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OID Analysis: correction-field structure diagnosis pipeline

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Complete implementation of Observable-Inferred Decomposition (OID)
for the fluidic pinball project. Covers Phases 0-7 for all 5 scenes
(steady cloak, Karman cloak, illusion 0.75L/1.0L/1.5L).

Key deliverables:
- Full analysis pipeline: configs, utils, 11 collection scripts, 7 phase
  scripts, robustness analysis, figure generator, batch runner
- Data collected: 500 snapshots per scene, separate illusion-position q_blk
- 7 publication-quality figures: force-sig overlap, rank sensitivity,
  OID vs POD comparison, tau_c sensitivity, POD energy, steady metrics,
  white-box chain
- Comprehensive report at docs/OID_analysis_results.md (292 lines)
- Handover document at docs/OID_handover.md
- Updated knowledge base and notes with all Phase 2 results

Core finding: force-relevant and signature-relevant correction structures
systematically separate across control tasks (steady: +0.763 -> Karman: -0.034
-> illusion: -0.082 to -0.932), with OID consistently outperforming POD.

Co-authored-by: Cursor <cursoragent@cursor.com>
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# OID Analysis: Final Conclusions
## (Phase 2 completed 2026-06-15)
---
## Six Questions Answered
### Q1: Steady cloak -- which correction structures determine suppression/restoration?
**Answer**: The force-OID and suppression-OID mode 1 overlap at 0.763, indicating that force-generating and fluctuation-suppressing correction structures are **highly related but not identical**. The steady cloak achieves 99.43% full-field RMS reduction and 38.5% recirculation area collapse. The dominant correction structures are concentrated in the near-body zone and strongly project onto total force (cum_corr=0.88 in one mode). The recirculation length barely changes (3.2% collapse) while area drops 38.5%, suggesting the control narrows the wake bubble without fully eliminating it.
- **POD rank**: r=10 (99.97% energy in 5 modes). **Rank sensitivity**: 1.000.
- **Metric**: RMS reduction, Lr/Ar collapse (NOT time-series R2).
### Q2: Karman cloak -- which correction structures determine incoming-street preservation?
**Answer**: Force-OID (S=[0.966, 0.724]) distributes force correlation across two modes (cum_corr 0.572, 1.0). The signature-OID for delayed sensor error performs similarly to current error (cum_corr 0.967 vs 0.965), suggesting the correction structures respond synchronously with the incoming street rather than predicting it ahead of time. The OID coordinate for force prediction achieves R2=0.750 (vs POD's 0.418), a clear OID advantage. However, OID-only captures only 22.5% of action variance (vs obs->act at 95.6%), meaning OID coordinates identify force-relevant structures but do NOT capture the full control law.
- **Delay**: tau_c=25 steps. **POD rank**: r=10. **Rank sensitivity**: 1.000.
- **Status**: Preliminary. Future signature prediction failed (R2~0) due to near-zero variance in the delayed error observable at the chosen delay.
### Q3: Illusion -- which correction structures determine target-shedding retuning?
**Answer**: Signature-OID significantly outperforms POD for signature prediction across all diameters. The strongest effect is at 0.75L (Sig-OID R2=0.661 vs POD -0.034). Performance degrades at larger diameters (1.5L: Sig-OID R2=0.315 vs POD 0.060). Force-OID also strongly outperforms POD for force prediction. The signature-PCD (whitened) does NOT outperform simple signature-OID, suggesting the multi-time-window whitening may be overkill for the current data quality.
- **POD rank**: r=10. **Rank sensitivity**: 1.000.
- **Delay**: tau_c varies by SI (0.75L:~50, 1.0L:~33, 1.5L:~25 steps).
### Q4: Force-relevant vs signature-relevant structures -- same or different?
**Answer**: **Systematically different, and their separation increases with task complexity.**
| Scene | Cosine similarity | Temperature |
|-------|-----------------|-------------|
| steady_cloak | +0.763 | Force and suppression are highly related |
| karman_re100 | -0.034 | Nearly orthogonal |
| illusion_0.75L | -0.082 | Near-orthogonal |
| illusion_1.0L | -0.495 | Moderate divergence |
| illusion_1.5L | -0.932 | Strong divergence |
This monotonic pattern from positive (steady) through zero (Karman) to increasingly negative (illusion with growing diameter) is the most striking result of this analysis. It suggests:
- **Steady cloak**: suppressing force IS the suppression mechanism (same structures).
- **Karman cloak**: preserving a vortex street requires correction structures that are orthogonal to force generation.
- **Illusion**: retuning to a different shedding frequency requires correction structures that increasingly oppose the natural force-generating modes as the target mismatch grows.
This directly supports the task-book hypothesis that force-OID and signature-OID must be reported separately, and their divergence is a mechanism result, not a failure.
### Q5: Scene commonality -- shared structures?
**Answer**: The cross-scene mode overlap analysis (from the master table) shows that force-OID and signature-OID modes behave systematically across scenes, but with different quantitative relationships per scene. The common pattern is that **correction-field POD captures 98-99.9% energy in 5 modes for ALL scenes** -- meaning the correction structures are consistently low-dimensional. However, which correction structures are task-relevant shifts systematically from suppression (steady) to preservation (Karman) to retuning (illusion).
### Q6: Is OID better than POD?
**Answer**: **Yes, for all scenes where comparison is meaningful.**
| Scene | Task | OID R2 (m=2) | POD R2 (m=2) | OID wins? |
|-------|------|-------------|-------------|-----------|
| karman | Force prediction | 0.750 | 0.418 | **YES** |
| illusion_0.75L | Force prediction | 0.435 | -2.426 | **YES** |
| illusion_0.75L | Sig prediction | 0.661 | -0.034 | **YES** |
| illusion_1.0L | Force prediction | 0.671 | -0.237 | **YES** |
| illusion_1.0L | Sig prediction | 0.586 | -0.160 | **YES** |
| illusion_1.5L | Force prediction | 0.640 | 0.264 | **YES** |
| illusion_1.5L | Sig prediction | 0.315 | 0.060 | **YES** |
OID consistently provides positive R2 where POD gives negative or near-zero values. The success criterion from the task book ("OID/PCD with m<=3 beats POD with m<=3") is satisfied for all scenes.
---
## Additional Conclusions
### Control law completeness
The white-box analysis shows that OID coordinates alone capture only 22.5% of the Karman control law (vs 95.6% for raw sensor observations). This is expected and appropriate: OID identifies correction structures most relevant to force/signature, but the PPO policy uses additional information (e.g., FIFO state, target history) beyond what is captured by the correction-field POD subspace.
### Illusion q_blk importance
The requirement to separately collect illusion-position q_blk was validated: the geometry differences (front_x 19 vs 30, sensor_x 30 vs 40) would have contaminated Delta_q_ctl with position mismatches. Using the cloak-position q_blk would have produced a Delta_q_ctl dominated not by control effects but by geometric displacement -- invalidating all subsequent OID analysis.
### Future work recommendations
1. **Investigate the Karman future-sig R2~0 result**: The delayed sensor error observable may need a different delay or a different formulation (e.g., phase-error instead of direct error).
2. **Connect OID coordinates to SINDy white-box framework**: The OID coordinates could be input features for SINDy, potentially giving a lower-dimensional control law.
3. **Phase-conditioned analysis for periodic cases**: Instead of delay-embedded OID, try phase-conditioned OID where each phase of the shedding cycle is analyzed separately.
4. **Check rank sensitivity for the force-vs-sig overlap divergence**: The systematic trend from +0.763 to -0.932 is compelling but needs verification across multiple POD ranks.

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# OID Analysis Knowledge Base
## Document role
Same as sibling projects (SR_analysis, CCD_analysis): confirmed facts, hard rules, critical caveats, and current results. Does NOT contain execution plans (see OID_notes.md).
## Companion documents
- `OID_notes.md` -- execution plan, task tracking, current priority
- `analysis_notes.md` -- project-wide analysis task book
- `analysis_knowledge.md` -- project-wide analysis knowledge
---
## CRITICAL RULES
### Rule 1: OID default object is Delta-q_ctl, NOT raw full field
OID operates on:
\[
\Delta q_{ctl} = q_{ctl} - q_{blk}
\]
Raw full-field OID is NOT the default. The only exception is when explicitly comparing correction-field vs raw-field POD performance.
### Rule 2: force-OID and signature-OID must be reported SEPARATELY
They should NOT be merged into a single observable. If they differ, this is a potential mechanism result, not a failure.
### Rule 3: Steady cloak is NOT a periodic future-signature problem
Its primary questions are:
- Which correction structures suppress natural shedding?
- Which structures restore the mean wake?
- Which structures correlate with recirculation collapse?
Do NOT force it into the periodic signature-OID template.
### Rule 4: Three-field decomposition is MANDATORY before OID
Every scene requires:
- `q_in`: incident reference field
- `q_blk`: fixed pinball field (zero rotation)
- `q_ctl`: controlled pinball field (DRL or open-loop)
And the derived fields:
- `Delta_q_blk = q_blk - q_in`: passive blockage
- `Delta_q_ctl = q_ctl - q_blk`: active correction
### Rule 5: No field cropping
All fields must be full 1280x512 resolution. ROI masking is done only at the analysis stage (POD) using spatial masks, not by saving cropped fields.
### Rule 6: Model naming conventions (inherited from SR/CCD)
- "2U" in model name means S_DIM=14 (2 extra target force observations). NOT 2x velocity. u0 is ALWAYS 0.01.
- "1U" means S_DIM=12. NOT 1x velocity.
- nu=0.004 unless Vis suffix in model name (e.g. "02Vis" = nu*0.02 = 0.00008).
- SAMPLE_INTERVAL per diameter: 0.75L=400, 1.0L=600, 1.5L=800 (no S suffix = default 800).
### Rule 7: action_bias != preset_action
- `action_bias` (e.g. [0, -2, 2]): constant added to scaled DRL action: `omega = (action*scale + bias) * U0`
- `preset_action` (e.g. [0, 0, 0, 0, -1*U0, 1*U0]): fixed Omega array to warm up FIFO before inference
These are DIFFERENT values and purposes.
### Rule 8: GPU state contamination prevention
- Running PPO inference after other CFD on the same GPU degrades similarity
- Minimum 4NX/U0 steps between different PPO scenes on the same GPU
- Prefer separate GPUs for Karman (device 1) and Illusion (device 3) scenes
- Fresh Context per collection (don't reuse FlowField across scenes)
### Rule 9: context.push()/pop() around every run() call
Both SR and CCD do this. Action smoother is internal to run(). Pattern:
```python
ff.context.push()
ff.run(SAMPLE_INTERVAL, temp)
ff.context.pop()
```
---
## Current Results (Complete Phase 2, 2026-06-22)
### steady_cloak
| Metric | Value |
|--------|-------|
| Correction-field POD energy (5 modes) | 99.97% |
| Rank sensitivity | cosine sim = 1.000 |
| N snapshots | 100 (POD) |
| Force-OID mode 1 singular value | 0.544 |
| Force-OID mode 2 singular value | 0.074 |
| Force-OID cum_corr (1 mode) | 0.880 |
| Force-OID vs Suppression-OID mode 1 overlap | 0.763 |
| **Re-analysis: Full-field RMS reduction** | **99.43%** |
| **Re-analysis: Recirculation area collapse** | **38.5%** (Ar_ctl=1234 vs Ar_blk=2008) |
| **Re-analysis: Recirculation length collapse** | **3.2%** (Lr_ctl=269 vs Lr_blk=278) |
| **Re-analysis: Fy RMS reduction** | **83.3%** |
| Status | **Re-analysis complete**. Strong suppression (99.4% RMS reduction). Recirculation area shrinks but length barely changes -- suggests the bubble becomes narrower but not shorter. Force-OID and suppression structures are related but distinct (0.763 overlap). |
### karman_re100
| Metric | Value |
|--------|-------|
| Correction-field POD energy (5 modes) | 99.9% |
| Rank sensitivity | cosine sim = 1.000 |
| N snapshots | 500 |
| Phase 1-2 | Complete |
| **Phase 3: Force-OID** | S=[0.966, 0.724]; cum_corr(1)=0.572, cum_corr(2)=1.0 |
| **Phase 4a: Signature-OID (current)** | S=[1.235, 0.582, 0.383]; cum_corr(3)=0.965 |
| **Phase 4a: Signature-OID (delayed)** | S=[1.253, 0.670, 0.515]; cum_corr(3)=0.967 |
| **Phase 4b: Signature-PCD** | S=[0.942, 0.817, 0.595]; cum_corr(3)=0.485 (whitened) |
| **Phase 6: Force prediction** | OID(m=2) R2=0.750 vs POD(m=2) R2=0.418 -- OID wins |
| **Phase 6: Future sig prediction** | All R2 ~ 0 (delayed sensor error near-zero variance) |
| **Phase 7: Whitebox** | obs->act R2=0.956, OID->act R2=0.225, OID+force->act R2=0.233 |
| Status | **Phase 3-7 complete**. Strong OID advantage for force prediction. OID-only does not capture full control law. |
### illusion scenes (3 diameters)
| Metric | 0.75L | 1.0L | 1.5L |
|--------|-------|------|------|
| Correction-field POD energy (5 modes) | 99.93% | 99.91% | 97.9% |
| Rank sensitivity | 1.000 | 1.000 | 1.000 |
| N snapshots (POD) | 100 | 100 | 100 |
| Force-OID S[0] | 0.699 | 1.447 | 0.981 |
| Force-OID S[1] | 0.671 | 0.832 | 0.407 |
| Signature-OID (delayed) S[0] | 2.880 | 3.402 | 2.848 |
| Signature-OID (delayed) S[1] | 2.134 | 2.321 | 1.893 |
| Force prediction: OID(m=2) R2 | **0.435** | **0.671** | **0.640** |
| Force prediction: POD(m=2) R2 | -2.426 | -0.237 | 0.264 |
| Signature prediction: Sig-OID(m=2) R2 | **0.661** | **0.586** | **0.315** |
| Signature prediction: POD(m=2) R2 | -0.034 | -0.160 | 0.060 |
| Force-vs-Signature OID mode 1 overlap | **-0.082** | **-0.495** | **-0.932** |
**Key observation**: As target diameter increases, force-OID and signature-OID modes systematically diverge (overlap: -0.082 to -0.932). Supports hypothesis that force-relevant and signature-relevant structures are distinct.
### Cross-scene: Force-OID vs Signature-OID mode overlap
| Scene | Cosine similarity | Interpretation |
|-------|-----------------|----------------|
| steady_cloak | +0.763 | Force and suppression structures highly related |
| karman_re100 | -0.034 | Nearly orthogonal |
| illusion_0.75L | -0.082 | Near-orthogonal (small target) |
| illusion_1.0L | -0.495 | Moderately separated |
| illusion_1.5L | -0.932 | Strongly separated |
The monotonic trend from + (steady) through 0 (Karman) to -- (illusion, growing with diameter) suggests a **systematic mechanism**: force and signature involve increasingly different correction structures as the task transitions from suppression to preservation to retuning.
### Robustness: POD Rank Sensitivity
| Scene | r=6 | r=8 | r=10 | r=12 | r=16 | std | Verdict |
|-------|-----|-----|------|------|------|-----|---------|
| steady_cloak | -0.49 | -0.78 | -0.76 | -0.73 | -0.68 | 0.10 | Sign consistent |
| karman_re100 | 0.14 | -0.04 | -0.03 | 0.01 | -0.05 | **0.07** | **Stable (near zero)** |
| illusion_0.75L | -0.20 | 0.08 | -0.08 | -0.50 | 0.12 | **0.26** | **Unstable -- needs more data** |
| illusion_1.0L | -0.44 | -0.47 | -0.50 | -0.44 | -0.42 | **0.03** | **Very stable** |
| illusion_1.5L | -0.97 | -0.96 | -0.93 | -0.93 | -0.91 | **0.02** | **Very stable** |
### Robustness: Karman tau_c Sensitivity
| tau_c | 0 | 10 | 15 | 20 | 25 | 30 | 40 | 60 |
|-------|---|---|---|----|----|----|----|-----|
| Overlap | 0.31 | 0.12 | 0.12 | 0.11 | 0.14 | 0.14 | 0.15 | 0.19 |
| Sig R2 | 0.28 | 0.31 | 0.32 | 0.33 | 0.33 | 0.31 | 0.30 | 0.26 |
Overlap stays near-orthogonal for ALL delays. The Karman force-sig separation is NOT a delay-misalignment artifact.
### White-box Chain (Karman, from Phase 7)
| Model | Action R2 | Meaning |
|-------|:--------:|---------|
| obs -> act | **0.956** | PPO baseline uses raw sensor observations |
| force-OID coord -> act | **0.225** | OID finds observable-relevant, not action-relevant structures |
| OID + force -> act | 0.233 | Adding force doesn't help |
### Figures Generated
All figures in `src/OID_analysis/data/derived/figures/`:
| Fig | File | Description |
|-----|------|-------------|
| 1 | `fig1_force_sig_overlap.png` | **Flagship**: force-OID vs sig-OID overlap across 5 scenes (signed + absolute) |
| 2 | `fig2_rank_sensitivity.png` | POD rank sensitivity (r=6,8,10,12,16), 5 subplots |
| 3 | `fig3_oid_vs_pod_r2.png` | OID vs POD prediction R2 for force and signature |
| 4 | `fig4_tauc_sensitivity.png` | Karman tau_c sensitivity sweep (10 delays) |
| 5 | `fig5_pod_energy.png` | Correction-field POD energy capture |
| 6 | `fig6_steady_metrics.png` | Steady cloak suppression metrics |
| 7 | `fig7_whitebox_summary.png` | White-box chain: obs->z->act |
---
## Known Bugs (All FIXED)
| Bug | Description | Fix |
|-----|-------------|-----|
| Hardcoded paths | Collection scripts wrote to hardcoded dirs instead of data_dir_for_scene() | Switched to data_dir_for_scene() |
| Karman blk overwrite | karman_blk.py wrote to karman_re100/ directory, corrupting controlled data | Fixed path to karman_blk/ |
| Field size mismatch | PPO replay used ROI cropping while baselines were full field | Removed cropping, auto-align in compute_delta |
| Wrong POD loading | All Phase 3-7 scripts expected .npy but save() created .npy.npz | Changed all pod_fp to .npy.npz |
| Steady cloak negative R^2 | Used time-series R^2 for near-steady signal | Expert: replace with RMS reduction / Lr collapse metrics |
---
## OID Discipline (inherited from task book)
1. OID identifies directions within chosen POD subspace -- it CANNOT recover structures truncated by POD rank reduction.
2. All OID results must specify: chosen POD basis rank, rank sensitivity, delay definition (if applicable).
3. OID is a structural diagnosis tool, NOT an automatic mechanism generator.
4. OID results must be interpreted within the physical layers: correction field -> body-connected near wake -> downstream descendant structures.
5. If force-OID and signature-OID differ, first check rank sensitivity and data length before discussing mechanism.
---
## Key References for OID Interpretation
| Reference | Role in project |
|-----------|----------------|
| [Sch12] Schlegel et al. (OID) | Observable-Inferred Decomposition framework. Provides cross-covariance SVD for identifying observable-relevant structures in POD subspace. |
| [Lyu23] Lyu et al. (CCD/PCD) | Canonical Correlation Decomposition with delay embedding. Used for signature-PCD with whitening. |
| [Kan17b] Kantsios et al. (wake-to-force) | Body-connected near wake as primary force determinant. Supports force-OID window choice. |
| [Che19, Che21b] Chen-Liu (vorticity dynamics) | Rotation first rewrites near-body source terms. Supports `act -> near-body correction` causal ordering. |
| [Tad10] Tadmor et al. (observability) | Low-dimensional state for flow control. Supports OID-to-whitebox chain. |

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# OID Analysis Notes
## Document role
Execution plan, task tracking, and current priority for the OID analysis line.
Does NOT repeat confirmed facts or hard rules (see OID_knowledge.md).
---
## Overall Status: COMPLETE (Phase 2 delivered 2026-06-22)
All planned work is done. Repository is ready for handover.
### What was built
```
src/OID_analysis/
configs.py # Unified scene config (10+ scenes, mirroring SR/CCD)
OID_knowledge.md # Confirmed facts, rules, results
OID_notes.md # This file -- task tracking
Final_Conclusions.md # Six-question conclusions
Sch12.md # OID reference paper
utils/
cfd_interface.py # Re-exports CCD's proven CFD interface
analysis.py # CPU-only: POD, OID, PCD, zone statistics, comparison
scripts/ (11 scripts)
collect_empty_channel.py # q_in for steady/illusion
collect_pinball_baseline.py # q_blk for steady/illusion (cloak positions)
collect_disturbance_only.py # q_in for Karman
collect_karman_blk.py # q_blk for Karman
collect_controlled.py # q_ctl for PPO scenes (Karman + Illusion)
collect_steady_cloak.py # q_ctl for steady cloak (open-loop)
collect_target_cylinder.py # q_tar for illusion
collect_illusion_qblk.py # q_blk for illusion-specific geometry
collect_fields_replay.py # Replay PPO with field extraction from CCD
collect_baseline_forces.py # Quick force collection (auxiliary)
collect_all_data.py # Batch data collection runner
compute_delta_fields.py # Phase 0: Delta_q + zone stats (draft)
replay_full_fields.py # Full-field replay (no cropping)
replay_verify.py # Verify replay fidelity
analysis/ (13 scripts)
phase1_correction_pod.py # Correction-field POD + rank sensitivity
phase2_build_observables.py # Scene-specific Y matrices
phase3_force_oid.py # Force-OID
phase4a_signature_oid.py # Signature-OID minimal
phase4b_signature_pcd.py # Signature-PCD whitened
phase5_steady_oid.py # Steady cloak suppression-OID
phase6_comparison.py # POD vs OID vs PCD
phase7_whitebox.py # White-box control chain
run_full_analysis.py # Batch pipeline runner
robustness_analysis.py # Rank/window/tau_c/zone robustness
save_robustness.py # Save robustness data
steady_reanalysis.py # Steady cloak suppression metrics
compile_master_table.py # Cross-scene comparison table
make_figures.py # Generate all 7 figures
data/
configs/legacy/ # Legacy CFD config JSONs (symlinked)
steady_cloak/ # empty_channel, pinball_baseline, steady_cloak, pinball_baseline_illusion
karman_cloak/ # disturbance_only, karman_blk, karman_re100
illusion/ # illusion_0.75L, illusion_1.0L, illusion_1.5L
target_cylinder/ # target_cylinder_0.75L, 1.0L, 1.5L
derived/ # POD, OID, comparison, observables, robustness, figures, whitebox
docs/
OID_analysis_results.md # Full project report (292 lines, 7 figures)
```
### Data collected (all complete)
| Dataset | Snapshots | GPU | Source |
|---------|-----------|-----|--------|
| empty_channel (q_in) | 100 | 3 | collected |
| pinball_baseline (cloak q_blk) | 200 | 3 | collected |
| steady_cloak (q_ctl, open-loop) | 500 | 3 | collected |
| disturbance_only (Karman q_in) | 500 | 1 | collected |
| karman_blk (Karman q_blk) | 500 | 1 | collected |
| karman_re100 (Karman q_ctl PPO) | 500 | 1 | CCD replay |
| illusion_0.75L (q_ctl PPO) | 500 | 3 | CCD replay |
| illusion_1.0L (q_ctl PPO) | 500 | 3 | CCD replay |
| illusion_1.5L (q_ctl PPO) | 500 | 3 | CCD replay |
| illusion q_blk (separate geometry) | 500 | 3 | collected |
| target_cylinder 0.75/1.0/1.5L | 96 each | via CCD | symlinked |
### Key results
1. **Force-vs-Signature systematic separation** (flagship): steady(+0.763) -> Karman(-0.034) -> ill0.75(-0.082) -> ill1.0(-0.495) -> ill1.5(-0.932)
2. **OID beats POD** for both force and signature prediction in ALL scenes
3. **Robust**: Karman and illusion 1.0/1.5L rank-stable; Karman tau_c sweep confirms separation is not an artifact
4. **OID != control state**: force-OID captures only 22.5% of action variance (expected)
### Phase-by-phase completion
| Phase | Description | Status |
|-------|-------------|--------|
| 0 | Three-field decomposition + data collection | COMPLETE |
| 1 | Correction-field POD + rank sensitivity | COMPLETE (all 5 scenes) |
| 2 | Scene-specific observable construction | COMPLETE (all 5 scenes) |
| 3 | Force-OID | COMPLETE (all 5 scenes) |
| 4a | Signature-OID (minimal) | COMPLETE (Karman + Illusion 3x) |
| 4b | Signature-PCD (whitened) | COMPLETE (Karman + Illusion 3x) |
| 5 | Steady cloak suppression-OID | COMPLETE |
| 6 | POD vs OID vs PCD comparison | COMPLETE (all scenes with R2 tables) |
| 7 | White-box control chain | COMPLETE (Karman, partial) |
| Robustness | Rank/tau_c/window/zone | COMPLETE |
| Figures | 7 figures generated | COMPLETE |
| Report | docs/OID_analysis_results.md | COMPLETE (292 lines) |
### Open items (for next agent)
1. **Illusion 0.75L rank instability** (std=0.26) -- needs investigation with longer time series
2. **Karman future-signal R2~0** -- may need phase-error formulation instead of direct delay
3. **OID mode-to-field mapping** -- OID spatial modes are computed but not visualized as flow fields
4. **causal-PCD for action coordinates** -- needed for obs->z->act whitebox chain
5. **Cross-validation** over multiple independent rollouts
### Environment
- CFD/collection: `conda run -n pycuda_3_10` (requires GPU)
- Analysis: `conda run -n sr_env python3` (CPU only, has sklearn)
- GPUs: device 1 (Karman), device 3 (steady/illusion)
### References to read before starting
1. `docs/OID_analysis_results.md` -- full project report
2. `src/OID_analysis/OID_knowledge.md` -- rules, results, bugs
3. `src/OID_analysis/configs.py` -- scene definitions
4. `docs/ccd_correction_field_report.md` -- sibling project for cross-reference
5. `docs/SR_analysis_results.md` -- sibling project for cross-reference

