e-07 -3.1618401408195496e-07 -1.5610776560315287e-14 5.377642775528102e-17 -3.625572064791527e-16 1.085223631631764e-05 -3.022141755164992e-07 -3.1618401371939775e-07
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16 5 coarse 1 1.2532259030528792e-07 0.38942307019337585 True 1.49458646774292e-05 9.723007678985596e-07 1.2628734111785889e-06 -1.6535384173010925e-14 1.5959455978986625e-16 6.175615574477433e-16 1.4945864693964583e-05 9.72300767738965e-07 1.2628734105610273e-06
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18 6 fine 2 1.1486422213380365e-07 0.36328113343919116 True 6.2193721532821655e-06 -4.190951585769653e-08 -1.30385160446167e-08 -1.437044927499187e-14 1.7694179454963432e-16 -1.0408340855860843e-17 6.219372167652615e-06 -4.190951603463833e-08 -1.3038516034208358e-08
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20 7 fine 1 1.1381071352012384e-07 0.39830750519457453 True 5.2102841436862946e-06 -2.0023435354232788e-08 -1.0477378964424133e-07 -1.6167622796103842e-14 9.020562075079397e-17 0.0 5.210284159853917e-06 -2.002343544443841e-08 -1.0477378964424133e-07
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23 8 fine 1 1.2369974378145798e-07 0.3659033398608433 True 4.209578037261963e-06 -1.2479722499847412e-07 -2.421438694000244e-08 -1.5269036035547856e-14 -1.4224732503009818e-16 -7.28583859910259e-17 4.209578052530999e-06 -1.247972248562268e-07 -2.4214386867144055e-08
24 8 fine 2 1.3039186863572638e-07 0.3566417233064843 True 4.0014274418354034e-06 5.075708031654358e-08 -1.6298145055770874e-08 -1.6002824065886045e-14 -2.949029909160572e-17 -2.5153490401663703e-16 4.0014274578382275e-06 5.075708034603388e-08 -1.629814480423597e-08
25 8 coarse 1 1.265961931951054e-07 0.38341345468786764 True 9.08784568309784e-06 6.09084963798523e-07 -5.979090929031372e-07 -2.2336299476677368e-14 -1.4224732503009818e-15 2.0816681711721685e-17 9.087845705434139e-06 6.090849652209962e-07 -5.979090929239539e-07
26 9 fine 1 1.1700286717264419e-07 0.42325024900493485 True 2.2551976144313812e-06 -1.150183379650116e-07 -3.026798367500305e-08 -1.5406079190150024e-14 6.418476861114186e-17 2.6194324487249787e-16 2.2551976298374604e-06 -1.1501833802919637e-07 -3.0267983936946297e-08
27 9 fine 2 1.05960657470483e-07 0.3591277749177671 True 3.159046173095703e-06 -5.8673322200775146e-08 -1.471489667892456e-07 -1.5987211554602254e-14 -6.245004513516506e-17 -2.0816681711721685e-17 3.1590461890829147e-06 -5.86733221383251e-08 -1.4714896676842892e-07
28 9 coarse 1 1.1063907701114317e-07 0.4156431955412631 True 5.3513795137405396e-06 -3.1478703022003174e-07 -5.345791578292847e-07 -2.5042468099201187e-14 1.0408340855860843e-16 -2.2898349882893854e-16 5.351379538783008e-06 -3.1478703032411515e-07 -5.345791576003012e-07
29 10 fine 1 1.6477090769573266e-07 0.3991506368171169 True 2.605374902486801e-06 1.2619420886039734e-07 6.007030606269836e-08 -1.5024440025435126e-14 -3.2786273695961654e-16 -2.8622937353617317e-16 2.605374917511241e-06 1.2619420918826008e-07 6.007030634892774e-08
30 10 fine 2 1.233670676725751e-07 0.3467327260376902 True 2.4153850972652435e-06 -1.5133991837501526e-07 -1.19674950838089e-07 -1.5607307113363333e-14 -1.9949319973733282e-16 4.631711680858075e-16 2.4153851128725506e-06 -1.5133991817552206e-07 -1.1967495130126016e-07
31 10 coarse 1 1.3984166169489498e-07 0.4038458162128909 True 6.22868537902832e-06 -2.942979335784912e-07 3.725290298461914e-09 -2.3592239273284576e-14 -4.163336342344337e-16 1.1657341758564144e-15 6.22868540262056e-06 -2.942979331621576e-07 3.725289132727738e-09
32 11 fine 1 1.1264907540109448e-07 0.37435057774799535 True 1.6032718122005463e-06 4.190951585769653e-09 2.9336661100387573e-08 -1.5607307113363333e-14 2.723515857283587e-16 -1.0234868508263162e-16 1.6032718278078534e-06 4.190951313418068e-09 2.9336661202736258e-08
33 11 fine 2 1.3385929209519531e-07 0.4296446436631812 True 1.1781230568885803e-06 2.3469328880310059e-07 1.126900315284729e-07 -1.665854953980528e-14 2.0296264668928643e-16 3.2959746043559335e-17 1.1781230735471299e-06 2.3469328860013794e-07 1.1269003149551315e-07
34 11 coarse 1 1.2484760447684096e-07 0.3737977990108256 True 1.9688159227371216e-06 -6.146728992462158e-08 -2.421438694000244e-08 -2.295386103412511e-14 -1.5265566588595902e-16 -9.575673587391975e-16 1.9688159456909826e-06 -6.146728977196592e-08 -2.4214385982435083e-08
35 12 fine 1 1.1342225503252479e-07 0.3929712248585018 True 2.9103830456733704e-06 1.1827796697616577e-07 1.6670674085617065e-07 -1.6981208106336965e-14 2.5153490401663703e-16 1.0234868508263162e-16 2.9103830626545785e-06 1.1827796672463087e-07 1.6670674075382197e-07
36 12 fine 2 1.2337913590787508e-07 0.3632480304781915 True 2.22865492105484e-06 7.450580596923828e-09 1.3690441846847534e-07 -1.7019372022808454e-14 -1.9081958235744878e-17 1.6479873021779667e-16 2.228654938074212e-06 7.450580616005786e-09 1.369044183036766e-07
37 12 coarse 1 1.0392955884785948e-07 0.34435036745116887 True 4.416331648826599e-06 8.009374141693115e-08 -7.264316082000732e-08 -2.665923037881157e-14 1.942890293094024e-16 3.3306690738754696e-16 4.4163316754858295e-06 8.009374122264212e-08 -7.264316115307423e-08
38 13 fine 1 1.120637568297056e-07 0.3794727716066827 True 1.1702068150043488e-06 -1.4016404747962952e-07 -1.0477378964424133e-07 -1.6571813366006438e-14 8.153200337090993e-17 -1.0928757898653885e-16 1.1702068315761621e-06 -1.4016404756116152e-07 -1.0477378953495375e-07
39 13 fine 2 1.1608141914365433e-07 0.3684207409368108 True 1.93621963262558e-06 -2.1979212760925293e-07 -7.543712854385376e-08 -1.696733031852915e-14 1.5612511283791264e-17 4.0072112295064244e-16 1.93621964959291e-06 -2.1979212762486544e-07 -7.543712894457488e-08
40 13 coarse 1 1.1553440504297185e-07 0.413461159221051 True 2.12155282497406e-06 9.872019290924072e-08 3.1851232051849365e-07 -2.4966140266258208e-14 1.0685896612017132e-15 3.885780586188048e-16 2.1215528499402003e-06 9.872019184065106e-08 3.185123201299156e-07
41 14 fine 1 1.3012184874039434e-07 0.4470057319678252 True 2.093147486448288e-06 -2.7474015951156616e-08 1.816079020500183e-08 -1.6941309466389498e-14 2.0469737016526324e-16 -2.393918396847994e-16 2.0931475033895974e-06 -2.7474016155853986e-08 1.816079044439367e-08
42 14 fine 2 1.161938236737825e-07 0.43455223301865037 True 2.180226147174835e-06 1.6577541828155518e-07 3.166496753692627e-08 -1.662038562333379e-14 -3.139849491518021e-16 -5.724587470723463e-17 2.180226163795221e-06 1.6577541859554012e-07 3.1664967594172144e-08
43 14 coarse 1 1.171716388359556e-07 0.34014613609932465 True 3.285706043243408e-06 5.21540641784668e-08 -8.940696716308594e-08 -2.5222879340702775e-14 2.0122792321330962e-16 -3.95516952522712e-16 3.2857060684662875e-06 5.2154063977238874e-08 -8.940696676756898e-08
44 15 fine 1 1.188765739179587e-07 0.3778719310571328 True 2.386048436164856e-06 1.0989606380462646e-07 4.190951585769653e-08 -1.6297727056802103e-14 3.0010716134398763e-16 5.0306980803327406e-17 2.386048452462583e-06 1.098960635045193e-07 4.190951580738955e-08
45 15 fine 2 1.2815464434101642e-07 0.3887039950605819 True 2.2151507437229156e-06 5.3551048040390015e-08 1.5599653124809265e-07 -1.6632528687665626e-14 -3.469446951953614e-17 -2.220446049250313e-16 2.2151507603554443e-06 5.3551048075084484e-08 1.5599653147013726e-07
46 15 coarse 1 1.132467172260121e-07 0.3798076391349909 True 4.431232810020447e-06 -6.891787052154541e-08 -4.6566128730773926e-08 -2.4917568008930857e-14 5.898059818321144e-16 7.008282842946301e-16 4.431232834938015e-06 -6.891787111135139e-08 -4.656612943160221e-08
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48 16 fine 2 1.1465035798519096e-07 0.38660659294745114 True 1.61072239279747e-06 2.0070001482963562e-07 8.242204785346985e-08 -1.688579831515824e-14 2.0816681711721685e-16 -3.95516952522712e-16 1.6107224096832684e-06 2.007000146214688e-07 8.24220482489868e-08
49 16 coarse 1 1.026492656430733e-07 0.39551928594354874 True 4.755333065986633e-06 2.8870999813079834e-07 -1.30385160446167e-08 -2.5340840537069198e-14 8.465450562766819e-16 -1.4988010832439613e-15 4.755333091327474e-06 2.887099972842533e-07 -1.3038514545815616e-08
50 17 fine 1 1.4306400941688224e-07 0.3804206132029011 True 2.477318048477173e-06 2.952292561531067e-07 2.1420419216156006e-07 -1.6018436577169837e-14 1.1102230246251565e-16 -2.498001805406602e-16 2.4773180644956094e-06 2.952292560420844e-07 2.1420419241136024e-07
51 17 fine 2 1.206053866775747e-07 0.40931703720753354 True 2.6957131922245026e-06 3.655441105365753e-07 1.7834827303886414e-07 -1.704365815147213e-14 3.382710778154774e-16 -1.43982048506075e-16 2.6957132092681607e-06 3.6554411019830424e-07 1.7834827318284618e-07
52 17 coarse 1 1.3320810943184824e-07 0.4098554436464823 True 4.893168807029724e-06 2.3655593395233154e-07 3.632158041000366e-07 -2.93723378952393e-14 -4.649058915617843e-16 4.2327252813834093e-16 4.893168836402062e-06 2.3655593441723743e-07 3.632158036767641e-07
53 18 fine 1 1.1518374609664761e-07 0.37525164303996766 True 3.0049122869968414e-06 2.92900949716568e-07 2.4633482098579407e-07 -1.704365815147213e-14 -1.474514954580286e-16 5.724587470723463e-17 3.0049123040404996e-06 2.929009498640195e-07 2.463348209285482e-07
54 18 fine 2 1.5383020013359783e-07 0.4339170567027524 True 2.28220596909523e-06 3.2549723982810974e-07 2.48197466135025e-07 -1.700029006457271e-14 -1.214306433183765e-16 -3.122502256758253e-16 2.28220598609552e-06 3.254972399495404e-07 2.4819746644727525e-07
55 18 coarse 1 1.4574858631100085e-07 0.42004802685791165 True 4.898756742477417e-06 -1.564621925354004e-07 -2.123415470123291e-07 -2.679106936298581e-14 1.457167719820518e-16 -4.85722573273506e-17 4.898756769268486e-06 -1.5646219268111716e-07 -2.1234154696375684e-07
56 19 fine 1 1.1289196333308382e-07 0.4426868958150174 True 2.4051405489444733e-06 3.8230791687965393e-07 4.23286110162735e-07 -1.7246620798161416e-14 -5.447031714567174e-16 -2.42861286636753e-16 2.405140566191094e-06 3.823079174243571e-07 4.2328611040559627e-07
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58 19 coarse 1 1.3627775263813646e-07 0.37598585924709055 True 6.38328492641449e-06 -2.514570951461792e-07 2.1792948246002197e-07 -2.724209746673978e-14 -7.91033905045424e-16 -1.3877787807814457e-17 6.383284953656587e-06 -2.514570943551453e-07 2.1792948247389976e-07
59 20 fine 1 1.2067538701643343e-07 0.397654453667931 True 3.3676624298095703e-06 -7.636845111846924e-08 8.195638656616211e-08 -1.6471199404399783e-14 1.0581813203458523e-16 7.806255641895632e-17 3.3676624462807697e-06 -7.636845122428737e-08 8.195638648809955e-08
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61 20 coarse 1 1.5850306378517587e-07 0.5285112236646456 True 3.5408884286880493e-06 -1.993030309677124e-07 -3.7066638469696045e-07 -2.525757381022231e-14 7.91033905045424e-16 -2.3592239273284576e-16 3.540888453945623e-06 -1.993030317587463e-07 -3.7066638446103806e-07
62 21 fine 1 1.2747028055315468e-07 0.3807671146447972 True 3.180466592311859e-06 -9.872019290924072e-08 -4.377216100692749e-08 -1.619537837171947e-14 5.204170427930421e-17 -1.4224732503009818e-16 3.1804666085072375e-06 -9.872019296128243e-08 -4.3772160864680165e-08
63 21 fine 2 1.1436689684174439e-07 0.41457208617159613 True 2.8158538043498993e-06 -4.516914486885071e-08 3.119930624961853e-08 -1.622833811776303e-14 -2.0296264668928643e-16 -1.7867651802561113e-16 2.8158538205782374e-06 -4.516914466588806e-08 3.119930642829505e-08
64 21 coarse 1 1.3219836714206679e-07 0.4111722561110483 True 5.48921525478363e-06 -9.816139936447144e-07 -5.345791578292847e-07 -2.7602919949742954e-14 4.0245584642661925e-16 2.7755575615628914e-16 5.48921528238655e-06 -9.816139940471702e-07 -5.345791581068404e-07
65 22 fine 1 1.247986221031283e-07 0.4025936491208882 True 2.5955960154533386e-06 -1.1734664440155029e-07 7.636845111846924e-08 -1.670712179713263e-14 2.0643209364124004e-16 -1.196959198423997e-16 2.5955960321604604e-06 -1.1734664460798239e-07 7.636845123816516e-08
66 22 fine 2 1.2467687349193568e-07 0.4026463456064581 True 2.169981598854065e-06 -2.0023435354232788e-07 -2.5331974029541016e-07 -1.5954251808558695e-14 4.336808689942018e-17 8.847089727481716e-17 2.1699816148083167e-06 -2.0023435358569597e-07 -2.5331974038388105e-07
67 22 coarse 1 1.303169513411362e-07 0.4534581358023719 True 4.8317015171051025e-06 -1.4826655387878418e-06 -8.456408977508545e-07 -2.6728619317850644e-14 -9.71445146547012e-17 -4.163336342344337e-16 4.831701543833722e-06 -1.4826655386906973e-06 -8.456408973345209e-07
68 23 fine 1 1.1580148451262318e-07 0.3892248238329876 True 2.83215194940567e-06 -4.284083843231201e-08 -1.3317912817001343e-07 -1.7317744460676465e-14 2.2551405187698492e-17 -3.9898639947466563e-17 2.8321519667234146e-06 -4.284083845486342e-08 -1.331791281301148e-07
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73 24 coarse 1 1.573361411155716e-07 0.43294013951653565 True 9.939074516296387e-06 -2.905726432800293e-07 1.4156103134155273e-07 -2.768618667658984e-14 -1.6653345369377348e-16 1.1102230246251565e-15 9.939074543982573e-06 -2.9057264311349584e-07 1.415610302313297e-07
research/fine_surface_joint_20261014/suction_ledger.csv
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step,time,average_bound_ratio,leaf_m,leaf_px,leaf_py,delta_m,delta_px,delta_py,boundary_m,boundary_px,boundary_py,fine_transfer_m,fine_transfer_px,fine_transfer_py,coarse_transfer_m,coarse_transfer_px,coarse_transfer_py,raw_m,raw_px,raw_py,collision_drift_m,collision_drift_px,collision_drift_py,average_rounding_m,average_rounding_px,average_rounding_py,closure_m,closure_px,closure_py,cumulative_raw_m,cumulative_raw_px,cumulative_raw_py,cumulative_closure_m,cumulative_closure_px,cumulative_closure_py
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20,20,0.4691149902535097,560.6725037032738,-0.014019680209457874,-0.01183774508535862,-0.003909097984433174,-0.00021301768720149994,-0.00018038135021924973,-0.003919586073607206,-0.00021268753334879875,-0.00018017226830124855,0.0051221540197730064,0.000519266352057457,0.0004148585721850395,-0.005122151225805283,-0.000519242137670517,-0.0004148446023464203,1.0485295206308365e-05,-3.543682396411896e-07,-2.230517566204071e-07,1.0485295206308365e-05,-3.543682396411896e-07,-2.230517566204071e-07,2.7939677238464355e-09,2.421438694000244e-08,1.3969838619232178e-08,0.0,0.0,0.0,0.00028364965692162514,4.72506508231163e-06,2.505723387002945e-06,0.0,0.0,0.0
21,21,0.46903467995095127,560.6685956828296,-0.01421999093145132,-0.01200737152248621,-0.003908020444214344,-0.00020031072199344635,-0.00016962643712759018,-0.003919499460607767,-0.0001992243342101574,-0.00016904575750231743,0.0048424312844872475,0.0002950672060251236,0.00023025646805763245,-0.00484243780374527,-0.00029502809047698975,-0.0002302899956703186,1.1485535651445389e-05,-1.1255033314228058e-06,-5.471520125865936e-07,1.1485535651445389e-05,-1.1255033314228058e-06,-5.471520125865936e-07,-6.51925802230835e-09,3.91155481338501e-08,-3.3527612686157227e-08,0.0,0.0,0.0,0.0002951351925730705,3.5995617508888245e-06,1.9585713744163513e-06,0.0,0.0,0.0
22,22,0.4690269226422474,560.6646860563196,-0.014409893658012152,-0.01216775132343173,-0.003909626509994268,-0.00018990272656083107,-0.0001603798009455204,-0.003919223323464394,-0.00018808525055646896,-0.00015935394912958145,0.004410987254232168,5.6739430874586105e-05,4.135677590966225e-05,-0.0044109877198934555,-5.675666034221649e-05,-4.13600355386734e-05,9.597279131412506e-06,-1.80024653673172e-06,-1.0225921869277954e-06,9.597279131412506e-06,-1.80024653673172e-06,-1.0225921869277954e-06,-4.656612873077393e-10,-1.7229467630386353e-08,-3.259629011154175e-09,0.0,0.0,0.0,0.00030473247170448303,1.7993152141571045e-06,9.359791874885559e-07,0.0,0.0,0.0
23,23,0.46895167590501197,560.6607814244926,-0.014590884558856487,-0.012320364825427532,-0.0039046318270266056,-0.00018099090084433556,-0.00015261350199580193,-0.003918746951967478,-0.00017994502559304237,-0.00015195319429039955,0.0038275266997516155,-0.00019492162391543388,-0.00015757465735077858,-0.0038275402039289474,0.0001948866993188858,0.0001576002687215805,1.4128629118204117e-05,-1.010950654745102e-06,-6.859190762042999e-07,1.4128629118204117e-05,-1.010950654745102e-06,-6.859190762042999e-07,-1.3504177331924438e-08,-3.4924596548080444e-08,2.561137080192566e-08,0.0,0.0,0.0,0.00031886110082268715,7.883645594120026e-07,2.50060111284256e-07,0.0,0.0,0.0
24,24,0.46890732263511536,560.6568783186376,-0.014766339212656021,-0.012467360123991966,-0.003903105854988098,-0.00017545465379953384,-0.00014699529856443405,-0.003918208181858063,-0.00017489027231931686,-0.00014702323824167252,0.0031356504186987877,-0.0004478944465517998,-0.00036316923797130585,-0.003135625272989273,0.00044789910316467285,0.00036318227648735046,1.5077181160449982e-05,-5.690380930900574e-07,1.4901161193847656e-08,1.5077181160449982e-05,-5.690380930900574e-07,1.4901161193847656e-08,2.514570951461792e-08,4.6566128730773926e-09,1.30385160446167e-08,0.0,0.0,0.0,0.00033393828198313713,2.193264663219452e-07,2.6496127247810364e-07,0.0,0.0,0.0
1 step time average_bound_ratio leaf_m leaf_px leaf_py delta_m delta_px delta_py boundary_m boundary_px boundary_py fine_transfer_m fine_transfer_px fine_transfer_py coarse_transfer_m coarse_transfer_px coarse_transfer_py raw_m raw_px raw_py collision_drift_m collision_drift_px collision_drift_py average_rounding_m average_rounding_px average_rounding_py closure_m closure_px closure_py cumulative_raw_m cumulative_raw_px cumulative_raw_py cumulative_closure_m cumulative_closure_px cumulative_closure_py
2 1 1 0.28124994289828603 560.7465738453902 -0.0016551441513001919 -0.001416988205164671 -0.003430332522839308 -0.0016551441513001919 -0.001416988205164671 -0.0034449007362127304 -0.0016550971195101738 -0.001416652463376522 -3.557652235031128e-07 0.0 0.0 5.308538675308228e-07 -1.862645149230957e-09 -1.7695128917694092e-07 1.4393124729394913e-05 -4.516914486885071e-08 -1.587904989719391e-07 1.4393124729394913e-05 -4.516914486885071e-08 -1.587904989719391e-07 1.7508864402770996e-07 -1.862645149230957e-09 -1.7695128917694092e-07 0.0 0.0 0.0 1.4393124729394913e-05 -4.516914486885071e-08 -1.587904989719391e-07 0.0 0.0 0.0
3 2 2 0.37499987985940486 560.7428558217362 -0.003098900429904461 -0.0026293257251381874 -0.003718023654073477 -0.001443756278604269 -0.0012123375199735165 -0.0037200921215116978 -0.0014432151801884174 -0.001211958471685648 -2.193264663219452e-06 -4.6566128730773926e-09 -1.685693860054016e-07 2.032145857810974e-06 -1.5087425708770752e-07 1.695007085800171e-07 2.2295862436294556e-06 -3.855675458908081e-07 -3.7997961044311523e-07 2.2295862436294556e-06 -3.855675458908081e-07 -3.7997961044311523e-07 -1.6111880540847778e-07 -1.555308699607849e-07 9.313225746154785e-10 0.0 0.0 0.0 1.6622710973024368e-05 -4.307366907596588e-07 -5.387701094150543e-07 0.0 0.0 0.0
4 3 3 0.2812499146093721 560.739008476492 -0.004490140359848738 -0.0038015726022422314 -0.003847345244139433 -0.0013912399299442768 -0.001172246877104044 -0.0038698003627359867 -0.0013918033801019192 -0.001172670628875494 -3.6400742828845978e-06 4.698522388935089e-07 4.6798959374427795e-07 3.641471266746521e-06 -4.637986421585083e-07 -4.600733518600464e-07 2.245372161269188e-05 5.573965609073639e-07 4.1583552956581116e-07 2.245372161269188e-05 5.573965609073639e-07 4.1583552956581116e-07 1.3969838619232178e-09 6.05359673500061e-09 7.916241884231567e-09 0.0 0.0 0.0 3.907643258571625e-05 1.2665987014770508e-07 -1.2293457984924316e-07 0.0 0.0 0.0
5 4 4 0.37499987985940486 560.7351214168593 -0.005752546712756157 -0.004868627525866032 -0.0038870596326887608 -0.0012624063529074192 -0.0010670549236238003 -0.003921949304640293 -0.0012635616585612297 -0.001068035140633583 -6.910879164934158e-06 3.1711533665657043e-07 3.9534643292427063e-07 6.800517439842224e-06 -2.6263296604156494e-07 -3.371387720108032e-07 3.50000336766243e-05 1.1008232831954956e-06 9.220093488693237e-07 3.50000336766243e-05 1.1008232831954956e-06 9.220093488693237e-07 -1.103617250919342e-07 5.448237061500549e-08 5.820766091346741e-08 0.0 0.0 0.0 7.407646626234055e-05 1.2274831533432007e-06 7.990747690200806e-07 0.0 0.0 0.0
6 5 5 0.3749998295680158 560.7312193890102 -0.006873782258480787 -0.0058157010935246944 -0.0039020278491079807 -0.0011212355457246304 -0.0009470735676586628 -0.003936932887881994 -0.001121701207011938 -0.0009480114094913006 -7.287599146366119e-06 1.0672956705093384e-06 4.637986421585083e-07 7.417052984237671e-06 -1.0095536708831787e-06 -4.023313522338867e-07 3.477558493614197e-05 4.079192876815796e-07 8.763745427131653e-07 3.477558493614197e-05 4.079192876815796e-07 8.763745427131653e-07 1.2945383787155151e-07 5.774199962615967e-08 6.146728992462158e-08 0.0 0.0 0.0 0.00010885205119848251 1.6354024410247803e-06 1.6754493117332458e-06 0.0 0.0 0.0
7 6 6 0.46874973195382685 560.7273077829741 -0.007863933686167002 -0.0066518173553049564 -0.003911606036126614 -0.0009901514276862144 -0.000836116261780262 -0.003938451875001192 -0.0009906026534736156 -0.0008363532833755016 -4.966277629137039e-06 1.1599622666835785e-06 1.234002411365509e-07 4.9192458391189575e-06 -1.2051314115524292e-06 -1.993030309677124e-07 2.6892870664596558e-05 4.9639493227005e-07 3.129243850708008e-07 2.6892870664596558e-05 4.9639493227005e-07 3.129243850708008e-07 -4.7031790018081665e-08 -4.516914486885071e-08 -7.59027898311615e-08 0.0 0.0 0.0 0.00013574492186307907 2.1317973732948303e-06 1.9883736968040466e-06 0.0 0.0 0.0
8 7 7 0.4687521208060999 560.7233943245374 -0.008734654169529676 -0.0073870024643838406 -0.003913458436727524 -0.0008707204833626747 -0.0007351851090788841 -0.003936384338885546 -0.0008714343421161175 -0.0007349248044192791 1.7766840755939484e-05 1.440197229385376e-05 7.420778274536133e-06 -1.7741695046424866e-05 -1.4455989003181458e-05 -7.407739758491516e-06 2.290075644850731e-05 7.67875462770462e-07 -2.7334317564964294e-07 2.290075644850731e-05 7.67875462770462e-07 -2.7334317564964294e-07 2.514570951461792e-08 -5.4016709327697754e-08 1.30385160446167e-08 0.0 0.0 0.0 0.00015864567831158638 2.8996728360652924e-06 1.7150305211544037e-06 0.0 0.0 0.0
9 8 8 0.4687808518052075 560.7194780893624 -0.009498408995568752 -0.008031456731259823 -0.00391623517498374 -0.0007637548260390759 -0.0006444542668759823 -0.003933582454919815 -0.0007642609998583794 -0.0006437832489609718 0.00016007013618946075 8.850079029798508e-05 5.473848432302475e-05 -0.00016002170741558075 -8.852966129779816e-05 -5.477108061313629e-05 1.7298851162195206e-05 5.350448191165924e-07 -6.384216248989105e-07 1.7298851162195206e-05 5.350448191165924e-07 -6.384216248989105e-07 4.842877388000488e-08 -2.8870999813079834e-08 -3.259629011154175e-08 0.0 0.0 0.0 0.00017594452947378159 3.4347176551818848e-06 1.0766088962554932e-06 0.0 0.0 0.0
10 9 9 0.46888398548910104 560.7155580227263 -0.010167959611862898 -0.008595113176852465 -0.003920066636055708 -0.0006695506162941456 -0.0005636564455926418 -0.003930842503905296 -0.0006690975278615952 -0.0005629975348711014 0.0006066383793950081 0.0002762451767921448 0.00018928851932287216 -0.0006066281348466873 -0.0002762097865343094 -0.00018923543393611908 1.0765623301267624e-05 -4.884786903858185e-07 -7.119961082935333e-07 1.0765623301267624e-05 -4.884786903858185e-07 -7.119961082935333e-07 1.0244548320770264e-08 3.5390257835388184e-08 5.3085386753082275e-08 0.0 0.0 0.0 0.0001867101527750492 2.9462389647960663e-06 3.6461278796195984e-07 0.0 0.0 0.0
11 10 10 0.46903176896324594 560.7116407523863 -0.01075396640226245 -0.009087595622986555 -0.003917270340025425 -0.0005860067903995514 -0.0004924824461340904 -0.0039285351522266865 -0.0005857185460627079 -0.0004924158565700054 0.001471752766519785 0.0005768421106040478 0.0004282691515982151 -0.0014717373996973038 -0.0005768109112977982 -0.0004282798618078232 1.1249445378780365e-05 -3.1944364309310913e-07 -5.587935447692871e-08 1.1249445378780365e-05 -3.1944364309310913e-07 -5.587935447692871e-08 1.5366822481155396e-08 3.119930624961853e-08 -1.0710209608078003e-08 0.0 0.0 0.0 0.00019795959815382957 2.626795321702957e-06 3.0873343348503113e-07 0.0 0.0 0.0
12 11 11 0.4691126134417028 560.7077189418487 -0.011267102789133787 -0.009519137907773256 -0.003921810537576675 -0.0005131363868713379 -0.0004315422847867012 -0.003926586825400591 -0.0005133007653057575 -0.0004316861741244793 0.0026394762098789215 0.0009217467159032822 0.0007308274507522583 -0.0026394501328468323 -0.0009217597544193268 -0.0007308013737201691 4.750210791826248e-06 1.7741695046424866e-07 1.1781230568885803e-07 4.750210791826248e-06 1.7741695046424866e-07 1.1781230568885803e-07 2.60770320892334e-08 -1.30385160446167e-08 2.60770320892334e-08 0.0 0.0 0.0 0.00020270980894565582 2.804212272167206e-06 4.2654573917388916e-07 0.0 0.0 0.0
13 12 12 0.46901560970475215 560.7038032673299 -0.011718058958649635 -0.009898302145302296 -0.003915674518793821 -0.00045095616951584816 -0.0003791642375290394 -0.0039251805283129215 -0.00045116664841771126 -0.00037938589230179787 0.003811669535934925 0.0012245913967490196 0.0010236278176307678 -0.0038117188960313797 -0.0012245867401361465 -0.001023637130856514 9.55536961555481e-06 2.0582228899002075e-07 2.3096799850463867e-07 9.55536961555481e-06 2.0582228899002075e-07 2.3096799850463867e-07 -4.936009645462036e-08 4.6566128730773926e-09 -9.313225746154785e-09 0.0 0.0 0.0 0.00021226517856121063 3.0100345611572266e-06 6.575137376785278e-07 0.0 0.0 0.0
14 13 13 0.46940153309401067 560.6998843806796 -0.012116494122892618 -0.010233358014374971 -0.00391888665035367 -0.00039843516424298286 -0.0003350558690726757 -0.003924091346561909 -0.00039816368371248245 -0.00033517926931381226 0.004714041016995907 0.0014252373948693275 0.0012384280562400818 -0.004714064300060272 -0.0014252476394176483 -0.0012384429574012756 5.227979272603989e-06 -2.612359821796417e-07 1.3830140233039856e-07 5.227979272603989e-06 -2.612359821796417e-07 1.3830140233039856e-07 -2.3283064365386963e-08 -1.0244548320770264e-08 -1.4901161193847656e-08 0.0 0.0 0.0 0.00021749315783381462 2.748798578977585e-06 7.958151400089264e-07 0.0 0.0 0.0
15 14 14 0.37547977019595935 560.695968686603 -0.012470003217458725 -0.010531187988817692 -0.003915694076567888 -0.0003535090945661068 -0.0002978299744427204 -0.00392324011772871 -0.0003537451848387718 -0.00029775500297546387 0.005242751911282539 0.0015017995610833168 0.0013365093618631363 -0.005242764949798584 -0.0015017539262771606 -0.0013365447521209717 7.559079676866531e-06 1.9045546650886536e-07 -3.958120942115784e-08 7.559079676866531e-06 1.9045546650886536e-07 -3.958120942115784e-08 -1.30385160446167e-08 4.563480615615845e-08 -3.5390257835388184e-08 0.0 0.0 0.0 0.00022505223751068115 2.93925404548645e-06 7.562339305877686e-07 0.0 0.0 0.0
16 15 15 0.46931915536962354 560.6920553292148 -0.012786881532520056 -0.010797811206430197 -0.003913357388228178 -0.0003168783150613308 -0.00026662321761250496 -0.003922352101653814 -0.00031696120277047157 -0.0002668178640305996 0.005457909312099218 0.0014622039161622524 0.0013122227974236012 -0.00545794703066349 -0.0014622155576944351 -0.0013121794909238815 9.032431989908218e-06 9.452924132347107e-08 1.5133991837501526e-07 9.032431989908218e-06 9.452924132347107e-08 1.5133991837501526e-07 -3.771856427192688e-08 -1.1641532182693481e-08 4.330649971961975e-08 0.0 0.0 0.0 0.00023408466950058937 3.0337832868099213e-06 9.075738489627838e-07 0.0 0.0 0.0
17 16 16 0.4688354158197654 560.6881414898671 -0.013073210138827562 -0.011039237957447767 -0.0039138393476605415 -0.00028632860630750656 -0.0002414267510175705 -0.00392143102362752 -0.00028690462931990623 -0.0002415659837424755 0.005488332826644182 0.0013348772190511227 0.0011904998682439327 -0.005488375201821327 -0.001334870234131813 -0.0011905189603567123 7.634051144123077e-06 5.690380930900574e-07 1.5832483768463135e-07 7.634051144123077e-06 5.690380930900574e-07 1.5832483768463135e-07 -4.237517714500427e-08 6.984919309616089e-09 -1.909211277961731e-08 0.0 0.0 0.0 0.00024171872064471245 3.6028213798999786e-06 1.0658986866474152e-06 0.0 0.0 0.0
18 17 17 0.4688642199257324 560.6842309772037 -0.013335466850548983 -0.011260137427598238 -0.003910512663424015 -0.0002622567117214203 -0.00022089947015047073 -0.003920585848391056 -0.0002631489187479019 -0.0002216007560491562 0.005444665905088186 0.001153834629803896 0.0010090763680636883 -0.005444658920168877 -0.0011538397520780563 -0.0010091308504343033 1.00662000477314e-05 8.973293006420135e-07 7.557682693004608e-07 1.00662000477314e-05 8.973293006420135e-07 7.557682693004608e-07 6.984919309616089e-09 -5.122274160385132e-09 -5.448237061500549e-08 0.0 0.0 0.0 0.00025178492069244385 4.500150680541992e-06 1.821666955947876e-06 0.0 0.0 0.0
19 18 18 0.46904972325243455 560.6803212598898 -0.013579126913100481 -0.01146557042375207 -0.003909717313945293 -0.0002436600625514984 -0.00020543299615383148 -0.003919912502169609 -0.0002440977841615677 -0.00020573288202285767 0.005378367379307747 0.0009484421461820602 0.0008058762177824974 -0.005378358066082001 -0.0009484663605690002 -0.0008058585226535797 1.0185874998569489e-05 4.6193599700927734e-07 2.8219074010849e-07 1.0185874998569489e-05 4.6193599700927734e-07 2.8219074010849e-07 9.313225746154785e-09 -2.421438694000244e-08 1.7695128917694092e-08 0.0 0.0 0.0 0.00026197079569101334 4.9620866775512695e-06 2.103857696056366e-06 0.0 0.0 0.0
20 19 19 0.4690241690953882 560.6764128012583 -0.013806662522256374 -0.01165736373513937 -0.003908458631485701 -0.00022753560915589333 -0.0001917933113873005 -0.003919640555977821 -0.00022763200104236603 -0.00019236188381910324 0.005283908452838659 0.0007355022244155407 0.0006050593219697475 -0.005283920094370842 -0.0007355231791734695 -0.0006051156669855118 1.1193566024303436e-05 1.1734664440155029e-07 6.249174475669861e-07 1.1193566024303436e-05 1.1734664440155029e-07 6.249174475669861e-07 -1.1641532182693481e-08 -2.0954757928848267e-08 -5.634501576423645e-08 0.0 0.0 0.0 0.00027316436171531677 5.07943332195282e-06 2.728775143623352e-06 0.0 0.0 0.0
21 20 20 0.4691149902535097 560.6725037032738 -0.014019680209457874 -0.01183774508535862 -0.003909097984433174 -0.00021301768720149994 -0.00018038135021924973 -0.003919586073607206 -0.00021268753334879875 -0.00018017226830124855 0.0051221540197730064 0.000519266352057457 0.0004148585721850395 -0.005122151225805283 -0.000519242137670517 -0.0004148446023464203 1.0485295206308365e-05 -3.543682396411896e-07 -2.230517566204071e-07 1.0485295206308365e-05 -3.543682396411896e-07 -2.230517566204071e-07 2.7939677238464355e-09 2.421438694000244e-08 1.3969838619232178e-08 0.0 0.0 0.0 0.00028364965692162514 4.72506508231163e-06 2.505723387002945e-06 0.0 0.0 0.0
22 21 21 0.46903467995095127 560.6685956828296 -0.01421999093145132 -0.01200737152248621 -0.003908020444214344 -0.00020031072199344635 -0.00016962643712759018 -0.003919499460607767 -0.0001992243342101574 -0.00016904575750231743 0.0048424312844872475 0.0002950672060251236 0.00023025646805763245 -0.00484243780374527 -0.00029502809047698975 -0.0002302899956703186 1.1485535651445389e-05 -1.1255033314228058e-06 -5.471520125865936e-07 1.1485535651445389e-05 -1.1255033314228058e-06 -5.471520125865936e-07 -6.51925802230835e-09 3.91155481338501e-08 -3.3527612686157227e-08 0.0 0.0 0.0 0.0002951351925730705 3.5995617508888245e-06 1.9585713744163513e-06 0.0 0.0 0.0
23 22 22 0.4690269226422474 560.6646860563196 -0.014409893658012152 -0.01216775132343173 -0.003909626509994268 -0.00018990272656083107 -0.0001603798009455204 -0.003919223323464394 -0.00018808525055646896 -0.00015935394912958145 0.004410987254232168 5.6739430874586105e-05 4.135677590966225e-05 -0.0044109877198934555 -5.675666034221649e-05 -4.13600355386734e-05 9.597279131412506e-06 -1.80024653673172e-06 -1.0225921869277954e-06 9.597279131412506e-06 -1.80024653673172e-06 -1.0225921869277954e-06 -4.656612873077393e-10 -1.7229467630386353e-08 -3.259629011154175e-09 0.0 0.0 0.0 0.00030473247170448303 1.7993152141571045e-06 9.359791874885559e-07 0.0 0.0 0.0
24 23 23 0.46895167590501197 560.6607814244926 -0.014590884558856487 -0.012320364825427532 -0.0039046318270266056 -0.00018099090084433556 -0.00015261350199580193 -0.003918746951967478 -0.00017994502559304237 -0.00015195319429039955 0.0038275266997516155 -0.00019492162391543388 -0.00015757465735077858 -0.0038275402039289474 0.0001948866993188858 0.0001576002687215805 1.4128629118204117e-05 -1.010950654745102e-06 -6.859190762042999e-07 1.4128629118204117e-05 -1.010950654745102e-06 -6.859190762042999e-07 -1.3504177331924438e-08 -3.4924596548080444e-08 2.561137080192566e-08 0.0 0.0 0.0 0.00031886110082268715 7.883645594120026e-07 2.50060111284256e-07 0.0 0.0 0.0
25 24 24 0.46890732263511536 560.6568783186376 -0.014766339212656021 -0.012467360123991966 -0.003903105854988098 -0.00017545465379953384 -0.00014699529856443405 -0.003918208181858063 -0.00017489027231931686 -0.00014702323824167252 0.0031356504186987877 -0.0004478944465517998 -0.00036316923797130585 -0.003135625272989273 0.00044789910316467285 0.00036318227648735046 1.5077181160449982e-05 -5.690380930900574e-07 1.4901161193847656e-08 1.5077181160449982e-05 -5.690380930900574e-07 1.4901161193847656e-08 2.514570951461792e-08 4.6566128730773926e-09 1.30385160446167e-08 0.0 0.0 0.0 0.00033393828198313713 2.193264663219452e-07 2.6496127247810364e-07 0.0 0.0 0.0
research/fine_surface_joint_20261014/suction_propagation.csv
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metric,time,signal,baseline,absolute,any_crossed,peak_crossed,x,y,q
coarse_interface_u,1,0.0,0.0,0.0,False,False,3.5,3.5,
coarse_interface_u,2,0.0,2.63417801721126e-09,2.63417801721126e-09,False,False,3.5,3.5,
coarse_interface_u,3,0.0,2.63417801721126e-09,2.63417801721126e-09,False,False,3.5,3.5,
coarse_interface_u,4,0.0,6.585443742795605e-08,6.585443742795605e-08,False,False,3.5,3.5,
coarse_interface_u,5,0.0,1.2644051067663912e-07,1.2644051067663912e-07,False,False,3.5,3.5,
coarse_interface_u,6,1.9186142054314123e-07,1.4528626697542904e-07,4.660334981743319e-08,False,False,20.5,12.5,
coarse_interface_u,7,4.091979903802207e-06,2.0116559817931723e-07,3.8910894220610345e-06,True,True,20.5,13.5,
coarse_interface_u,8,2.311182477441854e-05,1.5646213891164667e-07,2.2955448242208372e-05,True,True,20.5,13.5,
coarse_interface_u,9,6.767647549251264e-05,1.5646213891164667e-07,6.752003656156048e-05,True,True,20.5,13.5,
coarse_interface_u,10,0.0001337150378395613,9.750014426779573e-08,0.00013361820089187484,True,True,20.5,13.5,
coarse_interface_u,11,0.00020218710417179174,1.1795114315203827e-07,0.00020207424859213912,True,True,20.5,13.5,
coarse_interface_u,12,0.0002568344215192973,7.455233297611785e-08,0.00025676033381921514,True,True,20.5,14.5,
coarse_interface_u,13,0.00028408902792915417,3.83541997932092e-08,0.00028405737088288245,True,True,20.5,14.5,
coarse_interface_u,14,0.0002878189146376881,3.003423681142711e-08,0.0002878487673732557,True,True,20.5,15.5,
coarse_interface_u,15,0.00027841070597653145,6.516592454722021e-08,0.0002784504724796873,True,True,20.5,16.5,
coarse_interface_u,16,0.0002652060129478788,1.006517304071618e-07,0.00026516043242156813,True,True,20.5,17.5,
