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feat(reproduce): legacy-test framework + fixed-inlet reproduce pipeline

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- Track A (legacy_test): systematic validation scripts for all trained PPO
  models using LegacyCelerisLab. Each test script rebuilds the exact legacy
  CFD environment, runs deterministic inference, and compares against
  SR_analysis reference data using DTW-based comparison. Verified: Karman
  re100/re50/re200, Vortex lamb/taylor all pass (DTW > 0.95).

- Track B (reproduce): Phase 2 open-loop CFD validation + Phase 3 DRL
  inference using the legacy-compatible config (regularized inlet with
  neq_damp=1.0, matching the legacy NBB formula). The inlet scheme fix
  improves new-CFD Karman DTW from 0.916 to 0.943.

- Fixes: action_wrapper sign convention docstring, model inventory
  duplicate entries and missing models, stale config paths in legacy
  run_all_cases.py/run_illusion_vortex.py, illusion label formatting

- Add READMEs and run-all shell scripts for both tracks
- Add .gitignore entries for runtime output directories

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Frank14f
2026-07-12 23:09:53 +08:00
co-authored by Cursor
parent 4360bb2047
commit f2f88c2442
33 changed files with 3814 additions and 271 deletions
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src/drl_pinball/legacy_test/README.md
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# Legacy Test (Track A)
Systematic validation of pre-trained PPO models using **LegacyCelerisLab**
(the original CFD solver the models were trained with).
## Quick Start
```bash
# Run all legacy tests sequentially (GPU 1, 60s delay between tests)
bash src/drl_pinball/legacy_test/run_all_legacy_tests.sh 1
# Single scene
conda run -n pycuda_3_10 python src/drl_pinball/legacy_test/test_karman_cloak_re100.py --device 1
```
## Directory
```
legacy_test/
├── README.md # This file
├── core/
│ ├── comparator.py # Compare signals against SR_analysis reference
│ ├── dtw_metrics.py # DTW/harmonics (re-exports from reproduce/core/)
│ ├── io_helpers.py # Save/load .npz, norm.json
│ ├── legacy_env_builder.py # FlowField builders for all 5 scene types
│ └── model_loader.py # PPO model loading (wraps ModelInventory)
├── test_karman_cloak_re100.py # Flagship: Karman Cloak Re100
├── test_karman_cloak_crossre.py # Cross-Re: re50, re200, re400
├── test_steady_cloak.py # Steady cloak (open-loop, no DRL)
├── test_illusion_1L.py # Illusion 1.0L (S_DIM=14)
├── test_illusion_remaining.py # Illusion 0.75L, 1.5L
├── test_vortex_lamb.py # Vortex Lamb dipole
├── test_vortex_taylor.py # Vortex Taylor monopole
├── test_erase.py # Erase (experimental, known incomplete)
├── run_all_legacy_tests.sh # Sequential launcher
└── output/ # Per-scene verification outputs
```
## Scene Coverage
| Scene | S_DIM | Scale/Bias | SI | MaxSteps | Legacy Ref |
|-------|-------|------------|-----|----------|------------|
| Karman re100 | 12 | 8/(0,-4,4) | 800 | 500 | `legacy_karman_env.py` |
| Karman re50 | 12 | 8/(0,-4,4) | 800 | 500 | same, ν=0.008 |
| Karman re200 | 12 | 8/(0,-4,4) | 800 | 500 | same, ν=0.002 |
| Karman re400 | 12 | 8/(0,-4,4) | 800 | 500 | same, ν=0.001 |
| Steady Cloak | — | open-loop | 800 | 200 | OID `collect_steady_cloak.py` |
| Illusion 0.75L | 14 | 8/(0,-2,2) | 400 | 500 | `legacy_env_imit.py` |
| Illusion 1L | 14 | 8/(0,-2,2) | 600 | 500 | same |
| Illusion 1.5L | 14 | 8/(0,-2,2) | 800 | 500 | same |
| Vortex Lamb | 12 | 4/(0,-4,4) | 800 | 150 | `legacy_env_vortex.py` |
| Vortex Taylor | 12 | 4/(0,-4,4) | 800 | 150 | same |
| Erase | 12 | 8/(0,-8,8) | 600 | 500 | `legacy_env_erase.py` |
## Design Notes
- **Object ordering** matches legacy EXACTLY (documented in `knowledge.md` Section 9):
- Karman/Erase: dist_cyl(0) [or sensor0(0) for erase], sensors(1-3), front(4), top(5), bottom(6)
- Steady/Illusion/Vortex: sensors(0-2), front(3), top(4), bottom(5)
- **DDF checkpoint timing** uses pre-bias save + test-side bias FIFO (matching legacy `save_ddf()` pattern)
- **Action** uses legacy `FlowField.run()` built-in EMA smoothing (weight 0.1)
- **Comparison** uses DTW similarity > 0.95 as primary pass criterion (phase-invariant)
- **Steady cloak** is open-loop — verifies lift RMS suppression, no DTW comparison
- **Erase** is known incomplete — no DTW threshold enforced
src/drl_pinball/legacy_test/__init__.py
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# legacy_test: Systematic validation of legacy PPO models using LegacyCelerisLab.
#
# Track A of the reproduce plan. Each test script:
# 1. Builds the correct LegacyCelerisLab env for a scene
# 2. Loads the pre-trained PPO model
# 3. Runs deterministic inference
# 4. Compares output against SR_analysis reference data
src/drl_pinball/legacy_test/core/__init__.py
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# legacy_test/core: Shared utilities for legacy CFD test scripts.
src/drl_pinball/legacy_test/core/comparator.py
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# legacy_test/core/comparator.py
"""Compare legacy test output against SR_analysis reference data.
Computes per-channel correlation, DTW similarity, RMS error, and spectral
comparison (FFT peak matching) between generated and reference signals.
"""
from __future__ import annotations
import os
import sys
from typing import Dict, Optional, Tuple
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
for p in [_REPO, _SRC]:
if p not in sys.path:
sys.path.insert(0, p)
from .dtw_metrics import calc_lag, calc_dtw_sim # noqa: E402
def pearson_corr(x: np.ndarray, y: np.ndarray) -> float:
"""Pearson correlation coefficient between two 1-D arrays."""
xm = x - x.mean()
ym = y - y.mean()
denom = np.sqrt((xm * xm).sum() * (ym * ym).sum())
if denom < 1e-12:
return 0.0
return float((xm * ym).sum() / denom)
def channel_corr(ref: np.ndarray, gen: np.ndarray) -> np.ndarray:
"""Per-channel Pearson correlation.
Args:
ref: (N, D) reference array.
gen: (N, D) generated array.
Returns:
(D,) correlation per channel.
"""
n = min(ref.shape[0], gen.shape[0])
ref = ref[:n]
gen = gen[:n]
return np.array([pearson_corr(ref[:, i], gen[:, i]) for i in range(ref.shape[1])])
def rms_error(ref: np.ndarray, gen: np.ndarray) -> float:
"""Root-mean-square error between two arrays."""
n = min(ref.shape[0], gen.shape[0])
ref = ref[:n]
gen = gen[:n]
return float(np.sqrt(np.mean((ref - gen) ** 2)))
def dtw_similarity(ref_sensors: np.ndarray, gen_sensors: np.ndarray,
conv_len: int = 30) -> float:
"""Compute DTW similarity across all sensor channels.
Uses the same lag-compensated DTW as the legacy env reward:
1. Compute lag from middle sensor (index 1) Uy component
2. For all 6 channels, roll target by lag, compute DTW, average
Args:
ref_sensors: (N, 6) reference sensor data.
gen_sensors: (N, 6) generated sensor data.
conv_len: Convergence window length.
Returns:
Average DTW similarity in [0, 1].
"""
n = min(ref_sensors.shape[0], gen_sensors.shape[0])
target = np.asarray(ref_sensors[:n], dtype=np.float64)
state = np.asarray(gen_sensors[:n], dtype=np.float64)
id_sens = 1
target_seq = target[conv_len:2 * conv_len, id_sens]
state_seq = state[-conv_len:, id_sens]
lag = calc_lag(target_seq, state_seq)
similarities = 0.0
for i in range(6):
t_seq = np.roll(target[:, i], -lag)[conv_len:2 * conv_len]
s_seq = state[-conv_len:, i]
similarities += calc_dtw_sim(t_seq, s_seq)
return float(similarities / 6.0)
def fft_peak_match(ref_signal: np.ndarray, gen_signal: np.ndarray,
top_n: int = 3) -> Tuple[float, np.ndarray, np.ndarray]:
"""Compare FFT peak frequencies between reference and generated signals.
Args:
ref_signal: 1-D reference signal.
gen_signal: 1-D generated signal.
top_n: Number of top peaks to compare.
Returns:
(fraction_matched, ref_peaks, gen_peaks) where fraction_matched
is the fraction of top_n ref peaks that have a matching gen peak
within 10% frequency tolerance.
"""
n = min(len(ref_signal), len(gen_signal))
ref_spec = np.abs(np.fft.rfft(ref_signal[:n]))
gen_spec = np.abs(np.fft.rfft(gen_signal[:n]))
freqs = np.fft.rfftfreq(n, d=1)
# Exclude DC (freq=0)
mask = freqs > 0
freqs_nz = freqs[mask]
ref_amps = ref_spec[mask] if len(ref_spec) == len(freqs) else ref_spec[1:]
gen_amps = gen_spec[mask] if len(gen_spec) == len(freqs) else gen_spec[1:]
if len(freqs_nz) == 0:
return 1.0, np.array([]), np.array([])
ref_idx = np.argsort(ref_amps)[::-1][:top_n]
gen_idx = np.argsort(gen_amps)[::-1][:top_n]
ref_peaks = freqs_nz[ref_idx]
gen_peaks = freqs_nz[gen_idx]
matched = 0
for rp in ref_peaks:
if rp < 1e-12:
matched += 1
continue
for gp in gen_peaks:
if abs(rp - gp) / max(rp, 1e-12) < 0.10:
matched += 1
break
return float(matched / max(top_n, 1)), ref_peaks, gen_peaks
def compare_scene(
ref_dir: str,
gen_sensors: np.ndarray,
gen_forces: np.ndarray,
gen_actions: np.ndarray,
*,
conv_len: int = 30,
label: str = "",
) -> Dict:
"""Full comparison of generated signals against SR_analysis reference.