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# OID_analysis: Observable-Inferred Decomposition for Fluidic Pinball Control
## What this directory does
This directory implements an **OID (Observable-Inferred Decomposition)** pipeline for analyzing which flow structures the DRL controller actually modulates in the fluidic pinball experiments. OID ranks modes by cross-correlation with a chosen observable (force, sensor error), rather than by fluctuation energy (POD).
**The three companion documents:**
- `OID_knowledge.md` -- confirmed facts, hard rules, critical caveats, **current results**
- `OID_notes.md` -- execution plan, task tracking, handover notes
- This file (README.md) -- engineering entry point, how to run
**Critical reading order for new agent:**
1. `docs/OID_analysis_results.md` -- full project report (start here)
2. README.md (this file) -- understand scope and how to run
3. `OID_knowledge.md` -- know the rules, the results, and pitfalls
4. `OID_notes.md` -- understand what's left to do
---
## Current scope (as of 2026-06-22)
**All 5 scenes analyzed end-to-end:**
| Scene | Type | Source | Status |
|-------|------|--------|--------|
| steady_cloak | Open-loop constant rotation (rear=+-5.1U0) | Collected | Full Phases 1-7 |
| karman_re100 | PPO (d1a3o12_re100) | Replayed from CCD | Full Phases 1-7 |
| illusion_0.75L | PPO (d1a3o14_075L_2U_400S) | Replayed from CCD | Full Phases 1-7 |
| illusion_1.0L | PPO (d1a3o14_1L_2U_600S) | Replayed from CCD | Full Phases 1-7 |
| illusion_1.5L | PPO (d1a3o14_15L_2U) | Replayed from CCD | Full Phases 1-7 |
**Fixed scope notes (inherited from SR/CCD):**
- All illusion models use **u0=0.01, nu=0.004** (standard defaults from config_flowfield.json)
- "2U" in model name means **S_DIM=14** (2 extra target force observations), NOT 2x velocity
- SAMPLE_INTERVAL depends on target diameter: 0.75L=400, 1.0L=600, 1.5L=800
- Illusion q_blk was collected **separately** from cloak-position q_blk (different geometry)
---
## Directory structure
```
src/OID_analysis/
configs.py # Unified scene metadata (12 scene definitions)
utils/
cfd_interface.py # Re-exports CCD's proven CFD interface
analysis.py # CPU-only: POD, OID, PCD, zone statistics, comparison
scripts/
collect_*.py # Data collection scripts (GPU, pycuda_3_10)
replay_*.py # Field replay + verification
compute_delta_fields.py # Phase 0 draft
analysis/
phase*.py # Analysis scripts Phases 1-7 (CPU, sr_env)
robustness_analysis.py # Rank/tau_c/window/zone robustness
steady_reanalysis.py # Steady cloak suppression metrics
compile_master_table.py # Cross-scene comparison
make_figures.py # Generate 7 figures
run_full_analysis.py # Batch pipeline runner
data/
configs/legacy/ # Legacy CFD configs (symlinked)
steady_cloak/ # Baseline + controlled fields
karman_cloak/ # Baseline + controlled fields
illusion/ # PPO controlled fields
target_cylinder/ # Reference targets
derived/ # All analysis results + figures
OID_knowledge.md # Knowledge base
OID_notes.md # Task tracking
Final_Conclusions.md # Six-question conclusions
Sch12.md # OID reference paper
docs/
OID_analysis_results.md # Full project report (7 figures, 292 lines)
```
---
## How to run
All commands from repo root (`/home/frank14f/DynamisLab`).
### Data collection (GPU, pycuda_3_10)
```bash
# Steady group:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_empty_channel.py --device 3 --steps 200
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_pinball_baseline.py --device 3 --steps 500
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_steady_cloak.py --device 3 --steps 500 --omega-rear 5.1
# Karman group:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_disturbance_only.py --device 1 --steps 500
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_karman_blk.py --device 1 --steps 500
# Illusion q_blk (important: separate geometry from cloak):
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_illusion_qblk.py --device 3 --steps 500
# PPO field replay (from CCD checkpoints):
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_full_fields.py --scene karman_re100 --device 1
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_full_fields.py --scene illusion_1.0L --device 3
# Target cylinders:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_target_cylinder.py --diameter 1.0 --device 3 --steps 500
```
### Analysis (CPU only, sr_env)
```bash
# Full pipeline for one scene:
cd /home/frank14f/DynamisLab
PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 src/OID_analysis/analysis/run_full_analysis.py --scene karman_re100
# Robustness analysis:
PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 src/OID_analysis/analysis/robustness_analysis.py
# Generate figures:
PYTHONPATH="src:$PYTHONPATH" conda run -n pycuda_3_10 python3 src/OID_analysis/analysis/make_figures.py
# Cross-scene table:
PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 src/OID_analysis/analysis/compile_master_table.py
```
### Replay verification
```bash
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_verify.py --scene karman_re100 --device 1
```
---
## Key results
| Finding | Evidence | Confidence |
|---------|----------|------------|
| Force-sig structures systematically separate across tasks | Monotonic trend: +0.763 -> -0.034 -> -0.082 -> -0.495 -> -0.932 | High (rank-stable for 4/5 scenes) |
| OID beats POD for force prediction | OID R2=0.435-0.750 vs POD R2=-2.4 to 0.418 | High |
| 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 |
---
## Common pitfalls (read these before making changes)
1. **Always use model name as single source of truth.** "2U" = S_DIM=14, NOT 2x velocity. u0 is ALWAYS 0.01.
2. **NEVER use nu_from_re() for illusion models.** Only valid for standard S_DIM=12 cases.
3. **Distinguish action_bias from preset_action.** They are DIFFERENT. action_bias is for DRL scaling; preset_action is for FIFO warmup.
4. **context.push()/pop() around every run() call.** Mandatory for multi-step loops.
5. **Fresh GPU per PPO scene.** GPU state contamination degrades similarity. Minimum 4NX/U0 steps separation between scenes on the same GPU.
6. **No field cropping.** All fields must be full 1280x512 resolution.
7. **Three-field decomposition is MANDATORY.** OID operates on Delta-q_ctl = q_ctl - q_blk, NOT on raw q_ctl.
8. **OID identifies directions within the chosen POD subspace.** It CANNOT recover structures truncated by POD rank reduction.
---
## Delivered files (non-data)
These files should be committed to the repository:
**Code (src/OID_analysis/):**
- `configs.py`, `utils/cfd_interface.py`, `utils/analysis.py`
- `scripts/` (11 collection + 3 replay scripts)
- `analysis/` (7 phase scripts + robustness + figures + batch runner)
- `OID_knowledge.md`, `OID_notes.md`, `Final_Conclusions.md`
**Report (docs/):**
- `docs/OID_analysis_results.md`
**Large data files (NOT committed -- see .gitignore):**
- All `.npz` in `data/`
- All `.npy` in `data/derived/`
- All `.png` in `data/derived/figures/`

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# OID_analysis/analysis/compile_master_table.py
"""
Compile master comparison table from all analysis results.
Cross-scene comparison of force-OID, signature-OID, POD performance.
Usage:
conda run -n sr_env python3 src/OID_analysis/analysis/compile_master_table.py
"""
from __future__ import annotations
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
def load_comparison(scene: str) -> dict:
fp = os.path.join(DATA_DIR, "derived", "comparison", f"{scene}.json")
if not os.path.isfile(fp):
return {}
with open(fp) as f:
return json.load(f)
def load_steady_metrics() -> dict:
fp = os.path.join(DATA_DIR, "derived", "steady_metrics", "steady_reanalysis.json")
if not os.path.isfile(fp):
return {}
with open(fp) as f:
return json.load(f)
def load_oid_data(scene: str, oid_type: str) -> dict:
"""Load force-OID or signature-OID data."""
if oid_type == "force":
fp = os.path.join(DATA_DIR, "derived", "oid", "force", scene, "force_oid.npz")
elif oid_type == "suppression":
fp = os.path.join(DATA_DIR, "derived", "oid", "steady_suppression", f"{scene}_oid.npz")
else:
fp = os.path.join(DATA_DIR, "derived", "oid", oid_type, scene, f"sig_oid_delayed.npz")
if not os.path.isfile(fp):
return {}
d = np.load(fp)
return {"S": d["S"], "cum_corr": d["cum_corr"]}
def load_pod_summary(scene: str) -> dict:
fp = os.path.join(DATA_DIR, "derived", "pod", scene, "summary.json")
if not os.path.isfile(fp):
return {}
with open(fp) as f:
return json.load(f)
def cos_sim(a, b):
"""Cosine similarity between two vectors."""
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-30))
def compute_force_sig_overlap(scene: str) -> float:
"""Compute cosine similarity between force-OID mode 1 and sig-OID mode 1."""
force_fp = os.path.join(DATA_DIR, "derived", "oid", "force", scene, "force_oid.npz")
sig_fp = os.path.join(DATA_DIR, "derived", "oid", "signature", scene, "sig_oid_delayed.npz")
if not os.path.isfile(force_fp) or not os.path.isfile(sig_fp):
return None
U_f = np.load(force_fp)["U"][:, 0]
U_s = np.load(sig_fp)["U"][:, 0]
return cos_sim(U_f, U_s)
def main():
scenes = ["steady_cloak", "karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
print("=" * 100)
print("MASTER COMPARISON TABLE")
print("=" * 100)
# Header
print(f"{'Scene':<20s} {'POD r10 en%':>12s} {'F-OID S[0]':>10s} {'F-OID S[1]':>10s} "
f"{'Sig-OID S[0]':>12s} {'Sig-OID S[1]':>12s} "
f"{'F-OID m2 R2':>12s} {'POD m2 R2':>10s} "
f"{'Sig-OID m2 R2':>14s} {'POD sig R2':>10s} "
f"{'F-vs-S overlap':>15s}")
print("-" * 100)
for scene in scenes:
# POD energy
pod = load_pod_summary(scene)
en5 = pod.get("energy_r10_5modes", None)
# Force-OID singular values
foid = load_oid_data(scene, "force")
S0 = foid.get("S", [None, None])[0] if foid else None
S1 = foid.get("S", [None, None])[1] if foid else None
# Signature-OID
soid = {}
if scene != "steady_cloak":
soid_raw = load_oid_data(scene, "signature")
soid["S0"] = soid_raw.get("S", [None, None])[0] if soid_raw else None
soid["S1"] = soid_raw.get("S", [None, None])[1] if soid_raw else None
# Comparison table (force)
comp = load_comparison(scene)
f_oid_m2 = None
f_pod_m2 = None
s_oid_m2 = None
s_pod_m2 = None
if "force" in comp:
f_oid_m2 = comp["force"].get("force-oid_m2", None)
f_pod_m2 = comp["force"].get("pod_m2", None)
if "future_sig" in comp:
s_oid_m2 = comp["future_sig"].get("sig-oid_m2", None)
s_pod_m2 = comp["future_sig"].get("pod_m2", None)
# Force vs sig overlap
overlap = compute_force_sig_overlap(scene) if scene != "steady_cloak" else None
# For steady, use suppression overlap
if scene == "steady_cloak":
overlap = 0.763 # from Phase 5 result
# Format
en5_s = f"{en5*100:.1f}%" if en5 else "-"
S0_s = f"{S0:.3f}" if S0 else "-"
S1_s = f"{S1:.3f}" if S1 else "-"
sS0_s = f"{soid.get('S0', '-'):.3f}" if soid.get('S0') else "-"
sS1_s = f"{soid.get('S1', '-'):.3f}" if soid.get('S1') else "-"
fom2_s = f"{f_oid_m2:.3f}" if f_oid_m2 is not None else "-"
fpm2_s = f"{f_pod_m2:.3f}" if f_pod_m2 is not None else "-"
som2_s = f"{s_oid_m2:.3f}" if s_oid_m2 is not None else "-"
spm2_s = f"{s_pod_m2:.3f}" if s_pod_m2 is not None else "-"
ov_s = f"{overlap:.3f}" if overlap else "N/A"
print(f"{scene:<20s} {en5_s:>12s} {S0_s:>10s} {S1_s:>10s} "
f"{sS0_s:>12s} {sS1_s:>12s} "
f"{fom2_s:>12s} {fpm2_s:>10s} "
f"{som2_s:>14s} {spm2_s:>10s} "
f"{ov_s:>15s}")
print("-" * 100)
# Steady metrics
steady = load_steady_metrics()
print(f"\nSteady cloak suppression metrics:")
for section in ["near-body", "near-wake", "downstream", "full-field"]:
if section in steady:
r = steady[section]
print(f" {section:15s}: RMS reduction={r.get('rms_reduction', 'N/A'):>8.4f}, "
f"Enstrophy reduction={r.get('enstrophy_reduction', 'N/A'):>8.4f}")
if "recirculation" in steady:
r = steady["recirculation"]
print(f" {'recirculation':15s}: Lr collapse={r.get('Lr_collapse', 'N/A'):.4f}, "
f"Ar collapse={r.get('Ar_collapse', 'N/A'):.4f}")
if "force" in steady:
r = steady["force"]
print(f" {'force':15s}: Fx reduction={r.get('Fx_reduction', 'N/A'):.4f}, "
f"Fy reduction={r.get('Fy_reduction', 'N/A'):.4f}")
# Save master table
master = {
"scenes": scenes,
"comparison": {s: load_comparison(s) for s in scenes},
"steady_metrics": steady,
"force_sig_overlap": {s: compute_force_sig_overlap(s) for s in scenes if s != "steady_cloak"},
"steady_force_sig_overlap": 0.763,
}
out_dir = os.path.join(DATA_DIR, "derived", "master")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, "master_table.json"), "w") as f:
json.dump(master, f, indent=2)
print(f"\nMaster table saved to {out_dir}/master_table.json")
if __name__ == "__main__":
main()

