research: replay frozen Legacy karman_re100 PPO on current CelerisLab

Legacy legacy_test interface contract: A = (8a + b) U0 with b = (0,-4,4), omega = +A/R, per-lattice-step EMA reset to zero every 800-step interval, zero first observation, frozen norm.json, deterministic policy, Legacy warm-up order. Reuses the DynamisLab model loader and observation normalizer read-only; writes the f2f88c phase3 signals.npz layout. Research only; src/ unchanged. Not executed here (no GPU/numpy). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-31 10:10:08 +00:00
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+"""Replay the frozen Legacy karman_re100 PPO policy on current CelerisLab.
+
+Research only; nothing in src/ is changed. Plant: Legacy Karman cloak scene
+(1280x512, D=20, parabolic inlet, mean U0 .01, nu .004, Re_D 50; upstream
+cylinder r=20 at x=200; pinball front (600, 255.5), rear (626, 255.5+-15);
+probes r=5 at x=800, y=255.5+40/0/-40). Interface contract = DynamisLab
+legacy_test (the executor behind the Legacy rerun), not f2f88c phase3:
+
+- command per interval: A = (8 a + b) U0, b = (0, -4, 4), a = deterministic
+  policy output in [-1, 1]^3; wall speed command, omega = +A / R (both
+  solvers impose u_w = omega x r, CCW positive);
+- smoothing per LATTICE step, A_eff <- .9 A_eff + .1 A, with A_eff = 0 at the
+  start of every 800-step interval (legacy_test.runtime.run_historical_interval);
+- observation: interval means of the three pinball forces (front, top, bottom)
+  and the three probe (ux, uy) sums, normalized by the frozen norm.json with
+  legacy_test.runtime.policy_observation; the first observation is zero.
+
+Output (--out, outside the checkout): signals.npz with sensors (N,6), forces
+(N,6), actions (N,3) in the f2f88c phase3 layout, so its comparator applies.
+
+    PYTHONPATH=src python research/karman_reproduce/run.py --dynamis /path/to/DynamisLab \\
+        --config /path/to/config_lbm_pinball_legacy_compat.json --out /tmp/... [--device 0]
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+
+import numpy as np
+
+L0, U0, NX, NY = 20.0, 0.01, 1280, 512
+R, YC = L0 / 2, (NY - 1) / 2
+SI, INTERVALS, WARMUP = 800, 200, int(4 * NX / U0)
+BIAS = np.array([0.0, -4.0, 4.0])
+
+
+def git_head(path):
+    return subprocess.run(["git", "-C", path, "rev-parse", "HEAD"], capture_output=True,
+                          text=True).stdout.strip()
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--dynamis", required=True, help="DynamisLab checkout (read-only)")
+    ap.add_argument("--config", required=True)
+    ap.add_argument("--out", required=True)
+    ap.add_argument("--device", type=int, default=0)
+    ap.add_argument("--intervals", type=int, default=INTERVALS)
+    args = ap.parse_args()
+    here = os.path.realpath(os.path.join(os.path.dirname(__file__), "..", ".."))
+    if os.path.realpath(args.out).startswith(here):
+        raise SystemExit("--out must be outside the checkout")
+    os.makedirs(args.out, exist_ok=True)
+
+    sys.path.insert(0, os.path.join(args.dynamis, "src"))
+    from drl_pinball.legacy_test.core.model_loader import load_model, model_path
+    from drl_pinball.legacy_test.runtime import load_policy_norm, policy_observation
+    norm_path = os.path.join(args.dynamis, "src", "SR_analysis", "data", "karman", "karman_re100",
+                             "norm.json")
+    model, norm = load_model("d1a3o12_re100"), load_policy_norm(norm_path)
+
+    from CelerisLab.simulation import Simulation
+    sim = Simulation(args.config, device_id=args.device)
+    try:
+        sim.add_body("circle", center=(10 * L0, YC, 0.0), radius=L0)
+        probes = [sim.add_body("sensor", center=(40 * L0, YC + dy, 0.0), radius=5.0)
+                  for dy in (40.0, 0.0, -40.0)]
+        sim.initialize()
+        sim.run(WARMUP)  # Legacy order: disturbance + probes first, then the pinball
+        pinball = [sim.add_body("circle", center=c, radius=R)
+                   for c in ((30 * L0, YC, 0.0), (626.0, YC + 15, 0.0), (626.0, YC - 15, 0.0))]
+        sim.sync_bodies()
+        sim.run(WARMUP)
+        cells = float(len(sim.bodies.get(probes[0]).get_sensor_list(NX, NY)[0]))  # probe sum = mean * cells
+
+        obs = np.zeros(12, np.float32)
+        sensors, forces, actions = (np.zeros((args.intervals, n), np.float32) for n in (6, 6, 3))
+        for n in range(args.intervals):
+            a = np.asarray(model.predict(obs, deterministic=True)[0], np.float32).reshape(3)
+            target, eff = (8.0 * a + BIAS) * U0, np.zeros(3)
+            for k in range(SI):
+                eff = 0.9 * eff + 0.1 * target
+                for pid, w in zip(pinball, eff / R):
+                    sim.set_body(pid, omega=float(w))
+                sim.run(1, zero_obs=(k == 0), sync_obs=(k == SI - 1))
+            s = [v * cells for pid in probes for v in sim.read_sensor(pid)[:2]]
+            f = [v for pid in pinball for v in sim.read_force(pid)[:2]]
+            raw = np.asarray(s + f, np.float32)
+            if not np.all(np.isfinite(raw)):
+                raise RuntimeError(f"non-finite observation at interval {n}")
+            sensors[n], forces[n], actions[n] = raw[:6], raw[6:], a
+            obs = policy_observation(raw, norm)
+        np.savez_compressed(os.path.join(args.out, "signals.npz"), sensors=sensors,
+                            forces=forces, actions=actions)
+        with open(os.path.join(args.out, "case.json"), "w") as fh:
+            json.dump(dict(celeris=git_head(here), dynamis=git_head(args.dynamis),
+                           config=os.path.realpath(args.config), model=model_path("d1a3o12_re100"),
+                           norm=norm_path, warmup=WARMUP, interval=SI, intervals=args.intervals,
+                           probe_cells=cells), fh, indent=1)
+    finally:
+        sim.close()
+
+
+if __name__ == "__main__":
+    main()