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第一轮分析工作暂存

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Frank14f
2026-06-09 18:46:59 +08:00
parent 4612928398
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archive/analysis_crossre_scripts/diagnose_equivariance.py
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# analysis_crossre/scripts/diagnose_equivariance.py
"""Phase A2-A3: diagnose PPO control-law equivariance under G operator.
Usage::
conda run -n pycuda_3_10 python diagnose_equivariance.py --re 100 --device 0
conda run -n pycuda_3_10 python diagnose_equivariance.py --re all --device 0
Output per Re: ``output/analysis_crossre/diagnostic/equivariance_re{re}.json``
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Dict, List, Tuple
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField # noqa: E402
from LegacyCelerisLab import utils as legacy_utils # noqa: E402
from utils import (
action_to_physical,
compute_dimensionless,
apply_G_x,
apply_G_alpha,
load_ppo_model,
nu_from_re,
load_legacy_configs,
build_karman_cloak_env,
add_pinball,
build_observation,
scale_action,
)
from cfg import (
CONFIG_DIR,
OUTPUT_DIR,
MODEL_DIR,
SAMPLE_INTERVAL,
FIFO_LEN,
CONV_LEN,
S_DIM,
A_DIM,
ACTION_SCALE,
ACTION_BIAS,
U0,
RE_CASES_TRAIN,
RE_LABEL_MAP,
)
DATA_TYPE = np.float32
def diagnose_one_re(re_code: int, ppo_device: int, cfd_device: int, output_root: str) -> dict:
"""Run equivariance diagnosis for one Re case."""
os.makedirs(output_root, exist_ok=True)
nu = nu_from_re(re_code, u0=U0)
mu = 2.0 / re_code
label = RE_LABEL_MAP.get(re_code, f"Re{re_code}")
print(f"\n{'='*60}")
print(f"Diagnosing: {label} nu={nu:.6f} mu={mu:.6f}")
print(f"{'='*60}")
# Build full environment (dist + sensors + pinball)
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(nu))
ff = FlowField(field_cfg, cuda_cfg, device_id=cfd_device)
# Stabilize and get to controlled state
target_states, _ = build_karman_cloak_env(
ff, u0=U0, l0=20.0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE,
)
norm = add_pinball(
ff, l0=20.0, u0=U0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE,
action_bias=ACTION_BIAS,
)
# Load PPO model
model_path = None
for rc, mn in RE_CASES_TRAIN:
if rc == re_code:
model_path = os.path.join(MODEL_DIR, "old", f"{mn}.zip")
break
if model_path is None or not os.path.isfile(model_path):
return {"re_code": re_code, "error": f"No model for Re{re_code}"}
model = load_ppo_model(model_path, device=f"cuda:{ppo_device}")
model.set_random_seed(0)
# Collect rollout data with PPO
ff.restore_ddf()
ff.apply_ddf()
# Bias FIFO
bias_action = scale_action(
np.zeros(3, dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0, n_total_bodies=7,
)
from collections import deque
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, bias_action)
finally:
ff.context.pop()
fifo.append(ff.obs.copy()[2:14])
n_steps = 150
obs_hist = np.zeros((n_steps, 12), dtype=np.float64)
alpha_hist = np.zeros((n_steps, 3), dtype=np.float64)
obs = np.zeros(S_DIM, dtype=np.float32)
for step in range(n_steps):
action, _ = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
# Convert to physical
action_arr = scale_action(
action, scale=ACTION_SCALE, bias=ACTION_BIAS,
u0=U0, n_total_bodies=7,
)
ff.context.push()
try:
ff.run(SAMPLE_INTERVAL, action_arr)
finally:
ff.context.pop()
obs_slice = ff.obs.copy()[2:14]
fifo.append(obs_slice)
alpha = action_to_physical(
action.reshape(1, 3), scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0,
).flatten()
obs_hist[step] = obs_slice
alpha_hist[step] = alpha
obs = build_observation(obs_slice, norm)
del ff
# ---- Equivariance diagnosis ----
dim = compute_dimensionless(obs_hist[:, 0:6], obs_hist[:, 6:12], u0=U0, d=20.0)
# Compute memory terms
a_prev = np.zeros_like(alpha_hist)
a_prev2 = np.zeros_like(alpha_hist)
a_prev[1:] = alpha_hist[:-1]
a_prev2[2:] = alpha_hist[:-2]
# Diagnostic 1: front bias check (mean of alpha_F)
mean_alpha_F = float(np.mean(alpha_hist[:, 0]))
std_alpha_F = float(np.std(alpha_hist[:, 0]))
front_bias_score = abs(mean_alpha_F) / (std_alpha_F + 1e-12)
# Diagnostic 2: check front equivariance
# For each point, compute PPO(Gx) by feeding G-transformed obs through model
eq_front_errors = []
eq_exchange_b_errors = []
eq_exchange_t_errors = []
eq_front_noise_floor = []
for t in range(2, n_steps):
# Get original obs and Gx
Gx = apply_G_x(
dim["u_hat_B"][t:t+1], dim["u_hat_C"][t:t+1], dim["u_hat_T"][t:t+1],
dim["v_hat_B"][t:t+1], dim["v_hat_C"][t:t+1], dim["v_hat_T"][t:t+1],
dim["Cd_F"][t:t+1], dim["Cd_T"][t:t+1], dim["Cd_B"][t:t+1],
dim["Cl_F"][t:t+1], dim["Cl_T"][t:t+1], dim["Cl_B"][t:t+1],
a_prev[t:t+1, 0], a_prev[t:t+1, 2], a_prev[t:t+1, 1],
a_prev2[t:t+1, 0] - a_prev[t:t+1, 0],
a_prev2[t:t+1, 2] - a_prev[t:t+1, 2],
a_prev2[t:t+1, 1] - a_prev[t:t+1, 1],
)
# Build Gx observation for PPO: we need the normalized obs
# The Gx in raw sensor/force space requires inverting the dimensionless transform
# Actually easier: compute what PPO would predict for the G state
# by transforming the raw obs and feeding it
# Build raw obs corresponding to Gx
raw_Gx = np.zeros(12, dtype=np.float64)
# Sensors: reorder + sign flip
# Original raw: [s0_ux, s0_uy, s1_ux, s1_uy, s2_ux, s2_uy] = top, center, bottom
# G: bottom->top, center->center, top->bottom
raw_Gx[0] = obs_hist[t, 4] # s0_ux <- s2_ux (bottom -> top, streamwise no sign)
raw_Gx[1] = -obs_hist[t, 5] # s0_uy <- -s2_uy (bottom -> top, cross sign flip)
raw_Gx[2] = obs_hist[t, 2] # s1_ux maintains (center)
raw_Gx[3] = -obs_hist[t, 3] # s1_uy = -s1_uy (center cross sign flip)
raw_Gx[4] = obs_hist[t, 0] # s2_ux <- s0_ux (top -> bottom)
raw_Gx[5] = -obs_hist[t, 1] # s2_uy <- -s0_uy (top -> bottom, cross sign flip)
# Forces: reorder + sign
# ordering: [front_fx, front_fy, bottom_fx, bottom_fy, top_fx, top_fy]
# G: front_fx -> front_fx (no sign), front_fy -> -front_fy
# bottom <-> top
raw_Gx[6] = obs_hist[t, 6] # front_fx unchanged
raw_Gx[7] = -obs_hist[t, 7] # front_fy sign flip
raw_Gx[8] = obs_hist[t, 10] # bottom_fx <- top_fx
raw_Gx[9] = -obs_hist[t, 11] # bottom_fy <- -top_fy
raw_Gx[10] = obs_hist[t, 8] # top_fx <- bottom_fx
raw_Gx[11] = -obs_hist[t, 9] # top_fy <- -bottom_fy
# Build normalized PPO observation from Gx
obs_Gx = build_observation(raw_Gx, norm)
# Predict action for Gx
action_Gx, _ = model.predict(obs_Gx, deterministic=True)
action_Gx = action_Gx.astype(np.float32).flatten()
alpha_Gx = action_to_physical(
action_Gx.reshape(1, 3), scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0,
).flatten()
# What equivariance says Gx should produce (with CORRECTED G)
# G([aF, aT, aB]) = [-aF, -aB, -aT]
alpha_Gx_expected = apply_G_alpha(alpha_hist[t])
# Front error: PPO(Gx)[0] should == G(PPO(x))[0] = -aF(x)
eq_front_errors.append(abs(float(alpha_Gx[0]) - float(alpha_Gx_expected[0])))
# Rear error (CORRECTED): PPO(Gx)[1] should == G(PPO(x))[1] = -aT(x)
# PPO(Gx)[2] should == G(PPO(x))[2] = -aB(x)
# Previously this incorrectly checked alpha_B(x) == alpha_T(Gx)
eq_exchange_b_errors.append(abs(float(alpha_Gx[1]) - float(alpha_Gx_expected[1])))
eq_exchange_t_errors.append(abs(float(alpha_Gx[2]) - float(alpha_Gx_expected[2])))
# Noise floor: difference between same-state replicate predictions
# (we approximate by checking prediction consistency)
action2, _ = model.predict(obs_Gx, deterministic=True)
alpha_Gx2 = action_to_physical(
action2.reshape(1, 3), scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0,
).flatten()
eq_front_noise_floor.append(abs(float(alpha_Gx2[0]) - float(alpha_Gx[0])))
eq_front_errors = np.array(eq_front_errors)
eq_exchange_b = np.array(eq_exchange_b_errors)
eq_exchange_t = np.array(eq_exchange_t_errors)
eq_noise = np.array(eq_front_noise_floor)
# Scale equivariance errors by action range for relative measure
alpha_range = float(np.max(np.abs(alpha_hist[2:])))
rel_front_err = float(np.mean(eq_front_errors) / (alpha_range + 1e-12))
rel_exchange_b_err = float(np.mean(eq_exchange_b) / (alpha_range + 1e-12))
rel_exchange_t_err = float(np.mean(eq_exchange_t) / (alpha_range + 1e-12))
# Combined rear error (max of bottom and top)
rel_exchange_err = max(rel_exchange_b_err, rel_exchange_t_err)
# Diagnostic 3: cross-correlation between alpha_T and -alpha_B
if len(alpha_hist) > 10:
# After initial transient
tail = n_steps // 2
corr_TB = float(np.corrcoef(alpha_hist[tail:, 2], -alpha_hist[tail:, 1])[0, 1])
else:
corr_TB = float("nan")
result = {
"re_code": re_code,
"mu": mu,
"n_samples": n_steps,
"alpha_range": alpha_range,
"front_bias": {
"mean_alpha_F": mean_alpha_F,
"std_alpha_F": std_alpha_F,
"bias_over_std": front_bias_score,
"bias_significant": front_bias_score > 2.0,
},
"equivariance_front": {
"mean_abs_error": float(np.mean(eq_front_errors)),
"max_abs_error": float(np.max(eq_front_errors)),
"relative_error": rel_front_err,
"noise_floor": float(np.mean(eq_noise)),
"signal_to_noise": float(np.mean(eq_front_errors) / (np.mean(eq_noise) + 1e-12)),
},
"equivariance_rear_bottom": {
"mean_abs_error": float(np.mean(eq_exchange_b)),
"max_abs_error": float(np.max(eq_exchange_b)),
"relative_error": rel_exchange_b_err,
},
"equivariance_rear_top": {
"mean_abs_error": float(np.mean(eq_exchange_t)),
"max_abs_error": float(np.max(eq_exchange_t)),
"relative_error": rel_exchange_t_err,
},
"top_bottom_correlation": {
"corr_alphaT_vs_negAlphaB": corr_TB,
},
"equivariance_verdict": "PASS" if (rel_front_err < 0.20 and rel_exchange_err < 0.20) else "REVIEW",
}
print(f" Front bias: mean_alpha_F={mean_alpha_F:.6f} |bias|/std={front_bias_score:.3f}")
print(f" Front equiv err: mean={np.mean(eq_front_errors):.6f} rel={rel_front_err:.3%}")
print(f" Rear-bot err: mean={np.mean(eq_exchange_b):.6f} rel={rel_exchange_b_err:.3%}")
print(f" Rear-top err: mean={np.mean(eq_exchange_t):.6f} rel={rel_exchange_t_err:.3%}")
print(f" T vs -B corr: {corr_TB:.4f}")
print(f" Verdict: {result['equivariance_verdict']}")
with open(os.path.join(output_root, f"equivariance_re{re_code}.json"), "w") as f:
json.dump(result, f, indent=2)
print(f" Saved to {output_root}/equivariance_re{re_code}.json")
return result
def main():
ap = argparse.ArgumentParser(description="Equivariance diagnosis for PPO cloak control")
ap.add_argument("--re", type=str, default="all",
help='Re case: 50,100,200,400, or "all"')
ap.add_argument("--device", type=int, default=0, help="GPU device for PPO model")
ap.add_argument("--cfd-device", type=int, default=2, help="GPU device for CFD simulation")
args = ap.parse_args()
if args.re.lower() == "all":
re_list = [rc for rc, _ in RE_CASES_TRAIN]
else:
re_list = [int(args.re)]
# Store device args for use in diagnose_one_re
device_id = args.device
cfd_device = args.cfd_device
diag_root = os.path.join(OUTPUT_DIR, "diagnostic")
os.makedirs(diag_root, exist_ok=True)
all_results = []
for re_code in re_list:
res = diagnose_one_re(re_code, device_id, cfd_device, diag_root)
all_results.append(res)
summary = {
"summary": {
"equivariance_verdicts": {r["re_code"]: r.get("equivariance_verdict", "ERROR")
for r in all_results}
},
"details": all_results,
}
with open(os.path.join(diag_root, "equivariance_summary.json"), "w") as f:
json.dump(summary, f, indent=2)
print(f"\nSummary saved to {diag_root}/equivariance_summary.json")
if __name__ == "__main__":
main()
archive/analysis_crossre_scripts/phase2_ablation.py
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# analysis_crossre/scripts/phase2_ablation.py
"""Ablation runner: v2 baseline -> v2.1 -> v2.2 -> v2.3 -> v2.4.
Each version differs by exactly one change from the previous.
Run specific versions via --mode.
Usage::
conda run -n pycuda_3_10 python phase2_ablation.py \\
--mode all --out-dir output/analysis_crossre/sindy
conda run -n pycuda_3_10 python phase2_ablation.py \\
--mode v21 --out-dir output/analysis_crossre/sindy
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Dict, List, Tuple
import numpy as np
from utils import (
action_to_physical,
compute_dimensionless,
compute_physical_symbols,
fit_channel,
print_control_law,
)
from cfg import (
OUTPUT_DIR,
RE_CASES_TRAIN,
ACTION_SCALE,
ACTION_BIAS,
U0,
)
THRESHOLDS = [0.0, 0.001, 0.002, 0.005, 0.01, 0.015, 0.02, 0.03, 0.05, 0.1]
def load_case_data(re_code: int) -> Tuple:
case_dir = os.path.join(OUTPUT_DIR, f"re{re_code}")
npz_path = os.path.join(case_dir, "controlled.npz")
if not os.path.isfile(npz_path):
raise FileNotFoundError(f"Missing {npz_path}")
data = np.load(npz_path)
sensors = data["sensors"].astype(np.float64)
forces = data["forces"].astype(np.float64)
actions_norm = data["actions"].astype(np.float64)
rewards = data.get("rewards", np.zeros(sensors.shape[0])).astype(np.float64)
actions_phys = action_to_physical(
actions_norm, scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0)
mu = 2.0 / re_code
return sensors, forces, actions_phys, rewards, mu
def make_features_v2(sensors, forces, actions_prev, actions_prev2, include_raw_lattice=True):
"""v2 / v2.1 features: raw lattice physical symbols."""
if include_raw_lattice:
sym = compute_physical_symbols(sensors, forces, actions_prev, actions_prev2)
else:
sym = {}
T = sensors.shape[0]
# Always build v2 physical symbols even for lattice version
s = sensors.astype(np.float64)
f = forces.astype(np.float64)
u0, u1, u2 = s[:, 0], s[:, 2], s[:, 4]
v0, v1, v2 = s[:, 1], s[:, 3], s[:, 5]
# Add derived symbols (v2 style)
sym["u_m"] = (u0 + u1 + u2) / 3.0
sym["u_a"] = (u2 - u0) / 2.0
sym["u_c"] = u1.copy()
sym["u_curv"] = u0 - 2.0 * u1 + u2
sym["v_m"] = (v0 + v1 + v2) / 3.0
sym["v_a"] = (v2 - v0) / 2.0
sym["v_c"] = v1.copy()
sym["v_curv"] = v0 - 2.0 * v1 + v2
sym["sin_ua"] = np.sin(np.pi * sym["u_a"])
sym["cos_ua"] = np.cos(np.pi * sym["u_a"])
fx0, fy0 = f[:, 0], f[:, 1]
fx1, fy1 = f[:, 2], f[:, 3]
fx2, fy2 = f[:, 4], f[:, 5]
sym["Fx_tot"] = fx0 + fx1 + fx2
sym["Fx_rear"] = fx1 + fx2
sym["Fx_diff"] = fx2 - fx1
sym["Fy_tot"] = fy0 + fy1 + fy2
sym["Fy_rear"] = fy1 + fy2
sym["Fy_diff"] = fy2 - fy1
sym["a0_lag1"] = actions_prev[:, 0]
sym["a1_lag1"] = actions_prev[:, 1]
sym["a2_lag1"] = actions_prev[:, 2]
sym["da0"] = actions_prev[:, 0] - actions_prev2[:, 0]
sym["da1"] = actions_prev[:, 1] - actions_prev2[:, 1]
sym["da2"] = actions_prev[:, 2] - actions_prev2[:, 2]
return sym
def make_features_dimensionless(sensors, forces, actions_prev, actions_prev2, mu):
"""v2.2 features: fully dimensionless."""
dim = compute_dimensionless(sensors, forces, u0=U0, d=20.0)
T = actions_prev.shape[0]
# Nondim actions: alpha = omega_phys / U0
T = actions_prev.shape[0]
a_prev = np.zeros((T, 3), dtype=np.float64)
a_prev2 = np.zeros((T, 3), dtype=np.float64)
a_prev[1:] = actions_prev[1:] / U0
a_prev2[2:] = actions_prev2[2:] / U0
da = a_prev - a_prev2
# Sensor (nondim)
u_B, u_C, u_T = dim["u_hat_B"], dim["u_hat_C"], dim["u_hat_T"]
v_B, v_C, v_T = dim["v_hat_B"], dim["v_hat_C"], dim["v_hat_T"]
sym = {}
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["u_curv"] = u_B - 2.0 * u_C + u_T
sym["v_m"] = (v_B + v_C + v_T) / 3.0
sym["v_a"] = (v_T - v_B) / 2.0
sym["v_c"] = v_C.copy()
sym["v_curv"] = v_B - 2.0 * v_C + v_T
sym["sin_ua"] = np.sin(np.pi * sym["u_a"])
sym["cos_ua"] = np.cos(np.pi * sym["u_a"])
# Force (nondim Cd/Cl)
sym["Cd_tot"] = dim["Cd_F"] + dim["Cd_T"] + dim["Cd_B"]
sym["Cd_rear"] = dim["Cd_T"] + dim["Cd_B"]
sym["Cd_diff"] = dim["Cd_T"] - dim["Cd_B"]
sym["Cl_tot"] = dim["Cl_F"] + dim["Cl_T"] + dim["Cl_B"]
sym["Cl_rear"] = dim["Cl_T"] + dim["Cl_B"]
sym["Cl_diff"] = dim["Cl_T"] - dim["Cl_B"]
# Memory (nondim alpha)
sym["a0_lag1"] = a_prev[:, 0] # front
sym["a1_lag1"] = a_prev[:, 1] # bottom
sym["a2_lag1"] = a_prev[:, 2] # top
sym["da0"] = da[:, 0]
sym["da1"] = da[:, 1]
sym["da2"] = da[:, 2]
# Mu modulation
sym["mu"] = np.full(T, mu, dtype=np.float64)
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Cd_tot"] = sym["Cd_tot"] * mu
sym["mu_Cl_diff"] = sym["Cl_diff"] * mu
return sym
def build_theta(sym, feature_keys, add_bias=True):
"""Build feature matrix from symbol dict."""
T = sym[feature_keys[0]].shape[0]
cols = []
if add_bias:
cols.append(np.ones(T, dtype=np.float64))
for k in feature_keys:
cols.append(sym[k])
return np.column_stack(cols)
# Feature set definitions
V2_BASE_KEYS = [
"u_m", "u_a", "u_c", "u_curv", "v_a", "v_curv",
"Fx_tot", "Fx_rear", "Fx_diff", "Fy_tot", "Fy_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
]
V2_WITH_MU = V2_BASE_KEYS + [
"mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff",
]
V2DIM_KEYS = [
"u_m", "u_a", "u_c", "u_curv", "v_a", "v_curv",
"Cd_tot", "Cd_rear", "Cd_diff", "Cl_tot", "Cl_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
"mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff",
]
def build_dataset(re_codes, mode, use_mu=True, use_mu_nondim=True):
"""Build dataset for given mode.
Modes:
- "v2_baseline": raw lattice + all 3 with bias
- "v21": same but front no-bias
- "v22": dimensionless + front no-bias
- "v23": v22 + rear shared head
- "v24": v23 + mild weighting
"""
all_Theta = []
all_Y = []
all_W = []
all_re = []
for rc in re_codes:
sensors, forces, actions_phys, rewards, mu = load_case_data(rc)
if mode in ("v2_baseline", "v21"):
# raw lattice features
a_prev = np.zeros_like(actions_phys)
a_prev2 = np.zeros_like(actions_phys)
a_prev[1:] = actions_phys[:-1]
a_prev2[2:] = actions_phys[:-2]
sym = make_features_v2(sensors, forces, a_prev, a_prev2)
feature_keys = V2_WITH_MU if use_mu else V2_BASE_KEYS
sym["mu"] = np.full(sensors.shape[0], mu, dtype=np.float64)
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
if use_mu:
# Add mu modulated forces
sym["mu_Cd_tot"] = sym["Fx_tot"] * mu
sym["mu_Cl_diff"] = sym["Fy_diff"] * mu
Y = actions_phys.copy()
else:
# dimensionless features
a_prev_d = np.zeros_like(actions_phys)
a_prev2_d = np.zeros_like(actions_phys)
a_prev_d[1:] = actions_phys[:-1]
a_prev2_d[2:] = actions_phys[:-2]
sym = make_features_dimensionless(sensors, forces, a_prev_d, a_prev2_d, mu)
feature_keys = V2DIM_KEYS
# Y is nondim alpha = omega/U0
Y = actions_phys / U0
# Compute quality weight if needed
if mode == "v24":
late_mean = float(np.mean(rewards[-80:]))
weight = np.clip(0.3 + 0.7 * late_mean / 0.7, 0.2, 1.0)
W = np.full(sensors.shape[0], weight, dtype=np.float64)
else:
W = np.ones(sensors.shape[0], dtype=np.float64)
# Store for stacking (will trim warmup later)
all_Theta.append((sym, feature_keys, Y, W, rc))
# Stack all Re data with warmup removed
Theta_list = []
Y_list = []
W_list = []
re_list = []
for sym, feature_keys, Y, W, rc in all_Theta:
T = Y.shape[0]
theta = build_theta(sym, feature_keys, add_bias=True)
# Remove first 2 warmup steps
theta = theta[2:]
Y_t = Y[2:]
W_t = W[2:]
Theta_list.append(theta)
Y_list.append(Y_t)
W_list.append(W_t)
re_list.append(np.full(theta.shape[0], rc, dtype=np.int64))
Theta_stacked = np.vstack(Theta_list)
Y_stacked = np.vstack(Y_list)
W_stacked = np.concatenate(W_list)
Re_stacked = np.concatenate(re_list)
# For front no-bias versions: remove bias column (column 0)
front_bias = mode not in ("v21", "v22", "v23", "v24")
if front_bias:
Theta_front = Theta_stacked
Theta_other = Theta_stacked
else:
Theta_front = Theta_stacked[:, 1:] # remove bias column for front
Theta_other = Theta_stacked # keep bias for bottom/top
return Theta_front, Theta_other, Y_stacked, W_stacked, Re_stacked
def fit_weighted(Theta, y, w, thresholds):
"""Weighted STLSQ fit."""
import pysindy as ps
std = np.sqrt(np.average((Theta - np.average(Theta, axis=0, weights=w))**2,
axis=0, weights=w))
std = np.where(std < 1e-8, 1.0, std)
Theta_s = Theta / std
best = None
rows = []
for th in thresholds:
opt = ps.STLSQ(threshold=th, alpha=1e-4, max_iter=25)
opt.fit(Theta_s, y, sample_weight=w)
coef = np.asarray(opt.coef_, dtype=np.float64).flatten() / std
y_pred = Theta @ coef
y_mean = np.average(y, weights=w)
ssr = np.sum(w * (y - y_pred)**2)
sst = np.sum(w * (y - y_mean)**2) + 1e-12
r2 = 1.0 - ssr / sst
mae = float(np.average(np.abs(y - y_pred), weights=w))
nz = int(np.sum(np.abs(coef) > 1e-8))
entry = {"threshold": float(th), "nz": nz, "r2": r2, "mae": mae, "coef": coef}
rows.append(entry)
if best is None or r2 > best["r2"]:
best = entry
return rows, best
def run_ablation(mode, train_re, out_dir):
"""Run full ablation for given mode."""
print(f"\n{'='*60}")
print(f"Mode: {mode}")
print(f"{'='*60}")
ThetaF, ThetaO, Y, W, Re = build_dataset(train_re, mode)
# Determine which cylinders use which feature matrix
if mode == "v23":
# rear shared-head: only fit front and top. bottom = -top(Gx)
# For simplicity, still fit all 3 but check rear consistency separately
cylinders = [
("front", ThetaF, False), # front: no bias
("bottom", ThetaO, True), # bottom: has bias
("top", ThetaO, True), # top: has bias
]
elif mode in ("v21", "v22", "v24"):
cylinders = [
("front", ThetaF, False), # front: no bias
("bottom", ThetaO, True), # bottom: has bias
("top", ThetaO, True), # top: has bias
]
else: # v2_baseline
cylinders = [
("front", ThetaO, True), # front: has bias
("bottom", ThetaO, True), # bottom: has bias
("top", ThetaO, True), # top: has bias
]
channels = []
for name, theta, has_bias in cylinders:
ci = {"front": 0, "bottom": 1, "top": 2}[name]
print(f"\n --- {name} ---")
rows, best = fit_weighted(theta, Y[:, ci], W, THRESHOLDS)
coef = best["coef"]
nz = int(np.sum(np.abs(coef) > 1e-8))
print(f" {name}: R2={best['r2']:.6f} MAE={best['mae']:.6f} nz={nz}")
# Get feature names for this mode
if mode in ("v2_baseline", "v21"):
feat_names = [
"bias", "u_m", "u_a", "u_c", "u_curv", "v_a", "v_curv",
"Fx_tot", "Fx_rear", "Fx_diff", "Fy_tot", "Fy_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
"mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff",
]
has_mu = True
else:
feat_names = [
"bias", "u_m", "u_a", "u_c", "u_curv", "v_a", "v_curv",
"Cd_tot", "Cd_rear", "Cd_diff", "Cl_tot", "Cl_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
"mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff",
]
has_mu = True
# Trim feat_names to match actual theta dimensions
actual_nf = theta.shape[1]
if len(feat_names) != actual_nf:
# Feature names don't include mu if not included, etc.
# Just use generic names
feat_names = [f"f{i}" for i in range(actual_nf)]
# Per-Re breakdown
print(f"\n --- Per-Re breakdown ---")
breakdown = {}
for rc in set(Re.tolist()):
mask = Re == rc
ch_b = []
for name, theta, has_bias in cylinders:
ci = {"front": 0, "bottom": 1, "top": 2}[name]
th_r = theta[mask]
yr = Y[mask, ci]
wr = W[mask]
coef = np.array([ch["best_coef"][ci] for ch in channels], dtype=np.float64).flatten()
# Actually get the right coefficient for this cylinder
coef_c = np.array(channels[ci]["best_coef"], dtype=np.float64)
y_pred = th_r @ coef_c
y_mean = np.average(yr, weights=wr)
ssr = np.sum(wr * (yr - y_pred)**2)
sst = np.sum(wr * (yr - y_mean)**2) + 1e-12
r2 = 1.0 - ssr / sst
mae = float(np.average(np.abs(yr - y_pred), weights=wr))
ch_b.append({"cylinder": name, "r2": float(r2), "mae": mae})
breakdown[f"re{int(rc)}"] = ch_b
r2s = ", ".join([f"{b['cylinder']}={b['r2']:.4f}" for b in ch_b])
print(f" Re{int(rc)}: {r2s}")
return {
"mode": mode,
"train_re": train_re,
"channels": channels,
"per_re_breakdown": breakdown,
}
def main():
ap = argparse.ArgumentParser(description="Ablation runner v2->v2.4")
ap.add_argument("--mode", type=str, default="all",
choices=["v2_baseline", "v21", "v22", "v23", "v24", "all"])
ap.add_argument("--out-dir", type=str, default=os.path.join(OUTPUT_DIR, "sindy"))
ap.add_argument("--train-re", type=str, default="50,100,200")
args = ap.parse_args()
train_re = [int(r) for r in args.train_re.split(",")]
os.makedirs(args.out_dir, exist_ok=True)
modes = ["v2_baseline", "v21", "v22", "v23", "v24"] if args.mode == "all" else [args.mode]
results = {"metadata": {"thresholds": THRESHOLDS, "train_re": train_re}}
for mode in modes:
results[mode] = run_ablation(mode, train_re, args.out_dir)
out_path = os.path.join(args.out_dir, "ablation_results.json")
with open(out_path, "w") as f:
json.dump(results, f, indent=2)
print(f"\nSaved: {out_path}")
if __name__ == "__main__":
main()
archive/analysis_crossre_scripts/phase2_control_fit.py
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# analysis_crossre/scripts/phase2_control_fit.py
"""Phase 2 v3: dimensionless + front-no-bias + quality-weighted SINDy fitting.
Usage::
conda run -n pycuda_3_10 python phase2_control_fit.py \\
--cross-re --out-dir output/analysis_crossre/sindy
conda run -n pycuda_3_10 python phase2_control_fit.py \\
--leave-one-out --out-dir output/analysis_crossre/sindy
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from typing import Dict, List, Tuple
import numpy as np
from utils import (
action_to_physical,
compute_dimensionless,
compute_v3_symbols,
fit_channel,
print_control_law,
)
from cfg import (
OUTPUT_DIR,
RE_CASES_TRAIN,
ACTION_SCALE,
ACTION_BIAS,
U0,
)
THRESHOLDS = [0.0, 0.001, 0.002, 0.005, 0.01, 0.015, 0.02, 0.03, 0.05, 0.1]
def load_case_data(re_code: int) -> Tuple:
"""Load controlled NPZ for a single Re.
Returns (sensors, forces, actions_phys, rewards, mu).
"""
case_dir = os.path.join(OUTPUT_DIR, f"re{re_code}")
npz_path = os.path.join(case_dir, "controlled.npz")
if not os.path.isfile(npz_path):
raise FileNotFoundError(f"Missing {npz_path}")
data = np.load(npz_path)
sensors = data["sensors"].astype(np.float64)
forces = data["forces"].astype(np.float64)
actions_norm = data["actions"].astype(np.float64)
rewards = data.get("rewards", np.zeros(sensors.shape[0])).astype(np.float64)
actions_phys = action_to_physical(
actions_norm, scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0)
mu = 2.0 / re_code # 1 / Re_D
return sensors, forces, actions_phys, rewards, mu
def compute_trajectory_weights(rewards: np.ndarray, late_window: int = 80) -> float:
"""Compute a single quality weight for this trajectory.
Uses the mean reward over the last ``late_window`` steps.
Maps to weight via quantile-based scheme.
"""
n = len(rewards)
if n < late_window:
late_mean = float(np.mean(rewards))
else:
late_mean = float(np.mean(rewards[-late_window:]))
# Map reward to weight via sigmoid-like scheme:
# reward 0.0 -> weight 0.1, reward 0.3 -> 0.3, reward 0.5 -> 0.6, reward 0.7 -> 0.9
weight = 1.0 / (1.0 + np.exp(-8.0 * (late_mean - 0.4)))
return float(np.clip(weight, 0.05, 1.0))
def build_dataset_v3(
re_code: int,
include_mu: bool = True,
) -> Tuple:
"""Build v3 data: dimensionless features, front-no-bias, quality-weighted.
Returns
-------
Theta_front : (T, nf_f) for front model (no bias)
Theta_other : (T, nf_o) for top/bottom model (with bias)
Y : (T, 3) physical omegas
W : (T,) quality weight per sample
names : feature names (without "bias")
"""
sensors, forces, actions_phys, rewards, mu = load_case_data(re_code)
# Dimensionless
dim = compute_dimensionless(sensors, forces, u0=U0, d=20.0)
# Memory terms
a_prev = np.zeros_like(actions_phys)
a_prev2 = np.zeros_like(actions_phys)
a_prev[1:] = actions_phys[:-1]
a_prev2[2:] = actions_phys[:-2]
# Build v3 features
Theta_f, Theta_top, names = compute_v3_symbols(
dim, a_prev, a_prev2, mu=mu, include_mu=include_mu)
# Quality weight per sample: inherit trajectory weight
traj_weight = compute_trajectory_weights(rewards)
W = np.full(Theta_f.shape[0], traj_weight, dtype=np.float64)
# Remove warmup (need 2 steps of memory)
Theta_f = Theta_f[2:]
Theta_top = Theta_top[2:]
Y = actions_phys[2:]
W = W[2:]
print(f" Re{re_code}: {Theta_f.shape[0]} samples, {Theta_f.shape[1]} feats, "
f"mu={mu:.6f}, traj_weight={traj_weight:.4f}")
return Theta_f, Theta_top, Y, W, names, mu
def fit_channel_weighted(
Theta: np.ndarray,
y: np.ndarray,
w: np.ndarray,
thresholds: list,
alpha: float = 1e-4,
max_iter: int = 25,
) -> tuple:
"""Weighted STLSQ fit."""
import pysindy as ps
# Weighted normalisation
std = np.sqrt(np.average((Theta - np.average(Theta, axis=0, weights=w)) ** 2,
axis=0, weights=w))
std = np.where(std < 1e-8, 1.0, std)
Theta_s = Theta / std
best = None
rows = []
for th in thresholds:
opt = ps.STLSQ(threshold=th, alpha=alpha, max_iter=max_iter)
opt.fit(Theta_s, y, sample_weight=w)
coef = np.asarray(opt.coef_, dtype=np.float64).flatten() / std
y_pred = Theta @ coef
# Weighted R2
y_mean = np.average(y, weights=w)
ssr = np.sum(w * (y - y_pred) ** 2)
sst = np.sum(w * (y - y_mean) ** 2) + 1e-12
r2 = 1.0 - ssr / sst
mae = float(np.average(np.abs(y - y_pred), weights=w))
nz = int(np.sum(np.abs(coef) > 1e-8))
entry = {"threshold": float(th), "nz": nz, "r2": r2, "mae": mae, "coef": coef}
rows.append(entry)
if best is None or r2 > best["r2"]:
best = entry
return rows, best
def build_cross_re_v3(
train_re_codes: List[int],
include_mu: bool = True,
) -> Tuple:
"""Stack multiple Re datasets with v3 features.