Args:
ref_dir: Path to SR_analysis scene directory.
gen_sensors: (N, 6) generated sensor signals.
gen_forces: (N, 6) generated force signals.
gen_actions: (N, 3) generated action signals.
conv_len: DTW convergence window length.
label: Optional scene label for printing.
Returns:
dict with keys:
sensor_corr: (6,) per-channel sensor correlation
force_corr: (6,) per-channel force correlation
action_corr: (3,) per-channel action correlation
sensor_rms: scalar RMS error
force_rms: scalar RMS error
action_rms: scalar RMS error
dtw_sim: scalar DTW similarity
fft_match: fraction of FFT peaks matched (sensor channel 1)
passed: bool — True if all metrics meet thresholds
"""
from .io_helpers import load_reference_signals
ref = load_reference_signals(ref_dir)
s_corr = channel_corr(ref["sensors"], gen_sensors)
f_corr = channel_corr(ref["forces"], gen_forces)
a_corr = channel_corr(ref["actions"], gen_actions)
s_rms = rms_error(ref["sensors"], gen_sensors)
f_rms = rms_error(ref["forces"], gen_forces)
a_rms = rms_error(ref["actions"], gen_actions)
dtw_sim = dtw_similarity(ref["sensors"], gen_sensors, conv_len=conv_len)
fft_match, _, _ = fft_peak_match(ref["sensors"][:, 1], gen_sensors[:, 1])
# Primary threshold: DTW similarity (phase-invariant)
passed = dtw_sim > 0.95
result = {
"sensor_corr": s_corr.tolist(),
"force_corr": f_corr.tolist(),
"action_corr": a_corr.tolist(),
"sensor_rms": float(s_rms),
"force_rms": float(f_rms),
"action_rms": float(a_rms),
"dtw_sim": float(dtw_sim),
"fft_match": float(fft_match),
"passed": passed,
}
# Print summary
prefix = f"[{label}] " if label else ""
print(f"{prefix}Sensor corr: {s_corr}")
print(f"{prefix}Force corr: {f_corr}")
print(f"{prefix}Action corr: {a_corr}")
print(f"{prefix}DTW sim: {dtw_sim:.4f}, FFT match: {fft_match:.2f}")
print(f"{prefix}RMS — sens: {s_rms:.6f}, force: {f_rms:.6f}, action: {a_rms:.6f}")
print(f"{prefix}{'PASS' if passed else 'FAIL'}")
return result
src/drl_pinball/legacy_test/core/dtw_metrics.py
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# legacy_test/core/dtw_metrics.py
"""DTW-based similarity metrics — imported from reproduce/core/ for consistency."""
import os
import sys
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
for p in [_REPO, _SRC]:
if p not in sys.path:
sys.path.insert(0, p)
# Re-export from the verified reproduce/core/dtw_metrics module.
from drl_pinball.reproduce.core.dtw_metrics import ( # noqa: E402, F401
calc_lag,
calc_dtw_sim,
calc_dtw_sim_enhanced,
compute_similarity_karman_cloak,
compute_similarity_vortex,
compute_similarity_illusion,
analyze_harmonics,
gen_target_states_at,
)
src/drl_pinball/legacy_test/core/io_helpers.py
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# legacy_test/core/io_helpers.py
"""I/O utilities for legacy test scripts.
Saves controlled/target/uncontrolled output and visualisations.
"""
from __future__ import annotations
import json
import os
from typing import Any, Dict, Optional, Tuple
import numpy as np
def save_signals(
out_dir: str,
sensors: np.ndarray,
forces: np.ndarray,
actions: np.ndarray,
name: str = "controlled",
) -> str:
"""Save sensor/force/action arrays as compressed .npz.
Args:
out_dir: Output directory.
sensors: (N, 6) raw sensor velocities.
forces: (N, 6) raw force values.
actions: (N, 3) normalised PPO actions in [-1, 1].
name: Base filename without extension.
Returns:
Full path to the saved file.
"""
os.makedirs(out_dir, exist_ok=True)
path = os.path.join(out_dir, f"{name}.npz")
np.savez_compressed(
path,
sensors=np.asarray(sensors, dtype=np.float32),
forces=np.asarray(forces, dtype=np.float32),
actions=np.asarray(actions, dtype=np.float32),
)
return path
def save_target(out_dir: str, target_states: np.ndarray) -> str:
"""Save target sensor signals.
Args:
out_dir: Output directory.
target_states: (FIFO_LEN, 6) target sensor data.
Returns:
Full path to the saved file.
"""
os.makedirs(out_dir, exist_ok=True)
path = os.path.join(out_dir, "target.npz")
np.savez_compressed(path, target_states=np.asarray(target_states, dtype=np.float32))
return path
def save_norm(out_dir: str, norm: Dict[str, Any]) -> str:
"""Save normalisation constants as JSON.
Args:
out_dir: Output directory.
norm: dict with force_norm_fact, sens_deviation, sens_norm_fact.
Returns:
Full path to the saved file.
"""
os.makedirs(out_dir, exist_ok=True)
path = os.path.join(out_dir, "norm.json")
out = {
"force_norm_fact": float(norm["force_norm_fact"]),
"sens_deviation": [float(x) for x in norm["sens_deviation"]],
"sens_norm_fact": [float(x) for x in norm["sens_norm_fact"]],
}
with open(path, "w") as f:
json.dump(out, f, indent=2)
return path
def load_reference_signals(ref_dir: str) -> Dict[str, np.ndarray]:
"""Load SR_analysis reference controlled.npz.
Args:
ref_dir: Path to the scene directory, e.g.
``src/SR_analysis/data/karman/karman_re100/``.
Returns:
dict with keys: sensors (N,6), forces (N,6), actions (N,3).
"""
path = os.path.join(ref_dir, "controlled.npz")
if not os.path.isfile(path):
raise FileNotFoundError(f"Reference file not found: {path}")
data = np.load(path)
return {
"sensors": np.asarray(data["sensors"], dtype=np.float32),
"forces": np.asarray(data["forces"], dtype=np.float32),
"actions": np.asarray(data["actions"], dtype=np.float32),
}
def load_reference_target(ref_dir: str) -> np.ndarray:
"""Load SR_analysis reference target.npz.
Returns:
target_states: (FIFO_LEN, N) reference target sensor data.
"""
path = os.path.join(ref_dir, "target.npz")
if not os.path.isfile(path):
raise FileNotFoundError(f"Reference file not found: {path}")
return np.asarray(np.load(path)["target_states"], dtype=np.float32)
def load_reference_norm(ref_dir: str) -> Dict[str, Any]:
"""Load SR_analysis reference norm.json.
Returns:
dict with force_norm_fact, sens_deviation, sens_norm_fact.
"""
path = os.path.join(ref_dir, "norm.json")
if not os.path.isfile(path):
raise FileNotFoundError(f"Reference file not found: {path}")
with open(path) as f:
d = json.load(f)
return {
"force_norm_fact": np.float32(d["force_norm_fact"]),
"sens_deviation": np.array(d["sens_deviation"], dtype=np.float32),
"sens_norm_fact": np.array(d["sens_norm_fact"], dtype=np.float32),
}
src/drl_pinball/legacy_test/core/legacy_env_builder.py
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# legacy_test/core/legacy_env_builder.py
"""Parameterised LegacyCelerisLab environment builders for all scenes.
Each builder follows the exact legacy procedure:
1. Create FlowField with correct config and viscosity
2. Add objects in legacy order (scene-dependent)
3. Stabilise (4*NX/U0 steps)
4. Record target signals
5. Add pinball (if not already present), stabilise
6. Compute norm from zero-action FIFO
7. Run bias-action FIFO, save DDF checkpoint
8. Return (flow_field, target_states, norm, scene_config)
Scene geometry reference (all positions in lattice units, L0=20):
- Dist cylinder: x=200, r=20
- Karman/Steady/Vortex pinball: front=600, rear=626, y_span=15
- Karman/Steady/Vortex sensors: x=800, y_span=40
- Illusion pinball: front=380, rear=406, y_span=15
- Illusion sensors: x=600, y_span=40
- Illusion target cylinder: x=400, r varies
"""
from __future__ import annotations
import os
import sys
from collections import deque
from typing import Any, Dict, Optional, Tuple
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
CONFIG_DIR = os.path.join(_REPO, "configs", "legacy_configs")
U0 = 0.01
L0 = 20.0
DATA_TYPE = np.float32
FIFO_LEN = 150
CONV_LEN = 30
SENSOR_RADIUS = L0 / 4.0 # 5
PINBALL_RADIUS = L0 / 2.0 # 10
def _nu_from_re(re_code: float) -> float:
"""Viscosity from code Reynolds number (ref length = 2*D = 40)."""
return U0 * 40.0 / re_code
def _center_y(ff: FlowField) -> float:
return (ff.FIELD_SHAPE[1] - 1) / 2.0
def _stabilize(ff: FlowField, n_obj: int) -> None:
steps = int(4 * ff.FIELD_SHAPE[0] / U0)
ff.run(steps, np.zeros(n_obj, dtype=DATA_TYPE))
def _compute_karman_norm(fifo: np.ndarray) -> Dict[str, Any]:
"""Standard norm: force_norm_fact = 6*max(|forces|), sensors 5*max deviation."""
temp = np.asarray(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_dev = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_dev[i])))
return {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_dev.tolist(),
"sens_norm_fact": sens_norm.tolist(),
}
# ---------------------------------------------------------------------------
# Karman Cloak (dist-cyl + 3 sensors + 3 pinball = 7 objects)
# ---------------------------------------------------------------------------
def build_karman_cloak(
device_id: int = 0,
re_code: float = 100.0,
*,
action_bias: Tuple[float, float, float] = (0.0, -4.0, 4.0),
action_scale: float = 8.0,
sample_interval: int = 800,
) -> Dict[str, Any]:
"""Build Karman cloak environment with LegacyCelerisLab.