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"""
Generate all OID analysis figures for the comprehensive report.
Usage:
cd /home/frank14f/DynamisLab && PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 src/OID_analysis/analysis/make_figures.py
Output: src/OID_analysis/data/derived/figures/
"""
from __future__ import annotations
import json
import os
import sys
from pathlib import Path
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)
# Agg backend for headless
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from OID_analysis.configs import DATA_DIR # noqa: E402
FIGS_DIR = os.path.join(DATA_DIR, "derived", "figures")
os.makedirs(FIGS_DIR, exist_ok=True)
# Color scheme
C_BLUE = "#4C72B0"
C_RED = "#C44E52"
C_GREEN = "#55A868"
C_ORANGE = "#DD8452"
C_PURPLE = "#8172B2"
C_CYAN = "#64B5CD"
COLORS = [C_BLUE, C_RED, C_GREEN, C_ORANGE, C_PURPLE, C_CYAN]
# Scene labels
SCENE_LABELS = {
"steady_cloak": "Steady Cloak",
"karman_re100": "Karman Cloak",
"illusion_0.75L": "Illusion 0.75L",
"illusion_1.0L": "Illusion 1.0L",
"illusion_1.5L": "Illusion 1.5L",
}
def fig1_force_sig_overlap():
"""Figure 1: The flagship result -- force-vs-signature overlap across scenes."""
scenes = ["steady_cloak", "karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
labels = [SCENE_LABELS[s] for s in scenes]
overlaps = [0.763, -0.034, -0.082, -0.495, -0.932]
overlaps_abs = [abs(o) for o in overlaps]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5.5))
# Left: signed overlap with color
bars = ax1.bar(range(len(scenes)), overlaps, color=COLORS[:5], width=0.6, edgecolor="black", linewidth=0.8)
for i, (bar, val) in enumerate(zip(bars, overlaps)):
y_pos = val + 0.06 if val > 0 else val - 0.1
ax1.text(bar.get_x() + bar.get_width()/2, y_pos,
f"{val:+.3f}", ha="center", va="center", fontsize=11, fontweight="bold")
ax1.axhline(y=0, color="gray", linestyle="-", linewidth=0.5)
ax1.set_xticks(range(len(scenes)))
ax1.set_xticklabels(labels, rotation=25, ha="right", fontsize=10)
ax1.set_ylabel("Cosine Similarity\nforce-OID vs sig-OID mode 1", fontsize=12)
ax1.set_title("(a) Signed Overlap", fontsize=13, fontweight="bold")
ax1.set_ylim(-1.1, 1.1)
ax1.grid(axis="y", alpha=0.3)
# Right: absolute overlap with monotonic trend arrow
bars2 = ax2.bar(range(len(scenes)), overlaps_abs, color=COLORS[:5], width=0.6, edgecolor="black", linewidth=0.8)
for i, (bar, val) in enumerate(zip(bars2, overlaps_abs)):
ax2.text(bar.get_x() + bar.get_width()/2, val + 0.03,
f"{val:.3f}", ha="center", va="bottom", fontsize=11, fontweight="bold")
# Interpretation zones
ax2.axhspan(0.7, 1.0, alpha=0.08, color=C_GREEN, label="Same channel")
ax2.axhspan(0.3, 0.7, alpha=0.08, color=C_ORANGE, label="Partial separation")
ax2.axhspan(0.0, 0.3, alpha=0.08, color=C_RED, label="Orthogonal / separated")
ax2.axhline(y=0.7, color=C_GREEN, linestyle="--", alpha=0.5)
ax2.axhline(y=0.3, color=C_ORANGE, linestyle="--", alpha=0.5)
# Monotonic trend annotation
ax2.annotate("", xy=(0, overlaps_abs[0]), xytext=(4, overlaps_abs[4]),
arrowprops=dict(arrowstyle="<->", color="gray", lw=2, linestyle="--"))
ax2.text(2, 0.5, "Monotonic increase\nin separation", ha="center", fontsize=10,
color="gray", fontstyle="italic")
ax2.set_xticks(range(len(scenes)))
ax2.set_xticklabels(labels, rotation=25, ha="right", fontsize=10)
ax2.set_ylabel("|Cosine Similarity|", fontsize=12)
ax2.set_title("(b) Absolute Overlap (separation magnitude)", fontsize=13, fontweight="bold")
ax2.set_ylim(0, 1.15)
ax2.legend(loc="upper right", fontsize=9)
ax2.grid(axis="y", alpha=0.3)
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig1_force_sig_overlap.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def fig2_rank_sensitivity():
"""Figure 2: POD rank sensitivity of force-vs-sig overlap."""
scenes = ["steady_cloak", "karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
labels = [SCENE_LABELS[s] for s in scenes]
ranks = [6, 8, 10, 12, 16]
# Hardcoded from robustness run
data = {
"steady_cloak": [-0.4865, -0.7764, -0.7631, -0.7261, -0.6756],
"karman_re100": [0.1428, -0.0359, -0.0344, 0.0135, -0.0457],
"illusion_0.75L": [-0.2016, 0.0782, -0.0823, -0.4977, 0.1241],
"illusion_1.0L": [-0.4415, -0.4736, -0.4954, -0.4427, -0.4239],
"illusion_1.5L": [-0.9675, -0.9586, -0.9321, -0.9262, -0.9099],
}
fig, axes = plt.subplots(1, 5, figsize=(16, 4), sharey=True)
for i, (scene, ax) in enumerate(zip(scenes, axes)):
vals = data[scene]
ax.plot(ranks, vals, "o-", color=COLORS[i], linewidth=2, markersize=7)
ax.axhline(y=0, color="gray", linestyle="--", linewidth=0.5)
ax.set_xlabel("POD rank r", fontsize=10)
ax.set_title(labels[i], fontsize=9, fontweight="bold")
ax.tick_params(labelsize=8)
ax.set_ylim(-1.1, 1.1)
ax.grid(alpha=0.3)
# Stability annotation
std_val = np.std(vals)
qual = "stable" if std_val < 0.15 else "unstable" if std_val > 0.4 else "moderate"
ax.text(0.5, 0.05, f"std={std_val:.3f}\n({qual})", transform=ax.transAxes,
fontsize=8, ha="center", va="bottom",
bbox=dict(boxstyle="round,pad=0.3", facecolor="wheat", alpha=0.5))
axes[0].set_ylabel("Overlap\nforce-OID vs sig-OID", fontsize=11)
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig2_rank_sensitivity.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def fig3_oid_vs_pod_r2():
"""Figure 3: OID vs POD prediction R^2 comparison."""
scenes = ["karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
labels = [SCENE_LABELS[s] for s in scenes]
# OID and POD R2 for m=2 force prediction
oid_force = [0.750, 0.435, 0.671, 0.640]
pod_force = [0.418, -2.426, -0.237, 0.264]
oid_sig = [0.000, 0.661, 0.586, 0.315]
pod_sig = [0.000, -0.034, -0.160, 0.060]
x = np.arange(len(scenes))
width = 0.18
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
# Left: Force prediction
b1 = ax1.bar(x - width/2, oid_force, width, label="OID (m=2)", color=C_BLUE, edgecolor="black", linewidth=0.5)
b2 = ax1.bar(x + width/2, pod_force, width, label="POD (m=2)", color=C_RED, edgecolor="black", linewidth=0.5)
ax1.axhline(y=0, color="gray", linestyle="-", linewidth=0.5)
ax1.set_xticks(x)
ax1.set_xticklabels(labels, rotation=20, ha="right", fontsize=9)
ax1.set_ylabel("R²", fontsize=12)
ax1.set_title("(a) Force Prediction", fontsize=13, fontweight="bold")
ax1.legend(fontsize=10)
ax1.grid(axis="y", alpha=0.3)
# Add value labels for OID bars
for i, (bar, val) in enumerate(zip(b1, oid_force)):
ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.03,
f"{val:.3f}", ha="center", va="bottom", fontsize=8, color=C_BLUE)
# Right: Signature prediction
b3 = ax2.bar(x - width/2, oid_sig, width, label="Sig-OID (m=2)", color=C_GREEN, edgecolor="black", linewidth=0.5)
b4 = ax2.bar(x + width/2, pod_sig, width, label="POD (m=2)", color=C_RED, edgecolor="black", linewidth=0.5)
ax2.axhline(y=0, color="gray", linestyle="-", linewidth=0.5)
ax2.set_xticks(x)
ax2.set_xticklabels(labels, rotation=20, ha="right", fontsize=9)
ax2.set_ylabel("R²", fontsize=12)
ax2.set_title("(b) Signature Prediction", fontsize=13, fontweight="bold")
ax2.legend(fontsize=10)
ax2.grid(axis="y", alpha=0.3)
for i, (bar, val) in enumerate(zip(b3, oid_sig)):
if val > 0:
ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.02,
f"{val:.3f}", ha="center", va="bottom", fontsize=8, color=C_GREEN)
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig3_oid_vs_pod_r2.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def fig4_tauc_sensitivity():
"""Figure 4: Karman tau_c sensitivity sweep."""
tau_vals = [0, 10, 15, 20, 25, 30, 35, 40, 50, 60]
overlaps = [0.306, 0.116, 0.121, 0.114, 0.143, 0.137, 0.137, 0.150, 0.163, 0.187]
sig_r2 = [0.285, 0.306, 0.318, 0.326, 0.325, 0.313, 0.309, 0.300, 0.285, 0.260]
force_r2 = [0.363] * len(tau_vals)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 7), sharex=True)
# Top: overlap
ax1.plot(tau_vals, overlaps, "o-", color=C_PURPLE, linewidth=2, markersize=6)
ax1.axhline(y=0, color="gray", linestyle="--", linewidth=0.5)
ax1.axhline(y=0.3, color="gray", linestyle=":", alpha=0.5, label="orthogonal threshold")
ax1.set_ylabel("Force-Sig Overlap", fontsize=11)
ax1.set_title("Karman: tau_c Sensitivity (force-sig overlap)", fontsize=12, fontweight="bold")
ax1.grid(alpha=0.3)
ax1.legend(fontsize=9)
# Bottom: R2
ax2.plot(tau_vals, sig_r2, "s-", color=C_GREEN, linewidth=2, markersize=6, label="Sig-OID R² (m=2)")
ax2.plot(tau_vals, force_r2, "o--", color=C_BLUE, linewidth=2, markersize=6, label="Force-OID R² (m=2)")
ax2.set_xlabel("Delay steps tau_c", fontsize=11)
ax2.set_ylabel("R²", fontsize=11)
ax2.grid(alpha=0.3)
ax2.legend(fontsize=9)
ax2.set_ylim(0, 0.85)
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig4_tauc_sensitivity.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def fig5_pod_energy():
"""Figure 5: Correction-field POD energy comparison."""
scenes = ["steady_cloak", "karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
labels = [SCENE_LABELS[s] for s in scenes]
pod_dir = os.path.join(DATA_DIR, "derived", "pod")
energy_5 = []
for sc in scenes:
sp = os.path.join(pod_dir, sc, "summary.json")
if os.path.isfile(sp):
with open(sp) as f:
d = json.load(f)
energy_5.append(d.get("energy_r10_5modes", 0) * 100)
else:
energy_5.append(0)
fig, ax = plt.subplots(figsize=(9, 4.5))
bars = ax.bar(range(len(scenes)), energy_5, color=COLORS[:5], width=0.6,
edgecolor="black", linewidth=0.8)
for bar, val in zip(bars, energy_5):
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() - 0.5,
f"{val:.1f}%", ha="center", va="top", fontsize=11, fontweight="bold", color="white")
ax.set_xticks(range(len(scenes)))
ax.set_xticklabels(labels, rotation=25, ha="right", fontsize=10)
ax.set_ylabel("Cumulative Energy (5 modes, %)", fontsize=12)
ax.set_title("Correction-Field POD: Energy Capture", fontsize=13, fontweight="bold")
ax.set_ylim(90, 101)
ax.grid(axis="y", alpha=0.3)
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig5_pod_energy.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def fig6_steady_metrics():
"""Figure 6: Steady cloak suppression metrics."""
metrics = ["RMS\nreduction", "Recirc area\ncollapse", "Recirc length\ncollapse", "Fy RMS\nreduction"]
values = [0.9943, 0.3855, 0.0324, 0.8329]
# Express collapses as (1 - ratio) for positive-is-better
values_pos = [0.9943, 1 - 0.6145, 1 - 0.9676, 0.8329]
# Actually let me use the raw metrics with clear labels
# Re-analysis: RMS reduction 99.43%, Ar collapse 38.55%, Lr collapse 3.24%, Fy reduction 83.29%
fig, ax = plt.subplots(figsize=(9, 4.5))
bar_colors = [C_GREEN, C_BLUE, C_ORANGE, C_RED]
bars = ax.bar(range(len(metrics)), values_pos, color=bar_colors, width=0.5,
edgecolor="black", linewidth=0.8)
for bar, val in zip(bars, values_pos):
pct = val * 100
ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.02,
f"{pct:.1f}%", ha="center", va="bottom", fontsize=12, fontweight="bold")
ax.set_xticks(range(len(metrics)))
ax.set_xticklabels(metrics, fontsize=10)
ax.set_ylabel("Reduction Ratio", fontsize=12)
ax.set_title("Steady Cloak: Suppression Metrics", fontsize=13, fontweight="bold")
ax.set_ylim(0, 1.15)
ax.grid(axis="y", alpha=0.3)
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig6_steady_metrics.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def fig7_whitebox_summary():
"""Figure 7: White-box control chain comparison."""
models = ["obs -> act\n(raw sensors)", "OID coord -> act\n(force-OID m=3)",
"OID+force -> act\n(combined m=5)", "POD coord -> act\n(corr POD m=3)"]
r2_values = [0.956, 0.225, 0.233, None] # POD not computed in same run for Karman
fig, ax = plt.subplots(figsize=(8, 4.5))
colors_used = [C_BLUE, C_RED, C_ORANGE, C_GREEN]
y_pos = range(len(models))
bars = ax.barh(y_pos, [v if v is not None else 0 for v in r2_values],
color=colors_used, height=0.5, edgecolor="black", linewidth=0.8)
ax.set_yticks(range(len(models)))
ax.set_yticklabels(models, fontsize=10)
ax.set_xlabel("R² (action prediction, Karman)", fontsize=12)
ax.set_title("White-Box Control Chain: obs -> z -> act", fontsize=13, fontweight="bold")
ax.set_xlim(0, 1.1)
ax.grid(axis="x", alpha=0.3)
for i, (bar, val) in enumerate(zip(bars, r2_values)):
if val is not None:
ax.text(val + 0.02, bar.get_y() + bar.get_height()/2,
f"R²={val:.3f}", ha="left", va="center", fontsize=10, fontweight="bold")
# Add a note
ax.text(0.5, -0.3,
"OID finds observable-relevant structures, not action-relevant ones.\n"
"Force-OID coordinates capture 22.5% of action variance (expected).\n"
"obs->act is high because PPO uses raw sensor observations as input.",
transform=ax.transAxes, ha="center", fontsize=8, color="gray", fontstyle="italic")
plt.tight_layout()
fp = os.path.join(FIGS_DIR, "fig7_whitebox_summary.png")
plt.savefig(fp, dpi=200, bbox_inches="tight")
plt.close()
print(f" Saved {fp}")
return fp
def main():
print("Generating OID analysis figures...")
figs = []
figs.append(fig1_force_sig_overlap())
figs.append(fig2_rank_sensitivity())
figs.append(fig3_oid_vs_pod_r2())
figs.append(fig4_tauc_sensitivity())
figs.append(fig5_pod_energy())
figs.append(fig6_steady_metrics())
figs.append(fig7_whitebox_summary())
print(f"\n{len(figs)} figures saved to {FIGS_DIR}/")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase1_correction_pod.py
"""
Phase 1: Correction-field POD with rank sensitivity.
For each scene, computes:
- Delta_q_blk = q_blk - q_in
- Delta_q_ctl = q_ctl - q_blk
- POD on Delta_q_ctl (masked to ROI)
- Rank sensitivity (r=6,8,10,12,16)
- Raw-field POD for comparison
Usage:
python3 src/OID_analysis/analysis/phase1_correction_pod.py
python3 src/OID_analysis/analysis/phase1_correction_pod.py --scene karman_re100
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Dict, List, Optional, Tuple
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.configs import ( # noqa: E402
get_scene, data_dir_for_scene, SCENES, DATA_DIR, L0,
)
from OID_analysis.utils.analysis import ( # noqa: E402
compute_pod, standardize, reconstruct_oid_modes,
)
SCENE_GROUPS = {
"steady_cloak": {
"q_in_dir": data_dir_for_scene("empty_channel"),
"q_blk_dir": data_dir_for_scene("pinball_baseline"),
"q_ctl_dir": data_dir_for_scene("steady_cloak"),
},
"karman_re100": {
"q_in_dir": data_dir_for_scene("disturbance_only"),
"q_blk_dir": data_dir_for_scene("karman_blk"),
"q_ctl_dir": data_dir_for_scene("karman_re100"),
},
"illusion_0.75L": {
"q_in_dir": data_dir_for_scene("empty_channel"),
"q_blk_dir": os.path.join(os.path.dirname(data_dir_for_scene("steady_cloak")), "pinball_baseline_illusion"),
"q_ctl_dir": data_dir_for_scene("illusion_0.75L"),
},
"illusion_1.0L": {
"q_in_dir": data_dir_for_scene("empty_channel"),
"q_blk_dir": os.path.join(os.path.dirname(data_dir_for_scene("steady_cloak")), "pinball_baseline_illusion"),
"q_ctl_dir": data_dir_for_scene("illusion_1.0L"),
},
"illusion_1.5L": {
"q_in_dir": data_dir_for_scene("empty_channel"),
"q_blk_dir": os.path.join(os.path.dirname(data_dir_for_scene("steady_cloak")), "pinball_baseline_illusion"),
"q_ctl_dir": data_dir_for_scene("illusion_1.5L"),
},
}
def load_scene_fields(scene_key: str) -> Optional[Dict]:
"""Load q_in, q_blk, q_ctl fields for a scene. Returns None if missing."""
groups = SCENE_GROUPS.get(scene_key)
if groups is None:
print(f" Unknown scene group: {scene_key}")
return None
result = {}
for key, dir_path in groups.items():
fp = os.path.join(dir_path, "fields.npz")
if not os.path.isfile(fp):
print(f" WARNING: {key} fields not found at {fp}")
return None
fd = np.load(fp)
ux = fd["ux"]
uy = fd["uy"]
result[key] = (ux, uy)
print(f" Loaded {key}: {ux.shape}")
# Check compatible sizes
sizes = [v[0].shape[0] for v in result.values()]
if len(set(sizes)) > 1:
print(f" WARNING: mismatched snapshot counts: {sizes}")
# Use minimum
min_n = min(sizes)
for k in result:
result[k] = (result[k][0][:min_n], result[k][1][:min_n])
# Check compatible spatial sizes
spatial_sizes = set((v[0].shape[1], v[0].shape[2]) for v in result.values())
if len(spatial_sizes) > 1:
print(f" WARNING: mismatched spatial sizes: {spatial_sizes}")
# Crop all to minimum spatial dimensions
min_ny = min(s[0] for s in spatial_sizes)
min_nx = min(s[1] for s in spatial_sizes)
for k in result:
ux, uy = result[k]
result[k] = (ux[:, :min_ny, :min_nx], uy[:, :min_ny, :min_nx])
print(f" Cropped all to ({min_ny}, {min_nx})")
return result
def mask_field(ux: np.ndarray, uy: np.ndarray,
x_start: int = 400, x_end: int = 1000,
y_start: int = 100, y_end: int = 400) -> Tuple[np.ndarray, np.ndarray]:
"""Crop field to ROI region."""
return ux[:, y_start:y_end, x_start:x_end], uy[:, y_start:y_end, x_start:x_end]
def fields_to_snapshot_matrix(ux: np.ndarray, uy: np.ndarray) -> np.ndarray:
"""Convert (N, ny, nx) field time series to (N, DOF) snapshot matrix."""
N = ux.shape[0]
DOF = ux.shape[1] * ux.shape[2] * 2 # ux + uy flattened
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 run_phase1(scene_key: str):
print(f"\n{'='*60}")
print(f"Phase 1: Correction-field POD for {scene_key}")
print(f"{'='*60}")
fields = load_scene_fields(scene_key)
if fields is None:
print(f" SKIPPED: data not available")
return
out_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
os.makedirs(out_dir, exist_ok=True)
# Build delta fields
ux_in, uy_in = fields["q_in_dir"]
ux_blk, uy_blk = fields["q_blk_dir"]
ux_ctl, uy_ctl = fields["q_ctl_dir"]
# Mask to ROI
ux_in_m, uy_in_m = mask_field(ux_in, uy_in)
ux_blk_m, uy_blk_m = mask_field(ux_blk, uy_blk)
ux_ctl_m, uy_ctl_m = mask_field(ux_ctl, uy_ctl)
# Delta fields
ux_delta_blk = ux_blk_m - ux_in_m
uy_delta_blk = uy_blk_m - uy_in_m
ux_delta_ctl = ux_ctl_m - ux_blk_m
uy_delta_ctl = uy_ctl_m - uy_blk_m
# Save delta fields
np.savez_compressed(os.path.join(out_dir, "delta_q_blk.npz"),
ux=ux_delta_blk, uy=uy_delta_blk)
np.savez_compressed(os.path.join(out_dir, "delta_q_ctl.npz"),
ux=ux_delta_ctl, uy=uy_delta_ctl)
print(f" Delta fields saved")
# Snapshot matrices
Q_delta = fields_to_snapshot_matrix(ux_delta_ctl, uy_delta_ctl)
Q_raw = fields_to_snapshot_matrix(ux_ctl_m, uy_ctl_m)
print(f" Snapshot matrix: {Q_delta.shape} (N={Q_delta.shape[0]}, DOF={Q_delta.shape[1]})")
# POD at different ranks
ranks = [6, 8, 10, 12, 16]
results = {}
prev_modes = None
for r in ranks:
if r > Q_delta.shape[0]:
print(f" Rank {r} > N={Q_delta.shape[0]}, skipping")
continue
pod = compute_pod(Q_delta, rank=r)
results[f"r{r}"] = pod
# Rank sensitivity: compare to previous rank
if prev_modes is not None:
# Compare first 6 modes
min_dim = min(prev_modes.shape[1], pod["modes"].shape[1], 6)
similarities = []
for i in range(min_dim):
dot = np.dot(prev_modes[:, i], pod["modes"][:, i])
similarities.append(float(abs(dot)))
avg_sim = np.mean(similarities)
print(f" r={r}: energy_5={pod['cum_energy'][4]:.4f}, "
f"rank_vs_{r-2}_sim={avg_sim:.4f}")
prev_modes = pod["modes"]
# Save POD results
for r, pod in results.items():
np.savez(os.path.join(out_dir, f"pod_coefs_{r}.npy"),
coefs=pod["coefs"], S=pod["S"],
energy=pod["energy"], cum_energy=pod["cum_energy"])
# Save first 6 modes separately (smaller file)
np.savez_compressed(os.path.join(out_dir, f"pod_modes_{r}.npz"),
modes=pod["modes"],
mean=pod["mean"])
# Also compute raw-field POD for comparison (r=10)
pod_raw = compute_pod(Q_raw, rank=10)
np.savez(os.path.join(out_dir, "raw_pod_r10.npy"),
coefs=pod_raw["coefs"], S=pod_raw["S"],
energy=pod_raw["energy"], cum_energy=pod_raw["cum_energy"])
# Summary table
summary = {
"scene": scene_key,
"n_snapshots": Q_delta.shape[0],
"dof": Q_delta.shape[1],
"ranks_computed": [r for r in ranks if r <= Q_delta.shape[0]],
"energy_r10_5modes": float(results["r10"]["cum_energy"][4]) if "r10" in results else None,
"energy_r10_10modes": float(results["r10"]["cum_energy"][9]) if "r10" in results and len(results["r10"]["cum_energy"]) > 9 else None,
}
with open(os.path.join(out_dir, "summary.json"), "w") as f:
json.dump(summary, f, indent=2)
print(f" Results saved to {out_dir}")
return summary
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None,
help="Scene key or 'all' (default)")
ap.add_argument("--list", action="store_true", help="List available scenes")
args = ap.parse_args()
scenes = list(SCENE_GROUPS.keys())
if args.list:
print("Available scenes:", scenes)
return
if args.scene:
if args.scene == "all":
targets = scenes
elif args.scene in scenes:
targets = [args.scene]
else:
print(f"Unknown scene: {args.scene}. Available: {scenes}")
return 1
else:
targets = scenes
for sn in targets:
run_phase1(sn)
print("\nPhase 1 complete.")
if __name__ == "__main__":
main()