Front model uses Theta_front (no bias).
Top/Bottom models use Theta_other (with bias).
Returns three stacked datasets.
"""
all_ThetaF, all_ThetaO, all_Y, all_W, all_re = [], [], [], [], []
names = None
for rc in train_re_codes:
tf, to, y, w, fn, mu = build_dataset_v3(rc, include_mu=include_mu)
all_ThetaF.append(tf)
all_ThetaO.append(to)
all_Y.append(y)
all_W.append(w)
all_re.append(np.full(tf.shape[0], rc, dtype=np.int64))
if names is None:
names = fn
ThetaF = np.vstack(all_ThetaF)
ThetaO = np.vstack(all_ThetaO)
Y = np.vstack(all_Y)
W = np.concatenate(all_W)
Re = np.concatenate(all_re)
print(f"\n Cross-Re: {ThetaF.shape[0]} samples, "
f"front={ThetaF.shape[1]} feats (no bias), "
f"other={ThetaO.shape[1]} feats (w/ bias)")
return ThetaF, ThetaO, Y, W, names, Re
def run_cross_re_fit(
train_re: List[int],
tag: str = "",
include_mu: bool = True,
) -> dict:
"""Fit all 3 cylinders independently.
Front: no bias.
Top/Bottom: with bias.
"""
ThetaF, ThetaO, Y, W, names, re_labels = build_cross_re_v3(train_re, include_mu)
cylinders = [
{"name": "front", "theta": ThetaF, "label": "front (no bias)"},
{"name": "bottom", "theta": ThetaO, "label": "bottom (w/ bias)"},
{"name": "top", "theta": ThetaO, "label": "top (w/ bias)"},
]
channels = []
for ci, cyl in enumerate(cylinders):
print(f"\n --- {tag} {cyl['label']} ---")
rows, best = fit_channel_weighted(cyl["theta"], Y[:, ci], W, THRESHOLDS)
coef = best["coef"]
print_control_law(names, coef, channel_label=f"{cyl['name']}")
print(f" R2={best['r2']:.6f} MAE={best['mae']:.6f}")
channels.append({
"cylinder": cyl["name"],
"has_bias": cyl["name"] != "front",
"n_features": cyl["theta"].shape[1],
"best": {k: float(v) if isinstance(v, (np.floating, float)) else v
for k, v in best.items() if k != "coef"},
"best_coef": [float(c) for c in coef],
"grid": [{k: float(v) for k, v in row.items() if k != "coef"}
for row in rows],
"feature_names": names,
})
# Per-Re breakdown
print(f"\n --- {tag} per-Re breakdown ---")
breakdown = {}
for re_code in set(re_labels.tolist()):
mask = re_labels == re_code
Yr, Wr = Y[mask], W[mask]
ch_b = []
for ci, cyl in enumerate(cylinders):
th = cyl["theta"][mask]
coef = np.array(channels[ci]["best_coef"], dtype=np.float64)
y_pred = th @ coef
y_t = Yr[:, ci]
y_mean = np.average(y_t, weights=Wr)
ssr = np.sum(Wr * (y_t - y_pred) ** 2)
sst = np.sum(Wr * (y_t - y_mean) ** 2) + 1e-12
r2 = 1.0 - ssr / sst
mae = float(np.average(np.abs(y_t - y_pred), weights=Wr))
ch_b.append({"cylinder": cyl["name"], "r2": float(r2), "mae": mae})
breakdown[f"re{int(re_code)}"] = ch_b
r2s = ", ".join([f"{b['cylinder']}={b['r2']:.4f}" for b in ch_b])
print(f" Re{int(re_code)}: {r2s}")
return {
"tag": tag,
"train_re": train_re,
"n_samples": int(ThetaF.shape[0]),
"n_features_front": int(ThetaF.shape[1]),
"n_features_other": int(ThetaO.shape[1]),
"channels": channels,
"per_re_breakdown": breakdown,
}
def main():
ap = argparse.ArgumentParser(description="Phase 2 v3: dimensionless + constrained fitting")
ap.add_argument("--cross-re", action="store_true")
ap.add_argument("--leave-one-out", action="store_true")
ap.add_argument("--out-dir", type=str, default=os.path.join(OUTPUT_DIR, "sindy"))
ap.add_argument("--train-re", type=str, default="50,100,200")
ap.add_argument("--no-mu", action="store_true")
args = ap.parse_args()
if not (args.cross_re or args.leave_one_out):
print("ERROR: specify --cross-re and/or --leave-one-out")
return 1
train_re = [int(r) for r in args.train_re.split(",")]
include_mu = not args.no_mu
os.makedirs(args.out_dir, exist_ok=True)
results = {
"metadata": {
"method": "v3_dimensionless_front_nobias_weighted",
"thresholds": THRESHOLDS,
"include_mu": include_mu,
}
}
if args.cross_re:
print("\n" + "=" * 60)
print("v3 Cross-Re unified (dimensionless + front no-bias + weighted)")
print("=" * 60)
results["cross_re"] = run_cross_re_fit(
train_re, tag="v3-cross", include_mu=include_mu)
if args.leave_one_out:
print("\n" + "=" * 60)
print("v3 Leave-one-out cross-validation")
print("=" * 60)
loo_results = {}
for held_out in train_re:
train_set = [r for r in train_re if r != held_out]
print(f"\n--- LOO: train={train_set}, test={held_out} ---")
loo = run_cross_re_fit(
train_set, tag=f"v3-loo-{held_out}", include_mu=include_mu)
loo_results[f"holdout_{held_out}"] = loo
results["leave_one_out"] = loo_results
out_path = os.path.join(args.out_dir, "sindy_results_v3.json")
with open(out_path, "w") as f:
json.dump(results, f, indent=2)
print(f"\nSaved: {out_path}")
if __name__ == "__main__":
main()
archive/analysis_crossre_scripts/phase3_ablation_val.py
+267
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"""Phase 3: closed-loop for ablation modes.
Usage::
conda run -n pycuda_3_10 python phase3_ablation_val.py \\
--ablation-json output/analysis_crossre/sindy/ablation_results.json \\
--mode v21 --validate-re 70 --device 2
"""
from __future__ import annotations
import argparse
import json
import os
import sys
from collections import deque
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField
from utils import (
nu_from_re, load_legacy_configs, build_karman_cloak_env, add_pinball,
build_observation, scale_action, action_to_physical,
compute_dimensionless, compute_physical_symbols,
save_vorticity_png, vorticity_from_ddf, compute_similarity,
)
from cfg import (
CONFIG_DIR, OUTPUT_DIR, SAMPLE_INTERVAL, FIFO_LEN, CONV_LEN,
S_DIM, ACTION_SCALE, ACTION_BIAS, U0,
)
DATA_TYPE = np.float32
def load_ablation_coef(ablation_path, mode, channels_to_load=("front", "bottom", "top")):
"""Load coefficients for a specific ablation mode."""
with open(ablation_path) as f:
data = json.load(f)
mode_data = data[mode]
coefs = {}
for ch in mode_data["channels"]:
name = ch["cylinder"]
if name in channels_to_load:
coefs[name] = {
"coef": np.array(ch["best_coef"], dtype=np.float64),
"has_bias": ch["has_bias"],
}
return coefs
def predict_ablation(obs_slice, actions_prev, actions_prev2, coefs, mu, mode, u0=0.01):
"""Predict action using ablation mode coefficients.
obs_slice: (12,) raw lattice [sensor(6), force(6)]
"""
sensors = obs_slice[0:6].astype(np.float64).reshape(1, 6)
forces = obs_slice[6:12].astype(np.float64).reshape(1, 6)
a_prev = actions_prev.astype(np.float64).reshape(1, 3)
a_prev2 = actions_prev2.astype(np.float64).reshape(1, 3)
is_dim = "v22" in mode or "v23" in mode or "v24" in mode or mode in ("v2_dimless",)
if is_dim:
dim = compute_dimensionless(sensors, forces, u0=u0, d=20.0)
# Build dimensionless features
sym = _build_dimensionless_features(dim, a_prev[0], a_prev2[0], mu)
Y_scale = 1.0 / u0 # predict nondim alpha = omega / U0
else:
# Lattice features (v2/v21)
a_prev_f = np.zeros((1, 3), dtype=np.float64)
a_prev2_f = np.zeros((1, 3), dtype=np.float64)
a_prev_f[0] = a_prev
a_prev2_f[0] = a_prev2
sym = _build_lattice_features(sensors, forces, a_prev_f, a_prev2_f, mu)
Y_scale = 1.0 # predict raw omega
# Build feature vector
feat_keys = [k for k in sym.keys() if k not in ("mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff")]
feat_keys_mu = ["mu", "mu_u_a", "mu_v_a", "mu_Cd_tot", "mu_Cl_diff"]
feats = []
for k in feat_keys:
feats.append(float(sym[k][0]) if isinstance(sym[k], np.ndarray) else float(sym[k]))
if mu > 0:
for k in feat_keys_mu:
feats.append(float(sym[k][0]) if isinstance(sym[k], np.ndarray) else float(sym[k]))
omega = np.zeros(3, dtype=np.float64)
for ci, name in enumerate(["front", "bottom", "top"]):
c = coefs.get(name)
if c is None:
continue
coef_arr = c["coef"]
has_bias = c["has_bias"]
if has_bias:
feat_vec = np.array([1.0] + feats) if len(coef_arr) == len(feats) + 1 else np.array(feats)
else:
feat_vec = np.array(feats) if len(coef_arr) == len(feats) else np.array([1.0] + feats)
if len(feat_vec) != len(coef_arr):
feat_vec = np.array(feats) # fallback
pred = float(feat_vec @ coef_arr) * Y_scale
omega[ci] = pred
return omega
def _build_lattice_features(sensors, forces, a_prev, a_prev2, mu):
"""Build v2-style lattice features. All args 2D (1, N)."""
# Ensure 2D
if sensors.ndim == 1:
sensors = sensors.reshape(1, -1)
if forces.ndim == 1:
forces = forces.reshape(1, -1)
if a_prev.ndim == 1:
a_prev = a_prev.reshape(1, -1)
if a_prev2.ndim == 1:
a_prev2 = a_prev2.reshape(1, -1)
sym = compute_physical_symbols(sensors, forces, a_prev, a_prev2)
sym["mu"] = np.array([mu])
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Cd_tot"] = sym["Fx_tot"] * mu
sym["mu_Cl_diff"] = sym["Fy_diff"] * mu
return sym
def _build_dimensionless_features(dim, a_prev, a_prev2, mu):
"""Build dimensionless features. a_prev/a_prev2 are 1D (3,) arrays."""
if a_prev.ndim > 1:
a_prev = a_prev.flatten()
if a_prev2.ndim > 1:
a_prev2 = a_prev2.flatten()
"""Build dimensionless features."""
T = 1
u_B, u_C, u_T = dim["u_hat_B"][0], dim["u_hat_C"][0], dim["u_hat_T"][0]
v_B, v_C, v_T = dim["v_hat_B"][0], dim["v_hat_C"][0], dim["v_hat_T"][0]
sym = {}
sym["u_m"] = np.array([(u_B + u_C + u_T) / 3.0])
sym["u_a"] = np.array([(u_T - u_B) / 2.0])
sym["u_c"] = np.array([u_C])
sym["u_curv"] = np.array([u_B - 2.0*u_C + u_T])
sym["v_a"] = np.array([(v_T - v_B) / 2.0])
sym["v_curv"] = np.array([v_B - 2.0*v_C + v_T])
sym["sin_ua"] = np.sin(np.pi * sym["u_a"])
sym["cos_ua"] = np.cos(np.pi * sym["u_a"])
sym["Cd_tot"] = np.array([dim["Cd_F"][0] + dim["Cd_T"][0] + dim["Cd_B"][0]])
sym["Cd_rear"] = np.array([dim["Cd_T"][0] + dim["Cd_B"][0]])
sym["Cd_diff"] = np.array([dim["Cd_T"][0] - dim["Cd_B"][0]])
sym["Cl_tot"] = np.array([dim["Cl_F"][0] + dim["Cl_T"][0] + dim["Cl_B"][0]])
sym["Cl_diff"] = np.array([dim["Cl_T"][0] - dim["Cl_B"][0]])
# Nondim actions
a_prev_n = a_prev / U0
a_prev2_n = a_prev2 / U0
sym["a0_lag1"] = np.array([a_prev_n[0]])
sym["a1_lag1"] = np.array([a_prev_n[1]])
sym["a2_lag1"] = np.array([a_prev_n[2]])
sym["da0"] = np.array([a_prev_n[0] - a_prev2_n[0]])
sym["da1"] = np.array([a_prev_n[1] - a_prev2_n[1]])
sym["da2"] = np.array([a_prev_n[2] - a_prev2_n[2]])
sym["mu"] = np.array([mu])
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Cd_tot"] = sym["Cd_tot"] * mu
sym["mu_Cl_diff"] = sym["Cl_diff"] * mu
return sym
def run_closed_loop(re_code, coefs, mode, device_id, output_root, n_steps=100):
"""Run closed-loop for one ablation mode."""
os.makedirs(output_root, exist_ok=True)
nu = nu_from_re(re_code, u0=U0)
mu = 2.0 / re_code
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_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=20.0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE)
norm = add_pinball(
ff, l0=20.0, u0=U0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE, action_bias=ACTION_BIAS)
np.savez(os.path.join(output_root, "target.npz"), target_states=target_states)
# Controlled rollout
ff.restore_ddf()
ff.apply_ddf()
fifo = deque(maxlen=FIFO_LEN)
bias_action = scale_action(
np.zeros(3, dtype=np.float32), scale=ACTION_SCALE,
bias=ACTION_BIAS, u0=U0, n_total_bodies=7)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_action)
fifo.append(ff.obs.copy()[2:14])
sens_sc = []
actions_prev = action_to_physical(
np.zeros((1,3), dtype=np.float32), scale=ACTION_SCALE,
bias=ACTION_BIAS, u0=U0).flatten()
actions_prev2 = actions_prev.copy()
for step in range(n_steps):
obs_slice = fifo[-1] if len(fifo) > 0 else np.zeros(12, dtype=np.float32)
omega_pred = predict_ablation(obs_slice, actions_prev, actions_prev2, coefs, mu, mode, u0=U0)
norm_action = (omega_pred / U0 - np.array(ACTION_BIAS, dtype=np.float64)) / ACTION_SCALE
norm_action = np.clip(norm_action, -1.0, 1.0).astype(np.float32)
action_arr = scale_action(
norm_action, scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0, n_total_bodies=7)
ff.run(SAMPLE_INTERVAL, action_arr)
obs_slice_new = ff.obs.copy()[2:14]
fifo.append(obs_slice_new)
sens_sc.append(obs_slice_new[0:6])
actions_prev2 = actions_prev.copy()
actions_prev = omega_pred.copy()
sens_arr = np.array(sens_sc, dtype=np.float32)
sim = compute_similarity(target_states, sens_arr, CONV_LEN)
omega_vort = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(os.path.join(output_root, f"vorticity_{mode}.png"),
omega_vort, title=f"Re{re_code} {mode}")
del ff
result = {"re_code": re_code, "mode": mode, "similarity": sim}
with open(os.path.join(output_root, "result.json"), "w") as f:
json.dump(result, f, indent=2)
print(f" Re{re_code} {mode}: similarity={sim:.4f}")
return result
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--ablation-json", type=str, required=True)
ap.add_argument("--mode", type=str, default="v21")
ap.add_argument("--validate-re", type=str, default="70")
ap.add_argument("--device", type=int, default=2)
ap.add_argument("--steps", type=int, default=100)
ap.add_argument("--out-dir", type=str, default=os.path.join(OUTPUT_DIR, "sindy_val"))
args = ap.parse_args()
validate_re = [int(r) for r in args.validate_re.split(",")]
coefs = load_ablation_coef(args.ablation_json, args.mode)
os.makedirs(args.out_dir, exist_ok=True)
for rc in validate_re:
out_sub = os.path.join(args.out_dir, f"re{rc}")
run_closed_loop(rc, coefs, args.mode, args.device, out_sub, n_steps=args.steps)
if __name__ == "__main__":
main()
archive/analysis_crossre_scripts/phase3_validate.py
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# analysis_crossre/scripts/phase3_validate.py
"""Phase 3: closed-loop validation using cross-Re SINDy control law.
Usage::
conda run -n pycuda_3_10 python phase3_validate.py \\
--device 2 --out-dir output/analysis_crossre/sindy_val
conda run -n pycuda_3_10 python phase3_validate.py \\
--validate-re 35,70,150 --device 2
conda run -n pycuda_3_10 python phase3_validate.py \\
--baseline-only --validate-re 35 --device 2
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from collections import deque
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField # noqa: E402
from utils import (
nu_from_re,
load_legacy_configs,
build_karman_cloak_env,
add_pinball,
build_observation,
scale_action,
action_to_physical,
compute_dimensionless,
compute_v3_symbols,
save_vorticity_png,
vorticity_from_ddf,
compute_similarity,
)
from cfg import (
CONFIG_DIR,
OUTPUT_DIR,
MODEL_DIR,
SAMPLE_INTERVAL,
FIFO_LEN,
CONV_LEN,
S_DIM,
A_DIM,
ACTION_SCALE,
ACTION_BIAS,
U0,
RE_CASES_TRAIN,
RE_CASES_VALIDATION,
RE_LABEL_MAP,
)
DATA_TYPE = np.float32
def load_cross_re_coef(sindy_results_path: str, threshold: float) -> dict:
"""Load v3 cross-Re coefficients.
Returns dict ``{cylinder_name: {"coef": np.ndarray, "feat_names": list, "has_bias": bool}}``
"""
with open(sindy_results_path) as f:
data = json.load(f)
cross = data["cross_re"]
coefs = {}
for ch_entry in cross["channels"]:
name = ch_entry["cylinder"]
feat_names = ch_entry["feature_names"]
coef_full = np.array(ch_entry["best_coef"], dtype=np.float64)
has_bias = ch_entry["has_bias"]
scale = np.max(np.abs(coef_full))
if scale > 0 and threshold > 0:
mask = np.abs(coef_full) / scale >= threshold
else:
mask = np.ones_like(coef_full, dtype=bool)
coef = coef_full * mask
nz = int(np.sum(mask))
print(f" {name}: total={len(coef_full)} nz={nz} threshold={threshold} "
f"R2={ch_entry['best']['r2']:.4f}")
coefs[name] = {"coef": coef, "feat_names": feat_names,
"has_bias": has_bias, "nz": nz, "r2": ch_entry["best"]["r2"]}
return coefs
def predict_omega_v3(
obs_slice: np.ndarray,
actions_prev: np.ndarray,
actions_prev2: np.ndarray,
coefs: dict,
mu: float,
u0: float = 0.01,
) -> np.ndarray:
"""Predict physical omega using v3 dimensionless features.
Front: no bias term.
Bottom/Top: with bias term.
All 3 independently (no exchange symmetry constraint).
Parameters
----------
obs_slice : (12,) raw [sensor(6), force(6)] in lattice units
actions_prev : (3,) omega(t-1)
actions_prev2 : (3,) omega(t-2)
"""
sensors = obs_slice[0:6].astype(np.float64).reshape(1, 6)
forces = obs_slice[6:12].astype(np.float64).reshape(1, 6)
a_prev = actions_prev.astype(np.float64).reshape(1, 3)
a_prev2 = actions_prev2.astype(np.float64).reshape(1, 3)
# Dimensionless
dim = compute_dimensionless(sensors, forces, u0=u0, d=20.0)
# Build v3 features
Theta_f, Theta_top, names = compute_v3_symbols(
dim, a_prev, a_prev2, mu=mu, include_mu=(mu > 0))
# Predict
omega = np.zeros(3, dtype=np.float64)
omega[0] = float(Theta_f[0] @ coefs["front"]["coef"]) # front (no bias)
omega[1] = float(Theta_top[0] @ coefs["bottom"]["coef"]) # bottom
omega[2] = float(Theta_top[0] @ coefs["top"]["coef"]) # top
return omega
def run_sindy_controlled(
re_code: int,
coefs: dict,
device_id: int,
output_root: str,
*,
n_steps: int = 150,
) -> dict:
"""Run closed-loop validation with SINDy control law."""
os.makedirs(output_root, exist_ok=True)
nu = nu_from_re(re_code, u0=U0)
mu = 2.0 / re_code # 1 / Re_D
label = RE_LABEL_MAP.get(re_code, f"Re{re_code}")
print(f"\n{'='*60}")
print(f"SINDy Validation: {label} nu={nu:.6f} mu={mu:.6f}")
print(f"{'='*60}")
# Build environment (same as Phase 1)
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(nu))
# Phase 1: dist + sensors + target
ff = FlowField(field_cfg, cuda_cfg, device_id=device_id)
target_states, _ = build_karman_cloak_env(
ff, u0=U0, l0=20.0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE,
)
# Phase 2: pinball + norm
norm = add_pinball(
ff, l0=20.0, u0=U0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE,
action_bias=ACTION_BIAS,
)
np.savez(os.path.join(output_root, "target.npz"), target_states=target_states)
# --- Uncontrolled rollout ---
print(" uncontrolled rollout ...")
ff.restore_ddf()
ff.apply_ddf()
sens_unc, forc_unc = [], []
for _ in range(n_steps):
ff.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE))
obs_slice = ff.obs.copy()[2:14]
sens_unc.append(obs_slice[0:6])
forc_unc.append(obs_slice[6:12])
np.savez(os.path.join(output_root, "uncontrolled.npz"),
sensors=np.array(sens_unc, dtype=np.float32),
forces=np.array(forc_unc, dtype=np.float32))
# Uncontrolled vorticity
omega_unc = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(os.path.join(output_root, "vorticity_uncontrolled.png"),
omega_unc, title=f"{label} uncontrolled")
# --- SINDy controlled rollout ---
print(f" SINDy controlled rollout ({n_steps} steps) ...")
ff.restore_ddf()
ff.apply_ddf()
# Bias FIFO
fifo = deque(maxlen=FIFO_LEN)
bias_action = scale_action(
np.zeros(3, dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0, n_total_bodies=7,
)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_action)
fifo.append(ff.obs.copy()[2:14])
sens_sc, forc_sc, omega_sc = [], [], []
omega_bias = action_to_physical(
np.zeros((1, 3), dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0,
).flatten()
actions_prev = omega_bias.copy()
actions_prev2 = omega_bias.copy()
for step in range(n_steps):
obs_slice = fifo[-1] if len(fifo) > 0 else np.zeros(12, dtype=np.float32)
omega_pred = predict_omega_v3(obs_slice, actions_prev, actions_prev2, coefs, mu, u0=U0)
omega_sc.append(omega_pred.copy())
# Convert action to legacy array and apply
norm_action = (omega_pred / U0 - np.array(ACTION_BIAS, dtype=np.float64)) / ACTION_SCALE
norm_action = np.clip(norm_action, -1.0, 1.0).astype(np.float32)
action_arr = scale_action(
norm_action,
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0, n_total_bodies=7,
)
ff.run(SAMPLE_INTERVAL, action_arr)
obs_slice_new = ff.obs.copy()[2:14]
fifo.append(obs_slice_new)
sens_sc.append(obs_slice_new[0:6])
forc_sc.append(obs_slice_new[6:12])
actions_prev = omega_pred
sens_sc_arr = np.array(sens_sc, dtype=np.float32)
forc_sc_arr = np.array(forc_sc, dtype=np.float32)
omega_sc_arr = np.array(omega_sc, dtype=np.float32)
np.savez(os.path.join(output_root, "sindy_controlled.npz"),
sensors=sens_sc_arr, forces=forc_sc_arr,
omegas=omega_sc_arr)
# Vorticity
omega_vort = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(os.path.join(output_root, "vorticity_sindy_controlled.png"),
omega_vort, title=f"{label} SINDy-controlled")
# Similarity
sim = compute_similarity(target_states, sens_sc_arr, CONV_LEN)
print(f" SINDy similarity: {sim:.4f}")
del ff
result = {"re_code": re_code, "mu": mu,
"sindy_similarity": sim,
"n_steps": n_steps}
with open(os.path.join(output_root, "result.json"), "w") as f:
json.dump(result, f, indent=2)
return result
def main():
ap = argparse.ArgumentParser(description="Phase 3: cross-Re SINDy validation")
ap.add_argument("--sindy-results", type=str,
default=os.path.join(OUTPUT_DIR, "sindy", "sindy_results_v3.json"),
help="Path to Phase 2 SINDy results JSON (v3 dimensionless)")
ap.add_argument("--validate-re", type=str, default="35,70,150",
help="Comma-separated validation Re codes")
ap.add_argument("--device", type=int, default=0, help="GPU device ID")
ap.add_argument("--steps", type=int, default=150,
help="Number of inference steps")
ap.add_argument("--threshold", type=float, default=0.002,
help="SINDy sparsity threshold (default: 0.002)")
ap.add_argument("--out-dir", type=str,
default=os.path.join(OUTPUT_DIR, "sindy_val"),
help="Output root for validation results")
args = ap.parse_args()
validate_re = [int(r) for r in args.validate_re.split(",")]
os.makedirs(args.out_dir, exist_ok=True)
# Load cross-Re coefficients
print(f"\nLoading cross-Re coefficients from {args.sindy_results}")
coefs = load_cross_re_coef(args.sindy_results, args.threshold)
for name in ["front", "bottom", "top"]:
print(f" {name}: nz={coefs[name]['nz']}, R2={coefs[name]['r2']:.4f}, "
f"threshold={args.threshold}")
t_start = time.time()
# Run for each validation Re
for re_code in validate_re:
out_dir = os.path.join(args.out_dir, f"re{re_code}")
result = run_sindy_controlled(
re_code, coefs, args.device, out_dir, n_steps=args.steps,
)
print(f" Done: Re{re_code} -> {out_dir}")
elapsed = time.time() - t_start
print(f"\nTotal time: {elapsed:.1f}s")
return 0
if __name__ == "__main__":
sys.exit(main())
archive/analysis_crossre_scripts/validate_v22.py
+197
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# analysis_crossre/scripts/validate_v22.py
"""Validate v22: v2 coefficients + front bias zeroed.
Direct standalone script to avoid JSON format issues.
Usage:
conda run -n pycuda_3_10 python validate_v22.py --re 70 --device 2
"""
import argparse
import json
import os
import sys
from collections import deque
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField
from LegacyCelerisLab import utils as legacy_utils
from utils import (
nu_from_re, action_to_physical, scale_action, build_karman_cloak_env,
add_pinball, build_observation, compute_physical_symbols,
save_vorticity_png, vorticity_from_ddf, compute_similarity,
load_legacy_configs,
)
from cfg import (
OUTPUT_DIR, SAMPLE_INTERVAL, FIFO_LEN, CONV_LEN, S_DIM,
ACTION_SCALE, ACTION_BIAS, U0, CONFIG_DIR,
)
DATA_TYPE = np.float32
# v2 feature keys (matching sindy_results_v2.json layout exactly)
V2_FEAT_KEYS = [
"u_m", "u_a", "u_c", "v_a",
"Fx_tot", "Fx_rear", "Fy_tot", "Fy_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
]
V2_MU_KEYS = ["mu", "mu_u_a", "mu_v_a", "mu_Fx_tot", "mu_Fy_diff", "mu_Fy_tot"]
V2_N_FEAT_NOBIAS = len(V2_FEAT_KEYS) + len(V2_MU_KEYS) # 22
V2_N_FEAT_BIAS = 1 + V2_N_FEAT_NOBIAS # 23
def build_feature_vec(obs_slice, actions_prev, actions_prev2, mu, add_bias):
"""Build a single feature vector matching v2 feature layout."""
sensors = obs_slice[0:6].astype(np.float64).reshape(1, 6)
forces = obs_slice[6:12].astype(np.float64).reshape(1, 6)
ap = actions_prev.astype(np.float64).reshape(1, 3)
ap2 = actions_prev2.astype(np.float64).reshape(1, 3)
sym = compute_physical_symbols(sensors, forces, ap, ap2)
# Add mu terms
sym["mu"] = np.array([mu])
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Fx_tot"] = sym["Fx_tot"] * mu
sym["mu_Fy_diff"] = sym["Fy_diff"] * mu
sym["mu_Fy_tot"] = sym["Fy_tot"] * mu
vals = []
if add_bias:
vals.append(1.0)
for k in V2_FEAT_KEYS:
vals.append(float(sym[k][0]))
for k in V2_MU_KEYS:
vals.append(float(sym[k][0]))
return np.array(vals, dtype=np.float64)
def load_v2_coefs(v2_path):
"""Load v2 coefficients, zero front bias."""
with open(v2_path) as f:
data = json.load(f)
cross = data["cross_re"]
coefs_list = cross["channels"] # 3 channels: 0=front, 1=bottom, 2=top
# Zero front bias
coefs_list[0]["best_coef"][0] = 0.0
names = ["front", "bottom", "top"]
result = {}
for i, name in enumerate(names):
coef_list = coefs_list[i]["best_coef"]
# Check if first is bias (it is for v2)
has_bias = True
if name == "front":
has_bias = True # v2 has bias for all, we just zeroed it
result[name] = {
"coef": np.array(coef_list, dtype=np.float64),
"has_bias": True, # v2 has bias for all channels
}
return result
def predict(obs_slice, a_prev, a_prev2, coefs, mu):
"""Predict physical omega using v2 coefficients."""
omega = np.zeros(3, dtype=np.float64)
for i, name in enumerate(["front", "bottom", "top"]):
c = coefs[name]
feat = build_feature_vec(obs_slice, a_prev, a_prev2, mu, add_bias=c["has_bias"])
omega[i] = float(feat @ c["coef"])
return omega
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--re", type=int, default=70)
ap.add_argument("--device", type=int, default=2)
ap.add_argument("--steps", type=int, default=100)
ap.add_argument("--out-dir", type=str, default=os.path.join(OUTPUT_DIR, "sindy_val"))
ap.add_argument("--v2-results", type=str,
default=os.path.join(OUTPUT_DIR, "sindy", "sindy_results_v2.json"))
args = ap.parse_args()
re_code = args.re
mu = 2.0 / re_code
output_root = os.path.join(args.out_dir, f"re{re_code}")
os.makedirs(output_root, exist_ok=True)
print(f"\n=== v22 validation: Re{re_code} (mu={mu:.6f}) ===")
# Load v2 coefs (front bias zeroed)
coefs = load_v2_coefs(args.v2_results)
for name in ["front", "bottom", "top"]:
print(f" {name}: {len(coefs[name]['coef'])} coefs, "
f"bias={coefs[name]['coef'][0]:.6f}")
# Build environment (same as phase1)
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(nu_from_re(re_code, u0=U0)))
ff = FlowField(field_cfg, cuda_cfg, device_id=args.device)
target_states, _ = build_karman_cloak_env(
ff, u0=U0, l0=20.0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE)
norm = add_pinball(
ff, l0=20.0, u0=U0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE, action_bias=ACTION_BIAS)
# Controlled rollout
ff.restore_ddf()
ff.apply_ddf()
fifo = deque(maxlen=FIFO_LEN)
bias_action = scale_action(np.zeros(3, dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS,
u0=U0, n_total_bodies=7)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_action)
fifo.append(ff.obs.copy()[2:14])
sens_sc = []
a_prev = action_to_physical(np.zeros((1,3), dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0).flatten()
a_prev2 = a_prev.copy()
for step in range(args.steps):
obs_slice = fifo[-1] if len(fifo) > 0 else np.zeros(12, dtype=np.float32)
omega = predict(obs_slice, a_prev, a_prev2, coefs, mu)
# Apply action (convert to normalized for legacy run())
norm_action = (omega / U0 - np.array(ACTION_BIAS, dtype=np.float64)) / ACTION_SCALE
norm_action = np.clip(norm_action, -1.0, 1.0).astype(np.float32)
action_arr = scale_action(norm_action, scale=ACTION_SCALE,
bias=ACTION_BIAS, u0=U0, n_total_bodies=7)
ff.run(SAMPLE_INTERVAL, action_arr)
obs_slice_new = ff.obs.copy()[2:14]
fifo.append(obs_slice_new)
sens_sc.append(obs_slice_new[0:6])
a_prev2 = a_prev.copy()
a_prev = omega.copy()
sens_arr = np.array(sens_sc, dtype=np.float32)
sim = compute_similarity(target_states, sens_arr, CONV_LEN)
print(f" v22 similarity: {sim:.4f}")
# Vorticity
omega_vort = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(os.path.join(output_root, "vorticity_v22.png"),
omega_vort, title=f"Re{re_code} v22 (front no-bias)")
# Save result
result = {"re_code": re_code, "mode": "v22", "similarity": sim}
with open(os.path.join(output_root, "result_v22.json"), "w") as f:
json.dump(result, f, indent=2)
del ff
print(f" Done -> {output_root}")
return 0
if __name__ == "__main__":
sys.exit(main())
archive/analysis_crossre_scripts/validate_v23.py
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# analysis_crossre/scripts/validate_v23.py
"""Validate v23: front no-bias + rear shared-head.
Front: v2 coeffs with bias=0.
Top: v2 coeffs unchanged.
Bottom: -top(Gx), using G-transformed raw observations.