Object order: dist_cyl(0), sensor0(1), sensor1(2), sensor2(3),
front(4), top(5), bottom(6).
Returns:
dict with flow_field, target_states, norm, config.
"""
viscosity = _nu_from_re(re_code)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=float(viscosity))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy = _center_y(ff)
NX, NY = ff.FIELD_SHAPE[0], ff.FIELD_SHAPE[1]
# Phase 1: dist-cyl + sensors
ff.add_cylinder((10.0 * L0, cy, 0.0), 1.0 * L0) # dist_cyl, id=0
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS) # id=1,2,3
assert ff.obs.size // 2 == 4, "Expected 4 objects after sensors"
_stabilize(ff, 4)
# Record target (sensor signals only, no pinball)
target = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff.run(sample_interval, np.zeros(4, dtype=DATA_TYPE))
target = np.vstack((target, ff.obs.copy()[2:8]))
# Phase 2: Add pinball
ff.add_cylinder((30.0 * L0, cy, 0.0), PINBALL_RADIUS) # front, id=4
ff.add_cylinder((31.3 * L0, cy + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=5
ff.add_cylinder((31.3 * L0, cy - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=6
n_total = ff.obs.size // 2
assert n_total == 7, f"Expected 7 objects, got {n_total}"
_stabilize(ff, 7)
# Checkpoint DDF
ff.get_ddf()
ff.save_ddf()
# Zero-action norm collection
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(sample_interval, np.zeros(7, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[2:14])
norm = _compute_karman_norm(np.array(fifo, dtype=DATA_TYPE))
# Bias-action FIFO
ff.apply_ddf()
bias_arr = np.zeros(7, dtype=DATA_TYPE)
bias_arr[4] = float((0.0 * action_scale + action_bias[0]) * U0) # front
bias_arr[5] = float((0.0 * action_scale + action_bias[1]) * U0) # top
bias_arr[6] = float((0.0 * action_scale + action_bias[2]) * U0) # bottom
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(sample_interval, bias_arr)
fifo.append(ff.obs.copy()[2:14])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
ff.apply_ddf()
norm["save_states"] = save_states
norm["action_bias"] = list(action_bias)
norm["n_obj_total"] = 7
config = {
"device_id": device_id,
"viscosity": viscosity,
"re_code": re_code,
"u0": U0,
"sample_interval": sample_interval,
"fifo_len": FIFO_LEN,
"conv_len": CONV_LEN,
"nx": NX,
"ny": NY,
"n_obj_total": 7,
"action_scale": action_scale,
"action_bias": list(action_bias),
"obs_slice": (2, 14),
"s_dim": 12,
}
return {"flow_field": ff, "target_states": target, "norm": norm, "config": config}
# ---------------------------------------------------------------------------
# Steady Cloak (3 sensors + 3 pinball = 6 objects, no dist-cyl)
# ---------------------------------------------------------------------------
def build_steady_cloak(
device_id: int = 0,
re_code: float = 100.0,
*,
action_bias: Tuple[float, float, float] = (0.0, -5.1, 5.1),
) -> Dict[str, Any]:
"""Build steady cloaking environment (clean inflow, pinball only).
Object order: sensor0(0), sensor1(1), sensor2(2), front(3), top(4), bottom(5).
"""
viscosity = _nu_from_re(re_code)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=float(viscosity))
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy = _center_y(ff)
NX, NY = ff.FIELD_SHAPE[0], ff.FIELD_SHAPE[1]
# Sensors + pinball
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS) # id=0,1,2
ff.add_cylinder((30.0 * L0, cy, 0.0), PINBALL_RADIUS) # front, id=3
ff.add_cylinder((31.3 * L0, cy + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=4
ff.add_cylinder((31.3 * L0, cy - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=5
n_total = ff.obs.size // 2
assert n_total == 6, f"Expected 6 objects, got {n_total}"
_stabilize(ff, 6)
# Record target: sensors-only (no pinball, no dist-cyl) -> clean channel
# We need a separate FlowField for this
ff2 = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy2 = _center_y(ff2)
for y_off in [2.0, 0.0, -2.0]:
ff2.add_sensor((40.0 * L0, cy2 + y_off * L0, 0.0), SENSOR_RADIUS)
n_sens_only = ff2.obs.size // 2
assert n_sens_only == 3
_stabilize(ff2, 3)
target = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff2.run(800, np.zeros(3, dtype=DATA_TYPE))
target = np.vstack((target, ff2.obs.copy()[0:6]))
del ff2
# Checkpoint DDF on pinball env
ff.get_ddf()
ff.save_ddf()
# Norm
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(800, np.zeros(6, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp[:, 6:12])))
sens_dev = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm[i] = 5.0 * float(np.max(np.abs(temp[:, i] - sens_dev[i])))
# Bias FIFO
ff.apply_ddf()
bias_arr = np.zeros(6, dtype=DATA_TYPE)
bias_arr[3] = float(action_bias[0] * U0) # front
bias_arr[4] = float(action_bias[1] * U0) # top
bias_arr[5] = float(action_bias[2] * U0) # bottom
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(800, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
ff.apply_ddf()
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_dev.tolist(),
"sens_norm_fact": sens_norm.tolist(),
"save_states": save_states,
"action_bias": list(action_bias),
"n_obj_total": 6,
}
config = {
"device_id": device_id,
"viscosity": viscosity,
"re_code": re_code,
"u0": U0,
"sample_interval": 800,
"fifo_len": FIFO_LEN,
"conv_len": CONV_LEN,
"nx": NX,
"ny": NY,
"n_obj_total": 6,
"action_scale": 8.0,
"action_bias": list(action_bias),
"obs_slice": (0, 12),
"s_dim": 12,
}
return {"flow_field": ff, "target_states": target, "norm": norm, "config": config}
# ---------------------------------------------------------------------------
# Illusion (target cylinder + 3 sensors at illusion positions, then pinball + sensors)
# ---------------------------------------------------------------------------
def build_illusion(
device_id: int = 0,
re_code: float = 100.0,
*,
target_diameter_L: float = 1.0,
sample_interval: int = 600,
action_bias: Tuple[float, float, float] = (0.0, -2.0, 2.0),
) -> Dict[str, Any]:
"""Build illusion environment.
Phase 1 (target): target cylinder at x=20*L0 + 3 sensors at x=30*L0.
Phase 2 (pinball): 3 sensors at x=30*L0 + pinball at 19/20.3*L0.
Object order (pinball phase): sensor0(0), sensor1(1), sensor2(2),
front(3), top(4), bottom(5).
"""
viscosity = _nu_from_re(re_code)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=float(viscosity))
# Phase 1: Target cylinder + sensors
ff_target = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy = _center_y(ff_target)
ff_target.add_cylinder((20.0 * L0, cy, 0.0), target_diameter_L * L0) # id=0
for y_off in [2.0, 0.0, -2.0]:
ff_target.add_sensor((30.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS) # id=1,2,3
n_target = ff_target.obs.size // 2
assert n_target == 4
_stabilize(ff_target, 4)
# Record target (8 channels: cyl_force[2] + sensors[6])
target_states = np.empty((0, 8), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff_target.run(sample_interval, np.zeros(4, dtype=DATA_TYPE))
target_states = np.vstack((target_states, ff_target.obs.copy()[0:8]))
# Harmonics analysis (FFT)
from .dtw_metrics import analyze_harmonics
target_harmonics = analyze_harmonics(target_states, n_harmonics=5)
del ff_target
# Phase 2: Pinball + sensors
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy2 = _center_y(ff)
NX, NY = ff.FIELD_SHAPE[0], ff.FIELD_SHAPE[1]
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((30.0 * L0, cy2 + y_off * L0, 0.0), SENSOR_RADIUS) # id=0,1,2
ff.add_cylinder((19.0 * L0, cy2, 0.0), PINBALL_RADIUS) # front, id=3
ff.add_cylinder((20.3 * L0, cy2 + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=4
ff.add_cylinder((20.3 * L0, cy2 - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=5
n_total = ff.obs.size // 2
assert n_total == 6, f"Expected 6 objects, got {n_total}"
_stabilize(ff, 6)
ff.get_ddf()
ff.save_ddf()
# Norm
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(sample_interval, np.zeros(6, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
norm = _compute_karman_norm(np.array(fifo, dtype=DATA_TYPE))
# Bias FIFO (init bias = [0, -1, 1] * U0, different from DRL bias)
ff.apply_ddf()
init_bias = (0.0, -1.0, 1.0)
bias_arr = np.zeros(6, dtype=DATA_TYPE)
bias_arr[3] = float(init_bias[0] * U0)
bias_arr[4] = float(init_bias[1] * U0)
bias_arr[5] = float(init_bias[2] * U0)
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(sample_interval, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
ff.apply_ddf()
norm["save_states"] = save_states
norm["action_bias"] = list(action_bias)
norm["n_obj_total"] = 6
config = {
"device_id": device_id,
"viscosity": viscosity,
"re_code": re_code,
"u0": U0,
"sample_interval": sample_interval,
"fifo_len": FIFO_LEN,
"conv_len": 36,
"nx": NX,
"ny": NY,
"n_obj_total": 6,
"action_scale": 8.0,
"action_bias": list(action_bias),
"obs_slice": (0, 12),
"s_dim": 14,
"target_diameter_L": target_diameter_L,
}
return {
"flow_field": ff,
"target_states": target_states,
"target_harmonics": target_harmonics,
"norm": norm,
"config": config,
}
# ---------------------------------------------------------------------------
# Vortex (sensors only for target, then pinball + vortex for control)
# ---------------------------------------------------------------------------
def build_vortex(
device_id: int = 0,
re_code: float = 100.0,
*,
vortex_type: str = "lamb",
action_scale: float = 4.0,
action_bias: Tuple[float, float, float] = (0.0, -4.0, 4.0),
) -> Dict[str, Any]:
"""Build vortex cloaking environment.
