Ship the learned-key pipeline, without which six figures cannot be rebuilt

The package was missing every script behind the KM (lrn.) curves and
both learned table rows: exp_learned's driver, the merge that folds the
learned rows into sec_compare.csv and refresh_summary.csv, and the
report that reads the learned numbers back. It was also missing
sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the
two diagnostics that answer why a fixed key beats a learned one here and
where a learned mask would win instead.

The learned artifacts themselves are regenerated. They were trained on
the cross-entropy alone, which drifts to disjoint sparse supports: 99
percent of each key's energy on about six of the 64 entries, so a digit
is decided over a sixth of its period and the key set is a choice of
support rather than a dense direction in R^L. They are now the
regularized keys of Section V-C, and check_consistency asserts which of
the two families the figures draw.

Verified from inside this repository: replot_security.py rebuilds all
seven result figures, make_tables.py reproduces both result tables, and
check_consistency.py passes every check that does not need the
manuscript.

The README now lists what ships. Its run list, layout and figure map had
none of the learned pipeline, named two tables the manuscript renders as
prose, and gave Fig. 3 no data file for its permutation curve.

code/replot_security.py

@@ -365,7 +365,18 @@ def fig_keylen():
     ax.semilogy(col(rl, "L", int), col(rl, "legit_ser"), **STY["km_lrn"], label=LBL["legit_learned"])
     op = [(l, v) for l, v in zip(x, col(r, "oma")) if not math.isnan(v)]
     ax.semilogy([p[0] for p in op], [p[1] for p in op], **STY["oma"], label=LBL["oma"])
-    ax.semilogy(x, col(r, "eve_ser"), **STY["eve"], label=LBL["eve_key"])
+    ax.semilogy(x, col(r, "eve_ser"), **STY["eve"], markevery=(0, 2),
+                label=LBL["eve_key"], **UNDER)
+    # the permutation key shares this physical layer, so its legitimate
+    # curve lies on the structured one and appears at every key length
+    # rather than only in the tables. Its outsider measures 0.99997 to
+    # 0.99999 and would lie on the outsider curve already drawn, in the
+    # same style as this one and with no legend entry of its own, so the
+    # caption says where it sits instead.
+    rp = load("sec_keylen_perm.csv")
+    assert min(float(r["eve_ser"]) for r in rp) > 0.999,         "the permutation outsider left the random-guess level"
+    ax.semilogy(col(rp, "L", int), col(rp, "legit_ser"), **STY["perm"],
+                markevery=(1, 2), label=LBL["perm"], **OVER)
     ax.set_ylim(top=22.0)    # headroom above the flat eavesdropper curve
     # an error rate cannot exceed one, and the room below the data holds
     # the legend, since every curve decays to the right
@@ -409,7 +420,7 @@ def fig_jam():
     # axis-spanning lines, so it cannot move the legend off this one, and
     # a reference drawn along the legend frame reads as part of the box.
     ax.axhline(nojam, color=C_OMA, ls=(0, (1, 3)), lw=0.9, zorder=0)
-    ax.set_ylim(6e-3, 1.4)
+    ax.set_ylim(2.5e-2, 1.4)
     # a log axis spanning little more than a decade prints minor labels
     # like 6x10^-1 that consume the left margin, so only the decades are
     # labelled
@@ -478,20 +489,20 @@ def fig_real():
     r = load("real_sec_ter.csv")
     x = col(r, "snr_db")
     fig, ax = plt.subplots()
-    # insider and outsider still nearly coincide and are layered; the
-    # legitimate and OMA curves are separate at this frame
+    # two pairs nearly coincide here, the two legitimate realizations
+    # within a fifth of each other and the two adversaries both at the
+    # top, so each pair is layered and its markers staggered
     ax.semilogy(x, col(r, "ter_legit"), **STY["km_str"],
-                markevery=(0, 2), label=LBL["legit"], **UNDER)
+                markevery=(0, 3), label=LBL["legit"], **UNDER)
     rt = load("real_sec_ter_learned.csv")
-    ax.semilogy(col(rt, "snr_db"), col(rt, "ter_legit"),
-                **STY["km_lrn"], markevery=(1, 2),
-                label=LBL["legit_learned"])
+    ax.semilogy(col(rt, "snr_db"), col(rt, "ter_legit"), **STY["km_lrn"],
+                markevery=(1, 3), label=LBL["legit_learned"], **OVER)
     ax.semilogy(x, col(r, "ter_oma"), **STY["oma"],
-                markevery=(1, 2), label=LBL["oma"], **OVER)
+                markevery=(2, 3), label=LBL["oma"])
     ax.semilogy(x, col(r, "ter_insider"), **STY["insider"],
-                markevery=(0, 2), label=LBL["insider"], **UNDER)
+                markevery=(0, 3), label=LBL["insider"], **UNDER)
     ax.semilogy(x, col(r, "ter_eve"), **STY["eve"],
-                markevery=(1, 2), label=LBL["outsider"], **OVER)
+                markevery=(2, 3), label=LBL["outsider"], **OVER)
     ax.set_xlabel("SNR (dB)")
     ax.set_ylabel("TER")
     ax.set_xlim(min(x), max(x))