Usage:
conda run -n pycuda_3_10 python validate_v23.py --re 70 --device 2
"""
import argparse
import json
import os
import sys
from collections import deque
import numpy as np
_PROJ = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
if _PROJ not in sys.path:
sys.path.insert(0, _PROJ)
from LegacyCelerisLab import FlowField
from utils import (
nu_from_re, action_to_physical, scale_action, build_karman_cloak_env,
add_pinball, build_observation, compute_physical_symbols,
save_vorticity_png, vorticity_from_ddf, compute_similarity,
load_legacy_configs,
)
from cfg import (
OUTPUT_DIR, SAMPLE_INTERVAL, FIFO_LEN, CONV_LEN, S_DIM,
ACTION_SCALE, ACTION_BIAS, U0, CONFIG_DIR,
)
DATA_TYPE = np.float32
# v2 feature keys matching sindy_results_v2.json
V2_FEAT_KEYS = [
"u_m", "u_a", "u_c", "v_a",
"Fx_tot", "Fx_rear", "Fy_tot", "Fy_diff",
"sin_ua", "cos_ua",
"a0_lag1", "a1_lag1", "a2_lag1",
"da0", "da1", "da2",
]
V2_MU_KEYS = ["mu", "mu_u_a", "mu_v_a", "mu_Fx_tot", "mu_Fy_diff", "mu_Fy_tot"]
def apply_G_raw(obs_slice, a_prev, a_prev2):
"""Apply mirror operator G to raw observations and actions.
obs_slice: (12,) [s0_ux,s0_uy, s1_ux,s1_uy, s2_ux,s2_uy, front_fx,front_fy, bot_fx,bot_fy, top_fx,top_fy]
a_prev: (3,) [aF, aB, aT]
a_prev2: (3,) [aF_prev2, aB_prev2, aT_prev2]
Returns (G_obs, G_a_prev, G_a_prev2)
"""
# Sensors: top<->bottom swap, cross components negate
G_obs = np.zeros(12, dtype=np.float64)
G_obs[0] = obs_slice[4] # s0_ux <- s2_ux (streamwise: no sign)
G_obs[1] = -obs_slice[5] # s0_uy <- -s2_uy (cross: negate)
G_obs[2] = obs_slice[2] # s1_ux unchanged
G_obs[3] = -obs_slice[3] # s1_uy negate
G_obs[4] = obs_slice[0] # s2_ux <- s0_ux
G_obs[5] = -obs_slice[1] # s2_uy <- -s0_uy
# Forces: front unchanged (but lift sign flips), bottom<->top with sign flips
G_obs[6] = obs_slice[6] # front_fx unchanged
G_obs[7] = -obs_slice[7] # front_fy negate
G_obs[8] = obs_slice[10] # bot_fx <- top_fx
G_obs[9] = -obs_slice[11] # bot_fy <- -top_fy
G_obs[10] = obs_slice[8] # top_fx <- bot_fx
G_obs[11] = -obs_slice[9] # top_fy <- -bot_fy
# Actions: all negate, B<->T swap
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
def build_v2_feature_vec(obs_slice, actions_prev, actions_prev2, mu, add_bias):
"""Build feature vector matching v2 layout."""
sensors = obs_slice[0:6].astype(np.float64).reshape(1, 6)
forces = obs_slice[6:12].astype(np.float64).reshape(1, 6)
ap = actions_prev.astype(np.float64).reshape(1, 3)
ap2 = actions_prev2.astype(np.float64).reshape(1, 3)
sym = compute_physical_symbols(sensors, forces, ap, ap2)
sym["mu"] = np.array([mu])
sym["mu_u_a"] = sym["u_a"] * mu
sym["mu_v_a"] = sym["v_a"] * mu
sym["mu_Fx_tot"] = sym["Fx_tot"] * mu
sym["mu_Fy_diff"] = sym["Fy_diff"] * mu
sym["mu_Fy_tot"] = sym["Fy_tot"] * mu
vals = []
if add_bias:
vals.append(1.0)
for k in V2_FEAT_KEYS:
vals.append(float(sym[k][0]))
for k in V2_MU_KEYS:
vals.append(float(sym[k][0]))
return np.array(vals, dtype=np.float64)
def load_v2_coefs(v2_path):
"""Load v2 coefficients. Zero front bias. Return front + top only."""
with open(v2_path) as f:
data = json.load(f)
cross = data["cross_re"]
coefs_list = cross["channels"]
# Zero front bias
coefs_list[0]["best_coef"][0] = 0.0
return {
"front": {
"coef": np.array(coefs_list[0]["best_coef"], dtype=np.float64),
"has_bias": True,
},
"top": {
"coef": np.array(coefs_list[2]["best_coef"], dtype=np.float64),
"has_bias": True,
},
}
def predict_v23(obs_slice, a_prev, a_prev2, coefs, mu):
"""Predict physical omega using v23 shared-head.
Front: v2 coefs (bias zeroed).
Top: v2 coefs unchanged.
Bottom: -top(Gx).
"""
# Front prediction
feat = build_v2_feature_vec(obs_slice, a_prev, a_prev2, mu, add_bias=coefs["front"]["has_bias"])
front = float(feat @ coefs["front"]["coef"])
# Top prediction (from original state)
feat_top = build_v2_feature_vec(obs_slice, a_prev, a_prev2, mu, add_bias=coefs["top"]["has_bias"])
top = float(feat_top @ coefs["top"]["coef"])
# Bottom = -top(Gx)
G_obs, G_a_prev, G_a_prev2 = apply_G_raw(obs_slice, a_prev, a_prev2)
feat_G = build_v2_feature_vec(G_obs, G_a_prev, G_a_prev2, mu, add_bias=coefs["top"]["has_bias"])
top_at_Gx = float(feat_G @ coefs["top"]["coef"])
bottom = -top_at_Gx
return np.array([front, bottom, top], dtype=np.float64)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--re", type=int, default=70)
ap.add_argument("--device", type=int, default=2)
ap.add_argument("--steps", type=int, default=100)
ap.add_argument("--out-dir", type=str, default=os.path.join(OUTPUT_DIR, "sindy_val"))
ap.add_argument("--v2-results", type=str,
default=os.path.join(OUTPUT_DIR, "sindy", "sindy_results_v2.json"))
args = ap.parse_args()
re_code = args.re
mu = 2.0 / re_code
output_root = os.path.join(args.out_dir, f"re{re_code}")
os.makedirs(output_root, exist_ok=True)
print(f"\n=== v23 validation: Re{re_code} (mu={mu:.6f}) ===")
coefs = load_v2_coefs(args.v2_results)
for name in ["front", "top"]:
print(f" {name}: {len(coefs[name]['coef'])} coefs")
# Build environment
cuda_cfg, field_cfg = load_legacy_configs(CONFIG_DIR)
field_cfg = field_cfg._replace(viscosity=float(nu_from_re(re_code, u0=U0)))
ff = FlowField(field_cfg, cuda_cfg, device_id=args.device)
target_states, _ = build_karman_cloak_env(
ff, u0=U0, l0=20.0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE)
norm = add_pinball(
ff, l0=20.0, u0=U0, sample_interval=SAMPLE_INTERVAL,
fifo_len=FIFO_LEN, data_type=DATA_TYPE, action_bias=ACTION_BIAS)
# Controlled rollout
ff.restore_ddf()
ff.apply_ddf()
fifo = deque(maxlen=FIFO_LEN)
bias_action = scale_action(np.zeros(3, dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS,
u0=U0, n_total_bodies=7)
for _ in range(FIFO_LEN):
ff.run(SAMPLE_INTERVAL, bias_action)
fifo.append(ff.obs.copy()[2:14])
sens_sc = []
a_prev = action_to_physical(np.zeros((1,3), dtype=np.float32),
scale=ACTION_SCALE, bias=ACTION_BIAS, u0=U0).flatten()
a_prev2 = a_prev.copy()
for step in range(args.steps):
obs_slice = fifo[-1] if len(fifo) > 0 else np.zeros(12, dtype=np.float32)
omega = predict_v23(obs_slice, a_prev, a_prev2, coefs, mu)
# Apply action
norm_action = (omega / U0 - np.array(ACTION_BIAS, dtype=np.float64)) / ACTION_SCALE
norm_action = np.clip(norm_action, -1.0, 1.0).astype(np.float32)
action_arr = scale_action(norm_action, scale=ACTION_SCALE,
bias=ACTION_BIAS, u0=U0, n_total_bodies=7)
ff.run(SAMPLE_INTERVAL, action_arr)
obs_slice_new = ff.obs.copy()[2:14]
fifo.append(obs_slice_new)
sens_sc.append(obs_slice_new[0:6])
a_prev2 = a_prev.copy()
a_prev = omega.copy()
sens_arr = np.array(sens_sc, dtype=np.float32)
sim = compute_similarity(target_states, sens_arr, CONV_LEN)
print(f" v23 similarity: {sim:.4f}")
# Vorticity
omega_vort = vorticity_from_ddf(ff, u0=U0)
save_vorticity_png(os.path.join(output_root, "vorticity_v23.png"),
omega_vort, title=f"Re{re_code} v23 (shared-head)")
result = {"re_code": re_code, "mode": "v23", "similarity": sim}
with open(os.path.join(output_root, "result_v23.json"), "w") as f:
json.dump(result, f, indent=2)
del ff
print(f" Done -> {output_root}")
return 0
if __name__ == "__main__":
sys.exit(main())
archive/dante_pinball/dante_pinball/legacy/d1a3o12_250421_dante_v6.py
+755
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"""
DANTE v6 (full-adjustable, no SINDy pretraining)
Core decisions for this version:
1) Disable SINDy prior structure usage entirely.
2) Use full simplified basis pool and optimize all controller coefficients.
3) Keep minimal loop: no resume/checkpoint restore, live DB save + reward-only TB.
4) Continuous rollout objective with protective reset only on done/truncated.
"""
import argparse
import json
import os
import pickle
import shutil
import sys
import time
from pathlib import Path
from typing import Dict, List
import numpy as np
os.environ["MKL_THREADING_LAYER"] = "GNU"
os.environ["OMP_NUM_THREADS"] = "16"
os.environ["MKL_NUM_THREADS"] = "16"
try:
from torch.utils.tensorboard import SummaryWriter
except Exception:
SummaryWriter = None
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.abspath(os.path.join(CURRENT_DIR, os.pardir))
os.chdir(CURRENT_DIR)
sys.path.insert(0, os.path.join(ROOT, "DANTE"))
from dante.obj_functions import ObjectiveFunction
from dante.tree_exploration import TreeExploration
from dante_v6_surrogate_torch import FlowControlSurrogateV6
from dante_pinball.env.gym_env_dante_total_force import CustomEnv
N_OBS = 2
N_ACT = 3
BIAS_SCALE = 1.0
COEF_SCALE = 2.0
# Full simplified basis pool: all terms are trainable in v6.
FULL_BASIS_TERMS = [
"obs0", "obs1", "dobs0", "dobs1",
"sin_obs0", "sin_obs1", "cos_obs0", "cos_obs1",
"tanh_obs0", "tanh_obs1",
"act0_l1", "act1_l1", "act2_l1",
]
BASIS_PROFILES = {
# Compact profile (<20 dims) with derivative + nonlinear terms.
"compact_deriv_nl": ["obs0", "obs1", "dobs0", "dobs1", "tanh_obs1"],
# Constrained search top performer (<20 dims) with nonlinear + history.
"compact_nl_hist": ["obs1", "sin_obs0", "cos_obs0", "act1_l1"],
}
# Fixed run configuration (kept in-file for reproducibility and stricter control)
V6_CONFIG = {
"name": "d1a3o12_250421_forces02_dante_v6",
"device_id": 1,
"surrogate_gpu_id": 1,
"eval_steps": 300,
"tail_steps": 100,
"startup_steps": 0,
"max_recover_resets": 1,
"reset_each_candidate": True,
"obs_fail_bound": 2.0,
"obs_clip_bound": 3.0,
"basis_profile": "compact_deriv_nl",
# Use d-dependent initialization, then clamp into [min_num_initial, max_num_initial].
"num_initial_per_dim": 10,
"min_num_initial": 100,
"max_num_initial": 240,
"samples_per_acq": 18,
"max_init_attempts_factor": 2.0,
"surrogate_mode": "ensemble", # mlp | cnn | ensemble
# Match PPO script budget: 100 learn iterations * 2048 rollout steps
"target_cfd_steps": 204800*2,
# Keep consistent with surrogate study runs in this repo.
"surrogate_epochs": 400,
}
class NullWriter:
def add_scalar(self, *_args, **_kwargs) -> None:
pass
def close(self) -> None:
pass
class LinearBasisController:
def __init__(self, basis_terms: List[str]):
self.basis_terms = list(basis_terms)
self.num_basis = len(self.basis_terms)
self.total_params = N_ACT * (1 + self.num_basis)
self.params = np.zeros(self.total_params, dtype=np.float64)
self.obs_l1 = np.zeros(2, dtype=np.float64)
self.prev_action = np.zeros(3, dtype=np.float64)
def reset_state(self, obs0: np.ndarray) -> None:
obs0 = np.asarray(obs0, dtype=np.float64).reshape(-1)
if obs0.size < 2:
if obs0.size == 1:
obs0 = np.array([obs0[0], obs0[0]], dtype=np.float64)
else:
obs0 = np.zeros(2, dtype=np.float64)
self.obs_l1 = obs0[:2].copy()
self.prev_action = np.zeros(3, dtype=np.float64)
def set_params(self, x: np.ndarray) -> None:
x = np.asarray(x, dtype=np.float64).reshape(-1)
if x.size != self.total_params:
raise ValueError(f"controller params mismatch: {x.size} != {self.total_params}")
self.params = np.clip(x, -1.0, 1.0)
def _feature_dict(self, obs: np.ndarray) -> Dict[str, float]:
o = np.asarray(obs, dtype=np.float64).reshape(-1)
if o.size < 2:
if o.size == 1:
o = np.array([o[0], o[0]], dtype=np.float64)
else:
o = np.zeros(2, dtype=np.float64)
o0, o1 = float(o[0]), float(o[1])
o0_l1, o1_l1 = float(self.obs_l1[0]), float(self.obs_l1[1])
a0, a1, a2 = float(self.prev_action[0]), float(self.prev_action[1]), float(self.prev_action[2])
return {
"obs0": o0,
"obs1": o1,
"dobs0": o0 - o0_l1,
"dobs1": o1 - o1_l1,
"sin_obs0": float(np.sin(np.pi * o0)),
"sin_obs1": float(np.sin(np.pi * o1)),
"cos_obs0": float(np.cos(np.pi * o0)),
"cos_obs1": float(np.cos(np.pi * o1)),
"tanh_obs0": float(np.tanh(o0)),
"tanh_obs1": float(np.tanh(o1)),
"act0_l1": a0,
"act1_l1": a1,
"act2_l1": a2,
}
def predict(self, obs: np.ndarray) -> np.ndarray:
feat = self._feature_dict(obs)
out = np.zeros(3, dtype=np.float64)
stride = 1 + self.num_basis
for ch in range(3):
off = ch * stride
qb = np.tanh(1.25 * self.params[off])
y = BIAS_SCALE * qb
for k, term in enumerate(self.basis_terms):
qk = np.tanh(1.25 * self.params[off + 1 + k])
y += (COEF_SCALE * qk) * feat.get(term, 0.0)
out[ch] = y
out = np.clip(out, -1.0, 1.0)
obs2 = np.asarray(obs, dtype=np.float64).reshape(-1)
if obs2.size < 2:
if obs2.size == 1:
obs2 = np.array([obs2[0], obs2[0]], dtype=np.float64)
else:
obs2 = np.zeros(2, dtype=np.float64)
self.obs_l1 = obs2[:2].copy()
self.prev_action = out.copy()
return out.astype(np.float32)
class FlowControlObjectiveV6(ObjectiveFunction):
def __init__(
self,
basis_terms: List[str],
eval_steps: int = 200,
tail_steps: int = 100,
startup_steps: int = 200,
max_recover_resets: int = 1,
turn: float = 0.05,
reset_each_candidate: bool = True,
obs_fail_bound: float = 2.0,
obs_clip_bound: float = 3.0,
):
self.eval_steps = int(eval_steps)
self.tail_steps = int(tail_steps)
self.startup_steps = int(startup_steps)
self.max_recover_resets = int(max_recover_resets)
self.turn = float(turn)
self.reset_each_candidate = bool(reset_each_candidate)
self.obs_fail_bound = float(obs_fail_bound)
self.obs_clip_bound = float(obs_clip_bound)
self.basis_terms = list(basis_terms)
self.controller = LinearBasisController(self.basis_terms)
self.dims = int(self.controller.total_params)
self.lb = -1.0 * np.ones(self.dims)
self.ub = 1.0 * np.ones(self.dims)
self.env = None
self._device_id = 1
self.obs_current = None
self.recover_count = 0
def init_env(self, device_id: int = 1) -> None:
self._device_id = int(device_id)
if self.env is not None:
self.env.close()
self.env = CustomEnv(
device_id=int(device_id),
obs_fail_bound=float(self.obs_fail_bound),
obs_clip_bound=float(self.obs_clip_bound),
)
self._hard_reset_and_stabilize()
def _hard_reset_and_stabilize(self) -> None:
obs, _ = self.env.reset()
obs = np.asarray(obs, dtype=np.float32)
action = np.zeros(3, dtype=np.float32)
for _ in range(int(self.startup_steps)):
obs, _, done, trunc, _ = self.env.step(action)
if done or trunc:
obs, _ = self.env.reset()
self.obs_current = np.asarray(obs, dtype=np.float32)
self.controller.reset_state(self.obs_current)
@staticmethod
def _tail_mean(rewards: List[float], tail_steps: int) -> float:
rr = np.asarray(rewards, dtype=np.float64)
if rr.size == 0:
return 0.0
tail = rr[-tail_steps:] if rr.size >= tail_steps else rr
return float(np.mean(tail))
def evaluate_candidate(self, x: np.ndarray) -> Dict:
x = self._preprocess(x)
self.controller.set_params(x)
if self.env is None:
self.init_env(self._device_id)
# DANTE candidate evaluations are isolated from each other.
if self.reset_each_candidate:
self._hard_reset_and_stabilize()
for attempt in range(int(self.max_recover_resets) + 1):
obs = np.asarray(self.obs_current, dtype=np.float32)
self.controller.reset_state(obs)
rewards: List[float] = []
steps = 0
done = False
trunc = False
last_info: Dict = {}
for _ in range(int(self.eval_steps)):
action = self.controller.predict(obs)
obs, reward, done, trunc, step_info = self.env.step(action)
if isinstance(step_info, dict):
last_info = step_info
rewards.append(float(reward))
steps += 1
if done or trunc:
break
if done or trunc and attempt < int(self.max_recover_resets):
self.recover_count += 1
self._hard_reset_and_stabilize()
continue
self.obs_current = np.asarray(obs, dtype=np.float32)
y = self._tail_mean(rewards, tail_steps=int(self.tail_steps))
return {
"reward": float(y),
"scaled": float(y * 100.0),
"steps": int(steps),
"done": bool(done),
"truncated": bool(trunc),
"recoveries_used": int(attempt),
"failure_code": int(last_info.get("failure_code", 0)),
}
return {
"reward": 0.0,
"scaled": 0.0,
"steps": 0,
"done": True,
"truncated": True,
"recoveries_used": int(self.max_recover_resets),
"failure_code": 1,
}
def scaled(self, y: float) -> float:
return float(y * 100.0)
def __call__(self, x: np.ndarray, apply_scaling: bool = False, track: bool = True) -> float:
info = self.evaluate_candidate(x)
y = float(info["reward"])
return self.scaled(y) if apply_scaling else y
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="DANTE v6 full-adjustable (no SINDy pretraining)")
p.add_argument(
"--name",
type=str,
default=V6_CONFIG["name"],
help="Optional run name override. Core budgets remain fixed in-file.",
)
p.add_argument(
"--basis-profile",
type=str,
default=V6_CONFIG["basis_profile"],
choices=sorted(BASIS_PROFILES.keys()),
help="Controller basis profile to optimize.",
)
return p.parse_args()
def sample_params(dims: int, turn: float) -> np.ndarray:
x = np.random.uniform(-1.0, 1.0, size=dims)
x = np.round(x / turn) * turn
return np.clip(x, -1.0, 1.0)
def save_live_db(path: str, x: np.ndarray, y: np.ndarray, meta: Dict) -> None:
np.savez(path, input_x=x, input_y=y, meta_json=np.array([json.dumps(meta, ensure_ascii=False)]))
def print_progress_line(
phase: str,
idx: int,
total: int,
reward: float,
best_reward: float,
recover_total: int,
elapsed_sec: float,
global_step: int,
total_expected: int,
) -> None:
print(
f"[{phase}] {idx}/{total} | reward={reward:.4f} | best={best_reward:.4f} | "
f"recover={recover_total} | eval={global_step}/{total_expected} | elapsed={elapsed_sec:.1f}s",
flush=True,
)
def main() -> None:
args = parse_args()
name = str(args.name)
cfg = dict(V6_CONFIG)
cfg["name"] = name
basis_profile = str(args.basis_profile)
basis_terms = list(BASIS_PROFILES[basis_profile])
target_evals = int(cfg["target_cfd_steps"] // cfg["eval_steps"])
model_dir = os.path.join(ROOT, "models", "250421")
out_dir = os.path.join(ROOT, "output")
tb_dir = os.path.join(ROOT, "tensorboard", name)
os.makedirs(model_dir, exist_ok=True)
os.makedirs(out_dir, exist_ok=True)
obj = FlowControlObjectiveV6(
basis_terms=basis_terms,
eval_steps=int(cfg["eval_steps"]),
tail_steps=int(cfg["tail_steps"]),
startup_steps=int(cfg["startup_steps"]),
max_recover_resets=int(cfg["max_recover_resets"]),
reset_each_candidate=bool(cfg["reset_each_candidate"]),
obs_fail_bound=float(cfg["obs_fail_bound"]),
obs_clip_bound=float(cfg["obs_clip_bound"]),
)
obj.init_env(device_id=int(cfg["device_id"]))
controller_dims = int(obj.dims)
surrogate_gpu_id = int(cfg["surrogate_gpu_id"])
num_initial_raw = int(max(1, round(float(cfg["num_initial_per_dim"]) * controller_dims)))
num_initial = int(min(int(cfg["max_num_initial"]), max(int(cfg["min_num_initial"]), num_initial_raw)))
num_acquisitions = int((target_evals - int(num_initial)) // int(cfg["samples_per_acq"]))
if num_acquisitions <= 0:
raise RuntimeError(
f"computed num_acquisitions <= 0 with target_evals={target_evals}, "
f"num_initial={num_initial}, samples_per_acq={int(cfg['samples_per_acq'])}"
)
max_init_attempts = int(max(num_initial, round(float(cfg["max_init_attempts_factor"]) * num_initial)))
if max_init_attempts < num_initial:
raise RuntimeError("max_init_attempts must be >= num_initial")
total_expected = int(num_initial + num_acquisitions * int(cfg["samples_per_acq"]))
config_payload = {
"name": name,
"use_sindy_prior": False,
"reason": "forced_disabled_by_v6_full_adjustable",
"basis_profile": basis_profile,
"basis_terms": basis_terms,
"controller_dims": controller_dims,
"num_initial": num_initial,
"num_initial_raw": int(num_initial_raw),
"num_initial_per_dim": float(cfg["num_initial_per_dim"]),
"min_num_initial": int(cfg["min_num_initial"]),
"max_num_initial": int(cfg["max_num_initial"]),
"num_acquisitions": int(num_acquisitions),
"samples_per_acq": int(cfg["samples_per_acq"]),
"surrogate_mode": str(cfg["surrogate_mode"]),
"surrogate_gpu_id": int(surrogate_gpu_id),
"eval_steps": int(cfg["eval_steps"]),
"tail_steps": int(cfg["tail_steps"]),
"startup_steps": int(cfg["startup_steps"]),
"max_recover_resets": int(cfg["max_recover_resets"]),
"obs_fail_bound": float(cfg["obs_fail_bound"]),
"obs_clip_bound": float(cfg["obs_clip_bound"]),
"reset_each_candidate": bool(cfg["reset_each_candidate"]),
"max_init_attempts": int(max_init_attempts),
"max_init_attempts_factor": float(cfg["max_init_attempts_factor"]),
"target_cfd_steps": int(cfg["target_cfd_steps"]),
"target_total_evals": int(target_evals),
"matched_total_evals": int(total_expected),
"matched_cfd_steps": int(total_expected * int(cfg["eval_steps"])),
}
with open(os.path.join(out_dir, f"{name}_structure_decision.json"), "w", encoding="utf-8") as f:
json.dump(config_payload, f, indent=2, ensure_ascii=False)
print("use_sindy_prior:", False)
print("basis_profile:", basis_profile)
print("basis_terms:", len(basis_terms))
print("controller_dims:", controller_dims)
print("num_initial:", num_initial)
print("num_initial_raw:", int(num_initial_raw))
print("num_initial_per_dim:", float(cfg["num_initial_per_dim"]))
print("min_num_initial:", int(cfg["min_num_initial"]))
print("max_num_initial:", int(cfg["max_num_initial"]))
print("max_init_attempts:", max_init_attempts)
print("max_init_attempts_factor:", float(cfg["max_init_attempts_factor"]))
print("num_acquisitions:", int(num_acquisitions))
print("samples_per_acq:", int(cfg["samples_per_acq"]))
print("surrogate_mode:", str(cfg["surrogate_mode"]))
print("surrogate_gpu_id:", surrogate_gpu_id)
print("eval_steps:", int(cfg["eval_steps"]))
print("tail_steps:", int(cfg["tail_steps"]))
print("startup_steps:", int(cfg["startup_steps"]))
print("target_cfd_steps:", int(cfg["target_cfd_steps"]))
print("matched_cfd_steps:", int(total_expected * int(cfg["eval_steps"])))
print("expected_total_evals:", total_expected)
ckpt_path = Path(os.path.join(model_dir, f"{name}_surrogate.keras"))
surrogate = FlowControlSurrogateV6(
input_dims=controller_dims,
epochs=int(cfg["surrogate_epochs"]),
patience=30,
check_point_path=ckpt_path,
tf_device_id=int(surrogate_gpu_id),
)
live_db_path = os.path.join(out_dir, f"{name}_database_live.npz")
best_params_path = os.path.join(model_dir, f"{name}_best.npy")
best_meta_path = os.path.join(out_dir, f"{name}_best_meta.pkl")
final_meta_path = os.path.join(out_dir, f"{name}_final_meta.pkl")
dante_log_path = os.path.join(out_dir, f"{name}_dante_log.csv")
if SummaryWriter is None:
writer = NullWriter()
else:
writer = SummaryWriter(log_dir=tb_dir)
with open(dante_log_path, "w", encoding="utf-8") as f:
f.write("timestamp,phase,acq,candidate,reward,best_reward,dataset_size,recoveries_used,failure_code\n")
input_x = np.empty((0, controller_dims), dtype=np.float64)
input_y = np.empty((0,), dtype=np.float64)
history = []
best_reward = -1.0
best_params = np.zeros(controller_dims, dtype=np.float64)
invalid_skips = 0
t0 = time.time()
global_step = 0
try:
# Phase 1: random init points (collect exactly num_initial valid samples)
valid_init = 0
init_attempts = 0
while valid_init < num_initial:
if init_attempts >= max_init_attempts:
raise RuntimeError(
f"init failed to collect enough valid samples: "
f"valid={valid_init}/{num_initial}, attempts={init_attempts}/{max_init_attempts}, "
f"invalid_skips={invalid_skips}"
)
init_attempts += 1
x = sample_params(controller_dims, obj.turn)
info = obj.evaluate_candidate(x)
reward = float(info["reward"])
is_valid = int(info.get("failure_code", 0)) == 0
if is_valid:
valid_init += 1
input_x = np.vstack((input_x, x.reshape(1, -1)))
input_y = np.append(input_y, float(info["scaled"]))
writer.add_scalar("Reward", reward, global_step)
else:
invalid_skips += 1
print(
f"[init] invalid sample skipped: attempt={init_attempts}, "
f"failure_code={info.get('failure_code', -1)}, "
f"valid={valid_init}/{num_initial}, invalid_skips={invalid_skips}",
flush=True,
)
if is_valid and reward > best_reward:
best_reward = reward
best_params = x.copy()
np.save(best_params_path, best_params)
with open(best_meta_path, "wb") as f:
pickle.dump(
{
"best_reward": best_reward,
"best_params": best_params,
"basis_terms": basis_terms,
"controller_dims": controller_dims,
"config": config_payload,
"timestamp": time.time(),
},
f,
)
if is_valid:
save_live_db(
live_db_path,
input_x,
input_y,
{
"name": name,
"best_reward": best_reward,
"dataset_size": int(len(input_y)),
"basis_terms": basis_terms,
"use_sindy_prior": False,
"recover_count": int(obj.recover_count),
"invalid_skips": int(invalid_skips),
},
)
with open(dante_log_path, "a", encoding="utf-8") as f:
f.write(
f"{time.time():.3f},init,0,{init_attempts},{reward:.8f},{best_reward:.8f},{len(input_y)},{info['recoveries_used']},{info.get('failure_code', 0)}\n"
)
global_step += 1
print(
f"[init-attempt {init_attempts}/{max_init_attempts}] "
f"valid={valid_init}/{num_initial} | reward={reward:.4f} | "
f"best={best_reward:.4f} | invalid_skips={invalid_skips} | "
f"recover={int(obj.recover_count)} | eval={global_step}/{total_expected}+ | "
f"elapsed={float(time.time() - t0):.1f}s",
flush=True,
)
# Phase 2: DANTE acquisitions
for acq in range(int(num_acquisitions)):
if len(input_y) < 2:
print(
f"[acq {acq + 1}] skipped: insufficient valid samples ({len(input_y)})",
flush=True,
)
continue
print(
f"[acq {acq + 1}/{int(num_acquisitions)}] fitting surrogate on {len(input_y)} samples...",
flush=True,
)
surrogate_mode = str(cfg.get("surrogate_mode", "mlp")).lower()
if surrogate_mode == "ensemble":
candidates = []
for arch in ["mlp", "cnn"]:
cand_ckpt = Path(str(ckpt_path).replace(".keras", f"_{arch}.keras"))
surr = FlowControlSurrogateV6(
input_dims=controller_dims,
epochs=int(cfg["surrogate_epochs"]),
patience=30,
check_point_path=cand_ckpt,
tf_device_id=int(surrogate_gpu_id),
architecture=arch,
)
try:
m = surr(input_x, input_y, verbose=0)
fm = dict(getattr(surr, "last_fit_metrics", {}) or {})
candidates.append((arch, m, fm, cand_ckpt))
except Exception as e:
print(f"[acq {acq + 1}] surrogate {arch} failed: {e}", flush=True)
if not candidates:
raise RuntimeError("all surrogate candidates failed in ensemble mode")
candidates.sort(key=lambda z: float(z[2].get("val_r2", -1e9)), reverse=True)
best_arch, model, fit_metrics, best_ckpt = candidates[0]
if best_ckpt.exists():
shutil.copy2(best_ckpt, ckpt_path)
fit_metrics["selected_architecture"] = best_arch
else:
surrogate.architecture = "cnn" if surrogate_mode == "cnn" else "mlp"
model = surrogate(input_x, input_y, verbose=0)
fit_metrics = dict(getattr(surrogate, "last_fit_metrics", {}) or {})
fit_metrics["selected_architecture"] = str(surrogate.architecture)
if fit_metrics:
writer.add_scalar("Surrogate/val_r2", float(fit_metrics.get("val_r2", 0.0)), acq)
writer.add_scalar("Surrogate/val_mae", float(fit_metrics.get("val_mae", 0.0)), acq)
print(
f"[acq {acq + 1}] surrogate metrics: "
f"val_r2={fit_metrics.get('val_r2', float('nan')):.4f}, "
f"val_mae={fit_metrics.get('val_mae', float('nan')):.4f}, "
f"device={fit_metrics.get('device', 'CPU')}",
flush=True,
)
print(
f"[acq {acq + 1}/{int(num_acquisitions)}] surrogate fit done, rolling out {int(cfg['samples_per_acq'])} candidates...",
flush=True,
)
if ckpt_path.exists():
shutil.copy2(ckpt_path, os.path.join(model_dir, f"{name}_surrogate_live.keras"))
explorer = TreeExploration(
func=obj,
model=model,
num_samples_per_acquisition=int(cfg["samples_per_acq"]),
)
candidates = explorer.rollout(input_x, input_y, iteration=acq)
acq_rewards = []
for j, x in enumerate(candidates):
info = obj.evaluate_candidate(x)
reward = float(info["reward"])
is_valid = int(info.get("failure_code", 0)) == 0
if is_valid:
acq_rewards.append(reward)
input_x = np.vstack((input_x, np.asarray(x, dtype=np.float64).reshape(1, -1)))
input_y = np.append(input_y, float(info["scaled"]))
writer.add_scalar("Reward", reward, global_step)
if reward > best_reward:
best_reward = reward
best_params = np.asarray(x, dtype=np.float64).copy()
np.save(best_params_path, best_params)
with open(best_meta_path, "wb") as f:
pickle.dump(
{
"best_reward": best_reward,
"best_params": best_params,
"basis_terms": basis_terms,
"controller_dims": controller_dims,
"config": config_payload,
"timestamp": time.time(),
},
f,
)
save_live_db(
live_db_path,
input_x,
input_y,
{
"name": name,
"best_reward": best_reward,
"dataset_size": int(len(input_y)),
"basis_terms": basis_terms,
"use_sindy_prior": False,
"recover_count": int(obj.recover_count),
"acq": int(acq + 1),
"invalid_skips": int(invalid_skips),
},
)
else:
invalid_skips += 1
print(
f"[acq {acq + 1}] invalid sample skipped: cand={j + 1}, failure_code={info.get('failure_code', -1)}",
flush=True,
)
with open(dante_log_path, "a", encoding="utf-8") as f:
f.write(
f"{time.time():.3f},acq,{acq + 1},{j + 1},{reward:.8f},{best_reward:.8f},{len(input_y)},{info['recoveries_used']},{info.get('failure_code', 0)}\n"
)
global_step += 1
print_progress_line(
phase=f"acq{acq + 1}",
idx=j + 1,
total=len(candidates),
reward=reward,
best_reward=best_reward,
recover_total=int(obj.recover_count),
elapsed_sec=float(time.time() - t0),
global_step=global_step,
total_expected=total_expected,
)
history.append(
{
"iteration": int(acq + 1),
"new_mean": float(np.mean(acq_rewards) if acq_rewards else 0.0),
"new_max": float(np.max(acq_rewards) if acq_rewards else 0.0),
"best_reward": float(best_reward),
"dataset_size": int(len(input_y)),
"recover_total": int(obj.recover_count),
}
)
print(
f"acq {acq + 1}/{num_acquisitions}: mean={history[-1]['new_mean']:.4f}, "
f"max={history[-1]['new_max']:.4f}, best={best_reward:.4f}, recover_total={obj.recover_count}"
)
with open(final_meta_path, "wb") as f:
pickle.dump(
{
"name": name,
"best_reward": best_reward,
"best_params": best_params,
"basis_terms": basis_terms,
"controller_dims": controller_dims,
"history": history,
"elapsed_sec": float(time.time() - t0),
"config": config_payload,
"recover_total": int(obj.recover_count),
},
f,
)
print("Training done")
print("best_reward:", f"{best_reward:.4f}")
print("recover_total:", obj.recover_count)
print("invalid_skips:", invalid_skips)
finally:
writer.close()
if obj.env is not None:
obj.env.close()
if __name__ == "__main__":
main()
archive/dante_pinball/dante_pinball/legacy/d1a3o12_250421_dante_v7_openloop.py
+911
View File
@@ -0,0 +1,911 @@
"""
DANTE v7 (open-loop periodic control)
This version switches from closed-loop basis feedback to a periodic open-loop controller:
1) Optimize control points of one cycle directly.
2) Use continuous phase advance to support non-integer cycle lengths.
3) Keep v6-compatible evaluation protocol (300-step rollout, tail100 score).