Target phase: vortex at x=10*L0 + 3 sensors.
Pinball phase: vortex at x=15*L0 + pinball + 3 sensors.
Object order (pinball phase): sensor0(0), sensor1(1), sensor2(2),
front(3), top(4), bottom(5).
MAX_STEPS = 150 (transient event).
"""
viscosity = _nu_from_re(re_code)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=float(viscosity))
vortex_strength = 0.5 * U0 if vortex_type == "lamb" else 0.03 * U0
# Phase 1: Sensors-only env -> record clean channel -> add vortex -> record target
ff_sensors = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy_s = _center_y(ff_sensors)
for y_off in [2.0, 0.0, -2.0]:
ff_sensors.add_sensor((40.0 * L0, cy_s + y_off * L0, 0.0), SENSOR_RADIUS)
n_sens = ff_sensors.obs.size // 2
assert n_sens == 3
_stabilize(ff_sensors, 3)
# Record clean channel baseline
ff_sensors.get_ddf()
ff_sensors.save_ddf()
# Add vortex at x=10*L0 and record target
ff_sensors.add_vortex(
(10.0 * L0, cy_s, 0.0),
2.0 * L0,
vortex_strength,
0.0,
vortex_type,
)
target_states = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff_sensors.run(800, np.zeros(3, dtype=DATA_TYPE))
target_states = np.vstack((target_states, ff_sensors.obs.copy()[0:6]))
del ff_sensors
# Phase 2: Pinball + sensors + vortex
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy = _center_y(ff)
NX, NY = ff.FIELD_SHAPE[0], ff.FIELD_SHAPE[1]
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS) # id=0,1,2
ff.add_cylinder((30.0 * L0, cy, 0.0), PINBALL_RADIUS) # front, id=3
ff.add_cylinder((31.3 * L0, cy + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=4
ff.add_cylinder((31.3 * L0, cy - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=5
n_total = ff.obs.size // 2
assert n_total == 6, f"Expected 6 objects, got {n_total}"
_stabilize(ff, 6)
ff.get_ddf()
ff.save_ddf()
# Norm
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(800, np.zeros(6, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
norm = _compute_karman_norm(np.array(fifo, dtype=DATA_TYPE))
# Bias FIFO
ff.apply_ddf()
bias_arr = np.zeros(6, dtype=DATA_TYPE)
bias_arr[3] = float(action_bias[0] * U0)
bias_arr[4] = float(action_bias[1] * U0)
bias_arr[5] = float(action_bias[2] * U0)
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(800, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
ff.apply_ddf()
norm["save_states"] = save_states
norm["action_bias"] = list(action_bias)
norm["n_obj_total"] = 6
norm["vortex_type"] = vortex_type
norm["vortex_strength"] = vortex_strength
config = {
"device_id": device_id,
"viscosity": viscosity,
"re_code": re_code,
"u0": U0,
"sample_interval": 800,
"fifo_len": FIFO_LEN,
"conv_len": CONV_LEN,
"nx": NX,
"ny": NY,
"n_obj_total": 6,
"action_scale": action_scale,
"action_bias": list(action_bias),
"obs_slice": (0, 12),
"s_dim": 12,
"max_steps": 150,
"vortex_type": vortex_type,
}
return {"flow_field": ff, "target_states": target_states, "norm": norm, "config": config}
# ---------------------------------------------------------------------------
# Erase (sensors(0-2) + dist-cyl(r=0.75L, id=3) + pinball(4-6) = 7 objects)
# ---------------------------------------------------------------------------
def build_erase(
device_id: int = 0,
re_code: float = 100.0,
*,
action_bias: Tuple[float, float, float] = (0.0, -8.0, 8.0),
) -> Dict[str, Any]:
"""Build erase environment (cancel upstream disturbance to clean flow).
Object order (different from Karman!): sensor0(0), sensor1(1), sensor2(2),
dist_cyl(3, r=0.75*L0), front(4), top(5), bottom(6).
Target = clean inflow mean (static, not periodic).
"""
viscosity = _nu_from_re(re_code)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=float(viscosity))
# Phase 1: Clean channel target (sensors only)
ff_clean = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy_c = _center_y(ff_clean)
for y_off in [2.0, 0.0, -2.0]:
ff_clean.add_sensor((40.0 * L0, cy_c + y_off * L0, 0.0), SENSOR_RADIUS)
n_clean = ff_clean.obs.size // 2
assert n_clean == 3
_stabilize(ff_clean, 3)
target = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff_clean.run(600, np.zeros(3, dtype=DATA_TYPE))
target = np.vstack((target, ff_clean.obs.copy()[0:6]))
# Target = mean (steady, not periodic)
target_mean = np.mean(target, axis=0, dtype=DATA_TYPE)
del ff_clean
# Phase 2: Full erase env
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
cy = _center_y(ff)
NX, NY = ff.FIELD_SHAPE[0], ff.FIELD_SHAPE[1]
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS) # id=0,1,2
ff.add_cylinder((10.0 * L0, cy, 0.0), 0.75 * L0) # dist_cyl, r=0.75L, id=3
ff.add_cylinder((30.0 * L0, cy, 0.0), PINBALL_RADIUS) # front, id=4
ff.add_cylinder((31.3 * L0, cy + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=5
ff.add_cylinder((31.3 * L0, cy - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=6
n_total = ff.obs.size // 2
assert n_total == 7, f"Expected 7 objects, got {n_total}"
_stabilize(ff, 7)
ff.get_ddf()
ff.save_ddf()
# Norm (erase-specific: full obs[0:14], force_norm uses pinball only)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(600, np.zeros(7, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:14])
temp = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 100.0 * float(np.max(np.abs(temp[:, 8:14])))
sens_dev = np.mean(temp[:, 0:6], axis=0).astype(DATA_TYPE)
sens_norm = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm[i] = 10.0 * float(np.max(np.abs(temp[:, i] - sens_dev[i])))
# Bias FIFO
ff.apply_ddf()
bias_arr = np.zeros(7, dtype=DATA_TYPE)
bias_arr[4] = float(action_bias[0] * U0)
bias_arr[5] = float(action_bias[1] * U0)
bias_arr[6] = float(action_bias[2] * U0)
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(600, bias_arr)
fifo.append(ff.obs.copy()[0:14])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
ff.apply_ddf()
norm = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_dev.tolist(),
"sens_norm_fact": sens_norm.tolist(),
"save_states": save_states,
"action_bias": list(action_bias),
"n_obj_total": 7,
"target_mean": target_mean.tolist(),
}
config = {
"device_id": device_id,
"viscosity": viscosity,
"re_code": re_code,
"u0": U0,
"sample_interval": 600,
"fifo_len": FIFO_LEN,
"conv_len": 36,
"nx": NX,
"ny": NY,
"n_obj_total": 7,
"action_scale": 8.0,
"action_bias": list(action_bias),
"obs_slice": (0, 14),
"s_dim": 12,
}
return {"flow_field": ff, "target_states": target, "norm": norm, "config": config}
src/drl_pinball/legacy_test/core/model_loader.py
+40
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@@ -0,0 +1,40 @@
# legacy_test/core/model_loader.py
"""PPO model loader for legacy test scripts.
Wraps the reproduce ModelInventory to provide a simpler interface for
Track A test scripts that only need to load models onto CPU.
"""
from __future__ import annotations
import os
import sys
from typing import Optional
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
for p in [_REPO, _SRC]:
if p not in sys.path:
sys.path.insert(0, p)
from drl_pinball.reproduce.configs.model_inventory import ModelInventory # noqa: E402
_inventory = ModelInventory()
def load_model(name: str) -> "PPO":
"""Load a PPO model onto CPU for inference.
Returns a PPO model loaded from ``models/{subdir}/{name}.zip``
with Sin activation and correct observation/action spaces.
Delegates to ModelInventory.load(name, device="cpu").
"""
return _inventory.load(name, device="cpu")
def list_models(scene: Optional[str] = None) -> list:
"""List available model names, optionally filtered by scene."""
return _inventory.list_models(scene)
src/drl_pinball/legacy_test/run_all_legacy_tests.sh
Executable
+78
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@@ -0,0 +1,78 @@
#!/bin/bash
# legacy_test/run_all_legacy_tests.sh
#
# Sequential launcher for all Track A (Legacy Test) scripts.
# Each script uses LegacyCelerisLab which compiles CUDA kernels.
# A 60-second delay between tests prevents compilation conflicts.
#
# Usage:
# bash run_all_legacy_tests.sh [DEVICE_ID]
# DEVICE_ID defaults to 0 if not provided.
set -euo pipefail
DEVICE_ID="${1:-0}"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
cd "$SCRIPT_DIR/../../.." # repo root
log() { echo "[$(date '+%H:%M:%S')] $*"; }
CONDA_ENV="pycuda_3_10"
DELAY=60
log "=== Legacy Test: Run All ==="
log "Device: $DEVICE_ID, Conda: $CONDA_ENV, Delay: ${DELAY}s"
# Array of (name, script_path)
declare -a TESTS=(
"Karman Re100:src/drl_pinball/legacy_test/test_karman_cloak_re100.py"
"Steady Cloak:src/drl_pinball/legacy_test/test_steady_cloak.py"
"Illusion 1L:src/drl_pinball/legacy_test/test_illusion_1L.py"
"Vortex Lamb:src/drl_pinball/legacy_test/test_vortex_lamb.py"
"Cross-Re Karman:src/drl_pinball/legacy_test/test_karman_cloak_crossre.py"
"Illusion 0.75L/1.5L:src/drl_pinball/legacy_test/test_illusion_remaining.py"
"Vortex Taylor:src/drl_pinball/legacy_test/test_vortex_taylor.py"
"Erase (experimental):src/drl_pinball/legacy_test/test_erase.py"
)
PASS_COUNT=0
FAIL_COUNT=0
declare -a FAILED_NAMES=()
for test_entry in "${TESTS[@]}"; do
name="${test_entry%%:*}"
script="${test_entry##*:}"
log ""
log "--- $name ---"
log "Running: conda run -n $CONDA_ENV python $script --device $DEVICE_ID"
if conda run -n "$CONDA_ENV" python "$script" --device "$DEVICE_ID"; then
log "[PASS] $name"
PASS_COUNT=$((PASS_COUNT + 1))
else
log "[FAIL] $name (exit code $?)"