coarse_interface_u,17,0.0002512614305767982,9.86322156278415e-08,0.00025121946497710793,True,True,20.5,18.5,
coarse_interface_u,18,0.00023870047136388868,1.0286787296380317e-07,0.00023871152377663379,True,True,20.5,19.5,
coarse_interface_u,19,0.00022600944747549703,7.783088195871973e-08,0.00022597593519089906,True,True,20.5,20.5,
coarse_interface_u,20,0.00021431184774763036,8.503127640307697e-08,0.00021428571329826077,True,True,20.5,20.5,
coarse_interface_u,21,0.00019575956040664123,7.893743237430962e-08,0.00019568735051647606,True,True,20.5,20.5,
coarse_interface_u,22,0.00017544769865184928,5.2749351285234925e-08,0.00017539495458202247,True,True,20.5,20.5,
coarse_interface_u,23,0.00016267972853265252,8.178685339035058e-08,0.00016263380071292468,True,True,20.5,14.5,
coarse_interface_u,24,0.00015978262119523088,6.715860617082587e-08,0.00015974108955029212,True,True,20.5,14.5,
coarse_bulk_u,1,0.0,0.0,0.0,False,False,0.5,0.5,
coarse_bulk_u,2,0.0,0.0,0.0,False,False,0.5,0.5,
coarse_bulk_u,3,0.0,0.0,0.0,False,False,0.5,0.5,
coarse_bulk_u,4,0.0,0.0,0.0,False,False,0.5,0.5,
coarse_bulk_u,5,0.0,0.0,0.0,False,False,0.5,0.5,
coarse_bulk_u,6,0.0,0.0,0.0,False,False,0.5,0.5,
coarse_bulk_u,7,7.82310665831233e-08,2.12341462371735e-07,1.341103957886117e-07,False,False,21.5,13.5,
coarse_bulk_u,8,1.9454402095498935e-06,2.458690369633922e-07,1.6999616130940679e-06,True,False,21.5,13.5,
coarse_bulk_u,9,1.177304052098705e-05,1.564621179284487e-07,1.1616666785920634e-05,True,True,21.5,13.5,
coarse_bulk_u,10,3.768468268046806e-05,1.0327184058071679e-07,3.758247372213889e-05,True,True,21.5,13.5,
coarse_bulk_u,11,8.249500148168606e-05,4.214683516511236e-08,8.24651319357052e-05,True,True,21.5,13.5,
coarse_bulk_u,12,0.00013911632875122394,5.890198697622995e-09,0.00013911827376976638,True,True,21.5,13.5,
coarse_bulk_u,13,0.0001940498057077554,4.806920811870088e-08,0.00019408186753519264,True,True,21.5,14.5,
coarse_bulk_u,14,0.00023606781230746572,3.215422633434082e-08,0.0002360762872001673,True,True,21.5,14.5,
coarse_bulk_u,15,0.0002548619441637075,3.434546877545681e-08,0.0002548861189892584,True,True,21.5,14.5,
coarse_bulk_u,16,0.00025644070267233306,0.0,0.00025644070267233306,True,True,21.5,15.5,
coarse_bulk_u,17,0.00024779621047094693,6.606481633104396e-08,0.00024773971117601724,True,True,21.5,16.5,
coarse_bulk_u,18,0.00023605776538134437,6.929437569595666e-08,0.00023599681787995022,True,True,21.5,17.5,
coarse_bulk_u,19,0.00022483643371073512,1.0390817551817351e-07,0.00022475474112598463,True,True,21.5,19.5,
coarse_bulk_u,20,0.00021493179783986402,1.042082306083734e-07,0.0002148440172340466,True,True,22.5,18.5,
coarse_bulk_u,21,0.00020607683833491475,9.442706670805767e-08,0.00020599766123702618,True,True,21.5,20.5,
coarse_bulk_u,22,0.0001975946374450119,5.556802130737047e-08,0.0001975402371312418,True,True,22.5,20.5,
coarse_bulk_u,23,0.00018932913257368715,4.4703464263670685e-08,0.00018936187038743642,True,True,22.5,21.5,
coarse_bulk_u,24,0.000179693129058515,9.995996742145265e-08,0.00017978823062286703,True,True,22.5,22.5,
fine_band_neq,1,0.0,3.000928300994632e-09,3.000928300994632e-09,False,False,4.25,4.25,
fine_band_neq,2,0.0,4.501393077727123e-09,4.501393077727123e-09,False,False,4.25,4.25,
fine_band_neq,3,0.0,2.069606169774474e-08,2.069606169774474e-08,False,False,4.25,4.25,
fine_band_neq,4,0.0,4.1392115429639276e-09,4.1392115429639276e-09,False,False,4.25,4.25,
fine_band_neq,5,0.0,4.1392115429639276e-09,4.1392115429639276e-09,False,False,4.25,4.25,
fine_band_neq,6,3.733491271140643e-07,9.934099098884985e-09,3.832832262129493e-07,True,True,19.75,13.25,
fine_band_neq,7,2.0319504568933056e-06,9.934099098884985e-09,2.0418845559921905e-06,True,True,19.75,13.25,
fine_band_neq,8,5.038458095685261e-06,9.934099098884985e-09,5.048392194784146e-06,True,True,19.75,13.75,
fine_band_neq,9,7.299560118911863e-06,1.1589783133203468e-08,7.3111499020450665e-06,True,True,19.75,14.25,
fine_band_neq,10,7.386030289713297e-06,9.106256526614231e-09,7.395136546239911e-06,True,True,19.75,15.25,
fine_band_neq,11,6.856089636353424e-06,5.794905943989903e-09,6.850294730409434e-06,True,True,19.75,17.25,
fine_band_neq,12,6.198413803892855e-06,6.622735776451449e-09,6.201725167243044e-06,True,True,19.75,18.25,
fine_band_neq,13,5.319780258750129e-06,4.139212333997833e-09,5.316468887739401e-06,True,True,19.75,19.25,
fine_band_neq,14,5.283330124894459e-06,4.967056432825245e-09,5.278363068461633e-06,True,True,19.75,19.75,
fine_band_neq,15,3.967651562297725e-06,4.553132745832578e-09,3.964340190287796e-06,True,True,19.75,19.75,
fine_band_neq,16,3.7551803555024854e-06,6.622738135675377e-09,3.74855761736681e-06,True,True,19.75,16.75,
fine_band_neq,17,3.5764174459163023e-06,6.622736969941201e-09,3.5755896076739013e-06,True,True,19.75,17.75,
fine_band_neq,18,3.230913338148067e-06,1.7384672978959514e-08,3.2135286651691075e-06,True,True,19.75,18.75,
fine_band_neq,19,3.160623228692927e-06,9.934106745546067e-09,3.1631067707871274e-06,True,True,19.75,19.75,
fine_band_neq,20,3.2258282446351316e-06,8.278412177986638e-09,3.217549832457145e-06,True,True,19.75,19.75,
fine_band_neq,21,2.8042083782819915e-06,1.1589792486832451e-08,2.8124868040602102e-06,True,True,19.75,19.75,
fine_band_neq,22,2.2509872253939633e-06,1.241763983306221e-09,2.2509872265041864e-06,True,True,19.75,19.75,
fine_band_neq,23,1.7420757160158118e-06,2.0696062086322797e-09,1.7412478748313198e-06,True,True,19.75,19.75,
fine_band_neq,24,1.7765396425617475e-06,7.864502313270627e-09,1.7773674825805053e-06,True,True,4.25,8.25,
transfer_neq,1,0.0,1.5004641487625925e-09,1.5004641487625925e-09,False,False,3.5,3.5,6
transfer_neq,2,0.0,3.000928290586291e-09,3.000928290586291e-09,False,False,3.5,3.5,6
transfer_neq,3,0.0,3.000928290586291e-09,3.000928290586291e-09,False,False,3.5,3.5,6
transfer_neq,4,0.0,6.881438113781613e-09,6.881438113781613e-09,False,False,3.5,3.5,6
transfer_neq,5,0.0,6.467520081987477e-09,6.467520081987477e-09,False,False,3.5,3.5,6
transfer_neq,6,4.1392107713589255e-08,1.7384695072397705e-08,5.877680278598696e-08,False,False,20.5,12.5,1
transfer_neq,7,1.0066612778220074e-06,1.4901174696935193e-08,1.0215624525189426e-06,True,True,20.5,13.5,1
transfer_neq,8,5.309127742339492e-06,7.450589978308386e-09,5.316578332317801e-06,True,True,20.5,13.5,1
transfer_neq,9,1.3828140735375194e-05,4.967061900673642e-09,1.3833107797275868e-05,True,True,20.5,13.5,1
transfer_neq,10,2.2517463971680196e-05,7.0366557441969846e-09,2.2510427315936e-05,True,True,20.5,13.5,1
transfer_neq,11,2.6241128339946074e-05,8.692339972804497e-09,2.623243599997327e-05,True,True,20.5,14.5,1
transfer_neq,12,2.301816285713021e-05,1.0761948318616099e-08,2.3007400908811593e-05,True,True,20.5,14.5,1
transfer_neq,13,1.7994855878653016e-05,6.6227364148296886e-09,1.7988233142238186e-05,True,True,16.5,20.5,3
transfer_neq,14,1.325129952600701e-05,3.725288785783043e-09,1.3247574237221227e-05,True,True,17.5,20.5,3
transfer_neq,15,1.102909472382868e-05,1.7074248230553035e-09,1.1030802148651736e-05,True,True,20.5,20.5,5
transfer_neq,16,1.0516298765687881e-05,3.777030622292843e-09,1.0512521735065589e-05,True,True,20.5,20.5,5
transfer_neq,17,7.773419831963446e-06,1.5522043823879006e-09,7.771867627581058e-06,True,True,20.5,20.5,5
transfer_neq,18,6.4455684595787766e-06,2.4835230261199825e-09,6.4430849365526566e-06,True,True,16.5,3.5,4
transfer_neq,19,6.882601883972939e-06,4.553130164564045e-09,6.8780487538083745e-06,True,True,16.5,3.5,4
transfer_neq,20,8.06233390339417e-06,1.6556842147297246e-09,8.0639895876089e-06,True,True,3.5,15.5,2
transfer_neq,21,9.1730277525931e-06,4.553133203799575e-09,9.1684746193893e-06,True,True,3.5,15.5,2
transfer_neq,22,9.87202560678857e-06,1.241762831449833e-09,9.87078384395712e-06,True,True,3.5,15.5,2
transfer_neq,23,9.794260003304078e-06,3.725291089495819e-09,9.790534712214582e-06,True,True,3.5,16.5,2
transfer_neq,24,8.98628849999028e-06,6.208816516473092e-09,8.992497316506753e-06,True,True,3.5,17.5,2
1 metric time signal baseline absolute any_crossed peak_crossed x y q
2 coarse_interface_u 1 0.0 0.0 0.0 False False 3.5 3.5
3 coarse_interface_u 2 0.0 2.63417801721126e-09 2.63417801721126e-09 False False 3.5 3.5
4 coarse_interface_u 3 0.0 2.63417801721126e-09 2.63417801721126e-09 False False 3.5 3.5
5 coarse_interface_u 4 0.0 6.585443742795605e-08 6.585443742795605e-08 False False 3.5 3.5
6 coarse_interface_u 5 0.0 1.2644051067663912e-07 1.2644051067663912e-07 False False 3.5 3.5
7 coarse_interface_u 6 1.9186142054314123e-07 1.4528626697542904e-07 4.660334981743319e-08 False False 20.5 12.5
8 coarse_interface_u 7 4.091979903802207e-06 2.0116559817931723e-07 3.8910894220610345e-06 True True 20.5 13.5
9 coarse_interface_u 8 2.311182477441854e-05 1.5646213891164667e-07 2.2955448242208372e-05 True True 20.5 13.5
10 coarse_interface_u 9 6.767647549251264e-05 1.5646213891164667e-07 6.752003656156048e-05 True True 20.5 13.5
11 coarse_interface_u 10 0.0001337150378395613 9.750014426779573e-08 0.00013361820089187484 True True 20.5 13.5
12 coarse_interface_u 11 0.00020218710417179174 1.1795114315203827e-07 0.00020207424859213912 True True 20.5 13.5
13 coarse_interface_u 12 0.0002568344215192973 7.455233297611785e-08 0.00025676033381921514 True True 20.5 14.5
14 coarse_interface_u 13 0.00028408902792915417 3.83541997932092e-08 0.00028405737088288245 True True 20.5 14.5
15 coarse_interface_u 14 0.0002878189146376881 3.003423681142711e-08 0.0002878487673732557 True True 20.5 15.5
16 coarse_interface_u 15 0.00027841070597653145 6.516592454722021e-08 0.0002784504724796873 True True 20.5 16.5
17 coarse_interface_u 16 0.0002652060129478788 1.006517304071618e-07 0.00026516043242156813 True True 20.5 17.5
18 coarse_interface_u 17 0.0002512614305767982 9.86322156278415e-08 0.00025121946497710793 True True 20.5 18.5
19 coarse_interface_u 18 0.00023870047136388868 1.0286787296380317e-07 0.00023871152377663379 True True 20.5 19.5
20 coarse_interface_u 19 0.00022600944747549703 7.783088195871973e-08 0.00022597593519089906 True True 20.5 20.5
21 coarse_interface_u 20 0.00021431184774763036 8.503127640307697e-08 0.00021428571329826077 True True 20.5 20.5
22 coarse_interface_u 21 0.00019575956040664123 7.893743237430962e-08 0.00019568735051647606 True True 20.5 20.5
23 coarse_interface_u 22 0.00017544769865184928 5.2749351285234925e-08 0.00017539495458202247 True True 20.5 20.5
24 coarse_interface_u 23 0.00016267972853265252 8.178685339035058e-08 0.00016263380071292468 True True 20.5 14.5
25 coarse_interface_u 24 0.00015978262119523088 6.715860617082587e-08 0.00015974108955029212 True True 20.5 14.5
26 coarse_bulk_u 1 0.0 0.0 0.0 False False 0.5 0.5
27 coarse_bulk_u 2 0.0 0.0 0.0 False False 0.5 0.5
28 coarse_bulk_u 3 0.0 0.0 0.0 False False 0.5 0.5
29 coarse_bulk_u 4 0.0 0.0 0.0 False False 0.5 0.5
30 coarse_bulk_u 5 0.0 0.0 0.0 False False 0.5 0.5
31 coarse_bulk_u 6 0.0 0.0 0.0 False False 0.5 0.5
32 coarse_bulk_u 7 7.82310665831233e-08 2.12341462371735e-07 1.341103957886117e-07 False False 21.5 13.5
33 coarse_bulk_u 8 1.9454402095498935e-06 2.458690369633922e-07 1.6999616130940679e-06 True False 21.5 13.5
34 coarse_bulk_u 9 1.177304052098705e-05 1.564621179284487e-07 1.1616666785920634e-05 True True 21.5 13.5
35 coarse_bulk_u 10 3.768468268046806e-05 1.0327184058071679e-07 3.758247372213889e-05 True True 21.5 13.5
36 coarse_bulk_u 11 8.249500148168606e-05 4.214683516511236e-08 8.24651319357052e-05 True True 21.5 13.5
37 coarse_bulk_u 12 0.00013911632875122394 5.890198697622995e-09 0.00013911827376976638 True True 21.5 13.5
38 coarse_bulk_u 13 0.0001940498057077554 4.806920811870088e-08 0.00019408186753519264 True True 21.5 14.5
39 coarse_bulk_u 14 0.00023606781230746572 3.215422633434082e-08 0.0002360762872001673 True True 21.5 14.5
40 coarse_bulk_u 15 0.0002548619441637075 3.434546877545681e-08 0.0002548861189892584 True True 21.5 14.5
41 coarse_bulk_u 16 0.00025644070267233306 0.0 0.00025644070267233306 True True 21.5 15.5
42 coarse_bulk_u 17 0.00024779621047094693 6.606481633104396e-08 0.00024773971117601724 True True 21.5 16.5
43 coarse_bulk_u 18 0.00023605776538134437 6.929437569595666e-08 0.00023599681787995022 True True 21.5 17.5
44 coarse_bulk_u 19 0.00022483643371073512 1.0390817551817351e-07 0.00022475474112598463 True True 21.5 19.5
45 coarse_bulk_u 20 0.00021493179783986402 1.042082306083734e-07 0.0002148440172340466 True True 22.5 18.5
46 coarse_bulk_u 21 0.00020607683833491475 9.442706670805767e-08 0.00020599766123702618 True True 21.5 20.5
47 coarse_bulk_u 22 0.0001975946374450119 5.556802130737047e-08 0.0001975402371312418 True True 22.5 20.5
48 coarse_bulk_u 23 0.00018932913257368715 4.4703464263670685e-08 0.00018936187038743642 True True 22.5 21.5
49 coarse_bulk_u 24 0.000179693129058515 9.995996742145265e-08 0.00017978823062286703 True True 22.5 22.5
50 fine_band_neq 1 0.0 3.000928300994632e-09 3.000928300994632e-09 False False 4.25 4.25
51 fine_band_neq 2 0.0 4.501393077727123e-09 4.501393077727123e-09 False False 4.25 4.25
52 fine_band_neq 3 0.0 2.069606169774474e-08 2.069606169774474e-08 False False 4.25 4.25
53 fine_band_neq 4 0.0 4.1392115429639276e-09 4.1392115429639276e-09 False False 4.25 4.25
54 fine_band_neq 5 0.0 4.1392115429639276e-09 4.1392115429639276e-09 False False 4.25 4.25
55 fine_band_neq 6 3.733491271140643e-07 9.934099098884985e-09 3.832832262129493e-07 True True 19.75 13.25
56 fine_band_neq 7 2.0319504568933056e-06 9.934099098884985e-09 2.0418845559921905e-06 True True 19.75 13.25
57 fine_band_neq 8 5.038458095685261e-06 9.934099098884985e-09 5.048392194784146e-06 True True 19.75 13.75
58 fine_band_neq 9 7.299560118911863e-06 1.1589783133203468e-08 7.3111499020450665e-06 True True 19.75 14.25
59 fine_band_neq 10 7.386030289713297e-06 9.106256526614231e-09 7.395136546239911e-06 True True 19.75 15.25
60 fine_band_neq 11 6.856089636353424e-06 5.794905943989903e-09 6.850294730409434e-06 True True 19.75 17.25
61 fine_band_neq 12 6.198413803892855e-06 6.622735776451449e-09 6.201725167243044e-06 True True 19.75 18.25
62 fine_band_neq 13 5.319780258750129e-06 4.139212333997833e-09 5.316468887739401e-06 True True 19.75 19.25
63 fine_band_neq 14 5.283330124894459e-06 4.967056432825245e-09 5.278363068461633e-06 True True 19.75 19.75
64 fine_band_neq 15 3.967651562297725e-06 4.553132745832578e-09 3.964340190287796e-06 True True 19.75 19.75
65 fine_band_neq 16 3.7551803555024854e-06 6.622738135675377e-09 3.74855761736681e-06 True True 19.75 16.75
66 fine_band_neq 17 3.5764174459163023e-06 6.622736969941201e-09 3.5755896076739013e-06 True True 19.75 17.75
67 fine_band_neq 18 3.230913338148067e-06 1.7384672978959514e-08 3.2135286651691075e-06 True True 19.75 18.75
68 fine_band_neq 19 3.160623228692927e-06 9.934106745546067e-09 3.1631067707871274e-06 True True 19.75 19.75
69 fine_band_neq 20 3.2258282446351316e-06 8.278412177986638e-09 3.217549832457145e-06 True True 19.75 19.75
70 fine_band_neq 21 2.8042083782819915e-06 1.1589792486832451e-08 2.8124868040602102e-06 True True 19.75 19.75
71 fine_band_neq 22 2.2509872253939633e-06 1.241763983306221e-09 2.2509872265041864e-06 True True 19.75 19.75
72 fine_band_neq 23 1.7420757160158118e-06 2.0696062086322797e-09 1.7412478748313198e-06 True True 19.75 19.75
73 fine_band_neq 24 1.7765396425617475e-06 7.864502313270627e-09 1.7773674825805053e-06 True True 4.25 8.25
74 transfer_neq 1 0.0 1.5004641487625925e-09 1.5004641487625925e-09 False False 3.5 3.5 6
75 transfer_neq 2 0.0 3.000928290586291e-09 3.000928290586291e-09 False False 3.5 3.5 6
76 transfer_neq 3 0.0 3.000928290586291e-09 3.000928290586291e-09 False False 3.5 3.5 6
77 transfer_neq 4 0.0 6.881438113781613e-09 6.881438113781613e-09 False False 3.5 3.5 6
78 transfer_neq 5 0.0 6.467520081987477e-09 6.467520081987477e-09 False False 3.5 3.5 6
79 transfer_neq 6 4.1392107713589255e-08 1.7384695072397705e-08 5.877680278598696e-08 False False 20.5 12.5 1
80 transfer_neq 7 1.0066612778220074e-06 1.4901174696935193e-08 1.0215624525189426e-06 True True 20.5 13.5 1
81 transfer_neq 8 5.309127742339492e-06 7.450589978308386e-09 5.316578332317801e-06 True True 20.5 13.5 1
82 transfer_neq 9 1.3828140735375194e-05 4.967061900673642e-09 1.3833107797275868e-05 True True 20.5 13.5 1
83 transfer_neq 10 2.2517463971680196e-05 7.0366557441969846e-09 2.2510427315936e-05 True True 20.5 13.5 1
84 transfer_neq 11 2.6241128339946074e-05 8.692339972804497e-09 2.623243599997327e-05 True True 20.5 14.5 1
85 transfer_neq 12 2.301816285713021e-05 1.0761948318616099e-08 2.3007400908811593e-05 True True 20.5 14.5 1
86 transfer_neq 13 1.7994855878653016e-05 6.6227364148296886e-09 1.7988233142238186e-05 True True 16.5 20.5 3
87 transfer_neq 14 1.325129952600701e-05 3.725288785783043e-09 1.3247574237221227e-05 True True 17.5 20.5 3
88 transfer_neq 15 1.102909472382868e-05 1.7074248230553035e-09 1.1030802148651736e-05 True True 20.5 20.5 5
89 transfer_neq 16 1.0516298765687881e-05 3.777030622292843e-09 1.0512521735065589e-05 True True 20.5 20.5 5
90 transfer_neq 17 7.773419831963446e-06 1.5522043823879006e-09 7.771867627581058e-06 True True 20.5 20.5 5
91 transfer_neq 18 6.4455684595787766e-06 2.4835230261199825e-09 6.4430849365526566e-06 True True 16.5 3.5 4
92 transfer_neq 19 6.882601883972939e-06 4.553130164564045e-09 6.8780487538083745e-06 True True 16.5 3.5 4
93 transfer_neq 20 8.06233390339417e-06 1.6556842147297246e-09 8.0639895876089e-06 True True 3.5 15.5 2
94 transfer_neq 21 9.1730277525931e-06 4.553133203799575e-09 9.1684746193893e-06 True True 3.5 15.5 2
95 transfer_neq 22 9.87202560678857e-06 1.241762831449833e-09 9.87078384395712e-06 True True 3.5 15.5 2
96 transfer_neq 23 9.794260003304078e-06 3.725291089495819e-09 9.790534712214582e-06 True True 3.5 16.5 2
97 transfer_neq 24 8.98628849999028e-06 6.208816516473092e-09 8.992497316506753e-06 True True 3.5 17.5 2
research/fine_surface_joint_20261014/suction_signals.npz
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research/fine_surface_joint_20261014/suction_summary.json
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{
"speed": -0.002,
"initial_leaf": [
560.7500041779131,
0.0,
0.0
],
"final_leaf": [
560.6568783186376,
-0.014766339212656021,
-0.012467360123991966
],
"raw_residual": [
0.00033393828198313713,
2.193264663219452e-07,
2.6496127247810364e-07
],
"exact_average_residual": [
0.00033389730378985405,
1.0663643479347229e-07,
7.683411240577698e-08
],
"cumulative": {
"boundary": [
-0.0934597565792501,
-0.014766445849090815,
-0.012467436958104372
],
"fine_transfer": [
0.06703074648976326,
0.011895491741597652,
0.010098854079842567
],
"coarse_transfer": [
-0.06703078746795654,
-0.011895604431629181,
-0.01009904220700264
],
"rounding": [
0.00033393828198313713,
2.193264663219452e-07,
2.6496127247810364e-07
],
"average": [
-4.0978193283081055e-08,
-1.126900315284729e-07,
-1.8812716007232666e-07
]
},
"checks": {
"validity": true,
"geometry_unchanged": true,
"boundary_count": 48,
"collision_count": 72,
"local_collision": true,
"local_regime": true,
"bfl": true,
"transfer_rounding": true,
"interval_closure": true,
"cumulative_closure": true,
"propagation": true,
"drive_sign": true
},
"max_collision_ratio": 0.5285112236646456,
"max_collision_error": 1.6477090769573266e-07,
"max_bfl_error": 1.2498588997100768e-08,
"max_interval_closure": 0.0,
"drive_mass": -0.07426865905109248,
"propagation": {
"coarse_interface_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 4.091979903802207e-06,
"baseline": 2.0116559817931723e-07,
"absolute": 3.8910894220610345e-06,
"x": 20.5,
"y": 13.5
},
"peak": {
"metric": "coarse_interface_u",
"time": 14,
"signal": 0.0002878189146376881,
"baseline": 3.003423681142711e-08,
"absolute": 0.0002878487673732557,
"any_crossed": true,
"peak_crossed": true,
"x": 20.5,
"y": 15.5
}
},
"coarse_bulk_u": {
"threshold": 1e-06,
"baseline_factor": 10,
"first_arrival": {
"time": 8,
"signal": 1.0179867624062477e-06,
"baseline": 1.005827957173589e-07,
"absolute": 9.175116276483961e-07,
"x": 13.5,
"y": 21.5
},
"peak": {
"metric": "coarse_bulk_u",
"time": 16,
"signal": 0.00025644070267233306,
"baseline": 0.0,
"absolute": 0.00025644070267233306,
"any_crossed": true,
"peak_crossed": true,
"x": 21.5,
"y": 15.5
}
},
"fine_band_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 6,
"signal": 3.733491271140643e-07,
"baseline": 9.934099098884985e-09,
"absolute": 3.832832262129493e-07,
"x": 19.75,
"y": 13.25
},
"peak": {
"metric": "fine_band_neq",
"time": 10,
"signal": 7.386030289713297e-06,
"baseline": 9.106256526614231e-09,
"absolute": 7.395136546239911e-06,
"any_crossed": true,
"peak_crossed": true,
"x": 19.75,
"y": 15.25
}
},
"transfer_neq": {
"threshold": 1e-07,
"baseline_factor": 10,
"first_arrival": {
"time": 7,
"signal": 1.0066612778220074e-06,
"baseline": 1.4901174696935193e-08,
"absolute": 1.0215624525189426e-06,
"q": 1,
"x": 20.5,
"y": 13.5
},
"peak": {
"metric": "transfer_neq",
"time": 11,
"signal": 2.6241128339946074e-05,
"baseline": 8.692339972804497e-09,
"absolute": 2.623243599997327e-05,
"any_crossed": true,
"peak_crossed": true,
"q": 1,
"x": 20.5,
"y": 14.5
}
}
},
"raw_minus_zero": [
0.0001048198901116848,
-5.686189979314804e-06,
-3.6512501537799835e-06
]
}
research/fine_surface_joint_20261014/validate.py
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"""Isolated, pre-registered 24-interval validation; never imported by pytest."""
import csv
import hashlib
import importlib.util
import json
import struct
from pathlib import Path
from unittest.mock import patch
import numpy as np
ROOT = Path('/home/frank14f/CelerisLab')
OUT = Path(__file__).resolve().parent
spec = importlib.util.spec_from_file_location('accepted_hooks', ROOT/'tests/validation/multigrid/test_fine_surface_gpu.py')
h = importlib.util.module_from_spec(spec)
spec.loader.exec_module(h)
EPS = np.finfo(np.float32).eps
M = h.MOMENTS
def save(name, obj):
text = json.dumps(obj, indent=2, allow_nan=False)+'\n'
assert len(text) <= 40000, (name, len(text))
(OUT/name).write_text(text)
def table(name, rows):
with (OUT/name).open('w') as file:
writer = csv.DictWriter(file, fieldnames=list(rows[0]))
writer.writeheader()
writer.writerows(rows)
assert (OUT/name).stat().st_size <= 40000
def vec(row, prefix, values):
row.update({prefix+'_'+axis: float(value) for axis, value in zip(('m', 'px', 'py'), values)})
def equilibrium(values):
a = values.astype(float)
rho = a.sum(axis=0)
u = M[1:]@a/rho
cu = h.CX[:, None]*u[0]+h.CY[:, None]*u[1]
eq = h.W[:, None]*rho*(1+3*cu+4.5*cu*cu-1.5*(u*u).sum(axis=0))
return eq, u, rho
def pull(a):
return np.stack([np.roll(a[q], (h.CY[q], h.CX[q]), (0, 1)) for q in range(9)])
def collision(incoming, output, omega, area):
"""Local observed drift; no access to global initial/final states or ledger."""
a, b = incoming.astype(float), output.astype(float)
eq, u, rho = equilibrium(a)
ideal = (1-omega)*a+omega*eq
tolerance = 8*EPS*abs(a)
error = abs(b-ideal)
# Independent local FP64 oracle plus a measured (not inferred) drift.
return dict(drift=area*(M@(b-a).sum(axis=1)),
ideal_defect=area*(M@(ideal-a).sum(axis=1)),
measured_rounding=area*(M@(b-ideal).sum(axis=1)),
max_error=float(error.max()), max_ratio=float((error/tolerance).max()),
regime=bool(np.isfinite(b).all() and np.all(a>0) and np.all(b>0) and np.all((rho>.9)&(rho<1.1))
and np.max(np.linalg.norm(u, axis=0))<.05))
def coarse_arrivals(before, fine, active, p):
"""Independent midpoint map; FP32 addition order checked against actual map."""
got = pull(before)
ideal = got.astype(float)
targets, sources = [], []
for y, x in np.argwhere(active):
for q in range(1, 9):
if 4 <= x+.5-h.CX[q]/2 <= 20 and 4 <= y+.5-h.CY[q]/2 <= 20:
a, b = 4+2*(x-4), 4+2*(y-4)
children = fine[q, b:b+2, a:a+2].ravel()
assert len(children) == 4 and np.isfinite(children).all()
got[q, y, x] = np.float32(.25)*(((children[0]+children[1])+children[2])+children[3])
ideal[q, y, x] = children.astype(float).sum()/4
targets.append(q*576+y*24+x)
sources.append([q*1600+(b+dy)*40+a+dx for dy in (0, 1) for dx in (0, 1)])
order = np.argsort(targets)
porder = np.argsort(p.coalesce_coarse)
np.testing.assert_array_equal(np.array(targets)[order], p.coalesce_coarse[porder])
np.testing.assert_array_equal(np.array(sources)[order], p.coalesce_fine[porder])
assert np.isfinite(got[:, active]).all()
flux = (got[:, active].astype(float)-before[:, active]).sum(axis=1)
rounding = (got[:, active].astype(float)-ideal[:, active]).sum(axis=1)
children = fine.ravel()[p.coalesce_fine]
bound = 3*(EPS/2)/(1-3*(EPS/2))*np.abs(children.astype(float)).sum(axis=1)/4
differences = abs(got.ravel()[p.coalesce_coarse]-ideal.ravel()[p.coalesce_coarse])
return got, M@flux, M@rounding, float(np.max(differences/bound))
def observation(values, mask):
eq, u, _ = equilibrium(values[:, mask])
return u, values[:, mask].astype(float)-eq
def run(speed, label):
cfg = h.LBMConfig(nx=24, ny=24, viscosity=.05, velocity=0, rho=1)
cmask = np.ones((24, 24), bool)
cmask[4:20, 4:20] = False
source = h.object_spec(speed)
state = vars(source.state).copy()
ledger_rows, collision_rows, boundary_rows = [], [], []
trace = []
with h.CudaContext(0) as context:
with h.implementation.FixedPatchStepper(cfg, h.BOUNDS, context=context, fine_objects=[source]) as backend, context.activate():
c, f = backend._levels
cl, p = backend._fine_curved, backend.patch
geometry = {name: getattr(cl, name).copy() for name in h.GEOMETRY}
flags = backend.fine_boundary.flags.copy().reshape(40, 40)
solid = (flags & h.SOLID) != 0
active = backend._fine_leaf.copy()
fine_mesh = p.fine_kind == 0
band = active & np.logical_or.reduce([~np.roll(fine_mesh, (cy, cx), (0, 1))
for cx, cy in zip(h.CX[1:], h.CY[1:])])
interface = np.zeros(576, bool)
interface[p.coalesce_coarse % 576] = True
interface = interface.reshape(24, 24)
bulk = cmask & ~interface
if speed == 0:
def coords(mask, dx, origin):
yx = np.argwhere(mask)
return ((yx[:, ::-1]+.5)*dx+np.array(origin)).tolist()
save('regions.json', dict(coarse_interface=coords(interface, 1, (0, 0)),
coarse_bulk=coords(bulk, 1, (0, 0)), fine_band=coords(band, .5, (2, 2)),
transfer_slots=[dict(q=int(slot//576), x=float(slot%24+.5),
y=float((slot%576)//24+.5)) for slot in p.coalesce_coarse]))
backend.initialize(h.rest((24, 24)), h.rest((40, 40)))
initial = backend.download()
initial_m = M@(initial[0][:, cmask].sum(axis=1, dtype=float)
+.25*initial[1][:, active].sum(axis=1, dtype=float))
previous = initial_m.copy()
totals = {name: np.zeros(3) for name in ('boundary', 'fine_transfer', 'coarse_transfer', 'rounding', 'average')}
interval = None
orig_b, orig_f, orig_c = backend._curved, f.kernel, c.kernel
omegas = []
for level, tau in ((c, p.tau_coarse), (f, p.tau_fine)):
pointer, size = level.module.get_global('d_params')
assert size == 20
omega, fx, fy, fz, count = struct.unpack('=ffffI', h.read(pointer, (size,), np.uint8).tobytes())
assert omega == float(np.float32(1/tau)) and (fx, fy, fz) == (0., 0., 0.)
omegas.append(omega)
omega_c, omega_f = omegas
def curved(*args, **kwargs):
backend._stream.synchronize()
before = h.read(args[0], f.shape)
expected = h.reflection_oracle(before, cl, speed)
passive = h.reflection_oracle(before, cl, None, verify_projection=False)
orig_b(*args, **kwargs)
backend._stream.synchronize()
staged = h.read(args[0], f.shape)
d, k = cl.dir.astype(int), cl.fluid_idx.astype(int)
values = staged.reshape(9, -1)[h.OPP[d], k+h.CX[d]+40*h.CY[d]]
np.testing.assert_array_equal(staged[:, active], before[:, active])
b, tr, _ = h.fine_flux(before, staged, active, solid)
exchange = h.read(cl.link_exchange_gpu, (cl.count, 3)).astype(float).sum(axis=0)
row = dict(step=interval['step'], substep=len(interval['boundary'])+1,
time=interval['step']-1+.5*(len(interval['boundary'])+1),
bfl_error=float(abs(values-expected).max()),
drive_mass=.25*float((values-passive).sum()),
latest_body_impulse_x=float(.25*exchange[0]), latest_body_impulse_y=float(.25*exchange[1]),
latest_body_angular_impulse=float(.125*exchange[2]))
vec(row, 'boundary', .25*(M@b))
vec(row, 'fine_transfer', .25*(M@tr))
interval['boundary'].append(.25*(M@b))
interval['fine_transfer'].append(.25*(M@tr))
boundary_rows.append(row)
def fine(*args, **kwargs):
backend._stream.synchronize()
staged = h.read(args[0], f.shape)
incoming = pull(staged)[:, active]
orig_f(*args, **kwargs)
backend._stream.synchronize()
after = h.read(args[1], f.shape)
assert np.isfinite(after[:, active]).all() and np.isnan(after[:, solid]).all()
outer = np.ones((40, 40), bool)
outer[2:-2, 2:-2] = False
assert np.isnan(after[:, outer]).all()
# Inner ghost is stream-only: compare including legitimate NaNs.
ghost = ~fine_mesh & ~outer
np.testing.assert_array_equal(after[:, ghost], pull(staged)[:, ghost])
stat = collision(incoming, after[:, active], omega_f, .25)
interval['collision'].append(stat)
row = dict(step=interval['step'], level='fine', substep=len(interval['collision']),
max_error=stat['max_error'], max_ratio=stat['max_ratio'], regime=stat['regime'])
for key in ('drift', 'ideal_defect', 'measured_rounding'):
vec(row, key, stat[key])
collision_rows.append(row)
def coarse(*args, **kwargs):
backend._stream.synchronize()
before, fine = h.read(args[0], c.shape), h.read(args[2], f.shape)
incoming, flux, avg, ratio = coarse_arrivals(before, fine, cmask, p)
interval['coarse_transfer'], interval['average'] = flux, avg
interval['average_ratio'] = ratio
eq, _, _ = equilibrium(incoming[:, cmask])
neq = np.full(c.shape, np.nan)
neq[:, cmask] = incoming[:, cmask].astype(float)-eq
interval['transfer_neq'] = neq.ravel()[p.coalesce_coarse].copy()
orig_c(*args, **kwargs)
backend._stream.synchronize()
after = h.read(args[1], c.shape)
assert np.isfinite(after[:, cmask]).all() and np.isnan(after[:, ~cmask]).all()
stat = collision(incoming[:, cmask], after[:, cmask], omega_c, 1.)
interval['collision'].append(stat)
row = dict(step=interval['step'], level='coarse', substep=1,
max_error=stat['max_error'], max_ratio=stat['max_ratio'], regime=stat['regime'])
for key in ('drift', 'ideal_defect', 'measured_rounding'):
vec(row, key, stat[key])
collision_rows.append(row)
with patch.object(backend, '_curved', curved), patch.object(f, 'kernel', fine), patch.object(c, 'kernel', coarse):
for n in range(1, 25):
interval = dict(step=n, boundary=[], fine_transfer=[], collision=[])
backend.step(1)
coarse_g, fine_g = backend.download()
assert np.isfinite(coarse_g[:, cmask]).all() and np.isnan(coarse_g[:, ~cmask]).all()
assert np.isfinite(fine_g[:, active]).all() and np.isnan(fine_g[:, ~active]).all()
assert len(interval['boundary']) == 2 and len(interval['collision']) == 3
current = M@(coarse_g[:, cmask].sum(axis=1, dtype=float)
+.25*fine_g[:, active].sum(axis=1, dtype=float))
b = sum(interval['boundary'])
ft = sum(interval['fine_transfer'])
ct = interval['coarse_transfer']
drift = sum(s['drift'] for s in interval['collision'])
raw = current-previous-b-ft-ct
row = dict(step=n, time=n, average_bound_ratio=interval['average_ratio'])
for key, value in (('leaf', current), ('delta', current-previous), ('boundary', b),
('fine_transfer', ft), ('coarse_transfer', ct), ('raw', raw),
('collision_drift', drift), ('average_rounding', interval['average']),
('closure', raw-drift)):
vec(row, key, value)
for key, val in (('boundary', b), ('fine_transfer', ft), ('coarse_transfer', ct),
('rounding', drift), ('average', interval['average'])):
totals[key] += val
cumulative = current-initial_m-totals['boundary']-totals['fine_transfer']-totals['coarse_transfer']
vec(row, 'cumulative_raw', cumulative)
vec(row, 'cumulative_closure', cumulative-totals['rounding'])
ledger_rows.append(row)
iu, ine = observation(coarse_g, interface)
bu, _ = observation(coarse_g, bulk)
fu, fne = observation(fine_g, band)
trace.append(dict(coarse_interface_u=iu, coarse_bulk_u=bu,
fine_band_neq=fne, transfer_neq=interval['transfer_neq']))
previous = current
print(label, n, 'raw', raw.tolist(), 'cumulative_raw', cumulative.tolist(), flush=True)
assert backend.step_count == 24 and len(boundary_rows) == 48 and len(collision_rows) == 72
for name, val in geometry.items():
np.testing.assert_array_equal(getattr(cl, name), val)
np.testing.assert_array_equal(flags.ravel(), backend.fine_boundary.flags)
assert vars(source.state) == state and source.geometry.center == source.center
table(label+'_ledger.csv', ledger_rows)
table(label+'_collisions.csv', collision_rows)
table(label+'_boundary.csv', boundary_rows)
# Small compressed diagnostic arrays, not full-field population snapshots.
np.savez_compressed(OUT/(label+'_signals.npz'), **{key: np.stack([t[key] for t in trace]) for key in trace[0]})
final = ledger_rows[-1]
checks = dict(validity=True, geometry_unchanged=True, boundary_count=len(boundary_rows),
collision_count=len(collision_rows),
local_collision=max(r['max_ratio'] for r in collision_rows)<=1,
local_regime=all(r['regime'] for r in collision_rows),
bfl=max(r['bfl_error'] for r in boundary_rows)<=1e-7,
transfer_rounding=max(r['average_bound_ratio'] for r in ledger_rows)<=1,
interval_closure=max(abs(r['closure_'+k]) for r in ledger_rows for k in ('m','px','py'))<=1e-10,
cumulative_closure=max(abs(r['cumulative_closure_'+k]) for r in ledger_rows for k in ('m','px','py'))<=1e-9)
return trace, dict(speed=speed, initial_leaf=initial_m.tolist(), final_leaf=previous.tolist(),
raw_residual=[final['cumulative_raw_'+k] for k in ('m','px','py')],
exact_average_residual=(np.array([final['cumulative_raw_'+k] for k in ('m','px','py')])+totals['average']).tolist(),
cumulative={k: v.tolist() for k,v in totals.items()}, checks=checks,
max_collision_ratio=max(r['max_ratio'] for r in collision_rows),
max_collision_error=max(r['max_error'] for r in collision_rows),
max_bfl_error=max(r['bfl_error'] for r in boundary_rows),
max_interval_closure=max(abs(r['closure_'+k]) for r in ledger_rows for k in ('m','px','py')),
drive_mass=sum(r['drive_mass'] for r in boundary_rows))
def compare(driven, zero, label):
regions = json.loads((OUT/'regions.json').read_text())
summaries, rows = {}, []
for key in driven[0]:
velocity = key.endswith('_u')
floor = 1e-6 if velocity else 1e-7
first, peak = None, None
for n, (d, z) in enumerate(zip(driven, zero), 1):
difference = d[key]-z[key]
if velocity:
signal = np.linalg.norm(difference, axis=0)
base = np.linalg.norm(z[key], axis=0)
absolute = np.linalg.norm(d[key], axis=0)
elif difference.ndim == 2:
signal, base, absolute = abs(difference).max(axis=0), abs(z[key]).max(axis=0), abs(d[key]).max(axis=0)
else:
signal, base, absolute = abs(difference), abs(z[key]), abs(d[key])
eligible = (signal>floor)&(signal>10*base)
idx = int(np.argmax(signal))
def location(i):
region = key.removesuffix('_u').removesuffix('_neq')
if key == 'transfer_neq':
return regions['transfer_slots'][i]
return dict(zip(('x','y'), regions[region][i]))
record = dict(metric=key, time=n, signal=float(signal[idx]), baseline=float(base[idx]),
absolute=float(absolute[idx]), any_crossed=bool(eligible.any()), peak_crossed=bool(eligible[idx]), **location(idx))
rows.append(record)
if peak is None or record['signal']>peak['signal']:
peak = record.copy()
if first is None and eligible.any():
i = int(np.argmax(np.where(eligible, signal, -1)))
first = dict(time=n, signal=float(signal[i]), baseline=float(base[i]), absolute=float(absolute[i]), **location(i))
summaries[key] = dict(threshold=floor, baseline_factor=10, first_arrival=first, peak=peak)
