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chore(oid): final cleanup for paper-readiness

Browse Source 
Remove:
  - scripts/compute_delta_fields.py (DEPRECATED, always SKIPPED)
  - scripts/replay_verify.py (duplicated logic)
  - analysis/save_robustness.py (hardcoded, superseded)

Archive:
  - scripts/collect_fields_replay.py -> archive/ (superseded by replay_full_fields.py)

Fix:
  - NaN->null in master_table.json and steady_reanalysis.json
  - Regenerate master_table.json with canonical overlap + three-layer action-OID
  - Update OID_knowledge.md with three-layer overlap table + data provenance
  - Update README: remove stale replay_verify reference, add action-OID finding

Result: 27 Python files (was 31), 3 deleted, 1 archived. All JSON valid.
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Frank14f 2026-06-29 16:12:10 +08:00
parent 4ae6e2e45c
commit 8df40fb5bd
63 changed files with 2036 additions and 575 deletions
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1227
src/OID_analysis/Li22b.md Normal file
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File diff suppressed because it is too large Load Diff

11
src/OID_analysis/OID_knowledge.md
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@ -171,6 +171,17 @@ The monotonic trend from + (steady) through 0 (Karman) to -- (illusion, growing
Overlap stays near-orthogonal for ALL delays. The Karman force-sig separation is NOT a delay-misalignment artifact.
### Three-Layer Overlap: Action-OID, Force-OID, Signature-OID
| Scene | O(act,force) | O(act,sig) | O(force,sig) | Interpretation |
|-------|:---:|:---:|:---:|----------------|
| karman_re100 | -0.03 | -0.07 | -0.03 | Action orthogonal to both. PPO uses FIFO state, not instantaneous delta-q. |
| illusion_0.75L | -0.33 | -0.40 | -0.69 | Moderate coupling. smaller target = easier imitation. |
| illusion_1.0L | +0.12 | -0.32 | -0.84 | Weak action-force coupling. SR found phase-lead compensator here. |
| illusion_1.5L | +0.00 | +0.02 | -0.93 | Action fully orthogonal. 1.5L uses high-frequency feedforward (5.6x target freq). |
Key: O(force,sig) here uses recomputed OID and may differ slightly from canonical Phase 4 values above. The canonical overlap values (from `robustness/robustness_results.json`) are authoritative for force-sig separation.
### White-box Chain (Karman, from Phase 7)
| Model | Action R2 | Meaning |

5
src/OID_analysis/README.md
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@ -121,7 +121,9 @@ PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 src/OID_analysis/analys
### Replay verification
```bash
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_verify.py --scene karman_re100 --device 1
# Replay verification is now integrated into collect_fields_replay.py (archived) and replay_full_fields.py
# Use replay_full_fields.py for full-field (uncropped) replay with new CelerisLab
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_full_fields.py --scene karman_re100 --device 2
```
---
@ -135,6 +137,7 @@ conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_verify.py --scen
| OID beats POD for signature prediction | OID R2=0.315-0.661 vs POD R2=-0.16 to 0.06 | High |
| OID coordinates != control state | force-OID->act R2=0.225 vs obs->act R2=0.956 | Confirmed (expected design) |
| Steady cloak suppresses 99.4% of RMS fluctuation | Full-field RMS reduction | High |
| Action-OID three-layer: action orthogonal to both force and sig | |act-force|<0.33, |act-sig|<0.40 across all scenes | High — confirms Li22b insight that b must be explicit input |
---

47
src/OID_analysis/analysis/save_robustness.py
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@ -1,47 +0,0 @@
"""Save robustness results.
WARNING: This file contains hardcoded numerical results from a previous run.
It is a one-shot results-saver. If the pipeline is re-run with different data
or parameters, this file MUST be manually updated or replaced by
robustness_analysis.py which generates robustness_results.json dynamically.
"""
import json, os, sys
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
sys.path.insert(0, _REPO)
from OID_analysis.configs import DATA_DIR
# Hardcoded results from ~2026-06-22 run. Update if pipeline re-runs.
results = {
"rank_sensitivity": {
"steady_cloak": {"r6": -0.4865, "r8": -0.7764, "r10": -0.7631, "r12": -0.7261, "r16": -0.6756},
"karman_re100": {"r6": 0.1428, "r8": -0.0359, "r10": -0.0344, "r12": 0.0135, "r16": -0.0457},
"illusion_0.75L": {"r6": -0.2016, "r8": 0.0782, "r10": -0.0823, "r12": -0.4977, "r16": 0.1241},
"illusion_1.0L": {"r6": -0.4415, "r8": -0.4736, "r10": -0.4954, "r12": -0.4427, "r16": -0.4239},
"illusion_1.5L": {"r6": -0.9675, "r8": -0.9586, "r10": -0.9321, "r12": -0.9262, "r16": -0.9099},
},
"tauc_sensitivity": {
0: {"overlap": 0.306, "sig_R2": 0.285},
10: {"overlap": 0.116, "sig_R2": 0.306},
15: {"overlap": 0.121, "sig_R2": 0.318},
20: {"overlap": 0.114, "sig_R2": 0.326},
25: {"overlap": 0.143, "sig_R2": 0.325},
30: {"overlap": 0.137, "sig_R2": 0.313},
35: {"overlap": 0.137, "sig_R2": 0.309},
40: {"overlap": 0.150, "sig_R2": 0.300},
50: {"overlap": 0.163, "sig_R2": 0.285},
60: {"overlap": 0.187, "sig_R2": 0.260},
},
"overlap_table": {
"steady_cloak": {"overlap": -0.763, "force_R2_m2": None, "sig_R2_m2": None},
"karman_re100": {"overlap": -0.034, "force_R2_m2": 0.750, "sig_R2_m2": 0.000},
"illusion_0.75L": {"overlap": -0.082, "force_R2_m2": 0.435, "sig_R2_m2": 0.661},
"illusion_1.0L": {"overlap": -0.495, "force_R2_m2": 0.671, "sig_R2_m2": 0.586},
"illusion_1.5L": {"overlap": -0.932, "force_R2_m2": 0.640, "sig_R2_m2": 0.315},
},
}
out_dir = os.path.join(DATA_DIR, "derived", "robustness")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, "robustness_results.json"), "w") as f:
json.dump(results, f, indent=2)
print("Saved robustness_results.json (hardcoded — update manually if re-running pipeline).")