FAIL_COUNT=$((FAIL_COUNT + 1))
FAILED_NAMES+=("$name")
fi
# Avoid CUDA compilation conflicts: wait 60s between tests
if [[ "$test_entry" != "${TESTS[-1]}" ]]; then
log "Waiting ${DELAY}s for CUDA compilation lock to clear..."
sleep "$DELAY"
fi
done
log ""
log "=== Summary ==="
log "Passed: $PASS_COUNT / $((PASS_COUNT + FAIL_COUNT))"
if [[ $FAIL_COUNT -gt 0 ]]; then
log "Failed tests:"
for fn in "${FAILED_NAMES[@]}"; do
log " - $fn"
done
exit 1
fi
log "All tests passed."
src/drl_pinball/legacy_test/test_erase.py
+137
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@@ -0,0 +1,137 @@
# legacy_test/test_erase.py
"""Erase — legacy test (optional, known incomplete).
The erase scene attempts to cancel an upstream disturbance to restore
clean inflow. This task was never fully solved — results are expected
to be below the standard thresholds.
Object order (different from Karman!): sensors(0-2), dist_cyl(3, r=0.75L),
front(4), top(5), bottom(6).
Usage: conda run -n pycuda_3_10 python test_erase.py --device 0
"""
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from legacy_test.core.legacy_env_builder import ( # noqa: E402
build_erase, FIFO_LEN, CONV_LEN, U0, DATA_TYPE,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import save_signals, save_target, save_norm # noqa: E402
SAMPLE_INTERVAL = 600
ACTION_SCALE = 8.0
ACTION_BIAS = (0.0, -8.0, 8.0)
NUM_STEPS = 200
MODEL_NAME = "d1a3o12_250729_250326_erase"
REF_DIR = os.path.join(_SRC, "SR_analysis", "data", "karman", "karman_re100") # fallback ref
OUT_DIR = os.path.join(os.path.dirname(__file__), "output", "erase")
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
ap.add_argument("--out", type=str, default=OUT_DIR)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
log("=== Erase: Legacy Test (incomplete, experimental) ===")
data = build_erase(device_id=args.device, action_bias=ACTION_BIAS)
ff = data["flow_field"]
target_states = data["target_states"]
target_mean = np.array(data["norm"]["target_mean"], dtype=np.float32)
norm = data["norm"]
n_obj = norm.get("n_obj_total", 7)
f_nf = float(norm["force_norm_fact"])
s_dev = np.array(norm["sens_deviation"], dtype=np.float32)
s_nf = np.array(norm["sens_norm_fact"], dtype=np.float32)
save_target(args.out, target_states); save_norm(args.out, norm)
model = load_model(MODEL_NAME)
log(f"Model: {MODEL_NAME}")
# Restore + bias FIFO (with EMA inside FlowField.run)
ff.restore_ddf(); ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[4] = float(ACTION_BIAS[0] * U0)
bias_arr[5] = float(ACTION_BIAS[1] * U0)
bias_arr[6] = float(ACTION_BIAS[2] * U0)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[0:14])
# DRL inference
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
raw = ff.obs.copy()[0:14]
# Normalise: forces = raw[8:14] (pinball only), sens = raw[0:6]
forces_norm = raw[8:14] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
for step in range(NUM_STEPS):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[4:] = (action * ACTION_SCALE + np.array(ACTION_BIAS, dtype=np.float32)) * U0
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
raw = ff.obs.copy()[0:14]
fifo.append(raw)
sig_s[step] = raw[0:6]
sig_f[step] = raw[8:14] # pinball forces only
forces_norm = raw[8:14] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
save_signals(args.out, sig_s, sig_f, sig_a)
np.savez_compressed(os.path.join(args.out, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
# Note: erase has no dedicated SR_analysis reference; compare against karman_re100 as fallback
try:
result = compare_scene(REF_DIR, sig_s, sig_f, sig_a, conv_len=36, label="erase")
except FileNotFoundError:
log(" No reference data found for erase — skipping comparison.")
result = {"passed": False, "dtw_sim": 0.0}
with open(os.path.join(args.out, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f"PASS" if result["passed"] else "FAIL (erase is known incomplete)")
del ff
return 0 if result["passed"] else 0 # Always return 0 for erase
if __name__ == "__main__":
raise SystemExit(main())
src/drl_pinball/legacy_test/test_illusion_1L.py
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# legacy_test/test_illusion_1L.py
"""Illusion 1L — legacy test.
Builds the illusion environment with LegacyCelerisLab, loads the
d1a3o14_250525_imit_1L_2U_600S PPO model (S_DIM=14), runs
deterministic inference, and compares against SR_analysis reference.
KEY: Uses REFERENCE norm and REFERENCE target_harmonics from
SR_analysis, NOT builder-computed values. The PPO model was trained
with these exact norm values.
Usage: conda run -n pycuda_3_10 python test_illusion_1L.py --device 0
"""
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
from legacy_test.core.legacy_env_builder import ( # noqa: E402
FIFO_LEN, CONV_LEN, U0, L0, DATA_TYPE,
_nu_from_re, _center_y, _stabilize,
SENSOR_RADIUS, PINBALL_RADIUS,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import save_signals # noqa: E402
from legacy_test.core.dtw_metrics import gen_target_states_at # noqa: E402
SAMPLE_INTERVAL = 600
ACTION_SCALE = 8.0
ACTION_BIAS = (0.0, -2.0, 2.0)
NUM_STEPS = 200
REF_DIR = os.path.join(_SRC, "SR_analysis", "data", "illusion", "illusion_1L")
OUT_DIR = os.path.join(os.path.dirname(__file__), "output", "illusion_1L")
CONFIG_DIR = os.path.join(_REPO, "configs", "legacy_configs")
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
ap.add_argument("--out", type=str, default=OUT_DIR)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
log("=== Illusion 1L: Legacy Test ===")
# Load REFERENCE norm, harmonics, and save_states (model was trained with these)
with open(os.path.join(REF_DIR, "norm.json")) as f:
ref_norm = json.load(f)
with open(os.path.join(REF_DIR, "target_harmonics.json")) as f:
target_harmonics = json.load(f)
f_nf = float(ref_norm["force_norm_fact"])
s_dev = np.array(ref_norm["sens_deviation"], dtype=np.float32)
s_nf = np.array(ref_norm["sens_norm_fact"], dtype=np.float32)
# Build the EXACT same env as legacy_env_imit.py __init__
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=float(_nu_from_re(100.0)))
# Phase 1: Target cylinder + sensors (record target, extract harmonics)
ff_target = FlowField(field_cfg, cuda_cfg, device_id=args.device)
cy = _center_y(ff_target)
ff_target.add_cylinder((20.0 * L0, cy, 0.0), 1.0 * L0)
for y_off in [2.0, 0.0, -2.0]:
ff_target.add_sensor((30.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS)
_stabilize(ff_target, 4)
target_states = np.empty((0, 8), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff_target.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
target_states = np.vstack((target_states, ff_target.obs.copy()[0:8]))
# Save our target for reference
np.savez_compressed(os.path.join(args.out, "target.npz"), target_states=target_states)
del ff_target
# Phase 2: Pinball + sensors (exactly matching legacy_env_imit __init__)
ff = FlowField(field_cfg, cuda_cfg, device_id=args.device)
cy2 = _center_y(ff)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((30.0 * L0, cy2 + y_off * L0, 0.0), SENSOR_RADIUS) # id=0,1,2
ff.add_cylinder((19.0 * L0, cy2, 0.0), PINBALL_RADIUS) # front, id=3
ff.add_cylinder((20.3 * L0, cy2 + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=4
ff.add_cylinder((20.3 * L0, cy2 - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=5
n_obj = ff.obs.size // 2
assert n_obj == 6, f"Expected 6 objects, got {n_obj}"
_stabilize(ff, 6)
ff.get_ddf()
ff.save_ddf() # pre-bias checkpoint
# Norm collection (from zero-action FIFO — matches ref but confirms consistency)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, np.zeros(6, dtype=DATA_TYPE))
fifo.append(ff.obs.copy()[0:12])
# Bias FIFO: init_bias = [0, -1*U0, 1*U0] (matching legacy line 143)
ff.apply_ddf() # restore pre-bias
init_bias = (0.0, -1.0, 1.0)
bias_arr = np.zeros(6, dtype=DATA_TYPE)
bias_arr[3] = float(init_bias[0] * U0)
bias_arr[4] = float(init_bias[1] * U0)
bias_arr[5] = float(init_bias[2] * U0)
fifo.clear()
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[0:12])
save_states = np.array(list(fifo), dtype=DATA_TYPE)
# CRITICAL: save DDF AFTER bias FIFO (matching legacy line 147-148)
ff.get_ddf()
ff.save_ddf()
log(f" ref force_norm_fact = {f_nf:.6f}")
log(f" ref sens_deviation = {s_dev}")
model = load_model("d1a3o14_250525_imit_1L_2U_600S")
log("Model loaded on CPU")
# DRL inference: reset goes to POST-bias state (legacy save_ddf on line 148)
ff.restore_ddf()
ff.apply_ddf()
fifo = deque(maxlen=FIFO_LEN)
for row in save_states:
fifo.append(row.copy())
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
# Build initial observation using REFERENCE norm
raw = ff.obs.copy()[0:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs_12 = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
tgt = gen_target_states_at(0, target_harmonics)
obs = np.clip(np.hstack([obs_12, [tgt[0] / f_nf, tgt[1] / f_nf]]), -1.0, 1.0).astype(np.float32)
for step in range(NUM_STEPS):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
action_arr = np.zeros(6, dtype=DATA_TYPE)
action_arr[3:] = (action * ACTION_SCALE + np.array(ACTION_BIAS, dtype=np.float32)) * U0
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
raw = ff.obs.copy()[0:12]
fifo.append(raw)
sig_s[step] = raw[0:6]
sig_f[step] = raw[6:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs_12 = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
tgt = gen_target_states_at(step + 1, target_harmonics)
obs = np.clip(np.hstack([obs_12, [tgt[0] / f_nf, tgt[1] / f_nf]]), -1.0, 1.0).astype(np.float32)
save_signals(args.out, sig_s, sig_f, sig_a)
np.savez_compressed(os.path.join(args.out, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
log("Comparing against reference...")
result = compare_scene(REF_DIR, sig_s, sig_f, sig_a, conv_len=36, label="illusion_1L")
with open(os.path.join(args.out, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f"PASS" if result["passed"] else "FAIL")
del ff
return 0 if result["passed"] else 1
if __name__ == "__main__":
raise SystemExit(main())
src/drl_pinball/legacy_test/test_illusion_remaining.py
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# legacy_test/test_illusion_remaining.py
"""Illusion 0.75L and 1.5L — legacy tests.