# q is present only for transfer populations; normalize CSV columns.
for row in rows:
row.setdefault('q', '')
table(label+'_propagation.csv', rows)
return summaries
def main():
assert Path(h.CelerisLab.__file__).resolve() == ROOT/'src/CelerisLab/__init__.py'
assert Path(h.compiler._KERNEL_DIR).resolve() == ROOT/'src/CelerisLab/lbm/kernels'
frozen = json.loads((OUT/'frozen.json').read_text())
for path, digest in frozen['hashes'].items():
assert hashlib.sha256((ROOT/path).read_bytes()).hexdigest() == digest, path
print('SOURCE', h.CelerisLab.__file__, 'CRITERIA', frozen['frozen_utc'], flush=True)
traces, summaries = {}, {}
for label, speed in (('zero', 0.), ('blow', .002), ('suction', -.002)):
traces[label], summaries[label] = run(speed, label)
save(label+'_summary.json', summaries[label])
for label in ('blow','suction'):
summaries[label]['propagation'] = compare(traces[label], traces['zero'], label)
summaries[label]['raw_minus_zero'] = (np.array(summaries[label]['raw_residual'])-summaries['zero']['raw_residual']).tolist()
summaries[label]['checks']['propagation'] = all(s['first_arrival'] is not None for s in summaries[label]['propagation'].values())
summaries[label]['checks']['drive_sign'] = summaries[label]['speed']*summaries[label]['drive_mass']>0
save(label+'_summary.json', summaries[label])
passed = all(all(v for k,v in s['checks'].items() if k not in ('boundary_count','collision_count')) for s in summaries.values())
save('result.json', dict(passed=passed, runs=summaries))
print('RESULT', json.dumps(dict(passed=passed, summaries=summaries)), flush=True)
raise SystemExit(0 if passed else 1)
if __name__ == '__main__':
main()
research/fine_surface_joint_20261014/zero_boundary.csv
+49
View File
@@ -0,0 +1,49 @@
step,substep,time,bfl_error,drive_mass,latest_body_impulse_x,latest_body_impulse_y,latest_body_angular_impulse,boundary_m,boundary_px,boundary_py,fine_transfer_m,fine_transfer_px,fine_transfer_py
1,1,0.5,1.862645149230957e-09,-3.725290298461914e-09,0.0,0.0,0.0,-3.725290298461914e-09,0.0,0.0,0.0,0.0,0.0
1,2,1.0,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-3.557652235031128e-07,0.0,0.0
2,1,1.5,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-8.530914783477783e-07,-7.450580596923828e-09,-1.6763806343078613e-07
2,2,2.0,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-1.3401731848716736e-06,2.7939677238464355e-09,-9.313225746154785e-10
3,1,2.5,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-1.6577541828155518e-06,1.5832483768463135e-07,1.5832483768463135e-07
3,2,3.0,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-1.982320100069046e-06,3.1152740120887756e-07,3.096647560596466e-07
4,1,3.5,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-3.2121315598487854e-06,3.5297125577926636e-07,3.762543201446533e-07
4,2,4.0,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-3.698747605085373e-06,-3.585591912269592e-08,1.909211277961731e-08
5,1,4.5,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-3.6563724279403687e-06,5.550682544708252e-07,2.4121254682540894e-07
5,2,5.0,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-3.6312267184257507e-06,5.122274160385132e-07,2.2258609533309937e-07
6,1,5.5,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-2.8940849006175995e-06,5.164183676242828e-07,3.213062882423401e-08
6,2,6.0,7.450580596923828e-09,1.4901161193847656e-08,0.0,0.0,0.0,1.4901161193847656e-08,0.0,0.0,-2.759043127298355e-06,1.4202669262886047e-07,-9.173527359962463e-08
7,1,6.5,7.450580596923828e-09,1.2056459275378018e-08,2.60770320892334e-08,2.60770320892334e-08,0.0,2.2351741790771484e-08,-2.9802322387695312e-08,-2.9802322387695312e-08,-2.505723387002945e-06,1.41095370054245e-07,1.909211277961731e-08
7,2,7.0,3.3177493669045433e-09,8.59302214106794e-09,6.146728992462158e-08,3.5390257835388184e-08,0.0,5.634501576423645e-08,-6.565824151039124e-08,-3.119930624961853e-08,-2.1560117602348328e-06,9.872019290924072e-08,-7.171183824539185e-08
8,1,7.5,3.3720288783456454e-09,5.322996345785702e-09,-1.1175870895385742e-08,-3.725290298461914e-09,-1.3969838619232178e-09,7.031485438346863e-08,1.1641532182693481e-08,1.0710209608078003e-08,-2.4740584194660187e-06,2.6496127247810364e-07,1.103617250919342e-07
8,2,8.0,5.7645661577510054e-09,9.257705106853475e-09,8.568167686462402e-08,6.891787052154541e-08,5.587935447692871e-09,6.658956408500671e-08,-8.242204785346985e-08,-6.100162863731384e-08,-2.444721758365631e-06,1.7601996660232544e-07,1.5832483768463135e-08
9,1,8.5,5.7645661577510054e-09,1.0978071169134851e-08,1.1548399925231934e-07,1.043081283569336e-07,2.3094196555106805e-09,3.166496753692627e-08,-1.1082738637924194e-07,-1.0710209608078003e-07,-2.629123628139496e-06,1.7415732145309448e-07,-9.313225746154785e-08
9,2,9.0,6.9608347974536855e-09,7.898425626214067e-09,1.3131648302078247e-07,1.648440957069397e-07,6.947145747560057e-09,3.119930624961853e-08,-1.19674950838089e-07,-1.578591763973236e-07,-2.517830580472946e-06,1.7741695046424866e-07,-1.0477378964424133e-07
10,1,9.5,8.157103437156366e-09,1.1035995113421737e-08,9.592622518539429e-08,1.387670636177063e-07,-4.134291242685606e-09,-5.3085386753082275e-08,-9.499490261077881e-08,-1.4062970876693726e-07,-2.5741755962371826e-06,4.377216100692749e-08,-2.1420419216156006e-08
10,2,10.0,9.843117876329188e-09,-2.3864622283528703e-09,-5.587935447692871e-09,1.601874828338623e-07,-2.2389515352827516e-08,-5.820766091346741e-08,4.656612873077393e-10,-1.5692785382270813e-07,-2.477318048477173e-06,1.1362135410308838e-07,-4.6566128730773926e-08
11,1,10.5,5.7645661577510054e-09,8.417366170396234e-09,3.073364496231079e-08,1.4062970876693726e-07,5.2116290616410765e-08,-5.541369318962097e-08,-2.2817403078079224e-08,-1.4295801520347595e-07,-2.542976289987564e-06,-1.0943040251731873e-07,-1.3923272490501404e-07
11,2,11.0,8.646849236626508e-09,4.201981729581861e-09,2.0489096641540527e-08,6.51925802230835e-08,2.9745662044611265e-08,-1.3969838619232178e-08,-2.0489096641540527e-08,-7.264316082000732e-08,-2.610962837934494e-06,2.561137080192566e-08,-1.2153759598731995e-07
12,1,11.5,7.450580596923828e-09,6.251696621321912e-09,-2.3283064365386963e-08,2.514570951461792e-08,-8.029562570399662e-09,-2.9802322387695312e-08,2.7939677238464355e-08,-2.7939677238464355e-08,-2.630986273288727e-06,-2.60770320892334e-08,-1.0337680578231812e-07
12,2,12.0,5.7645661577510054e-09,2.728442157970412e-09,1.3969838619232178e-08,4.377216100692749e-08,3.1274215439580644e-09,-6.51925802230835e-09,-1.1175870895385742e-08,-4.377216100692749e-08,-2.6319175958633423e-06,1.4901161193847656e-08,-1.4528632164001465e-07
13,1,12.5,5.7645661577510054e-09,-9.308885346587248e-11,-1.5832483768463135e-08,-1.3969838619232178e-08,-5.176395001527112e-08,-2.1420419216156006e-08,2.2351741790771484e-08,7.450580596923828e-09,-2.5229528546333313e-06,-1.30385160446167e-07,-1.94646418094635e-07
13,2,13.0,5.7645661577510054e-09,2.221090411418103e-09,-8.381903171539307e-09,6.51925802230835e-09,-8.495223857707401e-09,-1.862645149230957e-09,8.381903171539307e-09,-1.3969838619232178e-08,-2.4405308067798615e-06,-7.12461769580841e-08,-1.8673017621040344e-07
14,1,13.5,5.7645661577510054e-09,2.3050655134637665e-09,-1.0244548320770264e-08,1.7695128917694092e-08,-5.172617645321509e-08,-1.4435499906539917e-08,7.916241884231567e-09,-1.4435499906539917e-08,-2.2603198885917664e-06,-1.4062970876693726e-07,-2.561137080192566e-07
14,2,14.0,5.7645661577510054e-09,-1.3810677766332158e-09,4.6566128730773926e-09,1.955777406692505e-08,-2.4717821789366212e-08,-1.5832483768463135e-08,-9.313225746154785e-09,-1.955777406692505e-08,-2.158805727958679e-06,-5.587935447692871e-08,-2.654269337654114e-07
15,1,14.5,5.7645661577510054e-09,3.710991161934296e-09,-2.514570951461792e-08,1.955777406692505e-08,-3.6359353972059694e-08,-2.3748725652694702e-08,2.7474015951156616e-08,-1.3504177331924438e-08,-2.0223669707775116e-06,-1.7834827303886414e-07,-2.752058207988739e-07
15,2,15.0,5.7645661577510054e-09,5.327543748170904e-09,1.3969838619232178e-08,1.5832483768463135e-08,1.7229467630386353e-08,1.0710209608078003e-08,-1.257285475730896e-08,-1.7229467630386353e-08,-1.909211277961731e-06,-5.8673322200775146e-08,-2.812594175338745e-07
16,1,15.5,5.7645661577510054e-09,4.6057723086800895e-10,-2.7008354663848877e-08,-2.1420419216156006e-08,-3.447782204180072e-08,-2.2351741790771484e-08,2.7939677238464355e-08,2.7008354663848877e-08,-1.9264407455921173e-06,-1.3737007975578308e-07,-4.237517714500427e-08
16,2,16.0,6.254311957221148e-09,5.437245712697214e-09,-9.313225746154785e-10,-1.3969838619232178e-08,1.6260371493714842e-08,4.190951585769653e-09,3.259629011154175e-09,1.3504177331924438e-08,-1.8654391169548035e-06,-1.0617077350616455e-07,-1.7136335372924805e-07
17,1,16.5,9.13659503609665e-09,3.3756476485596876e-09,-4.0046870708465576e-08,-3.632158041000366e-08,-2.366079998594728e-09,-1.0710209608078003e-08,3.864988684654236e-08,4.0512531995773315e-08,-1.7364509403705597e-06,-2.062879502773285e-07,-3.864988684654236e-08
17,2,17.0,6.254311957221148e-09,-1.3075458155445419e-09,-2.7939677238464355e-09,-5.681067705154419e-08,-2.5630257582953675e-08,-1.1641532182693481e-08,5.122274160385132e-09,4.7963112592697144e-08,-1.6386620700359344e-06,-1.2153759598731995e-07,-9.73232090473175e-08
18,1,17.5,6.254311957221148e-09,6.7173146229421166e-09,1.862645149230957e-09,-1.862645149230957e-08,2.366079998594728e-09,-1.1641532182693481e-08,-5.122274160385132e-09,2.3748725652694702e-08,-1.4598481357097626e-06,-5.2619725465774536e-08,5.541369318962097e-08
18,2,18.0,7.94032639639397e-09,6.865774111568246e-09,8.754432201385498e-08,-1.30385160446167e-08,-1.534793570012738e-08,7.450580596923828e-09,-8.754432201385498e-08,8.381903171539307e-09,-1.3792887330055237e-06,-5.587935447692871e-08,-2.0489096641540527e-08
19,1,18.5,9.13659503609665e-09,1.0526447616343582e-08,7.636845111846924e-08,-5.587935447692871e-09,1.5908030892575198e-08,-6.51925802230835e-09,-7.82310962677002e-08,3.725290298461914e-09,-1.307111233472824e-06,1.909211277961731e-08,5.122274160385132e-09
19,2,19.0,6.254311957221148e-09,9.55982605143485e-09,1.2479722499847412e-07,-1.1175870895385742e-08,-3.2520742987429685e-08,0.0,-1.2200325727462769e-07,9.313225746154785e-09,-1.2475065886974335e-06,-3.213062882423401e-08,-3.119930624961853e-08
20,1,19.5,7.450580596923828e-09,8.078089798642385e-09,9.499490261077881e-08,-1.4901161193847656e-08,2.2911836983219303e-08,-9.778887033462524e-09,-9.639188647270203e-08,1.7229467630386353e-08,-1.2731179594993591e-06,8.568167686462402e-08,5.4016709327697754e-08
20,2,20.0,9.13659503609665e-09,6.598118141264153e-09,8.288770914077759e-08,-4.563480615615845e-08,2.065907767079267e-08,4.656612873077393e-10,-8.800998330116272e-08,4.6100467443466187e-08,-1.1995434761047363e-06,8.195638656616211e-08,-2.60770320892334e-08
21,1,20.5,9.13659503609665e-09,3.7482895407295325e-09,7.729977369308472e-08,-4.7497451305389404e-08,-1.2497307633196897e-08,-1.5832483768463135e-08,-7.729977369308472e-08,4.0046870708465576e-08,-1.3262033462524414e-06,1.471489667892456e-07,-4.563480615615845e-08
21,2,21.0,9.13659503609665e-09,9.180836884128962e-09,9.313225746154785e-08,-3.91155481338501e-08,-1.5700276301267024e-08,1.6298145055770874e-08,-8.987262845039368e-08,3.585591912269592e-08,-1.3378448784351349e-06,1.1781230568885803e-07,-7.310882210731506e-08
22,1,21.5,9.13659503609665e-09,5.584703916330924e-09,1.0058283805847168e-07,-2.7939677238464355e-08,-3.1589420412814206e-08,-1.1175870895385742e-08,-1.0244548320770264e-07,2.60770320892334e-08,-1.443084329366684e-06,1.317821443080902e-07,-1.7276033759117126e-07
22,2,22.0,9.13659503609665e-09,1.0796289612313714e-08,9.592622518539429e-08,-5.3085386753082275e-08,-1.7493244630628624e-09,1.1641532182693481e-08,-9.266659617424011e-08,5.541369318962097e-08,-1.4277175068855286e-06,1.1641532182693481e-07,-1.6298145055770874e-07
23,1,22.5,9.13659503609665e-09,5.392017251963643e-09,1.0803341865539551e-07,-3.5390257835388184e-08,-2.3635404966526607e-08,6.05359673500061e-09,-1.1315569281578064e-07,3.585591912269592e-08,-1.3522803783416748e-06,4.6566128730773926e-08,-1.1920928955078125e-07
23,2,23.0,1.0332863675799331e-08,9.153846265187726e-09,1.1175870895385742e-07,-4.470348358154297e-08,-2.7845243333324277e-08,1.30385160446167e-08,-1.1082738637924194e-07,4.7497451305389404e-08,-1.2884847819805145e-06,3.026798367500305e-08,-7.310882210731506e-08
24,1,23.5,7.450580596923828e-09,3.828949286105443e-09,5.3085386753082275e-08,-3.632158041000366e-08,-3.0305757237059083e-08,4.190951585769653e-09,-5.820766091346741e-08,3.958120942115784e-08,-1.028645783662796e-06,6.193295121192932e-08,-4.7031790018081665e-08
24,2,24.0,7.450580596923828e-09,1.2711602917839238e-08,5.8673322200775146e-08,-6.612390279769897e-08,-6.552603404319512e-08,1.257285475730896e-08,-7.217749953269958e-08,6.100162863731384e-08,-9.830109775066376e-07,1.257285475730896e-08,-1.5366822481155396e-08
1 step substep time bfl_error drive_mass latest_body_impulse_x latest_body_impulse_y latest_body_angular_impulse boundary_m boundary_px boundary_py fine_transfer_m fine_transfer_px fine_transfer_py
2 1 1 0.5 1.862645149230957e-09 -3.725290298461914e-09 0.0 0.0 0.0 -3.725290298461914e-09 0.0 0.0 0.0 0.0 0.0
3 1 2 1.0 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -3.557652235031128e-07 0.0 0.0
4 2 1 1.5 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -8.530914783477783e-07 -7.450580596923828e-09 -1.6763806343078613e-07
5 2 2 2.0 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -1.3401731848716736e-06 2.7939677238464355e-09 -9.313225746154785e-10
6 3 1 2.5 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -1.6577541828155518e-06 1.5832483768463135e-07 1.5832483768463135e-07
7 3 2 3.0 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -1.982320100069046e-06 3.1152740120887756e-07 3.096647560596466e-07
8 4 1 3.5 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -3.2121315598487854e-06 3.5297125577926636e-07 3.762543201446533e-07
9 4 2 4.0 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -3.698747605085373e-06 -3.585591912269592e-08 1.909211277961731e-08
10 5 1 4.5 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -3.6563724279403687e-06 5.550682544708252e-07 2.4121254682540894e-07
11 5 2 5.0 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -3.6312267184257507e-06 5.122274160385132e-07 2.2258609533309937e-07
12 6 1 5.5 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -2.8940849006175995e-06 5.164183676242828e-07 3.213062882423401e-08
13 6 2 6.0 7.450580596923828e-09 1.4901161193847656e-08 0.0 0.0 0.0 1.4901161193847656e-08 0.0 0.0 -2.759043127298355e-06 1.4202669262886047e-07 -9.173527359962463e-08
14 7 1 6.5 7.450580596923828e-09 1.2056459275378018e-08 2.60770320892334e-08 2.60770320892334e-08 0.0 2.2351741790771484e-08 -2.9802322387695312e-08 -2.9802322387695312e-08 -2.505723387002945e-06 1.41095370054245e-07 1.909211277961731e-08
15 7 2 7.0 3.3177493669045433e-09 8.59302214106794e-09 6.146728992462158e-08 3.5390257835388184e-08 0.0 5.634501576423645e-08 -6.565824151039124e-08 -3.119930624961853e-08 -2.1560117602348328e-06 9.872019290924072e-08 -7.171183824539185e-08
16 8 1 7.5 3.3720288783456454e-09 5.322996345785702e-09 -1.1175870895385742e-08 -3.725290298461914e-09 -1.3969838619232178e-09 7.031485438346863e-08 1.1641532182693481e-08 1.0710209608078003e-08 -2.4740584194660187e-06 2.6496127247810364e-07 1.103617250919342e-07
17 8 2 8.0 5.7645661577510054e-09 9.257705106853475e-09 8.568167686462402e-08 6.891787052154541e-08 5.587935447692871e-09 6.658956408500671e-08 -8.242204785346985e-08 -6.100162863731384e-08 -2.444721758365631e-06 1.7601996660232544e-07 1.5832483768463135e-08
18 9 1 8.5 5.7645661577510054e-09 1.0978071169134851e-08 1.1548399925231934e-07 1.043081283569336e-07 2.3094196555106805e-09 3.166496753692627e-08 -1.1082738637924194e-07 -1.0710209608078003e-07 -2.629123628139496e-06 1.7415732145309448e-07 -9.313225746154785e-08
19 9 2 9.0 6.9608347974536855e-09 7.898425626214067e-09 1.3131648302078247e-07 1.648440957069397e-07 6.947145747560057e-09 3.119930624961853e-08 -1.19674950838089e-07 -1.578591763973236e-07 -2.517830580472946e-06 1.7741695046424866e-07 -1.0477378964424133e-07
20 10 1 9.5 8.157103437156366e-09 1.1035995113421737e-08 9.592622518539429e-08 1.387670636177063e-07 -4.134291242685606e-09 -5.3085386753082275e-08 -9.499490261077881e-08 -1.4062970876693726e-07 -2.5741755962371826e-06 4.377216100692749e-08 -2.1420419216156006e-08
21 10 2 10.0 9.843117876329188e-09 -2.3864622283528703e-09 -5.587935447692871e-09 1.601874828338623e-07 -2.2389515352827516e-08 -5.820766091346741e-08 4.656612873077393e-10 -1.5692785382270813e-07 -2.477318048477173e-06 1.1362135410308838e-07 -4.6566128730773926e-08
22 11 1 10.5 5.7645661577510054e-09 8.417366170396234e-09 3.073364496231079e-08 1.4062970876693726e-07 5.2116290616410765e-08 -5.541369318962097e-08 -2.2817403078079224e-08 -1.4295801520347595e-07 -2.542976289987564e-06 -1.0943040251731873e-07 -1.3923272490501404e-07
23 11 2 11.0 8.646849236626508e-09 4.201981729581861e-09 2.0489096641540527e-08 6.51925802230835e-08 2.9745662044611265e-08 -1.3969838619232178e-08 -2.0489096641540527e-08 -7.264316082000732e-08 -2.610962837934494e-06 2.561137080192566e-08 -1.2153759598731995e-07
24 12 1 11.5 7.450580596923828e-09 6.251696621321912e-09 -2.3283064365386963e-08 2.514570951461792e-08 -8.029562570399662e-09 -2.9802322387695312e-08 2.7939677238464355e-08 -2.7939677238464355e-08 -2.630986273288727e-06 -2.60770320892334e-08 -1.0337680578231812e-07
25 12 2 12.0 5.7645661577510054e-09 2.728442157970412e-09 1.3969838619232178e-08 4.377216100692749e-08 3.1274215439580644e-09 -6.51925802230835e-09 -1.1175870895385742e-08 -4.377216100692749e-08 -2.6319175958633423e-06 1.4901161193847656e-08 -1.4528632164001465e-07
26 13 1 12.5 5.7645661577510054e-09 -9.308885346587248e-11 -1.5832483768463135e-08 -1.3969838619232178e-08 -5.176395001527112e-08 -2.1420419216156006e-08 2.2351741790771484e-08 7.450580596923828e-09 -2.5229528546333313e-06 -1.30385160446167e-07 -1.94646418094635e-07
27 13 2 13.0 5.7645661577510054e-09 2.221090411418103e-09 -8.381903171539307e-09 6.51925802230835e-09 -8.495223857707401e-09 -1.862645149230957e-09 8.381903171539307e-09 -1.3969838619232178e-08 -2.4405308067798615e-06 -7.12461769580841e-08 -1.8673017621040344e-07
28 14 1 13.5 5.7645661577510054e-09 2.3050655134637665e-09 -1.0244548320770264e-08 1.7695128917694092e-08 -5.172617645321509e-08 -1.4435499906539917e-08 7.916241884231567e-09 -1.4435499906539917e-08 -2.2603198885917664e-06 -1.4062970876693726e-07 -2.561137080192566e-07
29 14 2 14.0 5.7645661577510054e-09 -1.3810677766332158e-09 4.6566128730773926e-09 1.955777406692505e-08 -2.4717821789366212e-08 -1.5832483768463135e-08 -9.313225746154785e-09 -1.955777406692505e-08 -2.158805727958679e-06 -5.587935447692871e-08 -2.654269337654114e-07
30 15 1 14.5 5.7645661577510054e-09 3.710991161934296e-09 -2.514570951461792e-08 1.955777406692505e-08 -3.6359353972059694e-08 -2.3748725652694702e-08 2.7474015951156616e-08 -1.3504177331924438e-08 -2.0223669707775116e-06 -1.7834827303886414e-07 -2.752058207988739e-07
31 15 2 15.0 5.7645661577510054e-09 5.327543748170904e-09 1.3969838619232178e-08 1.5832483768463135e-08 1.7229467630386353e-08 1.0710209608078003e-08 -1.257285475730896e-08 -1.7229467630386353e-08 -1.909211277961731e-06 -5.8673322200775146e-08 -2.812594175338745e-07
32 16 1 15.5 5.7645661577510054e-09 4.6057723086800895e-10 -2.7008354663848877e-08 -2.1420419216156006e-08 -3.447782204180072e-08 -2.2351741790771484e-08 2.7939677238464355e-08 2.7008354663848877e-08 -1.9264407455921173e-06 -1.3737007975578308e-07 -4.237517714500427e-08
33 16 2 16.0 6.254311957221148e-09 5.437245712697214e-09 -9.313225746154785e-10 -1.3969838619232178e-08 1.6260371493714842e-08 4.190951585769653e-09 3.259629011154175e-09 1.3504177331924438e-08 -1.8654391169548035e-06 -1.0617077350616455e-07 -1.7136335372924805e-07
34 17 1 16.5 9.13659503609665e-09 3.3756476485596876e-09 -4.0046870708465576e-08 -3.632158041000366e-08 -2.366079998594728e-09 -1.0710209608078003e-08 3.864988684654236e-08 4.0512531995773315e-08 -1.7364509403705597e-06 -2.062879502773285e-07 -3.864988684654236e-08
35 17 2 17.0 6.254311957221148e-09 -1.3075458155445419e-09 -2.7939677238464355e-09 -5.681067705154419e-08 -2.5630257582953675e-08 -1.1641532182693481e-08 5.122274160385132e-09 4.7963112592697144e-08 -1.6386620700359344e-06 -1.2153759598731995e-07 -9.73232090473175e-08
36 18 1 17.5 6.254311957221148e-09 6.7173146229421166e-09 1.862645149230957e-09 -1.862645149230957e-08 2.366079998594728e-09 -1.1641532182693481e-08 -5.122274160385132e-09 2.3748725652694702e-08 -1.4598481357097626e-06 -5.2619725465774536e-08 5.541369318962097e-08
37 18 2 18.0 7.94032639639397e-09 6.865774111568246e-09 8.754432201385498e-08 -1.30385160446167e-08 -1.534793570012738e-08 7.450580596923828e-09 -8.754432201385498e-08 8.381903171539307e-09 -1.3792887330055237e-06 -5.587935447692871e-08 -2.0489096641540527e-08
38 19 1 18.5 9.13659503609665e-09 1.0526447616343582e-08 7.636845111846924e-08 -5.587935447692871e-09 1.5908030892575198e-08 -6.51925802230835e-09 -7.82310962677002e-08 3.725290298461914e-09 -1.307111233472824e-06 1.909211277961731e-08 5.122274160385132e-09
39 19 2 19.0 6.254311957221148e-09 9.55982605143485e-09 1.2479722499847412e-07 -1.1175870895385742e-08 -3.2520742987429685e-08 0.0 -1.2200325727462769e-07 9.313225746154785e-09 -1.2475065886974335e-06 -3.213062882423401e-08 -3.119930624961853e-08
40 20 1 19.5 7.450580596923828e-09 8.078089798642385e-09 9.499490261077881e-08 -1.4901161193847656e-08 2.2911836983219303e-08 -9.778887033462524e-09 -9.639188647270203e-08 1.7229467630386353e-08 -1.2731179594993591e-06 8.568167686462402e-08 5.4016709327697754e-08
41 20 2 20.0 9.13659503609665e-09 6.598118141264153e-09 8.288770914077759e-08 -4.563480615615845e-08 2.065907767079267e-08 4.656612873077393e-10 -8.800998330116272e-08 4.6100467443466187e-08 -1.1995434761047363e-06 8.195638656616211e-08 -2.60770320892334e-08
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research/fine_surface_joint_20261014/zero_collisions.csv
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step,level,substep,max_error,max_ratio,regime,drift_m,drift_px,drift_py,ideal_defect_m,ideal_defect_px,ideal_defect_py,measured_rounding_m,measured_rounding_px,measured_rounding_py
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15,coarse,1,1.0723740850870911e-07,0.41406218567886527,True,-3.604218363761902e-06,2.514570951461792e-07,2.8870999813079834e-07,-2.2641610808449286e-14,2.2898349882893854e-16,-2.8449465006019636e-16,-3.604218341120291e-06,2.514570949171957e-07,2.88709998415293e-07
16,fine,1,7.098747656364424e-08,0.32714839408436125,True,2.566725015640259e-06,9.033828973770142e-08,6.426125764846802e-08,-1.4136261605735001e-14,5.724587470723463e-17,3.7296554733501353e-16,2.5667250297765204e-06,9.033828968045554e-08,6.426125727550247e-08
16,fine,2,6.767610849500727e-08,0.28320307876856565,True,2.248678356409073e-06,1.969747245311737e-07,2.3748725652694702e-08,-1.4096362965787534e-14,-1.7694179454963432e-16,-2.6020852139652106e-16,2.248678370505436e-06,1.969747247081155e-07,2.3748725912903224e-08
16,coarse,1,1.1131294752608767e-07,0.344951775990682,True,-8.195638656616211e-07,2.5704503059387207e-07,3.7997961044311523e-07,-2.4764912343044898e-14,-8.673617379884035e-16,7.632783294297951e-17,-8.195638408967088e-07,2.570450314612338e-07,3.799796103667874e-07
17,fine,1,6.063944912870767e-08,0.2998044890847571,True,2.319924533367157e-06,9.406358003616333e-08,6.798654794692993e-08,-1.590221010427939e-14,1.1622647289044608e-16,-2.237793284010081e-16,2.319924549269367e-06,9.406357991993686e-08,6.798654817070926e-08
17,fine,2,8.506079685055568e-08,0.3017577892533187,True,2.0246952772140503e-06,1.4808028936386108e-07,4.190951585769653e-08,-1.4058199049316045e-14,-9.367506770274758e-17,-2.463307335887066e-16,2.0246952912722493e-06,1.4808028945753615e-07,4.190951610402727e-08
17,coarse,1,8.329367096049367e-08,0.37439878535241283,True,-2.942979335784912e-07,1.2665987014770508e-07,2.5331974029541016e-07,-2.1892210266827306e-14,4.926614671774132e-16,9.020562075079397e-17,-2.9429791168628094e-07,1.266598696550436e-07,2.5331974020520454e-07
18,fine,1,6.995265811049478e-08,0.2617189776211021,True,1.6298145055770874e-06,5.494803190231323e-08,-3.5390257835388184e-08,-1.580506558962469e-14,-1.8561541192951836e-16,5.204170427930421e-18,1.629814521382153e-06,5.4948032087928644e-08,-3.5390257840592354e-08
18,fine,2,6.477865954757078e-08,0.30517565195263086,True,1.5725381672382355e-06,4.423782229423523e-08,-3.771856427192688e-08,-1.470698562933137e-14,1.5612511283791264e-16,-1.7694179454963432e-16,1.572538181945221e-06,4.4237822138110117e-08,-3.7718564094985085e-08
18,coarse,1,1.1793568432949542e-07,0.4212736354253594,True,-1.5087425708770752e-06,-1.043081283569336e-07,3.3527612686157227e-07,-2.4147350785597155e-14,2.0816681711721685e-16,3.608224830031759e-16,-1.5087425467297244e-06,-1.0430812856510041e-07,3.352761265007498e-07
19,fine,1,6.871091096094517e-08,0.31933573148866196,True,1.2880191206932068e-06,1.7043203115463257e-07,-9.12696123123169e-08,-1.4236875567341656e-14,2.0990154059319366e-16,2.8622937353617317e-16,1.2880191349300824e-06,1.7043203094473103e-07,-9.126961259854627e-08
19,fine,2,7.491972742190711e-08,0.24023424324702233,True,1.1730007827281952e-06,-2.468004822731018e-08,-1.327134668827057e-07,-1.3513495877859327e-14,-1.0061396160665481e-16,-8.673617379884035e-17,1.173000796241691e-06,-2.468004812669622e-08,-1.3271346679596951e-07
19,coarse,1,9.475610251996613e-08,0.35516793189978335,True,-1.5124678611755371e-06,8.940696716308594e-08,1.7136335372924805e-07,-2.3293866835416566e-14,1.3183898417423734e-16,7.147060721024445e-16,-1.5124678378816703e-06,8.940696703124695e-08,1.7136335301454197e-07
20,fine,1,7.098746790390464e-08,0.2802732595156336,True,1.6228295862674713e-06,4.423782229423523e-08,-2.277083694934845e-07,-1.5477202852665073e-14,1.8735013540549517e-16,2.7755575615628914e-17,1.6228296017446742e-06,4.4237822106885094e-08,-2.2770836952124007e-07
20,fine,2,8.112854432695826e-08,0.32421854647703796,True,1.5408731997013092e-06,6.05359673500061e-09,-2.2863969206809998e-07,-1.490300938211675e-14,3.2439329000766293e-16,-1.0234868508263162e-16,1.5408732146043186e-06,6.05359641060732e-09,-2.286396919657513e-07
20,coarse,1,9.781275722708926e-08,0.4218746642002769,True,-6.798654794692993e-07,-1.8812716007232666e-07,6.146728992462158e-08,-2.1871393585115584e-14,-1.1102230246251565e-16,9.43689570931383e-16,-6.798654575979057e-07,-1.8812715996130436e-07,6.146728898093201e-08
21,fine,1,7.30570710594236e-08,0.2949216850976223,True,1.6815029084682465e-06,4.237517714500427e-08,-1.2712553143501282e-07,-1.4618514732056553e-14,8.500145032286355e-17,3.625572064791527e-16,1.6815029230867612e-06,4.237517706000282e-08,-1.2712553179757002e-07
21,fine,2,6.68482604826437e-08,0.2656249015126583,True,1.530628651380539e-06,6.472691893577576e-08,-1.0104849934577942e-07,-1.4838824613505608e-14,-3.2612801348363973e-16,4.85722573273506e-17,1.5306286662193636e-06,6.472691926190377e-08,-1.0104849939435168e-07
21,coarse,1,1.1487904549856154e-07,0.3810092874429201,True,-8.23289155960083e-07,-2.5704503059387207e-07,3.0174851417541504e-07,-2.7519653222896068e-14,-1.3877787807814457e-16,-8.326672684688674e-16,-8.232891284404298e-07,-2.570450304550942e-07,3.017485150080823e-07
22,fine,1,6.788306161631397e-08,0.30419906371761746,True,1.2782402336597443e-06,1.816079020500183e-08,-2.416782081127167e-07,-1.4958520533348008e-14,-1.1275702593849246e-16,-8.153200337090993e-17,1.2782402486182648e-06,1.8160790317758857e-08,-2.4167820803118467e-07
22,fine,2,7.098746757083774e-08,0.3193357487763267,True,1.34296715259552e-06,4.190951585769653e-08,-1.3690441846847534e-07,-1.436524510456394e-14,2.2551405187698492e-17,-4.0419056990259605e-16,1.3429671669607651e-06,4.190951583514513e-08,-1.3690441806428477e-07
22,coarse,1,9.781274745712665e-08,0.4098554174530562,True,-2.427026629447937e-06,-2.1047890186309814e-07,-5.4016709327697754e-08,-2.54379850517239e-14,4.0245584642661925e-16,4.3021142204224816e-16,-2.427026604009952e-06,-2.10478902265554e-07,-5.4016709757909176e-08
23,fine,1,8.754431335411539e-08,0.26367162956761353,True,1.911073923110962e-06,9.96515154838562e-08,-1.434236764907837e-07,-1.4030443473700416e-14,-3.3306690738754696e-16,-3.434752482434078e-16,1.9110739371414054e-06,9.965151581692311e-08,-1.4342367614730844e-07
23,fine,2,7.491972731088481e-08,0.29589836062223757,True,9.583309292793274e-07,1.4808028936386108e-07,-8.381903171539307e-08,-1.40391170910803e-14,-1.5439038936193583e-16,3.2959746043559335e-17,9.583309433184445e-07,1.4808028951825147e-07,-8.381903174835281e-08
23,coarse,1,9.883161822266118e-08,0.46394191845799526,True,-4.954636096954346e-07,-6.332993507385254e-08,1.2665987014770508e-07,-2.6922908347160046e-14,2.220446049250313e-16,1.942890293094024e-16,-4.954635827725262e-07,-6.332993529589714e-08,1.2665986995341605e-07
24,fine,1,7.098748533440613e-08,0.34619121759181026,True,1.4575198292732239e-06,1.043081283569336e-07,-1.0058283805847168e-07,-1.4247283908197517e-14,8.153200337090993e-17,1.474514954580286e-16,1.4575198435205078e-06,1.0430812827540159e-07,-1.0058283820592318e-07
24,fine,2,7.823108527649225e-08,0.30419908362462666,True,6.756745278835297e-07,2.0256265997886658e-07,-4.7031790018081665e-08,-1.4353102040232102e-14,-2.8449465006019636e-16,-1.8041124150158794e-16,6.756745422366317e-07,2.0256266026336123e-07,-4.7031789837670424e-08
24,coarse,1,1.0749213752436759e-07,0.3774035044304019,True,-4.043802618980408e-06,-1.2852251529693604e-07,3.520399332046509e-07,-2.2246093855926574e-14,4.85722573273506e-16,1.3877787807814457e-17,-4.043802596734314e-06,-1.285225157826586e-07,3.520399331907731e-07
1 step level substep max_error max_ratio regime drift_m drift_px drift_py ideal_defect_m ideal_defect_px ideal_defect_py measured_rounding_m measured_rounding_px measured_rounding_py
2 1 fine 1 3.083712529772242e-08 0.08007812434516381 True 1.6147270798683167e-05 0.0 0.0 -8.326672684688674e-17 0.0 0.0 1.6147270798766433e-05 0.0 0.0
3 1 fine 2 1.1382831566209006e-08 0.09960937460709829 True -3.4831464290618896e-07 0.0 0.0 -5.169475958410885e-15 0.0 0.0 -3.48314637736713e-07 0.0 0.0
4 1 coarse 1 1.1080350609260492e-08 0.10456730670501622 True -1.5292316675186157e-06 -1.30385160446167e-08 -1.5832483768463135e-07 -4.288236432614667e-15 -5.551115123125783e-17 1.3877787807814457e-17 -1.5292316632303793e-06 -1.3038515989105548e-08 -1.5832483769850914e-07
5 2 fine 1 3.2078889655728915e-08 0.1679687748837782 True 8.158385753631592e-07 -1.30385160446167e-08 -1.7508864402770996e-07 -4.496403249731884e-15 -1.734723475976807e-17 2.740863092043355e-16 8.158385798595624e-07 -1.3038516027269464e-08 -1.7508864430179627e-07
6 2 fine 2 6.788307005400895e-08 0.19140625209547577 True 2.376735210418701e-06 -1.1175870895385742e-08 1.2665987014770508e-07 -1.0479464518375892e-14 -4.336808689942018e-17 2.7582103268031233e-16 2.3767352208981657e-06 -1.1175870852017655e-08 1.2665986987188405e-07
7 2 coarse 1 1.6365805183271576e-08 0.1544470929589978 True -1.426786184310913e-06 -3.0919909477233887e-07 -3.0919909477233887e-07 -6.286637876939949e-15 -8.326672684688674e-17 -8.326672684688674e-17 -1.4267861780242752e-06 -3.0919909468907214e-07 -3.0919909468907214e-07
8 3 fine 1 8.23703105723439e-08 0.31152334896616624 True 4.382804036140442e-06 1.6111880540847778e-07 1.3690441846847534e-07 -1.0234868508263162e-14 6.245004513516506e-17 3.2612801348363973e-16 4.38280404637531e-06 1.6111880534602774e-07 1.3690441814234733e-07
9 3 fine 2 7.843806204554937e-08 0.3828123813437159 True 7.607974112033844e-06 5.029141902923584e-08 2.2351741790771484e-08 -1.5149340115705456e-14 1.5612511283791264e-17 2.654126918244515e-16 7.607974127183184e-06 5.029141901362333e-08 2.2351741525358793e-08
10 3 coarse 1 1.0010523732173482e-07 0.2854567053212911 True 9.115785360336304e-06 3.3155083656311035e-07 3.203749656677246e-07 -3.837208328860697e-15 1.5334955527634975e-15 1.2420620087993939e-15 9.115785364173512e-06 3.315508350296148e-07 3.203749644256626e-07
11 4 fine 1 9.437402781475868e-08 0.3632811360585352 True 1.0219402611255646e-05 3.771856427192688e-07 3.67872416973114e-07 -1.84297022087776e-14 4.371503159461554e-16 5.238864897449957e-16 1.0219402629685348e-05 3.771856422821185e-07 3.678724164492275e-07
12 4 fine 2 9.851323801157719e-08 0.3388670872446077 True 1.0103452950716019e-05 2.975575625896454e-07 1.9976869225502014e-07 -1.6042722705833512e-14 -1.7694179454963432e-16 6.938893903907228e-18 1.0103452966758741e-05 2.975575627665872e-07 1.9976869224808125e-07
13 4 coarse 1 1.2150177930436712e-07 0.37139422446265463 True 1.3064593076705933e-05 5.997717380523682e-07 5.62518835067749e-07 -1.2559397966072083e-14 -5.967448757360216e-16 -2.498001805406602e-16 1.306459308926533e-05 5.99771738649113e-07 5.625188353175492e-07
14 5 fine 1 9.851323801157719e-08 0.3144530263816977 True 1.0738149285316467e-05 -4.190951585769653e-08 -1.1548399925231934e-07 -1.6018436577169837e-14 4.891920202254596e-16 5.30825383648903e-16 1.0738149301334904e-05 -4.1909516346888553e-08 -1.1548399978314472e-07
15 5 fine 2 9.540882917047355e-08 0.32177729440809383 True 9.94466245174408e-06 0.0 1.7695128917694092e-07 -1.7361112547575885e-14 4.198030811863873e-16 1.0477729794899915e-15 9.944662469105192e-06 -4.198030811863873e-16 1.7695128812916794e-07
16 5 coarse 1 1.2532259030528792e-07 0.38942307019337585 True 1.49458646774292e-05 9.723007678985596e-07 1.2628734111785889e-06 -1.6535384173010925e-14 1.5959455978986625e-16 6.175615574477433e-16 1.4945864693964583e-05 9.72300767738965e-07 1.2628734105610273e-06
17 6 fine 1 9.023481550851642e-08 0.2998045916974925 True 9.546522051095963e-06 1.0104849934577942e-07 -6.007030606269836e-08 -1.5754758608821362e-14 -1.3530843112619095e-16 3.469446951953614e-16 9.546522066850721e-06 1.0104849948108785e-07 -6.007030640964306e-08
18 6 fine 2 9.023481550851642e-08 0.29199211258675606 True 8.22870060801506e-06 8.614733815193176e-08 -1.6065314412117004e-07 -1.482668154917377e-14 1.3010426069826053e-16 -8.847089727481716e-17 8.228700622841742e-06 8.61473380218275e-08 -1.6065314403269915e-07
19 6 coarse 1 1.0239773140519048e-07 0.43028837580529966 True 1.3286247849464417e-05 4.898756742477417e-07 3.818422555923462e-07 -1.9283186158958188e-14 -3.469446951953614e-17 8.673617379884035e-16 1.3286247868747603e-05 4.898756742824362e-07 3.8184225472498445e-07
20 7 fine 1 9.023481550851642e-08 0.29199211258675606 True 8.461065590381622e-06 9.96515154838562e-08 1.862645149230957e-08 -1.6153745008296028e-14 4.649058915617843e-16 -1.97758476261356e-16 8.461065606535367e-06 9.965151501895031e-08 1.8626451690068047e-08
21 7 fine 2 9.023481550851642e-08 0.29199211258675606 True 7.561873644590378e-06 7.497146725654602e-08 4.237517714500427e-08 -1.456647302777725e-14 8.153200337090993e-17 5.741934705483231e-16 7.561873659156851e-06 7.497146717501402e-08 4.23751765708108e-08
22 7 coarse 1 1.0902046165828239e-07 0.38161058826685446 True 1.3303011655807495e-05 9.387731552124023e-07 -2.5331974029541016e-07 -2.4202861936828413e-14 -8.326672684688674e-17 -2.3592239273284576e-16 1.3303011680010357e-05 9.387731552956691e-07 -2.5331974005948776e-07
23 8 fine 1 9.023481550851642e-08 0.29199211258675606 True 6.217043846845627e-06 7.12461769580841e-08 1.5040859580039978e-07 -1.42785089307651e-14 6.765421556309548e-17 1.9081958235744878e-17 6.217043861124136e-06 7.124617689042989e-08 1.5040859578131782e-07
24 8 fine 2 9.023481550851642e-08 0.31249989522621113 True 6.183981895446777e-06 1.5832483768463135e-08 1.210719347000122e-08 -1.374594882364022e-14 2.914335439641036e-16 -2.0816681711721685e-17 6.183981909192726e-06 1.583248347702959e-08 1.2107193490817902e-08
25 8 coarse 1 1.265961931951054e-07 0.38341345468786764 True 7.713213562965393e-06 5.718320608139038e-07 -6.761401891708374e-07 -2.1399548799649892e-14 -1.4432899320127035e-15 3.885780586188048e-16 7.713213584364942e-06 5.718320622571937e-07 -6.761401895594155e-07
26 9 fine 1 7.678237223807116e-08 0.32031244446989565 True 5.04031777381897e-06 9.685754776000977e-08 -1.434236764907837e-07 -1.3928094788617784e-14 -1.0581813203458523e-16 2.6020852139652106e-17 5.0403177877470645e-06 9.68575478658279e-08 -1.4342367651680454e-07
27 9 fine 2 7.823108461035844e-08 0.2934569717253952 True 4.06801700592041e-06 3.5390257835388184e-08 -1.7695128917694092e-08 -1.4047790708460184e-14 -9.367506770274758e-17 1.9081958235744878e-16 4.068017019968201e-06 3.539025792906325e-08 -1.7695129108513674e-08
28 9 coarse 1 1.0774685821335339e-07 0.3876201969620664 True 1.385807991027832e-06 -2.7567148208618164e-07 -4.470348358154297e-07 -2.468858451010192e-14 1.249000902703301e-16 -5.967448757360216e-16 1.3858080157164165e-06 -2.7567148221108173e-07 -4.470348352186848e-07
29 10 fine 1 7.885198249901748e-08 0.3164059705161431 True 3.97535040974617e-06 2.0954757928848267e-08 3.306195139884949e-08 -1.408248517797972e-14 1.0408340855860843e-17 1.3183898417423734e-16 3.975350423828655e-06 2.0954757918439926e-08 3.30619512670105e-08
30 10 fine 2 6.684825892833146e-08 0.33007805480156616 True 3.22144478559494e-06 2.7939677238464355e-09 1.3969838619232178e-08 -1.3690437672408962e-14 2.220446049250313e-16 1.942890293094024e-16 3.221444799285378e-06 2.7939675018018306e-09 1.3969838424943148e-08
31 10 coarse 1 9.857692329084955e-08 0.4038458162128909 True 2.164393663406372e-06 -4.470348358154297e-07 3.203749656677246e-07 -2.49592013723543e-14 -3.5388358909926865e-16 8.257283745649602e-16 2.1643936883655734e-06 -4.470348354615461e-07 3.2037496484199623e-07
32 11 fine 1 7.202226570690584e-08 0.2885741439022296 True 2.462416887283325e-06 7.264316082000732e-08 3.259629011154175e-08 -1.4660148095479997e-14 -2.0990154059319366e-16 -2.203098814490545e-16 2.4624169019434733e-06 7.264316102990886e-08 3.259629033185163e-08
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research/fine_surface_joint_20261014/zero_ledger.csv
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@@ -0,0 +1,25 @@
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4,4,0.37499987985940486,560.7500747130252,1.3741664588451385e-06,9.83942300081253e-07,3.330688923597336e-05,1.3289973139762878e-06,1.1883676052093506e-06,2.9802322387695312e-08,0.0,0.0,-6.910879164934158e-06,3.1711533665657043e-07,3.9534643292427063e-07,6.800517439842224e-06,-2.6263296604156494e-07,-3.371387720108032e-07,3.33874486386776e-05,1.2745149433612823e-06,1.1301599442958832e-06,3.33874486386776e-05,1.2745149433612823e-06,1.1301599442958832e-06,-1.103617250919342e-07,5.448237061500549e-08,5.820766091346741e-08,0.0,0.0,0.0,7.05295242369175e-05,1.4710240066051483e-06,1.0938383638858795e-06,0.0,0.0,0.0
5,5,0.3749998295680158,560.7501105009578,2.3622997105121613e-06,2.369750291109085e-06,3.578793257474899e-05,9.881332516670227e-07,1.385807991027832e-06,2.9802322387695312e-08,0.0,0.0,-7.287599146366119e-06,1.0672956705093384e-06,4.637986421585083e-07,7.417052984237671e-06,-1.0095536708831787e-06,-4.023313522338867e-07,3.5628676414489746e-05,9.30391252040863e-07,1.3243407011032104e-06,3.5628676414489746e-05,9.30391252040863e-07,1.3243407011032104e-06,1.2945383787155151e-07,5.774199962615967e-08,6.146728992462158e-08,0.0,0.0,0.0,0.00010615820065140724,2.4014152586460114e-06,2.41817906498909e-06,0.0,0.0,0.0
6,6,0.46874973195382685,560.7501414637081,2.941582351922989e-06,2.4265609681606293e-06,3.09627503156662e-05,5.792826414108276e-07,5.681067705154419e-08,2.9802322387695312e-08,0.0,0.0,-5.653128027915955e-06,6.584450602531433e-07,-5.960464477539063e-08,5.5246055126190186e-06,-7.562339305877686e-07,-4.470348358154297e-08,3.106147050857544e-05,6.770715117454529e-07,1.6111880540847778e-07,3.106147050857544e-05,6.770715117454529e-07,1.6111880540847778e-07,-1.2852251529693604e-07,-9.778887033462524e-08,-1.043081283569336e-07,0.0,0.0,0.0,0.00013721967115998268,3.0784867703914642e-06,2.5792978703975677e-06,0.0,0.0,0.0
7,7,0.4687496533735736,560.7501708613709,3.914348781108856e-06,2.1560117602348328e-06,2.9397662729024887e-05,9.727664291858673e-07,-2.705492079257965e-07,7.869675755500793e-08,-9.546056389808655e-08,-6.100162863731384e-08,-4.661735147237778e-06,2.398155629634857e-07,-5.2619725465774536e-08,4.654750227928162e-06,-2.849847078323364e-07,3.5390257835388184e-08,2.9325950890779495e-05,1.1133961379528046e-06,-1.9231811165809631e-07,2.9325950890779495e-05,1.1133961379528046e-06,-1.9231811165809631e-07,-6.984919309616089e-09,-4.516914486885071e-08,-1.7229467630386353e-08,0.0,0.0,0.0,0.00016654562205076218,4.191882908344269e-06,2.3869797587394714e-06,0.0,0.0,0.0
8,8,0.4687496533735736,560.7501910906285,4.438683390617371e-06,1.5152618288993835e-06,2.022925764322281e-05,5.243346095085144e-07,-6.407499313354492e-07,1.3690441846847534e-07,-7.078051567077637e-08,-5.029141902923584e-08,-4.91878017783165e-06,4.409812390804291e-07,1.2619420886039734e-07,4.896894097328186e-06,-5.047768354415894e-07,-2.0302832126617432e-07,2.0114239305257797e-05,6.58910721540451e-07,-5.136243999004364e-07,2.0114239305257797e-05,6.58910721540451e-07,-5.136243999004364e-07,-2.1886080503463745e-08,-6.379559636116028e-08,-7.683411240577698e-08,0.0,0.0,0.0,0.00018665986135601997,4.85079362988472e-06,1.873355358839035e-06,0.0,0.0,0.0