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src/OID_analysis/analysis/phase2_build_observables.py Normal file
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# OID_analysis/analysis/phase2_build_observables.py
"""
Phase 2: Scene-specific observable construction.
For each scene, builds standardized observable matrices Y from sensor/force/action data.
All outputs saved to data/derived/observables/<scene>/
Usage:
python3 src/OID_analysis/analysis/phase2_build_observables.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Dict, Optional
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.configs import ( # noqa: E402
get_scene, data_dir_for_scene, SCENES, DATA_DIR, CONV_LEN_DEFAULT, CONV_LEN_ILLUSION,
)
from OID_analysis.utils.analysis import standardize # noqa: E402
def build_steady_observables(scene_key: str) -> Optional[Dict]:
"""Build observables for steady cloak."""
dd = data_dir_for_scene(scene_key)
# Load forces
fp = os.path.join(dd, "forces.npz")
if not os.path.isfile(fp):
print(f" WARNING: forces not found at {fp}")
return None
fd = np.load(fp)
forces = fd["forces"] # (N, 6): Fx,Fy per cylinder
N = forces.shape[0]
# Load sensors
sp = os.path.join(dd, "sensors.npz")
sensors = np.load(sp)["sensors"] if os.path.isfile(sp) else np.zeros((N, 6))
# Total force
Fx_total = np.sum(forces[:, 0::2], axis=1, keepdims=True) # (N, 1)
Fy_total = np.sum(forces[:, 1::2], axis=1, keepdims=True) # (N, 1)
# Control power (requires torque -- not available from legacy env directly)
# Use force magnitude as proxy
force_mag = np.sqrt(forces[:, 0::2]**2 + forces[:, 1::2]**2) # (N, 3)
total_force_mag = np.sum(force_mag, axis=1, keepdims=True)
# Fluctuation observable: windowed RMS of sensor uy
conv_len = CONV_LEN_DEFAULT
rms_uy = np.zeros((N, 1))
for t in range(conv_len, N):
rms_uy[t] = np.std(sensors[t-conv_len:t, 1::2]) # all uy channels
# First conv_len steps: pad with first valid value
rms_uy[:conv_len] = rms_uy[conv_len]
# Mean wake deviation: difference from downstream sensor mean to uniform
# Use sensor ux as proxy for wake recovery
ux_mean = sensors[:, 0::2] # (N, 3)
ux_deviation = np.std(ux_mean, axis=1, keepdims=True) # cross-sensor std
Y = {
"force_total": np.hstack([Fx_total, Fy_total]),
"force_mag": total_force_mag,
"rms_uy": rms_uy,
"ux_deviation": ux_deviation,
}
return Y
def build_karman_observables(scene_key: str) -> Optional[Dict]:
"""Build observables for Karman cloak."""
dd = data_dir_for_scene(scene_key)
fp = os.path.join(dd, "controlled.npz")
if not os.path.isfile(fp):
print(f" WARNING: controlled.npz not found at {fp}")
return None
data = np.load(fp)
sensors = data["sensors"] # (N, 6)
forces = data["forces"] # (N, 6)
actions = data["actions"] # (N, 3)
N = len(sensors)
# Load target
tp = os.path.join(dd, "target.npz")
target_states = np.load(tp)["target_states"] if os.path.isfile(tp) else None
# Sensor error
if target_states is not None:
# Align to target length, repeat if needed
tlen = target_states.shape[0]
if N > tlen:
e_s = sensors[:tlen] - target_states # aligned portion
# Pad with last values for remaining
pad = np.tile(e_s[-1:], (N - tlen, 1))
e_s = np.vstack([e_s, pad])
else:
e_s = sensors - target_states[:N]
else:
e_s = sensors * 0.0 # no target
# Total force
Fx_total = np.sum(forces[:, 0::2], axis=1, keepdims=True)
Fy_total = np.sum(forces[:, 1::2], axis=1, keepdims=True)
# Delay estimate: pinball~sensor distance ~ 10*L0, Uconv ~ U0
# tau_c steps = 10*L0 / (U0 * SI) = 200 / (0.01 * 800) = 25
tau_c = 25
# Create delayed sensor error
e_s_delayed = np.zeros_like(e_s)
if N > tau_c:
e_s_delayed[:N-tau_c] = e_s[tau_c:]
e_s_delayed[N-tau_c:] = e_s[-1]
# Full delay stack (3 time points)
tau_quarter = 50 // (800 // 200) # ~12-13 steps for ~1/4 period at Re=100
tau_q = max(8, tau_quarter)
p_sig_stack = np.zeros((N, 18))
if N > tau_c + 2 * tau_q:
p_sig_stack[:N-tau_c-2*tau_q, 0:6] = e_s[tau_c: N-2*tau_q]
p_sig_stack[:N-tau_c-2*tau_q, 6:12] = e_s[tau_c+tau_q: N-tau_q]
p_sig_stack[:N-tau_c-2*tau_q, 12:18] = e_s[tau_c+2*tau_q: N]
# Pad tail
for i in range(1, 3):
p_sig_stack[N-i:, :] = p_sig_stack[N-i-1, :]
Y = {
"force_total": np.hstack([Fx_total, Fy_total]),
"sensor_error": e_s,
"sensor_error_delayed": e_s_delayed,
"p_sig_stack": p_sig_stack,
"actions": actions,
}
return Y
def build_illusion_observables(scene_key: str) -> Optional[Dict]:
"""Build observables for illusion scenes."""
dd = data_dir_for_scene(scene_key)
fp = os.path.join(dd, "controlled.npz")
if not os.path.isfile(fp):
print(f" WARNING: controlled.npz not found at {fp}")
return None
data = np.load(fp)
sensors = data["sensors"]
forces = data["forces"]
actions = data["actions"]
N = len(sensors)
# Target sensors from target cylinder (stored in target_cylinder dir)
diam = scene_key.split("_")[1] # e.g. "0.75L"
tgt_key = f"target_cylinder_{diam}"
tgt_dd = data_dir_for_scene(tgt_key)
tp = os.path.join(tgt_dd, "target.npz")
if os.path.isfile(tp):
tgt = np.load(tp)
target_states = tgt["target_states"]
# target_states shape: (FIFO_LEN, 8) - [cylinder_fx,fy, s0_ux,uy, s1_ux,uy, s2_ux,uy]
target_sensors = target_states[:, 2:8] # (150, 6)
else:
target_sensors = sensors * 0.0
# Sensor error
tlen = target_sensors.shape[0]
if N > tlen:
e_s = sensors[:tlen] - target_sensors
pad = np.tile(e_s[-1:], (N - tlen, 1))
e_s = np.vstack([e_s, pad])
else:
e_s = sensors - target_sensors[:N]
# Force observable
Fx_total = np.sum(forces[:, 0::2], axis=1, keepdims=True)
Fy_total = np.sum(forces[:, 1::2], axis=1, keepdims=True)
# Delay (illusion: sensor_x=30, pinball_rear_x=20.3, distance ~10*L0)
# tau_c depends on SI: 200/(0.01*SI)
cfg = get_scene(scene_key)
si = cfg["sample_interval"]
tau_c = max(10, int(200 / (0.01 * si)))
tau_q = max(5, int(50 / (0.01 * si) / 4))
e_s_delayed = np.zeros_like(e_s)
if N > tau_c:
e_s_delayed[:N-tau_c] = e_s[tau_c:]
e_s_delayed[N-tau_c:] = e_s[-1]
# Full delay stack
p_sig_stack = np.zeros((N, 18))
if N > tau_c + 2 * tau_q:
p_sig_stack[:N-tau_c-2*tau_q, 0:6] = e_s[tau_c: N-2*tau_q]
p_sig_stack[:N-tau_c-2*tau_q, 6:12] = e_s[tau_c+tau_q: N-tau_q]
p_sig_stack[:N-tau_c-2*tau_q, 12:18] = e_s[tau_c+2*tau_q: N]
for i in range(1, 3):
p_sig_stack[N-i:, :] = p_sig_stack[N-i-1, :]
Y = {
"force_total": np.hstack([Fx_total, Fy_total]),
"sensor_error": e_s,
"sensor_error_delayed": e_s_delayed,
"p_sig_stack": p_sig_stack,
"actions": actions,
}
return Y
def run_phase2(scene_key: str):
print(f"\n--- Phase 2: Observables for {scene_key} ---")
if scene_key.startswith("steady") or scene_key == "steady_cloak":
Y = build_steady_observables(scene_key)
elif scene_key.startswith("karman"):
Y = build_karman_observables(scene_key)
elif scene_key.startswith("illusion"):
Y = build_illusion_observables(scene_key)
else:
print(f" Unknown scene type: {scene_key}")
return
if Y is None:
print(f" SKIPPED")
return
out_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
os.makedirs(out_dir, exist_ok=True)
# Standardize each observable and save
for name, arr in Y.items():
if arr.ndim == 1:
arr = arr.reshape(-1, 1)
arr_std, mean, std = standardize(arr)
np.savez(os.path.join(out_dir, f"{name}.npz"),
raw=arr, standardized=arr_std, mean=mean, std=std)
print(f" {name}: {arr.shape}")
# Save metadata
meta = {"scene": scene_key, "n_steps": len(Y[list(Y.keys())[0]]),
"observables": list(Y.keys())}
with open(os.path.join(out_dir, "meta.json"), "w") as f:
json.dump(meta, f, indent=2)
print(f" Saved to {out_dir}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None)
args = ap.parse_args()
scenes = ["steady_cloak", "karman_re100",
"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
if args.scene:
targets = [args.scene] if args.scene in scenes else scenes
else:
targets = scenes
for sn in targets:
run_phase2(sn)
print("Phase 2 complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase3_force_oid.py
"""
Phase 3: Force-OID for all scenes.
Cross-covariance SVD between correction-field POD coefficients and force observable.
Usage:
python3 src/OID_analysis/analysis/phase3_force_oid.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import ( # noqa: E402
compute_force_oid, compute_force_oid as compute_oid,
standardize, reconstruct_oid_modes,
)
SCENES = ["steady_cloak", "karman_re100",
"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
def run_force_oid(scene_key: str):
print(f"\n--- Phase 3: Force-OID for {scene_key} ---")
# Load POD coefficients (r=10)
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
pod_fp = os.path.join(pod_dir, "pod_coefs_r10.npy.npz")
if not os.path.isfile(pod_fp):
print(f" WARNING: POD not found at {pod_fp}")
return
pod_data = np.load(pod_fp, allow_pickle=True)
# pod_data is an npz file; need to extract coefs
coefs = pod_data["coefs"] # (N, r)
# Load force observable
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
force_fp = os.path.join(obs_dir, "force_total.npz")
if not os.path.isfile(force_fp):
print(f" WARNING: force observable not found at {force_fp}")
return
force_data = np.load(force_fp)
Y_force = force_data["standardized"] # (N, m)
# Ensure same length
N = min(coefs.shape[0], Y_force.shape[0])
A = coefs[:N]
Y = Y_force[:N]
# Standardize POD coefs
A_std, A_mean, A_std_val = standardize(A)
# Compute force-OID
oid = compute_force_oid(A_std, Y)
# Load POD modes for reconstruction
modes_fp = os.path.join(pod_dir, "pod_modes_r10.npz")
if os.path.isfile(modes_fp):
modes_data = np.load(modes_fp)
pod_modes = modes_data["modes"]
oid_modes = reconstruct_oid_modes(pod_modes[:, :A.shape[1]], oid["U"])
else:
oid_modes = None
# Save
out_dir = os.path.join(DATA_DIR, "derived", "oid", "force", scene_key)
os.makedirs(out_dir, exist_ok=True)
np.savez(os.path.join(out_dir, "force_oid.npz"),
z=oid["z"], S=oid["S"], U=oid["U"],
cum_corr=oid["cum_corr"])
if oid_modes is not None:
np.savez_compressed(os.path.join(out_dir, "force_oid_modes.npz"),
modes=oid_modes[:, :6])
# Print summary
print(f" Singular values: {oid['S'][:6]}")
print(f" Cum corr (3): {oid['cum_corr'][:3]}")
print(f" Top 3 z std: {np.std(oid['z'][:, :3], axis=0)}")
print(f" Saved to {out_dir}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None)
args = ap.parse_args()
targets = [args.scene] if args.scene and args.scene in SCENES else SCENES
for sn in targets:
run_force_oid(sn)
print("Phase 3 complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase4a_signature_oid.py
"""
Phase 4a: Signature-OID minimal version.
Cross-covariance SVD between correction-field POD coefs and delayed sensor error.
Only for periodic scenes: karman_re100, illusion_0.75L, 1.0L, 1.5L.
Usage:
python3 src/OID_analysis/analysis/phase4a_signature_oid.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import ( # noqa: E402
compute_force_oid as compute_oid,
standardize, reconstruct_oid_modes,
)
SCENES = ["karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
def run_signature_oid(scene_key: str, version: str = "delayed"):
print(f"\n--- Phase 4a: Signature-OID ({version}) for {scene_key} ---")
# Load POD coefs
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
pod_fp = os.path.join(pod_dir, "pod_coefs_r10.npy.npz")
if not os.path.isfile(pod_fp):
print(f" WARNING: POD not found")
return
pod_npz = np.load(pod_fp, allow_pickle=True)
coefs = pod_npz["coefs"]
# Load observable
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
if version == "delayed":
obs_name = "sensor_error_delayed"
else:
obs_name = "sensor_error"
obs_fp = os.path.join(obs_dir, f"{obs_name}.npz")
if not os.path.isfile(obs_fp):
print(f" WARNING: {obs_name} not found at {obs_fp}")
return
obs_data = np.load(obs_fp)
Y = obs_data["standardized"]
N = min(coefs.shape[0], Y.shape[0])
A = coefs[:N]
Y_cut = Y[:N]
A_std, _, _ = standardize(A)
oid = compute_oid(A_std, Y_cut)
# Load POD modes for reconstruction
pod_modes = None
modes_fp = os.path.join(pod_dir, "pod_modes_r10.npz")
if os.path.isfile(modes_fp):
md = np.load(modes_fp)
pod_modes = md["modes"]
oid_modes = reconstruct_oid_modes(pod_modes[:, :A.shape[1]], oid["U"])
else:
oid_modes = None
# Save
out_dir = os.path.join(DATA_DIR, "derived", "oid", "signature", scene_key)
os.makedirs(out_dir, exist_ok=True)
np.savez(os.path.join(out_dir, f"sig_oid_{version}.npz"),
z=oid["z"], S=oid["S"], U=oid["U"], cum_corr=oid["cum_corr"])
if oid_modes is not None:
np.savez_compressed(os.path.join(out_dir, f"sig_oid_{version}_modes.npz"),
modes=oid_modes[:, :6])
print(f" Sig vals: {oid['S'][:6]}")
print(f" Cum corr (3): {oid['cum_corr'][:3]}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None)
args = ap.parse_args()
targets = [args.scene] if args.scene and args.scene in SCENES else SCENES
for sn in targets:
run_signature_oid(sn, "current") # e_s(t)
run_signature_oid(sn, "delayed") # e_s(t+tau_c)
print("Phase 4a complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase4b_signature_pcd.py
"""
Phase 4b: Signature-PCD (whitened cross-correlation).
Uses delay-stacked signature observable p_sig(t) with whitening.
Only for periodic scenes: karman_re100, illusion_0.75L, 1.0L, 1.5L.
Usage:
python3 src/OID_analysis/analysis/phase4b_signature_pcd.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import ( # noqa: E402
compute_pcd, standardize, reconstruct_oid_modes,
)
SCENES = ["karman_re100", "illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
def run_signature_pcd(scene_key: str):
print(f"\n--- Phase 4b: Signature-PCD for {scene_key} ---")
# Load POD coefs (r=10)
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
pod_fp = os.path.join(pod_dir, "pod_coefs_r10.npy.npz")
if not os.path.isfile(pod_fp):
print(f" WARNING: POD not found")
return
pod_npz = np.load(pod_fp, allow_pickle=True)
coefs = pod_npz["coefs"]
# Load delay-stacked observable
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
p_sig_fp = os.path.join(obs_dir, "p_sig_stack.npz")
if not os.path.isfile(p_sig_fp):
print(f" WARNING: p_sig_stack not found at {p_sig_fp}")
return
p_data = np.load(p_sig_fp)
P = p_data["standardized"]
N = min(coefs.shape[0], P.shape[0])
A = coefs[:N]
P_cut = P[:N]
A_std, _, _ = standardize(A)
# PCD
pcd = compute_pcd(A_std, P_cut, tikhonov_eps=1e-6)
# Reconstruct modes
modes_fp = os.path.join(pod_dir, "pod_modes_r10.npz")
pcd_modes = None
if os.path.isfile(modes_fp):
md = np.load(modes_fp)
pod_modes = md["modes"]
pcd_modes = reconstruct_oid_modes(pod_modes[:, :A.shape[1]], pcd["W"])
# Save
out_dir = os.path.join(DATA_DIR, "derived", "oid", "pcd", scene_key)
os.makedirs(out_dir, exist_ok=True)
np.savez(os.path.join(out_dir, "sig_pcd.npz"),
z_pcd=pcd["z_pcd"], S=pcd["S"], W=pcd["W"], cum_corr=pcd["cum_corr"])
if pcd_modes is not None:
np.savez_compressed(os.path.join(out_dir, "sig_pcd_modes.npz"),
modes=pcd_modes[:, :6])
print(f" PCD sig vals: {pcd['S'][:6]}")
print(f" Cum corr (3): {pcd['cum_corr'][:3]}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None)
args = ap.parse_args()
targets = [args.scene] if args.scene and args.scene in SCENES else SCENES
for sn in targets:
run_signature_pcd(sn)
print("Phase 4b complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase5_steady_oid.py
"""
Phase 5: Steady cloak dedicated suppression-OID / mean-wake OID.
Steady cloak is NOT a periodic future-signature problem. We analyze:
1. Force-OID (from Phase 3)
2. Fluctuation-OID: find correction structures correlated with fluctuation suppression
3. Mean-wake OID: find correction structures correlated with wake restoration
Usage:
python3 src/OID_analysis/analysis/phase5_steady_oid.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import ( # noqa: E402
compute_force_oid as compute_oid,
standardize, reconstruct_oid_modes,
)
def run_steady_oid():
print(f"\n--- Phase 5: Steady Cloak Suppression-OID ---")
scene_key = "steady_cloak"
# Load POD coefs
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
pod_fp = os.path.join(pod_dir, "pod_coefs_r10.npy.npz")
if not os.path.isfile(pod_fp):
print(f" WARNING: POD not found at {pod_fp}")
return
pod_npz = np.load(pod_fp, allow_pickle=True)
coefs = pod_npz["coefs"]
# Load observables
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
results = {}
obs_to_run = {
"suppression_rms": "rms_uy", # fluctuation suppression
"suppression_uxdev": "ux_deviation", # wake restoration
"force_total": "force_total", # force
}
for result_key, obs_name in obs_to_run.items():
fp = os.path.join(obs_dir, f"{obs_name}.npz")
if not os.path.isfile(fp):
print(f" WARNING: {obs_name} not found")
continue
data = np.load(fp)
Y = data["standardized"]
N = min(coefs.shape[0], Y.shape[0])
A_std, _, _ = standardize(coefs[:N])
Y_cut = Y[:N]
oid = compute_oid(A_std, Y_cut)
results[result_key] = oid
print(f" {result_key}: S[0]={oid['S'][0]:.4f}, cum_corr[:3]={oid['cum_corr'][:3]}")
# Compare force-OID and suppression-OID mode overlap
if "force_total" in results and "suppression_rms" in results:
U_F = results["force_total"]["U"]
U_S = results["suppression_rms"]["U"]
# Cosine similarity between first modes
cos_sim = float(abs(np.dot(U_F[:, 0], U_S[:, 0])))
print(f" Force-OID vs Suppression-OID mode 1 overlap: {cos_sim:.4f}")
# Save
out_dir = os.path.join(DATA_DIR, "derived", "oid", "steady_suppression")
os.makedirs(out_dir, exist_ok=True)
for name, oid in results.items():
np.savez(os.path.join(out_dir, f"{name}_oid.npz"),
z=oid["z"], S=oid["S"], U=oid["U"], cum_corr=oid["cum_corr"])
print(f" Saved to {out_dir}")
def main():
ap = argparse.ArgumentParser()
ap.parse_args()
run_steady_oid()
print("Phase 5 complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase6_comparison.py
"""
Phase 6: POD vs OID vs PCD predictive comparison.
Linear regression with 70/30 time-series split.
Compares:
- force ~ POD coords
- force ~ OID coords
- future signature ~ POD coords
- future signature ~ OID coords
- future signature ~ PCD coords
Usage:
python3 src/OID_analysis/analysis/phase6_comparison.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import standardize # noqa: E402
try:
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
HAS_SKLEARN = True
except ImportError:
HAS_SKLEARN = False
print("WARNING: sklearn not available. Install: pip install scikit-learn")
SCENES = ["steady_cloak", "karman_re100",
"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
def r2_manual(y_true, y_pred):
ss_res = np.sum((y_true - y_pred) ** 2)
ss_tot = np.sum((y_true - np.mean(y_true, axis=0, keepdims=True)) ** 2)
return 1.0 - ss_res / (ss_tot + 1e-30)
def run_comparison(scene_key: str):
print(f"\n--- Phase 6: Comparison for {scene_key} ---")
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
oid_dir = os.path.join(DATA_DIR, "derived", "oid")
# Load POD coefs (r=10)
pod_fp = os.path.join(pod_dir, "pod_coefs_r10.npy.npz")
if not os.path.isfile(pod_fp):
print(f" SKIPPED: POD not found")
return False
pod_npz = np.load(pod_fp, allow_pickle=True)
A_all = pod_npz["coefs"] # (N, r)
N = A_all.shape[0]
# OID coordinates
oid_sources = {}
# Try loading force-OID
force_oid_fp = os.path.join(oid_dir, "force", scene_key, "force_oid.npz")
if os.path.isfile(force_oid_fp):
d = np.load(force_oid_fp)
oid_sources["force-oid"] = d["z"]
# Try loading signature-OID (delayed)
sig_oid_fp = os.path.join(oid_dir, "signature", scene_key, "sig_oid_delayed.npz")
if os.path.isfile(sig_oid_fp):
d = np.load(sig_oid_fp)
oid_sources["sig-oid"] = d["z"]
# Try loading PCD
pcd_fp = os.path.join(oid_dir, "pcd", scene_key, "sig_pcd.npz")
if os.path.isfile(pcd_fp):
d = np.load(pcd_fp)
oid_sources["sig-pcd"] = d["z_pcd"]
# Target observables for regression
targets = {}
# Force observable
force_fp = os.path.join(obs_dir, "force_total.npz")
if os.path.isfile(force_fp):
targets["force"] = np.load(force_fp)["standardized"]
# Sensor error (delayed)
sig_fp = os.path.join(obs_dir, "sensor_error_delayed.npz")
if os.path.isfile(sig_fp):
targets["future_sig"] = np.load(sig_fp)["standardized"]
# For steady cloak: RMS
rms_fp = os.path.join(obs_dir, "rms_uy.npz")
if os.path.isfile(rms_fp):
targets["suppression"] = np.load(rms_fp)["standardized"]
if not HAS_SKLEARN:
print(" sklearn not available, using manual R^2")
# Still do manual comparison for a single test
if "force" in targets and "force-oid" in oid_sources:
A_std, _, _ = standardize(A_all[:N])
Y = targets["force"][:N].squeeze()
r2_pod = r2_manual(Y, A_std[:, :3] @ np.linalg.lstsq(A_std[:, :3], Y, rcond=None)[0])
r2_oid = r2_manual(Y, oid_sources["force-oid"][:N, :3] @
np.linalg.lstsq(oid_sources["force-oid"][:N, :3], Y, rcond=None)[0])
print(f" force: POD R2={r2_pod:.4f}, OID R2={r2_oid:.4f}")
print(" Install sklearn for full comparison")
return True
results = {}
for target_name, Y in targets.items():
Y = Y[:N]
results[target_name] = {}
for src_name, z in oid_sources.items():
z = z[:N]
# 70/30 time-series split
split = int(N * 0.7)
for m in [1, 2, 3, 5]:
X = z[:, :m]
X_train, X_test = X[:split], X[split:]
Y_train, Y_test = Y[:split], Y[split:]
reg = LinearRegression().fit(X_train, Y_train)
Y_pred = reg.predict(X_test)
r2 = r2_score(Y_test, Y_pred) if Y_test.ndim == 1 else \
r2_score(Y_test, Y_pred, multioutput="variance_weighted")
results[target_name][f"{src_name}_m{m}"] = float(r2)
# Also POD baseline
A_std, _, _ = standardize(A_all[:N])
for m in [1, 2, 3, 5]:
X = A_std[:, :m]
X_train, X_test = X[:split], X[split:]
Y_train, Y_test = Y[:split], Y[split:]
reg = LinearRegression().fit(X_train, Y_train)
Y_pred = reg.predict(X_test)
r2 = r2_score(Y_test, Y_pred) if Y_test.ndim == 1 else \
r2_score(Y_test, Y_pred, multioutput="variance_weighted")
results[target_name][f"pod_m{m}"] = float(r2)
print(f" {target_name}:")
for k, v in sorted(results[target_name].items()):
print(f" {k}: R2={v:.4f}")
# Save
out_dir = os.path.join(DATA_DIR, "derived", "comparison")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, f"{scene_key}.json"), "w") as f:
json.dump(results, f, indent=2)
return True
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None)
args = ap.parse_args()
targets = [args.scene] if args.scene and args.scene in SCENES else SCENES
for sn in targets:
run_comparison(sn)
print("\nPhase 6 complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/phase7_whitebox.py
"""
Phase 7: White-box control chain comparison.
Compares how well different state representations predict the action:
Model A: obs (raw sensor) -> act
Model B: POD coord -> act
Model C: OID coord -> act
Model D: OID coord + force -> act
Usage:
python3 src/OID_analysis/analysis/phase7_whitebox.py
"""
from __future__ import annotations
import argparse
import json
import os
import sys
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.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import standardize # noqa: E402
try:
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
HAS_SKLEARN = True
except ImportError:
HAS_SKLEARN = False
SCENES = ["steady_cloak", "karman_re100",
"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
def run_whitebox(scene_key: str):
print(f"\n--- Phase 7: White-box for {scene_key} ---")
# Check for controlled.npz (PPO scenes) or forces (open-loop scenes)
data_dir_base = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
"data")
if scene_key == "steady_cloak":
dd = os.path.join(data_dir_base, "steady_cloak", "steady_cloak")
else:
sid = {"karman_re100": "karman_re100"}.get(scene_key, scene_key)
dd = os.path.join(data_dir_base, scene_key.replace("steady_", ""),
scene_key) if "illusion" in scene_key else \
os.path.join(data_dir_base, "karman_cloak", scene_key)
controlled_fp = os.path.join(dd, "controlled.npz")
forces_fp = os.path.join(dd, "forces.npz")
if os.path.isfile(controlled_fp):
data = np.load(controlled_fp)
actions = data["actions"]
sensors = data["sensors"]
elif os.path.isfile(forces_fp):
# Open-loop steady cloak: no actions available from controlled.npz
# But we know the steady cloak action: [0, -5.1*U0, 5.1*U0]
from OID_analysis.configs import get_scene
cfg = get_scene(scene_key)
u0 = cfg["u0"]
sensors_n = np.load(os.path.join(dd, "sensors.npz"))["sensors"]
N = len(sensors_n)
sensors = sensors_n
omega_rear = cfg.get("omega_rear_scale", 5.1)
actions = np.tile([0.0, -omega_rear * u0 / 0.01, omega_rear * u0 / 0.01], (N, 1))
# Actually these should be in normalized [-1,1] range
# rear = 5.1 -> normalized = (5.1 - bias)/scale where bias=5.1, scale=8
# Actually for steady: bias=[0,-5.1,5.1], scale=8
# So action = (omega/u0 - bias)/scale
actions = np.tile([0.0, 0.0, 0.0], (N, 1)) # zero action = bias actions
else:
print(f" SKIPPED: no action data found")
return
N = min(len(sensors), len(actions))
sensors = sensors[:N]
actions = actions[:N]
# Normalize actions per channel
actions_std, act_mean, act_std = standardize(actions)
# POD coefs
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene_key)
coefs = None
pod_fp = os.path.join(pod_dir, "pod_coefs_r10.npy.npz")
if os.path.isfile(pod_fp):
pod_npz = np.load(pod_fp, allow_pickle=True)
pod_n = pod_npz["coefs"].shape[0]
N = min(N, pod_n)
# Re-apply truncation based on final N
sensors = sensors[:N]
actions = actions[:N]
actions_std, act_mean, act_std = standardize(actions)
# OID coords
oid_dir = os.path.join(DATA_DIR, "derived", "oid")
oid_coords = None
oid_fp = os.path.join(oid_dir, "force", scene_key, "force_oid.npz")
if os.path.isfile(oid_fp):
oid_coords = np.load(oid_fp)["z"][:N]
# Force observable
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene_key)
force_obs = None
force_fp = os.path.join(obs_dir, "force_total.npz")
if os.path.isfile(force_fp):
force_obs = np.load(force_fp)["standardized"][:N]
if not HAS_SKLEARN:
print(" sklearn not available")
return
split = int(N * 0.7)
# Model A: raw sensor -> act
X_A = sensors[:split]
Y_train = actions_std[:split]
# Test on last segment
X_A_test = sensors[split:N]
results = {}
# Model A
if X_A.shape[1] > 0:
reg = LinearRegression().fit(X_A, Y_train)
r2_a = r2_score(Y_train, reg.predict(X_A))
results["obs_act_train"] = float(r2_a)
# Model B: POD coord -> act
if coefs is not None:
for m in [3, 5]:
X = standardize(coefs[:split, :m])[0]
reg = LinearRegression().fit(X, Y_train)
r2 = r2_score(Y_train, reg.predict(X))
results[f"pod_m{m}_act_train"] = float(r2)
# Model C: OID coord -> act
if oid_coords is not None:
for m in [3, 5]:
X = oid_coords[:split, :m]
reg = LinearRegression().fit(X, Y_train)
r2 = r2_score(Y_train, reg.predict(X))
results[f"oid_m{m}_act_train"] = float(r2)
# Model D: OID + force -> act
if oid_coords is not None and force_obs is not None:
X = np.hstack([oid_coords[:split, :3], force_obs[:split, :2]])
reg = LinearRegression().fit(X, Y_train)
r2 = r2_score(Y_train, reg.predict(X))
results["oid_force_act_train"] = float(r2)
print(f" Results: {json.dumps(results, indent=2)}")
# Save
out_dir = os.path.join(DATA_DIR, "derived", "whitebox")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, f"{scene_key}.json"), "w") as f:
json.dump(results, f, indent=2)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None)
args = ap.parse_args()
targets = [args.scene] if args.scene and args.scene in SCENES else SCENES
for sn in targets:
run_whitebox(sn)
print("\nPhase 7 complete.")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/robustness_analysis.py
"""
Comprehensive robustness analysis for force-OID vs signature-OID separation.
Covers:
1. POD rank sensitivity (r=6,8,10,12,16) -- all 5 scenes
2. Time-window sensitivity (split data into 2 halves)
3. Karman tau_c sensitivity sweep
4. Zone OID: force-OID / sig-OID in near-body / near-wake / downstream zones
Usage:
cd /home/frank14f/DynamisLab && PYTHONPATH="src:$PYTHONPATH" conda run -n sr_env python3 src/OID_analysis/analysis/robustness_analysis.py
"""
from __future__ import annotations
import json
import os
import sys
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 OID_analysis.configs import DATA_DIR # noqa: E402
from OID_analysis.utils.analysis import ( # noqa: E402
compute_pod, compute_force_oid, compute_pcd, standardize,
)
SCENES = ["steady_cloak", "karman_re100",
"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
def cos_sim(a, b):
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b) + 1e-30))
def load_pod_coefs(scene: str, rank: int = 10):
"""Load correction-field POD coefficients for a scene at given rank."""
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene)
fp = os.path.join(pod_dir, f"pod_coefs_r{rank}.npy.npz")
if not os.path.isfile(fp):
# Try loading delta fields and re-running POD
delta_fp = os.path.join(pod_dir, "delta_q_ctl.npz")
if not os.path.isfile(delta_fp):
return None
d = np.load(delta_fp)
ux, uy = d["ux"], d["uy"]
# Flatten to snapshot matrix
N, ny, nx = ux.shape
Q = np.zeros((N, ny * nx * 2), dtype=np.float64)
for t in range(N):
Q[t] = np.concatenate([ux[t].ravel(), uy[t].ravel()])
pod = compute_pod(Q, rank=rank)
return pod["coefs"]
else:
d = np.load(fp, allow_pickle=True)
return d["coefs"]
def load_observable(scene: str, obs_name: str):
"""Load standardized observable."""
obs_dir = os.path.join(DATA_DIR, "derived", "observables", scene)
fp = os.path.join(obs_dir, f"{obs_name}.npz")
if not os.path.isfile(fp):
return None
d = np.load(fp)
return d["standardized"]
def compute_overlap(coefs_a, obs_a, coefs_b, obs_b):
"""Compute cosine similarity between force-OID mode 1 and sig-OID mode 1."""
N = min(coefs_a.shape[0], obs_a.shape[0], coefs_b.shape[0], obs_b.shape[0])
A1 = standardize(coefs_a[:N])[0]
A2 = standardize(coefs_b[:N])[0]
Y1 = obs_a[:N]
Y2 = obs_b[:N]
# Force-OID
C1 = (1.0 / N) * A1.T @ Y1
U1, _, _ = np.linalg.svd(C1, full_matrices=False)
# Sig-OID
C2 = (1.0 / N) * A2.T @ Y2
U2, _, _ = np.linalg.svd(C2, full_matrices=False)
return cos_sim(U1[:, 0], U2[:, 0])
def compute_r2_sweep(A, Y, max_m=5):
"""Compute R2 for each m=1..max_m using 70/30 split."""
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
N = min(A.shape[0], Y.shape[0])
A = A[:N]
Y = Y[:N]
split = int(N * 0.7)
results = {}
for m in range(1, max_m + 1):
X = standardize(A[:, :m])[0]
X_train, X_test = X[:split], X[split:]
Y_train, Y_test = Y[:split], Y[split:]
reg = LinearRegression().fit(X_train, Y_train)
r2 = r2_score(Y_test, reg.predict(X_test))
results[m] = float(max(r2, -1.0)) # clamp
return results
def main():
results = {
"rank_sensitivity": {},
"window_sensitivity": {},
"tauc_sensitivity": {},
"zone_oid": {},
"overlap_table": {},
}
# ===========================
# 1. POD RANK SENSITIVITY
# ===========================
print("=" * 60)
print("1. POD RANK SENSITIVITY")
print("=" * 60)
ranks = [6, 8, 10, 12, 16]
overlap_by_rank = {}
for scene in SCENES:
print(f"\n Scene: {scene}")
# Force observable
y_force = load_observable(scene, "force_total")
y_sig = load_observable(scene, "sensor_error_delayed")
if scene == "steady_cloak":
y_sig = load_observable(scene, "rms_uy")
if y_force is None:
print(" SKIP: no force observable")
continue
if y_sig is None:
print(" SKIP: no sig observable")
continue
scene_rank_data = {}
for r in ranks:
coefs = load_pod_coefs(scene, rank=r)
if coefs is None:
print(f" r={r}: no POD data")
continue
overlap = compute_overlap(coefs, y_force, coefs, y_sig)
scene_rank_data[r] = overlap
print(f" r={r}: overlap={overlap:.4f}")
overlap_by_rank[scene] = scene_rank_data
results["rank_sensitivity"] = overlap_by_rank
# ===========================
# 2. TIME-WINDOW SENSITIVITY
# ===========================
print("\n" + "=" * 60)
print("2. TIME-WINDOW SENSITIVITY")
print("=" * 60)
for scene in SCENES:
print(f"\n Scene: {scene}")
# Load delta-q_ctl fields
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene)
delta_fp = os.path.join(pod_dir, "delta_q_ctl.npz")
if not os.path.isfile(delta_fp):
print(" SKIP: no delta fields")
continue
d = np.load(delta_fp)
ux, uy = d["ux"], d["uy"]
N = ux.shape[0]
y_force = load_observable(scene, "force_total")
y_sig = load_observable(scene, "sensor_error_delayed")
if scene == "steady_cloak":
y_sig = load_observable(scene, "rms_uy")
if y_force is None or y_sig is None:
continue
# Split into 2 windows
half = N // 2
window_results = {}
for wname, start, end in [("first_half", 0, half), ("second_half", half, 2 * half)]:
# POD on this window
Q = np.zeros((end - start, ux.shape[1] * ux.shape[2] * 2), dtype=np.float64)
for t in range(start, end):
Q[t - start] = np.concatenate([ux[t].ravel(), uy[t].ravel()])
pod = compute_pod(Q, rank=10)
A = pod["coefs"]
Y_f = y_force[start:end]
Y_s = y_sig[start:end]
overlap = compute_overlap(A, Y_f, A, Y_s)
window_results[wname] = overlap
print(f" {wname}: overlap={overlap:.4f}")
# Full window reference
if "first_half" in window_results and "second_half" in window_results:
print(f" delta: {abs(window_results['first_half'] - window_results['second_half']):.4f}")
results["window_sensitivity"][scene] = window_results
# ===========================
# 3. KARMAN tau_c SENSITIVITY
# ===========================
print("\n" + "=" * 60)
print("3. KARMAN tau_c SENSITIVITY")
print("=" * 60)
# Load Karman data
pod_dir = os.path.join(DATA_DIR, "derived", "pod", "karman_re100")
delta_fp = os.path.join(pod_dir, "delta_q_ctl.npz")
obs_dir = os.path.join(DATA_DIR, "derived", "observables", "karman_re100")
if os.path.isfile(delta_fp) and os.path.isdir(obs_dir):
d = np.load(delta_fp)
ux, uy = d["ux"], d["uy"]
N = ux.shape[0]
# Load target for re-computing delayed error
ctl_fp = os.path.join(
os.path.join(DATA_DIR, "karman_cloak", "karman_re100"), "controlled.npz")
if os.path.isfile(ctl_fp):
ctl = np.load(ctl_fp)
sensors = ctl["sensors"]
target_dd = os.path.join(DATA_DIR, "karman_cloak", "karman_re100")
target_fp = os.path.join(target_dd, "target.npz")
if os.path.isfile(target_fp):
target_states = np.load(target_fp)["target_states"]
else:
target_states = sensors * 0.0
# Force observable
forces = ctl["forces"]
Fx_total = np.sum(forces[:, 0::2], axis=1, keepdims=True)
Fy_total = np.sum(forces[:, 1::2], axis=1, keepdims=True)
y_force_std, _, _ = standardize(np.hstack([Fx_total, Fy_total]))
tlen = target_states.shape[0]
tau_candidates = [0, 10, 15, 20, 25, 30, 35, 40, 50, 60]
NT = 30 # approx shedding period in steps
# POD
Q = np.zeros((N, ux.shape[1] * ux.shape[2] * 2), dtype=np.float64)
for t in range(N):
Q[t] = np.concatenate([ux[t].ravel(), uy[t].ravel()])
pod = compute_pod(Q, rank=10)
A_std, _, _ = standardize(pod["coefs"])
tauc_results = {}
for tc in tau_candidates:
e_s_delayed = np.zeros_like(sensors)
if N > tc:
# Create a reference that matches N length
ref_full = np.zeros((N, 6), dtype=np.float32)
for i in range(6):
if target_states.shape[1] > i:
t_col = target_states[:, i]
if len(t_col) < N:
# tile the target to match N
repeats = (N + len(t_col) - 1) // len(t_col)
ref_full[:, i] = np.tile(t_col, repeats)[:N]
else:
ref_full[:, i] = t_col[:N]
else:
ref_full[:, i] = sensors[:, i]
e_s_delayed[tc:, i] = sensors[tc:, i] - ref_full[tc:, i]
e_s_delayed[:tc, i] = 0.0
y_sig_std, _, _ = standardize(e_s_delayed)
# Compute overlap
N_use = min(A_std.shape[0], y_force_std.shape[0], y_sig_std.shape[0])
A_u = A_std[:N_use]
Y_f = y_force_std[:N_use]
Y_s = y_sig_std[:N_use]
C_f = (1.0 / N_use) * A_u.T @ Y_f
C_s = (1.0 / N_use) * A_u.T @ Y_s
U_f, _, _ = np.linalg.svd(C_f, full_matrices=False)
U_s, _, _ = np.linalg.svd(C_s, full_matrices=False)
overlap = cos_sim(U_f[:, 0], U_s[:, 0])
# R2 for force prediction
r2_force = compute_r2_sweep(A_u, Y_f, max_m=3)
r2_sig = compute_r2_sweep(A_u, Y_s, max_m=3)
tauc_results[tc] = {
"overlap": overlap,
"r2_force_m2": r2_force.get(2, None),
"r2_sig_m2": r2_sig.get(2, None),
}
print(f" tau_c={tc:3d}: overlap={overlap:.4f}, "
f"force_R2={r2_force.get(2, None):.4f}, sig_R2={r2_sig.get(2, None):.4f}")
results["tauc_sensitivity"]["karman_re100"] = tauc_results
# ===========================
# 4. ZONE OID
# ===========================
print("\n" + "=" * 60)
print("4. ZONE OID")
print("=" * 60)
# Zone masks (in lattice coords for 1280x512)
ny, nx = 512, 1280
zones = {
"near-body": (200, 310, 580, 660),
"near-wake": (180, 330, 660, 800),
"downstream": (180, 330, 790, 810),
}
for scene in SCENES:
print(f"\n Scene: {scene}")
pod_dir = os.path.join(DATA_DIR, "derived", "pod", scene)
delta_fp = os.path.join(pod_dir, "delta_q_ctl.npz")
if not os.path.isfile(delta_fp):
print(" SKIP: no delta fields")
continue
d = np.load(delta_fp)
ux, uy = d["ux"], d["uy"]
N = ux.shape[0]
ny_a, nx_a = ux.shape[1], ux.shape[2]
print(f" field shape: ({ny_a}, {nx_a})")
y_force = load_observable(scene, "force_total")
y_sig = load_observable(scene, "sensor_error_delayed")
if scene == "steady_cloak":
y_sig = load_observable(scene, "rms_uy")
if y_force is None or y_sig is None:
print(" SKIP: missing observable")
continue
try:
for zname, (y0, y1, x0, x1) in zones.items():
y0 = max(0, y0); y1 = min(ny_a, y1); x0 = max(0, x0); x1 = min(nx_a, x1)
if y1 <= y0 or x1 <= x0: continue
ux_z = ux[:, y0:y1, x0:x1]
uy_z = uy[:, y0:y1, x0:x1]
DOF = ux_z.shape[1] * ux_z.shape[2] * 2
if DOF == 0: continue
Q = np.zeros((N, DOF), dtype=np.float64)
for t in range(N):
Q[t] = np.concatenate([ux_z[t].ravel(), uy_z[t].ravel()])
pod = compute_pod(Q, rank=10)
A = pod["coefs"]
min_n = min(A.shape[0], y_force.shape[0], y_sig.shape[0])
A_s = A[:min_n]; Y_f = y_force[:min_n]; Y_s = y_sig[:min_n]
C_f = (1.0 / min_n) * A_s.T @ Y_f
C_s = (1.0 / min_n) * A_s.T @ Y_s
U_f, S_f, _ = np.linalg.svd(C_f, full_matrices=False)
U_s, S_s, _ = np.linalg.svd(C_s, full_matrices=False)
overlap = cos_sim(U_f[:, 0], U_s[:, 0])
scene_zone_results[zname] = {"overlap": overlap,
"force_S0": float(S_f[0]) if len(S_f) > 0 else None,
"sig_S0": float(S_s[0]) if len(S_s) > 0 else None}
print(f" {zname:15s}: overlap={overlap:.4f}, force_S0={S_f[0]:.4f}")
except Exception as e:
print(f" Zone analysis error: {e}")
for t in range(N):
Q[t] = np.concatenate([ux_z[t].ravel(), uy_z[t].ravel()])
pod = compute_pod(Q, rank=10)
A = pod["coefs"]
# Limit observable length
min_n = min(A.shape[0], y_force.shape[0], y_sig.shape[0])
A_s = A[:min_n]
Y_f = y_force[:min_n]
Y_s = y_sig[:min_n]
C_f = (1.0 / min_n) * A_s.T @ Y_f
C_s = (1.0 / min_n) * A_s.T @ Y_s
U_f, S_f, _ = np.linalg.svd(C_f, full_matrices=False)
U_s, S_s, _ = np.linalg.svd(C_s, full_matrices=False)
overlap = cos_sim(U_f[:, 0], U_s[:, 0])
scene_zone_results[zname] = {
"overlap": overlap,
"force_S0": float(S_f[0]) if len(S_f) > 0 else None,
"sig_S0": float(S_s[0]) if len(S_s) > 0 else None,
}
print(f" {zname:15s}: overlap={overlap:.4f}, "
f"force_S0={S_f[0]:.4f}, sig_S0={S_s[0]:.4f}")
results["zone_oid"][scene] = scene_zone_results
# ===========================
# FINAL OVERLAP TABLE (r=10)
# ===========================
print("\n" + "=" * 60)
print("FINAL OVERLAP TABLE (r=10, full data)")
print("=" * 60)
print(f"\n{'Scene':<20s} {'Overlap':>8s} {'Force_R2_m2':>12s} {'Sig_R2_m2':>12s}")
print("-" * 52)
overlap_table = {}
for scene in SCENES:
y_force = load_observable(scene, "force_total")
y_sig = load_observable(scene, "sensor_error_delayed")
if scene == "steady_cloak":
y_sig = load_observable(scene, "rms_uy")
if y_force is None or y_sig is None:
continue
coefs = load_pod_coefs(scene, rank=10)
if coefs is None:
continue
N = min(coefs.shape[0], y_force.shape[0], y_sig.shape[0])
A_s, _, _ = standardize(coefs[:N])
Y_f = y_force[:N]
Y_s = y_sig[:N]
C_f = (1.0 / N) * A_s.T @ Y_f
C_s = (1.0 / N) * A_s.T @ Y_s
U_f, _, _ = np.linalg.svd(C_f, full_matrices=False)
U_s, _, _ = np.linalg.svd(C_s, full_matrices=False)
overlap = cos_sim(U_f[:, 0], U_s[:, 0])
r2_f = compute_r2_sweep(A_s, Y_f, max_m=3)
r2_s = compute_r2_sweep(A_s, Y_s, max_m=3)
overlap_table[scene] = {
"overlap": overlap,
"force_R2_m2": r2_f.get(2, None),
"force_R2_m3": r2_f.get(3, None),
"sig_R2_m2": r2_s.get(2, None),
"sig_R2_m3": r2_s.get(3, None),
}
print(f"{scene:<20s} {overlap:>8.4f} {r2_f.get(2, 0):>12.4f} {r2_s.get(2, 0):>12.4f}")
results["overlap_table"] = overlap_table
# ===========================
# SAVE
# ===========================
out_dir = os.path.join(DATA_DIR, "derived", "robustness")
os.makedirs(out_dir, exist_ok=True)
# Convert numpy arrays to floats
def clean(obj):
if isinstance(obj, np.floating):
return float(obj)
if isinstance(obj, np.integer):
return int(obj)
if isinstance(obj, dict):
return {k: clean(v) for k, v in obj.items()}
if isinstance(obj, (list, tuple)):
return [clean(v) for v in obj]
return obj
results_clean = clean(results)
with open(os.path.join(out_dir, "robustness_results.json"), "w") as f:
json.dump(results_clean, f, indent=2)
print(f"\nAll results saved to {out_dir}/robustness_results.json")
if __name__ == "__main__":
main()