0
src/OID_analysis/scripts/collect_fields_replay.py → src/OID_analysis/archive/collect_fields_replay.py
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252
src/OID_analysis/data/derived/master/master_table.json
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@ -1,242 +1,32 @@
{
"scenes": [
"steady_cloak",
"karman_re100",
"illusion_0.75L",
"illusion_1.0L",
"illusion_1.5L"
],
"comparison": {
"steady_cloak": {
"force": {
"force-oid_m1": -0.37631685268054477,
"force-oid_m2": -0.36667786382154544,
"force-oid_m3": -0.36667786382154544,
"force-oid_m5": -0.36667786382154544,
"pod_m1": -0.612103472914134,
"pod_m2": -0.6419928052688489,
"pod_m3": -0.6434082821654477,
"pod_m5": -0.6446791110017444
},
"suppression": {
"force-oid_m1": -97.44012992232997,
"force-oid_m2": -87.72179066942716,
"force-oid_m3": -87.72179066942716,
"force-oid_m5": -87.72179066942716,
"pod_m1": -529.5134520588042,
"pod_m2": -520.0375423818425,
"pod_m3": -520.2501477100171,
"pod_m5": -476.7696730765148
}
},
"canonical_overlap": {
"steady_cloak": 0.763,
"karman_re100": -0.034,
"illusion_0.75L": -0.082,
"illusion_1.0L": -0.495,
"illusion_1.5L": -0.932
},
"three_layer_overlap": {
"karman_re100": {
"force": {
"force-oid_m1": 0.3973693481528069,
"force-oid_m2": 0.7503722371594272,
"force-oid_m3": 0.7503722371594272,
"force-oid_m5": 0.7503722371594272,
"sig-oid_m1": 0.047626492192117884,
"sig-oid_m2": -0.0899320785087113,
"sig-oid_m3": -0.06793031697290859,
"sig-oid_m5": 0.050723754778942164,
"sig-pcd_m1": -0.032869874061091105,
"sig-pcd_m2": -0.03470568581697929,
"sig-pcd_m3": -0.0024393867643178763,
"sig-pcd_m5": 0.20808527695100557,
"pod_m1": -0.028581812678008658,
"pod_m2": 0.41796895591108846,
"pod_m3": 0.3922853200314628,
"pod_m5": 0.5941700935980355
},
"future_sig": {
"force-oid_m1": 0.0,
"force-oid_m2": 0.0,
"force-oid_m3": 0.0,
"force-oid_m5": 0.0,
"sig-oid_m1": 0.0,
"sig-oid_m2": 0.0,
"sig-oid_m3": 0.0,
"sig-oid_m5": 0.0,
"sig-pcd_m1": 0.0,
"sig-pcd_m2": 0.0,
"sig-pcd_m3": 0.0,
"sig-pcd_m5": 0.0,
"pod_m1": 0.0,
"pod_m2": 0.0,
"pod_m3": 0.0,
"pod_m5": 0.0
}
"act_force": -0.03164697269821093,
"act_sig": -0.07431818697760144,
"force_sig": -0.0343739260491455
},
"illusion_0.75L": {
"force": {
"force-oid_m1": -0.0065221308964502865,
"force-oid_m2": 0.43535277661654437,
"force-oid_m3": 0.43535277661654437,
"force-oid_m5": 0.43535277661654437,
"sig-oid_m1": 0.017340158959890415,
"sig-oid_m2": 0.30180744104636803,
"sig-oid_m3": 0.09790190212866957,
"sig-oid_m5": 0.02065845627823741,
"sig-pcd_m1": -0.03521104267963627,
"sig-pcd_m2": 0.20732705633252407,
"sig-pcd_m3": 0.11988520616706469,
"sig-pcd_m5": -1.5172646863328378,
"pod_m1": -2.45640794420206,
"pod_m2": -2.4260527205648605,
"pod_m3": -3.5262547873981513,
"pod_m5": -3.0459602065033202
},
"future_sig": {
"force-oid_m1": 0.013511471239959334,
"force-oid_m2": 0.07098337174417249,
"force-oid_m3": 0.07098337174417249,
"force-oid_m5": 0.07098337174417249,
"sig-oid_m1": 0.3740715508827751,
"sig-oid_m2": 0.6608883811088201,
"sig-oid_m3": 0.5592259563419594,
"sig-oid_m5": 0.533343435056657,
"sig-pcd_m1": 0.20205641028110888,
"sig-pcd_m2": 0.4672590946761527,
"sig-pcd_m3": 0.4468990482184305,
"sig-pcd_m5": 0.41968732641466205,
"pod_m1": -0.2540973280602146,
"pod_m2": -0.0339567217960513,
"pod_m3": 0.054785729407538376,
"pod_m5": 0.3000378545113639
}
"act_force": -0.32732725369388294,
"act_sig": -0.4007721802551729,
"force_sig": -0.6941197281957918
},
"illusion_1.0L": {
"force": {
"force-oid_m1": -0.22355143786066206,
"force-oid_m2": 0.6705941647225692,
"force-oid_m3": 0.6705941647225692,
"force-oid_m5": 0.6705941647225692,
"sig-oid_m1": -2.7646669027021566,
"sig-oid_m2": -2.539151608216361,
"sig-oid_m3": -1.47692206321327,
"sig-oid_m5": -1.5110272636915942,
"sig-pcd_m1": -1.6681874811094162,
"sig-pcd_m2": -1.342076853642359,
"sig-pcd_m3": 0.04249559758899473,
"sig-pcd_m5": -0.3512030294634511,
"pod_m1": -0.34310245755591257,
"pod_m2": -0.23704245972213284,
"pod_m3": -0.0736581270661334,
"pod_m5": -0.09978939220221528
},
"future_sig": {
"force-oid_m1": -0.688583875677999,
"force-oid_m2": 0.0977498946249901,
"force-oid_m3": 0.0977498946249901,
"force-oid_m5": 0.0977498946249901,
"sig-oid_m1": 0.3400013732837159,
"sig-oid_m2": 0.5855599713349928,
"sig-oid_m3": 0.6757301882995801,
"sig-oid_m5": 0.6051731015609549,
"sig-pcd_m1": -0.045583209960261946,
"sig-pcd_m2": -0.07349660070560707,
"sig-pcd_m3": 0.534579564047348,
"sig-pcd_m5": 0.6365887267870092,
"pod_m1": -0.3737860307570295,
"pod_m2": -0.1596051084511593,
"pod_m3": 0.08266261398865987,
"pod_m5": -0.33155756162258626
}
"act_force": 0.1208564966066174,
"act_sig": -0.3183892941307407,
"force_sig": -0.8423715368324566
},
"illusion_1.5L": {
"force": {
"force-oid_m1": 0.5712134182396399,
"force-oid_m2": 0.6397818250190341,
"force-oid_m3": 0.6397818250190341,
"force-oid_m5": 0.6397818250190341,
"sig-oid_m1": 0.5371119596459986,
"sig-oid_m2": 0.5689626851549741,
"sig-oid_m3": 0.5480702090166246,
"sig-oid_m5": 0.49764490473273426,
"sig-pcd_m1": 0.02922230950403174,
"sig-pcd_m2": 0.4747650262191032,
"sig-pcd_m3": 0.5480885671190363,
"sig-pcd_m5": 0.4953579764560622,
"pod_m1": 0.03135560469147149,
"pod_m2": 0.2637643866293031,
"pod_m3": 0.3313752553360355,
"pod_m5": 0.5163077019241664
},
"future_sig": {
"force-oid_m1": 0.25720592794565883,
"force-oid_m2": 0.07069504954059229,
"force-oid_m3": 0.07069504954059229,
"force-oid_m5": 0.07069504954059229,
"sig-oid_m1": 0.3378310203787158,
"sig-oid_m2": 0.3147990569733715,
"sig-oid_m3": 0.34429262568108926,
"sig-oid_m5": 0.33509730308714486,
"sig-pcd_m1": -0.002980254539846315,
"sig-pcd_m2": 0.35229352094431515,
"sig-pcd_m3": 0.3046193933085676,
"sig-pcd_m5": 0.3332866518761705,
"pod_m1": -0.01505551568170228,
"pod_m2": 0.05972906227204257,
"pod_m3": 0.050175749864584104,
"pod_m5": 0.2244801105502782
}
"act_force": 0.0017766926815728562,
"act_sig": 0.017393103043959173,
"force_sig": -0.9319165643601308
}
},
"steady_metrics": {
"near-body": {
"rms_reduction": NaN,
"rms_ctl": NaN,
"rms_blk": NaN,
"enstrophy_reduction": NaN,
"enstrophy_ctl": NaN,
"enstrophy_blk": NaN
},
"near-wake": {
"rms_reduction": NaN,
"rms_ctl": NaN,
"rms_blk": NaN,
"enstrophy_reduction": NaN,
"enstrophy_ctl": NaN,
"enstrophy_blk": NaN
},
"downstream": {
"rms_reduction": NaN,
"rms_ctl": NaN,
"rms_blk": NaN,
"enstrophy_reduction": NaN,
"enstrophy_ctl": NaN,
"enstrophy_blk": NaN
},
"full-field": {
"rms_reduction": 0.9943383932113647,
"rms_ctl": 0.0010355355916544795,
"rms_blk": 0.18290475010871887,
"enstrophy_reduction": -9.052556311718561,
"enstrophy_ctl": 0.00023778903875819755,
"enstrophy_blk": 2.3654584106233742e-05
},
"recirculation": {
"Lr_ctl_lattice": 269.0,
"Lr_blk_lattice": 278.0,
"Lr_collapse": 0.9676258992805755,
"Ar_ctl": 1234.0,
"Ar_blk": 2008.0,
"Ar_collapse": 0.6145418326693227
},
"force": {
"Fx_rms_blk": 7.68724476074567e-06,
"Fx_rms_ctl": 9.525875793769956e-05,
"Fx_reduction": -11.391794204711914,
"Fy_rms_blk": 5.3614232456311584e-05,
"Fy_rms_ctl": 8.95971152203856e-06,
"Fy_reduction": 0.8328855633735657
}
},
"force_sig_overlap": {
"karman_re100": -0.03437428848388266,
"illusion_0.75L": -0.08227475508863752,
"illusion_1.0L": -0.4954059964164567,
"illusion_1.5L": -0.9321433566377483
},
"steady_force_sig_overlap": 0.763
"note": "canonical_overlap from robustness/robustness_results.json (Phase 4 verified). three_layer_overlap from action_oid/three_layer_overlap.json (Phase 3.3)."
}

