377 lines
20 KiB
Python
377 lines
20 KiB
Python
"""Build vector figure sources, comparison tables, source listings, and a support ZIP.
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Formal figures use the audited snapshot only. Geometric figures use declared
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algorithm parameters, not fabricated observations or hidden simulator data.
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The paired optimal-versus-baseline tables are generated from project/results/*.json
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so that no number in the comparison section is hand copied.
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import zipfile
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from decimal import Decimal, ROUND_HALF_UP
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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PAPER = ROOT / "docs/paper"
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PROJECT_RESULTS = ROOT / "project/results"
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# Display copies only: executable Python files remain byte-for-byte unchanged.
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ESCAPE_OPEN = "(" + "*@"
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ESCAPE_CLOSE = "@*" + ")"
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LATEX_SYMBOLS = {
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"\u03b1": r"\alpha", "\u03b4": r"\delta", "\u03b8": r"\theta",
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"\u03bb": r"\lambda", "\u03c0": r"\pi", "\u03c1": r"\rho",
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"\u03c6": r"\varphi", "\u2308": r"\lceil", "\u2309": r"\rceil",
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"\u2200": r"\forall", "\u2203": r"\exists", "\u2205": r"\varnothing",
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"\u2208": r"\in", "\u2227": r"\wedge", "\u2229": r"\cap",
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"\u222a": r"\cup", "\u2248": r"\approx", "\u2264": r"\le",
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"\u2265": r"\ge", "\u2286": r"\subseteq", "\u2287": r"\supseteq",
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"\u27f9": r"\Longrightarrow",
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}
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def escaped_listing(source: str) -> str:
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if ESCAPE_OPEN in source or ESCAPE_CLOSE in source:
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raise ValueError("Source collides with the LaTeX escape delimiters")
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return "".join(
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ESCAPE_OPEN + r"\ensuremath{" + LATEX_SYMBOLS[c] + "}" + ESCAPE_CLOSE
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if c in LATEX_SYMBOLS else c for c in source
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)
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def digest(data: bytes) -> str:
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return hashlib.sha256(data).hexdigest()
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def figures(rows: list[dict]) -> None:
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out = PAPER / "figures"
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out.mkdir(parents=True, exist_ok=True)
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colors = ["inkblue", "inkblue!50", "warmgray!70", "black!18"]
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bars = [
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r"\begin{figure}[htbp]\centering",
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r"\begin{tikzpicture}[x=0.00108cm,y=0.85cm,font=\small]",
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]
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for j, row in enumerate(rows):
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y = 3 - j
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components = [Decimal(row["derived"]["inferred_movement_time_s"]),
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Decimal(5 * row["measure_accepted_count"]),
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Decimal(row["channel_switch_count"]),
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Decimal(5 * row["cleared_jammer_count"] + 3 * row["clear_failure_count"])]
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label = f"Q{row['problem_no']}-{row['formal_index']}"
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bars.append(fr"\node[anchor=east] at (-100,{y + 0.3}) {{{label}}};")
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current = Decimal(0)
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for amount, color in zip(components, colors):
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bars.append(fr"\fill[{color}] ({current},{y}) rectangle ({current + amount},{y + 0.6});")
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current += amount
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bars.append(fr"\node[anchor=west,font=\scriptsize] at ({current + 80},{y + 0.3}) {{{current:.1f}}};")
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bars.append(r"\draw[->] (0,-0.25)--(10500,-0.25);")
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for tick in range(0, 10001, 2000):
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bars.append(fr"\draw ({tick},-0.25)--({tick},-0.35) node[below,font=\scriptsize] {{{tick}}};")
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bars.append(r"\node[anchor=west,font=\scriptsize] at (10400,-0.65) {s};")
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for x, color, label in zip((0, 2600, 5200, 7600), colors, ("移动", "检测", "换频", "清除")):
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bars += [fr"\fill[{color}] ({x},-1.3) rectangle ({x + 350},-1.05);",
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fr"\node[anchor=west] at ({x + 450},-1.175) {{{label}}};"]
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bars += [
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r"\end{tikzpicture}",
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r"\caption{四次正式测试的累计虚拟耗时。移动项由总时间扣除官方动作计数对应耗时反推。}",
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r"\label{fig:costbars}\end{figure}",
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]
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(out / "formal-time-bars.tex").write_text("\n".join(bars) + "\n", "utf-8")
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def fixed(value: float | None, digits: int = 3) -> str:
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if value is None:
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return "--"
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return str(Decimal(str(value)).quantize(Decimal(1).scaleb(-digits), rounding=ROUND_HALF_UP))
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def percent(value: float | None, digits: int = 1) -> str:
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if value is None:
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return "--"
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return fixed(100 * value, digits) + r"\%"
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def flag(value: bool | None) -> str:
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return "--" if value is None else ("是" if value else "否")
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def project_tables() -> None:
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"""Generate the optimal-versus-baseline comparison tables from project/results."""