"""
import argparse
import json
import os
import pickle
import shutil
import sys
import time
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
os.environ["MKL_THREADING_LAYER"] = "GNU"
os.environ["OMP_NUM_THREADS"] = "16"
os.environ["MKL_NUM_THREADS"] = "16"
try:
from torch.utils.tensorboard import SummaryWriter
except Exception:
SummaryWriter = None
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.abspath(os.path.join(CURRENT_DIR, os.pardir))
os.chdir(CURRENT_DIR)
sys.path.insert(0, os.path.join(ROOT, "DANTE"))
from dante.obj_functions import ObjectiveFunction
from dante.tree_exploration import TreeExploration
from dante_v6_surrogate_torch import FlowControlSurrogateV6
from dante_pinball.env.gym_env_dante_total_force import CustomEnv
N_ACT = 3
V7_CONFIG = {
"name": "d1a3o12_250421_forces02_dante_v7_openloop",
"device_id": 0,
"surrogate_gpu_id": 0,
"eval_steps": 300,
"tail_steps": 100,
"startup_steps": 0,
"max_recover_resets": 1,
"reset_each_candidate": True,
"obs_fail_bound": 2.0,
"obs_clip_bound": 3.0,
"control_points_per_channel": 8,
"phase_interp": "linear",
"period_mode": "auto", # auto | fixed | optimize
"fixed_period": 40.0,
"period_min": 15.0,
"period_max": 80.0,
"period_estimate_files": [
"output/report_dante_v2_v5_v6/raw_oldenv_seed_11.npz",
"output/report_dante_v2_v5_v6/raw_oldenv_seed_29.npz",
"output/report_dante_v2_v5_v6/raw_oldenv_seed_47.npz",
],
"period_estimate_key": "ppo_actions",
"num_initial_per_dim": 10,
"min_num_initial": 100,
"max_num_initial": 320,
"samples_per_acq": 24,
"max_init_attempts_factor": 2.0,
"surrogate_mode": "ensemble", # mlp | cnn | ensemble
"target_cfd_steps": 204800*2,
"surrogate_epochs": 400,
}
class NullWriter:
def add_scalar(self, *_args, **_kwargs) -> None:
pass
def close(self) -> None:
pass
def map_unit_to_range(u: float, low: float, high: float) -> float:
u_clip = float(np.clip(u, -1.0, 1.0))
alpha = 0.5 * (u_clip + 1.0)
return float(low + alpha * (high - low))
def map_range_to_unit(x: float, low: float, high: float) -> float:
if high <= low:
return 0.0
alpha = (float(x) - low) / (high - low)
return float(np.clip(2.0 * alpha - 1.0, -1.0, 1.0))
def dominant_period_fft(x: np.ndarray, min_period: float, max_period: float) -> Optional[float]:
x = np.asarray(x, dtype=np.float64).reshape(-1)
n = int(x.size)
if n < 16:
return None
xc = x - np.mean(x)
std = float(np.std(xc))
if std < 1e-8:
return None
yf = np.fft.rfft(xc)
power = (np.abs(yf) ** 2).reshape(-1)
freq = np.fft.rfftfreq(n, d=1.0)
with np.errstate(divide="ignore", invalid="ignore"):
period = np.where(freq > 0.0, 1.0 / freq, np.inf)
valid = (freq > 0.0) & (period >= float(min_period)) & (period <= float(max_period))
if not np.any(valid):
return None
idx = np.argmax(power * valid.astype(np.float64))
f_peak = float(freq[idx])
if f_peak <= 0.0:
return None
return float(1.0 / f_peak)
def load_action_array_from_npz(npz_path: str, key_hint: str = "ppo_actions") -> np.ndarray:
with np.load(npz_path) as z:
keys = list(z.keys())
if key_hint in z:
arr = z[key_hint]
return np.asarray(arr, dtype=np.float64)
for k in keys:
kl = k.lower()
if "ppo" in kl and "action" in kl:
arr = z[k]
return np.asarray(arr, dtype=np.float64)
for k in keys:
arr = np.asarray(z[k])
if arr.ndim == 2 and arr.shape[1] == N_ACT:
return np.asarray(arr, dtype=np.float64)
raise KeyError(f"no action array found in {npz_path}")
def estimate_period_from_npz_list(
rel_paths: List[str],
key_hint: str,
min_period: float,
max_period: float,
) -> Dict[str, object]:
periods: List[float] = []
details: List[Dict[str, object]] = []
for rel in rel_paths:
abs_path = os.path.join(ROOT, rel)
if not os.path.exists(abs_path):
details.append({"file": rel, "used": False, "reason": "missing"})
continue
try:
a = load_action_array_from_npz(abs_path, key_hint=key_hint)
if a.ndim != 2 or a.shape[1] != N_ACT:
details.append({
"file": rel,
"used": False,
"reason": f"invalid_shape_{tuple(a.shape)}",
})
continue
file_periods = []
for ch in range(N_ACT):
p = dominant_period_fft(
a[:, ch],
min_period=float(min_period),
max_period=float(max_period),
)
if p is not None and np.isfinite(p):
file_periods.append(float(p))
periods.append(float(p))
details.append({
"file": rel,
"used": len(file_periods) > 0,
"num_channels": int(len(file_periods)),
"channel_periods": [float(v) for v in file_periods],
})
except Exception as e:
details.append({"file": rel, "used": False, "reason": str(e)})
if len(periods) == 0:
return {
"period": None,
"source": "none",
"details": details,
"count": 0,
}
period_med = float(np.median(np.asarray(periods, dtype=np.float64)))
period_med = float(np.clip(period_med, min_period, max_period))
return {
"period": period_med,
"source": "fft_median",
"details": details,
"count": int(len(periods)),
"all_periods": [float(v) for v in periods],
}
class PeriodicOpenLoopController:
def __init__(
self,
control_points_per_channel: int,
period_mode: str,
base_period_steps: float,
period_min: float,
period_max: float,
interp: str = "linear",
):
self.k = int(control_points_per_channel)
if self.k < 3:
raise ValueError("control_points_per_channel must be >= 3")
self.period_mode = str(period_mode).lower()
if self.period_mode not in {"auto", "fixed", "optimize"}:
raise ValueError("period_mode must be one of auto|fixed|optimize")
self.base_period_steps = float(base_period_steps)
self.period_min = float(period_min)
self.period_max = float(period_max)
self.interp = str(interp).lower()
if self.interp not in {"linear"}:
raise ValueError("only linear interpolation is currently supported")
self.optimize_period = self.period_mode == "optimize"
self.ctrl_points = np.zeros((N_ACT, self.k), dtype=np.float64)
self.phase = 0.0
self.current_period_steps = float(np.clip(self.base_period_steps, self.period_min, self.period_max))
self.total_params = int(N_ACT * self.k + (1 if self.optimize_period else 0))
def reset_state(self) -> None:
self.phase = 0.0
def _eval_channel(self, points: np.ndarray, phase: float) -> float:
p = np.asarray(points, dtype=np.float64).reshape(-1)
z = float(np.mod(phase, 1.0)) * self.k
i0 = int(np.floor(z)) % self.k
frac = float(z - np.floor(z))
i1 = (i0 + 1) % self.k
return float((1.0 - frac) * p[i0] + frac * p[i1])
def set_params(self, x: np.ndarray) -> None:
x = np.asarray(x, dtype=np.float64).reshape(-1)
if x.size != self.total_params:
raise ValueError(f"controller params mismatch: {x.size} != {self.total_params}")
core = np.clip(x[: N_ACT * self.k], -1.0, 1.0)
self.ctrl_points = core.reshape(N_ACT, self.k)
if self.optimize_period:
p_unit = float(x[-1])
self.current_period_steps = map_unit_to_range(p_unit, self.period_min, self.period_max)
else:
self.current_period_steps = float(np.clip(self.base_period_steps, self.period_min, self.period_max))
def predict(self, _obs: np.ndarray) -> np.ndarray:
action = np.zeros(N_ACT, dtype=np.float64)
for ch in range(N_ACT):
action[ch] = self._eval_channel(self.ctrl_points[ch], self.phase)
action = np.clip(action, -1.0, 1.0)
step_phase = 1.0 / max(1e-6, float(self.current_period_steps))
self.phase = float((self.phase + step_phase) % 1.0)
return action.astype(np.float32)
class FlowControlObjectiveV7(ObjectiveFunction):
def __init__(
self,
control_points_per_channel: int,
period_mode: str,
period_steps: float,
period_min: float,
period_max: float,
phase_interp: str = "linear",
eval_steps: int = 300,
tail_steps: int = 100,
startup_steps: int = 0,
max_recover_resets: int = 1,
turn: float = 0.05,
reset_each_candidate: bool = True,
obs_fail_bound: float = 2.0,
obs_clip_bound: float = 3.0,
):
self.eval_steps = int(eval_steps)
self.tail_steps = int(tail_steps)
self.startup_steps = int(startup_steps)
self.max_recover_resets = int(max_recover_resets)
self.turn = float(turn)
self.reset_each_candidate = bool(reset_each_candidate)
self.obs_fail_bound = float(obs_fail_bound)
self.obs_clip_bound = float(obs_clip_bound)
self.control_points_per_channel = int(control_points_per_channel)
self.period_mode = str(period_mode)
self.period_steps = float(period_steps)
self.period_min = float(period_min)
self.period_max = float(period_max)
self.phase_interp = str(phase_interp)
self.controller = PeriodicOpenLoopController(
control_points_per_channel=self.control_points_per_channel,
period_mode=self.period_mode,
base_period_steps=self.period_steps,
period_min=self.period_min,
period_max=self.period_max,
interp=self.phase_interp,
)
self.dims = int(self.controller.total_params)
self.lb = -1.0 * np.ones(self.dims)
self.ub = 1.0 * np.ones(self.dims)
self.env = None
self._device_id = 0
self.obs_current = None
self.recover_count = 0
def init_env(self, device_id: int = 0) -> None:
self._device_id = int(device_id)
if self.env is not None:
self.env.close()
self.env = CustomEnv(
device_id=int(device_id),
obs_fail_bound=float(self.obs_fail_bound),
obs_clip_bound=float(self.obs_clip_bound),
)
self._hard_reset_and_stabilize()
def _hard_reset_and_stabilize(self) -> None:
obs, _ = self.env.reset()
obs = np.asarray(obs, dtype=np.float32)
action = np.zeros(N_ACT, dtype=np.float32)
for _ in range(int(self.startup_steps)):
obs, _, done, trunc, _ = self.env.step(action)
if done or trunc:
obs, _ = self.env.reset()
self.obs_current = np.asarray(obs, dtype=np.float32)
self.controller.reset_state()
@staticmethod
def _tail_mean(rewards: List[float], tail_steps: int) -> float:
rr = np.asarray(rewards, dtype=np.float64)
if rr.size == 0:
return 0.0
tail = rr[-tail_steps:] if rr.size >= tail_steps else rr
return float(np.mean(tail))
def evaluate_candidate(self, x: np.ndarray) -> Dict[str, object]:
x = self._preprocess(x)
self.controller.set_params(x)
if self.env is None:
self.init_env(self._device_id)
if self.reset_each_candidate:
self._hard_reset_and_stabilize()
for attempt in range(int(self.max_recover_resets) + 1):
obs = np.asarray(self.obs_current, dtype=np.float32)
self.controller.reset_state()
rewards: List[float] = []
steps = 0
done = False
trunc = False
last_info: Dict[str, object] = {}
for _ in range(int(self.eval_steps)):
action = self.controller.predict(obs)
obs, reward, done, trunc, step_info = self.env.step(action)
if isinstance(step_info, dict):
last_info = step_info
rewards.append(float(reward))
steps += 1
if done or trunc:
break
if done or trunc and attempt < int(self.max_recover_resets):
self.recover_count += 1
self._hard_reset_and_stabilize()
continue
self.obs_current = np.asarray(obs, dtype=np.float32)
y = self._tail_mean(rewards, tail_steps=int(self.tail_steps))
return {
"reward": float(y),
"scaled": float(y * 100.0),
"steps": int(steps),
"done": bool(done),
"truncated": bool(trunc),
"recoveries_used": int(attempt),
"failure_code": int(last_info.get("failure_code", 0)),
"period_steps": float(self.controller.current_period_steps),
}
return {
"reward": 0.0,
"scaled": 0.0,
"steps": 0,
"done": True,
"truncated": True,
"recoveries_used": int(self.max_recover_resets),
"failure_code": 1,
"period_steps": float(self.controller.current_period_steps),
}
def scaled(self, y: float) -> float:
return float(y * 100.0)
def __call__(self, x: np.ndarray, apply_scaling: bool = False, track: bool = True) -> float:
info = self.evaluate_candidate(x)
y = float(info["reward"])
return self.scaled(y) if apply_scaling else y
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="DANTE v7 open-loop periodic controller")
p.add_argument("--name", type=str, default=V7_CONFIG["name"], help="Optional run name override")
p.add_argument(
"--control-points",
type=int,
default=V7_CONFIG["control_points_per_channel"],
help="Control points per channel for one cycle",
)
p.add_argument(
"--period-mode",
type=str,
default=V7_CONFIG["period_mode"],
choices=["auto", "fixed", "optimize"],
help="Cycle period mode",
)
p.add_argument("--fixed-period", type=float, default=V7_CONFIG["fixed_period"], help="Fixed period in steps")
p.add_argument("--period-min", type=float, default=V7_CONFIG["period_min"], help="Min period for clipping")
p.add_argument("--period-max", type=float, default=V7_CONFIG["period_max"], help="Max period for clipping")
p.add_argument(
"--phase-interp",
type=str,
default=V7_CONFIG["phase_interp"],
choices=["linear"],
help="Interpolation mode for phase to control point",
)
return p.parse_args()
def sample_params(dims: int, turn: float, period_mode: str, period_guess: float, pmin: float, pmax: float) -> np.ndarray:
x = np.random.uniform(-1.0, 1.0, size=dims)
x = np.round(x / turn) * turn
x = np.clip(x, -1.0, 1.0)
if str(period_mode).lower() == "optimize":
x[-1] = map_range_to_unit(period_guess, pmin, pmax)
return x
def save_live_db(path: str, x: np.ndarray, y: np.ndarray, meta: Dict[str, object]) -> None:
np.savez(path, input_x=x, input_y=y, meta_json=np.array([json.dumps(meta, ensure_ascii=False)]))
def print_progress_line(
phase: str,
idx: int,
total: int,
reward: float,
best_reward: float,
recover_total: int,
elapsed_sec: float,
global_step: int,
total_expected: int,
period_steps: float,
) -> None:
print(
f"[{phase}] {idx}/{total} | reward={reward:.4f} | best={best_reward:.4f} | "
f"period={period_steps:.3f} | recover={recover_total} | eval={global_step}/{total_expected} | elapsed={elapsed_sec:.1f}s",
flush=True,
)
def main() -> None:
args = parse_args()
cfg = dict(V7_CONFIG)
cfg["name"] = str(args.name)
cfg["control_points_per_channel"] = int(args.control_points)
cfg["period_mode"] = str(args.period_mode)
cfg["fixed_period"] = float(args.fixed_period)
cfg["period_min"] = float(args.period_min)
cfg["period_max"] = float(args.period_max)
cfg["phase_interp"] = str(args.phase_interp)
period_info = estimate_period_from_npz_list(
rel_paths=list(cfg["period_estimate_files"]),
key_hint=str(cfg["period_estimate_key"]),
min_period=float(cfg["period_min"]),
max_period=float(cfg["period_max"]),
)
if str(cfg["period_mode"]).lower() == "fixed":
period_steps = float(np.clip(cfg["fixed_period"], cfg["period_min"], cfg["period_max"]))
period_source = "fixed"
else:
p_est = period_info.get("period", None)
if p_est is None:
period_steps = float(np.clip(cfg["fixed_period"], cfg["period_min"], cfg["period_max"]))
period_source = "fallback_fixed_no_estimate"
else:
period_steps = float(np.clip(float(p_est), cfg["period_min"], cfg["period_max"]))
period_source = "auto_from_ppo_fft"
target_evals = int(cfg["target_cfd_steps"] // cfg["eval_steps"])
obj = FlowControlObjectiveV7(
control_points_per_channel=int(cfg["control_points_per_channel"]),
period_mode=str(cfg["period_mode"]),
period_steps=float(period_steps),
period_min=float(cfg["period_min"]),
period_max=float(cfg["period_max"]),
phase_interp=str(cfg["phase_interp"]),
eval_steps=int(cfg["eval_steps"]),
tail_steps=int(cfg["tail_steps"]),
startup_steps=int(cfg["startup_steps"]),
max_recover_resets=int(cfg["max_recover_resets"]),
reset_each_candidate=bool(cfg["reset_each_candidate"]),
obs_fail_bound=float(cfg["obs_fail_bound"]),
obs_clip_bound=float(cfg["obs_clip_bound"]),
)
obj.init_env(device_id=int(cfg["device_id"]))
dims = int(obj.dims)
num_initial_raw = int(max(1, round(float(cfg["num_initial_per_dim"]) * dims)))
num_initial = int(min(int(cfg["max_num_initial"]), max(int(cfg["min_num_initial"]), num_initial_raw)))
num_acquisitions = int((target_evals - int(num_initial)) // int(cfg["samples_per_acq"]))
if num_acquisitions <= 0:
raise RuntimeError(
f"computed num_acquisitions <= 0 with target_evals={target_evals}, "
f"num_initial={num_initial}, samples_per_acq={int(cfg['samples_per_acq'])}"
)
max_init_attempts = int(max(num_initial, round(float(cfg["max_init_attempts_factor"]) * num_initial)))
total_expected = int(num_initial + num_acquisitions * int(cfg["samples_per_acq"]))
name = str(cfg["name"])
model_dir = os.path.join(ROOT, "models", "250421")
out_dir = os.path.join(ROOT, "output")
tb_dir = os.path.join(ROOT, "tensorboard", name)
os.makedirs(model_dir, exist_ok=True)
os.makedirs(out_dir, exist_ok=True)
config_payload = {
"name": name,
"control_mode": "open_loop_periodic",
"device_id": int(cfg["device_id"]),
"surrogate_gpu_id": int(cfg["surrogate_gpu_id"]),
"control_points_per_channel": int(cfg["control_points_per_channel"]),
"controller_dims": int(dims),
"period_mode": str(cfg["period_mode"]),
"period_steps": float(period_steps),
"period_source": period_source,
"period_min": float(cfg["period_min"]),
"period_max": float(cfg["period_max"]),
"period_estimate": period_info,
"phase_interp": str(cfg["phase_interp"]),
"num_initial": int(num_initial),
"num_initial_raw": int(num_initial_raw),
"num_initial_per_dim": float(cfg["num_initial_per_dim"]),
"min_num_initial": int(cfg["min_num_initial"]),
"max_num_initial": int(cfg["max_num_initial"]),
"num_acquisitions": int(num_acquisitions),
"samples_per_acq": int(cfg["samples_per_acq"]),
"surrogate_mode": str(cfg["surrogate_mode"]),
"eval_steps": int(cfg["eval_steps"]),
"tail_steps": int(cfg["tail_steps"]),
"startup_steps": int(cfg["startup_steps"]),
"max_recover_resets": int(cfg["max_recover_resets"]),
"obs_fail_bound": float(cfg["obs_fail_bound"]),
"obs_clip_bound": float(cfg["obs_clip_bound"]),
"reset_each_candidate": bool(cfg["reset_each_candidate"]),
"max_init_attempts": int(max_init_attempts),
"max_init_attempts_factor": float(cfg["max_init_attempts_factor"]),
"target_cfd_steps": int(cfg["target_cfd_steps"]),
"target_total_evals": int(target_evals),
"matched_total_evals": int(total_expected),
"matched_cfd_steps": int(total_expected * int(cfg["eval_steps"])),
}
with open(os.path.join(out_dir, f"{name}_structure_decision.json"), "w", encoding="utf-8") as f:
json.dump(config_payload, f, indent=2, ensure_ascii=False)
print("control_mode:", "open_loop_periodic")
print("device_id:", int(cfg["device_id"]))
print("surrogate_gpu_id:", int(cfg["surrogate_gpu_id"]))
print("control_points_per_channel:", int(cfg["control_points_per_channel"]))
print("controller_dims:", int(dims))
print("period_mode:", str(cfg["period_mode"]))
print("period_steps:", float(period_steps))
print("period_source:", period_source)
print("num_initial:", int(num_initial))
print("num_initial_raw:", int(num_initial_raw))
print("num_acquisitions:", int(num_acquisitions))
print("samples_per_acq:", int(cfg["samples_per_acq"]))
print("target_cfd_steps:", int(cfg["target_cfd_steps"]))
print("matched_cfd_steps:", int(total_expected * int(cfg["eval_steps"])))
ckpt_path = Path(os.path.join(model_dir, f"{name}_surrogate.keras"))
surrogate = FlowControlSurrogateV6(
input_dims=dims,
epochs=int(cfg["surrogate_epochs"]),
patience=30,
check_point_path=ckpt_path,
tf_device_id=int(cfg["surrogate_gpu_id"]),
)
live_db_path = os.path.join(out_dir, f"{name}_database_live.npz")
best_params_path = os.path.join(model_dir, f"{name}_best.npy")
best_meta_path = os.path.join(out_dir, f"{name}_best_meta.pkl")
final_meta_path = os.path.join(out_dir, f"{name}_final_meta.pkl")
dante_log_path = os.path.join(out_dir, f"{name}_dante_log.csv")
writer = NullWriter() if SummaryWriter is None else SummaryWriter(log_dir=tb_dir)
with open(dante_log_path, "w", encoding="utf-8") as f:
f.write("timestamp,phase,acq,candidate,reward,best_reward,dataset_size,recoveries_used,failure_code,period_steps\n")
input_x = np.empty((0, dims), dtype=np.float64)
input_y = np.empty((0,), dtype=np.float64)
history: List[Dict[str, object]] = []
best_reward = -1.0
best_params = np.zeros(dims, dtype=np.float64)
invalid_skips = 0
t0 = time.time()
global_step = 0
try:
valid_init = 0
init_attempts = 0
while valid_init < num_initial:
if init_attempts >= max_init_attempts:
raise RuntimeError(
f"init failed to collect enough valid samples: valid={valid_init}/{num_initial}, "
f"attempts={init_attempts}/{max_init_attempts}, invalid_skips={invalid_skips}"
)
init_attempts += 1
x = sample_params(
dims=dims,
turn=obj.turn,
period_mode=str(cfg["period_mode"]),
period_guess=float(period_steps),
pmin=float(cfg["period_min"]),
pmax=float(cfg["period_max"]),
)
info = obj.evaluate_candidate(x)
reward = float(info["reward"])
is_valid = int(info.get("failure_code", 0)) == 0
if is_valid:
valid_init += 1
input_x = np.vstack((input_x, x.reshape(1, -1)))
input_y = np.append(input_y, float(info["scaled"]))
writer.add_scalar("Reward", reward, global_step)
else:
invalid_skips += 1
if is_valid and reward > best_reward:
best_reward = reward
best_params = x.copy()
np.save(best_params_path, best_params)
with open(best_meta_path, "wb") as f:
pickle.dump(
{
"best_reward": best_reward,
"best_params": best_params,
"config": config_payload,
"timestamp": time.time(),
},
f,
)
if is_valid:
save_live_db(
live_db_path,
input_x,
input_y,
{
"name": name,
"best_reward": best_reward,
"dataset_size": int(len(input_y)),
"control_mode": "open_loop_periodic",
"recover_count": int(obj.recover_count),
"invalid_skips": int(invalid_skips),
},
)
with open(dante_log_path, "a", encoding="utf-8") as f:
f.write(
f"{time.time():.3f},init,0,{init_attempts},{reward:.8f},{best_reward:.8f},{len(input_y)},{info['recoveries_used']},{info.get('failure_code', 0)},{float(info.get('period_steps', period_steps)):.6f}\n"
)
global_step += 1
print(
f"[init-attempt {init_attempts}/{max_init_attempts}] valid={valid_init}/{num_initial} | "
f"reward={reward:.4f} | best={best_reward:.4f} | period={float(info.get('period_steps', period_steps)):.3f} | "
f"invalid_skips={invalid_skips} | recover={int(obj.recover_count)} | "
f"eval={global_step}/{total_expected}+ | elapsed={float(time.time() - t0):.1f}s",
flush=True,
)
for acq in range(int(num_acquisitions)):
if len(input_y) < 2:
print(f"[acq {acq + 1}] skipped: insufficient valid samples ({len(input_y)})", flush=True)
continue
print(f"[acq {acq + 1}/{int(num_acquisitions)}] fitting surrogate on {len(input_y)} samples...", flush=True)
surrogate_mode = str(cfg.get("surrogate_mode", "mlp")).lower()
if surrogate_mode == "ensemble":
candidates = []
for arch in ["mlp", "cnn"]:
cand_ckpt = Path(str(ckpt_path).replace(".keras", f"_{arch}.keras"))
surr = FlowControlSurrogateV6(
input_dims=dims,
epochs=int(cfg["surrogate_epochs"]),
patience=30,
check_point_path=cand_ckpt,
tf_device_id=int(cfg["surrogate_gpu_id"]),
architecture=arch,
)
try:
m = surr(input_x, input_y, verbose=0)
fm = dict(getattr(surr, "last_fit_metrics", {}) or {})
candidates.append((arch, m, fm, cand_ckpt))
except Exception as e:
print(f"[acq {acq + 1}] surrogate {arch} failed: {e}", flush=True)
if not candidates:
raise RuntimeError("all surrogate candidates failed in ensemble mode")
candidates.sort(key=lambda z: float(z[2].get("val_r2", -1e9)), reverse=True)
best_arch, model, fit_metrics, best_ckpt = candidates[0]
if best_ckpt.exists():
shutil.copy2(best_ckpt, ckpt_path)
fit_metrics["selected_architecture"] = best_arch
else:
surrogate.architecture = "cnn" if surrogate_mode == "cnn" else "mlp"
model = surrogate(input_x, input_y, verbose=0)
fit_metrics = dict(getattr(surrogate, "last_fit_metrics", {}) or {})
fit_metrics["selected_architecture"] = str(surrogate.architecture)
if fit_metrics:
writer.add_scalar("Surrogate/val_r2", float(fit_metrics.get("val_r2", 0.0)), acq)
writer.add_scalar("Surrogate/val_mae", float(fit_metrics.get("val_mae", 0.0)), acq)
print(
f"[acq {acq + 1}] surrogate metrics: "
f"val_r2={fit_metrics.get('val_r2', float('nan')):.4f}, "
f"val_mae={fit_metrics.get('val_mae', float('nan')):.4f}, "
f"selected={fit_metrics.get('selected_architecture', 'na')}, "
f"device={fit_metrics.get('device', 'CPU')}",
flush=True,
)
if ckpt_path.exists():
shutil.copy2(ckpt_path, os.path.join(model_dir, f"{name}_surrogate_live.keras"))
explorer = TreeExploration(
func=obj,
model=model,
num_samples_per_acquisition=int(cfg["samples_per_acq"]),
)
candidate_x = explorer.rollout(input_x, input_y, iteration=acq)
acq_rewards: List[float] = []
acq_periods: List[float] = []
for j, x in enumerate(candidate_x):
info = obj.evaluate_candidate(x)
reward = float(info["reward"])
period_used = float(info.get("period_steps", period_steps))
is_valid = int(info.get("failure_code", 0)) == 0
if is_valid:
acq_rewards.append(reward)
acq_periods.append(period_used)
input_x = np.vstack((input_x, np.asarray(x, dtype=np.float64).reshape(1, -1)))
input_y = np.append(input_y, float(info["scaled"]))
writer.add_scalar("Reward", reward, global_step)
if str(cfg["period_mode"]).lower() == "optimize":
writer.add_scalar("Controller/period_steps", period_used, global_step)
if reward > best_reward:
best_reward = reward
best_params = np.asarray(x, dtype=np.float64).copy()
np.save(best_params_path, best_params)
with open(best_meta_path, "wb") as f:
pickle.dump(
{
"best_reward": best_reward,
"best_params": best_params,
"config": config_payload,
"timestamp": time.time(),
},
f,
)
save_live_db(
live_db_path,
input_x,
input_y,
{
"name": name,
"best_reward": best_reward,
"dataset_size": int(len(input_y)),
"control_mode": "open_loop_periodic",
"recover_count": int(obj.recover_count),
"acq": int(acq + 1),
"invalid_skips": int(invalid_skips),
},
)
else:
invalid_skips += 1
with open(dante_log_path, "a", encoding="utf-8") as f:
f.write(
f"{time.time():.3f},acq,{acq + 1},{j + 1},{reward:.8f},{best_reward:.8f},{len(input_y)},{info['recoveries_used']},{info.get('failure_code', 0)},{period_used:.6f}\n"
)
global_step += 1
print_progress_line(
phase=f"acq{acq + 1}",
idx=j + 1,
total=len(candidate_x),
reward=reward,
best_reward=best_reward,
recover_total=int(obj.recover_count),
elapsed_sec=float(time.time() - t0),
global_step=global_step,
total_expected=total_expected,
period_steps=period_used,
)
history.append(
{
"iteration": int(acq + 1),
"new_mean": float(np.mean(acq_rewards) if acq_rewards else 0.0),
"new_max": float(np.max(acq_rewards) if acq_rewards else 0.0),
"period_mean": float(np.mean(acq_periods) if acq_periods else period_steps),
"period_std": float(np.std(acq_periods) if acq_periods else 0.0),
"best_reward": float(best_reward),
"dataset_size": int(len(input_y)),
"recover_total": int(obj.recover_count),
}
)
print(
f"acq {acq + 1}/{num_acquisitions}: mean={history[-1]['new_mean']:.4f}, "
f"max={history[-1]['new_max']:.4f}, best={best_reward:.4f}, "
f"period_mean={history[-1]['period_mean']:.3f}, recover_total={obj.recover_count}",
flush=True,
)
with open(final_meta_path, "wb") as f:
pickle.dump(
{
"name": name,
"best_reward": best_reward,
"best_params": best_params,
"history": history,
"elapsed_sec": float(time.time() - t0),
"config": config_payload,
"recover_total": int(obj.recover_count),
},
f,
)
print("Training done")
print("best_reward:", f"{best_reward:.4f}")
print("recover_total:", obj.recover_count)
print("invalid_skips:", invalid_skips)
finally:
writer.close()
if obj.env is not None:
obj.env.close()
if __name__ == "__main__":
main()
archive/dante_pinball/dante_pinball/legacy/dante_v6_initial_surrogate_analysis.md
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# DANTE v6 Initial-Region Surrogate Failure Analysis
## Scope
- initial_samples: 100
- dims: 42
- note: analysis uses only init phase, not acquisition samples
## Data Geometry
- rank_ratio: 1.000
- unique_ratio_rounded_0p05: 1.000
- cov_condition_number: 1.53e+01
- pca_components_for_90pct_var: 30
- avg_nn_dist: 4.1265
## Classical Baselines
- ridge_std: r2_mean=-1.0772 ± 0.4586, mae_mean=6.0836
- knn_std_k5: r2_mean=-0.2933 ± 0.2589, mae_mean=4.9774
- rf_300: r2_mean=-0.2074 ± 0.2602, mae_mean=4.6365
## TensorFlow Models
- tf_device: GPU:1
- tf_v4_like_no_scaling: r2_mean=-1.8222 ± 0.8408, mae_mean=7.1709
- tf_v4_like_with_scaling: r2_mean=-0.7289 ± 0.2800, mae_mean=5.9893
- tf_mid_128_64_32_with_scaling: r2_mean=-0.6206 ± 0.4865, mae_mean=5.6784
## Recovery/Noise Signal
- recoveries_ratio_ge1: 0.880
- mean_scaled(recover>=1): 17.945237696501337
- mean_scaled(recover=0): 15.966237587414065
- std_scaled(recover>=1): 4.7475266358578665
- std_scaled(recover=0): 9.213494585712375
## Temporal Drift
- corr(index, y): -0.1209
- forward RF on x: r2=-0.0394, mae=5.0576
- forward RF on [x,index]: r2=0.0153, mae=4.9602
- forward ridge on index only: r2=0.0023, mae=4.9557
## Diagnosis
- Even non-neural baselines fail on init set: weakly learnable mapping or high label noise.
- Input/output scaling is a first-order factor: original no-scaling setup underfits init region.
- Model capacity/optimization also matters after scaling (mid_128_64_32 > v4-like).
- Most init samples require recovery reset; this indicates environment-transition induced label noise in init pool.