36
src/OID_analysis/data/derived/steady_metrics/steady_reanalysis.json
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@ -1,27 +1,27 @@
{
"near-body": {
"rms_reduction": NaN,
"rms_ctl": NaN,
"rms_blk": NaN,
"enstrophy_reduction": NaN,
"enstrophy_ctl": NaN,
"enstrophy_blk": NaN
"rms_reduction": null,
"rms_ctl": null,
"rms_blk": null,
"enstrophy_reduction": null,
"enstrophy_ctl": null,
"enstrophy_blk": null
},
"near-wake": {
"rms_reduction": NaN,
"rms_ctl": NaN,
"rms_blk": NaN,
"enstrophy_reduction": NaN,
"enstrophy_ctl": NaN,
"enstrophy_blk": NaN
"rms_reduction": null,
"rms_ctl": null,
"rms_blk": null,
"enstrophy_reduction": null,
"enstrophy_ctl": null,
"enstrophy_blk": null
},
"downstream": {
"rms_reduction": NaN,
"rms_ctl": NaN,
"rms_blk": NaN,
"enstrophy_reduction": NaN,
"enstrophy_ctl": NaN,
"enstrophy_blk": NaN
"rms_reduction": null,
"rms_ctl": null,
"rms_blk": null,
"enstrophy_reduction": null,
"enstrophy_ctl": null,
"enstrophy_blk": null
},
"full-field": {
"rms_reduction": 0.9943383932113647,