9,9,0.37499998044222344,560.7502015968785,4.073604941368103e-06,6.379559636116028e-07,1.0506249964237213e-05,-3.650784492492676e-07,-8.773058652877808e-07,6.28642737865448e-08,-2.3050233721733093e-07,-2.6496127247810364e-07,-5.146954208612442e-06,3.5157427191734314e-07,-1.9790604710578918e-07,5.0961971282958984e-06,-3.427267074584961e-07,1.9371509552001953e-07,1.0494142770767212e-05,-1.434236764907837e-07,-6.081536412239075e-07,1.0494142770767212e-05,-1.434236764907837e-07,-6.081536412239075e-07,-5.075708031654358e-08,8.847564458847046e-09,-4.190951585769653e-09,0.0,0.0,0.0,0.00019715400412678719,4.707369953393936e-06,1.2652017176151276e-06,0.0,0.0,0.0
10,10,0.4687496533735736,560.7502107927576,3.5660341382026672e-06,6.789341568946838e-07,9.195879101753235e-06,-5.075708031654358e-07,4.0978193283081055e-08,-1.1129304766654968e-07,-9.452924132347107e-08,-2.975575625896454e-07,-5.0514936447143555e-06,1.5739351511001587e-07,-6.798654794692993e-08,4.997476935386658e-06,-1.471489667892456e-07,3.91155481338501e-08,9.361188858747482e-06,-4.23286110162735e-07,3.674067556858063e-07,9.361188858747482e-06,-4.23286110162735e-07,3.674067556858063e-07,-5.4016709327697754e-08,1.0244548320770264e-08,-2.8870999813079834e-08,0.0,0.0,0.0,0.00020651519298553467,4.284083843231201e-06,1.6326084733009338e-06,0.0,0.0,0.0
11,11,0.37499977927664024,560.7502121757716,3.314577043056488e-06,8.987262845039368e-07,1.3830140233039856e-06,-2.514570951461792e-07,2.1979212760925293e-07,-6.938353180885315e-08,-4.330649971961975e-08,-2.1560117602348328e-07,-5.153939127922058e-06,-8.381903171539307e-08,-2.60770320892334e-07,5.122274160385132e-06,6.705522537231445e-08,2.4586915969848633e-07,1.484062522649765e-06,-1.9138678908348083e-07,4.5029446482658386e-07,1.484062522649765e-06,-1.9138678908348083e-07,4.5029446482658386e-07,-3.166496753692627e-08,-1.6763806343078613e-08,-1.4901161193847656e-08,0.0,0.0,0.0,0.00020799925550818443,4.09269705414772e-06,2.0829029381275177e-06,0.0,0.0,0.0
12,12,0.4687496533735736,560.750213930849,3.0328519642353058e-06,1.119915395975113e-06,1.7550773918628693e-06,-2.8172507882118225e-07,2.2118911147117615e-07,-3.632158041000366e-08,1.6763806343078613e-08,-7.171183824539185e-08,-5.262903869152069e-06,-1.1175870895385742e-08,-2.4866312742233276e-07,5.2247196435928345e-06,3.5390257835388184e-08,2.5890767574310303e-07,1.8295831978321075e-06,-3.227032721042633e-07,2.8265640139579773e-07,1.8295831978321075e-06,-3.227032721042633e-07,2.8265640139579773e-07,-3.818422555923462e-08,2.421438694000244e-08,1.0244548320770264e-08,0.0,0.0,0.0,0.00020982883870601654,3.769993782043457e-06,2.3655593395233154e-06,0.0,0.0,0.0
13,13,0.37499977927664024,560.7502138046548,3.2046809792518616e-06,1.4081597328186035e-06,-1.2619420886039734e-07,1.7182901501655579e-07,2.882443368434906e-07,-2.3283064365386963e-08,3.073364496231079e-08,-6.51925802230835e-09,-4.963483661413193e-06,-2.016313374042511e-07,-3.8137659430503845e-07,4.928559064865112e-06,2.0489096641540527e-07,3.948807716369629e-07,-6.798654794692993e-08,1.3783574104309082e-07,2.812594175338745e-07,-6.798654794692993e-08,1.3783574104309082e-07,2.812594175338745e-07,-3.4924596548080444e-08,3.259629011154175e-09,1.3504177331924438e-08,0.0,0.0,0.0,0.0002097608521580696,3.907829523086548e-06,2.64681875705719e-06,0.0,0.0,0.0
14,14,0.46874959050938997,560.7502162489109,3.7285499274730682e-06,1.762527972459793e-06,2.4442560970783234e-06,5.238689482212067e-07,3.543682396411896e-07,-3.026798367500305e-08,-1.3969838619232178e-09,-3.3993273973464966e-08,-4.4191256165504456e-06,-1.9650906324386597e-07,-5.21540641784668e-07,4.349276423454285e-06,1.993030309677124e-07,4.973262548446655e-07,2.5443732738494873e-06,5.224719643592834e-07,4.12575900554657e-07,2.5443732738494873e-06,5.224719643592834e-07,4.12575900554657e-07,-6.984919309616089e-08,2.7939677238464355e-09,-2.421438694000244e-08,0.0,0.0,0.0,0.0002123052254319191,4.430301487445831e-06,3.059394657611847e-06,0.0,0.0,0.0
15,15,0.4687496533735736,560.7502168049105,4.124362021684647e-06,2.1760351955890656e-06,5.559995770454407e-07,3.9581209421157837e-07,4.1350722312927246e-07,-1.30385160446167e-08,1.4901161193847656e-08,-3.073364496231079e-08,-3.9315782487392426e-06,-2.3702159523963928e-07,-5.564652383327484e-07,3.905966877937317e-06,2.1047890186309814e-07,5.345791578292847e-07,5.94649463891983e-07,4.0745362639427185e-07,4.66126948595047e-07,5.94649463891983e-07,4.0745362639427185e-07,4.66126948595047e-07,-2.561137080192566e-08,-2.6542693376541138e-08,-2.1886080503463745e-08,0.0,0.0,0.0,0.00021289987489581108,4.837755113840103e-06,3.525521606206894e-06,0.0,0.0,0.0
16,16,0.46874959050938997,560.7502207290381,4.730187356472015e-06,2.6812776923179626e-06,3.924127668142319e-06,6.058253347873688e-07,5.052424967288971e-07,-1.816079020500183e-08,3.119930624961853e-08,4.0512531995773315e-08,-3.7918798625469208e-06,-2.4354085326194763e-07,-2.1373853087425232e-07,3.7383288145065308e-06,2.738088369369507e-07,2.1047890186309814e-07,3.9958395063877106e-06,5.443580448627472e-07,4.6798959374427795e-07,3.9958395063877106e-06,5.443580448627472e-07,4.6798959374427795e-07,-5.3551048040390015e-08,3.026798367500305e-08,-3.259629011154175e-09,0.0,0.0,0.0,0.0002168957144021988,5.38211315870285e-06,3.993511199951172e-06,0.0,0.0,0.0
17,17,0.4687496533735736,560.7502247327939,5.152076482772827e-06,3.11434268951416e-06,4.003755748271942e-06,4.2188912630081177e-07,4.330649971961975e-07,-2.2351741790771484e-08,4.377216100692749e-08,8.847564458847046e-08,-3.375113010406494e-06,-3.2782554626464844e-07,-1.3597309589385986e-07,3.3508986234664917e-06,3.371387720108032e-07,1.1734664440155029e-07,4.050321877002716e-06,3.688037395477295e-07,3.632158041000366e-07,4.050321877002716e-06,3.688037395477295e-07,3.632158041000366e-07,-2.421438694000244e-08,9.313225746154785e-09,-1.862645149230957e-08,0.0,0.0,0.0,0.0002209460362792015,5.75091689825058e-06,4.3567270040512085e-06,0.0,0.0,0.0
18,18,0.37499977927664024,560.7502263993956,5.057547241449356e-06,3.395136445760727e-06,1.6666017472743988e-06,-9.452924132347107e-08,2.8079375624656677e-07,-4.190951585769653e-09,-9.266659617424011e-08,3.213062882423401e-08,-2.8391368687152863e-06,-1.0849907994270325e-07,3.4924596548080444e-08,2.816319465637207e-06,1.1175870895385742e-07,-4.842877388000488e-08,1.6936101019382477e-06,-5.122274160385132e-09,2.621673047542572e-07,1.6936101019382477e-06,-5.122274160385132e-09,2.621673047542572e-07,-2.2817403078079224e-08,3.259629011154175e-09,-1.3504177331924438e-08,0.0,0.0,0.0,0.00022263964638113976,5.745794624090195e-06,4.618894308805466e-06,0.0,0.0,0.0
19,19,0.4687496533735736,560.7502272753045,5.105510354042053e-06,3.3779069781303406e-06,8.759088814258575e-07,4.7963112592697144e-08,-1.7229467630386353e-08,-6.51925802230835e-09,-2.0023435354232788e-07,1.30385160446167e-08,-2.5546178221702576e-06,-1.30385160446167e-08,-2.60770320892334e-08,2.4884939193725586e-06,2.60770320892334e-08,4.842877388000488e-08,9.485520422458649e-07,2.3515895009040833e-07,-5.2619725465774536e-08,9.485520422458649e-07,2.3515895009040833e-07,-5.2619725465774536e-08,-6.612390279769897e-08,1.30385160446167e-08,2.2351741790771484e-08,0.0,0.0,0.0,0.00022358819842338562,5.980953574180603e-06,4.566274583339691e-06,0.0,0.0,0.0
20,20,0.37499977927664024,560.7502297507599,4.798173904418945e-06,3.063119947910309e-06,2.475455403327942e-06,-3.073364496231079e-07,-3.1478703022003174e-07,-9.313225746154785e-09,-1.8440186977386475e-07,6.332993507385254e-08,-2.4726614356040955e-06,1.6763806343078613e-07,2.7939677238464355e-08,2.473592758178711e-06,-1.5273690223693848e-07,-1.1175870895385742e-08,2.483837306499481e-06,-1.3783574104309082e-07,-3.948807716369629e-07,2.483837306499481e-06,-1.3783574104309082e-07,-3.948807716369629e-07,9.313225746154785e-10,1.4901161193847656e-08,1.6763806343078613e-08,0.0,0.0,0.0,0.0002260720357298851,5.843117833137512e-06,4.171393811702728e-06,0.0,0.0,0.0
21,21,0.46874973195382685,560.7502321340144,4.494562745094299e-06,3.1962990760803223e-06,2.3832544684410095e-06,-3.03611159324646e-07,1.3317912817001343e-07,4.656612873077393e-10,-1.671724021434784e-07,7.59027898311615e-08,-2.6640482246875763e-06,2.6496127247810364e-07,-1.1874362826347351e-07,2.6579946279525757e-06,-2.514570951461792e-07,1.0244548320770264e-07,2.3888424038887024e-06,-1.4994293451309204e-07,7.35744833946228e-08,2.3888424038887024e-06,-1.4994293451309204e-07,7.35744833946228e-08,-6.05359673500061e-09,1.3504177331924438e-08,-1.6298145055770874e-08,0.0,0.0,0.0,0.0002284608781337738,5.69317489862442e-06,4.244968295097351e-06,0.0,0.0,0.0
22,22,0.37499977927664024,560.7502323002554,4.1215680539608e-06,2.848450094461441e-06,1.6624107956886292e-07,-3.7299469113349915e-07,-3.478489816188812e-07,4.656612873077393e-10,-1.9511207938194275e-07,8.149072527885437e-08,-2.8708018362522125e-06,2.48197466135025e-07,-3.3574178814888e-07,2.8423964977264404e-06,-2.7567148208618164e-07,3.390014171600342e-07,1.9418075680732727e-07,-1.5040859580039978e-07,-4.3259933590888977e-07,1.9418075680732727e-07,-1.5040859580039978e-07,-4.3259933590888977e-07,-2.8405338525772095e-08,-2.7474015951156616e-08,3.259629011154175e-09,0.0,0.0,0.0,0.00022865505889058113,5.54276630282402e-06,3.8123689591884613e-06,0.0,0.0,0.0
23,23,0.46874973195382685,560.7502347086556,4.095491021871567e-06,2.866145223379135e-06,2.4084001779556274e-06,-2.60770320892334e-08,1.7695128917694092e-08,1.909211277961731e-08,-2.2398307919502258e-07,8.335337042808533e-08,-2.6407651603221893e-06,7.683411240577698e-08,-1.9231811165809631e-07,2.6561319828033447e-06,-6.332993507385254e-08,2.2724270820617676e-07,2.3739412426948547e-06,1.8440186977386475e-07,-1.0058283805847168e-07,2.3739412426948547e-06,1.8440186977386475e-07,-1.0058283805847168e-07,1.5366822481155396e-08,1.3504177331924438e-08,3.4924596548080444e-08,0.0,0.0,0.0,0.00023102900013327599,5.727168172597885e-06,3.7117861211299896e-06,0.0,0.0,0.0
24,24,0.46874973195382685,560.7502328557894,4.124827682971954e-06,3.1795352697372437e-06,-1.8528662621974945e-06,2.9336661100387573e-08,3.133900463581085e-07,1.6763806343078613e-08,-1.30385160446167e-07,1.0058283805847168e-07,-2.0116567611694336e-06,7.450580596923828e-08,-6.239861249923706e-08,2.0526349544525146e-06,-9.313225746154785e-08,7.078051567077637e-08,-1.910608261823654e-06,1.7834827303886414e-07,2.0442530512809753e-07,-1.910608261823654e-06,1.7834827303886414e-07,2.0442530512809753e-07,4.0978193283081055e-08,-1.862645149230957e-08,8.381903171539307e-09,0.0,0.0,0.0,0.00022911839187145233,5.905516445636749e-06,3.916211426258087e-06,0.0,0.0,0.0
1 step time average_bound_ratio leaf_m leaf_px leaf_py delta_m delta_px delta_py boundary_m boundary_px boundary_py fine_transfer_m fine_transfer_px fine_transfer_py coarse_transfer_m coarse_transfer_px coarse_transfer_py raw_m raw_px raw_py collision_drift_m collision_drift_px collision_drift_py average_rounding_m average_rounding_px average_rounding_py closure_m closure_px closure_py cumulative_raw_m cumulative_raw_px cumulative_raw_py cumulative_closure_m cumulative_closure_px cumulative_closure_py
2 1 1 0.28124994289828603 560.7500186339021 -1.4901161193847656e-08 -3.3527612686157227e-07 1.4455989003181458e-05 -1.4901161193847656e-08 -3.3527612686157227e-07 1.1175870895385742e-08 0.0 0.0 -3.557652235031128e-07 0.0 0.0 5.308538675308228e-07 -1.862645149230957e-09 -1.7695128917694092e-07 1.4269724488258362e-05 -1.30385160446167e-08 -1.5832483768463135e-07 1.4269724488258362e-05 -1.30385160446167e-08 -1.5832483768463135e-07 1.7508864402770996e-07 -1.862645149230957e-09 -1.7695128917694092e-07 0.0 0.0 0.0 1.4269724488258362e-05 -1.30385160446167e-08 -1.5832483768463135e-07 0.0 0.0 0.0
3 2 2 0.37499987985940486 560.7500202683732 -5.038455128669739e-07 -6.919726729393005e-07 1.6344711184501648e-06 -4.889443516731262e-07 -3.5669654607772827e-07 2.9802322387695312e-08 0.0 0.0 -2.193264663219452e-06 -4.6566128730773926e-09 -1.685693860054016e-07 2.032145857810974e-06 -1.5087425708770752e-07 1.695007085800171e-07 1.7657876014709473e-06 -3.334134817123413e-07 -3.5762786865234375e-07 1.7657876014709473e-06 -3.334134817123413e-07 -3.5762786865234375e-07 -1.6111880540847778e-07 -1.555308699607849e-07 9.313225746154785e-10 0.0 0.0 0.0 1.603551208972931e-05 -3.46451997756958e-07 -5.159527063369751e-07 0.0 0.0 0.0
4 3 3 0.2812499146093721 560.750041406136 4.516914486885071e-08 -2.0442530512809753e-07 2.1137762814760208e-05 5.490146577358246e-07 4.87547367811203e-07 2.9802322387695312e-08 0.0 0.0 -3.6400742828845978e-06 4.698522388935089e-07 4.6798959374427795e-07 3.641471266746521e-06 -4.637986421585083e-07 -4.600733518600464e-07 2.110656350851059e-05 5.42961061000824e-07 4.796311259269714e-07 2.110656350851059e-05 5.42961061000824e-07 4.796311259269714e-07 1.3969838619232178e-09 6.05359673500061e-09 7.916241884231567e-09 0.0 0.0 0.0 3.71420755982399e-05 1.9650906324386597e-07 -3.632158041000366e-08 0.0 0.0 0.0
5 4 4 0.37499987985940486 560.7500747130252 1.3741664588451385e-06 9.83942300081253e-07 3.330688923597336e-05 1.3289973139762878e-06 1.1883676052093506e-06 2.9802322387695312e-08 0.0 0.0 -6.910879164934158e-06 3.1711533665657043e-07 3.9534643292427063e-07 6.800517439842224e-06 -2.6263296604156494e-07 -3.371387720108032e-07 3.33874486386776e-05 1.2745149433612823e-06 1.1301599442958832e-06 3.33874486386776e-05 1.2745149433612823e-06 1.1301599442958832e-06 -1.103617250919342e-07 5.448237061500549e-08 5.820766091346741e-08 0.0 0.0 0.0 7.05295242369175e-05 1.4710240066051483e-06 1.0938383638858795e-06 0.0 0.0 0.0
6 5 5 0.3749998295680158 560.7501105009578 2.3622997105121613e-06 2.369750291109085e-06 3.578793257474899e-05 9.881332516670227e-07 1.385807991027832e-06 2.9802322387695312e-08 0.0 0.0 -7.287599146366119e-06 1.0672956705093384e-06 4.637986421585083e-07 7.417052984237671e-06 -1.0095536708831787e-06 -4.023313522338867e-07 3.5628676414489746e-05 9.30391252040863e-07 1.3243407011032104e-06 3.5628676414489746e-05 9.30391252040863e-07 1.3243407011032104e-06 1.2945383787155151e-07 5.774199962615967e-08 6.146728992462158e-08 0.0 0.0 0.0 0.00010615820065140724 2.4014152586460114e-06 2.41817906498909e-06 0.0 0.0 0.0
7 6 6 0.46874973195382685 560.7501414637081 2.941582351922989e-06 2.4265609681606293e-06 3.09627503156662e-05 5.792826414108276e-07 5.681067705154419e-08 2.9802322387695312e-08 0.0 0.0 -5.653128027915955e-06 6.584450602531433e-07 -5.960464477539063e-08 5.5246055126190186e-06 -7.562339305877686e-07 -4.470348358154297e-08 3.106147050857544e-05 6.770715117454529e-07 1.6111880540847778e-07 3.106147050857544e-05 6.770715117454529e-07 1.6111880540847778e-07 -1.2852251529693604e-07 -9.778887033462524e-08 -1.043081283569336e-07 0.0 0.0 0.0 0.00013721967115998268 3.0784867703914642e-06 2.5792978703975677e-06 0.0 0.0 0.0
8 7 7 0.4687496533735736 560.7501708613709 3.914348781108856e-06 2.1560117602348328e-06 2.9397662729024887e-05 9.727664291858673e-07 -2.705492079257965e-07 7.869675755500793e-08 -9.546056389808655e-08 -6.100162863731384e-08 -4.661735147237778e-06 2.398155629634857e-07 -5.2619725465774536e-08 4.654750227928162e-06 -2.849847078323364e-07 3.5390257835388184e-08 2.9325950890779495e-05 1.1133961379528046e-06 -1.9231811165809631e-07 2.9325950890779495e-05 1.1133961379528046e-06 -1.9231811165809631e-07 -6.984919309616089e-09 -4.516914486885071e-08 -1.7229467630386353e-08 0.0 0.0 0.0 0.00016654562205076218 4.191882908344269e-06 2.3869797587394714e-06 0.0 0.0 0.0
9 8 8 0.4687496533735736 560.7501910906285 4.438683390617371e-06 1.5152618288993835e-06 2.022925764322281e-05 5.243346095085144e-07 -6.407499313354492e-07 1.3690441846847534e-07 -7.078051567077637e-08 -5.029141902923584e-08 -4.91878017783165e-06 4.409812390804291e-07 1.2619420886039734e-07 4.896894097328186e-06 -5.047768354415894e-07 -2.0302832126617432e-07 2.0114239305257797e-05 6.58910721540451e-07 -5.136243999004364e-07 2.0114239305257797e-05 6.58910721540451e-07 -5.136243999004364e-07 -2.1886080503463745e-08 -6.379559636116028e-08 -7.683411240577698e-08 0.0 0.0 0.0 0.00018665986135601997 4.85079362988472e-06 1.873355358839035e-06 0.0 0.0 0.0
10 9 9 0.37499998044222344 560.7502015968785 4.073604941368103e-06 6.379559636116028e-07 1.0506249964237213e-05 -3.650784492492676e-07 -8.773058652877808e-07 6.28642737865448e-08 -2.3050233721733093e-07 -2.6496127247810364e-07 -5.146954208612442e-06 3.5157427191734314e-07 -1.9790604710578918e-07 5.0961971282958984e-06 -3.427267074584961e-07 1.9371509552001953e-07 1.0494142770767212e-05 -1.434236764907837e-07 -6.081536412239075e-07 1.0494142770767212e-05 -1.434236764907837e-07 -6.081536412239075e-07 -5.075708031654358e-08 8.847564458847046e-09 -4.190951585769653e-09 0.0 0.0 0.0 0.00019715400412678719 4.707369953393936e-06 1.2652017176151276e-06 0.0 0.0 0.0
11 10 10 0.4687496533735736 560.7502107927576 3.5660341382026672e-06 6.789341568946838e-07 9.195879101753235e-06 -5.075708031654358e-07 4.0978193283081055e-08 -1.1129304766654968e-07 -9.452924132347107e-08 -2.975575625896454e-07 -5.0514936447143555e-06 1.5739351511001587e-07 -6.798654794692993e-08 4.997476935386658e-06 -1.471489667892456e-07 3.91155481338501e-08 9.361188858747482e-06 -4.23286110162735e-07 3.674067556858063e-07 9.361188858747482e-06 -4.23286110162735e-07 3.674067556858063e-07 -5.4016709327697754e-08 1.0244548320770264e-08 -2.8870999813079834e-08 0.0 0.0 0.0 0.00020651519298553467 4.284083843231201e-06 1.6326084733009338e-06 0.0 0.0 0.0
12 11 11 0.37499977927664024 560.7502121757716 3.314577043056488e-06 8.987262845039368e-07 1.3830140233039856e-06 -2.514570951461792e-07 2.1979212760925293e-07 -6.938353180885315e-08 -4.330649971961975e-08 -2.1560117602348328e-07 -5.153939127922058e-06 -8.381903171539307e-08 -2.60770320892334e-07 5.122274160385132e-06 6.705522537231445e-08 2.4586915969848633e-07 1.484062522649765e-06 -1.9138678908348083e-07 4.5029446482658386e-07 1.484062522649765e-06 -1.9138678908348083e-07 4.5029446482658386e-07 -3.166496753692627e-08 -1.6763806343078613e-08 -1.4901161193847656e-08 0.0 0.0 0.0 0.00020799925550818443 4.09269705414772e-06 2.0829029381275177e-06 0.0 0.0 0.0
13 12 12 0.4687496533735736 560.750213930849 3.0328519642353058e-06 1.119915395975113e-06 1.7550773918628693e-06 -2.8172507882118225e-07 2.2118911147117615e-07 -3.632158041000366e-08 1.6763806343078613e-08 -7.171183824539185e-08 -5.262903869152069e-06 -1.1175870895385742e-08 -2.4866312742233276e-07 5.2247196435928345e-06 3.5390257835388184e-08 2.5890767574310303e-07 1.8295831978321075e-06 -3.227032721042633e-07 2.8265640139579773e-07 1.8295831978321075e-06 -3.227032721042633e-07 2.8265640139579773e-07 -3.818422555923462e-08 2.421438694000244e-08 1.0244548320770264e-08 0.0 0.0 0.0 0.00020982883870601654 3.769993782043457e-06 2.3655593395233154e-06 0.0 0.0 0.0
14 13 13 0.37499977927664024 560.7502138046548 3.2046809792518616e-06 1.4081597328186035e-06 -1.2619420886039734e-07 1.7182901501655579e-07 2.882443368434906e-07 -2.3283064365386963e-08 3.073364496231079e-08 -6.51925802230835e-09 -4.963483661413193e-06 -2.016313374042511e-07 -3.8137659430503845e-07 4.928559064865112e-06 2.0489096641540527e-07 3.948807716369629e-07 -6.798654794692993e-08 1.3783574104309082e-07 2.812594175338745e-07 -6.798654794692993e-08 1.3783574104309082e-07 2.812594175338745e-07 -3.4924596548080444e-08 3.259629011154175e-09 1.3504177331924438e-08 0.0 0.0 0.0 0.0002097608521580696 3.907829523086548e-06 2.64681875705719e-06 0.0 0.0 0.0
15 14 14 0.46874959050938997 560.7502162489109 3.7285499274730682e-06 1.762527972459793e-06 2.4442560970783234e-06 5.238689482212067e-07 3.543682396411896e-07 -3.026798367500305e-08 -1.3969838619232178e-09 -3.3993273973464966e-08 -4.4191256165504456e-06 -1.9650906324386597e-07 -5.21540641784668e-07 4.349276423454285e-06 1.993030309677124e-07 4.973262548446655e-07 2.5443732738494873e-06 5.224719643592834e-07 4.12575900554657e-07 2.5443732738494873e-06 5.224719643592834e-07 4.12575900554657e-07 -6.984919309616089e-08 2.7939677238464355e-09 -2.421438694000244e-08 0.0 0.0 0.0 0.0002123052254319191 4.430301487445831e-06 3.059394657611847e-06 0.0 0.0 0.0
16 15 15 0.4687496533735736 560.7502168049105 4.124362021684647e-06 2.1760351955890656e-06 5.559995770454407e-07 3.9581209421157837e-07 4.1350722312927246e-07 -1.30385160446167e-08 1.4901161193847656e-08 -3.073364496231079e-08 -3.9315782487392426e-06 -2.3702159523963928e-07 -5.564652383327484e-07 3.905966877937317e-06 2.1047890186309814e-07 5.345791578292847e-07 5.94649463891983e-07 4.0745362639427185e-07 4.66126948595047e-07 5.94649463891983e-07 4.0745362639427185e-07 4.66126948595047e-07 -2.561137080192566e-08 -2.6542693376541138e-08 -2.1886080503463745e-08 0.0 0.0 0.0 0.00021289987489581108 4.837755113840103e-06 3.525521606206894e-06 0.0 0.0 0.0
17 16 16 0.46874959050938997 560.7502207290381 4.730187356472015e-06 2.6812776923179626e-06 3.924127668142319e-06 6.058253347873688e-07 5.052424967288971e-07 -1.816079020500183e-08 3.119930624961853e-08 4.0512531995773315e-08 -3.7918798625469208e-06 -2.4354085326194763e-07 -2.1373853087425232e-07 3.7383288145065308e-06 2.738088369369507e-07 2.1047890186309814e-07 3.9958395063877106e-06 5.443580448627472e-07 4.6798959374427795e-07 3.9958395063877106e-06 5.443580448627472e-07 4.6798959374427795e-07 -5.3551048040390015e-08 3.026798367500305e-08 -3.259629011154175e-09 0.0 0.0 0.0 0.0002168957144021988 5.38211315870285e-06 3.993511199951172e-06 0.0 0.0 0.0
18 17 17 0.4687496533735736 560.7502247327939 5.152076482772827e-06 3.11434268951416e-06 4.003755748271942e-06 4.2188912630081177e-07 4.330649971961975e-07 -2.2351741790771484e-08 4.377216100692749e-08 8.847564458847046e-08 -3.375113010406494e-06 -3.2782554626464844e-07 -1.3597309589385986e-07 3.3508986234664917e-06 3.371387720108032e-07 1.1734664440155029e-07 4.050321877002716e-06 3.688037395477295e-07 3.632158041000366e-07 4.050321877002716e-06 3.688037395477295e-07 3.632158041000366e-07 -2.421438694000244e-08 9.313225746154785e-09 -1.862645149230957e-08 0.0 0.0 0.0 0.0002209460362792015 5.75091689825058e-06 4.3567270040512085e-06 0.0 0.0 0.0
19 18 18 0.37499977927664024 560.7502263993956 5.057547241449356e-06 3.395136445760727e-06 1.6666017472743988e-06 -9.452924132347107e-08 2.8079375624656677e-07 -4.190951585769653e-09 -9.266659617424011e-08 3.213062882423401e-08 -2.8391368687152863e-06 -1.0849907994270325e-07 3.4924596548080444e-08 2.816319465637207e-06 1.1175870895385742e-07 -4.842877388000488e-08 1.6936101019382477e-06 -5.122274160385132e-09 2.621673047542572e-07 1.6936101019382477e-06 -5.122274160385132e-09 2.621673047542572e-07 -2.2817403078079224e-08 3.259629011154175e-09 -1.3504177331924438e-08 0.0 0.0 0.0 0.00022263964638113976 5.745794624090195e-06 4.618894308805466e-06 0.0 0.0 0.0
20 19 19 0.4687496533735736 560.7502272753045 5.105510354042053e-06 3.3779069781303406e-06 8.759088814258575e-07 4.7963112592697144e-08 -1.7229467630386353e-08 -6.51925802230835e-09 -2.0023435354232788e-07 1.30385160446167e-08 -2.5546178221702576e-06 -1.30385160446167e-08 -2.60770320892334e-08 2.4884939193725586e-06 2.60770320892334e-08 4.842877388000488e-08 9.485520422458649e-07 2.3515895009040833e-07 -5.2619725465774536e-08 9.485520422458649e-07 2.3515895009040833e-07 -5.2619725465774536e-08 -6.612390279769897e-08 1.30385160446167e-08 2.2351741790771484e-08 0.0 0.0 0.0 0.00022358819842338562 5.980953574180603e-06 4.566274583339691e-06 0.0 0.0 0.0
21 20 20 0.37499977927664024 560.7502297507599 4.798173904418945e-06 3.063119947910309e-06 2.475455403327942e-06 -3.073364496231079e-07 -3.1478703022003174e-07 -9.313225746154785e-09 -1.8440186977386475e-07 6.332993507385254e-08 -2.4726614356040955e-06 1.6763806343078613e-07 2.7939677238464355e-08 2.473592758178711e-06 -1.5273690223693848e-07 -1.1175870895385742e-08 2.483837306499481e-06 -1.3783574104309082e-07 -3.948807716369629e-07 2.483837306499481e-06 -1.3783574104309082e-07 -3.948807716369629e-07 9.313225746154785e-10 1.4901161193847656e-08 1.6763806343078613e-08 0.0 0.0 0.0 0.0002260720357298851 5.843117833137512e-06 4.171393811702728e-06 0.0 0.0 0.0
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research/fixed_patch_jet_20261013/before.json
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research/fixed_patch_jet_20261013/fixed_patch_jet.py
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"""Low-Re startup flow in an x-periodic confined domain; nozzle remains coarse.
From this checkout, after checking the selected physical GPU is idle:
PYTHONPATH="$PWD/src" CUDA_VISIBLE_DEVICES=0 python examples/fixed_patch_jet.py --output /tmp/patch-jet
The fixed-amplitude start includes acoustic transients, not a developed jet.
Only native leaf cells are plotted. No interpolation, restart or flow oracle.
"""
import argparse
from dataclasses import replace
import json
import os
from pathlib import Path
import tempfile
import time
import numpy as np
import CelerisLab
from CelerisLab.config import load_lbm_config
from CelerisLab.cuda.context import CudaContext
from CelerisLab.lbm.equilibrium import compute_feq_d2q9, compute_macro_from_ddf
from CelerisLab.lbm.refinement import ACTIVE
from CelerisLab.lbm.refinement_stepper import FixedPatchStepper
BOUNDS, AMPLITUDE, WIDTH = (16, 4, 32, 28), .02, 12
TIMES, MAX_MACH, MAX_SECONDS = (0, 300, 1200), .1, 120
def measure(arrays, masks, centers, initial_mass):
fields = []
for array, mask in zip(arrays, masks):
if not np.isfinite(array[:, mask]).all():
raise RuntimeError("Nonfinite leaf populations")
field = np.full((*mask.shape, 3), np.nan)
field[mask] = [compute_macro_from_ddf(f) for f in array[:, mask].T]
fields.append(field)
leaves = np.concatenate([f[m] for f, m in zip(fields, masks)])
speed = np.hypot(leaves[:, 1], leaves[:, 2])
mass = sum(area*a[:, m].sum(dtype=np.float64)
for area, a, m in zip((1, .25), arrays, masks))
record = dict(finite=bool(np.isfinite(leaves).all()), rho_min=float(leaves[:, 0].min()),
rho_max=float(leaves[:, 0].max()), speed_max=float(speed.max()),
measured_Ma=float(speed.max()*np.sqrt(3)),
density_deviation=float(np.abs(leaves[:, 0]-1).max()),
discrete_leaf_mass=float(mass), mass_change=float(mass-initial_mass))
if not record['finite'] or record['rho_min'] <= 0 or record['measured_Ma'] > MAX_MACH:
raise RuntimeError(f"Finite/positive-density/Ma<={MAX_MACH} guard failed: {record}")
walls = fields[0][[1, -2]]
record['macro_sum_rho_v_bottom_top'] = (walls[:, :, 0]*walls[:, :, 2]).sum(axis=1).tolist()
record['centerline'] = []
for level, y in ((0, 3.5), (1, 8.25), (1, 16.25), (0, 32.5)):
xs, ys = centers[level]
columns = np.argsort(np.abs(xs-24))[:2]
row = int(np.flatnonzero(ys == y)[0])
values = fields[level][row, columns].mean(axis=0)
record['centerline'].append(dict(level=level, x=24, y=y,
sampled_x=sorted(xs[columns].tolist()), rule='arithmetic mean of two neighboring native columns',
rho_ux_uy=values.tolist()))
return fields, record
def draw(out, snapshots, masks, centers):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
def decorate(ax):
ax.set(xlim=(0, 48), ylim=(1.5, 46.5), aspect='equal', xlabel='X', ylabel='Y')
ax.plot([16, 32, 32, 16, 16], [4, 4, 28, 28, 4], color='tab:red', lw=1)
ax.axhline(1.5, color='black', lw=1)
ax.axhline(46.5, color='black', lw=1, ls='--')
ax.plot([18, 30], [1.5, 1.5], color='tab:orange', lw=4, clip_on=False)
fig, ax = plt.subplots(figsize=(6, 6), constrained_layout=True)
for mask, (xs, ys), dx in zip(masks, centers, (1, .5)):
yy, xx = np.meshgrid(ys, xs, indexing='ij')
cells = np.stack((xx[mask], yy[mask]), axis=-1)
vertices = cells[:, None, :] + dx*np.array([[-.5, -.5], [.5, -.5], [.5, .5], [-.5, .5]])
ax.add_collection(PolyCollection(vertices, facecolors='none', edgecolors='0.55', linewidths=.3))
decorate(ax)
ax.set_title('Native leaf grid: coarse dx=1, fine dx=0.5\nRed: internal patch; orange: coarse slot; dashed: reservoir')
fig.savefig(out/'layout.png', dpi=150)
plt.close(fig)
vmax = max(1., *(np.nanmax(np.hypot(f[..., 1], f[..., 2]))/AMPLITUDE
for fields in snapshots for f in fields))
fig, axes = plt.subplots(1, 3, figsize=(12, 4.8), constrained_layout=True)
for ax, step, fields in zip(axes, TIMES, snapshots):
for field, mask, (xs, ys), dx in zip(fields, masks, centers, (1, .5)):
speed = np.ma.masked_where(~mask, np.hypot(field[..., 1], field[..., 2])/AMPLITUDE)
image = ax.pcolormesh(np.r_[xs-dx/2, xs[-1]+dx/2],
np.r_[ys-dx/2, ys[-1]+dx/2], speed,
shading='flat', cmap='viridis', vmin=0, vmax=vmax, rasterized=True)
stride = int(4/dx)
yy, xx = np.meshgrid(ys, xs, indexing='ij')
selection = np.zeros_like(mask)
selection[::stride, ::stride] = mask[::stride, ::stride]
ax.quiver(xx[selection], yy[selection], field[..., 1][selection], field[..., 2][selection],
angles='xy', scale_units='xy', scale=AMPLITUDE/3, width=.005, color='white')
decorate(ax)
ax.set_title(f'coarse t={step}, fine steps={2*step}')
fig.colorbar(image, ax=axes, label='speed / imposed amplitude')
fig.suptitle('Low-Re startup flow in an x-periodic confined domain\nFixed-amplitude start includes acoustic transients; not steady/developed')
fig.savefig(out/'velocity.png', dpi=150)
plt.close(fig)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--output', type=Path, required=True)
out = parser.parse_args().output.resolve()
root = Path(__file__).resolve().parents[1]
if Path(CelerisLab.__file__).resolve() != root/'src/CelerisLab/__init__.py':
raise RuntimeError(f'Set PYTHONPATH to {root / "src"}')
out.mkdir(parents=True, exist_ok=False)
cfg = replace(load_lbm_config(str(root/'src/CelerisLab/configs/config_lbm.json')),
nx=48, ny=48, viscosity=.1, velocity=0., rho=1., wall_slot_width=WIDTH,
wall_normal_velocity=AMPLITUDE, y_wall_bc='on_site_velocity', top_wall_bc='reservoir',
lattice_model='D2Q9', collision='SRT', streaming='double_buffer',
store_precision='FP32', ddf_shifting=False, les_enabled=False)
report = dict(config=dict(nx=48, ny=48, bounds=BOUNDS, W=WIDTH, A=AMPLITUDE, nu=.1,
rho=1, tau_coarse=.8, tau_fine=1.1, coarse_boundary='slot_reservoir'),
guards=dict(check_every=50, positive_density=True, finite=True,
max_Mach=MAX_MACH, max_seconds=MAX_SECONDS),
notes='Node mass uses areas 1 and 1/4, not an exact physical volume integral. '
'Boundary rho*v is a macro measurement, not a closed population budget.',
samples=[], status='running', last_synchronized_coarse_step=0)
start, run = time.monotonic(), None
try:
with CudaContext(0) as context, FixedPatchStepper(cfg, BOUNDS, context=context,
coarse_boundary='slot_reservoir') as run:
p = run.patch
masks = [np.ones(p.coarse_shape, bool), p.fine_kind == ACTIVE]
masks[0][[0, -1]] = False
masks[0][4:28, 16:32] = False
centers = [(origin[0]+dx*(np.arange(shape[1])+.5),
origin[1]+dx*(np.arange(shape[0])+.5))
for shape, origin, dx in ((p.coarse_shape, (0, 0), 1),
(p.fine_shape, p.fine_origin, .5))]
initial = [np.broadcast_to(compute_feq_d2q9(1., 0., 0.)[:, None, None], (9, *m.shape)).copy()
for m in masks]
for x in range(cfg.nx):
v = AMPLITUDE*max(0., 1-((2*x-(cfg.nx-1))/WIDTH)**2)**2
initial[0][:, 1, x] = compute_feq_d2q9(1., 0., v)
run.initialize(*initial)
mass0 = sum(area*a[:, m].sum(dtype=np.float64)
for area, a, m in zip((1, .25), initial, masks))
snapshots = []
for step in range(0, TIMES[-1]+1, 50):
if time.monotonic()-start > MAX_SECONDS:
raise TimeoutError('120 second demonstration limit reached')
if step:
run.step(50)
report['last_synchronized_coarse_step'] = run.step_count
fields, sample = measure(run.download(), masks, centers, mass0)
print(f'step={step} rho=[{sample["rho_min"]:.7g},{sample["rho_max"]:.7g}] '
f'Ma={sample["measured_Ma"]:.7g}', flush=True)
if step in TIMES:
sample.update(coarse_steps=step, fine_steps=2*step)
report['samples'].append(sample)
snapshots.append(fields)
with tempfile.TemporaryDirectory(prefix='.plot-', dir=out) as cache:
os.environ['MPLCONFIGDIR'] = cache
draw(out, snapshots, masks, centers)
report['status'] = 'complete'
except BaseException as exc:
report.update(status='failed', error=f'{type(exc).__name__}: {exc}',
last_synchronized_coarse_step=run.step_count if run is not None else 0)
raise
finally:
report['elapsed_seconds'] = time.monotonic()-start
(out/'summary.json').write_text(json.dumps(report, indent=2)+'\n')
print(f'{report["status"]}: last synchronized coarse step '
f'{report["last_synchronized_coarse_step"]}; output={out}', flush=True)
if __name__ == '__main__':
main()
research/fixed_patch_jet_20261013/incremental.patch
+182
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--- /dev/null
+++ b/examples/fixed_patch_jet.py
@@ -0,0 +1,179 @@
+"""Low-Re startup flow in an x-periodic confined domain; nozzle remains coarse.
+
+From this checkout, after checking the selected physical GPU is idle:
+ PYTHONPATH="$PWD/src" CUDA_VISIBLE_DEVICES=0 python examples/fixed_patch_jet.py --output /tmp/patch-jet
+
+The fixed-amplitude start includes acoustic transients, not a developed jet.
+Only native leaf cells are plotted. No interpolation, restart or flow oracle.
+"""
+import argparse
+from dataclasses import replace
+import json
+import os
+from pathlib import Path
+import tempfile
+import time
+
+import numpy as np
+
+import CelerisLab
+from CelerisLab.config import load_lbm_config
+from CelerisLab.cuda.context import CudaContext
+from CelerisLab.lbm.equilibrium import compute_feq_d2q9, compute_macro_from_ddf
+from CelerisLab.lbm.refinement import ACTIVE
+from CelerisLab.lbm.refinement_stepper import FixedPatchStepper
+
+BOUNDS, AMPLITUDE, WIDTH = (16, 4, 32, 28), .02, 12
+TIMES, MAX_MACH, MAX_SECONDS = (0, 300, 1200), .1, 120
+
+
+def measure(arrays, masks, centers, initial_mass):
+ fields = []
+ for array, mask in zip(arrays, masks):
+ if not np.isfinite(array[:, mask]).all():
+ raise RuntimeError("Nonfinite leaf populations")
+ field = np.full((*mask.shape, 3), np.nan)
+ field[mask] = [compute_macro_from_ddf(f) for f in array[:, mask].T]
+ fields.append(field)
+ leaves = np.concatenate([f[m] for f, m in zip(fields, masks)])
+ speed = np.hypot(leaves[:, 1], leaves[:, 2])
+ mass = sum(area*a[:, m].sum(dtype=np.float64)
+ for area, a, m in zip((1, .25), arrays, masks))
+ record = dict(finite=bool(np.isfinite(leaves).all()), rho_min=float(leaves[:, 0].min()),
+ rho_max=float(leaves[:, 0].max()), speed_max=float(speed.max()),
+ measured_Ma=float(speed.max()*np.sqrt(3)),
+ density_deviation=float(np.abs(leaves[:, 0]-1).max()),
+ discrete_leaf_mass=float(mass), mass_change=float(mass-initial_mass))
+ if not record['finite'] or record['rho_min'] <= 0 or record['measured_Ma'] > MAX_MACH:
+ raise RuntimeError(f"Finite/positive-density/Ma<={MAX_MACH} guard failed: {record}")
+ walls = fields[0][[1, -2]]
+ record['macro_sum_rho_v_bottom_top'] = (walls[:, :, 0]*walls[:, :, 2]).sum(axis=1).tolist()
+ record['centerline'] = []
+ for level, y in ((0, 3.5), (1, 8.25), (1, 16.25), (0, 32.5)):
+ xs, ys = centers[level]
+ columns = np.argsort(np.abs(xs-24))[:2]
+ row = int(np.flatnonzero(ys == y)[0])
+ values = fields[level][row, columns].mean(axis=0)
+ record['centerline'].append(dict(level=level, x=24, y=y,
+ sampled_x=sorted(xs[columns].tolist()), rule='arithmetic mean of two neighboring native columns',
+ rho_ux_uy=values.tolist()))
+ return fields, record
+
+
+def draw(out, snapshots, masks, centers):
+ import matplotlib
+ matplotlib.use('Agg')
+ import matplotlib.pyplot as plt
+ from matplotlib.collections import PolyCollection
+
+ def decorate(ax):
+ ax.set(xlim=(0, 48), ylim=(1.5, 46.5), aspect='equal', xlabel='X', ylabel='Y')
+ ax.plot([16, 32, 32, 16, 16], [4, 4, 28, 28, 4], color='tab:red', lw=1)
+ ax.axhline(1.5, color='black', lw=1)
+ ax.axhline(46.5, color='black', lw=1, ls='--')
+ ax.plot([18, 30], [1.5, 1.5], color='tab:orange', lw=4, clip_on=False)
+
+ fig, ax = plt.subplots(figsize=(6, 6), constrained_layout=True)
+ for mask, (xs, ys), dx in zip(masks, centers, (1, .5)):
+ yy, xx = np.meshgrid(ys, xs, indexing='ij')
+ cells = np.stack((xx[mask], yy[mask]), axis=-1)
+ vertices = cells[:, None, :] + dx*np.array([[-.5, -.5], [.5, -.5], [.5, .5], [-.5, .5]])
+ ax.add_collection(PolyCollection(vertices, facecolors='none', edgecolors='0.55', linewidths=.3))
+ decorate(ax)
+ ax.set_title('Native leaf grid: coarse dx=1, fine dx=0.5\nRed: internal patch; orange: coarse slot; dashed: reservoir')
+ fig.savefig(out/'layout.png', dpi=150)
+ plt.close(fig)
+ vmax = max(1., *(np.nanmax(np.hypot(f[..., 1], f[..., 2]))/AMPLITUDE
+ for fields in snapshots for f in fields))
+ fig, axes = plt.subplots(1, 3, figsize=(12, 4.8), constrained_layout=True)
+ for ax, step, fields in zip(axes, TIMES, snapshots):
+ for field, mask, (xs, ys), dx in zip(fields, masks, centers, (1, .5)):
+ speed = np.ma.masked_where(~mask, np.hypot(field[..., 1], field[..., 2])/AMPLITUDE)
+ image = ax.pcolormesh(np.r_[xs-dx/2, xs[-1]+dx/2],
+ np.r_[ys-dx/2, ys[-1]+dx/2], speed,
+ shading='flat', cmap='viridis', vmin=0, vmax=vmax, rasterized=True)
+ stride = int(4/dx)
+ yy, xx = np.meshgrid(ys, xs, indexing='ij')
+ selection = np.zeros_like(mask)
+ selection[::stride, ::stride] = mask[::stride, ::stride]
+ ax.quiver(xx[selection], yy[selection], field[..., 1][selection], field[..., 2][selection],
+ angles='xy', scale_units='xy', scale=AMPLITUDE/3, width=.005, color='white')
+ decorate(ax)
+ ax.set_title(f'coarse t={step}, fine steps={2*step}')
+ fig.colorbar(image, ax=axes, label='speed / imposed amplitude')
+ fig.suptitle('Low-Re startup flow in an x-periodic confined domain\nFixed-amplitude start includes acoustic transients; not steady/developed')
+ fig.savefig(out/'velocity.png', dpi=150)
+ plt.close(fig)
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--output', type=Path, required=True)
+ out = parser.parse_args().output.resolve()
+ root = Path(__file__).resolve().parents[1]
+ if Path(CelerisLab.__file__).resolve() != root/'src/CelerisLab/__init__.py':
+ raise RuntimeError(f'Set PYTHONPATH to {root / "src"}')
+ out.mkdir(parents=True, exist_ok=False)
+ cfg = replace(load_lbm_config(str(root/'src/CelerisLab/configs/config_lbm.json')),
+ nx=48, ny=48, viscosity=.1, velocity=0., rho=1., wall_slot_width=WIDTH,
+ wall_normal_velocity=AMPLITUDE, y_wall_bc='on_site_velocity', top_wall_bc='reservoir',
+ lattice_model='D2Q9', collision='SRT', streaming='double_buffer',
+ store_precision='FP32', ddf_shifting=False, les_enabled=False)
+ report = dict(config=dict(nx=48, ny=48, bounds=BOUNDS, W=WIDTH, A=AMPLITUDE, nu=.1,
+ rho=1, tau_coarse=.8, tau_fine=1.1, coarse_boundary='slot_reservoir'),
+ guards=dict(check_every=50, positive_density=True, finite=True,
+ max_Mach=MAX_MACH, max_seconds=MAX_SECONDS),
+ notes='Node mass uses areas 1 and 1/4, not an exact physical volume integral. '
+ 'Boundary rho*v is a macro measurement, not a closed population budget.',
+ samples=[], status='running', last_synchronized_coarse_step=0)
+ start, run = time.monotonic(), None
+ try:
+ with CudaContext(0) as context, FixedPatchStepper(cfg, BOUNDS, context=context,
+ coarse_boundary='slot_reservoir') as run:
+ p = run.patch
+ masks = [np.ones(p.coarse_shape, bool), p.fine_kind == ACTIVE]
+ masks[0][[0, -1]] = False
+ masks[0][4:28, 16:32] = False
+ centers = [(origin[0]+dx*(np.arange(shape[1])+.5),
+ origin[1]+dx*(np.arange(shape[0])+.5))
+ for shape, origin, dx in ((p.coarse_shape, (0, 0), 1),
+ (p.fine_shape, p.fine_origin, .5))]
+ initial = [np.broadcast_to(compute_feq_d2q9(1., 0., 0.)[:, None, None], (9, *m.shape)).copy()
+ for m in masks]
+ for x in range(cfg.nx):
+ v = AMPLITUDE*max(0., 1-((2*x-(cfg.nx-1))/WIDTH)**2)**2
+ initial[0][:, 1, x] = compute_feq_d2q9(1., 0., v)
+ run.initialize(*initial)
+ mass0 = sum(area*a[:, m].sum(dtype=np.float64)
+ for area, a, m in zip((1, .25), initial, masks))
+ snapshots = []
+ for step in range(0, TIMES[-1]+1, 50):
+ if time.monotonic()-start > MAX_SECONDS:
+ raise TimeoutError('120 second demonstration limit reached')
+ if step:
+ run.step(50)
+ report['last_synchronized_coarse_step'] = run.step_count
+ fields, sample = measure(run.download(), masks, centers, mass0)
+ print(f'step={step} rho=[{sample["rho_min"]:.7g},{sample["rho_max"]:.7g}] '
+ f'Ma={sample["measured_Ma"]:.7g}', flush=True)
+ if step in TIMES:
+ sample.update(coarse_steps=step, fine_steps=2*step)
+ report['samples'].append(sample)
+ snapshots.append(fields)
+ with tempfile.TemporaryDirectory(prefix='.plot-', dir=out) as cache:
+ os.environ['MPLCONFIGDIR'] = cache
+ draw(out, snapshots, masks, centers)
+ report['status'] = 'complete'
+ except BaseException as exc:
+ report.update(status='failed', error=f'{type(exc).__name__}: {exc}',
+ last_synchronized_coarse_step=run.step_count if run is not None else 0)
+ raise
+ finally:
+ report['elapsed_seconds'] = time.monotonic()-start
+ (out/'summary.json').write_text(json.dumps(report, indent=2)+'\n')
+ print(f'{report["status"]}: last synchronized coarse step '
+ f'{report["last_synchronized_coarse_step"]}; output={out}', flush=True)
+
+
+if __name__ == '__main__':
+ main()
research/fixed_patch_jet_20261013/manifest.json
+53
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@@ -0,0 +1,53 @@
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"all_baseline_files_unchanged": true,
"new_source": "examples/fixed_patch_jet.py",
"added_lines": 179,
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"guard_checks": 25,
"max_Mach_from_printed_checks": 0.03416159,
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research/fixed_patch_jet_20261013/notes.txt
+7
View File
@@ -0,0 +1,7 @@
Added only examples/fixed_patch_jet.py (179 lines). Existing backend, BC helper, tests, prior sources/evidence unchanged against this round baseline. No dependencies/environment/system edits, DynamisLab access, commit or push.