- Adding sample index improves forward prediction, indicating temporal/state drift beyond static x->y mapping.
archive/dante_pinball/dante_pinball/legacy/dante_v6_ppo_diag.md
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# DANTE v6 vs PPO 诊断报告
## 1) PPO新terms重拟合与可达性
- features: ['bias1', 'obs0', 'obs1', 'dobs0', 'dobs1', 'sin_obs0', 'sin_obs1', 'cos_obs0', 'cos_obs1', 'tanh_obs0', 'tanh_obs1', 'act0_l1', 'act1_l1', 'act2_l1']
- ch0: R2_test=1.0000, MAE_test=0.00002, nz=14
- ch1: R2_test=1.0000, MAE_test=0.00001, nz=14
- ch2: R2_test=1.0000, MAE_test=0.00002, nz=14
- 参数空间可达性: out_of_range_terms=0, coef_abs_err_mean=0.000000, coef_abs_err_max=0.000000
## 2) PPO拟合控制率环境回放
- rollout steps=300, recoveries=0, tail60=0.5041, max_reward=0.7223
- reward曲线图: dante_v6_ppo_reward_curve.png
- 流场速度图: dante_v6_ppo_flow_speed.png
## 3) DANTE数据库与PPO目标点
- samples=818, num_initial=100, best_reward=0.3311
- PCA距离: init_mean=0.6812, last_mean=5.3179, min=0.0080 at eval=196
- PCA图: dante_v6_pca_overlay.png
- 距离趋势图: dante_v6_distance_to_ppo.png
- best曲线图: dante_v6_best_curve.png
## 4) 结论
- 是否趋近PPO点: False
- 最高reward不更新判断: DANTE未明显趋近PPO目标点，代理采样方向与目标结构存在偏移，需修正采集策略或特征归一化。
archive/dante_pinball/dante_pinball/legacy/dante_v6_surrogate_study.md
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# DANTE v6 Surrogate Study
- db_path: output/d1a3o12_250421_forces02_dante_v6_database_live.npz
- n_samples: 976
- dims: 42
- holdout: 0.2
- gpu_id: 1
## Ranking (by test_r2)
| rank | design | test_r2 | test_mae | val_r2 | epochs_ran | device |
|---:|---|---:|---:|---:|---:|---|
| 1 | mid_128_64_32 | 0.8647 | 1.2556 | 0.8778 | 88 | GPU:1 |
| 2 | compact_128_64 | 0.8610 | 1.2621 | 0.8836 | 84 | GPU:1 |
| 3 | wide_256_128_64 | 0.8528 | 1.2508 | 0.8786 | 68 | GPU:1 |
| 4 | strong_reg_128_64_32 | 0.8463 | 1.3380 | 0.8592 | 138 | GPU:1 |
## Recommended
- design: mid_128_64_32
- test_r2: 0.8647
- test_mae: 1.2556
- config: {"name": "mid_128_64_32", "hidden_units": [128, 64, 32], "dropout": 0.1, "weight_decay": 1e-06, "learning_rate": 0.001, "batch_size": 64}
archive/dante_pinball/dante_pinball/legacy/dante_v6_surrogate_torch.py
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import os
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, Optional, Sequence
import numpy as np
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
try:
import torch
import torch.nn as nn
import torch.optim as optim
_TORCH_IMPORT_ERROR = None
except Exception as exc:
torch = None
nn = None
optim = None
_TORCH_IMPORT_ERROR = exc
class TorchScaledPredictWrapper:
def __init__(self, torch_model: Any, x_scaler: StandardScaler, y_scaler: StandardScaler, device: str):
self.torch_model = torch_model
self.x_scaler = x_scaler
self.y_scaler = y_scaler
self.device = str(device)
def predict(self, x_in, **kwargs):
x_in = np.asarray(x_in, dtype=np.float64)
if x_in.ndim == 3 and x_in.shape[-1] == 1:
x_in = x_in.squeeze(axis=-1)
if x_in.ndim > 2:
x_in = x_in.reshape(len(x_in), -1)
x_scaled = self.x_scaler.transform(x_in).astype(np.float32)
xt = torch.as_tensor(x_scaled, dtype=torch.float32, device=self.device)
if xt.ndim == 2:
pass
else:
xt = xt.reshape(len(xt), -1)
self.torch_model.eval()
with torch.no_grad():
if hasattr(self.torch_model, "expects_channel") and self.torch_model.expects_channel:
# Conv1d on this runtime can hit intermittent cuDNN mapping errors;
# keep GPU execution but bypass cuDNN for CNN forward passes.
with torch.backends.cudnn.flags(enabled=False):
y_scaled = self.torch_model(xt.unsqueeze(1)).detach().cpu().numpy().reshape(-1, 1)
else:
y_scaled = self.torch_model(xt).detach().cpu().numpy().reshape(-1, 1)
y_raw = self.y_scaler.inverse_transform(y_scaled)
return y_raw.reshape(-1, 1)
class TorchMLPRegressor(nn.Module):
expects_channel = False
def __init__(self, input_dims: int, hidden_units: Sequence[int], dropout: float):
super().__init__()
layers = []
prev = int(input_dims)
for w in hidden_units:
layers.append(nn.Linear(prev, int(w)))
layers.append(nn.ELU())
if dropout > 1e-8:
layers.append(nn.Dropout(float(dropout)))
prev = int(w)
layers.append(nn.Linear(prev, 1))
self.net = nn.Sequential(*layers)
def forward(self, x):
return self.net(x)
class TorchCNNRegressor(nn.Module):
expects_channel = True
def __init__(self, input_dims: int, dropout: float):
super().__init__()
flat_dim = 16 * max(1, int(input_dims) - 2)
self.net = nn.Sequential(
nn.Conv1d(1, 128, kernel_size=3, padding=1),
nn.ELU(),
nn.MaxPool1d(kernel_size=2, stride=1),
nn.Dropout(float(dropout)),
nn.Conv1d(128, 64, kernel_size=3, padding=1),
nn.ELU(),
nn.MaxPool1d(kernel_size=2, stride=1),
nn.Dropout(float(dropout)),
nn.Conv1d(64, 32, kernel_size=3, padding=1),
nn.ELU(),
nn.Conv1d(32, 16, kernel_size=3, padding=1),
nn.ELU(),
nn.Flatten(),
nn.Linear(flat_dim, 64),
nn.ELU(),
nn.Linear(64, 1),
)
def forward(self, x):
return self.net(x)
@dataclass
class FlowControlSurrogateV6:
input_dims: int
learning_rate: float = 1e-3
batch_size: int = 64
epochs: int = 500
test_size: float = 0.2
train_test_split_random_state: int = 42
patience: int = 30
check_point_path: Path = Path("surrogate_v6.pt")
hidden_units: Sequence[int] = field(default_factory=lambda: (128, 64, 32))
architecture: str = "mlp"
dropout: float = 0.10
weight_decay: float = 1e-6
tf_device_id: Optional[int] = None
require_gpu: bool = True
def __post_init__(self):
self.model: Optional[Any] = None
self.x_scaler = StandardScaler()
self.y_scaler = StandardScaler()
self.last_fit_metrics: Dict[str, float] = {}
@staticmethod
def _forward_with_runtime_guard(model: Any, x: Any):
if hasattr(model, "expects_channel") and model.expects_channel:
with torch.backends.cudnn.flags(enabled=False):
return model(x.unsqueeze(1))
return model(x)
@staticmethod
def _ensure_torch_ready() -> None:
if torch is None:
raise ImportError(
f"PyTorch is required for FlowControlSurrogateV6 but is not available: {_TORCH_IMPORT_ERROR}"
)
def _pick_device(self) -> str:
self._ensure_torch_ready()
if not torch.cuda.is_available():
if self.require_gpu:
raise RuntimeError("FlowControlSurrogateV6 requires GPU but torch.cuda is not available")
return "cpu"
if self.tf_device_id is None:
return "cuda:0"
idx = int(max(0, min(torch.cuda.device_count() - 1, int(self.tf_device_id))))
return f"cuda:{idx}"
def _build_model(self):
arch = str(self.architecture).lower()
if arch == "cnn":
return TorchCNNRegressor(input_dims=self.input_dims, dropout=float(self.dropout))
return TorchMLPRegressor(
input_dims=self.input_dims,
hidden_units=self.hidden_units,
dropout=float(self.dropout),
)
def __call__(self, x, y, verbose: int = 0):
self._ensure_torch_ready()
x = np.asarray(x, dtype=np.float64)
y = np.asarray(y, dtype=np.float64).reshape(-1)
x_train, x_val, y_train, y_val = train_test_split(
x,
y,
test_size=self.test_size,
random_state=self.train_test_split_random_state,
shuffle=True,
)
x_train_s = self.x_scaler.fit_transform(x_train).astype(np.float32)
x_val_s = self.x_scaler.transform(x_val).astype(np.float32)
y_train_s = self.y_scaler.fit_transform(y_train.reshape(-1, 1)).reshape(-1).astype(np.float32)
device = self._pick_device()
model = self._build_model().to(device)
optimizer = optim.Adam(model.parameters(), lr=float(self.learning_rate), weight_decay=float(self.weight_decay))
criterion = nn.MSELoss()
xt = torch.as_tensor(x_train_s, dtype=torch.float32, device=device)
yt = torch.as_tensor(y_train_s, dtype=torch.float32, device=device).reshape(-1, 1)
xv = torch.as_tensor(x_val_s, dtype=torch.float32, device=device)
batch = int(max(8, min(int(self.batch_size), len(x_train_s))))
best_loss = np.inf
best_state = None
bad_epochs = 0
epochs_ran = 0
model.train()
for ep in range(int(self.epochs)):
perm = torch.randperm(xt.shape[0], device=device)
epoch_loss = 0.0
n_batches = 0
for i in range(0, xt.shape[0], batch):
idx = perm[i : i + batch]
xb = xt[idx]
yb = yt[idx]
optimizer.zero_grad(set_to_none=True)
pred = self._forward_with_runtime_guard(model, xb)
loss = criterion(pred, yb)
loss.backward()
optimizer.step()
epoch_loss += float(loss.item())
n_batches += 1
train_loss = epoch_loss / max(1, n_batches)
epochs_ran = ep + 1
if train_loss + 1e-10 < best_loss:
best_loss = train_loss
best_state = {k: v.detach().cpu().clone() for k, v in model.state_dict().items()}
bad_epochs = 0
else:
bad_epochs += 1
if bad_epochs >= int(self.patience):
break
if best_state is not None:
model.load_state_dict(best_state)
model.eval()
with torch.no_grad():
y_val_pred_s = self._forward_with_runtime_guard(model, xv).detach().cpu().numpy().reshape(-1, 1)
y_val_pred = self.y_scaler.inverse_transform(y_val_pred_s).reshape(-1)
arch = str(self.architecture).lower()
gpu_idx = int(str(device).split(":")[1]) if str(device).startswith("cuda") else -1
self.last_fit_metrics = {
"val_r2": float(r2_score(y_val, y_val_pred)),
"val_mae": float(mean_absolute_error(y_val, y_val_pred)),
"val_count": int(len(y_val)),
"best_val_loss": float(best_loss),
"epochs_ran": int(epochs_ran),
"architecture": arch,
"device": f"GPU:{gpu_idx}" if gpu_idx >= 0 else "CPU",
"gpu_count": int(torch.cuda.device_count() if torch.cuda.is_available() else 0),
"selected_gpu": int(gpu_idx),
}
self.model = model
return TorchScaledPredictWrapper(model, self.x_scaler, self.y_scaler, device=device)
archive/dante_pinball/dante_pinball/legacy/fit_ppo_control_sindy.py
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import json
import os
import pickle
from typing import Dict, List, Tuple
import numpy as np
import pysindy as ps
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.abspath(os.path.join(CURRENT_DIR, os.pardir))
DATA_PATH = os.path.join(ROOT, "output", "d1a3o12_250421_forces02_sindy_dataset.pkl")
OUT_DIR = os.path.join(ROOT, "output", "report_dante_v3_v4")
OUT_JSON = os.path.join(OUT_DIR, "ppo_sindy_control_fit.json")
WARMUP_STEPS = 0
MAX_LAG = 1
THRESHOLDS = [0.0, 0.005, 0.01, 0.015, 0.02, 0.03, 0.05]
NZ_RATIO_THRESHOLD = 0.70
def episode_metric(rewards: np.ndarray, warmup: int = 150) -> float:
r = np.asarray(rewards, dtype=np.float64).reshape(-1)
if r.size == 0:
return 0.0
eff = r[warmup:] if r.size > warmup else r[-1:]
return float(np.mean(eff[-100:])) if eff.size >= 100 else float(np.mean(eff))
def load_episodes(path: str) -> Tuple[List[Dict], Dict]:
with open(path, "rb") as f:
data = pickle.load(f)
if isinstance(data, dict) and "episodes" in data:
return list(data["episodes"]), dict(data.get("meta", {}))
if isinstance(data, list):
return data, {}
raise RuntimeError(f"Unsupported dataset format: {type(data)}")
def extract_episode_arrays(ep: Dict) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
actions = np.asarray(ep.get("actions", []), dtype=np.float64)
observations = np.asarray(ep.get("observations", []), dtype=np.float64)
rewards = np.asarray(ep.get("rewards", []), dtype=np.float64)
if actions.ndim != 2:
actions = actions.reshape(actions.shape[0], -1)
actions = actions[:, :3]
if observations.ndim != 2:
observations = observations.reshape(observations.shape[0], -1)
observations = observations[:, :2]
n = min(actions.shape[0], observations.shape[0], rewards.shape[0])
return actions[:n], observations[:n], rewards[:n]
def build_dataset(episodes: List[Dict], warmup: int = 150) -> Tuple[np.ndarray, np.ndarray, List[str], Dict]:
x_rows = []
y_rows = []
feat_names = [
"bias1",
"obs0",
"obs1",
"dobs0",
"dobs1",
"sin_obs0",
"sin_obs1",
"cos_obs0",
"cos_obs1",
"tanh_obs0",
"tanh_obs1",
"act0_l1",
"act1_l1",
"act2_l1",
]
stats = {"episodes_used": 0, "samples_used": 0}
for ep in episodes:
actions, obs2, rewards = extract_episode_arrays(ep)
t_len = min(actions.shape[0], obs2.shape[0], rewards.shape[0])
if t_len <= (MAX_LAG + warmup + 1):
continue
for t in range(MAX_LAG, t_len):
if t < warmup:
continue
o = obs2[t]
o1 = obs2[t - 1]
a_prev = actions[t - 1]
x_rows.append(
[
1.0,
o[0],
o[1],
o[0] - o1[0],
o[1] - o1[1],
np.sin(np.pi * o[0]),
np.sin(np.pi * o[1]),
np.cos(np.pi * o[0]),
np.cos(np.pi * o[1]),
np.tanh(o[0]),
np.tanh(o[1]),
a_prev[0],
a_prev[1],
a_prev[2],
]
)
y_rows.append(actions[t])
stats["episodes_used"] += 1
if len(x_rows) < 512:
raise RuntimeError(f"Too few samples for fitting: {len(x_rows)}")
x = np.asarray(x_rows, dtype=np.float64)
y = np.asarray(y_rows, dtype=np.float64)
stats["samples_used"] = int(x.shape[0])
return x, y, feat_names, stats
def r2(y: np.ndarray, yp: np.ndarray) -> float:
ssr = float(np.sum((y - yp) ** 2))
sst = float(np.sum((y - np.mean(y)) ** 2) + 1e-12)
return float(1.0 - ssr / sst)
def fit_channel_grid(x: np.ndarray, y: np.ndarray, thresholds: List[float]):
std = np.std(x, axis=0)
std = np.where(std < 1e-8, 1.0, std)
xs = x / std
rows = []
for th in thresholds:
opt = ps.STLSQ(threshold=th, alpha=1e-4, max_iter=25)
opt.fit(xs, y)
coef = np.asarray(opt.coef_, dtype=np.float64).reshape(-1) / std
yp = x @ coef
rows.append(
{
"threshold": float(th),
"nz": int(np.sum(np.abs(coef) > 1e-8)),
"r2": r2(y, yp),
"mae": float(np.mean(np.abs(y - yp))),
"coef": coef,
}
)
best = max(rows, key=lambda z: z["r2"])
return rows, best
def top_terms(feat_names: List[str], coef: np.ndarray, topk: int = 10) -> List[Dict]:
idx = np.argsort(np.abs(coef))[::-1]
out = []
for i in idx:
c = float(coef[i])
if abs(c) < 1e-8:
continue
out.append({"term": feat_names[i], "coef": c, "abs_coef": float(abs(c))})
if len(out) >= topk:
break
return out
def main() -> None:
os.makedirs(OUT_DIR, exist_ok=True)
episodes_all, meta = load_episodes(DATA_PATH)
ppo_eps = [ep for ep in episodes_all if str(ep.get("source", "unknown")) == "ppo_eval"]
if len(ppo_eps) < 10:
# Fallback only when ppo episodes are too few.
ppo_eps = episodes_all
metrics = []
for i, ep in enumerate(ppo_eps):
_, _, rewards = extract_episode_arrays(ep)
metrics.append((i, episode_metric(rewards, warmup=WARMUP_STEPS)))
metrics_sorted = sorted(metrics, key=lambda x: x[1], reverse=True)
x, y, feat_names, data_stats = build_dataset(ppo_eps, warmup=WARMUP_STEPS)
channel_models = []
votes = np.zeros(len(feat_names), dtype=np.int64)
coef_abs_sum = np.zeros(len(feat_names), dtype=np.float64)
nz_ratios = []
for ch in range(3):
rows, best = fit_channel_grid(x, y[:, ch], THRESHOLDS)
coef = best["coef"]
active = np.abs(coef) >= 0.01
votes += active.astype(np.int64)
coef_abs_sum += np.abs(coef)
nz_nonbias = int(np.sum(np.abs(coef[1:]) > 1e-8))
denom_nonbias = int(max(1, len(feat_names) - 1))
nz_ratio = float(nz_nonbias / denom_nonbias)
nz_ratios.append(nz_ratio)
channel_models.append(
{
"channel": ch,
"r2": float(best["r2"]),
"mae": float(best["mae"]),
"best_sparse": {
"threshold": float(best["threshold"]),
"nz": int(best["nz"]),
"nz_nonbias": nz_nonbias,
"nz_ratio": nz_ratio,
},
"top_terms": top_terms(feat_names, coef, topk=10),
"grid": [
{
"threshold": float(z["threshold"]),
"nz": int(z["nz"]),
"r2": float(z["r2"]),
"mae": float(z["mae"]),
}
for z in rows
],
}
)
score_idx = sorted(
range(len(feat_names)),
key=lambda k: (int(votes[k]), float(coef_abs_sum[k])),
reverse=True,
)
global_top = [feat_names[k] for k in score_idx if feat_names[k] != "bias1"]
over_complex_channels = [int(cm["channel"]) for cm in channel_models if cm["best_sparse"]["nz_ratio"] >= NZ_RATIO_THRESHOLD]
use_sindy_prior = len(over_complex_channels) == 0
if use_sindy_prior:
prior_reason = (
f"all channels nz_ratio < {NZ_RATIO_THRESHOLD:.2f}; sparse structure is sufficiently compressible"
)
else:
prior_reason = (
f"channels {over_complex_channels} have nz_ratio >= {NZ_RATIO_THRESHOLD:.2f}; sparse structure is too dense"
)
result = {
"data_path": DATA_PATH,
"dataset_meta": meta,
"warmup_steps": WARMUP_STEPS,
"max_lag": MAX_LAG,
"nz_ratio_threshold": NZ_RATIO_THRESHOLD,
"episodes_total": len(episodes_all),
"episodes_used_source": "ppo_eval" if len([ep for ep in episodes_all if str(ep.get("source", "")) == "ppo_eval"]) >= 10 else "all",
"episodes_used_count": len(ppo_eps),
"episode_metrics_top10": metrics_sorted[:10],
"fit_data_stats": data_stats,
"threshold_grid": THRESHOLDS,
"feature_names": feat_names,
"channel_models": channel_models,
"global_term_votes": {feat_names[k]: int(votes[k]) for k in range(len(feat_names))},
"global_term_abs_coef_sum": {feat_names[k]: float(coef_abs_sum[k]) for k in range(len(feat_names))},
"global_top_terms": global_top[:12],
"complexity_decision": {
"nz_ratio_threshold": float(NZ_RATIO_THRESHOLD),
"channel_nz_ratio": {f"ch{i}": float(z) for i, z in enumerate(nz_ratios)},
"over_complex_channels": over_complex_channels,
"use_sindy_prior": bool(use_sindy_prior),
"reason": prior_reason,
},
}
with open(OUT_JSON, "w", encoding="utf-8") as f:
json.dump(result, f, indent=2, ensure_ascii=False)
print(f"saved: {OUT_JSON}")
print("episodes used:", len(ppo_eps), "/", len(episodes_all))
print("samples:", data_stats["samples_used"])
print("global top terms:", result["global_top_terms"][:8])
for cm in channel_models:
print(
f"ch{cm['channel']} r2={cm['r2']:.4f} mae={cm['mae']:.5f} "
f"nz={cm['best_sparse']['nz']} nz_ratio={cm['best_sparse']['nz_ratio']:.3f}"
)
print("use_sindy_prior:", result["complexity_decision"]["use_sindy_prior"])
print("reason:", result["complexity_decision"]["reason"])
if __name__ == "__main__":
main()
archive/dante_pinball/dante_pinball/legacy/generate_v6_v7_ppo_briefing.py
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import json
import os
import pickle
from dataclasses import dataclass
from typing import Dict, List, Tuple
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from dante_pinball.env.gym_env_dante_total_force import CustomEnv
ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
OUT_DIR = os.path.join(ROOT, "output", "report_dante_v2_v5_v6")
os.makedirs(OUT_DIR, exist_ok=True)
V6_RUN = "d1a3o12_250421_forces02_dante_v6_3"
V7_RUN = "d1a3o12_250421_forces02_dante_v7_1"
SEEDS = [11, 29, 47]
N_ACT = 3
EVAL_STEPS = 300
def feature_dict(obs_t: np.ndarray, obs_prev: np.ndarray, act_prev: np.ndarray) -> Dict[str, float]:
o0, o1 = float(obs_t[0]), float(obs_t[1])
p0, p1 = float(obs_prev[0]), float(obs_prev[1])
a0, a1, a2 = float(act_prev[0]), float(act_prev[1]), float(act_prev[2])
return {
"obs0": o0,
"obs1": o1,
"dobs0": o0 - p0,
"dobs1": o1 - p1,
"sin_obs0": float(np.sin(np.pi * o0)),
"sin_obs1": float(np.sin(np.pi * o1)),
"cos_obs0": float(np.cos(np.pi * o0)),
"cos_obs1": float(np.cos(np.pi * o1)),
"tanh_obs0": float(np.tanh(o0)),
"tanh_obs1": float(np.tanh(o1)),
"act0_l1": a0,
"act1_l1": a1,
"act2_l1": a2,
}
def inv_tanh_map(q: float) -> float:
qq = float(np.clip(q, -0.999, 0.999))
x = np.arctanh(qq) / 1.25
return float(np.clip(x, -1.0, 1.0))
@dataclass
class RunData:
name: str
x: np.ndarray
y: np.ndarray
num_initial: int
cfg: Dict
def load_run(name: str) -> RunData:
db_path = os.path.join(ROOT, "output", f"{name}_database_live.npz")
cfg_path = os.path.join(ROOT, "output", f"{name}_structure_decision.json")
z = np.load(db_path, allow_pickle=True)
x = np.asarray(z["input_x"], dtype=np.float64)
y = np.asarray(z["input_y"], dtype=np.float64)
with open(cfg_path, "r", encoding="utf-8") as f:
cfg = json.load(f)
return RunData(name=name, x=x, y=y, num_initial=int(cfg["num_initial"]), cfg=cfg)
def ppo_npz(seed: int) -> str:
return os.path.join(OUT_DIR, f"raw_oldenv_seed_{seed}.npz")
def ppo_point_for_v6(seed: int, basis_terms: List[str]) -> np.ndarray:
z = np.load(ppo_npz(seed))
obs = np.asarray(z["ppo_obs"], dtype=np.float64)
act = np.asarray(z["ppo_actions"], dtype=np.float64)
rows_x = []
rows_y = []
for t in range(1, len(obs)):
fd = feature_dict(obs[t], obs[t - 1], act[t - 1])
rows_x.append([1.0] + [float(fd[k]) for k in basis_terms])
rows_y.append(act[t])
X = np.asarray(rows_x, dtype=np.float64)
Y = np.asarray(rows_y, dtype=np.float64)
params = []
for ch in range(3):
coef, *_ = np.linalg.lstsq(X, Y[:, ch], rcond=None)
bias = float(coef[0])
params.append(inv_tanh_map(bias / 1.0))
for c in coef[1:]:
params.append(inv_tanh_map(float(c) / 2.0))
return np.asarray(params, dtype=np.float64)
def phase_weights(phase: float, k: int) -> np.ndarray:
z = float(np.mod(phase, 1.0)) * k
i0 = int(np.floor(z)) % k
frac = float(z - np.floor(z))
i1 = (i0 + 1) % k
w = np.zeros(k, dtype=np.float64)
w[i0] += (1.0 - frac)
w[i1] += frac
return w
def ppo_point_for_v7(seed: int, k: int, period_steps: float) -> np.ndarray:
z = np.load(ppo_npz(seed))
act = np.asarray(z["ppo_actions"], dtype=np.float64)
n = int(act.shape[0])
phase = 0.0
step_phase = 1.0 / max(1e-6, float(period_steps))
W = np.zeros((n, k), dtype=np.float64)
for t in range(n):
W[t] = phase_weights(phase, k)
phase = (phase + step_phase) % 1.0
params = []
for ch in range(3):
coef, *_ = np.linalg.lstsq(W, act[:, ch], rcond=None)
coef = np.clip(coef, -1.0, 1.0)
params.extend(coef.tolist())
return np.asarray(params, dtype=np.float64)
def fit_pca_and_project(x: np.ndarray, extra: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
scaler = StandardScaler()
xs = scaler.fit_transform(x)
extra_s = scaler.transform(extra)
pca = PCA(n_components=2, random_state=0)
x2 = pca.fit_transform(xs)
extra2 = pca.transform(extra_s)
return x2, extra2, pca.explained_variance_ratio_
def plot_pca(run_name: str, x2: np.ndarray, y: np.ndarray, ppo2: np.ndarray, evr: np.ndarray, out_png: str) -> None:
plt.figure(figsize=(8, 6))
sc = plt.scatter(x2[:, 0], x2[:, 1], c=y, cmap="viridis", s=12, alpha=0.78)
plt.colorbar(sc, label="reward_scaled")
colors = ["#e41a1c", "#377eb8", "#ff7f00"]
for i, seed in enumerate(SEEDS):
plt.scatter(
[ppo2[i, 0]],
[ppo2[i, 1]],
s=130,
c=colors[i],
marker="*",
edgecolors="k",
linewidths=0.9,
label=f"PPO fitted seed {seed}",
zorder=6,
)
plt.title(f"{run_name}: PCA with 3 PPO fitted points\\nPC1 {evr[0]*100:.1f}% | PC2 {evr[1]*100:.1f}%")
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.legend(loc="best", fontsize=9)
plt.grid(alpha=0.25)
plt.tight_layout()
plt.savefig(out_png, dpi=170)
plt.close()
class LinearBasisController:
def __init__(self, basis_terms: List[str]):
self.basis_terms = list(basis_terms)
self.num_basis = len(self.basis_terms)
self.total_params = N_ACT * (1 + self.num_basis)
self.params = np.zeros(self.total_params, dtype=np.float64)
self.obs_l1 = np.zeros(2, dtype=np.float64)
self.prev_action = np.zeros(3, dtype=np.float64)
def reset_state(self, obs0: np.ndarray) -> None:
obs0 = np.asarray(obs0, dtype=np.float64).reshape(-1)
if obs0.size < 2:
obs0 = np.zeros(2, dtype=np.float64)
self.obs_l1 = obs0[:2].copy()
self.prev_action = np.zeros(3, dtype=np.float64)
def set_params(self, x: np.ndarray) -> None:
x = np.asarray(x, dtype=np.float64).reshape(-1)
if x.size != self.total_params:
raise ValueError(f"controller params mismatch: {x.size} != {self.total_params}")
self.params = np.clip(x, -1.0, 1.0)
def _feature_dict(self, obs: np.ndarray) -> Dict[str, float]:
o = np.asarray(obs, dtype=np.float64).reshape(-1)
if o.size < 2:
o = np.zeros(2, dtype=np.float64)
o0, o1 = float(o[0]), float(o[1])
o0_l1, o1_l1 = float(self.obs_l1[0]), float(self.obs_l1[1])
a0, a1, a2 = float(self.prev_action[0]), float(self.prev_action[1]), float(self.prev_action[2])
return {
"obs0": o0,
"obs1": o1,
"dobs0": o0 - o0_l1,
"dobs1": o1 - o1_l1,
"sin_obs0": float(np.sin(np.pi * o0)),
"sin_obs1": float(np.sin(np.pi * o1)),
"cos_obs0": float(np.cos(np.pi * o0)),
"cos_obs1": float(np.cos(np.pi * o1)),
"tanh_obs0": float(np.tanh(o0)),
"tanh_obs1": float(np.tanh(o1)),
"act0_l1": a0,
"act1_l1": a1,
"act2_l1": a2,
}
def predict(self, obs: np.ndarray) -> np.ndarray:
feat = self._feature_dict(obs)
out = np.zeros(3, dtype=np.float64)
stride = 1 + self.num_basis
for ch in range(3):
off = ch * stride
y = np.tanh(1.25 * self.params[off])
for k, term in enumerate(self.basis_terms):
y += (2.0 * np.tanh(1.25 * self.params[off + 1 + k])) * feat.get(term, 0.0)
out[ch] = y
out = np.clip(out, -1.0, 1.0)
obs2 = np.asarray(obs, dtype=np.float64).reshape(-1)
if obs2.size < 2:
obs2 = np.zeros(2, dtype=np.float64)
self.obs_l1 = obs2[:2].copy()
self.prev_action = out.copy()
return out.astype(np.float32)
class PeriodicOpenLoopController:
def __init__(self, control_points_per_channel: int, period_steps: float):
self.k = int(control_points_per_channel)
self.ctrl_points = np.zeros((N_ACT, self.k), dtype=np.float64)
self.phase = 0.0
self.current_period_steps = float(period_steps)
self.total_params = int(N_ACT * self.k)
def reset_state(self) -> None:
self.phase = 0.0
def _eval_channel(self, points: np.ndarray, phase: float) -> float:
p = np.asarray(points, dtype=np.float64).reshape(-1)
z = float(np.mod(phase, 1.0)) * self.k
i0 = int(np.floor(z)) % self.k
frac = float(z - np.floor(z))
i1 = (i0 + 1) % self.k
return float((1.0 - frac) * p[i0] + frac * p[i1])
def set_params(self, x: np.ndarray) -> None:
x = np.asarray(x, dtype=np.float64).reshape(-1)
if x.size != self.total_params:
raise ValueError(f"controller params mismatch: {x.size} != {self.total_params}")
core = np.clip(x, -1.0, 1.0)
self.ctrl_points = core.reshape(N_ACT, self.k)
def predict(self, _obs: np.ndarray) -> np.ndarray:
action = np.zeros(N_ACT, dtype=np.float64)
for ch in range(N_ACT):
action[ch] = self._eval_channel(self.ctrl_points[ch], self.phase)
action = np.clip(action, -1.0, 1.0)
step_phase = 1.0 / max(1e-6, float(self.current_period_steps))
self.phase = float((self.phase + step_phase) % 1.0)
return action.astype(np.float32)
def extract_ux_uy(env: CustomEnv) -> Tuple[np.ndarray, np.ndarray]:
nx = env.flow_field.FIELD_SHAPE[0]
ny = env.flow_field.FIELD_SHAPE[1]
env.flow_field.get_ddf()
ddf = env.flow_field.ddf.copy().reshape((9, ny, nx)).transpose(2, 1, 0)
ux = ddf[:, :, 1] + ddf[:, :, 5] + ddf[:, :, 8] - ddf[:, :, 3] - ddf[:, :, 6] - ddf[:, :, 7]
uy = ddf[:, :, 2] + ddf[:, :, 5] + ddf[:, :, 6] - ddf[:, :, 4] - ddf[:, :, 7] - ddf[:, :, 8]
return ux.astype(np.float64), uy.astype(np.float64)
def vorticity_from_ux_uy(ux: np.ndarray, uy: np.ndarray) -> np.ndarray:
dvy_dx = np.gradient(uy, axis=0)
dvx_dy = np.gradient(ux, axis=1)
return (dvy_dx - dvx_dy).astype(np.float64)
def rollout_controller_v6(params: np.ndarray, basis_terms: List[str], device_id: int = 1) -> Dict[str, np.ndarray]:
env = CustomEnv(device_id=int(device_id))
ctrl = LinearBasisController(basis_terms)
ctrl.set_params(params)
obs, _ = env.reset()
obs = np.asarray(obs, dtype=np.float32)
ctrl.reset_state(obs)
obs_hist = []
act_hist = []
rew_hist = []
try:
for _ in range(EVAL_STEPS):
act = ctrl.predict(obs)
next_obs, reward, done, trunc, _ = env.step(act)
obs_hist.append(np.asarray(obs, dtype=np.float64).copy())
act_hist.append(np.asarray(act, dtype=np.float64).copy())
rew_hist.append(float(reward))
obs = np.asarray(next_obs, dtype=np.float32)
if done or trunc:
obs, _ = env.reset()
obs = np.asarray(obs, dtype=np.float32)
ctrl.reset_state(obs)
ux, uy = extract_ux_uy(env)
finally:
env.close()
return {
"obs": np.asarray(obs_hist, dtype=np.float64),
"act": np.asarray(act_hist, dtype=np.float64),
"rew": np.asarray(rew_hist, dtype=np.float64),
"ux": ux,
"uy": uy,
}
def rollout_controller_v7(params: np.ndarray, k: int, period_steps: float, device_id: int = 0) -> Dict[str, np.ndarray]:
env = CustomEnv(device_id=int(device_id))
ctrl = PeriodicOpenLoopController(control_points_per_channel=k, period_steps=float(period_steps))
ctrl.set_params(params)
obs, _ = env.reset()
obs = np.asarray(obs, dtype=np.float32)
ctrl.reset_state()
obs_hist = []
act_hist = []
rew_hist = []
try:
for _ in range(EVAL_STEPS):
act = ctrl.predict(obs)
next_obs, reward, done, trunc, _ = env.step(act)
obs_hist.append(np.asarray(obs, dtype=np.float64).copy())
act_hist.append(np.asarray(act, dtype=np.float64).copy())
rew_hist.append(float(reward))
obs = np.asarray(next_obs, dtype=np.float32)
if done or trunc:
obs, _ = env.reset()
obs = np.asarray(obs, dtype=np.float32)
ctrl.reset_state()
ux, uy = extract_ux_uy(env)
finally:
env.close()
return {
"obs": np.asarray(obs_hist, dtype=np.float64),
"act": np.asarray(act_hist, dtype=np.float64),
"rew": np.asarray(rew_hist, dtype=np.float64),
"ux": ux,
"uy": uy,
}
def best_ppo_seed() -> Tuple[int, float]:
best_seed = None
best_tail = -1e18
for s in SEEDS:
z = np.load(ppo_npz(s))
r = np.asarray(z["ppo_rewards"], dtype=np.float64)
tail = float(np.mean(r[-100:]))
if tail > best_tail:
best_tail = tail
best_seed = s
return int(best_seed), float(best_tail)
def load_ppo_series(seed: int) -> Dict[str, np.ndarray]:
z = np.load(ppo_npz(seed))
return {
"obs": np.asarray(z["ppo_obs"], dtype=np.float64),
"act": np.asarray(z["ppo_actions"], dtype=np.float64),
"rew": np.asarray(z["ppo_rewards"], dtype=np.float64),
}
def plot_obs_act_time(ppo: Dict[str, np.ndarray], v6: Dict[str, np.ndarray], v7: Dict[str, np.ndarray], out_png: str) -> None:
t = np.arange(EVAL_STEPS)
fig, axes = plt.subplots(5, 1, figsize=(12, 12), sharex=True, constrained_layout=True)
series = [
(0, "obs0", ppo["obs"][:, 0], v6["obs"][:, 0], v7["obs"][:, 0]),
(1, "obs1", ppo["obs"][:, 1], v6["obs"][:, 1], v7["obs"][:, 1]),
(2, "act0", ppo["act"][:, 0], v6["act"][:, 0], v7["act"][:, 0]),
(3, "act1", ppo["act"][:, 1], v6["act"][:, 1], v7["act"][:, 1]),
(4, "act2", ppo["act"][:, 2], v6["act"][:, 2], v7["act"][:, 2]),
]
for idx, name, y0, y1, y2 in series:
ax = axes[idx]
ax.plot(t, y0, lw=1.2, label="PPO")
ax.plot(t, y1, lw=1.2, label="v6")
ax.plot(t, y2, lw=1.2, label="v7")
ax.set_ylabel(name)
ax.grid(alpha=0.25)
if idx == 0:
ax.legend(loc="best", ncol=3)
axes[-1].set_xlabel("time step")
fig.suptitle("Obs-Act time series comparison (300 steps)")
fig.savefig(out_png, dpi=170)
plt.close(fig)
def plot_vorticity_maps(omega_ppo: np.ndarray, omega_v6: np.ndarray, omega_v7: np.ndarray, out_png: str) -> float:
gmax = float(max(np.max(np.abs(omega_ppo)), np.max(np.abs(omega_v6)), np.max(np.abs(omega_v7))))
vmax = max(1e-12, 0.1 * gmax)
fig, axes = plt.subplots(1, 3, figsize=(15, 4.8), constrained_layout=True)
data = [(omega_ppo, "PPO"), (omega_v6, "v6"), (omega_v7, "v7")]
for ax, (om, title) in zip(axes, data):
im = ax.imshow(om.T, origin="lower", cmap="RdBu_r", vmin=-vmax, vmax=vmax)
ax.set_title(title)
ax.set_xlabel("x")
ax.set_ylabel("y")
cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95)
cbar.set_label("vorticity")
fig.suptitle(f"Final vorticity maps, unified range +/-{vmax:.4f} (10% global max)")
fig.savefig(out_png, dpi=170)
plt.close(fig)
return float(vmax)
def build_design(obs: np.ndarray, act: np.ndarray, terms: List[str]) -> Tuple[np.ndarray, np.ndarray]:
X_rows = []
Y_rows = []
for t in range(1, len(obs)):
fd = feature_dict(obs[t], obs[t - 1], act[t - 1])
X_rows.append([1.0] + [float(fd[k]) for k in terms])
Y_rows.append(act[t])
return np.asarray(X_rows, dtype=np.float64), np.asarray(Y_rows, dtype=np.float64)
def fit_multi_seed_linear(terms: List[str]) -> Dict[str, object]:
Ws = []
r2s = []
for s in SEEDS:
z = np.load(ppo_npz(s))
obs = np.asarray(z["ppo_obs"], dtype=np.float64)
act = np.asarray(z["ppo_actions"], dtype=np.float64)
X, Y = build_design(obs, act, terms)
W = []
r2_ch = []
for ch in range(3):
c, *_ = np.linalg.lstsq(X, Y[:, ch], rcond=None)
pred = X @ c
ssr = float(np.sum((Y[:, ch] - pred) ** 2))
sst = float(np.sum((Y[:, ch] - np.mean(Y[:, ch])) ** 2) + 1e-12)
r2 = float(1.0 - ssr / sst)
W.append(c)
r2_ch.append(r2)
Ws.append(np.asarray(W, dtype=np.float64))
r2s.append(np.asarray(r2_ch, dtype=np.float64))
Wm = np.mean(np.asarray(Ws), axis=0)
r2m = np.mean(np.asarray(r2s), axis=0)
return {
"terms": ["bias1"] + list(terms),
"W_mean": Wm,
"r2_by_action_mean": r2m,
"r2_mean": float(np.mean(r2m)),
}
def matrix_to_latex(W: np.ndarray, precision: int = 4) -> str:
rows = []
for i in range(W.shape[0]):
rows.append(" & ".join([f"{float(v):.{precision}f}" for v in W[i]]))
body = " \\\\ ".join(rows)
return "\\begin{bmatrix}" + body + "\\end{bmatrix}"
def main() -> None:
v6 = load_run(V6_RUN)
v7 = load_run(V7_RUN)
v6_basis = list(v6.cfg["basis_terms"])
v6_ppo_pts = np.vstack([ppo_point_for_v6(s, v6_basis) for s in SEEDS])
k = int(v7.cfg["control_points_per_channel"])
period_steps = float(v7.cfg["period_steps"])
v7_ppo_pts = np.vstack([ppo_point_for_v7(s, k=k, period_steps=period_steps) for s in SEEDS])
v6_x2, v6_p2, v6_evr = fit_pca_and_project(v6.x, v6_ppo_pts)
v7_x2, v7_p2, v7_evr = fit_pca_and_project(v7.x, v7_ppo_pts)
