"""Plot CDF + RMSE comparison for the 4 SIM physical-axis ckpts. Output: research_paper/sensing_cdf_compare.png (CDF curves) research_paper/sensing_rmse_bars.png (per-axis RMSE bar chart) """ import math, os, torch import matplotlib.pyplot as plt import numpy as np from joint_dual_sim import ( JointDualSIM, PortReadout, PerBinPort, LinearReadout, soft_position_estimate, make_range_edges, ) from rate_aware_gen import make_config from updated_SIM_0413_multi_user import hadamard_matrix CKPTS = [ ("baseline (5λ, 4L+3N)", "experiments_v2/checkpoints/sense_port_LLNLNLN_S64Q10_Pt10_SNR20.pt"), ("thick=0.10m (9.3λ)", "experiments_v2/checkpoints/phys_thick0p10.pt"), ("thick=0.20m (18.7λ)", "experiments_v2/checkpoints/phys_thick0p20.pt"), ("6L+5N (default 5λ)", "experiments_v2/checkpoints/phys_layout6L5N.pt"), ] DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") CACHE = "experiments_v2/caches/v2_K2_15k.pt" def argmax_position(logits, cfg, device): idx = logits.argmax(dim=-1) s = idx % cfg.S q = idx // cfg.S az_edges = torch.linspace(cfg.az_min_deg, cfg.az_max_deg, cfg.S + 1, device=device) r_edges = make_range_edges(cfg, device) az_hat = (az_edges[:-1] + az_edges[1:]) / 2.0 * math.pi / 180.0 r_hat = (r_edges[:-1] + r_edges[1:]) / 2.0 return r_hat[q], az_hat[s] def eval_one(ckpt_path): ckpt = torch.load(ckpt_path, weights_only=False, map_location="cpu") cfg_dict = ckpt['cfg'] d = torch.load(CACHE, weights_only=False, map_location="cpu") geos = d['geos'] M = geos.shape[0]; K = geos.shape[1] // 2 cfg = make_config("large", K) cfg.range_grid = ckpt['range_grid'] for k, v in cfg_dict.items(): setattr(cfg, k, v) Pt_dBm = getattr(cfg, 'Pt_UE_dBm', cfg.Pt_dBm) pt_w = 10 ** (Pt_dBm / 10) / 1000 sqrt_pt = math.sqrt(pt_w) r_all = geos[:, ::2] * cfg.r_max az_all = geos[:, 1::2] * math.pi g = torch.Generator().manual_seed(2027) perm = torch.randperm(M, generator=g) n_tr = int(0.8 * M); te = perm[n_tr:] sim = JointDualSIM(cfg, layout=ckpt['layout'], share_mask=tuple(int(c) == 1 for c in ckpt['share_mask'])).to(DEVICE) sim.load_state_dict(ckpt['sim']); sim.eval() n_bins = cfg.S * cfg.Q if ckpt['readout'] == "port": head = PortReadout(n_bins).to(DEVICE) elif ckpt['readout'] == "perbin": head = PerBinPort(n_bins).to(DEVICE) else: head = LinearReadout(n_bins).to(DEVICE) head.load_state_dict(ckpt['head']); head.eval() pilot_H = hadamard_matrix(max(cfg.T, K), DEVICE) pilot = pilot_H[:K, :cfg.T].to(torch.float32) * sqrt_pt rt, at, rs, as_, ra, aa = [], [], [], [], [], [] bs = 64 with torch.no_grad(): for s_ in range(0, len(te), bs): idx = te[s_:s_+bs] r_b = r_all[idx].to(DEVICE); az_b = az_all[idx].to(DEVICE) B = r_b.shape[0] for k in range(K): x_pilot = pilot[k:k+1].expand(B, -1) y = sim.forward_ul_signal(r_b[:, k], az_b[:, k], x_pilot) logits = head(y.abs()) r_s, az_s = soft_position_estimate(logits, cfg) r_a, az_a = argmax_position(logits, cfg, DEVICE) rt.append(r_b[:, k]); at.append(az_b[:, k]) rs.append(r_s); as_.append(az_s) ra.append(r_a); aa.append(az_a) r_true = torch.cat(rt); az_true = torch.cat(at) r_s = torch.cat(rs); az_s = torch.cat(as_) r_a = torch.cat(ra); az_a = torch.cat(aa) def err_xy(r_hat, az_hat): x_t, y_t = r_true*torch.cos(az_true), r_true*torch.sin(az_true) x_h, y_h = r_hat *torch.cos(az_hat), r_hat *torch.sin(az_hat) return torch.sqrt((x_t-x_h)**2 + (y_t-y_h)**2).cpu().numpy() return { "argmax": err_xy(r_a, az_a), "soft": err_xy(r_s, az_s), "r_rmse_arg": float((r_true-r_a).pow(2).mean().sqrt()), "r_rmse_soft": float((r_true-r_s).pow(2).mean().sqrt()), "az_rmse_arg": float(((az_true-az_a)*180/math.pi).pow(2).mean().sqrt()), "az_rmse_soft": float(((az_true-az_s)*180/math.pi).pow(2).mean().sqrt()), } def main(): os.makedirs("research_paper", exist_ok=True) results = {} for label, ck in CKPTS: print(f"Eval: {label}") results[label] = eval_one(ck) # ── CDF figure ──────────────────────────────────────────────── fig, axes = plt.subplots(1, 2, figsize=(11, 4.2)) colors = ["#888888", "#1f77b4", "#2ca02c", "#d62728"] for (label, _), col in zip(CKPTS, colors): for ax, key, sub in zip(axes, ["argmax", "soft"], ["argmax (hard)", "soft expected"]): err = np.sort(results[label][key]) cdf = np.arange(1, len(err)+1) / len(err) ax.plot(err, cdf, label=label, color=col, lw=1.8) for ax, sub in zip(axes, ["argmax (hard)", "soft expected"]): ax.axhline(0.5, ls=":", c="k", alpha=0.4) ax.axhline(0.9, ls=":", c="k", alpha=0.4) ax.set_xlabel("Position error (m)") ax.set_ylabel("CDF") ax.set_xlim(0, 6); ax.set_ylim(0, 1) ax.set_title(f"Position error CDF — {sub} estimator") ax.grid(alpha=0.3); ax.legend(loc="lower right", fontsize=8) plt.tight_layout() out1 = "research_paper/sensing_cdf_compare.png" plt.savefig(out1, dpi=150); plt.close() print(f"saved {out1}") # ── RMSE bar chart (best-of-{argmax,soft} per ckpt) ─────────── fig, axes = plt.subplots(1, 3, figsize=(13, 4)) labels = [l for l, _ in CKPTS] short = ["baseline", "thick=0.10", "thick=0.20", "6L+5N"] range_rmse = []; az_rmse = []; pos_rmse = [] for label, _ in CKPTS: r = results[label] # pick the better of arg/soft per-axis range_rmse.append(min(r["r_rmse_arg"], r["r_rmse_soft"])) az_rmse.append(min(r["az_rmse_arg"], r["az_rmse_soft"])) # for pos, take best-estimator p_rmse derived from CDF pos_rmse.append(min(np.sqrt((r["argmax"]**2).mean()), np.sqrt((r["soft"]**2).mean()))) for ax, vals, name, unit in zip( axes, [range_rmse, az_rmse, pos_rmse], ["Range RMSE", "Azimuth RMSE", "Position RMSE"], ["m", "deg", "m"]): bars = ax.bar(short, vals, color=colors) for b, v in zip(bars, vals): ax.text(b.get_x()+b.get_width()/2, v+0.02, f"{v:.2f}", ha="center", fontsize=9) ax.set_ylabel(f"{name} ({unit})") ax.set_title(name) ax.grid(axis="y", alpha=0.3) plt.tight_layout() out2 = "research_paper/sensing_rmse_bars.png" plt.savefig(out2, dpi=150); plt.close() print(f"saved {out2}") if __name__ == "__main__": main()