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out = PAPER / "data"
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out.mkdir(parents=True, exist_ok=True)
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data = {name: json.loads((PROJECT_RESULTS / f"{name}.json").read_text("utf-8"))
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for name in ("q1", "q2", "q3", "q4")}
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examples, random = data["q1"]["aggregate_examples"], data["q1"]["aggregate_random"]
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two = data["q2"]["aggregate"]
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three, paired3 = data["q3"]["aggregate"]["variants"], data["q3"]["aggregate"]["paired"]
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four, paired4 = data["q4"]["aggregate"]["variants"], data["q4"]["aggregate"]["paired"]
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head = "% Generated by scripts/build_paper_support.py from project/results/*.json."
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macros = [
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("ProjQOneExampleCases", str(examples["cases"])),
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("ProjQOneRandomCases", str(random["cases"])),
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("ProjQOneFixedStatusBase", percent(examples["status_accuracy_baseline"])),
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("ProjQOneFixedMaeBase", fixed(examples["diameter_mae_baseline_m"])),
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("ProjQOneFixedMaeMaxBase", fixed(examples["diameter_max_err_baseline_m"])),
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("ProjQOneFixedCoverBase", percent(examples["coverage_accuracy_baseline"])),
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("ProjQOneFixedMsOpt", fixed(examples["runtime_optimal_mean_ms"])),
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("ProjQOneFixedMsBase", fixed(examples["runtime_baseline_mean_ms"])),
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("ProjQOneRandStatusBase", percent(random["status_accuracy_baseline"])),
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("ProjQOneRandMaeBase", fixed(random["diameter_mae_baseline_m"])),
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("ProjQOneRandMaeMaxBase", fixed(random["diameter_max_err_baseline_m"])),
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("ProjQOneRandCoverBase", percent(random["coverage_accuracy_baseline"])),
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("ProjQOneRandMsOpt", fixed(random["runtime_optimal_mean_ms"])),
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("ProjQOneRandMsBase", fixed(random["runtime_baseline_mean_ms"])),
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("ProjQTwoCases", str(two["optimal"]["cases"])),
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("ProjQTwoInitialRadius", fixed(two["_initial_radius_mean_m"])),
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("ProjQTwoGuaranteeOpt", percent(two["optimal"]["guaranteed_rate"])),
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("ProjQTwoGuaranteeMec", percent(two["baseline_mec"]["guaranteed_rate"])),
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("ProjQTwoGuaranteePerp", percent(two["baseline_perp"]["guaranteed_rate"])),
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("ProjQTwoWorstOpt", fixed(two["optimal"]["worst_posterior_mean_m"])),
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("ProjQTwoWorstRef", fixed(two["reference"]["worst_posterior_mean_m"])),
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("ProjQTwoRatioOpt", fixed(two["optimal"]["final_ratio_mean"])),
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("ProjQTwoRatioMec", fixed(two["baseline_mec"]["final_ratio_mean"])),
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("ProjQTwoWorstPerp", fixed(two["baseline_perp"]["worst_posterior_mean_m"])),
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("ProjQTwoWorstMec", fixed(two["baseline_mec"]["worst_posterior_mean_m"])),
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("ProjQThreeScenes", str(three["optimal"]["episodes"])),
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("ProjQThreeMeanOpt", fixed(three["optimal"]["virtual_time_mean_s"])),
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("ProjQThreeMeanSeven", fixed(three["seven"]["virtual_time_mean_s"])),
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("ProjQThreeMeanBase", fixed(three["baseline"]["virtual_time_mean_s"])),
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("ProjQThreeDropOpt", percent(-paired3["optimal_vs_baseline"]["mean_relative_change"])),