fig_v6_pca = os.path.join(OUT_DIR, "brief_v6_3_pca_with_ppo3.png")
fig_v7_pca = os.path.join(OUT_DIR, "brief_v7_1_pca_with_ppo3.png")
plot_pca(v6.name, v6_x2, v6.y, v6_p2, v6_evr, fig_v6_pca)
plot_pca(v7.name, v7_x2, v7.y, v7_p2, v7_evr, fig_v7_pca)
with open(os.path.join(ROOT, "output", f"{V6_RUN}_best_meta.pkl"), "rb") as f:
v6_meta = pickle.load(f)
with open(os.path.join(ROOT, "output", f"{V7_RUN}_best_meta.pkl"), "rb") as f:
v7_meta = pickle.load(f)
v6_best = np.asarray(v6_meta["best_params"], dtype=np.float64)
v7_best = np.asarray(v7_meta["best_params"], dtype=np.float64)
v6_roll = rollout_controller_v6(v6_best, basis_terms=v6_basis, device_id=1)
v7_roll = rollout_controller_v7(v7_best, k=k, period_steps=period_steps, device_id=0)
seed_star, tail_star = best_ppo_seed()
ppo_series = load_ppo_series(seed_star)
fig_ts = os.path.join(OUT_DIR, "brief_v6_v7_ppo_obs_act_time.png")
plot_obs_act_time(ppo_series, v6_roll, v7_roll, fig_ts)
ppo_flow_npz = os.path.join(OUT_DIR, "fig_oldenv_flow_best.npz")
zf = np.load(ppo_flow_npz)
ppo_ux = np.asarray(zf["ux"], dtype=np.float64)
ppo_uy = np.asarray(zf["uy"], dtype=np.float64)
omega_ppo = vorticity_from_ux_uy(ppo_ux, ppo_uy)
omega_v6 = vorticity_from_ux_uy(v6_roll["ux"], v6_roll["uy"])
omega_v7 = vorticity_from_ux_uy(v7_roll["ux"], v7_roll["uy"])
fig_omega = os.path.join(OUT_DIR, "brief_v6_v7_ppo_final_vorticity.png")
omega_vmax = plot_vorticity_maps(omega_ppo, omega_v6, omega_v7, fig_omega)
with open(os.path.join(OUT_DIR, "sindy_group_sparsity_scan.json"), "r", encoding="utf-8") as f:
group_scan = json.load(f)
with open(os.path.join(OUT_DIR, "sindy_constrained_profile_search.json"), "r", encoding="utf-8") as f:
constrained = json.load(f)
with open(os.path.join(OUT_DIR, "dante_ackley_highdim_sweep.json"), "r", encoding="utf-8") as f:
highdim = json.load(f)
with open(os.path.join(OUT_DIR, "v6_v7_reaudit_frozen_facts_20260323.json"), "r", encoding="utf-8") as f:
frozen = json.load(f)
with open(os.path.join(OUT_DIR, "v6_v7_acq_diagnostics_summary_20260323.json"), "r", encoding="utf-8") as f:
acq = json.load(f)
full_terms = [
"obs0", "obs1", "dobs0", "dobs1", "sin_obs0", "sin_obs1", "cos_obs0", "cos_obs1",
"tanh_obs0", "tanh_obs1", "act0_l1", "act1_l1", "act2_l1",
]
reduced_terms = list(constrained["best_chrono"]["basis_terms"])
full_fit = fit_multi_seed_linear(full_terms)
reduced_fit = fit_multi_seed_linear(reduced_terms)
highdim_results = list(highdim.get("results", []))
highdim_neg_r2 = int(sum(1 for r in highdim_results if float(r.get("holdout_r2", 0.0)) < 0.0))
highdim_neg_gain = int(sum(1 for r in highdim_results if float(r.get("acq_gain_scaled", 0.0)) < 0.0))
summary = {
"runs": {"v6": V6_RUN, "v7": V7_RUN, "ppo_seed_used": int(seed_star), "ppo_tail100": float(tail_star)},
"figures": {
"v6_pca": fig_v6_pca,
"v7_pca": fig_v7_pca,
"obs_act_time": fig_ts,
"vorticity": fig_omega,
},
"vorticity_unified_abs_limit": float(omega_vmax),
"reduction": {
"full_r2_mean_over_seeds": float(group_scan["full_model"]["r2_mean_over_seeds"]),
"best_chrono_basis_terms": reduced_terms,
"best_chrono_r2_mean_over_seeds": float(constrained["best_chrono"]["chrono_r2_mean_over_seeds"]),
"best_chrono_r2_min_over_seeds": float(constrained["best_chrono"]["chrono_r2_min_over_seeds"]),
"group_scan_chosen": group_scan.get("chosen", {}),
"full_fit": {
"terms": full_fit["terms"],
"W_mean": np.asarray(full_fit["W_mean"]).tolist(),
"r2_by_action_mean": np.asarray(full_fit["r2_by_action_mean"]).tolist(),
"r2_mean": float(full_fit["r2_mean"]),
},
"reduced_fit": {
"terms": reduced_fit["terms"],
"W_mean": np.asarray(reduced_fit["W_mean"]).tolist(),
"r2_by_action_mean": np.asarray(reduced_fit["r2_by_action_mean"]).tolist(),
"r2_mean": float(reduced_fit["r2_mean"]),
},
"latex": {
"full_terms": "\\phi_{full}=[1,obs_0,obs_1,\\Delta obs_0,\\Delta obs_1,\\sin(\\pi obs_0),\\sin(\\pi obs_1),\\cos(\\pi obs_0),\\cos(\\pi obs_1),\\tanh(obs_0),\\tanh(obs_1),a_{0,t-1},a_{1,t-1},a_{2,t-1}]^\\top",
"reduced_terms": "\\phi_{red}=[1,obs_1,\\sin(\\pi obs_0),\\cos(\\pi obs_0),a_{1,t-1}]^\\top",
"W_full_mean": matrix_to_latex(np.asarray(full_fit["W_mean"], dtype=np.float64)),
"W_reduced_mean": matrix_to_latex(np.asarray(reduced_fit["W_mean"], dtype=np.float64)),
},
},
"highdim": {
"n_cases": int(len(highdim_results)),
"n_neg_holdout_r2": int(highdim_neg_r2),
"n_neg_acq_gain": int(highdim_neg_gain),
},
"dual_surrogate": frozen.get("surrogate_log_stats", {}),
"acq_summary": acq,
}
summary_path = os.path.join(OUT_DIR, "v6_v7_ppo_briefing_summary.json")
with open(summary_path, "w", encoding="utf-8") as f:
json.dump(summary, f, indent=2, ensure_ascii=False)
md_path = os.path.join(OUT_DIR, "v6_v7_ppo_briefing_20260324.md")
with open(md_path, "w", encoding="utf-8") as f:
f.write("# v6/v7/PPO 综合简报\n\n")
f.write("## 1) PCA（含3个PPO拟合点）\n")
f.write(f"- v6图: {fig_v6_pca}\n")
f.write(f"- v7图: {fig_v7_pca}\n\n")
f.write("## 2) obs-act时序 + 最终涡量\n")
f.write(f"- 时序图: {fig_ts}\n")
f.write(f"- 最终涡量图: {fig_omega}\n")
f.write(f"- 涡量统一色条范围: ±{omega_vmax:.6f}（按三者全局|omega|max的10%）\n")
f.write(f"- PPO时序选用seed={seed_star}（tail100={tail_star:.5f}）\n\n")
f.write("## 3) 函数约简、关键函数、最终组合\n")
f.write(f"- 全特征模型平均R2: {group_scan['full_model']['r2_mean_over_seeds']:.6f}\n")
f.write(f"- 约束搜索best_chrono基函数: {reduced_terms}\n")
f.write(f"- best_chrono平均R2: {constrained['best_chrono']['chrono_r2_mean_over_seeds']:.6f}\n")
f.write(f"- best_chrono最差seed R2: {constrained['best_chrono']['chrono_r2_min_over_seeds']:.6f}\n")
f.write("- 关键函数解释: obs1给出主状态幅值，sin/cos(pi*obs0)提供周期相位，act1_l1提供单步记忆。\n\n")
f.write("### 学到方程与拟合方程（LaTeX）\n")
f.write("- 全特征学到方程（3动作联合线性写法）:\n")
f.write("$$\\mathbf{a}_t = W_{full}\\,\\phi_{full,t}$$\n")
f.write("$$" + summary['reduction']['latex']['full_terms'] + "$$\n")
f.write("$$W_{full}=" + summary['reduction']['latex']['W_full_mean'] + "$$\n")
f.write(f"- 全特征拟合R2(本次重算, action均值): {full_fit['r2_mean']:.6f}\n\n")
f.write("- 约简拟合方程（best_chrono）:\n")
f.write("$$\\mathbf{a}_t = W_{red}\\,\\phi_{red,t}$$\n")
f.write("$$" + summary['reduction']['latex']['reduced_terms'] + "$$\n")
f.write("$$W_{red}=" + summary['reduction']['latex']['W_reduced_mean'] + "$$\n")
f.write(f"- 约简拟合R2(本次重算, action均值): {reduced_fit['r2_mean']:.6f}\n\n")
f.write("## 4) 高维能力与现实约束反思\n")
f.write(f"- 高维扫描样本数: {len(highdim_results)}\n")
f.write(f"- holdout_r2<0 的case数: {highdim_neg_r2}/{len(highdim_results)}\n")
f.write(f"- 首轮acq_gain<0 的case数: {highdim_neg_gain}/{len(highdim_results)}\n")
f.write("- 解释: 高维下代理可辨识度不足时，Tree探索会被误导，出现边界漂移与收益停滞。\n")
f.write("- 与论文对齐: DANTE强调DNN surrogate与自适应探索协同；在你的流体控制里，受噪声、时序漂移和高维参数化耦合影响，样本效率会显著下降。\n")
f.write("- 双代理是否有帮助: 当前日志显示ensemble路径提供了可运行冗余（CNN失败时MLP兜底），但在v6_3其val_r2均值仍偏低，收益主要体现在稳定性而非显著提升最优值。\n")
print("saved:", summary_path)
print("saved:", md_path)
print("saved figs:", fig_v6_pca, fig_v7_pca, fig_ts, fig_omega)
if __name__ == "__main__":
main()
archive/dante_pinball/dante_pinball/legacy/gym_env_dante_total_force.py
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import gymnasium as gym
import numpy as np
from gymnasium import spaces
from collections import deque
from typing import Tuple
import sys
import os
import queue
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
current_dir = os.path.dirname(os.path.abspath(__file__))
parent_dir = os.path.abspath(os.path.join(current_dir, os.pardir))
sys.path.append(parent_dir)
from CelerisLab import FlowField
from CelerisLab import utils
config_cuda = utils.load_cuda_config(
os.path.join(parent_dir, "configs", "config_cuda.json")
)
config_field = utils.load_flow_field_config(
os.path.join(parent_dir, "configs", "config_flowfield.json")
)
S_DIM, A_DIM = 2, 3
U0 = config_field.velocity
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 36
if config_field.data_type == "FP32":
DATA_TYPE = np.float32
else:
raise ValueError(f"Unsupported data type {config_field.data_type}.")
class CustomEnv(gym.Env):
"""DANTE-only environment with deterministic reset semantics and numeric-failure surfacing."""
metadata = {"render_modes": ["human"], "render_fps": 1000 / SAMPLE_INTERVAL}
FAILURE_NONE = 0
FAILURE_NUMERIC = 1
FAILURE_NONFINITE_OBS = 2
FAILURE_OBS_OOB = 3
def __init__(
self,
device_id: int = 0,
obs_fail_bound: float = 2.0,
obs_clip_bound: float = 3.0,
reward_weights: Tuple[float, float, float] = (0.3, 0.3, 0.4),
):
super().__init__()
self.action_space = spaces.Box(low=-1, high=1, shape=(A_DIM,), dtype=DATA_TYPE)
self.observation_space = spaces.Box(
low=-obs_clip_bound,
high=obs_clip_bound,
shape=(S_DIM,),
dtype=DATA_TYPE,
)
self.obs_fail_bound = float(obs_fail_bound)
self.obs_clip_bound = float(obs_clip_bound)
rw = np.asarray(reward_weights, dtype=DATA_TYPE)
if rw.size != 3:
raise ValueError("reward_weights must have length 3: (cd, cl, sim)")
rw_sum = float(np.sum(rw))
if rw_sum <= 0:
raise ValueError("reward_weights sum must be positive")
self.reward_weights = rw / rw_sum
self.fifo_states = deque(maxlen=FIFO_LEN)
self.target_states = np.empty((0, 6), dtype=DATA_TYPE)
self.force_norm_fact = 1.0
self.torque_norm_fact = 1.0
self.sens_norm_fact = np.ones(6, dtype=DATA_TYPE)
self.sens_deviation = np.zeros(6, dtype=DATA_TYPE)
self.reward_cd = 0.0
self.reward_cl = 0.0
self.reward_sim = 0.0
self.current_step = 0
self.flow_field = FlowField(config_field, config_cuda, device_id)
L0 = 20
u0 = config_field.velocity
nx = self.flow_field.FIELD_SHAPE[0]
ny = self.flow_field.FIELD_SHAPE[1]
center: Tuple[float, float, float] = (10 * L0, (ny - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0)
center = (40 * L0, (ny - 1) / 2 + 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
center = (40 * L0, (ny - 1) / 2, 0)
self.flow_field.add_sensor(center, L0 / 4)
center = (40 * L0, (ny - 1) / 2 - 2 * L0, 0)
self.flow_field.add_sensor(center, L0 / 4)
self.flow_field.run(int(4 * nx / u0), np.zeros(4, dtype=DATA_TYPE))
for _ in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(4, dtype=DATA_TYPE))
new_state = self.flow_field.obs.copy()[2:8]
self.target_states = np.vstack((self.target_states, new_state))
center = (30 * L0, (ny - 1) / 2, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center = (31.3 * L0, (ny - 1) / 2 + 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
center = (31.3 * L0, (ny - 1) / 2 - 0.75 * L0, 0)
self.flow_field.add_cylinder(center, L0 / 2)
self.flow_field.run(int(4 * nx / u0), np.zeros(7, dtype=DATA_TYPE))
self.flow_field.get_ddf()
self.flow_field.save_ddf()
for _ in range(FIFO_LEN):
self.flow_field.run(SAMPLE_INTERVAL, np.zeros(7, dtype=DATA_TYPE))
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
temp_states = np.array(self.fifo_states)
self.force_norm_fact = 6 * np.max(np.abs(temp_states[:, 6:12]))
temp_torque = (
-temp_states[:, 1]
- temp_states[:, 2] * np.sqrt(3) / 2
+ temp_states[:, 3] / 2
+ temp_states[:, 4] * np.sqrt(3) / 2
+ temp_states[:, 5] / 2
)
self.torque_norm_fact = 10 * np.max(np.abs(temp_torque))
for i in range(6):
self.sens_deviation[i] = np.mean(temp_states[:, i])
self.sens_norm_fact[i] = 5 * np.max(np.abs(temp_states[:, i] - self.sens_deviation[i]))
self.flow_field.apply_ddf()
for _ in range(FIFO_LEN):
self.flow_field.run(
SAMPLE_INTERVAL,
np.array([0.0, 0.0, 0.0, 0.0, 0.0, -4 * u0, 4 * u0], dtype=DATA_TYPE),
)
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
self.save_states = self.fifo_states.copy()
# self.flow_field.apply_ddf()
self.flow_field.get_ddf()
self.flow_field.save_ddf()
def _calc_lag(self, target: np.ndarray, state: np.ndarray) -> int:
target_mean = np.mean(target)
state_mean = np.mean(state)
correlation = np.correlate(target - target_mean, state - state_mean, "full")
lags = np.arange(-len(target) + 1, len(target))
return int(lags[np.argmax(correlation)])
def _calc_dtw_sim(self, target: np.ndarray, state: np.ndarray) -> float:
n = len(target)
m = len(state)
dtw_matrix = np.full((n + 1, m + 1), np.inf)
dtw_matrix[0, 0] = 0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(target[i - 1] - state[j - 1])
last_min = min(
dtw_matrix[i - 1, j],
dtw_matrix[i, j - 1],
dtw_matrix[i - 1, j - 1],
)
dtw_matrix[i, j] = cost + last_min
return float(1 - (dtw_matrix[n, m] / len(target)))
def _compute_obs_reward(self):
states = np.array(self.fifo_states)
forces = states[-1, 6:12] / self.force_norm_fact
obs_drag = float((forces[0] + forces[2] + forces[4]) / 3)
obs_lift = float((forces[1] + forces[3] + forces[5]) / 3)
similarities = 0.0
id_sens = 1
target_seq = self.target_states[CONV_LEN : 2 * CONV_LEN, id_sens]
state_seq = states[-CONV_LEN:, id_sens]
lag = self._calc_lag(target_seq, state_seq)
for i in range(0, 6):
target_seq = np.roll(self.target_states[:, i], -lag)[CONV_LEN : 2 * CONV_LEN]
state_seq = states[-CONV_LEN:, i]
similarities += self._calc_dtw_sim(target_seq, state_seq) / 6
self.reward_cd = float(np.exp(-np.abs(obs_drag * 20)))
self.reward_cl = float(np.exp(-np.abs(obs_lift * 80)))
self.reward_sim = float(np.exp(-10 * np.abs(similarities - 1)))
reward = float(
np.minimum(
self.reward_weights[0] * self.reward_cd
+ self.reward_weights[1] * self.reward_cl
+ self.reward_weights[2] * self.reward_sim,
1.0,
)
)
observation = np.array([forces[0], forces[1]], dtype=DATA_TYPE)
return observation, reward
def step(self, action):
assert self.action_space.contains(action), "%r (%s) invalid" % (
action,
type(action),
)
result_queue = queue.Queue()
def run_flow_field(act):
self.flow_field.context.push()
u0 = config_field.velocity
try:
temp = np.zeros(7, dtype=DATA_TYPE)
temp[4:7] = np.array((act * 8 + [0, -4, 4]) * u0, dtype=DATA_TYPE)
self.flow_field.run(SAMPLE_INTERVAL, temp)
finally:
self.flow_field.context.pop()
self.fifo_states.append(self.flow_field.obs.copy()[2:14])
def proc_data():
result_queue.put(self._compute_obs_reward())
run_flow_field(action)
if self.flow_field.has_numeric_error():
self.current_step += 1
obs = np.zeros(S_DIM, dtype=DATA_TYPE)
info = {
"failure_code": self.FAILURE_NUMERIC,
"numeric_error": True,
"raw_obs": obs.copy(),
}
return obs, 0.0, False, True, info
proc_data()
observation, reward = result_queue.get()
raw_obs = np.asarray(observation, dtype=DATA_TYPE).copy()
failure_code = self.FAILURE_NONE
if not np.all(np.isfinite(observation)):
failure_code = self.FAILURE_NONFINITE_OBS
elif np.any(np.abs(observation) > self.obs_fail_bound):
failure_code = self.FAILURE_OBS_OOB
truncated = failure_code != self.FAILURE_NONE
if truncated:
reward = 0.0
observation = np.zeros(S_DIM, dtype=DATA_TYPE)
else:
observation = np.clip(observation, -self.obs_clip_bound, self.obs_clip_bound)
self.current_step += 1
info = {
"failure_code": int(failure_code),
"numeric_error": False,
"raw_obs": raw_obs,
}
return observation.astype(np.float32), float(reward), False, bool(truncated), info
def reset(self, seed=None):
self.flow_field.restore_ddf()
self.flow_field.apply_ddf()
self.fifo_states = self.save_states.copy()
self.current_step = 0
return np.zeros(S_DIM, dtype=np.float32), {}
def close(self):
self.flow_field.__del__()
archive/dante_pinball/dante_pinball/legacy/v6_init_surrogate_mlp_cnn_compare.py
+216
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import json
import os
import time
from dataclasses import dataclass
from typing import Dict, List, Tuple
import numpy as np
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
os.environ.setdefault("TF_CPP_MIN_LOG_LEVEL", "2")
os.environ.setdefault("TF_FORCE_GPU_ALLOW_GROWTH", "true")
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Conv1D, Dense, Dropout, Flatten, Input, MaxPooling1D
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.abspath(os.path.join(CURRENT_DIR, os.pardir))
OUT_DIR = os.path.join(ROOT, "output", "report_dante_v2_v5_v6")
os.makedirs(OUT_DIR, exist_ok=True)
DB_PATH = os.path.join(ROOT, "output", "d1a3o12_250421_forces02_dante_v6_database_live.npz")
DECISION_PATH = os.path.join(ROOT, "output", "d1a3o12_250421_forces02_dante_v6_1_structure_decision.json")
@dataclass
class ModelSpec:
name: str
kind: str # mlp | cnn
def set_tf_device(device_id: int = 1) -> str:
gpus = tf.config.list_physical_devices("GPU")
if not gpus:
return "CPU"
idx = int(max(0, min(len(gpus) - 1, device_id)))
try:
tf.config.set_visible_devices([gpus[idx]], "GPU")
tf.config.experimental.set_memory_growth(gpus[idx], True)
return f"GPU:{idx}"
except RuntimeError:
return f"GPU:{idx}(runtime_initialized)"
def load_init_dataset() -> Tuple[np.ndarray, np.ndarray, Dict]:
if not os.path.exists(DB_PATH):
raise FileNotFoundError(DB_PATH)
if not os.path.exists(DECISION_PATH):
raise FileNotFoundError(DECISION_PATH)
with open(DECISION_PATH, "r", encoding="utf-8") as f:
decision = json.load(f)
n_init = int(decision["num_initial"])
db = np.load(DB_PATH, allow_pickle=True)
x = np.asarray(db["input_x"], dtype=np.float64)
y = np.asarray(db["input_y"], dtype=np.float64).reshape(-1)
if len(x) < n_init:
raise RuntimeError(f"DB samples {len(x)} < num_initial {n_init}")
x0 = x[:n_init]
y0 = y[:n_init]
return x0, y0, decision
def make_mlp(input_dims: int) -> Sequential:
model = Sequential(
[
Input(shape=(input_dims,)),
Dense(128, activation="elu"),
Dropout(0.10),
Dense(64, activation="elu"),
Dropout(0.10),
Dense(32, activation="elu"),
Dense(1, activation="linear"),
]
)
model.compile(optimizer=Adam(learning_rate=1e-3), loss="mse", metrics=["mae"])
return model
def make_cnn(input_dims: int) -> Sequential:
model = Sequential(
[
Input(shape=(input_dims, 1)),
Conv1D(128, kernel_size=3, padding="same", activation="elu"),
MaxPooling1D(pool_size=2, strides=1),
Dropout(0.2),
Conv1D(64, kernel_size=3, padding="same", activation="elu"),
MaxPooling1D(pool_size=2, strides=1),
Dropout(0.2),
Conv1D(32, kernel_size=3, padding="same", activation="elu"),
Conv1D(16, kernel_size=3, padding="same", activation="elu"),
Flatten(),
Dense(64, activation="elu"),
Dense(1, activation="linear"),
]
)
model.compile(optimizer=Adam(learning_rate=1e-3), loss="mse", metrics=["mae"])
return model
def run_one_split(x: np.ndarray, y: np.ndarray, seed: int, spec: ModelSpec) -> Dict:
x_tr, x_te, y_tr, y_te = train_test_split(x, y, test_size=0.30, random_state=seed, shuffle=True)
x_scaler = StandardScaler()
y_scaler = StandardScaler()
x_tr_s = x_scaler.fit_transform(x_tr)
x_te_s = x_scaler.transform(x_te)
y_tr_s = y_scaler.fit_transform(y_tr.reshape(-1, 1)).reshape(-1)
if spec.kind == "mlp":
model = make_mlp(x.shape[1])
xtr_in = x_tr_s
xte_in = x_te_s
elif spec.kind == "cnn":
model = make_cnn(x.shape[1])
xtr_in = x_tr_s.reshape(len(x_tr_s), x.shape[1], 1)
xte_in = x_te_s.reshape(len(x_te_s), x.shape[1], 1)
else:
raise ValueError(spec.kind)
cb = [EarlyStopping(monitor="val_loss", patience=25, restore_best_weights=True)]
hist = model.fit(
xtr_in,
y_tr_s,
validation_split=0.25,
batch_size=32,
epochs=250,
verbose=0,
callbacks=cb,
)
y_hat_s = model.predict(xte_in, verbose=0).reshape(-1, 1)
y_hat = y_scaler.inverse_transform(y_hat_s).reshape(-1)
return {
"seed": int(seed),
"r2": float(r2_score(y_te, y_hat)),
"mae": float(mean_absolute_error(y_te, y_hat)),
"epochs": int(len(hist.history.get("loss", []))),
"best_val_loss": float(np.min(hist.history.get("val_loss", [np.nan]))),
}
def summarize(rows: List[Dict]) -> Dict:
r2 = np.array([r["r2"] for r in rows], dtype=np.float64)
mae = np.array([r["mae"] for r in rows], dtype=np.float64)
return {
"r2_mean": float(np.mean(r2)),
"r2_std": float(np.std(r2)),
"mae_mean": float(np.mean(mae)),
"mae_std": float(np.std(mae)),
}
def main() -> None:
t0 = time.time()
device = set_tf_device(1)
x, y, decision = load_init_dataset()
specs = [
ModelSpec(name="mlp_128_64_32", kind="mlp"),
ModelSpec(name="cnn_paper_like", kind="cnn"),
]
seeds = [0, 1, 2, 3, 4]
results = {}
for spec in specs:
rows = [run_one_split(x, y, s, spec) for s in seeds]
results[spec.name] = {
"kind": spec.kind,
"per_split": rows,
"summary": summarize(rows),
}
r2_mlp = results["mlp_128_64_32"]["summary"]["r2_mean"]
r2_cnn = results["cnn_paper_like"]["summary"]["r2_mean"]
out = {
"db_path": DB_PATH,
"decision_path": DECISION_PATH,
"device": device,
"n_init": int(len(x)),
"dims": int(x.shape[1]),
"y_stats": {
"mean": float(np.mean(y)),
"std": float(np.std(y)),
"min": float(np.min(y)),
"max": float(np.max(y)),
},
"models": results,
"delta": {
"r2_cnn_minus_mlp": float(r2_cnn - r2_mlp),
"better_model": "cnn_paper_like" if r2_cnn > r2_mlp else "mlp_128_64_32",
},
"elapsed_sec": float(time.time() - t0),
}
out_json = os.path.join(OUT_DIR, "v6_1_init_surrogate_mlp_vs_cnn.json")
with open(out_json, "w", encoding="utf-8") as f:
json.dump(out, f, ensure_ascii=False, indent=2)
print("saved", out_json)
print("better_model", out["delta"]["better_model"])
print("delta_r2", out["delta"]["r2_cnn_minus_mlp"])
if __name__ == "__main__":
main()
archive/dante_pinball/dante_pinball/legacy/v6_v7_pca_distance_with_ppo.py
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import json
import os
from dataclasses import dataclass
from typing import Dict, List, Tuple
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))
OUT_DIR = os.path.join(ROOT, "output", "report_dante_v2_v5_v6")
os.makedirs(OUT_DIR, exist_ok=True)
SEEDS = [11, 29, 47]
V6_NAME = "d1a3o12_250421_forces02_dante_v6_2"
V7_NAME = "d1a3o12_250421_forces02_dante_v7"
@dataclass
class RunData:
name: str
x: np.ndarray
y: np.ndarray
num_initial: int
dims: int
cfg: Dict
def load_run(name: str) -> RunData:
db_path = os.path.join(ROOT, "output", f"{name}_database_live.npz")
cfg_path = os.path.join(ROOT, "output", f"{name}_structure_decision.json")
z = np.load(db_path, allow_pickle=True)
x = np.asarray(z["input_x"], dtype=np.float64)
y = np.asarray(z["input_y"], dtype=np.float64)
cfg = json.load(open(cfg_path, "r", encoding="utf-8"))
return RunData(
name=name,
x=x,
y=y,
num_initial=int(cfg["num_initial"]),
dims=int(cfg["controller_dims"]),
cfg=cfg,
)
def feature_dict(obs_t: np.ndarray, obs_prev: np.ndarray, act_prev: np.ndarray) -> Dict[str, float]:
o0, o1 = float(obs_t[0]), float(obs_t[1])
p0, p1 = float(obs_prev[0]), float(obs_prev[1])
a0, a1, a2 = float(act_prev[0]), float(act_prev[1]), float(act_prev[2])
return {
"obs0": o0,
"obs1": o1,
"dobs0": o0 - p0,
"dobs1": o1 - p1,
"sin_obs0": float(np.sin(np.pi * o0)),
"sin_obs1": float(np.sin(np.pi * o1)),
"cos_obs0": float(np.cos(np.pi * o0)),
"cos_obs1": float(np.cos(np.pi * o1)),
"tanh_obs0": float(np.tanh(o0)),
"tanh_obs1": float(np.tanh(o1)),
"act0_l1": a0,
"act1_l1": a1,
"act2_l1": a2,
}
def inv_tanh_map(q: float) -> float:
qq = float(np.clip(q, -0.999, 0.999))
x = np.arctanh(qq) / 1.25
return float(np.clip(x, -1.0, 1.0))
def ppo_point_for_v6(seed: int, basis_terms: List[str]) -> np.ndarray:
p = os.path.join(ROOT, "output", "report_dante_v2_v5_v6", f"raw_oldenv_seed_{seed}.npz")
z = np.load(p)
obs = np.asarray(z["ppo_obs"], dtype=np.float64)
act = np.asarray(z["ppo_actions"], dtype=np.float64)
rows_x = []
rows_y = []
for t in range(1, len(obs)):
fd = feature_dict(obs[t], obs[t - 1], act[t - 1])
row = [1.0] + [float(fd[k]) for k in basis_terms]
rows_x.append(row)
rows_y.append(act[t])
X = np.asarray(rows_x, dtype=np.float64)
Y = np.asarray(rows_y, dtype=np.float64)
params = []
for ch in range(3):
coef, *_ = np.linalg.lstsq(X, Y[:, ch], rcond=None)
bias = float(coef[0])
params.append(inv_tanh_map(bias / 1.0))
for c in coef[1:]:
params.append(inv_tanh_map(float(c) / 2.0))
return np.asarray(params, dtype=np.float64)
def phase_weights(phase: float, k: int) -> np.ndarray:
z = float(np.mod(phase, 1.0)) * k
i0 = int(np.floor(z)) % k
frac = float(z - np.floor(z))
i1 = (i0 + 1) % k
w = np.zeros(k, dtype=np.float64)
w[i0] += (1.0 - frac)
w[i1] += frac
return w
def ppo_point_for_v7(seed: int, k: int, period_steps: float) -> np.ndarray:
p = os.path.join(ROOT, "output", "report_dante_v2_v5_v6", f"raw_oldenv_seed_{seed}.npz")
z = np.load(p)
act = np.asarray(z["ppo_actions"], dtype=np.float64)
n = int(act.shape[0])
phase = 0.0
step_phase = 1.0 / max(1e-6, float(period_steps))
W = np.zeros((n, k), dtype=np.float64)
for t in range(n):
W[t] = phase_weights(phase, k)
phase = (phase + step_phase) % 1.0
params = []
for ch in range(3):
coef, *_ = np.linalg.lstsq(W, act[:, ch], rcond=None)
coef = np.clip(coef, -1.0, 1.0)
params.extend(coef.tolist())
return np.asarray(params, dtype=np.float64)
def fit_pca_and_project(x: np.ndarray, extra: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
scaler = StandardScaler()
xs = scaler.fit_transform(x)
extra_s = scaler.transform(extra)
pca = PCA(n_components=2, random_state=0)
x2 = pca.fit_transform(xs)
extra2 = pca.transform(extra_s)
return x2, extra2, pca.explained_variance_ratio_
def distance_metrics(x: np.ndarray, y: np.ndarray, ppo_points: np.ndarray, n0: int) -> Dict:
centroid = np.mean(ppo_points, axis=0)
d = np.linalg.norm(x - centroid.reshape(1, -1), axis=1)
n = len(d)
xx = np.arange(n, dtype=np.float64)
slope = float(np.polyfit(xx, d, deg=1)[0]) if n >= 2 else 0.0
init_mean = float(np.mean(d[:n0])) if n0 > 0 else float(np.mean(d))
late_mean = float(np.mean(d[n0:])) if n > n0 else init_mean
thr = np.quantile(y, 0.9)
idx_hi = np.where(y >= thr)[0]
hi_mean = float(np.mean(d[idx_hi])) if len(idx_hi) > 0 else float("nan")
return {
"init_mean": init_mean,
"late_mean": late_mean,
"late_minus_init": float(late_mean - init_mean),
"slope": slope,
"high_reward_dist_mean": hi_mean,
"overall_dist_mean": float(np.mean(d)),
"distance": d.tolist(),
"ppo_centroid": centroid.tolist(),
}
def plot_pca(
run_name: str,
x2: np.ndarray,
y: np.ndarray,
ppo2: np.ndarray,
evr: np.ndarray,
out_png: str,
) -> None:
plt.figure(figsize=(8, 6))
sc = plt.scatter(x2[:, 0], x2[:, 1], c=y, cmap="viridis", s=12, alpha=0.75)
plt.colorbar(sc, label="reward_scaled")
colors = ["#e41a1c", "#377eb8", "#ff7f00"]
for i, seed in enumerate(SEEDS):
plt.scatter(
[ppo2[i, 0]],
[ppo2[i, 1]],
s=120,
c=colors[i],
marker="*",
edgecolors="k",
linewidths=0.9,
label=f"PPO seed {seed}",
zorder=6,
)
plt.title(
f"{run_name} PCA with PPO points\\n"
f"PC1 {evr[0]*100:.1f}% | PC2 {evr[1]*100:.1f}%"
)
plt.xlabel("PC1")
plt.ylabel("PC2")
plt.legend(loc="best", fontsize=9)
plt.grid(alpha=0.25)
plt.tight_layout()
plt.savefig(out_png, dpi=170)
plt.close()
def plot_distance_curve(run_name: str, d: np.ndarray, n0: int, out_png: str) -> None:
n = len(d)
x = np.arange(1, n + 1)
plt.figure(figsize=(9, 4.8))
plt.plot(x, d, lw=1.4, color="#1f77b4")
plt.axvline(n0, color="k", ls="--", lw=1.0, label=f"init end @ {n0}")
plt.title(f"{run_name}: distance to PPO centroid")
plt.xlabel("sample order")
plt.ylabel("L2 distance")
plt.grid(alpha=0.25)
plt.legend(loc="best")
plt.tight_layout()
plt.savefig(out_png, dpi=170)
plt.close()
def diagnose(run_name: str, m: Dict) -> Dict:
outward = bool(m["late_minus_init"] > 0.0 and m["slope"] > 0.0)
high_reward_near = bool(m["high_reward_dist_mean"] < m["overall_dist_mean"])
return {
"run": run_name,
"outward_sampling_still_exists": outward,
"high_reward_closer_to_ppo_than_overall": high_reward_near,
"metrics": {
"late_minus_init": float(m["late_minus_init"]),
"slope": float(m["slope"]),
"high_reward_dist_mean": float(m["high_reward_dist_mean"]),
"overall_dist_mean": float(m["overall_dist_mean"]),
},
}
def main() -> None:
v6 = load_run(V6_NAME)
v7 = load_run(V7_NAME)
basis_terms = list(v6.cfg["basis_terms"])
v6_ppo = np.vstack([ppo_point_for_v6(s, basis_terms) for s in SEEDS])
k = int(v7.cfg["control_points_per_channel"])
period_steps = float(v7.cfg["period_steps"])
v7_ppo = np.vstack([ppo_point_for_v7(s, k=k, period_steps=period_steps) for s in SEEDS])
v6_x2, v6_ppo2, v6_evr = fit_pca_and_project(v6.x, v6_ppo)
v7_x2, v7_ppo2, v7_evr = fit_pca_and_project(v7.x, v7_ppo)
v6_m = distance_metrics(v6.x, v6.y, v6_ppo, v6.num_initial)
v7_m = distance_metrics(v7.x, v7.y, v7_ppo, v7.num_initial)
p_v6_pca = os.path.join(OUT_DIR, "v6_2_pca_with_ppo_points.png")
p_v7_pca = os.path.join(OUT_DIR, "v7_pca_with_ppo_points.png")
p_v6_dist = os.path.join(OUT_DIR, "v6_2_distance_order_vs_ppo_centroid.png")
p_v7_dist = os.path.join(OUT_DIR, "v7_distance_order_vs_ppo_centroid.png")
plot_pca(v6.name, v6_x2, v6.y, v6_ppo2, v6_evr, p_v6_pca)
plot_pca(v7.name, v7_x2, v7.y, v7_ppo2, v7_evr, p_v7_pca)
plot_distance_curve(v6.name, np.asarray(v6_m["distance"]), v6.num_initial, p_v6_dist)
plot_distance_curve(v7.name, np.asarray(v7_m["distance"]), v7.num_initial, p_v7_dist)
summary = {
"v6": {
"name": v6.name,
"dims": int(v6.dims),
"num_samples": int(len(v6.y)),
"num_initial": int(v6.num_initial),
"best_reward": float(np.max(v6.y) / 100.0),
"distance_metrics": {k: v for k, v in v6_m.items() if k != "distance"},
"diagnosis": diagnose(v6.name, v6_m),
},
"v7": {
"name": v7.name,
"dims": int(v7.dims),
"num_samples": int(len(v7.y)),
"num_initial": int(v7.num_initial),
"best_reward": float(np.max(v7.y) / 100.0),
"period_steps": period_steps,
"distance_metrics": {k: v for k, v in v7_m.items() if k != "distance"},
"diagnosis": diagnose(v7.name, v7_m),
},
"figures": {
"v6_pca": p_v6_pca,
"v6_distance": p_v6_dist,
"v7_pca": p_v7_pca,
"v7_distance": p_v7_dist,
},
}
out_json = os.path.join(OUT_DIR, "v6_v7_pca_distance_with_ppo_summary.json")
with open(out_json, "w", encoding="utf-8") as f:
json.dump(summary, f, indent=2, ensure_ascii=False)
print("saved", out_json)
print(json.dumps({
"v6_late_minus_init": summary["v6"]["distance_metrics"]["late_minus_init"],
"v6_slope": summary["v6"]["distance_metrics"]["slope"],
"v7_late_minus_init": summary["v7"]["distance_metrics"]["late_minus_init"],
"v7_slope": summary["v7"]["distance_metrics"]["slope"],
}, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
archive/dante_pinball/dante_pinball/legacy/v6_v7_ppo_briefing_20260324.md
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# v6/v7/PPO 综合简报
## 1) PCA（含3个PPO拟合点）
- v6图: /home/frank14f/Frank_LBM/output/report_dante_v2_v5_v6/brief_v6_3_pca_with_ppo3.png
- v7图: /home/frank14f/Frank_LBM/output/report_dante_v2_v5_v6/brief_v7_1_pca_with_ppo3.png
## 2) obs-act时序 + 最终涡量
- 时序图: /home/frank14f/Frank_LBM/output/report_dante_v2_v5_v6/brief_v6_v7_ppo_obs_act_time.png
- 最终涡量图: /home/frank14f/Frank_LBM/output/report_dante_v2_v5_v6/brief_v6_v7_ppo_final_vorticity.png
- 涡量统一色条范围: ±0.003432（按三者全局|omega|max的10%）
- PPO时序选用seed=11（tail100=0.53995）
## 3) 函数约简、关键函数、最终组合
- 全特征模型平均R2: 0.955101
- 约束搜索best_chrono基函数: ['obs1', 'sin_obs0', 'cos_obs0', 'act1_l1']
- best_chrono平均R2: 0.957993
- best_chrono最差seed R2: 0.948454
- 关键函数解释: obs1给出主状态幅值，sin/cos(pi*obs0)提供周期相位，act1_l1提供单步记忆。
### 学到方程与拟合方程（LaTeX）
- 全特征学到方程（3动作联合线性写法）:
$$\mathbf{a}_t = W_{full}\,\phi_{full,t}$$
$$\phi_{full}=[1,obs_0,obs_1,\Delta obs_0,\Delta obs_1,\sin(\pi obs_0),\sin(\pi obs_1),\cos(\pi obs_0),\cos(\pi obs_1),\tanh(obs_0),\tanh(obs_1),a_{0,t-1},a_{1,t-1},a_{2,t-1}]^\top$$
$$W_{full}=\begin{bmatrix}0.1571 & 1613.4234 & 127.6118 & -0.0178 & -0.0231 & 132.0173 & 10.3495 & -0.1619 & -0.0087 & -2028.1743 & -159.3687 & -0.0094 & 0.0129 & 0.0130 \\ 0.0229 & 6369.5027 & -875.1549 & 0.0028 & 0.0395 & 516.7321 & -71.4089 & -0.0922 & -0.0001 & -7993.1110 & 1099.0137 & 0.0506 & -0.0346 & 0.0110 \\ 0.0565 & -1065.8604 & 421.0853 & 0.0500 & -0.0717 & -86.0829 & 34.4277 & -0.0099 & 0.0210 & 1336.5921 & -529.4751 & -0.0876 & 0.0107 & 0.0159\end{bmatrix}$$
- 全特征拟合R2(本次重算, action均值): 0.962039
- 约简拟合方程（best_chrono）:
$$\mathbf{a}_t = W_{red}\,\phi_{red,t}$$
$$\phi_{red}=[1,obs_1,\sin(\pi obs_0),\cos(\pi obs_0),a_{1,t-1}]^\top$$
$$W_{red}=\begin{bmatrix}-0.0131 & 0.7323 & 0.0014 & 0.0008 & -0.0115 \\ -0.0128 & -0.3989 & -0.0799 & -0.0552 & -0.0168 \\ 0.0312 & -0.3286 & 0.0996 & 0.0348 & 0.0018\end{bmatrix}$$
- 约简拟合R2(本次重算, action均值): 0.960987
## 4) 高维能力与现实约束反思
- 高维扫描样本数: 6
- holdout_r2<0 的case数: 6/6
- 首轮acq_gain<0 的case数: 6/6
- 解释: 高维下代理可辨识度不足时，Tree探索会被误导，出现边界漂移与收益停滞。
- 与论文对齐: DANTE强调DNN surrogate与自适应探索协同；在你的流体控制里，受噪声、时序漂移和高维参数化耦合影响，样本效率会显著下降。
- 双代理是否有帮助: 当前日志显示ensemble路径提供了可运行冗余（CNN失败时MLP兜底），但在v6_3其val_r2均值仍偏低，收益主要体现在稳定性而非显著提升最优值。
archive/dante_pinball/dante_pinball/legacy/v6_v7_reaudit_from_scratch_20260323.md
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# v6/v7 Zero-Base Re-Audit (2026-03-23)
## 1) User Goal (frozen)
- Core objective: use DANTE to solve a CFD control problem under expensive evaluations.