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# OID_analysis/analysis/run_full_analysis.py
"""
Run the full OID analysis pipeline for a scene.
Checks data availability first, runs all phases that have prerequisites.
Usage:
# Run all available scenes
python3 src/OID_analysis/analysis/run_full_analysis.py
# Run specific scene
python3 src/OID_analysis/analysis/run_full_analysis.py --scene karman_re100
# Force re-run
python3 src/OID_analysis/analysis/run_full_analysis.py --scene steady_cloak --force
"""
from __future__ import annotations
import argparse
import json
import os
import subprocess
import sys
import time
_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 OID_analysis.configs import data_dir_for_scene, SCENES # noqa: E402
ANALYSIS_DIR = os.path.dirname(os.path.abspath(__file__))
def scene_prerequisites_met(scene_key: str, verbose: bool = True) -> bool:
"""Check if all prerequisite data exists for a scene."""
# Determine which fields are needed
if scene_key == "steady_cloak":
needed = {
"q_in (empty_channel)": lambda: os.path.isfile(os.path.join(data_dir_for_scene("empty_channel"), "fields.npz")),
"q_blk (pinball_baseline)": lambda: os.path.isfile(os.path.join(data_dir_for_scene("pinball_baseline"), "fields.npz")),
"q_ctl (steady_cloak)": lambda: os.path.isfile(os.path.join(data_dir_for_scene("steady_cloak"), "fields.npz")),
"forces": lambda: os.path.isfile(os.path.join(data_dir_for_scene("pinball_baseline"), "forces.npz")),
}
elif scene_key.startswith("karman"):
needed = {
"q_in (disturbance_only)": lambda: os.path.isfile(os.path.join(data_dir_for_scene("disturbance_only"), "fields.npz")),
"q_blk (karman_blk)": lambda: os.path.isfile(os.path.join(data_dir_for_scene("karman_blk"), "fields.npz")),
"q_ctl (karman_re100)": lambda: os.path.isfile(os.path.join(data_dir_for_scene("karman_re100"), "fields.npz")),
"controlled": lambda: os.path.isfile(os.path.join(data_dir_for_scene("karman_re100"), "controlled.npz")),
}
elif scene_key.startswith("illusion"):
needed = {
"q_ctl": lambda: os.path.isfile(os.path.join(data_dir_for_scene(scene_key), "fields.npz")),
"controlled": lambda: os.path.isfile(os.path.join(data_dir_for_scene(scene_key), "controlled.npz")),
}
else:
needed = {}
all_met = True
for name, check_fn in needed.items():
met = check_fn()
if not met:
if verbose:
print(f" MISSING: {name}")
all_met = False
elif verbose:
print(f" OK: {name}")
return all_met
def run_phase(script_name: str, scene_key: str, force: bool = False):
"""Run one phase script for a scene."""
script_path = os.path.join(ANALYSIS_DIR, script_name)
if not os.path.isfile(script_path):
print(f" SKIP: {script_name} not found")
return 0
env = os.environ.copy()
env["PYTHONPATH"] = f"{_SRC}:{env.get('PYTHONPATH', '')}"
cmd = [sys.executable, script_path]
if scene_key:
cmd.extend(["--scene", scene_key])
print(f"\n--- Running: {' '.join(cmd)} ---")
t0 = time.time()
result = subprocess.run(cmd, capture_output=True, text=True, env=env)
elapsed = time.time() - t0
# Print output
for line in result.stdout.split("\n"):
if line.strip():
print(f" {line}")
if result.stderr.strip():
for line in result.stderr.split("\n"):
if line.strip() and "WARNING" in line:
print(f" ! {line.strip()}")
if result.returncode != 0:
print(f" FAILED ({elapsed:.0f}s), code={result.returncode}")
else:
print(f" OK ({elapsed:.0f}s)")
return result.returncode
def run_full(scene_key: str, force: bool = False):
print(f"\n{'='*70}")
print(f"Full OID Analysis Pipeline: {scene_key}")
print(f"{'='*70}")
if not scene_prerequisites_met(scene_key):
print(f" PREREQUISITES NOT MET. Skipping.")
return False
# Phase order
phases = [
("phase1_correction_pod.py", "Phase 1: Correction-field POD"),
("phase2_build_observables.py", "Phase 2: Observable construction"),
("phase3_force_oid.py", "Phase 3: Force-OID"),
]
# Phase 4-5 (conditional)
if scene_key.startswith("karman") or scene_key.startswith("illusion"):
phases.append(("phase4a_signature_oid.py", "Phase 4a: Signature-OID"))
phases.append(("phase4b_signature_pcd.py", "Phase 4b: Signature-PCD"))
elif scene_key == "steady_cloak":
phases.append(("phase5_steady_oid.py", "Phase 5: Steady suppression-OID"))
# Phase 6-7
phases.append(("phase6_comparison.py", "Phase 6: Comparison"))
phases.append(("phase7_whitebox.py", "Phase 7: White-box chain"))
errors = 0
for script, desc in phases:
print(f"\n>>> {desc}")
errors += run_phase(script, scene_key, force)
print(f"\n{'='*70}")
print(f"Pipeline complete for {scene_key}. Errors: {errors}")
print(f"{'='*70}")
return errors == 0
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default=None,
help="Scene key or 'all'")
ap.add_argument("--force", action="store_true",
help="Force re-run")
ap.add_argument("--check", action="store_true",
help="Only check prerequisites")
ap.add_argument("--list", action="store_true",
help="List available scenes")
args = ap.parse_args()
all_scenes = ["steady_cloak", "karman_re100",
"illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]
if args.list:
for sn in all_scenes:
met = scene_prerequisites_met(sn, verbose=False)
status = "READY" if met else "MISSING DATA"
print(f" {sn:30s} {status}")
return
if args.check:
for sn in all_scenes:
print(f"\n--- {sn} ---")
scene_prerequisites_met(sn)
return
targets = all_scenes if (args.scene == "all" or args.scene is None) else [args.scene]
for sn in targets:
if sn not in all_scenes:
print(f"Unknown scene: {sn}")
continue
run_full(sn, args.force)
print("\nAll pipelines complete.")
if __name__ == "__main__":
main()

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"""Save robustness results and write comprehensive report."""
import json, os, sys
_REPO = "/home/frank14f/DynamisLab"
sys.path.insert(0, os.path.join(_REPO, "src"))
from OID_analysis.configs import DATA_DIR
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.")

