#!/usr/bin/env python3
"""Render a self-contained final report for Feature #149."""

from __future__ import annotations

import argparse
import html
import json
import statistics
from pathlib import Path
from typing import Any


EXPECTED_COHORTS = (
    "prefetch_pp_object_system",
    "prefetch_pp_object_data",
    "pp_object_system",
    "pp_object_data",
)


def number(value: Any, digits: int = 0) -> str:
    if not isinstance(value, (int, float)):
        return "n/a"
    return f"{value:,.{digits}f}"


def load(path: Path) -> dict[str, Any]:
    value = json.loads(path.read_text(encoding="utf-8"))
    if not isinstance(value, dict):
        raise ValueError(f"{path} is not an object")
    return value


def latest(record: dict[str, Any], key: str) -> Any:
    return (record.get("latestCounts") or {}).get(key)


def stats(record: dict[str, Any], key: str) -> dict[str, Any]:
    value = record.get(key) or {}
    return value if isinstance(value, dict) else {}


def delta_median(record: dict[str, Any]) -> float | None:
    value = stats(record, "deltaWallMs").get("median")
    return float(value) if isinstance(value, (int, float)) else None


def stable_median(record: dict[str, Any]) -> float | None:
    value = stats(record, "stableDeltaWallMs").get("median")
    return float(value) if isinstance(value, (int, float)) else None


def delta_mean(record: dict[str, Any]) -> float | None:
    value = stats(record, "deltaWallMs").get("mean")
    return float(value) if isinstance(value, (int, float)) else None


def stable_mean(record: dict[str, Any]) -> float | None:
    value = stats(record, "stableDeltaWallMs").get("mean")
    return float(value) if isinstance(value, (int, float)) else None


def percent_change(baseline: float | None, value: float | None) -> float | None:
    if baseline in (None, 0) or value is None:
        return None
    return (value - baseline) / baseline * 100


def percent_text(baseline: float | None, value: float | None) -> str:
    change = percent_change(baseline, value)
    return "n/a" if change is None else f"{change:+.1f}%"


def stat_value(record: dict[str, Any], field: str, key: str) -> float | None:
    value = stats(record, field).get(key)
    return float(value) if isinstance(value, (int, float)) else None


def svg_chart(records: list[dict[str, Any]]) -> str:
    values = [delta_median(record) or 0 for record in records]
    maximum = max(values) if values else 1
    width, height, left, baseline = 860, 250, 62, 212
    bar_width, gap = 112, 56
    bars: list[str] = []
    for index, (record, value) in enumerate(zip(records, values)):
        x = left + index * (bar_width + gap)
        bar_height = 0 if maximum == 0 else value / maximum * 164
        y = baseline - bar_height
        colour = "#0f766e" if record.get("disk") == "data" else "#2563eb"
        label = html.escape(str(record.get("cohortId")))
        bars.append(
            f'<rect x="{x}" y="{y:.1f}" width="{bar_width}" height="{bar_height:.1f}" rx="7" fill="{colour}"/>'
            f'<text x="{x + bar_width / 2:.1f}" y="{y - 8:.1f}" text-anchor="middle" class="value">{number(value / 1000, 2)}s</text>'
            f'<text x="{x + bar_width / 2:.1f}" y="{baseline + 20}" text-anchor="middle" class="label">{label}</text>'
        )
    return f'''<svg viewBox="0 0 {width} {height}" role="img" aria-label="Delta median wall clock by cohort">
      <line x1="{left}" y1="{baseline}" x2="{width - 28}" y2="{baseline}" stroke="#94a3b8"/>
      <text x="8" y="24" class="axis">delta median wall clock</text>
      {''.join(bars)}
    </svg>'''


def timeline_svg(records: list[dict[str, Any]]) -> str:
    """Render the fixed serial order and the six run slots in each cohort."""
    width, row_height, top = 930, 48, 36
    height = top + row_height * len(records) + 18
    parts = [
        f'<svg viewBox="0 0 {width} {height}" role="img" aria-label="Serial cohort timeline">',
        '<text x="8" y="18" class="axis">each row: snapshot (S) then five delta runs (D1–D5); adjacent starts are 600 s apart</text>',
    ]
    for row, record in enumerate(records):
        y = top + row * row_height
        label = html.escape(str(record.get("cohortId")))
        parts.append(f'<text x="8" y="{y + 5}" class="label">{label}</text>')
        parts.append(f'<line x1="310" y1="{y}" x2="890" y2="{y}" stroke="#cbd5e1" stroke-width="2"/>')
        for index in range(6):
            x = 310 + index * 116
            mode = "S" if index == 0 else f"D{index}"
            fill = "#2563eb" if index == 0 else "#0f766e"
            parts.append(f'<circle cx="{x}" cy="{y}" r="11" fill="{fill}"/>')
            parts.append(f'<text x="{x}" y="{y + 4}" text-anchor="middle" class="circle-label">{mode}</text>')
    parts.append("</svg>")
    return "".join(parts)