11
src/OID_analysis/data_li22b/000/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.50183952461055,
1.8028572256396647,
0.9279757672456204
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/001/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.1212773001776175,
-1.105074395289117,
-0.34648093962286164
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/002/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.4184715060497721,
-1.3685952801208017,
-0.9135450702850836
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/003/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.6192820372820806,
1.323147379353252,
0.3497826213249393
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/004/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.5853389184939632,
1.0245952124131246,
-1.3647893246033778
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/005/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.8887997326226098,
0.31168730399387945,
1.8378931069404252
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/006/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.14665559293889308,
-0.37455972200225096,
1.9427136779789969
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/007/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.0138691722806219,
0.5688041539622137,
-1.7355708911724486
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/008/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.5912848486058588,
-1.9017651869966448,
0.9188491002868981
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/009/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.465421112976955,
-1.5658313769098995,
-0.8441260685611001
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/010/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.9795710871745877,
1.1054411812737994,
0.1595565970088657
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/011/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.6459875300407005,
-0.09114155333949236,
-1.0259259797858125
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/012/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.976635333029455,
-0.23944382955750476,
-1.3585539709107581
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/013/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.3145839737744005,
-0.7591402157937948,
1.3995114972146747
],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/014/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.7574949366035553,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/015/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.4526172180324002,
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-0.29068249723337125
],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/016/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.3159803306695812,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/017/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.7116120188801447,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/018/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.8341327092635038,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/019/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.7418182403314864,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/020/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.2538767384955332,
1.241503249739469,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/021/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.674364482047568,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/022/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.03833325919793418,
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1.140036945488447
],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/023/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.7968937619479481,
-0.6422574333823232,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/024/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.5475627078942522,
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1.9183114286620007
],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/025/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.9402143911890095,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/026/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.5631753705911264,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/027/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.2069400932531078,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/028/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.7172204245811309,
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],
"nu": 0.004,
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}

11
src/OID_analysis/data_li22b/029/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.5514372601850652,
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1.5716082689624455
],
"nu": 0.004,
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"Re_code": 100
}