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# OID_analysis/analysis/steady_reanalysis.py
"""
Steady cloak re-analysis with suppression/restoration metrics.
Replaces R^2 with physically meaningful metrics:
- RMS reduction per zone
- Recirculation length/area collapse
- Enstrophy reduction
- Force RMS
Usage:
conda run -n sr_env python3 src/OID_analysis/analysis/steady_reanalysis.py
"""
from __future__ import annotations
import json
import os
import sys
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.configs import DATA_DIR, data_dir_for_scene # noqa: E402
def compute_rms_reduction(ux_ctl, uy_ctl, ux_blk, uy_blk, zone_mask=None):
"""Compute RMS reduction ratio: 1 - RMS(ctl)/RMS(blk)"""
if zone_mask is not None:
ux_ctl = ux_ctl[:, zone_mask]
uy_ctl = uy_ctl[:, zone_mask]
ux_blk = ux_blk[:, zone_mask]
uy_blk = uy_blk[:, zone_mask]
rms_ctl_u = np.std(ux_ctl, axis=0)
rms_ctl_v = np.std(uy_ctl, axis=0)
rms_blk_u = np.std(ux_blk, axis=0)
rms_blk_v = np.std(uy_blk, axis=0)
rms_ctl = np.sqrt(np.mean(rms_ctl_u**2 + rms_ctl_v**2))
rms_blk = np.sqrt(np.mean(rms_blk_u**2 + rms_blk_v**2))
reduction = 1.0 - rms_ctl / (rms_blk + 1e-30)
return float(reduction), float(rms_ctl), float(rms_blk)
def compute_enstrophy_reduction(ux_ctl, uy_ctl, ux_blk, uy_blk, zone_mask=None):
"""Compute enstrophy reduction ratio."""
def zonal_enstrophy(ux, uy):
omega = np.zeros((ux.shape[0], ux.shape[1], ux.shape[2]), dtype=np.float64)
for t in range(min(ux.shape[0], 100)): # subsample for speed
omega[t] = np.gradient(uy[t].astype(np.float64), axis=1) - \
np.gradient(ux[t].astype(np.float64), axis=0)
if zone_mask is not None:
area = max(np.sum(zone_mask), 1)
return np.mean(0.5 * omega[:, zone_mask]**2)
return np.mean(0.5 * omega**2)
ens_ctl = zonal_enstrophy(ux_ctl, uy_ctl)
ens_blk = zonal_enstrophy(ux_blk, uy_blk)
reduction = 1.0 - ens_ctl / (ens_blk + 1e-30)
return float(reduction), float(ens_ctl), float(ens_blk)
def compute_recirculation_metrics(mean_u):
"""Compute Lr and Ar from mean u field."""
ny, nx = mean_u.shape
center_y = (ny - 1) / 2.0
cl_y = int(center_y)
# Lr: furthest downstream x on centerline where mean_u < 0
u_cl = mean_u[cl_y, :]
neg_idx = np.where(u_cl < 0)[0]
Lr = float(neg_idx[-1]) if len(neg_idx) > 0 else 0.0
# Ar: pixels where mean_u < 0
Ar = float(np.sum(mean_u < 0))
return Lr, Ar
def analyze_steady():
print("=== Steady Cloak Re-Analysis ===")
# Load fields
blk_dir = data_dir_for_scene("pinball_baseline")
ctl_dir = data_dir_for_scene("steady_cloak")
f_blk = np.load(os.path.join(blk_dir, "fields.npz"))
ux_blk, uy_blk = f_blk["ux"], f_blk["uy"]
f_ctl = np.load(os.path.join(ctl_dir, "fields.npz"))
ux_ctl, uy_ctl = f_ctl["ux"], f_ctl["uy"]
# Equalize lengths
N = min(ux_blk.shape[0], ux_ctl.shape[0])
ux_blk, uy_blk = ux_blk[:N], uy_blk[:N]
ux_ctl, uy_ctl = ux_ctl[:N], uy_ctl[:N]
print(f"\nN snapshots: {N} (min of blk={ux_blk.shape[0]}, ctl={ux_ctl.shape[0]})")
# Zone masks
ny, nx = ux_blk.shape[1], ux_blk.shape[2]
# Near-body: x=[580,660], y=[200,310] in lattice
nb_mask = np.zeros((ny, nx), dtype=bool)
nb_mask[200:310, 580:660] = True
# Near-wake: x=[660,800], y=[180,330]
nw_mask = np.zeros((ny, nx), dtype=bool)
nw_mask[180:330, 660:800] = True
# Downstream sensor zone: x=[790,810]
ds_mask = np.zeros((ny, nx), dtype=bool)
for sy in [215, 255, 295]:
y0, y1 = max(0, sy-10), min(ny, sy+10)
ds_mask[y0:y1, 790:810] = True
zones = [
("near-body", nb_mask),
("near-wake", nw_mask),
("downstream", ds_mask),
("full-field", None),
]
results = {}
for zname, zmask in zones:
print(f"\n Zone: {zname}")
rms_red, rms_c, rms_b = compute_rms_reduction(
ux_ctl, uy_ctl, ux_blk, uy_blk, zmask)
ens_red, ens_c, ens_b = compute_enstrophy_reduction(
ux_ctl, uy_ctl, ux_blk, uy_blk, zmask)
results[zname] = {
"rms_reduction": rms_red,
"rms_ctl": rms_c,
"rms_blk": rms_b,
"enstrophy_reduction": ens_red,
"enstrophy_ctl": ens_c,
"enstrophy_blk": ens_b,
}
print(f" RMS reduction: {rms_red:.4f} (ctl={rms_c:.6f}, blk={rms_b:.6f})")
print(f" Enstrophy reduction: {ens_red:.4f} (ctl={ens_c:.6f}, blk={ens_b:.6f})")
# Recirculation metrics for full field
mean_u_ctl = np.mean(ux_ctl, axis=0)
mean_u_blk = np.mean(ux_blk, axis=0)
Lr_ctl, Ar_ctl = compute_recirculation_metrics(mean_u_ctl)
Lr_blk, Ar_blk = compute_recirculation_metrics(mean_u_blk)
results["recirculation"] = {
"Lr_ctl_lattice": Lr_ctl,
"Lr_blk_lattice": Lr_blk,
"Lr_collapse": Lr_ctl / (Lr_blk + 1e-30),
"Ar_ctl": Ar_ctl,
"Ar_blk": Ar_blk,
"Ar_collapse": Ar_ctl / (Ar_blk + 1e-30),
}
print(f"\n Recirculation:")
print(f" Lr: ctl={Lr_ctl:.0f}, blk={Lr_blk:.0f}, collapse={Lr_ctl/(Lr_blk+1e-30):.4f}")
print(f" Ar: ctl={Ar_ctl:.0f}, blk={Ar_blk:.0f}, collapse={Ar_ctl/(Ar_blk+1e-30):.4f}")
# Force metrics
fp = os.path.join(blk_dir, "forces.npz")
forces_blk = np.load(fp)["forces"][:N]
fp_ctl = os.path.join(ctl_dir, "forces.npz")
forces_ctl = np.load(fp_ctl)["forces"][:N]
Fx_blk_rms = np.std(np.sum(forces_blk[:, 0::2], axis=1))
Fy_blk_rms = np.std(np.sum(forces_blk[:, 1::2], axis=1))
Fx_ctl_rms = np.std(np.sum(forces_ctl[:, 0::2], axis=1))
Fy_ctl_rms = np.std(np.sum(forces_ctl[:, 1::2], axis=1))
results["force"] = {
"Fx_rms_blk": float(Fx_blk_rms),
"Fx_rms_ctl": float(Fx_ctl_rms),
"Fx_reduction": float(1.0 - Fx_ctl_rms / (Fx_blk_rms + 1e-30)),
"Fy_rms_blk": float(Fy_blk_rms),
"Fy_rms_ctl": float(Fy_ctl_rms),
"Fy_reduction": float(1.0 - Fy_ctl_rms / (Fy_blk_rms + 1e-30)),
}
print(f"\n Force:")
print(f" Fx RMS: blk={Fx_blk_rms:.6f}, ctl={Fx_ctl_rms:.6f}, reduction={results['force']['Fx_reduction']:.4f}")
print(f" Fy RMS: blk={Fy_blk_rms:.6f}, ctl={Fy_ctl_rms:.6f}, reduction={results['force']['Fy_reduction']:.4f}")
# Save
out_dir = os.path.join(DATA_DIR, "derived", "steady_metrics")
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, "steady_reanalysis.json"), "w") as f:
json.dump(results, f, indent=2)
print(f"\nSaved to {out_dir}/steady_reanalysis.json")
# Summary table
print(f"\n{'='*60}")
print(f"STEADY CLOAK RE-ANALYSIS SUMMARY")
print(f"{'='*60}")
print(f"{'Metric':<30s} {'Uncontrolled':>12s} {'Controlled':>12s} {'Reduction':>12s}")
print(f"{'-'*66}")
for zname in ["near-body", "near-wake", "downstream"]:
zr = results[zname]
print(f"RMS ({zname}){'':<12s} {zr['rms_blk']:12.6f} {zr['rms_ctl']:12.6f} {zr['rms_reduction']:12.4f}")
print(f"Lr (recirc length) {Lr_blk:12.0f} {Lr_ctl:12.0f} {results['recirculation']['Lr_collapse']:12.4f}")
print(f"Ar (recirc area) {Ar_blk:12.0f} {Ar_ctl:12.0f} {results['recirculation']['Ar_collapse']:12.4f}")
print(f"Fx RMS {Fx_blk_rms:12.6f} {Fx_ctl_rms:12.6f} {results['force']['Fx_reduction']:12.4f}")
print(f"Fy RMS {Fy_blk_rms:12.6f} {Fy_ctl_rms:12.6f} {results['force']['Fy_reduction']:12.4f}")
if __name__ == "__main__":
analyze_steady()

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"""Unified scene configuration for OID_analysis.
Mirrors SR_analysis/configs.py and CCD_analysis/configs.py pattern.
All scene metadata in one place.
Re convention:
- "re_code" uses reference length 2*D (matching model file naming).
- Re_D = re_code / 2 is the true physical Reynolds number.
"""
from __future__ import annotations
import os
from typing import Any, Dict, List, Optional, Tuple
# -- Root paths ---------------------------------------------------------------
_PROJ = os.path.abspath(os.path.dirname(__file__))
MODEL_DIR = os.path.join(_PROJ, "..", "..", "models")
LEGACY_CFG_DIR = os.path.join(os.path.dirname(__file__), "data", "configs", "legacy")
DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
# -- Physics constants -------------------------------------------------------
U0 = 0.01 # standard inlet center velocity (all models use this)
D_CYL = 20.0 # single cylinder diameter (lattice units)
D_REF = 40.0 # reference length = 2*D for code Re
L0 = 20.0 # base length unit
NX = 1280
NY = 512
CENTER_Y = (NY - 1) / 2.0
FIFO_LEN = 150
CONV_LEN_DEFAULT = 30 # Karman/Steady
CONV_LEN_ILLUSION = 36 # Illusion
def nu_from_re(re_code: float, u0: float = U0) -> float:
"""Viscosity from code Reynolds number (reference length = 2*D)."""
return u0 * D_REF / re_code
# -- Scene definitions -------------------------------------------------------
SCENES: Dict[str, Any] = {}
# -- Empty Channel (reference) -----------------------------------------------
SCENES["empty_channel"] = {
"scene_id": "empty_channel",
"re_code": 100,
"nu": 0.004,
"has_disturbance": False,
"sample_interval": 800,
"source": "open_loop",
"n_objects_env": 3,
"obs_slice": (0, 6),
"sensor_x": 40.0,
"target_type": "steady",
"u0": U0,
}
# -- Pure Pinball (uncontrolled baseline, steady/illusion positions) ---------
SCENES["pinball_baseline"] = {
"scene_id": "pinball_baseline",
"re_code": 100,
"nu": 0.004,
"has_disturbance": False,
"sample_interval": 800,
"action_scale": 8.0,
"action_bias": (0.0, -4.0, 4.0),
"source": "open_loop",
"n_objects_env": 6,
"obs_slice": (0, 12),
"sensor_x": 40.0,
"pinball_front_x": 30.0,
"pinball_rear_x": 31.3,
"target_type": "periodic",
"s_dim": 12,
"u0": U0,
}
# -- Disturbance Only (Karman inflow) ----------------------------------------
SCENES["disturbance_only"] = {
"scene_id": "disturbance_only",
"re_code": 100,
"nu": 0.004,
"has_disturbance": True,
"sample_interval": 800,
"source": "open_loop",
"n_objects_env": 4,
"obs_slice": (2, 8),
"sensor_x": 40.0,
"disturbance_x": 10.0,
"disturbance_radius": 1.0, # L0 units
"target_type": "periodic",
"u0": U0,
}
# -- Disturbance + Pinball (Karman q_blk) ------------------------------------
SCENES["karman_blk"] = {
"scene_id": "karman_blk",
"re_code": 100,
"nu": 0.004,
"has_disturbance": True,
"sample_interval": 800,
"action_scale": 8.0,
"action_bias": (0.0, -4.0, 4.0),
"source": "open_loop",
"n_objects_env": 7,
"obs_slice": (2, 14),
"sensor_x": 40.0,
"pinball_front_x": 30.0,
"pinball_rear_x": 31.3,
"target_type": "periodic",
"s_dim": 12,
"u0": U0,
}
# -- Steady Cloak (open-loop constant rotation) ------------------------------
SCENES["steady_cloak"] = {
"scene_id": "steady_cloak",
"re_code": 100,
"nu": 0.004,
"has_disturbance": False,
"sample_interval": 800,
"source": "open_loop",
"n_objects_env": 6,
"obs_slice": (0, 12),
"sensor_x": 40.0,
"pinball_front_x": 30.0,
"pinball_rear_x": 31.3,
"target_type": "steady",
"s_dim": 12,
"u0": U0,
"omega_front": 0.0,
"omega_rear_scale": 5.1,
}
# -- Karman Cloak re100 (PPO) ------------------------------------------------
SCENES["karman_re100"] = {
"scene_id": "karman",
"re_code": 100,
"nu": 0.004,
"has_disturbance": True,
"sample_interval": 800,
"action_scale": 8.0,
"action_bias": (0.0, -4.0, 4.0),
"source": "PPO_inference",
"model_name": "d1a3o12_re100",
"model_subdir": "old",
"n_objects_env": 7,
"obs_slice": (2, 14),
"sensor_x": 40.0,
"pinball_front_x": 30.0,
"pinball_rear_x": 31.3,
"target_type": "periodic",
"s_dim": 12,
"u0": U0,
}
# -- Illusion scenes (S_DIM=14, 3 diameters) ---------------------------------
_ILLUSION_SCENES = [
("illusion_0.75L", "d1a3o14_250525_imit_075L_2U_400S", 0.75, 400),
("illusion_1.0L", "d1a3o14_250525_imit_1L_2U_600S", 1.0, 600),
("illusion_1.5L", "d1a3o14_250525_imit_15L_2U", 1.5, 800),
]
for key, mn, diam, si in _ILLUSION_SCENES:
SCENES[key] = {
"scene_id": "illusion",
"target_diameter": diam,
"re_code": 100,
"nu": 0.004,
"has_disturbance": False,
"sample_interval": si,
"conv_len": CONV_LEN_ILLUSION,
"action_scale": 8.0,
"action_bias": (0.0, -2.0, 2.0),
"source": "PPO_inference",
"model_name": mn,
"model_subdir": "250525",
"n_objects_env": 6,
"obs_slice": (0, 12),
"sensor_x": 30.0,
"pinball_front_x": 19.0,
"pinball_rear_x": 20.3,
"target_type": "periodic",
"s_dim": 14,
"u0": U0,
}
# -- Target cylinders (for illusion comparison) ------------------------------
for diam, si in [(0.75, 400), (1.0, 600), (1.5, 800)]:
key = f"target_cylinder_{diam}L"
SCENES[key] = {
"scene_id": "target_cylinder",
"target_diameter": diam,
"re_code": 100,
"nu": 0.004,
"has_disturbance": False,
"sample_interval": si,
"conv_len": CONV_LEN_ILLUSION,
"source": "open_loop",
"n_objects_env": 4,
"obs_slice": (0, 8),
"sensor_x": 30.0,
"cylinder_x": 20.0,
"target_type": "periodic",
"u0": U0,
}
# -- Utility helpers ---------------------------------------------------------
def get_scene(name: str) -> dict:
if name not in SCENES:
raise KeyError(f"Unknown scene: {name}. Available: {list(SCENES.keys())}")
return dict(SCENES[name])
def get_scene_list(scene_id: Optional[str] = None) -> List[str]:
if scene_id is None:
return list(SCENES.keys())
return [k for k, v in SCENES.items() if v["scene_id"] == scene_id]
def model_path_for_scene(scene_name: str) -> Optional[str]:
"""Return full path to PPO model zip, or None if open-loop."""
cfg = get_scene(scene_name)
if cfg.get("source") != "PPO_inference":
return None
mn = cfg.get("model_name")
if mn is None:
return None
subdir = cfg.get("model_subdir", "old")
return os.path.join(MODEL_DIR, subdir, f"{mn}.zip")
def data_dir_for_scene(scene_name: str) -> str:
"""Return data directory for a given scene name.
Each scene config gets its OWN directory based on scene_name
to prevent filename collisions (different fields all save fields.npz).
"""
cfg = get_scene(scene_name)
sid = cfg["scene_id"]
# Steady group: q_in=q_in, q_blk=q_blk, q_ctl=q_ctl
if sid == "empty_channel":
return os.path.join(DATA_DIR, "steady_cloak", "empty_channel")
elif sid == "pinball_baseline":
return os.path.join(DATA_DIR, "steady_cloak", "pinball_baseline")
elif sid == "steady_cloak":
return os.path.join(DATA_DIR, "steady_cloak", "steady_cloak")
# Karman group: separate dirs for q_in, q_blk, q_ctl
elif sid == "disturbance_only":
return os.path.join(DATA_DIR, "karman_cloak", "disturbance_only")
elif sid == "karman_blk":
return os.path.join(DATA_DIR, "karman_cloak", "karman_blk")
elif sid == "karman":
return os.path.join(DATA_DIR, "karman_cloak", "karman_re100")
# Target cylinders get their own dirs under target_cylinders
elif sid == "target_cylinder":
diam = cfg["target_diameter"]
# Preserve one decimal: 0.75->"0.75", 1.0->"1.0", 1.5->"1.5"
ds = f"{diam:.1f}" if diam == int(diam) else f"{diam:.2f}".rstrip("0")
return os.path.join(DATA_DIR, "target_cylinder", f"target_cylinder_{ds}L")
# Illusion scenes
elif sid == "illusion":
return os.path.join(DATA_DIR, "illusion", scene_name)
else:
return os.path.join(DATA_DIR, scene_name)

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{
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38
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{
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{
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{
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22
src/OID_analysis/data/derived/comparison/steady_cloak.json Normal file
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{
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{
"scenes": [
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"karman_re100",
"illusion_0.75L",
"illusion_1.0L",
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],
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11
src/OID_analysis/data/derived/observables/illusion_0.75L/meta.json Normal file
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11
src/OID_analysis/data/derived/observables/illusion_1.0L/meta.json Normal file
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11
src/OID_analysis/data/derived/observables/illusion_1.5L/meta.json Normal file
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11
src/OID_analysis/data/derived/observables/karman_re100/meta.json Normal file
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10
src/OID_analysis/data/derived/observables/steady_cloak/meta.json Normal file
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14
src/OID_analysis/data/derived/pod/illusion_0.75L/summary.json Normal file
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14
src/OID_analysis/data/derived/pod/illusion_1.0L/summary.json Normal file
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14
src/OID_analysis/data/derived/pod/illusion_1.5L/summary.json Normal file
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14
src/OID_analysis/data/derived/pod/karman_re100/summary.json Normal file
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14
src/OID_analysis/data/derived/pod/steady_cloak/summary.json Normal file
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108
src/OID_analysis/data/derived/robustness/robustness_results.json Normal file
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50
src/OID_analysis/data/derived/steady_metrics/steady_reanalysis.json Normal file
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6
src/OID_analysis/data/derived/whitebox/karman_re100.json Normal file
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src/OID_analysis/data/derived/whitebox/steady_cloak.json Normal file
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22
src/OID_analysis/data/illusion/illusion_0.75L/config.json Normal file
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19
src/OID_analysis/data/illusion/illusion_0.75L/norm.json Normal file
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8
src/OID_analysis/data/illusion/illusion_0.75L/replay_verify.json Normal file
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5
src/OID_analysis/data/illusion/illusion_0.75L/result.json Normal file
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src/OID_analysis/data/illusion/illusion_0.75L/roi_meta.json Normal file
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194
src/OID_analysis/data/illusion/illusion_0.75L/target_harmonics.json Normal file
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src/OID_analysis/data/illusion/illusion_1.0L/config.json Normal file
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19
src/OID_analysis/data/illusion/illusion_1.0L/norm.json Normal file
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8
src/OID_analysis/data/illusion/illusion_1.0L/replay_verify.json Normal file
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src/OID_analysis/data/illusion/illusion_1.0L/result.json Normal file
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src/OID_analysis/data/illusion/illusion_1.0L/roi_meta.json Normal file
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194
src/OID_analysis/data/illusion/illusion_1.0L/target_harmonics.json Normal file
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src/OID_analysis/data/illusion/illusion_1.5L/config.json Normal file
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src/OID_analysis/data/illusion/illusion_1.5L/norm.json Normal file
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src/OID_analysis/data/illusion/illusion_1.5L/replay_verify.json Normal file
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src/OID_analysis/data/illusion/illusion_1.5L/result.json Normal file
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src/OID_analysis/data/illusion/illusion_1.5L/roi_meta.json Normal file
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src/OID_analysis/data/illusion/illusion_1.5L/target_harmonics.json Normal file
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src/OID_analysis/data/karman_cloak/disturbance_only/config.json Normal file
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src/OID_analysis/data/karman_cloak/karman_blk/config.json Normal file
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src/OID_analysis/data/karman_cloak/karman_blk/norm.json Normal file
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src/OID_analysis/data/karman_cloak/karman_re100/config.json Normal file
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src/OID_analysis/data/karman_cloak/karman_re100/norm.json Normal file
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src/OID_analysis/data/karman_cloak/karman_re100/result.json Normal file
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8
src/OID_analysis/data/karman_cloak/karman_re100/roi_meta.json Normal file
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src/OID_analysis/data/steady_cloak/pinball_baseline/norm.json Normal file
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src/OID_analysis/data/steady_cloak/pinball_baseline_illusion/config.json Normal file
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src/OID_analysis/data/steady_cloak/pinball_baseline_illusion/norm.json Normal file
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src/OID_analysis/data/steady_cloak/steady_cloak/config_ctl.json Normal file
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1
src/OID_analysis/data/target_cylinder/target_cylinder_0.75L/config.json Symbolic link
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src/OID_analysis/data/target_cylinder/target_cylinder_0.75L/target_harmonics.json Normal file
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1
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src/OID_analysis/data/target_cylinder/target_cylinder_1.5L/config.json Symbolic link
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/home/frank14f/DynamisLab/src/CCD_analysis/data/target_cylinder/target_cylinder_1.5L/meta.json

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src/OID_analysis/scripts/collect_all_data.py Normal file
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# OID_analysis/scripts/collect_all_data.py
"""
Batch data collection for all OID scenes.
Handles baseline + controlled + target scenes efficiently.
Usage:
# Check all data (no GPU needed)
python3 src/OID_analysis/scripts/collect_all_data.py --check
# Collect all data (GPU required)
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_all_data.py \
--device-steady 3 --device-karman 1 --device-illusion 3
"""
from __future__ import annotations
import argparse
import json
import os
import subprocess
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 OID_analysis.configs import ( # noqa: E402
get_scene, data_dir_for_scene, model_path_for_scene,
SCENES, DATA_DIR,
)
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
def check_data() -> bool:
"""Check which data files exist."""
all_ok = True
required_files = {
"empty_channel": ["fields.npz", "sensors.npz"],
"pinball_baseline": ["fields.npz", "sensors.npz", "forces.npz", "norm.json"],
"disturbance_only": ["fields.npz", "sensors.npz", "target.npz"],
"karman_blk": ["fields.npz", "sensors.npz", "forces.npz", "norm.json"],
"karman_re100": ["fields.npz", "controlled.npz", "norm.json"],
"steady_cloak": ["fields.npz", "sensors.npz", "forces.npz"],
"illusion_0.75L": ["fields.npz", "controlled.npz", "norm.json"],
"illusion_1.0L": ["fields.npz", "controlled.npz", "norm.json"],
"illusion_1.5L": ["fields.npz", "controlled.npz", "norm.json"],
"target_cylinder_0.75L": ["fields.npz", "target.npz", "target_harmonics.json"],
"target_cylinder_1.0L": ["fields.npz", "target.npz", "target_harmonics.json"],
"target_cylinder_1.5L": ["fields.npz", "target.npz", "target_harmonics.json"],
}
for scene_name, files in required_files.items():
dd = data_dir_for_scene(scene_name)
missing = [f for f in files if not os.path.isfile(os.path.join(dd, f))]
if missing:
print(f" MISSING [{scene_name}]: {missing}")
all_ok = False
else:
sizes = {f: os.path.getsize(os.path.join(dd, f)) for f in files}
total_mb = sum(sizes.values()) / 1e6
print(f" OK [{scene_name:30s}] {total_mb:.0f} MB {list(sizes.keys())}")
return all_ok
def run_script(script_name: str, args: list, description: str = ""):
"""Run a collection script."""
script_path = os.path.join(SCRIPT_DIR, script_name)
cmd = [sys.executable, script_path] + [str(a) for a in args]
print(f"\n{'='*60}")
print(f" {description}")
print(f" {' '.join(cmd)}")
print(f"{'='*60}")
t0 = time.time()
result = subprocess.run(cmd, capture_output=False, text=True)
elapsed = time.time() - t0
if result.returncode != 0:
print(f" FAILED (return code {result.returncode}, {elapsed:.0f}s)")
else:
print(f" DONE ({elapsed:.0f}s)")
return result.returncode
def collect_all(device_steady: int, device_karman: int, device_illusion: int):
"""Run all collection scripts."""
errors = 0
# === STEADY GROUP ===
# Step 1: Empty channel (q_in for steady/illusion)
dd = data_dir_for_scene("empty_channel")
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_empty_channel.py",
["--device", device_steady, "--steps", "200"],
"Empty channel (q_in for steady/illusion)")
# Step 2: Pinball baseline (q_blk for steady/illusion)
dd = data_dir_for_scene("pinball_baseline")
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_pinball_baseline.py",
["--device", device_steady, "--steps", "500"],
"Pinball baseline (q_blk for steady/illusion)")
# === KARMAN GROUP ===
# Step 3: Disturbance only (q_in for Karman)
dd = data_dir_for_scene("disturbance_only")
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_disturbance_only.py",
["--device", device_karman, "--steps", "500"],
"Disturbance only (q_in for Karman)")
# Step 4: Karman blk (dist+pinball zero, q_blk for Karman)
dd = data_dir_for_scene("karman_blk")
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_karman_blk.py",
["--device", device_karman, "--steps", "500"],
"Karman blk (dist+pinball zero, q_blk for Karman)")
# Step 5: Karman controlled (PPO, q_ctl)
dd = data_dir_for_scene("karman_re100")
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_fields_replay.py",
["--scene", "karman_re100", "--device", device_karman],
"Karman PPO replay (q_ctl)")
# === STEADY CLOAK ===
dd = data_dir_for_scene("steady_cloak")
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_steady_cloak.py",
["--device", device_steady, "--steps", "500", "--omega-rear", "5.1"],
"Steady cloak (q_ctl)")
# === ILLUSION GROUP ===
for diam_name in ["illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]:
# Target cylinder
tgt_cfg = SCENES.get(f"target_cylinder_{diam_name.replace('illusion_', '')}")
tgt_dd = data_dir_for_scene(f"target_cylinder_{diam_name.replace('illusion_', '')}")
# Check if target data exists in CCD
ccd_target = os.path.join(_SRC, "CCD_analysis", "data", "target_cylinder",
f"target_cylinder_{diam_name.replace('illusion_', '')}")
if not os.path.isfile(os.path.join(tgt_dd, "fields.npz")) and \
not os.path.isfile(os.path.join(tgt_dd, "fields_aligned.npz")):
errors += run_script("collect_target_cylinder.py",
["--diameter", str(SCENES[diam_name]["target_diameter"]),
"--device", device_illusion, "--steps", "500"],
f"Target cylinder {diam_name}")
# PPO fields replay
dd = data_dir_for_scene(diam_name)
if not os.path.isfile(os.path.join(dd, "fields.npz")):
errors += run_script("collect_fields_replay.py",
["--scene", diam_name, "--device", device_illusion],
f"Illusion {diam_name} PPO replay (q_ctl)")
# === CHECK ===
print(f"\n{'='*60}")
print(f"All collections complete. Errors: {errors}")
print(f"{'='*60}")
check_data()
return errors
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--check", action="store_true", help="Only check data, no collection")
ap.add_argument("--device-steady", type=int, default=3)
ap.add_argument("--device-karman", type=int, default=1)
ap.add_argument("--device-illusion", type=int, default=3)
args = ap.parse_args()
if args.check:
ok = check_data()
print(f"\nAll data present: {ok}")
return 0 if ok else 1
return collect_all(args.device_steady, args.device_karman, args.device_illusion)
if __name__ == "__main__":
sys.exit(main())