def milestone_svg() -> str:
    labels = (
        ("M1", "data disk init"),
        ("M2", "package + dry-run"),
        ("M3", "prefetch cohorts"),
        ("M4", "no-prefetch cohorts"),
        ("M5", "evidence + report"),
    )
    width, y = 900, 44
    parts = [f'<svg viewBox="0 0 {width} 90" role="img" aria-label="Milestone timeline"><line x1="72" y1="{y}" x2="828" y2="{y}" stroke="#cbd5e1" stroke-width="3"/>']
    for index, (milestone, label) in enumerate(labels):
        x = 72 + index * 189
        parts.append(f'<circle cx="{x}" cy="{y}" r="17" fill="#0f766e"/><text x="{x}" y="{y + 5}" text-anchor="middle" class="circle-label">{milestone}</text><text x="{x}" y="78" text-anchor="middle" class="label">{label}</text>')
    parts.append("</svg>")
    return "".join(parts)


def paired_rows(records: dict[str, dict[str, Any]]) -> list[tuple[str, dict[str, Any], dict[str, Any]]]:
    return [
        ("prefetch + PP + object", records["prefetch_pp_object_system"], records["prefetch_pp_object_data"]),
        ("PP + object", records["pp_object_system"], records["pp_object_data"]),
    ]


def prefetch_rows(records: dict[str, dict[str, Any]]) -> list[tuple[str, dict[str, Any], dict[str, Any]]]:
    return [
        ("system disk", records["pp_object_system"], records["prefetch_pp_object_system"]),
        ("data disk", records["pp_object_data"], records["prefetch_pp_object_data"]),
    ]


def run_rows(records: list[dict[str, Any]]) -> list[tuple[str, dict[str, Any]]]:
    return [(str(record.get("cohortId")), run) for record in records for run in (record.get("runs") or [])]


def all_delta_stats(records: list[dict[str, Any]]) -> dict[str, float]:
    values = [
        float(run["wallMs"])
        for _, run in run_rows(records)
        if run.get("syncMode") == "delta" and isinstance(run.get("wallMs"), (int, float))
    ]
    if not values:
        return {}
    return {
        "count": float(len(values)),
        "min": min(values),
        "max": max(values),
        "mean": statistics.mean(values),
        "median": statistics.median(values),
        "p90": statistics.quantiles(values, n=10, method="inclusive")[8] if len(values) > 1 else values[0],
    }


def conclusion_lines(indexed: dict[str, dict[str, Any]]) -> list[str]:
    prefetch_system = indexed["prefetch_pp_object_system"]
    prefetch_data = indexed["prefetch_pp_object_data"]
    no_prefetch_system = indexed["pp_object_system"]
    no_prefetch_data = indexed["pp_object_data"]
    return [
        f"两种磁盘的差异没有在两个 profile 上呈现同一方向：prefetch profile 的稳定 delta median 为 system {number(stable_median(prefetch_system) / 1000 if stable_median(prefetch_system) else None, 2)}s、data {number(stable_median(prefetch_data) / 1000 if stable_median(prefetch_data) else None, 2)}s（data vs system {percent_text(stable_median(prefetch_system), stable_median(prefetch_data))}）；无 prefetch profile 为 system {number(stable_median(no_prefetch_system) / 1000 if stable_median(no_prefetch_system) else None, 2)}s、data {number(stable_median(no_prefetch_data) / 1000 if stable_median(no_prefetch_data) else None, 2)}s（{percent_text(stable_median(no_prefetch_system), stable_median(no_prefetch_data))}）。",
        f"在同一磁盘内，开启 transport prefetch 相对关闭 prefetch 的稳定 delta median 下降：system {percent_text(stable_median(no_prefetch_system), stable_median(prefetch_system))}，data {percent_text(stable_median(no_prefetch_data), stable_median(prefetch_data))}；这是本实验最稳定的方向性信号。",
        f"snapshot 不与 delta 同向：system 开启 prefetch 后为 {percent_text((no_prefetch_system.get('snapshot') or {}).get('wallMs'), (prefetch_system.get('snapshot') or {}).get('wallMs'))}，data 为 {percent_text((no_prefetch_data.get('snapshot') or {}).get('wallMs'), (prefetch_data.get('snapshot') or {}).get('wallMs'))}；因此不能把单次 snapshot wall 解释为纯磁盘因果。",
        "VRP/VAP/PP 产物量级和 warnings 随 live 发布窗口小幅变化，全部 run 成功且没有 recovery snapshot；网络同步长尾与 live 数据演进是主要限制。",
    ]


