Sync data and code after the per-digit re-audit

code/exp_users_csi.py

@@ -27,6 +27,7 @@ sys.path.insert(0, str(Path(__file__).resolve().parent))
 import sse_lib as L
 from sse_lib import DATA, DEVICE, rayleigh_gain, snr_to_sigma2, eval_ser_sse
 from exp_full import (main_model, base_keys, get_model, eve_wrong_mask,
+                      oma_ser_keylen,
                       eval_ser_eve, mean_abs_xcorr, MAIN_D)
 
 SNR = 10.0
@@ -112,12 +113,15 @@ def main():
         ew = eve_wrong_mask(U, Lp, seed=20260813)
         ev = eval_ser_eve(m, ew, [SNR], frames=FRAMES)[0]
         xc = mean_abs_xcorr(m.masks().detach())
-        rows.append((U, "%.6f" % lg, "%.6f" % ev, "%.6f" % xc))
+        # OMA gets its own d/U dimensions per user at this load
+        oma = oma_ser_keylen(MAIN_D // U, SNR)
+        rows.append((U, "%.6f" % lg, "%.6f" % ev, "%.6f" % xc,
+                     "%.6f" % oma))
         print("  U=%2d  legit %.4f  eve %.5f  xcorr %.2e"
               % (U, lg, ev, xc), flush=True)
     with open(DATA / "users.csv", "w", newline="") as f:
         w = csv.writer(f)
-        w.writerow(["users", "legit_ser", "eve_ser", "mask_xcorr"])
+        w.writerow(["users", "legit_ser", "eve_ser", "mask_xcorr", "oma"])
         w.writerows(rows)
     print("[csv]", DATA / "users.csv", flush=True)
 

code/replot_security.py

@@ -347,7 +347,6 @@ def fig_jam():
     ax.set_xlabel("JSR (dB)")
     ax.set_ylabel("SER")
     ax.set_xlim(min(x), max(x))
-    ax.set_ylim(0.0, 1.02)      # keep the reference line off the spine
     place_legend(ax)
     save(fig, "fig_sec_jam")
 
@@ -386,11 +385,11 @@ def fig_brute():
     x = col(r, "K")
     fig, ax = plt.subplots()
     ax.semilogx(x, col(r, "ser_perm"), color=C_EVE, marker="s", ls="--",
-                label=LBL["perm"], **UNDER)
+                markevery=(0, 3), label=LBL["perm"], **UNDER)
     ax.semilogx(x, col(r, "ser_pad"), color=C_PUB, marker="v", ls="-.",
-                label=LBL["pad"], **OVER)
+                markevery=(1, 3), label=LBL["pad"], **OVER)
     ax.semilogx(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
-                label=LBL["mask"])
+                markevery=(2, 3), label=LBL["mask"])
     legit = main_legit()
     ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
                label=LBL["legit"])
@@ -430,16 +429,18 @@ def fig_kpa():
     fig, ax = plt.subplots()
     sty = {0.0: (C_LEGIT, "o"), 10.0: (C_EVE, "s"),
            20.0: (C_PUB, "v")}
-    for snr, (c, mk) in sty.items():
+    for off, (snr, (c, mk)) in enumerate(sty.items()):
         rows = [row for row in r if float(row["snr_db"]) == snr]
         n = [float(row["n_frames"]) for row in rows]
         ser = [float(row["eve_ser"]) for row in rows]
         ax.semilogx(n, ser, color=c, marker=mk, ls="-",
+                    markevery=(off, 4), markerfacecolor="none" if off else c,
                     label=LBL["mask"] + f", {int(snr)} dB")
     try:
         p = load("pkpa.csv")
         ax.semilogx(col(p, "n_frames"), col(p, "eve_ser"), color=C_MATCH,
-                    marker="P", ls="--", label=LBL["perm"] + ", 20 dB")
+                    marker="P", ls="--", markevery=(3, 4),
+                    label=LBL["perm"] + ", 20 dB")
     except FileNotFoundError:
         print("[skip] pkpa.csv not present yet")
     # legitimate reference measured with the SAME estimator as the

code/verify_math.py

@@ -130,7 +130,7 @@ def v3_leakage_vs_correlation():
         corrs.append(abs((m @ mt) / D))
     emp = float(np.mean(corrs))
     claim = float(np.sqrt(2.0 / (np.pi * D)))
-    ok_b = report("V3b random mask E|corr|", claim, emp, 0.1 * claim)
+    ok_b = report("V3b random mask E|corr|", claim, emp, 0.03 * claim)
     return lin_ok and ok_b
 
 
@@ -158,7 +158,7 @@ def v4_blind_jammer_spread():
     # full contribution scales this by (hJ^2/hu^2) rho.
     ok2 = report("V4b blind jammer projection variance",
                  float(np.mean(var_cl)), float(np.mean(var_emp)),
-                 0.05 * float(np.mean(var_cl)))
+                 0.01 * float(np.mean(var_cl)))
     return ok1 and ok2
 
 
@@ -282,10 +282,10 @@ def v8_cross_period_terms():
         diag = float((a ** 2).sum())
         rel.append((float((a.sum(0) ** 2).sum()) - diag) / diag)
     mean = sum(rel) / len(rel)
-    ok = abs(mean) < 0.01
+    ok = abs(mean) < 0.0005
     print("V8 cross-period remainder, mean %+.4f of the retained term" % mean)
     ROWS.append(("V8 cross-period remainder", "0.0", "%.6f" % mean,
-                 "%.6f" % abs(mean), "0.01", "PASS" if ok else "FAIL"))
+                 "%.6f" % abs(mean), "0.0005", "PASS" if ok else "FAIL"))
     return ok