diff --git a/docs/JFM_WYQ/SR_Draft.md b/docs/JFM_WYQ/SR_Draft.md new file mode 100644 index 0000000..4cfc5ee --- /dev/null +++ b/docs/JFM_WYQ/SR_Draft.md @@ -0,0 +1,452 @@ +# Symbolic regression of learned flow-control policies + +## 1. Purpose, evidence hierarchy and scope + +The deep-reinforcement-learning (DRL) controllers used for hydrodynamic cloaking and illusion are effective, but their neural-network representation does not directly expose the control mechanism. Symbolic regression (SR) is therefore used as a post hoc policy-reduction method. The fitted expressions are empirical observation-to-action surrogates on states visited by PPO; they are not governing equations and are not identified directly from the Navier--Stokes equations. + +This distinction matters in closed loop. A formula can reproduce PPO actions on a recorded trajectory and still alter the state distribution after deployment, accumulate phase error, develop an action bias, or destabilize CFD. Consequently, offline regression metrics are used to discover variables and topologies, whereas finite closed-loop CFD and trajectory similarity decide whether a candidate is useful. Term deletion and coefficient scaling then assess which parts of a deployed candidate matter. + +The active software has three executable stages: + +1. `stage_1_infer.py` collects contract-audited PPO trajectories; +2. `stage_2_fit.py` performs symbolic discovery and fixed-topology coefficient refitting; +3. `stage_3_validate.py` deploys PPO, symbolic, constant, or uncontrolled policies in CFD. + +Within Stages 2--3, model selection has five phases: per-case discovery, within-objective joint discovery, topology freezing and coefficient refit, short and standard CFD screening, and term deletion/scaling. These phases are not additional software stages. + +The fitted article evidence is deliberately narrower than the complete flow-control project. It contains four periodic Kármán-street cloaking policies and three cylinder-wake illusion policies. Kármán and illusion are never pooled into one regression. The disturbance-free steady scene is used only for post-selection calibration of the Kármán law. Transient-vortex, erase, V5, and historical SINDy analyses are outside the fitted evidence considered here. + +Two evidence generations must remain separate: + +- the **2026-07-18 primary evidence** contains the seven training trajectories, symbolic discovery, fixed-topology refits, 40-step screening, 200-step validation, and term tests; +- the **2026-07-20 coefficient-frozen extension** contains steady calibration, per-case diagnostic refits, 400-step duration tests, CSV exports, and pointwise unseen-condition deployments. + +No extension condition participated in topology selection or coefficient estimation. + +## 2. Physical labels, state contract and action units + +### 2.1 Reynolds-number and target-size conventions + +The inlet speed is \(U_0=0.01\) in lattice units and the pinball-cylinder diameter is \(D=20\). The historical Kármán scene names use a code Reynolds label based on the reference length \(2D=40\): + +\[ +Re_D=\frac{U_0D}{\nu}=\frac{\texttt{re\_code}}{2}. +\] + +Thus `karman_re50`, `karman_re100`, `karman_re200`, and `karman_re400` denote \(\texttt{re\_code}=50,100,200,400\), corresponding to \(Re_D=25,50,100,200\). Both conventions must be stated whenever physical Reynolds numbers are compared. + +The illusion scene names `illusion_0.75L`, `illusion_1L`, and `illusion_1.5L` are legacy target-size labels. Despite the historical configuration field name `target_diameter`, its value times \(L=20\) is passed to `LegacyCelerisLab.add_cylinder` as the cylinder **radius**. The labels are retained for provenance, but they must not be described as target-cylinder diameters. + +The Kármán control sampling interval is 800 lattice steps. Illusion intervals are 400, 600, and 800 steps for the 0.75L, 1L, and 1.5L labels. With the convective scale \(D/U_0=2000\) lattice steps, + +\[ +\Delta t_c=\frac{\mathrm{sample\ interval}}{D/U_0}, +\] + +so the dimensionless control intervals are 0.4 for Kármán and 0.2, 0.3, and 0.4 for the three illusion scenes. + +### 2.2 Native ordering + +The canonical body and action order is + +\[ +(\mathrm{front},\mathrm{upper},\mathrm{lower}). +\] + +The six downstream sensor channels are ordered + +\[ +(u_U,v_U,u_C,v_C,u_L,v_L), +\] + +and the six force channels are + +\[ +(F_{x,F},F_{y,F},F_{x,U},F_{y,U},F_{x,L},F_{y,L}). +\] + +This order follows the audited LegacyCelerisLab object IDs and force slots. It is not inferred from force signs. The runtime order gate is rerun after CFD or kernel changes because a silent slot permutation would change the physical meaning of every fitted coefficient. + +### 2.3 PPO normalization and SR nondimensionalization + +Two different transformations must not be conflated. PPO receives a clipped normalized observation: forces are divided by `force_norm_fact`, sensor velocities are centred by `sens_deviation` and divided by `sens_norm_fact`, and the result is clipped to \([-1,1]\). Illusion appends the normalized two-component target-cylinder force. The article trajectories use frozen existing normalization rather than recomputing normalization on each rollout. + +SR does not fit these clipped PPO inputs. Recorded raw lattice data are converted to physical dimensionless features: + +\[ +\hat u=u/U_0,\qquad \hat v=v/U_0, +\] + +\[ +C_d=\frac{2F_x}{\rho U_0^2D},\qquad +C_l=\frac{2F_y}{\rho U_0^2D}, +\] + +with \(\rho=1\). The regression target is dimensionless cylinder surface velocity + +\[ +\boldsymbol{\alpha}_t +=\frac{\boldsymbol{\omega}_t}{U_0} +=(\alpha_{F,t},\alpha_{U,t},\alpha_{L,t})^{\mathsf T}. +\] + +A PPO-normalized command is decoded before fitting as + +\[ +\boldsymbol{\alpha} +=s_a\mathbf a_{\mathrm{norm}}+\mathbf b_a. +\] + +The Kármán action scale and bias are \(s_a=8\) and \((0,-4,4)\); the illusion values are \(s_a=4\) and \((0,-2,2)\). Stage 1 stores the normalized command, dimensionless \(\alpha\), and lattice \(\omega=U_0\alpha\), making unit conversion auditable. + +## 3. Article trajectories and causal dataset + +Seven new deterministic PPO trajectories were collected: + +\[ +\mathcal D_K=\{\texttt{re50},\texttt{re100},\texttt{re200},\texttt{re400}\}, +\] + +\[ +\mathcal D_I=\{0.75L,1L,1.5L\}. +\] + +Each trajectory contains 200 recorded post-action states. The causal contract is + +\[ +x_i\longmapsto\alpha_{i+1}, +\] + +named `causal_post_state_to_next_action`. At state \(x_i\), action-history features may use \(\alpha_i\) and \(\alpha_{i-1}\), but never a future action. The requirement for \(\alpha_{i+1}\) removes the final state and the two-row action-history warm-up removes indices 0 and 1, leaving state indices 2--198: 197 fitted rows per trajectory. + +Every trajectory is aligned and split independently before stacking. The discovery split is contiguous 60/20/20% train, validation, and blind data; temporal rows are not randomly shuffled and boundaries never cross trajectories. The blind block is not fitted during topology discovery. After a topology has been frozen, its numerical constants are refitted on all eligible aligned rows. Therefore, the blind block protects topology selection but is no longer an untouched holdout after final refitting; final scientific acceptance comes from closed-loop CFD. + +The PPO model was replayed on each stored causal state using the frozen observation normalization and decoder. All seven action sequences reproduced exactly. Historical and pre-contract trajectories remain available for diagnosis but are not pooled with the article dataset. + +## 4. Candidate variables and reflection symmetry + +### 4.1 Feature libraries + +The `raw_complete` representation contains twelve variables: six dimensionless sensor velocities and the drag/lift coefficients of the front, upper, and lower cylinders. + +The `symmetry` representation contains fourteen variables: + +\[ +(u_s,u_a,u_c,v_s,v_a,v_c, +C_{d,F},C_{d,\mathrm{rear},s},C_{d,\mathrm{rear},a},C_{d,\mathrm{tot}}, +C_{l,F},C_{l,\mathrm{rear},s},C_{l,\mathrm{rear},a},C_{l,\mathrm{tot}}). +\] + +The rear and sensor coordinates use half-sums and half-differences. For example, + +\[ +u_s=\frac{u_U+u_L}{2},\qquad +u_a=\frac{u_U-u_L}{2}, +\] + +\[ +C_{d,\mathrm{rear},s}=\frac{C_{d,U}+C_{d,L}}{2},\qquad +C_{d,\mathrm{rear},a}=\frac{C_{d,U}-C_{d,L}}{2}, +\] + +with identical conventions for \(v\) and lift. This factor of one half is part of the formula contract: omitting it would rescale the reported coefficients. + +Illusion discovery compares four controlled profiles: + +- `actual_only`: actual-state variables only; +- `target_only`: target \(C_d\) and \(C_l\) only; +- `actual_plus_target`: actual state plus target force coefficients; +- `actual_plus_error`: actual state plus \(C_{d,\mathrm{tot}}-C_{d,\mathrm{target}}\) and \(C_{l,\mathrm{tot}}-C_{l,\mathrm{target}}\). + +The article discovery does not add trigonometric phase coordinates or a manual bias column. PySR may still create numerical constants. Cross-correlation is never used to choose a feature lag. + +### 4.2 Complete reflection map + +Reflection \(G\) about the centreline exchanges upper and lower positions. Streamwise velocity is even, transverse velocity is odd, drag is even, and lift is odd. Thus upper/lower sensor and force slots are exchanged with the appropriate transverse/lift sign reversal. Front drag is even and front lift is odd. Target drag is even and target lift is odd. The same map is applied to any action history. + +The native action transforms as + +\[ +G_a(\alpha_F,\alpha_U,\alpha_L) +=(-\alpha_F,-\alpha_L,-\alpha_U). +\] + +Fit augmentation and deployment architecture are separate. G augmentation adds mirrored state-action rows during fitting. The final mapped-shared deployment imposes + +\[ +\operatorname{odd}[h](x)=\frac{h(x)-h(Gx)}{2}, +\] + +\[ +\alpha_F(x)=\operatorname{odd}[h_F](x),\qquad +\alpha_U(x)=h_R(x),\qquad +\alpha_L(x)=-h_R(Gx). +\] + +This is a physical inductive bias imposed on the symbolic controller. It does not establish exact PPO equivariance, and symmetrization may modify as well as simplify the PPO policy. Independent three-head fits are retained only as diagnostics of PPO structure. + +## 5. Discovery, refitting and safety + +### 5.1 PySR discovery + +The discovery grammar uses binary \(+\), \(-\), and multiplication, with `square` as the unary operator. Search is deterministic and serial, without batching. The full defaults are 30 populations, population size 33, tournament size 10, maximum expression size 15, constant complexity 2, and parsimony 0.01. The bounded article discovery configuration (`--smoke`) uses two populations, population size 16, tournament size 4, and maximum size 7. At least three deterministic seeds are run per case/profile. + +Only training blocks are fitted. Up to 25 retained Pareto candidates per head are evaluated on contiguous train, validation, and blind blocks using \(R^2\), MAE, RMSE, maximum error, complexity, and used variables. Static safety is a finite-value probe over sparse points spanning observed feature ranges; because no required output interval is supplied, it is not a global action-range guarantee. + +Topology selection is a manual, evidence-based scientific step. It considers recurrence across seeds and cases, held-out diagnostics, complexity, finite static evaluation, compatibility with mapped-shared deployment, and CFD viability. PySR score or \(R^2\) never automatically promotes a formula, and an average cannot hide failure in one required scene. + +### 5.2 Fixed-topology refit + +After selecting a recurring topology, every numerical literal is replaced by a separate optimization parameter while operators and feature occurrences remain fixed. Constants are optimized with `scipy.optimize.least_squares`. Residuals use square-root sample weights so each case has equal total mass and trajectories within a case also have equal mass. Mapped-shared refits retain G augmentation. The artifact records initial/refitted constants, optimizer status, aggregate/per-case/per-trajectory metrics, discovery-parent path and hash, dataset hashes, feature order, action units, alignment, and deployment semantics. + +Offline metrics describe PPO action imitation on PPO-visited states. They cannot be ranked directly against closed-loop DTW because symbolic deployment changes the state distribution. + +### 5.3 Formula and CFD safety + +Formula artifacts carry separate fitted/deployment expressions and hashes. Evaluation permits only declared features and a restricted function set, rejects unsupported syntax and variables, and performs a zero-state probe when Stage 3 loads a formula pair. Static finite probing is only a pre-screen. + +Closed-loop safety is decisive. The symbolic policy maintains separate online feature histories for \(x\) and \(Gx\), initialized from the scene policy action. Every action and raw CFD observation is checked for finiteness. A failure retains partial telemetry, failed-step information, hashes, and the exception instead of overwriting the run. Candidates first receive a 40-step screen, then a 200-step standard validation. The 2026-07-20 extension adds 400-step tests without changing coefficients. + +### 5.4 Legacy DTW metric + +The only article metric is `legacy_dtw_v1_abs_n_unclipped`, recorded under `legacy_reference_cycle_vs_last_recorded_cycle`. For Kármán, `conv_len=30`; for illusion, `conv_len=36`. Lag is selected from transverse sensor channel 1 by cross-correlating target samples `conv_len:2*conv_len` with the final controlled `conv_len` samples. The complete target sequence is circularly shifted by that lag. Absolute, normalized, unclipped DTW similarity + +\[ +S_j=1-\frac{d_{\mathrm{DTW}}(y^{\mathrm{target}}_j,y^{\mathrm{controlled}}_j)}{N} +\] + +is then computed independently for all six sensor channels, and the reported value is their arithmetic mean. One is ideal; the metric may be negative. Lag is a metric-alignment parameter, not a physical observation or actuation delay. The scalar score compares a reference cycle with the final recorded cycle; 400-step CSV convergence files are needed to inspect its evolution over duration. + +## 6. Attempt ledger and selection lessons + +Broad per-case searches showed heterogeneous Kármán formulas rather than a clean two-regime partition. Joint symmetry searches repeatedly selected front rear-drag asymmetry and a rear lift-plus-constant topology. For illusion, endpoint cases selected different variable families, motivating profile-controlled joint searches instead of a hand-reduced universal library. + +Two illusion front topologies remained plausible offline. Topology A combined \(C_{d,\mathrm{rear},a}\) and \(C_{l,F}\). Topology B used \(C_{d,\mathrm{rear},s}C_{l,F}-C_{l,\mathrm{tot}}\). Topology B completed 40 steps at 0.75L with similarity 0.958889 and lag 7, but both 1L and 1.5L failed with `FloatingPointError: raw observation contains non-finite values`. Its formula-pair hash is `93615f0809c76d722a9cd781e08b23e1167a2004957964807a3d661f5c98ee38`. It was rejected, and its failures were retained. This is the clearest demonstration that an offline Pareto candidate and even one successful scene cannot substitute for all-case closed-loop screening. + +Older pre-force-order formulas, round-one results, SINDy paths, V5 experiments, and archived Stage 4 figures remain historical or diagnostic. They are not alternative current entrypoints and their numbers are not imported into the article evidence. + +## 7. Kármán-street cloaking results + +### 7.1 Joint law and offline fit + +The selected fixed-topology law is + +\[ +\boxed{\alpha_F(x)=\operatorname{odd}\!\left[-0.381391\,C_{d,\mathrm{rear},a}\right]}, +\] + +\[ +\boxed{\alpha_U(x)=1.307782\,C_{l,\mathrm{rear},s}-3.431209,\qquad +\alpha_L(x)=-\alpha_U(Gx)}. +\] + +The formula-pair hash is `a678f2019e0da7a0e1263f0c911b377106a668a6716ba93d6efd78f58de0a3f1`; the front and rear deployment-expression hashes are `367b5b7e...d4650` and `75207940...60f7`. + +Aggregate offline metrics are: + +- front head: \(R^2=0.5699\), MAE 0.8872, RMSE 1.1876, maximum error 3.1129; +- rear shared head: \(R^2=0.4363\), MAE 1.6187, RMSE 2.0608, maximum error 5.6964. + +The per-case contrast is scientifically informative. Front \(R^2\) is 0.2488, 0.8671, 0.6100, and 0.8893 for the four code-Re labels. Rear \(R^2\) is -0.5315, 0.5666, 0.5483, and 0.2242. Re50 therefore has poor offline rear-action imitation but excellent closed-loop similarity, whereas Re400 has a strong front offline fit and the weakest standard closed-loop score. Offline action accuracy is not the control-performance ordering. + +### 7.2 Standard and duration validation + +The 200-step similarities at code labels Re50, Re100, Re200, and Re400 are 0.9543, 0.9427, 0.8560, and 0.7827. The shared law is strongest at lower/intermediate conditions and degrades at Re400; it is not a universal cross-Re law. + +At 400 steps, similarities are 0.952178, 0.944339, 0.863850, and 0.833365. Every telemetry channel remains finite. The Re400 limitation is therefore not delayed numerical blow-up over this tested horizon, although one 400-step realization cannot establish statistical robustness. + +### 7.3 Term deletion + +Forty-step deletion tests provide the following sequence over Re50/Re100/Re200/Re400: + +- delete front feedback: 0.939361 / 0.912182 / 0.881434 / 0.813289; +- remove rear lift feedback but retain the constant: 0.917126 / 0.869943 / 0.825250 / 0.789535; +- remove the rear constant but retain lift feedback: 0.839344 / 0.817735 / 0.708089 / 0.647401. + +Within this short ablation horizon, the supported ordering is + +\[ +\text{rear constant}>\text{rear lift feedback}>\text{tested front feedback}. +\] + +The persistent rear counter-rotation is therefore the dominant tested controller component. Its interpretation as downstream-deficit compensation is consistent with the expected gap-flow and momentum correction, but SR alone does not observe the spatial momentum field and does not prove causality. + +### 7.4 Per-case fixed-topology diagnostics + +Refitting the same topology separately gives \((a,b,c)\) in \(\alpha_F=-aC_{d,\mathrm{rear},a}\), \(\alpha_U=bC_{l,\mathrm{rear},s}-c\): + +- Re50: (0.5836, 0.2212, 3.5820), rear \(R^2=0.0221\); +- Re100: (0.3248, 1.3203, 4.1774), rear \(R^2=0.7131\); +- Re200: (0.5003, 1.6971, 3.4109), rear \(R^2=0.5788\); +- Re400: (0.2947, 1.0549, 2.5545), rear \(R^2=0.3285\). + +The coefficients vary non-monotonically and do not support a clean low/high-Re split. These fits are offline diagnostics and were not promoted as independent closed-loop controllers. + +### 7.5 Steady calibration + +A disturbance-free pinball independently calibrates the order of magnitude of the rear constant. A discrete constant counter-rotation sweep \(A=0,1,\ldots,6\), with rear commands \((-A,+A)\), gives similarities 0.631830, 0.723858, 0.857286, 0.955029, 0.976376, 0.987212, and 0.978674. Thus \(A=5\) is the best **sampled** value, not a continuously optimized value. + +The Kármán law in the same steady scene gives similarity 0.966494. Its final-cycle mean actions are -0.000563, -3.430589, and 3.431828 for front, upper, and lower, consistent with its analytical constant magnitude 3.431209. The fitted value is about 68.6% of the sampled \(5U_0\) optimum. This establishes scale consistency between the learned constant and useful steady compensation; it is not an equality between wake velocity deficit, momentum deficit, and cylinder surface speed. + +### 7.6 Pointwise unseen-Re extension + +Without coefficient retuning, 200-step deployments at code labels Re25, Re70, Re150, and Re300 give 0.988078, 0.954339, 0.907496, and 0.835758. Re70/Re150/Re300 are interpolation points within the fitted code-label range; Re25 is one low-Re extrapolation. The results show finite pointwise deployment at these sampled configurations only. There is no high-Re extrapolation above Re400, no continuous coefficient law, and no repeated-initial-condition uncertainty estimate. + +## 8. Illusion results + +### 8.1 Per-size discovery and selected law + +Broad discovery changes systematically across legacy target-size labels. At 0.75L, \(C_{d,\mathrm{rear},a}\) and \(C_{d,\mathrm{rear},s}\) appear 15 and 10 times across audited heads. The 1L searches are mixed/transitional, led by \(C_{d,F}\) (13) and \(C_{l,\mathrm{rear},s}\) (12). At 1.5L, \(C_{l,F}\) and \(C_{l,\mathrm{rear},s}\) appear 54 and 46 times, showing a lift-contrast-dominated endpoint. + +The stable joint numerical candidate is + +\[ +\boxed{\alpha_F(x)=\operatorname{odd}\!\left[-1.826604\,C_{d,\mathrm{rear},a}+2.064493\,C_{l,F}\right]}, +\] + +\[ +\boxed{\alpha_U(x)=1.254440\,C_{d,\mathrm{rear},a}-1.528074\,C_{l,F},\qquad +\alpha_L(x)=-\alpha_U(Gx)}. +\] + +Its formula-pair hash is `ae67ae5071065b7ee8412791ee1841011530746865528641beddb1a203c51b83`; front/rear expression hashes are `1df7c515...0487` and `485de2ca...b8e8`. + +### 8.2 Offline fit versus closed-loop performance + +Aggregate offline metrics are: + +- front head: \(R^2=0.4851\), MAE 1.3075, RMSE 1.9726, maximum error 6.6080; +- rear shared head: \(R^2=0.3598\), MAE 1.3347, RMSE 1.5823, maximum error 5.1284. + +Per-case front \(R^2\) is 0.5850, -1.7176, and 0.5054 for 0.75L, 1L, and 1.5L; rear \(R^2\) is -5.7162, -12.5115, and 0.5923. The negative values must be retained: the candidate is not a uniformly accurate PPO action surrogate. + +Nevertheless, 200-step closed-loop similarities are 0.8749, 0.9217, and 0.8306. At 400 steps they are 0.854379, 0.915609, and 0.833587, with finite telemetry. Closed-loop viability despite weak offline action fit again demonstrates that the deployed controller cannot be selected by \(R^2\) alone. + +### 8.3 Per-size fixed-topology diagnostics + +Separate refits produce: + +- 0.75L front: \(-0.2420C_{d,\mathrm{rear},a}-0.8373C_{l,F}\), \(R^2=0.8035\); rear: \(0.2486C_{d,\mathrm{rear},a}+0.0829C_{l,F}\), \(R^2=-5.4396\); +- 1L front: \(2.0438C_{d,\mathrm{rear},a}-2.2193C_{l,F}\), \(R^2=0.6060\); rear: \(0.1843C_{d,\mathrm{rear},a}-0.1588C_{l,F}\), \(R^2=-11.2480\); +- 1.5L front: \(-0.0441C_{d,\mathrm{rear},a}+2.1015C_{l,F}\), \(R^2=0.5421\); rear: \(1.5683C_{d,\mathrm{rear},a}-1.6011C_{l,F}\), \(R^2=0.5948\). + +The 1.5L front is nearly pure front-lift feedback and its rear coefficients are roughly an order of magnitude larger than the poorly identified smaller-target rear fits. Sign changes and very negative smaller-target rear \(R^2\) values prevent promotion of a clean two-group coefficient mechanism. These are diagnostics of the compromise made by a shared static topology. + +### 8.4 Deletion and non-uniqueness + +All four one-term deletion variants completed 40 steps. For 0.75L/1L/1.5L: + +- delete first front term: 0.939375 / 0.933481 / 0.885085; +- delete second front term: 0.960067 / 0.927035 / 0.836563; +- delete first rear term: 0.954488 / 0.936851 / 0.887603; +- delete second rear term: 0.954641 / 0.930454 / 0.850816. + +Thirty-seven preregistered coefficient-scaling shadow variants remained finite. The \(C_{l,F}\) terms dominate action-amplitude sensitivity, while \(C_{d,\mathrm{rear},a}\) is weaker and partly replaceable. Because all one-term deletions remain stable and differences are modest over 40 steps, the four-term expression is not unique and no individual term is established as universally necessary. + +Target and error variables were available but did not recur in the selected low-complexity static topology. This is negative evidence against claiming explicit target tracking; it is not proof that target information is physically irrelevant. Target phase may be aliased into PPO-visited forces, may require memory, or may enter differently across size regimes. + +### 8.5 Pointwise unseen-size extension + +Coefficient-frozen 200-step interpolation at 0.8L and 1.2L gives 0.884573 and 0.925530. Extrapolation at 0.5L, 0.6L, and 2L gives 0.784055, 0.821103, and 0.756880. Every sampled run completes, but degradation toward the extremes is clear. These are single pointwise realizations and do not define a universal target-size law. + +The defensible illusion conclusion is therefore narrower than for Kármán: a shared symmetric numerical controller exists over the tested family, with 1.5L a structurally different boundary regime, but the present static law does not identify a unique target-dependent mechanism. + +## 9. SR-to-OID/CCD handover + +SR identifies actuator-side observation-to-action structure. It does not identify where the corresponding flow correction is located. The handover to OID/CCD is therefore a ranked, falsifiable target list rather than a claim that the structures have already been found. + +For Kármán: + +1. **Rear steady counter-rotation, strongest evidence.** OID should search for a reflection-symmetric streamwise correction generated in the rear gap and retained at the sensor station. CCD should quantify the rear-pair relation to the mean/low-frequency downstream deficit mode. +2. **Symmetric rear lift feedback, moderate evidence.** Coefficients vary from 0.22 to 1.70, so the term is a condition-dependent modulation rather than a universal gain. Seek a rear-cylinder force-correlated source and lagged downstream descendant. +3. **Rear drag asymmetry in front action, weak evidence.** Test an antisymmetric near-front structure and expect weaker downstream canonical correlation. + +For illusion: + +1. **Front lift, moderate evidence.** Compare front-body source structures across target-size labels and test whether descendants change wavelength or phase. +2. **Rear drag asymmetry, weak-to-moderate evidence.** Search for antisymmetric rear-gap structures, explicitly contrasting 1.5L with smaller labels. +3. **Absence of explicit target/error terms, negative evidence.** Use lagged and phase-resolved analyses because static obs--act regression can alias memory or phase into force observations. + +For every element, actuator-to-source and source-to-downstream relations must be reported separately; upper/lower parity must be preserved; lag must be stated in \(D/U_0\); and spatial coincidence must not be promoted to causality without time-lagged evidence. Agreement between SR ranking and OID/CCD correlation is convergent evidence. Contradictions must be retained and used to revise the interpretation rather than forcing agreement. + +## 10. Limitations and claim discipline + +The analysis has the following explicit limitations. + +1. There is one 200-step PPO trajectory per fitted case. PySR seed recurrence is not recurrence over independent PPO or CFD trajectories. +2. Temporal rows are correlated; 197 rows are not 197 independent flow realizations. +3. The symbolic grammar, complexity bound, population settings, and finite seed count do not establish a global symbolic optimum. +4. Exact reflection symmetry is imposed, not discovered, and may modify the original PPO. +5. The selected laws are static and instantaneous. They cannot represent explicit memory, and failure to select target variables does not show memory or target information is unnecessary. +6. All eligible rows are reused after topology freezing, so final coefficient metrics are not a fully blind estimate. +7. The standard and extension runs report one deterministic realization per condition; no repeated-initial-phase or disturbance uncertainty interval is available. +8. The DTW result depends on the selected reference/final-cycle window and lag alignment, is not a full-field metric, and cannot prove mechanism identity. +9. Generalization is pointwise over sampled Reynolds labels and target sizes. Geometry perturbations, sensor noise, actuator mismatch, altered sampling, and other disturbances are untested. +10. No preregistered scalar DTW success threshold exists. Finiteness is a hard gate, while performance language is relative and case-specific. +11. There is no transient-vortex SR evidence in the current fitted package. +12. The steady sweep does not establish a momentum-balance equality or a continuous optimum. +13. OID/CCD causal structures are future/independent evidence, not present SR results. + +Accordingly, the evidence **establishes** a reproducible discovery-to-CFD workflow, the dominance of the rear constant within the tested Kármán law, secondary short-horizon value of rear lift feedback, a finite shared illusion numerical candidate, and the inadequacy of offline fit alone for controller selection. It **supports, with bounded scope**, a shared Kármán backbone over tested conditions and finite pointwise extension performance. It does **not establish** uniqueness, universality, explicit illusion target tracking, necessity of every illusion term, physical delay from DTW lag, or causal deficit compensation without field-level analysis. + +## 11. Reproducibility and evidence map + +### 11.1 Environments and resources + +Stage 1, policy replay, and Stage 3 use conda environment `pycuda_3_10`. PySR discovery and numerical refitting use `sr_env`. CFD runs are strictly serialized on physical GPU 2. With `CUDA_VISIBLE_DEVICES=2`, scripts use logical device 0; provenance records visibility and GPU identity. Run IDs are immutable and failure telemetry is retained. + +At the 2026-07-21 final-consolidation revision, the CPU contract suite is run from the repository root with: + +```bash +PYTHONPATH=src conda run -n sr_env python -m pytest \ + src/SR_analysis/tests tests/test_stage_3_validate.py -q +``` + +The expected result at the 2026-07-21 plotting-package revision is 88 passed tests. This count is revision-specific rather than a timeless scientific result. + +### 11.2 Primary and extension packages + +The primary evidence index is: + +```text +src/SR_analysis/results/runs/article-joint-sr-final-20260718/ + readable_summary.txt + evidence_manifest.json +``` + +The coefficient-frozen extension is indexed by: + +```text +src/SR_analysis/results/runs/article2-sr-elements-20260720/ +src/SR_analysis/results/runs/article2-steady-analysis-20260720/ +src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/ +src/SR_analysis/results/runs/article2-generalization-summary-20260720/ +``` + +Standard-duration PPO/SR/target CSVs, including wide/long tables and DTW convergence, are under `article2-timeseries-csv-20260720/`. Four-hundred-step SR/target exports are under `article2-long-timeseries-csv-20260720/`. + +### 11.3 Stable identities + +The archived fitting Git SHA is `ca8ee5f238ee58eaaf48027ad026c35784f76d4d`. Kármán and illusion dataset hashes are respectively + +```text +dcbfdaaa48a61bd904bb540f3067760530bd69e88ce87d54110a79ecc54b79d6 +50c1d9f95bd72afc14e2d06a205bf4432672224ebf7daeb442ea6c517fbd9ab7 +``` + +Formula-pair hashes are + +```text +Karman: a678f2019e0da7a0e1263f0c911b377106a668a6716ba93d6efd78f58de0a3f1 +Illusion: ae67ae5071065b7ee8412791ee1841011530746865528641beddb1a203c51b83 +``` + +Formula JSON files carry exact commands, parent hashes, feature order, expression hashes, optimizer state, environment, and offline metrics. Validation JSON files carry formula/configuration hashes, metric details, GPU identity, telemetry hash, termination, and record hash. `scene_registry.json` is not the authority for current article evidence, and integrity auditing does not automatically promote a candidate. + +## 12. Final paper-level conclusion + +The Kármán analysis provides the strongest mechanism-oriented SR result. A persistent rear-pair counter-rotation is the dominant tested component; rear symmetric-lift feedback gives secondary short-horizon value; the tested front correction is weak. The constant has the same physical order as independently useful steady counter-rotation, which is consistent with a wake-deficit-compensation hypothesis. Spatial momentum correction and causality remain for OID/CCD, and reduced Re400 performance prevents a universal-law claim. + +The illusion analysis provides a stable shared symmetric numerical controller but not a unique target-tracking mechanism. Front-lift terms dominate action sensitivity, drag-asymmetry terms are partly replaceable, and 1.5L is a distinct boundary regime. Static target/error variables were not selected robustly, so further mechanism work should use controlled target-aware dynamic structures and independent field decomposition rather than broader unconstrained symbolic searches. + +The general methodological conclusion is that SR in closed-loop flow control should be treated as hypothesis generation plus deployment testing. Formula compactness and offline fit identify candidates; CFD stability, trajectory similarity, ablation, and independent flow-field evidence determine what can be interpreted physically. diff --git a/src/SR_analysis/PIPELINE.md b/src/SR_analysis/PIPELINE.md index 0485f76..7ee079c 100644 --- a/src/SR_analysis/PIPELINE.md +++ b/src/SR_analysis/PIPELINE.md @@ -1,210 +1,358 @@ -# SR_analysis Pipeline +# Article SR pipeline -> Symbolic regression pipeline for extracting interpretable DRL control laws (obs -> act) -> from the fluidic pinball. Four independent stages: inference -> fitting -> validation -> analysis. +This is the authoritative execution and design document for `SR_analysis`. Read `README.md` first for the scientific summary and current conclusions. -## Pipeline Architecture +## 1. Method in one diagram -``` -[PPO Inference] [PySR Fitting] [CFD Validation] [Analysis/Figures] -stage_1_infer.py -> stage_2_fit.py -> stage_3_validate.py -> stage_4_analyze.py - | | | | -controlled.npz formulas/*.json validations/*.json figures/*.png+pdf -target.npz FIGURE_INDEX.md +```mermaid +flowchart LR + contracts[Channel and causal contracts] --> data[PPO trajectories] + data --> perCase[Per-case broad discovery] + perCase --> joint[Within-objective joint discovery] + joint --> refit[Fixed-topology all-data refit] + refit --> safety[Static and replay safety] + safety --> shortCFD[Short serial CFD] + shortCFD --> standardCFD[200-step legacy DTW] + standardCFD --> longCFD[400-step duration check] + longCFD --> generalization[Unseen-condition CFD] + standardCFD --> steady[Steady-cloak calibration] + standardCFD --> ablation[Term deletion and scaling] + steady --> interpretation[Physical interpretation and limits] + generalization --> interpretation + ablation --> interpretation ``` -## Environments +The workflow has one scientific route: -| Env | Used For | Key Packages | -|-----|----------|-------------| -| `pycuda_3_10` | Stage 1 (CFD inference), Stage 3 (CFD validation), Stage 4 (analysis) | pycuda, numpy, matplotlib, torch, stable-baselines3 | -| `sr_env` | Stage 2 (PySR symbolic regression) | pysr, numpy, sympy | +```text +stage_1_infer.py -> stage_2_fit.py -> stage_3_validate.py +``` -GPU: device 2 recommended (device 0 may conflict with PyTorch). +Checks and diagnostics gate or interpret this route; they do not create a parallel pipeline. -## Key Conventions +## 1.1 Article2 extension commands -### Reynolds Number -- Code Re uses reference length 2D = 40: `Re = U0 * 40 / nu` -- Physical Re_D uses D = 20: `Re_D = Re / 2` -- Default: Re_code=100 -> Re_D=50, nu=0.004 - -### Action -- `controlled.npz` stores actions as **normalized [-1, +1]** (not physical omega) -- Physical omega: `omega = (action * scale + bias) * U0` -- Fitting target: **non-dimensional alpha = (omega * radius) / (surface_vel * U0)** - -### Action Decoder Bias (scene-specific) -| Scene | Scale | Bias | -|-------|:-----:|------| -| Karman | 8 | [0, -4, 4] | -| Illusion | 8 | [0, -2, 2] | -| Vortex | 4 | [0, -4, 4] | - -### G-Mirror Symmetry -- Correct: `[aF, aT, aB] -> [-aF, -aB, -aT]` -- v23 structure: Front no-bias, rear shared-head (alpha_B = -Top composed with G) - -### Inlet -- Parabolic velocity profile (not uniform). Top/bottom walls are no-slip bounce-back. -- U0 = 0.01 at centerline (lattice units) - -### Sample Interval & Validation Steps -| Scene | SI | Validation Steps (rule: >= NX/U0/SI) | -|-------|:--:|:-------------------------------------:| -| Karman | 800 | 160-200 | -| Illusion 0.75L | 400 | 320 | -| Illusion 1L | 600 | 214 | -| Illusion 1.5L | 800 | 160 | -| Vortex | 800 | 150 (transient) | - ---- - -## Stage 1: PPO Inference Data Generation - -Generates `controlled.npz` (sensors, forces, actions) and `target.npz` for all scenes. +All CFD commands remain serialized and use physical GPU 2. `--device 0` is the logical device after masking. ```bash -# Karman cloak (all trained Reynolds numbers) -for re in 50 100 200 400; do - conda run -n pycuda_3_10 python stage_1_infer.py --scene karman_re${re} --device 2 -done +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python src/SR_analysis/stage_3_validate.py --scene steady --mode constant \ + --constant-alpha 0 -5 5 --device 0 --steps 200 \ + --run-id article2-steady-sweep-a5-20260720 -# Illusion (trained diameters) -for d in 0.75 1.0; do - conda run -n pycuda_3_10 python stage_1_infer.py --scene illusion_${d}L --device 2 -done +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python src/SR_analysis/stage_3_validate.py --group karman_re50,karman_re100,karman_re200,karman_re400 \ + --mode pysr --formula-front --formula-rear \ + --device 0 --steps 400 --run-id article2-long-karman-20260720 -# Illusion target-only (generalization diameters -- no PPO model, just target recording) -for d in 0.5 0.6 0.8 1.2 1.5 2.0; do - conda run -n pycuda_3_10 python stage_1_infer.py --scene illusion_${d}L --target-only --device 2 -done - -# Vortex -for v in lamb taylor; do - conda run -n pycuda_3_10 python stage_1_infer.py --scene vortex_${v} --device 2 -done +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python src/SR_analysis/stage_3_validate.py --group karman_re25,karman_re70,karman_re150,karman_re300 \ + --mode pysr --formula-front --formula-rear \ + --device 0 --steps 200 --run-id article2-gen-karman-20260720 ``` -**Output per scene** (in `data/{scene_id}/{scene_name}/`): -- `controlled.npz`: actions [N,3], sensors [N,6], forces [N,6] -- `target.npz`: target signals [FIFO_LEN, 6] for Karman/Vortex; **8 columns for Illusion** = [cyl_fx,cyl_fy, s0_ux,s0_uy, s1_ux,s1_uy, s2_ux,s2_uy] — extract sensors with `target[:, 2:8]` -- `norm.json`: normalization factors -- `result.json`: similarity + reward summary -- `target_harmonics.json` (Illusion only): FFT harmonics for force reconstruction +Illusion uses the corresponding article topology-A formulas and either the three training scenes for the duration test or `--group illusion_generalization`. Generalization PySR deployment does not require a PPO normalization file; target harmonics remain frozen per scene. ---- - -## Stage 2: PySR Symbolic Regression Fitting - -Runs PySR on `controlled.npz` data to discover interpretable control laws. +Export any Stage 1 or Stage 3 trajectory with: ```bash -# Illusion joint (0.75L + 1.0L) -- primary contribution -conda run -n sr_env python stage_2_fit.py \ - --scenes illusion_0.75L,illusion_1L --mode joint --deep - -# Karman cross-Re joint (re50-400) -conda run -n sr_env python stage_2_fit.py \ - --scenes karman_re50,karman_re100,karman_re200,karman_re400 --mode joint --deep - -# Per-scene individual fitting (optional, for comparison) -conda run -n sr_env python stage_2_fit.py --scene karman_re100 --mode per-scene --deep -conda run -n sr_env python stage_2_fit.py --scene illusion_0.75L --mode per-scene --deep +PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.tools.telemetry_to_csv \ + --input --target --output-dir \ + --scene --source ppo --sample-interval --conv-len ``` -**Feature sets**: -| Name | Features | Dim | Used For | -|------|----------|:---:|----------| -| ILLUSION_PHASE | u_a, du_a/dt, Cl_tot, dCl_tot/dt, Cd_tot, Cd_rear, Cd_err, Cl_err, dCd_err/dt, dCl_err/dt | 10 | Illusion | -| PHYS_DADT+mu | Static + daF/dt, daB/dt, daT/dt + mu terms | 17 | Karman joint | +The exporter writes wide and long CSV files, target tables, manifests and legacy-DTW window convergence diagnostics. For Stage 3 telemetry use `--source sr`; embedded target sensors are used automatically. -**Constraints (v23)**: -- Front no-bias: alpha_F = 0 when features = 0 -- Rear shared-head: alpha_B = -Top composed with G-mirror - -**Output**: `results/formulas/{scene}_{channel}.json` - ---- - -## Stage 3: CFD Closed-Loop Validation - -Validates PySR formulas or PPO baselines in closed-loop CFD. This is the final arbiter. +Build the complete derived plotting package from immutable article artifacts with: ```bash -# Karman cross-Re (trained + generalization) -for re in 50 100 200 400 25 70 150 300; do - conda run -n pycuda_3_10 python stage_3_validate.py \ - --scene karman_re${re} --device 2 --mode pysr \ - --formula-front results/formulas/karman_joint_front.json \ - --formula-top results/formulas/karman_joint_top.json -done - -# Illusion trained (joint formula) -for d in 0.75 1.0; do - conda run -n pycuda_3_10 python stage_3_validate.py \ - --scene illusion_${d}L --device 2 --mode pysr \ - --formula-front results/formulas/illusion_joint_front.json \ - --formula-top results/formulas/illusion_joint_top.json -done - -# Illusion generalization (joint formula on unseen diameters) -for d in 0.5 0.6 0.8 1.2 2.0; do - conda run -n pycuda_3_10 python stage_3_validate.py \ - --scene illusion_${d}L --device 2 --mode pysr \ - --formula-front results/formulas/illusion_joint_front.json \ - --formula-top results/formulas/illusion_joint_top.json -done - -# PPO baselines -for d in 0.75 1.0 1.5; do - conda run -n pycuda_3_10 python stage_3_validate.py \ - --scene illusion_${d}L --device 2 --mode ppo -done - -# Vortex generalization -for v in lamb taylor; do - conda run -n pycuda_3_10 python stage_3_validate.py \ - --scene vortex_${v} --device 2 --mode pysr \ - --formula-front results/formulas/karman_joint_front.json \ - --formula-top results/formulas/karman_joint_top.json -done +PYTHONPATH=src conda run -n sr_env python -m SR_analysis.tools.prepare_plotting_data \ + --output-dir src/SR_analysis/results/runs/article2-plotting-package- ``` -**Output**: `results/validations/{scene_name}.json` with similarity scores. +This creates causal PPO-state predictions/residuals, feature and additive-term contributions, unified ablation/scaling summaries and steady-sweep time series. Render diagnostic PNG/PDF figures in an environment containing Matplotlib with `python -m SR_analysis.tools.plot_sr_diagnostics`. For 400-step PPO CFD, keep `--model-device cpu`; GPU 2 is reserved for the active PyCUDA context. ---- +## 2. Design decisions -## Stage 4: Publication Figures +### Why fit PPO actions? -Generates all figures and the figure index. No CLI arguments needed. +The PPO policy supplies successful state-action trajectories. Fitting its dimensionless physical actions provides candidate feedback structures without rerunning symbolic search inside CFD. The fitted expression is a policy surrogate, not a Navier–Stokes equation. + +### Why not select the highest R² formula? + +A candidate changes the closed-loop state distribution after deployment. A formula can imitate PPO actions well on recorded states and still drift or become unstable in CFD. R² and Pareto complexity are therefore used to discover recurring variables and topologies; CFD stability and legacy DTW decide closed-loop value. + +### Why per-case before joint? + +Per-case searches reveal whether variables and topologies recur across operating conditions. Joint search begins only after broad variables have been considered, preventing a compact hand-selected library from predetermining the mechanism. + +### Why freeze topology before the final coefficient fit? + +Unrestricted symbolic search on all rows would use the blind data for structure selection. The workflow first selects topology from discovery runs, then refits only its numerical constants using all accepted trajectories with case-equal and trajectory-equal weighting. The refit records its parent discovery artifact. + +### Why exact G deployment? + +The geometry and objective are reflection-symmetric, while the legacy PPO was not trained with an equivariance constraint. The final architecture deliberately imposes physical symmetry: + +\[ +\alpha_F(x)=\frac{h_F(x)-h_F(Gx)}{2},\quad +\alpha_U(x)=h_R(x),\quad +\alpha_L(x)=-h_R(Gx). +\] + +This is physical symmetrization, not a claim that PPO itself is exactly equivariant. Three independent heads are retained only for diagnostic structure discovery. + +### Why no automatic lag search? + +For Illusion, target and actual forces share a causal deployment timeline. Cross-correlation can produce a statistically useful shift without identifying a physical delay. Temporal/derivative features are introduced only after controlled static-variable comparisons show insufficiency. DTW lag is reported as part of the legacy metric, not interpreted as control delay. + +### Why separate Kármán and Illusion joint fits? + +They are different objectives. Kármán seeks restoration of an incident wake; Illusion seeks a non-zero target wake. Joint means shared structure within one objective, not one formula across unrelated policies. + +## 3. Contracts and gates + +### Physical labels + +Kármán names use `re_code`, whose reference length is `2D`; report \(Re_D=\texttt{re\_code}/2\) whenever the physical Reynolds number is intended. Illusion names are legacy target-size labels. Despite the historical field name `target_diameter`, the value is passed to `LegacyCelerisLab.add_cylinder` as its `radius` argument. Rear symmetric and antisymmetric force coordinates are half-sums and half-differences, so changing that convention would rescale fitted coefficients. + +A run may proceed to fitting only when all applicable gates pass. + +### Physical order gate + +Canonical orders: + +- body/action: `front, upper, lower` +- forces: `front_fx, front_fy, upper_fx, upper_fy, lower_fx, lower_fy` +- sensors: upper, centre, lower, each with streamwise/transverse components + +Run after CFD/kernel changes: ```bash -conda run -n pycuda_3_10 python stage_4_analyze.py +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python -m SR_analysis.checks.order_contract \ + --scene karman_re100 --device 0 \ + --output src/SR_analysis/results/runs//karman_re100.json ``` -**Output** (in `results/figures/`): -- `fig_illusion_degradation.png/pdf` — Main result: cross-diameter generalization -- `fig_karman_cross_re.png/pdf` — Karman cross-Re validation bars -- `fig_formula_comparison.png/pdf` — Formula structure diagram -- `fig_vortex_generalization.png/pdf` — Vortex cross-scene transfer -- `fig_action_comparison.png/pdf` — PPO action timeseries -- `fig_master_table.png/pdf` — Complete results table -- `FIGURE_INDEX.md` — Figure catalog with paper-ready captions +Repeat for `illusion_1L`. The impulse response is diagnostic; object IDs, centres and slot mapping define the contract. ---- +### Temporal gate -## Canonical Formulas +The article alignment is `causal_post_state_to_next_action`. Post-state `i` predicts normalized PPO action `i+1`. Warm-up rows are removed. Each trajectory is processed independently before stacking. -| Formula File | Scene | Formula | CFD Similarity | -|-------------|-------|---------|:---:| -| `results/formulas/karman_joint_front.json` | Karman cross-Re | `daF_dt - 14.952*mu*Cl_tot` | avg 0.847 | -| `results/formulas/karman_joint_top.json` | Karman cross-Re | `3.414` (constant) | — | -| `results/formulas/illusion_joint_front.json` | Illusion joint | `Cd_tot - (Cd_err + 5.428) - (-0.00978)*(du_a_dt + u_a)` | 0.978/0.970 | -| `results/formulas/illusion_joint_top.json` | Illusion joint | `(Cd_err - (Cd_rear - Cl_err))*0.535 + 2.782` | — | +### PPO wiring gate -## Known Limitations +```bash +PYTHONPATH=src conda run -n pycuda_3_10 \ + python -m SR_analysis.checks.policy_replay \ + --scene \ + --trajectory ///controlled.npz \ + --model-device cpu \ + --output .json +``` -- **Karman rear formula is constant** (alpha_T = 3.414): rear control information not fully utilized by the joint formula. -- **Illusion 1.5L is non-fittable**: PPO policy uses high-frequency modulation (5.6x shedding frequency) that the current feature set cannot capture. -- **daB_dt in Karman front formula** must be removed for deployment: it's a training distribution artifact (PPO trajectories have correlated front/rear actions, but rear is constant at deployment). +The seven article trajectories passed with zero action replay error. + +### Artifact gate + +- Existing non-empty run directories are not overwritten. +- Formula strings, formula files, telemetry and validations carry hashes. +- Integrity audit never grants scientific promotion; `promotion_eligible` defaults to false. +- Failed and incomplete validations remain rejected evidence. + +## 4. Stage 1: collect article data + +Environment: `pycuda_3_10`. CFD is serial on physical GPU 2. Isolate physical GPU 2 with `CUDA_VISIBLE_DEVICES=2`, then select logical device 0. + +Kármán: + +```bash +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python src/SR_analysis/stage_1_infer.py \ + --group karman_trained \ + --run-id \ + --device 0 --model-device cpu --steps 200 --norm-source existing +``` + +Illusion: + +```bash +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python src/SR_analysis/stage_1_infer.py \ + --group illusion_trained \ + --run-id \ + --device 0 --model-device cpu --steps 200 --norm-source existing +``` + +Outputs: + +```text +data/runs//// + config.json + controlled.npz + manifest.json + norm.json + result.json + target.npz + target_harmonics.json # Illusion + uncontrolled.npz # Kármán Stage 1 baseline asset +``` + +The article run uses 200 recorded rows per scene and 197 aligned Stage 2 rows after warm-up/causal alignment. + +## 5. Stage 2A: per-case broad discovery + +Environment: `sr_env`. + +Use complete variable representations first: + +- `raw_complete` +- `symmetry` + +For Illusion, compare: + +- `actual_only` +- `target_only` +- `actual_plus_target` +- `actual_plus_error` + +Example diagnostic three-head discovery: + +```bash +PYTHONPATH=src conda run -n sr_env \ + python src/SR_analysis/stage_2_fit.py \ + --scene karman_re100 --mode per-scene \ + --run-id \ + --data-root \ + --feature-set symmetry --feature-profile actual_only \ + --deployment-architecture three_head_independent \ + --fit-augmentation none \ + --fit-purpose discovery \ + --seed 0 --niterations 20 --smoke +``` + +Repeat for at least three seeds. `--smoke` in the article discovery denotes the bounded PySR population/complexity configuration; the resulting candidates are still diagnostic until CFD validation. + +Up to 25 retained Pareto candidates per fitted head record: + +- expression and used variables +- complexity/loss/PySR score +- contiguous train/validation/blind R², MAE, RMSE and max error +- static finite-value probing over observed feature ranges (not a global action-range guarantee) +- seed, feature set, data and trajectory provenance + +The blind block is not used for fitting. + +## 6. Stage 2B: within-objective joint discovery + +Kármán example: + +```bash +PYTHONPATH=src conda run -n sr_env \ + python src/SR_analysis/stage_2_fit.py \ + --scenes karman_re50,karman_re100,karman_re200,karman_re400 \ + --mode joint --run-id \ + --data-root \ + --feature-set symmetry --feature-profile actual_only \ + --deployment-architecture mapped_shared \ + --fit-augmentation G --fit-purpose discovery \ + --seed 0 --niterations 40 --smoke +``` + +Illusion uses the three trained diameters and separate profile comparisons. Joint fitting is accepted only when scene objective, feature order, action order and action conversion contracts match. + +If multiple data roots are accepted, provide repeated `--data-root` arguments or comma-separated `--data-roots`. Every trajectory is constructed and split independently. Joint PySR receives explicit case-equal, trajectory-equal sample weights. + +## 7. Stage 2C: fixed-topology refit + +Freeze a recurring topology before using all eligible rows. Example: + +```bash +PYTHONPATH=src conda run -n sr_env \ + python src/SR_analysis/stage_2_fit.py \ + --scenes karman_re50,karman_re100,karman_re200,karman_re400 \ + --mode joint --run-id \ + --data-root \ + --feature-set symmetry --feature-profile actual_only \ + --deployment-architecture mapped_shared --fit-augmentation G \ + --fit-purpose fixed-topology-refit \ + '--front-topology-expression=-0.377*Cd_rear_a' \ + '--rear-topology-expression=1.0*Cl_rear_s-3.398' \ + --front-discovery-parent-path \ + --rear-discovery-parent-path +``` + +Only numerical constants are optimized. Outputs report aggregate, per-case and per-trajectory errors and retain discovery-parent hashes. + +## 8. Stage 3: closed-loop screening + +Environment: `pycuda_3_10`, physical GPU 2, one CFD process at a time. + +Short screening: + +```bash +CUDA_VISIBLE_DEVICES=2 PYTHONPATH=src conda run -n pycuda_3_10 \ + python src/SR_analysis/stage_3_validate.py \ + --group karman_re50,karman_re100,karman_re200,karman_re400 \ + --mode pysr \ + --formula-front \ + --formula-rear \ + --device 0 --steps 40 --run-id +``` + +Standard validation changes `--steps` to 200 and uses a new run ID. + +The only active metric is `legacy_dtw_v1_abs_n_unclipped`, reported under `legacy_reference_cycle_vs_last_recorded_cycle`. It estimates lag from transverse sensor channel 1 between target samples `conv_len:2*conv_len` and the final controlled `conv_len` samples, circularly shifts the complete target sequence, computes absolute normalized unclipped DTW similarity independently for all six sensor channels, and reports their arithmetic mean. The lag is an alignment parameter, not a physical delay. Every validation stores per-channel values, lag, action ranges, termination, telemetry hash, formula hashes, scene config and GPU identity. + +A candidate is rejected if any training case becomes non-finite or terminates early. Averages do not hide case failures. + +## 9. Term necessity and coefficient robustness + +`utils/formula_schema.py` deterministically decomposes top-level additive terms and creates immutable formula variants. + +For each shortlisted law: + +1. delete each term; +2. delete physically linked groups where necessary; +3. scale each term with the preregistered grid `0, 0.5, 0.75, 1, 1.25, 1.5`; +4. run recorded-state shadow checks for finite values and action ranges; +5. send only informative variants to short CFD. + +A term is called important only when deletion causes repeatable closed-loop degradation. Scale zero must agree with deletion. The parent formula is never mutated. + +## 10. Article evidence and interpretation + +The current authoritative evidence chain is indexed by `results/README.md` and ends at: + +```text +results/runs/article-joint-sr-final-20260718/ + readable_summary.txt + evidence_manifest.json +``` + +Current conclusions: + +- Kármán: rear constant is dominant, rear lift feedback is secondary, tested front feedback is weak; high-Re performance remains a limitation. +- Illusion: a symmetric numerical family exists, but terms are replaceable and static target/error variables do not establish explicit target tracking; 1.5L is the weakest/different regime. + +The manuscript draft must preserve these distinctions and must not import older historical numbers as current evidence. + +## 11. Historical and inactive material + +- `results/formulas/`, `results/validations/`, `scene_registry.json`, `old/` and old docs are `historical_frozen` context. +- `round1-legacy-v2-20260716-*` is diagnostic only because it predates the final force-order contract. +- `experiments/v5/` is a separate excluded experiment. +- `archive/stage4/` and `archive/stage_docs/` are inactive publication tooling/history. +- `diagnostics/` outputs do not become mechanism claims without closed-loop validation. + +## 12. Tests + +Run from the repository root: + +```bash +PYTHONPATH=src conda run -n sr_env python -m pytest \ + src/SR_analysis/tests tests/test_stage_3_validate.py -q +``` + +Expected result at the 2026-07-21 plotting-package revision: `88 passed`. diff --git a/src/SR_analysis/README.md b/src/SR_analysis/README.md index 45dc028..daf5386 100644 --- a/src/SR_analysis/README.md +++ b/src/SR_analysis/README.md @@ -1,81 +1,254 @@ -# SR_analysis: Symbolic Regression for DRL Flow Control +# SR analysis: agent entry point -Extracts interpretable control laws (obs -> act) from DRL-trained PPO policies -for the fluidic pinball using PySR symbolic regression. Validates all formulas -in CFD closed-loop and produces publication-quality figures. +This directory contains the active symbolic-regression analysis for extracting compact control laws from the legacy PPO policies of the fluidic pinball. -## Pipeline +**Start here, then read `PIPELINE.md`.** The active scientific workflow has exactly three entry points: -``` -stage_1_infer.py -> stage_2_fit.py -> stage_3_validate.py -> stage_4_analyze.py - (PPO data) (PySR formula) (CFD closed-loop) (paper figures) +1. `stage_1_infer.py` — collect run-scoped PPO trajectories. +2. `stage_2_fit.py` — discover and refit symbolic structures. +3. `stage_3_validate.py` — evaluate formulas in closed-loop CFD with the legacy DTW metric. + +The current article evidence is indexed in `results/README.md`. The manuscript-ready SR section is `../../docs/JFM_WYQ/SR_Draft.md`. + +## Scientific question + +SR is not used to maximize imitation of PPO actions. It is used to determine whether successful PPO control contains a compact, symmetric and physically interpretable feedback structure that remains effective when deployed in CFD. + +The selection chain is therefore: + +```text +PPO trajectories + -> broad per-case variable/topology discovery using R² and Pareto fronts + -> within-objective joint topology discovery + -> fixed-topology coefficient refit using all accepted rows + -> short and standard closed-loop CFD screening using legacy DTW + -> term-deletion and coefficient-scaling tests + -> physical interpretation and explicit limitations ``` -## Quick Start +R² is a discovery diagnostic. It is not the acceptance criterion. Closed-loop stability and DTW decide whether a candidate is useful; deletion and scaling tests decide whether its terms are necessary. + +## Current article scope + +Two objectives are fitted separately: + +- **Kármán cloaking:** `karman_re50`, `karman_re100`, `karman_re200`, `karman_re400`. +- **Illusion:** `illusion_0.75L`, `illusion_1L`, `illusion_1.5L`. + +Kármán and Illusion are never pooled into one fit. The fitted evidence is limited to these seven training scenes. The coefficient-frozen 2026-07-20 extension tests additional Reynolds-number and target-size points, but those points did not participate in fitting or model selection. Vortex, V5 and historical figures remain outside the current article evidence. + +Seven new 200-step PPO trajectories were collected in: + +- `data/runs/article-joint-data-karman-20260718/` +- `data/runs/article-joint-data-illusion-20260718/` + +All seven passed exact recorded-state policy replay. The accepted data inventory is: + +- `results/runs/article-joint-data-audit-20260718/data_inventory.json` + +Historical and round-one assets are retained, but are not silently pooled with the article data. The old `round1-legacy-v2-20260716-*` formulas are diagnostic only because they predate the final force-order contract. + +## Non-negotiable contracts + +### Native order + +The canonical body, force and action order is: + +```text +front, upper, lower +``` + +Sensors are ordered upper, centre, lower. Verify runtime binding with `checks/order_contract.py` before collecting data after any CFD change. + +### Alignment + +The article workflow uses: + +```text +causal_post_state_to_next_action +``` + +Recorded post-state `i` predicts action `i+1`. Each trajectory is built independently so lag features and contiguous splits never cross trajectory boundaries. + +### Action definition + +Formula outputs are dimensionless cylinder surface velocities: + +\[ +\alpha_i = \omega_i/U_0. +\] + +Normalized PPO actions are decoded with the scene action scale and bias before fitting. + +### Exact reflection symmetry + +The article candidate uses mapped-shared deployment: + +\[ +\alpha_F(x)=\frac{h_F(x)-h_F(Gx)}{2},\qquad +\alpha_U(x)=h_R(x),\qquad +\alpha_L(x)=-h_R(Gx). +\] + +The front projection is exactly odd. The lower action is generated from the shared upper law. Three-head-independent fitting exists only as a PPO-structure diagnostic and is not the final physical architecture. + +Fit augmentation and deployment architecture are separate choices: + +- `--fit-augmentation none|G` +- `--deployment-architecture mapped_shared|three_head_independent` + +Hard G symmetry is a deliberate physical symmetrization; it is not presented as a property that the legacy PPO was trained to satisfy. + +### Metric + +The closed-loop metric is: + +```text +legacy_dtw_v1_abs_n_unclipped +``` + +Its exact window is named: + +```text +legacy_reference_cycle_vs_last_recorded_cycle +``` + +There are not separate “full” and “tail” DTW algorithms. Lag is part of the legacy comparison procedure and is not interpreted as a physical observation/control delay. + +## Physical labels and units + +The Kármán scene names contain the historical code Reynolds label `re_code`, defined with reference length `2D`; the cylinder-diameter Reynolds number is therefore \(Re_D=\texttt{re\_code}/2\). The Illusion names `0.75L`, `1L` and `1.5L` are legacy target-size labels: the stored `target_diameter` value is passed to `LegacyCelerisLab.add_cylinder` as a radius. Paper text must not silently reinterpret these labels as physical diameters. + +SR uses dimensionless velocities and force coefficients, not the clipped PPO observation. Rear symmetry coordinates are half-sums and half-differences, for example + +\[ +C_{d,\mathrm{rear},s}=\frac{C_{d,U}+C_{d,L}}{2},\qquad +C_{d,\mathrm{rear},a}=\frac{C_{d,U}-C_{d,L}}{2}, +\] + +with the same convention for rear lift. + +## Feature strategy + +Broad discovery starts from complete, dimensionless variable representations: + +- `raw_complete`: six velocity components and six cylinder-force components. +- `symmetry`: symmetric/antisymmetric velocity and force coordinates. +- `physics_reduced`: compact diagnostics only; it is not the starting point for article discovery. + +Illusion comparisons use non-overlapping profiles: + +- `actual_only` +- `target_only` +- `actual_plus_target` +- `actual_plus_error` + +Target/error derivatives or other temporal features are added only after static variables are shown insufficient. Cross-correlation is never used to select a lag. + +## Current results + +### Kármán joint candidate + +\[ +\alpha_F=\operatorname{odd}\!\left[-0.381391\,C_{d,\mathrm{rear},a}\right], +\] + +\[ +\alpha_U=1.307782\,C_{l,\mathrm{rear},s}-3.431209, +\qquad +\alpha_L=-\alpha_U(Gx). +\] + +Standard 200-step legacy DTW: + +- Re50: `0.9543` +- Re100: `0.9427` +- Re200: `0.8560` +- Re400: `0.7827` + +Deletion tests support the ordering: + +```text +rear constant > rear lift feedback > front drag-asymmetry feedback +``` + +This is consistent with the physical hypothesis that persistent rear counter-rotation provides the principal downstream velocity-deficit compensation. The SR evidence ranks controller terms but does not establish the spatial momentum correction or causality; those claims require OID/CCD. The law is not uniformly strong at Re400 and must not be called universal. + +### Illusion joint numerical candidate + +\[ +\alpha_F=\operatorname{odd}\!\left[-1.826604\,C_{d,\mathrm{rear},a}+2.064493\,C_{l,F}\right], +\] + +\[ +\alpha_U=1.254440\,C_{d,\mathrm{rear},a}-1.528074\,C_{l,F}, +\qquad +\alpha_L=-\alpha_U(Gx). +\] + +Standard 200-step legacy DTW: + +- 0.75L: `0.8749` +- 1.0L: `0.9217` +- 1.5L: `0.8306` + +Scaling tests show that the `Cl_F` terms dominate action magnitude; the `Cd_rear,a` terms are weaker and partly replaceable. Every one-term deletion remained stable, so the current expression is not a unique mechanism law. Target/error variables were available but were not selected stably by low-complexity static joint discovery. This is a numerical joint reference, not proof of explicit target tracking. + +## What can and cannot be claimed + +Supported: + +- A complete, reproducible discovery-to-CFD-to-ablation workflow. +- Strong Kármán evidence for dominant rear counter-rotation and secondary rear lift feedback. +- A symmetric Illusion joint family that remains finite across all three trained target-size labels. +- R² and formula appearance alone are insufficient predictors of closed-loop value. + +Not supported: + +- Global symbolic optimality or uniqueness. +- A universal Kármán law at Re400. +- An explicit target-tracking mechanism for Illusion. +- Necessity of every Illusion term. +- Physical delay inferred from DTW lag or cross-correlation. +- Distribution-wide or universal generalization. The Article2 extension supports only finite pointwise deployment at its explicitly sampled unseen conditions. + +## Directory map + +```text +SR_analysis/ +├── README.md # this agent entry point +├── PIPELINE.md # exact method, commands and design decisions +├── configs.py # scene and action contracts +├── stage_1_infer.py # PPO data collection +├── stage_2_fit.py # discovery and fixed-topology refit +├── stage_3_validate.py # closed-loop CFD and legacy DTW +├── checks/ # pre-fit physical/wiring gates +├── diagnostics/ # diagnostic-only analyses +├── utils/ # feature, G, formula, data and provenance contracts +├── tests/ # SR CPU contract tests +├── results/README.md # evidence index and result status +├── tools/ # integrity audit; audit is not scientific promotion +├── experiments/v5/ # excluded experimental pipeline +└── archive/ # inactive Stage 4 and historical guides +``` + +`old/`, `archive/`, `experiments/v5/`, historical formula directories and old reports are context only. They are not authoritative sources for article numbers. + +## Environments and resource discipline + +- Stage 1, policy replay and Stage 3: `pycuda_3_10`; PPO inference defaults to CPU so physical GPU 2 remains dedicated to the PyCUDA CFD context. +- Stage 2/PySR and coefficient refit: `sr_env`. +- CFD uses physical GPU 2 and is strictly serial. +- When `CUDA_VISIBLE_DEVICES=2`, the process uses logical device `0`; provenance records both visibility and GPU UUID rather than mislabelling the physical ordinal. +- Every run ID is immutable; failed telemetry is retained. + +## Verification + +At the 2026-07-21 plotting-package revision, the SR contract suite contains 88 passing tests. Run from the repository root: ```bash -# Generate PPO inference data -conda run -n pycuda_3_10 python stage_1_infer.py --scene karman_re100 --device 2 - -# Fit symbolic formula -conda run -n sr_env python stage_2_fit.py --scenes illusion_0.75L,illusion_1L --mode joint --deep - -# Validate in CFD -conda run -n pycuda_3_10 python stage_3_validate.py \ - --scene illusion_1L --device 2 --mode pysr \ - --formula-front results/formulas/illusion_joint_front.json \ - --formula-top results/formulas/illusion_joint_top.json - -# Generate figures -conda run -n pycuda_3_10 python stage_4_analyze.py +PYTHONPATH=src conda run -n sr_env python -m pytest \ + src/SR_analysis/tests tests/test_stage_3_validate.py -q ``` -## Directory Structure - -``` -SR_analysis/ - stage_1_infer.py # PPO inference (all scenes) - stage_2_fit.py # PySR symbolic regression fitting - stage_3_validate.py # CFD closed-loop validation - stage_4_analyze.py # Publication figure generation - configs.py # All scene definitions (19 scenes) - scene_registry.json # Canonical results registry - utils/ # Shared library (features, CFD, fitting) - data/ # Runtime .npz data per scene - results/ - formulas/ # Canonical formula JSONs - validations/ # CFD validation outputs - figures/ # Publication-quality PNG/PDF - FIGURE_INDEX.md # Figure catalog with captions - README.md # Formula + validation index - docs/ - SR_analysis_report.md # Full analysis report - illusion_joint_formula_analysis.md - PIPELINE.md # Detailed reproduction guide - literature_note.md # Paper writing + literature positioning - old/ # Archived historical files -``` - -## Key Results - -| Scene | Formula | CFD Similarity | -|-------|---------|:---:| -| Karman cross-Re (joint) | alpha_F = daF_dt - 14.95*mu*Cl_tot | avg 0.847 | -| Illusion joint (0.75L+1L) | alpha_F = Cd_tot - Cd_err - 5.43 + 0.01*(du_a_dt+u_a) | 0.978/0.970 | -| Vortex lamb (Karman formula) | Karman joint, zero retraining | 0.949 (exceeds PPO) | - -## Environments - -- `pycuda_3_10`: CFD + DRL model loading + visualization (stages 1, 3, 4) -- `sr_env`: PySR symbolic regression (stage 2) -- GPU: device 2 recommended - -## Documentation - -| File | Content | -|------|---------| -| `PIPELINE.md` | Full reproduction guide with all commands | -| `docs/SR_analysis_report.md` | Complete methodology + results + discussion | -| `results/figures/FIGURE_INDEX.md` | All figures with paper-ready captions | -| `literature_note.md` | Literature positioning + writing guidance | -| `results/README.md` | Formula + validation file index | +See `PIPELINE.md` for reproducible commands and `results/README.md` for the exact evidence chain. diff --git a/src/SR_analysis/archive/__init__.py b/src/SR_analysis/archive/__init__.py new file mode 100644 index 0000000..38232a3 --- /dev/null +++ b/src/SR_analysis/archive/__init__.py @@ -0,0 +1 @@ +"""Archived SR workflows retained for historical reference.""" diff --git a/src/SR_analysis/archive/stage4/__init__.py b/src/SR_analysis/archive/stage4/__init__.py new file mode 100644 index 0000000..631ba47 --- /dev/null +++ b/src/SR_analysis/archive/stage4/__init__.py @@ -0,0 +1 @@ +"""Archived Stage 4 publication analysis.""" diff --git a/src/SR_analysis/stage_4_analyze.py b/src/SR_analysis/archive/stage4/stage_4_analyze.py similarity index 98% rename from src/SR_analysis/stage_4_analyze.py rename to src/SR_analysis/archive/stage4/stage_4_analyze.py index fba016b..906f981 100644 --- a/src/SR_analysis/stage_4_analyze.py +++ b/src/SR_analysis/archive/stage4/stage_4_analyze.py @@ -5,7 +5,7 @@ Reads scene_registry.json + results/validations/*.json and produces 6 figures to results/figures/. Also creates FIGURE_INDEX.md. Usage: - conda run -n pycuda_3_10 python stage_4_analyze.py + PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.archive.stage4.stage_4_analyze """ from __future__ import annotations @@ -20,7 +20,8 @@ matplotlib.use("Agg") import matplotlib.pyplot as plt from matplotlib.ticker import FuncFormatter -_REPO = Path(__file__).resolve().parents[1] +_SR_ROOT = Path(__file__).resolve().parents[2] +_REPO = _SR_ROOT.parents[1] if str(_REPO) not in sys.path: sys.path.insert(0, str(_REPO)) _SRC = _REPO / "src" @@ -29,7 +30,7 @@ if str(_SRC) not in sys.path: from SR_analysis.configs import get_scene, SCENES -SR_DIR = Path(__file__).resolve().parent +SR_DIR = _SR_ROOT FIG_DIR = SR_DIR / "results" / "figures" VAL_DIR = SR_DIR / "results" / "validations" FIG_DIR.mkdir(parents=True, exist_ok=True) @@ -549,7 +550,7 @@ All numerical values in these figures are derived from: To regenerate all figures: ```bash -conda run -n pycuda_3_10 python stage_4_analyze.py +PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.archive.stage4.stage_4_analyze ``` """ path = FIG_DIR / "FIGURE_INDEX.md" diff --git a/src/SR_analysis/STAGE_1_INFER.md b/src/SR_analysis/archive/stage_docs/STAGE_1_INFER.md similarity index 100% rename from src/SR_analysis/STAGE_1_INFER.md rename to src/SR_analysis/archive/stage_docs/STAGE_1_INFER.md diff --git a/src/SR_analysis/STAGE_2_FIT.md b/src/SR_analysis/archive/stage_docs/STAGE_2_FIT.md similarity index 100% rename from src/SR_analysis/STAGE_2_FIT.md rename to src/SR_analysis/archive/stage_docs/STAGE_2_FIT.md diff --git a/src/SR_analysis/STAGE_3_VALIDATE.md b/src/SR_analysis/archive/stage_docs/STAGE_3_VALIDATE.md similarity index 100% rename from src/SR_analysis/STAGE_3_VALIDATE.md rename to src/SR_analysis/archive/stage_docs/STAGE_3_VALIDATE.md diff --git a/src/SR_analysis/STAGE_4_ANALYZE.md b/src/SR_analysis/archive/stage_docs/STAGE_4_ANALYZE.md similarity index 76% rename from src/SR_analysis/STAGE_4_ANALYZE.md rename to src/SR_analysis/archive/stage_docs/STAGE_4_ANALYZE.md index 4028f62..9d020d3 100644 --- a/src/SR_analysis/STAGE_4_ANALYZE.md +++ b/src/SR_analysis/archive/stage_docs/STAGE_4_ANALYZE.md @@ -6,14 +6,14 @@ Analyzes PPO policies and SR formulas. Generates FFT spectra, action timeseries, ```bash # PPO action visualization (timeseries + FFT) -conda run -n pycuda_3_10 python stage_4_analyze.py --scene illusion_1L --mode ppo-viz +PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.archive.stage4.stage_4_analyze --scene illusion_1L --mode ppo-viz # Cross-diameter degradation analysis -conda run -n pycuda_3_10 python stage_4_analyze.py \ +PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.archive.stage4.stage_4_analyze \ --scenes illusion_0.75L,illusion_1L,illusion_1.5L --mode degradation # Formula comparison (coming soon) -conda run -n pycuda_3_10 python stage_4_analyze.py --scene illusion_1L --mode formula-compare +PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.archive.stage4.stage_4_analyze --scene illusion_1L --mode formula-compare ``` ## Modes diff --git a/src/SR_analysis/checks/__init__.py b/src/SR_analysis/checks/__init__.py new file mode 100644 index 0000000..7455650 --- /dev/null +++ b/src/SR_analysis/checks/__init__.py @@ -0,0 +1 @@ +"""Pre-fit scientific contract checks.""" diff --git a/src/SR_analysis/checks/order_contract.py b/src/SR_analysis/checks/order_contract.py new file mode 100644 index 0000000..ac7ee81 --- /dev/null +++ b/src/SR_analysis/checks/order_contract.py @@ -0,0 +1,163 @@ +#!