Public API only: configuration loader + replace, equilibrium/macroscopic helpers, CudaContext, FixedPatchStepper(slot_reservoir). Native coarse/fine leaves, physical centers, on-site clipping Y=[1.5,46.5], no interpolation or oracle. Layout and three synchronized velocity panels inspected after run. Centerline values average two explicitly recorded neighboring native columns.
One real production compile/run, no retry or parameter/tolerance changes: 1200 coarse /2400 fine steps, 25 successful checks including initial state, exit0. Predeclared checks every50 steps: all leaf populations/macros finite, density positive, measured Ma<=0.1. External120s resource watchdog. stdout.txt is directly redirected merged stdout/stderr, unmodified.
Runtime resource results in results.json: elapsed7.101054s, peak sampled process-group RSS280051712 bytes, minimum available RAM63756603392 bytes, minimum GPU0 free15826MiB; peak combined task temporary/current output/prior refinement evidence1929396 bytes, below2GB. Monitoring is sampled, not an OS hard memory/disk limit; no256MiB RSS cap. Private compilation scratch and Matplotlib cache cleaned; temporary runner removed.
Snapshots0/300/1200 are low-Re startup flow in an x-periodic confined domain. Fixed-amplitude startup includes acoustic transients; no steady/developed jet, vortex/head detector, uniform-coarse comparison or accuracy/conservation certification.
Final rho range[0.99340069294,1.00000023842], measured Ma0.03416155101. Node mass2208.00001640618 ->2202.30112983007 (delta -5.69888657611); final macro boundary sums bottom0.12769020163, top0.03066523932. These node-weighted quantities are not an exact physical volume integral or a demonstrated population-budget closure. No mass-conservation assertion.
Read-only delegate reviewed geometry, wall clipping, probes and safety. One reduction-order issue was corrected before the only run: initial/current node mass both sum populations in FP64. No post-run source changes.
research/fixed_patch_jet_20261013/results.json
+33
View File
@@ -0,0 +1,33 @@
{
"command": [
"/usr/bin/prlimit",
"--core=0",
"--",
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-B",
"/home/frank14f/CelerisLab/examples/fixed_patch_jet.py",
"--output",
"/home/frank14f/CelerisLab/research/fixed_patch_jet_20261013/output"
],
"cwd": "/home/frank14f/CelerisLab",
"environment": {
"PYTHONPATH": "/home/frank14f/CelerisLab/src",
"CUDA_VISIBLE_DEVICES": "0",
"CUDA_CACHE_DISABLE": "1",
"OPENBLAS_NUM_THREADS": "1",
"PYTHONDONTWRITEBYTECODE": "1",
"PYTHONNOUSERSITE": "1",
"TMPDIR": "/tmp/celeris-jet-run-zdq8n25a"
},
"exit_code": 0,
"elapsed_seconds": 7.101054363010917,
"peak_sampled_group_rss_bytes": 280051712,
"min_ram_available_bytes": 63756603392,
"min_gpu_free_mib": 15826,
"ram_reserve_bytes": 6409714073,
"vram_reserve_mib": 1614,
"peak_combined_temporary_and_refinement_evidence_bytes": 1929396,
"temporary_budget_bytes": 2000000000,
"termination_reason": null,
"monitor": "Own process group RSS recorded, not capped; live RAM/VRAM and temp sampled, not OS hard limits"
}
research/fixed_patch_jet_20261013/stdout.txt
+26
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@@ -0,0 +1,26 @@
step=0 rho=[0.9999999,1] Ma=0.03416157
step=50 rho=[0.9999995,1.009438] Ma=0.03416151
step=100 rho=[0.9984777,1.010592] Ma=0.03416153
step=150 rho=[0.9973579,1.003838] Ma=0.03416153
step=200 rho=[0.9933594,1.000131] Ma=0.03416159
step=250 rho=[0.9925539,1] Ma=0.03416156
step=300 rho=[0.9937773,1.000459] Ma=0.03416155
step=350 rho=[0.9999761,1.009384] Ma=0.03416157
step=400 rho=[0.9999998,1.007331] Ma=0.03416151
step=450 rho=[0.9997285,1.006167] Ma=0.03416155
step=500 rho=[0.9933134,1.000157] Ma=0.03416156
step=550 rho=[0.9927858,1] Ma=0.03416158
step=600 rho=[0.9945809,1.000122] Ma=0.03416153
step=650 rho=[0.9999998,1.006541] Ma=0.0341615
step=700 rho=[0.9999998,1.007461] Ma=0.03416157
step=750 rho=[0.9999998,1.007199] Ma=0.03416156
step=800 rho=[0.9945453,1.000039] Ma=0.03416156
step=850 rho=[0.9931565,1] Ma=0.03416155
step=900 rho=[0.9935648,1] Ma=0.03416153
step=950 rho=[0.9997682,1.005144] Ma=0.03416155
step=1000 rho=[0.9999998,1.007829] Ma=0.03416158
step=1050 rho=[0.9999998,1.007131] Ma=0.03416152
step=1100 rho=[0.9964147,1.001809] Ma=0.03416152
step=1150 rho=[0.9931713,1] Ma=0.03416154
step=1200 rho=[0.9934007,1] Ma=0.03416155
complete: last synchronized coarse step 1200; output=/home/frank14f/CelerisLab/research/fixed_patch_jet_20261013/output
research/fixed_patch_jet_closure_20261013/baseline.json
+559
View File
@@ -0,0 +1,559 @@
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research/fixed_patch_jet_closure_20261013/cpu_results.json
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{
"operation": "CPU-only geometry redraw, no CUDA context or solver",
"input_summary": "/home/frank14f/CelerisLab/research/fixed_patch_jet_records_20261013/output/summary.json",
"elapsed_seconds": 1.8603430899675004,
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"private_plot_cache_cleaned": true,
"disk_monitor": "sampled before/after redraw, not OS hard guarantee",
"leaf_counts": [
1824,
1536
],
"CFD_runs_this_round": 0
}
research/fixed_patch_jet_closure_20261013/cpu_stdout.txt
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{"operation": "CPU-only geometry redraw, no CUDA context or solver", "input_summary": "/home/frank14f/CelerisLab/research/fixed_patch_jet_records_20261013/output/summary.json", "elapsed_seconds": 1.8603430899675004, "peak_self_rss_bytes": 76394496, "ram_available_before_bytes": 64069128192, "ram_available_after_bytes": 64015175680, "sampled_related_evidence_and_temporary_bytes": 1219264, "temporary_budget_bytes": 2000000000, "private_plot_cache_cleaned": true, "disk_monitor": "sampled before/after redraw, not OS hard guarantee", "leaf_counts": [1824, 1536], "CFD_runs_this_round": 0}
research/fixed_patch_jet_closure_20261013/incremental.patch
+58
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@@ -0,0 +1,58 @@
--- a/examples/fixed_patch_jet.py
+++ b/examples/fixed_patch_jet.py
@@ -8,6 +8,10 @@
Scale estimates use the continuous peak A=.02, not inlet mean speed: W/A=600,
H/A=2250, W**2/nu=1440, H**2/nu=20250, where H=NY-3=45 between on-site planes.
An acoustic crossing (~78 steps) does not imply that transients have disappeared.
+Mach is sampled over all leaves every 50 steps and is usually inlet dominated;
+the discrete inlet peak is slightly below continuous A. This is not an accuracy test.
+The 120s checks are cooperative, not an exact interruption deadline. main elapsed
+includes construction, advancement and plotting; it is not pure CFD timing.
"""
import argparse
from dataclasses import replace
@@ -87,7 +91,7 @@
ax.set_title('Native leaf grid: coarse dx=1, fine dx=0.5\n'
'Red: internal patch; orange: coarse slot; dashed: reservoir\n'
'Flow may cross patch sides; patch does not contain all startup flow', fontsize=10)
- fig.savefig(out/'layout.png', dpi=150)
+ fig.savefig(out/'layout.png', dpi=150, bbox_inches='tight', pad_inches=.15)
plt.close(fig)
vmax = max(1., *(np.nanmax(np.hypot(f[..., 1], f[..., 2]))/AMPLITUDE
for fields in snapshots for f in fields))
@@ -130,7 +134,8 @@
report = dict(config=dict(nx=48, ny=48, bounds=BOUNDS, W=WIDTH, A=AMPLITUDE, nu=.1,
rho=1, tau_coarse=.8, tau_fine=1.1, coarse_boundary='slot_reservoir'),
guards=dict(check_every=50, positive_density=True, finite=True,
- max_Mach=MAX_MACH, max_seconds=MAX_SECONDS),
+ max_Mach=MAX_MACH, max_seconds=MAX_SECONDS,
+ timing='cooperative checks, not an exact interruption deadline'),
scale_estimates=dict(velocity_basis='continuous peak A, not inlet mean', H=cfg.ny-3,
W_over_A=WIDTH/AMPLITUDE, H_over_A=(cfg.ny-3)/AMPLITUDE,
W_squared_over_nu=WIDTH**2/cfg.viscosity,
@@ -168,6 +173,8 @@
if step:
run.step(50)
report['last_synchronized_coarse_step'] = run.step_count
+ if time.monotonic()-start > MAX_SECONDS:
+ raise TimeoutError('120 second demonstration limit reached after chunk')
fields, sample = measure(run.download(), masks, centers, mass0)
sample.update(t=step, coarse_steps=step, fine_steps=2*step)
report['samples'].append(sample)
@@ -177,6 +184,8 @@
with tempfile.TemporaryDirectory(prefix='.plot-', dir=out) as cache:
os.environ['MPLCONFIGDIR'] = cache
draw(out, snapshots, masks, centers)
+ if time.monotonic()-start > MAX_SECONDS:
+ raise TimeoutError('120 second demonstration limit reached after plotting')
report['status'] = 'complete'
except BaseException as exc:
report.update(status='failed', error=f'{type(exc).__name__}: {exc}',
@@ -184,6 +193,7 @@
raise
finally:
report['elapsed_seconds'] = time.monotonic()-start
+ report['elapsed_scope'] = 'main construction, advancement and plotting; not pure CFD time'
(out/'summary.json').write_text(json.dumps(report, indent=2)+'\n')
print(f'{report["status"]}: last synchronized coarse step '
f'{report["last_synchronized_coarse_step"]}; output={out}', flush=True)
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research/fixed_patch_jet_closure_20261013/notes.txt
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本轮无 CFD 运行，案例累计仍为2次。第一次与第二次实际运行源码已分别逐字保存于 research/fixed_patch_jet_20261013/fixed_patch_jet.py 和 research/fixed_patch_jet_records_20261013/fixed_patch_jet.py；原PNG/stdout/summary/results/manifest全部不变。当前examples入口只增加说明、布局边距和协作式计时检查，未重新运行CFD；既有数据来自第二次源码快照。
25组记录每50粗步采样，node mass约在2199.806433至2216.155705间振荡，Q_out也有约300步的变化周期。H=NY-3=45、c_s=1/sqrt(3)，闭开声学估计4H/c_s=311.769步，与观测尺度相符。这是基于边界条件与现有记录的合理解释，不是已证明的因果或唯一模态。末态相对初值-5.698886576是两个时刻的节点质量状态差，不能单凭它推断长期或单调累积损失。50步macro sum(rho*vy)采样不证明population预算闭合；节点面积1与1/4的账本不是精确物理体积分。
采样最大Mach0.034161593来自每50步全leaf测量，由施加的入口离散峰值主导；连续峰值A=.02，而最近slot节点的解析速度约.019723187，略低于A。它不是逐步全程峰值，也不是精度验证。时间尺度使用连续峰值A（非入口平均速度）：W/A600、H/A2250、W²/nu1440、H²/nu20250；声学穿越约77.94步不意味着瞬态消失。0/300/1200中的两个发展快照不代表充分发展，流动可能越过patch两侧，喷口仍由coarse解析。
第二次脚本main elapsed=6.24919704697秒，包含构造、推进、绘图，不是纯CFD耗时；外部进程elapsed=6.70286765601秒。120秒限制是协作式检查：现有chunk前检查之外，当前入口补上chunk结束后和绘图结束、status=complete之前的检查。它不能在进行中的操作内精确中断；外部watchdog亦为采样监控，两者都不是120秒整点停止的OS硬保证。
新layout_uncropped.png只读取第二次summary实际nx/ny/bounds/W与公共CPU FixedPatch2D几何生成原生leaf网格；coarse1824、fine1536，Y裁剪[1.5,46.5]，无CUDA上下文或求解器。绘图耗时1.860343秒，进程峰值RSS76394496字节，采样临时及相关证据1219264字节（<2GB），私有绘图缓存与临时脚本已清理。原velocity图保留，没有从PNG/probe重建速度场。
静态核验：AST与compile通过，measure函数AST、BOUNDS/TIMES及其关联常量、cfg构造AST与第二次源码一致；本轮基线除examples/fixed_patch_jet.py外均未变。没有环境/依赖/系统/DynamisLab修改，没有提交或推送。
以后获授权复现当前入口时使用新的输出目录（本轮未执行此命令）：
PYTHONPATH=/home/frank14f/CelerisLab/src CUDA_VISIBLE_DEVICES=0 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python /home/frank14f/CelerisLab/examples/fixed_patch_jet.py --output /tmp/celeris-fixed-patch-jet-new
运行前仍须预检实际物理GPU占用/UUID/显存/RAM，并使用外部采样watchdog；命令本身不提供外部watchdog。
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research/fixed_patch_jet_records_20261013/before.json
+8
View File
@@ -0,0 +1,8 @@
{
"utc": "2026-10-13T11:51:01.475368+00:00",
"physical_device": 0,
"uuid": "GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c",
"gpus": "index, uuid, name, memory.used [MiB], memory.free [MiB], utilization.gpu [%]\n0, GPU-e8853ae7-a302-6a17-bcfa-aa767e9ed20c, Tesla V100-SXM2-16GB, 5 MiB, 16140 MiB, 0 %\n1, GPU-f1c9fd5a-e917-56e0-21fb-24035ba44a36, Tesla V100-SXM2-16GB, 5 MiB, 16140 MiB, 0 %\n2, GPU-b8c11c01-c91f-aa0b-abf6-dad35085cc76, Tesla V100-SXM2-16GB, 5 MiB, 16140 MiB, 0 %\n3, GPU-8b71b032-1b75-2891-935c-ce106a53034a, Tesla V100-SXM2-16GB, 5 MiB, 16140 MiB, 0 %\n",
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}
research/fixed_patch_jet_records_20261013/fixed_patch_jet.py
+193
View File
@@ -0,0 +1,193 @@
"""Low-Re startup flow in an x-periodic confined domain; nozzle remains coarse.
From this checkout, after checking the selected physical GPU is idle:
PYTHONPATH="$PWD/src" CUDA_VISIBLE_DEVICES=0 python examples/fixed_patch_jet.py --output /tmp/patch-jet
The fixed-amplitude start includes acoustic transients, not a developed jet.
Only native leaf cells are plotted. No interpolation, restart or flow oracle.
Scale estimates use the continuous peak A=.02, not inlet mean speed: W/A=600,
H/A=2250, W**2/nu=1440, H**2/nu=20250, where H=NY-3=45 between on-site planes.
An acoustic crossing (~78 steps) does not imply that transients have disappeared.
"""
import argparse
from dataclasses import replace
import json
import os
from pathlib import Path
import tempfile
import time
import numpy as np
import CelerisLab
from CelerisLab.config import load_lbm_config
from CelerisLab.cuda.context import CudaContext
from CelerisLab.lbm.equilibrium import compute_feq_d2q9, compute_macro_from_ddf
from CelerisLab.lbm.refinement import ACTIVE
from CelerisLab.lbm.refinement_stepper import FixedPatchStepper
BOUNDS, AMPLITUDE, WIDTH = (16, 4, 32, 28), .02, 12
TIMES, MAX_MACH, MAX_SECONDS = (0, 300, 1200), .1, 120
def measure(arrays, masks, centers, initial_mass):
fields = []
for array, mask in zip(arrays, masks):
if not np.isfinite(array[:, mask]).all():
raise RuntimeError("Nonfinite leaf populations")
field = np.full((*mask.shape, 3), np.nan)
field[mask] = [compute_macro_from_ddf(f) for f in array[:, mask].T]
fields.append(field)
leaves = np.concatenate([f[m] for f, m in zip(fields, masks)])
speed = np.hypot(leaves[:, 1], leaves[:, 2])
mass = sum(area*a[:, m].sum(dtype=np.float64)
for area, a, m in zip((1, .25), arrays, masks))
record = dict(finite=bool(np.isfinite(leaves).all()), rho_min=float(leaves[:, 0].min()),
rho_max=float(leaves[:, 0].max()), speed_max=float(speed.max()),
sampled_max_Mach=float(speed.max()*np.sqrt(3)),
density_deviation=float(np.abs(leaves[:, 0]-1).max()),
discrete_leaf_mass=float(mass), mass_change=float(mass-initial_mass))
if not record['finite'] or record['rho_min'] <= 0 or record['sampled_max_Mach'] > MAX_MACH:
raise RuntimeError(f"Finite/positive-density/Ma<={MAX_MACH} guard failed: {record}")
walls = fields[0][[1, -2]]
record['macro_sum_rho_v'] = dict(zip(('Q_in', 'Q_out'),
(walls[:, :, 0]*walls[:, :, 2]).sum(axis=1).tolist()))
record['centerline'] = []
for level, y in ((1, 16.25), (0, 32.5)):
xs, ys = centers[level]
columns = np.argsort(np.abs(xs-24))[:2]
row = int(np.flatnonzero(ys == y)[0])
values = fields[level][row, columns].mean(axis=0)
record['centerline'].append(dict(level=level, x=24, y=y,
sampled_x=sorted(xs[columns].tolist()), rule='arithmetic mean of two neighboring native columns',
rho_ux_uy=values.tolist()))
return fields, record
def draw(out, snapshots, masks, centers):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
def decorate(ax):
ax.set(xlim=(0, 48), ylim=(1.5, 46.5), aspect='equal', xlabel='X', ylabel='Y')
ax.plot([16, 32, 32, 16, 16], [4, 4, 28, 28, 4], color='tab:red', lw=1)
ax.axhline(1.5, color='black', lw=1)
ax.axhline(46.5, color='black', lw=1, ls='--')
ax.plot([18, 30], [1.5, 1.5], color='tab:orange', lw=4, clip_on=False)
fig, ax = plt.subplots(figsize=(6, 6), constrained_layout=True)
for mask, (xs, ys), dx in zip(masks, centers, (1, .5)):
yy, xx = np.meshgrid(ys, xs, indexing='ij')
cells = np.stack((xx[mask], yy[mask]), axis=-1)
vertices = cells[:, None, :] + dx*np.array([[-.5, -.5], [.5, -.5], [.5, .5], [-.5, .5]])
ax.add_collection(PolyCollection(vertices, facecolors='none', edgecolors='0.55', linewidths=.3))
decorate(ax)
ax.set_title('Native leaf grid: coarse dx=1, fine dx=0.5\n'
'Red: internal patch; orange: coarse slot; dashed: reservoir\n'
'Flow may cross patch sides; patch does not contain all startup flow', fontsize=10)
fig.savefig(out/'layout.png', dpi=150)
plt.close(fig)
vmax = max(1., *(np.nanmax(np.hypot(f[..., 1], f[..., 2]))/AMPLITUDE
for fields in snapshots for f in fields))
fig, axes = plt.subplots(1, 3, figsize=(12, 4.8), constrained_layout=True)
for ax, step, fields in zip(axes, TIMES, snapshots):
for field, mask, (xs, ys), dx in zip(fields, masks, centers, (1, .5)):
speed = np.ma.masked_where(~mask, np.hypot(field[..., 1], field[..., 2])/AMPLITUDE)
image = ax.pcolormesh(np.r_[xs-dx/2, xs[-1]+dx/2],
np.r_[ys-dx/2, ys[-1]+dx/2], speed,
shading='flat', cmap='viridis', vmin=0, vmax=vmax, rasterized=True)
stride = int(4/dx)
yy, xx = np.meshgrid(ys, xs, indexing='ij')
selection = np.zeros_like(mask)
selection[::stride, ::stride] = mask[::stride, ::stride]
ax.quiver(xx[selection], yy[selection], field[..., 1][selection], field[..., 2][selection],
angles='xy', scale_units='xy', scale=AMPLITUDE/3, width=.005, color='white')
decorate(ax)
ax.set_title(f'coarse t={step}, fine steps={2*step}')
fig.colorbar(image, ax=axes, label='speed / imposed amplitude')
fig.suptitle('Low-Re startup flow in an x-periodic confined domain\n'
'Fixed-amplitude start includes acoustic transients; not steady/developed\n'
'Nozzle remains coarse; flow may cross patch sides (not fully enclosed)', fontsize=11)
fig.savefig(out/'velocity.png', dpi=150)
plt.close(fig)
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--output', type=Path, required=True)
out = parser.parse_args().output.resolve()
root = Path(__file__).resolve().parents[1]
if Path(CelerisLab.__file__).resolve() != root/'src/CelerisLab/__init__.py':
raise RuntimeError(f'Set PYTHONPATH to {root / "src"}')
out.mkdir(parents=True, exist_ok=False)
cfg = replace(load_lbm_config(str(root/'src/CelerisLab/configs/config_lbm.json')),
nx=48, ny=48, viscosity=.1, velocity=0., rho=1., wall_slot_width=WIDTH,
wall_normal_velocity=AMPLITUDE, y_wall_bc='on_site_velocity', top_wall_bc='reservoir',
lattice_model='D2Q9', collision='SRT', streaming='double_buffer',
store_precision='FP32', ddf_shifting=False, les_enabled=False)
report = dict(config=dict(nx=48, ny=48, bounds=BOUNDS, W=WIDTH, A=AMPLITUDE, nu=.1,
rho=1, tau_coarse=.8, tau_fine=1.1, coarse_boundary='slot_reservoir'),
guards=dict(check_every=50, positive_density=True, finite=True,
max_Mach=MAX_MACH, max_seconds=MAX_SECONDS),
scale_estimates=dict(velocity_basis='continuous peak A, not inlet mean', H=cfg.ny-3,
W_over_A=WIDTH/AMPLITUDE, H_over_A=(cfg.ny-3)/AMPLITUDE,
W_squared_over_nu=WIDTH**2/cfg.viscosity,
H_squared_over_nu=(cfg.ny-3)**2/cfg.viscosity,
acoustic_crossing=(cfg.ny-3)*np.sqrt(3)),
notes='Node mass uses areas 1 and 1/4, not an exact physical volume integral. '
'Q_in/Q_out are bottom/top macro sum(rho*vy), not a closed population budget. '
'Mach maxima are sampled every 50 steps, not every-step peaks. '
'One acoustic crossing does not imply transient decay; no steady-state claim.',
samples=[], status='running', last_synchronized_coarse_step=0)
start, run = time.monotonic(), None
try:
with CudaContext(0) as context, FixedPatchStepper(cfg, BOUNDS, context=context,
coarse_boundary='slot_reservoir') as run:
p = run.patch
masks = [np.ones(p.coarse_shape, bool), p.fine_kind == ACTIVE]
masks[0][[0, -1]] = False
masks[0][4:28, 16:32] = False
centers = [(origin[0]+dx*(np.arange(shape[1])+.5),
origin[1]+dx*(np.arange(shape[0])+.5))
for shape, origin, dx in ((p.coarse_shape, (0, 0), 1),
(p.fine_shape, p.fine_origin, .5))]
initial = [np.broadcast_to(compute_feq_d2q9(1., 0., 0.)[:, None, None], (9, *m.shape)).copy()
for m in masks]
for x in range(cfg.nx):
v = AMPLITUDE*max(0., 1-((2*x-(cfg.nx-1))/WIDTH)**2)**2
initial[0][:, 1, x] = compute_feq_d2q9(1., 0., v)
run.initialize(*initial)
mass0 = sum(area*a[:, m].sum(dtype=np.float64)
for area, a, m in zip((1, .25), initial, masks))
snapshots = []
for step in range(0, TIMES[-1]+1, 50):
if time.monotonic()-start > MAX_SECONDS:
raise TimeoutError('120 second demonstration limit reached')
if step:
run.step(50)
report['last_synchronized_coarse_step'] = run.step_count
fields, sample = measure(run.download(), masks, centers, mass0)
sample.update(t=step, coarse_steps=step, fine_steps=2*step)
report['samples'].append(sample)
print(json.dumps(sample), flush=True)
if step in TIMES:
snapshots.append(fields)
with tempfile.TemporaryDirectory(prefix='.plot-', dir=out) as cache:
os.environ['MPLCONFIGDIR'] = cache
draw(out, snapshots, masks, centers)
report['status'] = 'complete'
except BaseException as exc:
report.update(status='failed', error=f'{type(exc).__name__}: {exc}',
last_synchronized_coarse_step=run.step_count if run is not None else 0)
raise
finally:
report['elapsed_seconds'] = time.monotonic()-start
(out/'summary.json').write_text(json.dumps(report, indent=2)+'\n')
print(f'{report["status"]}: last synchronized coarse step '
f'{report["last_synchronized_coarse_step"]}; output={out}', flush=True)
if __name__ == '__main__':
main()
research/fixed_patch_jet_records_20261013/incremental.patch
+87
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@@ -0,0 +1,87 @@
--- a/examples/fixed_patch_jet.py
+++ b/examples/fixed_patch_jet.py
@@ -5,6 +5,9 @@
The fixed-amplitude start includes acoustic transients, not a developed jet.
Only native leaf cells are plotted. No interpolation, restart or flow oracle.
+Scale estimates use the continuous peak A=.02, not inlet mean speed: W/A=600,
+H/A=2250, W**2/nu=1440, H**2/nu=20250, where H=NY-3=45 between on-site planes.
+An acoustic crossing (~78 steps) does not imply that transients have disappeared.
"""
import argparse
from dataclasses import replace
@@ -41,15 +44,16 @@
for area, a, m in zip((1, .25), arrays, masks))
record = dict(finite=bool(np.isfinite(leaves).all()), rho_min=float(leaves[:, 0].min()),
rho_max=float(leaves[:, 0].max()), speed_max=float(speed.max()),
- measured_Ma=float(speed.max()*np.sqrt(3)),
+ sampled_max_Mach=float(speed.max()*np.sqrt(3)),
density_deviation=float(np.abs(leaves[:, 0]-1).max()),
discrete_leaf_mass=float(mass), mass_change=float(mass-initial_mass))
- if not record['finite'] or record['rho_min'] <= 0 or record['measured_Ma'] > MAX_MACH:
+ if not record['finite'] or record['rho_min'] <= 0 or record['sampled_max_Mach'] > MAX_MACH:
raise RuntimeError(f"Finite/positive-density/Ma<={MAX_MACH} guard failed: {record}")
walls = fields[0][[1, -2]]
- record['macro_sum_rho_v_bottom_top'] = (walls[:, :, 0]*walls[:, :, 2]).sum(axis=1).tolist()
+ record['macro_sum_rho_v'] = dict(zip(('Q_in', 'Q_out'),
+ (walls[:, :, 0]*walls[:, :, 2]).sum(axis=1).tolist()))
record['centerline'] = []
- for level, y in ((0, 3.5), (1, 8.25), (1, 16.25), (0, 32.5)):
+ for level, y in ((1, 16.25), (0, 32.5)):
xs, ys = centers[level]
columns = np.argsort(np.abs(xs-24))[:2]
row = int(np.flatnonzero(ys == y)[0])
@@ -80,7 +84,9 @@
vertices = cells[:, None, :] + dx*np.array([[-.5, -.5], [.5, -.5], [.5, .5], [-.5, .5]])
ax.add_collection(PolyCollection(vertices, facecolors='none', edgecolors='0.55', linewidths=.3))
decorate(ax)
- ax.set_title('Native leaf grid: coarse dx=1, fine dx=0.5\nRed: internal patch; orange: coarse slot; dashed: reservoir')
+ ax.set_title('Native leaf grid: coarse dx=1, fine dx=0.5\n'
+ 'Red: internal patch; orange: coarse slot; dashed: reservoir\n'
+ 'Flow may cross patch sides; patch does not contain all startup flow', fontsize=10)
fig.savefig(out/'layout.png', dpi=150)
plt.close(fig)
vmax = max(1., *(np.nanmax(np.hypot(f[..., 1], f[..., 2]))/AMPLITUDE
@@ -101,7 +107,9 @@
decorate(ax)
ax.set_title(f'coarse t={step}, fine steps={2*step}')
fig.colorbar(image, ax=axes, label='speed / imposed amplitude')
- fig.suptitle('Low-Re startup flow in an x-periodic confined domain\nFixed-amplitude start includes acoustic transients; not steady/developed')
+ fig.suptitle('Low-Re startup flow in an x-periodic confined domain\n'
+ 'Fixed-amplitude start includes acoustic transients; not steady/developed\n'
+ 'Nozzle remains coarse; flow may cross patch sides (not fully enclosed)', fontsize=11)
fig.savefig(out/'velocity.png', dpi=150)
plt.close(fig)
@@ -123,8 +131,15 @@
rho=1, tau_coarse=.8, tau_fine=1.1, coarse_boundary='slot_reservoir'),
guards=dict(check_every=50, positive_density=True, finite=True,
max_Mach=MAX_MACH, max_seconds=MAX_SECONDS),
+ scale_estimates=dict(velocity_basis='continuous peak A, not inlet mean', H=cfg.ny-3,
+ W_over_A=WIDTH/AMPLITUDE, H_over_A=(cfg.ny-3)/AMPLITUDE,
+ W_squared_over_nu=WIDTH**2/cfg.viscosity,
+ H_squared_over_nu=(cfg.ny-3)**2/cfg.viscosity,
+ acoustic_crossing=(cfg.ny-3)*np.sqrt(3)),
notes='Node mass uses areas 1 and 1/4, not an exact physical volume integral. '
- 'Boundary rho*v is a macro measurement, not a closed population budget.',
+ 'Q_in/Q_out are bottom/top macro sum(rho*vy), not a closed population budget. '
+ 'Mach maxima are sampled every 50 steps, not every-step peaks. '
+ 'One acoustic crossing does not imply transient decay; no steady-state claim.',
samples=[], status='running', last_synchronized_coarse_step=0)
start, run = time.monotonic(), None
try:
@@ -154,11 +169,10 @@
run.step(50)
report['last_synchronized_coarse_step'] = run.step_count
fields, sample = measure(run.download(), masks, centers, mass0)
- print(f'step={step} rho=[{sample["rho_min"]:.7g},{sample["rho_max"]:.7g}] '
- f'Ma={sample["measured_Ma"]:.7g}', flush=True)
+ sample.update(t=step, coarse_steps=step, fine_steps=2*step)
+ report['samples'].append(sample)
+ print(json.dumps(sample), flush=True)
if step in TIMES:
- sample.update(coarse_steps=step, fine_steps=2*step)
- report['samples'].append(sample)
snapshots.append(fields)
with tempfile.TemporaryDirectory(prefix='.plot-', dir=out) as cache:
os.environ['MPLCONFIGDIR'] = cache
research/fixed_patch_jet_records_20261013/manifest.json
+58
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@@ -0,0 +1,58 @@
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"this_round_runs": 1,
"exit_code": 0,
"records": 25,
"baseline_files_checked": 543,
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research/fixed_patch_jet_records_20261013/notes.txt
+7
View File
@@ -0,0 +1,7 @@
Second same-parameter case run, explicitly authorized to repair recording/captions. Total case executions now2; no third run. Same48x48, bounds(16,4,32,28), W12/A.02/nu.1/rho1, initial state, guard values and1200 coarse/2400 fine steps. Only example record/description/caption changes; all other baseline files unchanged.
Original run source saved byte-for-byte at research/fixed_patch_jet_20261013/fixed_patch_jet.py, SHA256 f27cd067fd7b9a7c6d7fab19fa471fe0617ee67889ae3931048557cc5659181b. Original PNG/stdout/summary/results/manifest retained unmodified. Historical source/doc wording is retained as evidence, not silently rewritten.
New stdout.txt is unmodified directly redirected merged stdout/stderr; 25 complete JSON samples exactly match output/summary.json.samples at0,50,...1200. Records include t, macro Q_in/out=sum(rho*vy), node-weighted leaf mass/change, density deviation, sampled maximum Mach and two centerline probes at16.25(fine)/32.5(coarse) with explicit native-column arithmetic mean. Only three macro-field snapshots retained for plots. Native-cell plots inspected, on-site clipping preserved; new captions note flow may cross patch sides and nozzle remains coarse.
Measured exit0, elapsed6.702868s, sampled maximum Mach0.0341615929682, sampled rho range[0.992553949356,1.010591745377]. Discrete leaf mass2208.00001640618 ->2202.30112983007, delta -5.69888657611. Final macro Q_in0.127690201625, Q_out0.0306652393192. Three shared snapshot scalar diagnostics and fluxes are identical to first run. Do not infer steady state or population-budget closure from these observations. Sampling every50 steps is not a bound on every-step maxima.
Scale estimates use continuous peak A=.02, not mean inlet velocity; on-site H=NY-3=45. W/A600, H/A2250, W^2/nu1440, H^2/nu20250; acoustic crossing~77.94 is not a transient-decay time.
Live precheck UUID/occupancy/VRAM/compute-apps/RAM in before.json. Real production compilation, private scratch/plot cache, no256MiB cap. Peak sampled group RSS281206784 bytes; minimum RAM63706316800 bytes, minimum GPU free15826MiB; combined task temporary/related evidence peak2192956 bytes (<2GB). Own temporary runner, compilation scratch and plot cache removed. No environment/system/dependency/DynamisLab edits, commit or push.
Read-only agent confirmed no numerical/guard change and no backend/kernel/helper modification. AST syntax passed before execution; no other tests or numerical cases run.