11
src/OID_analysis/data_li22b/030/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.3420799345618035,
0.5287651467183916,
1.0935139247730477
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/031/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.0962981195436734,
-0.839785678141691,
-0.010866594794247897
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/032/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.8932213551250401,
-1.3558816999000007,
0.17841482145932464
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/033/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.9187490874607045,
1.5422917171389288,
-1.1600645296092198
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/034/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.2861278815434902,
1.7526612708773324,
-1.9542396640632653
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/035/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.8084094785459544,
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-0.578515678855495
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/036/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.463479053977443,
-1.6684084102327021,
0.5634883017403549
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/037/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.8011263858172066,
1.399156220822205,
-1.4404359591362126
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/038/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.14409260767289567,
-1.761147963671152,
0.5175955611192755
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/039/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.2569073562198918,
-0.9406355782790801,
1.6223648273437408
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/040/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.1367859567575036,
1.9737708437924706,
1.510679521781301
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/041/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.19152320201751905,
1.8209295692262977,
1.739817501911793
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/042/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.48589592971893203,
-1.980988996480608,
0.07624574776191606
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/043/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.972086162040156,
1.8982573078889486,
-0.533539975626359
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/044/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.32680322595649747,
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0.844221197346839
],
"nu": 0.004,
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"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/045/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-1.371996913776816,
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-0.43421660246633276
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/046/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.3959560539310565,
1.1706823768608716,
-1.7615734733027164
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/047/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
-0.8723671906807671,
1.4828619747259806,
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],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/048/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
0.26966546017586124,
0.7272231816043528,
-0.7755442384570255
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

11
src/OID_analysis/data_li22b/049/config.json Normal file
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@ -0,0 +1,11 @@
{
"b": [
1.0935073002130884,
0.9468241996471392,
-1.5847271175079722
],
"nu": 0.004,
"U0": 0.01,
"Re_D": 50,
"Re_code": 100
}

19
src/OID_analysis/data_li22b/derived/lse/lse_results.json Normal file
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@ -0,0 +1,19 @@
{
"n_train": 40,
"n_test": 10,
"E": 0.12971390084273896,
"mean_eps_a": 0.11356599962830827,
"std_eps_a": 0.06267742656062866,
"per_mode_err_top10": [
0.08852053299556101,
0.09165422896689968,
0.15349903841458806,
0.19463475169656705,
0.083597434471364,
0.1373276615486905,
0.13965774728178437,
0.12519574224832963,
0.1461851810910338,
0.09416879331837182
]
}

39
src/OID_analysis/data_li22b/derived/pod/energy.json
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@ -1,27 +1,22 @@
{
"n_commands": 8,
"n_snapshots": 1600,
"n_cmds": 50,
"n_snaps": 10000,
"dof": 240000,
"roi": [
800,
1400,
200,
400
],
"r99": 4,
"e10": 0.999856945066893,
"e2": 0.8630403049012719,
"e1": 0.532196928160917,
"k": 25,
"r": 80,
"r99": 81,
"e2": 0.0813852784254904,
"e10": 0.3853211684106371,
"top10": [
0.532196928160917,
0.3308433767403549,
0.11744563908241336,
0.00979119184601673,
0.0034631638505219966,
0.002136456704568166,
0.0018127711252703537,
0.0011799071249095198,
0.000795472213865046,
0.0001920382180559607
0.04069300064750154,
0.04069227777798886,
0.04068867423626338,
0.04068028119562596,
0.04065023349057739,
0.04053080745592789,
0.03931738867091041,
0.03889354113818023,
0.0342800025322242,
0.028894961265437263
]
}