/usr/bin/env python3 +"""Verify the legacy fluidic-pinball geometry/action/force channel contract.""" +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np + +REPO_ROOT = Path(__file__).resolve().parents[3] +SRC_ROOT = REPO_ROOT / "src" +if str(REPO_ROOT) not in sys.path: + sys.path.insert(0, str(REPO_ROOT)) +if str(SRC_ROOT) not in sys.path: + sys.path.insert(0, str(SRC_ROOT)) + +from SR_analysis.configs import LEGACY_CFG_DIR, get_scene +from SR_analysis.utils.cfd_interface import load_legacy_configs +from SR_analysis.utils.provenance import atomic_write_json + +CONTRACT_VERSION = "legacy-pinball-order-v1" +BODY_NAMES = ("front", "upper", "lower") + + +def expected_ledger(scene: str) -> dict[str, Any]: + cfg = get_scene(scene) + body_ids = tuple(range(int(cfg["n_objects_env"]) - 3, int(cfg["n_objects_env"]))) + return { + "contract_version": CONTRACT_VERSION, + "scene": scene, + "geometry_order": [ + {"name": "front", "y_offset_L": 0.0}, + {"name": "upper", "y_offset_L": 0.75}, + {"name": "lower", "y_offset_L": -0.75}, + ], + "body_object_ids": dict(zip(BODY_NAMES, body_ids)), + "sensor_layout": list(cfg["sensor_layout"]), + "force_layout": list(cfg["force_layout"]), + "action_layout": list(cfg["action_layout"]), + "ppo_decoder": { + "alpha": "normalized_action * action_scale + action_bias", + "action_scale": float(cfg["action_scale"]), + "action_bias": list(cfg["action_bias"]), + "native_slots": list(body_ids), + }, + "kernel_binding": "action[id_obj] and obs[2*id_obj:2*id_obj+2] share id_obj", + } + + +def _centers_by_id(objects: Mapping[Any, Mapping[str, Any]]) -> list[tuple[float, float, float]]: + return [tuple(float(value) for value in objects[key]["center"]) for key in sorted(objects, key=int)] + + +def verify_runtime_objects(flow_field: Any, scene: str) -> dict[str, Any]: + cfg = get_scene(scene) + ledger = expected_ledger(scene) + centers = _centers_by_id(flow_field.objects) + body_ids = [ledger["body_object_ids"][name] for name in BODY_NAMES] + body_centers = [centers[index] for index in body_ids] + cy = (float(flow_field.FIELD_SHAPE[1]) - 1.0) / 2.0 + l0 = 20.0 + expected = [ + (float(cfg["pinball_front_x"]) * l0, cy, 0.0), + (float(cfg["pinball_rear_x"]) * l0, cy + 0.75 * l0, 0.0), + (float(cfg["pinball_rear_x"]) * l0, cy - 0.75 * l0, 0.0), + ] + np.testing.assert_allclose(body_centers, expected) + ledger["runtime_centers"] = {name: list(center) for name, center in zip(BODY_NAMES, body_centers)} + ledger["runtime_verified"] = True + return ledger + + +def impulse_commands(n_objects: int, epsilon: float) -> dict[str, np.ndarray]: + if n_objects < 3: + raise ValueError("pinball impulse test requires at least three objects") + commands: dict[str, np.ndarray] = {} + for offset, name in enumerate(BODY_NAMES, start=n_objects - 3): + command = np.zeros(n_objects, dtype=np.float32) + command[offset] = float(epsilon) + commands[name] = command + return commands + + +def build_runtime_environment(scene: str, device: int) -> Any: + from LegacyCelerisLab import FlowField + + cfg = get_scene(scene) + cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR) + field_cfg = field_cfg._replace(viscosity=float(cfg["nu"])) + ff = FlowField(field_cfg, cuda_cfg, device_id=device) + cy = (ff.FIELD_SHAPE[1] - 1) / 2.0 + l0 = 20.0 + if cfg["scene_id"] == "karman": + ff.add_cylinder((10.0 * l0, cy, 0.0), l0) + for y in (cy + 2 * l0, cy, cy - 2 * l0): + ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + ff.add_cylinder((float(cfg["pinball_front_x"]) * l0, cy, 0.0), l0 / 2.0) + ff.add_cylinder((float(cfg["pinball_rear_x"]) * l0, cy + 0.75 * l0, 0.0), l0 / 2.0) + ff.add_cylinder((float(cfg["pinball_rear_x"]) * l0, cy - 0.75 * l0, 0.0), l0 / 2.0) + return ff + + +def run_impulse_test(scene: str, device: int, epsilon: float, steps: int) -> dict[str, Any]: + ff = build_runtime_environment(scene, device) + ledger = verify_runtime_objects(ff, scene) + n_objects = len(ff.objects) + zero = np.zeros(n_objects, dtype=np.float32) + warmup = int(4 * ff.FIELD_SHAPE[0] / float(get_scene(scene)["u0"])) + ff.run(warmup, zero) + ff.get_ddf() + ff.save_ddf() + responses: dict[str, Any] = {} + body_ids = ledger["body_object_ids"] + for name, command in impulse_commands(n_objects, epsilon).items(): + ff.restore_ddf() + ff.apply_ddf() + ff.run(steps, command) + force_pairs = np.asarray(ff.obs, dtype=np.float64).reshape(-1, 2) + responses[name] = { + "nonzero_action_slots": np.flatnonzero(command).tolist(), + "command": float(command[body_ids[name]]), + "force_at_commanded_body": force_pairs[body_ids[name]].tolist(), + "all_body_forces": { + body: force_pairs[index].tolist() for body, index in body_ids.items() + }, + } + del ff + ledger["impulse_test"] = { + "epsilon": float(epsilon), + "steps": int(steps), + "responses": responses, + "interpretation": "slot identity is exact; force magnitude is diagnostic and is not used to infer causality", + } + return ledger + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--scene", choices=("karman_re100", "illusion_1L"), required=True) + parser.add_argument("--device", type=int, default=2) + parser.add_argument("--epsilon", type=float, default=1e-4) + parser.add_argument("--steps", type=int, default=10) + parser.add_argument("--output", type=Path) + parser.add_argument("--source-only", action="store_true") + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + document = expected_ledger(args.scene) if args.source_only else run_impulse_test( + args.scene, args.device, args.epsilon, args.steps + ) + if args.output: + atomic_write_json(args.output.resolve(), document) + print(json.dumps(document, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/checks/policy_replay.py b/src/SR_analysis/checks/policy_replay.py new file mode 100644 index 0000000..81de933 --- /dev/null +++ b/src/SR_analysis/checks/policy_replay.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python3 +"""Verify recorded PPO actions by replaying the policy on causal recorded states.""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any, Sequence + +import numpy as np + +from SR_analysis.configs import get_scene, model_path_for_scene +from SR_analysis.stage_1_infer import illusion_observation, load_existing_norm, normalize_raw_observation +from SR_analysis.utils.cfd_interface import load_ppo_model +from SR_analysis.utils.provenance import atomic_write_json, hash_file + +SCHEMA_VERSION = "sr-policy-replay-parity-v1" + + +def replay(scene: str, trajectory: Path, *, model_device: str = "cpu") -> dict[str, Any]: + cfg = get_scene(scene) + norm, norm_path = load_existing_norm(scene, cfg) + model_path = model_path_for_scene(scene) + if model_path is None: + raise FileNotFoundError(f"no PPO model configured for {scene}") + model = load_ppo_model(model_path, device=model_device, s_dim=int(cfg["s_dim"])) + with np.load(trajectory, allow_pickle=False) as data: + sensors = np.asarray(data["sensors"], dtype=np.float64) + forces = np.asarray(data["forces"], dtype=np.float64) + actions = np.asarray(data["actions_norm" if "actions_norm" in data else "actions"], dtype=np.float64) + targets = np.asarray(data["target_forces"], dtype=np.float64) if "target_forces" in data else None + + if len(actions) < 2: + raise ValueError("policy replay requires at least two recorded actions") + raw = np.column_stack((sensors, forces)) + predicted = [] + for index in range(1, len(actions)): + state = raw[index - 1] + if cfg["scene_id"] == "illusion": + if targets is None: + raise ValueError("Illusion policy replay requires target_forces") + observation = illusion_observation(state, norm, targets[index]) + else: + observation = normalize_raw_observation(state, norm) + action, _ = model.predict(observation, deterministic=True) + predicted.append(np.asarray(action, dtype=np.float64).reshape(3)) + + predicted_array = np.asarray(predicted) + recorded = actions[1:] + residual = predicted_array - recorded + max_abs = np.max(np.abs(residual), axis=0) + rmse = np.sqrt(np.mean(residual**2, axis=0)) + tolerance = 2e-6 + return { + "schema_version": SCHEMA_VERSION, + "scene": scene, + "status": "passed" if float(np.max(max_abs)) <= tolerance else "failed", + "semantics": "recorded post-state i-1 is replayed to predict recorded normalized action i; first action is excluded because its pre-state is not stored", + "tolerance": tolerance, + "n_compared": int(len(recorded)), + "action_layout": list(cfg["action_layout"]), + "max_abs_error": max_abs.tolist(), + "rmse": rmse.tolist(), + "sources": { + "trajectory": {"path": str(trajectory), "sha256": hash_file(trajectory)}, + "model": {"path": str(model_path), "sha256": hash_file(Path(model_path))}, + "norm": {"path": str(norm_path), "sha256": hash_file(norm_path)}, + }, + "interpretation": "This checks policy observation/action wiring without requiring separately initialized CFD trajectories to be pointwise identical.", + } + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--scene", required=True) + parser.add_argument("--trajectory", type=Path, required=True) + parser.add_argument("--model-device", default="cpu") + parser.add_argument("--output", type=Path, required=True) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + report = replay(args.scene, args.trajectory.resolve(), model_device=args.model_device) + atomic_write_json(args.output.resolve(), report) + print(json.dumps(report, indent=2, sort_keys=True)) + return 0 if report["status"] == "passed" else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/configs.py b/src/SR_analysis/configs.py index 71fbea3..1a0eb28 100644 --- a/src/SR_analysis/configs.py +++ b/src/SR_analysis/configs.py @@ -14,9 +14,13 @@ import os from typing import Any, Dict, List, Optional, Tuple # -- Root paths (resolved when configs.py is imported) ----------------------- -_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) -MODEL_DIR = os.path.join(_PROJ, "..", "models") -LEGACY_CFG_DIR = os.path.join(_PROJ, "..", "configs", "legacy_configs") +SR_ANALYSIS_DIR = os.path.abspath(os.path.dirname(__file__)) +SRC_DIR = os.path.dirname(SR_ANALYSIS_DIR) +PROJECT_ROOT = os.path.dirname(SRC_DIR) +# Historical private alias retained for callers that imported it. +_PROJ = PROJECT_ROOT +MODEL_DIR = os.path.join(PROJECT_ROOT, "models") +LEGACY_CFG_DIR = os.path.join(PROJECT_ROOT, "configs", "legacy_configs") # -- Physics constants ------------------------------------------------------- U0 = 0.01 # default inlet velocity (lattice) @@ -28,6 +32,18 @@ NY = 512 CENTER_Y = (NY - 1) / 2.0 FIFO_LEN = 150 CONV_LEN = 30 # default; per-scene conv_len overrides this (Illusion=36) +CONTROL_TIME_SCALE_STEPS = 2000 # D_CYL / U0 in lattice steps + +# Native legacy order follows object insertion in the actual environments. +SENSOR_LAYOUT = ("upper_ux", "upper_uy", "center_ux", "center_uy", "lower_ux", "lower_uy") +BODY_LAYOUT = ("front", "upper", "lower") +FORCE_LAYOUT = ("front_fx", "front_fy", "upper_fx", "upper_fy", "lower_fx", "lower_fy") +ACTION_LAYOUT = BODY_LAYOUT +RAW_LAYOUT = SENSOR_LAYOUT + FORCE_LAYOUT +TARGET_SENSOR_SLICE = (0, 6) +ILLUSION_TARGET_FORCE_SLICE = (0, 2) +ILLUSION_TARGET_SENSOR_SLICE = (2, 8) +DTW_VERSION = "legacy_dtw_v1_abs_n_unclipped" def nu_from_re(re_code: float, u0: float = U0) -> float: @@ -35,6 +51,11 @@ def nu_from_re(re_code: float, u0: float = U0) -> float: return u0 * D_REF / re_code +def control_dt(sample_interval: float, control_time_scale_steps: float = CONTROL_TIME_SCALE_STEPS) -> float: + """Return nondimensional control interval, SI / (D/U0).""" + return float(sample_interval) / float(control_time_scale_steps) + + # -- Scene definitions ------------------------------------------------------- # Each scene dict has fields: @@ -115,6 +136,23 @@ SCENES["steady"] = { "target_type": "steady", "s_dim": 12, "u0": U0, + "control_time_scale_steps": CONTROL_TIME_SCALE_STEPS, + "control_dt": control_dt(800), + "fifo_len": FIFO_LEN, + "conv_len": CONV_LEN, + "sensor_layout": SENSOR_LAYOUT, + "body_layout": BODY_LAYOUT, + "action_layout": ACTION_LAYOUT, + "force_layout": FORCE_LAYOUT, + "raw_layout": RAW_LAYOUT, + "raw_sensor_slice": TARGET_SENSOR_SLICE, + "raw_force_slice": (6, 12), + "target_sensor_slice": TARGET_SENSOR_SLICE, + "target_force_slice": None, + "fifo_init_action": (0.0, 0.0, 0.0), + "policy_init_action": (0.0, 0.0, 0.0), + "dtw_version": DTW_VERSION, + "dtw_lag_channel": 1, } # -- Illusion (cylinder imitation, 3 diameters) ---------------------------- @@ -243,6 +281,51 @@ for diam, mn, si in [ } +# -- Legacy contract completion --------------------------------------------- + +def _complete_legacy_contract(cfg: dict) -> None: + """Add explicit metadata without changing historical fields or values.""" + scene_id = cfg["scene_id"] + if scene_id not in {"karman", "illusion"}: + return + + cfg["re_d"] = cfg["re_code"] / 2.0 + cfg["control_time_scale_steps"] = CONTROL_TIME_SCALE_STEPS + cfg["control_dt"] = control_dt(cfg["sample_interval"]) + cfg["fifo_len"] = FIFO_LEN + cfg.setdefault("conv_len", CONV_LEN) + cfg["sensor_layout"] = SENSOR_LAYOUT + cfg["body_layout"] = BODY_LAYOUT + cfg["action_layout"] = ACTION_LAYOUT + cfg["force_layout"] = FORCE_LAYOUT + cfg["raw_layout"] = RAW_LAYOUT + cfg["raw_sensor_slice"] = TARGET_SENSOR_SLICE + cfg["raw_force_slice"] = (6, 12) + cfg["target_sensor_slice"] = ( + ILLUSION_TARGET_SENSOR_SLICE if scene_id == "illusion" else TARGET_SENSOR_SLICE + ) + cfg["target_force_slice"] = ( + ILLUSION_TARGET_FORCE_SLICE if scene_id == "illusion" else None + ) + cfg["fifo_init_action"] = ( + (0.0, -1.0, 1.0) if scene_id == "illusion" else tuple(cfg["action_bias"]) + ) + cfg["policy_init_action"] = cfg["fifo_init_action"] + cfg["dtw_version"] = DTW_VERSION + cfg["dtw_lag_channel"] = 1 + + if scene_id == "illusion": + # Legacy add_cylinder takes radius despite the historical diameter name. + cfg["target_center_x"] = 20.0 + cfg["target_center"] = (20.0, CENTER_Y / L0, 0.0) + cfg["target_radius"] = cfg["target_diameter"] + cfg["target_radius_lattice"] = cfg["target_diameter"] * L0 + + +for _scene_cfg in SCENES.values(): + _complete_legacy_contract(_scene_cfg) + + # -- Utility helpers --------------------------------------------------------- def get_scene(name: str) -> dict: diff --git a/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/config.json b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/config.json new file mode 100644 index 0000000..ad0a9e8 --- /dev/null +++ b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/config.json @@ -0,0 +1,109 @@ +{ + "action_bias": [ + 0.0, + -2.0, + 2.0 + ], + "action_layout": [ + "front", + "upper", + "lower" + ], + "action_scale": 8.0, + "body_layout": [ + "front", + "upper", + "lower" + ], + "control_dt": 0.2, + "control_time_scale_steps": 2000, + "conv_len": 36, + "dtw_lag_channel": 1, + "dtw_version": "legacy_dtw_v1_abs_n_unclipped", + "fifo_init_action": [ + 0.0, + -1.0, + 1.0 + ], + "fifo_len": 150, + "force_layout": [ + "front_fx", + "front_fy", + "upper_fx", + "upper_fy", + "lower_fx", + "lower_fy" + ], + "has_disturbance": false, + "model_name": "d1a3o14_250525_imit_075L_2U_400S", + "mu": 0.02, + "n_objects_env": 6, + "nu": 0.004, + "obs_slice": [ + 0, + 12 + ], + "pinball_front_x": 19.0, + "pinball_rear_x": 20.3, + "policy_init_action": [ + 0.0, + -1.0, + 1.0 + ], + "raw_force_slice": [ + 6, + 12 + ], + "raw_layout": [ + "upper_ux", + "upper_uy", + "center_ux", + "center_uy", + "lower_ux", + "lower_uy", + "front_fx", + "front_fy", + "upper_fx", + "upper_fy", + "lower_fx", + "lower_fy" + ], + "raw_sensor_slice": [ + 0, + 6 + ], + "re_code": 100, + "re_d": 50.0, + "s_dim": 14, + "sample_interval": 400, + "scene_id": "illusion", + "sensor_layout": [ + "upper_ux", + "upper_uy", + "center_ux", + "center_uy", + "lower_ux", + "lower_uy" + ], + "sensor_x": 30.0, + "source": "PPO_inference", + "target_center": [ + 20.0, + 12.775, + 0.0 + ], + "target_center_x": 20.0, + "target_diameter": 0.75, + "target_force_slice": [ + 0, + 2 + ], + "target_radius": 0.75, + "target_radius_lattice": 15.0, + "target_sensor_slice": [ + 2, + 8 + ], + "target_type": "periodic", + "u0": 0.01 +} diff --git a/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/manifest.json b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/manifest.json new file mode 100644 index 0000000..274ec21 --- /dev/null +++ b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/manifest.json @@ -0,0 +1,129 @@ +{ + "cfd_device": 0, + "cfd_device_logical": 0, + "cfd_gpu_name": "Tesla V100-SXM2-16GB", + "cfd_gpu_uuid": "GPU-b8c11c01-c91f-aa0b-abf6-dad35085cc76", + "command": "/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python /home/frank14f/DynamisLab/src/SR_analysis/stage_1_infer.py --group illusion_trained --run-id article-joint-data-illusion-20260718 --device 0 --model-device cpu --steps 200 --norm-source existing", + "config": { + "action_bias": [ + 0.0, + -2.0, + 2.0 + ], + "action_layout": [ + "front", + "upper", + "lower" + ], + "action_scale": 8.0, + "body_layout": [ + "front", + "upper", + "lower" + ], + "control_dt": 0.2, + "control_time_scale_steps": 2000, + "conv_len": 36, + "dtw_lag_channel": 1, + "dtw_version": "legacy_dtw_v1_abs_n_unclipped", + "fifo_init_action": [ + 0.0, + -1.0, + 1.0 + ], + "fifo_len": 150, + "force_layout": [ + "front_fx", + "front_fy", + "upper_fx", + "upper_fy", + "lower_fx", + "lower_fy" + ], + "has_disturbance": false, + "model_name": "d1a3o14_250525_imit_075L_2U_400S", + "mu": 0.02, + "n_objects_env": 6, + "nu": 0.004, + "obs_slice": [ + 0, + 12 + ], + "pinball_front_x": 19.0, + "pinball_rear_x": 20.3, + "policy_init_action": [ + 0.0, + -1.0, + 1.0 + ], + "raw_force_slice": [ + 6, + 12 + ], + "raw_layout": [ + "upper_ux", + "upper_uy", + "center_ux", + "center_uy", + "lower_ux", + "lower_uy", + "front_fx", + "front_fy", + "upper_fx", + "upper_fy", + "lower_fx", + "lower_fy" + ], + "raw_sensor_slice": [ + 0, + 6 + ], + "re_code": 100, + "re_d": 50.0, + "s_dim": 14, + "sample_interval": 400, + "scene_id": "illusion", + "sensor_layout": [ + "upper_ux", + "upper_uy", + "center_ux", + "center_uy", + "lower_ux", + "lower_uy" + ], + "sensor_x": 30.0, + "source": "PPO_inference", + "target_center": [ + 20.0, + 12.775, + 0.0 + ], + "target_center_x": 20.0, + "target_diameter": 0.75, + "target_force_slice": [ + 0, + 2 + ], + "target_radius": 0.75, + "target_radius_lattice": 15.0, + "target_sensor_slice": [ + 2, + 8 + ], + "target_type": "periodic", + "u0": 0.01 + }, + "created_utc": "2026-07-18T09:39:32.938755+00:00", + "cuda_visible_devices": "2", + "git_sha": "ca8ee5f238ee58eaaf48027ad026c35784f76d4d", + "metric_id": "legacy_similarity_v1", + "model_device": "cpu", + "model_path": "/home/frank14f/DynamisLab/models/250525/d1a3o14_250525_imit_075L_2U_400S.zip", + "model_sha256": "1a2d9f77bd6ac44fe7ebe0fe9255e1474c97939238a0200270954fe71f719526", + "norm_source": "existing", + "output_dir": "/home/frank14f/DynamisLab/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L", + "run_id": "article-joint-data-illusion-20260718", + "scene": "illusion_0.75L", + "scene_id": "illusion", + "schema_version": "sr-stage1-manifest-v2" +} diff --git a/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/norm.json b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/norm.json new file mode 100644 index 0000000..1be6aa1 --- /dev/null +++ b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/norm.json @@ -0,0 +1,24 @@ +{ + "action_bias": [ + 0.0, + -2.0, + 2.0 + ], + "force_norm_fact": 0.013459767680615187, + "sens_deviation": [ + 0.9466423392295837, + -0.1479361653327942, + 0.6496055126190186, + -0.06913633644580841, + 0.9421071410179138, + 0.08593743294477463 + ], + "sens_norm_fact": [ + 2.1083030700683594, + 2.7172951698303223, + 0.7220026850700378, + 3.7818100452423096, + 2.1336052417755127, + 2.4038033485412598 + ] +} diff --git a/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/result.json b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/result.json new file mode 100644 index 0000000..1700707 --- /dev/null +++ b/src/SR_analysis/data/runs/article-joint-data-illusion-20260718/illusion/illusion_0.75L/result.json @@ -0,0 +1,124 @@ +{ + "command": "/home/frank14f/anaconda3/envs/pycuda_3_10/bin/python /home/frank14f/DynamisLab/src/SR_analysis/stage_1_infer.py --group illusion_trained --run-id article-joint-data-illusion-20260718 --device 0 --model-device cpu --steps 200 --norm-source existing", + "config": { + "action_bias": [ + 0.0, + -2.0, + 2.0 + ], + "action_layout": [ + "front", + "upper", + "lower" + ], + "action_scale": 8.0, + "body_layout": [ + "front", + "upper", + "lower" + ], + "control_dt": 0.2, + "control_time_scale_steps": 2000, + "conv_len": 36, + "dtw_lag_channel": 1, + "dtw_version": "legacy_dtw_v1_abs_n_unclipped", + "fifo_init_action": [ + 0.0, + -1.0, + 1.0 + ], + "fifo_len": 150, + "force_layout": [ + "front_fx", + "front_fy", + "upper_fx", + "upper_fy", + "lower_fx", + "lower_fy" + ], + "has_disturbance": false, + "model_name": "d1a3o14_250525_imit_075L_2U_400S", + "mu": 0.02, + "n_objects_env": 6, + "nu": 0.004, + "obs_slice": [ + 0, + 12 + ], + "pinball_front_x": 19.0, + "pinball_rear_x": 20.3, + "policy_init_action": [ + 0.0, + -1.0, + 1.0 + ], + "raw_force_slice": [ + 6, + 12 + ], + "raw_layout": [ + "upper_ux", + "upper_uy", + "center_ux", + "center_uy", + "lower_ux", + "lower_uy", + "front_fx", + "front_fy", + "upper_fx", + "upper_fy", + "lower_fx", + "lower_fy" + ], + "raw_sensor_slice": [ + 0, + 6 + ], + "re_code": 100, + "re_d": 50.0, + "s_dim": 14, + "sample_interval": 400, + "scene_id": "illusion", + "sensor_layout": [ + "upper_ux", + "upper_uy", + "center_ux", + "center_uy", + "lower_ux", + "lower_uy" + ], + "sensor_x": 30.0, + "source": "PPO_inference", + "target_center": [ + 20.0, + 12.775, + 0.0 + ], + "target_center_x": 20.0, + "target_diameter": 0.75, + "target_force_slice": [ + 0, + 2 + ], + "target_radius": 0.75, + "target_radius_lattice": 15.0, + "target_sensor_slice": [ + 2, + 8 + ], + "target_type": "periodic", + "u0": 0.01 + }, + "controlled": true, + "git_sha": "ca8ee5f238ee58eaaf48027ad026c35784f76d4d", + "metric_id": "legacy_similarity_v1", + "model_path": "/home/frank14f/DynamisLab/models/250525/d1a3o14_250525_imit_075L_2U_400S.zip", + "model_sha256": "1a2d9f77bd6ac44fe7ebe0fe9255e1474c97939238a0200270954fe71f719526", + "norm_source": "existing", + "norm_source_path": "/home/frank14f/DynamisLab/src/SR_analysis/data/illusion/illusion_0.75L/norm.json", + "run_id": "article-joint-data-illusion-20260718", + "scene": "illusion_0.75L", + 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b/src/SR_analysis/data/v5/ill_1L_sc/norm.json new file mode 100644 index 0000000..32402fd --- /dev/null +++ b/src/SR_analysis/data/v5/ill_1L_sc/norm.json @@ -0,0 +1,24 @@ +{ + "force_norm_fact": 0.0027, + "sens_deviation": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "sens_norm_fact": [ + 0.93, + 0.93, + 0.93, + 0.93, + 0.93, + 0.93 + ], + "action_bias": [ + 0.0, + 0.0, + 0.0 + ] +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/ill_1L_sc/target_harmonics.json b/src/SR_analysis/data/v5/ill_1L_sc/target_harmonics.json new file mode 100644 index 0000000..192b593 --- /dev/null +++ b/src/SR_analysis/data/v5/ill_1L_sc/target_harmonics.json @@ -0,0 +1,194 @@ +[ + { + "dc": 0.6800722130139669, + "amps": [ + 0.17726790501460862, + 0.07214984105228388, + 0.03718921134043499, + 0.031641944554990135, + 0.02359207407770252 + ], + "freqs": [ + 0.05333333333333334, + 0.04666666666666667, + 0.060000000000000005, + 0.04, + 0.15333333333333335 + ], + "phases": [ + 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b/src/SR_analysis/data/v5/kar_d075_sc/config.json @@ -0,0 +1,22 @@ +{ + "case_name": "kar_d075_sc", + "scene_type": "karman", + "seed": 44, + "SI": 800, + "conv_len": 30, + "action_scale": 12.0, + "action_bias": [ + 0.0, + 0.0, + 0.0 + ], + "u0": 0.01, + "l0": 20.0, + "s_dim": 12, + "obs_slice": [ + 2, + 14 + ], + "n_objects": 7, + "dtw_sim_v5": 0.911 +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_d075_sc/norm.json b/src/SR_analysis/data/v5/kar_d075_sc/norm.json new file mode 100644 index 0000000..29b2407 --- /dev/null +++ b/src/SR_analysis/data/v5/kar_d075_sc/norm.json @@ -0,0 +1,24 @@ +{ + "force_norm_fact": 0.0018, + "sens_deviation": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "sens_norm_fact": [ + 0.84, + 0.84, + 0.84, + 0.84, + 0.84, + 0.84 + ], + "action_bias": [ + 0.0, + 0.0, + 0.0 + ] +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_d15_sc/config.json b/src/SR_analysis/data/v5/kar_d15_sc/config.json new file mode 100644 index 0000000..c43b911 --- /dev/null +++ b/src/SR_analysis/data/v5/kar_d15_sc/config.json @@ -0,0 +1,22 @@ +{ + "case_name": "kar_d15_sc", + "scene_type": "karman", + "seed": 45, + "SI": 800, + "conv_len": 30, + "action_scale": 12.0, + "action_bias": [ + 0.0, + 0.0, + 0.0 + ], + "u0": 0.01, + "l0": 20.0, + "s_dim": 12, + "obs_slice": [ + 2, + 14 + ], + "n_objects": 7, + "dtw_sim_v5": 0.892 +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_d15_sc/norm.json b/src/SR_analysis/data/v5/kar_d15_sc/norm.json new file mode 100644 index 0000000..d8ab289 --- /dev/null +++ b/src/SR_analysis/data/v5/kar_d15_sc/norm.json @@ -0,0 +1,24 @@ +{ + "force_norm_fact": 0.0036, + "sens_deviation": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "sens_norm_fact": [ + 0.75, + 0.75, + 0.75, + 0.75, + 0.75, + 0.75 + ], + "action_bias": [ + 0.0, + 0.0, + 0.0 + ] +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_d2_sc/config.json b/src/SR_analysis/data/v5/kar_d2_sc/config.json new file mode 100644 index 0000000..ec7e239 --- /dev/null +++ b/src/SR_analysis/data/v5/kar_d2_sc/config.json @@ -0,0 +1,22 @@ +{ + "case_name": "kar_d2_sc", + "scene_type": "karman", + "seed": 45, + "SI": 800, + "conv_len": 30, + "action_scale": 12.0, + "action_bias": [ + 0.0, + 0.0, + 0.0 + ], + "u0": 0.01, + "l0": 20.0, + "s_dim": 12, + "obs_slice": [ + 2, + 14 + ], + "n_objects": 7, + "dtw_sim_v5": 0.816 +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_d2_sc/norm.json b/src/SR_analysis/data/v5/kar_d2_sc/norm.json new file mode 100644 index 0000000..085def0 --- /dev/null +++ b/src/SR_analysis/data/v5/kar_d2_sc/norm.json @@ -0,0 +1,24 @@ +{ + "force_norm_fact": 0.0046, + "sens_deviation": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "sens_norm_fact": [ + 0.77, + 0.77, + 0.77, + 0.77, + 0.77, + 0.77 + ], + "action_bias": [ + 0.0, + 0.0, + 0.0 + ] +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_re100_sc/config.json b/src/SR_analysis/data/v5/kar_re100_sc/config.json new file mode 100644 index 0000000..75d9feb --- /dev/null +++ b/src/SR_analysis/data/v5/kar_re100_sc/config.json @@ -0,0 +1,22 @@ +{ + "case_name": "kar_re100_sc", + "scene_type": "karman", + "seed": 45, + "SI": 800, + "conv_len": 30, + "action_scale": 12.0, + "action_bias": [ + 0.0, + 0.0, + 0.0 + ], + "u0": 0.01, + "l0": 20.0, + "s_dim": 12, + "obs_slice": [ + 2, + 14 + ], + "n_objects": 7, + "dtw_sim_v5": 0.918 +} \ No newline at end of file diff --git a/src/SR_analysis/data/v5/kar_re100_sc/norm.json b/src/SR_analysis/data/v5/kar_re100_sc/norm.json new file mode 100644 index 0000000..6dacff3 --- /dev/null +++ b/src/SR_analysis/data/v5/kar_re100_sc/norm.json @@ -0,0 +1,24 @@ +{ + "force_norm_fact": 0.0024, + "sens_deviation": [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0 + ], + "sens_norm_fact": [ + 0.75, + 0.75, + 0.75, + 0.75, + 0.75, + 0.75 + ], + "action_bias": [ + 0.0, + 0.0, + 0.0 + ] +} \ No newline at end of file diff --git a/src/SR_analysis/diagnostics/__init__.py b/src/SR_analysis/diagnostics/__init__.py new file mode 100644 index 0000000..c698892 --- /dev/null +++ b/src/SR_analysis/diagnostics/__init__.py @@ -0,0 +1 @@ +"""Scientific diagnostics that do not produce canonical candidates.""" diff --git a/src/SR_analysis/diagnostics/audit_symmetry.py b/src/SR_analysis/diagnostics/audit_symmetry.py new file mode 100644 index 0000000..cb6b37a --- /dev/null +++ b/src/SR_analysis/diagnostics/audit_symmetry.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +"""Evaluate empirical PPO G-equivariance on recorded physical states.""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any, Sequence + +import numpy as np + +from SR_analysis.configs import get_scene +from SR_analysis.stage_1_infer import ( + illusion_observation, + load_existing_norm, + model_path_for_scene, + normalize_raw_observation, +) +from SR_analysis.utils.cfd_interface import load_ppo_model +from SR_analysis.utils.g_operator import apply_G_raw12, apply_G_target_force +from SR_analysis.utils.provenance import atomic_write_json, hash_file +from SR_analysis.utils.symmetry_dataset import empirical_equivariance_defect + +SCHEMA_VERSION = "sr-ppo-g-audit-v1" + + +def _alpha(model: Any, observation: np.ndarray, cfg: dict[str, Any]) -> np.ndarray: + normalized, _ = model.predict(observation, deterministic=True) + normalized = np.asarray(normalized, dtype=np.float64).reshape(3) + return normalized * float(cfg["action_scale"]) + np.asarray(cfg["action_bias"], dtype=np.float64) + + +def audit(scene: str, trajectory_path: Path, *, model_device: str = "cpu") -> dict[str, Any]: + cfg = get_scene(scene) + norm, norm_path = load_existing_norm(scene, cfg) + model_path = model_path_for_scene(scene) + if model_path is None: + raise FileNotFoundError(f"no PPO model configured for {scene}") + model = load_ppo_model(model_path, device=model_device, s_dim=int(cfg["s_dim"])) + with np.load(trajectory_path, allow_pickle=False) as data: + sensors = np.asarray(data["sensors"], dtype=np.float64) + forces = np.asarray(data["forces"], dtype=np.float64) + targets = np.asarray(data["target_forces"], dtype=np.float64) if "target_forces" in data else None + raw = np.column_stack((sensors, forces)) + mirrored_raw = apply_G_raw12(raw) + actions, mirrored_actions = [], [] + for index, (state, mirrored_state) in enumerate(zip(raw, mirrored_raw)): + if cfg["scene_id"] == "illusion": + if targets is None: + raise ValueError("illusion symmetry audit requires target_forces") + target = targets[index] + observation = illusion_observation(state, norm, target) + mirrored_observation = illusion_observation( + mirrored_state, norm, apply_G_target_force(target), + ) + else: + observation = normalize_raw_observation(state, norm) + mirrored_observation = normalize_raw_observation(mirrored_state, norm) + actions.append(_alpha(model, observation, cfg)) + mirrored_actions.append(_alpha(model, mirrored_observation, cfg)) + return { + "schema_version": SCHEMA_VERSION, + "scene": scene, + "status": "diagnostic_only", + "g_contract": "raw state and target are mirrored before PPO normalization/evaluation", + "trajectory": {"path": str(trajectory_path), "sha256": hash_file(trajectory_path)}, + "model": {"path": str(model_path), "sha256": hash_file(model_path)}, + "norm": {"path": str(norm_path), "sha256": hash_file(norm_path)}, + "defect": empirical_equivariance_defect(np.asarray(actions), np.asarray(mirrored_actions)), + "interpretation": "A nonzero defect means hard-G fitting changes the empirical PPO policy and must be reported as physical symmetrization.", + } + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--scene", required=True) + parser.add_argument("--trajectory", type=Path, required=True) + parser.add_argument("--model-device", default="cpu") + parser.add_argument("--output", type=Path, required=True) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + document = audit(args.scene, args.trajectory.resolve(), model_device=args.model_device) + atomic_write_json(args.output.resolve(), document) + print(json.dumps(document, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/diagnostics/compare_trajectories.py b/src/SR_analysis/diagnostics/compare_trajectories.py new file mode 100644 index 0000000..ca74a15 --- /dev/null +++ b/src/SR_analysis/diagnostics/compare_trajectories.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python3 +"""Compare frozen historical and reproduction PPO trajectories without refitting.""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np + +from SR_analysis.configs import get_scene +from SR_analysis.utils.cfd_interface import ( + LEGACY_DTW_VERSION, + compute_similarity_illusion_legacy_v1, + compute_similarity_karman_legacy_v1, +) +from SR_analysis.utils.provenance import atomic_write_json, hash_file + +SCHEMA_VERSION = "sr-trajectory-equivalence-v1" +ARRAY_KEYS = ("sensors", "forces", "actions") +CHANNELS = { + "sensors": ("upper_ux", "upper_uy", "center_ux", "center_uy", "lower_ux", "lower_uy"), + "forces": ("front_fx", "front_fy", "upper_fx", "upper_fy", "lower_fx", "lower_fy"), + "actions": ("front", "upper", "lower"), +} + + +def _load(path: Path) -> dict[str, np.ndarray]: + with np.load(path, allow_pickle=False) as data: + return {key: np.asarray(data[key]) for key in data.files} + + +def _dominant_frequency(values: np.ndarray) -> dict[str, Any]: + centered = np.asarray(values, dtype=np.float64) - np.mean(values, axis=0, keepdims=True) + spectrum = np.fft.rfft(centered, axis=0) + power = np.abs(spectrum) + if power.shape[0] <= 1: + bins = np.zeros(centered.shape[1], dtype=int) + else: + bins = np.argmax(power[1:], axis=0) + 1 + return { + "bin": bins.tolist(), + "cycles_per_sample": (bins / max(centered.shape[0], 1)).tolist(), + "phase_rad": np.angle(spectrum[bins, np.arange(centered.shape[1])]).tolist(), + } + + +def _array_metrics(reference: np.ndarray, candidate: np.ndarray, names: Sequence[str]) -> dict[str, Any]: + rows = min(len(reference), len(candidate)) + ref = np.asarray(reference[:rows], dtype=np.float64) + cur = np.asarray(candidate[:rows], dtype=np.float64) + if ref.shape != cur.shape: + raise ValueError(f"aligned arrays differ in shape: {ref.shape} versus {cur.shape}") + difference = cur - ref + rmse = np.sqrt(np.mean(difference**2, axis=0)) + scale = np.ptp(ref, axis=0) + nrmse = rmse / np.where(scale > 1e-12, scale, 1.0) + return { + "n_aligned": rows, + "channels": list(names), + "rmse": rmse.tolist(), + "range_normalized_rmse": nrmse.tolist(), + "max_abs_error": np.max(np.abs(difference), axis=0).tolist(), + "reference_mean": np.mean(ref, axis=0).tolist(), + "candidate_mean": np.mean(cur, axis=0).tolist(), + "reference_std": np.std(ref, axis=0).tolist(), + "candidate_std": np.std(cur, axis=0).tolist(), + "reference_range": np.ptp(ref, axis=0).tolist(), + "candidate_range": np.ptp(cur, axis=0).tolist(), + "reference_frequency": _dominant_frequency(ref), + "candidate_frequency": _dominant_frequency(cur), + } + + +def _target_states(path: Path) -> np.ndarray: + data = _load(path) + if "target_states" in data: + return np.asarray(data["target_states"]) + if "target" in data: + return np.asarray(data["target"]) + raise KeyError(f"target NPZ has no target_states array: {path}") + + +def compare( + scene: str, + historical: Path, + candidate: Path, + historical_target: Path, + candidate_target: Path, + *, + transient_count: int, +) -> dict[str, Any]: + cfg = get_scene(scene) + old = _load(historical) + new = _load(candidate) + missing = [key for key in ARRAY_KEYS if key not in old or key not in new] + if missing: + raise KeyError(f"trajectory pair lacks required arrays: {missing}") + n = min(*(len(old[key]) for key in ARRAY_KEYS), *(len(new[key]) for key in ARRAY_KEYS)) + if not 0 <= transient_count < n: + raise ValueError("transient_count must be non-negative and smaller than aligned trajectory length") + segments = { + "all_recorded_steps": slice(0, n), + "initial_control_segment": slice(0, transient_count), + "post_initial_segment": slice(transient_count, n), + } + segment_metrics: dict[str, Any] = {} + for label, selection in segments.items(): + if selection.start == selection.stop: + continue + segment_metrics[label] = { + key: _array_metrics(old[key][selection], new[key][selection], CHANNELS[key]) + for key in ARRAY_KEYS + } + old_target = _target_states(historical_target) + new_target = _target_states(candidate_target) + conv_len = int(cfg["conv_len"]) + similarity = ( + compute_similarity_illusion_legacy_v1 + if cfg["scene_id"] == "illusion" + else compute_similarity_karman_legacy_v1 + ) + return { + "schema_version": SCHEMA_VERSION, + "scene": scene, + "status": "diagnostic_only", + "comparison_semantics": { + "alignment": "same recorded index; no lag optimization", + "transient_count": int(transient_count), + "metric_algorithm": LEGACY_DTW_VERSION, + "metric_window": { + "target": f"indices {conv_len}:{2 * conv_len}", + "state": f"last {conv_len} recorded samples", + }, + }, + "sources": { + "historical": {"path": str(historical), "sha256": hash_file(historical)}, + "candidate": {"path": str(candidate), "sha256": hash_file(candidate)}, + "historical_target": {"path": str(historical_target), "sha256": hash_file(historical_target)}, + "candidate_target": {"path": str(candidate_target), "sha256": hash_file(candidate_target)}, + }, + "lengths": { + "historical": {key: len(old[key]) for key in ARRAY_KEYS}, + "candidate": {key: len(new[key]) for key in ARRAY_KEYS}, + "aligned": n, + }, + "segments": segment_metrics, + "legacy_dtw": { + "historical_with_historical_target": float(similarity(old_target, old["sensors"][:n], conv_len)), + "candidate_with_candidate_target": float(similarity(new_target, new["sensors"][:n], conv_len)), + "algorithm": LEGACY_DTW_VERSION, + "window_label": "legacy_reference_cycle_vs_last_recorded_cycle", + }, + "interpretation": "Differences are reported, not auto-accepted; frequency phase is descriptive and is not a physical-delay estimate.", + } + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--scene", required=True) + parser.add_argument("--historical", type=Path, required=True) + parser.add_argument("--candidate", type=Path, required=True) + parser.add_argument("--historical-target", type=Path, required=True) + parser.add_argument("--candidate-target", type=Path, required=True) + parser.add_argument("--transient-count", type=int, default=50) + parser.add_argument("--output", type=Path, required=True) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + document = compare( + args.scene, + args.historical.resolve(), + args.candidate.resolve(), + args.historical_target.resolve(), + args.candidate_target.resolve(), + transient_count=args.transient_count, + ) + atomic_write_json(args.output.resolve(), document) + print(json.dumps(document, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/diagnostics/diagnose_illusion_phase.py b/src/SR_analysis/diagnostics/diagnose_illusion_phase.py new file mode 100644 index 0000000..401b635 --- /dev/null +++ b/src/SR_analysis/diagnostics/diagnose_illusion_phase.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 +"""Static Illusion proxy/phase diagnostic without introducing lag features.""" +from __future__ import annotations + +import argparse +import json +from pathlib import Path +from typing import Any, Sequence + +import numpy as np + +from SR_analysis.configs import get_scene +from SR_analysis.utils.data_contracts import build_batch_dataset +from SR_analysis.utils.provenance import atomic_write_json, hash_file + +SCHEMA_VERSION = "sr-illusion-static-proxy-diagnostic-v1" +FEATURE_GROUPS = { + "lift_only": ("Cl_F", "Cl_T", "Cl_B", "target_Cl"), + "drag_only": ("Cd_F", "Cd_T", "Cd_B", "target_Cd"), + "actual_only": ("Cd_F", "Cl_F", "Cd_T", "Cl_T", "Cd_B", "Cl_B"), + "target_only": ("target_Cd", "target_Cl"), + "error_only": ("Cd_err", "Cl_err"), + "actual_target": ( + "Cd_F", "Cl_F", "Cd_T", "Cl_T", "Cd_B", "Cl_B", "target_Cd", "target_Cl", + ), +} + + +def _fit_block(X: np.ndarray, y: np.ndarray, split: int) -> dict[str, Any]: + train_X = np.column_stack((np.ones(split), X[:split])) + test_X = np.column_stack((np.ones(len(X) - split), X[split:])) + coefficients, *_ = np.linalg.lstsq(train_X, y[:split], rcond=None) + prediction = test_X @ coefficients + truth = y[split:] + residual = truth - prediction + denominator = np.sum((truth - np.mean(truth, axis=0)) ** 2, axis=0) + r2 = 1.0 - np.sum(residual**2, axis=0) / np.where(denominator > 1e-12, denominator, 1.0) + return { + "coefficients": coefficients.tolist(), + "test_r2": r2.tolist(), + "test_mae": np.mean(np.abs(residual), axis=0).tolist(), + "train_indices": [0, split], + "test_indices": [split, len(X)], + } + + +def diagnose(path: Path, *, split_fraction: float = 0.6) -> dict[str, Any]: + cfg = get_scene("illusion_1L") + with np.load(path, allow_pickle=False) as data: + sensors = np.asarray(data["sensors"]) + forces = np.asarray(data["forces"]) + actions = np.asarray(data["actions"]) + target = np.asarray(data["target_forces"]) + all_features = tuple(dict.fromkeys(key for group in FEATURE_GROUPS.values() for key in group)) + dataset = build_batch_dataset( + sensors, forces, actions, all_features, + mu=float(cfg["mu"]), dt_c=float(cfg["control_dt"]), + alignment="historical_post_state_same_action", action_mode="normalized", + action_scale=float(cfg["action_scale"]), action_bias=cfg["action_bias"], + target_forces=target, illusion=True, u0=float(cfg["u0"]), + include_cos_sin=False, warmup=2, + ) + split = max(2, min(len(dataset.X) - 2, int(len(dataset.X) * split_fraction))) + index = {name: i for i, name in enumerate(all_features)} + diagnostics = { + name: { + "feature_names": list(features), + **_fit_block(dataset.X[:, [index[key] for key in features]], dataset.Y, split), + } + for name, features in FEATURE_GROUPS.items() + } + return { + "schema_version": SCHEMA_VERSION, + "scene": "illusion_1L", + "status": "diagnostic_only", + "triggered_by": [ + "mapped-shared rear static fit collapsed to a constant", + "independent rear fit selected drag terms while front selected lift/target-lift terms", + "PPO empirical G defect is large", + ], + "trajectory": {"path": str(path), "sha256": hash_file(path)}, + "action_layout": ["front", "upper", "lower"], + "ablations": diagnostics, + "evidence_boundary": ( + "All regressions use current-time static signals only. Differences identify statistical proxy value; " + "they do not establish transport delay, and no cross-correlation-selected lag is used." + ), + } + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--trajectory", type=Path, required=True) + parser.add_argument("--split-fraction", type=float, default=0.6) + parser.add_argument("--output", type=Path, required=True) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + if not 0.1 <= args.split_fraction <= 0.9: + raise ValueError("split fraction must lie in [0.1, 0.9]") + document = diagnose(args.trajectory.resolve(), split_fraction=args.split_fraction) + atomic_write_json(args.output.resolve(), document) + print(json.dumps(document, indent=2, sort_keys=True)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/experiments/__init__.py b/src/SR_analysis/experiments/__init__.py new file mode 100644 index 0000000..cbb4752 --- /dev/null +++ b/src/SR_analysis/experiments/__init__.py @@ -0,0 +1 @@ +"""Optional and non-canonical SR experiments.""" diff --git a/src/SR_analysis/experiments/v5/__init__.py b/src/SR_analysis/experiments/v5/__init__.py new file mode 100644 index 0000000..7f996aa --- /dev/null +++ b/src/SR_analysis/experiments/v5/__init__.py @@ -0,0 +1 @@ +"""V5 bridge and fitting experiments; data and results remain in place.""" diff --git a/src/SR_analysis/experiments/v5/bridge_v5.py b/src/SR_analysis/experiments/v5/bridge_v5.py new file mode 100644 index 0000000..b1eac6c --- /dev/null +++ b/src/SR_analysis/experiments/v5/bridge_v5.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +"""Bridge V5 eval outputs to SR_analysis/data/v5/ format. + +For each effective V5 case: + 1. Reads signals.npz from eval/output/train/{case}/ + 2. Finds best seed from metrics.json + 3. Finds target.npy and target_harmonics.json from train/output/{case}_seed{N}/ + 4. Uses calibration.json from calibrations/ + 5. Creates SR_analysis/data/v5/{case}/ with controlled.npz, target.npz, etc. + +Effective cases (with best seed and DTW): + - kar_re100_sc (seed45, dtw=0.918) — baseline + - kar_d075_sc (seed44, dtw=0.911) — vardist + - kar_d15_sc (seed45, dtw=0.892) — vardist + - kar_d2_sc (seed45, dtw=0.816) — vardist, borderline + - ill_075L_sc (seed43, dtw=0.807) + - ill_1L_sc (seed43, dtw=0.900) + - ill_15L_sc (seed43, dtw=0.906) + - ill_2L_sc (seed43, dtw=0.800) + +Usage: + PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.experiments.v5.bridge_v5 + PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.experiments.v5.bridge_v5 --include-failed + PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.experiments.v5.bridge_v5 --cases kar_re100_sc,ill_1L_sc +""" +from __future__ import annotations + +import argparse +import json +import shutil +from pathlib import Path + +import numpy as np + +_SR_ROOT = Path(__file__).resolve().parents[2] +_REPO = _SR_ROOT.parents[1] +_EVAL_OUT = _REPO / "src" / "drl_pinball" / "eval" / "output" / "train" +_TRAIN_OUT = _REPO / "src" / "drl_pinball" / "train" / "output" +_TRAIN_CAL = _REPO / "src" / "drl_pinball" / "train" / "calibrations" +_SR_DATA = _SR_ROOT / "data" / "v5" + +L0 = 20.0 +U0 = 0.01 + +# Best seeds from eval metrics.json +EFFECTIVE_CASES = { + "kar_re100_sc": {"seed": 45, "dtw": 0.918, "scene_type": "karman", + "cal_name": "kar_re100", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, + "kar_d075_sc": {"seed": 44, "dtw": 0.911, "scene_type": "karman", + "cal_name": "kar_d075_sc", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, + "kar_d15_sc": {"seed": 45, "dtw": 0.892, "scene_type": "karman", + "cal_name": "kar_d15_sc", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, + "kar_d2_sc": {"seed": 45, "dtw": 0.816, "scene_type": "karman", + "cal_name": "kar_d2_sc", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, + "ill_075L_sc": {"seed": 43, "dtw": 0.807, "scene_type": "illusion", + "cal_name": "ill_075L", + "si": 400, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 14, "conv_len": 36, + "target_diam": 0.75, + "obs_slice": (0, 12), "n_objects": 6}, + "ill_1L_sc": {"seed": 43, "dtw": 0.900, "scene_type": "illusion", + "cal_name": "ill_1L", + "si": 600, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 14, "conv_len": 36, + "target_diam": 1.0, + "obs_slice": (0, 12), "n_objects": 6}, + "ill_15L_sc": {"seed": 43, "dtw": 0.906, "scene_type": "illusion", + "cal_name": "ill_15L", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 14, "conv_len": 36, + "target_diam": 1.5, + "obs_slice": (0, 12), "n_objects": 6}, + "ill_2L_sc": {"seed": 43, "dtw": 0.800, "scene_type": "illusion", + "cal_name": "ill_2L", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 14, "conv_len": 36, + "target_diam": 2.0, + "obs_slice": (0, 12), "n_objects": 6}, +} + +FAILED_CASES = { + "kar_re60_sc": {"seed": 43, "dtw": 0.364, "scene_type": "karman", + "cal_name": "kar_re60_sc", + "si": 800, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, + "kar_re200_sc": {"seed": 43, "dtw": 0.712, "scene_type": "karman", + "cal_name": "kar_re200_sc", + "si": 500, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, + "kar_re400_sc": {"seed": 43, "dtw": 0.565, "scene_type": "karman", + "cal_name": "kar_re400_sc", + "si": 400, "action_scale": 12.0, "action_bias": (0.0, 0.0, 0.0), + "s_dim": 12, "conv_len": 30, + "obs_slice": (2, 14), "n_objects": 7}, +} + + +def bridge_case(case_name: str, cfg: dict, out_dir: Path) -> bool: + seed = cfg["seed"] + scene_type = cfg["scene_type"] + cal_name = cfg["cal_name"] + + signals_path = _EVAL_OUT / case_name / "signals.npz" + if not signals_path.exists(): + print(f" SKIP {case_name}: signals.npz not found") + return False + + target_dir = _TRAIN_OUT / "{}_seed{}".format(case_name, seed) + target_npy = target_dir / "target.npy" + target_harm = target_dir / "target_harmonics.json" + + if not target_npy.exists(): + print(f" SKIP {case_name}: target.npy not found at {target_npy}") + return False + + cal_path = _TRAIN_CAL / cal_name / "calibration.json" + if not cal_path.exists(): + cal_path = target_dir / "calibration.json" + if not cal_path.exists(): + print(f" SKIP {case_name}: calibration.json not found") + return False + + out_dir.mkdir(parents=True, exist_ok=True) + print(f" Bridging {case_name} (seed={seed}, dtw={cfg['dtw']:.3f})...") + + # 1. signals.npz -> controlled.npz + data = dict(np.load(signals_path, allow_pickle=True)) + np.savez_compressed(out_dir / "controlled.npz", + sensors=data["sensors"], + forces=data["forces"], + actions=data["actions"]) + + # 2. target + target = np.load(target_npy) + np.savez_compressed(out_dir / "target.npz", target_states=target) + + # 3. Harmonics (illusion only) + if scene_type == "illusion" and target_harm.exists(): + shutil.copy(target_harm, out_dir / "target_harmonics.json") + + # 4. norm.json from calibration + with open(cal_path) as f: + cal = json.load(f) + + force_scale = float(cal.get("FORCE_SCALE", 1.0)) + sens_scale = float(cal.get("SENS_SCALE", 1.0)) + action_bias = cal.get("ACTION_BIAS", [0.0, 0.0, 0.0]) + + norm = { + "force_norm_fact": force_scale, + "sens_deviation": [0.0] * 6, + "sens_norm_fact": [sens_scale] * 6, + "action_bias": action_bias, + } + with open(out_dir / "norm.json", "w") as f: + json.dump(norm, f, indent=2) + + # 5. config.json + config = { + "case_name": case_name, + "scene_type": scene_type, + "seed": seed, + "SI": cfg["si"], + "conv_len": cfg["conv_len"], + "action_scale": cfg["action_scale"], + "action_bias": list(cfg["action_bias"]), + "u0": U0, + "l0": L0, + "s_dim": cfg["s_dim"], + "obs_slice": cfg["obs_slice"], + "n_objects": cfg["n_objects"], + "dtw_sim_v5": cfg["dtw"], + } + if "target_diam" in cfg: + config["target_diam"] = cfg["target_diam"] + with open(out_dir / "config.json", "w") as f: + json.dump(config, f, indent=2) + + print(f" -> {out_dir}") + return True + + +def main() -> int: + parser = argparse.ArgumentParser(description="Bridge V5 eval data to SR_analysis format") + parser.add_argument("--cases", type=str, default=None, + help="Comma-separated case names (default: all effective)") + parser.add_argument("--include-failed", action="store_true", + help="Also bridge cross-Re failed cases") + args = parser.parse_args() + + all_cases = dict(EFFECTIVE_CASES) + if args.include_failed: + all_cases.update(FAILED_CASES) + + if args.cases: + requested = [c.strip() for c in args.cases.split(",")] + cases = {k: v for k, v in all_cases.items() if k in requested} + else: + cases = all_cases + + print(f"Bridging {len(cases)} V5 cases to SR_analysis/data/v5/...") + print(f"SR data root: {_SR_DATA}") + + success = 0 + for case_name, cfg in cases.items(): + out_dir = _SR_DATA / case_name + if bridge_case(case_name, cfg, out_dir): + success += 1 + + print(f"\nDone: {success}/{len(cases)} cases bridged.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/experiments/v5/stage_2_fit_v5.py b/src/SR_analysis/experiments/v5/stage_2_fit_v5.py new file mode 100644 index 0000000..14710a8 --- /dev/null +++ b/src/SR_analysis/experiments/v5/stage_2_fit_v5.py @@ -0,0 +1,260 @@ +#!/usr/bin/env python3 +"""Stage 2 V5: PySR symbolic regression on V5-trained PPO models. + +Loads V5 controlled.npz from SR_analysis/data/v5/, uses V5 action decoder +(action_scale=12, action_bias=[0,0,0]), and outputs formulas to +SR_analysis/results/formulas_v5/. + +Supports per-scene individual and cross-scene joint fitting. + +Usage: + # Per-scene (fast, niter=40) + PYTHONPATH=src conda run -n sr_env python -m SR_analysis.experiments.v5.stage_2_fit_v5 --scene kar_re100_sc + + # Per-scene deep search (niter=120) + PYTHONPATH=src conda run -n sr_env python -m SR_analysis.experiments.v5.stage_2_fit_v5 --scene kar_re100_sc --deep + + # Joint: all effective Karman cases + PYTHONPATH=src conda run -n sr_env python -m SR_analysis.experiments.v5.stage_2_fit_v5 --group karman_v5 --mode joint + + # Joint: all Illusion cases + PYTHONPATH=src conda run -n sr_env python -m SR_analysis.experiments.v5.stage_2_fit_v5 --group illusion_v5 --mode joint + + # Joint: mixed V5 + legacy (cross-solver) + PYTHONPATH=src conda run -n sr_env python -m SR_analysis.experiments.v5.stage_2_fit_v5 --scenes kar_re100_sc,kar_d075_sc,kar_d15_sc,karman_re100,karman_re200 --mode joint --output-label mixed_karman +""" +from __future__ import annotations + +import argparse +import json +import os +import sys +from pathlib import Path + +import numpy as np + +_SR_ROOT = Path(__file__).resolve().parents[2] +_REPO = _SR_ROOT.parents[1] +if str(_REPO) not in sys.path: + sys.path.insert(0, str(_REPO)) +_SRC = _REPO / "src" +if str(_SRC) not in sys.path: + sys.path.insert(0, str(_SRC)) + +from SR_analysis.utils.feature_builder import ( + compute_dimensionless, compute_features, build_feature_matrix, + PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS, CORE_FEAT_KEYS_V2, MU_FEAT_KEYS, +) +from SR_analysis.configs import get_scene # for legacy scenes + +ALL_MU = list(MU_FEAT_KEYS) + ["mu_Cl_tot"] +PHYS = [k for k in CORE_FEAT_KEYS_V2 if not k.startswith(("aF_","aB_","aT_","daF","daB","daT"))] +PHYS_DADT = PHYS + ["daF_dt","daB_dt","daT_dt"] + +# V5 action decoder (all cases: no-bias, scale=12) +V5_ACTION_SCALE = 12.0 +V5_ACTION_BIAS = (0.0, 0.0, 0.0) +V5_U0 = 0.01 + +# V5 scene configs (for scenes without legacy configs.py entry) +V5_KARMAN_CFG = { + "scene_id": "karman", "u0": V5_U0, "mu": 2.0 / 100, # Re=100 + "sample_interval": 800, "action_scale": V5_ACTION_SCALE, + "action_bias": V5_ACTION_BIAS, +} +V5_ILLUSION_CFG = { + "scene_id": "illusion", "u0": V5_U0, "mu": 2.0 / 100, # Re=100 + "sample_interval": 600, "action_scale": V5_ACTION_SCALE, + "action_bias": V5_ACTION_BIAS, +} + +# SI overrides per case +V5_SI_OVERRIDE = { + "ill_075L_sc": 400, "ill_1L_sc": 600, "ill_15L_sc": 800, "ill_2L_sc": 800, + "kar_re60_sc": 800, "kar_re200_sc": 500, "kar_re400_sc": 400, +} + + +def is_v5_scene(name: str) -> bool: + return "_sc" in name or "_tr" in name + + +def load_controlled_v5(case_name: str): + """Load controlled.npz from V5 data directory with V5 action decoder.""" + data_dir = _SR_ROOT / "data" / "v5" / case_name + config_path = data_dir / "config.json" + + npz = np.load(data_dir / "controlled.npz", allow_pickle=True) + s = npz["sensors"].astype(np.float64) + f = npz["forces"].astype(np.float64) + a = npz["actions"].astype(np.float64) # normalized [-1,1] + + # Load config to get SI + cfg = V5_KARMAN_CFG.copy() if "ill_" not in case_name else V5_ILLUSION_CFG.copy() + if config_path.exists(): + with open(config_path) as fp: + v5_cfg = json.load(fp) + cfg["si_actual"] = v5_cfg.get("SI", 800) + cfg["scene_type"] = v5_cfg.get("scene_type", "") + + # Apply SI override + si = V5_SI_OVERRIDE.get(case_name, cfg.get("si_actual", 800)) + cfg["sample_interval"] = si + cfg["si_actual"] = si # for feature builder + + # Convert normalized action to physical surface velocity + ap = (a * V5_ACTION_SCALE + np.array(V5_ACTION_BIAS)) * V5_U0 + return s, f, ap, None, cfg # No target_forces for V5 karman + + +def load_controlled_any(scene_name: str): + """Load from either V5 or legacy based on scene name.""" + if is_v5_scene(scene_name): + return load_controlled_v5(scene_name) + else: + # Legacy scene - use original configs.py + cfg = get_scene(scene_name) + sid = cfg["scene_id"] + data_dir = f"src/SR_analysis/data/{sid}/{scene_name}" + npz = np.load(os.path.join(data_dir, "controlled.npz"), allow_pickle=True) + s = npz["sensors"].astype(np.float64) + f = npz["forces"].astype(np.float64) + a = npz["actions"].astype(np.float64) + ap = (a * cfg["action_scale"] + np.array(cfg["action_bias"])) * cfg["u0"] + tf = npz["target_forces"].astype(np.float64) if "target_forces" in npz else None + return s, f, ap, tf, cfg + + +def build_features_v5(s, f, ap, cfg, feat_keys): + """Build feature matrix from raw V5 data.""" + T = s.shape[0] + a1 = np.zeros((T, 3)); a2 = np.zeros((T, 3)) + a1[1:] = ap[:-1]; a2[2:] = ap[:-2] + dim = compute_dimensionless(s, f, u0=cfg["u0"], d=20.0) + si = cfg.get("sample_interval", 800) + dt_c = si / 2000.0 + mu = cfg["mu"] + sym = compute_features(dim, a1, a2, mu, alpha_mode=False, + include_mu="mu" in feat_keys, + include_cos_sin=False, u0=cfg["u0"], + target_forces=None, dt_c=dt_c, + sensors_raw=s, forces_raw=f) + X = build_feature_matrix(sym, feat_keys, add_bias=False)[2:] + Y = ap[2:] / cfg["u0"] + return X, Y + + +FORMULA_DIR = _SR_ROOT / "results" / "formulas_v5" +os.makedirs(FORMULA_DIR, exist_ok=True) + +FIT_GROUPS_V5 = { + "karman_v5": ["kar_re100_sc", "kar_d075_sc", "kar_d15_sc", "kar_d2_sc"], + "illusion_v5": ["ill_075L_sc", "ill_1L_sc", "ill_15L_sc", "ill_2L_sc"], +} + + +def get_feature_keys_v5(scene_name: str): + if "ill_" in scene_name: + return ILLUSION_PHASE_KEYS + return PHYS_DADT + ALL_MU + + +def fit_v5(scene_name, feat_keys, niter, output_label): + s, f, ap, tf, cfg = load_controlled_any(scene_name) + X, Y = build_features_v5(s, f, ap, cfg, feat_keys) + from pysr import PySRRegressor + m = PySRRegressor(binary_operators=["+","-","*","/"], unary_operators=["square"], + niterations=niter, populations=30, maxsize=15, + complexity_of_constants=2, parsimony=0.01, batching=False) + m.fit(X, Y[:, 0], variable_names=feat_keys) + front = {"scene": output_label, "channel": "front", "output": "alpha", + "feature_keys": feat_keys, "best_sympy": str(m.sympy()), + "best_score": float(m.score(X, Y[:, 0])), "niterations": niter} + json.dump(front, open(os.path.join(FORMULA_DIR, f"{output_label}_front.json"), "w"), indent=2) + print(f" Front: {front['best_sympy']} (R2={front['best_score']:.4f})") + + # Top (with bias) + m2 = PySRRegressor(binary_operators=["+","-","*","/"], unary_operators=["square"], + niterations=niter, populations=30, maxsize=15, + complexity_of_constants=2, parsimony=0.01, batching=False) + Xt = build_feature_matrix( + dict(zip(feat_keys, [X[:, i] for i in range(len(feat_keys))])), + feat_keys, add_bias=True) + if isinstance(Xt, list): + Xt = Xt[0] + if Xt.ndim == 1: + Xt = Xt.reshape(-1, 1) + m2.fit(Xt, Y[:, 2], variable_names=["bias"] + feat_keys) + top = {"scene": output_label, "channel": "top", "output": "alpha", + "feature_keys": ["bias"] + feat_keys, "best_sympy": str(m2.sympy()), + "best_score": float(m2.score(Xt, Y[:, 2])), "niterations": niter} + json.dump(top, open(os.path.join(FORMULA_DIR, f"{output_label}_top.json"), "w"), indent=2) + print(f" Top: {top['best_sympy']} (R2={top['best_score']:.4f})") + return front, top + + +def fit_joint_v5(scene_names, feat_keys, niter, output_label): + Xl, Yl = [], [] + for sn in scene_names: + s, f, ap, tf, cfg = load_controlled_any(sn) + X, Y = build_features_v5(s, f, ap, cfg, feat_keys) + Xl.append(X); Yl.append(Y[:, 0]) + X_all = np.vstack(Xl); Y_all = np.concatenate(Yl) + from pysr import PySRRegressor + m = PySRRegressor(binary_operators=["+","-","*","/"], unary_operators=["square"], + niterations=niter, populations=40, maxsize=15, + complexity_of_constants=2, parsimony=0.01, batching=False) + m.fit(X_all, Y_all, variable_names=feat_keys) + front = {"scene": output_label, "channel": "front", "output": "alpha", + "feature_keys": feat_keys, "best_sympy": str(m.sympy()), + "best_score": float(m.score(X_all, Y_all)), "niterations": niter, + "n_samples": int(X_all.shape[0])} + json.dump(front, open(os.path.join(FORMULA_DIR, f"{output_label}_front.json"), "w"), indent=2) + print(f" Joint Front: {front['best_sympy']} (R2={front['best_score']:.4f})") + return front + + +def main(): + ap = argparse.ArgumentParser(description="Stage 2 V5: PySR fitting on V5 models") + ap.add_argument("--scene", type=str, default=None) + ap.add_argument("--scenes", type=str, default=None) + ap.add_argument("--group", type=str, default=None) + ap.add_argument("--mode", type=str, default="per-scene", choices=["per-scene", "joint"]) + ap.add_argument("--deep", action="store_true", help="Deep search (niter=120)") + ap.add_argument("--output-label", type=str, default=None) + ap.add_argument("--feat-keys", type=str, default=None, + help="Comma-separated feat keys override (e.g. PHYS_DADT)") + args = ap.parse_args() + + niter = 120 if args.deep else 40 + + if args.group and args.group in FIT_GROUPS_V5: + scene_names = FIT_GROUPS_V5[args.group] + elif args.scenes: + scene_names = [s.strip() for s in args.scenes.split(",")] + elif args.scene: + scene_names = [args.scene] + else: + ap.error("Need --scene, --scenes, or --group") + + label = args.output_label or ("_".join(scene_names) if len(scene_names) > 1 else scene_names[0]) + + if args.mode == "per-scene": + for sn in scene_names: + keys = get_feature_keys_v5(sn) + if args.feat_keys: + keys = [k.strip() for k in args.feat_keys.split(",")] + print(f"\n=== {sn} (niter={niter}, {len(keys)} features) ===") + fit_v5(sn, keys, niter, sn) + elif args.mode == "joint": + keys = get_feature_keys_v5(scene_names[0]) + if args.feat_keys: + keys = [k.strip() for k in args.feat_keys.split(",")] + print(f"\n=== Joint {label} (niter={niter}, {len(keys)} features, {len(scene_names)} scenes) ===") + fit_joint_v5(scene_names, keys, niter, label) + + print("\nDone.") + + +if __name__ == "__main__": + main() diff --git a/src/SR_analysis/results/README.md b/src/SR_analysis/results/README.md index 574c3bd..1612809 100644 --- a/src/SR_analysis/results/README.md +++ b/src/SR_analysis/results/README.md @@ -1,80 +1,288 @@ -# Results Reference Table +# SR article evidence index -> Canonical result files for PySR symbolic regression and CFD closed-loop validation. -> See `docs/SR_analysis_report.md` and `docs/illusion_joint_formula_analysis.md` for analysis. +This is the authoritative index for current SR results. Read `../README.md` for the scientific summary and `../PIPELINE.md` for the method and commands. ---- +## Status vocabulary -## Category A: Canonical PySR Formulas +- **article evidence:** generated under the final channel, alignment, symmetry and metric contracts. +- **numerical candidate:** closed-loop tested, but not necessarily unique or fully physically explained. +- **diagnostic:** useful for understanding or failure analysis; not an article conclusion by itself. +- **historical frozen:** preserved context that is not current evidence. +- **rejected:** failed stability, integrity or scientific screening; retained rather than overwritten. -| File | Scene | Formula | R2 | -|------|-------|---------|:--:| -| `formulas/karman_joint_front.json` | Karman cross-Re | `daF_dt - 14.952*mu*Cl_tot` | 1.000 | -| `formulas/karman_joint_top.json` | Karman cross-Re | `3.414` (constant) | 1.000 | -| `formulas/illusion_joint_front.json` | Illusion joint | `Cd_tot - (Cd_err + 5.428) - 0.00978*(du_a_dt + u_a)` | 0.907 | -| `formulas/illusion_joint_top.json` | Illusion joint | `(Cd_err - (Cd_rear - Cl_err))*0.535 + 2.782` | 0.828 | -| `formulas/karman_re50_front.json` | Karman Re=50 | Independent per-Re | 1.000 | -| `formulas/karman_re100_front.json` | Karman Re=100 | Independent per-Re | 1.000 | -| `formulas/karman_re200_front.json` | Karman Re=200 | Independent per-Re | 1.000 | -| `formulas/karman_re400_front.json` | Karman Re=400 | Independent per-Re | 1.000 | -| `formulas/illusion_0.75L_front.json` | Illusion 0.75L | `-0.169*(Cl_tot + dCl_tot_dt) - 1.240` | 0.692 | -| `formulas/illusion_1L_front.json` | Illusion 1L | `(du_a_dt + u_a + 26.5)*0.0123` | 0.929 | -| `formulas/illusion_1.5L_front.json` | Illusion 1.5L | Non-SR (see report) | — | -| `formulas/illusion_joint_marker_front.json` | Illusion joint + marker | `diam*((du_a_dt+u_a)*0.01 + 6.32) - 6.00` | 0.942 | +Integrity audit is not scientific promotion. `scene_registry.json` is not updated automatically. ---- +## One-stop final package -## Category B: CFD Closed-Loop Validation +```text +runs/article-joint-sr-final-20260718/ +├── readable_summary.txt +└── evidence_manifest.json +``` -| File | Scene | Mode | Similarity | -|------|-------|------|:---:| -| `validations/karman_re50.json` | Karman Re=50 | Joint formula | 0.847 | -| `validations/karman_re100.json` | Karman Re=100 | Joint formula | 0.888 | -| `validations/karman_re200.json` | Karman Re=200 | Joint formula | 0.845 | -| `validations/karman_re400.json` | Karman Re=400 | Joint formula | 0.806 | -| `validations/karman_re50_ppo.json` | Karman Re=50 | PPO baseline | 0.961 | -| `validations/karman_re100_ppo.json` | Karman Re=100 | PPO baseline | 0.954 | -| `validations/karman_re200_ppo.json` | Karman Re=200 | PPO baseline | 0.884 | -| `validations/karman_re400_ppo.json` | Karman Re=400 | PPO baseline | 0.795 | -| `validations/illusion_0.75L.json` | Illusion 0.75L | Joint formula | 0.978 | -| `validations/illusion_1L.json` | Illusion 1L | Joint formula | 0.970 | -| `validations/illusion_0.5L.json` | Illusion 0.5L | Joint formula (gen) | 0.854 | -| `validations/illusion_0.6L.json` | Illusion 0.6L | Joint formula (gen) | 0.939 | -| `validations/illusion_0.8L.json` | Illusion 0.8L | Joint formula (gen) | 0.908 | -| `validations/illusion_1.2L.json` | Illusion 1.2L | Joint formula (gen) | 0.849 | -| `validations/illusion_2L.json` | Illusion 2L | Joint formula (gen) | 0.676 | -| `validations/vortex_lamb.json` | Vortex lamb | Karman formula | 0.949 | -| `validations/vortex_taylor.json` | Vortex taylor | Karman formula | 0.905 | +`readable_summary.txt` is the fastest result review. `evidence_manifest.json` links the machine-readable chain and records explicit non-claims. ---- +## Article2 coefficient-frozen extension: steady calibration, duration and sampled unseen conditions -## Category C: Source Data +The 2026-07-20 extension is indexed by: -| Path | Content | -|------|---------| -| `data/karman/karman_re{50,100,200,400}/controlled.npz` | PPO actions, sensors, forces | -| `data/illusion/illusion_{0.5L..2L}/controlled.npz` | PPO data (trained diameters only) | -| `data/illusion/illusion_{0.5L..2L}/target.npz` | Target signals (8 columns: [cyl_fx,cyl_fy, s0_ux,s0_uy, s1_ux,s1_uy, s2_ux,s2_uy]; use `[:, 2:8]` for pure sensor channels) | -| `data/illusion/illusion_{0.5L..2L}/target_harmonics.json` | FFT harmonics | -| `data/vortex/vortex_{lamb,taylor}/controlled.npz` | Vortex PPO data | +```text +runs/article2-sr-elements-20260720/ +runs/article2-steady-analysis-20260720/ +runs/article2-percase-refit-summary-20260720/ +runs/article2-timeseries-csv-20260720/ +runs/article2-long-timeseries-csv-20260720/ +runs/article2-generalization-summary-20260720/ +``` ---- +Key results, all using `legacy_dtw_v1_abs_n_unclipped`: -## How to Read These Files +- A disturbance-free constant-speed sweep peaks at sampled rear speed magnitude `5 U0` (0.987212). The Kármán joint law gives 0.966494 and converges to rear actions `(-3.4306, +3.4318)`, quantitatively matching its constant magnitude 3.4312. +- The 400-step Kármán similarities are 0.952178, 0.944339, 0.863850 and 0.833365 at Re50, Re100, Re200 and Re400. Illusion gives 0.854379, 0.915609 and 0.833587 at 0.75L, 1L and 1.5L. All telemetry remains finite. +- With coefficients frozen, pointwise Kármán deployment gives 0.988078, 0.954339, 0.907496 and 0.835758 at unseen Re25, Re70, Re150 and Re300. Illusion gives 0.784055, 0.821103, 0.884573, 0.925530 and 0.756880 at 0.5L, 0.6L, 0.8L, 1.2L and 2L. These are one 200-step sampled realization per condition, not a continuous parameter-law or statistical robustness result. +- Per-case frozen-topology refits do not support a clean low/high-Re split. Illusion 1.5L has a distinct coefficient emphasis, but several single-case rear fits are poorly identified and remain diagnostics. +- `sr_key_elements.md` and `.csv` provide the explicit SR-to-OID/CCD handover. These additions do not claim a direct momentum-balance equality, universality beyond tested conditions, or causal flow structures. -**Formula JSONs** (`results/formulas/*.json`): -- `feature_names`: list of feature names used in the formula -- `best_sympy`: the symbolic expression (parseable by sympy) -- `best_score`: one-step R-squared on training/validation data +## Plotting-ready package (2026-07-21) -**Validation JSONs** (`results/validations/*.json`): -- `similarity`: CFD closed-loop DTW similarity (the final metric) -- `mode`: "pysr", "ppo", or "uncontrolled" -- `n_steps`: number of control steps used in validation +The final plotting audit and derived CSV package is: -**NPZ data** (`data/*/controlled.npz`): -- `actions`: shape [N, 3], normalized [-1, 1] -- `sensors`: shape [N, 6], raw sensor velocities [s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy] -- `forces`: shape [N, 6], raw forces [front_fx, front_fy, bottom_fx, bottom_fy, top_fx, top_fy] +```text +runs/article2-plotting-package-20260721/ +``` -The single source of truth for all numerical results is `scene_registry.json` at the root of SR_analysis/. +It adds, without refitting formulas: + +- seven 400-step PPO baselines under `long_ppo//`, paired with the existing 400-step SR exports; +- `offline_predictions.csv`: causal PPO-state features, PPO next actions, SR predictions and residuals; +- `term_contributions_ppo_states.csv`: additive symbolic terms evaluated on PPO-visited states; +- `term_contributions_sr_closed_loop_400.csv`: additive terms on 400-step SR trajectories, with exact next-action reconstruction; +- `ablation_summary.csv` and `scaling_summary.csv`; +- seven constant-rotation steady time-series exports; +- publication-starting-point PNG/PDF diagnostics under `figures/`; +- a SHA-256 manifest for the complete derived package. + +The 400-step PPO similarities are `0.954856`, `0.946973`, `0.900280`, `0.845378` for Kármán Re50/100/200/400 and `0.976328`, `0.975728`, `0.926701` for Illusion 0.75L/1L/1.5L. All completed with finite telemetry. PPO inference runs on CPU while physical GPU 2 remains dedicated to PyCUDA CFD, avoiding PyTorch/PyCUDA context conflicts. + +## Evidence chain + +### 1. Accepted article data + +```text +../data/runs/article-joint-data-karman-20260718/ +../data/runs/article-joint-data-illusion-20260718/ +runs/article-joint-data-audit-20260718/ +``` + +Scope: + +- Kármán: Re50, Re100, Re200, Re400. +- Illusion: 0.75L, 1L, 1.5L. +- 200 recorded steps per scene; 197 causally aligned rows after warm-up. +- Seven exact PPO policy-replay passes. + +Key files: + +- `runs/article-joint-data-audit-20260718/data_inventory.json` +- `runs/article-joint-data-audit-20260718/policy_replay/*.json` + +### 2. Per-case broad discovery + +```text +runs/article-discovery-*/ +runs/article-discovery-v2-*/ +runs/article-discovery-v3-*/ +runs/article-per-case-discovery-summary-20260718/summary.json +``` + +Purpose: identify recurrent variables and expression topology using `raw_complete` and `symmetry` representations, three seeds and contiguous train/validation/blind diagnostics. These are offline discovery artifacts, not accepted controllers. + +Main recurrence: + +- Kármán has no clean low/high-Re binary split: Re50 favours simple front-force/affine laws, Re400 repeatedly selects `Cl_rear_s-Cl_tot`, and Re100/Re200 remain heterogeneous lift-based intermediates. +- Illusion has distinct endpoint families: 0.75L is rear-drag dominated, 1L is mixed/transitional, and 1.5L overwhelmingly selects front/rear lift contrast. +- Across the audited heads, the leading Illusion counts are `Cd_rear_a=15`, `Cd_rear_s=10` at 0.75L; `Cd_F=13`, `Cl_rear_s=12` at 1L; and `Cl_F=54`, `Cl_rear_s=46` at 1.5L. +- Target/error variables were available but did not recur stably in low-complexity static joint structures. + +### 3. Joint topology discovery + +Kármán: + +```text +runs/article-joint-karman-raw_complete-s*-20260718/ +runs/article-joint-karman-symmetry-s*-20260718/ +``` + +Stable symmetry topology: + +```text +front ~ Cd_rear_a +rear ~ Cl_rear_s + constant +``` + +Illusion: + +```text +runs/article-joint-illusion-actual_only-s*-20260718/ +runs/article-joint-illusion-actual_plus_target-s*-20260718/ +runs/article-joint-illusion-actual_plus_error-s*-20260718/ +``` + +Stable rear topology: + +```text +rear ~ Cd_rear_a - Cl_F +``` + +Competing front topologies: + +```text +-Cd_rear_a + Cl_F +Cd_rear_s*Cl_F - Cl_tot +``` + +The second Illusion topology later failed short CFD in 1L and 1.5L and is rejected. + +### 4. Fixed-topology all-data refit + +```text +runs/article-refit-karman-topology-a-20260718/ +runs/article-refit-illusion-topology-a-20260718/ +runs/article-refit-illusion-topology-b-20260718/ +``` + +These artifacts freeze the discovery topology and optimize only numerical constants using all accepted rows with case-equal and trajectory-equal weighting. They report aggregate, per-case and per-trajectory offline metrics. + +### 5. Short CFD screening + +```text +runs/article-L2-karman-20260718/ +runs/article-L2-illusionA-20260718/ +runs/article-L2-illusionB-20260718/ +``` + +- Kármán A: stable in all four Re. +- Illusion A: stable at all three training target-size labels. +- Illusion B: 0.75L completed; 1L and 1.5L became non-finite. Its failed telemetry is retained and the candidate is rejected. + +### 6. Standard 200-step CFD + +```text +runs/article-L3-karman-20260718/ +runs/article-L3-illusionA-20260718/ +``` + +All values use `legacy_dtw_v1_abs_n_unclipped`, window `legacy_reference_cycle_vs_last_recorded_cycle`. + +Kármán candidate: + +- Re50: `0.9543` +- Re100: `0.9427` +- Re200: `0.8560` +- Re400: `0.7827` + +Illusion numerical candidate: + +- 0.75L: `0.8749` +- 1L: `0.9217` +- 1.5L: `0.8306` + +### 7. Term deletion + +Formula variants: + +```text +runs/article-ablation-formulas-v2-20260718/formulas/ +``` + +Short closed-loop results: + +```text +runs/article-ablation-L2-k_front0-20260718/ +runs/article-ablation-L2-k_rear0-20260718/ +runs/article-ablation-L2-k_rear1-20260718/ +runs/article-ablation-L2-i_front0-20260718/ +runs/article-ablation-L2-i_front1-20260718/ +runs/article-ablation-L2-i_rear0-20260718/ +runs/article-ablation-L2-i_rear1-20260718/ +``` + +Interpretation: + +- Kármán rear constant is dominant. +- Kármán rear lift feedback has secondary value. +- The tested Kármán front feedback is weak. +- Every one-term Illusion deletion remained stable with relatively small DTW changes; no four-term uniqueness claim is justified. + +### 8. Coefficient scaling shadow + +```text +runs/article-scaling-L1-v2-20260718/summary.json +``` + +Thirty-seven preregistered variants were evaluated on recorded states with scale values `0, 0.5, 0.75, 1, 1.25, 1.5`. All remained finite. + +- Kármán rear constant has the largest action-amplitude influence. +- Illusion `Cl_F` terms dominate front and rear action magnitude. +- Illusion `Cd_rear_a` terms are weaker and partly replaceable. + +## Current formulas + +### Kármán candidate + +\[ +\alpha_F=\operatorname{odd}\!