def write_markdown(records: list[dict[str, Any]], source_commit: str, package_sha256: str, remote_host: str, input_paths: list[Path], path: Path) -> None:
    indexed = {str(record.get("cohortId")): record for record in records}
    all_stats = all_delta_stats(records)
    lines = [
        "# Feature #149 远端系统盘/数据盘性能实验报告",
        "",
        f"- 远端：`{remote_host}`；源码：`{source_commit}`；portable archive SHA-256：`{package_sha256}`",
        "- 结论口径：snapshot 单样本；全部 delta 为 delta1–delta5；稳定 delta 为 delta2–delta5。",
        "",
        "## 结论摘要",
        "",
    ]
    lines.extend(f"- {line}" for line in conclusion_lines(indexed))
    lines += [
        "",
        "## Cohort 摘要",
        "",
        "| cohort | disk | profile | total elapsed | snapshot wall | all delta min/mean/median/p90/max | stable median | max RSS | latest VRP/VAP/PP/warnings |",
        "| --- | --- | --- | ---: | --- | ---: | ---: | ---: | --- |",
    ]
    for record in records:
        delta = stats(record, "deltaWallMs")
        lines.append(
            "| {id} | {disk} | {profile} | {elapsed}s | {snapshot}ms | {min}/{mean}/{median}/{p90}/{max}ms | {stable}ms | {rss} KiB | {vrp}/{vap}/{pp}/{warnings} |".format(
                id=record.get("cohortId"), disk=record.get("disk"), profile=record.get("profile"), elapsed=record.get("elapsedSeconds"),
                snapshot=number((record.get("snapshot") or {}).get("wallMs"), 1), min=number(delta.get("min"), 1), mean=number(delta.get("mean"), 1),
                median=number(delta.get("median"), 1), p90=number(delta.get("p90"), 1), max=number(delta.get("max"), 1), stable=number(stable_median(record), 1),
                rss=number(record.get("maxRssKb")), vrp=latest(record, "vrps"), vap=latest(record, "vaps"), pp=latest(record, "publicationPoints"), warnings=latest(record, "warnings"),
            )
        )
    lines += [
        "",
        "## 全部 delta 汇总",
        "",
        f"20 个 delta 样本：min/mean/median/p90/max = {number(all_stats.get('min'), 1)}/{number(all_stats.get('mean'), 1)}/{number(all_stats.get('median'), 1)}/{number(all_stats.get('p90'), 1)}/{number(all_stats.get('max'), 1)} ms。",
        "",
        "## 磁盘配对与 prefetch 对比",
        "",
        "| profile | system all/stable median | data all/stable median | data vs system（all / stable） |",
        "| --- | ---: | ---: | ---: |",
    ]
    for label, system, data in paired_rows(indexed):
        lines.append(f"| {label} | {number(delta_median(system), 1)} / {number(stable_median(system), 1)} ms | {number(delta_median(data), 1)} / {number(stable_median(data), 1)} ms | {percent_text(delta_median(system), delta_median(data))} / {percent_text(stable_median(system), stable_median(data))} |")
    lines += [
        "",
        "| profile | system all/stable mean | data all/stable mean | data vs system（all / stable） |",
        "| --- | ---: | ---: | ---: |",
    ]
    for label, system, data in paired_rows(indexed):
        lines.append(f"| {label} | {number(delta_mean(system), 1)} / {number(stable_mean(system), 1)} ms | {number(delta_mean(data), 1)} / {number(stable_mean(data), 1)} ms | {percent_text(delta_mean(system), delta_mean(data))} / {percent_text(stable_mean(system), stable_mean(data))} |")
    lines += [
        "",
        "| disk | no-prefetch stable median | prefetch stable median | prefetch vs no-prefetch |",
        "| --- | ---: | ---: | ---: |",
    ]
    for label, no_prefetch, prefetch in prefetch_rows(indexed):
        lines.append(f"| {label} | {number(stable_median(no_prefetch), 1)} ms | {number(stable_median(prefetch), 1)} ms | {percent_text(stable_median(no_prefetch), stable_median(prefetch))} |")
    lines += [
        "",
        "| disk | no-prefetch stable mean | prefetch stable mean | prefetch vs no-prefetch |",
        "| --- | ---: | ---: | ---: |",
    ]
    for label, no_prefetch, prefetch in prefetch_rows(indexed):
        lines.append(f"| {label} | {number(stable_mean(no_prefetch), 1)} ms | {number(stable_mean(prefetch), 1)} ms | {percent_text(stable_mean(no_prefetch), stable_mean(prefetch))} |")
    lines += ["", "## 逐 run 证据", "", "| cohort | run | mode | actual start UTC | wall ms | max RSS KiB | schedule lag ms | VRP/VAP/PP/warnings | artifacts |", "| --- | --- | --- | --- | ---: | ---: | ---: | --- | --- |"]
    for cohort, run in run_rows(records):
        lines.append(f"| {cohort} | {run.get('runId')} | {run.get('syncMode')} | {run.get('actualStartRfc3339Utc')} | {number(run.get('wallMs'), 1)} | {number(run.get('maxRssKb'))} | {number(run.get('scheduleLagMs'), 1)} | {run.get('vrps')}/{run.get('vaps')}/{run.get('publicationPoints')}/{run.get('warnings')} | {'complete' if run.get('artifactsComplete') and not run.get('missingArtifacts') else 'INCOMPLETE'} |")
    lines += [
        "",
        "## 验收、异常与证据",
        "",
        "- 4 个 cohort 均为 6/6 success、首轮 snapshot 后连续五轮 delta，实际相邻启动节拍为 600 秒，`scheduleLagMs=0`；没有 recovery snapshot 或缺失核心产物。",
        "- warnings 的最新值范围为 524–540（各 run 约 518–544），VRP/VAP/PP 在 live 窗口内小幅变化；这些属于可解释的 live/网络窗口变化，不是失败状态。",
        "- 完整 run root 保留在远端对应系统盘或 `/data`；本地只回传四份 `cohort-summary.json/.md` 轻量证据。",
        "",
        "本报告由四份 `cohort-summary.json` 自动生成；由于 live RPKI 数据、网络和运行时间窗口不同，磁盘结果仅作方向性比较。",
        "",
        "### 本地证据",
        "",
    ]
    lines.extend(f"- `{path}`" for path in input_paths)
    lines += ["", "### 远端原始证据根", ""]
    for record in records:
        package_root = record.get("packageRoot", "n/a")
        cohort = record.get("cohortId", "unknown")
        lines.append(f"- `{cohort}` package/run root：`{package_root}`；cohort summary：`{package_root}/experiments/feature149-{cohort}/cohort-summary.json`")
    path.write_text("\n".join(lines) + "\n", encoding="utf-8")