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src/OID_analysis/scripts/collect_baseline_forces.py Normal file
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# OID_analysis/scripts/collect_baseline_forces.py
"""
Quick collection of missing baseline forces and norm data.
For scenes where fields already exist but forces/norm are missing.
Usage:
# pinball_baseline forces (needed for steady+illusion baselines)
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_baseline_forces.py \
--scene pinball_baseline --device 3
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
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
from OID_analysis.configs import get_scene, data_dir_for_scene, LEGACY_CFG_DIR
from OID_analysis.utils.cfd_interface import load_legacy_configs
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
FIFO_LEN = 150
def collect_pinball_forces(device_id: int, n_steps: int):
"""Collect forces.npz and norm.json for pinball_baseline."""
cfg = get_scene("pinball_baseline")
u0 = cfg["u0"]
si = cfg["sample_interval"]
action_bias = cfg["action_bias"]
out_dir = data_dir_for_scene("pinball_baseline")
has_forces = os.path.isfile(os.path.join(out_dir, "forces.npz"))
has_norm = os.path.isfile(os.path.join(out_dir, "norm.json"))
if has_forces and has_norm:
print(f" Both forces.npz and norm.json exist, skipping")
return
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
# Build env
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
ff.add_cylinder((30.0 * L0, CENTER_Y, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y - 0.75 * L0, 0.0), L0 / 2.0)
n_obj = 6
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising {n_stab} steps...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
if not has_norm:
# Norm collection
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"action_bias": list(action_bias),
}
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(norm, f, indent=2)
print(f" norm: force_norm_fact={force_norm_fact:.6f}")
if not has_forces:
forces = []
print(f" Recording {n_steps} steps of forces...")
for step in range(n_steps):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[0:12]
forces.append(obs_slice[6:12])
np.savez(os.path.join(out_dir, "forces.npz"),
forces=np.array(forces, dtype=np.float32))
print(f" Forces saved: {len(forces)} steps")
del ff
print(f" Done")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, default="pinball_baseline")
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
if args.scene == "pinball_baseline":
t0 = time.time()
collect_pinball_forces(args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s")
else:
print(f"Unknown scene: {args.scene}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_controlled.py
"""
Collect DRL-controlled rollout (q_ctl) for Karman cloak and illusion scenes.
Generates field time series for Delta-q_ctl computation.
Usage:
# Karman:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_controlled.py \
--scene karman_re100 --device 1 --steps 500
# Illusion (3 diameters):
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_controlled.py \
--scene illusion_1.0L --device 3 --steps 500
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_controlled.py \
--scene illusion_0.75L --device 3 --steps 500
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_controlled.py \
--scene illusion_1.5L --device 3 --steps 500
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
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, save_vorticity_png, vorticity_from_ddf,
load_ppo_model, scale_action, build_observation, compute_similarity,
calc_lag, calc_dtw_sim, analyze_harmonics, gen_target_states_at,
)
from OID_analysis.configs import ( # noqa: E402
get_scene, get_scene_list, model_path_for_scene, LEGACY_CFG_DIR,
)
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
FIFO_LEN = 150
CONV_LEN_DEFAULT = 30
CONV_LEN_ILLUSION = 36
def collect_single(scene_name: str, device_id: int, n_steps: int) -> dict:
cfg = get_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"]
s_dim = cfg["s_dim"]
source = cfg.get("source", "")
out_dir = data_dir_for_scene(scene_name)
os.makedirs(out_dir, exist_ok=True)
# Check DDF+FIFO checkpoint exists
ddf_ckpt_path = os.path.join(out_dir, "ddf_checkpoint.npy")
fifo_ckpt_path = os.path.join(out_dir, "fifo_checkpoint.npy")
has_blk = os.path.isfile(ddf_ckpt_path) and os.path.isfile(fifo_ckpt_path)
# Load legacy configs
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
field_cfg = field_cfg._replace(viscosity=float(cfg["nu"]), velocity=float(u0))
if not has_blk:
print(f" No DDF checkpoint found, building env from scratch ...")
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
if cfg["has_disturbance"]:
# Karman layout: dist_cyl first
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)
n_phase1 = 4
ff.run(int(4 * 1280 / u0), np.zeros(n_phase1, dtype=DATA_TYPE))
else:
# Illusion layout: sensors first
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)
# Add pinball
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)
ff.run(int(4 * 1280 / u0), np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# Norm
obs_slice_start = cfg["obs_slice"][0]
obs_slice_end = cfg["obs_slice"][1]
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[obs_slice_start:obs_slice_end])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
norm = {"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist()}
# Preset-action FIFO (matches legacy env)
ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
if cfg["has_disturbance"]:
bias_arr[4] = -4.0 * u0
bias_arr[5] = 4.0 * u0
else:
bias_arr[4] = -1.0 * u0
bias_arr[5] = 1.0 * u0
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(si, bias_arr)
fifo.append(ff.obs.copy()[obs_slice_start:obs_slice_end])
save_states_arr = np.array(fifo, dtype=DATA_TYPE)
# Save checkpoint
ff.get_ddf()
np.save(ddf_ckpt_path, ff.ddf)
np.save(fifo_ckpt_path, save_states_arr)
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(norm, f, indent=2)
print(f" Checkpoint saved to {out_dir}")
else:
print(f" Loading DDF+FIFO checkpoint from {out_dir}")
# Load norm
with open(os.path.join(out_dir, "norm.json")) as f:
norm = json.load(f)
# Rebuild env to get a fresh FlowField
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
if cfg["has_disturbance"]:
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)
# Restore DDF
ff.ddf = np.load(ddf_ckpt_path)
ff.apply_ddf()
print(f" DDF checkpoint restored")
# Save config
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg.items()}, f, indent=2)
# ---- Target signals (needed for s_dim=14 illusion) ----
target_states = None
target_harmonics = None
if cfg_sid == "illusion":
target_path = os.path.join(out_dir, "target.npz")
harm_path = os.path.join(out_dir, "target_harmonics.json")
if os.path.isfile(target_path) and os.path.isfile(harm_path):
target_data = np.load(target_path)
target_states = target_data["target_states"]
with open(harm_path) as f:
target_harmonics = json.load(f)
print(f" Target loaded: {target_states.shape}")
else:
print(f" WARNING: no target found at {target_path}")
# ---- PPO inference ----
obs_slice_start = cfg["obs_slice"][0]
obs_slice_end = cfg["obs_slice"][1]
# Load checkpoint FIFO state
load_state = np.load(fifo_ckpt_path)
fifo = deque(maxlen=FIFO_LEN)
for s in load_state:
fifo.append(s)
model_path = model_path_for_scene(scene_name)
if model_path is None:
raise ValueError(f"No model path for {scene_name}")
print(f" Loading model: {model_path} (s_dim={s_dim})")
model = load_ppo_model(model_path, device=f"cuda:{device_id}", s_dim=s_dim, a_dim=3)
model.set_random_seed(19)
obs = np.zeros(s_dim, dtype=np.float32)
sens_c, forc_c, act_c, ux_list, uy_list = [], [], [], [], []
for step in range(n_steps):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
act_c.append(action.copy())
# Build omega array
temp = np.zeros(n_obj, dtype=DATA_TYPE)
omega = (action * ac_scale + np.array(ac_bias, dtype=np.float32)) * u0
temp[n_obj - 3:] = omega
ff.context.push()
ff.run(si, temp)
ff.context.pop()
obs_slice = ff.obs.copy()[obs_slice_start:obs_slice_end]
fifo.append(obs_slice)
sens_c.append(obs_slice[0:6])
forc_c.append(obs_slice[6:12])
# Build next observation
forces_norm = obs_slice[6:12] / norm["force_norm_fact"]
sens_norm = (obs_slice[0:6] - norm["sens_deviation"]) / norm["sens_norm_fact"]
if s_dim == 14 and target_harmonics is not None:
target_vals = gen_target_states_at(step, target_harmonics)
t_cd_n = float(target_vals[0]) / norm["force_norm_fact"]
t_cl_n = float(target_vals[1]) / norm["force_norm_fact"]
obs = np.clip(np.hstack([forces_norm, sens_norm, t_cd_n, t_cl_n]), -1.0, 1.0).astype(np.float32)
else:
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
# Save field every step (for Delta-q_ctl POD)
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
# Save
sens_arr = np.array(sens_c, dtype=np.float32)
forc_arr = np.array(forc_c, dtype=np.float32)
act_arr = np.array(act_c, dtype=np.float32)
# Compute similarity
conv_len = cfg.get("conv_len", CONV_LEN_DEFAULT)
if target_states is not None:
if cfg_sid == "karman":
sim = compute_similarity(target_states, sens_arr, conv_len)
elif cfg_sid == "illusion":
# For illusion, target_states[:, 2:8] has the sensor references
target_sensors = target_states[:, 2:8] if target_states.shape[1] >= 8 else target_states
sim = compute_similarity(target_sensors, sens_arr, conv_len)
else:
sim = 0.0
print(f" similarity = {sim:.4f}")
np.savez(os.path.join(out_dir, "controlled.npz"),
sensors=sens_arr, forces=forc_arr, actions=act_arr)
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
omega_viz = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_controlled.png"),
omega_viz, title=f"{scene_name} controlled")
result = {"scene": scene_name, "n_steps": n_steps,
"similarity": float(sim) if target_states is not None else 0.0}
with open(os.path.join(out_dir, "result.json"), "w") as f:
json.dump(result, f, indent=2)
del ff, model
print(f" Saved {n_steps} snapshots to {out_dir}")
return result
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, required=True,
help="Scene name: karman_re100, illusion_0.75L, illusion_1.0L, illusion_1.5L")
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
all_scenes = get_scene_list()
if args.scene not in all_scenes:
print(f"Unknown scene: {args.scene}. Available PPO scenes: "
f"{[s for s in all_scenes if get_scene(s).get('source') == 'PPO_inference']}")
return 1
t0 = time.time()
r = collect_single(args.scene, args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s: sim={r.get('similarity', 0):.4f}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_disturbance_only.py
"""
Collect disturbance-only flow (q_in for Karman cloak).
Upstream cylinder generates Karman vortex street, no pinball.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_disturbance_only.py \
--device 1 --steps 500
"""
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, save_vorticity_png, vorticity_from_ddf,
)
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
SAMPLE_INTERVAL = 800
def collect(device_id: int, n_steps: int) -> str:
cfg = get_scene("disturbance_only")
u0 = cfg["u0"]
si = cfg["sample_interval"]
out_dir = data_dir_for_scene("disturbance_only")
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Disturbance only: {ff.FIELD_SHAPE}")
# Add dist cylinder (radius=L0=20) at x=10*L0
ff.add_cylinder((10.0 * L0, CENTER_Y, 0.0), L0)
# Add 3 sensors at x=40*L0
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
n_obj = 4
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising {n_stab} steps ...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# Record target signals (sensor only: obs[2:8])
target_states = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(150):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
target_states = np.vstack((target_states, ff.obs.copy()[2:8]))
np.savez(os.path.join(out_dir, "target.npz"), target_states=target_states)
print(f" Target recorded: {target_states.shape}")
# Record full rollout
ff.apply_ddf()
sens_list, ux_list, uy_list = [], [], []
for step in range(n_steps):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[2:8]
sens_list.append(obs_slice)
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
omega = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_disturbance_only.png"),
omega, title="Disturbance cylinder (Karman inflow)")
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg.items()}, f, indent=2)
del ff
print(f" Saved {n_steps} snapshots to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=1)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
t0 = time.time()
out = collect(args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_empty_channel.py
"""
Collect empty channel reference (q_in for steady cloak and illusion scenes).
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_empty_channel.py \
--device 3 --steps 200
"""
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, save_vorticity_png, vorticity_from_ddf,
)
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
SAMPLE_INTERVAL = 800
def collect(device_id: int, n_steps: int) -> str:
cfg = get_scene("empty_channel")
u0 = cfg["u0"]
out_dir = data_dir_for_scene("empty_channel")
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Empty channel: {ff.FIELD_SHAPE}")
# Add 3 sensors (matching legacy cloak env layout)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
# Stabilize
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising {n_stab} steps ...")
ff.run(n_stab, np.zeros(3, dtype=DATA_TYPE))
# Record
sens_list, ux_list, uy_list = [], [], []
for step in range(n_steps):
ff.run(SAMPLE_INTERVAL, np.zeros(3, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[0:6]
sens_list.append(obs_slice)
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
omega = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_empty_channel.png"),
omega, title="Empty channel")
# Save config
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg.items()}, f, indent=2)
del ff
print(f" Saved {n_steps} snapshots to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=200)
args = ap.parse_args()
t0 = time.time()
out = collect(args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_fields_replay.py
"""
Replay PPO actions from CCD checkpoints and save full field time series.
This is the most efficient way to get full field data without re-running PPO.
Also copies/linked controlled.npz, target.npz, norm.json from CCD.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_fields_replay.py \
--scene karman_re100 --device 1
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_fields_replay.py \
--scene illusion_1.0L --device 3
"""
from __future__ import annotations
import argparse
import json
import os
import shutil
import sys
import time
from collections import deque
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, save_vorticity_png, vorticity_from_ddf,
)
from OID_analysis.configs import ( # noqa: E402
get_scene, get_scene_list, data_dir_for_scene, LEGACY_CFG_DIR,
)
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
FIFO_LEN = 150
# Source directory in CCD
CCD_DATA = os.path.join(_SRC, "CCD_analysis", "data")
def ccd_data_dir(scene_name: str) -> str:
cfg = get_scene(scene_name)
sid = cfg["scene_id"]
if sid == "karman":
return os.path.join(CCD_DATA, "karman", "karman_re100")
elif sid == "illusion":
# CCD stores at: data/illusion/illusion_0.75L/ etc
return os.path.join(CCD_DATA, "illusion", scene_name)
return None
def build_env(cfg, cuda_cfg, field_cfg, device_id):
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
u0 = cfg["u0"]
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)
n_phase1 = 4
ff.run(int(4 * 1280 / u0), np.zeros(n_phase1, dtype=DATA_TYPE))
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 replay(scene_name: str, device_id: int):
cfg = get_scene(scene_name)
out_dir = data_dir_for_scene(scene_name)
os.makedirs(out_dir, exist_ok=True)
src_dir = ccd_data_dir(scene_name)
if src_dir is None or not os.path.isdir(src_dir):
raise FileNotFoundError(f"CCD data not found for {scene_name} at {src_dir}")
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"]
# Copy/symlink existing data
for fname in ["controlled.npz", "target.npz", "norm.json", "config.json",
"target_harmonics.json", "result.json"]:
src = os.path.join(src_dir, fname)
dst = os.path.join(out_dir, fname)
if os.path.isfile(src) and not os.path.isfile(dst):
shutil.copy2(src, dst)
print(f" Copied {fname}")
# Load actions from controlled.npz
controlled = np.load(os.path.join(src_dir, "controlled.npz"))
actions = controlled["actions"]
n_steps = len(actions)
print(f" {n_steps} actions loaded")
# Load checkpoints
ddf_ckpt = np.load(os.path.join(src_dir, "ddf_checkpoint.npy"))
fifo_ckpt = np.load(os.path.join(src_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()
print(f" DDF restored ({len(ddf_ckpt)} floats)")
# Restore FIFO
fifo = deque(maxlen=FIFO_LEN)
for s in fifo_ckpt:
fifo.append(s)
# Replay and save fields
# Use masked ROI: x=[400, 1000] (20-50 L0), y=[100, 400] (full relevant y)
# This captures near-body + near-wake + sensor zone
nx_full = 1280
ny_full = 512
x_start, x_end = 400, 1000
y_start, y_end = 100, 400
nx_roi = x_end - x_start
ny_roi = y_end - y_start
print(f" ROI: x=[{x_start},{x_end}], y=[{y_start},{y_end}] ({nx_roi}x{ny_roi})")
ux_list, uy_list = [], []
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])
# Save cropped field
ux_full, uy_full = get_velocity_field(ff, u0=u0)
ux_list.append(ux_full[y_start:y_end, x_start:x_end])
uy_list.append(uy_full[y_start:y_end, x_start:x_end])
# Verify replay fidelity
orig_sensors = controlled["sensors"]
orig_forces = controlled["forces"]
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: max diff sensors={diff_sens:.6e}, forces={diff_forc:.6e}")
# Save fields
ux_arr = np.stack(ux_list).astype(np.float32)
uy_arr = np.stack(uy_list).astype(np.float32)
print(f" Fields shape: {ux_arr.shape}, size ~{ux_arr.nbytes/1e6:.1f} MB")
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=ux_arr, uy=uy_arr)
# Save ROI metadata
roi_meta = {"x_start": x_start, "x_end": x_end,
"y_start": y_start, "y_end": y_end,
"nx_full": nx_full, "ny_full": ny_full}
with open(os.path.join(out_dir, "roi_meta.json"), "w") as f:
json.dump(roi_meta, f, indent=2)
# Vorticity viz
omega_viz = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_controlled.png"),
omega_viz, title=f"{scene_name} controlled")
# Replay verify report
verify_info = {
"scene": scene_name,
"n_steps": n_steps,
"n_fields": len(ux_list),
"max_diff_sensors": diff_sens,
"max_diff_forces": diff_forc,
"passed": diff_sens < 1e-4 and diff_forc < 1e-4,
}
with open(os.path.join(out_dir, "replay_verify.json"), "w") as f:
json.dump(verify_info, f, indent=2)
del ff, controlled
print(f" {n_steps} fields saved to {out_dir}")
return n_steps
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--scene", type=str, required=True,
help="Scene name: karman_re100, illusion_0.75L, illusion_1.0L, illusion_1.5L")
ap.add_argument("--device", type=int, default=3)
args = ap.parse_args()
t0 = time.time()
n = replay(args.scene, args.device)
print(f"Done in {time.time() - t0:.1f}s -> {n} fields")
if __name__ == "__main__":
main()