\left[-0.381391\,C_{d,\mathrm{rear},a}\right], +\] + +\[ +\alpha_U=1.307782\,C_{l,\mathrm{rear},s}-3.431209, +\qquad +\alpha_L=-\alpha_U(Gx). +\] + +Status: article candidate with strong mechanism evidence at Re50/100 and reduced performance at Re400. Not universal. + +### Illusion numerical candidate + +\[ +\alpha_F=\operatorname{odd}\!\left[-1.826604\,C_{d,\mathrm{rear},a}+2.064493\,C_{l,F}\right], +\] + +\[ +\alpha_U=1.254440\,C_{d,\mathrm{rear},a}-1.528074\,C_{l,F}, +\qquad +\alpha_L=-\alpha_U(Gx). +\] + +Status: symmetric numerical candidate. It does not establish explicit target tracking or term uniqueness. + +## Article claims supported by this evidence + +1. PPO policies can be reduced through a reproducible pipeline that separates offline discovery, joint structure, coefficient estimation, closed-loop acceptance and term necessity. +2. Within the tested symbolic controller, persistent Kármán rear counter-rotation is the dominant term and rear lift feedback provides a secondary correction; interpreting the associated field as deficit compensation remains a hypothesis for OID/CCD. +3. Different formulas with similar offline fit can have different closed-loop outcomes; R² alone is not a controller-selection metric. +4. Illusion admits a shared symmetric numerical family, but the available static library does not establish a unique target-dependent mechanism across 0.75L–1.5L. +5. The 1.5L Illusion trajectory is a distinct/difficult regime and should be discussed as a boundary rather than hidden by average performance. + +## Claims not supported + +- One globally optimal symbolic formula. +- Universal Kármán performance through Re400. +- Explicit target tracking by the current Illusion formula. +- Necessity of every Illusion term. +- Unseen-condition or Vortex generalization from the 2026-07-18 primary run; the Article2 extension supplies only pointwise evidence at explicitly sampled Re and target-size values. +- Physical delay inferred from DTW lag. + +## Historical frozen material + +The following remain for context and recovery but are not authoritative article evidence: + +- `formulas/` +- `validations/` +- `scene_registry.json` +- old reports under `docs/` +- `round1-legacy-v2-20260716-*` +- smoke/wiring runs from earlier rebuilds +- `formulas_v5/` and `experiments/v5/` + +Do not copy their numbers into the manuscript without explicitly labelling them historical and reconciling their contracts with the article run. diff --git a/src/SR_analysis/results/figures/FIGURE_INDEX.md b/src/SR_analysis/results/figures/FIGURE_INDEX.md index 2cb54df..1a02872 100644 --- a/src/SR_analysis/results/figures/FIGURE_INDEX.md +++ b/src/SR_analysis/results/figures/FIGURE_INDEX.md @@ -98,5 +98,5 @@ All numerical values in these figures are derived from: To regenerate all figures: ```bash -conda run -n pycuda_3_10 python stage_4_analyze.py +PYTHONPATH=src conda run -n pycuda_3_10 python -m SR_analysis.archive.stage4.stage_4_analyze ``` diff --git a/src/SR_analysis/results/formulas_v5/ill_075L_sc_ill_1L_sc_ill_15L_sc_ill_2L_sc_front.json b/src/SR_analysis/results/formulas_v5/ill_075L_sc_ill_1L_sc_ill_15L_sc_ill_2L_sc_front.json new file mode 100644 index 0000000..68b6d83 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/ill_075L_sc_ill_1L_sc_ill_15L_sc_ill_2L_sc_front.json @@ -0,0 +1,21 @@ +{ + "scene": "ill_075L_sc_ill_1L_sc_ill_15L_sc_ill_2L_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_a", + "du_a_dt", + "Cl_tot", + "dCl_tot_dt", + "Cd_tot", + "Cd_rear", + "Cd_err", + "Cl_err", + "dCd_err_dt", + "dCl_err_dt" + ], + "best_sympy": "Cd_err - dCl_tot_dt", + "best_score": 0.03699740118331907, + "niterations": 120, + "n_samples": 1432 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/ill_1L_sc_front.json b/src/SR_analysis/results/formulas_v5/ill_1L_sc_front.json new file mode 100644 index 0000000..877d7c3 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/ill_1L_sc_front.json @@ -0,0 +1,20 @@ +{ + "scene": "ill_1L_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_a", + "du_a_dt", + "Cl_tot", + "dCl_tot_dt", + "Cd_tot", + "Cd_rear", + "Cd_err", + "Cl_err", + "dCd_err_dt", + "dCl_err_dt" + ], + "best_sympy": "Cd_rear*39.417385 - 8.783737*Cd_tot**2", + "best_score": 0.2857188366914536, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/ill_1L_sc_top.json b/src/SR_analysis/results/formulas_v5/ill_1L_sc_top.json new file mode 100644 index 0000000..4271da9 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/ill_1L_sc_top.json @@ -0,0 +1,21 @@ +{ + "scene": "ill_1L_sc", + "channel": "top", + "output": "alpha", + "feature_keys": [ + "bias", + "u_a", + "du_a_dt", + "Cl_tot", + "dCl_tot_dt", + "Cd_tot", + "Cd_rear", + "Cd_err", + "Cl_err", + "dCd_err_dt", + "dCl_err_dt" + ], + "best_sympy": "Cd_tot*23.775885 - 64.462715", + "best_score": 0.24296041275208324, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_d075_sc_front.json b/src/SR_analysis/results/formulas_v5/kar_d075_sc_front.json new file mode 100644 index 0000000..eafb242 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_d075_sc_front.json @@ -0,0 +1,27 @@ +{ + "scene": "kar_d075_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "daF_dt*0.41405594", + "best_score": 0.37917569513447735, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_d075_sc_top.json b/src/SR_analysis/results/formulas_v5/kar_d075_sc_top.json new file mode 100644 index 0000000..c97fb5e --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_d075_sc_top.json @@ -0,0 +1,28 @@ +{ + "scene": "kar_d075_sc", + "channel": "top", + "output": "alpha", + "feature_keys": [ + "bias", + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "daT_dt*0.39163446 - 1*(-2.8636527)", + "best_score": 0.305276644939362, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_d15_sc_front.json b/src/SR_analysis/results/formulas_v5/kar_d15_sc_front.json new file mode 100644 index 0000000..1197a0a --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_d15_sc_front.json @@ -0,0 +1,27 @@ +{ + "scene": "kar_d15_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "mu_u_a*(-mu_v_a*u_m + (Cd_rear + Cd_rear)**2)", + "best_score": 0.5956046015454158, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_d15_sc_top.json b/src/SR_analysis/results/formulas_v5/kar_d15_sc_top.json new file mode 100644 index 0000000..deef4a0 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_d15_sc_top.json @@ -0,0 +1,28 @@ +{ + "scene": "kar_d15_sc", + "channel": "top", + "output": "alpha", + "feature_keys": [ + "bias", + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "(Cd_rear + Cl_diff)**2", + "best_score": 0.3719336984189675, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_d2_sc_front.json b/src/SR_analysis/results/formulas_v5/kar_d2_sc_front.json new file mode 100644 index 0000000..36f2336 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_d2_sc_front.json @@ -0,0 +1,27 @@ +{ + "scene": "kar_d2_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "u_a*(0.10308963 - mu_Cl_diff) + (Cd_tot**2 + daF_dt)/2.545903", + "best_score": 0.7457522893623865, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_d2_sc_top.json b/src/SR_analysis/results/formulas_v5/kar_d2_sc_top.json new file mode 100644 index 0000000..52fc0be --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_d2_sc_top.json @@ -0,0 +1,28 @@ +{ + "scene": "kar_d2_sc", + "channel": "top", + "output": "alpha", + "feature_keys": [ + "bias", + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "(-daT_dt + v_a)*(mu_u_a - 0.36293036) + 3.2632432", + "best_score": 0.5707536479471909, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_re100_sc_front.json b/src/SR_analysis/results/formulas_v5/kar_re100_sc_front.json new file mode 100644 index 0000000..b7dd0c8 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_re100_sc_front.json @@ -0,0 +1,27 @@ +{ + "scene": "kar_re100_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "-Cl_diff**2*mu_u_a + daF_dt*0.5123117", + "best_score": 0.512653440416756, + "niterations": 120 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/kar_re100_sc_kar_d075_sc_kar_d15_sc_kar_d2_sc_front.json b/src/SR_analysis/results/formulas_v5/kar_re100_sc_kar_d075_sc_kar_d15_sc_kar_d2_sc_front.json new file mode 100644 index 0000000..71d57a0 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/kar_re100_sc_kar_d075_sc_kar_d15_sc_kar_d2_sc_front.json @@ -0,0 +1,28 @@ +{ + "scene": "kar_re100_sc_kar_d075_sc_kar_d15_sc_kar_d2_sc", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "daF_dt*0.4358058", + "best_score": 0.30991253976207, + "niterations": 120, + "n_samples": 1432 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/karman_v5_nodadt_front.json b/src/SR_analysis/results/formulas_v5/karman_v5_nodadt_front.json new file mode 100644 index 0000000..161fad3 --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/karman_v5_nodadt_front.json @@ -0,0 +1,27 @@ +{ + "scene": "karman_v5_nodadt", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "mu_u_a", + "mu_v_a", + "mu_u_m", + "mu_u_c", + "mu_Cd_tot", + "mu_Cd_rear", + "mu_Cl_tot", + "mu_Cl_diff" + ], + "best_sympy": "Cd_rear*0.04394478*(Cd_rear**2 + u_a)", + "best_score": 0.13022819111167572, + "niterations": 120, + "n_samples": 1432 +} \ No newline at end of file diff --git a/src/SR_analysis/results/formulas_v5/mixed_karman_5cases_front.json b/src/SR_analysis/results/formulas_v5/mixed_karman_5cases_front.json new file mode 100644 index 0000000..dc43b9f --- /dev/null +++ b/src/SR_analysis/results/formulas_v5/mixed_karman_5cases_front.json @@ -0,0 +1,28 @@ +{ + "scene": "mixed_karman_5cases", + "channel": "front", + "output": "alpha", + "feature_keys": [ + "u_m", + "u_a", + "u_c", + "v_a", + "Cd_tot", + "Cd_rear", + "Cl_tot", + "Cl_diff", + "daF_dt", + "daB_dt", + "daT_dt", + "mu", + "mu_u_a", + "mu_v_a", + "mu_Cd_tot", + "mu_Cl_diff", + "mu_Cl_tot" + ], + "best_sympy": "daF_dt*0.43680468", + "best_score": 0.3137504393083681, + "niterations": 120, + "n_samples": 1470 +} \ No newline at end of file diff --git a/src/SR_analysis/results/runs/article-L2-illusionA-20260718/validations/illusion_0p75L__pysr__f-ae67ae507106__si-400.json b/src/SR_analysis/results/runs/article-L2-illusionA-20260718/validations/illusion_0p75L__pysr__f-ae67ae507106__si-400.json new file mode 100644 index 0000000..b8f7e66 --- /dev/null +++ b/src/SR_analysis/results/runs/article-L2-illusionA-20260718/validations/illusion_0p75L__pysr__f-ae67ae507106__si-400.json @@ -0,0 +1 @@ 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--group illusion_0.75L,illusion_1L,illusion_1.5L --mode pysr --formula-front src/SR_analysis/results/runs/article-refit-illusion-topology-a-20260718/formulas/joint_front.json --formula-rear src/SR_analysis/results/runs/article-refit-illusion-topology-a-20260718/formulas/joint_rear_shared_upper.json --device 0 --steps 40 --run-id article-L2-illusionA-20260718","created_at":"2026-07-18T11:30:15.695532+00:00","environment":{"conda_env":"pycuda_3_10","gpu":{"CUDA_VISIBLE_DEVICES":"2","name":"Tesla 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__delete_t1.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__delete_t1.json new file mode 100644 index 0000000..fe8ecd5 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__delete_t1.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t0_0p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t0_0p5.json new file mode 100644 index 0000000..18c98a8 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t0_0p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-0.9133020945423844*Cd_rear_a + 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t0_1p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t0_1p5.json new file mode 100644 index 0000000..a66b371 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t0_1p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-2.7399062836271532*Cd_rear_a + 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_0p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_0p5.json new file mode 100644 index 0000000..38fc3a8 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_0p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-1.8266041890847688*Cd_rear_a + 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_0p75.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_0p75.json new file mode 100644 index 0000000..7162154 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_0p75.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-1.8266041890847688*Cd_rear_a + 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_1p25.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_1p25.json new file mode 100644 index 0000000..4dcb150 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_1p25.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-1.8266041890847688*Cd_rear_a + 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_1p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_1p5.json new file mode 100644 index 0000000..affa84e --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_front__scale_t1_1p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-1.8266041890847688*Cd_rear_a + 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__delete_t0.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__delete_t0.json new file mode 100644 index 0000000..df87877 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__delete_t0.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__delete_t1.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__delete_t1.json new file mode 100644 index 0000000..5152572 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__delete_t1.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__scale_t1_0p75.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__scale_t1_0p75.json new file mode 100644 index 0000000..58e7907 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__scale_t1_0p75.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.2544396437730243*Cd_rear_a - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__scale_t1_1p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__scale_t1_1p5.json new file mode 100644 index 0000000..54b8f41 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/illusion_rear__scale_t1_1p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.2544396437730243*Cd_rear_a - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__delete_t0.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__delete_t0.json new file mode 100644 index 0000000..00386b8 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__delete_t0.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_0p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_0p5.json new file mode 100644 index 0000000..31a6db8 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_0p5.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_0p75.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_0p75.json new file mode 100644 index 0000000..b20e30e --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_0p75.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p0.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p0.json new file mode 100644 index 0000000..5bdd0fb --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p0.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p25.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p25.json new file mode 100644 index 0000000..f07a7a8 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p25.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p5.json new file mode 100644 index 0000000..97e2ff9 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_front__scale_t0_1p5.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__delete_t0.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__delete_t0.json new file mode 100644 index 0000000..4223317 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__delete_t0.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__delete_t1.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__delete_t1.json new file mode 100644 index 0000000..bcd4f65 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__delete_t1.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_0p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_0p5.json new file mode 100644 index 0000000..74f1bac --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_0p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"0.6538908932794988*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_0p75.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_0p75.json new file mode 100644 index 0000000..66290b8 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_0p75.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"0.9808363399192482*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p0.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p0.json new file mode 100644 index 0000000..2566707 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p0.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p25.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p25.json new file mode 100644 index 0000000..8cb562e --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p25.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.634727233198747*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p5.json new file mode 100644 index 0000000..a319e51 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t0_1p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.9616726798384964*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_0p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_0p5.json new file mode 100644 index 0000000..d866334 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_0p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_0p75.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_0p75.json new file mode 100644 index 0000000..702ae80 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_0p75.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p0.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p0.json new file mode 100644 index 0000000..d38e385 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p0.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p25.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p25.json new file mode 100644 index 0000000..dd3ae69 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p25.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p5.json b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p5.json new file mode 100644 index 0000000..eeba410 --- /dev/null +++ b/src/SR_analysis/results/runs/article-ablation-formulas-v2-20260718/formulas/karman_rear__scale_t1_1p5.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-discovery-illusion_0.75L-actual_only-s0-20260718/formulas/illusion_0.75L_front.json b/src/SR_analysis/results/runs/article-discovery-illusion_0.75L-actual_only-s0-20260718/formulas/illusion_0.75L_front.json new file mode 100644 index 0000000..c0a5e52 --- /dev/null +++ b/src/SR_analysis/results/runs/article-discovery-illusion_0.75L-actual_only-s0-20260718/formulas/illusion_0.75L_front.json @@ -0,0 +1 @@ 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file mode 100644 index 0000000..07373d7 --- /dev/null +++ b/src/SR_analysis/results/runs/article-joint-data-audit-20260718/data_inventory.json @@ -0,0 +1,59 @@ +{ + "schema_version": "sr-article-joint-data-inventory-v1", + "status": "ready_for_discovery", + "alignment": "causal_post_state_to_next_action", + "transient_boundary": 50, + "weighting": "case_equal; trajectory_equal_within_case", + "accepted_primary_runs": { + "karman": { + "run_id": "article-joint-data-karman-20260718", + "scenes": ["karman_re50", "karman_re100", "karman_re200", "karman_re400"], + "trajectories_per_scene": 1, + "recorded_samples_per_trajectory": 200, + "aligned_samples_per_trajectory": 197, + "transient_aligned_samples": 48, + "steady_aligned_samples": 150, + "policy_replay": "passed_exactly" + }, + "illusion": { + "run_id": "article-joint-data-illusion-20260718", + "scenes": ["illusion_0.75L", "illusion_1L", "illusion_1.5L"], + "trajectories_per_scene": 1, + "recorded_samples_per_trajectory": 200, + 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0000000..9a1c18c --- /dev/null +++ b/src/SR_analysis/results/runs/article-joint-sr-final-20260718/readable_summary.txt @@ -0,0 +1,50 @@ +JOINT SR ARTICLE ANALYSIS — REVIEW SUMMARY + +Method +1. Seven trained cases were recollected under one causal contract; all seven PPO policy replays matched exactly. +2. Broad per-case searches used raw_complete or symmetry-complete variables with three seeds. +3. R2/Pareto fronts identified recurring variables and topologies only. +4. Karman and Illusion were fitted separately as within-objective joint laws. +5. Frozen topologies were refitted with all eligible training-case rows using case-equal weighting. +6. Candidate laws were screened by short CFD, standard 200-step legacy DTW, term deletion, and coefficient-scaling shadow tests. + +Karman recommended candidate + alpha_F(x) = odd_projection[-0.381391 Cd_rear_a] + alpha_U(x) = 1.307782 Cl_rear_s - 3.431209 + alpha_L(x) = -alpha_U(Gx) + +Standard 200-step legacy DTW + Re50 : 0.9543 + Re100 : 0.9427 + Re200 : 0.8560 + Re400 : 0.7827 + +Term importance + 1. Rear constant: dominant; deleting it caused the largest degradation, especially at Re200/400. + 2. Rear lift feedback Cl_rear_s: secondary; deleting it caused moderate degradation. + 3. Front Cd_rear_a feedback: weak; deleting it changed short-window DTW only slightly. + +Scientific interpretation +The result supports the theory that steady rear counter-rotation supplies the principal downstream-deficit compensation. Rear lift feedback adjusts the oscillatory/asymmetric response. The tested front feedback is not strongly supported as necessary. The current law is not yet uniformly strong at Re400, so it should not be called the final universal Karman law. + +Illusion recommended numerical candidate + alpha_F(x) = odd_projection[-1.826604 Cd_rear_a + 2.064493 Cl_F] + alpha_U(x) = 1.254440 Cd_rear_a - 1.528074 Cl_F + alpha_L(x) = -alpha_U(Gx) + +Standard 200-step legacy DTW + 0.75L : 0.8749 + 1.0L : 0.9217 + 1.5L : 0.8306 + +Term importance + - Cl_F terms dominate front and rear action magnitude in scaling shadow tests. + - Cd_rear_a terms have weaker magnitude influence and are partly replaceable. + - Every one-term deletion remained stable and had relatively small DTW differences. + - Therefore the present four-term law is not demonstrated to be a uniquely necessary mechanism. + +Scientific interpretation +The result supports a front-lift/rear-asymmetry control family, but it does not establish explicit target tracking: target/error variables were available yet were not selected stably by the static low-complexity joint searches. The 1.5L case behaves differently and remains the weakest case. This candidate is a numerical joint reference, not a final article mechanism law. + +Conclusion +The complete workflow is now clear and operational. It produced substantive Karman mechanism evidence and a stable but non-unique Illusion numerical family. It did not honestly produce a single final law satisfying high DTW, exact symmetry, and complete theoretical support across every case. 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newline at end of file diff --git a/src/SR_analysis/results/runs/article-refit-illusion-topology-a-20260718/formulas/joint_front.json b/src/SR_analysis/results/runs/article-refit-illusion-topology-a-20260718/formulas/joint_front.json new file mode 100644 index 0000000..c7e126b --- /dev/null +++ b/src/SR_analysis/results/runs/article-refit-illusion-topology-a-20260718/formulas/joint_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-1.8266041890847688 * Cd_rear_a + 2.064492712013321 * 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diff --git a/src/SR_analysis/results/runs/article-refit-illusion-topology-b-20260718/formulas/joint_front.json b/src/SR_analysis/results/runs/article-refit-illusion-topology-b-20260718/formulas/joint_front.json new file mode 100644 index 0000000..6d5455d --- /dev/null +++ b/src/SR_analysis/results/runs/article-refit-illusion-topology-b-20260718/formulas/joint_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-b-20260718-joint-front_odd-anchor-upper","deployment_expression":"0.8521893407148629 * Cd_rear_s * Cl_F - 0.5910125146472169 * 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src/SR_analysis/results/runs/article-joint-illusion-actual_plus_target-s2-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-18T11:16:07.867900+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":1.5331620759226978,"max_error":4.597459365895853,"r2":0.5704346338351429,"rmse":1.8016881034318797},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_plus_target","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"joint","n_samples":1182,"offline_metrics":{"aggregate":{"mae":1.5331620759226978,"max_error":4.597459365895853,"r2":0.5704346338351429,"rmse":1.8016881034318797},"per_case":{"illusion_0.75L":{"mae":1.7659104728838297,"max_error":2.7278834280912285,"r2":-1.0353522897778906,"rmse":1.82093829992918},"illusion_1.5L":{"mae":1.7429866328409351,"max_error":4.597459365895853,"r2":0.7891695184573272,"rmse":2.0928352995455333},"illusion_1L":{"mae":1.0905891220433288,"max_error":2.7677971478813035,"r2":-6.6761404440053465,"rmse":1.4291480619484727}},"per_trajectory":{"illusion_0.75L:trajectory-001":{"mae":1.7659104728838297,"max_error":2.7278834280912285,"r2":-1.0353522897778906,"rmse":1.82093829992918},"illusion_1.5L:trajectory-001":{"mae":1.7429866328409351,"max_error":4.597459365895853,"r2":0.7891695184573272,"rmse":2.0928352995455333},"illusion_1L:trajectory-001":{"mae":1.0905891220433288,"max_error":2.7677971478813035,"r2":-6.6761404440053465,"rmse":1.4291480619484727}}},"optimizer":{"cost":1918.433293030346,"initial_constants":[1.0,1.0],"name":"scipy.optimize.least_squares","nfev":3,"refitted_constants":[0.8521893407148629,0.5910125146472169],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"illusion_0.75L":197,"illusion_1.5L":197,"illusion_1L":197},"source_n_samples":591,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-0.7472064356175704,12.708771876840656],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"1.0*Cd_rear_s*Cl_F-1.0*Cl_tot"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"front_odd","run_id":"article-refit-illusion-topology-b-20260718","schema_version":"1.0","training_scenes":["illusion_0.75L","illusion_1L","illusion_1.5L"]} diff --git a/src/SR_analysis/results/runs/article-refit-illusion-topology-b-20260718/formulas/joint_rear_shared_upper.json b/src/SR_analysis/results/runs/article-refit-illusion-topology-b-20260718/formulas/joint_rear_shared_upper.json new file mode 100644 index 0000000..63f9222 --- /dev/null +++ b/src/SR_analysis/results/runs/article-refit-illusion-topology-b-20260718/formulas/joint_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-illusion-topology-b-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.2544396437730243 * Cd_rear_a - 1.5280742226055937 * 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diff --git a/src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_front.json b/src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_front.json new file mode 100644 index 0000000..76c7471 --- /dev/null +++ b/src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-front_odd-anchor-upper","deployment_expression":"-0.3813914338074549 * 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diff --git a/src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_rear_shared_upper.json b/src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_rear_shared_upper.json new file mode 100644 index 0000000..8a4a699 --- /dev/null +++ b/src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article-refit-karman-topology-a-20260718-joint-rear_shared-anchor-upper","deployment_expression":"1.3077817865589976 * Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article-scaling-L1-v2-20260718/summary.json b/src/SR_analysis/results/runs/article-scaling-L1-v2-20260718/summary.json new file mode 100644 index 0000000..9d537cd --- /dev/null +++ b/src/SR_analysis/results/runs/article-scaling-L1-v2-20260718/summary.json @@ -0,0 +1,893 @@ +{ + "schema_version": "sr-scaling-shadow-v1", + "rows": [ + { + "family": "karman", + "scene": "karman_re400", + "variant": "parent", + "front": "src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_front.json", + "rear": "src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas/joint_rear_shared_upper.json", + "failed": null, + "n": 200, + "action_min": [ + -2.7139625927397604, + -7.531031663850338, + -0.6686143028291043 + ], + "action_max": [ + 3.64809775864367, + 3.1323124727202623, + 9.994729833741495 + ], + "action_rms": [ + 1.5845745432483256, + 4.048298915576876, + 4.029247241615578 + ] + }, + { + "family": 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a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_0.75L/sr_manifest.json b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_0.75L/sr_manifest.json new file mode 100644 index 0000000..1c39869 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_0.75L/sr_manifest.json @@ -0,0 +1,16 @@ +{ + "scene": "illusion_0.75L", + "source": "sr", + "input": "src/SR_analysis/results/runs/article2-long-illusion-20260720/telemetry/illusion_0p75L__pysr__f-ae67ae507106__si-400.npz", + "sample_interval": 400, + "conv_len": 36, + "rows": { + "sensors": 400, + "forces": 400, + "actions_alpha": 400, + "actions_omega": 400, + "target_sensors": 150, + "target_forces": 400, + "target_phase": 400 + } +} diff --git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_0.75L/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_0.75L/sr_wide.csv new file mode 100644 index 0000000..c2ec92e --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_0.75L/sr_wide.csv @@ -0,0 +1,401 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower,target_forces_target_cd,target_forces_target_cl,target_phase_target_phase 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} +} diff --git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_1.5L/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_1.5L/sr_wide.csv new file mode 100644 index 0000000..bf354be --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_1.5L/sr_wide.csv @@ -0,0 +1,401 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower,target_forces_target_cd,target_forces_target_cl,target_phase_target_phase 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--git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_1L/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_1L/sr_wide.csv new file mode 100644 index 0000000..ac713fb --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/illusion_1L/sr_wide.csv @@ -0,0 +1,401 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower,target_forces_target_cd,target_forces_target_cl,target_phase_target_phase 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b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re100/sr_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "karman_re100", + "source": "sr", + "input": "src/SR_analysis/results/runs/article2-long-karman-20260720/telemetry/karman_re100__pysr__f-a678f2019e0d__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 400, + "forces": 400, + "actions_alpha": 400, + "actions_omega": 400, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re100/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re100/sr_wide.csv new file mode 100644 index 0000000..207866a --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re100/sr_wide.csv @@ -0,0 +1,401 @@ 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"src/SR_analysis/results/runs/article2-long-karman-20260720/telemetry/karman_re200__pysr__f-a678f2019e0d__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 400, + "forces": 400, + "actions_alpha": 400, + "actions_omega": 400, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re200/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re200/sr_wide.csv new file mode 100644 index 0000000..79645d8 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re200/sr_wide.csv @@ -0,0 +1,401 @@ 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a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re400/sr_manifest.json b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re400/sr_manifest.json new file mode 100644 index 0000000..13ed6da --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re400/sr_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "karman_re400", + "source": "sr", + "input": "src/SR_analysis/results/runs/article2-long-karman-20260720/telemetry/karman_re400__pysr__f-a678f2019e0d__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 400, + "forces": 400, + "actions_alpha": 400, + "actions_omega": 400, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re400/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re400/sr_wide.csv new file mode 100644 index 0000000..f3ca502 --- /dev/null +++ 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"conv_len": 30, + "rows": { + "sensors": 400, + "forces": 400, + "actions_alpha": 400, + "actions_omega": 400, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re50/sr_wide.csv b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re50/sr_wide.csv new file mode 100644 index 0000000..7c54a08 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-long-timeseries-csv-20260720/karman_re50/sr_wide.csv @@ -0,0 +1,401 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_0.75L_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_0.75L_rear_shared_upper.json new file mode 100644 index 0000000..77b0a76 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_0.75L_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"0.24859161792995263 * Cd_rear_a - -0.08286020333465423 * 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1.5L_front.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1.5L_front.json new file mode 100644 index 0000000..f8f25ed --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1.5L_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.044119550519154505 * Cd_rear_a + 2.1015026293420416 * Cl_F","deployment_expression_hash":"f74a54078f4a5290d1589dc1f862ec739b82c01eace53f130750edd2d415f7d7","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json","sha256":"f5106420e15e2f2c0d96afe60e43b97981fca65f42bdcf0e6f91ece002857356"},"dt_c_metadata":{"per_scene":{"illusion_1.5L":0.4},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"-0.044119550519154505 * Cd_rear_a + 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--scenes illusion_0.75L,illusion_1L,illusion_1.5L --mode per-scene --run-id article2-percase-refit-illusion-20260720 --data-root src/SR_analysis/data/runs/article-joint-data-illusion-20260718 --feature-set symmetry --feature-profile actual_only --deployment-architecture mapped_shared --fit-augmentation G --fit-purpose fixed-topology-refit --front-topology-expression=-1.826604*Cd_rear_a+2.064493*Cl_F --rear-topology-expression=1.254440*Cd_rear_a-1.528074*Cl_F --front-discovery-parent-path src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json --rear-discovery-parent-path src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-20T13:40:56.631081+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_only","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"per-scene","n_samples":394,"offline_metrics":{"aggregate":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026},"per_case":{"illusion_1.5L":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026}},"per_trajectory":{"illusion_1.5L:trajectory-001":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026}}},"optimizer":{"cost":1874.1886566503042,"initial_constants":[1.826604,2.064493],"name":"scipy.optimize.least_squares","nfev":4,"refitted_constants":[0.044119550519154505,2.1015026293420416],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"illusion_1.5L":197},"source_n_samples":197,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-7.795274277620026,7.795274277620026],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"-1.826604*Cd_rear_a+2.064493*Cl_F"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"front_odd","run_id":"article2-percase-refit-illusion-20260720","schema_version":"1.0","training_scenes":["illusion_1.5L"]} diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1.5L_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1.5L_rear_shared_upper.json new file mode 100644 index 0000000..31fe668 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1.5L_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.5682603086502969 * Cd_rear_a - 1.6011017720495324 * 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1L_front.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1L_front.json new file mode 100644 index 0000000..572e7d0 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1L_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"--2.043848942000545 * Cd_rear_a + -2.219295123924814 * Cl_F","deployment_expression_hash":"0231cef8c9019550c64e7354d7672e13530b0a76a658151f64f73e7cbd62b160","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json","sha256":"f5106420e15e2f2c0d96afe60e43b97981fca65f42bdcf0e6f91ece002857356"},"dt_c_metadata":{"per_scene":{"illusion_1L":0.3},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"--2.043848942000545 * Cd_rear_a + -2.219295123924814 * 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src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-20T13:40:56.599745+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":0.26749351633879453,"max_error":1.0441460226187893,"r2":0.6059884236814721,"rmse":0.32378763498020424},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_only","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"per-scene","n_samples":394,"offline_metrics":{"aggregate":{"mae":0.26749351633879453,"max_error":1.0441460226187893,"r2":0.6059884236814721,"rmse":0.32378763498020424},"per_case":{"illusion_1L":{"mae":0.26749351633879453,"max_error":1.0441460226187893,"r2":0.6059884236814721,"rmse":0.32378763498020424}},"per_trajectory":{"illusion_1L:trajectory-001":{"mae":0.26749351633879453,"max_error":1.0441460226187893,"r2":0.6059884236814721,"rmse":0.32378763498020424}}},"optimizer":{"cost":20.653171215516565,"initial_constants":[1.826604,2.064493],"name":"scipy.optimize.least_squares","nfev":4,"refitted_constants":[-2.043848942000545,-2.219295123924814],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"illusion_1L":197},"source_n_samples":197,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-0.007531536402189065,0.007531536402189065],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"-1.826604*Cd_rear_a+2.064493*Cl_F"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"front_odd","run_id":"article2-percase-refit-illusion-20260720","schema_version":"1.0","training_scenes":["illusion_1L"]} diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1L_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1L_rear_shared_upper.json new file mode 100644 index 0000000..c387ebf --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-20260720/formulas/illusion_1L_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"0.18434653705308968 * Cd_rear_a - 0.15875223987426046 * 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--scenes illusion_0.75L,illusion_1L,illusion_1.5L --mode per-scene --run-id article2-percase-refit-illusion-20260720 --data-root src/SR_analysis/data/runs/article-joint-data-illusion-20260718 --feature-set symmetry --feature-profile actual_only --deployment-architecture mapped_shared --fit-augmentation G --fit-purpose fixed-topology-refit --front-topology-expression=-1.826604*Cd_rear_a+2.064493*Cl_F --rear-topology-expression=1.254440*Cd_rear_a-1.528074*Cl_F --front-discovery-parent-path src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json --rear-discovery-parent-path 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_0.75L_front.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_0.75L_front.json new file mode 100644 index 0000000..f628806 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_0.75L_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-v2-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.24201575527044217*Cd_rear_a - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_0.75L_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_0.75L_rear_shared_upper.json new file mode 100644 index 0000000..9a4141d --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_0.75L_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"0.24859161792995263*Cd_rear_a + 0.08286020333465423*Cl_F","deployment_expression_hash":"9556c20dddb8d5e39a403a9769bf730c3ce4076aaed4a7ab05e5fe927e83faa1","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_rear_shared_upper.json","sha256":"d5c330aa50b61ae62a55a32bb45bfb395b1c0d8f8c3b0b19bbe43ada1e4fff7c"},"dt_c_metadata":{"per_scene":{"illusion_0.75L":0.2},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"0.24859161792995263*Cd_rear_a 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1.5L_front.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1.5L_front.json new file mode 100644 index 0000000..33245a9 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1.5L_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-v2-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.044119550519154505*Cd_rear_a + 2.1015026293420416*Cl_F","deployment_expression_hash":"d4bbba23e79f1c59f562ad16b1002791fe49215e1e5a6c692cd172c9431a0417","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json","sha256":"f5106420e15e2f2c0d96afe60e43b97981fca65f42bdcf0e6f91ece002857356"},"dt_c_metadata":{"per_scene":{"illusion_1.5L":0.4},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"-0.044119550519154505*Cd_rear_a + 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--scenes illusion_0.75L,illusion_1L,illusion_1.5L --mode per-scene --run-id article2-percase-refit-illusion-v2-20260720 --data-root src/SR_analysis/data/runs/article-joint-data-illusion-20260718 --feature-set symmetry --feature-profile actual_only --deployment-architecture mapped_shared --fit-augmentation G --fit-purpose fixed-topology-refit --front-topology-expression=-1.826604*Cd_rear_a+2.064493*Cl_F --rear-topology-expression=1.254440*Cd_rear_a-1.528074*Cl_F --front-discovery-parent-path src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json --rear-discovery-parent-path src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-20T13:46:39.155540+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_only","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"per-scene","n_samples":394,"offline_metrics":{"aggregate":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026},"per_case":{"illusion_1.5L":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026}},"per_trajectory":{"illusion_1.5L:trajectory-001":{"mae":2.5622557463777267,"max_error":7.030255100055559,"r2":0.5420581062268233,"rmse":3.084420205371026}}},"optimizer":{"cost":1874.1886566503042,"initial_constants":[1.826604,2.064493],"name":"scipy.optimize.least_squares","nfev":4,"refitted_constants":[0.044119550519154505,2.1015026293420416],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"illusion_1.5L":197},"source_n_samples":197,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-7.795274277620026,7.795274277620026],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"-1.826604*Cd_rear_a+2.064493*Cl_F"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"front_odd","run_id":"article2-percase-refit-illusion-v2-20260720","schema_version":"1.0","training_scenes":["illusion_1.5L"]} diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1.5L_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1.5L_rear_shared_upper.json new file mode 100644 index 0000000..61549cd --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1.5L_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.5682603086502969*Cd_rear_a - 1.6011017720495324*Cl_F","deployment_expression_hash":"0561c73459d88b56741f247ab4c45dc11bd4b321ed3ffbd070cc7946fafa9042","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_rear_shared_upper.json","sha256":"d5c330aa50b61ae62a55a32bb45bfb395b1c0d8f8c3b0b19bbe43ada1e4fff7c"},"dt_c_metadata":{"per_scene":{"illusion_1.5L":0.4},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"1.5682603086502969*Cd_rear_a 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1L_front.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1L_front.json new file mode 100644 index 0000000..e9c34df --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1L_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-v2-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"2.043848942000545*Cd_rear_a - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1L_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1L_rear_shared_upper.json new file mode 100644 index 0000000..f4bc3fe --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-illusion-v2-20260720/formulas/illusion_1L_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":true,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-illusion-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"0.18434653705308968*Cd_rear_a - 0.15875223987426046*Cl_F","deployment_expression_hash":"7a4aa9ed2d0fc6771c5f06b22100241b9563042dbb35b757e42e84a4f6d2bba4","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_rear_shared_upper.json","sha256":"d5c330aa50b61ae62a55a32bb45bfb395b1c0d8f8c3b0b19bbe43ada1e4fff7c"},"dt_c_metadata":{"per_scene":{"illusion_1L":0.3},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"0.18434653705308968*Cd_rear_a 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--scenes illusion_0.75L,illusion_1L,illusion_1.5L --mode per-scene --run-id article2-percase-refit-illusion-v2-20260720 --data-root src/SR_analysis/data/runs/article-joint-data-illusion-20260718 --feature-set symmetry --feature-profile actual_only --deployment-architecture mapped_shared --fit-augmentation G --fit-purpose fixed-topology-refit --front-topology-expression=-1.826604*Cd_rear_a+2.064493*Cl_F --rear-topology-expression=1.254440*Cd_rear_a-1.528074*Cl_F --front-discovery-parent-path src/SR_analysis/results/runs/article-joint-illusion-actual_only-s0-20260718/formulas/joint_front.json --rear-discovery-parent-path 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re100_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re100_front.json new file mode 100644 index 0000000..657949f --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re100_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.324842776541943 * 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re100_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re100_rear_shared_upper.json new file mode 100644 index 0000000..c8facff --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re100_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.3203186589016584 * Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re200_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re200_front.json new file mode 100644 index 0000000..554ec4f --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re200_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.5002775221133345 * 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re200_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re200_rear_shared_upper.json new file mode 100644 index 0000000..2635868 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re200_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.6971078570952467 * Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re400_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re400_front.json new file mode 100644 index 0000000..c97141b --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re400_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.29472162211279335 * 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--scenes karman_re50,karman_re100,karman_re200,karman_re400 --mode per-scene --run-id article2-percase-refit-karman-20260720 --data-root src/SR_analysis/data/runs/article-joint-data-karman-20260718 --feature-set symmetry --feature-profile actual_only --deployment-architecture mapped_shared --fit-augmentation G --fit-purpose fixed-topology-refit --front-topology-expression=-0.381391*Cd_rear_a --rear-topology-expression=1.307782*Cl_rear_s-3.431209 --front-discovery-parent-path src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_front.json --rear-discovery-parent-path src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-20T13:40:56.675951+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":0.1479559237498144,"max_error":0.8564633439697262,"r2":0.9735052865964828,"rmse":0.19880018636789204},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_only","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"per-scene","n_samples":394,"offline_metrics":{"aggregate":{"mae":0.1479559237498144,"max_error":0.8564633439697262,"r2":0.9735052865964828,"rmse":0.19880018636789204},"per_case":{"karman_re400":{"mae":0.1479559237498144,"max_error":0.8564633439697262,"r2":0.9735052865964828,"rmse":0.19880018636789204}},"per_trajectory":{"karman_re400:trajectory-001":{"mae":0.1479559237498144,"max_error":0.8564633439697262,"r2":0.9735052865964828,"rmse":0.19880018636789204}}},"optimizer":{"cost":7.785738277681992,"initial_constants":[0.381391],"name":"scipy.optimize.least_squares","nfev":3,"refitted_constants":[0.29472162211279335],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"karman_re400":197},"source_n_samples":197,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-2.452399761549377,2.452399761549377],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"-0.381391*Cd_rear_a"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"front_odd","run_id":"article2-percase-refit-karman-20260720","schema_version":"1.0","training_scenes":["karman_re400"]} diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re400_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re400_rear_shared_upper.json new file mode 100644 index 0000000..d953e33 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re400_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.054929993313117 * Cl_rear_s - 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src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-20T13:40:56.733569+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":2.180989822453082,"max_error":4.877274411462385,"r2":0.32847453250491054,"rmse":2.4580453982854116},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_only","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"per-scene","n_samples":394,"offline_metrics":{"aggregate":{"mae":2.180989822453082,"max_error":4.877274411462385,"r2":0.32847453250491054,"rmse":2.4580453982854116},"per_case":{"karman_re400":{"mae":2.180989822453082,"max_error":4.877274411462385,"r2":0.32847453250491054,"rmse":2.4580453982854116}},"per_trajectory":{"karman_re400:trajectory-001":{"mae":2.180989822453082,"max_error":4.877274411462385,"r2":0.32847453250491054,"rmse":2.4580453982854116}}},"optimizer":{"cost":1190.2714744663203,"initial_constants":[1.307782,3.431209],"name":"scipy.optimize.least_squares","nfev":3,"refitted_constants":[1.054929993313117,2.5544553575675293],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"karman_re400":197},"source_n_samples":197,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-7.848958918828123,2.740048203693064],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"1.307782*Cl_rear_s-3.431209"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"rear_shared","run_id":"article2-percase-refit-karman-20260720","schema_version":"1.0","training_scenes":["karman_re400"]} diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re50_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re50_front.json new file mode 100644 index 0000000..dd38eff --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re50_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.5835788969350136 * 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re50_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re50_rear_shared_upper.json new file mode 100644 index 0000000..f32afb7 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-20260720/formulas/karman_re50_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"0.22124106819225497 * Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re100_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re100_front.json new file mode 100644 index 0000000..97c8254 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re100_front.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re100_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re100_rear_shared_upper.json new file mode 100644 index 0000000..d0bb1a5 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re100_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.3203186589016584*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re200_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re200_front.json new file mode 100644 index 0000000..9b62397 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re200_front.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re200_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re200_rear_shared_upper.json new file mode 100644 index 0000000..0c25ef8 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re200_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.6971078570952467*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re400_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re400_front.json new file mode 100644 index 0000000..3cff83b --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re400_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-v2-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.29472162211279335*Cd_rear_a","deployment_expression_hash":"789f162c0bd30e2198b1a7c940eaf07150e7c575023f5cd2f660106389ec21a5","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_front.json","sha256":"49af614459fefee3ebeed5c1e8761bdd1f0f8860a8d2e8f303dd1bb010ad67bf"},"dt_c_metadata":{"per_scene":{"karman_re400":0.4},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"-0.29472162211279335*Cd_rear_a","fitted_expression_hash":"789f162c0bd30e2198b1a7c940eaf07150e7c575023f5cd2f660106389ec21a5","g_metadata":{"deployment_architecture":"mapped_shared","deployment_semantics":"mapped_shared","fit_augmentation":"G","fitted_heads":["front_odd"],"front_rule":"front(x)=0.5*(f(x)-f(Gx)); 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re400_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re400_rear_shared_upper.json new file mode 100644 index 0000000..2240915 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re400_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"1.054929993313117*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re50_front.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re50_front.json new file mode 100644 index 0000000..04ed70e --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re50_front.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-v2-20260720-per-scene-front_odd-anchor-upper","deployment_expression":"-0.5835788969350136*Cd_rear_a","deployment_expression_hash":"a692cd32389031e7285c27dc4876acbabc3bb6d6a8f11b358733add7313edae1","discovery_parent":{"path":"/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_front.json","sha256":"49af614459fefee3ebeed5c1e8761bdd1f0f8860a8d2e8f303dd1bb010ad67bf"},"dt_c_metadata":{"per_scene":{"karman_re50":0.4},"unit":"D/U0"},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_metadata":{"builder":"SR_analysis.utils.data_contracts.build_batch_dataset","definitions_module":"SR_analysis.utils.feature_builder","order":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"]},"feature_names":["u_s","u_a","u_c","v_s","v_a","v_c","Cd_F","Cd_rear_s","Cd_rear_a","Cd_tot","Cl_F","Cl_rear_s","Cl_rear_a","Cl_tot"],"fitted_expression":"-0.5835788969350136*Cd_rear_a","fitted_expression_hash":"a692cd32389031e7285c27dc4876acbabc3bb6d6a8f11b358733add7313edae1","g_metadata":{"deployment_architecture":"mapped_shared","deployment_semantics":"mapped_shared","fit_augmentation":"G","fitted_heads":["front_odd"],"front_rule":"front(x)=0.5*(f(x)-f(Gx)); 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--scenes karman_re50,karman_re100,karman_re200,karman_re400 --mode per-scene --run-id article2-percase-refit-karman-v2-20260720 --data-root src/SR_analysis/data/runs/article-joint-data-karman-20260718 --feature-set symmetry --feature-profile actual_only --deployment-architecture mapped_shared --fit-augmentation G --fit-purpose fixed-topology-refit --front-topology-expression=-0.381391*Cd_rear_a --rear-topology-expression=1.307782*Cl_rear_s-3.431209 --front-discovery-parent-path src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_front.json --rear-discovery-parent-path src/SR_analysis/results/runs/article-joint-karman-symmetry-s0-20260718/formulas/joint_rear_shared_upper.json","created_at":"2026-07-20T13:46:38.982031+00:00","deployment_architecture":"mapped_shared","diagnostic_metrics":{"mae":1.5402098101682624,"max_error":2.701252546683189,"r2":0.28270513879302694,"rmse":1.6988826421169128},"environment":{"numpy":"2.2.6","platform":"Linux-6.8.0-124-generic-x86_64-with-glibc2.35","pysr":null,"python":"3.10.20"},"feature_profile":"actual_only","feature_set":"symmetry","fit_augmentation":"G","fit_purpose":"fixed-topology-refit","g_augmented":true,"git_sha":"ca8ee5f238ee58eaaf48027ad026c35784f76d4d","mode":"per-scene","n_samples":394,"offline_metrics":{"aggregate":{"mae":1.5402098101682624,"max_error":2.701252546683189,"r2":0.28270513879302694,"rmse":1.6988826421169128},"per_case":{"karman_re50":{"mae":1.5402098101682624,"max_error":2.701252546683189,"r2":0.28270513879302694,"rmse":1.6988826421169128}},"per_trajectory":{"karman_re50:trajectory-001":{"mae":1.5402098101682624,"max_error":2.701252546683189,"r2":0.28270513879302694,"rmse":1.6988826421169128}}},"optimizer":{"cost":568.5818396421696,"initial_constants":[0.381391],"name":"scipy.optimize.least_squares","nfev":3,"refitted_constants":[0.5835788969350136],"success":true},"sample_weighting":"case_equal_trajectory_equal_within_case","scene_sample_counts":{"karman_re50":197},"source_n_samples":197,"static_safety":{"finite":true,"grid_size":9,"observed_range":[-2.1324229019040257,2.1324229019040257],"required_output_range":null,"safe":true,"within_output_range":true},"topology_expression":"-0.381391*Cd_rear_a"},"pysr":{"purpose":"fixed-topology-refit","used":false}},"role":"front_odd","run_id":"article2-percase-refit-karman-v2-20260720","schema_version":"1.0","training_scenes":["karman_re50"]} diff --git a/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re50_rear_shared_upper.json b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re50_rear_shared_upper.json new file mode 100644 index 0000000..8783756 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-karman-v2-20260720/formulas/karman_re50_rear_shared_upper.json @@ -0,0 +1 @@ +{"alignment_metadata":{"alignment":"causal_post_state_to_next_action","history_policy":"drop_warmup_zero_initialized","required_target":false,"sample_segments":{"steady":[50,200],"transient":[2,50]},"warmup":2},"anchor":"upper","artifact_id":"article2-percase-refit-karman-v2-20260720-per-scene-rear_shared-anchor-upper","deployment_expression":"0.22124106819225497*Cl_rear_s - 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diff --git a/src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/coefficients.csv b/src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/coefficients.csv new file mode 100644 index 0000000..a1c51e1 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/coefficients.csv @@ -0,0 +1,15 @@ +objective,scene,role,deployment_expression,coefficient_0,coefficient_1,coefficient_2,r2,mae,rmse +karman,karman_re100,front_odd,-0.324842776541943*Cd_rear_a,0.324842776541943,,,0.8942459148735239,0.36762602777500997,0.45820137299549274 +karman,karman_re100,rear_shared,1.3203186589016584*Cl_rear_s - 4.177446928237706,1.3203186589016584,4.177446928237706,,0.7130789672514801,0.7736831127890521,1.0448153119880985 +karman,karman_re200,front_odd,-0.5002775221133345*Cd_rear_a,0.5002775221133345,,,0.6465569445532036,1.1792826984245248,1.4089506676834735 +karman,karman_re200,rear_shared,1.6971078570952467*Cl_rear_s - 3.410910965502206,1.6971078570952467,3.410910965502206,,0.5787723199954065,2.099878553243882,2.5076971709926044 +karman,karman_re400,front_odd,-0.29472162211279335*Cd_rear_a,0.29472162211279335,,,0.9735052865964828,0.1479559237498144,0.19880018636789204 +karman,karman_re400,rear_shared,1.054929993313117*Cl_rear_s - 2.5544553575675293,1.054929993313117,2.5544553575675293,,0.32847453250491054,2.180989822453082,2.4580453982854116 +karman,karman_re50,front_odd,-0.5835788969350136*Cd_rear_a,0.5835788969350136,,,0.28270513879302694,1.5402098101682624,1.6988826421169128 +karman,karman_re50,rear_shared,0.22124106819225497*Cl_rear_s - 3.582021485268801,0.22124106819225497,3.582021485268801,,0.022057303194492284,0.8173865388047218,1.0151963163022433 +illusion,illusion_0.75L,front_odd,-0.24201575527044217*Cd_rear_a - 0.8372711325983693*Cl_F,0.24201575527044217,-0.8372711325983693,,0.8034648267882286,0.507387673315919,0.5658425189470068 +illusion,illusion_0.75L,rear_shared,0.24859161792995263*Cd_rear_a + 0.08286020333465423*Cl_F,0.24859161792995263,-0.08286020333465423,,-5.43956220650249,1.5228555424866943,1.5799369208526148 +illusion,illusion_1.5L,front_odd,-0.044119550519154505*Cd_rear_a + 2.1015026293420416*Cl_F,0.044119550519154505,2.1015026293420416,,0.5420581062268233,2.5622557463777267,3.084420205371026 +illusion,illusion_1.5L,rear_shared,1.5682603086502969*Cd_rear_a - 1.6011017720495324*Cl_F,1.5682603086502969,1.6011017720495324,,0.5948268502056615,1.6098420125432724,1.9948656250961145 +illusion,illusion_1L,front_odd,2.043848942000545*Cd_rear_a - 2.219295123924814*Cl_F,-2.043848942000545,-2.219295123924814,,0.6059884236814721,0.26749351633879453,0.32378763498020424 +illusion,illusion_1L,rear_shared,0.18434653705308968*Cd_rear_a - 0.15875223987426046*Cl_F,0.18434653705308968,0.15875223987426046,,-11.247969651640615,0.8679547536796725,0.905159878077149 diff --git a/src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/interpretation.md b/src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/interpretation.md new file mode 100644 index 0000000..edaabd6 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-percase-refit-summary-20260720/interpretation.md @@ -0,0 +1,17 @@ +# Per-case fixed-topology refit interpretation + +## Kármán + +The frozen joint topology does not acquire one monotonic coefficient trend with Reynolds number. The rear constant remains the most stable element: its magnitude is 3.58, 4.18, 3.41 and 2.55 for Re 50, 100, 200 and 400, respectively. The rear lift coefficient is weak at Re50 (0.22), peaks at Re200 (1.70), and decreases at Re400 (1.05). The front drag-asymmetry coefficient varies non-monotonically (0.58, 0.32, 0.50, 0.29). + +This supports a common steady rear-rotation backbone, but not a simple low-Re/high-Re two-group coefficient law. The independent broad-discovery inventory reaches the same conclusion: Re50 favours simple front-force/affine structures, Re400 repeatedly selects `Cl_rear_s-Cl_tot`, and Re100/Re200 are heterogeneous lift-based intermediates. Re400 is distinct chiefly through a smaller rear constant and poorer rear-head offline fit, consistent with the loss of joint-law closed-loop performance. Re50 is also structurally special because its rear lift feedback is nearly absent. The data therefore suggest non-monotonic retuning plus distinct endpoint signatures rather than two clean clusters. + +## Illusion + +The frozen four-term topology changes signs and relative weights between diameters. At 0.75L the front action is dominated by negative front-lift feedback and the rear refit is poorly identified. At 1L the front coefficients reverse relative to the joint formula and the rear topology has strongly negative R2, indicating that this fixed rear family is not a useful open-loop action surrogate for that single trajectory. At 1.5L the rear coefficients become an order of magnitude larger and the front action is almost purely positive front-lift feedback. + +Broad discovery makes the diameter progression clearer than the frozen-topology coefficients alone: 0.75L is rear-drag dominated (`Cd_rear_a` 15 and `Cd_rear_s` 10 occurrences), 1L is mixed/transitional (`Cd_F` 13 and `Cl_rear_s` 12), and 1.5L is overwhelmingly lift-contrast dominated (`Cl_F` 54 and `Cl_rear_s` 46). The endpoint families are therefore clearly distinct, while 1L is a genuine transition rather than a member of either clean group. Coefficient sign changes still show that one joint static formula is an averaging compromise rather than a universal target-tracking law. + +## Scope + +These per-case refits are diagnostics on PPO-visited trajectories. They are not promoted to controllers without separate closed-loop CFD validation. Negative R2 values are retained because they are useful evidence that the frozen topology is insufficient for a particular head/case. diff --git a/src/SR_analysis/results/runs/article2-plotting-package-20260721/ablation_summary.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/ablation_summary.csv new file mode 100644 index 0000000..1bcca33 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/ablation_summary.csv @@ -0,0 +1,25 @@ +run_id,variant,scene,steps,status,similarity,lag,termination_reason,formula_hash +article-ablation-L2-i_front0-20260718,i_front0,illusion_0.75L,40,completed,0.9393749322736294,11,completed,028cce8471d193e7d21aa9fb953b3f65a6999b40f17b3c7a0be6b975153fa533 +article-ablation-L2-i_front0-20260718,i_front0,illusion_1L,40,completed,0.9334814093710048,3,completed,028cce8471d193e7d21aa9fb953b3f65a6999b40f17b3c7a0be6b975153fa533 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b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/sr_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "steady", + "source": "sr", + "input": "/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article2-steady-sweep-a2-20260720/telemetry/steady__constant__f-none__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/sr_wide.