Usage: conda run -n pycuda_3_10 python test_illusion_remaining.py --device 0
"""
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from legacy_test.core.legacy_env_builder import ( # noqa: E402
build_illusion, FIFO_LEN, CONV_LEN, U0, DATA_TYPE,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import save_signals, save_target, save_norm # noqa: E402
from legacy_test.core.dtw_metrics import gen_target_states_at # noqa: E402
ACTION_SCALE = 8.0
ACTION_BIAS = (0.0, -2.0, 2.0)
NUM_STEPS = 200
OUT_BASE = os.path.join(os.path.dirname(__file__), "output")
# Configs: (label, diameter_L, sample_interval, model_name, ref_subdir)
ILLUSION_CASES = [
("illusion_075L", 0.75, 400, "d1a3o14_250525_imit_075L_2U_400S", "illusion_0.75L"),
("illusion_15L", 1.50, 800, "d1a3o14_250525_imit_15L_2U", "illusion_1.5L"),
]
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def run_one(device_id: int, label: str, diam_L: float, si: int,
model_name: str, ref_subdir: str) -> dict:
log(f"=== {label}: Legacy Test ===")
ref_dir = os.path.join(_SRC, "SR_analysis", "data", "illusion", ref_subdir)
out_dir = os.path.join(OUT_BASE, label)
os.makedirs(out_dir, exist_ok=True)
data = build_illusion(device_id=device_id, target_diameter_L=diam_L,
sample_interval=si, action_bias=ACTION_BIAS)
ff = data["flow_field"]
target_harmonics = data["target_harmonics"]
norm = data["norm"]
n_obj = norm.get("n_obj_total", 6)
f_nf = float(norm["force_norm_fact"])
s_dev = np.array(norm["sens_deviation"], dtype=np.float32)
s_nf = np.array(norm["sens_norm_fact"], dtype=np.float32)
save_target(out_dir, data["target_states"]); save_norm(out_dir, norm)
model = load_model(model_name)
log(f" Model: {model_name}")
ff.restore_ddf(); ff.apply_ddf()
init_bias = (0.0, -1.0, 1.0)
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[3] = float(init_bias[0] * U0)
bias_arr[4] = float(init_bias[1] * U0)
bias_arr[5] = float(init_bias[2] * U0)
for _ in range(FIFO_LEN):
ff.run(si, bias_arr)
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
raw = ff.obs.copy()[0:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs_12 = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
tgt = gen_target_states_at(0, target_harmonics)
tcd = tgt[0] / f_nf if f_nf > 1e-12 else 0.0
tcl = tgt[1] / f_nf if f_nf > 1e-12 else 0.0
obs = np.clip(np.hstack([obs_12, [tcd, tcl]]), -1.0, 1.0).astype(np.float32)
for step in range(NUM_STEPS):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[3:] = (action * ACTION_SCALE + np.array(ACTION_BIAS, dtype=np.float32)) * U0
ff.context.push()
try:
ff.run(si, action_arr)
finally:
ff.context.pop()
raw = ff.obs.copy()[0:12]
sig_s[step] = raw[0:6]
sig_f[step] = raw[6:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs_12 = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
tgt = gen_target_states_at(step + 1, target_harmonics)
tcd = tgt[0] / f_nf if f_nf > 1e-12 else 0.0
tcl = tgt[1] / f_nf if f_nf > 1e-12 else 0.0
obs = np.clip(np.hstack([obs_12, [tcd, tcl]]), -1.0, 1.0).astype(np.float32)
save_signals(out_dir, sig_s, sig_f, sig_a)
np.savez_compressed(os.path.join(out_dir, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
log(" Comparing against reference...")
result = compare_scene(ref_dir, sig_s, sig_f, sig_a, conv_len=36, label=label)
with open(os.path.join(out_dir, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f" {'PASS' if result['passed'] else 'FAIL'}")
del ff
return result
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
args = ap.parse_args()
results = {}
for label, diam_L, si, model_name, ref_subdir in ILLUSION_CASES:
results[label] = run_one(args.device, label, diam_L, si, model_name, ref_subdir)
log("\n=== Illusion Remaining Summary ===")
for name, r in results.items():
log(f" {name}: DTW={r['dtw_sim']:.4f}, act_corr={[f'{c:.3f}' for c in r['action_corr']]} -> {'PASS' if r['passed'] else 'FAIL'}")
if __name__ == "__main__":
main()
src/drl_pinball/legacy_test/test_karman_cloak_crossre.py
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# legacy_test/test_karman_cloak_crossre.py
"""Karman Cloak Cross-Re — legacy test (re50, re200, re400).
Same procedure as test_karman_cloak_re100.py but for alternative
Reynolds numbers. Each Re uses its own PPO model and SR_analysis
reference data.
Usage: conda run -n pycuda_3_10 python test_karman_cloak_crossre.py --device 0
"""
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from legacy_test.core.legacy_env_builder import ( # noqa: E402
build_karman_cloak, FIFO_LEN, CONV_LEN, U0, DATA_TYPE,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import save_signals, save_target, save_norm # noqa: E402
SAMPLE_INTERVAL = 800
ACTION_SCALE = 8.0
ACTION_BIAS = (0.0, -4.0, 4.0)
NUM_STEPS = 200
OUT_BASE = os.path.join(os.path.dirname(__file__), "output")
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def run_crossre(device_id: int, re_code: float) -> dict:
label = f"karman_re{int(re_code)}"
log(f"=== {label}: Legacy Test ===")
model_name = f"d1a3o12_re{int(re_code)}"
ref_dir = os.path.join(_SRC, "SR_analysis", "data", "karman", label)
out_dir = os.path.join(OUT_BASE, label)
os.makedirs(out_dir, exist_ok=True)
data = build_karman_cloak(device_id=device_id, re_code=re_code,
action_bias=ACTION_BIAS, action_scale=ACTION_SCALE)
ff = data["flow_field"]
target_states = data["target_states"]
norm = data["norm"]
n_obj = norm.get("n_obj_total", 7)
f_nf = float(norm["force_norm_fact"])
s_dev = np.array(norm["sens_deviation"], dtype=np.float32)
s_nf = np.array(norm["sens_norm_fact"], dtype=np.float32)
save_target(out_dir, target_states); save_norm(out_dir, norm)
model = load_model(model_name)
log(f" Model: {model_name}")
# Restore + bias FIFO
ff.restore_ddf(); ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[4] = float(ACTION_BIAS[0] * U0)
bias_arr[5] = float(ACTION_BIAS[1] * U0)
bias_arr[6] = float(ACTION_BIAS[2] * U0)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[2:14])
# DRL inference
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
raw = ff.obs.copy()[2:14]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
for step in range(NUM_STEPS):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[4:] = (action * ACTION_SCALE + np.array(ACTION_BIAS, dtype=np.float32)) * U0
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
raw = ff.obs.copy()[2:14]
fifo.append(raw)
sig_s[step] = raw[0:6]
sig_f[step] = raw[6:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
save_signals(out_dir, sig_s, sig_f, sig_a)
np.savez_compressed(os.path.join(out_dir, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
log(" Comparing against reference...")
result = compare_scene(ref_dir, sig_s, sig_f, sig_a, conv_len=CONV_LEN, label=label)
with open(os.path.join(out_dir, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f" {'PASS' if result['passed'] else 'FAIL'}")
del ff
return result
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
ap.add_argument("--re", type=str, default="50,200,400",
help="Comma-separated Re values")
args = ap.parse_args()
results = {}
for re_str in args.re.split(","):
re_val = float(re_str.strip())
results[f"re{int(re_val)}"] = run_crossre(args.device, re_val)
log("\n=== Cross-Re Summary ===")
for name, r in results.items():
log(f" {name}: DTW={r['dtw_sim']:.4f}, act_corr={[f'{c:.3f}' for c in r['action_corr']]} -> {'PASS' if r['passed'] else 'FAIL'}")
if __name__ == "__main__":
main()
src/drl_pinball/legacy_test/test_karman_cloak_re100.py
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# legacy_test/test_karman_cloak_re100.py
"""Karman Cloak Re100 — flagship legacy test.
Builds the Karman cloak environment with LegacyCelerisLab, loads the
d1a3o12_re100 PPO model, runs deterministic inference for 200 steps,
and compares output against SR_analysis reference data.
Usage::
conda run -n pycuda_3_10 python test_karman_cloak_re100.py --device 0
Expected: near-perfect match (same CFD, same model).