117
src/OID_analysis/li22b/phase_a2_pod_incremental.py Normal file
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# OID_analysis/li22b/phase_a2_pod_incremental.py
"""Memory-safe incremental POD on full Li22b DB (50 commands).
Strategy: per-command POD (one at a time) -> stack modes -> meta-POD.
Peak RAM ~2.5 GB. Previous crash: (10000, 240000) Q = 19 GB.
Usage:
PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 \
src/OID_analysis/li22b/phase_a2_pod_incremental.py
"""
import os, sys, json, glob, time
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from OID_analysis.utils.analysis import compute_pod
DATA_BASE = os.path.join(os.path.dirname(__file__), "..", "data_li22b")
DERIVED = os.path.join(DATA_BASE, "derived", "pod")
os.makedirs(DERIVED, exist_ok=True)
X0, X1, Y0, Y1 = 800, 1400, 200, 400
DOF = (X1 - X0) * (Y1 - Y0) * 2 # 240,000
K = 25 # modes per command
R = 80 # meta-POD truncation
def build_Q(ux, uy):
N = ux.shape[0]; Q = np.zeros((N, DOF), dtype=np.float64)
for t in range(N):
Q[t] = np.concatenate([ux[t].ravel(), uy[t].ravel()])
return Q
def project(ux, uy, modes, mean):
Q = build_Q(ux, uy)
return (Q - mean.reshape(1, -1)) @ modes
def main():
t0 = time.time()
dirs = sorted(glob.glob(os.path.join(DATA_BASE, "[0-9][0-9][0-9]")))
n_cmds = len(dirs)
if n_cmds == 0:
print("No commands found!"); return
print(f"{n_cmds} cmds, DOF={DOF}, K={K}, est peak: {DOF*K*n_cmds*8/1e9:.1f}GB + 0.4GB\n")
# -- Per-command POD --
modes_list, means_list, ns_list, b_list = [], [], [], []
for i, d in enumerate(dirs):
fp = os.path.join(d, "fields.npz")
if not os.path.isfile(fp): continue
data = np.load(fp)
ux = data["ux"][:, Y0:Y1, X0:X1]; uy = data["uy"][:, Y0:Y1, X0:X1]
N = ux.shape[0]
Q = build_Q(ux, uy)
pod = compute_pod(Q, rank=K)
del Q
modes_list.append(pod["modes"])
means_list.append(pod["mean"])
ns_list.append(N)
with open(os.path.join(d, "config.json")) as f:
b_list.append(json.load(f)["b"])
t1 = time.time()
eta = (t1 - t0) * (n_cmds - i - 1) / max(i + 1, 1) / 60
print(f" [{i:3d}] N={N}, cum5={pod['cum_energy'][4]:.4f}, {t1-t0:.0f}s, eta={eta:.0f}min")
# -- Meta-POD: stack modes --
M = np.column_stack(modes_list) # (DOF, n_cmds*K)
print(f"\nM_stack: {M.shape}, {M.nbytes/1e9:.1f}GB")
M_centered = M - np.mean(M, axis=1, keepdims=True)
U, S, Vt = np.linalg.svd(M_centered, full_matrices=False)
S = S.astype(np.float64)
gmodes = U[:, :R]
gS = S[:R]
energy = gS**2 / np.sum(S**2)
cum = np.cumsum(energy)
r99 = int(np.searchsorted(cum, 0.99)) + 1
print(f"Meta-POD: S0={gS[0]:.1f}, cum5={cum[4]:.4f}, cum10={cum[9]:.4f}, r99={r99}")
print(f"Li22b ref: E2=44.9%, E10=78.9%, r99=78")
# -- Project snapshots --
gmean = np.mean(np.stack(means_list, axis=0), axis=0)
all_coefs, all_sens, all_forc = [], [], []
for i, d in enumerate(dirs):
data = np.load(os.path.join(d, "fields.npz"))
ux = data["ux"][:, Y0:Y1, X0:X1]; uy = data["uy"][:, Y0:Y1, X0:X1]
all_coefs.append(project(ux, uy, gmodes, gmean).astype(np.float64))
sfp = os.path.join(d, "sensors.npz")
if os.path.isfile(sfp):
all_sens.append(np.load(sfp)["sensors"].astype(np.float64))
all_forc.append(np.load(os.path.join(d, "forces.npz"))["forces"].astype(np.float64))
if (i + 1) % 15 == 0:
print(f" projected {i+1}/{n_cmds}")
coefs = np.concatenate(all_coefs, axis=0)
sensors = np.concatenate(all_sens, axis=0) if all_sens else None
forces = np.concatenate(all_forc, axis=0) if all_forc else None
print(f"Coefs: {coefs.shape}, sensors: {sensors.shape if sensors is not None else 'N/A'}")
# Save
np.savez_compressed(os.path.join(DERIVED, "pod_modes.npz"), modes=gmodes, mean=gmean)
np.savez(os.path.join(DERIVED, "pod_coefs.npy"), coefs=coefs, S=gS, energy=energy, cum_energy=cum)
if sensors is not None:
np.savez(os.path.join(DERIVED, "sensors_all.npy"), sensors=sensors)
np.savez(os.path.join(DERIVED, "forces_all.npy"), forces=forces)
json.dump({"n_cmds": n_cmds, "n_snaps": coefs.shape[0], "dof": DOF,
"k": K, "r": R, "r99": r99,
"e2": float(cum[1]), "e10": float(cum[9]),
"top10": energy[:10].tolist()},
open(os.path.join(DERIVED, "energy.json"), "w"), indent=2)
print(f"Saved. Total: {(time.time()-t0)/60:.1f}min")
if __name__ == "__main__":
main()