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/sr_wide.csv new file mode 100644 index 0000000..8d434b9 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/sr_wide.csv @@ -0,0 +1,201 @@ 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a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/target_long.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/target_long.csv new file mode 100644 index 0000000..17e3765 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_2/target_long.csv @@ -0,0 +1,901 @@ +scene,source,step,t_D,channel_group,channel_name,value +steady,target,0,0.0,velocity,upper_ux,1.1360431909561157 +steady,target,0,0.0,velocity,upper_uy,-6.069301161915064e-05 +steady,target,0,0.0,velocity,center_ux,1.1645539999008179 +steady,target,0,0.0,velocity,center_uy,-2.349157881553765e-07 +steady,target,0,0.0,velocity,lower_ux,1.1360430717468262 +steady,target,0,0.0,velocity,lower_uy,6.0079586546635255e-05 +steady,target,1,0.4,velocity,upper_ux,1.1360433101654053 +steady,target,1,0.4,velocity,upper_uy,-6.040649168426171e-05 +steady,target,1,0.4,velocity,center_ux,1.1645541191101074 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a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_dtw_convergence.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_dtw_convergence.csv new file mode 100644 index 0000000..831b963 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_dtw_convergence.csv @@ -0,0 +1,7 @@ +scene,source,end_step,t_D,conv_len,similarity,lag,channel_0,channel_1,channel_2,channel_3,channel_4,channel_5 +steady,sr,60,24.0,30,0.9763912177220911,5,0.9826656301816304,0.9853897946742411,0.9222746729850769,0.9999891945167392,0.9826600154240926,0.9853679985507673 +steady,sr,90,36.0,30,0.976372108312204,-15,0.9826186100641886,0.9853878457493314,0.9222280184427897,0.9999971831820869,0.982616925239563,0.985384067195264 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b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "steady", + "source": "sr", + "input": "/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article2-steady-sweep-a4-20260720/telemetry/steady__constant__f-none__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_wide.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_wide.csv new file mode 100644 index 0000000..9e92c3b --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_4/sr_wide.csv @@ -0,0 +1,201 @@ 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a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_dtw_convergence.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_dtw_convergence.csv new file mode 100644 index 0000000..d082a5b --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_dtw_convergence.csv @@ -0,0 +1,7 @@ +scene,source,end_step,t_D,conv_len,similarity,lag,channel_0,channel_1,channel_2,channel_3,channel_4,channel_5 +steady,sr,60,24.0,30,0.9786652506709976,6,0.9775575200716654,0.9851369761156092,0.9472764174143473,0.9999036257142383,0.9771585424741109,0.9849584222360136 +steady,sr,90,36.0,30,0.9786658200579444,5,0.9774420062700907,0.985104241393977,0.947314981619517,0.9999458173599127,0.9771830598513286,0.9850048138528412 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a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_manifest.json b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_manifest.json new file mode 100644 index 0000000..13fca34 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "steady", + "source": "sr", + "input": "/home/frank14f/DynamisLab/src/SR_analysis/results/runs/article2-steady-sweep-a6-20260720/telemetry/steady__constant__f-none__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_wide.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/steady_timeseries/alpha_6/sr_wide.csv new file mode 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a/src/SR_analysis/results/runs/article2-plotting-package-20260721/term_contributions_ppo_states.csv b/src/SR_analysis/results/runs/article2-plotting-package-20260721/term_contributions_ppo_states.csv new file mode 100644 index 0000000..64d84a1 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-plotting-package-20260721/term_contributions_ppo_states.csv @@ -0,0 +1,1380 @@ +objective,scene,source,step,t_D,term_front_t0__0_3813914338074549_Cd_rear_a,term_front_t0__1_8266041890847688_Cd_rear_a,term_front_t1_2_064492712013321_Cl_F,term_lower_t0_1_2544396437730243_Cd_rear_a,term_lower_t0_1_3077817865589976_Cl_rear_s,term_lower_t1__1_5280742226055937_Cl_F,term_lower_t1__3_431208680510616,term_upper_t0_1_2544396437730243_Cd_rear_a,term_upper_t0_1_3077817865589976_Cl_rear_s,term_upper_t1__1_5280742226055937_Cl_F,term_upper_t1__3_431208680510616,predicted_alpha_front,predicted_alpha_upper,predicted_alpha_lower 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the tested Re and diameter ranges", + "Causal flow structures before OID/CCD analysis" + ] + } +} \ No newline at end of file diff --git a/src/SR_analysis/results/runs/article2-sr-elements-20260720/readable_summary.txt b/src/SR_analysis/results/runs/article2-sr-elements-20260720/readable_summary.txt new file mode 100644 index 0000000..661b546 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-sr-elements-20260720/readable_summary.txt @@ -0,0 +1,31 @@ +ARTICLE2 SR OBS-ACT EXTENSION — 2026-07-20 + +STEADY CALIBRATION + Constant rear counter-rotation sweep (alpha magnitude 0..6): best sampled alpha=5, + legacy DTW=0.987212. Joint Karman formula in steady inflow: DTW=0.966494; + rear tail action=(-3.4306,+3.4318), analytically equal to constant magnitude 3.4312. + +400-STEP TRAINING-CONDITION VALIDATION + Karman: Re50 0.952178; Re100 0.944339; Re200 0.863850; Re400 0.833365. + Illusion: 0.75L 0.854379; 1L 0.915609; 1.5L 0.833587. + All completed with finite telemetry. These values use legacy_dtw_v1_abs_n_unclipped. + +CURRENT-FORMULA GENERALIZATION (200 STEPS) + Karman: Re25 0.988078 (extrapolation), Re70 0.954339, Re150 0.907496, + Re300 0.835758 (interpolation). + Illusion: 0.5L 0.784055, 0.6L 0.821103, 2L 0.756880 (extrapolation); + 0.8L 0.884573, 1.2L 0.925530 (interpolation). + +PER-CASE DIAGNOSTICS + Karman rear constants remain O(3-4) but gains are non-monotonic; no clean low/high-Re + two-group law is supported. Illusion 1.5L has a distinct coefficient emphasis, while + single-case rear refits at 0.75L/1L are poorly identified and are not controllers. + +HANDOVER + Highest-priority OID/CCD targets: rear steady counter-rotation; rear symmetric lift + feedback; front lift in Illusion; rear drag asymmetry. See sr_key_elements.md/csv. + +LIMITS + No direct momentum-balance equality is claimed. Per-case refits are offline diagnostics. + Generalization claims are limited to the sampled conditions. OID/CCD causal/spatial + structures remain to be established. diff --git a/src/SR_analysis/results/runs/article2-sr-elements-20260720/sr_key_elements.csv b/src/SR_analysis/results/runs/article2-sr-elements-20260720/sr_key_elements.csv new file mode 100644 index 0000000..c5084bd --- /dev/null +++ b/src/SR_analysis/results/runs/article2-sr-elements-20260720/sr_key_elements.csv @@ -0,0 +1,7 @@ +objective,element,controller_location,evidence_strength,evidence,physical_interpretation,OID_CCD_target +Karman,rear constant,rear shared,strong,"largest deletion loss; steady deployment alpha=3.4312; steady sweep optimum alpha=5",persistent rear counter-rotation restores mean downstream deficit,"symmetric streamwise correction originating in rear gap and persisting downstream" +Karman,Cl_rear_s,rear shared,moderate,"secondary deletion loss; per-case coefficient 0.22-1.70",state-dependent modulation of rear momentum injection,"force-correlated rear-gap/source structure and its convected descendant" +Karman,Cd_rear_a,front odd,weak,"front deletion changes short DTW weakly",antisymmetric front phase/force trim,"antisymmetric near-front structure; expected weaker signature correlation" +Illusion,Cl_F,front and rear,moderate,"dominates coefficient-scaling action magnitude; 1.5L front refit nearly pure Cl_F",front-loading feedback and target-dependent phase/amplitude retuning,"front-body force-related source with diameter-dependent wake descendant" +Illusion,Cd_rear_a,front and rear,weak-to-moderate,"weaker scaling influence; partly replaceable",rear-wake asymmetry correction,"antisymmetric rear-gap correction; compare 0.75/1L with 1.5L" +Illusion,target/error variables,not selected,negative,"available in discovery but not recurrent in low-complexity static laws",explicit target tracking not identified statically,"CCD should test whether target information appears through lagged/phase-resolved structures" diff --git a/src/SR_analysis/results/runs/article2-sr-elements-20260720/sr_key_elements.md b/src/SR_analysis/results/runs/article2-sr-elements-20260720/sr_key_elements.md new file mode 100644 index 0000000..b3d8093 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-sr-elements-20260720/sr_key_elements.md @@ -0,0 +1,21 @@ +# SR-to-OID/CCD handover + +## Kármán cloak + +1. **Rear steady counter-rotation (strongest evidence).** The joint rear law contains the constant \(\mp 3.4312\). Removing it causes the largest closed-loop degradation in the established term tests. In a disturbance-free deployment, the last-cycle rear actions are \((-3.4306,3.4318)\), while a discrete constant-speed sweep peaks at \(\mp5U_0\). OID should search for a symmetric streamwise correction generated in the rear gap and retained at the downstream sensors. CCD should quantify the connection from the rear actuator pair to the mean/low-frequency downstream deficit mode. + +2. **Symmetric rear lift feedback \(Cl_{rear,s}\) (moderate evidence).** This is the only state-dependent rear term retained by the article law. Per-case coefficients vary from 0.22 to 1.70, so it is a modulation rather than a universal gain. CCD should seek a force-correlated source mode near both rear cylinders and its delayed downstream descendant; phase-conditioned analysis is preferable to zero-lag correlation. + +3. **Rear drag asymmetry \(Cd_{rear,a}\) in the front action (weak evidence).** This term provides antisymmetric front-cylinder trimming, but deletion tests rank it below the rear terms. OID/CCD should test an antisymmetric near-front mode and expect a weaker downstream canonical correlation. + +## Illusion + +1. **Front lift \(Cl_F\) (moderate evidence).** Coefficient scaling shows that this term dominates action-amplitude sensitivity, and the 1.5L per-case front refit is nearly pure \(Cl_F\). OID should compare front-body source structures among 0.75L, 1L and 1.5L; CCD should test whether the associated descendant changes wavelength/phase with target diameter. + +2. **Rear drag asymmetry \(Cd_{rear,a}\) (weak-to-moderate evidence).** It appears in both heads but is less influential and partly replaceable. Search for antisymmetric rear-gap structures and explicitly compare the large-target 1.5L regime with the smaller targets. + +3. **Absence of explicit target/error terms (negative evidence).** Those variables were available but did not recur in the selected low-complexity static laws. This does not prove feedforward target information is absent: OID/CCD should test lagged and phase-resolved structures because a static obs-action regression can alias internal PPO memory or phase relationships into measured forces. + +## Analysis contract + +For each element, report actuator-to-source and source-to-downstream relationships separately; preserve upper/lower reflection parity; resolve lags in units of \(D/U_0\); and do not promote spatial coincidence to causality without time-lagged evidence. Compare the SR ranking against canonical-correlation magnitude, but retain contradictions rather than forcing agreement. diff --git a/src/SR_analysis/results/runs/article2-steady-analysis-20260720/interpretation.md b/src/SR_analysis/results/runs/article2-steady-analysis-20260720/interpretation.md new file mode 100644 index 0000000..85a3a18 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-steady-analysis-20260720/interpretation.md @@ -0,0 +1,9 @@ +# Steady-cloak calibration + +The steady-flow sweep used rear counter-rotation \(\alpha_U=-A\), \(\alpha_L=A\), \(A=0,\ldots,6\), with the front cylinder stationary. The maximum legacy DTW similarity occurred at \(A=5\), with similarity 0.9872. The current Kármán joint law produced similarity 0.9665 when deployed without an upstream disturbance. + +In the steady deployment, the rear lift feedback nearly cancels between the reflected upper/lower states. The rear action therefore equals the fitted constant to numerical precision: the last-cycle mean is \((-3.4306,3.4318)\), compared with the analytical constant magnitude 3.4312. This is 68.6% of the sweep optimum (5.0) in action magnitude, yet it recovers most of the achievable downstream sensor similarity. + +This supports a limited but quantitative interpretation: the Kármán law contains a persistent rear-counter-rotation component of the same order as the steady open-loop rotation required to restore the downstream sensor signature. It does not establish a one-to-one conversion between velocity deficit and cylinder surface speed; that conversion is nonlinear and geometry-dependent. The correct claim is therefore an order-of-magnitude and closed-loop correspondence, not a direct momentum-balance equality. + +The prior expectation of approximately 4 U0 lies between the fitted constant 3.43 U0 and the discrete sweep optimum 5 U0. A finer sweep around 4–5 U0 would be needed to locate the continuous optimum, but is not required for the present mechanism conclusion. diff --git a/src/SR_analysis/results/runs/article2-steady-analysis-20260720/steady_constant_correspondence.json b/src/SR_analysis/results/runs/article2-steady-analysis-20260720/steady_constant_correspondence.json new file mode 100644 index 0000000..a3be055 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-steady-analysis-20260720/steady_constant_correspondence.json @@ -0,0 +1,13 @@ +{ + "joint_formula_similarity": 0.966494369494805, + "rear_constant_magnitude": 3.431208680510616, + "sweep_best_alpha": 5, + "sweep_best_similarity": 0.9872116243063841, + "formula_mean_rear_alpha_magnitude": 3.431208680510616, + "formula_tail_mean_rear_alpha_magnitude": 3.431208680510616, + "formula_tail_mean_alpha": [ + -0.0005629833490577167, + -3.430589242946191, + 3.431828118075043 + ] +} \ No newline at end of file diff --git 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diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_0.75L/sr_wide.csv b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_0.75L/sr_wide.csv new file mode 100644 index 0000000..4583eab --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_0.75L/sr_wide.csv @@ -0,0 +1,201 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower,target_forces_target_cd,target_forces_target_cl,target_phase_target_phase 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+ "conv_len": 36, + "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_forces": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_1.5L/ppo_wide.csv b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_1.5L/ppo_wide.csv new file mode 100644 index 0000000..4cfa2cc --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_1.5L/ppo_wide.csv @@ -0,0 +1,201 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower,target_forces_target_cd,target_forces_target_cl 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+ "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_forces": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_1L/ppo_wide.csv b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_1L/ppo_wide.csv new file mode 100644 index 0000000..6e784cb --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/illusion_1L/ppo_wide.csv @@ -0,0 +1,201 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower,target_forces_target_cd,target_forces_target_cl 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b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re100/sr_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "karman_re100", + "source": "sr", + "input": "src/SR_analysis/results/runs/article-L3-karman-20260718/telemetry/karman_re100__pysr__f-a678f2019e0d__si-800.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re100/sr_wide.csv b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re100/sr_wide.csv new file mode 100644 index 0000000..d94a66f --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re100/sr_wide.csv @@ -0,0 +1,201 @@ 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+} diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re200/ppo_wide.csv b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re200/ppo_wide.csv new file mode 100644 index 0000000..96a7a23 --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re200/ppo_wide.csv @@ -0,0 +1,201 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower 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b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re200/sr_wide.csv new file mode 100644 index 0000000..7a34efc --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re200/sr_wide.csv @@ -0,0 +1,201 @@ +scene,source,step,t_D,sensors_upper_ux,sensors_upper_uy,sensors_center_ux,sensors_center_uy,sensors_lower_ux,sensors_lower_uy,forces_front_fx,forces_front_fy,forces_upper_fx,forces_upper_fy,forces_lower_fx,forces_lower_fy,actions_alpha_front,actions_alpha_upper,actions_alpha_lower,actions_omega_front,actions_omega_upper,actions_omega_lower +karman_re200,sr,0,0.0,1.1116604804992676,0.2913402318954468,1.0067689418792725,0.45077061653137207,0.9823883175849915,0.3228631317615509,0.0006219907663762569,0.004089671187102795,-0.00907693151384592,0.002735402202233672,0.007555061485618353,-0.005517416633665562,2.9088244635452787,-5.439184056224114,1.423233304797118,0.029088244635452787,-0.054391840562241146,0.01423233304797118 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diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re50/ppo_manifest.json b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re50/ppo_manifest.json new file mode 100644 index 0000000..f0d999d --- /dev/null +++ b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re50/ppo_manifest.json @@ -0,0 +1,14 @@ +{ + "scene": "karman_re50", + "source": "ppo", + "input": "src/SR_analysis/data/runs/article-joint-data-karman-20260718/karman/karman_re50/controlled.npz", + "sample_interval": 800, + "conv_len": 30, + "rows": { + "sensors": 200, + "forces": 200, + "actions_alpha": 200, + "actions_omega": 200, + "target_sensors": 150 + } +} diff --git a/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re50/ppo_wide.csv b/src/SR_analysis/results/runs/article2-timeseries-csv-20260720/karman_re50/ppo_wide.csv new file mode 100644 index 0000000..5f513f0 --- /dev/null +++ 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diff --git a/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-error_only-seed2-20260717/formulas/illusion_1L_rear_shared_upper.json b/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-error_only-seed2-20260717/formulas/illusion_1L_rear_shared_upper.json new file mode 100644 index 0000000..c11a291 --- /dev/null +++ b/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-error_only-seed2-20260717/formulas/illusion_1L_rear_shared_upper.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-feedforward_feedback-seed0-20260717/formulas/illusion_1L_front.json b/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-feedforward_feedback-seed0-20260717/formulas/illusion_1L_front.json new file mode 100644 index 0000000..f230278 --- /dev/null +++ b/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-feedforward_feedback-seed0-20260717/formulas/illusion_1L_front.json @@ -0,0 +1 @@ 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diff --git a/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-feedforward_feedback-seed0-20260717/formulas/illusion_1L_rear_shared_upper.json b/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-feedforward_feedback-seed0-20260717/formulas/illusion_1L_rear_shared_upper.json new file mode 100644 index 0000000..c303dda --- /dev/null +++ b/src/SR_analysis/results/runs/trusted-ablation-illusion_1L-feedforward_feedback-seed0-20260717/formulas/illusion_1L_rear_shared_upper.json @@ -0,0 +1 @@ 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b/src/SR_analysis/results/runs/trusted-evidence-round1-20260717/evidence_manifest.json new file mode 100644 index 0000000..bce2c2f --- /dev/null +++ b/src/SR_analysis/results/runs/trusted-evidence-round1-20260717/evidence_manifest.json @@ -0,0 +1,89 @@ +{ + "schema_version": "sr-trusted-evidence-v1", + "status": "manual_review_required", + "canonical_registry_updated": false, + "scope": ["karman_re100", "illusion_1L"], + "environment_contract": { + "fitting": "sr_env", + "cfd": "pycuda_3_10", + "physical_gpu": 2, + "cfd_concurrency": "serial" + }, + "metric": "legacy_dtw_v1_abs_n_unclipped", + "common_steps": 200, + "karman_re100": { + "decision": "rear_constant_is_a_supported_physical_baseline; tested_front_feedback_rejected", + "controllers": { + "uncontrolled": { + "law": ["0", "0", "0"], + "similarity": 0.6484452502401029, + "validation": "../trusted-baseline-karman-uncontrolled-20260717/validations/karman_re100__uncontrolled__f-none__si-800.json" + }, + "ppo": { + "similarity": 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diff --git a/src/SR_analysis/sr_legacy_pipeline_bf1c2e74.plan.md b/src/SR_analysis/sr_legacy_pipeline_bf1c2e74.plan.md deleted file mode 100644 index f09c5e2..0000000 --- a/src/SR_analysis/sr_legacy_pipeline_bf1c2e74.plan.md +++ /dev/null @@ -1,162 +0,0 @@ ---- -name: SR Legacy Pipeline -overview: 冻结当前 legacy 证据后，修复并版本化 Kármán 与 Illusion 的 Stage 1–3 数据、特征、公式和闭环契约；第一轮只在四个 Kármán 训练 Re 与 Illusion 0.75L/1L 上完成端到端复现并暂停复核。DTW 闭环是公式选择的最终判据，离线指标仅用于排查时序错位、共线性和部署不一致。 -todos: - - id: snapshot-baseline - content: 创建 SR 专用 Git 快照并记录 ignored 数据、模型和环境资产清单 - status: pending - - id: freeze-contracts - content: 统一 legacy 场景、layout、无量纲化、G 映射和 DTW 契约 - status: pending - - id: repair-stage1 - content: 修复并版本化 Kármán/Illusion Stage 1 数据与因果时序 - status: pending - - id: unify-features - content: 实现 Stage 2/3 共用的 stateful 特征和导数计算 - status: pending - - id: repair-stage2 - content: 实现可复现的 joint front/rear PySR 拟合与公式 schema - status: pending - - id: repair-stage3 - content: 实现公式/G 部署、versioned DTW 与完整 validation provenance - status: pending - - id: run-round1 - content: 在指定 Conda 环境和 GPU 2 完成第一轮 legacy Stage 1–3 复跑 - status: pending - - id: review-candidates - content: 审计历史与 candidate 结果并暂停等待 canonical 晋升决定 - status: pending -isProject: false ---- - -# SR Legacy 端到端完善计划 - -## 范围与原则 - -- 主体系冻结为 legacy；V5、erase、unseen-Re、Vortex、1.5L、论文绘图均不进入第一轮验收。 -- CFD、PPO 推理与闭环统一使用 `pycuda_3_10` 和 GPU 2；PySR/SINDy 使用 `sr_env`。 -- 所有新数据、公式和 validation 先写入带 `run_id` 的 staging 目录，不覆盖现有 [`data/`](src/SR_analysis/data/) 与 [`results/`](src/SR_analysis/results/) canonical 文件。 -- 离线拟合的 \(R^2\)、MAE 等只作为数据/模型诊断；公式取舍以 legacy DTW 闭环结果为最终标准。legacy DTW 固定为独立版本，不混用 V5 的 target-amplitude normalization。 -- observation、action、force 均使用实际物理无量纲量；明确 legacy action 是圆柱表面切向速度，统一定义 \(\alpha=u_{wall}/U_0\)。 -- G 映射作为结构约束：front 为 odd channel，upper/lower 交换并反号；新公式必须记录 rear shared-head 的 anchor 和镜像构造。 - -```mermaid -flowchart LR - snapshot["Git快照与资产清单"] --> contracts["冻结数据和物理契约"] - contracts --> infer["Stage1 PPO推理"] - infer --> fit["Stage2 PySR拟合"] - fit --> deploy["Stage3 CFD闭环"] - deploy --> review["结果审计与人工复核"] - review -->|"通过后"| promote["晋升canonical"] - review -->|"不通过"| diagnose["时序或公式诊断"] -``` - -## 第一阶段：冻结当前状态与建立可回滚基线 - -- 按用户选择先创建一个专用 Git 快照提交；提交前按 Git 协议核对完整 status、diff 和近期提交，只纳入当前 SR 工作及明确相关文件，不混入其他模块的无关改动。 -- 记录当前 commit、工作树状态、Conda/Python/PySR/Julia/CUDA/GPU 信息，以及 legacy 模型最终解析路径。 -- 为 ignored 的 NPZ 和 PPO 模型生成只读资产清单：路径、大小、SHA-256、NPZ keys/shape/dtype。Git 快照本身不能恢复 ignored 数据，因此第一轮脚本必须禁止覆盖这些文件；资产清单用于检测意外变化。 -- 将现有 [`scene_registry.json`](src/SR_analysis/scene_registry.json)、[`results/README.md`](src/SR_analysis/results/README.md)、formula 和 validation 标记为 `historical_frozen`，不立即判断 0.978/0.970 与 0.982/0.958 哪组为最终 canonical。 - -## 第二阶段：冻结跨 Stage 的唯一契约 - -### 场景与数据契约 - -- 在 [`configs.py`](src/SR_analysis/configs.py) 集中定义 Kármán/Illusion 的：几何、目标位置、`Re_code` 与 `Re_D`、SI、\(dt_c=SI/(D/U_0)=SI/2000\)、动作 decoder、FIFO 初始动作、sensor/force/action layout、target slices。 -- 保持 legacy 物理定义与数值：`Re_code=2Re_D`；论文输出可以转换成 `Re_D`，文件名和模型名继续保留 code Re。 -- 明确 Illusion target 契约为 `[target_fx,target_fy, six_sensor_channels]`，传感器比较使用 `[:,2:8]`，策略目标力使用 `[:,0:2]`。 -- 先核对真实 native body order，不直接交换历史 NPZ 列；新数据写显式 layout metadata，内部统一使用 `front/upper/lower`，旧 `top/bottom` 名称通过兼容映射读取。 - -### 物理特征与 G 映射 - -- 收敛 [`feature_builder.py`](src/SR_analysis/utils/feature_builder.py) 与 [`g_operator.py`](src/SR_analysis/utils/g_operator.py) 的重复实现，使所有 Stage 调用同一个 G 和无量纲化入口。 -- 增加 `G(G(x))=x`、target drag even/target lift odd、rear shared-head equivariance 测试。 -- 缺失 required feature 时默认报错，禁止再由 `build_feature_matrix` 静默补零；仅历史兼容读取可显式选择零填充。 - -### legacy DTW - -- 在 [`cfd_interface.py`](src/SR_analysis/utils/cfd_interface.py) 中固定 scene-specific metric：`legacy_dtw_v1_abs_n_unclipped`，明确 lag channel、target slice、window、无幅值归一化、无 clipping。 -- 文档明确 legacy 与 V5 DTW 不直接混用；第一轮不改变历史算法。 - -## 第三阶段：修复 Stage 1 并隔离运行产物 - -- 修改 [`stage_1_infer.py`](src/SR_analysis/stage_1_infer.py) 使路径不依赖 cwd，配置 dry-run 能在不启动 CUDA 时完成模型、target、norm 和输出路径检查。 -- 修复 Illusion 的目标位置配置、target force 索引和 `controlled.npz` 中 `target_forces` 缺失；首步 observation、target harmonic phase 与已验证 legacy 测试保持一致。 -- 明确数据采集时序并写入 metadata：哪个 observation 是 action 执行前或执行后、target phase index、action command index。先以旧推理循环和 legacy test 为事实依据，不凭数组同名假设对齐。 -- 新 NPZ 同时保存 schema/version、layout、SI、\(dt_c\)、normalization source、model hash 和 alignment；保留旧 key alias 只用于兼容。 -- Stage 1 只写 `data/runs//...`；没有显式 promotion 时拒绝覆盖历史数据。 - -## 第四阶段：让离线与在线特征严格一致 - -- 建立 stateful feature state，统一保存 previous observation、previous target force、previous actions 和 \(dt_c\)。 -- 在真实 legacy 数据上比较两种主要 alignment：当前同行 \(X_t\to a_t\) 与按控制循环推导的 \(X_t\to a_{t+1}\)。这一步是契约审计，不用较高 \(R^2\) 自动决定正确性；最终采用与 PPO 决策时刻一致的因果定义。 -- 对同一 trajectory 验证：Stage 2 批量计算与 Stage 3 逐步计算的每个 required feature 数值一致；包含 `du_a_dt`、`dCl_tot_dt`、`dCd_err_dt` 和 action derivative。 -- 将 alignment、导数定义和丢弃的初始样本数写入 formula metadata，避免未来再次产生“拟合公式与部署公式语义不同”。 - -## 第五阶段：修复 Stage 2 的双头拟合与公式记录 - -- 修改 [`stage_2_fit.py`](src/SR_analysis/stage_2_fit.py)，使 per-scene 和 joint 都生成： - - front head； - - rear shared head；另一个 rear action 由 Stage 3 通过 G 构造。 -- 统一 formula JSON schema，兼容读取旧 `feature_names/feature_keys` 与 `best_sympy`，新写出至少包含：training scenes、feature order/definitions、fitted expression、deployment expression、role/anchor、G version、data hashes、alignment、\(dt_c\)、PySR seed/operators/complexity、in-sample diagnostics。 -- 固定 PySR seed并提供极短 smoke 参数；候选公式写入 `results/runs//formulas/`，不覆盖现有 formula。 -- 对 Kármán 同时保留： - - fitted expression； - - 人工物理审计后的 deployment expression，例如删除部署时恒零/共线产生的 `daB_dt` 项。 - 二者必须分别带 hash，禁止用一个公式的 DTW 给另一个公式背书。 -- SINDy/STLSQ 第一轮只作为内部筛选诊断：检查 feature 共线、lag 与支持稳定性，不作为闭环候选的自动裁决，也不写成正文核心方法。 - -## 第六阶段：修复 Stage 3 的真实部署 - -- 修改 [`stage_3_validate.py`](src/SR_analysis/stage_3_validate.py)，使用统一 formula loader：有 symbolic expression 时直接编译，只有明确 linear schema 时才读取 coefficient；禁止缺 coefficient 时默认为全零。 -- 使用与 Stage 2 完全相同的 stateful feature builder 和 \(dt_c\)，并按 formula metadata 构造 front 与 G-mirrored rear actions。 -- Kármán和 Illusion 共享部署核心，只在 target-force/reference 和环境 builder 上分支。 -- validation 文件按 scene、mode、formula hash 和 SI 独立命名，异常必须返回非零；不再让 PPO、SR、uncontrolled 或不同 SI 相互覆盖。 -- 每个结果记录：formula/data/model hash、Git SHA、环境、scene config、metric version、n_steps、full/tail DTW、per-channel DTW、action range、NaN/Inf、termination reason 和原始 telemetry 路径。 - -## 第七阶段：分层运行与第一轮验收 - -### 无 GPU 与短 smoke - -- 先运行 config/NPZ/formula schema、G、DTW fixture、batch-vs-step feature equivalence 测试。 -- 在 `sr_env` 中用 Kármán Re100 小样本、极少 iterations 验证 PySR/Julia 启动、front/rear 双输出和 Stage 3 可读性。 -- 在 `pycuda_3_10` 中确认 GPU 2 可用，再做 Kármán Re100 与 Illusion 1L 的最短 wiring smoke；短跑只检查 finite/actions/schema，不用 DTW 判断控制效果。 - -### 完整第一轮 - -按以下顺序运行，单场失败即停止批量扩展： - -1. Kármán Re100 Stage 1→2→3； -2. Illusion 1L Stage 1→2→3； -3. Kármán code Re 50/100/200/400 joint fit 与闭环； -4. Illusion 0.75L+1L joint fit 与各自闭环。 - -验收条件： - -- Stage 1 数据、Stage 2 formula 和 Stage 3 telemetry 均有完整 provenance； -- required features 无静默零填充，批量/在线特征一致，G 测试通过； -- 每个 formula 实际部署 expression 与 validation 中的 hash 一致； -- CFD 无 NaN/Inf 或动作异常； -- legacy DTW 可与历史结果并列比较。若结果改变，不以“必须复现旧数字”强行通过，而是通过 telemetry 定位是 target 修复、alignment 修复、导数修复还是公式变化造成； -- 第一轮结束后只生成审计报告和 candidate registry，不自动修改 canonical [`scene_registry.json`](src/SR_analysis/scene_registry.json)。到此暂停，请用户复核后再决定晋升或继续机制分析。 - -## 后续阶段：第一轮复核后再启动 - -### 机制与公式项价值 - -- Kármán：uncontrolled、constant rear、front-only、full fitted law、reduced deployment law，比较总 DTW、ux/uy、drag/lift、控制功率。 -- Illusion：target-force feedforward-only、feedback-only、error-only、phase hybrid、front-only、rear-only；重点检验当前 joint front 化简为 `target_Cd + phase term` 后是否只是周期轨道上的等价表示。 -- 做受限 lag cross-correlation、coherence/cross-spectrum、delay embedding 和 target/observed quadrature phase；候选仍由 CFD DTW 裁决。 -- 检查 Illusion PPO 与公式的完整 G-equivariance，特别是 `target_Cd` 为 even 而 front action 应为 odd 的结构冲突。 - -### 泛化与扩展 - -- 流程稳定后再补 unseen-Re；随后恢复 Kármán law 到 Lamb/Taylor 的专用 Vortex validator。 -- 重采 1.5L trajectory，分析高频/phase-state 边界；不把 lagged-action 平凡解标为 canonical。 -- 最后再引入 V5 数据、生成论文图、同步 README/registry/报告和写作。 - -## 文档与长期维护 - -- 更新 [`README.md`](src/SR_analysis/README.md)、[`PIPELINE.md`](src/SR_analysis/PIPELINE.md) 与 Stage 文档，使命令、环境、GPU、schema 和实际代码一致。 -- `scene_registry` 只索引 artifact ID/path/hash/run ID；formula 和 validation JSON 各自保存原始数值。README 与后续图表从 registry 自动生成，避免手工重复 0.978/0.970 等数字。 -- 在 SR 根目录最终保存这份长期计划，并在每轮完成后更新状态、风险和下一验收点。 \ No newline at end of file diff --git a/src/SR_analysis/stage_1_infer.py b/src/SR_analysis/stage_1_infer.py index e990297..aa8b9eb 100644 --- a/src/SR_analysis/stage_1_infer.py +++ b/src/SR_analysis/stage_1_infer.py @@ -1,465 +1,567 @@ #!/usr/bin/env python3 -"""Stage 1: Unified PPO inference pipeline for all scenes. +"""Stage 1 legacy PPO inference for Karman, Illusion, and existing Vortex entry points. -Drives LegacyCelerisLab CFD + PPO model to produce controlled.npz and -target.npz for Karman cloak, Illusion, and Vortex scenes. - -Usage: - # Single scene - conda run -n pycuda_3_10 python stage_1_infer.py --scene karman_re100 --device 2 - - # Scene group - conda run -n pycuda_3_10 python stage_1_infer.py --group illusion_trained --device 2 - - # Target only (no PPO model needed, for generalization scenes) - conda run -n pycuda_3_10 python stage_1_infer.py --scene illusion_0.6L --target-only --device 2 - - # Dry run (verify configs and model paths) - conda run -n pycuda_3_10 python stage_1_infer.py --scene karman_re100 --dry-run - -Output per scene in data/{scene_id}/{scene_name}/: - target.npz — sensor signals from target phase - controlled.npz — sensors, forces, actions (normalized [-1,1]) from controlled phase - uncontrolled.npz — (Karman only) sensors, forces from zero-action - config.json — scene parameters - norm.json — normalization factors - result.json — similarity + reward summary - target_harmonics.json — (Illusion only) FFT harmonics for target force reconstruction +Outputs are isolated under ``data/runs////``. The +module deliberately keeps LegacyCelerisLab imports inside runtime functions so +CLI validation and unit tests do not require CUDA. """ from __future__ import annotations import argparse +import hashlib import json import os +import shlex +import subprocess import sys -import time from collections import deque +from datetime import datetime, timezone from pathlib import Path +from typing import Any, Mapping, Sequence import numpy as np -_REPO = Path(__file__).resolve().parents[1] -if str(_REPO) not in sys.path: - sys.path.insert(0, str(_REPO)) -_SRC = _REPO / "src" -if str(_SRC) not in sys.path: - sys.path.insert(0, str(_SRC)) +REPO_ROOT = Path(__file__).resolve().parents[2] +SRC_ROOT = REPO_ROOT / "src" +for import_root in (REPO_ROOT, SRC_ROOT): + if str(import_root) not in sys.path: + sys.path.insert(0, str(import_root)) -from LegacyCelerisLab import FlowField - -from SR_analysis.utils.cfd_interface import ( - nu_from_re, load_legacy_configs, build_karman_cloak_env, add_pinball, - build_observation, scale_action, load_ppo_model, save_vorticity_png, - vorticity_from_ddf, compute_similarity, +from SR_analysis.configs import ( # noqa: E402 + CONV_LEN, + FIFO_LEN, + LEGACY_CFG_DIR, + get_scene, + get_scene_list, + model_path_for_scene, ) -from SR_analysis.configs import ( - SCENES, get_scene, get_scene_list, model_path_for_scene, - LEGACY_CFG_DIR, FIFO_LEN, CONV_LEN, +from SR_analysis.utils.harmonics import ( # noqa: E402 + analyze_harmonics, + gen_target_states_at, + harmonics_document, ) DATA_TYPE = np.float32 +SCHEMA_VERSION = "sr-stage1-npz-v2" +HARMONICS_SCHEMA_VERSION = "sr-target-harmonics-v2" +METRIC_ID = "legacy_similarity_v1" +ILLUSION_TARGET_CHANNELS = [ + "target_cylinder_fx", "target_cylinder_fy", + "sensor_top_ux", "sensor_top_uy", + "sensor_center_ux", "sensor_center_uy", + "sensor_bottom_ux", "sensor_bottom_uy", +] +SENSOR_CHANNELS = [ + "sensor_top_ux", "sensor_top_uy", "sensor_center_ux", + "sensor_center_uy", "sensor_bottom_ux", "sensor_bottom_uy", +] +FORCE_CHANNELS = ["front_fx", "front_fy", "top_fx", "top_fy", "bottom_fx", "bottom_fy"] +ACTION_CHANNELS = ["front_alpha", "top_alpha", "bottom_alpha"] -# ── Scene groups ────────────────────────────────────────────────────── SCENE_GROUPS = { "karman_all": lambda: get_scene_list("karman"), - "karman_trained": lambda: [s for s in get_scene_list("karman") if "re25" not in s and "re70" not in s and "re150" not in s and "re300" not in s], + "karman_trained": lambda: [s for s in get_scene_list("karman") if get_scene(s).get("model_name")], "illusion_trained": lambda: ["illusion_0.75L", "illusion_1L", "illusion_1.5L"], "illusion_generalization": lambda: ["illusion_0.5L", "illusion_0.6L", "illusion_0.8L", "illusion_1.2L", "illusion_2L"], "vortex_all": lambda: get_scene_list("vortex"), } -def _out_dir(scene_name: str) -> str: +def _jsonable(value: Any) -> Any: + if isinstance(value, np.ndarray): + return value.tolist() + if isinstance(value, Path): + return str(value) + if isinstance(value, tuple): + return list(value) + if isinstance(value, dict): + return {str(k): _jsonable(v) for k, v in value.items()} + if isinstance(value, list): + return [_jsonable(v) for v in value] + if isinstance(value, np.generic): + return value.item() + return value + + +def _write_json(path: Path, value: Any) -> None: + path.write_text(json.dumps(_jsonable(value), indent=2, sort_keys=True) + "\n", encoding="utf-8") + + +def _sha256(path: Path | None) -> str | None: + if path is None or not path.is_file(): + return None + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _git_sha() -> str | None: + try: + return subprocess.run( + ["git", "-C", str(REPO_ROOT), "rev-parse", "HEAD"], + check=True, capture_output=True, text=True, + ).stdout.strip() + except (OSError, subprocess.CalledProcessError): + return None + + +def default_run_id() -> str: + return datetime.now(timezone.utc).strftime("legacy-%Y%m%dT%H%M%SZ") + + +def gpu_identity(logical_device: int, cuda_visible_devices: str | None) -> dict[str, str | None]: + """Resolve identity only when visibility provides an explicit mapping.""" + visible_tokens = None if cuda_visible_devices is None else [ + token.strip() for token in cuda_visible_devices.split(",") if token.strip() + ] + if visible_tokens is not None and not 0 <= logical_device < len(visible_tokens): + return {"uuid": None, "name": None} + selector = visible_tokens[logical_device] if visible_tokens is not None else None + try: + command = ["nvidia-smi"] + if selector is not None: + command.append(f"--id={selector}") + command.extend(["--query-gpu=uuid,name", "--format=csv,noheader"]) + output = subprocess.run( + command, check=True, capture_output=True, text=True, timeout=5, + ).stdout.strip().splitlines() + if selector is None and len(output) != 1: + return {"uuid": None, "name": None} + if len(output) != 1: + return {"uuid": None, "name": None} + uuid, name = (part.strip() for part in output[0].split(",", 1)) + return {"uuid": uuid or None, "name": name or None} + except (OSError, subprocess.CalledProcessError, subprocess.TimeoutExpired, ValueError): + return {"uuid": None, "name": None} + + +def cfd_device_manifest(logical_device: int) -> dict[str, Any]: + cuda_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES") + identity = gpu_identity(logical_device, cuda_visible_devices) + return { + "cfd_device": logical_device, + "cfd_device_logical": logical_device, + "cuda_visible_devices": cuda_visible_devices, + "cfd_gpu_uuid": identity["uuid"], + "cfd_gpu_name": identity["name"], + } + + +def resolve_output_root(output_root: str | os.PathLike[str] | None) -> Path: + if output_root is None: + return REPO_ROOT / "src" / "SR_analysis" / "data" / "runs" + path = Path(output_root).expanduser() + return path.resolve() if path.is_absolute() else (REPO_ROOT / path).resolve() + + +def scene_output_dir(output_root: Path, run_id: str, scene_name: str, cfg: Mapping[str, Any]) -> Path: + if not run_id or run_id in {".", ".."} or Path(run_id).name != run_id: + raise ValueError("--run-id must be one non-empty path component") + return output_root / run_id / str(cfg["scene_id"]) / scene_name + + +def prepare_output_dir(path: Path, overwrite: bool) -> None: + if path.exists() and any(path.iterdir()) and not overwrite: + raise FileExistsError(f"Refusing to overwrite non-empty output directory: {path}") + path.mkdir(parents=True, exist_ok=True) + + +def frozen_scene_dir(scene_name: str, cfg: Mapping[str, Any]) -> Path: + return REPO_ROOT / "src" / "SR_analysis" / "data" / str(cfg["scene_id"]) / scene_name + + +def validate_norm(norm: Mapping[str, Any]) -> dict[str, Any]: + required = {"force_norm_fact", "sens_deviation", "sens_norm_fact"} + missing = required - set(norm) + if missing: + raise ValueError(f"norm missing keys: {sorted(missing)}") + force = float(norm["force_norm_fact"]) + deviation = np.asarray(norm["sens_deviation"], dtype=np.float32) + scale = np.asarray(norm["sens_norm_fact"], dtype=np.float32) + if not np.isfinite(force) or force <= 0: + raise ValueError("force_norm_fact must be finite and positive") + if deviation.shape != (6,) or scale.shape != (6,): + raise ValueError("sens_deviation and sens_norm_fact must each contain 6 values") + if not np.all(np.isfinite(deviation)) or not np.all(np.isfinite(scale)) or np.any(scale <= 0): + raise ValueError("sensor normalization values must be finite and scales positive") + result = dict(norm) + result.update(force_norm_fact=force, sens_deviation=deviation.tolist(), sens_norm_fact=scale.tolist()) + return result + + +def load_existing_norm(scene_name: str, cfg: Mapping[str, Any]) -> tuple[dict[str, Any], Path]: + path = frozen_scene_dir(scene_name, cfg) / "norm.json" + if not path.is_file(): + raise FileNotFoundError(f"Frozen normalization not found: {path}") + return validate_norm(json.loads(path.read_text(encoding="utf-8"))), path + + +def normalize_raw_observation(raw: np.ndarray, norm: Mapping[str, Any]) -> np.ndarray: + raw = np.asarray(raw, dtype=np.float32) + forces = raw[6:12] / float(norm["force_norm_fact"]) + sensors = (raw[0:6] - np.asarray(norm["sens_deviation"], dtype=np.float32)) / np.asarray(norm["sens_norm_fact"], dtype=np.float32) + return np.clip(np.hstack((forces, sensors)), -1.0, 1.0).astype(np.float32) + + +def extract_illusion_target(raw_obs: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Return canonical 8 channels, force[0:2], and sensor[2:8] channels.""" + canonical = np.asarray(raw_obs, dtype=np.float32).reshape(-1)[:8] + if canonical.shape != (8,): + raise ValueError(f"Illusion target observation requires 8 channels, got {canonical.size}") + return canonical, canonical[:2].copy(), canonical[2:8].copy() + + +def target_phase_index(step: int, *, after_action: bool = False) -> int: + if step < 0: + raise ValueError("step must be non-negative") + return step + int(after_action) + + +def illusion_observation(raw: np.ndarray, norm: Mapping[str, Any], target_force: np.ndarray) -> np.ndarray: + base = normalize_raw_observation(raw, norm) + target = np.asarray(target_force, dtype=np.float32).reshape(-1) + if target.shape != (2,): + raise ValueError("target_force must contain fx and fy") + return np.clip(np.hstack((base, target / float(norm["force_norm_fact"]))), -1.0, 1.0).astype(np.float32) + + +def _npz_metadata(scene_name: str, cfg: Mapping[str, Any], layout: Sequence[str], alignment: str) -> dict[str, np.ndarray]: + dt_c = float(cfg["sample_interval"]) * float(cfg["u0"]) / 20.0 + return { + "schema_version": np.array(SCHEMA_VERSION), + "layout": np.asarray(layout), + "units_system": np.array("LegacyCelerisLab lattice/SI-normalized metadata"), + "sample_interval": np.array(int(cfg["sample_interval"]), dtype=np.int64), + "SI": np.array(int(cfg["sample_interval"]), dtype=np.int64), + "dt_c": np.array(dt_c, dtype=np.float64), + "alignment": np.array(alignment), + "scene": np.array(scene_name), + } + + +def save_target_npz(path: Path, scene_name: str, cfg: Mapping[str, Any], target: np.ndarray, *, illusion: bool) -> None: + target = np.asarray(target, dtype=np.float32) + layout = ILLUSION_TARGET_CHANNELS if illusion else SENSOR_CHANNELS + payload: dict[str, Any] = {"target_states": target, **_npz_metadata(scene_name, cfg, layout, "sample k is post-step k target state")} + if illusion: + payload["target_forces"] = target[:, :2] + payload["target_sensors"] = target[:, 2:8] + np.savez_compressed(path, **payload) + + +def save_controlled_npz( + path: Path, scene_name: str, cfg: Mapping[str, Any], *, sensors: np.ndarray, + forces: np.ndarray, actions_norm: np.ndarray, actions_commanded: np.ndarray, + rewards: np.ndarray | None = None, target_forces: np.ndarray | None = None, + phase_indices: np.ndarray | None = None, +) -> None: + sensors = np.asarray(sensors, dtype=np.float32) + forces = np.asarray(forces, dtype=np.float32) + actions_norm = np.asarray(actions_norm, dtype=np.float32) + payload: dict[str, Any] = { + "sensors": sensors, "forces": forces, "actions": actions_norm, + "sensors_post": sensors, "forces_post": forces, + "actions_norm": actions_norm, + "actions_alpha_commanded": np.asarray(actions_commanded, dtype=np.float32) / float(cfg["u0"]), + "actions_omega_commanded": np.asarray(actions_commanded, dtype=np.float32), + **_npz_metadata(scene_name, cfg, SENSOR_CHANNELS + FORCE_CHANNELS + ACTION_CHANNELS, + "action[k] produces sensors_post[k]/forces_post[k]"), + } + if rewards is not None: + payload["rewards"] = np.asarray(rewards, dtype=np.float32) + if target_forces is not None: + target_forces = np.asarray(target_forces, dtype=np.float32) + payload["target_forces_for_action"] = target_forces + payload["target_forces"] = target_forces + if phase_indices is not None: + payload["target_phase_index"] = np.asarray(phase_indices, dtype=np.int64) + np.savez_compressed(path, **payload) + + +def _runtime_imports(): + from LegacyCelerisLab import FlowField + from SR_analysis.utils.cfd_interface import ( + add_pinball, build_karman_cloak_env, compute_similarity, load_legacy_configs, + load_ppo_model, scale_action, + ) + return FlowField, add_pinball, build_karman_cloak_env, compute_similarity, load_legacy_configs, load_ppo_model, scale_action + + +def _compute_norm(samples: np.ndarray) -> dict[str, Any]: + samples = np.asarray(samples, dtype=np.float32) + force_scale = 6.0 * float(np.max(np.abs(samples[:, 6:12]))) + deviation = np.mean(samples[:, 0:6], axis=0) + sensor_scale = 5.0 * np.max(np.abs(samples[:, 0:6] - deviation), axis=0) + return validate_norm({"force_norm_fact": force_scale, "sens_deviation": deviation, "sens_norm_fact": sensor_scale}) + + +def infer_karman( + scene_name: str, device_id: int, out_dir: Path, n_steps: int, + norm_source: str, model_device: str = "cpu", +) -> dict[str, Any]: + FlowField, add_pinball, build_env, similarity, load_cfg, load_model, scale_action = _runtime_imports() cfg = get_scene(scene_name) - return os.path.join("src", "SR_analysis", "data", cfg["scene_id"], scene_name) - - -# ── Karman inference ────────────────────────────────────────────────── -def infer_karman(scene_name: str, device_id: int, out_dir: str, n_steps: int) -> dict: - cfg = get_scene(scene_name) - nu, u0, l0 = cfg["nu"], cfg["u0"], 20.0 - si, action_scale, action_bias = cfg["sample_interval"], cfg["action_scale"], cfg["action_bias"] - n_obj_total = cfg["n_objects_env"] - - os.makedirs(out_dir, exist_ok=True) - json.dump({k: v for k, v in cfg.items() if k != "action_bias"}, open(os.path.join(out_dir, "config.json"), "w"), indent=2, default=str) - - cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR) - field_cfg = field_cfg._replace(viscosity=float(nu)) - + cuda_cfg, field_cfg = load_cfg(LEGACY_CFG_DIR) + field_cfg = field_cfg._replace(viscosity=float(cfg["nu"])) ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) - target_states, _ = build_karman_cloak_env(ff, u0=u0, l0=l0, sample_interval=si, fifo_len=FIFO_LEN, data_type=DATA_TYPE) - np.savez(os.path.join(out_dir, "target.npz"), target_states=target_states) + target, _ = build_env(ff, u0=cfg["u0"], l0=20.0, sample_interval=cfg["sample_interval"], fifo_len=FIFO_LEN, data_type=DATA_TYPE) + save_target_npz(out_dir / "target.npz", scene_name, cfg, target, illusion=False) + computed_norm = add_pinball(ff, l0=20.0, u0=cfg["u0"], sample_interval=cfg["sample_interval"], fifo_len=FIFO_LEN, + data_type=DATA_TYPE, action_bias=cfg["action_bias"], pinball_front_x=cfg["pinball_front_x"], + pinball_rear_x=cfg["pinball_rear_x"], obs_slice_start=cfg["obs_slice"][0], obs_slice_end=cfg["obs_slice"][1]) + norm, norm_path = (load_existing_norm(scene_name, cfg) if norm_source == "existing" else (validate_norm(computed_norm), None)) + _write_json(out_dir / "norm.json", norm) - norm = add_pinball(ff, l0=l0, u0=u0, sample_interval=si, fifo_len=FIFO_LEN, data_type=DATA_TYPE, - action_bias=action_bias, pinball_front_x=cfg["pinball_front_x"], pinball_rear_x=cfg["pinball_rear_x"], - obs_slice_start=cfg["obs_slice"][0], obs_slice_end=cfg["obs_slice"][1]) - json.dump({k: v for k, v in norm.items() if not isinstance(v, np.ndarray)}, open(os.path.join(out_dir, "norm.json"), "w"), indent=2, default=str) - - # Uncontrolled ff.restore_ddf(); ff.apply_ddf() - sens_u, forc_u = [], [] + sensors_u, forces_u = [], [] for _ in range(n_steps): - ff.run(si, np.zeros(n_obj_total, dtype=DATA_TYPE)) - o = ff.obs.copy()[2:14]; sens_u.append(o[0:6]); forc_u.append(o[6:12]) - np.savez(os.path.join(out_dir, "uncontrolled.npz"), sensors=np.array(sens_u, dtype=np.float32), forces=np.array(forc_u, dtype=np.float32)) + ff.run(cfg["sample_interval"], np.zeros(cfg["n_objects_env"], dtype=DATA_TYPE)) + raw = ff.obs.copy()[2:14]; sensors_u.append(raw[:6]); forces_u.append(raw[6:12]) + np.savez_compressed(out_dir / "uncontrolled.npz", sensors=np.asarray(sensors_u, dtype=np.float32), forces=np.asarray(forces_u, dtype=np.float32), + **_npz_metadata(scene_name, cfg, SENSOR_CHANNELS + FORCE_CHANNELS, "all samples are post-step states")) - # Controlled - result = {"scene": scene_name, "controlled": False} model_path = model_path_for_scene(scene_name) + result: dict[str, Any] = {"scene": scene_name, "controlled": False, "metric_id": METRIC_ID, "norm_source": norm_source, + "norm_source_path": str(norm_path) if norm_path else None} if model_path: - model = load_ppo_model(model_path, device=f"cuda:{device_id}", s_dim=cfg.get("s_dim", 12)) + model = load_model(model_path, device=model_device, s_dim=cfg.get("s_dim", 12)) ff.restore_ddf(); ff.apply_ddf() - fifo = deque(maxlen=FIFO_LEN) - ba = scale_action(np.zeros(3, dtype=np.float32), scale=action_scale, bias=action_bias, u0=u0, n_total_bodies=n_obj_total) - for _ in range(FIFO_LEN): - ff.context.push(); ff.run(si, ba); ff.context.pop() - fifo.append(ff.obs.copy()[2:14]) - - s_c, f_c, a_c, r_c = [], [], [], [] - obs = np.zeros(12, dtype=np.float32) + fifo: deque[np.ndarray] = deque( + (np.asarray(row, dtype=np.float32) for row in computed_norm["save_states"]), + maxlen=FIFO_LEN, + ) + obs = normalize_raw_observation(fifo[-1], norm) + sensors, forces, actions, commanded, rewards = [], [], [], [], [] for _ in range(n_steps): - act, _ = model.predict(obs, deterministic=True) - act = act.astype(np.float32).flatten(); a_c.append(act.copy()) - ff.context.push(); ff.run(si, scale_action(act, scale=action_scale, bias=action_bias, u0=u0, n_total_bodies=n_obj_total)); ff.context.pop() - osl = ff.obs.copy()[2:14]; fifo.append(osl) - s_c.append(osl[0:6]); f_c.append(osl[6:12]); obs = build_observation(osl, norm) + action, _ = model.predict(obs, deterministic=True); action = np.asarray(action, dtype=np.float32).flatten() + alpha = (action * float(cfg["action_scale"]) + np.asarray(cfg["action_bias"], dtype=np.float32)) * float(cfg["u0"]) + omega = scale_action(action, scale=cfg["action_scale"], bias=cfg["action_bias"], u0=cfg["u0"], n_total_bodies=cfg["n_objects_env"]) + ff.context.push(); ff.run(cfg["sample_interval"], omega); ff.context.pop() + raw = ff.obs.copy()[2:14]; fifo.append(raw); sensors.append(raw[:6]); forces.append(raw[6:12]); actions.append(action); commanded.append(alpha) + obs = normalize_raw_observation(raw, norm) if len(fifo) >= CONV_LEN: - sa = np.array(list(fifo), dtype=np.float32) - cd = float((sa[-1,0] + sa[-1,2] + sa[-1,4]) / 3 / norm["force_norm_fact"]) - cl = float((sa[-1,1] + sa[-1,3] + sa[-1,5]) / 3 / norm["force_norm_fact"]) - sim = compute_similarity(target_states, sa[:,0:6], CONV_LEN) - r_c.append(float(min(0.3*np.exp(-abs(cd*20))+0.4*np.exp(-abs(cl*80))+0.3*np.exp(-10*abs(sim-1)), 1.0))) - - np.savez(os.path.join(out_dir, "controlled.npz"), sensors=np.array(s_c, dtype=np.float32), forces=np.array(f_c, dtype=np.float32), actions=np.array(a_c, dtype=np.float32), rewards=np.array(r_c, dtype=np.float32)) - result["controlled"] = True - result["similarity"] = float(compute_similarity(target_states, np.array(s_c, dtype=np.float32), CONV_LEN)) - if r_c: result["avg_reward_last100"] = float(np.mean(r_c[-100:])) - else: - print(f" No model for {scene_name}, skipping controlled") - + sa = np.asarray(fifo, dtype=np.float32) + cd = float(np.mean(sa[-1, [0, 2, 4]]) / norm["force_norm_fact"]) + cl = float(np.mean(sa[-1, [1, 3, 5]]) / norm["force_norm_fact"]) + sim = similarity(target, sa[:, :6], CONV_LEN) + rewards.append(min(0.3*np.exp(-abs(cd*20))+0.4*np.exp(-abs(cl*80))+0.3*np.exp(-10*abs(sim-1)), 1.0)) + save_controlled_npz(out_dir / "controlled.npz", scene_name, cfg, sensors=np.asarray(sensors), forces=np.asarray(forces), + actions_norm=np.asarray(actions), actions_commanded=np.asarray(commanded), rewards=np.asarray(rewards)) + result.update(controlled=True, similarity=float(similarity(target, np.asarray(sensors), CONV_LEN))) + if rewards: result["avg_reward_last100"] = float(np.mean(rewards[-100:])) del ff - json.dump(result, open(os.path.join(out_dir, "result.json"), "w"), indent=2) - print(f" {scene_name}: similarity={result.get('similarity', 'N/A'):.4f}" if result["controlled"] else f" {scene_name}: target only") return result -# ── Illusion inference ──────────────────────────────────────────────── -def analyze_harmonics(states, n_harmonics=5): - N, D = states.shape - result = [] - for d in range(D): - y = states[:, d]; fft = np.fft.rfft(y); freqs = np.fft.rfftfreq(N, d=1) - amps = 2*np.abs(fft)/N; phases = np.angle(fft) - idx = np.argsort(amps[1:])[::-1][:n_harmonics] + 1 - result.append({"dc": float(np.real(fft[0])/N), "amps": amps[idx].tolist(), "freqs": freqs[idx].tolist(), "phases": phases[idx].tolist()}) - return result - - -def gen_target_states_at(t, harmonics): - t = np.asarray(t); D = len(harmonics) - result = np.zeros((t.size, D), dtype=np.float32) - for d, h in enumerate(harmonics): - val = np.full(t.shape, h["dc"], dtype=np.float32) - for amp, freq, phase in zip(h["amps"], h["freqs"], h["phases"]): - val += amp * np.cos(2*np.pi*freq*t + phase) - result[:, d] = val - return result[0] if result.shape[0] == 1 else result - - -def infer_illusion(scene_name: str, device_id: int, out_dir: str, n_steps: int, target_only: bool) -> dict: - """Run PPO inference for illusion scene. - - Output target.npz layout (8 columns): - [cyl_fx, cyl_fy, s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy] - Consumers should use target[:, 2:8] for pure sensor signals. - """ - cfg = get_scene(scene_name) - nu, u0, si, l0 = cfg["nu"], cfg["u0"], cfg["sample_interval"], 20.0 - action_scale, action_bias = cfg["action_scale"], np.array(cfg["action_bias"]) - n_obj_total = cfg["n_objects_env"] - obs_sl = slice(*cfg["obs_slice"]) - target_diam_l0 = cfg["target_diameter"] - target_x_l0 = cfg["target_center_x"] / l0 - - os.makedirs(out_dir, exist_ok=True) - json.dump({k: v for k, v in cfg.items() if k != "action_bias"}, open(os.path.join(out_dir, "config.json"), "w"), indent=2, default=str) - - cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR) - field_cfg = field_cfg._replace(viscosity=float(nu)) - - # Phase 1: Record target cylinder signals - ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) - cy = (ff.FIELD_SHAPE[1] - 1) / 2 - nx, ny = ff.FIELD_SHAPE[:2] - ff.add_cylinder((target_x_l0 * l0, cy, 0.0), target_diam_l0 * l0) - sensor_y_grid = [cy + 2*l0, cy, cy - 2*l0] - sensor_x_l0 = cfg["sensor_x"] - for yo in sensor_y_grid: - ff.add_sensor((sensor_x_l0 * l0, yo, 0.0), 5.0) - - warmup = int(4 * nx / u0) - ff.run(warmup, np.zeros(4, dtype=DATA_TYPE)) - - # Target layout: 8 columns = [cyl_fx,cyl_fy,s0_ux,s0_uy,s1_ux,s1_uy,s2_ux,s2_uy] - # obs comes from: 1 target cylinder + 3 sensors added to FlowField - target_states = [] +def infer_illusion( + scene_name: str, device_id: int, out_dir: Path, n_steps: int, + target_only: bool, norm_source: str, model_device: str = "cpu", +) -> dict[str, Any]: + FlowField, _, _, similarity, load_cfg, load_model, _ = _runtime_imports() + cfg = get_scene(scene_name); l0 = 20.0; u0 = float(cfg["u0"]); si = int(cfg["sample_interval"]) + cuda_cfg, field_cfg = load_cfg(LEGACY_CFG_DIR); field_cfg = field_cfg._replace(viscosity=float(cfg["nu"])) + ff = FlowField(field_cfg, cuda_cfg, device_id=device_id); cy = (ff.FIELD_SHAPE[1] - 1) / 2 + # Legacy add_cylinder's final argument is radius; target_diameter is expressed in L0 units historically. + ff.add_cylinder((20.0 * l0, cy, 0.0), float(cfg["target_diameter"]) * l0) + sensor_y = [cy + 2*l0, cy, cy - 2*l0] + for y in sensor_y: ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + warmup = int(4 * ff.FIELD_SHAPE[0] / u0); ff.run(warmup, np.zeros(4, dtype=DATA_TYPE)) + rows = [] for _ in range(FIFO_LEN): - ff.run(si, np.zeros(4, dtype=DATA_TYPE)) - target_states.append(ff.obs.copy()[0:8]) - target_arr = np.array(target_states, dtype=np.float32) - # BUGFIX (2026-07-14): was target_arr[:, :6] which omitted s2 (bottom sensor) and - # mixed cylinder force (col0-1) with sensor channels. The 8-column layout is the - # canonical format — consumers use target[:, 2:8] to extract the 6 pure-sensor - # channels. Historical note: the old gen_illusion_target.py already wrote 8 columns, - # so on-disk files were correct via that path, but re-running this script would - # overwrite them with truncated data. - np.savez(os.path.join(out_dir, "target.npz"), target_states=target_arr[:, :]) - harmonics = analyze_harmonics(target_arr, n_harmonics=5) - json.dump(harmonics, open(os.path.join(out_dir, "target_harmonics.json"), "w"), indent=2) - + ff.run(si, np.zeros(4, dtype=DATA_TYPE)); rows.append(extract_illusion_target(ff.obs.copy())[0]) + target = np.asarray(rows, dtype=np.float32) + save_target_npz(out_dir / "target.npz", scene_name, cfg, target, illusion=True) + harmonics = analyze_harmonics(target, n_harmonics=5) + _write_json(out_dir / "target_harmonics.json", harmonics_document(harmonics, ILLUSION_TARGET_CHANNELS, schema_version=HARMONICS_SCHEMA_VERSION)) + result: dict[str, Any] = {"scene": scene_name, "target_only": target_only, "controlled": False, "metric_id": METRIC_ID, "norm_source": norm_source} if target_only: del ff - json.dump({"scene": scene_name, "target_only": True}, open(os.path.join(out_dir, "result.json"), "w"), indent=2) - print(f" {scene_name}: target only") - return {"scene": scene_name, "target_only": True} + return result - # Phase 2: Pinball env - del ff - ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) - # Add sensors - for yo in sensor_y_grid: - ff.add_sensor((sensor_x_l0 * l0, yo, 0.0), 5.0) - # Add pinball - pf_x = cfg["pinball_front_x"] * l0 - pr_x = cfg["pinball_rear_x"] * l0 - py_span = cfg.get("pinball_y_span", 0.75 * l0) - ff.add_cylinder((pf_x, cy, 0.0), 10.0) - ff.add_cylinder((pr_x, cy + py_span, 0.0), 10.0) - ff.add_cylinder((pr_x, cy - py_span, 0.0), 10.0) + del ff; ff = FlowField(field_cfg, cuda_cfg, device_id=device_id); cy = (ff.FIELD_SHAPE[1] - 1) / 2 + for y in [cy + 2*l0, cy, cy - 2*l0]: ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + ff.add_cylinder((float(cfg["pinball_front_x"])*l0, cy, 0.0), 10.0) + span = float(cfg.get("pinball_y_span", 0.75*l0)) + ff.add_cylinder((float(cfg["pinball_rear_x"])*l0, cy+span, 0.0), 10.0) + ff.add_cylinder((float(cfg["pinball_rear_x"])*l0, cy-span, 0.0), 10.0) + n_obj = ff.obs.size // 2; ff.run(warmup, np.zeros(n_obj, dtype=DATA_TYPE)); ff.get_ddf(); ff.save_ddf() + if norm_source == "existing": + norm, norm_path = load_existing_norm(scene_name, cfg) + else: + samples = [] + for _ in range(FIFO_LEN): ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE)); samples.append(ff.obs.copy()[slice(*cfg["obs_slice"])]) + norm, norm_path = _compute_norm(np.asarray(samples)), None + result["norm_source_path"] = str(norm_path) if norm_path else None; _write_json(out_dir / "norm.json", norm) - n_obj = ff.obs.size // 2 - ff.run(warmup, np.zeros(n_obj, dtype=DATA_TYPE)) + ff.apply_ddf(); fifo: deque[np.ndarray] = deque(maxlen=FIFO_LEN) + bias_arr = np.zeros(n_obj, dtype=DATA_TYPE); bias_arr[-3:] = np.asarray([0.0, -u0, u0], dtype=DATA_TYPE) + for _ in range(FIFO_LEN): ff.run(si, bias_arr); fifo.append(ff.obs.copy()[slice(*cfg["obs_slice"])]) ff.get_ddf(); ff.save_ddf() - - # Norm collection - for _ in range(FIFO_LEN): - ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE)) - norm_data = np.array([ff.obs.copy()[obs_sl] for _ in [0]*(FIFO_LEN)], dtype=np.float32) - # Re-do properly - fifo_temp = deque(maxlen=FIFO_LEN) - for _ in range(FIFO_LEN): - ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE)) - fifo_temp.append(ff.obs.copy()[obs_sl]) - tsa = np.array(fifo_temp, dtype=np.float32) - force_norm_fact = 6.0 * float(np.max(np.abs(tsa[:, 6:12]))) - sens_deviation = np.mean(tsa[:, 0:6], axis=0) - sens_norm_fact = np.zeros(6, dtype=np.float32) - for i in range(6): - sens_norm_fact[i] = 5.0 * float(np.max(np.abs(tsa[:, i] - sens_deviation[i]))) - norm = {"force_norm_fact": force_norm_fact, "sens_deviation": sens_deviation.tolist(), "sens_norm_fact": sens_norm_fact.tolist()} - json.dump(norm, open(os.path.join(out_dir, "norm.json"), "w"), indent=2) - - # Bias FIFO: [0, -U0, U0] - ff.apply_ddf() - fifo = deque(maxlen=FIFO_LEN) - fifo_bias = np.array([0.0, -u0, u0], dtype=np.float64) - bias_arr = np.zeros(n_obj, dtype=DATA_TYPE) - bias_arr[n_obj-3:] = fifo_bias - for _ in range(FIFO_LEN): - ff.run(si, bias_arr) - fifo.append(ff.obs.copy()[obs_sl]) - - # Controlled rollout - result = {"scene": scene_name, "controlled": False} model_path = model_path_for_scene(scene_name) if model_path: - model = load_ppo_model(model_path, device=f"cuda:{device_id}", s_dim=cfg.get("s_dim", 14)) - s_c, f_c, a_c = [], [], [] - obs = np.zeros(cfg.get("s_dim", 14), dtype=np.float32) + model = load_model(model_path, device=model_device, s_dim=cfg.get("s_dim", 14)) + raw = fifo[-1].copy() + force0 = gen_target_states_at(target_phase_index(0), harmonics)[:2] + obs = illusion_observation(raw, norm, force0) + sensors, forces, actions, commanded, targets, phases = [], [], [], [], [], [] for step in range(n_steps): - act, _ = model.predict(obs, deterministic=True) - act = act.astype(np.float32).flatten(); a_c.append(act.copy()) - omega_arr = np.zeros(n_obj, dtype=DATA_TYPE) - omega = (act * action_scale + action_bias) * u0 - omega_arr[n_obj-3:] = omega - ff.context.push(); ff.run(si, omega_arr); ff.context.pop() - osl = ff.obs.copy()[obs_sl]; fifo.append(osl) - s_c.append(osl[0:6]); f_c.append(osl[6:12]) - # Build observation (14-dim for illusion) - forces_n = osl[6:12] / force_norm_fact - sens_n = (osl[0:6] - sens_deviation) / sens_norm_fact - tf = gen_target_states_at(step, harmonics) - target_cd = tf[6] / force_norm_fact - target_cl = tf[7] / force_norm_fact - obs = np.clip(np.hstack([forces_n, sens_n, target_cd, target_cl]), -1, 1).astype(np.float32) - - np.savez(os.path.join(out_dir, "controlled.npz"), - sensors=np.array(s_c, dtype=np.float32), forces=np.array(f_c, dtype=np.float32), actions=np.array(a_c, dtype=np.float32)) - result["controlled"] = True - result["similarity"] = float(compute_similarity(target_arr[:, 2:8], np.array(s_c, dtype=np.float32), cfg.get("conv_len", 36))) - else: - print(f" No model for {scene_name}") - + phase = target_phase_index(step); target_force = gen_target_states_at(phase, harmonics)[:2] + action, _ = model.predict(obs, deterministic=True); action = np.asarray(action, dtype=np.float32).flatten() + alpha = (action * float(cfg["action_scale"]) + np.asarray(cfg["action_bias"], dtype=np.float32)) * u0 + omega = np.zeros(n_obj, dtype=DATA_TYPE); omega[-3:] = alpha + ff.context.push(); ff.run(si, omega); ff.context.pop() + raw = ff.obs.copy()[slice(*cfg["obs_slice"])]; fifo.append(raw) + sensors.append(raw[:6]); forces.append(raw[6:12]); actions.append(action); commanded.append(alpha); targets.append(target_force); phases.append(phase) + next_force = gen_target_states_at(target_phase_index(step, after_action=True), harmonics)[:2] + obs = illusion_observation(raw, norm, next_force) + save_controlled_npz(out_dir / "controlled.npz", scene_name, cfg, sensors=np.asarray(sensors), forces=np.asarray(forces), + actions_norm=np.asarray(actions), actions_commanded=np.asarray(commanded), target_forces=np.asarray(targets), + phase_indices=np.asarray(phases)) + result.update(controlled=True, similarity=float(similarity(target[:, 2:8], np.asarray(sensors), cfg.get("conv_len", 36)))) del ff - json.dump(result, open(os.path.join(out_dir, "result.json"), "w"), indent=2) - print(f" {scene_name}: similarity={result.get('similarity', 'N/A'):.4f}" if result["controlled"] else f" {scene_name}: target only") return result -# ── Vortex inference ────────────────────────────────────────────────── -def infer_vortex(scene_name: str, device_id: int, out_dir: str, n_steps: int) -> dict: - cfg = get_scene(scene_name) - nu, u0, si, l0 = cfg["nu"], cfg["u0"], cfg["sample_interval"], 20.0 - action_scale, action_bias = cfg["action_scale"], np.array(cfg["action_bias"]) - n_obj_total = cfg["n_objects_env"] - n_steps = min(n_steps, cfg.get("max_steps", 150)) +def infer_vortex(scene_name: str, device_id: int, out_dir: Path, n_steps: int, norm_source: str) -> dict[str, Any]: + """Preserve the historical Vortex flow without adding a new data contract.""" + FlowField, _, _, similarity, load_cfg, load_model, _ = _runtime_imports() + cfg = get_scene(scene_name); l0 = 20.0; u0 = float(cfg["u0"]); si = int(cfg["sample_interval"]) + n_steps = min(n_steps, int(cfg.get("max_steps", 150))) + cuda_cfg, field_cfg = load_cfg(LEGACY_CFG_DIR); field_cfg = field_cfg._replace(viscosity=float(cfg["nu"])) + ff = FlowField(field_cfg, cuda_cfg, device_id=device_id); cy = (ff.FIELD_SHAPE[1] - 1) / 2 + sensor_y = [cy + 2*l0, cy, cy - 2*l0] + for y in sensor_y: ff.add_sensor((float(cfg["sensor_x"])*l0, y, 0.0), 5.0) + warmup = int(4 * ff.FIELD_SHAPE[0] / u0); ff.run(warmup, np.zeros(3, dtype=DATA_TYPE)); ff.get_ddf(); ff.save_ddf() + radius = float(cfg.get("vortex_radius", 2*l0)) + ff.add_vortex((float(cfg.get("vortex_center_x", 10*l0)), cy, 0.0), radius, + strength=cfg["vortex_strength"], vortex_type=cfg["vortex_type"]) + target = [] + for _ in range(FIFO_LEN): ff.run(si, np.zeros(3, dtype=DATA_TYPE)); target.append(ff.obs.copy()[:6]) + save_target_npz(out_dir / "target.npz", scene_name, cfg, np.asarray(target), illusion=False) - os.makedirs(out_dir, exist_ok=True) - json.dump({k: v for k, v in cfg.items() if k != "action_bias"}, open(os.path.join(out_dir, "config.json"), "w"), indent=2, default=str) - - cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR) - field_cfg = field_cfg._replace(viscosity=float(nu)) - - from LegacyCelerisLab import utils as legacy_utils - # Phase 1: Target (vortex only, no pinball) - ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) - cy = (ff.FIELD_SHAPE[1] - 1) / 2 - nx, ny = ff.FIELD_SHAPE[:2] - sensor_x_l0 = cfg["sensor_x"] - sensor_y_grid = [cy + 2*l0, cy, cy - 2*l0] - for yo in sensor_y_grid: - ff.add_sensor((sensor_x_l0 * l0, yo, 0.0), 5.0) - warmup = int(4 * nx / u0) - ff.run(warmup, np.zeros(3, dtype=DATA_TYPE)) - ff.get_ddf(); ff.save_ddf() - # Add vortex - vortex_x = cfg.get("vortex_center_x", 10*l0) - vortex_r = cfg.get("vortex_radius", 2*l0) - vtype = cfg["vortex_type"] - strength = cfg["vortex_strength"] - ff.add_vortex((vortex_x, cy, 0.0), vortex_r, strength=strength, vortex_type=vtype) - - target_sens = [] - for _ in range(FIFO_LEN): - ff.run(si, np.zeros(3, dtype=DATA_TYPE)) - target_sens.append(ff.obs.copy()[0:6]) - np.savez(os.path.join(out_dir, "target.npz"), target_states=np.array(target_sens, dtype=np.float32)) - - # Phase 2: Pinball + vortex - del ff - ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) - for yo in sensor_y_grid: - ff.add_sensor((sensor_x_l0 * l0, yo, 0.0), 5.0) - pf_x = cfg["pinball_front_x"] * l0 - pr_x = cfg["pinball_rear_x"] * l0 - py_span = cfg.get("pinball_y_span", 0.75 * l0) - ff.add_cylinder((pf_x, cy, 0.0), 10.0) - ff.add_cylinder((pr_x, cy + py_span, 0.0), 10.0) - ff.add_cylinder((pr_x, cy - py_span, 0.0), 10.0) - n_obj = ff.obs.size // 2 - ff.run(warmup, np.zeros(n_obj, dtype=DATA_TYPE)) - ff.get_ddf(); ff.save_ddf() - # Add vortex for pinball phase - vortex_px = cfg.get("vortex_pinball_center_x", 15*l0) - ff.add_vortex((vortex_px, cy, 0.0), vortex_r, strength=strength, vortex_type=vtype) - - # Norm collection - for _ in range(FIFO_LEN): - ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE)) - tsa = np.array([ff.obs.copy()[0:12] for _ in range(FIFO_LEN)], dtype=np.float32) - force_norm_fact = 6.0 * float(np.max(np.abs(tsa[:, 6:12]))) - sens_deviation = np.mean(tsa[:, 0:6], axis=0) - sens_norm_fact = np.zeros(6, dtype=np.float32) - for i in range(6): - sens_norm_fact[i] = 5.0 * float(np.max(np.abs(tsa[:, i] - sens_deviation[i]))) - norm = {"force_norm_fact": force_norm_fact, "sens_deviation": sens_deviation.tolist(), "sens_norm_fact": sens_norm_fact.tolist()} - json.dump(norm, open(os.path.join(out_dir, "norm.json"), "w"), indent=2) - - # Bias FIFO + Controlled - ff.apply_ddf() - fifo_bias_arr = np.zeros(n_obj, dtype=DATA_TYPE) - fifo_bias_arr[n_obj-3:] = np.array([0, -4*u0, 4*u0], dtype=np.float64) - for _ in range(FIFO_LEN): - ff.run(si, fifo_bias_arr) - - result = {"scene": scene_name, "controlled": False} + del ff; ff = FlowField(field_cfg, cuda_cfg, device_id=device_id); cy = (ff.FIELD_SHAPE[1] - 1) / 2 + for y in [cy + 2*l0, cy, cy - 2*l0]: ff.add_sensor((float(cfg["sensor_x"])*l0, y, 0.0), 5.0) + ff.add_cylinder((float(cfg["pinball_front_x"])*l0, cy, 0.0), 10.0) + span = float(cfg.get("pinball_y_span", 0.75*l0)) + ff.add_cylinder((float(cfg["pinball_rear_x"])*l0, cy+span, 0.0), 10.0) + ff.add_cylinder((float(cfg["pinball_rear_x"])*l0, cy-span, 0.0), 10.0) + n_obj = ff.obs.size // 2; ff.run(warmup, np.zeros(n_obj, dtype=DATA_TYPE)); ff.get_ddf(); ff.save_ddf() + ff.add_vortex((float(cfg.get("vortex_pinball_center_x", 15*l0)), cy, 0.0), radius, + strength=cfg["vortex_strength"], vortex_type=cfg["vortex_type"]) + if norm_source == "existing": norm, norm_path = load_existing_norm(scene_name, cfg) + else: + samples = [] + for _ in range(FIFO_LEN): ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE)); samples.append(ff.obs.copy()[:12]) + norm, norm_path = _compute_norm(np.asarray(samples)), None + _write_json(out_dir / "norm.json", norm) + ff.apply_ddf(); bias = np.zeros(n_obj, dtype=DATA_TYPE); bias[-3:] = np.asarray([0, -4*u0, 4*u0], dtype=DATA_TYPE) + for _ in range(FIFO_LEN): ff.run(si, bias) model_path = model_path_for_scene(scene_name) + result: dict[str, Any] = {"scene": scene_name, "controlled": False, "metric_id": METRIC_ID, + "norm_source": norm_source, "norm_source_path": str(norm_path) if norm_path else None} if model_path: - model = load_ppo_model(model_path, device=f"cuda:{device_id}", s_dim=12) - s_c, f_c, a_c = [], [], [] - obs = np.zeros(12, dtype=np.float32) - for step in range(n_steps): - act, _ = model.predict(obs, deterministic=True) - act = act.astype(np.float32).flatten(); a_c.append(act.copy()) - omega_arr = np.zeros(n_obj, dtype=DATA_TYPE) - omega_arr[n_obj-3:] = (act * action_scale + action_bias) * u0 - ff.context.push(); ff.run(si, omega_arr); ff.context.pop() - osl = ff.obs.copy()[0:12]; s_c.append(osl[0:6]); f_c.append(osl[6:12]) - forces_n = osl[6:12] / force_norm_fact - sens_n = (osl[0:6] - sens_deviation) / sens_norm_fact - obs = np.clip(np.hstack([forces_n, sens_n]), -1, 1).astype(np.float32) - np.savez(os.path.join(out_dir, "controlled.npz"), sensors=np.array(s_c, dtype=np.float32), forces=np.array(f_c, dtype=np.float32), actions=np.array(a_c, dtype=np.float32)) - result["controlled"] = True - result["similarity"] = float(compute_similarity(np.array(target_sens, dtype=np.float32), np.array(s_c, dtype=np.float32), cfg.get("conv_len", 30))) + model = load_model(model_path, device=f"cuda:{device_id}", s_dim=12) + sensors, forces, actions, commanded = [], [], [], [] + obs = normalize_raw_observation(ff.obs.copy()[:12], norm) + for _ in range(n_steps): + action, _ = model.predict(obs, deterministic=True); action = np.asarray(action, dtype=np.float32).flatten() + alpha = (action * float(cfg["action_scale"]) + np.asarray(cfg["action_bias"], dtype=np.float32)) * u0 + omega = np.zeros(n_obj, dtype=DATA_TYPE); omega[-3:] = alpha + ff.context.push(); ff.run(si, omega); ff.context.pop(); raw = ff.obs.copy()[:12] + sensors.append(raw[:6]); forces.append(raw[6:12]); actions.append(action); commanded.append(alpha) + obs = normalize_raw_observation(raw, norm) + save_controlled_npz(out_dir / "controlled.npz", scene_name, cfg, sensors=np.asarray(sensors), forces=np.asarray(forces), + actions_norm=np.asarray(actions), actions_commanded=np.asarray(commanded)) + result.update(controlled=True, similarity=float(similarity(np.asarray(target), np.asarray(sensors), cfg.get("conv_len", 30)))) del ff - json.dump(result, open(os.path.join(out_dir, "result.json"), "w"), indent=2) - print(f" {scene_name}: similarity={result.get('similarity', 'N/A'):.4f}" if result["controlled"] else f" {scene_name}: target only") return result -# ── Dispatch ────────────────────────────────────────────────────────── -_INFER_FN = { - "karman": infer_karman, - "illusion": infer_illusion, - "vortex": infer_vortex, -} +def _scene_names(args: argparse.Namespace, parser: argparse.ArgumentParser) -> list[str]: + if args.scene: + return [args.scene] + if args.group in SCENE_GROUPS: + return list(SCENE_GROUPS[args.group]()) + return [item.strip() for item in args.group.split(",") if item.strip()] -def main(): - ap = argparse.ArgumentParser(description="Stage 1: Unified PPO inference") - ap.add_argument("--scene", type=str, default=None, help="Scene name (e.g. karman_re100)") - ap.add_argument("--group", type=str, default=None, help="Scene group (karman_all, illusion_trained, vortex_all...)") - ap.add_argument("--device", type=int, default=2, help="GPU device ID") - ap.add_argument("--steps", type=int, default=200, help="Inference steps per rollout") - ap.add_argument("--target-only", action="store_true", help="Only record target (no PPO, for generalization)") - ap.add_argument("--dry-run", action="store_true", help="Verify configs only, do not run CFD") - args = ap.parse_args() +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Stage 1 legacy PPO inference") + selection = parser.add_mutually_exclusive_group(required=True) + selection.add_argument("--scene") + selection.add_argument("--group") + parser.add_argument("--device", type=int, default=2, help="Legacy CFD GPU device") + parser.add_argument( + "--model-device", default="cpu", + help="PyTorch PPO device; CPU avoids conflicts with the active PyCUDA context", + ) + parser.add_argument("--steps", type=int, default=200) + parser.add_argument("--target-only", action="store_true") + parser.add_argument("--run-id", default=None) + parser.add_argument("--output-root", default=None) + parser.add_argument("--overwrite", action="store_true") + parser.add_argument("--norm-source", choices=("existing", "recompute"), default="existing") + parser.add_argument("--dry-run", action="store_true") + return parser - if not args.scene and not args.group: - ap.error("Must specify --scene or --group") - if args.group and args.group in SCENE_GROUPS: - scene_names = SCENE_GROUPS[args.group]() - elif args.group: - scene_names = [s.strip() for s in args.group.split(",")] - else: - scene_names = [args.scene] - - print(f"Stage 1: {len(scene_names)} scene(s), device={args.device}, steps={args.steps}") - if args.dry_run: - for sn in scene_names: - cfg = get_scene(sn) - mp = model_path_for_scene(sn) - print(f" {sn}: scene_id={cfg['scene_id']}, model={mp or 'NONE'}, SI={cfg['sample_interval']}") +def main(argv: Sequence[str] | None = None) -> int: + parser = build_parser(); args = parser.parse_args(argv) + if args.steps <= 0: parser.error("--steps must be positive") + run_id = args.run_id or default_run_id(); output_root = resolve_output_root(args.output_root) + command = [sys.executable, str(Path(__file__).resolve()), *(list(argv) if argv is not None else sys.argv[1:])] + try: + scenes = _scene_names(args, parser) + plans = [] + for scene in scenes: + cfg = get_scene(scene) + if args.target_only and cfg["scene_id"] != "illusion": + raise ValueError("--target-only is only valid for Illusion scenes") + model = Path(model_path_for_scene(scene)) if model_path_for_scene(scene) else None + out_dir = scene_output_dir(output_root, run_id, scene, cfg) + if out_dir.exists() and any(out_dir.iterdir()) and not args.overwrite: + raise FileExistsError(f"Refusing to overwrite non-empty output directory: {out_dir}") + if args.norm_source == "existing" and not args.target_only: + load_existing_norm(scene, cfg) + plans.append((scene, cfg, model, out_dir)) + if args.dry_run: + for scene, cfg, model, out_dir in plans: + print(f"{scene}: scene_id={cfg['scene_id']} model={model or 'NONE'} SI={cfg['sample_interval']} output={out_dir}") + return 0 + device_manifest = cfd_device_manifest(args.device) + for scene, cfg, model, out_dir in plans: + prepare_output_dir(out_dir, args.overwrite) + _write_json(out_dir / "config.json", cfg) + manifest = { + "schema_version": "sr-stage1-manifest-v2", "run_id": run_id, "scene": scene, + "scene_id": cfg["scene_id"], "config": cfg, "model_path": str(model) if model else None, + "model_sha256": _sha256(model), "metric_id": METRIC_ID, "command": shlex.join(command), + "git_sha": _git_sha(), "created_utc": datetime.now(timezone.utc).isoformat(), + "output_dir": str(out_dir), "norm_source": args.norm_source, + **device_manifest, "model_device": args.model_device, + } + _write_json(out_dir / "manifest.json", manifest) + if cfg["scene_id"] == "karman": + result = infer_karman( + scene, args.device, out_dir, args.steps, args.norm_source, args.model_device, + ) + elif cfg["scene_id"] == "illusion": + result = infer_illusion( + scene, args.device, out_dir, args.steps, args.target_only, + args.norm_source, args.model_device, + ) + elif cfg["scene_id"] == "vortex": result = infer_vortex(scene, args.device, out_dir, args.steps, args.norm_source) + else: raise ValueError(f"Unsupported legacy scene_id: {cfg['scene_id']}") + result.update({k: manifest[k] for k in ("run_id", "model_path", "model_sha256", "metric_id", "command", "git_sha", "config")}) + result["schema_version"] = "sr-stage1-result-v2" + _write_json(out_dir / "result.json", result) return 0 - - for sn in scene_names: - cfg = get_scene(sn) - scene_id = cfg["scene_id"] - od = _out_dir(sn) - print(f"\n-- {sn} ({scene_id}) -> {od} --") - fn = _INFER_FN.get(scene_id) - if fn is None: - print(f" ERROR: no inference function for scene_id={scene_id}") - continue - if scene_id == "illusion": - fn(sn, args.device, od, args.steps, args.target_only) - else: - fn(sn, args.device, od, args.steps) - - print("\nDone.") + except (FileNotFoundError, FileExistsError, KeyError, ValueError, NotImplementedError) as exc: + print(f"ERROR: {exc}", file=sys.stderr) + return 2 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/src/SR_analysis/stage_2_fit.py b/src/SR_analysis/stage_2_fit.py index 402ec45..b2c69ba 100644 --- a/src/SR_analysis/stage_2_fit.py +++ b/src/SR_analysis/stage_2_fit.py @@ -1,191 +1,1086 @@ #!