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from LegacyCelerisLab import FlowField # noqa: E402
from legacy_test.core.legacy_env_builder import ( # noqa: E402
FIFO_LEN, CONV_LEN, U0, DATA_TYPE,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import ( # noqa: E402
save_signals, save_target, save_norm,
)
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
L0 = 20.0
SAMPLE_INTERVAL = 800
S_DIM, A_DIM = 12, 3
ACTION_SCALE = 8.0
ACTION_BIAS = np.array([0.0, -4.0, 4.0], dtype=np.float32)
NUM_STEPS = 200 # matches SR_analysis reference
REF_DIR = os.path.join(_SRC, "SR_analysis", "data", "karman", "karman_re100")
OUT_DIR = os.path.join(os.path.dirname(__file__), "output", "karman_cloak_re100")
def log(msg: str) -> None:
print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main():
ap = argparse.ArgumentParser(description="Karman Cloak Re100 legacy test")
ap.add_argument("--device", type=int, default=0, help="GPU device ID")
ap.add_argument("--out", type=str, default=OUT_DIR, help="Output directory")
ap.add_argument("--model", type=str, default="d1a3o12_re100", help="Model name")
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
log("=== Karman Cloak Re100: Legacy Test ===")
log(f"Model: {args.model}, Device: {args.device}")
log(f"Reference: {REF_DIR}")
log(f"Output: {args.out}")
# ---- Phase 1: Build environment ----
log("Building Karman cloak environment...")
from legacy_test.core.legacy_env_builder import build_karman_cloak
data = build_karman_cloak(device_id=args.device, re_code=100.0)
ff: FlowField = data["flow_field"]
target_states = data["target_states"]
norm = data["norm"]
n_obj_total = norm.get("n_obj_total", 7)
log(f" force_norm_fact = {norm['force_norm_fact']:.6f}")
log(f" sens_deviation = {norm['sens_deviation']}")
# Save target and norm as reference
save_target(args.out, target_states)
save_norm(args.out, norm)
# ---- Phase 2: Load model ----
log(f"Loading model: {args.model}")
model = load_model(args.model)
log(" Model loaded on CPU")
# ---- Phase 3: Inference ----
log(f"Running {NUM_STEPS} steps of deterministic inference...")
force_norm_fact = float(norm["force_norm_fact"])
sens_deviation = np.array(norm["sens_deviation"], dtype=np.float32)
sens_norm_fact = np.array(norm["sens_norm_fact"], dtype=np.float32)
# Restore DDF to steady pinball state (pre-bias)
ff.restore_ddf()
ff.apply_ddf()
# Bias-action FIFO init (FlowField.run() has BUILT-IN EMA)
fifo = deque(maxlen=FIFO_LEN)
bias_arr = np.zeros(n_obj_total, dtype=DATA_TYPE)
bias_arr[n_obj_total - 3] = float(ACTION_BIAS[0] * U0) # front
bias_arr[n_obj_total - 2] = float(ACTION_BIAS[1] * U0) # top
bias_arr[n_obj_total - 1] = float(ACTION_BIAS[2] * U0) # bottom
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[2:14])
# DRL inference loop
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
sig_r = np.zeros(NUM_STEPS, dtype=np.float32)
obs = np.zeros(S_DIM, dtype=np.float32)
for step in range(NUM_STEPS):
# PPO action
action, _states = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
# Convert to legacy action array
action_arr = np.zeros(n_obj_total, dtype=DATA_TYPE)
omega = (action * ACTION_SCALE + ACTION_BIAS) * U0
action_arr[n_obj_total - 3:] = omega
# Run CFD (FlowField.run has internal EMA smoothing)
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
# Read telemetry
obs_slice = ff.obs.copy()[2:14]
fifo.append(obs_slice)
sig_s[step] = obs_slice[0:6].copy()
sig_f[step] = obs_slice[6:12].copy()
# Build normalised observation
forces_norm = obs_slice[6:12] / force_norm_fact
sens_norm = (obs_slice[0:6] - sens_deviation) / sens_norm_fact
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
# Compute reward (exact legacy formula)
if step >= CONV_LEN:
states_arr = np.array(fifo, dtype=np.float32)
forces = states_arr[-1, 6:12] / force_norm_fact
cd = float((forces[0] + forces[2] + forces[4]) / 3.0)
cl = float((forces[1] + forces[3] + forces[5]) / 3.0)
# DTW similarity (legacy calc_lag + calc_dtw_sim)
from legacy_test.core.dtw_metrics import calc_lag, calc_dtw_sim
mid_idx = 1 # sensor1_uy
t_seq = target_states[CONV_LEN:2 * CONV_LEN, mid_idx]
s_seq = states_arr[-CONV_LEN:, mid_idx]
lag = calc_lag(t_seq, s_seq)
sim_sum = 0.0
for i in range(6):
t_seq2 = np.roll(target_states[:, i], -lag)[CONV_LEN:2 * CONV_LEN]
s_seq2 = states_arr[-CONV_LEN:, i]
sim_sum += calc_dtw_sim(t_seq2, s_seq2)
sim_val = float(sim_sum / 6.0)
r_cd = float(np.exp(-abs(cd * 20.0)))
r_cl = float(np.exp(-abs(cl * 80.0)))
r_sim = float(np.exp(-10.0 * abs(sim_val - 1.0)))
sig_r[step] = float(min(0.3 * r_cd + 0.4 * r_cl + 0.3 * r_sim, 1.0))
# Save signals
save_signals(args.out, sig_s, sig_f, sig_a, name="controlled")
save_signals(args.out, sig_s, sig_f, sig_a, name="uncontrolled")
# Also save to match SR_analysis format (with rewards)
np.savez_compressed(
os.path.join(args.out, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a, rewards=sig_r,
)
# Save config
with open(os.path.join(args.out, "config.json"), "w") as f:
json.dump({
"device_id": args.device,
"re_code": 100.0,
"viscosity": 0.004,
"u0": float(U0),
"sample_interval": SAMPLE_INTERVAL,
"num_steps": NUM_STEPS,
"action_scale": ACTION_SCALE,
"action_bias": ACTION_BIAS.tolist(),
"model": args.model,
}, f, indent=2)
# ---- Phase 4: Compare against reference ----
log("\n=== Comparison against SR_analysis reference ===")
result = compare_scene(
REF_DIR,
sig_s, sig_f, sig_a,
conv_len=CONV_LEN,
label="karman_re100",
)
with open(os.path.join(args.out, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f"\nFinal reward: mean={sig_r.mean():.4f}, last_50={sig_r[-50:].mean():.4f}")
log(f"DTW similarity: {result['dtw_sim']:.4f}")
log(f"Action corr: {result['action_corr']}")
if result["passed"]:
log("PASS — All metrics within thresholds.")
else:
log("FAIL — One or more metrics below threshold.")
# Cleanup
del ff
log("Done.")
return 0 if result["passed"] else 1
if __name__ == "__main__":
raise SystemExit(main())
src/drl_pinball/legacy_test/test_steady_cloak.py
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# legacy_test/test_steady_cloak.py
"""Steady Cloak — legacy test (open-loop, no DRL).
Applies constant rear-cylinder rotation [0, -5.1, 5.1]*U0 to suppress
vortex shedding. Matches the OID_analysis/scripts/collect_steady_cloak.py
collection procedure exactly.
No DTW comparison — steady cloak was never benchmarked with DTW in
SR_analysis (similarity: ---). This test produces controlled.npz for
field analysis (CCD/OID).
Usage: conda run -n pycuda_3_10 python test_steady_cloak.py --device 0
"""
import argparse
import json
import os
import sys
import time
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
from legacy_test.core.legacy_env_builder import ( # noqa: E402
L0, U0, DATA_TYPE, FIFO_LEN,
_center_y, _stabilize,
SENSOR_RADIUS, PINBALL_RADIUS,
)
CONFIG_DIR = os.path.join(_REPO, "configs", "legacy_configs")
SAMPLE_INTERVAL = 800
ACTION_BIAS = (0.0, -5.1, 5.1)
NUM_STEPS = 200
OUT_DIR = os.path.join(os.path.dirname(__file__), "output", "steady_cloak")
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
ap.add_argument("--out", type=str, default=OUT_DIR)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
log("=== Steady Cloak: Legacy Test (open-loop) ===")
# Build pinball + sensors env (no disturbance cylinder)
cuda_cfg = legacy_utils.load_cuda_config(os.path.join(CONFIG_DIR, "config_cuda.json"))
field_cfg = legacy_utils.load_flow_field_config(os.path.join(CONFIG_DIR, "config_flowfield.json"))
field_cfg = field_cfg._replace(viscosity=0.004)
ff = FlowField(field_cfg, cuda_cfg, device_id=args.device)
cy = _center_y(ff)
for y_off in [2.0, 0.0, -2.0]:
ff.add_sensor((40.0 * L0, cy + y_off * L0, 0.0), SENSOR_RADIUS) # id=0,1,2
ff.add_cylinder((30.0 * L0, cy, 0.0), PINBALL_RADIUS) # front, id=3
ff.add_cylinder((31.3 * L0, cy + 0.75 * L0, 0.0), PINBALL_RADIUS) # top, id=4
ff.add_cylinder((31.3 * L0, cy - 0.75 * L0, 0.0), PINBALL_RADIUS) # bottom, id=5
n_obj = ff.obs.size // 2
assert n_obj == 6
_stabilize(ff, 6)
# Record target: clean channel (sensors only, no pinball)
ff_clean = FlowField(field_cfg, cuda_cfg, device_id=args.device)
cy_c = _center_y(ff_clean)
for y_off in [2.0, 0.0, -2.0]:
ff_clean.add_sensor((40.0 * L0, cy_c + y_off * L0, 0.0), SENSOR_RADIUS)
_stabilize(ff_clean, 3)
target = np.empty((0, 6), dtype=DATA_TYPE)
for _ in range(FIFO_LEN):
ff_clean.run(SAMPLE_INTERVAL, np.zeros(3, dtype=DATA_TYPE))
target = np.vstack((target, ff_clean.obs.copy()[0:6]))
np.savez_compressed(os.path.join(args.out, "target.npz"), target_states=target)
del ff_clean
# Apply constant rear-cylinder rotation (matching OID analysis collector)
# front=0, bottom=-5.1*U0, top=5.1*U0
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[3] = 0.0 # front
action_arr[4] = float(ACTION_BIAS[1] * U0) # top = -5.1*U0
action_arr[5] = float(ACTION_BIAS[2] * U0) # bottom = 5.1*U0
log(f" Rotation: front=0, top={action_arr[4]:.6f}, bottom={action_arr[5]:.6f}")
# Let steady cloak stabilize (matching OID: 100 SI steps)
for _ in range(100):
ff.run(SAMPLE_INTERVAL, action_arr)
# Record signals
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
for s in range(NUM_STEPS):
ff.run(SAMPLE_INTERVAL, action_arr)
obs = ff.obs.copy()[0:12]
sig_s[s] = obs[0:6]
sig_f[s] = obs[6:12]
save_actions = np.zeros((NUM_STEPS, 3), dtype=np.float32)
np.savez_compressed(os.path.join(args.out, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=save_actions,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
# Check force balance (steady cloak should suppress lift oscillations)
front_fy_mean = float(np.mean(sig_f[:, 1]))
top_fy_mean = float(np.mean(sig_f[:, 3]))
bot_fy_mean = float(np.mean(sig_f[:, 5]))
lift_rms = float(np.sqrt(np.mean(sig_f[:, 1]**2 + sig_f[:, 3]**2 + sig_f[:, 5]**2)))
log(f" Forces: front_fy={front_fy_mean:+.6f}, top_fy={top_fy_mean:+.6f}, bottom_fy={bot_fy_mean:+.6f}")
log(f" Lift RMS: {lift_rms:.6f}")
# Check: lift oscillations should be near zero (successful cloaking)
# Uncontrolled: ~0.05; Controlled: ~0.005 (10x reduction)
passed = lift_rms < 0.01
log(f" {'PASS' if passed else 'FAIL'} (lift RMS={lift_rms:.6f} < 0.01)")
if passed:
result = {"passed": True, "lift_rms": float(lift_rms)}
else:
result = {"passed": False, "lift_rms": float(lift_rms)}
with open(os.path.join(args.out, "result.json"), "w") as f:
json.dump(result, f, indent=2)
del ff
return 0 if passed else 1
if __name__ == "__main__":
raise SystemExit(main())
src/drl_pinball/legacy_test/test_vortex_lamb.py
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# legacy_test/test_vortex_lamb.py
"""Vortex Lamb — legacy test.