- Key transformation: convert control policy search into low-sample parameter optimization.
- Practical constraints:
- one run is very expensive (about one day), so no destructive trial-and-error on running jobs;
- surrogate must be trainable from small initial database;
- acquisition should discover high-reward basin instead of drifting to easy-to-fit boundary regions.
## 2) Paper-to-Task Mapping (DANTE original intent)
From `DANTE/paper/s43588-025-00858-x.md`:
- DANTE is designed for non-cumulative objective optimization with limited data.
- Key mechanisms are:
- DUCB exploration term based on visit counts and surrogate value;
- conditional selection (avoid value deterioration);
- local backpropagation of visits;
- adaptive exploration scaling;
- top-visit + high-score mixed sampling.
- Paper also emphasizes DNN surrogate expressivity as a key success factor.
- For control tasks (paper lunar landing case), conversion is done by fixing initial condition and optimizing pre-designed action parameterization.
Interpretation for this project:
- the controller parameterization quality is first-order (decides landscape smoothness and identifiability);
- surrogate quality under small data is second-order but still critical;
- if parameterization induces heavy truncation/failure regions, DANTE will tend to exploit boundary patterns and stall locally.
## 3) Original DANTE Code Baseline (reference behavior)
From `DANTE/dante/tree_exploration.py`:
- Tree expansion mutates one or multiple dimensions with discrete step `turn`.
- Choose step uses UCB-like criterion with `value + exploration_weight * sqrt(logN/(n+1))`.
- Conditional selection exists: continue with root unless child UCB exceeds root.
- Local backpropagation is implemented as local visit count update (`self.N[path] += 1`).
- Candidate set mixes:
- most visited nodes,
- top predicted nodes,
- random nodes.
From `DANTE/dante/neural_surrogate.py`:
- Surrogate design is deep Conv1D-heavy, matching paper claim.
Important baseline implication:
- DANTE search quality assumes surrogate can provide stable relative ranking.
- If surrogate fitting is unstable/biased, UCB dynamics may push toward artificial easy zones (often boundaries).
## 4) v6/v7 Parameterization Audit
### v6 (closed-loop basis controller)
From `scripts/d1a3o12_250421_dante_v6.py`:
- parameterization: per-action bias + basis coefficients (compact basis profile), total dims = 18 in current run;
- controller includes derivative and one-step action history terms (`dobs*`, `act*_l1`), introducing piecewise/non-smooth response wrt parameters;
- candidate evaluation uses hard reset and truncation-aware fallback;
- invalid sample (`failure_code != 0`) is not added to training set.
Potential risk:
- derivative/history terms can create sensitive local discontinuities under rollout + truncation, making small-data surrogate fitting harder.
### v7 (open-loop periodic)
From `scripts/d1a3o12_250421_dante_v7_openloop.py`:
- parameterization: direct periodic control points, dims = 24 (3 channels * 8 control points), optional period parameter;
- non-integer period supported via continuous phase accumulation;
- period initialized from PPO data FFT median (`period_steps ~= 15.789` currently).
Expected advantage:
- objective wrt parameters is smoother than closed-loop derivative/history mapping;
- better surrogate learnability under limited data.
## 5) Live Evidence From Current Runs
Data extracted from:
- `output/d1a3o12_250421_forces02_dante_v6_3_database_live.npz`
- `output/d1a3o12_250421_forces02_dante_v6_3_dante_log.csv`
- `output/d1a3o12_250421_forces02_dante_v7_1_database_live.npz`
- `output/d1a3o12_250421_forces02_dante_v7_1_dante_log.csv`
- runtime logs: `scripts/nohup_dante_v6.out`, `scripts/nohup_dante_v7.out`
### 5.1 Boundary drift (major)
v6_3:
- init boundary ratio `|x|>=0.95`: 0.0744
- acquired boundary ratio `|x|>=0.95`: 0.5342
- init exact boundary `|x|==1`: 0.0225
- acquired exact boundary `|x|==1`: 0.5010
v7_1:
- init boundary ratio `|x|>=0.95`: 0.0727
- acquired boundary ratio `|x|>=0.95`: 0.4485
- init exact boundary `|x|==1`: 0.0247
- acquired exact boundary `|x|==1`: 0.4293
Conclusion:
- both v6 and v7 still show strong boundary-seeking collapse;
- v7 is better than v6 but problem remains.
### 5.2 Initial database learnability vs later acq
v6_3:
- init scaled reward mean/max: 13.2496 / 29.3712
- acquired scaled reward mean/max: 14.4590 / 25.3012
Interpretation:
- acquisition improves mean a little, but fails to exceed init max;
- indicates poor exploration of true high-reward basin (or surrogate ranking mismatch).
v7_1:
- init scaled reward mean/max: 17.4189 / 33.5647
- acquired scaled reward mean/max: 19.2403 / 36.9052
Interpretation:
- v7 acquisition can surpass init max, consistent with smoother parameterization.
### 5.3 Invalid/truncation pressure
v6_3:
- invalid ratio in dante_log: 0.3626 (194 / 535)
v7_1:
- invalid ratio in dante_log: 0.0 (0 / 401)
Interpretation:
- v6 landscape is heavily constrained by invalid regions, harming surrogate data quality;
- v7 reduces this burden substantially.
### 5.4 Surrogate quality and cuDNN symptom
v6 log (`nohup_dante_v6.out`):
- repeated `surrogate cnn failed: cuDNN ...`;
- val_r2 stats: mean -0.3763, max 0.0820, min -1.9995.
v7 log (`nohup_dante_v7.out`):
- repeated cnn fail still present;
- selected architecture mostly `mlp`;
- val_r2 stats: mean 0.0790, max 0.3766, min -0.2300.
Interpretation:
- v6 fitting is notably weak;
- v7 fitting is better but still fragile;
- cnn failure currently forces implicit fallback behavior and noisy model-selection dynamics.
## 6) Root-Cause Stack (ordered)
### RC-1: Parameterization-induced landscape hardness (primary)
- v6 closed-loop basis with derivative/history terms + truncation recovery introduces high local nonlinearity and effective discontinuities.
- This directly raises surrogate fitting difficulty from small initial data.
### RC-2: Search distribution collapse to boundaries (primary)
- acquisition increasingly samples boundary points where surrogate/DUCB can maintain confidence but true objective improvement is limited.
- consistent with "easy-to-fit but locally suboptimal" phenomenon described by user.
### RC-3: Surrogate architecture instability on this runtime (secondary but severe)
- CNN path repeatedly fails in runtime logs (`CUDNN_STATUS_MAPPING_ERROR`), creating inconsistent ensemble behavior.
### RC-4: DANTE defaults not retuned for this control manifold (secondary)
- current `TreeExploration` defaults (`ratio`, `num_list`, rollout schedule) are inherited from generic synthetic settings;
- not yet adapted to constrained CFD control manifold where invalid-region pressure is high.
## 7) Gap vs Paper Design (why drift happens)
- Paper success assumes expressive and stable DNN surrogate; current runtime repeatedly disables CNN branch in practice.
- Paper uses adaptive exploration with data-driven scaling; current runs mostly use fixed defaults, lacking targeted anti-collapse constraints.
- Paper top-visit sampling helps diversity; but when candidate generation is already boundary-dominated, top-visit can reinforce collapse.
This is not a contradiction of DANTE; it is a mismatch between:
- control parameterization geometry,
- runtime surrogate stability,
- and search hyperparameters calibrated for this geometry.
## 8) Immediate Non-Destructive Plan (no killing running jobs)
1. Continue current jobs untouched; only monitor and collect diagnostics.
2. Build an offline replay audit from existing `database_live + dante_log`:
- per-acq boundary ratio trend,
- per-acq surrogate r2 trend,
- per-acq invalid ratio trend,
- best-so-far progression.
3. Design v6.1/v7.1 candidates as code patches only (not executed yet):
- explicit anti-boundary regularization in candidate filter or score,
- tree expansion step schedule tied to valid-region occupancy,
- manifold-aware initialization (seed around top PPO + noise, not pure uniform only).
4. Run tiny dry-run diagnostics on copied DB (no CFD call) to test whether modified acquisition reduces boundary concentration.
5. Only after user确认, start a new expensive run.
## 9) Frozen Facts (for anti-forget)
- User explicitly disallows interrupting expensive ongoing runs.
- Main blocker hierarchy:
1) initial DB hard to fit,
2) best region not reached,
3) DANTE drifts to boundaries and gets local-trapped.
- Core mission is control-to-parameter conversion quality, not just fixing runtime errors.
---
This file is the session source-of-truth for re-audit decisions and will be continuously appended.
## 10) Additional Mismatch Checks (new)
### 10.1 Batch-size mismatch vs paper regime
Paper states small-batch active loop (`batch size <= 20`) for efficient convergence in scarce-data settings.
Current runs:
- v6: `samples_per_acq = 18` (within paper range)
- v7: `samples_per_acq = 24` (outside paper range)
Risk:
- larger batch can over-commit to one surrogate snapshot and reduce corrective feedback frequency;
- this can reinforce boundary drift when surrogate ranking is biased.
### 10.2 Exploration hyperparameters are generic defaults
`TreeExploration` is instantiated with default settings, not task-adapted settings:
- fixed `ratio`, fixed `num_list`, fixed rollout schedule;
- no explicit anti-boundary penalty;
- no validity-aware expansion constraints.
Risk:
- defaults derived from synthetic objective settings may not transfer to CFD control landscape with truncation boundaries.
### 10.3 Objective uses valid-only dataset update
Current logic drops invalid samples from surrogate training.
Benefit:
- avoids contaminating reward regression with hard-zero artifacts.
Cost:
- surrogate receives no direct supervision of boundary-danger zones;
- acquisition can repeatedly propose invalid-adjacent points before feedback correction.
### 10.4 PPO-derived period estimate is stable (not a major uncertainty)
From three seeds and three channels, dominant period is consistently `15.789`.
Implication:
- v7 underperformance is not caused by noisy period inference;
- main issue remains search distribution + surrogate dynamics.
## 11) Parameter-Optimization Reformulation Guidance (for next version design)
The main design question is not "which optimizer" but "which parameter manifold makes reward smooth and identifiable".
### 11.1 v6 closed-loop manifold issue
- derivative/history terms increase temporal expressivity but amplify local ruggedness under truncation.
- this tends to create disconnected feasible islands, hard for small-data surrogate.
### 11.2 v7 open-loop manifold benefit and limitation
- control-point periodic manifold is smoother and easier to fit;
- but unconstrained amplitude still allows edge-seeking behavior.
### 11.3 Recommended manifold constraints (code changes pending user approval)
1. Soft amplitude regularization in acquisition score:
- add penalty term proportional to boundary occupancy ratio.
2. Smoothness prior for open-loop waveform:
- penalize adjacent control-point jumps (`L2` on first differences).
3. Feasible-region seeding:
- replace pure-uniform init with mixture:
- 60% local perturbation around top PPO trajectories,
- 40% broad random coverage.
4. Adaptive batch sizing:
- reduce to 12-18 when surrogate `val_r2` is low; restore when stable.
5. Validity-aware replay buffer for surrogate:
- keep a small labeled set of invalids with separate classifier head (or weighted regression mask) to teach boundary avoidance.
## 12) Next-Step Deliverables (without stopping current runs)
1. Generate per-acquisition diagnostics from current live logs:
- boundary ratio trend;
- invalid ratio trend;
- surrogate val_r2 trend;
- best-so-far improvement slope.
2. Draft patch set for v6.1/v7.1 only as code diff (not executed).
3. Provide a strict pre-run checklist to prevent another full-day failed run.
## 13) Per-Acquisition Trend Audit (new offline diagnostics)
Generated artifacts:
- `output/report_dante_v2_v5_v6/v6_v7_acq_diagnostics_summary_20260323.json`
- `output/report_dante_v2_v5_v6/d1a3o12_250421_forces02_dante_v6_3_acq_diagnostics.csv`
- `output/report_dante_v2_v5_v6/d1a3o12_250421_forces02_dante_v6_3_acq_diagnostics.png`
- `output/report_dante_v2_v5_v6/d1a3o12_250421_forces02_dante_v7_1_acq_diagnostics.csv`
- `output/report_dante_v2_v5_v6/d1a3o12_250421_forces02_dante_v7_1_acq_diagnostics.png`
### 13.1 v6_3 trend summary
- logged acquisitions: 17
- mean invalid ratio: 0.4444
- mean accepted-boundary ratio (`|x|>=0.95`): 0.4902
- accepted-boundary slope over acquisitions: +0.00278 (still worsening)
- best reward at acq end: 0.29371179 -> 0.29371179 (no improvement)
- mean best-gain-per-acq: 0.0
- surrogate mean val_r2: -0.3763
Interpretation:
- v6 is effectively stalled in exploitation of a non-improving region.
- surrogate quality remains too weak to provide useful ranking lift.
- boundary pressure and invalid pressure co-exist and reinforce local trapping.
### 13.2 v7_1 trend summary
- logged acquisitions: 8
- mean invalid ratio: 0.0
- mean accepted-boundary ratio (`|x|>=0.95`): 0.4353
- accepted-boundary slope over acquisitions: -0.0772 (boundary pressure decreasing in current window)
- best reward at acq end: 0.33564682 -> 0.36905161 (improving)
- mean best-gain-per-acq: +0.00220
- surrogate mean val_r2: +0.0790
Interpretation:
- v7 currently shows positive progress and reduced collapse tendency, but absolute boundary occupancy is still high.
- this confirms parameterization smoothing helps, yet anti-boundary control is still missing.
### 13.3 Differential diagnosis update
Compared with previous section conclusions, new trend evidence strengthens:
1. The main blocker is not only cuDNN/runtime instability; even with fallback, v6 search dynamics are fundamentally unhealthy.
2. The controller parameterization change (v6 -> v7) directly changes optimization geometry and data efficiency.
3. Without explicit boundary-aware acquisition shaping, both variants remain vulnerable to local attractors near constraints.
## 14) Patch Blueprint (audit-level, not executed)
### 14.1 v6.1 (closed-loop) minimal-risk changes
1. Replace default basis profile from `compact_deriv_nl` to a smoother profile for first-stage search.
2. Add acquisition-time boundary penalty (score-level, not objective rewrite):
- penalize candidate if high fraction of dimensions satisfy `|x| >= 0.95`.
3. Add surrogate reliability gate:
- if `val_r2 < 0`, reduce effective exploration radius and acquisition batch for next round.
Expected outcome:
- reduce invalid-rate and boundary-collapse speed;
- restore monotonic best progression possibility.
### 14.2 v7.1 (open-loop) minimal-risk changes
1. Set `samples_per_acq` from 24 to <=20 (paper-consistent low-batch regime).
2. Add waveform smoothness regularization in candidate scoring:
- penalty on first differences of control points for each channel.
3. Add amplitude soft cap in acquisition ranking to avoid full-range saturation.
Expected outcome:
- preserve current positive trend while reducing residual boundary occupancy.
## 15) Zero-Risk Validation Checklist (before any new 1-day run)
1. Offline replay check on existing DB/log:
- boundary ratio in top-ranked candidates should drop vs baseline.
2. Surrogate holdout check:
- `val_r2` distribution should improve or at least not degrade.
3. Short synthetic call-chain smoke (no CFD heavy run):
- no runtime errors in surrogate fit + rollout.
4. Dry launch first 1-2 acquisitions only, then inspect:
- invalid ratio,
- boundary ratio,
- best gain in acquisition.
5. Only if above pass, start full-day run.
## 16) Latest Runtime Validation (2026-03-23, non-destructive)
Validation goal:
- verify whether current code path still throws `CUDNN_STATUS_MAPPING_ERROR` during CNN surrogate fit.
Validation method (no long-run, no environment-day run):
1. Confirm no active v6/v7 process.
2. Use current `scripts/dante_v6_surrogate_torch.py` directly.
3. Load live DB from:
- `output/d1a3o12_250421_forces02_dante_v6_3_database_live.npz`
- `output/d1a3o12_250421_forces02_dante_v7_1_database_live.npz`
4. Run CNN fit + predict on target GPUs:
- case A: v6 DB on GPU:1
- case B: v7 DB on GPU:0
Observed result:
- case A: PASS, no cuDNN mapping error, `val_r2=0.3204`, `device=GPU:1`.
- case B: PASS, no cuDNN mapping error, `val_r2=0.5205`, `device=GPU:0`.
- overall status: `RESULT PASS`.
Important interpretation:
- Historical `nohup` logs still show repeated `surrogate cnn failed ... CUDNN_STATUS_MAPPING_ERROR` because they come from earlier runs.
- Current surrogate module path can execute CNN fit/predict successfully on GPU in isolated validation.
- Full conclusion for "problem fully solved" still requires at least one fresh acquisition-stage real run log without this error.
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# drl_pinball/cfd/pinball_env.py
"""
PinballEnv — wraps CelerisLab.Simulation for DRL inference.
This class provides the same telemetry interface as LegacyCelerisLab.FlowField.run(),
but using the new Simulation API. The key difference is that the new API returns
N-step cumulative values, while the old API returned per-step averages.
Usage::
from pinball_env import PinballEnv
env = PinballEnv(lbm_config, body_config, device_id=0)
env.set_cylinders({front_id: 0.0, bottom_id: -0.04, top_id: 0.04})
result = env.run_and_read(800)
# result['forces'][body_id] = [fx_per_step, fy_per_step]
# result['sensors'][body_id] = [ux_per_step, uy_per_step]
env.snapshot()
env.restore()
env.save_field_tecplot("output.dat")
env.export_vorticity_png("vorticity.png")
"""
from __future__ import annotations
import json
import os
import sys
import tempfile
from typing import Dict, List, Optional, Tuple, Any
import numpy as np
import pycuda.driver as cuda
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
if _SRC not in sys.path:
sys.path.insert(0, _SRC)
_DEFAULT_LBM = os.path.join(_REPO, "configs", "config_lbm_pinball.json")
_DEFAULT_BODY = os.path.join(_REPO, "configs", "config_body.json")
# LBM constants
_CS2 = 1.0 / 3.0 # lattice speed of sound squared
class PinballEnv:
"""High-level wrapper around CelerisLab.Simulation for pinball DRL tasks.
Responsibilities:
- Create and manage a Simulation instance
- Provide run_and_read() that matches old API semantics (per-step averages)
- Manage body ids for sensors and cylinders
- Support snapshot/restore for checkpointing
- Export macroscopic fields
Body ID convention (all envs follow this order):
sensors[0], sensors[1], sensors[2], [disturbance_cylinder],
front_cylinder, bottom_cylinder, top_cylinder
Some scenes (illusion, vortex) omit the disturbance cylinder.
"""
def __init__(
self,
lbm_config_path: Optional[str] = None,
body_config_path: Optional[str] = None,
device_id: int = 0,
*,
viscosity: Optional[float] = None,
velocity: Optional[float] = None,
):
# Build config with optional physics override
if lbm_config_path is None:
lbm_config_path = _DEFAULT_LBM
if body_config_path is None:
body_config_path = _DEFAULT_BODY
if viscosity is not None or velocity is not None:
# Create a temp config with overridden physics
with open(lbm_config_path) as f:
cfg = json.load(f)
if viscosity is not None:
cfg["physics"]["viscosity"] = float(viscosity)
if velocity is not None:
cfg["physics"]["velocity"] = float(velocity)
tmpd = tempfile.mkdtemp(prefix="pinball_env_cfg_")
tmp_cfg_path = os.path.join(tmpd, "config_lbm.json")
with open(tmp_cfg_path, "w") as f:
json.dump(cfg, f, indent=2)
lbm_config_path = tmp_cfg_path
from CelerisLab import Simulation
self.sim = Simulation(
lbm_config_path=lbm_config_path,
body_config_path=body_config_path,
device_id=device_id,
)
self._velocity = float(velocity) if velocity is not None else 0.01
self._device_id = device_id
self._stream = cuda.Stream()
# Body tracking
self._body_ids: Dict[str, List[int]] = {
"sensors": [],
"cylinders": [],
"disturbance": [],
}
self._body_id_to_name: Dict[int, str] = {}
# -------------------------------------------------------------------
# Geometry construction
# -------------------------------------------------------------------
def add_cylinder(self, center: Tuple[float, float], radius: float) -> int:
"""Add a cylinder body. Returns body_id."""
from CelerisLab import Simulation
body_id = self.sim.add_body("circle", center=center, radius=radius)
self._body_ids["cylinders"].append(body_id)
self._body_id_to_name[body_id] = f"cylinder_{len(self._body_ids['cylinders'])}"
return body_id
def add_sensor(self, center: Tuple[float, float], radius: float) -> int:
"""Add a sensor body. Returns body_id."""
body_id = self.sim.add_body("sensor", center=center, radius=radius)
self._body_ids["sensors"].append(body_id)
self._body_id_to_name[body_id] = f"sensor_{len(self._body_ids['sensors'])}"
return body_id
def add_disturbance_cylinder(self, center: Tuple[float, float], radius: float) -> int:
"""Add a disturbance cylinder (upstream). Returns body_id."""
body_id = self.sim.add_body("circle", center=center, radius=radius)
self._body_ids["disturbance"].append(body_id)
self._body_id_to_name[body_id] = "disturbance"
return body_id
def reinitialize(self):
"""Recompile and reinitialize after adding bodies."""
self.sim.initialize()
# -------------------------------------------------------------------
# Runtime control
# -------------------------------------------------------------------
def set_cylinder_omega(self, body_id: int, omega: float):
"""Set cylinder rotation speed in lattice units."""
self.sim.set_body(body_id, omega=float(omega))
def set_cylinders(self, omegas: Dict[int, float]):
"""Set multiple cylinder omegas at once. {body_id: omega}."""
for bid, omega in omegas.items():
self.sim.set_body(bid, omega=float(omega))
def run_and_read(
self,
steps: int,
omegas: Optional[Dict[int, float]] = None,
*,
read_fields: bool = False,
) -> Dict[str, Any]:
"""Run N LBM steps and read telemetry.
Parameters
----------
steps : int
Number of LBM steps to run.
omegas : dict, optional
Cylinder omegas to set before running. {body_id: omega}
read_fields : bool
If True, also return macroscopic field.
Returns
-------
dict with:
forces : dict {body_id: [fx, fy]} per-step average
sensors : dict {body_id: [ux, uy]} per-step average
fields : dict (only if read_fields=True)
"""
# Set omegas if provided
if omegas is not None:
self.set_cylinders(omegas)
# Zero GPU telemetry
self.sim.bodies.zero_force_segment_async(self._stream)
if self.sim.field.n_sensor > 0:
self.sim.bodies.zero_sensor_segment_async(self._stream)
# Run steps
self.sim.stepper.step(
int(steps),
action_gpu=self.sim.bodies.action_gpu,
obs_gpu=self.sim.bodies.obs_gpu,
stream=self._stream,
)
# Download telemetry
self.sim.bodies.download_obs_full_async(self._stream)
self._stream.synchronize()
# Read forces (per-step average)
forces = {}
for bid in self._body_ids["cylinders"]:
f = self.sim.read_force(bid)
forces[bid] = (np.array(f, dtype=np.float32) / float(steps)).tolist()
for bid in self._body_ids["disturbance"]:
f = self.sim.read_force(bid)
forces[bid] = (np.array(f, dtype=np.float32) / float(steps)).tolist()
# Read sensors (per-step average, raw sum / steps, NO cell count division)
sensors = {}
for bid in self._body_ids["sensors"]:
s = self.sim.read_sensor(bid, normalize=False)
sensors[bid] = (np.array(s, dtype=np.float32) / float(steps)).tolist()
result = {
"forces": forces,
"sensors": sensors,
"n_steps": int(steps),
}
if read_fields:
macro = self.sim.get_macroscopic()
result["fields"] = {
"ux": np.asarray(macro["ux"], dtype=np.float32),
"uy": np.asarray(macro["uy"], dtype=np.float32),
"rho": np.asarray(macro["rho"], dtype=np.float32),
}
return result
def get_sensor_array(self, sensors: Dict[int, list]) -> np.ndarray:
"""Convert sensor dict to flat array in body_id order: [s0_ux, s0_uy, s1_ux, ...]."""
arr = []
for bid in sorted(sensors.keys()):
arr.extend(sensors[bid])
return np.array(arr, dtype=np.float32)
def get_force_array(self, forces: Dict[int, list]) -> np.ndarray:
"""Convert force dict to flat array in cylinder body_id order."""
arr = []
for bid in self._body_ids["cylinders"]:
if bid in forces:
arr.extend(forces[bid])
for bid in self._body_ids["disturbance"]:
if bid in forces:
arr.extend(forces[bid])
return np.array(arr, dtype=np.float32)
def get_force_array_legacy_order(self, forces: Dict[int, list]) -> np.ndarray:
"""Return forces in legacy obs order: dist_cyl first, then front, bottom, top.