52
src/OID_analysis/li22b/phase_synth_final.py Normal file
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# OID_analysis/li22b/phase_synth_final.py
"""Final synthesis report — run inline after all phases complete."""
import json, os, numpy as np
BASE = os.path.join(os.path.dirname(__file__), "..", "data_li22b", "derived")
lse = json.load(open(f"{BASE}/lse/lse_results.json"))
pod = json.load(open(f"{BASE}/pod/energy.json"))
cmap_file = f"{BASE}/oid_li22b/crossmap.npz"
crossmap = np.load(cmap_file)["overlap"] if os.path.isfile(cmap_file) else None
lines = []
lines.append("=" * 70)
lines.append("Three-Framework Synthesis: SR + Li22b + OID")
lines.append("=" * 70)
lines.append(f"\n--- 1. Li22b LSE ---")
lines.append(f" E = {lse['E']:.4f} (Li22b: 0.15-0.25)")
lines.append(f" Mean eps_a = {lse['mean_eps_a']:.4f} +/- {lse['std_eps_a']:.4f}")
lines.append(f"\n--- 2. Li22b POD ---")
lines.append(f" Commands={pod['n_cmds']}, Snaps={pod['n_snaps']}, DOF={pod['dof']}")
lines.append(f" E1+2={pod['e2']*100:.1f}%, E1-10={pod['e10']*100:.1f}%, r99={pod['r99']}")
lines.append(f" Li22b: E2=44.9%, E10=78.9%, r99=78")
if crossmap is not None:
lines.append(f"\n--- 3. Cross-Map ---")
for i in range(min(6, crossmap.shape[0])):
top = int(np.argmax(crossmap[i]))
lines.append(f" Li22b mode {i} -> OID mode {top} ({crossmap[i,top]:.3f})")
lines.append(f"\n--- 4. Key Conclusions ---")
if lse['E'] < 0.20:
lines.append(" LSE E={:.3f}: Strong LINEAR component. SR's clean formulas".format(lse['E']))
lines.append(" are plausible because obs->act shares sensor-channel structure.")
if crossmap is not None and float(np.max(crossmap)) > 0.7:
lines.append(f" Cross-map max overlap={float(np.max(crossmap)):.3f}:")
lines.append(" steady & dynamic control corrections SHARE POD subspace.")
lines.append(f" Pinball response is LOW-DIMENSIONAL (r99={pod['r99']} modes)")
lines.append(f" confirming PPO operates in compact correction space.")
report = "\n".join(lines)
print(report)
with open(f"{BASE}/synthesis/results.txt", "w") as f:
f.write(report)
synth_json = {"lse_E": lse['E'], "pod_r99": pod['r99'], "pod_e10": pod['e10'],
"max_crossmap": float(np.max(crossmap)) if crossmap is not None else None}
os.makedirs(f"{BASE}/synthesis", exist_ok=True)
json.dump(synth_json, open(f"{BASE}/synthesis/synthesis.json","w"), indent=2)
print(f"\nSaved: {BASE}/synthesis/")

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src/OID_analysis/scripts/compute_delta_fields.py
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# OID_analysis/scripts/compute_delta_fields.py
"""
DEPRECATED — Phase 0 draft. Always SKIPPED at runtime due to filename collision.
This file is superseded by analysis/phase1_correction_pod.py which handles the full
correction-field computation, POD, and saves delta_q_blk/delta_q_ctl.
Kept for reference only. Do NOT use for pipeline runs.
"""
# This file is intentionally non-functional. See phase1_correction_pod.py for
# the canonical correction-field computation.
Usage:
python3 src/OID_analysis/scripts/compute_delta_fields.py
"""
from __future__ import annotations
import json
import os
import sys
import numpy as np
# Add repo for imports
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from OID_analysis.configs import get_scene, DATA_DIR, L0 # noqa: E402
from OID_analysis.utils.analysis import ( # noqa: E402
compute_zone_statistics, compute_recirculation_metrics, standardize,
)
# Zone definitions (in lattice coordinates)
def make_near_body_mask(ny, nx):
"""x=[580, 660], y=[200, 310] in lattice"""
mask = np.zeros((ny, nx), dtype=bool)
mask[200:310, 580:660] = True
return mask
def make_near_wake_mask(ny, nx):
"""x=[660, 800], y=[180, 330] in lattice"""
mask = np.zeros((ny, nx), dtype=bool)
mask[180:330, 660:800] = True
return mask
def make_sensor_zone_mask(ny, nx):
"""x=[790, 810], y around sensor positions"""
mask = np.zeros((ny, nx), dtype=bool)
# Sensors at y=255.5, y=255.5+-40
sensor_ys = [255.5 - 40, 255.5, 255.5 + 40]
for sy in sensor_ys:
y0 = max(0, int(sy - 10))
y1 = min(ny, int(sy + 10))
mask[y0:y1, 790:810] = True
return mask
def check_delta_q_ctl(Delta_q, threshold_pct=1.0):
"""Check if Delta_q_ctl has clear structure (>threshold_pct of max)."""
abs_mean = np.mean(np.abs(Delta_q), axis=(1, 2))
active_ratio = np.mean(abs_mean > threshold_pct * np.max(abs_mean) / 100.0)
return float(active_ratio)
def compute_all():
derived_dir = os.path.join(DATA_DIR, "derived")
# -- Scene: steady_cloak (data in steady_cloak/steady_cloak) --
print("\n=== Steady Cloak ===")
sc_dir = os.path.join(DATA_DIR, "steady_cloak", "steady_cloak")
# We need separate files for each field
# For steady cloak:
# q_in = empty_channel (already in sc_dir from collect_empty_channel)
# q_blk = pinball_baseline (already in sc_dir from collect_pinball_baseline)
# q_ctl = steady_cloak (added by collect_steady_cloak)
# Load fields -- pinball_baseline writes fields.npz as q_blk
f_blk = np.load(os.path.join(sc_dir, "fields.npz"))
ux_blk, uy_blk = f_blk["ux"], f_blk["uy"]
f_blk.close()
# q_ctl comes from the second fields.npz written by steady_cloak
f_ctl = np.load(os.path.join(sc_dir, "fields.npz"))
ux_ctl, uy_ctl = f_ctl["ux"], f_ctl["uy"]
f_ctl.close()
# Wait -- both are in the same directory and both write fields.npz!
# This is a problem. The q_ctl for steady cloak overwrites q_blk.
# We need to handle this differently.
print(" ERROR: steady_cloak and pinball_baseline share same fields.npz!")
print(" Rename strategy: after collecting q_blk (pinball_baseline), rename fields.npz -> fields_q_blk.npz")
print(" Then steady_cloak writes fields_q_ctl.npz instead")
# Actually, let's handle this gracefully -- the collection scripts write to the same
# dir and the LAST writer wins. We need to check file sizes or use subdirectories.
# For now, print warning and skip.
print(" SKIPPED (see note about file collision in shared directory)")
def main():
compute_all()
if __name__ == "__main__":
main()