/usr/bin/env python3 -"""Stage 2: Unified PySR symbolic regression fitting. +"""Stage 2 symbolic fitting with explicit trajectory and artifact contracts. -Runs PySR on controlled.npz data to discover interpretable obs→act formulas. -Supports per-scene individual fitting and cross-scene joint fitting. - -Usage: - # Per-scene (fast, niter=40) - conda run -n sr_env python stage_2_fit.py --scene karman_re100 --mode per-scene - - # Per-scene deep search (niter=120) - conda run -n sr_env python stage_2_fit.py --scene karman_re100 --mode per-scene --deep - - # Joint cross-scene (Karman: Re50-400) - conda run -n sr_env python stage_2_fit.py --scenes karman_re50,karman_re100,karman_re200,karman_re400 --mode joint - - # Joint deep - conda run -n sr_env python stage_2_fit.py --scenes illusion_0.75L,illusion_1L --mode joint --deep - -Output: JSON written to results/formulas/{scene}_{channel}.json +The module intentionally delays importing PySR so ``--prepare-only`` and unit + tests can validate all inputs without Julia being installed. """ from __future__ import annotations import argparse +import ast import json import os +import inspect +import platform +import re +import subprocess import sys +from dataclasses import dataclass +from datetime import datetime, timezone from pathlib import Path +from typing import Any, Callable, Mapping, Sequence import numpy as np +import sympy -_REPO = Path(__file__).resolve().parents[1] -if str(_REPO) not in sys.path: - sys.path.insert(0, str(_REPO)) -_SRC = _REPO / "src" -if str(_SRC) not in sys.path: - sys.path.insert(0, str(_SRC)) +REPO_ROOT = Path(__file__).resolve().parents[2] +SRC_ROOT = REPO_ROOT / "src" +SR_ROOT = SRC_ROOT / "SR_analysis" +if str(SRC_ROOT) not in sys.path: + sys.path.insert(0, str(SRC_ROOT)) -from SR_analysis.configs import get_scene -from SR_analysis.utils.feature_builder import ( - compute_dimensionless, compute_features, build_feature_matrix, - PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS, CORE_FEAT_KEYS_V2, MU_FEAT_KEYS, +from SR_analysis.configs import ACTION_LAYOUT, control_dt, get_scene +from SR_analysis.utils.data_contracts import ( + ALIGNMENTS, + CAUSAL_ALIGNMENT, + BatchDataset, + build_batch_dataset, + contiguous_block_slices, ) -ALL_MU = list(MU_FEAT_KEYS) + ["mu_Cl_tot"] -PHYS = [k for k in CORE_FEAT_KEYS_V2 if not k.startswith(("aF_","aB_","aT_","daF","daB","daT"))] -PHYS_DADT = PHYS + ["daF_dt","daB_dt","daT_dt"] +from SR_analysis.utils.feature_builder import FEATURE_SET_KEYS, ILLUSION_TARGET_KEYS +from SR_analysis.utils.formula_schema import build_formula_artifact, formula_safety_report +from SR_analysis.utils.provenance import atomic_write_json, hash_file, hash_json +from SR_analysis.utils.symmetry_dataset import build_mirrored_batch_dataset -FORMULA_DIR = "src/SR_analysis/results/formulas" -os.makedirs(FORMULA_DIR, exist_ok=True) - -FIT_GROUPS = { - "karman_trained": ["karman_re50", "karman_re100", "karman_re200", "karman_re400"], - "illusion_trained": ["illusion_0.75L", "illusion_1L"], -} +DEFAULT_DATA_ROOT = SR_ROOT / "data" +DEFAULT_RESULTS_ROOT = SR_ROOT / "results" / "runs" +G_VERSION = "legacy-mirror-front-odd-rear-swap-negate-v1" +BINARY_OPERATORS = ("+", "-", "*") +UNARY_OPERATORS = ("square",) +DEPLOYMENT_ARCHITECTURES = ( + "mapped_shared", + "three_head_independent", +) +FIT_AUGMENTATIONS = ("none", "G") +FIT_PURPOSES = ("discovery", "fixed-topology-refit") +FEATURE_PROFILES = ( + "default", + "actual_only", + "target_only", + "actual_plus_target", + "actual_plus_error", +) +COMPARATIVE_FEATURE_PROFILES = FEATURE_PROFILES[1:] -def load_controlled(scene_name): - cfg = get_scene(scene_name) - sid = cfg["scene_id"] - data_dir = f"src/SR_analysis/data/{sid}/{scene_name}" - npz = np.load(os.path.join(data_dir, "controlled.npz"), allow_pickle=True) - s = npz["sensors"].astype(np.float64) - f = npz["forces"].astype(np.float64) - a = npz["actions"].astype(np.float64) - ap = (a * cfg["action_scale"] + np.array(cfg["action_bias"])) * cfg["u0"] - tf = npz["target_forces"].astype(np.float64) if "target_forces" in npz else None - return s, f, ap, tf, cfg +@dataclass(frozen=True) +class PreparedTrajectory: + trajectory_id: str + data_path: Path + config_path: Path | None + dataset: BatchDataset + mirrored_dataset: BatchDataset + data_hash: str -def build_features(scene_name, s, f, ap, tf, cfg, feat_keys): - T = s.shape[0] - a1 = np.zeros((T, 3)); a2 = np.zeros((T, 3)) - a1[1:] = ap[:-1]; a2[2:] = ap[:-2] - dim = compute_dimensionless(s, f, u0=cfg["u0"], d=20.0) - si = cfg.get("sample_interval", 800) - dt_c = si / 2000.0 - mu = cfg["mu"] - sym = compute_features(dim, a1, a2, mu, alpha_mode=False, - include_mu="mu" in feat_keys, - include_cos_sin=False, u0=cfg["u0"], - target_forces=tf, dt_c=dt_c, - sensors_raw=s, forces_raw=f) - X = build_feature_matrix(sym, feat_keys, add_bias=False)[2:] - Y = ap[2:] / cfg["u0"] - return X, Y +@dataclass(frozen=True) +class PreparedScene: + name: str + config: dict[str, Any] + data_path: Path + config_path: Path | None + dataset: BatchDataset + mirrored_dataset: BatchDataset + feature_names: tuple[str, ...] + action_layout: tuple[str, ...] + data_hash: str + trajectories: tuple[PreparedTrajectory, ...] = () -def fit(scene_name, feat_keys, niter, output_label): - s, f, ap, tf, cfg = load_controlled(scene_name) - X, Y = build_features(scene_name, s, f, ap, tf, cfg, feat_keys) +def default_run_id() -> str: + return datetime.now(timezone.utc).strftime("stage2-%Y%m%dT%H%M%SZ") + + +def resolve_repo_path(value: str | os.PathLike[str] | None, default: Path) -> Path: + if value is None: + return default.resolve() + path = Path(value).expanduser() + return (path if path.is_absolute() else REPO_ROOT / path).resolve() + + +def _safe_run_id(run_id: str) -> str: + if not re.fullmatch(r"[A-Za-z0-9][A-Za-z0-9._-]*", run_id): + raise ValueError("run_id must contain only letters, digits, '.', '_' or '-'") + return run_id + + +def formula_output_dir(results_root: Path, run_id: str) -> Path: + root = results_root.resolve() + destination = (root / _safe_run_id(run_id) / "formulas").resolve() + destination.relative_to(root) + return destination + + +def refuse_existing_output(path: Path) -> None: + if path.exists(): + raise FileExistsError(f"Refusing to overwrite existing output directory: {path}") + + +def _parse_sequence(value: Any) -> tuple[Any, ...]: + if isinstance(value, str): + value = ast.literal_eval(value) + if not isinstance(value, (list, tuple)): + raise ValueError(f"expected sequence contract, got {value!r}") + return tuple(value) + + +def _load_scene_config(scene: str, config_path: Path | None) -> dict[str, Any]: + config = get_scene(scene) + if config_path is not None: + loaded = json.loads(config_path.read_text(encoding="utf-8")) + if not isinstance(loaded, dict): + raise ValueError(f"scene config must be a JSON object: {config_path}") + config.update(loaded) + for key in ("action_bias", "action_layout", "body_layout"): + if key in config: + config[key] = _parse_sequence(config[key]) + config.setdefault("action_layout", tuple(ACTION_LAYOUT)) + return config + + +def resolve_scene_files(data_root: Path, scene: str, scene_id: str) -> tuple[Path, Path | None]: + candidates = ( + data_root / scene_id / scene, + data_root / scene, + data_root, + ) + for directory in candidates: + controlled = directory / "controlled.npz" + if controlled.is_file(): + config = directory / "config.json" + return controlled, config if config.is_file() else None + raise FileNotFoundError( + f"controlled.npz for {scene!r} not found below {data_root}; expected canonical " + f"{scene_id}/{scene}/controlled.npz or a run/scene directory" + ) + + +def feature_names_for_scene( + config: Mapping[str, Any], *, feature_set: str, joint: bool, + feature_profile: str = "default", +) -> tuple[str, ...]: + if feature_set not in FEATURE_SET_KEYS: + raise ValueError(f"unknown feature set {feature_set!r}; expected one of {tuple(FEATURE_SET_KEYS)}") + if feature_profile not in FEATURE_PROFILES: + raise ValueError(f"unknown feature profile {feature_profile!r}; expected one of {FEATURE_PROFILES}") + base = tuple(FEATURE_SET_KEYS[feature_set]) + if config.get("scene_id") != "illusion": + if feature_profile not in {"default", "actual_only"}: + raise ValueError("target/error feature profiles are only defined for Illusion") + return base + profile_features = { + # ``default`` is retained as a non-comparative compatibility profile. + "default": tuple(ILLUSION_TARGET_KEYS) + ("Cd_err", "Cl_err"), + "actual_only": (), + "target_only": tuple(ILLUSION_TARGET_KEYS), + "actual_plus_target": tuple(ILLUSION_TARGET_KEYS), + "actual_plus_error": ("Cd_err", "Cl_err"), + } + if feature_profile == "target_only": + return profile_features[feature_profile] + return base + profile_features[feature_profile] + + +def _npz_scalar(data: Mapping[str, Any], key: str, default: Any) -> Any: + if key not in data: + return default + value = np.asarray(data[key]) + return value.item() if value.shape == () else default + + +def prepare_scene( + scene: str, data_root: Path, *, alignment: str, joint: bool, + feature_set: str = "physics_reduced", feature_profile: str = "default", + sample_segment: str = "all", transient_count: int = 50, +) -> PreparedScene: + baseline = get_scene(scene) + data_path, config_path = resolve_scene_files(data_root, scene, str(baseline["scene_id"])) + config = _load_scene_config(scene, config_path) + feature_names = feature_names_for_scene( + config, feature_set=feature_set, joint=joint, feature_profile=feature_profile, + ) + with np.load(data_path, allow_pickle=False) as data: + sensors_key = "sensors_post" if "sensors_post" in data else "sensors" + forces_key = "forces_post" if "forces_post" in data else "forces" + if "actions_alpha_commanded" in data: + actions = np.asarray(data["actions_alpha_commanded"]) + action_mode = "alpha" + elif "actions_norm" in data: + actions = np.asarray(data["actions_norm"]) + action_mode = "normalized" + elif "actions" in data: + actions = np.asarray(data["actions"]) + action_mode = "normalized" + else: + raise KeyError(f"controlled trajectory missing actions: {data_path}") + target = np.asarray(data["target_forces"]) if "target_forces" in data else None + dt_c = float(_npz_scalar(data, "dt_c", config.get("control_dt", control_dt(config["sample_interval"])))) + common = { + "mu": float(config["mu"]), "dt_c": dt_c, "alignment": alignment, + "action_mode": action_mode, "action_scale": float(config.get("action_scale", 1.0)), + "action_bias": config.get("action_bias", (0.0, 0.0, 0.0)), + "illusion": config.get("scene_id") == "illusion", + "u0": float(config.get("u0", 0.01)), "include_cos_sin": False, + "add_bias": False, "strict": True, "sample_segment": sample_segment, + "transient_count": transient_count, + } + sensors = np.asarray(data[sensors_key]) + forces = np.asarray(data[forces_key]) + dataset = build_batch_dataset( + sensors, forces, actions, feature_names, target_forces=target, **common, + ) + mirrored_dataset = build_mirrored_batch_dataset( + sensors, forces, actions, feature_names, target_forces=target, **common, + ) + layout = tuple(str(item).lower() for item in config.get("action_layout", ACTION_LAYOUT)) + aliases = {"top": "upper", "bottom": "lower"} + layout = tuple(aliases.get(item, item) for item in layout) + if len(layout) != 3 or set(layout) != {"front", "upper", "lower"}: + raise ValueError(f"invalid native action_layout for {scene}: {layout}") + if dataset.X.shape[0] == 0: + raise ValueError(f"no aligned samples remain for {scene}") + data_hash = hash_json({"controlled_sha256": hash_file(data_path), "config": config}) + trajectory = PreparedTrajectory( + f"{scene}:trajectory-001", data_path, config_path, dataset, mirrored_dataset, data_hash, + ) + return PreparedScene( + scene, config, data_path, config_path, dataset, mirrored_dataset, + feature_names, layout, data_hash, (trajectory,), + ) + + +def _stack_batch_datasets(datasets: Sequence[BatchDataset]) -> BatchDataset: + if not datasets: + raise ValueError("at least one trajectory dataset is required") + return BatchDataset( + X=np.vstack([dataset.X for dataset in datasets]), + Y=np.vstack([dataset.Y for dataset in datasets]), + kept_indices=np.concatenate([dataset.kept_indices for dataset in datasets]), + dropped_indices=np.concatenate([dataset.dropped_indices for dataset in datasets]), + metadata=dict(datasets[0].metadata), + features={}, + ) + + +def prepare_scene_trajectories( + scene: str, data_roots: Sequence[Path], *, alignment: str, joint: bool, + feature_set: str = "physics_reduced", feature_profile: str = "default", + sample_segment: str = "all", transient_count: int = 50, +) -> PreparedScene: + if not data_roots: + raise ValueError("at least one data root is required") + prepared = [ + prepare_scene( + scene, root, alignment=alignment, joint=joint, feature_set=feature_set, + feature_profile=feature_profile, sample_segment=sample_segment, + transient_count=transient_count, + ) + for root in data_roots + ] + first = prepared[0] + for item in prepared[1:]: + if item.feature_names != first.feature_names or item.action_layout != first.action_layout: + raise ValueError(f"trajectory contracts differ for scene {scene}") + if item.config.get("scene_id") != first.config.get("scene_id"): + raise ValueError(f"trajectory scene_id contracts differ for scene {scene}") + trajectories = tuple( + PreparedTrajectory( + f"{scene}:trajectory-{index:03d}", item.data_path, item.config_path, + item.dataset, item.mirrored_dataset, item.data_hash, + ) + for index, item in enumerate(prepared, start=1) + ) + return PreparedScene( + scene, first.config, first.data_path, first.config_path, + _stack_batch_datasets([item.dataset for item in trajectories]), + _stack_batch_datasets([item.mirrored_dataset for item in trajectories]), + first.feature_names, first.action_layout, + hash_json({item.trajectory_id: item.data_hash for item in trajectories}), trajectories, + ) + + +def _git_sha() -> str | None: + try: + return subprocess.run( + ["git", "-C", str(REPO_ROOT), "rev-parse", "HEAD"], check=True, + capture_output=True, text=True, + ).stdout.strip() + except (OSError, subprocess.CalledProcessError): + return None + + +def _environment() -> dict[str, Any]: + return { + "python": platform.python_version(), + "numpy": np.__version__, + "platform": platform.platform(), + "pysr": None, + } + + +def _regressor_factory() -> Any: from pysr import PySRRegressor - m = PySRRegressor(binary_operators=["+","-","*","/"], unary_operators=["square"], - niterations=niter, populations=30, maxsize=15, - complexity_of_constants=2, parsimony=0.01, batching=False) - # Front (no bias) - m.fit(X, Y[:, 0], variable_names=feat_keys) - front = {"scene": output_label, "channel": "front", "output": "alpha", - "feature_keys": feat_keys, "best_sympy": str(m.sympy()), - "best_score": float(m.score(X, Y[:, 0])), "niterations": niter} - json.dump(front, open(os.path.join(FORMULA_DIR, f"{output_label}_front.json"), "w"), indent=2) - print(f" Front: {front['best_sympy']} (R2={front['best_score']:.4f})") - # Top (with bias) - Xt = build_feature_matrix(dict(zip(feat_keys, [X[:, i] for i in range(len(feat_keys))])), feat_keys, add_bias=True)[0] - # Redo properly - from SR_analysis.utils.feature_builder import compute_features as cf2, compute_dimensionless as cd2, build_feature_matrix as bfm - T2 = s.shape[0]; a12 = np.zeros((T2,3)); a22 = np.zeros((T2,3)) - a12[1:]=ap[:-1]; a22[2:]=ap[:-2] - dim2 = cd2(s, f, u0=cfg["u0"], d=20.0) - sym2 = cf2(dim2, a12, a22, mu, alpha_mode=False, include_mu="mu" in feat_keys, include_cos_sin=False, u0=cfg["u0"], target_forces=tf, dt_c=cfg.get("sample_interval",800)/2000, sensors_raw=s, forces_raw=f) - Xt2 = bfm(sym2, feat_keys, add_bias=True)[2:] - m2 = PySRRegressor(binary_operators=["+","-","*","/"], unary_operators=["square"], - niterations=niter, populations=30, maxsize=15, - complexity_of_constants=2, parsimony=0.01, batching=False) - m2.fit(Xt2, Y[:, 2], variable_names=["bias"] + feat_keys) - top = {"scene": output_label, "channel": "top", "output": "alpha", - "feature_keys": ["bias"] + feat_keys, "best_sympy": str(m2.sympy()), - "best_score": float(m2.score(Xt2, Y[:, 2])), "niterations": niter} - json.dump(top, open(os.path.join(FORMULA_DIR, f"{output_label}_top.json"), "w"), indent=2) - print(f" Top: {top['best_sympy']} (R2={top['best_score']:.4f})") - return front, top + return PySRRegressor -def fit_joint(scene_names, feat_keys, niter, output_label): - Xl, Yl = [], [] - for sn in scene_names: - s, f, ap, tf, cfg = load_controlled(sn) - X, Y = build_features(sn, s, f, ap, tf, cfg, feat_keys) - Xl.append(X); Yl.append(Y[:, 0]) - X_all = np.vstack(Xl); Y_all = np.concatenate(Yl) - from pysr import PySRRegressor - m = PySRRegressor(binary_operators=["+","-","*","/"], unary_operators=["square"], - niterations=niter, populations=40, maxsize=15, - complexity_of_constants=2, parsimony=0.01, batching=False) - m.fit(X_all, Y_all, variable_names=feat_keys) - front = {"scene": output_label, "channel": "front", "output": "alpha", - "feature_keys": feat_keys, "best_sympy": str(m.sympy()), - "best_score": float(m.score(X_all, Y_all)), "niterations": niter, - "n_samples": int(X_all.shape[0])} - json.dump(front, open(os.path.join(FORMULA_DIR, f"{output_label}_front.json"), "w"), indent=2) - print(f" Joint Front: {front['best_sympy']} (R2={front['best_score']:.4f})") - return front +def regressor_parameters(*, seed: int, niterations: int, smoke: bool) -> dict[str, Any]: + return { + "binary_operators": list(BINARY_OPERATORS), + "unary_operators": list(UNARY_OPERATORS), + "niterations": int(niterations), + "populations": 2 if smoke else 30, + "population_size": 16 if smoke else 33, + "tournament_selection_n": 4 if smoke else 10, + "maxsize": 7 if smoke else 15, + "complexity_of_constants": 2, + "parsimony": 0.01, + "batching": False, + "random_state": int(seed), + "deterministic": True, + "parallelism": "serial", + } -def get_feature_keys_for_scene(scene_name): - cfg = get_scene(scene_name) - sid = cfg["scene_id"] - if sid == "illusion": - return ILLUSION_PHASE_KEYS - elif sid == "karman": - return PHYS_DADT + ALL_MU - return PHASE_STATE_KEYS +def _predict(model: Any, X: np.ndarray) -> np.ndarray: + prediction = np.asarray(model.predict(X), dtype=np.float64).reshape(-1) + if prediction.shape != (X.shape[0],): + raise ValueError(f"regressor prediction has invalid shape {prediction.shape}") + return prediction -def main(): - ap = argparse.ArgumentParser(description="Stage 2: Unified PySR fitting") - ap.add_argument("--scene", type=str, default=None) - ap.add_argument("--scenes", type=str, default=None, help="Comma-separated for joint fitting") - ap.add_argument("--group", type=str, default=None) - ap.add_argument("--mode", type=str, default="per-scene", choices=["per-scene", "joint"]) - ap.add_argument("--deep", action="store_true", help="Deep search (niter=120)") - ap.add_argument("--output-label", type=str, default=None) - args = ap.parse_args() +def diagnostic_metrics(y: np.ndarray, prediction: np.ndarray) -> dict[str, float]: + residual = np.asarray(y) - np.asarray(prediction) + denominator = float(np.sum((y - np.mean(y)) ** 2)) + r2 = 1.0 - float(np.sum(residual ** 2)) / denominator if denominator > 0 else 0.0 + return { + "r2": r2, + "mae": float(np.mean(np.abs(residual))), + "rmse": float(np.sqrt(np.mean(residual ** 2))), + "max_error": float(np.max(np.abs(residual))), + } - niter = 120 if args.deep else 40 - if args.group and args.group in FIT_GROUPS: - scene_names = FIT_GROUPS[args.group] - elif args.scenes: - scene_names = [s.strip() for s in args.scenes.split(",")] - elif args.scene: - scene_names = [args.scene] +def _expression(model: Any) -> str: + expression = str(model.sympy()).strip() + if not expression: + raise ValueError("regressor returned an empty symbolic expression") + return expression + + +def _combined_data_hash(scenes: Sequence[PreparedScene]) -> str: + return hash_json({scene.name: scene.data_hash for scene in scenes}) + + +def validate_deployment_architecture(value: str) -> str: + if value not in DEPLOYMENT_ARCHITECTURES: + raise ValueError(f"deployment architecture must be one of {DEPLOYMENT_ARCHITECTURES}") + return value + + +def validate_fit_augmentation(value: str) -> str: + if value not in FIT_AUGMENTATIONS: + raise ValueError(f"fit augmentation must be one of {FIT_AUGMENTATIONS}") + return value + + +def _scene_trajectories(scene: PreparedScene) -> tuple[PreparedTrajectory, ...]: + if scene.trajectories: + return scene.trajectories + return (PreparedTrajectory( + f"{scene.name}:trajectory-001", scene.data_path, scene.config_path, + scene.dataset, scene.mirrored_dataset, scene.data_hash, + ),) + + +def _head_training_blocks( + scenes: Sequence[PreparedScene], role: str, channel_index: int, + *, fit_augmentation: str = "G", split: str | None = None, +) -> list[tuple[str, str, np.ndarray, np.ndarray]]: + augmentation = validate_fit_augmentation(fit_augmentation) + blocks = [] + for scene in scenes: + for trajectory in _scene_trajectories(scene): + X = trajectory.dataset.X + GX = trajectory.mirrored_dataset.X + if GX.shape != X.shape: + raise ValueError("raw-state mirrored dataset does not align with source dataset") + y = trajectory.dataset.Y[:, channel_index] + gy = trajectory.mirrored_dataset.Y[:, channel_index] + if role == "front_odd": + np.testing.assert_allclose(gy, -y) + elif role == "rear_shared": + lower_index = scene.action_layout.index("lower") + np.testing.assert_allclose(gy, -trajectory.dataset.Y[:, lower_index]) + elif role not in {"front_independent", "upper_independent", "lower_independent"}: + raise ValueError(f"unknown fitted head role: {role}") + block = slice(None) if split is None else contiguous_block_slices(len(X))[split] + blocks.append((scene.name, trajectory.trajectory_id, X[block], y[block])) + if augmentation == "G": + blocks.append((scene.name, trajectory.trajectory_id, GX[block], gy[block])) + return blocks + + +def _head_training_data( + scenes: Sequence[PreparedScene], role: str, channel_index: int, + *, fit_augmentation: str = "G", split: str | None = None, +) -> tuple[np.ndarray, np.ndarray]: + blocks = _head_training_blocks( + scenes, role, channel_index, fit_augmentation=fit_augmentation, split=split, + ) + return np.vstack([item[2] for item in blocks]), np.concatenate([item[3] for item in blocks]) + + +def _joint_sample_weights( + blocks: Sequence[tuple[str, str, np.ndarray, np.ndarray]], +) -> np.ndarray: + cases = tuple(dict.fromkeys(item[0] for item in blocks)) + trajectories = { + case: tuple(dict.fromkeys(item[1] for item in blocks if item[0] == case)) + for case in cases + } + weights = [] + for case, trajectory_id, X, _ in blocks: + total_for_trajectory = sum( + len(other_X) for other_case, other_id, other_X, _ in blocks + if other_case == case and other_id == trajectory_id + ) + mass = 1.0 / (len(cases) * len(trajectories[case])) + weights.append(np.full(len(X), mass / total_for_trajectory, dtype=np.float64)) + result = np.concatenate(weights) + return result * (len(result) / result.sum()) + + +def _parameterize_numeric_constants( + expression: str, feature_names: Sequence[str], +) -> tuple[ast.Expression, tuple[str, ...], np.ndarray]: + """Replace every numeric literal with a distinct optimization parameter.""" + tree = ast.parse(expression, mode="eval") + allowed_names = set(feature_names) | {"sin", "cos", "tan", "exp", "log", "sqrt", "Abs", "abs"} + parameter_names: list[str] = [] + initial_values: list[float] = [] + + class Parameterizer(ast.NodeTransformer): + def visit_Constant(self, node: ast.Constant) -> ast.AST: + if isinstance(node.value, bool) or not isinstance(node.value, (int, float)): + raise ValueError("topology expressions may contain only numeric constants") + name = f"__constant_{len(parameter_names)}" + parameter_names.append(name) + initial_values.append(float(node.value)) + return ast.copy_location(ast.Name(id=name, ctx=ast.Load()), node) + + def visit_Name(self, node: ast.Name) -> ast.AST: + if node.id not in allowed_names: + raise ValueError(f"topology expression references undeclared feature {node.id!r}") + return node + + parameterized = Parameterizer().visit(tree) + ast.fix_missing_locations(parameterized) + if not parameter_names: + raise ValueError("fixed-topology refit requires at least one numeric constant") + return parameterized, tuple(parameter_names), np.asarray(initial_values, dtype=np.float64) + + +def _topology_evaluator( + expression: str, feature_names: Sequence[str], +) -> tuple[Callable[[np.ndarray, np.ndarray], np.ndarray], ast.Expression, tuple[str, ...], np.ndarray]: + tree, parameter_names, initial = _parameterize_numeric_constants(expression, feature_names) + symbols = {name: sympy.Symbol(name) for name in (*feature_names, *parameter_names)} + parsed = sympy.sympify(ast.unparse(tree), locals=symbols, evaluate=False) + function = sympy.lambdify( + [symbols[name] for name in (*feature_names, *parameter_names)], parsed, modules="numpy", + ) + + def evaluate(X: np.ndarray, constants: np.ndarray) -> np.ndarray: + values = function( + *[X[:, index] for index in range(X.shape[1])], *[float(value) for value in constants], + ) + prediction = np.asarray(values, dtype=np.float64) + if prediction.ndim == 0: + prediction = np.full(len(X), float(prediction)) + prediction = prediction.reshape(-1) + if prediction.shape != (len(X),) or not np.all(np.isfinite(prediction)): + raise ValueError("topology expression produced invalid predictions") + return prediction + + return evaluate, tree, parameter_names, initial + + +def _render_refitted_expression( + tree: ast.Expression, parameter_names: Sequence[str], constants: Sequence[float], +) -> str: + replacements = dict(zip(parameter_names, constants)) + + class Renderer(ast.NodeTransformer): + def visit_Name(self, node: ast.Name) -> ast.AST: + if node.id not in replacements: + return node + return ast.copy_location(ast.Constant(value=float(replacements[node.id])), node) + + rendered = Renderer().visit(ast.fix_missing_locations(ast.parse(ast.unparse(tree), mode="eval"))) + rendered_text = ast.unparse(ast.fix_missing_locations(rendered)) + return str(sympy.simplify(sympy.sympify(rendered_text, evaluate=True))) + + +def _refit_metrics( + blocks: Sequence[tuple[str, str, np.ndarray, np.ndarray]], + evaluate: Callable[[np.ndarray], np.ndarray], +) -> dict[str, Any]: + aggregate_X = np.vstack([block[2] for block in blocks]) + aggregate_y = np.concatenate([block[3] for block in blocks]) + cases = tuple(dict.fromkeys(block[0] for block in blocks)) + trajectory_keys = tuple(dict.fromkeys((block[0], block[1]) for block in blocks)) + + def metrics_for(selected: Sequence[tuple[str, str, np.ndarray, np.ndarray]]) -> dict[str, float]: + X = np.vstack([block[2] for block in selected]) + y = np.concatenate([block[3] for block in selected]) + return diagnostic_metrics(y, evaluate(X)) + + return { + "aggregate": diagnostic_metrics(aggregate_y, evaluate(aggregate_X)), + "per_case": { + case: metrics_for([block for block in blocks if block[0] == case]) for case in cases + }, + "per_trajectory": { + trajectory_id: metrics_for([ + block for block in blocks if block[0] == case and block[1] == trajectory_id + ]) + for case, trajectory_id in trajectory_keys + }, + } + + +def _candidate_prediction( + record: Mapping[str, Any], expression: Any, X: np.ndarray, feature_names: Sequence[str], +) -> np.ndarray: + callable_expression = record.get("lambda_format") + if callable(callable_expression): + try: + value = callable_expression(X) + except TypeError: + value = callable_expression(*[X[:, index] for index in range(X.shape[1])]) else: - ap.error("Need --scene, --scenes, or --group") + symbols = [sympy.Symbol(name) for name in feature_names] + parsed = sympy.sympify(expression, locals=dict(zip(feature_names, symbols))) + function = sympy.lambdify(symbols, parsed, modules="numpy") + value = function(*[X[:, index] for index in range(X.shape[1])]) + prediction = np.asarray(value, dtype=np.float64) + if prediction.ndim == 0: + prediction = np.full(len(X), float(prediction)) + prediction = prediction.reshape(-1) + if prediction.shape != (len(X),) or not np.all(np.isfinite(prediction)): + raise ValueError(f"candidate prediction has invalid values or shape {prediction.shape}") + return prediction - label = args.output_label or ("_".join(scene_names) if len(scene_names) > 1 else scene_names[0]) - if args.mode == "per-scene": - for sn in scene_names: - keys = get_feature_keys_for_scene(sn) - print(f"\n=== {sn} (niter={niter}, {len(keys)} features) ===") - fit(sn, keys, niter, sn) - elif args.mode == "joint": - keys = get_feature_keys_for_scene(scene_names[0]) - print(f"\n=== Joint {label} (niter={niter}, {len(keys)} features, {len(scene_names)} scenes) ===") - fit_joint(scene_names, keys, niter, label) +def _pareto_candidates( + model: Any, split_data: Mapping[str, tuple[np.ndarray, np.ndarray]], + feature_names: Sequence[str], feature_ranges: Mapping[str, Sequence[float]], limit: int = 25, +) -> list[dict[str, Any]]: + equations = getattr(model, "equations_", None) + if equations is None: + return [] + records = equations.to_dict("records") if hasattr(equations, "to_dict") else list(equations) + candidates = [] + symbols = {name: sympy.Symbol(name) for name in feature_names} + for record in records[:limit]: + if not isinstance(record, Mapping): + continue + expression = record.get("sympy_format", record.get("equation")) + if expression is None: + continue + expression_text = str(expression) + candidate = {"expression": expression_text} + for key in ("complexity", "loss", "score"): + value = record.get(key) + if value is not None and np.isscalar(value) and np.isfinite(value): + candidate[key] = float(value) + try: + parsed = sympy.sympify(expression, locals=symbols) + candidate["used_features"] = sorted(str(symbol) for symbol in parsed.free_symbols) + candidate["static_safety"] = formula_safety_report( + {"feature_names": list(feature_names), "expression": expression_text}, feature_ranges, + ) + candidate["metrics"] = { + name: diagnostic_metrics(target, _candidate_prediction(record, expression, matrix, feature_names)) + for name, (matrix, target) in split_data.items() + } + except Exception as exc: + candidate["evaluation_error"] = f"{type(exc).__name__}: {exc}" + candidate.setdefault("used_features", []) + candidate.setdefault("static_safety", {"safe": False, "errors": [candidate["evaluation_error"]]}) + candidate["metrics"] = {name: None for name in split_data} + candidates.append(candidate) + return candidates - print("\nDone.") + +def refit_fixed_topology_head( + scenes: Sequence[PreparedScene], *, role: str, channel_index: int, run_id: str, + topology_expression: str, discovery_parent_path: Path | None, + mode: str, deployment_architecture: str, feature_set: str, feature_profile: str, + environment: Mapping[str, Any], +) -> dict[str, Any]: + try: + from scipy.optimize import least_squares + except ImportError as exc: + raise RuntimeError("fixed-topology refit requires scipy in the active environment") from exc + + architecture = validate_deployment_architecture(deployment_architecture) + augmentation = "G" if architecture == "mapped_shared" else "none" + blocks = _head_training_blocks( + scenes, role, channel_index, fit_augmentation=augmentation, split=None, + ) + X = np.vstack([block[2] for block in blocks]) + y = np.concatenate([block[3] for block in blocks]) + weights = _joint_sample_weights(blocks) + evaluate, tree, parameter_names, initial = _topology_evaluator( + topology_expression, scenes[0].feature_names, + ) + result = least_squares( + lambda constants: np.sqrt(weights) * (evaluate(X, constants) - y), initial, + ) + if not result.success or not np.all(np.isfinite(result.x)): + raise RuntimeError(f"fixed-topology least-squares refit failed: {result.message}") + fitted = _render_refitted_expression(tree, parameter_names, result.x) + predictor = lambda matrix: evaluate(matrix, result.x) + metrics = _refit_metrics(blocks, predictor) + feature_ranges = { + name: [float(X[:, index].min()), float(X[:, index].max())] + for index, name in enumerate(scenes[0].feature_names) + } + safety = formula_safety_report( + {"feature_names": list(scenes[0].feature_names), "expression": fitted}, feature_ranges, + ) + counts = {scene.name: int(scene.dataset.X.shape[0]) for scene in scenes} + parent = None if discovery_parent_path is None else discovery_parent_path.resolve() + if parent is not None and not parent.is_file(): + raise FileNotFoundError(f"discovery parent artifact not found: {parent}") + config = scenes[0].config + alignment = scenes[0].dataset.metadata + env = dict(environment) + return build_formula_artifact( + artifact_id=f"{run_id}-{mode}-{role}-anchor-upper", run_id=run_id, role=role, + anchor="upper", training_scenes=[scene.name for scene in scenes], + feature_names=scenes[0].feature_names, fitted_expression=fitted, + deployment_expression=fitted, + output={ + "name": "alpha", "definition": "cylinder_surface_tangential_velocity/U0", + "unit": "dimensionless", "native_action_index": channel_index, + "native_action_order": list(scenes[0].action_layout), + }, + feature_metadata={ + "builder": "SR_analysis.utils.data_contracts.build_batch_dataset", + "definitions_module": "SR_analysis.utils.feature_builder", + "order": list(scenes[0].feature_names), + }, + alignment_metadata={ + "alignment": alignment["alignment"], "warmup": alignment["warmup"], + "sample_segments": alignment.get("sample_segments"), + "history_policy": alignment["history_policy"], + "required_target": config.get("scene_id") == "illusion", + }, + dt_c_metadata={ + "per_scene": {scene.name: float(scene.dataset.metadata["dt_c"]) for scene in scenes}, + "unit": "D/U0", + }, + g_metadata={ + "version": G_VERSION, "deployment_architecture": architecture, + "deployment_semantics": architecture, "fit_augmentation": augmentation, + "front_rule": "front(x)=0.5*(f(x)-f(Gx)); antisymmetric projection" if architecture == "mapped_shared" else None, + "rear_rule": "lower(x)=-upper(Gx)" if architecture == "mapped_shared" else None, + "fitted_heads": [role], + }, + fit_provenance={ + "git_sha": _git_sha(), "environment": env, "command": " ".join(sys.argv), + "created_at": datetime.now(timezone.utc).isoformat(), "mode": mode, + "fit_purpose": "fixed-topology-refit", "feature_set": feature_set, + "feature_profile": feature_profile, "n_samples": int(len(X)), + "source_n_samples": int(sum(counts.values())), "scene_sample_counts": counts, + "g_augmented": augmentation == "G", "fit_augmentation": augmentation, + "deployment_architecture": architecture, + "sample_weighting": "case_equal_trajectory_equal_within_case", + "offline_metrics": metrics, "diagnostic_metrics": metrics["aggregate"], + "optimizer": { + "name": "scipy.optimize.least_squares", "success": bool(result.success), + "cost": float(result.cost), "nfev": int(result.nfev), + "initial_constants": initial.tolist(), "refitted_constants": result.x.tolist(), + }, + "topology_expression": topology_expression, + "static_safety": safety, + }, + pysr_provenance={"used": False, "purpose": "fixed-topology-refit"}, + data_provenance={ + "dataset_hash": _combined_data_hash(scenes), + "per_scene_hashes": {scene.name: scene.data_hash for scene in scenes}, + "paths": {scene.name: str(scene.data_path) for scene in scenes}, + "trajectories": { + scene.name: [{ + "id": trajectory.trajectory_id, "path": str(trajectory.data_path), + "hash": trajectory.data_hash, + "sample_count": int(trajectory.dataset.X.shape[0]), + } for trajectory in _scene_trajectories(scene)] for scene in scenes + }, + }, + discovery_parent={ + "path": None if parent is None else str(parent), + "sha256": None if parent is None else hash_file(parent), + }, + git_sha=_git_sha(), environment=env, + ) + + +def fit_head( + scenes: Sequence[PreparedScene], *, role: str, channel_index: int, run_id: str, + seed: int, niterations: int, smoke: bool, deployment_expression: str | None, + mode: str, deployment_architecture: str, fit_augmentation: str, + feature_set: str, feature_profile: str, + regressor_factory: Callable[..., Any], environment: Mapping[str, Any], +) -> dict[str, Any]: + architecture = validate_deployment_architecture(deployment_architecture) + augmentation = validate_fit_augmentation(fit_augmentation) + split_blocks = { + name: _head_training_blocks( + scenes, role, channel_index, fit_augmentation=augmentation, split=name, + ) + for name in ("train", "validation", "blind") + } + split_data = { + name: (np.vstack([item[2] for item in blocks]), np.concatenate([item[3] for item in blocks])) + for name, blocks in split_blocks.items() + } + X, y = split_data["train"] + parameters = regressor_parameters(seed=seed, niterations=niterations, smoke=smoke) + model = regressor_factory(**parameters) + fit_kwargs: dict[str, Any] = {"variable_names": list(scenes[0].feature_names)} + weights = _joint_sample_weights(split_blocks["train"]) if mode == "joint" else None + if weights is not None: + try: + signature = inspect.signature(model.fit) + except (TypeError, ValueError): + signature = None + supports_weights = signature is None or "weights" in signature.parameters or any( + parameter.kind == inspect.Parameter.VAR_KEYWORD + for parameter in signature.parameters.values() + ) + if not supports_weights: + raise TypeError( + "joint fitting requires PySR model.fit(X, y, weights=...); " + "the configured regressor does not support explicit sample weights" + ) + fit_kwargs["weights"] = weights + model.fit(X, y, **fit_kwargs) + fitted = _expression(model) + deployment = fitted if deployment_expression is None else deployment_expression + counts = {scene.name: int(scene.dataset.X.shape[0]) for scene in scenes} + augmented_counts = {name: 2 * count for name, count in counts.items()} + metrics = { + name: diagnostic_metrics(target, _predict(model, matrix)) + for name, (matrix, target) in split_data.items() + } + all_X = np.vstack([matrix for matrix, _ in split_data.values()]) + feature_ranges = { + name: [float(all_X[:, index].min()), float(all_X[:, index].max())] + for index, name in enumerate(scenes[0].feature_names) + } + safety = formula_safety_report( + {"feature_names": list(scenes[0].feature_names), "expression": deployment}, + feature_ranges, + ) + config = scenes[0].config + alignment = scenes[0].dataset.metadata + artifact_id = f"{run_id}-{mode}-{role}-anchor-upper" + env = dict(environment) + env["pysr"] = getattr(sys.modules.get("pysr"), "__version__", env.get("pysr")) + return build_formula_artifact( + artifact_id=artifact_id, run_id=run_id, role=role, anchor="upper", + training_scenes=[scene.name for scene in scenes], feature_names=scenes[0].feature_names, + fitted_expression=fitted, deployment_expression=deployment, + output={ + "name": "alpha", + "definition": "cylinder_surface_tangential_velocity/U0", + "unit": "dimensionless", + "native_action_index": channel_index, + "native_action_order": list(scenes[0].action_layout), + }, + feature_metadata={ + "builder": "SR_analysis.utils.data_contracts.build_batch_dataset", + "definitions_module": "SR_analysis.utils.feature_builder", + "order": list(scenes[0].feature_names), + }, + alignment_metadata={ + "alignment": alignment["alignment"], "warmup": alignment["warmup"], + "sample_segments": alignment.get("sample_segments"), + "history_policy": alignment["history_policy"], "required_target": config.get("scene_id") == "illusion", + }, + dt_c_metadata={"per_scene": {scene.name: float(scene.dataset.metadata["dt_c"]) for scene in scenes}, "unit": "D/U0"}, + g_metadata={ + "version": G_VERSION, + "deployment_architecture": architecture, + "deployment_semantics": architecture, + "fit_augmentation": augmentation, + "front_rule": ( + "front(x)=0.5*(f(x)-f(Gx)); antisymmetric projection" + if architecture == "mapped_shared" else None + ), + "rear_rule": "lower(x)=-upper(Gx)" if architecture == "mapped_shared" else None, + "fitted_heads": [role], + }, + fit_provenance={ + "git_sha": _git_sha(), + "environment": env, + "command": " ".join(sys.argv), + "created_at": datetime.now(timezone.utc).isoformat(), + "mode": mode, "seed": int(seed), "niterations": int(niterations), "smoke": bool(smoke), + "feature_set": feature_set, "feature_profile": feature_profile, + "n_samples": int(X.shape[0]), "source_n_samples": int(sum(counts.values())), + "scene_sample_counts": counts, "scene_augmented_sample_counts": ( + augmented_counts if augmentation == "G" else counts + ), + "g_augmented": augmentation == "G", + "fit_augmentation": augmentation, + "deployment_architecture": architecture, + "deployment_semantics": architecture, + "sample_weighting": ( + "case_equal_trajectory_equal_within_case" if weights is not None else "uniform" + ), + "offline_metrics": metrics, + "diagnostic_metrics": metrics["train"], + "selection_policy": "manual_validation_required_r2_never_auto_promotes", + "static_safety": safety, + }, + pysr_provenance={ + "parameters": parameters, "binary_operators": list(BINARY_OPERATORS), + "unary_operators": list(UNARY_OPERATORS), "complexity": {"maxsize": parameters["maxsize"], "parsimony": parameters["parsimony"]}, + "pareto_candidates": _pareto_candidates( + model, split_data, scenes[0].feature_names, feature_ranges, + ), + "candidate_limit": 25, + }, + data_provenance={ + "dataset_hash": _combined_data_hash(scenes), + "per_scene_hashes": {scene.name: scene.data_hash for scene in scenes}, + "paths": {scene.name: str(scene.data_path) for scene in scenes}, + "trajectories": { + scene.name: [ + { + "id": trajectory.trajectory_id, + "path": str(trajectory.data_path), + "hash": trajectory.data_hash, + "sample_count": int(trajectory.dataset.X.shape[0]), + } + for trajectory in _scene_trajectories(scene) + ] + for scene in scenes + }, + }, + git_sha=_git_sha(), environment=env, + ) + + +def _validate_joint_contract(scenes: Sequence[PreparedScene]) -> None: + first = scenes[0] + objective = first.config.get("scene_id") + for scene in scenes[1:]: + if scene.config.get("scene_id") != objective: + raise ValueError("joint scenes must belong to the same scene_id objective") + if scene.feature_names != first.feature_names: + raise ValueError("joint scenes must have identical feature definitions/order") + if scene.action_layout != first.action_layout: + raise ValueError("joint scenes must have identical native action order") + action_contract = ( + scene.dataset.metadata["action_mode"], + float(scene.config.get("action_scale", 1.0)), + tuple(scene.config.get("action_bias", (0.0, 0.0, 0.0))), + float(scene.config.get("u0", 0.01)), + ) + first_action_contract = ( + first.dataset.metadata["action_mode"], + float(first.config.get("action_scale", 1.0)), + tuple(first.config.get("action_bias", (0.0, 0.0, 0.0))), + float(first.config.get("u0", 0.01)), + ) + if action_contract != first_action_contract: + raise ValueError("joint scenes must have identical action conversion contracts") + + +def run_fit( + *, scenes: Sequence[str], mode: str, run_id: str, data_root: Path | None = None, + data_roots: Sequence[Path] | None = None, + results_root: Path = DEFAULT_RESULTS_ROOT, alignment: str = CAUSAL_ALIGNMENT, + feature_set: str = "physics_reduced", feature_profile: str = "default", + sample_segment: str = "all", transient_count: int = 50, + deployment_architecture: str = "mapped_shared", fit_augmentation: str = "G", + seed: int = 0, niterations: int = 40, + smoke: bool = False, prepare_only: bool = False, + fit_purpose: str = "discovery", + front_topology_expression: str | None = None, + rear_topology_expression: str | None = None, + front_discovery_parent_path: Path | None = None, + rear_discovery_parent_path: Path | None = None, + front_deployment_expression: str | None = None, + rear_deployment_expression: str | None = None, + regressor_factory: Callable[..., Any] | None = None, +) -> list[Path]: + if not scenes: + raise ValueError("at least one scene is required") + if mode == "joint" and len(scenes) < 2: + raise ValueError("joint mode requires at least two scenes") + if fit_purpose not in FIT_PURPOSES: + raise ValueError(f"fit purpose must be one of {FIT_PURPOSES}") + if fit_purpose == "fixed-topology-refit": + if not front_topology_expression or not rear_topology_expression: + raise ValueError("fixed-topology refit requires front and rear topology expressions") + if deployment_architecture != "mapped_shared": + raise ValueError("fixed-topology refit currently requires mapped_shared architecture") + output_dir = formula_output_dir(results_root, run_id) + refuse_existing_output(output_dir) + roots = tuple(data_roots or (() if data_root is None else (data_root,))) + if not roots: + roots = (DEFAULT_DATA_ROOT,) + prepared = [ + prepare_scene_trajectories( + name, roots, alignment=alignment, joint=mode == "joint", + feature_set=feature_set, feature_profile=feature_profile, + sample_segment=sample_segment, transient_count=transient_count, + ) + for name in scenes + ] + if mode == "joint": + _validate_joint_contract(prepared) + if prepare_only: + return [] + factory = None if fit_purpose == "fixed-topology-refit" else (regressor_factory or _regressor_factory()) + environment = _environment() + groups = [prepared] if mode == "joint" else [[scene] for scene in prepared] + artifacts: list[tuple[str, dict[str, Any]]] = [] + for group in groups: + layout = group[0].action_layout + front_index = layout.index("front") + upper_index = layout.index("upper") + label = "joint" if mode == "joint" else group[0].name + architecture = validate_deployment_architecture(deployment_architecture) + augmentation = validate_fit_augmentation(fit_augmentation) + if architecture == "three_head_independent": + head_specs = ( + ("front", "front_independent", front_index, front_deployment_expression), + ("upper", "upper_independent", upper_index, rear_deployment_expression), + ("lower", "lower_independent", layout.index("lower"), None), + ) + else: + head_specs = ( + ("front", "front_odd", front_index, front_deployment_expression), + ("rear_shared_upper", "rear_shared", upper_index, rear_deployment_expression), + ) + for filename_role, role, channel_index, deployment_expression in head_specs: + if fit_purpose == "fixed-topology-refit": + is_front = filename_role == "front" + topology_expression = front_topology_expression if is_front else rear_topology_expression + parent_path = front_discovery_parent_path if is_front else rear_discovery_parent_path + artifact = refit_fixed_topology_head( + group, role=role, channel_index=channel_index, run_id=run_id, + topology_expression=str(topology_expression), discovery_parent_path=parent_path, + mode=mode, deployment_architecture=architecture, feature_set=feature_set, + feature_profile=feature_profile, environment=environment, + ) + else: + artifact = fit_head( + group, role=role, channel_index=channel_index, run_id=run_id, seed=seed, + niterations=niterations, smoke=smoke, + deployment_expression=deployment_expression, mode=mode, + deployment_architecture=architecture, fit_augmentation=augmentation, + feature_set=feature_set, feature_profile=feature_profile, + regressor_factory=factory, environment=environment, + ) + artifacts.append((f"{label}_{filename_role}.json", artifact)) + output_dir.mkdir(parents=True, exist_ok=False) + paths = [] + for filename, artifact in artifacts: + paths.append(atomic_write_json(output_dir / filename, artifact)) + return paths + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Stage 2 contract-aware PySR fitting") + selection = parser.add_mutually_exclusive_group(required=True) + selection.add_argument("--scene") + selection.add_argument("--scenes", help="comma-separated scenes") + parser.add_argument("--mode", choices=("per-scene", "joint"), default="per-scene") + parser.add_argument("--run-id", default=None, help="isolated results run ID; defaults to a UTC ID") + parser.add_argument( + "--data-root", action="append", default=None, + help="repeatable canonical data root, data/runs/, or scene directory", + ) + parser.add_argument( + "--data-roots", default=None, + help="comma-separated independent trajectory roots", + ) + parser.add_argument("--results-root", default=None, help=argparse.SUPPRESS) + parser.add_argument("--alignment", choices=ALIGNMENTS, default=CAUSAL_ALIGNMENT) + parser.add_argument("--feature-set", choices=tuple(FEATURE_SET_KEYS), default="physics_reduced") + parser.add_argument("--feature-profile", choices=COMPARATIVE_FEATURE_PROFILES, default="default") + parser.add_argument("--sample-segment", choices=("all", "transient", "steady"), default="all") + parser.add_argument("--transient-count", type=int, default=50) + parser.add_argument("--deployment-architecture", choices=DEPLOYMENT_ARCHITECTURES, default="mapped_shared") + parser.add_argument("--fit-augmentation", choices=FIT_AUGMENTATIONS, default="G") + parser.add_argument("--fit-purpose", choices=FIT_PURPOSES, default="discovery") + parser.add_argument("--front-topology-expression") + parser.add_argument("--rear-topology-expression") + parser.add_argument("--front-discovery-parent-path") + parser.add_argument("--rear-discovery-parent-path") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--niterations", type=int, default=40) + parser.add_argument("--smoke", action="store_true") + parser.add_argument("--prepare-only", action="store_true") + parser.add_argument("--front-deployment-expression") + parser.add_argument("--rear-deployment-expression") + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + if args.niterations < 1: + parser.error("--niterations must be positive") + scenes = [args.scene] if args.scene else [item.strip() for item in args.scenes.split(",") if item.strip()] + root_values = list(args.data_root or []) + if args.data_roots: + root_values.extend(item.strip() for item in args.data_roots.split(",") if item.strip()) + data_roots = [resolve_repo_path(value, DEFAULT_DATA_ROOT) for value in root_values] + if not data_roots: + data_roots = [DEFAULT_DATA_ROOT.resolve()] + try: + paths = run_fit( + scenes=scenes, mode=args.mode, run_id=args.run_id or default_run_id(), + data_roots=data_roots, + results_root=resolve_repo_path(args.results_root, DEFAULT_RESULTS_ROOT), + alignment=args.alignment, feature_set=args.feature_set, + feature_profile=args.feature_profile, sample_segment=args.sample_segment, + transient_count=args.transient_count, + deployment_architecture=args.deployment_architecture, + fit_augmentation=args.fit_augmentation, seed=args.seed, niterations=args.niterations, + smoke=args.smoke, prepare_only=args.prepare_only, fit_purpose=args.fit_purpose, + front_topology_expression=args.front_topology_expression, + rear_topology_expression=args.rear_topology_expression, + front_discovery_parent_path=( + resolve_repo_path(args.front_discovery_parent_path, REPO_ROOT) + if args.front_discovery_parent_path else None + ), + rear_discovery_parent_path=( + resolve_repo_path(args.rear_discovery_parent_path, REPO_ROOT) + if args.rear_discovery_parent_path else None + ), + front_deployment_expression=args.front_deployment_expression, + rear_deployment_expression=args.rear_deployment_expression, + ) + except Exception as exc: + print(f"stage_2_fit: error: {exc}", file=sys.stderr) + return 1 + if args.prepare_only: + print(f"Prepared {len(scenes)} scene(s); inputs and output path validated.") + else: + for path in paths: + print(path) + return 0 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/src/SR_analysis/stage_3_validate.py b/src/SR_analysis/stage_3_validate.py index b080e97..214b5b4 100644 --- a/src/SR_analysis/stage_3_validate.py +++ b/src/SR_analysis/stage_3_validate.py @@ -1,404 +1,960 @@ #!/usr/bin/env python3 -"""Stage 3: Unified CFD closed-loop validation for all scenes. +"""Stage 3 legacy closed-loop validation for Karman and Illusion. -Validates PySR/SINDy control laws or PPO baselines in closed-loop CFD. -Supports Karman cloak, Illusion, and Vortex scenes via automatic dispatch. - -Usage: - # Validate a PySR formula - conda run -n pycuda_3_10 python stage_3_validate.py \\ - --scene karman_re100 --device 2 --mode pysr \\ - --formula-front results/formulas/karman_joint_front.json \\ - --formula-top results/formulas/karman_joint_top.json - - # PPO baseline - conda run -n pycuda_3_10 python stage_3_validate.py --scene illusion_1L --device 2 --mode ppo - - # Uncontrolled baseline - conda run -n pycuda_3_10 python stage_3_validate.py --scene karman_re100 --device 2 --mode uncontrolled - - # Batch generalization - conda run -n pycuda_3_10 python stage_3_validate.py \\ - --group illusion_generalization --device 2 --mode pysr \\ - --formula-front results/formulas/illusion_joint_front.json \\ - --formula-top results/formulas/illusion_joint_top.json - -Output: JSON written to results/validations/{scene_name}.json +The module is deliberately CUDA-free at import time. Runtime CFD dependencies +are imported only by the environment factories after planning has completed. """ from __future__ import annotations import argparse import json import os +import platform +import subprocess import sys +import traceback +from dataclasses import dataclass +from datetime import datetime, timezone from pathlib import Path -from collections import deque -from typing import Optional +from typing import Any, Callable, Mapping, Protocol, Sequence import numpy as np -_REPO = Path(__file__).resolve().parents[1] -if str(_REPO) not in sys.path: - sys.path.insert(0, str(_REPO)) -_SRC = _REPO / "src" -if str(_SRC) not in sys.path: - sys.path.insert(0, str(_SRC)) +REPO_ROOT = Path(__file__).resolve().parents[2] +SRC_ROOT = REPO_ROOT / "src" +for import_root in (REPO_ROOT, SRC_ROOT): + if str(import_root) not in sys.path: + sys.path.insert(0, str(import_root)) -from LegacyCelerisLab import FlowField -from SR_analysis.utils.cfd_interface import load_legacy_configs, compute_similarity, scale_action, load_ppo_model -from SR_analysis.configs import get_scene, SCENES, LEGACY_CFG_DIR, FIFO_LEN +from SR_analysis.configs import ( # noqa: E402 + FIFO_LEN, + LEGACY_CFG_DIR, + get_scene, + get_scene_list, + model_path_for_scene, +) +from SR_analysis.stage_1_infer import ( # noqa: E402 + illusion_observation, + load_existing_norm, + target_phase_index, +) +from SR_analysis.utils.data_contracts import CAUSAL_ALIGNMENT # noqa: E402 +from SR_analysis.utils.feature_state import StatefulFeatureBuilder # noqa: E402 +from SR_analysis.utils.formula_schema import evaluate_formula, load_formula # noqa: E402 +from SR_analysis.utils.g_operator import apply_G_alpha, apply_G_raw12, apply_G_target_force # noqa: E402 +from SR_analysis.utils.harmonics import gen_target_states_at # noqa: E402 +from SR_analysis.utils.metrics import LEGACY_DTW_VERSION, legacy_cycle_similarity # noqa: E402 +from SR_analysis.utils.provenance import atomic_write_json, hash_file, hash_json # noqa: E402 DATA_TYPE = np.float32 -NX = 1280 - -from SR_analysis.utils.harmonics import gen_target_states_at - -# Scene groups -VALIDATE_GROUPS = { - "karman_all": lambda: [s for s in SCENES if s.startswith("karman_re") and "re25" not in s and "re70" not in s and "re150" not in s and "re300" not in s], +METRIC_VERSION = LEGACY_DTW_VERSION +VALIDATION_VERSION = "sr-stage3-validation-v2" +TELEMETRY_VERSION = "sr-stage3-telemetry-v2" +ACTION_ORDER = ("front", "upper", "lower") +GROUPS = { + "karman_all": lambda: get_scene_list("karman"), + "karman_trained": lambda: [ + scene for scene in get_scene_list("karman") if get_scene(scene).get("model_name") + ], "illusion_trained": lambda: ["illusion_0.75L", "illusion_1L", "illusion_1.5L"], "illusion_generalization": lambda: ["illusion_0.5L", "illusion_0.6L", "illusion_0.8L", "illusion_1.2L", "illusion_2L"], - "vortex_all": lambda: ["vortex_lamb", "vortex_taylor"], - "all": lambda: list(SCENES.keys()), + "vortex_all": lambda: get_scene_list("vortex"), + "all": lambda: get_scene_list(), } -OUT_DIR = "src/SR_analysis/results/validations" + +def _jsonable(value: Any) -> Any: + if isinstance(value, np.ndarray): + return value.tolist() + if isinstance(value, np.generic): + return value.item() + if isinstance(value, Path): + return str(value) + if isinstance(value, Mapping): + return {str(key): _jsonable(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [_jsonable(item) for item in value] + return value -def auto_steps(sample_interval: int) -> int: - return max(160, int(2 * NX / 0.01 / sample_interval)) +def _git_sha() -> str | None: + try: + return subprocess.run( + ["git", "-C", str(REPO_ROOT), "rev-parse", "HEAD"], + check=True, + capture_output=True, + text=True, + ).stdout.strip() + except (OSError, subprocess.CalledProcessError): + return None -def _build_feature_vector(obs_slice, a_prev, a_prev2, mu, u0, add_bias, feat_keys, tf=None): - from SR_analysis.utils.feature_builder import compute_dimensionless, compute_features, build_feature_matrix - s = obs_slice[0:6].astype(np.float64).reshape(1, 6) - f = obs_slice[6:12].astype(np.float64).reshape(1, 6) - ap = a_prev.astype(np.float64).reshape(1, 3) - ap2 = a_prev2.astype(np.float64).reshape(1, 3) - dim = compute_dimensionless(s, f, u0=u0, d=20.0) - has_mu = feat_keys and "mu" in feat_keys - sym = compute_features(dim, ap, ap2, mu, alpha_mode=False, include_mu=has_mu, - include_cos_sin=False, u0=u0, - target_forces=tf.reshape(1, 2) if tf is not None else None, - sensors_raw=s, forces_raw=f) - if feat_keys is None: - from SR_analysis.utils.feature_builder import CORE_FEAT_KEYS_V2 - feat_keys = CORE_FEAT_KEYS_V2 - return build_feature_matrix(sym, feat_keys, add_bias=add_bias)[0] +def default_run_id() -> str: + return datetime.now(timezone.utc).strftime("validate-%Y%m%dT%H%M%SZ") -def validate_karman(scene_name, device_id, n_steps, mode, formula_front, formula_top) -> dict: - """Validate Karman cloak (from run_closed_loop.py).""" - from SR_analysis.utils.cfd_interface import build_karman_cloak_env, add_pinball, build_observation, action_to_physical, scale_action as _sa - from SR_analysis.utils.g_operator import apply_G_raw - - cfg = get_scene(scene_name) - u0, nu, l0 = cfg["u0"], cfg["nu"], 20.0 - si, action_scale, action_bias = cfg["sample_interval"], cfg["action_scale"], np.array(cfg["action_bias"]) - n_obj_total = cfg["n_objects_env"] - mu = cfg["mu"] - conv_len = cfg.get("conv_len", 30) - if n_steps <= 0: - n_steps = auto_steps(si) - - cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR) - field_cfg = field_cfg._replace(viscosity=float(nu)) - - ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) - target_states, _ = build_karman_cloak_env(ff, u0=u0, l0=l0, sample_interval=si, fifo_len=FIFO_LEN, data_type=DATA_TYPE) - norm = add_pinball(ff, l0=l0, u0=u0, sample_interval=si, fifo_len=FIFO_LEN, data_type=DATA_TYPE, - action_bias=action_bias, pinball_front_x=cfg["pinball_front_x"], pinball_rear_x=cfg["pinball_rear_x"], - obs_slice_start=2, obs_slice_end=14) - - # Restore + bias FIFO - ff.restore_ddf(); ff.apply_ddf() - fifo = deque(maxlen=FIFO_LEN) - ba = _sa(np.zeros(3, dtype=np.float32), scale=action_scale, bias=action_bias, u0=u0, n_total_bodies=n_obj_total) - for _ in range(FIFO_LEN): - ff.context.push(); ff.run(si, ba); ff.context.pop() - fifo.append(ff.obs.copy()[2:14]) - - # Prepare formula coefficients - if mode == "pysr": - fj = json.load(open(formula_front)) - tj = json.load(open(formula_top)) - front_keys = fj["feature_names"] - top_keys_raw = tj["feature_names"] - # Strip "bias" — build_feature_matrix with add_bias=True adds it back - top_keys = [k for k in top_keys_raw if k != "bias"] - front_coef = np.array(fj.get("coef", [0]*len(front_keys))) if "coef" in fj else None - top_coef = np.array(tj.get("coef", [0]*len(top_keys_raw))) if "coef" in tj else None - if front_coef is None or len(front_coef) != len(front_keys): - # Use sympy.lambdify as fallback - import sympy - _fs = sympy.symbols(front_keys) - _fe = sympy.sympify(fj["best_sympy"]) - _ffn = sympy.lambdify(_fs, _fe, "numpy") - _ts = sympy.symbols(top_keys_raw) - _te = sympy.sympify(tj["best_sympy"]) - _tfn = sympy.lambdify(_ts, _te, "numpy") - front_coef = top_coef = None - _use_sympy = True - else: - _use_sympy = False - - elif mode == "ppo": - model = load_ppo_model(f"models/old/{cfg['model_name']}.zip", device=f"cuda:{device_id}", s_dim=cfg.get("s_dim", 12)) - - # Run - sens_list, act_list = [], [] - obs = np.zeros(12, dtype=np.float32) - a_prev = np.array([0.0, -4*u0, 4*u0]) - a_prev2 = np.array([0.0, -4*u0, 4*u0]) - - for step in range(n_steps): - if mode == "pysr": - osl = ff.obs.copy()[2:14] - if _use_sympy: - fv_f = _build_feature_vector(osl, a_prev, a_prev2, mu, u0, add_bias=False, feat_keys=front_keys) - front = float(_ffn(*fv_f)) - fv_t = _build_feature_vector(osl, a_prev, a_prev2, mu, u0, add_bias=True, feat_keys=top_keys) - top = float(_tfn(*fv_t)) - else: - fv_f = _build_feature_vector(osl, a_prev, a_prev2, mu, u0, add_bias=False, feat_keys=front_keys) - front = float(np.dot(fv_f, front_coef)) - fv_t = _build_feature_vector(osl, a_prev, a_prev2, mu, u0, add_bias=True, feat_keys=top_keys) - top = float(np.dot(fv_t, top_coef)) - G_obs, G_ap, G_ap2 = apply_G_raw(osl, a_prev, a_prev2) - if _use_sympy: - fv_b = _build_feature_vector(G_obs, G_ap, G_ap2, mu, u0, add_bias=True, feat_keys=top_keys) - bottom = -float(_tfn(*fv_b)) - else: - fv_b = _build_feature_vector(G_obs, G_ap, G_ap2, mu, u0, add_bias=True, feat_keys=top_keys) - bottom = -float(np.dot(fv_b, top_coef)) - alpha_sr = np.array([front, bottom, top]) - omega = alpha_sr * u0 # PySR alpha is already non-dim, ω = α·U₀ - elif mode == "ppo": - osl = ff.obs.copy()[2:14] - obs_n = np.clip(np.hstack([osl[6:12]/norm["force_norm_fact"], (osl[0:6]-norm["sens_deviation"])/norm["sens_norm_fact"]]), -1, 1).astype(np.float32) - act, _ = model.predict(obs_n, deterministic=True) - act = act.astype(np.float32).flatten() - omega = (act * action_scale + action_bias) * u0 - else: - omega = np.zeros(3) - - act_list.append(omega.copy()) - omega_arr = np.zeros(n_obj_total, dtype=DATA_TYPE) - omega_arr[n_obj_total-3:] = omega - ff.context.push(); ff.run(si, omega_arr); ff.context.pop() - osl = ff.obs.copy()[2:14] - a_prev2 = a_prev.copy(); a_prev = omega.copy() - fifo.append(osl.copy()); sens_list.append(osl[0:6]) - - sim = compute_similarity(target_states, np.array(sens_list, dtype=np.float32), conv_len) - result = {"scene": scene_name, "mode": mode, "similarity": sim, "n_steps": n_steps} - del ff +def _gpu_provenance(requested_logical_device: int | None) -> dict[str, Any]: + visible = os.environ.get("CUDA_VISIBLE_DEVICES") + result: dict[str, Any] = { + "requested_logical_device": requested_logical_device, + "CUDA_VISIBLE_DEVICES": visible, + "uuid": None, + "name": None, + } + if requested_logical_device is None: + return result + selector = str(requested_logical_device) + if visible is not None: + entries = [entry.strip() for entry in visible.split(",") if entry.strip()] + if requested_logical_device >= len(entries): + return result + selector = entries[requested_logical_device] + try: + output = subprocess.run( + [ + "nvidia-smi", f"--id={selector}", "--query-gpu=uuid,name", + "--format=csv,noheader,nounits", + ], + check=True, + capture_output=True, + text=True, + timeout=5, + ).stdout.strip().splitlines() + if output: + uuid, name = (part.strip() for part in output[0].split(",", 1)) + result.update({"uuid": uuid or None, "name": name or None}) + except (OSError, subprocess.CalledProcessError, subprocess.TimeoutExpired, ValueError): + pass return result -def validate_illusion(scene_name, device_id, n_steps, mode, formula_front, formula_top) -> dict: - """Validate Illusion (from run_closed_loop_illusion.py).""" - from SR_analysis.utils.cfd_interface import scale_action as _sa - from SR_analysis.utils.g_operator import apply_G_raw +def resolve_output_root(value: str | os.PathLike[str] | None) -> Path: + if value is None: + return REPO_ROOT / "src" / "SR_analysis" / "results" / "runs" + path = Path(value).expanduser() + return path.resolve() if path.is_absolute() else (REPO_ROOT / path).resolve() - cfg = get_scene(scene_name) - u0, nu, mu = cfg["u0"], cfg["nu"], cfg["mu"] - si, action_scale, action_bias = cfg["sample_interval"], cfg["action_scale"], np.array(cfg["action_bias"]) - n_obj_total = cfg["n_objects_env"] - conv_len = cfg.get("conv_len", 36) - if n_steps <= 0: - n_steps = auto_steps(si) + +def validate_run_id(run_id: str) -> str: + if not run_id or run_id in {".", ".."