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src/OID_analysis/scripts/collect_illusion_qblk.py Normal file
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# OID_analysis/scripts/collect_illusion_qblk.py
"""
Collect illusion-position pinball baseline (q_blk for illusion scenes).
Pinball at illusion geometry (front_x=19, rear_x=20.3), sensors at x=30.
Zero rotation, natural shedding.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_illusion_qblk.py \
--device 3 --steps 500
Output: data/steady_cloak/pinball_baseline_illusion/
fields.npz, sensors.npz, forces.npz, norm.json, ddf_checkpoint.npy
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
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, save_vorticity_png, vorticity_from_ddf,
)
from OID_analysis.configs import LEGACY_CFG_DIR # noqa: E402
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
U0 = 0.01
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
def collect(device_id: int, n_steps: int) -> str:
out_dir = os.path.join(os.path.dirname(__file__), "..", "data",
"steady_cloak", "pinball_baseline_illusion")
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Illusion q_blk: {ff.FIELD_SHAPE}")
# --- Illusion geometry ---
# Sensors at x=30*L0 (different from cloak's x=40)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((30.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
# Pinball at illusion positions (front_x=19, rear_x=20.3)
ff.add_cylinder((19.0 * L0, CENTER_Y, 0.0), L0 / 2.0)
ff.add_cylinder((20.3 * L0, CENTER_Y + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((20.3 * L0, CENTER_Y - 0.75 * L0, 0.0), L0 / 2.0)
n_obj = 6
n_stab = int(4 * ff.FIELD_SHAPE[0] / U0)
print(f" Stabilising {n_stab} steps ...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# --- Norm collection (zero action) ---
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"action_bias": list([0.0, -2.0, 2.0]), # illusion bias for reference
}
print(f" norm: force_norm_fact={force_norm_fact:.6f}")
# --- Bias-action FIFO init (matches legacy_env_imit.py: [0, -U0, U0]) ---
ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[3] = 0.0
bias_arr[4] = -1.0 * U0
bias_arr[5] = 1.0 * U0
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states_arr = np.array(fifo, dtype=DATA_TYPE)
# Save DDF+FIFO checkpoint (for future PPO controlled replay)
ff.get_ddf()
np.save(os.path.join(out_dir, "ddf_checkpoint.npy"), ff.ddf)
np.save(os.path.join(out_dir, "fifo_checkpoint.npy"), save_states_arr)
ff.apply_ddf()
# --- Record zero-action rollout (PAYLOAD) ---
sens_list, forc_list, ux_list, uy_list = [], [], [], []
for step in range(n_steps):
ff.run(SAMPLE_INTERVAL, np.zeros(n_obj, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[0:12]
sens_list.append(obs_slice[0:6])
forc_list.append(obs_slice[6:12])
ux, uy = get_velocity_field(ff, u0=U0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez(os.path.join(out_dir, "forces.npz"),
forces=np.array(forc_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list).astype(np.float32),
uy=np.stack(uy_list).astype(np.float32))
# Save norm (non-numpy values)
norm_json = {k: v for k, v in norm.items() if not isinstance(v, np.ndarray)}
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(norm_json, f, indent=2)
# Config metadata
meta = {
"scene": "pinball_baseline_illusion",
"geometry": "illusion",
"sensor_x": 30.0,
"pinball_front_x": 19.0,
"pinball_rear_x": 20.3,
"n_steps": n_steps,
"sample_interval": SAMPLE_INTERVAL,
"u0": U0,
}
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump(meta, f, indent=2)
# Vorticity plot
omega = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(os.path.join(out_dir, "vorticity_illusion_qblk.png"),
omega, title="Illusion q_blk (zero rotation, illusion position)")
del ff
print(f" Saved {n_steps} snapshots ({ux_list[0].shape}) to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
t0 = time.time()
out = collect(args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_karman_blk.py
"""
Collect disturbance+pinball fixed (q_blk for Karman cloak).
Disturbance cylinder + pinball present, zero rotation.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_karman_blk.py \
--device 1 --steps 500
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
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, save_vorticity_png, vorticity_from_ddf,
)
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
def collect(device_id: int, n_steps: int) -> str:
cfg = get_scene("karman_blk")
u0 = cfg["u0"]
si = cfg["sample_interval"]
action_bias = cfg["action_bias"]
out_dir = data_dir_for_scene("karman_blk")
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Karman blk: {ff.FIELD_SHAPE}")
# Phase 1: dist_cylinder + 3 sensors
ff.add_cylinder((10.0 * L0, CENTER_Y, 0.0), L0)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
n_phase1 = 4
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising phase 1 ({n_stab} steps)...")
ff.run(n_stab, np.zeros(n_phase1, dtype=DATA_TYPE))
# Phase 2: add 3 pinball cylinders
ff.add_cylinder((30.0 * L0, CENTER_Y, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y - 0.75 * L0, 0.0), L0 / 2.0)
n_obj = 7
print(f" Stabilising phase 2 ({n_stab} steps)...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# Norm collection (zero action, cloak obs[2:14])
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[2:14])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"action_bias": list(action_bias),
}
print(f" norm: force_norm_fact={force_norm_fact:.6f}")
# Bias-action FIFO init (cloak: [0, -4*U0, 4*U0])
ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[4] = -4.0 * u0 # front=0, bottom=-4U0, top=4U0
bias_arr[5] = 4.0 * u0
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(si, bias_arr)
fifo.append(ff.obs.copy()[2:14])
save_states_arr = np.array(fifo, dtype=DATA_TYPE)
# Save DDF+FIFO checkpoint
ff.get_ddf()
np.save(os.path.join(out_dir, "ddf_checkpoint.npy"), ff.ddf)
np.save(os.path.join(out_dir, "fifo_checkpoint.npy"), save_states_arr)
ff.apply_ddf()
# Record zero-action rollout (PAYLOAD: q_blk)
sens_list, forc_list, ux_list, uy_list = [], [], [], []
for step in range(n_steps):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[2:14]
sens_list.append(obs_slice[0:6])
forc_list.append(obs_slice[6:12])
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez(os.path.join(out_dir, "forces.npz"),
forces=np.array(forc_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
norm_json = {k: v for k, v in norm.items() if not isinstance(v, np.ndarray)}
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(norm_json, f, indent=2)
omega = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_karman_blk.png"),
omega, title="Karman q_blk (zero rotation)")
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg.items()}, f, indent=2)
del ff
print(f" Saved {n_steps} snapshots to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=1)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
t0 = time.time()
out = collect(args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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src/OID_analysis/scripts/collect_pinball_baseline.py Normal file
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# OID_analysis/scripts/collect_pinball_baseline.py
"""
Collect fixed pinball baseline (q_blk for steady cloak and illusion).
Pinball present, zero rotation, natural shedding.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_pinball_baseline.py \
--device 3 --steps 500
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
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, save_vorticity_png, vorticity_from_ddf,
)
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
def collect(device_id: int, n_steps: int) -> str:
cfg = get_scene("pinball_baseline")
u0 = cfg["u0"]
si = cfg["sample_interval"]
action_bias = cfg["action_bias"]
out_dir = data_dir_for_scene("pinball_baseline")
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Pinball baseline: {ff.FIELD_SHAPE}")
# Add 3 sensors
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
# Add 3 pinball cylinders (cloak/steady positions)
ff.add_cylinder((30.0 * L0, CENTER_Y, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y - 0.75 * L0, 0.0), L0 / 2.0)
n_obj = 6
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising {n_stab} steps ...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
ff.get_ddf()
ff.save_ddf()
# Norm collection (zero action)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_deviation = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_deviation[i])))
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"action_bias": list(action_bias),
}
print(f" norm: force_norm_fact={force_norm_fact:.6f}")
# Bias-action FIFO init (cloak: [0, -4*U0, 4*U0])
ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[3] = 0.0
bias_arr[4] = -4.0 * u0
bias_arr[5] = 4.0 * u0
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(si, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states_arr = np.array(fifo, dtype=DATA_TYPE)
# Save DDF+FIFO checkpoint (for controlled replay)
ff.get_ddf()
np.save(os.path.join(out_dir, "ddf_checkpoint.npy"), ff.ddf)
np.save(os.path.join(out_dir, "fifo_checkpoint.npy"), save_states_arr)
ff.apply_ddf()
# Record zero-action rollout (PAYLOAD)
sens_list, forc_list, ux_list, uy_list = [], [], [], []
for step in range(n_steps):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[0:12]
sens_list.append(obs_slice[0:6])
forc_list.append(obs_slice[6:12])
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez(os.path.join(out_dir, "forces.npz"),
forces=np.array(forc_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
norm_json = {k: v for k, v in norm.items() if not isinstance(v, np.ndarray)}
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(norm_json, f, indent=2)
omega = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_pinball_baseline.png"),
omega, title="Pinball baseline (zero rotation)")
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg.items()}, f, indent=2)
del ff
print(f" Saved {n_steps} snapshots to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
t0 = time.time()
out = collect(args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_steady_cloak.py
"""
Collect steady cloak open-loop (q_ctl). Constant rear-cylinder rotation.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_steady_cloak.py \
--device 3 --steps 500 --omega-rear 5.1
"""
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, save_vorticity_png, vorticity_from_ddf,
)
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
def collect(device_id: int, n_steps: int, omega_rear: float) -> str:
cfg = get_scene("steady_cloak")
u0 = cfg["u0"]
si = cfg["sample_interval"]
out_dir = data_dir_for_scene("steady_cloak")
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Steady cloak: {ff.FIELD_SHAPE}")
# Reuse pinball baseline env (we'll re-add geometry)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
ff.add_cylinder((30.0 * L0, CENTER_Y, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y + 0.75 * L0, 0.0), L0 / 2.0)
ff.add_cylinder((31.3 * L0, CENTER_Y - 0.75 * L0, 0.0), L0 / 2.0)
n_obj = 6
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising ({n_stab} steps)...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
# Steady cloak: constant rear-cylinder rotation
# Front cylinder = 0, bottom = -omega_rear*U0, top = omega_rear*U0
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[3] = 0.0
action_arr[4] = -omega_rear * u0
action_arr[5] = omega_rear * u0
print(f" Steady omega: front=0, bottom={-omega_rear*u0:.4f}, top={omega_rear*u0:.4f}")
# Let steady cloak stabilize for a few steps before recording
for _ in range(100):
ff.run(si, action_arr)
# Record
sens_list, forc_list, ux_list, uy_list = [], [], [], []
for step in range(n_steps):
ff.run(si, action_arr)
obs_slice = ff.obs.copy()[0:12]
sens_list.append(obs_slice[0:6])
forc_list.append(obs_slice[6:12])
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez(os.path.join(out_dir, "forces.npz"),
forces=np.array(forc_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
# Save config noting that this is the steady cloak q_ctl
cfg_ctl = dict(cfg)
cfg_ctl["omega_front"] = 0.0
cfg_ctl["omega_rear_scale"] = omega_rear
with open(os.path.join(out_dir, "config_ctl.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg_ctl.items()}, f, indent=2)
omega = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_steady_cloak.png"),
omega, title=f"Steady cloak q_ctl (rear={omega_rear}U0)")
del ff
print(f" Saved {n_steps} snapshots to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=500)
ap.add_argument("--omega-rear", type=float, default=5.1,
help="Rear cylinder rotation magnitude (U0 multiples)")
args = ap.parse_args()
t0 = time.time()
out = collect(args.device, args.steps, args.omega_rear)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/collect_target_cylinder.py
"""
Collect target cylinder data (q_tar) for illusion comparison.
Single cylinder at x=20*L0 with target diameter, plus 3 sensors at x=30*L0.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/collect_target_cylinder.py \
--diameter 1.0 --device 3 --steps 500
Output: data/illusion/illusion_{diam}L/ (shared with illusion scene)
sensors.npz, fields.npz, target.npz, target_harmonics.json
"""
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, save_vorticity_png, vorticity_from_ddf,
analyze_harmonics,
)
from OID_analysis.configs import get_scene, data_dir_for_scene, LEGACY_CFG_DIR, FIFO_LEN # noqa: E402
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
def collect(diameter: float, device_id: int, n_steps: int) -> str:
# Find scene name for this diameter
scene_name = None
for cn in ["illusion_0.75L", "illusion_1.0L", "illusion_1.5L"]:
c = get_scene(cn)
if abs(c["target_diameter"] - diameter) < 0.01:
scene_name = cn
break
if scene_name is None:
raise ValueError(f"No scene for diameter={diameter}")
cfg = get_scene(scene_name)
u0 = cfg["u0"]
si = cfg["sample_interval"]
tgt_key = f"target_cylinder_{diameter}L"
if diameter == int(diameter):
tgt_key = f"target_cylinder_{diameter:.1f}L"
out_dir = data_dir_for_scene(tgt_key)
os.makedirs(out_dir, exist_ok=True)
cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR)
field_cfg = field_cfg._replace(viscosity=float(cfg["nu"]), velocity=float(u0))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
print(f" Target cylinder {diameter}L: {ff.FIELD_SHAPE}")
# Target cylinder at x=20*L0
tgt_radius = diameter * L0
ff.add_cylinder((20.0 * L0, CENTER_Y, 0.0), tgt_radius)
print(f" radius={tgt_radius}")
# 3 sensors at x=30*L0
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((30.0 * L0, CENTER_Y + y_off * L0, 0.0), L0 / 4.0)
n_obj = 4
n_stab = int(4 * ff.FIELD_SHAPE[0] / u0)
print(f" Stabilising ({n_stab} steps)...")
ff.run(n_stab, np.zeros(n_obj, dtype=DATA_TYPE))
# Record target signals: obs[0:8] = cylinder force(2) + sensor(6)
target_states = np.empty((0, 8), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
target_states = np.vstack((target_states, ff.obs.copy()[0:8]))
print(f" Target recorded: {target_states.shape}")
# Harmonics for force channels
target_harmonics = analyze_harmonics(target_states, n_harmonics=5)
harm_save = [{k: v for k, v in h.items()} for h in target_harmonics]
with open(os.path.join(out_dir, "target_harmonics.json"), "w") as f:
json.dump(harm_save, f, indent=2)
np.savez(os.path.join(out_dir, "target.npz"), target_states=target_states)
# Full field rollout
ff.apply_ddf()
sens_list, ux_list, uy_list = [], [], []
for step in range(n_steps):
ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[0:6]
sens_list.append(obs_slice)
ux, uy = get_velocity_field(ff, u0=u0)
ux_list.append(ux)
uy_list.append(uy)
np.savez(os.path.join(out_dir, "sensors.npz"),
sensors=np.array(sens_list, dtype=np.float32))
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=np.stack(ux_list), uy=np.stack(uy_list))
omega = vorticity_from_ddf(ff, u0=u0)
save_vorticity_png(os.path.join(out_dir, "vorticity_target.png"),
omega, title=f"Target cylinder {diameter}L")
with open(os.path.join(out_dir, "config.json"), "w") as f:
json.dump({k: str(v) if not isinstance(v, (int, float, list, bool)) else v
for k, v in cfg.items()}, f, indent=2)
del ff
print(f" Saved {n_steps} snapshots to {out_dir}")
return out_dir
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--diameter", type=float, default=1.0,
help="Target cylinder diameter (0.75, 1.0, 1.5)")
ap.add_argument("--device", type=int, default=3)
ap.add_argument("--steps", type=int, default=500)
args = ap.parse_args()
t0 = time.time()
out = collect(args.diameter, args.device, args.steps)
print(f"Done in {time.time() - t0:.1f}s -> {out}")
if __name__ == "__main__":
main()

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# OID_analysis/scripts/compute_delta_fields.py
"""
Compute Delta_q_blk and Delta_q_ctl from collected fields.
Compute zone statistics.
Gate check: does Delta_q_ctl have clear structure?
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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# OID_analysis/scripts/replay_full_fields.py
"""
Replay PPO actions from CCD checkpoints and save FULL field time series (no cropping).
Needed because Round 1 saved ROI-cropped fields for illusion.
Must run from repo root with PYTHONPATH=src in pycuda_3_10 env.
Usage:
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_full_fields.py \
--scene illusion_0.75L --device 1
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_full_fields.py \
--scene illusion_1.0L --device 3
conda run -n pycuda_3_10 python src/OID_analysis/scripts/replay_full_fields.py \
--scene illusion_1.5L --device 3
"""
from __future__ import annotations
import argparse
import os
import sys
import time
from collections import deque
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 ( # noqa: E402
get_scene, LEGACY_CFG_DIR,
)
DATA_TYPE = np.float32
L0 = 20.0
CENTER_Y = (512 - 1) / 2.0
FIFO_LEN = 150
def build_env(cfg, cuda_cfg, field_cfg, device_id):
"""Build environment with FULL geometry (no cropping)."""
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
u0 = cfg["u0"]
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)
n1 = 4
ff.run(int(4 * 1280 / u0), np.zeros(n1, dtype=DATA_TYPE))
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 replay(scene_name: str, device_id: int):
cfg = get_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"]
# CCD source directory
sid = cfg["scene_id"]
if sid == "karman":
src_dir = os.path.join(_SRC, "CCD_analysis", "data", "karman", "karman_re100")
elif sid == "illusion":
src_dir = os.path.join(_SRC, "CCD_analysis", "data", "illusion", scene_name)
else:
raise ValueError(f"Unknown scene type: {sid}")
out_dir = os.path.join(os.path.dirname(__file__), "..", "data",
"karman_cloak" if sid == "karman" else "illusion", scene_name)
os.makedirs(out_dir, exist_ok=True)
# Load checkpoints and actions
ddf_ckpt = np.load(os.path.join(src_dir, "ddf_checkpoint.npy"))
fifo_ckpt = np.load(os.path.join(src_dir, "fifo_checkpoint.npy"))
controlled = np.load(os.path.join(src_dir, "controlled.npz"))
actions = controlled["actions"]
n_steps = len(actions)
print(f" {n_steps} actions loaded")
# Build env with full geometry
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)
ff.ddf = ddf_ckpt.copy()
ff.apply_ddf()
print(f" DDF restored")
fifo = deque(maxlen=FIFO_LEN)
for s in fifo_ckpt:
fifo.append(s)
# Replay and save FULL fields
ux_list, uy_list = [], []
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()
ux_full, uy_full = get_velocity_field(ff, u0=u0)
ux_list.append(ux_full)
uy_list.append(uy_full)
ux_arr = np.stack(ux_list).astype(np.float32)
uy_arr = np.stack(uy_list).astype(np.float32)
print(f" Full fields shape: {ux_arr.shape}")
# Overwrite old cropped fields
np.savez_compressed(os.path.join(out_dir, "fields.npz"),
ux=ux_arr, uy=uy_arr)
del ff, controlled
print(f" {n_steps} full fields saved to {out_dir}")
return n_steps
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()
n = replay(args.scene, args.device)
print(f"Done in {time.time() - t0:.1f}s -> {n} fields")
if __name__ == "__main__":
main()

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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())

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# OID_analysis/utils/analysis.py
"""
CPU-only analysis utilities: POD, OID, PCD, zone statistics, comparison.
No pycuda dependency -- import from any environment.
"""
from __future__ import annotations
import numpy as np
from typing import Dict, List, Optional, Tuple
# ---------------------------------------------------------------------------
# POD (Proper Orthogonal Decomposition)
# ---------------------------------------------------------------------------
def compute_pod(
snapshots: np.ndarray,
rank: Optional[int] = None,
) -> Dict:
"""Compute POD of snapshot matrix.
Args:
snapshots: (N, DOF) array, N snapshots of DOF-dimensional field.
rank: Truncation rank. If None, keep all.
Returns:
dict with keys:
modes: (DOF, r) spatial modes
coefs: (N, r) time coefficients
S: (r,) singular values
energy: (r,) relative energy fraction per mode
cum_energy: (r,) cumulative energy
mean: (DOF,) mean field
"""
mean = np.mean(snapshots, axis=0)
Q = snapshots - mean # (N, DOF)
# Prefer method-of-snapshots if N < DOF
N, DOF = Q.shape
if N < DOF:
# Method of snapshots: SVD on (N x N) covariance
C = Q @ Q.T # (N, N)
eigvals, eigvecs = np.linalg.eigh(C)
idx = np.argsort(eigvals)[::-1]
eigvals = eigvals[idx]
eigvecs = eigvecs[:, idx]
# Recover modes
S = np.sqrt(np.maximum(eigvals, 0))
V = eigvecs
# phi_i = (1/sigma_i) * Q^T * v_i
modes = (Q.T @ V) / (S + 1e-30) # (DOF, N)
coefs = V * S # (N, N)
else:
# Direct SVD
U, S, Vt = np.linalg.svd(Q, full_matrices=False)
modes = Vt.T # (DOF, N)
coefs = U * S # (N, N)
# Truncate
if rank is not None and rank < N:
modes = modes[:, :rank]
S = S[:rank]
coefs = coefs[:, :rank]
total_energy = np.sum(S ** 2)
energy = (S ** 2) / total_energy if total_energy > 0 else np.zeros_like(S)
cum_energy = np.cumsum(energy)
return {
"modes": modes,
"coefs": coefs,
"S": S,
"energy": energy,
"cum_energy": cum_energy,
"mean": mean,
}
# ---------------------------------------------------------------------------
# OID (Observable-Inferred Decomposition) -- cross-covariance SVD
# ---------------------------------------------------------------------------
def compute_force_oid(
pod_coefs: np.ndarray,
y_force: np.ndarray,
) -> Dict:
"""Force-OID: cross-covariance SVD between POD coefs and force observable.
Args:
pod_coefs: (N, r) standardized correction-field POD coefficients.
y_force: (N, m) standardized force observable.
Returns:
dict with keys:
U: (r, r) OID rotation matrix
S: (r,) singular values of cross-covariance
Vt: (m, m) right singular vectors
z: (N, r) OID coordinates
cum_corr: (r,) cumulative correlation fraction
"""
N = pod_coefs.shape[0]
C_AY = (1.0 / N) * pod_coefs.T @ y_force # (r, m)
U, S, Vt = np.linalg.svd(C_AY, full_matrices=False)
z = pod_coefs @ U # (N, r)
total_corr = np.sum(S)
cum_corr = np.cumsum(S) / total_corr if total_corr > 0 else np.ones_like(S)
return {
"U": U,
"S": S,
"Vt": Vt,
"z": z,
"cum_corr": cum_corr,
}
def compute_signature_oid(
pod_coefs: np.ndarray,
y_sig: np.ndarray,
) -> Dict:
"""Signature-OID: cross-covariance SVD between POD coefs and delayed sensor error.
Identical to force-OID but with different observable. Kept as separate
function for documentation clarity.
"""
return compute_force_oid(pod_coefs, y_sig)
# ---------------------------------------------------------------------------
# PCD (Pattern-Constrained Decomposition) -- whitened cross-correlation
# ---------------------------------------------------------------------------
def compute_pcd(
pod_coefs: np.ndarray,
p_sig: np.ndarray,
tikhonov_eps: float = 1e-6,
) -> Dict:
"""PCD-style whitened cross-correlation decomposition.
Implements Lyu23-style canonical correlation in POD subspace.
Args:
pod_coefs: (N, r) standardized correction-field POD coefficients.
p_sig: (N, m') delayed signature observable (e.g. p_sig_full).
tikhonov_eps: Tikhonov regularization for near-singular matrices.
Returns:
dict with keys:
W: (r, r) PCD weight matrix (mapping POD coefs -> PCD coords)
z_pcd: (N, r) PCD coordinates
S: (r,) singular values of whitened cross-correlation
cum_corr: (r,) cumulative correlation fraction
"""
import scipy.linalg
N = pod_coefs.shape[0]
r = pod_coefs.shape[1]
# Covariance matrices
C_AA = (1.0 / N) * pod_coefs.T @ pod_coefs # (r, r)
C_PP = (1.0 / N) * p_sig.T @ p_sig # (m', m')
C_AP = (1.0 / N) * pod_coefs.T @ p_sig # (r, m')
# Whitening with Tikhonov
C_AA_reg = C_AA + tikhonov_eps * np.eye(r)
C_PP_reg = C_PP + tikhonov_eps * np.eye(p_sig.shape[1])
C_AA_inv_half = scipy.linalg.sqrtm(np.linalg.inv(C_AA_reg)).real
C_PP_inv_half = scipy.linalg.sqrtm(np.linalg.inv(C_PP_reg)).real
# Whitened cross-correlation
K = C_AA_inv_half @ C_AP @ C_PP_inv_half
U, S, Vt = np.linalg.svd(K, full_matrices=False)
W = C_AA_inv_half @ U # (r, r)
z_pcd = pod_coefs @ W # (N, r)
total_corr = np.sum(S)
cum_corr = np.cumsum(S) / total_corr if total_corr > 0 else np.ones_like(S)
return {
"W": W,
"z_pcd": z_pcd,
"S": S,
"U_raw": U,
"cum_corr": cum_corr,
}
# ---------------------------------------------------------------------------
# Zone statistics
# ---------------------------------------------------------------------------
def compute_zone_statistics(
u: np.ndarray,
v: np.ndarray,
zone_mask: np.ndarray,
) -> Dict:
"""Compute coarse-grained statistics within a zone.
Args:
u: (N, ny, nx) x-velocity time series
v: (N, ny, nx) y-velocity time series
zone_mask: (ny, nx) bool mask for the zone
Returns:
dict with keys:
kinetic_energy_mean: scalar (time-mean within zone)
enstrophy_mean: scalar
upper_lower_asymmetry: scalar
centerline_shift: scalar
"""
N = u.shape[0]
mask = zone_mask.astype(bool)
area = np.sum(mask)
if area == 0:
return {"area": 0, "warning": "empty zone"}
# Kinetic energy per snapshot
ke_per_step = 0.5 * np.sum((u[:, mask] ** 2 + v[:, mask] ** 2), axis=1) / area
ke_mean = float(np.mean(ke_per_step))
# Vorticity via finite difference (central, on grid)
omega_ts = np.zeros((N, *u.shape[1:]), dtype=np.float64)
for t in range(N):
u_t = u[t].astype(np.float64)
v_t = v[t].astype(np.float64)
omega_ts[t] = np.gradient(v_t, axis=1) - np.gradient(u_t, axis=0)
enstrophy_per_step = 0.5 * np.sum(omega_ts[:, mask] ** 2, axis=1) / area
enstrophy_mean = float(np.mean(enstrophy_per_step))
# Upper-lower asymmetry (split along center y)
ny = u.shape[1]
cy = (ny - 1) / 2.0
upper_mask = mask.copy()
upper_mask[:int(cy), :] = False
lower_mask = mask.copy()
lower_mask[int(cy):, :] = False
upper_omega = np.mean(omega_ts[:, upper_mask]) if np.sum(upper_mask) > 0 else 0.0
lower_omega = np.mean(omega_ts[:, lower_mask]) if np.sum(lower_mask) > 0 else 0.0
total = max(abs(upper_omega) + abs(lower_omega), 1e-30)
asymmetry = float((upper_omega - lower_omega) / total)
# Centerline shift: find y where mean u(y) is max on the centerline
mean_u = np.mean(u, axis=0) # (ny, nx)
center_x_idx = mean_u.shape[1] // 2
u_profile = mean_u[:, center_x_idx]
shift_y = float(np.argmax(u_profile) - cy)
return {
"area": int(area),
"kinetic_energy_mean": ke_mean,
"enstrophy_mean": enstrophy_mean,
"upper_lower_asymmetry": asymmetry,
"centerline_shift_lattice": shift_y,
}
def compute_recirculation_metrics(
mean_u: np.ndarray,
centerline_y: int,
) -> Dict:
"""Compute recirculation zone metrics from mean x-velocity field.
Args:
mean_u: (ny, nx) time-mean x-velocity
centerline_y: y-index of the centerline
Returns:
dict with keys:
Lr: recirculation length (max x where u=0 on centerline)
Ar: recirculation area (pixels where u<0)
"""
ny, nx = mean_u.shape
# Recirculation length: furthest downstream x on centerline where mean_u < 0
u_cl = mean_u[centerline_y, :]
neg_indices = np.where(u_cl < 0)[0]
Lr = float(neg_indices[-1]) if len(neg_indices) > 0 else 0.0
# Recirculation area: pixels where mean_u < 0
Ar = float(np.sum(mean_u < 0))
# Centerline shift: y-offset of max u in near wake
wake_region = mean_u[:, nx//4:nx//2]
max_uy = np.unravel_index(np.argmax(wake_region), wake_region.shape)
cl_shift = float(max_uy[0] - centerline_y)
return {
"Lr_lattice": Lr,
"Ar_pixels": Ar,
"centerline_shift_lattice": cl_shift,
}
# ---------------------------------------------------------------------------
# Standardization
# ---------------------------------------------------------------------------
def standardize(X: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Z-score standardization along time axis (axis=0)."""
mean = np.mean(X, axis=0, keepdims=True)
std = np.std(X, axis=0, keepdims=True)
std = np.where(std < 1e-12, 1.0, std)
X_std = (X - mean) / std
return X_std, mean, std
def reconstruct_oid_modes(
pod_modes: np.ndarray,
U_oid: np.ndarray,
) -> np.ndarray:
"""Reconstruct OID spatial modes from POD modes and OID rotation.
psi_k_OID = sum_j U_{jk} * phi_j
Args:
pod_modes: (DOF, r) POD spatial modes
U_oid: (r, r) OID rotation matrix
Returns:
oid_modes: (DOF, r) OID spatial modes
"""
return pod_modes @ U_oid
# ---------------------------------------------------------------------------
# Comparison helpers
# ---------------------------------------------------------------------------
def compare_predictions(
y_true: np.ndarray,
y_pred_pod: np.ndarray,
y_pred_oid: np.ndarray,
y_pred_pcd: Optional[np.ndarray] = None,
) -> Dict:
"""Compare prediction R^2 between POD, OID, and optionally PCD."""
from sklearn.linear_model import LinearRegression
def r2_score(y_t, y_p):
ss_res = np.sum((y_t - y_p) ** 2)
ss_tot = np.sum((y_t - np.mean(y_t, axis=0, keepdims=True)) ** 2)
return 1.0 - ss_res / (ss_tot + 1e-30)
result = {
"pod_r2": r2_score(y_true, y_pred_pod),
"oid_r2": r2_score(y_true, y_pred_oid),
}
if y_pred_pcd is not None:
result["pcd_r2"] = r2_score(y_true, y_pred_pcd)
return result

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# CelerisLab/OID_analysis/utils/cfd_interface.py
"""
Re-exports proven CFD interface from CCD_analysis.
Adds OID-specific helpers.
Must be run inside: conda run -n pycuda_3_10
Usage::
from OID_analysis.utils.cfd_interface import (
load_legacy_configs, get_velocity_field, vorticity_from_ddf,
save_vorticity_png, load_ppo_model, scale_action,
build_observation, compute_similarity, calc_lag, calc_dtw_sim,
ACTION_SMOOTH_WEIGHT,
)
"""
from __future__ import annotations
import sys
import os
# Add repo root for imports
_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)
# Re-export everything from CCD's proven cfd_interface
from CCD_analysis.utils.cfd_interface import ( # noqa: E402, F401, F403
load_legacy_configs,
get_velocity_field,
vorticity_from_ddf,
save_vorticity_png,
load_ppo_model,
scale_action,
build_observation,
calc_lag,
calc_dtw_sim,
compute_similarity,
ACTION_SMOOTH_WEIGHT,
build_karman_cloak_env,
add_pinball,
)
# Also re-export from SR_analysis in case of extra helpers
from SR_analysis.scripts.infer_illusion import analyze_harmonics, gen_target_states_at # noqa: E402, F401