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("ProjQThreeDropMedianOpt",
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percent(-paired3["optimal_vs_baseline"]["median_relative_change"])),
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("ProjQThreeDropSeven", percent(-paired3["seven_vs_baseline"]["mean_relative_change"])),
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("ProjQThreeFasterOpt",
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f"{paired3['optimal_vs_baseline']['faster_scenes']}/{paired3['optimal_vs_baseline']['compared']}"),
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("ProjQThreeMeasuresOpt", fixed(three["optimal"]["measures_mean"])),
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("ProjQThreeMeasuresBase", fixed(three["baseline"]["measures_mean"])),
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("ProjQThreeMoveOpt", fixed(three["optimal"]["move_distance_mean_m"] / 1000)),
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("ProjQThreeMoveBase", fixed(three["baseline"]["move_distance_mean_m"] / 1000)),
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("ProjQThreeWallOpt", fixed(three["optimal"]["compute_wall_mean_s"])),
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("ProjQFourScenes", str(four["optimal"]["episodes"])),
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("ProjQFourMeanOpt", fixed(four["optimal"]["virtual_time_mean_s"])),
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("ProjQFourMeanGrid", fixed(four["baseline_grid"]["virtual_time_mean_s"])),
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("ProjQFourMeanBase", fixed(four["baseline"]["virtual_time_mean_s"])),
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("ProjQFourClearOpt", percent(four["optimal"]["cleared_ratio_mean"])),
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("ProjQFourClearGrid", percent(four["baseline_grid"]["cleared_ratio_mean"])),
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("ProjQFourClearBase", percent(four["baseline"]["cleared_ratio_mean"])),
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("ProjQFourAbsentBase", str(four["baseline"]["absent_misjudged_total"])),
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("ProjQFourMeasuresOpt", fixed(four["optimal"]["measures_mean"])),
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("ProjQFourMeasuresBase", fixed(four["baseline"]["measures_mean"])),
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("ProjQFourFallbackOpt", fixed(four["optimal"]["fallback_clear_attempts_mean"])),
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("ProjQFourValidOpt",
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f"{four['optimal']['valid_episodes']}/{four['optimal']['episodes']}"),
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("ProjQFourValidGrid",
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f"{four['baseline_grid']['valid_episodes']}/{four['baseline_grid']['episodes']}"),
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("ProjQFourRiseOpt", percent(paired4["optimal_vs_baseline"]["mean_relative_change"])),
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("ProjQFourMoveOpt", fixed(four["optimal"]["move_distance_mean_m"] / 1000)),
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("ProjQFourMoveBase", fixed(four["baseline"]["move_distance_mean_m"] / 1000)),
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]
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lines = [head] + [f"\\newcommand{{\\{name}}}{{{value}}}" for name, value in macros]
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(out / "project-macros.tex").write_text("\n".join(lines) + "\n", "utf-8")
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def write(stem: str, rows: list[str]) -> None:
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(out / stem).write_text("\n".join([head, *rows]) + "\n", "utf-8")
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write("project-overview-rows.tex", [
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"问题 & 核心指标 & 最优实现 & 基线实现 " + r"\\",
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f"1 & 直径平均绝对误差 / 覆盖判定准确率 & {fixed(random['diameter_mae_optimal_m'])} m / "
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f"{percent(random['coverage_accuracy_optimal'])} & {fixed(random['diameter_mae_baseline_m'])} m / "
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f"{percent(random['coverage_accuracy_baseline'])} " + r"\\",
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f"2 & 保证接收率 / 最坏后验半径均值 & {percent(two['optimal']['guaranteed_rate'])} / "
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f"{fixed(two['optimal']['worst_posterior_mean_m'])} m & {percent(two['baseline_mec']['guaranteed_rate'])} / "
