KiHoLee
f828018160
check_texhealth.py flags control characters and bare macro stubs left by a shell heredoc, a class of corruption that LaTeX compiles without complaint. It skips when main.tex is absent, as in this package. fig_sec_snr now carries an inset with the OMA-to-proposed SER ratio, since the 1 to 9 percent advantage is invisible across two decades of log axis, and save() now guards inset overlap as it guards the legend. check_consistency covers the L=8 crossover, the inset ratio span, the stated secret sizes, and the Fig. 5 curve coincidence: 29 assertions.
169 lines
6.9 KiB
Python
169 lines
6.9 KiB
Python
# -*- coding: utf-8 -*-
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"""Final consistency check: every headline number vs its raw CSV.
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A quoted value that goes stale during a revision is the failure mode this
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guards against, so each assertion recomputes from data/ rather than from
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another quoted value. The manuscript-side assertions are skipped when
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main.tex is absent, which is the case in the reproducibility package.
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"""
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import csv
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import math
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import re
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from pathlib import Path
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base = Path(__file__).resolve().parents[1]
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_tex_path = base / "main.tex"
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HAVE_TEX = _tex_path.exists()
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tex = _tex_path.read_text(encoding="utf-8") if HAVE_TEX else ""
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def rows(name):
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with open(base / "data" / name) as f:
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return list(csv.DictReader(f))
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def col(name, k):
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return [float(r[k]) for r in rows(name) if r[k] != "nan"]
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ok = True
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def chk(label, cond, detail, needs_tex=False):
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global ok
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if needs_tex and not HAVE_TEX:
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print(" SKIP " + label + " :: main.tex not in this package")
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return
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print((" PASS " if cond else " FAIL ") + label + " :: " + detail)
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if not cond:
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ok = False
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print("headline numbers vs raw data")
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# --- Fig. 2: the proposal is below OMA -------------------------------
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sn = rows("sec_snr.csv")
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lg = [float(x["legit"]) for x in sn]
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om = [float(x["oma"]) for x in sn]
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rel = [(a - b) / b * 100 for a, b in zip(lg, om)]
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chk("legit below OMA at every SNR", max(rel) < 0, "max relative %+.2f%%" % max(rel))
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chk("gain 1.3 to 7.9 percent",
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round(-max(rel), 1) == 1.3 and round(-min(rel), 1) == 7.9,
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"%.2f to %.2f percent" % (-max(rel), -min(rel)))
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chk("1.3 and 7.9 in tex", "$1.3$ to\n$7.9$~percent" in tex or "$1.3$ to $7.9$~percent" in tex,
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"searched tex", needs_tex=True)
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ew = [float(x["eve_wrong"]) for x in sn]
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ch = float(sn[0]["chance"])
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chk("outsider at chance to 2e-5", max(abs(x - ch) for x in ew) < 2e-5,
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"max deviation %.2e" % max(abs(x - ch) for x in ew))
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# --- Fig. 3: key-length ratio ----------------------------------------
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k = rows("sec_keylen.csv")
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r64 = [x for x in k if int(x["L"]) == 64][0]
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ratio = float(r64["oma"]) / float(r64["legit_ser"])
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chk("key-length ratio 1.52", round(ratio, 2) == 1.52, "%.4f" % ratio)
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chk("1.52 in tex", tex.count("1.52") >= 2, "%d occurrences" % tex.count("1.52"),
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needs_tex=True)
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chk("keys exactly orthogonal in the sweep",
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max(float(x["mask_xcorr"]) for x in k) < 1e-6,
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"max xcorr %.2e" % max(float(x["mask_xcorr"]) for x in k))
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# --- Fig. 4: jamming --------------------------------------------------
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g = col("sec_jam_gap.csv", "gap_db")
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chk("gap 5.5-6.3 dB", round(min(g), 1) == 5.5 and round(max(g), 1) == 6.3,
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"%.3f to %.3f" % (min(g), max(g)))
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lin = (10 ** (min(g) / 10), 10 ** (max(g) / 10))
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chk("about four times power", lin[0] < 4.5 and lin[1] > 3.4,
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"%.2f to %.2f" % lin)
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j = rows("sec_jam_cmp.csv")
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dmax = max(abs(float(r["blind"]) - float(r["perm_blind"])) for r in j)
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chk("within 0.002", dmax <= 0.002, "%.5f" % dmax)
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chk("no stale 8.1 dB", "$8.1$~dB" not in tex, "searched tex", needs_tex=True)
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# --- Fig. 6: brute force ---------------------------------------------
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b = rows("sec_brute_cmp.csv")
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sm = float(b[-1]["ser_mask"])
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chk("brute 0.59 at 1e6", round(sm, 2) == 0.59, "%.4f" % sm)
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chk("0.59 in tex", "$0.59$" in tex, "searched tex", needs_tex=True)