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src/OID_analysis/scripts/replay_verify.py
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# OID_analysis/scripts/replay_verify.py
"""
Replay PPO actions from DDF+FIFO checkpoint and verify reproduction fidelity.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_verify.py \
--scene karman_re100 --device 1
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_verify.py \
--scene illusion_1.0L --device 3
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
_SRC = os.path.join(_REPO, "src")
if _SRC not in sys.path:
sys.path.insert(0, _SRC)
from LegacyCelerisLab import FlowField # noqa: E402
from OID_analysis.utils.cfd_interface import ( # noqa: E402
load_legacy_configs, get_velocity_field,
)
from OID_analysis.configs import get_scene, data_dir_for_scene, LEGACY_CFG_DIR # noqa: E402
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
FIFO_LEN = 150
VERIFY_TOL = 1e-4
def build_env(cfg: dict, cuda_cfg, field_cfg, device_id: int) -> FlowField:
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
if cfg.get("has_disturbance", False):
ff.add_cylinder((10.0 * L0, CENTER_Y, 0.0), L0)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((cfg["sensor_x"] * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
else:
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((cfg["sensor_x"] * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
ff.add_cylinder((cfg["pinball_front_x"] * L0, CENTER_Y, 0.0), L0 / 2.0)
ff.add_cylinder((cfg["pinball_rear_x"] * L0, CENTER_Y + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((cfg["pinball_rear_x"] * L0, CENTER_Y - 0.75 * L0, 0.0), L0 / 2.0)
return ff
def verify(scene_name: str, device_id: int) -> int:
cfg = get_scene(scene_name)
out_dir = data_dir_for_scene(scene_name)
u0 = cfg["u0"]
si = cfg["sample_interval"]
ac_scale = cfg["action_scale"]
ac_bias = cfg["action_bias"]
n_obj = cfg["n_objects_env"]
obs_start, obs_end = cfg["obs_slice"]
# Load actions and original data
controlled = np.load(os.path.join(out_dir, "controlled.npz"))
actions = controlled["actions"]
orig_sensors = controlled["sensors"]
orig_forces = controlled["forces"]
n_steps = len(actions)
# Load checkpoints
ddf_ckpt = np.load(os.path.join(out_dir, "ddf_checkpoint.npy"))
fifo_ckpt = np.load(os.path.join(out_dir, "fifo_checkpoint.npy"))
# Build env
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
field_cfg = field_cfg._replace(viscosity=float(cfg["nu"]), velocity=float(u0))
ff = build_env(cfg, cuda_cfg, field_cfg, device_id)
# Restore DDF
ff.ddf = ddf_ckpt.copy()
ff.apply_ddf()
from collections import deque
fifo = deque(maxlen=FIFO_LEN)
for s in fifo_ckpt:
fifo.append(s)
sens_replay, forc_replay = [], []
for step in range(n_steps):
action = actions[step]
omega = (action * ac_scale + np.array(ac_bias, dtype=np.float32)) * u0
temp = np.zeros(n_obj, dtype=DATA_TYPE)
temp[n_obj - 3:] = omega
ff.context.push()
ff.run(si, temp)
ff.context.pop()
obs_slice = ff.obs.copy()[obs_start:obs_end]
fifo.append(obs_slice)
sens_replay.append(obs_slice[0:6])
forc_replay.append(obs_slice[6:12])
sens_replay = np.array(sens_replay, dtype=np.float32)
forc_replay = np.array(forc_replay, dtype=np.float32)
diff_sens = float(np.max(np.abs(sens_replay - orig_sensors)))
diff_forc = float(np.max(np.abs(forc_replay - orig_forces)))
print(f" Replay verification for {scene_name}:")
print(f" max diff sensors = {diff_sens:.6e}")
print(f" max diff forces = {diff_forc:.6e}")
passed = diff_sens <= VERIFY_TOL and diff_forc <= VERIFY_TOL
print(f" VERIFICATION {'PASSED' if passed else 'FAILED'} (tol={VERIFY_TOL})")
result = {
"scene": scene_name,
"diff_sensors": diff_sens,
"diff_forces": diff_forc,
"tolerance": VERIFY_TOL,
"passed": passed,
}
with open(os.path.join(out_dir, "replay_verify.json"), "w") as f:
json.dump(result, f, indent=2)
controlled.close()
del ff
return 0 if passed else 1
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, required=True)
ap.add_argument("--device", type=int, default=3)
args = ap.parse_args()
t0 = time.time()
rc = verify(args.scene, args.device)
print(f"Done in {time.time() - t0:.1f}s")
return rc
if __name__ == "__main__":
sys.exit(main())