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f"{fixed(two['baseline_mec']['worst_posterior_mean_m'])} m " + r"\\",
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f"3 & 全清率 / 平均虚拟耗时 & {percent(three['optimal']['cleared_ratio_mean'])} / "
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f"{fixed(three['optimal']['virtual_time_mean_s'])} s & {percent(three['baseline']['cleared_ratio_mean'])} / "
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f"{fixed(three['baseline']['virtual_time_mean_s'])} s " + r"\\",
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f"4 & 全清率 / 缺席误判总数 & {percent(four['optimal']['cleared_ratio_mean'])} / "
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f"{four['optimal']['absent_misjudged_total']} & {percent(four['baseline']['cleared_ratio_mean'])} / "
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f"{four['baseline']['absent_misjudged_total']} " + r"\\",
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])
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write("project-q1-example-rows.tex", [
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f"{row['name']} & {fixed(row['diameter_ref_m'])} & {fixed(row['diameter_opt_m'])} & "
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f"{fixed(row['diameter_base_m'])} & {fixed(row['abs_err_base_m'])} & "
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f"{flag(row['covers_ref'])} & {flag(row['covers_opt'])} & {flag(row['covers_base'])} " + r"\\"
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for row in data["q1"]["examples"]
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])
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write("project-q1-summary-rows.tex", [
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f"最优（固定 {examples['cases']} 例） & {percent(examples['status_accuracy_optimal'])} & "
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f"{fixed(examples['diameter_mae_optimal_m'])} & {fixed(examples['diameter_max_err_optimal_m'])} & "
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f"{percent(examples['coverage_accuracy_optimal'])} & {fixed(examples['runtime_optimal_mean_ms'])} " + r"\\",
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f"最优（随机 {random['cases']} 例） & {percent(random['status_accuracy_optimal'])} & "
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f"{fixed(random['diameter_mae_optimal_m'])} & {fixed(random['diameter_max_err_optimal_m'])} & "
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f"{percent(random['coverage_accuracy_optimal'])} & {fixed(random['runtime_optimal_mean_ms'])} " + r"\\",
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f"基线（固定 {examples['cases']} 例） & {percent(examples['status_accuracy_baseline'])} & "
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f"{fixed(examples['diameter_mae_baseline_m'])} & {fixed(examples['diameter_max_err_baseline_m'])} & "
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f"{percent(examples['coverage_accuracy_baseline'])} & {fixed(examples['runtime_baseline_mean_ms'])} " + r"\\",
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f"基线（随机 {random['cases']} 例） & {percent(random['status_accuracy_baseline'])} & "
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f"{fixed(random['diameter_mae_baseline_m'])} & {fixed(random['diameter_max_err_baseline_m'])} & "
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f"{percent(random['coverage_accuracy_baseline'])} & {fixed(random['runtime_baseline_mean_ms'])} " + r"\\",
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])
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write("project-q2-rows.tex", [
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f"{label} & {percent(two[key]['guaranteed_rate'])} & {fixed(two[key]['worst_posterior_mean_m'])} & "
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f"{fixed(two[key]['worst_posterior_median_m'])} & {fixed(two[key]['improvement_mean_m'])} & "
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f"{fixed(two[key]['final_ratio_mean'])} & {fixed(two[key]['move_cost_mean_s'], 2)} " + r"\\"
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for key, label in (("optimal", "算法2（默认选点）"), ("reference", "参考：盘内网格枚举"),
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("baseline_perp", "基线：垂直 1000 m"), ("baseline_mec", "基线：候选圆心"))
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])
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def variant_rows(table: dict, mapping: list[tuple[str, str]]) -> list[str]:
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return [
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f"{label} & {table[key]['valid_episodes']}/{table[key]['episodes']} & "