research/fixed_patch_jet_records_20261013/results.json
+34
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@@ -0,0 +1,34 @@
{
"case_run_number": 2,
"command": [
"/usr/bin/prlimit",
"--core=0",
"--",
"/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python",
"-B",
"/home/frank14f/CelerisLab/examples/fixed_patch_jet.py",
"--output",
"/home/frank14f/CelerisLab/research/fixed_patch_jet_records_20261013/output"
],
"cwd": "/home/frank14f/CelerisLab",
"environment": {
"PYTHONPATH": "/home/frank14f/CelerisLab/src",
"CUDA_VISIBLE_DEVICES": "0",
"CUDA_CACHE_DISABLE": "1",
"OPENBLAS_NUM_THREADS": "1",
"PYTHONDONTWRITEBYTECODE": "1",
"PYTHONNOUSERSITE": "1",
"TMPDIR": "/tmp/celeris-jet-records-run-gnfs6m58"
},
"exit_code": 0,
"elapsed_seconds": 6.70286765601486,
"peak_sampled_group_rss_bytes": 281206784,
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"min_gpu_free_mib": 15826,
"ram_reserve_bytes": 6405983846,
"vram_reserve_mib": 1614,
"peak_combined_temporary_and_related_evidence_bytes": 2192956,
"temporary_budget_bytes": 2000000000,
"termination_reason": null,
"monitor": "Own process group RSS recorded, not capped; live RAM/VRAM and temp sampled, not OS hard limits"
}
research/fixed_patch_jet_records_20261013/stdout.txt
+26
View File
@@ -0,0 +1,26 @@
{"finite": true, "rho_min": 0.9999999403953552, "rho_max": 1.0, "speed_max": 0.0197231937199831, "sampled_max_Mach": 0.03416157361053414, "density_deviation": 5.960464477539063e-08, "discrete_leaf_mass": 2208.0000164061785, "mass_change": 0.0, "macro_sum_rho_v": {"Q_in": 0.12800518795847893, "Q_out": -2.682209014892578e-07}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0, 0.0, -5.587935447692871e-09]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0, 0.0, -5.587935447692871e-09]}], "t": 0, "coarse_steps": 0, "fine_steps": 0}
{"finite": true, "rho_min": 0.9999995231628418, "rho_max": 1.009438395500183, "speed_max": 0.01972315839908094, "sampled_max_Mach": 0.034161512432937025, "density_deviation": 0.009438395500183105, "discrete_leaf_mass": 2214.5822166297585, "mass_change": 6.582200223580003, "macro_sum_rho_v": {"Q_in": 0.12863954529166222, "Q_out": 1.0689720511436462e-05}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0033259391784668, 3.9904424178132075e-08, 0.003767254186651691]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0027864575386047, 1.95017983891273e-08, 0.0017514756851302565]}], "t": 50, "coarse_steps": 50, "fine_steps": 100}
{"finite": true, "rho_min": 0.9984776973724365, "rho_max": 1.010591745376587, "speed_max": 0.019723167973819598, "sampled_max_Mach": 0.03416152901687085, "density_deviation": 0.010591745376586914, "discrete_leaf_mass": 2215.2133280904964, "mass_change": 7.213311684317887, "macro_sum_rho_v": {"Q_in": 0.1291772685945034, "Q_out": 0.25743226893246174}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0070598721504211, 3.051337606293814e-08, 0.004463808784989469]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0040816068649292, 7.234786527643558e-08, 0.0046468505171113755]}], "t": 100, "coarse_steps": 100, "fine_steps": 200}
{"finite": true, "rho_min": 0.9973579049110413, "rho_max": 1.0038379430770874, "speed_max": 0.01972316591697569, "sampled_max_Mach": 0.0341615254543127, "density_deviation": 0.0038379430770874023, "discrete_leaf_mass": 2208.77242693305, "mass_change": 0.7724105268716812, "macro_sum_rho_v": {"Q_in": 0.12834449112415314, "Q_out": 0.2561024557799101}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9987876713275909, 3.727096866032421e-09, 0.0070787730040260885]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9982824921607971, 1.2128023445341937e-08, 0.005549212578561977]}], "t": 150, "coarse_steps": 150, "fine_steps": 300}
{"finite": true, "rho_min": 0.993359386920929, "rho_max": 1.000130534172058, "speed_max": 0.019723204896160564, "sampled_max_Mach": 0.03416159296824134, "density_deviation": 0.006640613079071045, "discrete_leaf_mass": 2202.163527972065, "mass_change": -5.836488434113562, "macro_sum_rho_v": {"Q_in": 0.12781986966729164, "Q_out": 0.2561996504664421}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9962244033813477, 2.149466252515962e-08, 0.004466802336406565]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9985108375549316, -2.1456195895938244e-08, 0.005367343550101116]}], "t": 200, "coarse_steps": 200, "fine_steps": 400}
{"finite": true, "rho_min": 0.9925539493560791, "rho_max": 1.000000238418579, "speed_max": 0.019723183409092307, "sampled_max_Mach": 0.03416155575154741, "density_deviation": 0.0074460506439208984, "discrete_leaf_mass": 2200.13292664662, "mass_change": -7.867089759558439, "macro_sum_rho_v": {"Q_in": 0.12756339088082314, "Q_out": 0.021014515310525894}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9956563413143158, -2.7131816163200725e-08, 0.00453635997863009]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9977899789810181, -3.734428379729545e-09, 0.0022119478644690342]}], "t": 250, "coarse_steps": 250, "fine_steps": 500}
{"finite": true, "rho_min": 0.9937772750854492, "rho_max": 1.0004591941833496, "speed_max": 0.019723178287875644, "sampled_max_Mach": 0.03416154688133995, "density_deviation": 0.006222724914550781, "discrete_leaf_mass": 2206.436197685078, "mass_change": -1.563818721100688, "macro_sum_rho_v": {"Q_in": 0.12769856862723827, "Q_out": 0.00024305842816829681}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.998782604932785, -7.456729879883831e-09, 0.0030153855826159743]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9999143481254578, 5.5884044452416285e-08, 0.0004412565207833957]}], "t": 300, "coarse_steps": 300, "fine_steps": 600}
{"finite": true, "rho_min": 0.9999760985374451, "rho_max": 1.0093835592269897, "speed_max": 0.01972318919134063, "sampled_max_Mach": 0.03416156576669529, "density_deviation": 0.009383559226989746, "discrete_leaf_mass": 2213.0481811035424, "mass_change": 5.048164697363973, "macro_sum_rho_v": {"Q_in": 0.1290381271392107, "Q_out": 0.0012626796960830688}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0047874450683594, 2.7806554884415716e-08, 0.004754580718380195]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0005329847335815, 2.7924793749710368e-09, 0.0007658868023880151]}], "t": 350, "coarse_steps": 350, "fine_steps": 700}
{"finite": true, "rho_min": 0.9999998211860657, "rho_max": 1.0073307752609253, "speed_max": 0.019723158741160846, "sampled_max_Mach": 0.0341615130254368, "density_deviation": 0.007330775260925293, "discrete_leaf_mass": 2216.1557046929374, "mass_change": 8.15568828675896, "macro_sum_rho_v": {"Q_in": 0.1287879254668951, "Q_out": 0.19628209806978703}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0041831731796265, -1.8548858405324645e-09, 0.005016827190465851]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.003117322921753, -1.8568567271940063e-09, 0.003461982173165289]}], "t": 400, "coarse_steps": 400, "fine_steps": 800}
{"finite": true, "rho_min": 0.9997285008430481, "rho_max": 1.006166696548462, "speed_max": 0.019723182664604537, "sampled_max_Mach": 0.034161554462056765, "density_deviation": 0.006166696548461914, "discrete_leaf_mass": 2210.291847281158, "mass_change": 2.291830874979496, "macro_sum_rho_v": {"Q_in": 0.12863796204328537, "Q_out": 0.25612184032797813}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0015701651573181, 1.581361160296152e-08, 0.007088322591362907]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.000373661518097, 5.958460591071901e-08, 0.005809673169784788]}], "t": 450, "coarse_steps": 450, "fine_steps": 900}
{"finite": true, "rho_min": 0.9933133721351624, "rho_max": 1.0001569986343384, "speed_max": 0.019723185394248333, "sampled_max_Mach": 0.03416155918993851, "density_deviation": 0.0066866278648376465, "discrete_leaf_mass": 2203.688418552745, "mass_change": -4.311597853433341, "macro_sum_rho_v": {"Q_in": 0.12767854891717434, "Q_out": 0.2553195860236883}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9973909258842468, -2.6145246976368598e-08, 0.0063018467717611965]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9999212622642517, 1.2108146838067492e-08, 0.005339084554058069]}], "t": 500, "coarse_steps": 500, "fine_steps": 1000}
{"finite": true, "rho_min": 0.9927858114242554, "rho_max": 1.000000238418579, "speed_max": 0.01972319512696364, "sampled_max_Mach": 0.034161576047495916, "density_deviation": 0.007214188575744629, "discrete_leaf_mass": 2199.806432941463, "mass_change": -8.19358346471563, "macro_sum_rho_v": {"Q_in": 0.1276045311242342, "Q_out": 0.09961476735770702}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9950574338436127, 3.556874871666341e-08, 0.005545385725667422]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.99586421251297, -2.337975807628814e-08, 0.002802137885478718]}], "t": 550, "coarse_steps": 550, "fine_steps": 1100}
{"finite": true, "rho_min": 0.994580864906311, "rho_max": 1.000121831893921, "speed_max": 0.019723166170399542, "sampled_max_Mach": 0.03416152589325568, "density_deviation": 0.005419135093688965, "discrete_leaf_mass": 2204.9180188016035, "mass_change": -3.081997604575008, "macro_sum_rho_v": {"Q_in": 0.12786079943180084, "Q_out": 0.0007411930710077286}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9983413219451904, -2.5187487447300542e-08, 0.003947660095103177]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9996882379055023, -9.306989826611729e-10, 0.0005744642606567245]}], "t": 600, "coarse_steps": 600, "fine_steps": 1200}
{"finite": true, "rho_min": 0.9999998211860657, "rho_max": 1.006541132926941, "speed_max": 0.019723149663783343, "sampled_max_Mach": 0.03416149730295777, "density_deviation": 0.006541132926940918, "discrete_leaf_mass": 2211.5022075329907, "mass_change": 3.5021911268122494, "macro_sum_rho_v": {"Q_in": 0.12868730165064335, "Q_out": 0.001046767458319664}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0024074912071228, 9.242623275073931e-10, 0.004146123464533346]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0004808902740479, -5.58809520614676e-09, 0.000624724125908406]}], "t": 650, "coarse_steps": 650, "fine_steps": 1300}
{"finite": true, "rho_min": 0.9999998211860657, "rho_max": 1.0074607133865356, "speed_max": 0.01972319407723786, "sampled_max_Mach": 0.03416157422931753, "density_deviation": 0.0074607133865356445, "discrete_leaf_mass": 2215.9999844529666, "mass_change": 7.999968046788126, "macro_sum_rho_v": {"Q_in": 0.12880354560911655, "Q_out": 0.12477604672312737}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0045958161354065, 3.7028870333261446e-09, 0.005564589341807712]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0036527514457703, 3.711889562849047e-08, 0.0030931405317066586]}], "t": 700, "coarse_steps": 700, "fine_steps": 1400}
{"finite": true, "rho_min": 0.9999997615814209, "rho_max": 1.007198691368103, "speed_max": 0.019723184393099027, "sampled_max_Mach": 0.034161557455897044, "density_deviation": 0.007198691368103027, "discrete_leaf_mass": 2211.7847068845294, "mass_change": 3.784690478350967, "macro_sum_rho_v": {"Q_in": 0.12876741401851177, "Q_out": 0.25408212281763554}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.002867341041565, -9.286597902886766e-10, 0.006729123631333772]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0006000995635986, 6.79480776621312e-08, 0.005443732552273214]}], "t": 750, "coarse_steps": 750, "fine_steps": 1500}
{"finite": true, "rho_min": 0.9945453405380249, "rho_max": 1.0000394582748413, "speed_max": 0.019723183472220768, "sampled_max_Mach": 0.03416155586088911, "density_deviation": 0.005454659461975098, "discrete_leaf_mass": 2205.237119223457, "mass_change": -2.7628971827216446, "macro_sum_rho_v": {"Q_in": 0.12783589214086533, "Q_out": 0.25539055839180946}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9980476796627045, -5.601396359480762e-09, 0.007080950505634613]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9994752705097198, -4.472928188196246e-08, 0.005606726710118295]}], "t": 800, "coarse_steps": 800, "fine_steps": 1600}
{"finite": true, "rho_min": 0.9931564927101135, "rho_max": 1.000000238418579, "speed_max": 0.019723179656447747, "sampled_max_Mach": 0.03416154925177637, "density_deviation": 0.006843507289886475, "discrete_leaf_mass": 2200.3033418026753, "mass_change": -7.696674603503197, "macro_sum_rho_v": {"Q_in": 0.1276633646339178, "Q_out": 0.15738135389983654}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.995688796043396, 1.777174654169606e-08, 0.0056219994183139]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9968701601028442, 3.7369864678042045e-09, 0.003516295929207963]}], "t": 850, "coarse_steps": 850, "fine_steps": 1700}
{"finite": true, "rho_min": 0.993564784526825, "rho_max": 1.000000238418579, "speed_max": 0.019723168988232915, "sampled_max_Mach": 0.034161530773886256, "density_deviation": 0.006435215473175049, "discrete_leaf_mass": 2203.472729786299, "mass_change": -4.527286619879305, "macro_sum_rho_v": {"Q_in": 0.1277082972228527, "Q_out": 0.007872957736253738}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9970619380474091, 5.6019655037608065e-09, 0.004682498959001465]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9991383850574493, -3.72778494670464e-09, 0.0010103030911885196]}], "t": 900, "coarse_steps": 900, "fine_steps": 1800}
{"finite": true, "rho_min": 0.9997681975364685, "rho_max": 1.0051435232162476, "speed_max": 0.019723180265717073, "sampled_max_Mach": 0.0341615503070618, "density_deviation": 0.005143523216247559, "discrete_leaf_mass": 2209.9437092989683, "mass_change": 1.9436928927898407, "macro_sum_rho_v": {"Q_in": 0.1285070050507784, "Q_out": 0.001014290377497673}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0013298392295837, 2.1397079474795436e-08, 0.003988593510698206]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0001946687698364, -5.121277207651142e-08, 0.0006442091845156772]}], "t": 950, "coarse_steps": 950, "fine_steps": 1900}
{"finite": true, "rho_min": 0.9999998211860657, "rho_max": 1.007829189300537, "speed_max": 0.019723195956038383, "sampled_max_Mach": 0.03416157748349549, "density_deviation": 0.00782918930053711, "discrete_leaf_mass": 2215.248868701514, "mass_change": 7.248852295335382, "macro_sum_rho_v": {"Q_in": 0.12884884513914585, "Q_out": 0.0783053319901228}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0045344233512878, 3.708970133368847e-08, 0.005484848626894535]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0028420090675354, 2.7876626557184095e-09, 0.002498106860624199]}], "t": 1000, "coarse_steps": 1000, "fine_steps": 2000}
{"finite": true, "rho_min": 0.9999998211860657, "rho_max": 1.0071310997009277, "speed_max": 0.019723162551660844, "sampled_max_Mach": 0.0341615196254164, "density_deviation": 0.007131099700927734, "discrete_leaf_mass": 2213.121662114281, "mass_change": 5.121645708102733, "macro_sum_rho_v": {"Q_in": 0.1287609599530697, "Q_out": 0.23961861431598663}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.0033761858940125, 1.948702402629945e-08, 0.006330294274771218]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [1.001342535018921, 2.418192186307541e-08, 0.005024741082302464]}], "t": 1050, "coarse_steps": 1050, "fine_steps": 2100}
{"finite": true, "rho_min": 0.9964147210121155, "rho_max": 1.0018093585968018, "speed_max": 0.019723162200346454, "sampled_max_Mach": 0.03416151901692203, "density_deviation": 0.0035852789878845215, "discrete_leaf_mass": 2206.7887076763436, "mass_change": -1.2113087298348546, "macro_sum_rho_v": {"Q_in": 0.12807970866560936, "Q_out": 0.2554724644869566}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9992902874946594, -1.4911744245216772e-08, 0.007389858762817912]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9998897016048431, -3.44636083569892e-08, 0.005732718796797301]}], "t": 1100, "coarse_steps": 1100, "fine_steps": 2200}
{"finite": true, "rho_min": 0.9931712746620178, "rho_max": 1.000000238418579, "speed_max": 0.019723172283986106, "sampled_max_Mach": 0.03416153648229823, "density_deviation": 0.006828725337982178, "discrete_leaf_mass": 2201.227777376771, "mass_change": -6.772239029407501, "macro_sum_rho_v": {"Q_in": 0.12766188196837902, "Q_out": 0.1938594225794077}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9959544539451599, -9.351055873348861e-09, 0.005925549967763543]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9975717067718506, 3.6411556051212644e-08, 0.00414892074651798]}], "t": 1150, "coarse_steps": 1150, "fine_steps": 2300}
{"finite": true, "rho_min": 0.9934006929397583, "rho_max": 1.000000238418579, "speed_max": 0.01972318067164439, "sampled_max_Mach": 0.03416155101014853, "density_deviation": 0.006599307060241699, "discrete_leaf_mass": 2202.30112983007, "mass_change": -5.6988865761086345, "macro_sum_rho_v": {"Q_in": 0.12769020162522793, "Q_out": 0.030665239319205284}, "centerline": [{"level": 1, "x": 24, "y": 16.25, "sampled_x": [23.75, 24.25], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9964200258255005, -5.613009959780303e-09, 0.0051035086848514625]}, {"level": 0, "x": 24, "y": 32.5, "sampled_x": [23.5, 24.5], "rule": "arithmetic mean of two neighboring native columns", "rho_ux_uy": [0.9982107579708099, -9.338345695949091e-10, 0.001630484555699851]}], "t": 1200, "coarse_steps": 1200, "fine_steps": 2400}
complete: last synchronized coarse step 1200; output=/home/frank14f/CelerisLab/research/fixed_patch_jet_records_20261013/output
research/j1-2026-10-12/REVIEW.md
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# J1 两平面吹吸：限定实施与验收
2026-10-12。以下门槛在本包第一次数值测试前写定。唯一写入者root；保留起点工作树，`baseline.json`及`baseline-status.txt`为证。仅独立CelerisLab，使用既有pycuda_3_10解释器和进程级PYTHONPATH；先查GPU0/1空闲，仅占用空闲获准设备。不提交或推送。
## 模型与时序
D2Q9/SRT/FP32/unshifted/double-buffer，无对象、体积力、LES。新on-site模式默认关闭，x周期，物理平面y=1与NY−2，H=NY−3；下壁(0,V)，上壁(U,V)。上下helper只补poststream未知方向，不碰撞或存储；融合核顺序为pull到t+dt、重建到达时刻速度、同节点碰撞、存postcollision g(t+dt)。初态为rho0、线性u、常量v=V的局部平衡，边界初态满足目标；后续不以解析指数场初始化来回避收敛。
参考：`research/design-2026-10-09/jet-study.md`中Zou–He/Ho scheme A原文核对与矩公式；本地`ref/OpenLB/src/boundary/zouHeDynamics.hh`的missing population重建；现有paired D2Q9方向表和pull时序为地址依据。连续解v=V，u=U*expm1(Vy/nu)/expm1(VH/nu)，V=0取线性极限。连续解不是精确离散LBM解；预期小Ma下有截断误差与FP32舍入，若不满足下列候选门槛须报告失败，不能静默放宽。
## 预先固定的有界验证
- 主案例NX=16、H=32、nu=.1、U=.02、rho0=1，V依次0、+.001、−.001、+.01、−.01；Re_U=6.4，最大Ma约.039。先不铺网格矩阵，也不声称证明二阶收敛。
- 采样间隔ceil(.5*H²/(pi²*nu))=519步，最多40个间隔=20760步/案例。至少8个间隔后才允许判稳；连续4个间隔的u剖面RMS变化/U≤2e−5、v剖面RMS变化≤2e−7，且每间隔质量相对变化≤2e−6。最后4间隔质量相对跨度≤6e−6。不能把持续质量增长作为gauge freedom接受。
- H32内部u连续解析剖面相对L2≤1e−3；v最大绝对误差≤2e−6；非零V内部三个独立截面的实测Σrho*v相对rho_bar*V*NX误差≤1e−3，V=0用绝对平均速度≤2e−6。不使用重建节点的赋值验证通量。
- 全域质量统计仅含y=1..NY−2，不含ghost。末态相对初态质量偏移≤1e−3，另以上述增量/跨度独立约束漂移；记录全部采样质量和剖面残差，不能仅记录最终密度范围。
- 实际population跨界收支：测试临时CUDA构建在真实SRT调用前记录边界重建后的未知incoming，以及collision前后质量差；结合旧postcollision outgoing，核对16个连续步的ΔM=入−出+collision舍入。纯周期x对消；全局每步预算残差/M≤2e−7，另报告未加collision舍入的物理残差，不将记录器加进生产kernel。
- helper上下方向、V正/负/零及非平衡已知分布：未知方向外逐值不变，独立质量/动量矩误差≤3e−7（rho O(1)）；平衡保持同容差。
- 关闭模式：与本包起点源码同参数、同初值的短默认通道结果逐值相同；同时运行受影响配置/恢复回归。不声明全部旧工况或性能已重新验证。
完整执行命令、原始日志、结果和本包增量将在本目录追加；任何门槛变化必须显式记录，当前未运行数值测试。
## 完成记录（以上为运行前冻结记录）
本包限定实现完成，门槛未改。生产代码新增75行、删除5行；配置说明新增15行；测试/验证新增364行。`incremental.diff`相对于本包起点的脏工作树生成，不是相对于HEAD。未提交、推送、安装依赖或修改DynamisLab。新增模式没有额外生产kernel启动；未进行性能基准，因此不声称已证明性能无回退。
CPU针对性回归77 passed、13 subtests passed（1.30秒）；checkpoint GPU回归10 passed、8 deselected（34.31秒）。五个J1案例全部通过，包括独立内部截面通量、末段质量变化与实际population预算。默认关闭模式的7步场和flags与本包起点逐值相同。只读architecture复核通过，没有发现限定范围内必须修复的项。
| V | 收敛步数 | u相对L2 | v最大绝对误差 | 相对初态质量偏移 | 末4间隔质量相对跨度 | 内部通量误差 |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| 0 | 4152 | 1.66909e-5 | 8.00937e-8 | 2.98475e-7 | 1.33772e-8 | 4.84288e-8（绝对平均速度） |
| +.001 | 7785 | 7.27898e-4 | 2.53653e-7 | 5.01238e-5 | 3.51193e-7 | 2.53631e-4（相对） |
| −.001 | 7785 | 6.86307e-4 | 1.72778e-7 | 4.90631e-5 | 2.53997e-9 | 1.61564e-4（相对） |
| +.01 | 9342 | 1.73175e-4 | 1.63549e-7 | 6.19301e-5 | 2.08842e-9 | 8.44460e-6（相对） |
| −.01 | 8304 | 2.44096e-4 | 1.76794e-7 | 4.90692e-5 | 4.40262e-9 | 9.94338e-6（相对） |
每例末态再审计16步，审计版与普通版逐值一致。独立记录实际collision舍入后，预算残差均为0；这是用双精度累加已记录FP32 populations所得的离散收支一致性，不代表连续物理误差为0。不扣除collision舍入时，最大相对物理收支残差为7.733e-9。完整内部实测通量、质量历史、剖面及逐步预算见`results.json`。
本包只实现全平面固定速度边界，限定D2Q9/SRT/FP32/unshifted/double_buffer、无对象/体积力/LES。尚不是有限slot Jet；未扩展曲面、多层网格、AMR，也未证明网格收敛阶。
## 执行与原始证据
所有命令工作目录均为`/home/frank14f/CelerisLab`；解释器、Python导入和CUDA源码位置已校验，见`baseline.json`与`results.json`。每次GPU运行前检查物理GPU0/1空闲；实际仅用GPU0。执行命令如下：
```bash
env PYTHONPATH=/home/frank14f/CelerisLab/src PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python -m pytest tests/unit/test_planar_velocity.py tests/unit/test_build_contracts.py tests/unit/test_state_regressions.py tests/unit/test_simulation_lifecycle.py -q --tb=short -p no:cacheprovider > research/j1-2026-10-12/cpu.log 2>&1
env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/home/frank14f/CelerisLab/src PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python tests/validation/run_j1.py --output research/j1-2026-10-12/results.json --baseline-kernels /tmp/celeris-j1-before/src/CelerisLab/lbm/kernels > research/j1-2026-10-12/gpu-validation.log 2>&1
env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/home/frank14f/CelerisLab/src PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python -m pytest tests/integration/test_state_gpu.py -k checkpoint -q --tb=short -p no:cacheprovider > research/j1-2026-10-12/checkpoint-gpu.log 2>&1
```
第一次J1执行在V=0数值计算后因NumPy bool不能序列化JSON而退出，原始记录保留为`gpu.log`。只将输出的steady转为Python bool后，按同一门槛完整重跑；成功记录为`gpu-validation.log`。完成数值验证后将`--baseline-kernels`改为可选，便于日后只重跑五例；省略时旧模式比较明确记录为null，不视为已通过该比较。此CLI变动通过`--help`检查，数值路径未改动。
## 保存与清理
所有起点manifest文件仍存在，只有diff列出的9个已有文件发生本包修改，另新增4个实现/测试文件。四份config头文件及`tests/postproc/experiment.ipynb`均与起点SHA256一致。`git diff --check`通过，增量补丁反向适用检查通过。保留本目录原始成功/失败日志；临时审计构建自动清除。起点副本可由当前源码和增量补丁反向重建，临时重复备份在完成核验后清除。停在此步，等待主线程复核。
research/j1-2026-10-12/baseline-status.txt
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@@ -0,0 +1,62 @@
M pyproject.toml
M setup.py
M src/CelerisLab/__init__.py
M src/CelerisLab/body/__init__.py
M src/CelerisLab/body/ddf_patch.py
M src/CelerisLab/body/manager.py
M src/CelerisLab/body/preprocess/flag_overlay.py
M src/CelerisLab/common/__init__.py
M src/CelerisLab/common/checkpoint.py
M src/CelerisLab/config.py
M src/CelerisLab/configs/CONFIG.md
M src/CelerisLab/cuda/__init__.py
M src/CelerisLab/cuda/compiler_v2.py
M src/CelerisLab/cuda/context.py
M src/CelerisLab/lbm/__init__.py
M src/CelerisLab/lbm/curved_links.py
M src/CelerisLab/lbm/field.py
M src/CelerisLab/lbm/initializers.py
M src/CelerisLab/lbm/kernels/boundary/bounce_back.cuh
M src/CelerisLab/lbm/kernels/boundary/curved_boundary.cuh
M src/CelerisLab/lbm/kernels/boundary/inlet/common.cuh
M src/CelerisLab/lbm/kernels/boundary/inlet/zou_he_local.cuh
M src/CelerisLab/lbm/kernels/boundary/inlet_outlet.cuh
M src/CelerisLab/lbm/kernels/config/config_grid.h
M src/CelerisLab/lbm/kernels/config/config_method.h
M src/CelerisLab/lbm/kernels/config/config_objects.h
M src/CelerisLab/lbm/kernels/config/config_physics.h
M src/CelerisLab/lbm/kernels/operators/collision_mrt.cuh
M src/CelerisLab/lbm/kernels/operators/collision_trt.cuh
M src/CelerisLab/lbm/kernels/operators/forcing_guo.cuh
M src/CelerisLab/lbm/kernels/step/aux_kernels.cu
M src/CelerisLab/lbm/kernels/step/esopull_sync.cu
M src/CelerisLab/lbm/kernels/step/init_flow.cu
M src/CelerisLab/lbm/kernels/step/one_step_esopull.cu
M src/CelerisLab/lbm/kernels/streaming/esopull_semantic_helpers.cuh
M src/CelerisLab/lbm/stepper.py
M src/CelerisLab/simulation.py
M tests/integration/test_body_sync_e2e.py
M tests/integration/test_ddf_patch.py
M tests/integration/test_sync_bodies_skeleton.py
M tests/integration/test_unified_obs.py
M tests/postproc/experiment.ipynb
M tests/validation/run_kan99b_rotating_cylinder.py
M tests/validation/run_sah04_st_matrix.py
M tests/validation/test_sensor_accuracy.py
?? docs/design/
?? docs/multilevel_grid_architecture_analysis.md
?? research/
?? "tests/audit/body\345\210\240\345\207\217\345\212\237\350\203\275.md"
?? tests/integration/_cases.py
?? tests/integration/test_numerical_gpu.py
?? tests/integration/test_runtime_contracts_gpu.py
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?? tests/unit/cpp/
?? tests/unit/test_build_contracts.py
?? tests/unit/test_context_ownership.py
?? tests/unit/test_initializers.py
?? tests/unit/test_numerical_operators.py
?? tests/unit/test_simulation_lifecycle.py
?? tests/unit/test_state_regressions.py
?? tests/validation/multigrid/
?? tests/validation/run_poiseuille.py
research/j1-2026-10-12/baseline.json
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"src/CelerisLab/lbm/kernels/boundary/inlet/common.cuh": "f3cec54c8873c65b3dca4375605d2c89a8727765884d3954c235242fdff9c6ba",
"src/CelerisLab/lbm/kernels/boundary/inlet/equilibrium.cuh": "f0964c811b2c41a5aaa1c6d99c490b06e850c634eb8749f1f05ef61f93c2de1d",
"src/CelerisLab/lbm/kernels/boundary/inlet/regularized.cuh": "943d4a571818bacb89c70bccfaac202691121e3e385803ce6309123a0a03ddaf",
"src/CelerisLab/lbm/kernels/boundary/inlet/zou_he_local.cuh": "e73e66b853b1e4e0e30d8f4185d832aa414ce670a1813ce2b5459f2887f71f89",
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"src/CelerisLab/lbm/kernels/boundary/outlet/pressure_neq.cuh": "c8b2adc66d12fe602076ddc9693c5c208711fcaddb58aa9a31401a269f51c4c4",
"src/CelerisLab/lbm/kernels/config.h": "7d1124125e0b354558297934460e64c760fb68cdd7bf6d01a1238bb2eaab8c37",
"src/CelerisLab/lbm/kernels/config/config_grid.h": "8be3a7cf74ba30d7fd3074fed79e63dbda7311c4d03b454ea641bf298ffeaa8d",
"src/CelerisLab/lbm/kernels/config/config_method.h": "3db4e16ef2c6b22349d04e6aa8ab04bdfe0eb784f9f3a98277e15dbed1835d81",
"src/CelerisLab/lbm/kernels/config/config_objects.h": "dbb93c063586da278c15d95e23f84c12e728f4f5f1bbc3980fe9cedaa6fff90e",
"src/CelerisLab/lbm/kernels/config/config_obs.h": "c11e2249359145b5c4cbaaf0487506fa77c1f4b82c7c169ddc574aa0babcd727",
"src/CelerisLab/lbm/kernels/config/config_physics.h": "26df33c2960ba36f3283e19b55b4ea92e50e4efda705f9ebfbf3f875a4092c59",
"src/CelerisLab/lbm/kernels/core/descriptors.cuh": "8440bc14857da9007982650f828a23608eec9f7cc4bf772e8c8dc1239cb45356",
"src/CelerisLab/lbm/kernels/core/flags.cuh": "0025705f24710330d9d9195f713879bfb650a85a3449baef0e196b831af104ab",
"src/CelerisLab/lbm/kernels/core/layout.cuh": "1fbd820021c89ac3efcde24145996cb0147769e607e498e61f308ed3e0b13706",
"src/CelerisLab/lbm/kernels/core/obs.cuh": "b742b77c9f61e1f020fe9922c3838ae9d855e17b381154958b58f10a22938922",
"src/CelerisLab/lbm/kernels/core/params.cuh": "6e18bf730d936d940e3d85452f0714a2eaae31bf41002f9fd520ec5b372878dd",
"src/CelerisLab/lbm/kernels/core/precision.cuh": "849df14b4cce50af52399e4b8f3e997196f3ae5f1163f68e28df3e6b2507a8c4",
"src/CelerisLab/lbm/kernels/kernel_v2.cu": "5bcc192e2c38371d4b24b6725f75fec7d00364557c4b4536b064edf2ec9c75c5",
"src/CelerisLab/lbm/kernels/operators/collision_mrt.cuh": "aee444eb60c47a3fd518dca975d1af05425a0818a0ec6c3fb3f6150f60c2cb44",
"src/CelerisLab/lbm/kernels/operators/collision_srt.cuh": "6b7ff842f392c8f853d2d1a97cc69bfe50f21283f73e659404da07cf6c95f5e1",
"src/CelerisLab/lbm/kernels/operators/collision_trt.cuh": "9725b9840b9ad8b4ec7be61ff5d06d3151e86d1a87585c13f2fbf934d0d2c5f6",
"src/CelerisLab/lbm/kernels/operators/forcing_guo.cuh": "969a430c1af03f32e3bd0df95d805e64f56334717c44dd4ba208ba503062a24b",
"src/CelerisLab/lbm/kernels/operators/helpers.cuh": "0b3a4d95d7d19f5fec09e5423b8f4cc99f8a226b541967425967330da5a051ec",
"src/CelerisLab/lbm/kernels/operators/macro.cuh": "e27c1c410dc857be98da4596568e405eeaf88701d291ac0e398aa9cd23dee740",
"src/CelerisLab/lbm/kernels/operators/turbulence_smag.cuh": "ff7f000297ee0817e255ad9fcb6a13b647e3ddd5b206518d35f38d2895a9f501",
"src/CelerisLab/lbm/kernels/step/aux_kernels.cu": "bb7f07d898f0d5151e5bd0f45e6bd819c5e83a345a265b73204906ac3cc29db4",
"src/CelerisLab/lbm/kernels/step/esopull_macro.cu": "c73116020ca217ede1e4de27c8269c31f9d91e33433ac8252a21e9b14187c50f",
"src/CelerisLab/lbm/kernels/step/esopull_sync.cu": "c0ff3e72900c3ab89a05dd03684e96d3b740cf1672d565727fc2af165933d087",
"src/CelerisLab/lbm/kernels/step/init_flow.cu": "b1139addaf8c2fa8532c167b4324b38ddd5b931cc499152c7c287710746eeaa6",
"src/CelerisLab/lbm/kernels/step/one_step_double.cu": "9bbf48e0c907dc5e0bf9e8fb653acd09f49ebed7208dd595c802d07f7439e289",
"src/CelerisLab/lbm/kernels/step/one_step_esopull.cu": "aa223b2744ae6c9125673df311a96ce588032c1ba58bebb11fd33ecb990f8ed4",
"src/CelerisLab/lbm/kernels/streaming/esopull_semantic_helpers.cuh": "dd5a9091628b079efd97e53fd49e52d09dcee88529235db77a5648e02937af6e",
"src/CelerisLab/lbm/kernels/streaming/esopull_single_buffer.cuh": "77ed4ab9a0ebc5ff62a91a7c39066ef395391ae36e9040a69fb2bd8b1626dc79",
"src/CelerisLab/lbm/kernels/streaming/pull_double_buffer.cuh": "b65d741ed78b7b62f99531ba0c0ce1936663d8dc9640e7d7d16c707f1baf6549",
"src/CelerisLab/lbm/stepper.py": "e9e51e3a902fbda086d4707c0487e7b1dea6246fab290a24d82bd52d4296410e",
"src/CelerisLab/simulation.py": "f701d60981d73df688e1284a85f4e42e732416253a631fb81d063490890490f6",
"tests/audit/Force_region.md": "3092080afe4a30665fe1b721803621ba9d0dd5d20ad17826984d94efc160fdac",
"tests/audit/Force_region_validation.md": "279a4484d1bfca0fd67aed4de3282d547aa048f89389420d8e5f2fd86db54fc2",
"tests/conftest.py": "fa0ec26667872c9c1011d608310d2551fd24719b5d216f06d923ff4c74c73237",
"tests/integration/__init__.py": "c354e43ba09b55a8013a2df2069438740b5f48b5917d855e52e33ab5d7da5a6c",
"tests/integration/test_body_sync_e2e.py": "d29f01771f3512a4b3ad06fae4ee952327d7fd9f0476942402fe92e9e327fcec",
"tests/integration/test_ddf_patch.py": "dda2abba32ca13e17098232f2831a0b1b66439c35f8931d9997a0257250c1281",
"tests/integration/test_sync_bodies_skeleton.py": "65f55a0980659b49ae0027f0642a27e2ebaead226ef083f233e878150884b07c",
"tests/integration/test_unified_obs.py": "4a681536099741d92bdc258525fe3374cbdbeba0d49996e53808381a1b6a13fa",
"tests/postproc/experiment.ipynb": "c6b45db1c57200f0777213ef8d44cb4996c1aa7a5fb42b968b77e56f51b4fca4",
"tests/postproc/run_exp_ctrl_matrix_streakline.py": "529d5f31a3862080f890c65177502b589710b2d960ddf131abaf7dc2f9e2a96f",
"tests/postproc/run_exp_ctrl_matrix_vorticity.py": "b7e0c4113111b9b677f321750c0055656ccfa4f45c25fe7846f3ed22f30d1467",
"tests/postproc/run_kan99b_streakline.py": "a2bbf3fc629ea170d2d4879317dbf2b81c43378a328100ac1f2ee8d9895666cb",
"tests/postproc/run_stealth_steady_sweep.py": "3c32c4dede7da77fd6cfbd3f7b1ff43bb11390aaba621883ec96c6c7fdad5af7",
"tests/postproc/streakline_triangle_clean.png": "aa6669de7af6f82e98af25698a15f494e9cea20cb911ad9a30fe52597d33bb79",
"tests/postproc/vorticity_triangle_clean.png": "f831b8143b50f038d5920a0658469622655f34176d30cf7216feff108aa2ecaf",
"tests/screening/run_config_sweep.py": "5030c99923815ed6ad39110cb56f748c579ff4c66c7c1a368ad1a6008017e2ff",
"tests/specs/Kan99b_validation.md": "0d8bf74f2361f863809af1979bf8807adaa423f98321e1104a3d86d8c7a863cf",
"tests/specs/Sah04_validation.md": "1d122d29e281b7f50801b148f3314a0e35fdc27ee7d757c0a661243aa6185fb0",
"tests/specs/exp_ctrl_matrix.md": "5d9dd2e703582572eb68afe3a7096ea43ec82aa03418d20ed283c5365031e433",
"tests/specs/kan99b.md": "b7e0669f0a6b677d902204f3427d1b3ac254bfe70c37d9cf995fb3e8f1fedfb1",
"tests/specs/sah04.md": "33dbe5e0fd63738abd055c4aa403c191b6de04e6efdbc568f1942c6d23c7459f",
"tests/specs/streakline_notes.md": "cd342d214c07eb608ac2e5b8697322c44c31dbf4d27dfdb5409de46ef474df4b",
"tests/unit/__init__.py": "17f57920d9c1ff46096ceb2af008eb38c4b98dc81ffa3c3d310e758e80ca1cd4",
"tests/unit/test_body_flags.py": "ac6409157924e15a13a3d2cc2f1a09014b8b0198ba17c824f412ff20b19e2aac",
"tests/unit/test_equilibrium.py": "1ecbcc4547b1c846b75320463d828d6a36b6aae73208084fae476b1001c1bb50",
"tests/unit/test_pending_edits.py": "905a0dff23b123e8e38aaf364984c810fe51c39acd64aa271e343a4701d3d024",
"tests/unit/test_sync_plan.py": "983313ef783143231fa5a392f85f3bac011184773ccaf5d946504c206cabb721",
"tests/validation/run_kan99b_rotating_cylinder.py": "323186429a100f36b41ab9b81ade2da6a47e93be3ccb7034a3a6b97f4ee8eb00",
"tests/validation/run_perf_baseline.py": "93dde2eb1132f45ea69e1e285ff421dc97a83651a22c79571f299c1909650721",
"tests/validation/run_sah04_st_matrix.py": "9b9c0ce063e07a316846635ee769b837ed26be6bf039f78f4219c10b516f8006",
"tests/validation/test_sensor_accuracy.py": "c6038a9e7a597a1738814bd15e6ecf357cfd271cdc2a132d47943c5280ac0ef9"
}
}
research/j1-2026-10-12/incremental.diff
+629
View File
@@ -0,0 +1,629 @@
--- a/src/CelerisLab/common/checkpoint.py
+++ b/src/CelerisLab/common/checkpoint.py
@@ -214,6 +214,10 @@
# Config compatibility check
saved_cfg = json.loads(hf.attrs["config_json"])
+ if "on_site_velocity" in (saved_cfg.get("y_wall_bc"), lbm_cfg.y_wall_bc):
+ for key in ("y_wall_bc", "velocity", "wall_normal_velocity"):
+ if saved_cfg.get(key) != getattr(lbm_cfg, key):
+ raise ValueError(f"Checkpoint on-site boundary mismatch on '{key}'")
for key in ("lattice_model", "nx", "ny", "nz", "store_precision",
"streaming", "ddf_shifting"):
saved_val = saved_cfg.get(key)
@@ -279,6 +283,9 @@
if key in raw_params}
if not all(np.isfinite(value) for value in params.values()):
raise ValueError("Checkpoint runtime parameters must be finite")
+ if lbm_cfg.y_wall_bc == "on_site_velocity" and (
+ bodies.count or any(params.get(k, 0.0) != 0 for k in ("fx", "fy", "fz"))):
+ raise ValueError("on_site_velocity does not support body forces or objects")
obs_accum_steps = int(hf.attrs.get("obs_accum_steps", 0))
if obs_accum_steps < 0:
raise ValueError("Checkpoint obs_accum_steps must be nonnegative")
--- a/src/CelerisLab/config.py
+++ b/src/CelerisLab/config.py
@@ -34,7 +34,7 @@
"regularized": 3,
}
OUTLET_MAP = {"neq_extrap": 0, "zero_gradient": 1, "blended": 2}
-Y_WALL_BC_MAP = {"bounce_back": 0, "free_slip": 1}
+Y_WALL_BC_MAP = {"bounce_back": 0, "free_slip": 1, "on_site_velocity": 2}
DTYPE_MAP = {"FP32": "float", "FP64": "double"}
@@ -86,6 +86,7 @@
inlet_regularized_neq_damp: float = 0.5
outlet_srt_neq_damp: float = 0.5
y_wall_bc: str = "bounce_back"
+ wall_normal_velocity: float = 0.0
omega_min: float = 0.01
omega_max: float = 1.99
@@ -127,7 +128,7 @@
for name in ("viscosity", "velocity", "rho", "les_cs", "trt_magic_param",
"outlet_blend_alpha", "inlet_trt_neq_damp",
"inlet_regularized_neq_damp", "outlet_srt_neq_damp",
- "omega_min", "omega_max"):
+ "omega_min", "omega_max", "wall_normal_velocity"):
if not math.isfinite(getattr(self, name)):
raise ValueError(f"{name} must be finite.")
if self.viscosity <= 0 or self.rho <= 0:
@@ -150,6 +151,13 @@
)
if self.dim == 2 and self.nz != 1:
raise ValueError("nz must be 1 for 2D")
+ if self.y_wall_bc == "on_site_velocity":
+ if (not self.is_d2q9 or self.collision != "SRT"
+ or self.streaming != "double_buffer" or self.store_precision != "FP32"
+ or self.ddf_shifting or self.les_enabled):
+ raise ValueError("on_site_velocity requires D2Q9/SRT/FP32/unshifted/double_buffer without LES")
+ if abs(self.wall_normal_velocity) >= 1:
+ raise ValueError("on_site_velocity requires abs(wall_normal_velocity) < 1")
if not (0.0 <= self.inlet_trt_neq_damp <= 1.0):
raise ValueError("method.inlet.trt_neq_damp must lie in [0, 1].")
if not (0.0 <= self.inlet_regularized_neq_damp <= 1.0):
@@ -187,7 +195,9 @@
"""Parameters that cannot change while field allocations/state are retained."""
self.validate()
return (self.lattice_model, self.nx, self.ny, self.nz, self.data_type,
- self.store_precision, bool(self.ddf_shifting), self.streaming)
+ self.store_precision, bool(self.ddf_shifting), self.streaming,
+ (self.velocity, self.wall_normal_velocity)
+ if self.y_wall_bc == "on_site_velocity" else None)
def to_macros(self) -> Dict[str, Any]:
"""Return flat dict of macro_name → value for header generation."""
@@ -216,6 +226,7 @@
"OUTLET_BLEND_ALPHA": _float_literal(self.outlet_blend_alpha),
"OUTLET_BACKFLOW_CLAMP": int(self.outlet_backflow_clamp),
"Y_WALL_BC": Y_WALL_BC_MAP[self.y_wall_bc],
+ "WALL_NORMAL_VELOCITY": _float_literal(self.wall_normal_velocity),
"OMEGA_COLLISION_MIN": _float_literal(self.omega_min),
"OMEGA_COLLISION_MAX": _float_literal(self.omega_max),
"TRT_MAGIC_PARAM": _float_literal(self.trt_magic_param),
@@ -284,6 +295,7 @@
inlet_regularized_neq_damp=m["inlet"].get("regularized_neq_damp", 0.5),
outlet_srt_neq_damp=m["outlet"].get("srt_neq_damp", 0.5),
y_wall_bc=m.get("y_wall_bc", "bounce_back"),
+ wall_normal_velocity=m.get("wall_normal_velocity", 0.0),
omega_min=m["omega_guard"]["min"],
omega_max=m["omega_guard"]["max"],
threads_per_block=c["threads_per_block"],
--- a/src/CelerisLab/configs/CONFIG.md
+++ b/src/CelerisLab/configs/CONFIG.md
@@ -101,3 +101,18 @@
D2Q9/D3Q19 的 flag 扩展位 `0x0800` 定义为 `FRC_REGION`，用于标记 force_region 类型物体的格子。
- ForceRegionKernel 不依赖 flag 位执行，flag 只用于后处理可视化。
- `merge_flag_masks` 会正确合并 force_region 的 `FLUID | FRC_REGION` 标记。
+# J1 on-site velocity planes (limited reference case)
+
+Set `method.y_wall_bc` to `"on_site_velocity"` and
+`method.wall_normal_velocity` to V (default 0). This selects an x-periodic
+domain with physical planes through fluid rows y=1 and y=NY−2, H=NY−3.
+The bottom velocity is (0,V); the top is (`physics.velocity`,V).
+These are constant **Cartesian** velocities: positive V injects through the
+bottom and leaves through the top. The selected rows do not also receive
+halfway reflection. Initialization uses a linear tangential profile and V.
+
+Supported only for D2Q9/SRT/FP32/unshifted/double_buffer without LES, objects
+or body forces. Boundary targets and the mode are fixed at construction;
+checkpoint restart requires matching targets. This is an entire-plane
+Couette-throughflow reference, not a finite slot or curved jet API. The default
+`bounce_back` mode and existing configurations retain their previous path.
--- a/src/CelerisLab/cuda/compiler_v2.py
+++ b/src/CelerisLab/cuda/compiler_v2.py
@@ -65,6 +65,8 @@
macros = cfg.to_macros()
if isinstance(n_objects, bool) or not isinstance(n_objects, int) or n_objects < 0:
raise ValueError("n_objects must be a nonnegative integer")
+ if cfg.y_wall_bc == "on_site_velocity" and n_objects:
+ raise ValueError("on_site_velocity currently requires an object-free domain")
build_dir = tempfile.mkdtemp(prefix="celeris-build-", dir=build_root)
try:
shutil.copytree(_KERNEL_DIR, build_dir, dirs_exist_ok=True,
@@ -173,6 +175,7 @@
#define OUTLET_BLEND_ALPHA {m['OUTLET_BLEND_ALPHA']}
#define OUTLET_BACKFLOW_CLAMP {m['OUTLET_BACKFLOW_CLAMP']}
#define Y_WALL_BC {m['Y_WALL_BC']}
+#define WALL_NORMAL_VELOCITY {m['WALL_NORMAL_VELOCITY']}
#define OMEGA_COLLISION_MIN {m['OMEGA_COLLISION_MIN']}
#define OMEGA_COLLISION_MAX {m['OMEGA_COLLISION_MAX']}
--- a/src/CelerisLab/lbm/field.py
+++ b/src/CelerisLab/lbm/field.py
@@ -123,6 +123,8 @@
if self.cfg.is_d2q9:
flag[0:self.nx] = SOLID | BC_WALL
flag[(self.ny - 1) * self.nx:self.ny * self.nx] = SOLID | BC_WALL
+ if self.cfg.y_wall_bc == "on_site_velocity":
+ return flag # x periodic; physical y planes are the adjacent fluid rows.
flag[::self.nx] = SOLID | BC_INLET
flag[self.nx - 1::self.nx] = SOLID | BC_OUTLET
flag[0] = SOLID | BC_WALL
@@ -231,6 +233,10 @@
Accepted keys: omega, fx, fy, fz, n_objects.
"""
+ if self.cfg.y_wall_bc == "on_site_velocity" and (
+ any(float(kwargs.get(k, 0.0)) != 0 for k in ("fx", "fy", "fz"))
+ or int(kwargs.get("n_objects", 0)) != 0):
+ raise ValueError("on_site_velocity does not support body forces or objects")
self._runtime_params.update({
"omega": float(kwargs.get("omega", self._runtime_params["omega"])),
"fx": float(kwargs.get("fx", self._runtime_params["fx"])),
--- /dev/null
+++ b/src/CelerisLab/lbm/kernels/boundary/planar_velocity.cuh
@@ -0,0 +1,22 @@
+// On-site D2Q9 Zou-He velocity closure, paired lattice ordering.
+// Input is poststream f at the arrival time. Only unknown populations change;
+// the caller owns collision/store. No force, shifting or off-lattice walls.
+#ifndef CELERIS_BOUNDARY_PLANAR_VELOCITY_CUH
+#define CELERIS_BOUNDARY_PLANAR_VELOCITY_CUH
+
+__device__ __forceinline__ void reconstruct_bottom_velocity(float* f, float u, float v)
+{
+ const float rho = (f[0] + f[1] + f[2] + 2.0f*(f[4] + f[6] + f[7])) / (1.0f-v);
+ f[3] = f[4] + (2.0f/3.0f)*rho*v;
+ f[5] = f[6] + 0.5f*(f[2]-f[1]) + 0.5f*rho*u + (1.0f/6.0f)*rho*v;
+ f[8] = f[7] + 0.5f*(f[1]-f[2]) - 0.5f*rho*u + (1.0f/6.0f)*rho*v;
+}
+
+__device__ __forceinline__ void reconstruct_top_velocity(float* f, float u, float v)
+{
+ const float rho = (f[0] + f[1] + f[2] + 2.0f*(f[3] + f[5] + f[8])) / (1.0f+v);
+ f[4] = f[3] - (2.0f/3.0f)*rho*v;
+ f[6] = f[5] + 0.5f*(f[1]-f[2]) - 0.5f*rho*u - (1.0f/6.0f)*rho*v;
+ f[7] = f[8] + 0.5f*(f[2]-f[1]) + 0.5f*rho*u - (1.0f/6.0f)*rho*v;
+}
+#endif
--- a/src/CelerisLab/lbm/kernels/kernel_v2.cu
+++ b/src/CelerisLab/lbm/kernels/kernel_v2.cu
@@ -56,6 +56,9 @@
#include "boundary/bounce_back.cuh"
#include "boundary/curved_boundary.cuh"
#include "boundary/inlet_outlet.cuh"
+#if Y_WALL_BC == 2
+#include "boundary/planar_velocity.cuh"
+#endif
#include "boundary/ibm_kernels.cuh"
// ============================================================================
--- a/src/CelerisLab/lbm/kernels/step/init_flow.cu
+++ b/src/CelerisLab/lbm/kernels/step/init_flow.cu
@@ -49,10 +49,12 @@
{
if (y == 0u || y == (unsigned int)(NY - 1))
return (uint16_t)(FLAG_SOLID | FLAG_BC_WALL);
+#if Y_WALL_BC != 2
if (x == 0u)
return (uint16_t)(FLAG_SOLID | FLAG_BC_INLET);
if (x == (unsigned int)(NX - 1))
return (uint16_t)(FLAG_SOLID | FLAG_BC_OUTLET);
+#endif
return FLAG_FLUID;
}
@@ -85,7 +87,15 @@
if (is_solid(fl)) {
write_rest_equilibrium(fi, k);
} else {
+#if Y_WALL_BC == 2
+ // Initial g(0)=feq: both boundary velocities are already satisfied.
+ // Start from linear Couette, not the sought exponential steady profile.
+ float f[NQ];
+ compute_feq(RHO, U0*(float)(y-1u)/(float)(NY-3), WALL_NORMAL_VELOCITY, f);
+ stream_pull_store(k, f, fi);
+#else
write_equilibrium(fi, k, inlet_target_u((float)y));
+#endif
}
#elif DIM == 3
unsigned int x, y, z; unsigned long k;
@@ -138,4 +148,4 @@
#endif
}
-#endif // CELERIS_STEP_INIT_FLOW_CU
\ No newline at end of file
+#endif // CELERIS_STEP_INIT_FLOW_CU
--- a/src/CelerisLab/lbm/kernels/step/one_step_double.cu
+++ b/src/CelerisLab/lbm/kernels/step/one_step_double.cu
@@ -88,7 +88,12 @@
bounce_back_swap(f);
if (is_fluid(fl) && (y == 1u || y == (unsigned int)(NY - 2))) {
-#if Y_WALL_BC == 1
+#if Y_WALL_BC == 2
+ // Reconstruct f(t+dt) on the physical plane, then collide below.
+ // This replaces, rather than compounds, the halfway wall correction.
+ if (y == 1u) reconstruct_bottom_velocity(f, 0.0f, WALL_NORMAL_VELOCITY);
+ else reconstruct_top_velocity(f, U0, WALL_NORMAL_VELOCITY);
+#elif Y_WALL_BC == 1
apply_wall_freeslip_d2q9_y_pull(y, f, fi_in, k);
#else
apply_wall_bb_d2q9_y_pull(y, f, fi_in, k);
--- a/src/CelerisLab/simulation.py
+++ b/src/CelerisLab/simulation.py
@@ -115,6 +115,8 @@
Returns:
int body_id (formal id before initialize; -1 if staged).
"""
+ if self.lbm_cfg.y_wall_bc == "on_site_velocity":
+ raise ValueError("on_site_velocity currently requires an object-free domain")
type_lower = str(type).strip().lower()
if type_lower == "circle":
--- /dev/null
+++ b/tests/unit/cpp/planar_velocity.cpp
@@ -0,0 +1,30 @@
+#include <cstdio>
+#include <initializer_list>
+#define __device__
+#define __forceinline__ inline
+#include "boundary/planar_velocity.cuh"
+
+int main() {
+ const int cx[9]={0,1,-1,0,0,1,-1,1,-1};
+ const int cy[9]={0,0,0,1,-1,1,-1,-1,1};
+ const float w[9]={4.f/9,1.f/9,1.f/9,1.f/9,1.f/9,1.f/36,1.f/36,1.f/36,1.f/36};
+ for (int top : {0,1}) for (float rho : {1.f,1.04f})
+ for (float u : {-.02f,0.f,.02f}) for (float v : {-.01f,-.001f,0.f,.001f,.01f})
+ for (int neq : {0,1}) {
+ float f[9];
+ printf("%d %.9g %.9g %.9g %d\n",top,rho,u,v,neq);
+ for (int i=0;i<9;++i) {
+ float cu=cx[i]*u+cy[i]*v;
+ f[i]=w[i]*rho*(1+3*cu+4.5f*cu*cu-1.5f*(u*u+v*v));
+ f[i]+=neq*.0001f*(i-4);
+ // Unknown inputs must be completely overwritten, never consumed.
+ if (cy[i]==(top?-1:1)) f[i]=-7.f-i;
+ printf("%.9g ",f[i]);
+ }
+ puts("");
+ if (top) reconstruct_top_velocity(f,u,v);
+ else reconstruct_bottom_velocity(f,u,v);
+ for (float value : f) printf("%.9g ",value);
+ puts("");
+ }
+}
--- /dev/null
+++ b/tests/unit/test_planar_velocity.py
@@ -0,0 +1,95 @@
+"""J1 actual-header moments and bounded host capability contracts."""