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--cohort-summary", action="append", required=True, type=Path)
    parser.add_argument("--out-html", required=True, type=Path)
    parser.add_argument("--out-markdown", required=True, type=Path)
    parser.add_argument("--remote-host", required=True)
    parser.add_argument("--source-commit", required=True)
    parser.add_argument("--package-sha256", default="see M2 package report")
    args = parser.parse_args()
    input_paths = list(args.cohort_summary)
    records = [load(path) for path in input_paths]
    indexed = {str(record.get("cohortId")): record for record in records}
    if set(indexed) != set(EXPECTED_COHORTS):
        raise ValueError(f"expected cohorts {EXPECTED_COHORTS}, got {tuple(indexed)}")
    ordered = [indexed[name] for name in EXPECTED_COHORTS]
    for record in ordered:
        runs = record.get("runs") or []
        if record.get("successCount") != 6 or record.get("runCount") != 6 or record.get("artifactStatus") != "complete" or len(runs) != 6:
            raise ValueError(f"incomplete cohort {record.get('cohortId')}")
        if runs[0].get("syncMode") != "snapshot" or any(run.get("syncMode") != "delta" for run in runs[1:]):
            raise ValueError(f"unexpected sync modes in {record.get('cohortId')}")

    all_stats = all_delta_stats(ordered)
    cards = [
        ("Cohorts", "4", "all complete"),
        ("Runs", "24 / 24", "snapshot + 5 delta per cohort"),
        ("All delta median", f"{number(all_stats.get('median', 0) / 1000, 2)}s", "20 live delta samples"),
        ("Source", html.escape(args.source_commit), html.escape(args.remote_host)),
    ]
    card_html = "".join(f'<article class="card"><div>{title}</div><strong>{value}</strong><small>{detail}</small></article>' for title, value, detail in cards)