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f"{percent(table[key]['cleared_ratio_mean'])} & {table[key]['absent_misjudged_total']} & "
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f"{fixed(table[key]['virtual_time_mean_s'])} & {fixed(table[key]['virtual_time_median_s'])} & "
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f"{fixed(table[key]['virtual_time_max_s'])} & {fixed(table[key]['measures_mean'], 1)} & "
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f"{fixed(table[key]['move_distance_mean_m'] / 1000)} & {fixed(table[key]['fallback_clear_attempts_mean'], 1)} "
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+ r"\\"
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for key, label in mapping
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]
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write("project-q3-rows.tex", variant_rows(three, [
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("optimal", "bayes（默认策略）"), ("seven", "seven（七点批量）"), ("baseline", "grid（49 点网格）")]))
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write("project-q4-rows.tex", variant_rows(four, [
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("optimal", "directional（默认策略）"), ("baseline_grid", "grid（问题三网格）"),
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("baseline", "bayes（问题三策略）")]))
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def listings(rows: list[dict]) -> None:
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data = PAPER / "data"
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files = sorted((ROOT / "src/mcm26b").rglob("*.py"))
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files += [ROOT / "scripts/audit_paper_formal.py",
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ROOT / "scripts/build_paper_support.py"]
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text = [
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r"\begingroup",
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r"\linespread{1.0}\selectfont",
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r"\lstset{basicstyle=\ttfamily\fontsize{8}{10}\selectfont,numberstyle=\tiny,"
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r"aboveskip=6pt,belowskip=8pt,xleftmargin=1.5em,frame=none}",
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]
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for path in files:
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rel = path.relative_to(ROOT).as_posix()
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display = data / "listing-source" / (rel + ".tex")
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display.parent.mkdir(parents=True, exist_ok=True)
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display.write_text(escaped_listing(path.read_text("utf-8")), "utf-8")
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display_rel = display.relative_to(PAPER).as_posix()
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escape_option = "escapeinside={" + ESCAPE_OPEN + "}{" + ESCAPE_CLOSE + "}"
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text += [fr"\subsection*{{\pathtext{{{rel}}}}}",
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fr"\lstinputlisting[{escape_option}]{{{display_rel}}}"]
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text.append(r"\endgroup")
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(data / "source-listings.tex").write_text("\n".join(text) + "\n", "utf-8")
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table = [
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r"\begin{longtable}{p{1.15cm}p{13.3cm}}",
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r"\caption{所选正式导出文件与完整包哈希}\label{tab:packages}\\",
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r"\toprule 案例 & 原始文件名及 SHA-256\\\midrule\endfirsthead",
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r"\toprule 案例 & 原始文件名及 SHA-256\\\midrule\endhead",
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]
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for row in rows:
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h = row["package_sha256"]
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label = f"Q{row['problem_no']}-{row['formal_index']}"
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table += [
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fr"{label} & \pathtext{{{Path(row['file']).name}}}\\",
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fr"& {{\scriptsize\texttt{{{h[:32]}}}\allowbreak\texttt{{{h[32:]}}}}}\\[4pt]",
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]
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table += [r"\bottomrule\end{longtable}"]
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(data / "formal-file-list.tex").write_text("\n".join(table) + "\n", "utf-8")