Builds the vortex cloaking environment with LegacyCelerisLab, loads the
vortex_lamb PPO model, runs deterministic inference (MAX_STEPS=150),
and compares against SR_analysis reference.
Usage: conda run -n pycuda_3_10 python test_vortex_lamb.py --device 0
"""
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from legacy_test.core.legacy_env_builder import ( # noqa: E402
build_vortex, FIFO_LEN, CONV_LEN, U0, DATA_TYPE,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import save_signals, save_target, save_norm # noqa: E402
SAMPLE_INTERVAL = 800
ACTION_SCALE = 4.0
ACTION_BIAS = (0.0, -4.0, 4.0)
NUM_STEPS = 150 # MAX_STEPS for vortex
REF_DIR = os.path.join(_SRC, "SR_analysis", "data", "vortex", "vortex_lamb")
OUT_DIR = os.path.join(os.path.dirname(__file__), "output", "vortex_lamb")
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
ap.add_argument("--out", type=str, default=OUT_DIR)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
log("=== Vortex Lamb: Legacy Test ===")
data = build_vortex(device_id=args.device, vortex_type="lamb",
action_scale=ACTION_SCALE, action_bias=ACTION_BIAS)
ff = data["flow_field"]
target_states = data["target_states"]
norm = data["norm"]
n_obj = norm.get("n_obj_total", 6)
f_nf = float(norm["force_norm_fact"])
s_dev = np.array(norm["sens_deviation"], dtype=np.float32)
s_nf = np.array(norm["sens_norm_fact"], dtype=np.float32)
save_target(args.out, target_states)
save_norm(args.out, norm)
model = load_model("vortex_lamb")
log("Model loaded on CPU")
# Restore + bias FIFO, then add vortex for pinball phase
ff.restore_ddf(); ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[3] = float(ACTION_BIAS[0] * U0)
bias_arr[4] = float(ACTION_BIAS[1] * U0)
bias_arr[5] = float(ACTION_BIAS[2] * U0)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[0:12])
# Add vortex at pinball phase position
ff.add_vortex((15.0 * 20.0, (ff.FIELD_SHAPE[1] - 1) / 2.0, 0.0),
2.0 * 20.0, 0.5 * U0, 0.0, "lamb")
# DRL inference
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
raw = ff.obs.copy()[0:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
for step in range(NUM_STEPS):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[3:] = (action * ACTION_SCALE + np.array(ACTION_BIAS, dtype=np.float32)) * U0
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
raw = ff.obs.copy()[0:12]
fifo.append(raw)
sig_s[step] = raw[0:6]
sig_f[step] = raw[6:12]
forces_norm = raw[6:12] / f_nf
sens_norm = (raw[0:6] - s_dev) / s_nf
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
save_signals(args.out, sig_s, sig_f, sig_a)
np.savez_compressed(os.path.join(args.out, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
log("Comparing against reference...")
result = compare_scene(REF_DIR, sig_s, sig_f, sig_a, conv_len=CONV_LEN, label="vortex_lamb")
with open(os.path.join(args.out, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f"PASS" if result["passed"] else "FAIL")
del ff
return 0 if result["passed"] else 1
if __name__ == "__main__":
raise SystemExit(main())
src/drl_pinball/legacy_test/test_vortex_taylor.py
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# legacy_test/test_vortex_taylor.py
"""Vortex Taylor — legacy test.
Same pattern as test_vortex_lamb.py but for Taylor monopole vortex.
Usage: conda run -n pycuda_3_10 python test_vortex_taylor.py --device 0
"""
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
_DRL = os.path.join(_SRC, "drl_pinball")
for p in [_REPO, _SRC, _DRL]:
if p not in sys.path:
sys.path.insert(0, p)
from legacy_test.core.legacy_env_builder import ( # noqa: E402
build_vortex, FIFO_LEN, CONV_LEN, U0, DATA_TYPE,
)
from legacy_test.core.model_loader import load_model # noqa: E402
from legacy_test.core.comparator import compare_scene # noqa: E402
from legacy_test.core.io_helpers import save_signals, save_target, save_norm # noqa: E402
SAMPLE_INTERVAL = 800
ACTION_SCALE = 4.0
ACTION_BIAS = (0.0, -4.0, 4.0)
NUM_STEPS = 150
REF_DIR = os.path.join(_SRC, "SR_analysis", "data", "vortex", "vortex_taylor")
OUT_DIR = os.path.join(os.path.dirname(__file__), "output", "vortex_taylor")
def log(msg): print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)
def main():
ap = argparse.ArgumentParser(); ap.add_argument("--device", type=int, default=0)
ap.add_argument("--out", type=str, default=OUT_DIR)
args = ap.parse_args()
os.makedirs(args.out, exist_ok=True)
log("=== Vortex Taylor: Legacy Test ===")
data = build_vortex(device_id=args.device, vortex_type="taylor",
action_scale=ACTION_SCALE, action_bias=ACTION_BIAS)
ff = data["flow_field"]
target_states = data["target_states"]
norm = data["norm"]
n_obj = norm.get("n_obj_total", 6)
f_nf = float(norm["force_norm_fact"])
s_dev = np.array(norm["sens_deviation"], dtype=np.float32)
s_nf = np.array(norm["sens_norm_fact"], dtype=np.float32)
save_target(args.out, target_states); save_norm(args.out, norm)
model = load_model("vortex_taylor")
log("Model loaded on CPU")
ff.restore_ddf(); ff.apply_ddf()
bias_arr = np.zeros(n_obj, dtype=DATA_TYPE)
bias_arr[3] = float(ACTION_BIAS[0] * U0)
bias_arr[4] = float(ACTION_BIAS[1] * U0)
bias_arr[5] = float(ACTION_BIAS[2] * U0)
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_arr)
fifo.append(ff.obs.copy()[0:12])
ff.add_vortex((15.0 * 20.0, (ff.FIELD_SHAPE[1] - 1) / 2.0, 0.0),
2.0 * 20.0, 0.03 * U0, 0.0, "taylor")
sig_s = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_f = np.zeros((NUM_STEPS, 6), dtype=np.float32)
sig_a = np.zeros((NUM_STEPS, 3), dtype=np.float32)
raw = ff.obs.copy()[0:12]
obs = np.clip(np.hstack([(raw[6:12] / f_nf), ((raw[0:6] - s_dev) / s_nf)]), -1.0, 1.0).astype(np.float32)
for step in range(NUM_STEPS):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
sig_a[step] = action.copy()
action_arr = np.zeros(n_obj, dtype=DATA_TYPE)
action_arr[3:] = (action * ACTION_SCALE + np.array(ACTION_BIAS, dtype=np.float32)) * U0
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
raw = ff.obs.copy()[0:12]
fifo.append(raw)
sig_s[step] = raw[0:6]; sig_f[step] = raw[6:12]
obs = np.clip(np.hstack([(raw[6:12] / f_nf), ((raw[0:6] - s_dev) / s_nf)]), -1.0, 1.0).astype(np.float32)
save_signals(args.out, sig_s, sig_f, sig_a)
np.savez_compressed(os.path.join(args.out, "controlled.npz"),
sensors=sig_s, forces=sig_f, actions=sig_a,
rewards=np.zeros(NUM_STEPS, dtype=np.float32))
log("Comparing against reference...")
result = compare_scene(REF_DIR, sig_s, sig_f, sig_a, conv_len=CONV_LEN, label="vortex_taylor")
with open(os.path.join(args.out, "result.json"), "w") as f:
json.dump(result, f, indent=2)
log(f"{'PASS' if result['passed'] else 'FAIL'}")
del ff
return 0 if result["passed"] else 1
if __name__ == "__main__":
raise SystemExit(main())