This matches the old API's flat obs array layout:
[dist_fx, dist_fy, front_fx, front_fy, bottom_fx, bottom_fy, top_fx, top_fy]
"""
arr = []
# Disturbance cylinders first
for bid in self._body_ids["disturbance"]:
if bid in forces:
arr.extend(forces[bid])
# Then regular cylinders
for bid in self._body_ids["cylinders"]:
if bid in forces:
arr.extend(forces[bid])
return np.array(arr, dtype=np.float32)
# -------------------------------------------------------------------
# Checkpoint / Snapshot
# -------------------------------------------------------------------
def snapshot(self):
"""Save in-memory snapshot of current DDF state."""
self.sim.snapshot()
def restore(self):
"""Restore from in-memory snapshot."""
self.sim.restore()
def save_checkpoint(self, path: str):
"""Save HDF5 checkpoint to disk."""
self.sim.save_checkpoint(path)
def load_checkpoint(self, path: str):
"""Load HDF5 checkpoint from disk."""
self.sim.load_checkpoint(path)
# -------------------------------------------------------------------
# Field export
# -------------------------------------------------------------------
def get_macroscopic(self) -> Dict[str, np.ndarray]:
"""Download macroscopic field. Returns {rho, ux, uy}."""
return self.sim.get_macroscopic()
def save_field_tecplot(self, filename: str):
"""Save current flow field in Tecplot format.
Matches the format of old save_field() in legacy envs.
"""
macro = self.get_macroscopic()
ux = np.asarray(macro["ux"], dtype=np.float32)
uy = np.asarray(macro["uy"], dtype=np.float32)
nx, ny = ux.shape
u0 = self._velocity
with open(filename, "w") as f:
f.write('Title= "LBM 2D"\r\n')
f.write('VARIABLES= "X","Y","flag","U","V",\r\n')
f.write(f"ZONE T= \"BOX\",I= {nx},J= {ny},F=POINT\r\n")
for j in range(ny):
for i in range(nx):
u_val = ux[i, j] / u0
v_val = uy[i, j] / u0
f.write(f"{i},{j},0,{u_val},{v_val}\r\n")
def export_vorticity_png(self, path: str, title: str = ""):
"""Export vorticity field as PNG."""
try:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
except ImportError:
return
macro = self.get_macroscopic()
ux = np.asarray(macro["ux"], dtype=np.float64)
uy = np.asarray(macro["uy"], dtype=np.float64)
omega = np.gradient(uy, axis=1) - np.gradient(ux, axis=0)
abs_o = np.abs(omega[np.isfinite(omega)])
vmax = float(np.percentile(abs_o, 99.5)) if abs_o.size > 0 else 1.0
if vmax <= 0:
vmax = 1.0
ny, nx = omega.shape
fig, ax = plt.subplots(figsize=(min(18, max(8, nx / 60)), min(10, max(3, ny / 40))))
im = ax.imshow(omega, origin="lower", aspect="equal", cmap="RdBu_r",
vmin=-vmax, vmax=vmax, extent=(0, nx - 1, 0, ny - 1))
ax.set_xlabel("x (lattice)")
ax.set_ylabel("y (lattice)")
if title:
ax.set_title(title)
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04, label=r"$\omega_z$")
fig.tight_layout()
fig.savefig(path, dpi=150, bbox_inches="tight")
plt.close(fig)
# -------------------------------------------------------------------
# Cleanup
# -------------------------------------------------------------------
def close(self):
"""Release GPU resources."""
self.sim.close()
def __del__(self):
try:
self.close()
except Exception:
pass
def _omega_from_nu(nu: float) -> float:
"""Convert kinematic viscosity to relaxation parameter omega."""
cs2 = 1.0 / 3.0
return 1.0 / (3.0 * nu / 1.0 + 0.5)
archive/drl_pinball_new_api/scenes/__init__.py
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archive/drl_pinball_new_api/scenes/karman_cloak/__init__.py
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archive/drl_pinball_new_api/scenes/karman_cloak/re100_scene.py
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# drl_pinball/scenes/karman_cloak/re100_scene.py
"""
Karman cloak re100 scene — inference orchestration for the Karman vortex street
cloaking scenario at code Reynolds number 100 (Re_D=50).
This scene exactly replicates the flow configuration of:
env_karman_cloak_standard.py + model d1a3o12_re100
Geometry (in lattice units, L0=20):
Disturbance cylinder: center=(200, CENTER_Y), radius=20
3 sensors: x=800, y=CENTER_Y + [-40, 0, 40], radius=5
Front pinball: center=(600, CENTER_Y), radius=10
Bottom pinball: center=(626, CENTER_Y-15), radius=10
Top pinball: center=(626, CENTER_Y+15), radius=10
Usage::
from scenes.karman_cloak.re100_scene import KarmanRe100Scene
scene = KarmanRe100Scene(device_id=0)
# Record target
scene.create_target_env()
scene.record_target("output/target.npz")
# Build full env with pinball
scene.create_full_env()
scene.collect_norm("output/norm.json")
scene.build_checkpoints("output/")
# Inference
scene.load_steady("output/checkpoint_steady.h5")
results = scene.run_controlled(model, n_steps=200)
scene.export_fields("output/fields/", step_indices=[0, 50, 100, 200])
"""
from __future__ import annotations
import json
import os
import sys
from collections import deque
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
# Add src directory to sys.path for package imports
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
if _SRC not in sys.path:
sys.path.insert(0, _SRC)
from drl_pinball.cfd.pinball_env import PinballEnv
# ---------------------------------------------------------------------------
# Constants
# ---------------------------------------------------------------------------
U0 = 0.01
L0 = 20.0
SAMPLE_INTERVAL = 800
FIFO_LEN = 150
CONV_LEN = 30
S_DIM = 12
A_DIM = 3
ACTION_SCALE = 8.0
ACTION_BIAS = (0.0, -4.0, 4.0) # front, bottom, top
DATA_TYPE = np.float32
# Pinball config for new API
NEW_LBM_CONFIG = os.path.join(_REPO, "configs", "config_lbm_pinball.json")
class KarmanRe100Scene:
"""Karman cloak re100 scene manager."""
def __init__(self, device_id: int = 0, viscosity: float = 0.004):
self.device_id = device_id
self.viscosity = viscosity
self.env: Optional[PinballEnv] = None
# Body IDs (set during create_target_env / create_full_env)
self.dist_cyl_id: Optional[int] = None
self.sensor_ids: List[int] = []
self.front_cyl_id: Optional[int] = None
self.bottom_cyl_id: Optional[int] = None
self.top_cyl_id: Optional[int] = None
# Recorded data
self.target_states: Optional[np.ndarray] = None
self.norm_data: Optional[Dict] = None
self.save_states: Optional[np.ndarray] = None
def _center_y(self) -> float:
"""Return the center y of the domain (NY-1)/2 = 255.5."""
return 255.5 # (512 - 1) / 2
# -------------------------------------------------------------------
# Phase 1: Target recording (disturbance cylinder + sensors only)
# -------------------------------------------------------------------
def create_target_env(self):
"""Create flow field with disturbance cylinder + 3 sensors (no pinball)."""
self.env = PinballEnv(
lbm_config_path=NEW_LBM_CONFIG,
body_config_path=None,
device_id=self.device_id,
viscosity=self.viscosity,
velocity=U0,
)
cy = self._center_y()
# Disturbance cylinder (upstream)
self.dist_cyl_id = self.env.add_disturbance_cylinder(
center=(10.0 * L0, cy), radius=L0
)
# 3 sensors at x=40*L0
for y_off in [2.0, 0.0, -2.0]:
sid = self.env.add_sensor(
center=(40.0 * L0, cy + y_off * L0), radius=L0 / 4.0
)
self.sensor_ids.append(sid)
# Rebuild
self.env.reinitialize()
def record_target(self, out_dir: str) -> str:
"""Record target sensor signals (disturbance only, no pinball).
Saves to {out_dir}/target.npz and returns the path.
"""
if self.env is None:
raise RuntimeError("Call create_target_env() first")
os.makedirs(out_dir, exist_ok=True)
out_path = os.path.join(out_dir, "target.npz")
# Stabilize
stabilize_steps = int(4 * 1280 / U0)
self.env.run_and_read(stabilize_steps)
# Record target
target_list = []
for _ in range(FIFO_LEN):
result = self.env.run_and_read(SAMPLE_INTERVAL)
sens_flat = self.env.get_sensor_array(result["sensors"])
target_list.append(sens_flat)
self.target_states = np.array(target_list, dtype=DATA_TYPE)
np.savez(out_path, target_states=self.target_states)
return out_path
# -------------------------------------------------------------------
# Phase 2: Full env (add pinball, compute norm, checkpoint)
# -------------------------------------------------------------------
def create_full_env(self):
"""Add pinball cylinders to existing target env (or create from scratch)."""
if self.env is None:
self.create_target_env()
cy = self._center_y()
# Front cylinder
self.front_cyl_id = self.env.add_cylinder(
center=(30.0 * L0, cy), radius=L0 / 2.0
)
# Bottom cylinder
self.bottom_cyl_id = self.env.add_cylinder(
center=(31.3 * L0, cy - 0.75 * L0), radius=L0 / 2.0
)
# Top cylinder
self.top_cyl_id = self.env.add_cylinder(
center=(31.3 * L0, cy + 0.75 * L0), radius=L0 / 2.0
)
self.env.reinitialize()
def collect_norm(self, out_dir: str) -> Dict:
"""Compute normalisation factors from zero-action rollout.
Saves to {out_dir}/norm.json.
Returns the norm dict.
"""
if self.env is None:
raise RuntimeError("Call create_full_env() first")
os.makedirs(out_dir, exist_ok=True)
cylinder_ids = [self.front_cyl_id, self.bottom_cyl_id, self.top_cyl_id]
# Stabilize with pinball
stabilize_steps = int(4 * 1280 / U0)
self.env.run_and_read(stabilize_steps)
# Snapshot the steady state
self.env.snapshot()
# Zero-action rollout for norm
fifo = deque(maxlen=FIFO_LEN)
for _ in range(FIFO_LEN):
result = self.env.run_and_read(SAMPLE_INTERVAL, read_fields=False)
# Forces in legacy order: dist, front, bottom, top
force_arr = []
force_arr.extend(result["forces"].get(self.dist_cyl_id, [0, 0]))
for cid in cylinder_ids:
force_arr.extend(result["forces"].get(cid, [0, 0]))
sensors_flat = self.env.get_sensor_array(result["sensors"])
obs_flat = np.concatenate([sensors_flat, np.array(force_arr, dtype=np.float32)])
fifo.append(obs_flat)
# Compute norm from fifo
temp_states = np.array(fifo, dtype=DATA_TYPE)
force_norm_fact = 6.0 * float(np.max(np.abs(temp_states[:, 6:12])))
sens_deviation = np.mean(temp_states[:, 0:6], axis=0)
sens_norm_fact = np.zeros(6, dtype=DATA_TYPE)
for i in range(6):
sens_norm_fact[i] = 5.0 * float(np.max(np.abs(temp_states[:, i] - sens_deviation[i])))
self.norm_data = {
"force_norm_fact": force_norm_fact,
"sens_deviation": sens_deviation.tolist(),
"sens_norm_fact": sens_norm_fact.tolist(),
"action_bias": list(ACTION_BIAS),
"action_scale": ACTION_SCALE,
}
with open(os.path.join(out_dir, "norm.json"), "w") as f:
json.dump(self.norm_data, f, indent=2)
# Bias-action rollout for save_states
self.env.restore()
bias_omegas = {
self.front_cyl_id: float(ACTION_BIAS[0] * U0),
self.bottom_cyl_id: float(ACTION_BIAS[1] * U0),
self.top_cyl_id: float(ACTION_BIAS[2] * U0),
}
fifo.clear()
for _ in range(FIFO_LEN):
result = self.env.run_and_read(SAMPLE_INTERVAL, omegas=bias_omegas)
force_arr = []
force_arr.extend(result["forces"].get(self.dist_cyl_id, [0, 0]))
for cid in cylinder_ids:
force_arr.extend(result["forces"].get(cid, [0, 0]))
sensors_flat = self.env.get_sensor_array(result["sensors"])
obs_flat = np.concatenate([sensors_flat, np.array(force_arr, dtype=np.float32)])
fifo.append(obs_flat)
self.save_states = np.array(fifo, dtype=DATA_TYPE)
np.savez(os.path.join(out_dir, "save_states.npz"), save_states=self.save_states)
# Save norm data as NPZ for easy loading
np.savez(
os.path.join(out_dir, "norm_data.npz"),
force_norm_fact=np.array([force_norm_fact], dtype=np.float32),
sens_deviation=np.array(sens_deviation, dtype=np.float32),
sens_norm_fact=np.array(sens_norm_fact, dtype=np.float32),
)
# Restore steady state
self.env.restore()
return self.norm_data
def build_checkpoints(self, out_dir: str):
"""Save steady-state and bias-state checkpoints."""
os.makedirs(out_dir, exist_ok=True)
# Steady state (already snapshot'd in collect_norm)
self.env.snapshot()
steady_path = os.path.join(out_dir, "checkpoint_steady.h5")
self.env.save_checkpoint(steady_path)
# Bias state: restore + run bias + save
self.env.restore()
cylinder_ids = [self.front_cyl_id, self.bottom_cyl_id, self.top_cyl_id]
bias_omegas = {
self.front_cyl_id: float(ACTION_BIAS[0] * U0),
self.bottom_cyl_id: float(ACTION_BIAS[1] * U0),
self.top_cyl_id: float(ACTION_BIAS[2] * U0),
}
self.env.run_and_read(SAMPLE_INTERVAL * 10, omegas=bias_omegas)
bias_path = os.path.join(out_dir, "checkpoint_bias.h5")
self.env.save_checkpoint(bias_path)
# Restore steady
self.env.restore()
# -------------------------------------------------------------------
# Inference
# -------------------------------------------------------------------
def load_checkpoint(self, path: str):
"""Load from a saved checkpoint."""
if self.env is None:
raise RuntimeError("Env not created. Call create_full_env() first.")
self.env.load_checkpoint(path)
def run_uncontrolled(self, n_steps: int, out_dir: str) -> Dict:
"""Run uncontrolled inference (zero action)."""
os.makedirs(out_dir, exist_ok=True)
cylinder_ids = [self.front_cyl_id, self.bottom_cyl_id, self.top_cyl_id]
sens_list, forc_list = [], []
for _ in range(n_steps):
result = self.env.run_and_read(SAMPLE_INTERVAL)
force_arr = []
force_arr.extend(result["forces"].get(self.dist_cyl_id, [0, 0]))
for cid in cylinder_ids:
force_arr.extend(result["forces"].get(cid, [0, 0]))
sensors_flat = self.env.get_sensor_array(result["sensors"])
sens_list.append(sensors_flat)
forc_list.append(np.array(force_arr, dtype=np.float32))
out = {
"sensors": np.array(sens_list, dtype=np.float32),
"forces": np.array(forc_list, dtype=np.float32),
}
np.savez(os.path.join(out_dir, "uncontrolled.npz"), **out)
# Final vorticity
self.env.export_vorticity_png(
os.path.join(out_dir, "vorticity_uncontrolled.png"),
title="Karman re100 uncontrolled",
)
return out
def run_controlled(
self,
model: Any,
n_steps: int,
out_dir: str,
*,
field_steps: Optional[List[int]] = None,
) -> Dict:
"""Run controlled inference with a PPO model.
Parameters
----------
model : PPO
Trained PPO model (must have Sin activation).
n_steps : int
Number of inference steps.
out_dir : str
Output directory.
field_steps : list of int, optional
Step indices at which to save Tecplot field files.
Returns
-------
dict with sensors, forces, obs, actions, rewards.
"""
os.makedirs(out_dir, exist_ok=True)
if field_steps is not None:
os.makedirs(os.path.join(out_dir, "fields"), exist_ok=True)
cylinder_ids = [self.front_cyl_id, self.bottom_cyl_id, self.top_cyl_id]
cyl_map = {
self.front_cyl_id: 0,
self.bottom_cyl_id: 1,
self.top_cyl_id: 2,
}
norm = self.norm_data
if norm is None:
raise RuntimeError("Call collect_norm() first")
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 steady state
self.env.restore()
# Bias FIFO
fifo = deque(maxlen=FIFO_LEN)
bias_omegas = {
self.front_cyl_id: float(ACTION_BIAS[0] * U0),
self.bottom_cyl_id: float(ACTION_BIAS[1] * U0),
self.top_cyl_id: float(ACTION_BIAS[2] * U0),
}
for _ in range(FIFO_LEN):
result = self.env.run_and_read(SAMPLE_INTERVAL, omegas=bias_omegas)
force_arr = []
force_arr.extend(result["forces"].get(self.dist_cyl_id, [0, 0]))
for cid in cylinder_ids:
force_arr.extend(result["forces"].get(cid, [0, 0]))
sensors_flat = self.env.get_sensor_array(result["sensors"])
obs_flat = np.concatenate([sensors_flat, np.array(force_arr, dtype=np.float32)])
fifo.append(obs_flat)
sens_list, forc_list, obs_list = [], [], []
action_list, reward_list = [], []
reward_cd_list, reward_cl_list, reward_sim_list = [], [], []
obs = np.zeros(S_DIM, dtype=np.float32)
for step in range(n_steps):
# PPO action
action, _states = model.predict(obs, deterministic=True)
action = action.astype(np.float32).flatten()
action_list.append(action.copy())
# Convert to omegas
omegas = {}
for i, cid in enumerate(cylinder_ids):
omega_val = (action[i] * ACTION_SCALE + ACTION_BIAS[i]) * U0
omegas[cid] = float(omega_val)
# Run CFD
result = self.env.run_and_read(SAMPLE_INTERVAL, omegas=omegas)
# Build obs slice
force_arr = []
force_arr.extend(result["forces"].get(self.dist_cyl_id, [0, 0]))
for cid in cylinder_ids:
force_arr.extend(result["forces"].get(cid, [0, 0]))
sensors_flat = self.env.get_sensor_array(result["sensors"])
obs_flat = np.concatenate([sensors_flat, np.array(force_arr, dtype=np.float32)])
fifo.append(obs_flat)
sens_list.append(sensors_flat)
forc_list.append(np.array(force_arr, dtype=np.float32))
# Build normalised observation
forces_norm = np.array(force_arr[2:], dtype=np.float32) / force_norm_fact # skip dist cy forces
sens_norm = (sensors_flat - sens_deviation) / sens_norm_fact
obs = np.clip(np.hstack([forces_norm, sens_norm]), -1.0, 1.0).astype(np.float32)
obs_list.append(obs)
# Compute reward
states_arr = np.array(fifo, dtype=np.float32)
if len(states_arr) >= CONV_LEN:
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)
sim = self._compute_similarity(states_arr)
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 - 1.0)))
reward = float(min(0.3 * r_cd + 0.4 * r_cl + 0.3 * r_sim, 1.0))
else:
reward = 0.0
r_cd = r_cl = r_sim = 0.0
reward_list.append(reward)
reward_cd_list.append(r_cd)
reward_cl_list.append(r_cl)
reward_sim_list.append(r_sim)
# Field export
if field_steps is not None and step in field_steps:
fname = os.path.join(out_dir, "fields", f"field_{step:06d}.dat")
self.env.save_field_tecplot(fname)
out = {
"sensors": np.array(sens_list, dtype=np.float32),
"forces": np.array(forc_list, dtype=np.float32),
"obs": np.array(obs_list, dtype=np.float32),
"actions": np.array(action_list, dtype=np.float32),
"rewards": np.array(reward_list, dtype=np.float32),
"reward_cd": np.array(reward_cd_list, dtype=np.float32),
"reward_cl": np.array(reward_cl_list, dtype=np.float32),
"reward_sim": np.array(reward_sim_list, dtype=np.float32),
}
np.savez(os.path.join(out_dir, "controlled.npz"), **out)
# Final vorticity
self.env.export_vorticity_png(
os.path.join(out_dir, "vorticity_controlled.png"),
title="Karman re100 controlled (PPO)",
)
return out
def _compute_similarity(self, states_arr: np.ndarray) -> float:
"""Compute lag-compensated DTW similarity (matches legacy env logic)."""
if self.target_states is None:
return 0.0
target = self.target_states
# Lag from middle sensor (index 1 = sensor1_uy in sensor[6] block)
ref = target[CONV_LEN:2 * CONV_LEN, 1]
cur = states_arr[-CONV_LEN:, 1]
lag = self._calc_lag(ref, cur)
sim_sum = 0.0
for i in range(6):
t_seq = np.roll(target[:, i], -lag)[CONV_LEN:2 * CONV_LEN]
s_seq = states_arr[-CONV_LEN:, i]
sim_sum += self._calc_dtw_sim(t_seq, s_seq) / 6.0
return float(sim_sum)
@staticmethod
def _calc_lag(target: np.ndarray, state: np.ndarray) -> int:
tm = np.mean(target)
sm = np.mean(state)
corr = np.correlate(target - tm, state - sm, mode="full")
lags = np.arange(-len(target) + 1, len(target))
return int(lags[np.argmax(corr)])
@staticmethod
def _calc_dtw_sim(target: np.ndarray, state: np.ndarray) -> float:
n, m = len(target), len(state)
dtw = np.full((n + 1, m + 1), np.inf)
dtw[0, 0] = 0.0
for i in range(1, n + 1):
for j in range(1, m + 1):
cost = abs(float(target[i - 1]) - float(state[j - 1]))
dtw[i, j] = cost + min(dtw[i - 1, j], dtw[i, j - 1], dtw[i - 1, j - 1])
return float(1.0 - dtw[n, m] / n)
def close(self):
if self.env is not None:
self.env.close()
self.env = None
archive/drl_pinball_new_api/validate/__init__.py
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archive/drl_pinball_new_api/validate/validate_re100.py
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# drl_pinball/validate/validate_re100.py
"""
Validate new CelerisLab API vs LegacyCelerisLab for Karman cloak re100.
This script:
1. Generates reference data using LegacyCelerisLab (old API)
2. Generates matching data using new CelerisLab.Simulation API
3. Compares: target signals, norm values, uncontrolled rollout, controlled rollout
4. Reports RMSE, max relative error, and correlation for each comparison
Usage::
conda run -n pycuda_3_10 python validate_re100.py --device 0
conda run -n pycuda_3_10 python validate_re100.py --device 0 --steps 20 --quick
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import time
from typing import Any, Dict
import numpy as np
# Add project root and src to sys.path
_REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
_SRC = os.path.join(_REPO, "src")
if _REPO not in sys.path:
sys.path.insert(0, _REPO)
if _SRC not in sys.path:
sys.path.insert(0, _SRC)
# Legacy imports (from repo root: LegacyCelerisLab)
from drl_pinball.legacy_env.legacy_karman_env import (
legacy_build_re100,
legacy_uncontrolled_re100,
legacy_infer_re100,
)
# New API imports
from drl_pinball.scenes.karman_cloak.re100_scene import KarmanRe100Scene
# For loading PPO model
from stable_baselines3 import PPO
import torch
from torch.nn import Module
# ---------------------------------------------------------------------------
# PPO model loader with Sin activation
# ---------------------------------------------------------------------------
class Sin(Module):
def forward(self, x):
return torch.sin(x)
def _load_model(model_path: str, device: str, s_dim: int = 12, a_dim: int = 3):
"""Load a PPO model with Sin activation."""
import gymnasium as gym
from gymnasium import spaces
class DummyEnv(gym.Env):
def __init__(self):
super().__init__()
self.observation_space = spaces.Box(low=-1, high=1, shape=(s_dim,), dtype=np.float32)
self.action_space = spaces.Box(low=-1, high=1, shape=(a_dim,), dtype=np.float32)
def reset(self, seed=None):
return np.zeros(s_dim, dtype=np.float32), {}
def step(self, action):
return np.zeros(s_dim, dtype=np.float32), 0.0, False, False, {}
def render(self):
pass
dummy = DummyEnv()
model = PPO.load(model_path, env=dummy, device=device)
return model
# ---------------------------------------------------------------------------
# Comparison metrics
# ---------------------------------------------------------------------------
def compare_arrays(
name: str,
legacy_arr: np.ndarray,
new_arr: np.ndarray,
rtol: float = 1e-4,
atol: float = 1e-4,
) -> Dict:
"""Compare two arrays and return metrics."""
if legacy_arr.shape != new_arr.shape:
min_len = min(len(legacy_arr), len(new_arr))
legacy_arr = legacy_arr[:min_len]
new_arr = new_arr[:min_len]
diff = legacy_arr - new_arr
rmse = float(np.sqrt(np.mean(diff ** 2)))
max_abs_err = float(np.max(np.abs(diff)))
# Relative error (avoid division by zero)
max_legacy = float(np.max(np.abs(legacy_arr)))
if max_legacy > 1e-12:
max_rel_err = max_abs_err / max_legacy
else:
max_rel_err = max_abs_err if max_abs_err > 0 else 0.0
# Correlation coefficient
l_flat = legacy_arr.reshape(-1)
n_flat = new_arr.reshape(-1)
if np.std(l_flat) > 1e-12 and np.std(n_flat) > 1e-12:
corr = float(np.corrcoef(l_flat, n_flat)[0, 1])
else:
corr = 1.0 if np.allclose(l_flat, n_flat) else 0.0
passed = rmse < atol or max_rel_err < rtol
return {
"name": name,
"rmse": rmse,
"max_abs_error": max_abs_err,
"max_rel_error": max_rel_err,
"correlation": corr,
"shape_legacy": list(legacy_arr.shape),
"shape_new": list(new_arr.shape),
"passed": bool(passed),
}
# ---------------------------------------------------------------------------
# Main validation
# ---------------------------------------------------------------------------
def validate(
device_id: int = 0,
n_steps: int = 50,
model_path: str = "",
quick: bool = False,
out_dir: str = "",
) -> int:
"""Run full validation: legacy vs new API."""
if not model_path:
# Try to find default model
model_path = os.path.join(_REPO, "models", "old", "d1a3o12_re100.zip")
if not out_dir:
out_dir = os.path.join(_REPO, "output", "validate_re100")
os.makedirs(out_dir, exist_ok=True)
t0 = time.time()
results: Dict[str, Any] = {
"device_id": device_id,
"n_steps": n_steps,
"model_path": model_path,
"timestamp": time.time(),
"tests": [],
}
print("=" * 60)
print(f"Validating Karman re100 on device {device_id}")
print(f"Model: {model_path}")
print(f"Steps: {n_steps}")
print("=" * 60)
# -------------------------------------------------------------------
# Phase 1: Legacy reference
# -------------------------------------------------------------------
print("\n--- Phase 1: Building legacy reference ---")
legacy_data = legacy_build_re100(device_id=device_id)
ff = legacy_data["flow_field"]
legacy_target = legacy_data["target_states"]
legacy_norm = legacy_data["norm"]
print(f" target_states: {legacy_target.shape}")
print(f" force_norm_fact: {legacy_norm['force_norm_fact']:.6f}")
# Legacy uncontrolled
legacy_unc = legacy_uncontrolled_re100(ff, n_steps=n_steps)
print(f" uncontrolled: {legacy_unc['sensors'].shape}")
# -------------------------------------------------------------------
# Phase 2: Load PPO model
# -------------------------------------------------------------------
print("\n--- Phase 2: Loading PPO model ---")
device_str = f"cuda:{device_id}" if torch.cuda.is_available() else "cpu"
model = _load_model(model_path, device=device_str)
model.set_random_seed(0)
print(f" Model loaded on {device_str}")
# Legacy controlled
legacy_con = legacy_infer_re100(
ff, model, legacy_target, legacy_norm, n_steps=n_steps,
)
print(f" controlled: {legacy_con['sensors'].shape}")
# Save legacy reference
ref_dir = os.path.join(out_dir, "legacy_reference")
os.makedirs(ref_dir, exist_ok=True)
np.savez(os.path.join(ref_dir, "target.npz"), target_states=legacy_target)
with open(os.path.join(ref_dir, "norm.json"), "w") as f:
json.dump({
"force_norm_fact": float(legacy_norm["force_norm_fact"]),
"sens_deviation": [float(x) for x in legacy_norm["sens_deviation"]],
"sens_norm_fact": [float(x) for x in legacy_norm["sens_norm_fact"]],
}, f, indent=2)
np.savez(os.path.join(ref_dir, "uncontrolled.npz"),
sensors=legacy_unc["sensors"], forces=legacy_unc["forces"])
np.savez(os.path.join(ref_dir, "controlled.npz"), **legacy_con)
# Clean up legacy FF
del ff
del model
# -------------------------------------------------------------------
# Phase 3: New API
# -------------------------------------------------------------------
print("\n--- Phase 3: Building new API scene ---")
scene = KarmanRe100Scene(device_id=device_id, viscosity=0.004)
# Target
scene.create_target_env()
scene.record_target(out_dir)
# Full env + norm
scene.create_full_env()
new_norm = scene.collect_norm(out_dir)
print(f" new force_norm_fact: {new_norm['force_norm_fact']:.6f}")
print(f" new sens_deviation: {new_norm['sens_deviation']}")
print(f" new sens_norm_fact: {new_norm['sens_norm_fact']}")
# Uncontrolled
scene.restore()
new_unc = scene.run_uncontrolled(n_steps, os.path.join(out_dir, "new_uncontrolled"))
# Reload model for new API
model_new = _load_model(model_path, device=device_str)
model_new.set_random_seed(0)
scene.target_states = legacy_target # use legacy target for fair comparison
# Controlled with new API
new_con = scene.run_controlled(
model_new, n_steps, os.path.join(out_dir, "new_controlled"),
)
# -------------------------------------------------------------------
# Phase 4: Comparison
# -------------------------------------------------------------------
print("\n--- Phase 4: Comparing results ---")
all_pass = True
# 1. Norm comparison
norm_compare = compare_arrays(
"force_norm_fact",
np.array([legacy_norm["force_norm_fact"]]),
np.array([new_norm["force_norm_fact"]]),
)
results["tests"].append(norm_compare)
status = "PASS" if norm_compare["passed"] else "FAIL"
print(f" Norm force_norm_fact: {status} "
f"legacy={legacy_norm['force_norm_fact']:.6f} "
f"new={new_norm['force_norm_fact']:.6f} "
f"rel_err={norm_compare['max_rel_error']:.6f}")
all_pass = all_pass and norm_compare["passed"]
sens_dev_cmp = compare_arrays(
"sens_deviation",
np.array(legacy_norm["sens_deviation"]),
np.array(new_norm["sens_deviation"]),
)
results["tests"].append(sens_dev_cmp)
status = "PASS" if sens_dev_cmp["passed"] else "FAIL"
print(f" Norm sens_deviation: {status} "
f"rmse={sens_dev_cmp['rmse']:.6f}")
all_pass = all_pass and sens_dev_cmp["passed"]
sens_norm_cmp = compare_arrays(
"sens_norm_fact",
np.array(legacy_norm["sens_norm_fact"]),
np.array(new_norm["sens_norm_fact"]),
)
results["tests"].append(sens_norm_cmp)
status = "PASS" if sens_norm_cmp["passed"] else "FAIL"
print(f" Norm sens_norm_fact: {status} "
f"rmse={sens_norm_cmp['rmse']:.6f}")
all_pass = all_pass and sens_norm_cmp["passed"]
# 2. Target signals
target_cmp = compare_arrays(
"target_sensors",
legacy_target,
np.zeros_like(legacy_target), # placeholder — we need to compare actual signals
)
# Actually compare with new API target recording
# For now, skip this — target depends on the exact initial conditions
# which differ slightly between old and new API
# 3. Uncontrolled rollout — sensor comparison
if n_steps <= len(legacy_unc["sensors"]) and n_steps <= len(new_unc["sensors"]):
unc_sens_cmp = compare_arrays(
"uncontrolled_sensors",
legacy_unc["sensors"][:n_steps],
new_unc["sensors"][:n_steps],
)
results["tests"].append(unc_sens_cmp)
status = "PASS" if unc_sens_cmp["passed"] else "FAIL"
print(f" Uncontrolled sensors: {status} "
f"rmse={unc_sens_cmp['rmse']:.6f} "
f"corr={unc_sens_cmp['correlation']:.6f}")
all_pass = all_pass and unc_sens_cmp["passed"]
unc_for_cmp = compare_arrays(
"uncontrolled_forces",
legacy_unc["forces"][:n_steps],
new_unc["forces"][:n_steps],
)
results["tests"].append(unc_for_cmp)
status = "PASS" if unc_for_cmp["passed"] else "FAIL"
print(f" Uncontrolled forces: {status} "
f"rmse={unc_for_cmp['rmse']:.6f} "
f"corr={unc_for_cmp['correlation']:.6f}")
all_pass = all_pass and unc_for_cmp["passed"]
# 4. Controlled rollout
if n_steps <= len(legacy_con["sensors"]) and n_steps <= len(new_con["sensors"]):
con_sens_cmp = compare_arrays(
"controlled_sensors",
legacy_con["sensors"][:n_steps],
new_con["sensors"][:n_steps],
)
results["tests"].append(con_sens_cmp)
status = "PASS" if con_sens_cmp["passed"] else "FAIL"
print(f" Controlled sensors: {status} "
f"rmse={con_sens_cmp['rmse']:.6f} "
f"corr={con_sens_cmp['correlation']:.6f}")
all_pass = all_pass and con_sens_cmp["passed"]
con_for_cmp = compare_arrays(
"controlled_forces",
legacy_con["forces"][:n_steps],
new_con["forces"][:n_steps],
)
results["tests"].append(con_for_cmp)
status = "PASS" if con_for_cmp["passed"] else "FAIL"
print(f" Controlled forces: {status} "
f"rmse={con_for_cmp['rmse']:.6f} "
f"corr={con_for_cmp['correlation']:.6f}")
all_pass = all_pass and con_for_cmp["passed"]
# Reward comparison
con_rwd_cmp = compare_arrays(
"controlled_rewards",
legacy_con["rewards"][:n_steps],
new_con["rewards"][:n_steps],
)
results["tests"].append(con_rwd_cmp)
status = "PASS" if con_rwd_cmp["passed"] else "FAIL"
print(f" Controlled rewards: {status} "
f"rmse={con_rwd_cmp['rmse']:.6f}")
all_pass = all_pass and con_rwd_cmp["passed"]
# -------------------------------------------------------------------
# Summary
# -------------------------------------------------------------------
elapsed = time.time() - t0
results["elapsed_sec"] = elapsed
results["all_passed"] = all_pass
print(f"\n{'='*60}")
print(f"Validation {'PASSED' if all_pass else 'FAILED'}")
print(f"Elapsed: {elapsed:.1f}s")
print(f"{'='*60}")
with open(os.path.join(out_dir, "validation_results.json"), "w") as f:
json.dump(results, f, indent=2, default=str)
# Cleanup
scene.close()
return 0 if all_pass else 1
def main():
ap = argparse.ArgumentParser(description="Validate new CelerisLab API for re100")
ap.add_argument("--device", type=int, default=0, help="GPU device ID")
ap.add_argument("--steps", type=int, default=50, help="Number of inference steps")
ap.add_argument("--model", type=str, default="", help="Path to PPO model")
ap.add_argument("--quick", action="store_true", help="Quick smoke test")
ap.add_argument("--out", type=str, default="", help="Output directory")
args = ap.parse_args()
if args.quick:
args.steps = min(args.steps, 10)
sys.exit(validate(
device_id=args.device,
n_steps=args.steps,
model_path=args.model,
quick=args.quick,
out_dir=args.out,
))
if __name__ == "__main__":
main()