KiHoLee
0645347685
The lower of two coinciding curves is the wide one, the random-guess reference is dotted everywhere and never borrows a scheme colour, the three collection SNRs in the known-plaintext figure differ by face and width rather than by dash alone, and the legend floor now prints at six points with an assertion that fails if any figure drags it lower. Both table generators list the proposal first.
76 lines
2.7 KiB
Python
76 lines
2.7 KiB
Python
# -*- coding: utf-8 -*-
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"""Fold the learned-key rows into the two table sources.
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Table IV reads sec_compare.csv and Table V reads refresh_summary.csv,
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and both are written by the structured stages, which know nothing about
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the learned family. Its rows were appended by hand, so a rerun of the
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learned stages left the tables behind. This does the fold, so both files
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are derived from data/ like every other table source.
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Run after code/run_learned_reg.py, before code/make_tables.py.
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"""
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from __future__ import annotations
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import csv
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from pathlib import Path
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DATA = Path(__file__).resolve().parents[1] / "data"
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def read(name):
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with open(DATA / name) as f:
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r = csv.DictReader(f)
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return r.fieldnames, list(r)
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def write(name, fields, rows):
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with open(DATA / name, "w", newline="") as f:
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w = csv.DictWriter(f, fieldnames=fields)
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w.writeheader()
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w.writerows(rows)
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def upsert(rows, key, value, row):
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"""Replace the row carrying key==value, or append it."""
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for i, r in enumerate(rows):
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if r[key] == value:
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rows[i] = row
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return rows
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rows.append(row)
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return rows
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def main():
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# Table IV: the learned scheme row, measured by exp_learned.compare
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fields, rows = read("sec_compare.csv")
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_, learned = read("compare_learned.csv")
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assert len(learned) == 1, "compare_learned.csv should carry one row"
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rows = upsert(rows, "scheme", "proposed_learned",
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{k: learned[0][k] for k in fields})
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write("sec_compare.csv", fields, rows)
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print("sec_compare.csv proposed_learned jam0 %s"
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% learned[0]["jam0_ser"])
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# Table V: the learned refresh row, averaged over the blocks that
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# exp_learned.refresh measured, at the same entropy as the
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# structured refresh because the invariance group is the same
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fields, rows = read("refresh_summary.csv")
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_, blocks = read("refresh_learned.csv")
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lg = sum(float(r["legit_ser"]) for r in blocks) / len(blocks)
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ev = sum(float(r["eve_ser"]) for r in blocks) / len(blocks)
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ent = next(r["entropy_bits"] for r in rows
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if r["scheme"] == "Invariant, KM (str.)")
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rows = upsert(rows, "scheme", "Invariant, KM (lrn.)",
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{"scheme": "Invariant, KM (lrn.)",
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"legit": "%.6f" % lg, "eve": "%.6f" % ev,
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"entropy_bits": ent})
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# 9.3: the proposal first, as every figure legend lists it
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rows.sort(key=lambda x: 0 if x["scheme"].startswith("Invariant") else 1)
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write("refresh_summary.csv", fields, rows)
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print("refresh_summary.csv Invariant, KM (lrn.) legit %.5f eve %.5f"
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% (lg, ev))
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if __name__ == "__main__":
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main()
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