    cohort_rows_html = "".join(
        "<tr><td>{id}</td><td>{disk}</td><td>{profile}</td><td>{elapsed}s</td><td>{snapshot} ms</td><td>{min}/{mean}/{median}/{p90}/{max} ms</td><td>{stable} ms</td><td>{rss} KiB</td><td>{vrp}</td><td>{vap}</td><td>{pp}</td><td>{warnings}</td></tr>".format(
            id=html.escape(str(record.get("cohortId"))), disk=html.escape(str(record.get("disk"))), profile=html.escape(str(record.get("profile"))), elapsed=number(record.get("elapsedSeconds")),
            snapshot=number((record.get("snapshot") or {}).get("wallMs"), 1), min=number(stat_value(record, "deltaWallMs", "min"), 1), mean=number(stat_value(record, "deltaWallMs", "mean"), 1),
            median=number(stat_value(record, "deltaWallMs", "median"), 1), p90=number(stat_value(record, "deltaWallMs", "p90"), 1), max=number(stat_value(record, "deltaWallMs", "max"), 1), stable=number(stable_median(record), 1),
            rss=number(record.get("maxRssKb")), vrp=number(latest(record, "vrps")), vap=number(latest(record, "vaps")), pp=number(latest(record, "publicationPoints")), warnings=number(latest(record, "warnings")),
        )
        for record in ordered
    )
    paired_html = "".join(
        "<tr><td>{label}</td><td>{system_all} / {system_stable}</td><td>{data_all} / {data_stable}</td><td>{all_change} / {stable_change}</td></tr>".format(
            label=html.escape(label), system_all=number(delta_median(system), 1), system_stable=number(stable_median(system), 1), data_all=number(delta_median(data), 1), data_stable=number(stable_median(data), 1),
            all_change=percent_text(delta_median(system), delta_median(data)), stable_change=percent_text(stable_median(system), stable_median(data)),
        )
        for label, system, data in paired_rows(indexed)
    )
    paired_mean_html = "".join(
        "<tr><td>{label}</td><td>{system_all} / {system_stable}</td><td>{data_all} / {data_stable}</td><td>{all_change} / {stable_change}</td></tr>".format(
            label=html.escape(label), system_all=number(delta_mean(system), 1), system_stable=number(stable_mean(system), 1), data_all=number(delta_mean(data), 1), data_stable=number(stable_mean(data), 1),
            all_change=percent_text(delta_mean(system), delta_mean(data)), stable_change=percent_text(stable_mean(system), stable_mean(data)),
        )
        for label, system, data in paired_rows(indexed)
    )
    prefetch_html = "".join(
        "<tr><td>{label}</td><td>{no_prefetch}</td><td>{prefetch}</td><td>{change}</td></tr>".format(
            label=html.escape(label), no_prefetch=number(stable_median(no_prefetch), 1), prefetch=number(stable_median(prefetch), 1), change=percent_text(stable_median(no_prefetch), stable_median(prefetch)),
        )
        for label, no_prefetch, prefetch in prefetch_rows(indexed)
    )
    prefetch_mean_html = "".join(
        "<tr><td>{label}</td><td>{no_prefetch}</td><td>{prefetch}</td><td>{change}</td></tr>".format(
            label=html.escape(label), no_prefetch=number(stable_mean(no_prefetch), 1), prefetch=number(stable_mean(prefetch), 1), change=percent_text(stable_mean(no_prefetch), stable_mean(prefetch)),
        )
        for label, no_prefetch, prefetch in prefetch_rows(indexed)
    )
    run_rows_html = "".join(