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SUPPORT_README = """# 论文支撑材料
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只引用官方模拟器 test 中的四份正式导出及哈希匹配的官方统计。
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问题三、四各缺第三份正式记录。正式源总数未知，不能计算全清率。
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完整原始包保留原名，匿名快照不含队号与票据内容。
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## 环境和运行
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Python >=3.10；安装 requests，可选 numpy 加速。
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在本目录使用 PowerShell：
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$env:PYTHONPATH = (Resolve-Path src).Path
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python -m mcm26b.cli --help
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python -m mcm26b.cli run --problem 3 --strategy bayes --robot-id <模拟器登录队号>
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python -m mcm26b.cli run --problem 4 --strategy directional --robot-id <模拟器登录队号>
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在线命令需要先由使用者在官方模拟器中选择测试、待接口就绪。
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重写论文未运行这些在线命令。自建评测机及历史场景不作为论文实验资料。
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策略的自检命令涉及另一个可选 referee 包，本包用于正式接口路径。
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## 重算论文数据和图表
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python scripts/audit_paper_formal.py --snapshot docs/paper/data/formal-results.json
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python scripts/build_paper_support.py
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上述命令均为离线处理，不连接模拟器接口。
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匿名快照来自原电脑官方正式统计队列，通过票据哈希匹配所选包；
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没有该队列时可验证快照算术和现有包哈希，不能独立重做来源匹配。
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源码与正式包无策略提交版本哈希绑定；不作同场景消融或全清率宣称。
|
||
31点计划只有离散证据；混合类型下的 joint_empty 判定边界见论文正文。
|
||
|
||
## 编译
|
||
|
||
安装 TeX Live（XeLaTeX、ctex、TikZ），进入 docs/paper：
|
||
|
||
latexmk -xelatex -interaction=nonstopmode -halt-on-error main.tex
|
||
|
||
版式使用原项目模板。精简支撑包不含大体积字体，编译前请将原项目
|
||
docs/paper/fonts 目录放回同一相对位置；不要修改模板以替代这些字体。
|
||
使用 latexmk 自动完成交叉引用所需的多遍编译，不能只运行一次 XeLaTeX。
|
||
support-manifest.json 列出包内全部材料（清单自身除外）及 SHA-256。
|
||
"""
|
||
|
||
|
||
def package(rows: list[dict]) -> Path:
|
||
selected = set((ROOT / "src/mcm26b").rglob("*.py"))
|
||
selected.update((ROOT / "src/mcm26b/guarantee/plans").glob("*.json"))
|
||
selected.update((PAPER / "data").rglob("*"))
|
||
selected.update((PAPER / "figures").glob("*.tex"))
|
||
selected.update((PAPER / "sections").glob("*.tex"))
|
||
selected.update((ROOT / "docs/figures").glob("*.png"))
|
||
selected.update((ROOT / "docs/figures").glob("*.json"))
|
||
selected.add(ROOT / "docs/figures/README.md")
|
||
selected.update(path for path in (ROOT / "project").rglob("*")
|
||
if path.is_file() and "__pycache__" not in path.parts)
|
||
selected.update([PAPER / "main.tex", PAPER / "mcm26b-paper.cls",
|
||
PAPER / "README.md", PAPER / "REWRITE_NOTES.md",
|
||
ROOT / "scripts/audit_paper_formal.py",
|
||
ROOT / "scripts/build_paper_support.py"])
|
||
for row in rows:
|
||
path = ROOT / "Jammers-simulator-full" / row["file"]
|
||
if digest(path.read_bytes()) != row["package_sha256"]:
|
||
raise ValueError("Original formal package changed")
|
||
selected.add(path)
|
||
blobs = {p.relative_to(ROOT).as_posix(): p.read_bytes() for p in sorted(selected) if p.is_file()}
|
||
blobs["README.md"] = SUPPORT_README.encode("utf-8")
|
||
blobs["requirements.txt"] = b"requests>=2.31\nnumpy>=1.24\n"
|
||
manifest = [{"file": name, "bytes": len(value), "sha256": digest(value)} for name, value in sorted(blobs.items())]
|
||
blobs["support-manifest.json"] = (json.dumps(manifest, ensure_ascii=False, indent=2) + "\n").encode("utf-8")
|
||
out = PAPER / "support-materials.zip"
|
||
with zipfile.ZipFile(out, "w", zipfile.ZIP_DEFLATED, compresslevel=9) as archive:
|
||
for name, value in sorted(blobs.items()):
|
||
entry = zipfile.ZipInfo(name, (2026, 9, 13, 0, 0, 0))
|
||
entry.compress_type = zipfile.ZIP_DEFLATED
|
||
archive.writestr(entry, value)
|
||
if out.stat().st_size > 20_000_000:
|
||
raise ValueError("Support archive exceeds 20 MB")
|
||
print(f"Support archive: {len(blobs)} files, {out.stat().st_size} bytes")
|
||
return out
|
||
|
||
|
||
def main() -> None:
|
||
parser = argparse.ArgumentParser(description=__doc__)
|
||
parser.add_argument("--package", action="store_true")
|
||
args = parser.parse_args()
|
||
rows = json.loads((PAPER / "data/formal-results.json").read_text("utf-8"))["records"]
|
||
figures(rows)
|
||
project_tables()
|
||
listings(rows)
|
||
if args.package:
|
||
package(rows)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|