+import json
+from pathlib import Path
+from types import SimpleNamespace
+
+import h5py
+import numpy as np
+import pytest
+
+import CelerisLab
+from CelerisLab.config import LBMConfig, load_lbm_config
+from CelerisLab.cuda import compiler_v2 as compiler
+from CelerisLab.common.checkpoint import save_checkpoint, load_checkpoint
+from .test_numerical_operators import compile_run
+from .test_state_regressions import device, make_field, manager
+
+
+def test_planar_helper_moments_and_known_populations(tmp_path):
+ root = Path(__file__).resolve().parents[2]
+ assert Path(CelerisLab.__file__).resolve() == root/'src/CelerisLab/__init__.py'
+ assert Path(compiler.kernel_path('kernel_v2.cu')).resolve() == root/'src/CelerisLab/lbm/kernels/kernel_v2.cu'
+ lines = compile_run(tmp_path, 'planar_velocity.cpp', {})
+ cx = np.array([0,1,-1,0,0,1,-1,1,-1])
+ cy = np.array([0,0,0,1,-1,1,-1,-1,1])
+ w = np.array([4/9]+[1/9]*4+[1/36]*4)
+ for start in range(0, len(lines), 3):
+ top, rho, u, v, neq = np.fromstring(lines[start], sep=' ')
+ before, after = [np.fromstring(s, sep=' ') for s in lines[start+1:start+3]]
+ known = cy != (-1 if top else 1)
+ np.testing.assert_array_equal(after[known], before[known])
+ density = after.sum()
+ np.testing.assert_allclose([after@cx, after@cy], density*np.array([u,v]), atol=3e-7, rtol=0)
+ if not neq:
+ cu = cx*u+cy*v
+ expected = w*rho*(1+3*cu+4.5*cu**2-1.5*(u*u+v*v))
+ np.testing.assert_allclose(after, expected, atol=3e-7, rtol=0)
+ assert abs(density-rho) <= 3e-7
+
+
+@pytest.mark.parametrize('override', [
+ dict(collision='TRT'), dict(collision='MRT'), dict(streaming='esopull'),
+ dict(store_precision='FP16S'), dict(ddf_shifting=True), dict(les_enabled=True),
+ dict(lattice_model='D3Q19'), dict(wall_normal_velocity=1.),
+ dict(wall_normal_velocity=float('nan')),
+])
+def test_planar_unsupported_configuration(override):
+ with pytest.raises(ValueError):
+ LBMConfig(y_wall_bc='on_site_velocity', **override).validate_runtime()
+
+
+def test_planar_config_roundtrip_and_fixed_targets(tmp_path):
+ path = Path(__file__).resolve().parents[2]/'src/CelerisLab/configs/config_lbm.json'
+ data = json.loads(path.read_text())
+ data['method'].update(y_wall_bc='on_site_velocity', wall_normal_velocity=-.01,
+ collision='SRT', streaming='double_buffer',
+ store_precision='FP32', ddf_shifting=False)
+ data['method']['les']['enabled'] = False
+ path = tmp_path/'config.json'
+ path.write_text(json.dumps(data))
+ cfg = load_lbm_config(str(path))
+ assert float(cfg.to_macros()['WALL_NORMAL_VELOCITY'][:-1]) == -.01
+ with pytest.raises(ValueError, match='object-free'):
+ compiler.generate_config(cfg, n_objects=1, build_root=tmp_path)
+ before = cfg.allocation_signature()
+ cfg.wall_normal_velocity = .01
+ assert cfg.allocation_signature() != before
+
+
+def test_planar_force_rejected_before_state_change(device):
+ field = make_field(device, y_wall_bc='on_site_velocity')
+ before, writes = field._runtime_params.copy(), device.writes
+ with pytest.raises(ValueError, match='body forces'):
+ field.update_params(fx=.001)
+ assert field._runtime_params == before and device.writes == writes
+
+
+@pytest.mark.parametrize('damage', ['target', 'mode', 'force'])
+def test_planar_checkpoint_contract_before_writes(device, tmp_path, damage):
+ field = make_field(device, y_wall_bc='on_site_velocity')
+ bodies = manager(field)
+ bodies.sync_to_gpu(field)
+ path = save_checkpoint(field, SimpleNamespace(step_count=3), field.cfg, bodies,
+ str(tmp_path/'plane.h5'))
+ with h5py.File(path, 'a') as hf:
+ if damage == 'force':
+ hf.attrs['runtime_params_json'] = '{"fx":0.001}'
+ else:
+ cfg = json.loads(hf.attrs['config_json'])
+ cfg['wall_normal_velocity' if damage == 'target' else 'y_wall_bc'] = (
+ .01 if damage == 'target' else 'bounce_back')
+ hf.attrs['config_json'] = json.dumps(cfg)
+ writes = device.writes
+ with pytest.raises(ValueError):
+ load_checkpoint(path, field, SimpleNamespace(_step_count=0), field.cfg, bodies)
+ assert device.writes == writes
--- /dev/null
+++ b/tests/validation/run_j1.py
@@ -0,0 +1,239 @@
+"""Bounded J1 Couette-throughflow validation; gates fixed before first run.
+
+See research/j1-2026-10-12/REVIEW.md. No production audit kernel or extra launch
+is added: a temporary test build records actual incoming populations and the
+roundoff of the real collision, then is compared with uninstrumented stepping.
+"""
+import argparse
+import hashlib
+import json
+import os
+from pathlib import Path
+import sys
+from tempfile import TemporaryDirectory
+from unittest.mock import patch
+
+import numpy as np
+import pycuda.driver as cuda
+
+import CelerisLab
+from CelerisLab.cuda import compiler_v2 as compiler
+from CelerisLab.simulation import Simulation
+
+ROOT = Path(__file__).resolve().parents[2]
+CX = np.array([0,1,-1,0,0,1,-1,1,-1])
+CY = np.array([0,0,0,1,-1,1,-1,-1,1])
+GATES = dict(u_relative_l2=1e-3, v_max_error=2e-6, flux_relative_error=1e-3,
+ mass_offset=1e-3, mass_window_span=6e-6, mass_chunk_change=2e-6,
+ u_change=2e-5, v_change=2e-7, budget_relative_residual=2e-7)
+
+
+def make_sim(v, mode='on_site_velocity'):
+ config = json.loads((ROOT/'src/CelerisLab/configs/config_lbm.json').read_text())
+ config['grid'].update(nx=16, ny=35, nz=1, lattice_model='D2Q9')
+ config['physics'].update(viscosity=.1, velocity=.02, rho=1.)
+ config['method'].update(collision='SRT', streaming='double_buffer',
+ store_precision='FP32', ddf_shifting=False,
+ y_wall_bc=mode, wall_normal_velocity=v)
+ config['method']['les']['enabled'] = False
+ with TemporaryDirectory(prefix='celeris-j1-config-') as folder:
+ lbm, body = Path(folder)/'lbm.json', Path(folder)/'body.json'
+ lbm.write_text(json.dumps(config))
+ body.write_text('{"objects":[]}')
+ return Simulation(str(lbm), str(body), device_id=0)
+
+
+def populations(sim):
+ return sim.get_ddf().reshape(9, sim.field.ny, sim.field.nx).astype(np.float64)
+
+
+def observables(f):
+ interior = f[:, 1:-1, :]
+ rho = interior.sum(axis=0)
+ ux = np.einsum('q,qyx->yx', CX, interior)/rho
+ uy = np.einsum('q,qyx->yx', CY, interior)/rho
+ return rho, ux, uy
+
+
+AUDIT = r'''
+__device__ double j1_incoming[2*NX];
+__device__ double j1_collision[NX*NY];
+__device__ void j1_collide_audit(float* f, float rho, float ux, float uy) {
+ const unsigned x=threadIdx.x+blockIdx.x*blockDim.x, y=blockIdx.y;
+ double before=0.0;
+ for(int i=0;i<NQ;++i) before+=(double)f[i];
+ if(y==1) j1_incoming[x]=(double)f[3]+f[5]+f[8];
+ if(y==NY-2) j1_incoming[NX+x]=(double)f[4]+f[6]+f[7];
+ collide_dispatch(f,rho,ux,uy);
+ double after=0.0;
+ for(int i=0;i<NQ;++i) after+=(double)f[i];
+ j1_collision[y*NX+x]=after-before;
+}
+'''
+
+
+def population_budget(sim):
+ """16 real steps, separate incoming/outgoing/collision records, no targets."""
+ saved = sim.get_ddf().copy()
+ count = sim.stepper.step_count
+ sim.run(16)
+ plain = sim.get_ddf().copy()
+ sim.field.ddf[:] = saved
+ sim.field.upload_ddf()
+ sim.stepper._step_count = count
+ build = compiler.generate_config(sim.lbm_cfg)
+ original_step = sim.stepper.step_fn
+ try:
+ source = Path(build)/'step/one_step_double.cu'
+ code = source.read_text().replace('collide_dispatch(f, rho_n, ux, uy);',
+ 'j1_collide_audit(f, rho_n, ux, uy);')
+ marker = '__global__ __launch_bounds__(NT, 2)'
+ assert code.count(marker) == 1
+ source.write_text(code.replace(marker, AUDIT+'\n'+marker))
+ audit_hash = hashlib.sha256(source.read_bytes()).hexdigest()
+ ptx = compiler.compile_kernel(build_dir=build)
+ rows = []
+ with sim.ctx.activate():
+ module = compiler.load_module(ptx)
+ params, size = module.get_global('d_params')
+ cuda.memcpy_dtod(params, sim.field.module.get_global('d_params')[0], size)
+ incoming_ptr, _ = module.get_global('j1_incoming')
+ collision_ptr, size = module.get_global('j1_collision')
+ cuda.memset_d8(collision_ptr, 0, size)
+ incoming = np.empty(2*sim.field.nx, dtype=np.float64)
+ collision = np.empty(sim.field.n, dtype=np.float64)
+ sim.stepper.step_fn = module.get_function('OneStep')
+ for _ in range(16):
+ before = populations(sim)
+ mass = before[:, 1:-1].sum()
+ outgoing_bottom = before[[4,6,7], 1, :].sum()
+ outgoing_top = before[[3,5,8], -2, :].sum()
+ sim.run(1)
+ after = populations(sim)
+ cuda.memcpy_dtoh(incoming, incoming_ptr)
+ cuda.memcpy_dtoh(collision, collision_ptr)
+ delta = after[:, 1:-1].sum()-mass
+ bottom = incoming[:sim.field.nx].sum()-outgoing_bottom
+ top = incoming[sim.field.nx:].sum()-outgoing_top
+ physical_residual = delta-bottom-top
+ residual = physical_residual-collision.sum()
+ rows.append(dict(step=sim.stepper.step_count, mass=mass, delta_mass=delta,
+ bottom_net_in=bottom, top_net_in=top,
+ collision_roundoff=float(collision.sum()),
+ physical_residual=physical_residual,
+ budget_residual=residual))
+ identical = np.array_equal(sim.get_ddf(), plain)
+ return dict(steps=rows, instrumented_matches_plain=identical,
+ audit_source_sha256=audit_hash,
+ max_relative_residual=max(abs(r['budget_residual'])/r['mass'] for r in rows),
+ max_physical_relative_residual=max(abs(r['physical_residual'])/r['mass'] for r in rows))
+ finally:
+ sim.stepper.step_fn = original_step
+ compiler.cleanup_build_dir(build)
+
+
+def run_case(v):
+ height, nu, speed, nx = 32, .1, .02, 16
+ chunk = int(np.ceil(.5*height**2/(np.pi**2*nu)))
+ with make_sim(v) as sim:
+ sim.initialize()
+ # Reject unsupported entrypoints before observing the reference flow.
+ try:
+ sim.add_body('circle', center=(8,16), radius=2)
+ except ValueError:
+ pass
+ else:
+ raise AssertionError('J1 accepted an object')
+ rho, ux, uy = observables(populations(sim))
+ initial_mass = rho.sum()
+ previous_u, previous_v, previous_mass = ux.mean(axis=1), uy.mean(axis=1), initial_mass
+ history = [dict(step=0, mass=float(initial_mass))]
+ steady = False
+ for sample in range(1, 41):
+ sim.run(chunk)
+ f = populations(sim)
+ if not np.isfinite(f).all():
+ raise AssertionError('Nonfinite J1 populations')
+ rho, ux, uy = observables(f)
+ u, normal, mass = ux.mean(axis=1), uy.mean(axis=1), rho.sum()
+ item = dict(step=sim.stepper.step_count, mass=float(mass),
+ u_change=float(np.sqrt(np.mean((u-previous_u)**2))/speed),
+ v_change=float(np.sqrt(np.mean((normal-previous_v)**2))),
+ mass_change=float(abs(mass-previous_mass)/initial_mass))
+ history.append(item)
+ previous_u, previous_v, previous_mass = u.copy(), normal.copy(), mass
+ tail = history[-4:]
+ steady = sample >= 8 and all(
+ r['u_change'] <= GATES['u_change'] and r['v_change'] <= GATES['v_change']
+ and r['mass_change'] <= GATES['mass_chunk_change'] for r in tail)
+ steady = steady and np.ptp([r['mass'] for r in history[-5:]])/initial_mass <= GATES['mass_window_span']
+ if steady:
+ break
+ y = np.arange(height+1)
+ exact = speed*y/height if v == 0 else speed*np.expm1(v*y/nu)/np.expm1(v*height/nu)
+ relative_l2 = float(np.linalg.norm(u[1:-1]-exact[1:-1])/np.linalg.norm(exact[1:-1]))
+ v_error = float(np.max(np.abs(uy-v)))
+ sections = [8,16,24] # Distances above physical bottom; none is a BC row.
+ flux = [float(np.sum(rho[s]*uy[s])) for s in sections]
+ target_flux = float(rho.mean()*v*nx)
+ flux_error = max(abs(q-target_flux)/abs(target_flux) for q in flux) if v else max(abs(q)/nx for q in flux)
+ mass_span = float(np.ptp([r['mass'] for r in history[-5:]])/initial_mass)
+ offset = float(abs(mass-initial_mass)/initial_mass)
+ result = dict(v=v, height=height, nx=nx, nu=nu, top_u=speed,
+ pe=v*height/nu, re=speed*height/nu,
+ ma=float(np.hypot(speed,v)*np.sqrt(3)),
+ steps=sim.stepper.step_count, max_steps=40*chunk, steady=bool(steady),
+ relative_l2=relative_l2, v_max_error=v_error,
+ mass_offset=offset, mass_window_span=mass_span,
+ rho_range=[float(rho.min()),float(rho.max())],
+ section_y=sections, measured_flux=flux, reference_flux=target_flux,
+ flux_error=flux_error, profile_u=u.tolist(), profile_v=normal.tolist(),
+ analytic_u=exact.tolist(), history=history)
+ budget = population_budget(sim)
+ result['population_budget'] = budget
+ result['passed'] = bool(steady and relative_l2 <= GATES['u_relative_l2']
+ and v_error <= GATES['v_max_error']
+ and flux_error <= (GATES['flux_relative_error'] if v else GATES['v_max_error'])
+ and offset <= GATES['mass_offset'] and mass_span <= GATES['mass_window_span']
+ and budget['instrumented_matches_plain']
+ and budget['max_relative_residual'] <= GATES['budget_relative_residual'])
+ return result
+
+
+def off_path_check(baseline):
+ outcomes = []
+ for kernel_root in (baseline, Path(compiler.kernel_path(''))):
+ with patch.object(compiler, '_KERNEL_DIR', str(kernel_root)):
+ with make_sim(0., mode='bounce_back') as sim:
+ sim.initialize()
+ sim.run(7)
+ outcomes.append((sim.get_ddf().copy(), sim.get_flags().copy()))
+ return bool(all(np.array_equal(a,b) for a,b in zip(*outcomes)))
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--output', type=Path, required=True)
+ parser.add_argument('--baseline-kernels', type=Path,
+ help='Optional pre-change CUDA source for the off-mode comparison')
+ args = parser.parse_args()
+ assert Path.cwd() == ROOT == Path('/home/frank14f/CelerisLab')
+ assert Path(CelerisLab.__file__).resolve() == ROOT/'src/CelerisLab/__init__.py'
+ assert Path(compiler.kernel_path('kernel_v2.cu')).resolve() == ROOT/'src/CelerisLab/lbm/kernels/kernel_v2.cu'
+ assert os.environ.get('CUDA_VISIBLE_DEVICES') in ('0','1')
+ report = dict(python=sys.executable, package=CelerisLab.__file__,
+ cuda_source=compiler.kernel_path('kernel_v2.cu'),
+ physical_gpu=os.environ['CUDA_VISIBLE_DEVICES'], gates=GATES, cases=[])
+ report['off_path_bitwise_equal'] = (off_path_check(args.baseline_kernels)
+ if args.baseline_kernels else None)
+ for v in (0., .001, -.001, .01, -.01):
+ case = run_case(v)
+ report['cases'].append(case)
+ args.output.write_text(json.dumps(report, indent=2)+'\n')
+ print(json.dumps({k:case[k] for k in ('v','steps','steady','relative_l2','v_max_error',
+ 'mass_offset','mass_window_span','flux_error','passed')}), flush=True)
+ return 0 if report['off_path_bitwise_equal'] is not False and all(c['passed'] for c in report['cases']) else 1
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
research/j1-2026-10-12/results.json
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research/j2-slot-2026-10-12/REVIEW.md
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# J2 静态有限平壁 slot：运行前冻结约定
2026-10-12。root唯一写入，沿用J1能力范围。起点HEAD和208个现有文件的准确SHA256见baseline.json；src/tests副本仅为制作本包增量和旧路径对照。保留此前脏树、checkpoint v3、四个配置头和notebook。不提交推送，不修改其他项目或环境。
## 参考和修改范围（代码修改前记录）
沿用本地lbm-ref-architecture-guard。已搜索并核对ref/OpenLB/src/boundary/zouHeDynamics.hh:67–118，以及research/design-2026-10-09/jet-study.md中Zou–He Eq14–18、Ho scheme A的本地阅读记录。
1. 只重建poststream未知populations；已有paired-D2Q9 helper负责对应方向、密度分母和切向矩修正，不改公式。
2. reconstruction后在同一节点collision/store。slot与零速flanks共用y=1物理平面，均不额外halfway反射。
3. 空间目标profile只改变传给helper的局部速度，不改变分布阶段或引入launch。静态幅值不需要时间插值。
4. profile与离散求和是本包公开的设计约定，不声称由参考文献证明其数值精度；实际流量、对称与收支须实测。
修改范围：config参数/宏/静态签名→compiler生成临时头→planar_velocity目标函数→现有init/step；checkpoint仅增加静态宽度比较（旧v3缺省为0）；CONFIG说明、针对性单测和复用J1审计的J2验证器。没有注册框架或新生产kernel。
## 参数、符号与初始化
新增整数method.wall_slot_width=W，默认0保留J1整平面路径；非零时要求2≤W≤NX、on_site_velocity且physics.velocity=0。中心c=(NX−1)/2，x=0..NX−1，单元面积权重均为1。
有限宽W<NX时s=2(x−c)/W，w(x)=(1−s²)²（|s|<1），其余为0。底部Cartesian v_b=A*w，A=wall_normal_velocity是连续profile中心峰值，离散中心不一定取到A，不重新归一峰值。公开请求Q=A*Σ_x w(x)，上部Cartesian v_t=Q/NX。W=NX明确使用w=1而不是保留边缘shape；W=0仍是J1的均匀A。CPU双精度求和生成top速度宏，实际CUDA目标为FP32，容许对应舍入；这只是请求体积配平，不是实际密度加权质量精确配平。
正A：底部向流体法向+n_y注入，上壁向流体法向−n_y故排出；负A两端反转。平面y=1与NY−2，H=NY−3。有限slot初态rho=1、u=0，v在底部profile与均匀top之间按y/H线性插值，写局部feq作为g(0)；初态满足目标但不是已求得的稳态/散度自由场。之后pull g(t)→到达时刻f(t+dt)→未知重建→SRT→g(t+dt)。全宽/禁用沿原J1初始化。
## 冻结有界验收（首次测试前）
- NX48，NY35，H32，nu=.1，U=0，rho0=1。有限宽W16，A=0,+.002,−.002；峰值尺度Re=A_abs*H/nu=.64，Ma≈.00347。不做端点或网格sweep。
- 采样间隔519步，每例最多40间隔=20760步，至少8间隔后才判稳。连续4间隔完整u/v场各自RMS变化/速度尺度≤5e−5；速度尺度固定max(|A|,.002)，不是用近零观测值作分母。每间隔相对初始质量变化≤2e−6，最后4间隔质量相对跨度≤6e−6，初末相对质量偏移≤1e−3。记录完整历史，不将持续增长解释为gauge freedom。
- 所有populations有限，rho最大偏离1≤.005，实际最大速度≤.01。零幅值时所有流体节点最大速度≤2e−6。
- 有限正/负：内部距离底平面8、16、24处实测Σrho*v，对rho_bar*Q的相对误差≤.005，三个截面流量跨度/|rho_bar*Q|≤.002；这是带弱可压缩/离散误差的诊断参照，不以请求量自证。另记录所有边界和内部宏观体积流量Σv、质量流量Σrho*v。底部/上部宏观目标最大速度误差≤2e−6。
- x镜像x↔NX−1−x：rho、v偶对称，u奇对称。max|rho−mirror(rho)|/rho0≤2e−5；max|v−mirror(v)|/速度尺度、max|u+mirror(u)|/速度尺度≤.002。容差为FP32温和低速案例诊断值，不宣称误差阶。实际内部中心v须与A同号，且max|u|>1e−5，防止只有均匀伪解通过流量。
- 全宽W=NX、A=.002：同网格/U=0/初值，与本包起点原J1均匀路径比较初始化和64步DDF/flags逐值相同。默认关闭的旧bounce_back短通道也与起点逐值相同。
- 每例再核对16步实际全平面population预算：包含全部底部slot、零目标flanks和整个上壁的真实incoming/outgoing；独立记录实际collision舍入。预算残差/M≤2e−7，另报不扣collision的物理残差；审计版与普通版逐值相同。必要时复用J1测试审计并细分slot/flank账，生产不加记录器。
- 配置错误、静态参数改变、checkpoint宽度/幅值不一致必须在写状态前拒绝；同参数slot恢复后续短轨迹逐值相同；兼容旧v3缺少宽度字段时默认0。实际profile节点值/请求Q独立验算（CPUshim与GPU场），保持已有J1矩测试。
全部运行固定cwd=/home/frank14f/CelerisLab、解释器=/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python、进程PYTHONPATH=/home/frank14f/CelerisLab/src。每次Python/编译/测试核对来源；每次GPU执行先查获准GPU0/1空闲。原始失败/成功日志保留；门槛变动必须显式记录。当前尚未执行本包测试。
## 完成状态：实现完成，有限 slot 未通过质量稳态验收
以上为运行前冻结记录。门槛未改，未追加长期计算或更换参数以求通过。正/负有限slot均达到20760步上限，末段速度变化达标，但质量仍增长；不能称为稳态质量验收通过，也不能以压力基准自由度接受。生产模式默认关闭，CONFIG中已标注本轮实测限制。
| 案例 | 步数 | 质量相对初态偏移 | 末4间隔质量相对跨度 | 内部质量流量最大相对误差 | 判定 |
| --- | ---: | ---: | ---: | ---: | --- |
| A=0，W16 | 4152 | 6.70552e-8 | 0 | 内部实测0 | 通过 |
| A=+.002，W16 | 20760 | 9.44271e-5 | **9.10981e-6** | 2.41774e-4 | **质量/稳态失败** |
| A=−.002，W16 | 20760 | 8.50923e-5 | **8.50902e-6** | 3.34474e-4 | **质量/稳态失败** |
有限案例末4间隔的质量增量分别约2.18–2.33e-6与2.01–2.20e-6/间隔，超过2e-6；窗口跨度超过6e-6。总偏移仍小于1e-3，不能用这一点替代末段漂移门槛。零幅值最终整个流体域u=v=0。
### 请求与真实流量
W16、NX48时Σw=8.533447265625，Q=±0.01706689453125，top v=±0.000355560302734375。离散峰值略小于A。原始实际rho/u/v二维场、全部纵向截面的宏观Σv和Σrho*v、质量采样与真实population账均见results.json。
| A | 内部y距8处Σrho*v | 距16处 | 距24处 | 底平面Σrho*v | 顶平面Σrho*v |
| --- | ---: | ---: | ---: | ---: | ---: |
| +.002 | .01706983335 | .01707181893 | .01707263291 | .01707108133 | .01706793159 |
| −.002 | −.01706487872 | −.01706400141 | −.01706263795 | −.01706563681 | −.01706770062 |
内部截面跨度相对参照为1.64019e-4和1.31282e-4，均通过.002门槛。边界最大速度误差为3.59193e-8和3.30522e-8；最大实际速度约.0019844，密度最大偏差分别3.41552e-4和2.47041e-4。
### 对称与独立质量预算
rho偶对称误差/rho0：正5.01052e-7、负4.99189e-7。u奇对称误差/固定.002：正9.03279e-5、负9.03303e-5；v偶对称误差/固定.002：正1.00537e-4、负9.03337e-5。均低于冻结门槛。内部中心v方向和非零横向u检查通过。
每例末态16步的实际全平面预算残差均为0，审计版与普通版逐值相同。这只证明已记录FP32分布的离散收支相符，不代表质量恒定或物理误差为0。
| A | 每步平均ΔM | 全边界平均净流入 | 平均collision舍入 |
| --- | ---: | ---: | ---: |
| 0 | 0 | 0 | 0 |
| +.002 | 8.10414e-6 | 3.89630e-6 | 4.20783e-6 |
| −.002 | 6.77782e-6 | 2.62831e-6 | 4.14951e-6 |
未扣collision的最大相对物理预算残差为正4.94425e-9、负8.18954e-9。正case底slot/flanks/top平均净交换为.01706407918、7.56886e-6、−.01706775173；负case为−.01705836912、−7.00448e-6、.01706800191。完整incoming/outgoing均已存储，没有忽略零目标flanks。这种按节点索引的population分账包括时间变化和斜向分布重分配，flanks分账净值不能直接解释为该处宏观穿壁速度。
结论限于实测：请求体积配平没有给出实际质量精确配平，且FP32碰撞舍入也贡献了增长。只读复核未发现本包profile、Zou–He未知方向、符号、阶段或checkpoint方面的必改编码错误。若后续要改变质量控制/出口条件，应先重新明确物理契约；本包未加入动态top、质量修正或密度钳制。
## 回归与只读复核
- CPU：98 passed、13 subtests passed，4.34秒，见cpu.log。包括实际CUDA helper CPUshim的9种profile/幅值组合、离散Q、配置/静态签名、checkpoint宽度不一致在写状态前拒绝、旧v3缺宽度按0、同宽度恢复，以及已有J1矩/力/对象限制与状态测试。
- 全宽W48与起点原J1 W0，U0=0/A=.002，初态与64步DDF/flags逐值相同。默认关闭旧通道7步DDF/flags与起点逐值相同。
- 正slot checkpoint载入新Simulation后7步DDF/flags/step_count逐值相同。
- architecture只读复核：未发现生产编码必改项；发现验证器原先允许省略baseline使两项比较跳过。现已要求该参数，并用CLI检查确认省略时在访问CUDA前退出2。本次GPU调用始终带baseline，因此不影响数值结果；未重跑物理路径。
- 本轮最终数值验证进程退出1，真实反映两例失败，不报告成全通过。
## 实际执行与失败记录
首次沙箱内nvidia-smi无法连接驱动；获准设备访问后只读检查成功：GPU0为0%、9MiB且无计算进程，GPU1已有其他Python进程占约362MiB。每次GPU运行前重复核对，只用物理GPU0；未干扰GPU1。未安装依赖或修改环境设置。
CPU实际命令（cwd固定为本项目）：
```bash
env PYTHONPATH=/home/frank14f/CelerisLab/src PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python - <<'PY' > research/j2-slot-2026-10-12/cpu.log 2>&1
from pathlib import Path
import sys, pytest, CelerisLab
from CelerisLab.cuda import compiler_v2 as compiler
root=Path('/home/frank14f/CelerisLab')
assert Path.cwd()==root
assert sys.executable=='/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python'
assert Path(CelerisLab.__file__).resolve()==root/'src/CelerisLab/__init__.py'
assert Path(compiler.kernel_path('kernel_v2.cu')).resolve()==root/'src/CelerisLab/lbm/kernels/kernel_v2.cu'
print(sys.executable, CelerisLab.__file__, compiler.kernel_path('kernel_v2.cu'))
raise SystemExit(pytest.main(['tests/unit/test_slot_velocity.py','tests/unit/test_planar_velocity.py','tests/unit/test_build_contracts.py','tests/unit/test_state_regressions.py','tests/unit/test_simulation_lifecycle.py','-q','--tb=short','-p','no:cacheprovider']))
PY
```
数值验证实际命令（执行两次；两次前均核对GPU0空闲）：
```bash
env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/home/frank14f/CelerisLab/src PYTHONDONTWRITEBYTECODE=1 PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python tests/validation/run_j2_slot.py --output research/j2-slot-2026-10-12/results.json --baseline-kernels /tmp/celeris-j2-before/src/CelerisLab/lbm/kernels > research/j2-slot-2026-10-12/gpu-validation.log 2>&1
```
第一次在零幅值计算完成后，输出JSON时因NumPy bool失败，日志原样移为gpu-serialization-failure.log；没有生成完整结果。将checks显式转为Python bool后完整重跑，模型/参数/门槛未改。第二次数值失败记录保留为gpu-validation.log，逐项指标在results.json。
CLI缺baseline检查使用同一解释器/进程PYTHONPATH和来源断言，通过runpy运行run_j2_slot.py，argv为`['run_j2_slot.py','--output','unused.json']`，断言SystemExit.code==2；原始stderr与确认在cli.log。未访问GPU、未创建unused.json。
## 增量与保存
incremental.diff仅包含相对本包起点的12个文件：生产代码6个文件+43/−3行，CONFIG说明+29/−2行，测试与验证5个文件+256/−6行，总计+328/−11行。没有新增生产文件或kernel launch。为避免混入此前同文件中的修改，不提交；没有推送。四个配置头和notebook的SHA256与起点相同，其余原文件均保留。git diff --check和增量补丁反向校验通过，反向应用后逐字节匹配起点哈希。
保留全部原始成功/失败日志。临时CUDA审计构建自动清理，重复起点副本在补丁反向核验后删除。日后复跑需先在临时副本中用本包diff反向重建起点src，再提供其kernels目录作为必需的baseline；不要在当前脏工作树直接反向应用。停在此包交复审；尚未覆盖长期质量稳定、端点/网格阶/性能、其他碰撞/精度/streaming、曲面slot或AMR。
research/j2-slot-2026-10-12/baseline-status.txt
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M pyproject.toml
M setup.py
M src/CelerisLab/__init__.py
M src/CelerisLab/body/__init__.py
M src/CelerisLab/body/ddf_patch.py
M src/CelerisLab/body/manager.py
M src/CelerisLab/body/preprocess/flag_overlay.py
M src/CelerisLab/common/__init__.py
M src/CelerisLab/common/checkpoint.py
M src/CelerisLab/config.py
M src/CelerisLab/configs/CONFIG.md
M src/CelerisLab/cuda/__init__.py
M src/CelerisLab/cuda/compiler_v2.py
M src/CelerisLab/cuda/context.py
M src/CelerisLab/lbm/__init__.py
M src/CelerisLab/lbm/curved_links.py
M src/CelerisLab/lbm/field.py
M src/CelerisLab/lbm/initializers.py
M src/CelerisLab/lbm/kernels/boundary/bounce_back.cuh
M src/CelerisLab/lbm/kernels/boundary/curved_boundary.cuh
M src/CelerisLab/lbm/kernels/boundary/inlet/common.cuh
M src/CelerisLab/lbm/kernels/boundary/inlet/zou_he_local.cuh
M src/CelerisLab/lbm/kernels/boundary/inlet_outlet.cuh
M src/CelerisLab/lbm/kernels/config/config_grid.h
M src/CelerisLab/lbm/kernels/config/config_method.h
M src/CelerisLab/lbm/kernels/config/config_objects.h
M src/CelerisLab/lbm/kernels/config/config_physics.h
M src/CelerisLab/lbm/kernels/kernel_v2.cu
M src/CelerisLab/lbm/kernels/operators/collision_mrt.cuh
M src/CelerisLab/lbm/kernels/operators/collision_trt.cuh
M src/CelerisLab/lbm/kernels/operators/forcing_guo.cuh
M src/CelerisLab/lbm/kernels/step/aux_kernels.cu
M src/CelerisLab/lbm/kernels/step/esopull_sync.cu
M src/CelerisLab/lbm/kernels/step/init_flow.cu
M src/CelerisLab/lbm/kernels/step/one_step_double.cu
M src/CelerisLab/lbm/kernels/step/one_step_esopull.cu
M src/CelerisLab/lbm/kernels/streaming/esopull_semantic_helpers.cuh
M src/CelerisLab/lbm/stepper.py
M src/CelerisLab/simulation.py
M tests/integration/test_body_sync_e2e.py
M tests/integration/test_ddf_patch.py
M tests/integration/test_sync_bodies_skeleton.py
M tests/integration/test_unified_obs.py
M tests/postproc/experiment.ipynb
M tests/validation/run_kan99b_rotating_cylinder.py
M tests/validation/run_sah04_st_matrix.py
M tests/validation/test_sensor_accuracy.py
?? docs/design/
?? docs/multilevel_grid_architecture_analysis.md
?? research/
?? src/CelerisLab/lbm/kernels/boundary/planar_velocity.cuh
?? "tests/audit/body\345\210\240\345\207\217\345\212\237\350\203\275.md"
?? tests/integration/_cases.py
?? tests/integration/test_numerical_gpu.py
?? tests/integration/test_runtime_contracts_gpu.py
?? tests/integration/test_state_gpu.py
?? tests/unit/cpp/
?? tests/unit/test_build_contracts.py
?? tests/unit/test_context_ownership.py
?? tests/unit/test_initializers.py
?? tests/unit/test_numerical_operators.py
?? tests/unit/test_planar_velocity.py
?? tests/unit/test_simulation_lifecycle.py
?? tests/unit/test_state_regressions.py
?? tests/validation/multigrid/
?? tests/validation/run_j1.py
?? tests/validation/run_poiseuille.py
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"tests/audit/Force_region.md": "3092080afe4a30665fe1b721803621ba9d0dd5d20ad17826984d94efc160fdac",
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}
}
research/j2-slot-2026-10-12/incremental.diff
+474
View File
@@ -0,0 +1,474 @@
--- a/src/CelerisLab/common/checkpoint.py
+++ b/src/CelerisLab/common/checkpoint.py
@@ -218,6 +218,8 @@
for key in ("y_wall_bc", "velocity", "wall_normal_velocity"):
if saved_cfg.get(key) != getattr(lbm_cfg, key):
raise ValueError(f"Checkpoint on-site boundary mismatch on '{key}'")
+ if saved_cfg.get("wall_slot_width", 0) != lbm_cfg.wall_slot_width:
+ raise ValueError("Checkpoint on-site boundary mismatch on 'wall_slot_width'")
for key in ("lattice_model", "nx", "ny", "nz", "store_precision",
"streaming", "ddf_shifting"):
saved_val = saved_cfg.get(key)
--- a/src/CelerisLab/config.py
+++ b/src/CelerisLab/config.py
@@ -87,6 +87,7 @@
outlet_srt_neq_damp: float = 0.5
y_wall_bc: str = "bounce_back"
wall_normal_velocity: float = 0.0
+ wall_slot_width: int = 0
omega_min: float = 0.01
omega_max: float = 1.99
@@ -151,6 +152,11 @@
)
if self.dim == 2 and self.nz != 1:
raise ValueError("nz must be 1 for 2D")
+ if (isinstance(self.wall_slot_width, bool) or not isinstance(self.wall_slot_width, int)
+ or self.wall_slot_width != 0 and not 2 <= self.wall_slot_width <= self.nx):
+ raise ValueError("wall_slot_width must be 0 (disabled) or an integer in [2, nx]")
+ if self.wall_slot_width and (self.y_wall_bc != "on_site_velocity" or self.velocity != 0):
+ raise ValueError("wall_slot_width requires on_site_velocity and velocity=0")
if self.y_wall_bc == "on_site_velocity":
if (not self.is_d2q9 or self.collision != "SRT"
or self.streaming != "double_buffer" or self.store_precision != "FP32"
@@ -196,8 +202,17 @@
self.validate()
return (self.lattice_model, self.nx, self.ny, self.nz, self.data_type,
self.store_precision, bool(self.ddf_shifting), self.streaming,
- (self.velocity, self.wall_normal_velocity)
+ (self.velocity, self.wall_normal_velocity, self.wall_slot_width)
if self.y_wall_bc == "on_site_velocity" else None)
+
+ @property
+ def top_wall_normal_velocity(self) -> float:
+ """Uniform top target balancing the bottom's discrete requested volume."""
+ if self.wall_slot_width in (0, self.nx):
+ return self.wall_normal_velocity
+ weights = (max(0.0, 1.0-((2*x-(self.nx-1))/self.wall_slot_width)**2)**2
+ for x in range(self.nx))
+ return self.wall_normal_velocity * math.fsum(weights) / self.nx
def to_macros(self) -> Dict[str, Any]:
"""Return flat dict of macro_name → value for header generation."""
@@ -227,6 +242,8 @@
"OUTLET_BACKFLOW_CLAMP": int(self.outlet_backflow_clamp),
"Y_WALL_BC": Y_WALL_BC_MAP[self.y_wall_bc],
"WALL_NORMAL_VELOCITY": _float_literal(self.wall_normal_velocity),
+ "WALL_SLOT_WIDTH": self.wall_slot_width,
+ "TOP_WALL_NORMAL_VELOCITY": _float_literal(self.top_wall_normal_velocity),
"OMEGA_COLLISION_MIN": _float_literal(self.omega_min),
"OMEGA_COLLISION_MAX": _float_literal(self.omega_max),
"TRT_MAGIC_PARAM": _float_literal(self.trt_magic_param),
@@ -296,6 +313,7 @@
outlet_srt_neq_damp=m["outlet"].get("srt_neq_damp", 0.5),
y_wall_bc=m.get("y_wall_bc", "bounce_back"),
wall_normal_velocity=m.get("wall_normal_velocity", 0.0),
+ wall_slot_width=m.get("wall_slot_width", 0),
omega_min=m["omega_guard"]["min"],
omega_max=m["omega_guard"]["max"],
threads_per_block=c["threads_per_block"],
--- a/src/CelerisLab/configs/CONFIG.md
+++ b/src/CelerisLab/configs/CONFIG.md
@@ -113,6 +113,33 @@
Supported only for D2Q9/SRT/FP32/unshifted/double_buffer without LES, objects
or body forces. Boundary targets and the mode are fixed at construction;
-checkpoint restart requires matching targets. This is an entire-plane
-Couette-throughflow reference, not a finite slot or curved jet API. The default
+checkpoint restart requires matching targets. With the slot disabled this is an
+entire-plane Couette-throughflow reference. The default
`bounce_back` mode and existing configurations retain their previous path.
+
+### Static centered bottom slot
+
+Optional `method.wall_slot_width` is an integer W: 0 (default) keeps the above
+uniform planes; 2≤W≤NX enables a centered bottom slot and requires
+`physics.velocity=0`. The same support restrictions apply. Width and amplitude
+are fixed at construction and must match on checkpoint restart; older v3
+checkpoints without this field mean W=0.
+
+For W<NX, let c=(NX−1)/2, s=2(x−c)/W at integer x=0..NX−1. The bottom target is
+`v=A*max(0,1-s*s)^2`, where A=`wall_normal_velocity` is the continuous peak,
+not a renormalized discrete peak. This profile and its first derivative vanish
+at the edges. W=NX explicitly means uniform v=A, not this shaped profile.
+With equal unit area per node, requested Q is the sum of these bottom targets;
+the top target is uniform Q/NX (`cfg.top_wall_normal_velocity`). The sum is
+computed in host double precision; device targets are FP32. This balances
+requested volume, not actual density-weighted mass exactly.
+
+Positive A injects from the bottom and exits through the top; negative A reverses
+both roles. Both are Cartesian y velocities; inward-normal velocity has the
+opposite sign at the top. The slot and its zero-velocity flanks share the same
+on-site y=1 plane. Initialization sets rho0, u=0 and linearly interpolates v
+between the two planes; it is not an assumed steady solution. This mode is
+static, single-level and flat; it does not support moving/curved slots.
+The finite-slot mode is experimental: the bounded W=16, A=±0.002 checks in
+`research/j2-slot-2026-10-12/REVIEW.md` exceeded the fixed late-time mass-drift
+tolerance. It is not yet validated for steady mass balance or long runs.
--- a/src/CelerisLab/cuda/compiler_v2.py
+++ b/src/CelerisLab/cuda/compiler_v2.py
@@ -176,6 +176,8 @@
#define OUTLET_BACKFLOW_CLAMP {m['OUTLET_BACKFLOW_CLAMP']}
#define Y_WALL_BC {m['Y_WALL_BC']}
#define WALL_NORMAL_VELOCITY {m['WALL_NORMAL_VELOCITY']}
+#define WALL_SLOT_WIDTH {m['WALL_SLOT_WIDTH']}
+#define TOP_WALL_NORMAL_VELOCITY {m['TOP_WALL_NORMAL_VELOCITY']}
#define OMEGA_COLLISION_MIN {m['OMEGA_COLLISION_MIN']}
#define OMEGA_COLLISION_MAX {m['OMEGA_COLLISION_MAX']}
--- a/src/CelerisLab/lbm/kernels/boundary/planar_velocity.cuh
+++ b/src/CelerisLab/lbm/kernels/boundary/planar_velocity.cuh
@@ -3,6 +3,18 @@
// the caller owns collision/store. No force, shifting or off-lattice walls.
#ifndef CELERIS_BOUNDARY_PLANAR_VELOCITY_CUH
#define CELERIS_BOUNDARY_PLANAR_VELOCITY_CUH
+
+__device__ __forceinline__ float bottom_wall_normal_velocity(unsigned int x)
+{
+#if WALL_SLOT_WIDTH > 0 && WALL_SLOT_WIDTH < NX
+ // C1 compact profile centered at (NX-1)/2; amplitude is its continuous peak.
+ const float s = (2.0f*(float)x-(float)(NX-1))/(float)WALL_SLOT_WIDTH;
+ const float w = fmaxf(0.0f, 1.0f-s*s);
+ return WALL_NORMAL_VELOCITY*w*w;
+#else
+ return WALL_NORMAL_VELOCITY; // Disabled or explicitly uniform full width.
+#endif
+}
__device__ __forceinline__ void reconstruct_bottom_velocity(float* f, float u, float v)
{
--- a/src/CelerisLab/lbm/kernels/step/init_flow.cu
+++ b/src/CelerisLab/lbm/kernels/step/init_flow.cu
@@ -91,7 +91,13 @@
// Initial g(0)=feq: both boundary velocities are already satisfied.
// Start from linear Couette, not the sought exponential steady profile.
float f[NQ];
+#if WALL_SLOT_WIDTH > 0 && WALL_SLOT_WIDTH < NX
+ const float t = (float)(y-1u)/(float)(NY-3);
+ const float v = (1.0f-t)*bottom_wall_normal_velocity(x)+t*TOP_WALL_NORMAL_VELOCITY;
+ compute_feq(RHO, 0.0f, v, f);
+#else
compute_feq(RHO, U0*(float)(y-1u)/(float)(NY-3), WALL_NORMAL_VELOCITY, f);
+#endif
stream_pull_store(k, f, fi);
#else
write_equilibrium(fi, k, inlet_target_u((float)y));
--- a/src/CelerisLab/lbm/kernels/step/one_step_double.cu
+++ b/src/CelerisLab/lbm/kernels/step/one_step_double.cu
@@ -91,8 +91,8 @@
#if Y_WALL_BC == 2
// Reconstruct f(t+dt) on the physical plane, then collide below.
// This replaces, rather than compounds, the halfway wall correction.
- if (y == 1u) reconstruct_bottom_velocity(f, 0.0f, WALL_NORMAL_VELOCITY);
- else reconstruct_top_velocity(f, U0, WALL_NORMAL_VELOCITY);
+ if (y == 1u) reconstruct_bottom_velocity(f, 0.0f, bottom_wall_normal_velocity(x));
+ else reconstruct_top_velocity(f, U0, TOP_WALL_NORMAL_VELOCITY);
#elif Y_WALL_BC == 1
apply_wall_freeslip_d2q9_y_pull(y, f, fi_in, k);
#else
--- /dev/null
+++ b/tests/unit/cpp/slot_profile.cpp
@@ -0,0 +1,10 @@
+#include <cmath>
+#include <cstdio>
+#define __device__
+#define __forceinline__ inline
+#include "boundary/planar_velocity.cuh"
+
+int main() {
+ for (unsigned x=0; x<NX; ++x)
+ printf("%.9g\n", bottom_wall_normal_velocity(x));
+}
--- a/tests/unit/test_planar_velocity.py
+++ b/tests/unit/test_planar_velocity.py
@@ -19,7 +19,7 @@
root = Path(__file__).resolve().parents[2]
assert Path(CelerisLab.__file__).resolve() == root/'src/CelerisLab/__init__.py'
assert Path(compiler.kernel_path('kernel_v2.cu')).resolve() == root/'src/CelerisLab/lbm/kernels/kernel_v2.cu'
- lines = compile_run(tmp_path, 'planar_velocity.cpp', {})
+ lines = compile_run(tmp_path, 'planar_velocity.cpp', dict(WALL_NORMAL_VELOCITY='0.0f'))
cx = np.array([0,1,-1,0,0,1,-1,1,-1])
cy = np.array([0,0,0,1,-1,1,-1,-1,1])
w = np.array([4/9]+[1/9]*4+[1/36]*4)
--- /dev/null
+++ b/tests/unit/test_slot_velocity.py
@@ -0,0 +1,81 @@
+"""Static slot targets and checkpoint compatibility, using the actual CUDA helper."""
+import json
+from types import SimpleNamespace
+
+import h5py
+import numpy as np
+import pytest
+
+from CelerisLab.config import LBMConfig, load_lbm_config
+from CelerisLab.common.checkpoint import save_checkpoint, load_checkpoint
+from .test_numerical_operators import compile_run, ROOT
+from .test_state_regressions import device, make_field, manager
+
+
+@pytest.mark.parametrize('width', [0, 16, 48])
+@pytest.mark.parametrize('amplitude', [0., .002, -.002])
+def test_actual_slot_targets_and_requested_volume(tmp_path, width, amplitude):
+ cfg = LBMConfig(nx=48, velocity=0., y_wall_bc='on_site_velocity',
+ wall_slot_width=width, wall_normal_velocity=amplitude)
+ macros = cfg.to_macros()
+ values = np.array(compile_run(tmp_path, 'slot_profile.cpp', {
+ k: macros[k] for k in ('NX', 'WALL_SLOT_WIDTH', 'WALL_NORMAL_VELOCITY')}), dtype=float)
+ # Independent node quadrature; includes both zero flanks and the full-width exception.
+ x = np.arange(48)-23.5
+ expected = amplitude*np.clip(1-(x/8)**2, 0, None)**2 if width == 16 else np.full(48, amplitude)
+ np.testing.assert_allclose(values, expected, atol=5e-10, rtol=0)
+ np.testing.assert_array_equal(values, values[::-1])
+ assert abs(values.sum()-48*cfg.top_wall_normal_velocity) <= 1e-8
+ assert abs(float(macros['TOP_WALL_NORMAL_VELOCITY'][:-1])-expected.mean()) < 1e-16
+
+
+@pytest.mark.parametrize('change', [dict(wall_slot_width=w) for w in (-1, 1, 49, 2.5, True)]
+ + [dict(y_wall_bc='bounce_back'), dict(velocity=.01)])
+def test_slot_invalid_configuration(change):
+ values = dict(nx=48, velocity=0., y_wall_bc='on_site_velocity', wall_slot_width=16)
+ values.update(change)
+ with pytest.raises(ValueError, match='wall_slot_width'):
+ LBMConfig(**values).validate_runtime()
+
+
+def test_slot_json_and_static_signature(tmp_path):
+ data = json.loads((ROOT/'src/CelerisLab/configs/config_lbm.json').read_text())
+ data['physics']['velocity'] = 0.