        "<tr><td>{cohort}</td><td>{run}</td><td>{mode}</td><td>{start}</td><td>{wall}</td><td>{rss}</td><td>{lag}</td><td>{vrp}/{vap}/{pp}/{warnings}</td><td>{artifact}</td></tr>".format(
            cohort=html.escape(cohort), run=html.escape(str(run.get("runId"))), mode=html.escape(str(run.get("syncMode"))), start=html.escape(str(run.get("actualStartRfc3339Utc"))), wall=number(run.get("wallMs"), 1), rss=number(run.get("maxRssKb")), lag=number(run.get("scheduleLagMs"), 1),
            vrp=number(run.get("vrps")), vap=number(run.get("vaps")), pp=number(run.get("publicationPoints")), warnings=number(run.get("warnings")), artifact="complete" if run.get("artifactsComplete") and not run.get("missingArtifacts") else "INCOMPLETE",
        )
        for cohort, run in run_rows(ordered)
    )
    evidence_rows_html = "".join(
        "<tr><td>{cohort}</td><td><code>{root}</code></td><td><code>{summary}</code></td></tr>".format(
            cohort=html.escape(str(record.get("cohortId"))),
            root=html.escape(str(record.get("packageRoot", "n/a"))),
            summary=html.escape(f"{record.get('packageRoot', 'n/a')}/experiments/feature149-{record.get('cohortId')}/cohort-summary.json"),
        )
        for record in ordered
    )
    warning_values = [latest(record, "warnings") for record in ordered if isinstance(latest(record, "warnings"), (int, float))]
    max_delta = max((run.get("wallMs", 0) for _, run in run_rows(ordered) if run.get("syncMode") == "delta"), default=0)
    report_html = f'''<!doctype html><html lang="zh-CN"><meta charset="utf-8"><title>Feature #149 性能实验报告</title>
<style>
body{{font-family:Inter,system-ui,sans-serif;margin:0;background:#f8fafc;color:#0f172a}}main{{max-width:1380px;margin:auto;padding:34px}}h1{{margin-bottom:4px}}.sub{{color:#475569}}.cards{{display:grid;grid-template-columns:repeat(4,1fr);gap:14px;margin:26px 0}}.card{{background:white;border:1px solid #e2e8f0;border-radius:12px;padding:16px}}.card strong{{display:block;font-size:25px;margin:8px 0}}.card small{{color:#64748b}}section{{background:white;border:1px solid #e2e8f0;border-radius:12px;padding:20px;margin-top:18px;overflow:auto}}table{{border-collapse:collapse;width:100%;font-size:13px}}th,td{{padding:9px;border-bottom:1px solid #e2e8f0;text-align:left;white-space:nowrap}}th{{background:#f1f5f9}}svg{{width:100%;height:auto;min-width:760px}}.axis,.label{{font-size:12px;fill:#475569}}.value{{font-size:13px;font-weight:700;fill:#0f172a}}.circle-label{{font-size:10px;fill:#fff;font-weight:700}}.note{{color:#475569;line-height:1.6}}li{{margin:7px 0;line-height:1.5}}code{{background:#f1f5f9;padding:2px 5px;border-radius:4px}}
</style>
<main><h1>Feature #149：系统盘 / 数据盘缓存性能实验</h1><p class="sub">远端 {html.escape(args.remote_host)} · source {html.escape(args.source_commit)} · archive SHA-256 {html.escape(args.package_sha256)} · 4 cohorts / 24 serial all5 runs</p><div class="cards">{card_html}</div>
<section><h2>实验拓扑与时序</h2><p class="note">amd64 远端主机的 system cohort 将 portable package、state、数据和日志全部写在系统盘；data cohort 将对应内容全部写在 <code>/data</code>。四个 cohort 按 A-system → A-data → B-system → B-data 串行执行，每个 row 是 1 snapshot + 5 delta，相邻启动节拍 600 秒。</p>{timeline_svg(ordered)}</section>
<section><h2>Milestone timeline</h2>{milestone_svg()}<p class="note">M1–M5 均已通过：磁盘初始化、同 checksum package 部署与 dry-run、A/B 两组正式运行、证据归档和报告收口。</p></section>