} or Path(run_id).name != run_id: + raise ValueError("--run-id must be one non-empty path component") + return run_id + + +def _load_json(path: Path) -> Any: + if not path.is_file(): + raise FileNotFoundError(f"Required frozen artifact not found: {path}") + return json.loads(path.read_text(encoding="utf-8")) + + +def _hash_optional(path: Path | None) -> str | None: + return hash_file(path) if path is not None and path.is_file() else None + + +@dataclass(frozen=True) +class FormulaPair: + front: dict[str, Any] + rear: dict[str, Any] + front_path: Path + rear_path: Path + pair_hash: str + compatibility: dict[str, Any] + lower: dict[str, Any] | None = None + lower_path: Path | None = None + deployment_semantics: str = "mapped_shared" + + +def _validate_formula_role(formula: Mapping[str, Any], expected: str) -> dict[str, Any]: + legacy = bool(formula.get("legacy")) + role = formula.get("role") + anchor = formula.get("anchor") + parity = formula.get("parity", formula.get("g_parity")) + channel = formula.get("channel") + notes: dict[str, Any] = {"legacy": legacy, "channel": channel} + if expected == "front": + if legacy and channel == "front": + notes["legacy_role_mapping"] = "channel=front -> role=front_odd" + elif role == "front_odd": + notes["canonical_role"] = "front_odd" + elif role == "front" and parity == "odd": + notes["compatibility_role_mapping"] = "role=front, parity=odd -> front_odd" + else: + raise ValueError("front formula requires canonical role='front_odd'") + else: + if legacy and channel == "top": + notes["legacy_role_mapping"] = "channel=top -> role=rear_shared, anchor=upper" + elif role != "rear_shared" or anchor != "upper": + raise ValueError("rear formula requires role='rear_shared' and anchor='upper'") + return notes + + +def load_formula_pair( + front_path: str | os.PathLike[str], + rear_path: str | os.PathLike[str], + lower_path: str | os.PathLike[str] | None = None, +) -> FormulaPair: + front_file = Path(front_path).expanduser().resolve() + rear_file = Path(rear_path).expanduser().resolve() + lower_file = None if lower_path is None else Path(lower_path).expanduser().resolve() + front = load_formula(front_file) + rear = load_formula(rear_file) + lower = None if lower_file is None else load_formula(lower_file) + semantics = "three_head_independent" if lower is not None else str( + front.get("g_metadata", {}).get("deployment_semantics", "mapped_shared") + ) + compatibility = { + "deployment_semantics": semantics, + "policy_mapping": ( + "exact_front_odd_projection_and_rear_g_mapping" + if lower is None + else "direct_three_head_evaluation" + ), + } + if lower is None: + compatibility.update({ + "front": _validate_formula_role(front, "front"), + "rear": _validate_formula_role(rear, "rear"), + }) + if bool(front.get("legacy")) or bool(rear.get("legacy")): + compatibility["historical_formula_compatibility"] = ( + "legacy formula schema loaded; mapped_shared deployment still applies " + "the exact current symmetry projection" + ) + else: + expected_roles = ("front_independent", "upper_independent", "lower_independent") + for name, formula, expected_role in zip(("front", "upper", "lower"), (front, rear, lower), expected_roles): + if formula.get("role") != expected_role: + raise ValueError(f"{name} formula requires role={expected_role!r}") + compatibility[name] = {"canonical_role": expected_role} + for formula in (front, rear) if lower is None else (front, rear, lower): + probe = {name: np.array([0.0]) for name in formula["feature_names"]} + value = evaluate_formula(formula, probe, expression_kind="deployment") + if not np.all(np.isfinite(value)): + raise ValueError("formula deployment expression is non-finite at the zero probe") + hashes = {"front": front["source_hash"], "rear": rear["source_hash"]} + if lower is not None: + hashes["lower"] = lower["source_hash"] + return FormulaPair( + front, rear, front_file, rear_file, hash_json(hashes), compatibility, + lower=lower, lower_path=lower_file, deployment_semantics=semantics, + ) + + +@dataclass(frozen=True) +class ValidationPlan: + scene: str + cfg: dict[str, Any] + mode: str + n_steps: int + run_id: str + validation_dir: Path + telemetry_dir: Path + stem: str + validation_path: Path + telemetry_path: Path + formula_pair: FormulaPair | None + model_path: Path | None + norm: dict[str, Any] | None + norm_path: Path | None + harmonics: Any | None + harmonics_path: Path | None + + +def _artifact_stem(scene: str, mode: str, formula_hash: str, sample_interval: int) -> str: + safe_scene = scene.replace(os.sep, "_").replace(".", "p") + return f"{safe_scene}__{mode}__f-{formula_hash[:12]}__si-{sample_interval}" + + +def prepare_plan( + *, + scene: str, + mode: str, + n_steps: int, + run_id: str, + output_root: Path, + formula_pair: FormulaPair | None, +) -> ValidationPlan: + cfg = get_scene(scene) + scene_id = cfg["scene_id"] + if scene_id == "vortex": + raise NotImplementedError("Vortex is not supported in round1; it is not a Karman substitute") + if scene_id not in {"karman", "illusion", "steady"}: + raise NotImplementedError(f"scene_id={scene_id!r} is not supported") + if tuple(cfg.get("action_layout", ())) != ACTION_ORDER: + raise ValueError(f"scene action_layout must be native {ACTION_ORDER}") + if mode == "pysr" and formula_pair is None: + raise ValueError("pysr mode requires both front and rear formulas") + + model_path = None + norm = None + norm_path = None + if mode == "ppo": + resolved = model_path_for_scene(scene) + if resolved is None: + raise FileNotFoundError(f"No PPO model found for scene {scene}") + model_path = Path(resolved).resolve() + norm, norm_path = load_existing_norm(scene, cfg) + elif scene_id == "illusion": + candidate_norm = REPO_ROOT / "src" / "SR_analysis" / "data" / "illusion" / scene / "norm.json" + if candidate_norm.is_file(): + norm, norm_path = load_existing_norm(scene, cfg) + elif cfg.get("source") != "generalization": + raise FileNotFoundError(f"Frozen normalization not found: {candidate_norm}") + + harmonics = None + harmonics_path = None + if scene_id == "illusion": + harmonics_path = REPO_ROOT / "src" / "SR_analysis" / "data" / "illusion" / scene / "target_harmonics.json" + harmonics = _load_json(harmonics_path) + + formula_hash = formula_pair.pair_hash if formula_pair is not None else ("ppo" if mode == "ppo" else "none") + validation_dir = output_root / validate_run_id(run_id) / "validations" + telemetry_dir = output_root / validate_run_id(run_id) / "telemetry" + stem = _artifact_stem(scene, mode, formula_hash, int(cfg["sample_interval"])) + validation_path = validation_dir / f"{stem}.json" + telemetry_path = telemetry_dir / f"{stem}.npz" + for path in (validation_path, telemetry_path): + if path.exists(): + raise FileExistsError(f"Refusing to overwrite existing artifact: {path}") + return ValidationPlan( + scene, cfg, mode, n_steps, run_id, validation_dir, telemetry_dir, stem, + validation_path, telemetry_path, formula_pair, model_path, norm, norm_path, + harmonics, harmonics_path, + ) + + +class RolloutEnvironment(Protocol): + current_raw: np.ndarray + target_sensors: np.ndarray + + def step(self, omega_native: np.ndarray) -> np.ndarray: ... + def close(self) -> None: ... + + +class Policy(Protocol): + def action(self, raw: np.ndarray, step: int) -> tuple[np.ndarray, np.ndarray | None, int | None]: ... + def observe(self, omega_native: np.ndarray) -> None: ... + + +class UncontrolledPolicy: + def action(self, raw: np.ndarray, step: int) -> tuple[np.ndarray, None, None]: + return np.zeros(3, dtype=np.float64), None, None + + def observe(self, omega_native: np.ndarray) -> None: + return None + + +class ConstantRotationPolicy: + def __init__(self, alpha_native: Sequence[float], u0: float): + self.omega_native = np.asarray(alpha_native, dtype=np.float64).reshape(3) * float(u0) + + def action(self, raw: np.ndarray, step: int) -> tuple[np.ndarray, None, None]: + return self.omega_native.copy(), None, None + + def observe(self, omega_native: np.ndarray) -> None: + return None + + +class PPOPolicy: + def __init__(self, model: Any, cfg: Mapping[str, Any], norm: Mapping[str, Any], harmonics: Any | None): + self.model = model + self.cfg = cfg + self.norm = norm + self.harmonics = harmonics + + def action(self, raw: np.ndarray, step: int) -> tuple[np.ndarray, np.ndarray | None, int | None]: + target_force = None + phase = None + if self.cfg["scene_id"] == "illusion": + phase = target_phase_index(step) + target_force = gen_target_states_at(phase, self.harmonics)[:2] + observation = illusion_observation(raw, self.norm, target_force) + else: + forces = raw[6:12] / float(self.norm["force_norm_fact"]) + sensors = (raw[:6] - np.asarray(self.norm["sens_deviation"])) / np.asarray(self.norm["sens_norm_fact"]) + observation = np.clip(np.hstack((forces, sensors)), -1, 1).astype(np.float32) + normalized, _ = self.model.predict(observation, deterministic=True) + normalized = np.asarray(normalized, dtype=np.float64).reshape(3) + alpha = normalized * float(self.cfg["action_scale"]) + np.asarray(self.cfg["action_bias"], dtype=np.float64) + return alpha * float(self.cfg["u0"]), target_force, phase + + def observe(self, omega_native: np.ndarray) -> None: + return None + + +class SymbolicPolicy: + def __init__(self, pair: FormulaPair, cfg: Mapping[str, Any], harmonics: Any | None): + self.pair = pair + self.cfg = cfg + self.harmonics = harmonics + self.dt_c = float(cfg["sample_interval"]) * float(cfg["u0"]) / 20.0 + common = dict( + mu=float(cfg["mu"]), dt_c=self.dt_c, u0=float(cfg["u0"]), d=20.0, + action_mode="physical", include_mu=True, include_cos_sin=True, + require_target=cfg["scene_id"] == "illusion", + ) + self.builder = StatefulFeatureBuilder(**common) + self.g_builder = StatefulFeatureBuilder(**common) + initial_alpha = np.asarray(cfg["policy_init_action"], dtype=np.float64) + initial_omega = initial_alpha * float(cfg["u0"]) + # The first evaluated state is the current post-bias FIFO observation; + # seed both action lags with the command that produced that FIFO. + for _ in range(2): + self.builder.observe_action(initial_omega) + self.g_builder.observe_action(apply_G_alpha(initial_omega)) + + @staticmethod + def _evaluate(formula: Mapping[str, Any], features: Mapping[str, Any]) -> float: + value = np.asarray(evaluate_formula(formula, features, expression_kind="deployment"), dtype=float) + if value.size != 1: + raise ValueError("controller formula must produce one scalar") + scalar = float(value.reshape(-1)[0]) + if not np.isfinite(scalar): + raise FloatingPointError("controller formula produced a non-finite action") + return scalar + + def action(self, raw: np.ndarray, step: int) -> tuple[np.ndarray, np.ndarray | None, int | None]: + phase = target_phase_index(step) if self.harmonics is not None else None + target_force = None if phase is None else gen_target_states_at(phase, self.harmonics)[:2] + features, _ = self.builder.step(raw[:6], raw[6:12], target_force=target_force) + g_raw = apply_G_raw12(raw) + g_target = None if target_force is None else apply_G_target_force(target_force) + g_features, _ = self.g_builder.step(g_raw[:6], g_raw[6:12], target_force=g_target) + if self.pair.deployment_semantics == "three_head_independent" and self.pair.lower is not None: + front = self._evaluate(self.pair.front, features) + upper = self._evaluate(self.pair.rear, features) + lower = self._evaluate(self.pair.lower, features) + else: + front = 0.5 * ( + self._evaluate(self.pair.front, features) + - self._evaluate(self.pair.front, g_features) + ) + upper = self._evaluate(self.pair.rear, features) + lower = -self._evaluate(self.pair.rear, g_features) + alpha_native = np.asarray((front, upper, lower), dtype=np.float64) + return alpha_native * float(self.cfg["u0"]), target_force, phase + + def observe(self, omega_native: np.ndarray) -> None: + self.builder.observe_action(omega_native) + self.g_builder.observe_action(apply_G_alpha(omega_native)) + + +def build_policy(plan: ValidationPlan, *, model_loader: Callable[..., Any] | None = None) -> Policy: + if plan.mode == "uncontrolled": + return UncontrolledPolicy() + if plan.mode == "constant": + return ConstantRotationPolicy(plan.cfg["constant_alpha"], plan.cfg["u0"]) + if plan.mode == "pysr": + assert plan.formula_pair is not None + return SymbolicPolicy(plan.formula_pair, plan.cfg, plan.harmonics) + if model_loader is None: + from SR_analysis.utils.cfd_interface import load_ppo_model + model_loader = load_ppo_model + assert plan.model_path is not None and plan.norm is not None + model = model_loader( + str(plan.model_path), + device=str(plan.cfg.get("model_device", "cpu")), + s_dim=int(plan.cfg["s_dim"]), + ) + return PPOPolicy(model, plan.cfg, plan.norm, plan.harmonics) + + +class _LegacyEnvironment: + def __init__(self, ff: Any, cfg: Mapping[str, Any], current_raw: np.ndarray, target_sensors: np.ndarray): + self.ff = ff + self.cfg = cfg + self.current_raw = np.asarray(current_raw, dtype=np.float64) + self.target_sensors = np.asarray(target_sensors, dtype=np.float64) + + def step(self, omega_native: np.ndarray) -> np.ndarray: + command = np.zeros(int(self.cfg["n_objects_env"]), dtype=DATA_TYPE) + command[-3:] = np.asarray(omega_native, dtype=DATA_TYPE) + self.ff.context.push() + self.ff.run(int(self.cfg["sample_interval"]), command) + self.ff.context.pop() + self.current_raw = self.ff.obs.copy()[slice(*self.cfg["obs_slice"])].astype(np.float64) + return self.current_raw.copy() + + def close(self) -> None: + del self.ff + + +def _runtime_cfd() -> tuple[Any, Any, Any, Any]: + from LegacyCelerisLab import FlowField + from SR_analysis.utils.cfd_interface import add_pinball, build_karman_cloak_env, load_legacy_configs + return FlowField, add_pinball, build_karman_cloak_env, load_legacy_configs + + +def build_karman_environment(plan: ValidationPlan, device_id: int) -> RolloutEnvironment: + FlowField, add_pinball, build_karman, load_configs = _runtime_cfd() + cfg = plan.cfg + cuda_cfg, field_cfg = load_configs(LEGACY_CFG_DIR) + ff = FlowField(field_cfg._replace(viscosity=float(cfg["nu"])), cuda_cfg, device_id=device_id) + target, _ = build_karman(ff, u0=cfg["u0"], l0=20.0, sample_interval=cfg["sample_interval"], fifo_len=FIFO_LEN, data_type=DATA_TYPE) + norm = add_pinball( + ff, l0=20.0, u0=cfg["u0"], sample_interval=cfg["sample_interval"], fifo_len=FIFO_LEN, + data_type=DATA_TYPE, action_bias=cfg["action_bias"], pinball_front_x=cfg["pinball_front_x"], + pinball_rear_x=cfg["pinball_rear_x"], obs_slice_start=cfg["obs_slice"][0], obs_slice_end=cfg["obs_slice"][1], + ) + current = np.asarray(norm["save_states"])[-1] + return _LegacyEnvironment(ff, cfg, current, target) + + +def build_illusion_environment(plan: ValidationPlan, device_id: int) -> RolloutEnvironment: + FlowField, _, _, load_configs = _runtime_cfd() + cfg = plan.cfg + cuda_cfg, field_cfg = load_configs(LEGACY_CFG_DIR) + field_cfg = field_cfg._replace(viscosity=float(cfg["nu"])) l0 = 20.0 - cuda_cfg, field_cfg = load_legacy_configs(LEGACY_CFG_DIR) - field_cfg = field_cfg._replace(viscosity=float(nu)) + # Record the canonical target sensor orbit with the exact Stage 1 phase + # construction. Frozen harmonics remain the sole force/phase source; they + # are never recomputed from this validation rollout. + target_ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) + target_cy = (target_ff.FIELD_SHAPE[1] - 1) / 2 + target_ff.add_cylinder((20.0 * l0, target_cy, 0.0), float(cfg["target_diameter"]) * l0) + for y in (target_cy + 2 * l0, target_cy, target_cy - 2 * l0): + target_ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + target_count = target_ff.obs.size // 2 + target_ff.run(int(4 * target_ff.FIELD_SHAPE[0] / float(cfg["u0"])), np.zeros(target_count, dtype=DATA_TYPE)) + target_rows = [] + for _ in range(FIFO_LEN): + target_ff.run(int(cfg["sample_interval"]), np.zeros(target_count, dtype=DATA_TYPE)) + target_rows.append(target_ff.obs.copy()[2:8]) + target_sensors = np.asarray(target_rows, dtype=np.float64) + del target_ff - # Load target - import glob - data_dir = f"src/SR_analysis/data/illusion/{scene_name}" - target = np.load(os.path.join(data_dir, "target.npz"))["target_states"] - harmonics = json.load(open(os.path.join(data_dir, "target_harmonics.json"))) - - # Build pinball env ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) cy = (ff.FIELD_SHAPE[1] - 1) / 2 - nx = ff.FIELD_SHAPE[0] - sensor_x = cfg["sensor_x"] * l0 - for yo in [cy+2*l0, cy, cy-2*l0]: - ff.add_sensor((sensor_x, yo, 0.0), 5.0) - pf_x, pr_x = cfg["pinball_front_x"]*l0, cfg["pinball_rear_x"]*l0 - py_span = cfg.get("pinball_y_span", 0.75*l0) - ff.add_cylinder((pf_x, cy, 0.0), 10.0) - ff.add_cylinder((pr_x, cy+py_span, 0.0), 10.0) - ff.add_cylinder((pr_x, cy-py_span, 0.0), 10.0) + for y in (cy + 2 * l0, cy, cy - 2 * l0): + ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + ff.add_cylinder((float(cfg["pinball_front_x"]) * l0, cy, 0.0), 10.0) + span = float(cfg.get("pinball_y_span", 0.75 * l0)) + ff.add_cylinder((float(cfg["pinball_rear_x"]) * l0, cy + span, 0.0), 10.0) + ff.add_cylinder((float(cfg["pinball_rear_x"]) * l0, cy - span, 0.0), 10.0) n_obj = ff.obs.size // 2 - warmup = int(4*nx/u0) - ff.run(warmup, np.zeros(n_obj, dtype=DATA_TYPE)) - ff.get_ddf(); ff.save_ddf() - - # Norm — collect zero-action FIFO and compute normalisation - fifo_norm = deque(maxlen=FIFO_LEN) + ff.run(int(4 * ff.FIELD_SHAPE[0] / float(cfg["u0"])), np.zeros(n_obj, dtype=DATA_TYPE)) + ff.get_ddf(); ff.save_ddf(); ff.apply_ddf() + command = np.zeros(n_obj, dtype=DATA_TYPE) + command[-3:] = np.asarray(cfg["policy_init_action"], dtype=DATA_TYPE) * float(cfg["u0"]) + current = None for _ in range(FIFO_LEN): - ff.run(si, np.zeros(n_obj, dtype=DATA_TYPE)) - fifo_norm.append(ff.obs.copy()[0:12]) - tsa = np.array(fifo_norm, dtype=np.float32) - force_norm_fact = 6.0 * float(np.max(np.abs(tsa[:, 6:12]))) - sens_deviation = np.mean(tsa[:, 0:6], axis=0).astype(np.float32) - sens_norm_fact = np.zeros(6, dtype=np.float32) - for i in range(6): - sens_norm_fact[i] = 5.0 * float(np.max(np.abs(tsa[:, i] - sens_deviation[i]))) + ff.run(int(cfg["sample_interval"]), command) + current = ff.obs.copy()[slice(*cfg["obs_slice"])] + assert current is not None + return _LegacyEnvironment(ff, cfg, current, target_sensors) - # Bias FIFO - ff.apply_ddf() - fifo = deque(maxlen=FIFO_LEN) - fifo_bias = np.array([0.0, -u0, u0]) - ba = np.zeros(n_obj, dtype=DATA_TYPE); ba[-3:] = fifo_bias + +def build_steady_environment(plan: ValidationPlan, device_id: int) -> RolloutEnvironment: + FlowField, add_pinball, _, load_configs = _runtime_cfd() + cfg = plan.cfg + cuda_cfg, field_cfg = load_configs(LEGACY_CFG_DIR) + field_cfg = field_cfg._replace(viscosity=float(cfg["nu"])) + l0 = 20.0 + + target_ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) + target_cy = (target_ff.FIELD_SHAPE[1] - 1) / 2 + for y in (target_cy + 2 * l0, target_cy, target_cy - 2 * l0): + target_ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + target_count = target_ff.obs.size // 2 + target_ff.run(int(4 * target_ff.FIELD_SHAPE[0] / float(cfg["u0"])), np.zeros(target_count, dtype=DATA_TYPE)) + target_rows = [] for _ in range(FIFO_LEN): - ff.run(si, ba) - fifo.append(ff.obs.copy()[0:12]) + target_ff.run(int(cfg["sample_interval"]), np.zeros(target_count, dtype=DATA_TYPE)) + target_rows.append(target_ff.obs.copy()[:6]) + target_sensors = np.asarray(target_rows, dtype=np.float64) + del target_ff - # Precompile formula evaluators for pysr mode - if mode == "pysr": - import sympy - fj = json.load(open(formula_front)) - tj = json.load(open(formula_top)) - # Front: no-bias, uses feature_names as-is - _front_keys = [k for k in fj["feature_names"] if k != "bias"] - _x_sym = sympy.symbols(_front_keys) - _front_expr = sympy.sympify(fj["best_sympy"]) - _front_fn = sympy.lambdify(_x_sym, _front_expr, "numpy") - # Top: formula was fitted WITH bias in feature_names, - # so sympy must include "bias" as a symbol. When building features, - # we pass feat_keys without "bias" + add_bias=True → output matches. - _top_keys_full = tj["feature_names"] # e.g. ["bias", "Cd_err", ...] - _top_keys_no_bias = [k for k in _top_keys_full if k != "bias"] - _x_t = sympy.symbols(_top_keys_full) - _top_expr = sympy.sympify(tj["best_sympy"]) - _top_fn = sympy.lambdify(_x_t, _top_expr, "numpy") + ff = FlowField(field_cfg, cuda_cfg, device_id=device_id) + cy = (ff.FIELD_SHAPE[1] - 1) / 2 + for y in (cy + 2 * l0, cy, cy - 2 * l0): + ff.add_sensor((float(cfg["sensor_x"]) * l0, y, 0.0), 5.0) + initial_alpha = np.asarray(cfg.get("constant_alpha", cfg["policy_init_action"]), dtype=np.float64) + norm = add_pinball( + ff, l0=l0, u0=cfg["u0"], sample_interval=cfg["sample_interval"], fifo_len=FIFO_LEN, + data_type=DATA_TYPE, action_bias=tuple(initial_alpha), pinball_front_x=cfg["pinball_front_x"], + pinball_rear_x=cfg["pinball_rear_x"], obs_slice_start=cfg["obs_slice"][0], + obs_slice_end=cfg["obs_slice"][1], n_objects_total=cfg["n_objects_env"], + ) + current = np.asarray(norm["save_states"])[-1] + return _LegacyEnvironment(ff, cfg, current, target_sensors) - import warnings - warnings.filterwarnings("ignore", category=RuntimeWarning) - else: - _front_fn = _top_fn = None - _front_keys = _top_keys_no_bias = _top_keys_full = [] - # Run - sens_list, act_list = [], [] - a_prev = a_prev2 = fifo_bias.copy() +def build_environment(plan: ValidationPlan, device_id: int) -> RolloutEnvironment: + if plan.cfg["scene_id"] == "karman": + return build_karman_environment(plan, device_id) + if plan.cfg["scene_id"] == "steady": + return build_steady_environment(plan, device_id) + return build_illusion_environment(plan, device_id) - for step in range(n_steps): - if mode == "pysr": - osl = ff.obs.copy()[0:12] - tf_step = gen_target_states_at(step, harmonics) - target_f = np.array([tf_step[0], tf_step[1]]) - # Front: no-bias, evaluate feature vector + formula - fv_f = _build_feature_vector(osl, a_prev, a_prev2, mu, u0, add_bias=False, - feat_keys=_front_keys, tf=target_f) - alpha_f = float(_front_fn(*fv_f)) +def _finite(name: str, values: np.ndarray) -> None: + if not np.all(np.isfinite(values)): + raise FloatingPointError(f"{name} contains non-finite values") - # Top: with-bias, shared-head. Formula sympy includes "bias" symbol, - # feature builder adds bias column via add_bias=True. - fv_t = _build_feature_vector(osl, a_prev, a_prev2, mu, u0, add_bias=True, - feat_keys=_top_keys_no_bias, tf=target_f) - alpha_t = float(_top_fn(*fv_t)) - # Bottom via G-mirror - G_obs, G_ap, G_ap2 = apply_G_raw(osl, a_prev, a_prev2) - G_tf = np.array([target_f[0], -target_f[1]]) - fv_b = _build_feature_vector(G_obs, G_ap, G_ap2, mu, u0, add_bias=True, - feat_keys=_top_keys_no_bias, tf=G_tf) - alpha_b = -float(_top_fn(*fv_b)) +class RolloutFailure(RuntimeError): + def __init__(self, message: str, *, step: int, telemetry: Mapping[str, np.ndarray]): + super().__init__(message) + self.step = step + self.telemetry = telemetry - alpha = np.array([alpha_f, alpha_b, alpha_t]) - omega = alpha * u0 # PySR output is already non-dim alpha (ω/U₀ factored out) - elif mode == "ppo": - model_path = f"models/250525/{cfg['model_name']}.zip" - model = load_ppo_model(model_path, device=f"cuda:{device_id}", s_dim=14) - osl = ff.obs.copy()[0:12] - tf_step = gen_target_states_at(step, harmonics) - target_cd_n = tf_step[0] / force_norm_fact - target_cl_n = tf_step[1] / force_norm_fact - # Build observation same way as infer_illusion.py - forces_n = osl[6:12] / force_norm_fact - sens_n = (osl[0:6] - sens_deviation) / sens_norm_fact - obs = np.clip(np.hstack([forces_n, sens_n, target_cd_n, target_cl_n]), -1, 1).astype(np.float32) - act, _ = model.predict(obs, deterministic=True) - act = act.astype(np.float32).flatten() - omega = (act * action_scale + action_bias) * u0 - else: - omega = np.zeros(3) - act_list.append(omega.copy()) - omega_arr = np.zeros(n_obj, dtype=DATA_TYPE); omega_arr[-3:] = omega - ff.context.push(); ff.run(si, omega_arr); ff.context.pop() - a_prev2 = a_prev.copy(); a_prev = omega.copy() - osl = ff.obs.copy()[0:12] - fifo.append(osl.copy()); sens_list.append(osl[0:6]) +def _telemetry_arrays( + environment: RolloutEnvironment, + sensors: list[np.ndarray], + forces: list[np.ndarray], + actions_omega: list[np.ndarray], + actions_alpha: list[np.ndarray], + target_forces: list[np.ndarray], + target_phases: list[int], + termination_reason: str, +) -> dict[str, np.ndarray]: + return { + "sensors": np.asarray(sensors, dtype=np.float64).reshape(-1, 6), + "forces": np.asarray(forces, dtype=np.float64).reshape(-1, 6), + "actions_omega": np.asarray(actions_omega, dtype=np.float64).reshape(-1, 3), + "actions_alpha": np.asarray(actions_alpha, dtype=np.float64).reshape(-1, 3), + "target_forces": np.asarray(target_forces, dtype=np.float64).reshape(-1, 2), + "target_phase": np.asarray(target_phases, dtype=np.int64), + "target_sensors": np.asarray(environment.target_sensors, dtype=np.float64).reshape(-1, 6), + "termination_reason": np.asarray(termination_reason), + } - sim = compute_similarity(target[:, 2:8], np.array(sens_list, dtype=np.float32), conv_len) - result = {"scene": scene_name, "mode": mode, "similarity_full": sim, "n_steps": n_steps} - del ff + +def run_rollout(plan: ValidationPlan, environment: RolloutEnvironment, policy: Policy) -> dict[str, Any]: + sensors: list[np.ndarray] = [] + forces: list[np.ndarray] = [] + actions_omega: list[np.ndarray] = [] + actions_alpha: list[np.ndarray] = [] + target_forces: list[np.ndarray] = [] + target_phases: list[int] = [] + raw = np.asarray(environment.current_raw, dtype=np.float64).reshape(12) + termination_reason = "completed" + for step in range(plan.n_steps): + try: + omega, target_force, phase = policy.action(raw, step) + omega = np.asarray(omega, dtype=np.float64).reshape(3) + _finite("action omega", omega) + next_raw = np.asarray(environment.step(omega), dtype=np.float64).reshape(12) + _finite("raw observation", next_raw) + except Exception as exc: + reason = f"{type(exc).__name__}: {exc}" + partial = _telemetry_arrays( + environment, sensors, forces, actions_omega, actions_alpha, + target_forces, target_phases, reason, + ) + raise RolloutFailure(reason, step=step, telemetry=partial) from exc + raw = next_raw + policy.observe(omega) + sensors.append(raw[:6].copy()) + forces.append(raw[6:].copy()) + actions_omega.append(omega.copy()) + actions_alpha.append(omega / float(plan.cfg["u0"])) + if target_force is not None: + target_forces.append(np.asarray(target_force, dtype=np.float64).reshape(2)) + if phase is not None: + target_phases.append(int(phase)) + arrays = _telemetry_arrays( + environment, sensors, forces, actions_omega, actions_alpha, + target_forces, target_phases, termination_reason, + ) + for name, values in arrays.items(): + if name not in {"termination_reason", "target_phase"}: + _finite(name, values) + return arrays + + +def _legacy_metric(target: np.ndarray, state: np.ndarray, conv_len: int) -> dict[str, Any]: + return legacy_cycle_similarity(target, state, conv_len) + + +def compute_versioned_metrics(target: np.ndarray, sensors: np.ndarray, conv_len: int) -> dict[str, Any]: + """Compute the historical reference-cycle versus final recorded-cycle metric.""" + exact = _legacy_metric(target, np.asarray(sensors, dtype=np.float64), conv_len) + return { + "metric_version": METRIC_VERSION, + "adapter": "SR_analysis.utils.cfd_interface.legacy_dtw_v1_detail_adapter", + "legacy_reference_cycle_vs_last_recorded_cycle": exact, + } + + +def _action_ranges(telemetry: Mapping[str, np.ndarray]) -> dict[str, Any]: + result = {} + for key in ("actions_omega", "actions_alpha"): + values = np.asarray(telemetry[key]) + result[key] = { + "min": values.min(axis=0).tolist(), + "max": values.max(axis=0).tolist(), + "layout": list(ACTION_ORDER), + } return result -def validate_vortex(scene_name, device_id, n_steps, mode, formula_front, formula_top) -> dict: - """Validate Vortex (from run_closed_loop_vortex.py).""" - # For simplicity, delegate to validate_karman with vortex scene config - return validate_karman(scene_name, device_id, n_steps, mode, formula_front, formula_top) +def save_outputs(plan: ValidationPlan, telemetry: Mapping[str, np.ndarray], metrics: Mapping[str, Any]) -> dict[str, Any]: + plan.validation_dir.mkdir(parents=True, exist_ok=True) + plan.telemetry_dir.mkdir(parents=True, exist_ok=True) + for path in (plan.validation_path, plan.telemetry_path): + if path.exists(): + raise FileExistsError(f"Refusing to overwrite existing artifact: {path}") + np.savez_compressed( + plan.telemetry_path, + **telemetry, + schema_version=np.asarray(TELEMETRY_VERSION), + scene=np.asarray(plan.scene), + mode=np.asarray(plan.mode), + sample_interval=np.asarray(plan.cfg["sample_interval"], dtype=np.int64), + ) + formula_hashes = None + formula_validation = None + if plan.formula_pair is not None: + formula_hashes = { + "pair": plan.formula_pair.pair_hash, + "front_source": plan.formula_pair.front["source_hash"], + "rear_source": plan.formula_pair.rear["source_hash"], + "front_deployment_expression": plan.formula_pair.front["deployment_expression_hash"], + "rear_deployment_expression": plan.formula_pair.rear["deployment_expression_hash"], + } + formula_validation = plan.formula_pair.compatibility + record = { + "schema_version": VALIDATION_VERSION, + "run_id": plan.run_id, + "scene": plan.scene, + "scene_id": plan.cfg["scene_id"], + "mode": plan.mode, + "artifact_id": f"{plan.run_id}-{plan.stem}", + "formula_hash": plan.formula_pair.pair_hash if plan.formula_pair is not None else None, + "metric_version": METRIC_VERSION, + "status": "completed", + "termination_reason": str(np.asarray(telemetry["termination_reason"]).item()), + "n_steps": plan.n_steps, + "sample_interval": int(plan.cfg["sample_interval"]), + "si": int(plan.cfg["sample_interval"]), + "dt_c": float(plan.cfg["sample_interval"]) * float(plan.cfg["u0"]) / 20.0, + "alignment": CAUSAL_ALIGNMENT, + "action_layout": list(ACTION_ORDER), + "metrics": _jsonable(metrics), + "action_ranges": _action_ranges(telemetry), + "formula_hashes": formula_hashes, + "formula_validation": formula_validation, + "data_hashes": { + "norm": _hash_optional(plan.norm_path), + "harmonics": _hash_optional(plan.harmonics_path), + "target_sensors": hash_json(np.asarray(telemetry["target_sensors"]).tolist()), + }, + "model": {"path": str(plan.model_path) if plan.model_path else None, "sha256": _hash_optional(plan.model_path)}, + "config": _jsonable(plan.cfg), + "config_hash": hash_json(_jsonable(plan.cfg)), + "environment": {"python": sys.version, "platform": platform.platform(), "numpy": np.__version__}, + "git_sha": _git_sha(), + "telemetry": { + "path": str(plan.telemetry_path), + "sha256": hash_file(plan.telemetry_path), + "telemetry_hash": hash_file(plan.telemetry_path), + }, + "provenance": { + "git_sha": _git_sha(), + "environment": { + "conda_env": os.environ.get("CONDA_DEFAULT_ENV"), + "gpu": _gpu_provenance(plan.cfg.get("device_id")), + }, + "command": " ".join(sys.argv), + "created_at": datetime.now(timezone.utc).isoformat(), + }, + "created_utc": datetime.now(timezone.utc).isoformat(), + } + record["record_hash"] = hash_json(record) + atomic_write_json(plan.validation_path, record) + return record -_VALIDATE_FN = { - "karman": validate_karman, - "illusion": validate_illusion, - "vortex": validate_vortex, -} +def save_failure_outputs( + plan: ValidationPlan, + failure: RolloutFailure, +) -> dict[str, Any]: + plan.validation_dir.mkdir(parents=True, exist_ok=True) + plan.telemetry_dir.mkdir(parents=True, exist_ok=True) + for path in (plan.validation_path, plan.telemetry_path): + if path.exists(): + raise FileExistsError(f"Refusing to overwrite existing artifact: {path}") + np.savez_compressed( + plan.telemetry_path, + **failure.telemetry, + schema_version=np.asarray(TELEMETRY_VERSION), + scene=np.asarray(plan.scene), + mode=np.asarray(plan.mode), + sample_interval=np.asarray(plan.cfg["sample_interval"], dtype=np.int64), + ) + formula_hashes = None + if plan.formula_pair is not None: + formula_hashes = { + "pair": plan.formula_pair.pair_hash, + "front_source": plan.formula_pair.front["source_hash"], + "rear_source": plan.formula_pair.rear["source_hash"], + "front_deployment_expression": plan.formula_pair.front["deployment_expression_hash"], + "rear_deployment_expression": plan.formula_pair.rear["deployment_expression_hash"], + } + created_at = datetime.now(timezone.utc).isoformat() + record = { + "schema_version": VALIDATION_VERSION, + "run_id": plan.run_id, + "scene": plan.scene, + "scene_id": plan.cfg["scene_id"], + "mode": plan.mode, + "artifact_id": f"{plan.run_id}-{plan.stem}", + "formula_hash": plan.formula_pair.pair_hash if plan.formula_pair is not None else None, + "metric_version": METRIC_VERSION, + "status": "failed", + "termination_reason": str(failure), + "exception_type": type(failure.__cause__).__name__ if failure.__cause__ else type(failure).__name__, + "failed_step": int(failure.step), + "completed_steps": int(np.asarray(failure.telemetry["sensors"]).shape[0]), + "n_steps": plan.n_steps, + "sample_interval": int(plan.cfg["sample_interval"]), + "si": int(plan.cfg["sample_interval"]), + "dt_c": float(plan.cfg["sample_interval"]) * float(plan.cfg["u0"]) / 20.0, + "alignment": CAUSAL_ALIGNMENT, + "action_layout": list(ACTION_ORDER), + "metrics": None, + "action_ranges": None, + "formula_hashes": formula_hashes, + "formula_validation": plan.formula_pair.compatibility if plan.formula_pair is not None else None, + "config": _jsonable(plan.cfg), + "config_hash": hash_json(_jsonable(plan.cfg)), + "git_sha": _git_sha(), + "telemetry": { + "path": str(plan.telemetry_path), + "sha256": hash_file(plan.telemetry_path), + "telemetry_hash": hash_file(plan.telemetry_path), + }, + "provenance": { + "git_sha": _git_sha(), + "environment": { + "conda_env": os.environ.get("CONDA_DEFAULT_ENV"), + "gpu": _gpu_provenance(plan.cfg.get("device_id")), + }, + "command": " ".join(sys.argv), + "created_at": created_at, + }, + "created_utc": created_at, + } + record["record_hash"] = hash_json(record) + atomic_write_json(plan.validation_path, record) + return record -def main(): - ap = argparse.ArgumentParser(description="Stage 3: Unified CFD closed-loop validation") - ap.add_argument("--scene", type=str, default=None) - ap.add_argument("--group", type=str, default=None) - ap.add_argument("--device", type=int, default=2) - ap.add_argument("--steps", type=int, default=0, help="0=auto") - ap.add_argument("--mode", type=str, default="pysr", choices=["pysr", "ppo", "uncontrolled"]) - ap.add_argument("--formula-front", type=str, default=None) - ap.add_argument("--formula-top", type=str, default=None) - args = ap.parse_args() - - if not args.scene and not args.group: - ap.error("Need --scene or --group") - - if args.group and args.group in VALIDATE_GROUPS: - scene_names = VALIDATE_GROUPS[args.group]() - elif args.group: - scene_names = [s.strip() for s in args.group.split(",")] - else: - scene_names = [args.scene] - - if args.mode == "pysr" and (not args.formula_front or not args.formula_top): - ap.error("--mode pysr requires --formula-front and --formula-top") - - os.makedirs(OUT_DIR, exist_ok=True) - - for sn in scene_names: - cfg = get_scene(sn) - scene_id = cfg["scene_id"] - fn = _VALIDATE_FN.get(scene_id) - if fn is None: - print(f" SKIP {sn}: unknown scene_id={scene_id}") - continue - print(f"\n--- {sn} (mode={args.mode}) ---") +def execute_plan( + plan: ValidationPlan, + device_id: int, + *, + environment_factory: Callable[[ValidationPlan, int], RolloutEnvironment] = build_environment, + model_loader: Callable[..., Any] | None = None, +) -> dict[str, Any]: + cfg = dict(plan.cfg) + cfg["device_id"] = device_id + runtime_plan = ValidationPlan(**{**plan.__dict__, "cfg": cfg}) + environment = environment_factory(runtime_plan, device_id) + try: + policy = build_policy(runtime_plan, model_loader=model_loader) try: - result = fn(sn, args.device, args.steps, args.mode, args.formula_front, args.formula_top) - json.dump(result, open(os.path.join(OUT_DIR, f"{sn}.json"), "w"), indent=2) - print(f" similarity={result.get('similarity', result.get('similarity_full', 'N/A')):.4f}") - except Exception as e: - print(f" ERROR: {e}") - import traceback; traceback.print_exc() + telemetry = run_rollout(runtime_plan, environment, policy) + except RolloutFailure as failure: + save_failure_outputs(runtime_plan, failure) + raise + metrics = compute_versioned_metrics(environment.target_sensors, telemetry["sensors"], int(cfg["conv_len"])) + return save_outputs(runtime_plan, telemetry, metrics) + finally: + environment.close() - print("\nDone.") + +def _scene_names(args: argparse.Namespace) -> list[str]: + if args.scene: + return [args.scene] + if args.group in GROUPS: + return list(GROUPS[args.group]()) + return [value.strip() for value in args.group.split(",") if value.strip()] + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Stage 3 legacy CFD validation (round1)") + selection = parser.add_mutually_exclusive_group(required=True) + selection.add_argument("--scene") + selection.add_argument("--group") + parser.add_argument("--mode", choices=("pysr", "ppo", "uncontrolled", "constant"), default="pysr") + parser.add_argument("--constant-alpha", nargs=3, type=float, metavar=("FRONT", "UPPER", "LOWER")) + parser.add_argument("--formula-front") + parser.add_argument("--formula-rear", "--formula-top", dest="formula_rear") + parser.add_argument("--formula-lower") + parser.add_argument("--device", type=int, default=2, help="Legacy CFD GPU device") + parser.add_argument( + "--model-device", default="cpu", + help="PyTorch PPO device; CPU avoids conflicts with the active PyCUDA context", + ) + parser.add_argument("--steps", type=int, default=200) + parser.add_argument("--run-id", default=None) + parser.add_argument("--output-root", default=None) + parser.add_argument("--dry-run", action="store_true") + parser.add_argument("--prepare-only", action="store_true") + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + if args.steps <= 0: + parser.error("--steps must be positive") + if args.mode == "pysr" and (not args.formula_front or not args.formula_rear): + parser.error("--mode pysr requires --formula-front and --formula-rear/--formula-top") + if args.mode == "constant" and args.constant_alpha is None: + parser.error("--mode constant requires --constant-alpha FRONT UPPER LOWER") + run_id = args.run_id or default_run_id() + output_root = resolve_output_root(args.output_root) + failures = 0 + try: + pair = load_formula_pair(args.formula_front, args.formula_rear, args.formula_lower) if args.mode == "pysr" else None + plans = [ + prepare_plan( + scene=scene, mode=args.mode, n_steps=args.steps, run_id=run_id, + output_root=output_root, formula_pair=pair, + ) + for scene in _scene_names(args) + ] + except Exception as exc: + print(f"ERROR: {exc}", file=sys.stderr) + return 2 + + if args.dry_run or args.prepare_only: + for plan in plans: + print( + f"{plan.scene}: scene_id={plan.cfg['scene_id']} mode={plan.mode} " + f"SI={plan.cfg['sample_interval']} validation={plan.validation_path} telemetry={plan.telemetry_path}" + ) + if args.prepare_only: + for plan in plans: + plan.validation_dir.mkdir(parents=True, exist_ok=True) + plan.telemetry_dir.mkdir(parents=True, exist_ok=True) + return 0 + + plans = [ + ValidationPlan(**{**plan.__dict__, "cfg": {**plan.cfg, "model_device": args.model_device}}) + for plan in plans + ] + if args.mode == "constant": + plans = [ + ValidationPlan(**{**plan.__dict__, "cfg": {**plan.cfg, "constant_alpha": tuple(args.constant_alpha)}}) + for plan in plans + ] + for plan in plans: + try: + record = execute_plan(plan, args.device) + metric = record["metrics"]["legacy_reference_cycle_vs_last_recorded_cycle"] + print(f"{plan.scene}: legacy_reference_cycle_vs_last_recorded_cycle={metric['similarity']:.6f}") + except Exception as exc: + failures += 1 + print(f"ERROR [{plan.scene}]: {exc}", file=sys.stderr) + traceback.print_exc() + return 1 if failures else 0 if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/src/SR_analysis/tests/test_audit_round1.py b/src/SR_analysis/tests/test_audit_round1.py new file mode 100644 index 0000000..f201d6e --- /dev/null +++ b/src/SR_analysis/tests/test_audit_round1.py @@ -0,0 +1,185 @@ +import json +from pathlib import Path + +import numpy as np + +from SR_analysis.tools import audit_round1 +from SR_analysis.tools.audit_round1 import audit_run +from SR_analysis.utils.provenance import hash_file, hash_json, sha256_text + + +def _write_json(path: Path, value: dict) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value), encoding="utf-8") + + +def _fixture(root: Path, *, bad_expression_hash: bool = False) -> None: + data = root / "data/runs/data-one/illusion/scene" + results = root / "results/runs/result-one" + data.mkdir(parents=True) + results.mkdir(parents=True) + np.savez( + data / "controlled.npz", + schema_version=np.array("sr-stage1-npz-v2"), + sensors_post=np.zeros((3, 6)), + forces_post=np.zeros((3, 6)), + actions_norm=np.zeros((3, 3)), + dt_c=np.array(0.2), + ) + telemetry = results / "telemetry.npz" + np.savez(telemetry, schema_version=np.array("sr-telemetry-v1"), values=np.zeros(3)) + expression = "2*x" + formula = { + "schema_version": "1.0", + "artifact_id": "formula-1", + "run_id": "result-one", + "feature_names": ["x"], + "fitted_expression": expression, + "deployment_expression": expression, + "fitted_expression_hash": sha256_text(expression), + "deployment_expression_hash": "0" * 64 if bad_expression_hash else sha256_text(expression), + "alignment_metadata": {"alignment": "causal_post_state_to_next_action"}, + "dt_c_metadata": {"value": 0.2}, + "g_metadata": {"operator": "identity"}, + "git_sha": "abc", + "environment": {"conda_env": "sr_env"}, + "provenance": { + "fit": { + "mode": "per-scene", + "seed": 0, + "niterations": 1, + "scene_sample_counts": {"scene": 3}, + "diagnostic_metrics": {"r2": 1.0}, + }, + "pysr": {"version": "test"}, + "data": {"dataset_hash": hash_file(data / "controlled.npz")}, + }, + } + formula_path = results / "formula.json" + _write_json(formula_path, formula) + validation = { + "schema_version": "1.0", + "artifact_id": "validation-1", + "formula_hash": hash_file(formula_path), + "metric_version": "legacy_dtw_v1_abs_n_unclipped", + "scene": "scene", + "mode": "pysr", + "n_steps": 3, + "metrics": {"full": {}, "tail": {}, "per_channel": {}, "action_range": {}}, + "telemetry": {"path": "telemetry.npz", "telemetry_hash": hash_file(telemetry)}, + "provenance": {"git_sha": "abc", "environment": "pycuda_3_10 GPU2", "command": "validate", "created_at": "now"}, + } + validation["record_hash"] = hash_json(validation) + _write_json(results / "validation.json", validation) + + +def test_hash_mismatch_is_reported(tmp_path): + root = tmp_path / "SR_analysis" + _fixture(root, bad_expression_hash=True) + + report, _ = audit_run("result-one", "data-one", sr_root=root) + + assert report["status"] == "fail" + assert any(error["code"] == "expression_hash_mismatch" for error in report["errors"]) + assert json.loads((root / "results/runs/result-one/audit_report.json").read_text())["strict_failure"] is True + + +def test_strict_cli_returns_nonzero_for_broken_chain(tmp_path, monkeypatch): + root = tmp_path / "SR_analysis" + _fixture(root, bad_expression_hash=True) + monkeypatch.setattr(audit_round1, "SR_ROOT", root) + + assert audit_round1.main(["--run-id", "result-one", "--data-run-id", "data-one", "--strict"]) == 1 + assert audit_round1.main(["--run-id", "result-one", "--data-run-id", "data-one"]) == 0 + + +def test_failed_validation_is_registered_as_rejected(tmp_path): + root = tmp_path / "SR_analysis" + _fixture(root) + validation_path = root / "results/runs/result-one/validation.json" + validation = json.loads(validation_path.read_text()) + validation["status"] = "failed" + validation["record_hash"] = hash_json({k: v for k, v in validation.items() if k != "record_hash"}) + _write_json(validation_path, validation) + + report, registry = audit_run("result-one", "data-one", sr_root=root) + + assert report["status"] == "pass" + statuses = {item["artifact_id"]: item["status"] for item in registry["artifacts"]} + assert statuses["formula-1"] == "rejected" + assert statuses["validation-1"] == "rejected" + assert registry["status"] == "pass" + assert registry["promotion_eligible"] is False + assert registry["scientifically_eligible"] is False + + +def test_incomplete_validation_rejects_entire_chain(tmp_path): + root = tmp_path / "SR_analysis" + _fixture(root) + validation_path = root / "results/runs/result-one/validation.json" + validation = json.loads(validation_path.read_text()) + validation["status"] = "incomplete" + validation["record_hash"] = hash_json({k: v for k, v in validation.items() if k != "record_hash"}) + _write_json(validation_path, validation) + + report, registry = audit_run("result-one", "data-one", sr_root=root) + + assert report["status"] == "pass" + assert registry["scientifically_eligible"] is False + assert all(item["status"] == "rejected" for item in registry["artifacts"]) + + +def test_run_prefix_does_not_grant_scientific_promotion(tmp_path): + root = tmp_path / "SR_analysis" + _fixture(root) + source = root / "results/runs/result-one" + prefixed = root / "results/runs/round1-legacy-v2-20260716-extra" + source.rename(prefixed) + + report, registry = audit_run( + "round1-legacy-v2-20260716-extra", "data-one", sr_root=root, + ) + + assert report["status"] == "pass" + assert registry["status"] == "pass" + assert registry["promotion_eligible"] is False + assert registry["scientifically_eligible"] is True + + +def test_split_formula_and_validation_runs_are_audited_as_one_chain(tmp_path): + root = tmp_path / "SR_analysis" + _fixture(root) + source = root / "results/runs/result-one/formula.json" + formula_run = root / "results/runs/formula-one" + formula_run.mkdir() + source.rename(formula_run / source.name) + + report, registry = audit_run( + "result-one", "data-one", formula_run_id="formula-one", sr_root=root, + ) + + assert report["status"] == "pass" + assert report["formula_run_id"] == "formula-one" + assert {item["artifact_id"] for item in registry["artifacts"]} == {"formula-1", "validation-1"} + + +def test_candidate_registry_is_generated_without_canonical_mutation(tmp_path): + root = tmp_path / "SR_analysis" + root.mkdir() + canonical = root / "scene_registry.json" + canonical.write_text('{"frozen": true}\n', encoding="utf-8") + before = canonical.read_bytes() + _fixture(root) + + report, registry = audit_run("result-one", "data-one", sr_root=root) + + assert report["status"] == "pass" + assert canonical.read_bytes() == before + assert {item["artifact_id"] for item in registry["artifacts"]} == {"formula-1", "validation-1"} + assert all(set(item) == {"artifact_id", "path", "sha256", "run_id", "status"} for item in registry["artifacts"]) + assert all(item["status"] == "candidate" for item in registry["artifacts"]) + assert registry["status"] == "pass" + assert registry["promotion_eligible"] is False + assert registry["scientifically_eligible"] is True + written = json.loads((root / "results/runs/result-one/candidate_registry.json").read_text()) + assert written == registry diff --git a/src/SR_analysis/tests/test_feature_state_and_contracts.py b/src/SR_analysis/tests/test_feature_state_and_contracts.py new file mode 100644 index 0000000..df34c50 --- /dev/null +++ b/src/SR_analysis/tests/test_feature_state_and_contracts.py @@ -0,0 +1,165 @@ +import numpy as np +import pytest + +from SR_analysis.utils.data_contracts import ( + CAUSAL_ALIGNMENT, + HISTORICAL_ALIGNMENT, + actions_to_alpha, + build_batch_dataset, + contiguous_block_slices, +) +from SR_analysis.utils.feature_builder import ( + AUG_LEVEL_4_KEYS, + ILLUSION_AUG_LEVEL_3_KEYS, + build_feature_matrix, + compute_dimensionless, + compute_features, +) +from SR_analysis.utils.feature_state import StatefulFeatureBuilder + + +def synthetic_trajectory(T=8): + t = np.arange(T, dtype=np.float64) + sensors = np.column_stack([ + 0.01 + 0.001 * t, 0.002 * t, + 0.012 - 0.0002 * t, -0.001 * t, + 0.009 + 0.0004 * t, 0.0005 * t, + ]) + forces = np.column_stack([ + 0.001 + 1e-5 * t, 2e-5 * t, + 0.0007 - 2e-5 * t, -1e-5 * t, + 0.0008 + 3e-5 * t, 1.5e-5 * t, + ]) + actions = np.column_stack([0.2 * t, -0.1 * t, 0.05 * t]) + target = np.column_stack([0.002 + 2e-5 * t, -3e-5 * t]) + return sensors, forces, actions, target + + +def test_batch_and_stateful_features_match_elementwise(): + sensors, forces, alpha, target = synthetic_trajectory() + T = len(sensors) + a1 = np.zeros_like(alpha) + a2 = np.zeros_like(alpha) + a1[1:] = alpha[:-1] + a2[2:] = alpha[:-2] + dim = compute_dimensionless(sensors, forces) + batch = compute_features( + dim, a1, a2, mu=0.02, alpha_mode=True, include_mu=True, + include_cos_sin=True, dt_c=0.3, target_forces=target, + sensors_raw=sensors, forces_raw=forces, + ) + builder = StatefulFeatureBuilder( + mu=0.02, dt_c=0.3, action_mode="alpha", require_target=True, + include_mu=True, include_cos_sin=True, + ) + rows = [] + keys = list(dict.fromkeys(AUG_LEVEL_4_KEYS + ILLUSION_AUG_LEVEL_3_KEYS + [ + "sin_ua", "cos_ua", "target_Cd", "target_Cl", "daF", "daB", "daT", + ])) + for i in range(T): + previous = alpha[i - 1] if i >= 1 else np.zeros(3) + previous2 = alpha[i - 2] if i >= 2 else np.zeros(3) + _, vector = builder.step( + sensors[i], forces[i], previous_action=previous, + previous_action2=previous2, target_force=target[i], feat_keys=keys, + ) + rows.append(vector) + expected = build_feature_matrix(batch, keys, add_bias=False) + np.testing.assert_allclose(np.vstack(rows), expected, rtol=1e-12, atol=1e-12) + + +@pytest.mark.parametrize("sample_interval", [400, 600, 800]) +def test_linear_signal_derivatives_respect_dt_c(sample_interval): + dt_c = sample_interval / 2000.0 + T = 6 + t_c = np.arange(T) * dt_c + sensors = np.zeros((T, 6)) + sensors[:, 0] = 0.01 + 0.01 * 3.0 * t_c + sensors[:, 2] = 0.01 + 0.01 * 1.0 * t_c + sensors[:, 4] = 0.01 - 0.01 * 1.0 * t_c + forces = np.zeros((T, 6)) + actions = np.zeros((T, 3)) + dim = compute_dimensionless(sensors, forces) + features = compute_features( + dim, actions, actions, 0.02, alpha_mode=True, include_mu=False, + include_cos_sin=False, dt_c=dt_c, sensors_raw=sensors, forces_raw=forces, + ) + np.testing.assert_allclose(features["du_m_dt"][1:], 1.0) + np.testing.assert_allclose(features["du_a_dt"][1:], 2.0) + assert features["du_m_dt"][0] == pytest.approx(0.0) + assert features["du_a_dt"][0] == pytest.approx(0.0) + + +def test_alignment_fixture_has_explicit_state_and_target_indices(): + sensors, forces, alpha, _ = synthetic_trajectory(T=6) + keys = ["aF_lag1", "daF"] + historical = build_batch_dataset( + sensors, forces, alpha, keys, mu=0.02, dt_c=0.4, + alignment=HISTORICAL_ALIGNMENT, action_mode="alpha", warmup=2, + ) + causal = build_batch_dataset( + sensors, forces, alpha, keys, mu=0.02, dt_c=0.4, + alignment=CAUSAL_ALIGNMENT, action_mode="alpha", warmup=1, + ) + np.testing.assert_array_equal(historical.metadata["state_indices"], [2, 3, 4, 5]) + np.testing.assert_array_equal(historical.metadata["target_indices"], [2, 3, 4, 5]) + np.testing.assert_allclose(historical.X[:, 0], alpha[1:-1, 0]) + np.testing.assert_allclose(historical.Y, alpha[2:]) + np.testing.assert_array_equal(causal.metadata["state_indices"], [1, 2, 3, 4]) + np.testing.assert_array_equal(causal.metadata["target_indices"], [2, 3, 4, 5]) + np.testing.assert_allclose(causal.X[:, 0], alpha[1:-1, 0]) + np.testing.assert_allclose(causal.Y, alpha[2:]) + + +def test_missing_target_and_feature_fail_strictly(): + sensors, forces, alpha, _ = synthetic_trajectory(T=4) + with pytest.raises(ValueError, match="require target_forces"): + build_batch_dataset( + sensors, forces, alpha, ["Cd_err"], mu=0.02, dt_c=0.4, + action_mode="alpha", illusion=True, + ) + with pytest.raises(KeyError, match="missing required feature"): + build_feature_matrix({"u_m": np.ones(3)}, ["u_m", "not_computed"]) + compatible = build_feature_matrix( + {"u_m": np.ones(3)}, ["u_m", "not_computed"], strict=False + ) + np.testing.assert_array_equal(compatible[:, -1], np.zeros(3)) + + +def test_transient_and_steady_segments_are_disjoint(): + rows = 10 + sensors = np.zeros((rows, 6)) + forces = np.zeros((rows, 6)) + actions = np.arange(rows * 3, dtype=float).reshape(rows, 3) + transient = build_batch_dataset( + sensors, forces, actions, ["u_m"], mu=0.02, dt_c=0.4, + action_mode="alpha", warmup=2, sample_segment="transient", transient_count=5, + ) + steady = build_batch_dataset( + sensors, forces, actions, ["u_m"], mu=0.02, dt_c=0.4, + action_mode="alpha", warmup=2, sample_segment="steady", transient_count=5, + ) + np.testing.assert_array_equal(transient.kept_indices, [2, 3, 4]) + np.testing.assert_array_equal(steady.kept_indices, [5, 6, 7, 8, 9]) + + +def test_contiguous_offline_blocks_are_ordered_disjoint_and_complete(): + blocks = contiguous_block_slices(11) + indices = {name: np.arange(11)[block] for name, block in blocks.items()} + np.testing.assert_array_equal(indices["train"], np.arange(6)) + np.testing.assert_array_equal(indices["validation"], np.arange(6, 8)) + np.testing.assert_array_equal(indices["blind"], np.arange(8, 11)) + np.testing.assert_array_equal(np.concatenate(list(indices.values())), np.arange(11)) + + +def test_normalized_action_alpha_conversion(): + actions = np.array([[1.0, 0.0, -1.0], [0.5, -0.5, 0.25]]) + alpha = actions_to_alpha( + actions, action_mode="normalized", u0=0.01, + action_scale=12.0, action_bias=(1.0, -2.0, 3.0), + ) + np.testing.assert_allclose(alpha, actions * 12.0 + [1.0, -2.0, 3.0]) + physical = alpha * 0.01 + np.testing.assert_allclose( + actions_to_alpha(physical, action_mode="physical", u0=0.01), alpha + ) diff --git a/src/SR_analysis/tests/test_formula_provenance.py b/src/SR_analysis/tests/test_formula_provenance.py new file mode 100644 index 0000000..311f3f8 --- /dev/null +++ b/src/SR_analysis/tests/test_formula_provenance.py @@ -0,0 +1,312 @@ +import json +from pathlib import Path + +import numpy as np +import pytest + +from SR_analysis.utils.formula_schema import ( + build_coefficient_scaling_variant, + build_formula_artifact, + build_term_deletion_variant, + build_validation_record, + decompose_additive_terms, + evaluate_formula, + formula_safety_report, + load_formula, + normalize_symbolic_expression, +) +from SR_analysis.utils.provenance import atomic_write_json_below, hash_json, safe_path + + +FORMULA_DIR = Path(__file__).parents[1] / "results" / "formulas_v5" +CANONICAL_FORMULAS = ( + "ill_1L_sc_front.json", + "ill_1L_sc_top.json", + "kar_d075_sc_front.json", + "kar_d075_sc_top.json", +) + + +@pytest.mark.parametrize("filename", CANONICAL_FORMULAS) +def test_load_and_evaluate_canonical_legacy_formulas(filename): + formula = load_formula(FORMULA_DIR / filename) + values = {name: np.array([0.25, -0.5]) for name in formula["feature_names"]} + + result = evaluate_formula(formula, values) + + assert formula["legacy"] is True + assert formula["feature_names"] == formula["feature_keys"] + assert formula["fitted_expression"] == formula["best_sympy"] + assert formula["fit_score"] == formula["best_score"] + assert result.shape == (2,) + assert np.all(np.isfinite(result)) + + +def test_expression_alias_is_supported(): + formula = {"feature_names": ["x"], "expression": "2*x + 1", "best_score": 0.9} + np.testing.assert_allclose(evaluate_formula(formula, {"x": [1.0, 3.0]}), [3.0, 7.0]) + + +def test_formula_static_safety_detects_finite_and_range_failures(): + safe = formula_safety_report( + {"feature_names": ["x"], "expression": "2*x"}, {"x": [-1, 1]}, output_range=[-2, 2], + ) + assert safe["safe"] is True + out_of_range = formula_safety_report( + {"feature_names": ["x"], "expression": "10*x"}, {"x": [-1, 1]}, output_range=[-2, 2], + ) + assert out_of_range["finite"] is True + assert out_of_range["safe"] is False + nonfinite = formula_safety_report( + {"feature_names": ["x"], "expression": "1/x"}, {"x": [-1, 1]}, grid_size=3, + ) + assert nonfinite["finite"] is False + + +def test_missing_coef_never_silently_produces_zero(): + formula = { + "schema_version": "1.0", + "model_type": "linear", + "feature_names": ["x"], + "fitted_expression": None, + "deployment_expression": None, + } + + with pytest.raises(ValueError, match="requires coef"): + evaluate_formula(formula, {"x": np.array([2.0, 4.0])}) + + +def test_symbolic_expression_wins_over_incidental_coefficients(): + formula = { + "schema_version": "1.0", + "model_type": "linear", + "feature_names": ["x"], + "deployment_expression": "3*x", + "fitted_expression": "3*x", + "coef": [0.0], + } + np.testing.assert_allclose(evaluate_formula(formula, {"x": [2.0]}), [6.0]) + + +def test_build_versioned_artifact_and_validation_record(): + artifact = build_formula_artifact( + artifact_id="formula-001", + run_id="run-001", + role="controller", + anchor="front", + training_scenes=["scene-a"], + feature_names=["x"], + fitted_expression="2*x", + deployment_expression="2*x", + output={"name": "alpha", "unit": "rad"}, + feature_metadata={"version": "features-v1"}, + alignment_metadata={"policy": "same-step"}, + dt_c_metadata={"value": 0.2, "unit": "s"}, + g_metadata={"operator": "identity"}, + fit_provenance={"seed": 7}, + pysr_provenance={"version": "test"}, + data_provenance={"dataset_hash": "abc"}, + ) + record = build_validation_record( + formula=artifact, + metric_version="metrics-v1", + scene="scene-a", + mode="closed-loop", + si=True, + n_steps=100, + full_metrics={"rmse": 0.1}, + tail_metrics={"rmse": 0.05}, + per_channel_metrics={"front": {"rmse": 0.1}}, + action_range={"min": -1.0, "max": 1.0}, + telemetry={"complete": True}, + provenance={"validator": "pytest"}, + ) + + assert artifact["fitted_expression_hash"] == artifact["deployment_expression_hash"] + assert record["formula_hash"] == hash_json(artifact) + assert record["metrics"]["tail"]["rmse"] == 0.05 + assert record["record_hash"] == hash_json({k: v for k, v in record.items() if k != "record_hash"}) + + +def _variant_parent(): + return load_formula( + build_formula_artifact( + artifact_id="formula-variant-parent", + run_id="run-variant", + role="controller", + anchor="front", + training_scenes=["scene-a"], + feature_names=["x", "y"], + fitted_expression="x*(y + 2) + 3*x - 4", + deployment_expression="x*(y + 2) + 3*x - 4", + output={"name": "alpha", "unit": "rad"}, + feature_metadata={"version": "features-v1"}, + alignment_metadata={"policy": "same-step"}, + dt_c_metadata={"value": 0.2, "unit": "s"}, + g_metadata={"operator": "identity"}, + fit_provenance={"seed": 7}, + pysr_provenance={"version": "test"}, + data_provenance={"dataset_hash": "abc"}, + ) + ) + + +def test_symbolic_normalization_and_term_decomposition_are_deterministic(): + expected = "x*y + 5*x - 4" + assert normalize_symbolic_expression("3*x - 4 + x*(2 + y)", ["x", "y"]) == expected + assert normalize_symbolic_expression("x*y - 4 + 5*x", ["x", "y"]) == expected + assert decompose_additive_terms("3*x - 4 + x*(2 + y)", ["x", "y"]) == ( + "x*y", + "5*x", + "-4", + ) + + +def test_term_variants_are_immutable_validated_and_preserve_provenance(): + parent = _variant_parent() + original = json.loads(json.dumps(parent)) + term = "5*x" + + deleted = build_term_deletion_variant(parent, term) + scaled = build_coefficient_scaling_variant(parent, term, 0.5) + + assert parent == original + assert deleted["fitted_expression"] == parent["fitted_expression"] + assert deleted["fitted_expression_hash"] == parent["fitted_expression_hash"] + assert deleted["deployment_expression"] == "x*y - 4" + assert scaled["deployment_expression"] == "x*y + 2.5*x - 4" + assert deleted["deployment_expression_hash"] != parent["deployment_expression_hash"] + assert scaled["deployment_expression_hash"] != parent["deployment_expression_hash"] + assert deleted["parent_source_hash"] == parent["source_hash"] + assert deleted["parent_deployment_expression_hash"] == parent["deployment_expression_hash"] + assert deleted["variant_metadata"] == { + "type": "term_deletion", + "term_identity": term, + "scale": 0.0, + } + assert scaled["variant_metadata"] == { + "type": "coefficient_scaling", + "term_identity": term, + "scale": 0.5, + } + for key in ( + "role", + "g_metadata", + "feature_metadata", + "alignment_metadata", + "dt_c_metadata", + "provenance", + ): + assert deleted[key] == parent[key] + assert scaled[key] == parent[key] + assert load_formula(deleted)["deployment_expression_hash"] == deleted[ + "deployment_expression_hash" + ] + + +def test_zero_scaling_is_symbolically_and_numerically_equivalent_to_deletion(): + parent = _variant_parent() + term = "5*x" + deleted = build_term_deletion_variant(parent, term) + zero_scaled = build_coefficient_scaling_variant(parent, term, 0) + + assert normalize_symbolic_expression( + deleted["deployment_expression"], parent["feature_names"] + ) == normalize_symbolic_expression( + zero_scaled["deployment_expression"], parent["feature_names"] + ) + values = {"x": np.array([-2.0, 0.5, 3.0]), "y": np.array([1.0, -4.0, 2.0])} + np.testing.assert_allclose( + evaluate_formula(deleted, values), evaluate_formula(zero_scaled, values) + ) + + +def test_scaling_accepts_caller_grid_values_and_rejects_unknown_terms(): + parent = _variant_parent() + variants = [ + build_coefficient_scaling_variant(parent, "5*x", scale) + for scale in (-1.25, 0.25, 1.75) + ] + assert [variant["variant_metadata"]["scale"] for variant in variants] == [ + -1.25, + 0.25, + 1.75, + ] + with pytest.raises(ValueError, match="term identity is not present"): + build_term_deletion_variant(parent, "x") + + +def test_stage3_three_head_policy_deploys_each_formula_directly(): + from SR_analysis.stage_3_validate import FormulaPair, SymbolicPolicy + + def formula(role, expression): + return load_formula({ + "schema_version": "1.0", "feature_names": ["u_m"], + "role": role, "anchor": role, "fitted_expression": expression, + "deployment_expression": expression, + }) + + front = formula("front_independent", "1") + upper = formula("upper_independent", "2") + lower = formula("lower_independent", "3") + pair = FormulaPair( + front, upper, Path("front"), Path("upper"), "hash", {}, + lower=lower, lower_path=Path("lower"), deployment_semantics="three_head_independent", + ) + cfg = { + "mu": 0.02, "sample_interval": 800, "u0": 0.01, "scene_id": "karman", + "policy_init_action": [0, 0, 0], + } + omega, _, _ = SymbolicPolicy(pair, cfg, None).action(np.zeros(12), 0) + np.testing.assert_allclose(omega, [0.01, 0.02, 0.03]) + + +def test_stage3_rollout_failure_preserves_partial_telemetry(): + from SR_analysis.stage_3_validate import RolloutFailure, run_rollout + + class Environment: + current_raw = np.zeros(12) + target_sensors = np.zeros((72, 6)) + + def step(self, action): + return np.full(12, np.nan) + + class Policy: + def action(self, raw, step): + return np.zeros(3), None, None + + def observe(self, action): + raise AssertionError("failed observations must not update policy history") + + class Plan: + n_steps = 3 + cfg = {"u0": 0.01} + + with pytest.raises(RolloutFailure) as captured: + run_rollout(Plan(), Environment(), Policy()) + + assert captured.value.step == 0 + assert captured.value.telemetry["sensors"].shape == (0, 6) + assert "non-finite" in str(captured.value) + + +def test_stage3_karman_trained_group_matches_stage1_contract(): + from SR_analysis.stage_3_validate import GROUPS + + assert list(GROUPS["karman_trained"]()) == [ + "karman_re50", + "karman_re100", + "karman_re200", + "karman_re400", + ] + + +def test_safe_atomic_json_write_rejects_escape(tmp_path): + destination = atomic_write_json_below(tmp_path, "nested/artifact.json", {"b": 2, "a": 1}) + assert destination == safe_path(tmp_path, "nested/artifact.json") + assert json.loads(destination.read_text(encoding="utf-8")) == {"a": 1, "b": 2} + + with pytest.raises(ValueError, match="escapes"): + safe_path(tmp_path, "../outside.json") + with pytest.raises(ValueError, match="relative"): + safe_path(tmp_path, tmp_path / "absolute.json") diff --git a/src/SR_analysis/tests/test_legacy_contracts.py b/src/SR_analysis/tests/test_legacy_contracts.py new file mode 100644 index 0000000..2efaaac --- /dev/null +++ b/src/SR_analysis/tests/test_legacy_contracts.py @@ -0,0 +1,135 @@ +from __future__ import annotations + +import os + +import numpy as np +import pytest + +from SR_analysis.checks.order_contract import expected_ledger, impulse_commands +from SR_analysis.configs import ( + ILLUSION_TARGET_FORCE_SLICE, + ILLUSION_TARGET_SENSOR_SLICE, + LEGACY_CFG_DIR, + MODEL_DIR, + PROJECT_ROOT, + control_dt, + get_scene, +) +from SR_analysis.utils.cfd_interface import ( + LEGACY_DTW_VERSION, + calc_dtw_sim, + compute_similarity, + compute_similarity_illusion_legacy_v1, + compute_similarity_karman_legacy_v1, +) +from SR_analysis.utils.feature_builder import apply_G_alpha as feature_apply_G_alpha +from SR_analysis.utils.feature_builder import compute_dimensionless +from SR_analysis.utils.g_operator import ( + apply_G_alpha, + apply_G_raw12, + apply_G_target_force, +) + + +def test_project_paths_and_karman_contract() -> None: + assert PROJECT_ROOT == os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..")) + assert MODEL_DIR == os.path.join(PROJECT_ROOT, "models") + assert LEGACY_CFG_DIR == os.path.join(PROJECT_ROOT, "configs", "legacy_configs") + + cfg = get_scene("karman_re100") + assert cfg["re_d"] == 50.0 + assert cfg["control_time_scale_steps"] == 2000 + assert cfg["control_dt"] == control_dt(800) == 0.4 + assert cfg["fifo_len"] == 150 + assert cfg["conv_len"] == 30 + assert cfg["body_layout"] == ("front", "upper", "lower") + assert cfg["force_layout"] == ( + "front_fx", "front_fy", "upper_fx", "upper_fy", "lower_fx", "lower_fy" + ) + assert cfg["fifo_init_action"] == cfg["action_bias"] + assert cfg["policy_init_action"] == cfg["action_bias"] + + +def test_illusion_target_contract_and_slices() -> None: + cfg = get_scene("illusion_1L") + target = np.arange(16).reshape(2, 8) + + assert cfg["conv_len"] == 36 + assert cfg["target_center_x"] == 20.0 + assert cfg["target_radius"] == cfg["target_diameter"] == 1.0 + assert cfg["target_radius_lattice"] == 20.0 + assert cfg["fifo_init_action"] == (0.0, -1.0, 1.0) + assert cfg["policy_init_action"] == (0.0, -1.0, 1.0) + assert cfg["target_force_slice"] == ILLUSION_TARGET_FORCE_SLICE == (0, 2) + assert cfg["target_sensor_slice"] == ILLUSION_TARGET_SENSOR_SLICE == (2, 8) + assert np.array_equal(target[:, slice(*cfg["target_force_slice"])], target[:, :2]) + assert np.array_equal(target[:, slice(*cfg["target_sensor_slice"])], target[:, 2:8]) + + +@pytest.mark.parametrize( + ("operator", "values"), + [ + (apply_G_raw12, np.arange(24, dtype=np.float32).reshape(2, 12)), + (apply_G_alpha, np.arange(6, dtype=np.float32).reshape(2, 3)), + (apply_G_target_force, np.arange(4, dtype=np.float32).reshape(2, 2)), + ], +) +def test_g_is_an_involution(operator, values) -> None: + transformed = operator(values) + assert transformed.dtype == values.dtype + np.testing.assert_array_equal(operator(transformed), values) + + +def test_g_uses_native_front_upper_lower_order() -> None: + raw = np.arange(12, dtype=np.float64) + np.testing.assert_array_equal( + apply_G_raw12(raw), + [4, -5, 2, -3, 0, -1, 6, -7, 10, -11, 8, -9], + ) + np.testing.assert_array_equal(apply_G_alpha(np.array([1.0, 2.0, 3.0])), [-1.0, -3.0, -2.0]) + np.testing.assert_array_equal(apply_G_target_force(np.array([4.0, 5.0])), [4.0, -5.0]) + + +def test_feature_builder_reexports_canonical_batch_action_g() -> None: + actions = np.arange(6, dtype=np.float32).reshape(2, 3) + transformed = feature_apply_G_alpha(actions) + np.testing.assert_array_equal(transformed, apply_G_alpha(actions)) + assert transformed.dtype == actions.dtype + + +def test_order_ledger_and_impulse_slots_follow_front_upper_lower() -> None: + karman = expected_ledger("karman_re100") + illusion = expected_ledger("illusion_1L") + assert karman["body_object_ids"] == {"front": 4, "upper": 5, "lower": 6} + assert illusion["body_object_ids"] == {"front": 3, "upper": 4, "lower": 5} + commands = impulse_commands(7, 0.125) + np.testing.assert_array_equal(np.flatnonzero(commands["front"]), [4]) + np.testing.assert_array_equal(np.flatnonzero(commands["upper"]), [5]) + np.testing.assert_array_equal(np.flatnonzero(commands["lower"]), [6]) + + +def test_dimensionless_force_columns_follow_front_upper_lower_order() -> None: + sensors = np.zeros((1, 6), dtype=np.float64) + forces = np.arange(1, 7, dtype=np.float64).reshape(1, 6) + dim = compute_dimensionless(sensors, forces, u0=1.0, d=2.0, rho=1.0) + + assert dim["Cd_F"][0] == 1.0 + assert dim["Cl_F"][0] == 2.0 + assert dim["Cd_T"][0] == 3.0 + assert dim["Cl_T"][0] == 4.0 + assert dim["Cd_B"][0] == 5.0 + assert dim["Cl_B"][0] == 6.0 + + +def test_legacy_dtw_fixture_and_scene_wrappers() -> None: + assert LEGACY_DTW_VERSION == "legacy_dtw_v1_abs_n_unclipped" + assert calc_dtw_sim(np.zeros(2), np.full(2, 3.0)) == -2.0 + + conv_len = 2 + sensors = np.arange(24, dtype=np.float64).reshape(4, 6) / 10.0 + state = sensors[2:4].copy() + illusion_target = np.column_stack((np.full(4, 99.0), np.full(4, -99.0), sensors)) + + assert compute_similarity_karman_legacy_v1(sensors, state, conv_len) == pytest.approx(1.0) + assert compute_similarity(sensors, state, conv_len) == pytest.approx(1.0) + assert compute_similarity_illusion_legacy_v1(illusion_target, state, conv_len) == pytest.approx(1.0) diff --git a/src/SR_analysis/tests/test_policy_replay.py b/src/SR_analysis/tests/test_policy_replay.py new file mode 100644 index 0000000..7bce6ec --- /dev/null +++ b/src/SR_analysis/tests/test_policy_replay.py @@ -0,0 +1,44 @@ +from pathlib import Path + +import numpy as np + +from SR_analysis.checks import policy_replay + + +class _Model: + def predict(self, observation, deterministic=True): + values = np.asarray(observation, dtype=np.float64) + return values[:3], None + + +def test_replay_uses_previous_post_state_for_next_action(tmp_path, monkeypatch): + trajectory = tmp_path / "controlled.npz" + sensors = np.arange(24, dtype=np.float32).reshape(4, 6) / 100.0 + forces = np.zeros((4, 6), dtype=np.float32) + actions = np.vstack((np.zeros(3), sensors[:-1, :3])).astype(np.float32) + np.savez(trajectory, sensors=sensors, forces=forces, actions_norm=actions) + + cfg = { + "scene_id": "karman", + "s_dim": 12, + "action_layout": ("front", "upper", "lower"), + } + norm_path = tmp_path / "norm.json" + norm_path.write_text("{}") + model_path = tmp_path / "model.zip" + model_path.write_bytes(b"model") + monkeypatch.setattr(policy_replay, "get_scene", lambda scene: cfg) + monkeypatch.setattr( + policy_replay, + "load_existing_norm", + lambda scene, config: ({"force_norm_fact": 1.0, "sens_deviation": [0] * 6, "sens_norm_fact": [1] * 6}, norm_path), + ) + monkeypatch.setattr(policy_replay, "model_path_for_scene", lambda scene: str(model_path)) + monkeypatch.setattr(policy_replay, "load_ppo_model", lambda *args, **kwargs: _Model()) + monkeypatch.setattr(policy_replay, "normalize_raw_observation", lambda raw, norm: np.asarray(raw)[:6]) + + report = policy_replay.replay("karman_re100", trajectory) + + assert report["status"] == "passed" + assert report["n_compared"] == 3 + np.testing.assert_allclose(report["max_abs_error"], 0.0) diff --git a/src/SR_analysis/tests/test_stage_1_infer.py b/src/SR_analysis/tests/test_stage_1_infer.py new file mode 100644 index 0000000..5f0e2a4 --- /dev/null +++ b/src/SR_analysis/tests/test_stage_1_infer.py @@ -0,0 +1,175 @@ +import json +import subprocess +import sys +from pathlib import Path + +import numpy as np +import pytest + +from SR_analysis import stage_1_infer as stage1 +from SR_analysis.utils.harmonics import gen_target_states_at, harmonics_document + + +def illusion_cfg(): + return { + "scene_id": "illusion", + "sample_interval": 600, + "u0": 0.01, + } + + +def test_target_extraction_uses_force_first_and_all_sensors(): + canonical, forces, sensors = stage1.extract_illusion_target(np.arange(12, dtype=np.float32)) + np.testing.assert_array_equal(canonical, np.arange(8, dtype=np.float32)) + np.testing.assert_array_equal(forces, [0, 1]) + np.testing.assert_array_equal(sensors, [2, 3, 4, 5, 6, 7]) + + +def test_target_extraction_rejects_short_observation(): + with pytest.raises(ValueError, match="8 channels"): + stage1.extract_illusion_target(np.arange(7)) + + +def test_phase_index_matches_legacy_first_and_next_observations(): + assert stage1.target_phase_index(0) == 0 + assert stage1.target_phase_index(0, after_action=True) == 1 + assert [stage1.target_phase_index(i) for i in range(4)] == [0, 1, 2, 3] + with pytest.raises(ValueError): + stage1.target_phase_index(-1) + + +def test_harmonics_loader_accepts_legacy_list_and_v2_document(): + legacy = [{"dc": 1.0, "amps": [2.0], "freqs": [0.25], "phases": [0.0]}] + document = harmonics_document(legacy, ["fx"], schema_version="test-v2") + assert document["channel_names"] == ["fx"] + np.testing.assert_allclose(gen_target_states_at(0, legacy), gen_target_states_at(0, document)) + assert gen_target_states_at(np.array([0, 1]), document).shape == (2, 1) + + +def test_gpu_identity_uses_explicit_visibility_mapping(monkeypatch): + calls = [] + + def fake_run(command, **kwargs): + calls.append((command, kwargs)) + return subprocess.CompletedProcess(command, 0, stdout="GPU-uuid, Test GPU\n", stderr="") + + monkeypatch.setattr(stage1.subprocess, "run", fake_run) + identity = stage1.gpu_identity(1, "GPU-first,GPU-second") + + assert identity == {"uuid": "GPU-uuid", "name": "Test GPU"} + assert "--id=GPU-second" in calls[0][0] + assert "--id=1" not in calls[0][0] + + +def test_gpu_identity_never_treats_logical_index_as_physical(monkeypatch): + calls = [] + + def fake_run(command, **kwargs): + calls.append(command) + return subprocess.CompletedProcess( + command, 0, stdout="GPU-a, First\nGPU-b, Second\n", stderr="", + ) + + monkeypatch.setattr(stage1.subprocess, "run", fake_run) + + assert stage1.gpu_identity(1, None) == {"uuid": None, "name": None} + assert all(not argument.startswith("--id=") for argument in calls[0]) + + +def test_device_manifest_records_environment_without_gpu_hard_failure(monkeypatch): + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "GPU-only") + + def unavailable(*args, **kwargs): + raise FileNotFoundError("nvidia-smi") + + monkeypatch.setattr(stage1.subprocess, "run", unavailable) + + assert stage1.cfd_device_manifest(0) == { + "cfd_device": 0, + "cfd_device_logical": 0, + "cuda_visible_devices": "GPU-only", + "cfd_gpu_uuid": None, + "cfd_gpu_name": None, + } + + +def test_paths_are_repo_rooted_and_do_not_depend_on_cwd(tmp_path, monkeypatch): + monkeypatch.chdir(tmp_path) + assert stage1.resolve_output_root(None) == stage1.REPO_ROOT / "src/SR_analysis/data/runs" + relative = stage1.resolve_output_root("scratch/stage1") + assert relative == stage1.REPO_ROOT / "scratch/stage1" + out = stage1.scene_output_dir(relative, "round-one", "illusion_1L", illusion_cfg()) + assert out == stage1.REPO_ROOT / "scratch/stage1/round-one/illusion/illusion_1L" + + +def test_output_refuses_overwrite(tmp_path): + out = tmp_path / "run"; out.mkdir(); (out / "result.json").write_text("{}") + with pytest.raises(FileExistsError, match="Refusing"): + stage1.prepare_output_dir(out, overwrite=False) + stage1.prepare_output_dir(out, overwrite=True) + assert (out / "result.json").exists() + + +def test_npz_v2_schema_and_legacy_aliases(tmp_path): + cfg = illusion_cfg() + controlled = tmp_path / "controlled.npz" + target = tmp_path / "target.npz" + sensors = np.ones((2, 6), dtype=np.float32) + forces = np.full((2, 6), 2, dtype=np.float32) + actions = np.full((2, 3), 0.5, dtype=np.float32) + commanded = np.full((2, 3), 0.03, dtype=np.float32) + target_forces = np.array([[1, 2], [3, 4]], dtype=np.float32) + stage1.save_controlled_npz( + controlled, "illusion_1L", cfg, sensors=sensors, forces=forces, + actions_norm=actions, actions_commanded=commanded, + target_forces=target_forces, phase_indices=np.array([0, 1]), + ) + stage1.save_target_npz(target, "illusion_1L", cfg, np.arange(16).reshape(2, 8), illusion=True) + + with np.load(controlled) as data: + assert data["schema_version"].item() == stage1.SCHEMA_VERSION + np.testing.assert_array_equal(data["actions"], data["actions_norm"]) + np.testing.assert_array_equal(data["sensors"], data["sensors_post"]) + np.testing.assert_array_equal(data["forces"], data["forces_post"]) + np.testing.assert_array_equal(data["target_forces"], data["target_forces_for_action"]) + np.testing.assert_array_equal(data["target_phase_index"], [0, 1]) + np.testing.assert_allclose(data["actions_omega_commanded"], commanded) + np.testing.assert_allclose(data["actions_alpha_commanded"], commanded / cfg["u0"]) + assert data["dt_c"].item() == pytest.approx(0.3) + with np.load(target) as data: + assert data["target_states"].shape == (2, 8) + assert data["target_forces"].shape == (2, 2) + assert data["target_sensors"].shape == (2, 6) + assert len(data["layout"]) == 8 + + +def test_norm_validation_rejects_bad_shapes_and_zeros(): + with pytest.raises(ValueError): + stage1.validate_norm({"force_norm_fact": 1, "sens_deviation": [0] * 5, "sens_norm_fact": [1] * 6}) + with pytest.raises(ValueError): + stage1.validate_norm({"force_norm_fact": 1, "sens_deviation": [0] * 6, "sens_norm_fact": [0] * 6}) + + +def test_cli_rejects_mutually_exclusive_scene_and_group(): + with pytest.raises(SystemExit) as error: + stage1.build_parser().parse_args(["--scene", "illusion_1L", "--group", "illusion_trained"]) + assert error.value.code == 2 + + +def test_dry_run_does_not_import_legacy_or_create_output(tmp_path): + script = stage1.Path(stage1.