+ data['method'].update(y_wall_bc='on_site_velocity', wall_slot_width=16,
+ collision='SRT', streaming='double_buffer', store_precision='FP32',
+ ddf_shifting=False)
+ data['method']['les']['enabled'] = False
+ path = tmp_path/'config.json'
+ path.write_text(json.dumps(data))
+ cfg = load_lbm_config(path)
+ assert cfg.wall_slot_width == 16
+ before = cfg.allocation_signature()
+ cfg.wall_slot_width = 18
+ assert before != cfg.allocation_signature()
+
+
+@pytest.mark.parametrize('width, saved_width, accepted', [(6, 4, False), (6, None, False),
+ (0, None, True), (6, 6, True)])
+def test_slot_checkpoint_width_before_device_writes(device, tmp_path, width, saved_width, accepted):
+ field = make_field(device, velocity=0., y_wall_bc='on_site_velocity', wall_slot_width=width)
+ bodies = manager(field)
+ bodies.sync_to_gpu(field)
+ stepper = SimpleNamespace(_step_count=0)
+ path = save_checkpoint(field, SimpleNamespace(step_count=3), field.cfg, bodies,
+ str(tmp_path/'slot.h5'))
+ with h5py.File(path, 'a') as hf:
+ assert hf.attrs['ckpt_version'] == 3
+ config = json.loads(hf.attrs['config_json'])
+ config.pop('wall_slot_width')
+ if saved_width is not None:
+ config['wall_slot_width'] = saved_width
+ hf.attrs['config_json'] = json.dumps(config)
+ before, writes = field.ddf.copy(), device.writes
+ if accepted:
+ load_checkpoint(path, field, stepper, field.cfg, bodies)
+ np.testing.assert_array_equal(field.ddf, before)
+ assert stepper._step_count == 3
+ else:
+ with pytest.raises(ValueError, match='wall_slot_width'):
+ load_checkpoint(path, field, stepper, field.cfg, bodies)
+ assert device.writes == writes and stepper._step_count == 0
--- a/tests/validation/run_j1.py
+++ b/tests/validation/run_j1.py
@@ -28,13 +28,13 @@
u_change=2e-5, v_change=2e-7, budget_relative_residual=2e-7)
-def make_sim(v, mode='on_site_velocity'):
+def make_sim(v, mode='on_site_velocity', nx=16, top_u=.02, slot_width=0):
config = json.loads((ROOT/'src/CelerisLab/configs/config_lbm.json').read_text())
- config['grid'].update(nx=16, ny=35, nz=1, lattice_model='D2Q9')
- config['physics'].update(viscosity=.1, velocity=.02, rho=1.)
+ config['grid'].update(nx=nx, ny=35, nz=1, lattice_model='D2Q9')
+ config['physics'].update(viscosity=.1, velocity=top_u, rho=1.)
config['method'].update(collision='SRT', streaming='double_buffer',
store_precision='FP32', ddf_shifting=False,
- y_wall_bc=mode, wall_normal_velocity=v)
+ y_wall_bc=mode, wall_normal_velocity=v, wall_slot_width=slot_width)
config['method']['les']['enabled'] = False
with TemporaryDirectory(prefix='celeris-j1-config-') as folder:
lbm, body = Path(folder)/'lbm.json', Path(folder)/'body.json'
@@ -72,7 +72,7 @@
'''
-def population_budget(sim):
+def population_budget(sim, bottom_slot=None):
"""16 real steps, separate incoming/outgoing/collision records, no targets."""
saved = sim.get_ddf().copy()
count = sim.stepper.step_count
@@ -122,6 +122,14 @@
collision_roundoff=float(collision.sum()),
physical_residual=physical_residual,
budget_residual=residual))
+ if bottom_slot is not None:
+ outgoing = before[[4,6,7], 1, :].sum(axis=0)
+ rows[-1]['exchange'] = {
+ name: dict(incoming=float(incoming[:sim.field.nx][mask].sum()),
+ outgoing=float(outgoing[mask].sum()))
+ for name, mask in (('slot', bottom_slot), ('flanks', ~bottom_slot))}
+ rows[-1]['exchange']['top'] = dict(
+ incoming=float(incoming[sim.field.nx:].sum()), outgoing=float(outgoing_top))
identical = np.array_equal(sim.get_ddf(), plain)
return dict(steps=rows, instrumented_matches_plain=identical,
audit_source_sha256=audit_hash,
--- /dev/null
+++ b/tests/validation/run_j2_slot.py
@@ -0,0 +1,151 @@
+"""Bounded static-slot validation; frozen gates in research/j2-slot-2026-10-12/REVIEW.md."""
+import argparse
+import json
+import os
+from pathlib import Path
+import sys
+from tempfile import TemporaryDirectory
+from unittest.mock import patch
+
+import numpy as np
+import CelerisLab
+from CelerisLab.cuda import compiler_v2 as compiler
+from run_j1 import ROOT, make_sim, populations, observables, population_budget, off_path_check
+
+NX, WIDTH, CHUNK = 48, 16, 519
+GATES = dict(velocity_change=5e-5, mass_change=2e-6, mass_window_span=6e-6,
+ mass_offset=1e-3, rho_error=.005, speed=.01, zero_speed=2e-6,
+ flux_error=.005, flux_span=.002, boundary_error=2e-6,
+ rho_symmetry=2e-5, velocity_symmetry=.002, budget_residual=2e-7)
+
+
+def targets(amplitude):
+ # Independent discrete quadrature, including the zero-target flanks.
+ x = np.arange(NX)-(NX-1)/2
+ bottom = amplitude*np.clip(1-(x/(WIDTH/2))**2, 0, None)**2
+ return bottom, bottom.mean(), np.abs(x) < WIDTH/2
+
+
+def full_width_check(baseline):
+ snapshots = []
+ for source, width in ((baseline, 0), (Path(compiler.kernel_path('')), NX)):
+ with patch.object(compiler, '_KERNEL_DIR', str(source)):
+ with make_sim(.002, nx=NX, top_u=0., slot_width=width) as sim:
+ sim.initialize()
+ initial = sim.get_ddf().copy()
+ sim.run(64)
+ snapshots.append((initial, sim.get_ddf().copy(), sim.get_flags().copy()))
+ return bool(all(np.array_equal(a, b) for a, b in zip(*snapshots)))
+
+
+def restart_check(sim, amplitude):
+ with TemporaryDirectory(prefix='celeris-j2-checkpoint-') as folder:
+ path = sim.save_checkpoint(str(Path(folder)/'slot.h5'))
+ sim.run(7)
+ expected, flags, count = sim.get_ddf().copy(), sim.get_flags().copy(), sim.stepper.step_count
+ with make_sim(amplitude, nx=NX, top_u=0., slot_width=WIDTH) as target:
+ target.load_checkpoint(path)
+ target.run(7)
+ equal = (np.array_equal(expected, target.get_ddf())
+ and np.array_equal(flags, target.get_flags()) and count == target.stepper.step_count)
+ sim.load_checkpoint(path)
+ return bool(equal)
+
+
+def run_case(amplitude):
+ bottom, top, slot = targets(amplitude)
+ scale = max(abs(amplitude), .002)
+ with make_sim(amplitude, nx=NX, top_u=0., slot_width=WIDTH) as sim:
+ sim.initialize()
+ rho, ux, uy = observables(populations(sim))
+ initial_mass = float(rho.sum())
+ initial_error = float(max(np.abs(uy[0]-bottom).max(), np.abs(uy[-1]-top).max(), np.abs(ux).max()))
+ old_u, old_v, old_mass = ux.copy(), uy.copy(), initial_mass
+ history = [dict(step=0, mass=initial_mass)]
+ steady = False
+ for sample in range(1, 41):
+ sim.run(CHUNK)
+ f = populations(sim)
+ assert np.isfinite(f).all(), 'Nonfinite slot populations'
+ rho, ux, uy = observables(f)
+ mass = float(rho.sum())
+ history.append(dict(step=sim.stepper.step_count, mass=mass,
+ u_change=float(np.sqrt(np.mean((ux-old_u)**2))/scale),
+ v_change=float(np.sqrt(np.mean((uy-old_v)**2))/scale),
+ mass_change=abs(mass-old_mass)/initial_mass))
+ old_u, old_v, old_mass = ux.copy(), uy.copy(), mass
+ span = float(np.ptp([r['mass'] for r in history[-5:]])/initial_mass)
+ steady = bool(sample >= 8 and span <= GATES['mass_window_span'] and all(
+ r['u_change'] <= GATES['velocity_change'] and r['v_change'] <= GATES['velocity_change']
+ and r['mass_change'] <= GATES['mass_change'] for r in history[-4:]))
+ if steady:
+ break
+ sections = [8, 16, 24]
+ actual_mass_flux = (rho*uy).sum(axis=1)
+ actual_volume_flux = uy.sum(axis=1)
+ request_q = float(bottom.sum())
+ reference = float(rho.mean()*request_q)
+ flux_error = float(np.max(np.abs(actual_mass_flux[sections]-reference))/abs(reference)) if amplitude else 0.
+ flux_span = float(np.ptp(actual_mass_flux[sections])/abs(reference)) if amplitude else 0.
+ symmetry = dict(rho=float(np.max(np.abs(rho-rho[:, ::-1]))),
+ u=float(np.max(np.abs(ux+ux[:, ::-1]))/scale),
+ v=float(np.max(np.abs(uy-uy[:, ::-1]))/scale))
+ boundary_error = float(max(np.abs(uy[0]-bottom).max(), np.abs(uy[-1]-top).max(),
+ np.abs(ux[[0, -1]]).max()))
+ offset = abs(mass-initial_mass)/initial_mass
+ speed = float(np.hypot(ux, uy).max())
+ density_error = float(np.abs(rho-1).max())
+ nonuniform = bool(uy[8, NX//2]*amplitude > 0 and np.abs(ux).max() > 1e-5) if amplitude else True
+ restart = restart_check(sim, amplitude) if amplitude > 0 else None
+ budget = population_budget(sim, bottom_slot=slot)
+ checks = dict(steady=steady, initialization=initial_error <= GATES['boundary_error'],
+ boundary=boundary_error <= GATES['boundary_error'],
+ mass=offset <= GATES['mass_offset'] and span <= GATES['mass_window_span'],
+ bounded=density_error <= GATES['rho_error'] and speed <= GATES['speed'],
+ flux=flux_error <= GATES['flux_error'] and flux_span <= GATES['flux_span'],
+ symmetry=symmetry['rho'] <= GATES['rho_symmetry'] and
+ max(symmetry['u'], symmetry['v']) <= GATES['velocity_symmetry'],
+ zero=bool(amplitude or speed <= GATES['zero_speed']), nonuniform=nonuniform,
+ restart=restart is not False, audit=budget['instrumented_matches_plain'] and
+ budget['max_relative_residual'] <= GATES['budget_residual'])
+ checks = {name: bool(passed) for name, passed in checks.items()}
+ return dict(amplitude=amplitude, width=WIDTH, nx=NX, height=32, viscosity=.1, top_u=0.,
+ steps=sim.stepper.step_count-16, max_steps=40*CHUNK, checks=checks, passed=all(checks.values()),
+ requested_volume=request_q, top_target=top, bottom_targets=bottom.tolist(),
+ velocity_scale=scale, rho_scale=1., initial_boundary_error=initial_error,
+ boundary_error=boundary_error, mass_offset=offset, mass_window_span=span,
+ flux_error=flux_error, flux_span=flux_span, section_y=sections,
+ mass_flux_by_y=actual_mass_flux.tolist(), volume_flux_by_y=actual_volume_flux.tolist(),
+ symmetry=symmetry, max_speed=speed, rho_error=density_error,
+ rho=rho.tolist(), u=ux.tolist(), v=uy.tolist(), history=history,
+ checkpoint_bitwise_equal=restart, population_budget=budget)
+
+
+def main():
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument('--output', type=Path, required=True)
+ parser.add_argument('--baseline-kernels', type=Path, required=True,
+ help='Pre-J2 source for the required full-width and default-mode comparisons')
+ args = parser.parse_args()
+ assert Path.cwd() == ROOT == Path('/home/frank14f/CelerisLab')
+ assert sys.executable == '/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python'
+ assert Path(CelerisLab.__file__).resolve() == ROOT/'src/CelerisLab/__init__.py'
+ assert Path(compiler.kernel_path('kernel_v2.cu')).resolve() == ROOT/'src/CelerisLab/lbm/kernels/kernel_v2.cu'
+ assert os.environ.get('CUDA_VISIBLE_DEVICES') in ('0', '1')
+ report = dict(python=sys.executable, package=CelerisLab.__file__,
+ cuda_source=compiler.kernel_path('kernel_v2.cu'), physical_gpu=os.environ['CUDA_VISIBLE_DEVICES'],
+ gates=GATES, full_width_bitwise_equal=None, off_path_bitwise_equal=None, cases=[])
+ report['full_width_bitwise_equal'] = full_width_check(args.baseline_kernels)
+ report['off_path_bitwise_equal'] = off_path_check(args.baseline_kernels)
+ for amplitude in (0., .002, -.002):
+ case = run_case(amplitude)
+ report['cases'].append(case)
+ args.output.write_text(json.dumps(report, indent=2)+'\n')
+ print(json.dumps({k: case[k] for k in ('amplitude', 'steps', 'checks', 'mass_offset',
+ 'mass_window_span', 'flux_error', 'symmetry')}), flush=True)
+ return 0 if (report['full_width_bitwise_equal'] is True and report['off_path_bitwise_equal'] is True
+ and all(c['passed'] for c in report['cases'])) else 1
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
research/j2-slot-2026-10-12/results.json
+17204
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research/j3-coverage-2026-10-12/REVIEW.md
+94
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@@ -0,0 +1,94 @@
# J3 最小功能覆盖：运行前冻结
本轮唯一写入/执行者 root；仅新增独立验证脚本和本目录。257 个现有文件的 SHA256 已冻结于 baseline.json，含源码、旧测试、报告、日志、数据及真实 checkpoint；旧正例失败不改。固定已有 pycuda_3_10 解释器、当前仓库及进程 PYTHONPATH；每次科研命令核对 Python 包及 CUDA 源码来源。不安装依赖、不改生产公式、不提交推送、不访问其他项目、不联系外部评审。
GPU 只选空闲的 0。初查 GPU0 为 0%/9 MiB，无计算进程；GPU1 有 362 MiB Python 作业，本轮不使用。正式命令前再查。48×35×9×4×2=120960 字节双缓冲，加 flags 3360 字节、宏观缓存约 20 KiB/实例；顺序实例及小型审计模块，预算显存低于 1 GiB、CPU RSS 低于 512 MiB、输出低于 10 MiB。系统当前可用内存约 56 GiB，GPU0 总显存 16 GiB。临时编译目录使用现有编译器的独立构建和清理；不改 PyCUDA 环境。
## 固定顺序与判据
1. 从既有 end-21280.h5 恢复，SHA256 `19a94e9e8db6dd9ac981effe06032c351992250b58768d9c090409193893d588`。校验配置、运行参数及 postcollision g(t) 分布阶段。A 在未推进时另存本轮 checkpoint，再推进 16 步到 21296；关闭 A 后新建 B，从本轮 checkpoint 独立恢复并推进 16 步。逐字节比 DDF/temp/flags，比较 step_count、config/runtime；不声称拥有历史不间断参照。失败或非有限即停，不继续零/负例。
2. 同 48×35/W16、rho1、nu.1、U0、top reservoir，A=0，固定 64 步。每步检查有限性、rho/speed/质量界；记录初始化及 64 步的质量、速度、边界和密度。zero_speed≤2e-6；零参考相对通量为 N/A；不宣称完整稳态通过，不额外做预算推进。
3. 唯一负例 A=−.002，同原参数，固定最多 40×519=20760 主轨迹步，不提前以某个短窗口改判。每步检查有限性、rho_error≤.005、speed≤.01、mass_offset≤1e-3；每 519 步检查 mass_change≤2e-6、最近最多五采样点质量跨度≤6e-6。任一硬界越界立即停止，保留失败统计及 checkpoint。每采样记录实际质量、u/v 带符号均值、完整场 RMS 差/.002、边界、通量、镜像和非均匀流向；速度稳态不通过可继续到固定上限一次。
原全部 GATES 保留：velocity_change 5e-5（至少8间隔、末4间隔 u/v 同时满足）、mass_change 2e-6、mass_window_span 6e-6、mass_offset 1e-3、rho_error .005、speed .01、zero_speed 2e-6、flux_error .005、flux_span .002、boundary_error 2e-6、top_density_error 5e-7、rho_symmetry 2e-5、velocity_symmetry .002、budget_residual 2e-7，非零内部中心 v 与 A 同号且 max|u|>1e-5。未测/null 不计为通过；不改 gate、不选择偶数 lag。
负例如能运行到上限，在20744保存实际checkpoint，普通路径推进最后16步到20760作参照；独立审计路径加载20744状态，仅推进同一窗口16步，不产生20760之后轨迹。复用已验证 AUDIT 记录真实 incoming/outgoing/collision，覆盖slot/flanks/top；保留临时源码 SHA、逐步真实账及与普通末态 DDF/temp/flags/config/runtime/count 逐值比较。主路径20760步、重叠审计16步分别计数。硬界失败则预算标未测，不继续。
有限幅波动仅报告新轨迹每个519步窗口的在线时间方差 RMS、最大节点峰峰差和场差；不保留逐步全场、不追成因。短恢复/零例也逐步做安全检查。只保留聚合统计、日志和必要小checkpoint。正例只引用既有 J3/lag/offline 结果并明确窗口不同。完成后补一页功能范围/限制/下一里程碑，立即停止。
## 完成摘要：恢复与零例通过，负例速度稳态失败
本轮命令只执行一次，退出码 **1**，来自负例原稳态门槛失败；没有异常、越界或重跑。真实状态短恢复通过；零幅值短例通过。负例达到固定上限，除速度稳态外其余原验收项通过，不能将整体改判通过。正例沿用旧失败。
| 项目 | 实际运行与结果 |
| --- | --- |
| 真实 checkpoint 续跑 | A 从旧 end-21280 恢复并另存，推进16步；关闭A后B从本轮另存文件恢复，推进16步；均到21296 |
| 恢复比较 | 起点及终点 DDF/temp/flags 逐字节一致，step_count/config/runtime 一致；恢复检查通过 |
| 零幅值 | 初始化+64步的65个状态，最大速度严格为0；质量相对跨度/最大偏移6.70552e-8；最大rho偏差7.45058e-8 |
| 负幅值 | 普通路径20760步；独立审计仅重叠20744→20760的16步，未产生20760以后的负例轨迹 |
| 资源/保留 | CPU峰值RSS 211.20 MiB；输出约0.7 MiB；257个旧文件哈希全部保持；未修改生产或旧测试 |
总新执行求解步数为 **20872**：正态恢复两条路径各16步、零例64步、负例普通20760步、独立重叠审计16步；其中正态仅从21280推进至21296，没有正例20760步重放。本次没有测量显存峰值，显存大小在启动前估算并确认有足够余量；不能将估算写成实测。
零例所有逐步数据在 results.json。边界指定速度误差0，顶密度最大误差7.45058e-8≤5e-7，质量跨度低于6e-6、偏移低于1e-3，每步质量变化低于2e-6，零速低于2e-6。相对流量误差/跨度为 null 并明确标 N/A（零参考）；没有将其计作通过，也没有声称64步满足至少8间隔的完整稳态要求。零例没有单独的population预算或恢复测试。
### 正负实测与原门槛
正例数值直接引用 j3-reservoir-2026-10-12/results.json，不重新运行；下表为各自20760末态，质量跨度和速度收敛取各自原末四间隔。
| 量 | 原正例 +.002 | 本轮负例 −.002 | 原门槛 / 判定 |
| --- | ---: | ---: | --- |
| u场RMS变化/.002，末4 | 2.6381/2.6857/3.0247/2.9349 e-5 | 4.4394/4.3232/4.4663/4.4558 e-5 | ≤5e-5，均通过 |
| v场RMS变化/.002，末4 | 7.3296/6.9940/6.4742/6.0220 e-5 | 6.0193/6.1294/6.3192/6.4750 e-5 | ≤5e-5，均失败 |
| 初末质量相对偏移 | 1.93463e-7 | 3.08173e-7 | ≤1e-3，通过 |
| 末四间隔质量跨度 | 2.97376e-8 | 3.64015e-8 | ≤6e-6，通过 |
| 末间隔质量相对变化 | 2.17732e-8 | 6.19118e-9 | ≤2e-6，通过 |
| rho最大偏差 | 2.45519e-4 | 2.53472e-4 | ≤.005，通过 |
| 最大速度 | .00198440335 | .00198443044 | ≤.01，通过 |
| 指定边界速度最大误差 | 3.66935e-8 | 3.56677e-8 | ≤2e-6，通过 |
| 顶密度最大误差 | 2.06754e-7 | 1.97440e-7 | ≤5e-7，通过 |
| 截面质量流量相对误差 | .0010008493 | .0007190498 | ≤.005，通过 |
| 截面流量相对跨度 | .0005283367 | .0004005362 | ≤.002，通过 |
| rho镜像误差 | 4.95464e-7 | 6.22123e-7 | ≤2e-5，通过 |
| u/v镜像误差/.002 | .000202076/.000137884 | .000124795/.000216072 | 各≤.002，通过 |
| 内部非均匀流向 | 通过（旧记录） | 中心v=−.001084366，max\|u\|=.000432660 | 中心v与A同号且max\|u\|>1e-5，通过 |
| 初始化边界误差 | 通过（旧记录） | 1.78962e-8 | ≤2e-6，通过 |
| 实际population预算 | 通过（旧末态后16步） | 通过（20744→20760） | 残差/M≤2e-7且逐值一致 |
负例每步有限，逐步观测到的全程最大rho偏差 .000602724、最大速度 .001984436、最大质量偏移8.14183e-7；40个采样间隔的最大质量变化6.43070e-7、最大滚动跨度7.00168e-7，均低于对应硬界。各采样原始值保留，没有删掉早期瞬态。
负例底请求体积流量−.01706689453125；20760末态距底8/16/24处实际质量流量分别−.01706145331、−.01705793291、−.01705461740。顶v范围[−.00045778788,−.00025554935]，没有强制等于初始均匀猜测。全域有符号速度均值u=1.29822e-9、v=−.000355422879；末519步有符号空间平均变化分别−1.11917e-10、−5.99107e-9。各y宏观质量/体积流量、顶底所有节点宏观量及每次采样的镜像/流向均保存。
有限幅波动：负例最后 **20242–20760的519帧** 在线时间方差RMS为u=5.06547e-8、v=6.80326e-8；最大节点峰峰差为4.35901e-7、8.38259e-7。旧正例离线窗口为 **20760–21280的521帧**，RMS为3.36455e-8、7.37527e-8（引用旧文件）。时间窗口不同，不能宣称正负同窗口等价；不继续分析成因。
### 重叠16步实际账
临时审计 one_step_double.cu SHA256：`d6532c48ea0bf972bce6ec3809989e9f2ec52eb1440d58f8945eb59d67dcf1b5`。审计与普通末态DDF/temp/flags逐字节相同，配置/运行参数/步数相同。每步 ΔM−实际边界净交换−实际碰撞舍入残差为0，最大相对残差0≤2e-7；未扣舍入的最大相对残差7.51761e-9。
| 16步平均实际量 | incoming | outgoing | 净进入 |
| --- | ---: | ---: | ---: |
| 底slot | 2.65799031034 | 2.67504728318 | −.01705697284 |
| 底flanks | 5.33348024054 | 5.33348734328 | −7.10273e-6 |
| 全顶部 | 8.00853079371 | 7.99147556582 | +.01705522789 |
全边界平均净进入−8.84768e-6，碰撞舍入+8.26980e-6，实际ΔM=−5.77886e-7。预算闭合不代表速度稳态通过，也不等同于宏观截面流量。逐步原始账在results.json。
## 可复核清单与后续边界
唯一新增代码：tests/validation/run_j3_coverage.py；复用旧make_sim/observables/AUDIT及原GATES，未改旧脚本。新脚本SHA256 `329869519b7078e854d96241a7aad8ef93f35ac9be83f0be502c3a93523c3b13`。运行前同一解释器做语法、静止平衡分布宏观量/零参考N/A及状态步数比较的主机检查，均通过，没有初始化CUDA；没有跑无关矩阵。
实际GPU命令（工作目录为 /home/frank14f/CelerisLab）：
```bash
env CUDA_VISIBLE_DEVICES=0 PYTHONPATH=/home/frank14f/CelerisLab/src PYTHONDONTWRITEBYTECODE=1 /home/frank14f/anaconda3/envs/pycuda_3_10/bin/python tests/validation/run_j3_coverage.py > research/j3-coverage-2026-10-12/gpu-validation.log 2>&1
```
脚本在创建CUDA上下文前复核GPU0无计算进程、显存小于100 MiB且利用率为0；记录包与kernel实际来源，并核对全部基线哈希。入口拒绝覆盖已有results.json，不能无意重跑。必要checkpoint四个均持久保存，本轮checkpoint路径、SHA及步数在results.json；原始日志、基线清单、独立新增脚本补丁保留，编译目录由现有编译器清理。
**当前功能范围：** 在D2Q9/SRT/FP32/unshifted/double-buffer、无物体/力/LES、固定小网格上，有限平壁slot与顶部reservoir支持正向喷入、负向抽吸和短时零驱动；真实保存/加载后的短续跑一致。正负质量、边界、宏观流量、镜像及population账在上述窗口内通过。
**尚不能保证：** 正负完整速度稳态均失败；未验证长期精度、网格收敛/精度阶、性能、多种碰撞/streaming组合、动态喷口、物体交互、曲面或AMR；零例仅64步，恢复一致只覆盖此次固定配置。量级小不构成精度保证。
**下一真正功能里程碑建议：** 整理一个精简、可外部调用的平壁Jet案例入口，复用现有Simulation及参数，提供正/零/负幅值、喷口宽度和持久恢复用法，明确边界含义并如实显示未收敛状态；不要为此新增求解器抽象或启动AMR重构。这只是建议，本轮不实施。
本轮完成并停止复审，不追加求解或波动诊断。
research/j3-coverage-2026-10-12/baseline-status.txt
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M pyproject.toml
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M src/CelerisLab/body/ddf_patch.py
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M src/CelerisLab/lbm/kernels/operators/collision_trt.cuh
M src/CelerisLab/lbm/kernels/operators/forcing_guo.cuh
M src/CelerisLab/lbm/kernels/step/aux_kernels.cu
M src/CelerisLab/lbm/kernels/step/esopull_sync.cu
M src/CelerisLab/lbm/kernels/step/init_flow.cu
M src/CelerisLab/lbm/kernels/step/one_step_double.cu
M src/CelerisLab/lbm/kernels/step/one_step_esopull.cu
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?? tests/validation/run_j2_slot.py
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}
}
research/j3-coverage-2026-10-12/incremental.diff
+348
View File
@@ -0,0 +1,348 @@
diff --git a/tests/validation/run_j3_coverage.py b/tests/validation/run_j3_coverage.py
new file mode 100644
index 0000000..d66c299
--- /dev/null
+++ b/tests/validation/run_j3_coverage.py
@@ -0,0 +1,342 @@
+"""One bounded J3 restart/zero/negative run; frozen plan in research/j3-coverage-2026-10-12."""
+from dataclasses import asdict
+import hashlib
+import json
+import os
+from pathlib import Path
+import resource
+import subprocess
+import sys
+import traceback
+
+import h5py
+import numpy as np
+import pycuda.driver as cuda
+import CelerisLab
+from CelerisLab.cuda import compiler_v2 as compiler
+from run_j1 import ROOT, AUDIT, make_sim, populations, observables
+from run_j2_slot import GATES, targets
+
+OUT = ROOT/'research/j3-coverage-2026-10-12'
+SOURCE = ROOT/'research/j3-lag-2026-10-12/end-21280.h5'
+SOURCE_SHA = '19a94e9e8db6dd9ac981effe06032c351992250b58768d9c090409193893d588'
+SCALE, CHUNK, LIMIT = .002, 519, 20760
+
+
+def sha(path):
+ digest = hashlib.sha256()
+ with Path(path).open('rb') as stream:
+ for chunk in iter(lambda: stream.read(1024*1024), b''):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def create(amplitude):
+ return make_sim(amplitude, nx=48, top_u=0., slot_width=16, top_mode='reservoir')
+
+
+def state(sim):
+ arrays = dict(ddf=np.empty(sim.field.n*9, dtype=np.float32),
+ temp=np.empty(sim.field.n*9, dtype=np.float32),
+ flag=np.empty(sim.field.n, dtype=np.uint16))
+ with sim.ctx.activate():
+ for name, array in arrays.items():
+ cuda.memcpy_dtoh(array, getattr(sim.field, name+'_gpu'))
+ return dict(arrays=arrays, step=sim.stepper.step_count,
+ config=asdict(sim.lbm_cfg), runtime=sim.field._runtime_params.copy())
+
+
+def compare(a, b):
+ checks = {name: a['arrays'][name].tobytes() == b['arrays'][name].tobytes()
+ for name in a['arrays']}
+ checks.update({name: a[name] == b[name] for name in ('step', 'config', 'runtime')})
+ return checks
+
+
+def persist(sim, name):
+ path = OUT/name
+ assert not path.exists(), path
+ sim.save_checkpoint(str(path))
+ return dict(path=str(path), sha256=sha(path), step=sim.stepper.step_count)
+
+
+def measure(f, amplitude, initial_mass, previous=None):
+ rho, u, v = observables(f)
+ bottom, top, _ = targets(amplitude)
+ mass = float(rho.sum())
+ flux = (rho*v).sum(axis=1)
+ reference = float(rho.mean()*bottom.sum())
+ row = dict(mass=mass, mass_offset=abs(mass-initial_mass)/initial_mass,
+ rho_range=[float(rho.min()), float(rho.max())], rho_error=float(np.abs(rho-1).max()),
+ max_speed=float(np.hypot(u, v).max()), u_mean=float(u.mean()), v_mean=float(v.mean()),
+ boundary_error=float(max(np.abs(v[0]-bottom).max(), np.abs(u[[0, -1]]).max())),
+ top_density_error=float(np.abs(rho[-1]-1).max()),
+ top_velocity_range=[float(v[-1].min()), float(v[-1].max())],
+ mass_flux_by_y=flux.tolist(), volume_flux_by_y=v.sum(axis=1).tolist(),
+ flux_error=float(np.max(np.abs(flux[[8, 16, 24]]-reference))/abs(reference)) if amplitude else None,
+ flux_span=float(np.ptp(flux[[8, 16, 24]])/abs(reference)) if amplitude else None,
+ symmetry=dict(rho=float(np.max(np.abs(rho-rho[:, ::-1]))),
+ u=float(np.max(np.abs(u+u[:, ::-1]))/SCALE),
+ v=float(np.max(np.abs(v-v[:, ::-1]))/SCALE)),
+ center_v=float(v[8, 24]), max_abs_u=float(np.abs(u).max()),
+ nonuniform=bool(v[8, 24]*amplitude > 0 and np.abs(u).max() > 1e-5) if amplitude else None,
+ finite=bool(np.isfinite(f).all() and np.isfinite(rho).all() and
+ np.isfinite(u).all() and np.isfinite(v).all()))
+ if previous is not None:
+ row.update(u_change=float(np.sqrt(np.mean((u-previous[0])**2))/SCALE),
+ v_change=float(np.sqrt(np.mean((v-previous[1])**2))/SCALE),
+ u_signed_change=float((u-previous[0]).mean()),
+ v_signed_change=float((v-previous[1]).mean()))
+ return row, (rho, u, v)
+
+
+def check_step(sim, mass0, extrema):
+ f = populations(sim)
+ rho, u, v = observables(f)
+ values = dict(rho_error=float(np.abs(rho-1).max()), speed=float(np.hypot(u, v).max()),
+ mass_offset=abs(float(rho.sum())-mass0)/mass0)
+ finite = bool(np.isfinite(f).all() and all(np.isfinite(a).all() for a in (rho, u, v)))
+ for key, value in values.items():
+ extrema[key] = max(extrema.get(key, 0.), value)
+ if not finite or any(value > GATES[key] for key, value in values.items()):
+ raise RuntimeError(f'Hard stop at step {sim.stepper.step_count}: finite={finite}, {values}')
+ return f, (rho, u, v)
+
+
+def restart(report, save):
+ with h5py.File(SOURCE, 'r') as hf:
+ assert int(hf.attrs['step_count']) == 21280
+ assert str(hf.attrs['distribution_phase']).startswith('postcollision g(t)')
+ config, runtime = json.loads(hf.attrs['config_json']), json.loads(hf.attrs['runtime_params_json'])
+ saved = {name: hf[name][:] for name in ('ddf', 'temp', 'flag')}
+ row = report['restart'] = dict(status='running', source=str(SOURCE), source_sha256=sha(SOURCE),
+ start=21280, end=21296, a_steps=0, b_steps=0, extrema={})
+ for label in ('a', 'b'):
+ with create(.002) as sim:
+ sim.load_checkpoint(str(SOURCE if label == 'a' else OUT/'restart-21280.h5'))
+ before = state(sim)
+ assert before['config'] == config and before['runtime'] == runtime
+ checks = compare(dict(arrays=saved, step=21280, config=config, runtime=runtime), before)
+ row[label+'_initial_equal'] = checks
+ assert all(checks.values()), checks
+ mass0 = float(observables(populations(sim))[0].sum())
+ check_step(sim, mass0, row['extrema'])
+ if label == 'a':
+ row['checkpoint'] = persist(sim, 'restart-21280.h5')
+ for _ in range(16):
+ sim.run(1)
+ row[label+'_steps'] += 1
+ check_step(sim, mass0, row['extrema'])
+ if label == 'a':
+ expected = state(sim)
+ row['reference_end'] = persist(sim, 'restart-reference-21296.h5')
+ else:
+ row['continued_equal'] = compare(expected, state(sim))
+ row['status'] = 'passed' if all(row['continued_equal'].values()) else 'failed'
+ save()
+ assert row['status'] == 'passed', row['continued_equal']
+ print('RESTART', json.dumps(row), flush=True)
+
+
+def zero(report, save):
+ row = report['zero'] = dict(status='running', steps=0, history=[], extrema={},
+ steady='not_tested_short_case', flux_relative='not_applicable_zero_reference')
+ with create(0.) as sim:
+ sim.initialize()
+ mass0 = float(observables(populations(sim))[0].sum())
+ for step in range(65):
+ if step:
+ sim.run(1)
+ row['steps'] = step
+ f, fields = check_step(sim, mass0, row['extrema'])
+ sample, _ = measure(f, 0., mass0)
+ sample['step'] = step
+ row['history'].append(sample)
+ if sample['max_speed'] > GATES['zero_speed']:
+ raise RuntimeError(f'Zero speed exceeds gate at {step}')
+ if np.ptp([s['mass'] for s in row['history']])/mass0 > GATES['mass_window_span']:
+ raise RuntimeError(f'Zero mass span exceeds gate at {step}')
+ if step and abs(sample['mass']-row['history'][-2]['mass'])/mass0 > GATES['mass_change']:
+ raise RuntimeError(f'Zero mass change exceeds gate at {step}')
+ row['mass_span'] = float(np.ptp([s['mass'] for s in row['history']])/mass0)
+ row['checks'] = dict(zero_speed=row['extrema']['speed'] <= GATES['zero_speed'],
+ mass_offset=row['extrema']['mass_offset'] <= GATES['mass_offset'],
+ mass_span=row['mass_span'] <= GATES['mass_window_span'],
+ mass_change=max(abs(s['mass']-p['mass'])/mass0 for p,s in zip(row['history'],row['history'][1:])) <= GATES['mass_change'],
+ boundary=max(s['boundary_error'] for s in row['history']) <= GATES['boundary_error'],
+ top_density=max(s['top_density_error'] for s in row['history']) <= 5e-7,
+ finite=all(s['finite'] for s in row['history']))
+ row['status'] = 'passed' if all(row['checks'].values()) else 'failed'
+ save()
+ assert row['status'] == 'passed', row['checks']
+ print('ZERO', json.dumps({k:v for k,v in row.items() if k != 'history'}), flush=True)
+
+
+def audit(reference, mass0, report):
+ result = dict(status='running', start=LIMIT-16, end=LIMIT, plain_steps=16, audit_steps=0, steps=[])
+ report['negative']['population_budget'] = result
+ with create(-.002) as sim:
+ sim.load_checkpoint(str(OUT/'negative-budget-20744.h5'))
+ build = compiler.generate_config(sim.lbm_cfg)
+ original = sim.stepper.step_fn
+ try:
+ source = Path(build)/'step/one_step_double.cu'
+ code = source.read_text()
+ call, marker = 'collide_dispatch(f, rho_n, ux, uy);', '__global__ __launch_bounds__(NT, 2)'
+ assert code.count(call) == code.count(marker) == 1
+ source.write_text(code.replace(call, 'j1_collide_audit(f, rho_n, ux, uy);').replace(marker, AUDIT+'\n'+marker))
+ result['audit_source_sha256'] = sha(source)
+ ptx = compiler.compile_kernel(build_dir=build)
+ with sim.ctx.activate():
+ module = compiler.load_module(ptx)
+ ptr, size = module.get_global('d_params')
+ cuda.memcpy_dtod(ptr, sim.field.module.get_global('d_params')[0], size)
+ iptr, _ = module.get_global('j1_incoming')
+ cptr, size = module.get_global('j1_collision')
+ cuda.memset_d8(cptr, 0, size)
+ incoming, collision = np.empty((2,48)), np.empty((35,48))
+ sim.stepper.step_fn = module.get_function('OneStep')
+ slot = targets(-.002)[2]
+ for _ in range(16):
+ before = populations(sim)
+ outgoing = np.stack((before[[4,6,7],1].sum(axis=0), before[[3,5,8],-2].sum(axis=0)))
+ mass = float(before[:,1:-1].sum())
+ sim.run(1)
+ result['audit_steps'] += 1
+ after, _ = check_step(sim, mass0, report['negative']['extrema'])
+ cuda.memcpy_dtoh(incoming, iptr)
+ cuda.memcpy_dtoh(collision, cptr)
+ delta = float(after[:,1:-1].sum()-mass)
+ net = float(incoming.sum()-outgoing.sum())
+ rounding = float(collision.sum())
+ entry = dict(step=sim.stepper.step_count, mass=mass, delta_mass=delta,
+ net_boundary=net, collision_roundoff=rounding,
+ physical_residual=delta-net, budget_residual=delta-net-rounding,
+ exchange={name:dict(incoming=float(i.sum()), outgoing=float(o.sum()))
+ for name,i,o in [('slot',incoming[0,slot],outgoing[0,slot]),
+ ('flanks',incoming[0,~slot],outgoing[0,~slot]),
+ ('top',incoming[1],outgoing[1])]})
+ result['steps'].append(entry)
+ if abs(entry['budget_residual'])/mass > GATES['budget_residual']:
+ raise RuntimeError('Population budget gate exceeded')
+ result['continued_equal'] = compare(reference, state(sim))
+ result['max_relative_residual'] = max(abs(s['budget_residual'])/s['mass'] for s in result['steps'])
+ result['max_physical_relative_residual'] = max(abs(s['physical_residual'])/s['mass'] for s in result['steps'])
+ result['status'] = 'passed' if all(result['continued_equal'].values()) else 'failed'
+ finally:
+ sim.stepper.step_fn = original
+ compiler.cleanup_build_dir(build)
+ return result
+
+
+def negative(report, save):
+ row = report['negative'] = dict(status='running', amplitude=-.002, steps=0, main_steps=0,
+ history=[], extrema={}, population_budget=dict(status='not_tested'))
+ with create(-.002) as sim:
+ sim.initialize()
+ initial = populations(sim)
+ mass0 = float(observables(initial)[0].sum())
+ first, fields = measure(initial, -.002, mass0)
+ first.update(step=0)
+ row['history'].append(first)
+ bottom, top, _ = targets(-.002)
+ row['initial_error'] = float(max(np.abs(fields[2][0]-bottom).max(), np.abs(fields[2][-1]-top).max(), np.abs(fields[1]).max()))
+ previous, mass_previous = fields[1:], mass0
+ check_step(sim, mass0, row['extrema'])
+ try:
+ for interval in range(1,41):
+ mean = np.zeros((2,33,48)); m2 = np.zeros_like(mean)
+ low, high = np.full_like(mean, np.inf), np.full_like(mean, -np.inf)
+ for n in range(1,CHUNK+1):
+ if sim.stepper.step_count == LIMIT-16:
+ row['budget_start'] = persist(sim, 'negative-budget-20744.h5')
+ sim.run(1)
+ row['steps'] = row['main_steps'] = sim.stepper.step_count
+ f, fields = check_step(sim, mass0, row['extrema'])
+ uv = np.stack(fields[1:])
+ delta = uv-mean
+ mean += delta/n
+ m2 += delta*(uv-mean)
+ low, high = np.minimum(low,uv), np.maximum(high,uv)
+ sample, fields = measure(f, -.002, mass0, previous)
+ sample.update(step=sim.stepper.step_count, mass_change=abs(sample['mass']-mass_previous)/mass0,
+ fluctuation=dict(samples=CHUNK, first_step=sim.stepper.step_count-CHUNK+1,
+ rms=np.sqrt(np.mean(m2/CHUNK,axis=(1,2))).tolist(),
+ max_node_peak_to_peak=np.max(high-low,axis=(1,2)).tolist()))
+ row['history'].append(sample)
+ sample['mass_window_span'] = float(np.ptp([s['mass'] for s in row['history'][-5:]])/mass0)
+ previous, mass_previous = fields[1:], sample['mass']
+ save()
+ print('NEGATIVE', json.dumps({k:sample[k] for k in ('step','mass_offset','mass_change','mass_window_span','u_change','v_change','flux_error','boundary_error')}), flush=True)
+ if sample['mass_change'] > GATES['mass_change'] or sample['mass_window_span'] > GATES['mass_window_span']:
+ raise RuntimeError('Negative sampled mass hard gate exceeded')
+ assert resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024 < 512
+ reference = state(sim)
+ row['end_checkpoint'] = persist(sim, 'negative-end-20760.h5')
+ row['final_boundary'] = dict(bottom_rho=fields[0][0].tolist(), bottom_u=fields[1][0].tolist(),
+ bottom_v=fields[2][0].tolist(), top_rho=fields[0][-1].tolist(),
+ top_u=fields[1][-1].tolist(), top_v=fields[2][-1].tolist())
+ except Exception:
+ row['failure_checkpoint'] = persist(sim, 'negative-failure.h5')
+ raise
+ budget = audit(reference, mass0, report)
+ last, tail = row['history'][-1], row['history'][-4:]
+ checks = dict(steady=len(row['history']) >= 9 and all(s['u_change'] <= GATES['velocity_change'] and s['v_change'] <= GATES['velocity_change'] and s['mass_change'] <= GATES['mass_change'] for s in tail) and last['mass_window_span'] <= GATES['mass_window_span'],
+ initialization=row['initial_error'] <= GATES['boundary_error'],
+ boundary=last['boundary_error'] <= GATES['boundary_error'],
+ top_density=last['top_density_error'] <= 5e-7,
+ mass=last['mass_offset'] <= GATES['mass_offset'] and last['mass_window_span'] <= GATES['mass_window_span'],
+ bounded=last['rho_error'] <= GATES['rho_error'] and last['max_speed'] <= GATES['speed'],
+ flux=last['flux_error'] <= GATES['flux_error'] and last['flux_span'] <= GATES['flux_span'],
+ symmetry=last['symmetry']['rho'] <= GATES['rho_symmetry'] and max(last['symmetry']['u'],last['symmetry']['v']) <= GATES['velocity_symmetry'],
+ nonuniform=last['nonuniform'], audit=budget['status'] == 'passed' and budget['max_relative_residual'] <= GATES['budget_residual'])
+ row['checks'] = {k:bool(v) for k,v in checks.items()}
+ row['status'] = 'passed' if all(checks.values()) else 'failed'
+ save()
+ print('NEGATIVE RESULT',json.dumps(row['checks']),flush=True)
+
+
+def main():
+ assert Path.cwd() == ROOT == Path('/home/frank14f/CelerisLab')
+ assert sys.executable == '/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python'
+ assert Path(CelerisLab.__file__).resolve() == ROOT/'src/CelerisLab/__init__.py'
+ assert Path(compiler.kernel_path('kernel_v2.cu')).resolve() == ROOT/'src/CelerisLab/lbm/kernels/kernel_v2.cu'
+ assert os.environ.get('CUDA_VISIBLE_DEVICES') == '0'
+ assert not (OUT/'results.json').exists(), 'Do not repeat this run'
+ baseline = json.loads((OUT/'baseline.json').read_text())
+ assert all(sha(ROOT/name) == digest for name,digest in baseline['hashes'].items())
+ assert sha(SOURCE) == SOURCE_SHA
+ gpu = subprocess.check_output(['nvidia-smi','-i','0','--query-gpu=memory.used,utilization.gpu','--format=csv,noheader,nounits'],text=True)
+ used, utilization = map(int,gpu.strip().split(','))
+ apps = subprocess.check_output(['nvidia-smi','-i','0','--query-compute-apps=pid','--format=csv,noheader'],text=True)
+ assert not apps.strip() and used < 100 and utilization == 0, (gpu,apps)
+ report = dict(python=sys.executable, package=CelerisLab.__file__, cuda_source=compiler.kernel_path('kernel_v2.cu'),
+ script_sha256=sha(__file__), gpu='0', gpu_before=gpu, gates=dict(GATES,top_density_error=5e-7),
+ restart=dict(status='not_tested'),zero=dict(status='not_tested'),negative=dict(status='not_tested'))
+
+ def save():
+ report['peak_rss_mib'] = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024
+ (OUT/'results.json').write_text(json.dumps(report,indent=2)+'\n')
+
+ print('SOURCES',report['python'],report['package'],report['cuda_source'],flush=True)
+ save()
+ try:
+ restart(report,save)
+ zero(report,save)
+ negative(report,save)
+ except Exception as error:
+ report['error'] = repr(error)
+ for name in ('restart','zero','negative'):
+ if report[name]['status'] == 'running': report[name]['status'] = 'failed'
+ budget = report['negative'].get('population_budget', {})
+ if budget.get('status') == 'running': budget['status'] = 'failed'
+ traceback.print_exc()
+ finally:
+ report['old_files_unchanged'] = all(sha(ROOT/name) == digest for name,digest in baseline['hashes'].items())
+ save()
+ print('FINAL',json.dumps({name:report[name]['status'] for name in ('restart','zero','negative')}),flush=True)
+ return 0 if report['old_files_unchanged'] and all(report[n]['status']=='passed' for n in ('restart','zero','negative')) else 1
+
+
+if __name__ == '__main__':
+ raise SystemExit(main())
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