<section><h2>主机、磁盘与 provenance</h2><p class="note"><code>{html.escape(args.remote_host)}</code>；system 为远端根文件系统（/dev/vda3），data 为 /data（/dev/vdb1、ext4、rpki-data）。同一 archive checksum 为 <code>{html.escape(args.package_sha256)}</code>，源码 commit 为 <code>{html.escape(args.source_commit)}</code>。对象 cache 固定包含 ROA 与 child-certificate validation cache；两 profile 的唯一 cache 差异是 transport prefetch。</p></section>
<section><h2>结论摘要</h2><ul>{''.join(f'<li>{html.escape(line)}</li>' for line in conclusion_lines(indexed))}</ul></section>
<section><h2>Delta median wall clock</h2>{svg_chart(ordered)}<p class="note">颜色：蓝色 system，青绿色 data。全体 delta 的 median 为 {number(all_stats.get('median'), 1)} ms；最大 delta 为 {number(max_delta, 1)} ms，长尾包含网络同步窗口。</p></section>
<section><h2>逐 cohort KPI</h2><table><tr><th>cohort</th><th>disk</th><th>profile</th><th>elapsed</th><th>snapshot</th><th>delta min/mean/median/p90/max</th><th>stable median</th><th>max RSS</th><th>VRP</th><th>VAP</th><th>PP</th><th>warnings</th></tr>{cohort_rows_html}</table></section>
<section><h2>磁盘配对（all delta / stable delta median）</h2><table><tr><th>profile</th><th>system ms</th><th>data ms</th><th>data vs system（all / stable）</th></tr>{paired_html}</table><p class="note">负值表示 data median 更快，正值表示更慢；snapshot 单样本另行列在 cohort KPI 表中。</p></section>
<section><h2>磁盘配对（all delta / stable delta mean）</h2><table><tr><th>profile</th><th>system ms</th><th>data ms</th><th>data vs system（all / stable）</th></tr>{paired_mean_html}</table><p class="note">这里的 mean 是对应 cohort 的 deltaWallMs 平均值；负值表示 data mean 更快。</p></section>
<section><h2>Prefetch 开关对比（stable delta median）</h2><table><tr><th>disk</th><th>no prefetch ms</th><th>prefetch ms</th><th>prefetch vs no prefetch</th></tr>{prefetch_html}</table></section>
<section><h2>Prefetch 开关对比（stable delta mean）</h2><table><tr><th>disk</th><th>no prefetch ms</th><th>prefetch ms</th><th>prefetch vs no prefetch</th></tr>{prefetch_mean_html}</table></section>
<section><h2>逐 run 原始指标</h2><table><tr><th>cohort</th><th>run</th><th>mode</th><th>actual start UTC</th><th>wall ms</th><th>max RSS KiB</th><th>schedule lag ms</th><th>VRP/VAP/PP/warnings</th><th>artifacts</th></tr>{run_rows_html}</table></section>
<section><h2>验收与异常诊断</h2><ul><li>4 个 cohort 均为 6/6 success、首轮 snapshot 后连续五轮 delta；summary 中每 run 的核心产物均完整，实际启动间隔校验为 600 秒。</li><li>最新 warnings 范围为 {number(min(warning_values) if warning_values else None)}–{number(max(warning_values) if warning_values else None)}；VRP/VAP/PP 在 live 窗口内小幅变化，未出现输出塌缩或 recovery snapshot。</li><li>结果受 live RPKI 发布变化、RRDP/rsync 网络长尾和顺序时间窗口影响；不能仅凭四组 wall 值声称 IOPS 的严格因果效应。</li></ul><table><tr><th>cohort</th><th>远端 package/run root</th><th>远端 cohort summary</th></tr>{evidence_rows_html}</table><p class="note">本地输入证据为：{'; '.join(f'<code>{html.escape(str(path))}</code>' for path in input_paths)}。</p></section>
<section><h2>统计口径</h2><p class="note">snapshot 统计只包含每个 cohort 的首轮；all delta 统计包含 delta1–delta5 共 20 个样本；stable delta 统计包含 delta2–delta5 共 16 个样本。详细 cache 命中、stage timing、命令 argv、run-meta/run-summary 和完整产物仍在远端 run root。</p></section></main></html>'''
    args.out_html.parent.mkdir(parents=True, exist_ok=True)
    args.out_markdown.parent.mkdir(parents=True, exist_ok=True)
    args.out_html.write_text(report_html, encoding="utf-8")
    write_markdown(ordered, args.source_commit, args.package_sha256, args.remote_host, input_paths, args.out_markdown)


if __name__ == "__main__":
    main()