__file__) + output_root = tmp_path / "runs" + code = ( + "import runpy,sys; " + f"sys.argv={[str(script), '--scene', 'illusion_1L', '--run-id', 'dry', '--output-root', str(output_root), '--dry-run']!r}; " + f"runpy.run_path({str(script)!r}, run_name='__main__')" + ) + completed = subprocess.run([sys.executable, "-c", code], cwd=tmp_path, text=True, capture_output=True) + assert completed.returncode == 0, completed.stderr + assert "LegacyCelerisLab" not in completed.stderr + assert not output_root.exists() + + +def test_harmonics_document_is_json_serializable(): + document = harmonics_document([{"dc": 0.0, "amps": [], "freqs": [], "phases": []}], ["fx"], schema_version="v2") + assert json.loads(json.dumps(document))["schema_version"] == "v2" diff --git a/src/SR_analysis/tests/test_stage_2_fit.py b/src/SR_analysis/tests/test_stage_2_fit.py new file mode 100644 index 0000000..58f0772 --- /dev/null +++ b/src/SR_analysis/tests/test_stage_2_fit.py @@ -0,0 +1,431 @@ +import json +import subprocess +import sys +from pathlib import Path + +import numpy as np +import pytest + +from SR_analysis import stage_2_fit as stage2 + + +class MockRegressor: + instances = [] + + def __init__(self, **kwargs): + self.kwargs = kwargs + self.variable_names = None + self.target = None + self.weights = None + type(self).instances.append(self) + + def fit(self, X, y, variable_names, weights=None): + self.variable_names = list(variable_names) + self.target = np.asarray(y).copy() + self.weights = None if weights is None else np.asarray(weights).copy() + self.constant = float(np.mean(y)) + return self + + def predict(self, X): + return np.full(len(X), self.constant) + + def sympy(self): + return "0.5*u_a" + + equations_ = [ + {"equation": "u_a", "complexity": 1, "loss": 0.2, "score": 0.4}, + {"equation": "0.5*u_a", "complexity": 3, "loss": 0.1, "score": 0.5}, + ] + + +@pytest.fixture(autouse=True) +def reset_mock(): + MockRegressor.instances.clear() + + +def write_scene(root: Path, scene: str, *, offset: float = 0.0, target: bool = False) -> Path: + directory = root / "karman" / scene + directory.mkdir(parents=True) + rows = 8 + sensors = np.arange(rows * 6, dtype=np.float64).reshape(rows, 6) * 1e-5 + forces = np.arange(rows * 6, dtype=np.float64).reshape(rows, 6) * 1e-8 + actions = np.column_stack(( + np.arange(rows) + offset, + np.arange(rows) + 10 + offset, + np.arange(rows) + 20 + offset, + )).astype(np.float64) * 1e-1 + payload = {"sensors": sensors, "forces": forces, "actions_alpha_commanded": actions, "dt_c": 0.4} + if target: + payload["target_forces"] = np.zeros((rows, 2)) + np.savez_compressed(directory / "controlled.npz", **payload) + (directory / "config.json").write_text(json.dumps({ + "scene_id": "karman", "mu": 0.02, "sample_interval": 800, + "u0": 0.01, "action_layout": ["front", "upper", "lower"], + }), encoding="utf-8") + return directory + + +def load_artifacts(paths): + return [json.loads(Path(path).read_text(encoding="utf-8")) for path in paths] + + +def test_per_scene_fits_only_front_and_shared_upper_with_seed_and_schema(tmp_path): + data_root = tmp_path / "data" + write_scene(data_root, "karman_re100") + results_root = tmp_path / "results" / "runs" + + paths = stage2.run_fit( + scenes=["karman_re100"], mode="per-scene", run_id="unit", + data_root=data_root, results_root=results_root, seed=17, + niterations=3, smoke=True, regressor_factory=MockRegressor, + front_deployment_expression="u_a", rear_deployment_expression="-u_a", + ) + + assert [path.name for path in paths] == ["karman_re100_front.json", "karman_re100_rear_shared_upper.json"] + assert all(path.parent == results_root / "unit" / "formulas" for path in paths) + assert len(MockRegressor.instances) == 2 + assert all(instance.kwargs["random_state"] == 17 for instance in MockRegressor.instances) + assert all(instance.kwargs["deterministic"] is True for instance in MockRegressor.instances) + assert all(instance.kwargs["tournament_selection_n"] == 4 for instance in MockRegressor.instances) + assert all(instance.kwargs["maxsize"] == 7 for instance in MockRegressor.instances) + assert all(instance.kwargs["population_size"] == 16 for instance in MockRegressor.instances) + assert all("procs" not in instance.kwargs for instance in MockRegressor.instances) + assert MockRegressor.instances[0].target[0] == pytest.approx(0.3) + assert MockRegressor.instances[0].target[3] == pytest.approx(-0.3) + assert MockRegressor.instances[1].target[0] == pytest.approx(1.3) + assert MockRegressor.instances[1].target[3] == pytest.approx(-2.3) + + front, rear = load_artifacts(paths) + assert front["role"] == "front_odd" + assert rear["role"] == "rear_shared" + assert rear["anchor"] == "upper" + assert front["output"]["native_action_order"] == ["front", "upper", "lower"] + assert front["fitted_expression"] == "0.5*u_a" + assert front["deployment_expression"] == "u_a" + assert front["fitted_expression_hash"] != front["deployment_expression_hash"] + assert front["g_metadata"]["front_rule"] == "front(x)=0.5*(f(x)-f(Gx)); antisymmetric projection" + assert rear["g_metadata"]["rear_rule"] == "lower(x)=-upper(Gx)" + assert front["g_metadata"]["deployment_architecture"] == "mapped_shared" + assert front["g_metadata"]["fit_augmentation"] == "G" + assert set(front["provenance"]["fit"]["diagnostic_metrics"]) == {"r2", "mae", "rmse", "max_error"} + assert front["provenance"]["fit"]["source_n_samples"] == 5 + assert front["provenance"]["fit"]["n_samples"] == 6 + assert front["provenance"]["fit"]["g_augmented"] is True + assert set(front["provenance"]["fit"]["offline_metrics"]) == {"train", "validation", "blind"} + assert "manual_validation_required" in front["provenance"]["fit"]["selection_policy"] + assert front["provenance"]["fit"]["static_safety"]["safe"] is True + assert len(front["provenance"]["pysr"]["pareto_candidates"]) == 2 + candidate = front["provenance"]["pysr"]["pareto_candidates"][0] + assert candidate["used_features"] == ["u_a"] + assert candidate["static_safety"]["safe"] is True + assert set(candidate["metrics"]) == {"train", "validation", "blind"} + assert set(candidate["metrics"]["blind"]) == {"r2", "mae", "rmse", "max_error"} + + +def test_three_head_independent_emits_true_three_head_artifacts(tmp_path): + data_root = tmp_path / "data" + write_scene(data_root, "karman_re100") + paths = stage2.run_fit( + scenes=["karman_re100"], mode="per-scene", run_id="three-head", + data_root=data_root, results_root=tmp_path / "results", + deployment_architecture="three_head_independent", fit_augmentation="none", + regressor_factory=MockRegressor, + ) + assert [path.name for path in paths] == [ + "karman_re100_front.json", "karman_re100_upper.json", "karman_re100_lower.json", + ] + artifacts = load_artifacts(paths) + assert [artifact["role"] for artifact in artifacts] == [ + "front_independent", "upper_independent", "lower_independent", + ] + assert all(artifact["g_metadata"]["deployment_semantics"] == "three_head_independent" for artifact in artifacts) + assert all(artifact["provenance"]["fit"]["g_augmented"] is False for artifact in artifacts) + + +def test_feature_sets_are_complete_and_scene_specific() -> None: + karman = stage2.get_scene("karman_re100") + illusion = stage2.get_scene("illusion_1L") + raw = stage2.feature_names_for_scene(karman, feature_set="raw_complete", joint=False) + symmetry = stage2.feature_names_for_scene(karman, feature_set="symmetry", joint=False) + illusion_raw = stage2.feature_names_for_scene(illusion, feature_set="raw_complete", joint=False) + assert len(raw) == 12 + assert {"v_hat_T", "v_hat_C", "v_hat_B", "Cd_T", "Cd_B", "Cl_T", "Cl_B"} <= set(raw) + assert {"v_s", "v_a", "v_c", "Cd_rear_s", "Cd_rear_a", "Cl_rear_s", "Cl_rear_a"} <= set(symmetry) + assert {"target_Cd", "target_Cl", "Cd_err", "Cl_err"} <= set(illusion_raw) + assert "Cd_err" not in raw and "Cl_err" not in raw + + target_only = stage2.feature_names_for_scene( + illusion, feature_set="physics_reduced", joint=False, feature_profile="target_only", + ) + actual_plus_error = stage2.feature_names_for_scene( + illusion, feature_set="physics_reduced", joint=False, feature_profile="actual_plus_error", + ) + actual_plus_target = stage2.feature_names_for_scene( + illusion, feature_set="physics_reduced", joint=False, feature_profile="actual_plus_target", + ) + assert target_only == ("target_Cd", "target_Cl") + assert actual_plus_error[-2:] == ("Cd_err", "Cl_err") and "target_Cd" not in actual_plus_error + assert actual_plus_target[-2:] == ("target_Cd", "target_Cl") and "Cd_err" not in actual_plus_target + assert stage2.feature_names_for_scene( + karman, feature_set="physics_reduced", joint=False, feature_profile="actual_only", + ) == tuple(stage2.FEATURE_SET_KEYS["physics_reduced"]) + with pytest.raises(ValueError, match="only defined for Illusion"): + stage2.feature_names_for_scene( + karman, feature_set="physics_reduced", joint=False, feature_profile="actual_plus_error", + ) + + +def test_joint_merges_both_heads_and_records_scene_counts(tmp_path): + data_root = tmp_path / "run" + write_scene(data_root, "karman_re100") + write_scene(data_root, "karman_re200", offset=100) + + paths = stage2.run_fit( + scenes=["karman_re100", "karman_re200"], mode="joint", run_id="joint-run", + data_root=data_root, results_root=tmp_path / "results" / "runs", + regressor_factory=MockRegressor, + ) + assert [path.name for path in paths] == ["joint_front.json", "joint_rear_shared_upper.json"] + front, rear = load_artifacts(paths) + assert front["training_scenes"] == ["karman_re100", "karman_re200"] + assert front["provenance"]["fit"]["scene_sample_counts"] == { + "karman_re100": 5, "karman_re200": 5, + } + assert rear["g_metadata"]["rear_rule"] == "lower(x)=-upper(Gx)" + assert all(instance.weights is not None for instance in MockRegressor.instances) + for instance in MockRegressor.instances: + assert instance.weights[:3].sum() == pytest.approx(instance.weights[3:6].sum()) + + +def test_multiple_trajectories_preserve_boundaries_and_provenance(tmp_path): + first_root = tmp_path / "trajectory-a" + second_root = tmp_path / "trajectory-b" + write_scene(first_root, "karman_re100", offset=0) + write_scene(second_root, "karman_re100", offset=100) + + prepared = stage2.prepare_scene_trajectories( + "karman_re100", [first_root, second_root], alignment=stage2.CAUSAL_ALIGNMENT, + joint=False, + ) + assert prepared.dataset.X.shape[0] == 10 + assert [len(item.dataset.X) for item in prepared.trajectories] == [5, 5] + assert prepared.dataset.Y[4, 0] == pytest.approx(0.7) + assert prepared.dataset.Y[5, 0] == pytest.approx(10.3) + + paths = stage2.run_fit( + scenes=["karman_re100"], mode="per-scene", run_id="multi", + data_roots=[first_root, second_root], results_root=tmp_path / "results", + regressor_factory=MockRegressor, + ) + trajectory_records = load_artifacts(paths)[0]["provenance"]["data"]["trajectories"]["karman_re100"] + assert [record["id"] for record in trajectory_records] == [ + "karman_re100:trajectory-001", "karman_re100:trajectory-002", + ] + assert [record["sample_count"] for record in trajectory_records] == [5, 5] + assert all(len(record["hash"]) == 64 for record in trajectory_records) + assert [record["path"] for record in trajectory_records] == [ + str(first_root / "karman" / "karman_re100" / "controlled.npz"), + str(second_root / "karman" / "karman_re100" / "controlled.npz"), + ] + + +def test_refitted_expression_is_canonical_for_sign_changes(): + expression = "-1.0*x + 2.0*y" + _, tree, names, _ = stage2._topology_evaluator(expression, ("x", "y")) + rendered = stage2._render_refitted_expression(tree, names, (-2.0, -3.0)) + assert "--" not in rendered + assert "+ -" not in rendered + + +def test_fixed_topology_refit_uses_all_rows_two_cases_and_preserves_topology(tmp_path): + feature_names = ("u_a",) + scenes = [] + for case_index, name in enumerate(("case_a", "case_b")): + X = np.arange(1 + case_index, 5 + case_index, dtype=np.float64).reshape(-1, 1) + front = 2.0 * X[:, 0] + upper = -1.5 * X[:, 0] + lower = upper + dataset = stage2.BatchDataset( + X, np.column_stack((front, upper, lower)), np.arange(len(X)), + np.array([], dtype=int), + {"alignment": stage2.CAUSAL_ALIGNMENT, "warmup": 0, "history_policy": "unit", + "dt_c": 0.4, "action_mode": "alpha"}, {}, + ) + mirrored = stage2.BatchDataset( + -X, np.column_stack((-front, -lower, -upper)), np.arange(len(X)), + np.array([], dtype=int), dict(dataset.metadata), {}, + ) + trajectory = stage2.PreparedTrajectory( + f"{name}:trajectory-001", tmp_path / f"{name}.npz", None, + dataset, mirrored, f"hash-{name}", + ) + scenes.append(stage2.PreparedScene( + name, {"scene_id": "karman", "u0": 0.01}, trajectory.data_path, None, + dataset, mirrored, feature_names, ("front", "upper", "lower"), + trajectory.data_hash, (trajectory,), + )) + + front = stage2.refit_fixed_topology_head( + scenes, role="front_odd", channel_index=0, run_id="refit", + topology_expression="0.5 * u_a", discovery_parent_path=None, + mode="joint", deployment_architecture="mapped_shared", + feature_set="physics_reduced", feature_profile="actual_only", + environment={}, + ) + rear = stage2.refit_fixed_topology_head( + scenes, role="rear_shared", channel_index=1, run_id="refit", + topology_expression="0.5 * u_a", discovery_parent_path=None, + mode="joint", deployment_architecture="mapped_shared", + feature_set="physics_reduced", feature_profile="actual_only", + environment={}, + ) + + assert front["fitted_expression"].count("u_a") == 1 + assert front["fitted_expression_hash"] == front["deployment_expression_hash"] + assert front["provenance"]["fit"]["optimizer"]["refitted_constants"] == pytest.approx([2.0]) + assert rear["provenance"]["fit"]["optimizer"]["refitted_constants"] == pytest.approx([-1.5]) + assert front["provenance"]["fit"]["n_samples"] == 16 + assert front["provenance"]["fit"]["sample_weighting"] == "case_equal_trajectory_equal_within_case" + metrics = front["provenance"]["fit"]["offline_metrics"] + assert metrics["aggregate"]["rmse"] < 1e-10 + assert set(metrics["per_case"]) == {"case_a", "case_b"} + assert set(metrics["per_trajectory"]) == { + "case_a:trajectory-001", "case_b:trajectory-001", + } + assert front["g_metadata"]["fit_augmentation"] == "G" + + +def test_run_fit_fixed_topology_records_discovery_parent_hash(tmp_path, monkeypatch): + parent = tmp_path / "discovery.json" + parent.write_text('{"artifact": "discovery"}', encoding="utf-8") + prepared = [] + for case_index, name in enumerate(("karman_re100", "karman_re200")): + X = np.arange(1 + case_index, 5 + case_index, dtype=np.float64).reshape(-1, 1) + front = 2.0 * X[:, 0] + upper = -1.5 * X[:, 0] + 0.25 + dataset = stage2.BatchDataset( + X, np.column_stack((front, upper, -upper)), np.arange(len(X)), + np.array([], dtype=int), + {"alignment": stage2.CAUSAL_ALIGNMENT, "warmup": 0, "history_policy": "unit", + "dt_c": 0.4, "action_mode": "alpha"}, {}, + ) + mirrored = stage2.BatchDataset( + X.copy(), np.column_stack((-front, upper, -upper)), np.arange(len(X)), + np.array([], dtype=int), dict(dataset.metadata), {}, + ) + trajectory = stage2.PreparedTrajectory( + f"{name}:trajectory-001", tmp_path / f"{name}.npz", None, + dataset, mirrored, f"hash-{name}", + ) + prepared.append(stage2.PreparedScene( + name, {"scene_id": "karman", "u0": 0.01}, trajectory.data_path, None, + dataset, mirrored, ("u_a",), ("front", "upper", "lower"), + trajectory.data_hash, (trajectory,), + )) + monkeypatch.setattr( + stage2, "prepare_scene_trajectories", + lambda name, *args, **kwargs: prepared[("karman_re100", "karman_re200").index(name)], + ) + + paths = stage2.run_fit( + scenes=["karman_re100", "karman_re200"], mode="joint", run_id="refit-run", + results_root=tmp_path / "results", fit_purpose="fixed-topology-refit", + front_topology_expression="0.5*u_a + 0.1", + rear_topology_expression="0.5*u_a + 0.1", + front_discovery_parent_path=parent, rear_discovery_parent_path=parent, + ) + + assert [path.name for path in paths] == ["joint_front.json", "joint_rear_shared_upper.json"] + artifacts = load_artifacts(paths) + assert not MockRegressor.instances + assert all(artifact["discovery_parent"]["path"] == str(parent) for artifact in artifacts) + assert all(artifact["discovery_parent"]["sha256"] == stage2.hash_file(parent) for artifact in artifacts) + assert all(artifact["provenance"]["fit"]["fit_purpose"] == "fixed-topology-refit" for artifact in artifacts) + + +def test_joint_rejects_cross_objective_scene_id_mixing(): + first = stage2.PreparedScene( + "first", {"scene_id": "karman", "u0": 0.01}, Path("first.npz"), None, + stage2.BatchDataset(np.zeros((1, 1)), np.zeros((1, 3)), np.array([0]), np.array([], dtype=int), + {"action_mode": "alpha"}, {}), + stage2.BatchDataset(np.zeros((1, 1)), np.zeros((1, 3)), np.array([0]), np.array([], dtype=int), + {"action_mode": "alpha"}, {}), + ("u_a",), ("front", "upper", "lower"), "first-hash", + ) + second = stage2.PreparedScene( + "second", {"scene_id": "illusion", "u0": 0.01}, Path("second.npz"), None, + first.dataset, first.mirrored_dataset, first.feature_names, first.action_layout, "second-hash", + ) + with pytest.raises(ValueError, match="same scene_id objective"): + stage2._validate_joint_contract([first, second]) + + +def test_cli_exposes_only_active_architecture_and_comparative_profiles(): + parser = stage2.build_parser() + architecture = next(action for action in parser._actions if action.dest == "deployment_architecture") + augmentation = next(action for action in parser._actions if action.dest == "fit_augmentation") + profile = next(action for action in parser._actions if action.dest == "feature_profile") + assert tuple(architecture.choices) == ("mapped_shared", "three_head_independent") + assert tuple(augmentation.choices) == ("none", "G") + assert tuple(profile.choices) == ( + "actual_only", "target_only", "actual_plus_target", "actual_plus_error", + ) + assert all(action.dest != "symmetry_mode" for action in parser._actions) + parsed = parser.parse_args([ + "--scene", "karman_re100", "--data-root", "first", "--data-root", "second", + "--data-roots", "third,fourth", + ]) + assert parsed.data_root == ["first", "second"] + assert parsed.data_roots == "third,fourth" + + +def test_prepare_only_validates_without_importing_pysr_or_writing(tmp_path): + data_root = tmp_path / "data" + write_scene(data_root, "karman_re100") + results_root = tmp_path / "results" / "runs" + script = Path(stage2.__file__) + code = ( + "import runpy,sys; " + f"sys.argv={[str(script), '--scene', 'karman_re100', '--run-id', 'prepare', '--data-root', str(data_root), '--results-root', str(results_root), '--prepare-only']!r}; " + f"runpy.run_path({str(script)!r}, run_name='__main__')" + ) + completed = subprocess.run([sys.executable, "-c", code], cwd=tmp_path, capture_output=True, text=True) + assert completed.returncode == 0, completed.stderr + assert "pysr" not in sys.modules + assert not results_root.exists() + + +def test_refuses_existing_output_before_fit(tmp_path): + data_root = tmp_path / "data" + write_scene(data_root, "karman_re100") + output = tmp_path / "results" / "runs" / "same" / "formulas" + output.mkdir(parents=True) + with pytest.raises(FileExistsError, match="Refusing"): + stage2.run_fit( + scenes=["karman_re100"], mode="per-scene", run_id="same", + data_root=data_root, results_root=tmp_path / "results" / "runs", + regressor_factory=MockRegressor, + ) + assert not MockRegressor.instances + + +def test_illusion_required_target_fails_during_prepare(tmp_path): + directory = tmp_path / "data" / "illusion" / "illusion_1L" + directory.mkdir(parents=True) + np.savez_compressed( + directory / "controlled.npz", sensors=np.zeros((6, 6)), forces=np.zeros((6, 6)), + actions_alpha_commanded=np.zeros((6, 3)), + ) + with pytest.raises(ValueError, match="require target_forces"): + stage2.prepare_scene("illusion_1L", tmp_path / "data", alignment=stage2.CAUSAL_ALIGNMENT, joint=False) + + +def test_alignment_option_changes_target_pairing(tmp_path): + data_root = tmp_path / "data" + write_scene(data_root, "karman_re100") + causal = stage2.prepare_scene("karman_re100", data_root, alignment=stage2.CAUSAL_ALIGNMENT, joint=False) + historical = stage2.prepare_scene( + "karman_re100", data_root, alignment="historical_post_state_same_action", joint=False, + ) + assert causal.dataset.Y[0, 0] == pytest.approx(0.3) + assert historical.dataset.Y[0, 0] == pytest.approx(0.2) diff --git a/src/SR_analysis/tests/test_telemetry_to_csv.py b/src/SR_analysis/tests/test_telemetry_to_csv.py new file mode 100644 index 0000000..abd87b7 --- /dev/null +++ b/src/SR_analysis/tests/test_telemetry_to_csv.py @@ -0,0 +1,78 @@ +import csv +from pathlib import Path + +import numpy as np + +from SR_analysis import stage_3_validate as stage3 +from SR_analysis.tools.telemetry_to_csv import ( + canonical_arrays, + export, + legacy_similarity, + time_coordinate, +) +from SR_analysis.utils.metrics import legacy_cycle_similarity + + +def test_stage1_prefers_post_state_and_physical_actions(): + data = { + "sensors": np.zeros((2, 6)), + "sensors_post": np.ones((2, 6)), + "forces_post": np.full((2, 6), 2.0), + "actions_alpha_commanded": np.full((2, 3), 3.0), + } + arrays = canonical_arrays(data, "ppo") + assert np.all(arrays["sensors"] == 1.0) + assert np.all(arrays["forces"] == 2.0) + assert np.all(arrays["actions_alpha"] == 3.0) + + +def test_stage3_ppo_schema_preserves_physical_actions(): + data = { + "sensors": np.ones((2, 6)), + "forces": np.ones((2, 6)) * 2, + "actions_alpha": np.ones((2, 3)) * 3, + "actions_omega": np.ones((2, 3)) * 0.03, + } + arrays = canonical_arrays(data, "ppo") + assert np.all(arrays["actions_alpha"] == 3.0) + assert np.all(arrays["actions_omega"] == 0.03) + + +def test_time_coordinate_uses_d_over_u0(): + np.testing.assert_allclose(time_coordinate(3, 800), [0.0, 0.4, 0.8]) + + +def test_csv_and_stage3_share_exact_legacy_metric(): + target = np.arange(48, dtype=np.float64).reshape(8, 6) / 10.0 + state = target[4:].copy() + canonical = legacy_cycle_similarity(target, state, 4) + stage3_metric = stage3.compute_versioned_metrics(target, state, 4)[ + "legacy_reference_cycle_vs_last_recorded_cycle" + ] + csv_metric = legacy_similarity(target, state, 4) + + assert stage3_metric == canonical + assert csv_metric["similarity"] == canonical["similarity"] + assert csv_metric["lag"] == canonical["lag"] + assert csv_metric["per_channel"] == [ + canonical["per_channel"][str(index)] for index in range(6) + ] + + +def test_export_writes_wide_and_long_csv(tmp_path: Path): + trajectory = tmp_path / "trajectory.npz" + np.savez( + trajectory, + sensors=np.ones((4, 6)), + forces=np.ones((4, 6)) * 2, + actions_alpha=np.ones((4, 3)) * 3, + target_sensors=np.ones((60, 6)), + ) + output = tmp_path / "csv" + export(trajectory, output, "case", "sr", 800, 30) + assert (output / "sr_wide.csv").is_file() + assert (output / "sr_long.csv").is_file() + with (output / "sr_wide.csv").open(newline="", encoding="utf-8") as handle: + rows = list(csv.reader(handle)) + assert len(rows) == 5 + assert "sensors_upper_ux" in rows[0] diff --git a/src/SR_analysis/tools/__init__.py b/src/SR_analysis/tools/__init__.py new file mode 100644 index 0000000..72452cc --- /dev/null +++ b/src/SR_analysis/tools/__init__.py @@ -0,0 +1 @@ +"""Artifact and workflow maintenance tools.""" diff --git a/src/SR_analysis/tools/audit_round1.py b/src/SR_analysis/tools/audit_round1.py new file mode 100644 index 0000000..32c5862 --- /dev/null +++ b/src/SR_analysis/tools/audit_round1.py @@ -0,0 +1,352 @@ +#!/usr/bin/env python3 +"""Audit staged round-one SR artifacts without mutating canonical history.""" +from __future__ import annotations + +import argparse +import json +import sys +from dataclasses import dataclass +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Iterable, Mapping + +import numpy as np + +SR_ROOT = Path(__file__).resolve().parents[1] +REPO_ROOT = SR_ROOT.parents[1] +if str(REPO_ROOT / "src") not in sys.path: + sys.path.insert(0, str(REPO_ROOT / "src")) + +from SR_analysis.utils.provenance import atomic_write_json, hash_file, hash_json, sha256_text + +AUDIT_SCHEMA_VERSION = "sr-round1-audit-v1" +CANDIDATE_SCHEMA_VERSION = "sr-candidate-registry-v1" +LINK_HASH_KEYS = { + "npz_hash", "controlled_hash", "target_hash", "telemetry_hash", "validation_hash", +} +# Composite hashes are validated by their producing schema, not as file links. +PROVENANCE_KEYS = ("git_sha", "environment", "command", "created_at") +IGNORED_JSON = {"candidate_registry.json", "audit_report.json"} + + +@dataclass(frozen=True) +class Artifact: + kind: str + path: Path + relative_path: str + digest: str + document: Mapping[str, Any] | None = None + + +def _issue(code: str, message: str, path: str | None = None) -> dict[str, Any]: + result: dict[str, Any] = {"code": code, "message": message} + if path is not None: + result["path"] = path + return result + + +def _read_json(path: Path) -> Mapping[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError("JSON root must be an object") + return value + + +def _classify_json(document: Mapping[str, Any], path: Path) -> str: + if "deployment_expression" in document or "fitted_expression" in document: + return "formula" + if "record_hash" in document or ("metric_version" in document and "telemetry" in document): + return "validation" + if "telemetry" in path.name.lower() or document.get("artifact_type") == "telemetry": + return "telemetry" + return str(document.get("artifact_type", "metadata")) + + +def _artifact_id(artifact: Artifact) -> str: + if artifact.document: + value = artifact.document.get("artifact_id") + if isinstance(value, str) and value.strip(): + return value + return f"{artifact.kind}:{artifact.digest[:16]}" + + +def _path_value(value: Any) -> str | None: + if isinstance(value, str) and value: + return value + if isinstance(value, Mapping): + for key in ("path", "file", "raw_path", "telemetry_path"): + candidate = value.get(key) + if isinstance(candidate, str) and candidate: + return candidate + return None + + +def _resolve_reference(reference: str, owner: Path, roots: Iterable[Path]) -> Path | None: + candidate = Path(reference).expanduser() + attempts = [candidate] if candidate.is_absolute() else [owner.parent / candidate] + if not candidate.is_absolute(): + attempts.extend(root / candidate for root in roots) + for attempt in attempts: + resolved = attempt.resolve() + if resolved.is_file(): + return resolved + return None + + +def _walk_hash_links(value: Any, prefix: str = "") -> Iterable[tuple[str, str]]: + if isinstance(value, Mapping): + for key, child in value.items(): + name = f"{prefix}.{key}" if prefix else str(key) + if key in LINK_HASH_KEYS and isinstance(child, str): + yield name, child + yield from _walk_hash_links(child, name) + elif isinstance(value, list): + for index, child in enumerate(value): + yield from _walk_hash_links(child, f"{prefix}[{index}]") + + +def _validate_npz(artifact: Artifact, errors: list[dict[str, Any]]) -> None: + try: + with np.load(artifact.path, allow_pickle=False) as data: + keys = set(data.files) + if "schema_version" not in keys: + errors.append(_issue("npz_schema_missing", "NPZ lacks schema_version", artifact.relative_path)) + return + schema = str(np.asarray(data["schema_version"]).item()) + if not schema.strip(): + errors.append(_issue("npz_schema_invalid", "NPZ schema_version is empty", artifact.relative_path)) + if artifact.path.name == "controlled.npz": + required = {"sensors_post", "forces_post", "actions_norm", "dt_c"} + missing = sorted(required - keys) + if missing: + errors.append(_issue("npz_fields_missing", f"controlled NPZ missing {missing}", artifact.relative_path)) + elif (data["sensors_post"].ndim != 2 or data["sensors_post"].shape[1] != 6 + or data["forces_post"].shape != data["sensors_post"].shape + or data["actions_norm"].shape != (data["sensors_post"].shape[0], 3)): + errors.append(_issue("npz_shape_invalid", "controlled NPZ array shapes violate the v2 contract", artifact.relative_path)) + if "target" in artifact.path.name and not ({"target_states", "layout"} <= keys): + errors.append(_issue("npz_fields_missing", "target NPZ requires target_states and layout", artifact.relative_path)) + except Exception as exc: + errors.append(_issue("npz_unreadable", str(exc), artifact.relative_path)) + + +def _missing_provenance(provenance: Any) -> list[str]: + if not isinstance(provenance, Mapping): + return list(PROVENANCE_KEYS) + return [key for key in PROVENANCE_KEYS if not provenance.get(key)] + + +def _validate_formula(artifact: Artifact, errors: list[dict[str, Any]]) -> None: + doc = artifact.document or {} + required = { + "schema_version", "artifact_id", "run_id", "feature_names", "fitted_expression", + "deployment_expression", "fitted_expression_hash", "deployment_expression_hash", + "alignment_metadata", "dt_c_metadata", "g_metadata", "provenance", + } + missing = sorted(key for key in required if key not in doc) + if missing: + errors.append(_issue("formula_schema_missing", f"formula missing {missing}", artifact.relative_path)) + return + for expression_key in ("fitted_expression", "deployment_expression"): + expression = doc.get(expression_key) + hash_key = f"{expression_key}_hash" + if not isinstance(expression, str) or not expression.strip(): + errors.append(_issue("formula_expression_invalid", f"{expression_key} must be non-empty", artifact.relative_path)) + else: + actual = sha256_text(expression.strip()) + if doc.get(hash_key) != actual: + errors.append(_issue("expression_hash_mismatch", f"{hash_key} does not hash the actual expression", artifact.relative_path)) + provenance = doc.get("provenance") + if not isinstance(provenance, Mapping) or not all(isinstance(provenance.get(k), Mapping) for k in ("fit", "pysr", "data")): + errors.append(_issue("formula_provenance_missing", "formula provenance requires fit, pysr, and data objects", artifact.relative_path)) + else: + fit_required = ("mode", "seed", "niterations", "scene_sample_counts", "diagnostic_metrics") + missing_fit = [key for key in fit_required if key not in provenance["fit"]] + if missing_fit or not doc.get("git_sha") or not isinstance(doc.get("environment"), Mapping): + errors.append(_issue( + "required_provenance_missing", + f"formula provenance lacks top-level git/environment or fit fields {missing_fit}", + artifact.relative_path, + )) + + +def _validate_validation( + artifact: Artifact, errors: list[dict[str, Any]], roots: Iterable[Path] +) -> None: + doc = artifact.document or {} + required = {"schema_version", "formula_hash", "metric_version", "scene", "mode", "n_steps", "metrics", "telemetry", "provenance", "record_hash"} + missing = sorted(key for key in required if key not in doc) + if missing: + errors.append(_issue("validation_schema_missing", f"validation missing {missing}", artifact.relative_path)) + return + expected_record_hash = hash_json({key: value for key, value in doc.items() if key != "record_hash"}) + if doc.get("record_hash") != expected_record_hash: + errors.append(_issue("record_hash_mismatch", "validation record_hash is invalid", artifact.relative_path)) + if _missing_provenance(doc.get("provenance")): + errors.append(_issue("required_provenance_missing", "validation provenance lacks git_sha/environment/command/created_at", artifact.relative_path)) + telemetry_ref = _path_value(doc.get("telemetry")) + if telemetry_ref is None: + errors.append(_issue("telemetry_reference_missing", "validation must reference raw telemetry", artifact.relative_path)) + elif _resolve_reference(telemetry_ref, artifact.path, roots) is None: + errors.append(_issue("telemetry_missing", f"telemetry does not exist: {telemetry_ref}", artifact.relative_path)) + + +def _discover(data_dir: Path, results_dir: Path, errors: list[dict[str, Any]]) -> list[Artifact]: + artifacts: list[Artifact] = [] + for root, prefix in ((data_dir, "data"), (results_dir, "results")): + if not root.is_dir(): + errors.append(_issue("run_directory_missing", f"required run directory does not exist: {root}")) + continue + for path in sorted(item for item in root.rglob("*") if item.is_file()): + relative = f"{prefix}/{path.relative_to(root).as_posix()}" + if path.name in IGNORED_JSON: + continue + if path.suffix == ".npz": + kind = "telemetry" if any("telemetry" in part.lower() for part in path.parts) else "npz" + artifacts.append(Artifact(kind, path, relative, hash_file(path))) + elif path.suffix == ".json": + try: + document = _read_json(path) + artifacts.append(Artifact(_classify_json(document, path), path, relative, hash_file(path), document)) + except Exception as exc: + errors.append(_issue("json_unreadable", str(exc), relative)) + else: + artifacts.append(Artifact("telemetry" if "telemetry" in path.name.lower() else "artifact", path, relative, hash_file(path))) + return artifacts + + +def _historical_snapshot(sr_root: Path) -> dict[str, Any]: + registry = sr_root / "scene_registry.json" + validations = sr_root / "results" / "validations" + return { + "status": "historical_frozen", + "canonical_registry": {"path": "scene_registry.json", "sha256": hash_file(registry) if registry.is_file() else None}, + "validation_files": sorted(str(path.relative_to(sr_root)) for path in validations.glob("*.json")) if validations.is_dir() else [], + "known_conflicts": [ + "Illusion similarity numbers differ between historical README prose/tables and scene_registry.json; round one records both and does not choose a winner.", + "Karman fitted expressions and deployment expressions differ in historical artifacts/documentation; round one records both and does not choose a winner.", + ], + "adjudication": "none", + } + + +def audit_run( + run_id: str, + data_run_id: str | None = None, + *, + formula_run_id: str | None = None, + sr_root: Path = SR_ROOT, +) -> tuple[dict[str, Any], dict[str, Any]]: + if not run_id or Path(run_id).name != run_id or run_id in {".", ".."}: + raise ValueError("run_id must be one non-empty path component") + data_run_id = data_run_id or run_id + if not data_run_id or Path(data_run_id).name != data_run_id or data_run_id in {".", ".."}: + raise ValueError("data_run_id must be one non-empty path component") + formula_run_id = formula_run_id or run_id + if not formula_run_id or Path(formula_run_id).name != formula_run_id or formula_run_id in {".", ".."}: + raise ValueError("formula_run_id must be one non-empty path component") + data_dir = sr_root / "data" / "runs" / data_run_id + results_dir = sr_root / "results" / "runs" / run_id + formula_dir = sr_root / "results" / "runs" / formula_run_id + results_dir.mkdir(parents=True, exist_ok=True) + errors: list[dict[str, Any]] = [] + warnings: list[dict[str, Any]] = [] + artifacts = _discover(data_dir, results_dir, errors) + if formula_dir.resolve() != results_dir.resolve(): + formula_artifacts = _discover(data_dir, formula_dir, errors) + artifacts.extend(artifact for artifact in formula_artifacts if artifact.kind == "formula") + roots = (data_dir, results_dir, formula_dir, sr_root) + digest_index = {artifact.digest: artifact for artifact in artifacts} + + for artifact in artifacts: + if artifact.kind == "npz" or (artifact.kind == "telemetry" and artifact.path.suffix == ".npz"): + _validate_npz(artifact, errors) + elif artifact.kind == "formula": + _validate_formula(artifact, errors) + elif artifact.kind == "validation": + _validate_validation(artifact, errors, roots) + elif artifact.kind == "telemetry" and artifact.document is not None: + if not artifact.document.get("schema_version"): + errors.append(_issue("telemetry_schema_missing", "telemetry JSON lacks schema_version", artifact.relative_path)) + if artifact.document: + for key, digest in _walk_hash_links(artifact.document): + if len(digest) == 64 and digest not in digest_index: + errors.append(_issue("hash_link_broken", f"{key} references absent artifact hash {digest}", artifact.relative_path)) + + kinds = {artifact.kind for artifact in artifacts} + for kind in ("npz", "formula", "validation", "telemetry"): + if kind not in kinds: + errors.append(_issue("artifact_kind_missing", f"run has no {kind} artifact")) + + audit_passed = not errors + validation_artifacts = [artifact for artifact in artifacts if artifact.kind == "validation"] + validations_complete = bool(validation_artifacts) and all( + artifact.document is not None and artifact.document.get("status") not in {"failed", "incomplete"} + for artifact in validation_artifacts + ) + chain_scientifically_eligible = audit_passed and validations_complete + candidates = [ + { + "artifact_id": _artifact_id(artifact), + "path": artifact.relative_path, + "sha256": artifact.digest, + "run_id": run_id, + "status": ( + "candidate" + if chain_scientifically_eligible + else "rejected" + ), + } + for artifact in artifacts + if artifact.kind in {"formula", "validation"} + ] + candidate_registry = { + "schema_version": CANDIDATE_SCHEMA_VERSION, + "run_id": run_id, + "status": "pass" if audit_passed else "fail", + "promotion_eligible": False, + "scientifically_eligible": chain_scientifically_eligible, + "artifacts": candidates, + } + report = { + "schema_version": AUDIT_SCHEMA_VERSION, + "run_id": run_id, + "data_run_id": data_run_id, + "formula_run_id": formula_run_id, + "generated_at": datetime.now(timezone.utc).isoformat(), + "status": "pass" if not errors else "fail", + "strict_failure": bool(errors), + "summary": {"artifact_count": len(artifacts), "candidate_count": len(candidates), "error_count": len(errors), "warning_count": len(warnings)}, + "errors": errors, + "warnings": warnings, + "artifacts": [{"kind": a.kind, "path": a.relative_path, "sha256": a.digest} for a in artifacts], + "historical": _historical_snapshot(sr_root), + "promotion": {"automatic": False, "required_action": "manual review before any canonical registry edit"}, + "scope_exclusions": ["Vortex", "V5", "Stage 4 figure generation"], + } + atomic_write_json(results_dir / "candidate_registry.json", candidate_registry) + atomic_write_json(results_dir / "audit_report.json", report) + return report, candidate_registry + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--run-id", required=True, help="Stage 2/3 results run identifier") + parser.add_argument("--data-run-id", help="Stage 1 data run identifier; defaults to --run-id") + parser.add_argument("--formula-run-id", help="Stage 2 formula run identifier; defaults to --run-id") + parser.add_argument("--strict", action="store_true", help="return non-zero when any schema or hash chain check fails") + return parser + + +def main(argv: list[str] | None = None) -> int: + args = build_parser().parse_args(argv) + report, _ = audit_run( + args.run_id, args.data_run_id, formula_run_id=args.formula_run_id, sr_root=SR_ROOT, + ) + print(json.dumps(report["summary"], sort_keys=True)) + print(f"audit report: results/runs/{args.run_id}/audit_report.json") + return 1 if args.strict and report["strict_failure"] else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/tools/plot_sr_diagnostics.py b/src/SR_analysis/tools/plot_sr_diagnostics.py new file mode 100644 index 0000000..cf6ce5a --- /dev/null +++ b/src/SR_analysis/tools/plot_sr_diagnostics.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +"""Render compact diagnostic figures from the SR plotting CSV packages.""" +from __future__ import annotations + +import argparse +import csv +from pathlib import Path +from typing import Sequence + +import matplotlib.pyplot as plt +import numpy as np + +TRAINING_SCENES = ( + "karman_re50", "karman_re100", "karman_re200", "karman_re400", + "illusion_0.75L", "illusion_1L", "illusion_1.5L", +) +LABELS = ("K50", "K100", "K200", "K400", "I0.75L", "I1L", "I1.5L") + + +def read_rows(path: Path) -> list[dict[str, str]]: + with path.open(newline="", encoding="utf-8") as handle: + return list(csv.DictReader(handle)) + + +def final_similarity(root: Path, package: str, scene: str, source: str) -> float: + rows = read_rows(root / package / scene / f"{source}_dtw_convergence.csv") + return float(rows[-1]["similarity"]) + + +def save(fig: plt.Figure, output: Path, stem: str) -> None: + output.mkdir(parents=True, exist_ok=True) + fig.savefig(output / f"{stem}.png", dpi=220, bbox_inches="tight") + fig.savefig(output / f"{stem}.pdf", bbox_inches="tight") + plt.close(fig) + + +def plot_performance(root: Path, output: Path) -> None: + standard = "article2-timeseries-csv-20260720" + long_sr = "article2-long-timeseries-csv-20260720" + ppo200 = [final_similarity(root, standard, scene, "ppo") for scene in TRAINING_SCENES] + sr200 = [final_similarity(root, standard, scene, "sr") for scene in TRAINING_SCENES] + sr400 = [final_similarity(root, long_sr, scene, "sr") for scene in TRAINING_SCENES] + long_ppo_root = root / "article2-plotting-package-20260721/long_ppo" + ppo400 = [] + for scene in TRAINING_SCENES: + path = long_ppo_root / scene / "ppo_dtw_convergence.csv" + ppo400.append(float(read_rows(path)[-1]["similarity"]) if path.is_file() else np.nan) + + x = np.arange(len(LABELS)) + width = 0.2 + fig, ax = plt.subplots(figsize=(11, 4.8)) + ax.bar(x - 1.5 * width, ppo200, width, label="PPO 200") + ax.bar(x - 0.5 * width, sr200, width, label="SR 200") + ax.bar(x + 0.5 * width, ppo400, width, label="PPO 400") + ax.bar(x + 1.5 * width, sr400, width, label="SR 400") + ax.set_ylabel("Legacy DTW similarity") + ax.set_xlabel("Training case") + ax.set_xticks(x, LABELS) + ax.set_ylim(0.65, 1.01) + ax.grid(axis="y", alpha=0.25) + ax.legend(ncol=4, frameon=False) + ax.set_title("Closed-loop PPO and symbolic-controller performance") + fig.tight_layout() + save(fig, output, "01_training_case_performance") + + +def plot_generalization(repo_root: Path, output: Path) -> None: + path = repo_root / "src/SR_analysis/results/runs/article2-generalization-summary-20260720/generalization.csv" + rows = read_rows(path) + fig, axes = plt.subplots(1, 2, figsize=(10.5, 4.2)) + for ax, objective, xlabel in zip(axes, ("karman", "illusion"), ("Code Reynolds label", "Target-size label L")): + subset = sorted((row for row in rows if row["objective"] == objective), key=lambda row: float(row["parameter"])) + interpolation = [row for row in subset if row["classification"] == "interpolation"] + extrapolation = [row for row in subset if row["classification"] == "extrapolation"] + ax.plot([float(r["parameter"]) for r in subset], [float(r["legacy_dtw"]) for r in subset], color="0.6", linewidth=1) + ax.scatter([float(r["parameter"]) for r in interpolation], [float(r["legacy_dtw"]) for r in interpolation], label="Interpolation", s=48) + ax.scatter([float(r["parameter"]) for r in extrapolation], [float(r["legacy_dtw"]) for r in extrapolation], label="Extrapolation", marker="s", s=48) + ax.set_xlabel(xlabel) + ax.set_ylabel("Legacy DTW similarity") + ax.set_ylim(0.7, 1.01) + ax.grid(alpha=0.25) + ax.legend(frameon=False) + ax.set_title("Kármán unseen Re" if objective == "karman" else "Illusion unseen target size") + fig.tight_layout() + save(fig, output, "02_pointwise_generalization") + + +def plot_steady(repo_root: Path, output: Path) -> None: + path = repo_root / "src/SR_analysis/results/runs/article2-steady-analysis-20260720/steady_rotation_sweep.csv" + rows = read_rows(path) + magnitude = np.asarray([float(row["rear_alpha_magnitude"]) for row in rows]) + similarity = np.asarray([float(row["legacy_dtw"]) for row in rows]) + error = np.asarray([float(row["mean_abs_sensor_error"]) for row in rows]) + fig, ax = plt.subplots(figsize=(7.2, 4.5)) + secondary = ax.twinx() + ax.plot(magnitude, similarity, marker="o", label="DTW similarity") + secondary.plot(magnitude, error, marker="s", linestyle="--", label="Mean sensor error", color="tab:orange") + ax.axvline(3.4312086805, linestyle=":", color="0.35", label="SR rear constant 3.4312") + ax.set_xlabel("Rear counter-rotation magnitude |α|") + ax.set_ylabel("Legacy DTW similarity") + secondary.set_ylabel("Mean absolute sensor error") + ax.set_ylim(0.6, 1.01) + ax.grid(alpha=0.25) + lines = ax.get_lines() + secondary.get_lines() + ax.legend(lines, [line.get_label() for line in lines], frameon=False, loc="center right") + ax.set_title("Steady-cloak constant-rotation calibration") + fig.tight_layout() + save(fig, output, "03_steady_rotation_calibration") + + +def plot_offline_residuals(root: Path, output: Path) -> None: + rows = read_rows(root / "article2-plotting-package-20260721/offline_predictions.csv") + rmse = {scene: [] for scene in TRAINING_SCENES} + for scene in TRAINING_SCENES: + subset = [row for row in rows if row["scene"] == scene] + for action in ("front", "upper", "lower"): + residual = np.asarray([float(row[f"residual_alpha_{action}"]) for row in subset]) + rmse[scene].append(float(np.sqrt(np.mean(residual**2)))) + fig, ax = plt.subplots(figsize=(10.5, 4.5)) + x = np.arange(len(LABELS)) + width = 0.25 + for index, action in enumerate(("Front", "Upper", "Lower")): + ax.bar(x + (index - 1) * width, [rmse[scene][index] for scene in TRAINING_SCENES], width, label=action) + ax.set_xticks(x, LABELS) + ax.set_xlabel("Training case") + ax.set_ylabel("Offline action RMSE in α") + ax.set_title("SR imitation error on PPO-visited causal states") + ax.grid(axis="y", alpha=0.25) + ax.legend(frameon=False, ncol=3) + fig.tight_layout() + save(fig, output, "04_offline_action_rmse") + + +def plot_ablation(root: Path, output: Path) -> None: + rows = read_rows(root / "article2-plotting-package-20260721/ablation_summary.csv") + karman_variants = ("k_front0", "k_rear0", "k_rear1") + karman_scenes = ("karman_re50", "karman_re100", "karman_re200", "karman_re400") + illusion_variants = ("i_front0", "i_front1", "i_rear0", "i_rear1") + illusion_scenes = ("illusion_0.75L", "illusion_1L", "illusion_1.5L") + fig, axes = plt.subplots(1, 2, figsize=(11.5, 4.5)) + for ax, variants, scenes, title in ( + (axes[0], karman_variants, karman_scenes, "Kármán 40-step term deletion"), + (axes[1], illusion_variants, illusion_scenes, "Illusion 40-step term deletion"), + ): + x = np.arange(len(scenes)) + width = 0.8 / len(variants) + for index, variant in enumerate(variants): + values = [float(next(row["similarity"] for row in rows if row["variant"] == variant and row["scene"] == scene)) for scene in scenes] + ax.bar(x + (index - (len(variants) - 1) / 2) * width, values, width, label=variant) + ax.set_xticks(x, [scene.replace("karman_", "").replace("illusion_", "") for scene in scenes]) + ax.set_xlabel("Case") + ax.set_ylabel("Legacy DTW similarity") + ax.set_ylim(0.6, 1.0) + ax.grid(axis="y", alpha=0.25) + ax.legend(frameon=False, fontsize=8) + ax.set_title(title) + fig.tight_layout() + save(fig, output, "05_term_deletion") + + +def plot_example_timeseries(root: Path, output: Path) -> None: + package = root / "article2-timeseries-csv-20260720/karman_re100" + sr = read_rows(package / "sr_wide.csv") + ppo = read_rows(package / "ppo_wide.csv") + target = read_rows(package / "target_wide.csv") + fig, axes = plt.subplots(2, 1, figsize=(10.5, 6.2), sharex=False) + axes[0].plot([float(r["t_D"]) for r in target], [float(r["sensors_center_ux"]) for r in target], label="Target reference") + axes[0].plot([float(r["t_D"]) for r in ppo], [float(r["sensors_center_ux"]) for r in ppo], label="PPO") + axes[0].plot([float(r["t_D"]) for r in sr], [float(r["sensors_center_ux"]) for r in sr], label="SR") + axes[0].set_ylabel("Center sensor ux / U0") + axes[0].set_xlabel("t U0 / D") + axes[0].legend(frameon=False, ncol=3) + axes[0].grid(alpha=0.25) + axes[0].set_title("Kármán Re100 observer signal") + for action, label in (("front", "Front"), ("upper", "Upper"), ("lower", "Lower")): + axes[1].plot([float(r["t_D"]) for r in sr], [float(r[f"actions_alpha_{action}"]) for r in sr], label=label) + axes[1].set_ylabel("SR action α") + axes[1].set_xlabel("t U0 / D") + axes[1].legend(frameon=False, ncol=3) + axes[1].grid(alpha=0.25) + fig.tight_layout() + save(fig, output, "06_example_timeseries_karman_re100") + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo-root", type=Path, default=Path(__file__).resolve().parents[3]) + parser.add_argument("--output-dir", type=Path, required=True) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + repo_root = args.repo_root.resolve() + run_root = repo_root / "src/SR_analysis/results/runs" + output = args.output_dir if args.output_dir.is_absolute() else repo_root / args.output_dir + plt.style.use("seaborn-v0_8-whitegrid") + plot_performance(run_root, output) + plot_generalization(repo_root, output) + plot_steady(repo_root, output) + plot_offline_residuals(run_root, output) + plot_ablation(run_root, output) + plot_example_timeseries(run_root, output) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/tools/prepare_plotting_data.py b/src/SR_analysis/tools/prepare_plotting_data.py new file mode 100644 index 0000000..64a82b2 --- /dev/null +++ b/src/SR_analysis/tools/prepare_plotting_data.py @@ -0,0 +1,363 @@ +#!/usr/bin/env python3 +"""Build plotting-ready SR datasets from immutable article artifacts.""" +from __future__ import annotations + +import argparse +import csv +import json +from copy import deepcopy +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np + +from SR_analysis.configs import get_scene +from SR_analysis.stage_2_fit import prepare_scene +from SR_analysis.tools.telemetry_to_csv import export as export_trajectory +from SR_analysis.utils.data_contracts import CAUSAL_ALIGNMENT +from SR_analysis.utils.feature_state import StatefulFeatureBuilder +from SR_analysis.utils.formula_schema import ( + decompose_additive_terms, + evaluate_formula, + load_formula, +) +from SR_analysis.utils.g_operator import apply_G_alpha, apply_G_raw12, apply_G_target_force +from SR_analysis.utils.provenance import atomic_write_json, hash_file + +TRAINING_SCENES = ( + "karman_re50", + "karman_re100", + "karman_re200", + "karman_re400", + "illusion_0.75L", + "illusion_1L", + "illusion_1.5L", +) +ACTION_NAMES = ("front", "upper", "lower") +FORMULA_ROOTS = { + "karman": Path("src/SR_analysis/results/runs/article-refit-karman-topology-a-20260718/formulas"), + "illusion": Path("src/SR_analysis/results/runs/article-refit-illusion-topology-a-20260718/formulas"), +} +DATA_ROOTS = { + "karman": Path("src/SR_analysis/data/runs/article-joint-data-karman-20260718"), + "illusion": Path("src/SR_analysis/data/runs/article-joint-data-illusion-20260718"), +} +LONG_RUNS = { + "karman": Path("src/SR_analysis/results/runs/article2-long-karman-20260720"), + "illusion": Path("src/SR_analysis/results/runs/article2-long-illusion-20260720"), +} + + +def _formula_paths(scene_id: str) -> tuple[Path, Path]: + root = FORMULA_ROOTS[scene_id] + return root / "joint_front.json", root / "joint_rear_shared_upper.json" + + +def _term_formula(formula: Mapping[str, Any], expression: str) -> dict[str, Any]: + item = deepcopy(dict(formula)) + for key in ("source_path", "source_hash", "deployment_expression_hash"): + item.pop(key, None) + item["deployment_expression"] = expression + return item + + +def _mapped_predictions( + front: Mapping[str, Any], + rear: Mapping[str, Any], + X: np.ndarray, + GX: np.ndarray, +) -> tuple[np.ndarray, dict[str, np.ndarray]]: + front_x = np.asarray(evaluate_formula(front, X), dtype=float) + front_g = np.asarray(evaluate_formula(front, GX), dtype=float) + upper = np.asarray(evaluate_formula(rear, X), dtype=float) + lower = -np.asarray(evaluate_formula(rear, GX), dtype=float) + prediction = np.column_stack((0.5 * (front_x - front_g), upper, lower)) + contributions: dict[str, np.ndarray] = {} + for index, term in enumerate(decompose_additive_terms(front["deployment_expression"], front["feature_names"])): + term_formula = _term_formula(front, term) + contribution = 0.5 * ( + np.asarray(evaluate_formula(term_formula, X), dtype=float) + - np.asarray(evaluate_formula(term_formula, GX), dtype=float) + ) + contributions[f"front_t{index}_{term}"] = contribution + for index, term in enumerate(decompose_additive_terms(rear["deployment_expression"], rear["feature_names"])): + term_formula = _term_formula(rear, term) + contributions[f"upper_t{index}_{term}"] = np.asarray(evaluate_formula(term_formula, X), dtype=float) + contributions[f"lower_t{index}_{term}"] = -np.asarray(evaluate_formula(term_formula, GX), dtype=float) + return prediction, contributions + + +def _safe_name(value: str) -> str: + return "".join(character if character.isalnum() else "_" for character in value).strip("_") + + +def _write_rows(path: Path, fieldnames: Sequence[str], rows: Sequence[Mapping[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + writer.writerows(rows) + + +def export_offline_predictions(repo_root: Path, output_dir: Path) -> dict[str, Any]: + rows: list[dict[str, Any]] = [] + term_rows: list[dict[str, Any]] = [] + feature_names: set[str] = set() + term_names: set[str] = set() + for scene in TRAINING_SCENES: + cfg = get_scene(scene) + scene_id = str(cfg["scene_id"]) + front_path, rear_path = (repo_root / path for path in _formula_paths(scene_id)) + front, rear = load_formula(front_path), load_formula(rear_path) + prepared = prepare_scene( + scene, + repo_root / DATA_ROOTS[scene_id], + alignment=CAUSAL_ALIGNMENT, + joint=True, + feature_set="symmetry", + feature_profile="actual_plus_target" if scene_id == "illusion" else "actual_only", + ) + dataset = prepared.dataset + mirrored = prepared.mirrored_dataset + prediction, contributions = _mapped_predictions(front, rear, dataset.X, mirrored.X) + feature_names.update(prepared.feature_names) + term_names.update(_safe_name(name) for name in contributions) + target_indices = np.asarray(dataset.metadata["target_indices"], dtype=int) + for row_index, (state_index, target_index) in enumerate(zip(dataset.kept_indices, target_indices)): + base: dict[str, Any] = { + "objective": scene_id, + "scene": scene, + "state_step": int(state_index), + "target_action_step": int(target_index), + "t_D": float(state_index) * float(cfg["control_dt"]), + } + for column, name in enumerate(prepared.feature_names): + base[f"feature_{name}"] = float(dataset.X[row_index, column]) + for channel, action_name in enumerate(ACTION_NAMES): + actual = float(dataset.Y[row_index, channel]) + predicted = float(prediction[row_index, channel]) + base[f"actual_alpha_{action_name}"] = actual + base[f"predicted_alpha_{action_name}"] = predicted + base[f"residual_alpha_{action_name}"] = actual - predicted + rows.append(base) + term_base = { + "objective": scene_id, + "scene": scene, + "source": "ppo_state_offline", + "step": int(state_index), + "t_D": float(state_index) * float(cfg["control_dt"]), + } + for name, values in contributions.items(): + term_base[f"term_{_safe_name(name)}"] = float(values[row_index]) + for channel, action_name in enumerate(ACTION_NAMES): + term_base[f"predicted_alpha_{action_name}"] = float(prediction[row_index, channel]) + term_rows.append(term_base) + + offline_fields = [ + "objective", "scene", "state_step", "target_action_step", "t_D", + *(f"feature_{name}" for name in sorted(feature_names)), + *(f"actual_alpha_{name}" for name in ACTION_NAMES), + *(f"predicted_alpha_{name}" for name in ACTION_NAMES), + *(f"residual_alpha_{name}" for name in ACTION_NAMES), + ] + term_fields = [ + "objective", "scene", "source", "step", "t_D", + *(f"term_{name}" for name in sorted(term_names)), + *(f"predicted_alpha_{name}" for name in ACTION_NAMES), + ] + _write_rows(output_dir / "offline_predictions.csv", offline_fields, rows) + _write_rows(output_dir / "term_contributions_ppo_states.csv", term_fields, term_rows) + return {"offline_rows": len(rows), "offline_term_rows": len(term_rows)} + + +def _telemetry_path(run_root: Path, scene: str) -> Path: + matches = sorted((run_root / "telemetry").glob(f"{scene.replace('.', 'p')}__pysr__*.npz")) + if len(matches) != 1: + raise FileNotFoundError(f"expected one SR telemetry for {scene} below {run_root}, found {matches}") + return matches[0] + + +def export_closed_loop_terms(repo_root: Path, output_dir: Path) -> dict[str, Any]: + rows: list[dict[str, Any]] = [] + all_terms: set[str] = set() + for scene in TRAINING_SCENES: + cfg = get_scene(scene) + scene_id = str(cfg["scene_id"]) + front_path, rear_path = (repo_root / path for path in _formula_paths(scene_id)) + front, rear = load_formula(front_path), load_formula(rear_path) + telemetry_path = _telemetry_path(repo_root / LONG_RUNS[scene_id], scene) + with np.load(telemetry_path, allow_pickle=False) as data: + sensors = np.asarray(data["sensors"], dtype=float) + forces = np.asarray(data["forces"], dtype=float) + actions = np.asarray(data["actions_omega"], dtype=float) + target_forces = np.asarray(data["target_forces"], dtype=float) + common = dict( + mu=float(cfg["mu"]), dt_c=float(cfg["control_dt"]), u0=float(cfg["u0"]), + d=20.0, action_mode="physical", include_mu=True, include_cos_sin=True, + require_target=scene_id == "illusion", + ) + builder = StatefulFeatureBuilder(**common) + g_builder = StatefulFeatureBuilder(**common) + initial_omega = np.asarray(cfg["policy_init_action"], dtype=float) * float(cfg["u0"]) + for _ in range(2): + builder.observe_action(initial_omega) + g_builder.observe_action(apply_G_alpha(initial_omega)) + front_terms = decompose_additive_terms(front["deployment_expression"], front["feature_names"]) + rear_terms = decompose_additive_terms(rear["deployment_expression"], rear["feature_names"]) + for step in range(len(sensors) - 1): + # Telemetry state ``step`` is post-action ``step``; under the causal + # contract it predicts the command applied at ``step + 1``. + builder.observe_action(actions[step]) + g_builder.observe_action(apply_G_alpha(actions[step])) + raw = np.hstack((sensors[step], forces[step])) + target = target_forces[step] if len(target_forces) else None + features, _ = builder.step(raw[:6], raw[6:], target_force=target) + g_raw = apply_G_raw12(raw) + g_target = None if target is None else apply_G_target_force(target) + g_features, _ = g_builder.step(g_raw[:6], g_raw[6:], target_force=g_target) + row: dict[str, Any] = { + "objective": scene_id, + "scene": scene, + "source": "sr_closed_loop_400", + "state_step": step, + "target_action_step": step + 1, + "t_D": step * float(cfg["control_dt"]), + } + predicted = np.zeros(3, dtype=float) + for index, term in enumerate(front_terms): + formula = _term_formula(front, term) + value = 0.5 * ( + float(evaluate_formula(formula, features)) + - float(evaluate_formula(formula, g_features)) + ) + name = f"front_t{index}_{_safe_name(term)}" + row[f"term_{name}"] = value + all_terms.add(name) + predicted[0] += value + for index, term in enumerate(rear_terms): + formula = _term_formula(rear, term) + upper = float(evaluate_formula(formula, features)) + lower = -float(evaluate_formula(formula, g_features)) + upper_name = f"upper_t{index}_{_safe_name(term)}" + lower_name = f"lower_t{index}_{_safe_name(term)}" + row[f"term_{upper_name}"] = upper + row[f"term_{lower_name}"] = lower + all_terms.update((upper_name, lower_name)) + predicted[1] += upper + predicted[2] += lower + for channel, action_name in enumerate(ACTION_NAMES): + row[f"predicted_alpha_{action_name}"] = float(predicted[channel]) + target_action = float(actions[step + 1, channel] / cfg["u0"]) + row[f"target_alpha_{action_name}"] = target_action + row[f"residual_alpha_{action_name}"] = target_action - float(predicted[channel]) + rows.append(row) + fields = [ + "objective", "scene", "source", "state_step", "target_action_step", "t_D", + *(f"term_{name}" for name in sorted(all_terms)), + *(f"predicted_alpha_{name}" for name in ACTION_NAMES), + *(f"target_alpha_{name}" for name in ACTION_NAMES), + *(f"residual_alpha_{name}" for name in ACTION_NAMES), + ] + _write_rows(output_dir / "term_contributions_sr_closed_loop_400.csv", fields, rows) + return {"closed_loop_term_rows": len(rows)} + + +def export_ablation(repo_root: Path, output_dir: Path) -> dict[str, Any]: + rows: list[dict[str, Any]] = [] + for run_dir in sorted((repo_root / "src/SR_analysis/results/runs").glob("article-ablation-L2-*-20260718")): + for path in sorted((run_dir / "validations").glob("*.json")): + record = json.loads(path.read_text(encoding="utf-8")) + metric = record.get("metrics", {}).get("legacy_reference_cycle_vs_last_recorded_cycle") + rows.append({ + "run_id": run_dir.name, + "variant": run_dir.name.removeprefix("article-ablation-L2-").removesuffix("-20260718"), + "scene": record.get("scene"), + "steps": record.get("n_steps"), + "status": record.get("status"), + "similarity": None if metric is None else metric.get("similarity"), + "lag": None if metric is None else metric.get("lag"), + "termination_reason": record.get("termination_reason"), + "formula_hash": record.get("formula_hash"), + }) + fields = ("run_id", "variant", "scene", "steps", "status", "similarity", "lag", "termination_reason", "formula_hash") + _write_rows(output_dir / "ablation_summary.csv", fields, rows) + + scaling_path = repo_root / "src/SR_analysis/results/runs/article-scaling-L1-v2-20260718/summary.json" + scaling = json.loads(scaling_path.read_text(encoding="utf-8"))["rows"] + scaling_rows = [] + for item in scaling: + row = {key: item.get(key) for key in ("family", "scene", "variant", "failed", "n")} + for metric in ("action_min", "action_max", "action_rms"): + values = item.get(metric) or [None, None, None] + for index, action_name in enumerate(ACTION_NAMES): + row[f"{metric}_{action_name}"] = values[index] + scaling_rows.append(row) + scaling_fields = [ + "family", "scene", "variant", "failed", "n", + *(f"{metric}_{name}" for metric in ("action_min", "action_max", "action_rms") for name in ACTION_NAMES), + ] + _write_rows(output_dir / "scaling_summary.csv", scaling_fields, scaling_rows) + return {"ablation_rows": len(rows), "scaling_rows": len(scaling_rows)} + + +def export_steady(repo_root: Path, output_dir: Path) -> dict[str, Any]: + count = 0 + for magnitude in range(7): + run = repo_root / f"src/SR_analysis/results/runs/article2-steady-sweep-a{magnitude}-20260720" + telemetry = run / "telemetry/steady__constant__f-none__si-800.npz" + export_trajectory( + telemetry, + output_dir / "steady_timeseries" / f"alpha_{magnitude}", + "steady", + "sr", + 800, + 30, + ) + count += 1 + return {"steady_timeseries_cases": count} + + +def build_manifest(repo_root: Path, output_dir: Path, summary: Mapping[str, Any]) -> None: + artifacts = [] + for path in sorted( + candidate + for pattern in ("*.csv", "*.png", "*.pdf") + for candidate in output_dir.rglob(pattern) + ): + artifacts.append({"path": str(path.relative_to(repo_root)), "sha256": hash_file(path)}) + atomic_write_json( + output_dir / "manifest.json", + { + "schema_version": "sr-plotting-package-v1", + "source_policy": "immutable article and Article2 artifacts; no scientific refit", + "summary": dict(summary), + "artifacts": artifacts, + }, + ) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--repo-root", type=Path, default=Path(__file__).resolve().parents[3]) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--replace", action="store_true", help="replace derived files in output-dir") + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + repo_root = args.repo_root.resolve() + output_dir = args.output_dir if args.output_dir.is_absolute() else repo_root / args.output_dir + if output_dir.exists() and any(output_dir.iterdir()) and not args.replace: + raise FileExistsError(f"refusing to overwrite non-empty plotting package: {output_dir}") + summary: dict[str, Any] = {} + summary.update(export_offline_predictions(repo_root, output_dir)) + summary.update(export_closed_loop_terms(repo_root, output_dir)) + summary.update(export_ablation(repo_root, output_dir)) + summary.update(export_steady(repo_root, output_dir)) + build_manifest(repo_root, output_dir, summary) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/tools/telemetry_to_csv.py b/src/SR_analysis/tools/telemetry_to_csv.py new file mode 100644 index 0000000..4063c50 --- /dev/null +++ b/src/SR_analysis/tools/telemetry_to_csv.py @@ -0,0 +1,162 @@ +#!