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pad0 = next((x["K"] for x in b if float(x["ser_pad"]) < 0.27), None)
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chk("index cipher collapses at 65536", pad0 == "65536", str(pad0))
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# --- Fig. 7: known plaintext -----------------------------------------
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kp = rows("kpa.csv")
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legit = float([x for x in k if int(x["L"]) == 16][0]["legit_ser"])
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thr = legit * 1.02
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first20 = next((x["n_frames"] for x in kp
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if int(x["snr_db"]) == 20 and float(x["eve_ser"]) <= thr), None)
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first10 = next((x["n_frames"] for x in kp
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if int(x["snr_db"]) == 10 and float(x["eve_ser"]) <= thr), None)
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chk("KPA five frames at 20 dB", first20 == "5", "first N = %s" % first20)
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chk("KPA twenty-four frames at 10 dB", first10 == "24", "first N = %s" % first10)
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pk = rows("pkpa.csv")
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p6 = float([x for x in pk if x["n_frames"] == "6"][0]["eve_ser"])
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chk("perm KPA at N=6 near its own 0.258", abs(p6 - 0.258) < 0.005, "%.4f" % p6)
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# --- refresh ----------------------------------------------------------
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rs = {x["scheme"]: x for x in rows("refresh_summary.csv")}
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chk("refresh 64.8 bits",
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round(float(rs["Invariant"]["entropy_bits"]), 1) == 64.8,
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"%.3f" % float(rs["Invariant"]["entropy_bits"]))
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chk("fixed key 15.0 bits",
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round(float(rs["None (fixed key)"]["entropy_bits"]), 1) == 15.0,
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"%.4f" % float(rs["None (fixed key)"]["entropy_bits"]))
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chk("invariant refresh free",
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abs(float(rs["Invariant"]["legit"]) - float(rs["None (fixed key)"]["legit"]))
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< 0.001, "%.4f vs %.4f" % (float(rs["Invariant"]["legit"]),
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float(rs["None (fixed key)"]["legit"])))
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# --- real tokens ------------------------------------------------------
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import json
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st = json.loads((base / "data" / "real_sec_stats.json").read_text())
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rec = st["recovery"]["28"]
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chk("headline recovery 78 vs 76 percent",
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round(rec["legit"] * 100) == 78 and round(rec["oma"] * 100) == 76,
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"%.1f vs %.1f" % (rec["legit"] * 100, rec["oma"] * 100))
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chk("legit leads OMA at every point",
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all(st["recovery"][s]["legit"] > st["recovery"][s]["oma"]
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for s in st["recovery"]),
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"checked %d points" % len(st["recovery"]))
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# --- the room argument of Fig. 2 and its evidence in Fig. 3 -----------
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kl = {int(r["L"]): r for r in rows("sec_keylen.csv")}
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r8 = kl[8]
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chk("L=8 crowding, proposal behind OMA",
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round(float(r8["legit_ser"]), 3) == 0.949
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and round(float(r8["oma"]), 3) == 0.685,
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"%.3f vs %.3f" % (float(r8["legit_ser"]), float(r8["oma"])))
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chk("0.949 and 0.685 in tex", "0.949" in tex and "0.685" in tex,
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"searched tex", needs_tex=True)
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# the Fig. 2 inset plots this ratio, so its stated span must hold
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sr = rows("sec_snr.csv")
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rt = [float(r["oma"]) / float(r["legit"]) for r in sr]
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chk("inset ratio spans 1.01 to 1.09", 1.005 < min(rt) and max(rt) < 1.095,
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"%.3f to %.3f" % (min(rt), max(rt)))
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# the three secrets named in the setup
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chk("secret sizes UL=64, perm 64, pad 16",
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all(t in tex for t in ["$UL=64$ key entries",
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"one permutation of $64$ positions",
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"$16$ pad\nbits per user"]),
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"searched tex", needs_tex=True)
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# Fig. 5 shows the permutation curve tracking the mask curve
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sc = rows("sec_sens_cmp.csv")
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dv = max(abs(float(r["ser_mask"]) - float(r["ser_perm"])) for r in sc)
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chk("permutation tracks mask in Fig. 5", dv < 0.06, "max gap %.3f" % dv)
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# --- abstract ---------------------------------------------------------
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a = (tex.split(r"\begin{abstract}")[1].split(r"\end{abstract}")[0].strip()
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if HAVE_TEX else "")
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w = len(re.split(r"\s+", a)) if a else 0
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chk("abstract <= 250 words", w <= 250, "%d words" % w, needs_tex=True)
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chk("abstract has no abbreviations",
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not re.findall(r"\b[A-Z]{2,}\b", a), str(re.findall(r"\b[A-Z]{2,}\b", a)),
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needs_tex=True)
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print()
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print("ALL CONSISTENT" if ok else "INCONSISTENCIES FOUND")
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