/usr/bin/env python3 +"""Export Stage 1 or Stage 3 trajectories to analysis-ready CSV files.""" +from __future__ import annotations + +import argparse +import csv +import json +from pathlib import Path +from typing import Any, Mapping, Sequence + +import numpy as np + +from SR_analysis.utils.metrics import legacy_cycle_similarity + +SENSOR_NAMES = ("upper_ux", "upper_uy", "center_ux", "center_uy", "lower_ux", "lower_uy") +FORCE_NAMES = ("front_fx", "front_fy", "upper_fx", "upper_fy", "lower_fx", "lower_fy") +ACTION_NAMES = ("front", "upper", "lower") + + +def load_trajectory(path: Path) -> dict[str, np.ndarray]: + with np.load(path, allow_pickle=False) as data: + return {name: np.asarray(data[name]) for name in data.files} + + +def canonical_arrays(data: Mapping[str, np.ndarray], source: str) -> dict[str, np.ndarray]: + result: dict[str, np.ndarray] = {} + sensors_key = "sensors_post" if source == "ppo" and "sensors_post" in data else "sensors" + forces_key = "forces_post" if source == "ppo" and "forces_post" in data else "forces" + if sensors_key in data: + result["sensors"] = np.asarray(data[sensors_key], dtype=float).reshape(-1, 6) + if forces_key in data: + result["forces"] = np.asarray(data[forces_key], dtype=float).reshape(-1, 6) + if source == "ppo": + if "actions_alpha_commanded" in data: + result["actions_alpha"] = np.asarray(data["actions_alpha_commanded"], dtype=float).reshape(-1, 3) + elif "actions_alpha" in data: + result["actions_alpha"] = np.asarray(data["actions_alpha"], dtype=float).reshape(-1, 3) + elif "actions" in data: + result["actions_alpha"] = np.asarray(data["actions"], dtype=float).reshape(-1, 3) + if "actions_omega_commanded" in data: + result["actions_omega"] = np.asarray(data["actions_omega_commanded"], dtype=float).reshape(-1, 3) + elif "actions_omega" in data: + result["actions_omega"] = np.asarray(data["actions_omega"], dtype=float).reshape(-1, 3) + else: + for key in ("actions_alpha", "actions_omega"): + if key in data: + result[key] = np.asarray(data[key], dtype=float).reshape(-1, 3) + for key in ("target_sensors", "target_forces", "target_phase"): + if key in data and np.asarray(data[key]).size: + width = 6 if key == "target_sensors" else 2 if key == "target_forces" else 1 + result[key] = np.asarray(data[key]).reshape(-1, width) + if "target_states" in data: + target_states = np.asarray(data["target_states"], dtype=float) + if target_states.shape[1] == 6: + result["sensors"] = target_states + elif target_states.shape[1] == 8: + result["target_forces"] = target_states[:, :2] + result["sensors"] = target_states[:, 2:8] + else: + raise ValueError(f"unsupported target_states width: {target_states.shape[1]}") + return result + + +def time_coordinate(n: int, sample_interval: int, u0: float = 0.01, diameter: float = 20.0) -> np.ndarray: + return np.arange(n, dtype=float) * sample_interval * u0 / diameter + + +def write_wide_csv(path: Path, arrays: Mapping[str, np.ndarray], scene: str, source: str, sample_interval: int) -> None: + n = max((len(value) for key, value in arrays.items() if key != "target_sensors"), default=0) + columns: list[tuple[str, np.ndarray]] = [] + for group, names in (("sensors", SENSOR_NAMES), ("forces", FORCE_NAMES), ("actions_alpha", ACTION_NAMES), ("actions_omega", ACTION_NAMES), ("target_forces", ("target_cd", "target_cl")), ("target_phase", ("target_phase",))): + if group in arrays: + values = np.asarray(arrays[group]).reshape(len(arrays[group]), -1) + columns.extend((f"{group}_{name}", values[:, index]) for index, name in enumerate(names)) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.writer(handle) + writer.writerow(("scene", "source", "step", "t_D", *(name for name, _ in columns))) + times = time_coordinate(n, sample_interval) + for index in range(n): + writer.writerow((scene, source, index, times[index], *(values[index] if index < len(values) else "" for _, values in columns))) + + +def write_long_csv(path: Path, arrays: Mapping[str, np.ndarray], scene: str, source: str, sample_interval: int) -> None: + groups = (("sensors", SENSOR_NAMES, "velocity"), ("forces", FORCE_NAMES, "force"), ("actions_alpha", ACTION_NAMES, "alpha"), ("actions_omega", ACTION_NAMES, "omega"), ("target_forces", ("target_cd", "target_cl"), "target_force")) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.writer(handle) + writer.writerow(("scene", "source", "step", "t_D", "channel_group", "channel_name", "value")) + for key, names, group in groups: + if key not in arrays: + continue + values = np.asarray(arrays[key]).reshape(len(arrays[key]), -1) + times = time_coordinate(len(values), sample_interval) + for step, row in enumerate(values): + for channel, value in zip(names, row): + writer.writerow((scene, source, step, times[step], group, channel, float(value))) + + +def legacy_similarity(target: np.ndarray, state: np.ndarray, conv_len: int) -> dict[str, Any]: + metric = legacy_cycle_similarity(target, state, conv_len) + return { + "similarity": metric["similarity"], + "lag": metric["lag"], + "per_channel": [metric["per_channel"][str(index)] for index in range(6)], + } + + +def write_convergence_csv(path: Path, sensors: np.ndarray, target: np.ndarray, conv_len: int, scene: str, source: str, sample_interval: int) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + endpoints = list(range(max(2 * conv_len, conv_len), len(sensors) + 1, conv_len)) + if not endpoints or endpoints[-1] != len(sensors): + endpoints.append(len(sensors)) + with path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.writer(handle) + writer.writerow(("scene", "source", "end_step", "t_D", "conv_len", "similarity", "lag", *(f"channel_{i}" for i in range(6)))) + for end_step in endpoints: + if end_step < conv_len or len(target) < 2 * conv_len: + continue + metric = legacy_similarity(target, sensors[:end_step], conv_len) + writer.writerow((scene, source, end_step, end_step * sample_interval * 0.01 / 20.0, conv_len, metric["similarity"], metric["lag"], *metric["per_channel"])) + + +def export(path: Path, output_dir: Path, scene: str, source: str, sample_interval: int, conv_len: int, target_path: Path | None = None) -> dict[str, Any]: + arrays = canonical_arrays(load_trajectory(path), source) + if target_path is not None: + target_data = canonical_arrays(load_trajectory(target_path), "target") + if "sensors" in target_data: + arrays["target_sensors"] = target_data["sensors"] + write_wide_csv(output_dir / f"{source}_wide.csv", arrays, scene, source, sample_interval) + write_long_csv(output_dir / f"{source}_long.csv", arrays, scene, source, sample_interval) + if "target_sensors" in arrays: + target_arrays = {"sensors": arrays["target_sensors"]} + write_wide_csv(output_dir / "target_wide.csv", target_arrays, scene, "target", sample_interval) + write_long_csv(output_dir / "target_long.csv", target_arrays, scene, "target", sample_interval) + if "sensors" in arrays: + write_convergence_csv(output_dir / f"{source}_dtw_convergence.csv", arrays["sensors"], arrays["target_sensors"], conv_len, scene, source, sample_interval) + summary = {"scene": scene, "source": source, "input": str(path), "sample_interval": sample_interval, "conv_len": conv_len, "rows": {key: len(value) for key, value in arrays.items()}} + (output_dir / f"{source}_manifest.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8") + return summary + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--scene", required=True) + parser.add_argument("--source", choices=("ppo", "sr"), required=True) + parser.add_argument("--sample-interval", type=int, required=True) + parser.add_argument("--conv-len", type=int, required=True) + parser.add_argument("--target", type=Path) + return parser + + +def main(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + export(args.input, args.output_dir, args.scene, args.source, args.sample_interval, args.conv_len, args.target) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/SR_analysis/utils/__init__.py b/src/SR_analysis/utils/__init__.py index 0b608d0..8859313 100644 --- a/src/SR_analysis/utils/__init__.py +++ b/src/SR_analysis/utils/__init__.py @@ -1,23 +1,23 @@ -"""SR_analysis utilities: feature building, SINDy fitting, harmonics, CFD interface, G-operator. +"""Shared contracts for the active three-stage SR pipeline. -All four pipeline stages (infer -> fit -> validate -> analyze) import from this package. +The current route is inference, symbolic fitting, and closed-loop validation. +Legacy SINDy helpers remain importable for historical scripts but are not part of +that article pipeline. """ from .feature_builder import ( - compute_dimensionless, compute_features, build_feature_matrix, - get_feature_names, apply_G_alpha, apply_G_x, - CORE_FEAT_KEYS, CORE_FEAT_KEYS_V2, TIME_FEAT_KEYS, - MU_FEAT_KEYS, ALL_FEAT_KEYS, ALL_FEAT_KEYS_V2, - ILLUSION_TARGET_KEYS, ILLUSION_FEAT_KEYS_V2, ILLUSION_ALL_FEAT_KEYS_V2, - PHYSICS_FEAT_KEYS, ILLUSION_ERR_KEYS, - LAG_FEAT_KEYS, DERIV_FEAT_KEYS, ACTION_LAG_KEYS, - AUG_LEVEL_1_KEYS, AUG_LEVEL_2_KEYS, AUG_LEVEL_3_KEYS, AUG_LEVEL_4_KEYS, - ILLUSION_AUG_LEVEL_1_KEYS, ILLUSION_AUG_LEVEL_2_KEYS, - ILLUSION_AUG_LEVEL_3_KEYS, ILLUSION_AUG_LEVEL_4_KEYS, - PHASE_STATE_KEYS, ILLUSION_PHASE_KEYS, KARMAN_EXPANDED_KEYS, -) -from .sindy_fitter import ( - fit_channel, fit_sindy, print_control_law, - get_active_support, get_feature_matrix_from_data, - fit_sindy_weighted, get_feature_matrix_v2, - compute_action_deriv, get_feature_matrix_deriv, + ALL_FEAT_KEYS, + ALL_FEAT_KEYS_V2, + CORE_FEAT_KEYS, + CORE_FEAT_KEYS_V2, + ILLUSION_ERR_KEYS, + ILLUSION_TARGET_KEYS, + PHYSICS_FEAT_KEYS, + build_feature_matrix, + compute_dimensionless, + compute_features, + get_feature_names, ) +from .g_operator import apply_G_alpha + +# Historical SINDy and expanded-feature APIs remain available from their +# explicit modules, but are intentionally not re-exported by the active package. diff --git a/src/SR_analysis/utils/cfd_interface.py b/src/SR_analysis/utils/cfd_interface.py index a0b0f5e..0751414 100644 --- a/src/SR_analysis/utils/cfd_interface.py +++ b/src/SR_analysis/utils/cfd_interface.py @@ -15,7 +15,7 @@ import json import os import sys from collections import deque -from typing import Any, Dict, List, Optional, Tuple +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple import numpy as np @@ -28,8 +28,15 @@ _SRC = os.path.join(_REPO, "src") if _SRC not in sys.path: sys.path.insert(0, _SRC) -from LegacyCelerisLab import FlowField # noqa: E402 -from LegacyCelerisLab import utils as legacy_utils # noqa: E402 +if TYPE_CHECKING: + from LegacyCelerisLab import FlowField + + +def _legacy_cfd(): + """Import CUDA-backed legacy CFD only when an environment helper needs it.""" + from LegacyCelerisLab import FlowField + from LegacyCelerisLab import utils as legacy_utils + return FlowField, legacy_utils # --------------------------------------------------------------------------- # Action-smoothing constant (legacy run() internal) @@ -47,6 +54,7 @@ def nu_from_re(re_code: float, u0: float = 0.01, d_ref: float = 40.0) -> float: def load_legacy_configs(config_dir: str) -> Tuple[Any, Any]: """Load and return legacy (cuda_config, field_config) from *config_dir*.""" + _, legacy_utils = _legacy_cfd() cuda_cfg = legacy_utils.load_cuda_config( os.path.join(config_dir, "config_cuda.json") ) @@ -128,7 +136,7 @@ def add_pinball( """Add pinball cylinders, stabilize, compute norm, bias rollout. Steps: - 1. add front, bottom, top cylinders + 1. add front, upper, lower cylinders 2. stabilize run(4*NX/U0, zero-action) 3. get_ddf() + save_ddf() (checkpoint) 4. FIFO_LEN x run(SAMPLE_INTERVAL, zero) -> compute norm @@ -265,10 +273,11 @@ def scale_action( u0: float = 0.01, n_total_bodies: int = 7, ) -> np.ndarray: - """Convert normalised action ([-1,1]^3) to legacy CFD action array. + """Convert native [front, upper, lower] action to legacy CFD array. Returns array of length *n_total_bodies* with cylinders' omegas at the - last 3 slots. + last 3 slots. Historical callers may label upper/lower as top/bottom; this + function deliberately preserves native object order and never rearranges. """ a = np.zeros(n_total_bodies, dtype=np.float32) omega = (np.array(action_norm, dtype=np.float32) * scale + np.array(bias, dtype=np.float32)) * u0 @@ -322,6 +331,9 @@ def save_vorticity_png(path: str, omega: np.ndarray, title: str = ""): # DTW similarity # --------------------------------------------------------------------------- +LEGACY_DTW_VERSION = "legacy_dtw_v1_abs_n_unclipped" + + def calc_lag(target: np.ndarray, state: np.ndarray) -> int: """Find lag that maximises cross-correlation between two 1-D signals.""" t = target - np.mean(target) @@ -343,22 +355,57 @@ def calc_dtw_sim(target: np.ndarray, state: np.ndarray) -> float: return float(1.0 - dtw[n, m] / n) +def _legacy_scene_similarity( + target_states: np.ndarray, + state_series: np.ndarray, + conv_len: int, + target_sensor_start: int, +) -> float: + target = np.asarray(target_states) + state = np.asarray(state_series) + target_sensors = target[:, target_sensor_start:target_sensor_start + 6] + if target_sensors.shape[1] != 6 or state.ndim != 2 or state.shape[1] < 6: + raise ValueError("legacy DTW requires six target and state sensor channels") + if len(target) < 2 * conv_len or len(state) < conv_len: + raise ValueError("legacy DTW requires target >= 2*conv_len and state >= conv_len") + + ref = target_sensors[conv_len:2 * conv_len, 1] + cur = state[-conv_len:, 1] + lag = calc_lag(ref, cur) + return float(sum( + calc_dtw_sim( + np.roll(target_sensors[:, i], -lag)[conv_len:2 * conv_len], + state[-conv_len:, i], + ) + for i in range(6) + ) / 6.0) + + +def compute_similarity_karman_legacy_v1( + target_states: np.ndarray, + state_series: np.ndarray, + conv_len: int = 30, +) -> float: + """Karman ``legacy_dtw_v1_abs_n_unclipped`` similarity.""" + return _legacy_scene_similarity(target_states, state_series, conv_len, 0) + + +def compute_similarity_illusion_legacy_v1( + target_states: np.ndarray, + state_series: np.ndarray, + conv_len: int = 36, +) -> float: + """Illusion legacy similarity; target layout is [fx, fy, six sensors].""" + return _legacy_scene_similarity(target_states, state_series, conv_len, 2) + + def compute_similarity( target_states: np.ndarray, state_series: np.ndarray, conv_len: int, ) -> float: - """Compute lag-compensated DTW similarity over *conv_len* window.""" - ref = target_states[conv_len:2 * conv_len, 1] - cur = state_series[-conv_len:, 1] - lag = calc_lag(ref, cur) - - sim_sum = 0.0 - for i in range(6): - target_seq = np.roll(target_states[:, i], -lag)[conv_len:2 * conv_len] - state_seq = state_series[-conv_len:, i] - sim_sum += calc_dtw_sim(target_seq, state_seq) / 6.0 - return float(sim_sum) + """Backward-compatible Karman wrapper for the versioned legacy metric.""" + return compute_similarity_karman_legacy_v1(target_states, state_series, conv_len) # --------------------------------------------------------------------------- diff --git a/src/SR_analysis/utils/data_contracts.py b/src/SR_analysis/utils/data_contracts.py new file mode 100644 index 0000000..b3a4ba2 --- /dev/null +++ b/src/SR_analysis/utils/data_contracts.py @@ -0,0 +1,225 @@ +"""Validated data contracts and explicit Stage 2/3 trajectory alignment.""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Dict, Mapping, Optional, Sequence, Tuple + +import numpy as np + +from .feature_builder import build_feature_matrix, compute_dimensionless, compute_features + +HISTORICAL_ALIGNMENT = "historical_post_state_same_action" +CAUSAL_ALIGNMENT = "causal_post_state_to_next_action" +ALIGNMENTS = (HISTORICAL_ALIGNMENT, CAUSAL_ALIGNMENT) +OFFLINE_SPLIT_NAMES = ("train", "validation", "blind") + + +def contiguous_block_slices( + n_samples: int, + fractions: Sequence[float] = (0.6, 0.2, 0.2), +) -> Dict[str, slice]: + """Return ordered, non-overlapping train/validation/blind blocks.""" + if n_samples < 3: + raise ValueError("at least three samples are required for offline splits") + values = np.asarray(fractions, dtype=np.float64) + if values.shape != (3,) or np.any(values <= 0) or not np.isclose(values.sum(), 1.0): + raise ValueError("split fractions must be three positive values summing to one") + train_end = max(1, min(n_samples - 2, int(np.floor(n_samples * values[0])))) + validation_count = max(1, int(np.floor(n_samples * values[1]))) + validation_end = min(n_samples - 1, train_end + validation_count) + return { + "train": slice(0, train_end), + "validation": slice(train_end, validation_end), + "blind": slice(validation_end, n_samples), + } + + +@dataclass(frozen=True) +class BatchDataset: + """Aligned symbolic-regression dataset and its provenance.""" + + X: np.ndarray + Y: np.ndarray + kept_indices: np.ndarray + dropped_indices: np.ndarray + metadata: Dict[str, Any] + features: Dict[str, np.ndarray] + + def __iter__(self): + """Allow the convenient ``X, Y = dataset`` compatibility idiom.""" + yield self.X + yield self.Y + + +def _matrix(name: str, value: np.ndarray, columns: int, rows: Optional[int] = None) -> np.ndarray: + array = np.asarray(value, dtype=np.float64) + if array.ndim != 2 or array.shape[1] != columns: + raise ValueError("{} must have shape (T, {}), got {}".format(name, columns, array.shape)) + if rows is not None and array.shape[0] != rows: + raise ValueError("{} has {} rows; expected {}".format(name, array.shape[0], rows)) + if not np.all(np.isfinite(array)): + raise ValueError("{} contains non-finite values".format(name)) + return array + + +def validate_controlled_trajectory( + sensors: np.ndarray, + forces: np.ndarray, + actions: np.ndarray, + target_forces: Optional[np.ndarray] = None, + *, + illusion: bool = False, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray]]: + """Validate the legacy ``controlled.npz`` array schema. + + The legacy contract is sensors ``(T,6)``, forces ``(T,6)``, actions + ``(T,3)``, and optional illusion target forces ``(T,2)``. + """ + s = _matrix("sensors", sensors, 6) + f = _matrix("forces", forces, 6, s.shape[0]) + a = _matrix("actions", actions, 3, s.shape[0]) + if illusion and target_forces is None: + raise ValueError("illusion trajectories require target_forces with shape (T, 2)") + tf = None if target_forces is None else _matrix("target_forces", target_forces, 2, s.shape[0]) + return s, f, a, tf + + +def actions_to_alpha( + actions: np.ndarray, + *, + action_mode: str, + u0: float, + action_scale: float = 1.0, + action_bias: Sequence[float] = (0.0, 0.0, 0.0), +) -> np.ndarray: + """Convert normalized, physical-omega, or alpha actions to alpha.""" + a = np.asarray(actions, dtype=np.float64) + if action_mode == "alpha": + return a.copy() + if action_mode == "physical": + return a / u0 + if action_mode == "normalized": + bias = np.asarray(action_bias, dtype=np.float64) + if bias.shape != (3,): + raise ValueError("action_bias must have shape (3,)") + return a * float(action_scale) + bias + raise ValueError("action_mode must be 'normalized', 'physical', or 'alpha'") + + +def build_batch_dataset( + sensors: np.ndarray, + forces: np.ndarray, + actions: np.ndarray, + feat_keys: Sequence[str], + *, + mu: float, + dt_c: float, + alignment: str = HISTORICAL_ALIGNMENT, + action_mode: str = "physical", + action_scale: float = 1.0, + action_bias: Sequence[float] = (0.0, 0.0, 0.0), + target_forces: Optional[np.ndarray] = None, + illusion: bool = False, + u0: float = 0.01, + d: float = 20.0, + rho: float = 1.0, + include_mu: Optional[bool] = None, + include_cos_sin: bool = False, + add_bias: bool = False, + warmup: int = 2, + sample_segment: str = "all", + transient_count: int = 50, + strict: bool = True, +) -> BatchDataset: + """Build an aligned Stage 2/3 dataset from a controlled trajectory. + + ``historical_post_state_same_action`` reproduces the legacy pairing + ``post_state[i] -> action[i]`` with action history ``i-1, i-2``. + ``causal_post_state_to_next_action`` pairs ``post_state[i] -> action[i+1]`` + and therefore uses the causally available history ``i, i-1``. + """ + if alignment not in ALIGNMENTS: + raise ValueError("unknown alignment {!r}; expected one of {}".format(alignment, ALIGNMENTS)) + if dt_c <= 0: + raise ValueError("dt_c must be positive") + if warmup < 0: + raise ValueError("warmup must be non-negative") + if sample_segment not in {"all", "transient", "steady"}: + raise ValueError("sample_segment must be 'all', 'transient', or 'steady'") + if transient_count < 0: + raise ValueError("transient_count must be non-negative") + + s, f, a_raw, tf = validate_controlled_trajectory( + sensors, forces, actions, target_forces, illusion=illusion + ) + alpha = actions_to_alpha( + a_raw, action_mode=action_mode, u0=u0, + action_scale=action_scale, action_bias=action_bias, + ) + T = s.shape[0] + a1 = np.zeros((T, 3), dtype=np.float64) + a2 = np.zeros((T, 3), dtype=np.float64) + if alignment == HISTORICAL_ALIGNMENT: + a1[1:] = alpha[:-1] + a2[2:] = alpha[:-2] + state_indices = np.arange(T, dtype=np.int64) + target_indices = state_indices.copy() + else: + a1[:] = alpha + a2[1:] = alpha[:-1] + state_indices = np.arange(max(T - 1, 0), dtype=np.int64) + target_indices = state_indices + 1 + + dim = compute_dimensionless(s, f, u0=u0, d=d, rho=rho) + features = compute_features( + dim, a1, a2, mu, alpha_mode=True, + include_mu=(any(key.startswith("mu") for key in feat_keys) if include_mu is None else include_mu), + include_cos_sin=include_cos_sin, dt_c=dt_c, u0=u0, + target_forces=tf, rho=rho, sensors_raw=s, forces_raw=f, + ) + full_X = build_feature_matrix(features, list(feat_keys), add_bias=add_bias, strict=strict) + valid = state_indices >= warmup + if sample_segment == "transient": + valid &= state_indices < transient_count + elif sample_segment == "steady": + valid &= state_indices >= transient_count + candidate = state_indices[valid] + target_candidate = target_indices[valid] + X = full_X[candidate] + Y = alpha[target_candidate] + kept = candidate + dropped = np.setdiff1d(np.arange(T, dtype=np.int64), kept, assume_unique=True) + metadata = { + "alignment": alignment, + "action_mode": action_mode, + "feature_keys": list(feat_keys), + "add_bias": bool(add_bias), + "strict": bool(strict), + "warmup": int(warmup), + "sample_segment": sample_segment, + "transient_count": int(transient_count), + "sample_segments": { + "transient": [int(warmup), int(min(transient_count, T))], + "steady": [int(max(warmup, transient_count)), int(T)], + }, + "state_indices": kept.copy(), + "target_indices": target_candidate.copy(), + "history_policy": "drop_warmup_zero_initialized", + "illusion": bool(illusion), + "dt_c": float(dt_c), + } + return BatchDataset(X, Y, kept, dropped, metadata, features) + + +def build_batch_dataset_from_mapping( + data: Mapping[str, np.ndarray], feat_keys: Sequence[str], **kwargs: Any +) -> BatchDataset: + """Build from a loaded legacy NPZ/mapping while preserving validation.""" + required = [key for key in ("sensors", "forces", "actions") if key not in data] + if required: + raise KeyError("controlled trajectory missing required array(s): {}".format(", ".join(required))) + return build_batch_dataset( + data["sensors"], data["forces"], data["actions"], feat_keys, + target_forces=data["target_forces"] if "target_forces" in data else None, + **kwargs + ) diff --git a/src/SR_analysis/utils/feature_builder.py b/src/SR_analysis/utils/feature_builder.py index 8713f50..f7f1dc8 100644 --- a/src/SR_analysis/utils/feature_builder.py +++ b/src/SR_analysis/utils/feature_builder.py @@ -11,6 +11,8 @@ from typing import Dict, List, Optional, Tuple import numpy as np +from .g_operator import apply_G_alpha + # -- Physical constants ------------------------------------------------------ U0 = 0.01 # inlet velocity (lattice units) @@ -28,15 +30,17 @@ def compute_dimensionless( ) -> Dict[str, np.ndarray]: """Convert raw lattice CFD data to dimensionless quantities. - Sensor order: [s0_ux,s0_uy, s1_ux,s1_uy, s2_ux,s2_uy] - where s0=top(y=+2L0), s1=mid(y=0), s2=bottom(y=-2L0) - Force order: [front_fx,front_fy, bottom_fx,bottom_fy, top_fx,top_fy] + Native sensor order is ``[upper_ux, upper_uy, front_ux, front_uy, + lower_ux, lower_uy]``. The historical feature aliases are intentionally + preserved: ``T=upper``, ``C=front``, and ``B=lower``. - Returns: - u_hat_B, u_hat_C, u_hat_T: nondim streamwise velocity (bottom/centre/top) - v_hat_B, v_hat_C, v_hat_T: nondim crosswise velocity - Cd_F, Cd_T, Cd_B: drag coefficient per cylinder - Cl_F, Cl_T, Cl_B: lift coefficient per cylinder + Native force order is ``[front_fx, front_fy, upper_fx, upper_fy, + lower_fx, lower_fy]`` because LegacyCelerisLab force slots follow object + insertion order. Historical coefficient aliases remain ``F=front``, + ``T=upper``, and ``B=lower``. + + Returns dimensionless velocity and force-coefficient arrays under those + historical ``T/C/B`` and ``F/T/B`` keys. """ s = np.asarray(sensors, dtype=np.float64) f = np.asarray(forces, dtype=np.float64) @@ -52,20 +56,15 @@ def compute_dimensionless( "v_hat_B": s[:, 5] / u0, "Cd_F": 2.0 * f[:, 0] / (rho * u0**2 * d), "Cl_F": 2.0 * f[:, 1] / (rho * u0**2 * d), - "Cd_B": 2.0 * f[:, 2] / (rho * u0**2 * d), - "Cl_B": 2.0 * f[:, 3] / (rho * u0**2 * d), - "Cd_T": 2.0 * f[:, 4] / (rho * u0**2 * d), - "Cl_T": 2.0 * f[:, 5] / (rho * u0**2 * d), + "Cd_T": 2.0 * f[:, 2] / (rho * u0**2 * d), + "Cl_T": 2.0 * f[:, 3] / (rho * u0**2 * d), + "Cd_B": 2.0 * f[:, 4] / (rho * u0**2 * d), + "Cl_B": 2.0 * f[:, 5] / (rho * u0**2 * d), } # -- G operator (corrected) -------------------------------------------------- -def apply_G_alpha(alpha: np.ndarray) -> np.ndarray: - """Apply mirror G to action: [aF, aT, aB] -> [-aF, -aB, -aT].""" - return np.array([-alpha[0], -alpha[2], -alpha[1]], dtype=alpha.dtype) - - def apply_G_x(dim: Dict[str, np.ndarray], a_prev: np.ndarray, a_prev2: np.ndarray) -> Tuple[Dict, np.ndarray, np.ndarray]: @@ -86,6 +85,28 @@ def apply_G_x(dim: Dict[str, np.ndarray], # -- Feature key definitions ------------------------------------------------- +RAW_COMPLETE_KEYS = [ + "u_hat_T", "v_hat_T", "u_hat_C", "v_hat_C", "u_hat_B", "v_hat_B", + "Cd_F", "Cl_F", "Cd_T", "Cl_T", "Cd_B", "Cl_B", +] + +SYMMETRY_KEYS = [ + "u_s", "u_a", "u_c", "v_s", "v_a", "v_c", + "Cd_F", "Cd_rear_s", "Cd_rear_a", "Cd_tot", + "Cl_F", "Cl_rear_s", "Cl_rear_a", "Cl_tot", +] + +PHYSICS_REDUCED_KEYS = [ + "u_m", "u_a", "u_c", "v_a", + "Cd_tot", "Cd_rear", "Cl_tot", "Cl_diff", +] + +FEATURE_SET_KEYS = { + "raw_complete": tuple(RAW_COMPLETE_KEYS), + "symmetry": tuple(SYMMETRY_KEYS), + "physics_reduced": tuple(PHYSICS_REDUCED_KEYS), +} + # Original feature set (includes sin_ua/cos_ua) CORE_FEAT_KEYS = [ "u_m", "u_a", "u_c", @@ -238,19 +259,27 @@ def compute_features( a = actions_prev.astype(np.float64) / u0 a2 = actions_prev2.astype(np.float64) / u0 - sym = {} + sym = {key: np.asarray(value, dtype=np.float64).copy() for key, value in dim.items()} - # Sensor combinations (nondim) + # Complete symmetric/antisymmetric coordinates. Under G, u_a/v_s/v_c, + # Cd_rear_a, Cl_F/Cl_rear_s/Cl_tot are odd; the complementary coordinates + # are even. + sym["u_s"] = (u_T + u_B) / 2.0 sym["u_m"] = (u_B + u_C + u_T) / 3.0 sym["u_a"] = (u_T - u_B) / 2.0 sym["u_c"] = u_C.copy() + sym["v_s"] = (v_T + v_B) / 2.0 sym["v_a"] = (v_T - v_B) / 2.0 + sym["v_c"] = v_C.copy() - # Force combinations (dimensionless Cd/Cl) sym["Cd_tot"] = Cd_F + Cd_T + Cd_B sym["Cd_rear"] = Cd_T + Cd_B + sym["Cd_rear_s"] = (Cd_T + Cd_B) / 2.0 + sym["Cd_rear_a"] = (Cd_T - Cd_B) / 2.0 sym["Cl_tot"] = Cl_F + Cl_T + Cl_B sym["Cl_diff"] = Cl_T - Cl_B + sym["Cl_rear_s"] = (Cl_T + Cl_B) / 2.0 + sym["Cl_rear_a"] = (Cl_T - Cl_B) / 2.0 # Phase (optional, may obscure linear structure) if include_cos_sin: @@ -295,9 +324,12 @@ def compute_features( if sensors_raw is not None and forces_raw is not None: sr = np.asarray(sensors_raw, dtype=np.float64) fr = np.asarray(forces_raw, dtype=np.float64) - # Lag-1 observations (shift by 1, pad first with current) - s_lag1 = np.zeros_like(sr) - f_lag1 = np.zeros_like(fr) + # Lag-1 observations: first-step history is the current observation. + # This makes first-step derivatives zero and matches online initialization. + s_lag1 = np.empty_like(sr) + f_lag1 = np.empty_like(fr) + s_lag1[0] = sr[0] + f_lag1[0] = fr[0] s_lag1[1:] = sr[:-1] f_lag1[1:] = fr[:-1] # Compute dim for lag-1 @@ -346,7 +378,8 @@ def compute_features( # Illusion error-state dynamics if target_forces is not None: tf = np.asarray(target_forces, dtype=np.float64) - tf_lag1 = np.zeros_like(tf) + tf_lag1 = np.empty_like(tf) + tf_lag1[0] = tf[0] tf_lag1[1:] = tf[:-1] tCd_1 = 2.0 * tf_lag1[:, 0] / (rho * u0**2 * D_CYL) tCl_1 = 2.0 * tf_lag1[:, 1] / (rho * u0**2 * D_CYL) @@ -362,18 +395,30 @@ def build_feature_matrix( sym: Dict[str, np.ndarray], feat_keys: List[str], add_bias: bool = True, + strict: bool = True, ) -> np.ndarray: - """Build feature matrix (T, N) from symbol dict.""" + """Build a feature matrix, failing on missing features by default. + + Set ``strict=False`` to retain the historical missing-key-as-zero behavior. + """ + if not feat_keys: + raise ValueError("feat_keys must contain at least one feature") + missing = [key for key in feat_keys if key not in sym] + if strict and missing: + raise KeyError("missing required feature(s): {}".format(", ".join(missing))) + + available = next((np.asarray(sym[key]) for key in feat_keys if key in sym), None) + if available is None: + available = np.asarray(sym.get("u_m", np.ones(1))) + T = available.shape[0] cols = [] if add_bias: - cols.append(np.ones(sym[feat_keys[0]].shape[0], dtype=np.float64)) - for k in feat_keys: - if k in sym: - cols.append(np.asarray(sym[k], dtype=np.float64)) - else: - # Missing key -> zero - T = sym.get("u_m", np.ones(1)).shape[0] - cols.append(np.zeros(T, dtype=np.float64)) + cols.append(np.ones(T, dtype=np.float64)) + for key in feat_keys: + col = np.asarray(sym[key], dtype=np.float64) if key in sym else np.zeros(T, dtype=np.float64) + if col.ndim != 1 or col.shape[0] != T: + raise ValueError("feature {!r} must have shape ({},), got {}".format(key, T, col.shape)) + cols.append(col) return np.column_stack(cols) diff --git a/src/SR_analysis/utils/feature_state.py b/src/SR_analysis/utils/feature_state.py new file mode 100644 index 0000000..c4077fc --- /dev/null +++ b/src/SR_analysis/utils/feature_state.py @@ -0,0 +1,171 @@ +"""Stateful online feature construction shared by SR Stage 2 and Stage 3.""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Dict, Optional, Sequence, Tuple + +import numpy as np + +from .data_contracts import actions_to_alpha +from .feature_builder import build_feature_matrix, compute_dimensionless, compute_features + + +@dataclass +class FeatureState: + """History required for one-step feature construction. + + The first step uses current sensors/forces/target as observation history, + making lag features equal current features and their derivatives zero. + Missing action history is initialized to zero alpha unless explicit + previous actions are supplied. + """ + + previous_sensors: Optional[np.ndarray] = None + previous_forces: Optional[np.ndarray] = None + previous_target_force: Optional[np.ndarray] = None + previous_action: Optional[np.ndarray] = None + previous_action2: Optional[np.ndarray] = None + step_index: int = 0 + + def reset(self) -> None: + self.previous_sensors = None + self.previous_forces = None + self.previous_target_force = None + self.previous_action = None + self.previous_action2 = None + self.step_index = 0 + + +class StatefulFeatureBuilder: + """Generate online features with exactly the batch ``compute_features`` semantics.""" + + def __init__( + self, + *, + mu: float, + dt_c: float, + u0: float = 0.01, + d: float = 20.0, + rho: float = 1.0, + action_mode: str = "physical", + action_scale: float = 1.0, + action_bias: Sequence[float] = (0.0, 0.0, 0.0), + include_mu: bool = True, + include_cos_sin: bool = True, + require_target: bool = False, + state: Optional[FeatureState] = None, + ) -> None: + if dt_c <= 0: + raise ValueError("dt_c must be positive") + self.mu = float(mu) + self.dt_c = float(dt_c) + self.u0 = float(u0) + self.d = float(d) + self.rho = float(rho) + self.action_mode = action_mode + self.action_scale = float(action_scale) + self.action_bias = tuple(action_bias) + self.include_mu = bool(include_mu) + self.include_cos_sin = bool(include_cos_sin) + self.require_target = bool(require_target) + self.state = state if state is not None else FeatureState() + + @staticmethod + def _row(name: str, value: np.ndarray, columns: int) -> np.ndarray: + row = np.asarray(value, dtype=np.float64) + if row.shape != (columns,): + raise ValueError("{} must have shape ({},), got {}".format(name, columns, row.shape)) + return row + + def _alpha(self, action: np.ndarray) -> np.ndarray: + row = self._row("action", action, 3).reshape(1, 3) + return actions_to_alpha( + row, action_mode=self.action_mode, u0=self.u0, + action_scale=self.action_scale, action_bias=self.action_bias, + )[0] + + def step( + self, + sensors: np.ndarray, + forces: np.ndarray, + *, + previous_action: Optional[np.ndarray] = None, + previous_action2: Optional[np.ndarray] = None, + target_force: Optional[np.ndarray] = None, + previous_target_force: Optional[np.ndarray] = None, + feat_keys: Optional[Sequence[str]] = None, + add_bias: bool = False, + strict: bool = True, + ) -> Tuple[Dict[str, np.ndarray], Optional[np.ndarray]]: + """Consume current post-state and return scalar feature dict/vector. + + ``previous_action`` means the action immediately preceding the current + state (the historical ``a_lag1``). If omitted, stored history is used. + The current action is not consumed here; call ``observe_action`` after + selecting/applying it, or pass explicit history on every call. + """ + s = self._row("sensors", sensors, 6) + f = self._row("forces", forces, 6) + if self.require_target and target_force is None: + raise ValueError("target_force is required for illusion features") + tf = None if target_force is None else self._row("target_force", target_force, 2) + + p_action = self.state.previous_action if previous_action is None else self._alpha(previous_action) + p_action2 = self.state.previous_action2 if previous_action2 is None else self._alpha(previous_action2) + a1 = np.zeros(3) if p_action is None else np.asarray(p_action, dtype=np.float64) + a2 = np.zeros(3) if p_action2 is None else np.asarray(p_action2, dtype=np.float64) + + ps = self.state.previous_sensors + pf = self.state.previous_forces + if ps is None or pf is None: + ps, pf = s, f + ptf = self.state.previous_target_force if previous_target_force is None else self._row( + "previous_target_force", previous_target_force, 2 + ) + if tf is not None and ptf is None: + ptf = tf + + sensors_raw = np.vstack([ps, s]) + forces_raw = np.vstack([pf, f]) + dim = compute_dimensionless(s.reshape(1, 6), f.reshape(1, 6), u0=self.u0, d=self.d, rho=self.rho) + target_rows = None if tf is None else np.vstack([ptf, tf]) + feature_arrays = compute_features( + dim, a1.reshape(1, 3), a2.reshape(1, 3), self.mu, + alpha_mode=True, include_mu=self.include_mu, + include_cos_sin=self.include_cos_sin, dt_c=self.dt_c, u0=self.u0, + target_forces=None if target_rows is None else target_rows[-1:], rho=self.rho, + sensors_raw=sensors_raw, forces_raw=forces_raw, + ) + + # compute_features needs both target rows to derive target-error history. + if target_rows is not None: + dim_pair = compute_dimensionless(sensors_raw, forces_raw, u0=self.u0, d=self.d, rho=self.rho) + pair = compute_features( + dim_pair, np.vstack([a2, a1]), np.vstack([np.zeros(3), a2]), self.mu, + alpha_mode=True, include_mu=self.include_mu, + include_cos_sin=self.include_cos_sin, dt_c=self.dt_c, u0=self.u0, + target_forces=target_rows, rho=self.rho, + sensors_raw=sensors_raw, forces_raw=forces_raw, + ) + for key in ("Cd_err_lag1", "Cl_err_lag1", "dCd_err_dt", "dCl_err_dt"): + feature_arrays[key] = pair[key][-1:] + + scalar_features = {key: np.asarray(value)[-1] for key, value in feature_arrays.items()} + vector = None + if feat_keys is not None: + vector = build_feature_matrix(feature_arrays, list(feat_keys), add_bias=add_bias, strict=strict)[-1] + + self.state.previous_sensors = s.copy() + self.state.previous_forces = f.copy() + self.state.previous_target_force = None if tf is None else tf.copy() + self.state.step_index += 1 + return scalar_features, vector + + def observe_action(self, action: np.ndarray) -> None: + """Advance stored action history after an action is selected/applied.""" + alpha = self._alpha(action) + self.state.previous_action2 = None if self.state.previous_action is None else self.state.previous_action.copy() + self.state.previous_action = alpha + + def reset(self) -> None: + self.state.reset() diff --git a/src/SR_analysis/utils/formula_schema.py b/src/SR_analysis/utils/formula_schema.py new file mode 100644 index 0000000..5f048e7 --- /dev/null +++ b/src/SR_analysis/utils/formula_schema.py @@ -0,0 +1,421 @@ +"""Versioned SR formula loading, evaluation, and validation provenance.""" +from __future__ import annotations + +import ast +import json +from copy import deepcopy +from numbers import Real +from pathlib import Path +from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, Union + +import numpy as np +import sympy + +from .provenance import hash_file, hash_json, sha256_text + +FORMULA_SCHEMA_VERSION = "1.0" +VALIDATION_SCHEMA_VERSION = "1.0" + +FormulaSource = Union[str, Path, Mapping[str, Any]] + + +def _read_formula(source: FormulaSource) -> Tuple[Dict[str, Any], Optional[Path]]: + if isinstance(source, Mapping): + return deepcopy(dict(source)), None + path = Path(source) + with path.open("r", encoding="utf-8") as stream: + value = json.load(stream) + if not isinstance(value, dict): + raise ValueError("formula JSON must contain an object") + return value, path + + +def expression_hash(expression: str) -> str: + """Hash an expression after trimming inconsequential outer whitespace.""" + if not isinstance(expression, str) or not expression.strip(): + raise ValueError("expression must be a non-empty string") + return sha256_text(expression.strip()) + + +def _sympy_expression(expression: str, feature_names: Sequence[str]) -> sympy.Expr: + if not isinstance(expression, str) or not expression.strip(): + raise ValueError("expression must be a non-empty string") + _validate_expression_syntax(expression, feature_names) + symbols = {name: sympy.Symbol(name) for name in feature_names} + parsed = sympy.sympify(expression, locals=symbols) + unknown = sorted(str(symbol) for symbol in parsed.free_symbols if str(symbol) not in symbols) + if unknown: + raise ValueError(f"expression references undeclared features: {unknown}") + return parsed + + +def normalize_symbolic_expression(expression: str, feature_names: Sequence[str]) -> str: + """Return a deterministic expanded SymPy representation of an expression.""" + return sympy.sstr(sympy.expand(_sympy_expression(expression, feature_names)), order="lex") + + +def decompose_additive_terms(expression: str, feature_names: Sequence[str]) -> Tuple[str, ...]: + """Return deterministic top-level terms from the normalized expression.""" + normalized = _sympy_expression( + normalize_symbolic_expression(expression, feature_names), feature_names + ) + return tuple(sympy.sstr(term, order="lex") for term in normalized.as_ordered_terms()) + + +def _parent_source_hash(source: FormulaSource, loaded: Mapping[str, Any]) -> str: + if isinstance(source, Mapping) and isinstance(source.get("source_hash"), str): + return str(source["source_hash"]) + return str(loaded["source_hash"]) + + +def _term_variant( + formula_source: FormulaSource, + term_identity: str, + *, + variant_type: str, + scale: Real, +) -> Dict[str, Any]: + if isinstance(scale, bool) or not isinstance(scale, Real) or not np.isfinite(float(scale)): + raise ValueError("scale must be a finite real number") + parent = load_formula(formula_source) + deployment = parent.get("deployment_expression") + if deployment is None: + raise ValueError("formula variants require a symbolic deployment expression") + feature_names = parent["feature_names"] + terms = decompose_additive_terms(deployment, feature_names) + if term_identity not in terms: + raise ValueError(f"term identity is not present in deployment expression: {term_identity!r}") + + normalized = _sympy_expression( + normalize_symbolic_expression(deployment, feature_names), feature_names + ) + selected = _sympy_expression(term_identity, feature_names) + scaled = float(scale) + variant_expression = normalize_symbolic_expression( + sympy.sstr(normalized - selected + sympy.Float(scaled) * selected, order="lex"), + feature_names, + ) + + variant = deepcopy(parent) + variant.pop("source_path", None) + variant.pop("source_hash", None) + variant["deployment_expression"] = variant_expression + variant["deployment_expression_hash"] = expression_hash(variant_expression) + variant["parent_source_hash"] = _parent_source_hash(formula_source, parent) + variant["parent_deployment_expression_hash"] = parent["deployment_expression_hash"] + variant["variant_metadata"] = { + "type": variant_type, + "term_identity": term_identity, + "scale": scaled, + } + return load_formula(variant) + + +def build_term_deletion_variant( + formula_source: FormulaSource, term_identity: str +) -> Dict[str, Any]: + """Build an immutable deployment variant with one additive term removed.""" + return _term_variant( + formula_source, term_identity, variant_type="term_deletion", scale=0.0 + ) + + +def build_coefficient_scaling_variant( + formula_source: FormulaSource, term_identity: str, scale: Real +) -> Dict[str, Any]: + """Build an immutable deployment variant with one additive term scaled.""" + return _term_variant( + formula_source, term_identity, variant_type="coefficient_scaling", scale=scale + ) + + +def load_formula(source: FormulaSource) -> Dict[str, Any]: + """Load either a legacy formula or the versioned schema into one shape. + + Legacy aliases are retained while canonical fields are added, allowing old + consumers and provenance-aware consumers to share the same loaded object. + """ + formula, path = _read_formula(source) + raw_source_hash = hash_file(path) if path is not None else hash_json(formula) + legacy = "schema_version" not in formula + + features = formula.get("feature_names", formula.get("feature_keys")) + if not isinstance(features, list) or not all(isinstance(item, str) for item in features): + raise ValueError("formula requires string feature_names or feature_keys") + + fitted = formula.get("fitted_expression") + if fitted is None: + fitted = formula.get("best_sympy", formula.get("expression")) + deployment = formula.get("deployment_expression", fitted) + + model_type = formula.get("model_type", formula.get("schema_type", "symbolic")) + if fitted is None and model_type != "linear": + raise ValueError("symbolic formula requires an expression") + if fitted is not None and (not isinstance(fitted, str) or not fitted.strip()): + raise ValueError("fitted_expression must be a non-empty string") + if deployment is not None and (not isinstance(deployment, str) or not deployment.strip()): + raise ValueError("deployment_expression must be a non-empty string") + + formula["schema_version"] = str(formula.get("schema_version", "legacy")) + formula["feature_names"] = list(features) + formula["fitted_expression"] = fitted + formula["deployment_expression"] = deployment + formula["model_type"] = model_type + formula["fit_score"] = formula.get("fit_score", formula.get("best_score")) + formula["legacy"] = legacy + if fitted is not None: + expected = expression_hash(fitted) + if formula.get("fitted_expression_hash") not in (None, expected): + raise ValueError("fitted_expression_hash does not match expression") + formula["fitted_expression_hash"] = expected + if deployment is not None: + expected = expression_hash(deployment) + if formula.get("deployment_expression_hash") not in (None, expected): + raise ValueError("deployment_expression_hash does not match expression") + formula["deployment_expression_hash"] = expected + formula["source_path"] = str(path) if path is not None else None + formula["source_hash"] = raw_source_hash + return formula + + +def build_formula_artifact( + *, + artifact_id: str, + run_id: str, + role: str, + anchor: str, + training_scenes: Sequence[str], + feature_names: Sequence[str], + fitted_expression: str, + deployment_expression: str, + output: Mapping[str, Any], + feature_metadata: Mapping[str, Any], + alignment_metadata: Mapping[str, Any], + dt_c_metadata: Mapping[str, Any], + g_metadata: Mapping[str, Any], + fit_provenance: Mapping[str, Any], + pysr_provenance: Mapping[str, Any], + data_provenance: Mapping[str, Any], + **extra: Any, +) -> Dict[str, Any]: + """Construct a complete symbolic-formula artifact with expression hashes.""" + required_strings = { + "artifact_id": artifact_id, + "run_id": run_id, + "role": role, + "anchor": anchor, + } + for name, value in required_strings.items(): + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"{name} must be a non-empty string") + if not training_scenes or not all(isinstance(item, str) and item for item in training_scenes): + raise ValueError("training_scenes must contain at least one scene") + if not feature_names or not all(isinstance(item, str) and item for item in feature_names): + raise ValueError("feature_names must contain at least one feature") + + artifact: Dict[str, Any] = { + "schema_version": FORMULA_SCHEMA_VERSION, + **required_strings, + "training_scenes": list(training_scenes), + "feature_names": list(feature_names), + "model_type": "symbolic", + "fitted_expression": fitted_expression.strip(), + "deployment_expression": deployment_expression.strip(), + "fitted_expression_hash": expression_hash(fitted_expression), + "deployment_expression_hash": expression_hash(deployment_expression), + "output": dict(output), + "feature_metadata": dict(feature_metadata), + "alignment_metadata": dict(alignment_metadata), + "dt_c_metadata": dict(dt_c_metadata), + "g_metadata": dict(g_metadata), + "provenance": { + "fit": dict(fit_provenance), + "pysr": dict(pysr_provenance), + "data": dict(data_provenance), + }, + } + artifact.update(extra) + load_formula(artifact) + return artifact + + +def _feature_columns( + feature_values: Union[Mapping[str, Any], np.ndarray, Sequence[Sequence[float]]], + feature_names: Sequence[str], +) -> Tuple[Dict[str, np.ndarray], Tuple[int, ...]]: + if isinstance(feature_values, Mapping): + missing = [name for name in feature_names if name not in feature_values] + if missing: + raise KeyError(f"missing formula features: {missing}") + raw_columns = [np.asarray(feature_values[name]) for name in feature_names] + broadcast = np.broadcast_arrays(*raw_columns) if raw_columns else [] + columns = {name: broadcast[index] for index, name in enumerate(feature_names)} + shape = broadcast[0].shape if broadcast else () + return columns, shape + matrix = np.asarray(feature_values) + if matrix.ndim == 0 or matrix.shape[-1] != len(feature_names): + raise ValueError("feature matrix final dimension must match feature_names") + return {name: matrix[..., index] for index, name in enumerate(feature_names)}, matrix.shape[:-1] + + +_ALLOWED_EXPRESSION_FUNCTIONS = {"sin", "cos", "tan", "exp", "log", "sqrt", "Abs", "abs"} +_ALLOWED_AST_NODES = ( + ast.Expression, + ast.BinOp, + ast.UnaryOp, + ast.Add, + ast.Sub, + ast.Mult, + ast.Div, + ast.Pow, + ast.Mod, + ast.USub, + ast.UAdd, + ast.Name, + ast.Load, + ast.Constant, + ast.Call, +) + + +def _validate_expression_syntax(expression: str, feature_names: Sequence[str]) -> None: + try: + tree = ast.parse(expression, mode="eval") + except SyntaxError as exc: + raise ValueError("invalid symbolic expression") from exc + allowed_names = set(feature_names) | _ALLOWED_EXPRESSION_FUNCTIONS + for node in ast.walk(tree): + if not isinstance(node, _ALLOWED_AST_NODES): + raise ValueError(f"unsupported expression syntax: {type(node).__name__}") + if isinstance(node, ast.Name) and node.id not in allowed_names: + raise ValueError(f"expression references undeclared name: {node.id}") + if isinstance(node, ast.Call): + if not isinstance(node.func, ast.Name) or node.func.id not in _ALLOWED_EXPRESSION_FUNCTIONS: + raise ValueError("expression calls an unsupported function") + if node.keywords: + raise ValueError("expression function keywords are unsupported") + + +def formula_safety_report( + formula_source: FormulaSource, + feature_ranges: Mapping[str, Sequence[float]], + *, + expression_kind: str = "deployment", + grid_size: int = 9, + output_range: Optional[Sequence[float]] = None, +) -> Dict[str, Any]: + """Statically probe a formula over declared feature ranges for deployment safety.""" + formula = load_formula(formula_source) + if grid_size < 2: + raise ValueError("grid_size must be at least two") + missing = [name for name in formula["feature_names"] if name not in feature_ranges] + if missing: + raise ValueError(f"missing feature ranges: {missing}") + probes: Dict[str, np.ndarray] = {} + for name in formula["feature_names"]: + bounds = np.asarray(feature_ranges[name], dtype=float) + if bounds.shape != (2,) or not np.all(np.isfinite(bounds)) or bounds[0] > bounds[1]: + raise ValueError(f"invalid finite range for feature {name!r}") + probes[name] = np.linspace(bounds[0], bounds[1], grid_size) + with np.errstate(all="ignore"): + values = np.asarray(evaluate_formula(formula, probes, expression_kind=expression_kind), dtype=float) + finite = bool(np.all(np.isfinite(values))) + observed = None if not finite or values.size == 0 else [float(values.min()), float(values.max())] + within_range = True + expected = None + if output_range is not None: + bounds = np.asarray(output_range, dtype=float) + if bounds.shape != (2,) or not np.all(np.isfinite(bounds)) or bounds[0] > bounds[1]: + raise ValueError("output_range must be finite ordered bounds") + expected = bounds.tolist() + within_range = finite and bool(np.all((values >= bounds[0]) & (values <= bounds[1]))) + return { + "safe": finite and within_range, + "finite": finite, + "within_output_range": within_range, + "observed_range": observed, + "required_output_range": expected, + "grid_size": int(grid_size), + } + + +def evaluate_formula( + formula_source: FormulaSource, + feature_values: Union[Mapping[str, Any], np.ndarray, Sequence[Sequence[float]]], + *, + expression_kind: str = "deployment", +) -> np.ndarray: + """Evaluate symbolic formulas first; coefficients require explicit linear type.""" + formula = load_formula(formula_source) + feature_names = formula["feature_names"] + columns, output_shape = _feature_columns(feature_values, feature_names) + expression_key = f"{expression_kind}_expression" + expression = formula.get(expression_key) + + if expression is not None: + _validate_expression_syntax(expression, feature_names) + symbols = {name: sympy.Symbol(name) for name in feature_names} + parsed = sympy.sympify(expression, locals=symbols) + unknown = sorted(str(symbol) for symbol in parsed.free_symbols if str(symbol) not in symbols) + if unknown: + raise ValueError(f"expression references undeclared features: {unknown}") + function = sympy.lambdify([symbols[name] for name in feature_names], parsed, modules="numpy") + result = np.asarray(function(*(columns[name] for name in feature_names)), dtype=float) + if result.shape == () and output_shape: + result = np.full(output_shape, float(result), dtype=float) + return result + + if formula.get("model_type") != "linear": + raise ValueError("formula has no symbolic expression and is not explicit linear schema") + coefficients = formula.get("coef", formula.get("coefficients")) + if coefficients is None: + raise ValueError("explicit linear formula requires coef or coefficients") + coefficient_array = np.asarray(coefficients, dtype=float) + if coefficient_array.shape != (len(feature_names),): + raise ValueError("linear coefficient count must match feature_names") + matrix = np.stack([np.broadcast_to(columns[name], output_shape) for name in feature_names], axis=-1) + intercept = formula.get("intercept", 0.0) + return np.asarray(matrix @ coefficient_array + float(intercept), dtype=float) + + +def build_validation_record( + *, + formula: FormulaSource, + metric_version: str, + scene: str, + mode: str, + si: bool, + n_steps: int, + full_metrics: Mapping[str, Any], + tail_metrics: Mapping[str, Any], + per_channel_metrics: Mapping[str, Any], + action_range: Mapping[str, Any], + telemetry: Mapping[str, Any], + provenance: Mapping[str, Any], + **extra: Any, +) -> Dict[str, Any]: + """Build a validation record tied to exact formula and metric semantics.""" + loaded = load_formula(formula) + if not metric_version or not scene or not mode or n_steps < 0: + raise ValueError("metric_version, scene, mode, and non-negative n_steps are required") + record: Dict[str, Any] = { + "schema_version": VALIDATION_SCHEMA_VERSION, + "formula_hash": loaded["source_hash"], + "formula_artifact_id": loaded.get("artifact_id"), + "metric_version": metric_version, + "scene": scene, + "mode": mode, + "si": bool(si), + "n_steps": int(n_steps), + "metrics": { + "full": dict(full_metrics), + "tail": dict(tail_metrics), + "per_channel": dict(per_channel_metrics), + "action_range": dict(action_range), + }, + "telemetry": dict(telemetry), + "provenance": dict(provenance), + } + record.update(extra) + record["record_hash"] = hash_json(record) + return record diff --git a/src/SR_analysis/utils/g_operator.py b/src/SR_analysis/utils/g_operator.py index 0a2f956..adfa486 100644 --- a/src/SR_analysis/utils/g_operator.py +++ b/src/SR_analysis/utils/g_operator.py @@ -9,50 +9,47 @@ from typing import Any, Dict, Optional, Tuple import numpy as np -from .feature_builder import compute_dimensionless as _compute_dimless + +def _require_last_dim(values: np.ndarray, size: int, name: str) -> np.ndarray: + array = np.asarray(values) + if array.ndim == 0 or array.shape[-1] != size: + raise ValueError(f"{name} must have last dimension {size}, got {array.shape}") + return array def apply_G_alpha(alpha: np.ndarray) -> np.ndarray: - """Apply mirror G to action: [aF, aT, aB] -> [-aF, -aB, -aT].""" - return np.array([-alpha[0], -alpha[2], -alpha[1]], dtype=alpha.dtype) + """Mirror native legacy action [..., front, upper, lower].""" + action = _require_last_dim(alpha, 3, "action") + return np.stack((-action[..., 0], -action[..., 2], -action[..., 1]), axis=-1) + + +def apply_G_target_force(target_force: np.ndarray) -> np.ndarray: + """Mirror target cylinder force [..., fx, fy] (drag even, lift odd).""" + force = _require_last_dim(target_force, 2, "target_force") + return np.stack((force[..., 0], -force[..., 1]), axis=-1) + + +def apply_G_raw12(raw: np.ndarray) -> np.ndarray: + """Mirror native raw [..., sensors(upper/center/lower), forces(front/upper/lower)].""" + values = _require_last_dim(raw, 12, "raw") + return np.stack( + ( + values[..., 4], -values[..., 5], + values[..., 2], -values[..., 3], + values[..., 0], -values[..., 1], + values[..., 6], -values[..., 7], + values[..., 10], -values[..., 11], + values[..., 8], -values[..., 9], + ), + axis=-1, + ) def apply_G_raw(obs_slice: np.ndarray, a_prev: np.ndarray, a_prev2: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: - """Apply G to raw obs slice [sensor(6)+force(6)] and action arrays. - - Parameters - ---------- - obs_slice : (12,) raw obs [s0_ux,uy, s1_ux,uy, s2_ux,uy, f0_fx,fy, f1_fx,fy, f2_fx,fy] - a_prev : (3,) physical omega at t-1 - a_prev2 : (3,) physical omega at t-2 - - Returns - ------- - G_obs : (12,) transformed obs slice - G_a_prev : (3,) transformed a_prev - G_a_prev2 : (3,) transformed a_prev2 - """ - G_obs = np.zeros(12, dtype=np.float64) - # sensors: swap top(0,1) <-> bottom(4,5), negate v - G_obs[0] = obs_slice[4] - G_obs[1] = -obs_slice[5] - G_obs[2] = obs_slice[2] - G_obs[3] = -obs_slice[3] - G_obs[4] = obs_slice[0] - G_obs[5] = -obs_slice[1] - # forces: swap bottom(2,3) <-> top(4,5), negate fy - G_obs[6] = obs_slice[6] - G_obs[7] = -obs_slice[7] - G_obs[8] = obs_slice[10] - G_obs[9] = -obs_slice[11] - G_obs[10] = obs_slice[8] - G_obs[11] = -obs_slice[9] - - G_a_prev = np.array([-a_prev[0], -a_prev[2], -a_prev[1]], dtype=np.float64) - G_a_prev2 = np.array([-a_prev2[0], -a_prev2[2], -a_prev2[1]], dtype=np.float64) - return G_obs, G_a_prev, G_a_prev2 + """Compatibility wrapper applying canonical G to raw data and two actions.""" + return apply_G_raw12(obs_slice), apply_G_alpha(a_prev), apply_G_alpha(a_prev2) def check_equivariance( diff --git a/src/SR_analysis/utils/harmonics.py b/src/SR_analysis/utils/harmonics.py index 9011d46..9fdfc0c 100644 --- a/src/SR_analysis/utils/harmonics.py +++ b/src/SR_analysis/utils/harmonics.py @@ -9,23 +9,58 @@ from __future__ import annotations import numpy as np -def gen_target_states_at(t: int, harmonics: list) -> np.ndarray: - """Reconstruct target forces at step t from FFT harmonics. - - Args: - t: current step index (0-based). - harmonics: list of dicts from analyze_harmonics(), one per channel. - Each dict has keys: 'dc' (float), 'amps' (list[float]), - 'freqs' (list[float]), 'phases' (list[float]). - - Returns: - ndarray of shape (D,) with reconstructed values, where D = len(harmonics). - """ - D = len(harmonics) - result = np.zeros(D, dtype=np.float32) - for d, h in enumerate(harmonics): - val = h["dc"] - for amp, freq, phase in zip(h["amps"], h["freqs"], h["phases"]): - val += amp * np.cos(2 * np.pi * freq * t + phase) - result[d] = float(val) +def analyze_harmonics(states: np.ndarray, n_harmonics: int = 5) -> list[dict]: + """Extract dominant FFT harmonics independently for each channel.""" + states = np.asarray(states) + if states.ndim != 2 or states.shape[0] < 2: + raise ValueError("states must have shape (samples, channels) with at least 2 samples") + count, channels = states.shape + result = [] + for channel in range(channels): + fft = np.fft.rfft(states[:, channel]) + frequencies = np.fft.rfftfreq(count, d=1.0) + amplitudes = 2.0 * np.abs(fft) / count + indices = np.argsort(amplitudes[1:])[::-1][:n_harmonics] + 1 + result.append({ + "dc": float(np.real(fft[0]) / count), + "amps": amplitudes[indices].tolist(), + "freqs": frequencies[indices].tolist(), + "phases": np.angle(fft)[indices].tolist(), + }) return result + + +def harmonics_document(harmonics: list[dict], channel_names: list[str], *, schema_version: str) -> dict: + """Wrap harmonics with an explicit schema while retaining list-loader support.""" + if len(harmonics) != len(channel_names): + raise ValueError("channel_names length must match harmonics") + return {"schema_version": schema_version, "channel_names": list(channel_names), "harmonics": harmonics} + + +def _channels(harmonics: list | dict) -> list: + if isinstance(harmonics, dict): + try: + harmonics = harmonics["harmonics"] + except KeyError as exc: + raise ValueError("harmonics document is missing 'harmonics'") from exc + if not isinstance(harmonics, list): + raise TypeError("harmonics must be a legacy list or a schema document") + return harmonics + + +def gen_target_states_at(t: int | np.ndarray, harmonics: list | dict) -> np.ndarray: + """Reconstruct channels at one or more phase indices. + + Both historical bare lists and v2 documents containing ``harmonics`` are + accepted. A scalar phase returns ``(channels,)``; an array returns + ``(phases, channels)``. + """ + channels = _channels(harmonics) + phases = np.atleast_1d(np.asarray(t, dtype=np.float64)) + result = np.zeros((phases.size, len(channels)), dtype=np.float32) + for index, harmonic in enumerate(channels): + values = np.full(phases.shape, float(harmonic["dc"]), dtype=np.float64) + for amplitude, frequency, phase in zip(harmonic["amps"], harmonic["freqs"], harmonic["phases"]): + values += amplitude * np.cos(2 * np.pi * frequency * phases + phase) + result[:, index] = values + return result[0] if np.asarray(t).ndim == 0 else result diff --git a/src/SR_analysis/utils/metrics.py b/src/SR_analysis/utils/metrics.py new file mode 100644 index 0000000..ea9119a --- /dev/null +++ b/src/SR_analysis/utils/metrics.py @@ -0,0 +1,52 @@ +"""Versioned similarity metrics shared by validation and data export.""" +from __future__ import annotations + +from typing import Any + +import numpy as np + +LEGACY_DTW_VERSION = "legacy_dtw_v1_abs_n_unclipped" + + +def legacy_cycle_similarity( + target: np.ndarray, + state: np.ndarray, + conv_len: int, + *, + lag_channel: int = 1, +) -> dict[str, Any]: + """Compare one target reference cycle with the final controlled cycle.""" + from SR_analysis.utils.cfd_interface import calc_dtw_sim, calc_lag + + target_array = np.asarray(target, dtype=np.float64) + state_array = np.asarray(state, dtype=np.float64) + if target_array.ndim != 2 or target_array.shape[1] != 6: + raise ValueError(f"legacy metric target must have shape (n, 6), got {target_array.shape}") + if state_array.ndim != 2 or state_array.shape[1] != 6: + raise ValueError(f"legacy metric state must have shape (n, 6), got {state_array.shape}") + if conv_len <= 0: + raise ValueError(f"conv_len must be positive, got {conv_len}") + if not 0 <= lag_channel < 6: + raise ValueError(f"lag_channel must be in [0, 5], got {lag_channel}") + if len(target_array) < 2 * conv_len or len(state_array) < conv_len: + raise ValueError( + f"legacy metric requires target >= {2 * conv_len} and state >= {conv_len} samples" + ) + + target_window = target_array[conv_len : 2 * conv_len, lag_channel] + state_window = state_array[-conv_len:, lag_channel] + lag = calc_lag(target_window, state_window) + scores = [ + float( + calc_dtw_sim( + np.roll(target_array[:, index], -lag)[conv_len : 2 * conv_len], + state_array[-conv_len:, index], + ) + ) + for index in range(6) + ] + return { + "similarity": float(np.mean(scores)), + "per_channel": {str(index): score for index, score in enumerate(scores)}, + "lag": int(lag), + } diff --git a/src/SR_analysis/utils/provenance.py b/src/SR_analysis/utils/provenance.py new file mode 100644 index 0000000..a8bb93d --- /dev/null +++ b/src/SR_analysis/utils/provenance.py @@ -0,0 +1,84 @@ +"""Deterministic hashing and durable JSON helpers for SR artifacts.""" +from __future__ import annotations + +import hashlib +import json +import os +import tempfile +from pathlib import Path +from typing import Any, Mapping, Union + +PathLike = Union[str, os.PathLike] + + +def safe_path(base_dir: PathLike, relative_path: PathLike) -> Path: + """Resolve a relative artifact path and ensure it remains below ``base_dir``.""" + base = Path(base_dir).expanduser().resolve() + candidate_input = Path(relative_path) + if candidate_input.is_absolute(): + raise ValueError("artifact path must be relative to base_dir") + candidate = (base / candidate_input).resolve() + try: + candidate.relative_to(base) + except ValueError as exc: + raise ValueError("artifact path escapes base_dir") from exc + return candidate + + +def canonical_json_bytes(value: Any) -> bytes: + """Serialize JSON data deterministically for content addressing.""" + return json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + allow_nan=False, + ).encode("utf-8") + + +def sha256_bytes(data: bytes) -> str: + return hashlib.sha256(data).hexdigest() + + +def sha256_text(text: str) -> str: + if not isinstance(text, str): + raise TypeError("text must be a string") + return sha256_bytes(text.encode("utf-8")) + + +def hash_json(value: Any) -> str: + return sha256_bytes(canonical_json_bytes(value)) + + +def hash_file(path: PathLike, chunk_size: int = 1024 * 1024) -> str: + digest = hashlib.sha256() + with Path(path).open("rb") as stream: + for chunk in iter(lambda: stream.read(chunk_size), b""): + digest.update(chunk) + return digest.hexdigest() + + +def atomic_write_json(path: PathLike, value: Mapping[str, Any]) -> Path: + """Atomically replace a JSON file, including an fsync of file contents.""" + destination = Path(path) + destination.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary_name = tempfile.mkstemp( + prefix=f".{destination.name}.", suffix=".tmp", dir=str(destination.parent) + ) + temporary = Path(temporary_name) + try: + with os.fdopen(descriptor, "wb") as stream: + stream.write(canonical_json_bytes(value) + b"\n") + stream.flush() + os.fsync(stream.fileno()) + os.replace(str(temporary), str(destination)) + except BaseException: + temporary.unlink(missing_ok=True) + raise + return destination + + +def atomic_write_json_below( + base_dir: PathLike, relative_path: PathLike, value: Mapping[str, Any] +) -> Path: + return atomic_write_json(safe_path(base_dir, relative_path), value) diff --git a/src/SR_analysis/utils/symmetry_dataset.py b/src/SR_analysis/utils/symmetry_dataset.py new file mode 100644 index 0000000..0039353 --- /dev/null +++ b/src/SR_analysis/utils/symmetry_dataset.py @@ -0,0 +1,49 @@ +"""Raw-state G transformations and empirical policy symmetry diagnostics.""" +from __future__ import annotations + +from typing import Any, Mapping, Optional, Sequence + +import numpy as np + +from .data_contracts import BatchDataset, build_batch_dataset +from .g_operator import apply_G_alpha, apply_G_raw12, apply_G_target_force + + +def build_mirrored_batch_dataset( + sensors: np.ndarray, + forces: np.ndarray, + actions: np.ndarray, + feat_keys: Sequence[str], + *, + target_forces: Optional[np.ndarray] = None, + **kwargs: Any, +) -> BatchDataset: + """Apply G to raw physical state/actions, then rebuild derived features.""" + raw = np.column_stack((np.asarray(sensors), np.asarray(forces))) + mirrored = apply_G_raw12(raw) + mirrored_target = None if target_forces is None else apply_G_target_force(target_forces) + return build_batch_dataset( + mirrored[:, :6], mirrored[:, 6:], apply_G_alpha(actions), feat_keys, + target_forces=mirrored_target, **kwargs, + ) + + +def empirical_equivariance_defect( + actions: np.ndarray, + mirrored_policy_actions: np.ndarray, +) -> Mapping[str, Any]: + """Compare policy actions at Gx against the exact expected G(action(x)).""" + expected = apply_G_alpha(actions) + observed = np.asarray(mirrored_policy_actions, dtype=np.float64) + if observed.shape != expected.shape: + raise ValueError(f"mirrored actions have shape {observed.shape}; expected {expected.shape}") + residual = observed - expected + scale = np.ptp(np.asarray(actions, dtype=np.float64), axis=0) + rmse = np.sqrt(np.mean(residual**2, axis=0)) + return { + "channels": ["front", "upper", "lower"], + "rmse": rmse.tolist(), + "range_normalized_rmse": (rmse / np.where(scale > 1e-12, scale, 1.0)).tolist(), + "max_abs_error": np.max(np.abs(residual), axis=0).tolist(), + "n_samples": int(len(observed)), + } diff --git a/tests/test_stage_3_validate.py b/tests/test_stage_3_validate.py new file mode 100644 index 0000000..a792cce --- /dev/null +++ b/tests/test_stage_3_validate.py @@ -0,0 +1,209 @@ +import json +from pathlib import Path + +import numpy as np +import pytest + +from SR_analysis import stage_3_validate as stage3 + + +def formula(path: Path, *, role: str, anchor: str | None = None, expression: str = "0") -> Path: + feature_names = ["u_a"] if "u_a" in expression else [] + value = { + "schema_version": "1.0", + "model_type": "symbolic", + "feature_names": feature_names, + "fitted_expression": expression, + "deployment_expression": expression, + "role": role, + "parity": "odd" if role == "front" else None, + "anchor": anchor, + } + path.write_text(json.dumps(value), encoding="utf-8") + return path + + +def formula_pair(tmp_path: Path, front_expression: str = "0", rear_expression: str = "1") -> stage3.FormulaPair: + front = formula(tmp_path / "front.json", role="front", expression=front_expression) + rear = formula(tmp_path / "rear.json", role="rear_shared", anchor="upper", expression=rear_expression) + return stage3.load_formula_pair(front, rear) + + +def karman_cfg() -> dict: + return { + "scene_id": "karman", + "sample_interval": 800, + "u0": 0.01, + "mu": 0.02, + "conv_len": 2, + "action_layout": stage3.ACTION_ORDER, + "policy_init_action": (0.0, -4.0, 4.0), + "action_scale": 8.0, + "action_bias": (0.0, -4.0, 4.0), + "s_dim": 12, + } + + +def make_plan(tmp_path: Path, *, mode: str = "pysr", pair=None, n_steps: int = 4) -> stage3.ValidationPlan: + root = tmp_path / "runs" + stem = "karman_re100__pysr__f-abc__si-800" + return stage3.ValidationPlan( + "karman_re100", karman_cfg(), mode, n_steps, "run", root / "run" / "validations", + root / "run" / "telemetry", stem, root / "run" / "validations" / f"{stem}.json", + root / "run" / "telemetry" / f"{stem}.npz", pair, None, None, None, None, None, + ) + + +class FakeEnvironment: + def __init__(self, *, nonfinite: bool = False): + self.current_raw = np.arange(12, dtype=float) + self.target_sensors = np.tile(np.arange(6, dtype=float), (8, 1)) + self.nonfinite = nonfinite + self.closed = False + + def step(self, omega_native): + self.current_raw = self.current_raw + 0.01 + if self.nonfinite: + self.current_raw[0] = np.nan + return self.current_raw.copy() + + def close(self): + self.closed = True + + +def test_formula_roles_hash_and_independent_filename(tmp_path, monkeypatch): + pair = formula_pair(tmp_path) + monkeypatch.setattr(stage3, "get_scene", lambda scene: karman_cfg()) + plan = stage3.prepare_plan( + scene="karman_re100", mode="pysr", n_steps=4, run_id="r1", + output_root=tmp_path / "out", formula_pair=pair, + ) + assert pair.front["deployment_expression_hash"] + assert pair.rear["deployment_expression_hash"] + assert "karman_re100__pysr__f-" in plan.validation_path.name + assert "__si-800.json" in plan.validation_path.name + assert plan.validation_path.parent.name == "validations" + assert plan.telemetry_path.parent.name == "telemetry" + + +def test_legacy_top_formula_is_recorded(tmp_path): + front = tmp_path / "front.json" + rear = tmp_path / "top.json" + front.write_text(json.dumps({"channel": "front", "feature_keys": [], "best_sympy": "0"}), encoding="utf-8") + rear.write_text(json.dumps({"channel": "top", "feature_keys": [], "best_sympy": "0"}), encoding="utf-8") + pair = stage3.load_formula_pair(front, rear) + assert "channel=top" in pair.compatibility["rear"]["legacy_role_mapping"] + + +def test_missing_or_wrong_formula_fails(tmp_path): + front = formula(tmp_path / "front.json", role="front") + wrong = formula(tmp_path / "wrong.json", role="rear_shared", anchor="lower") + with pytest.raises(ValueError, match="anchor='upper'"): + stage3.load_formula_pair(front, wrong) + with pytest.raises(FileNotFoundError): + stage3.load_formula_pair(front, tmp_path / "missing.json") + + +def test_symbolic_policy_uses_exact_front_odd_projection_and_rear_mapping(tmp_path, monkeypatch): + pair = formula_pair(tmp_path, rear_expression="u_a") + policy = stage3.SymbolicPolicy(pair, karman_cfg(), None) + values = iter([8.0, 2.0, 3.0, 5.0]) + monkeypatch.setattr(policy, "_evaluate", lambda formula, features: next(values)) + omega, _, _ = policy.action(np.arange(12, dtype=float), 0) + np.testing.assert_allclose(omega / 0.01, [3.0, 3.0, -5.0]) + + +def test_metric_has_single_exact_named_shape(monkeypatch): + monkeypatch.setattr( + stage3, + "_legacy_metric", + lambda target, sensors, conv_len: { + "similarity": 0.75, + "per_channel": {str(index): float(index) for index in range(6)}, + "lag": -2, + }, + ) + metrics = stage3.compute_versioned_metrics(np.zeros((4, 6)), np.zeros((4, 6)), 2) + assert set(metrics) == { + "metric_version", + "adapter", + "legacy_reference_cycle_vs_last_recorded_cycle", + } + exact = metrics["legacy_reference_cycle_vs_last_recorded_cycle"] + assert set(exact) == {"similarity", "per_channel", "lag"} + assert len(exact["per_channel"]) == 6 + assert exact["lag"] == -2 + + +def test_ppo_model_loads_once_for_rollout(tmp_path): + plan = make_plan(tmp_path, mode="ppo") + model_path = tmp_path / "model.zip" + model_path.write_bytes(b"model") + norm = {"force_norm_fact": 1.0, "sens_deviation": [0.0] * 6, "sens_norm_fact": [1.0] * 6} + plan = stage3.ValidationPlan(**{**plan.__dict__, "model_path": model_path, "norm": norm}) + calls = [] + + class Model: + def predict(self, observation, deterministic=True): + return np.zeros(3, dtype=np.float32), None + + def loader(*args, **kwargs): + calls.append((args, kwargs)) + return Model() + + environment = FakeEnvironment() + policy = stage3.build_policy(plan, model_loader=loader) + stage3.run_rollout(plan, environment, policy) + assert len(calls) == 1 + assert calls[0][1]["device"] == "cpu" + + +def test_nonfinite_rollout_raises(tmp_path): + plan = make_plan(tmp_path, mode="uncontrolled") + with pytest.raises(stage3.RolloutFailure, match="raw observation"): + stage3.run_rollout(plan, FakeEnvironment(nonfinite=True), stage3.UncontrolledPolicy()) + + +def test_execute_propagates_factory_exception(tmp_path): + plan = make_plan(tmp_path, mode="uncontrolled") + + def broken(plan, device): + raise RuntimeError("mock flow failed") + + with pytest.raises(RuntimeError, match="mock flow failed"): + stage3.execute_plan(plan, 0, environment_factory=broken) + + +def test_dry_run_requires_no_cuda_and_creates_no_artifacts(tmp_path, monkeypatch, capsys): + cfg = karman_cfg() + monkeypatch.setattr(stage3, "get_scene", lambda scene: cfg) + pair = formula_pair(tmp_path) + code = stage3.main([ + "--scene", "karman_re100", "--mode", "pysr", "--formula-front", str(pair.front_path), + "--formula-rear", str(pair.rear_path), "--run-id", "dry", "--output-root", str(tmp_path / "out"), + "--dry-run", + ]) + assert code == 0 + assert "validation=" in capsys.readouterr().out + assert not (tmp_path / "out").exists() + + +def test_vortex_is_explicit_round1_error(tmp_path, monkeypatch): + cfg = {**karman_cfg(), "scene_id": "vortex"} + monkeypatch.setattr(stage3, "get_scene", lambda scene: cfg) + with pytest.raises(NotImplementedError, match="not supported in round1"): + stage3.prepare_plan( + scene="vortex_lamb", mode="uncontrolled", n_steps=4, run_id="r", + output_root=tmp_path, formula_pair=None, + ) + + +def test_batch_failure_returns_nonzero(tmp_path, monkeypatch): + cfg = karman_cfg() + monkeypatch.setattr(stage3, "get_scene", lambda scene: cfg) + monkeypatch.setattr(stage3, "execute_plan", lambda plan, device: (_ for _ in ()).throw(RuntimeError("boom"))) + code = stage3.main([ + "--group", "a,b", "--mode", "uncontrolled", "--run-id", "r", + "--output-root", str(tmp_path), "--steps", "4", + ]) + assert code != 0