feat(review): 医生核对包入口；CEUS 运行目录的读取收进 runs
- 新模块 review（组织层，只依赖 library、answers、runs）：把一次构建和它装入的 CEUS 运行整理成医生可读的文件夹（CSV 带 BOM、说明与报告转 PDF、图片视频逐字节拷贝）， 入口 scripts/review_pack.py + config/review_pack.yaml。任务书 03。 - 读 CEUS 运行目录（failed.csv、结果目录、关键帧文件）从 build 挪进 runs，build 与 review 都经它读；failed.csv 读取在边界上收严（步号须为整数、视频号不重复、列数固定）。 - library、answers 补只读函数；import-linter 增 review 契约。 - 文档：guides/医生核对包.md（过一轮硕士生阅读），结构.md 第三至八部分同步。 Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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"""医生核对包：目录结构、CSV 规格、问题清单的每种来源、逐字节拷贝、TIC结果、没装 CEUS 的构建、PDF。
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预期值照任务书 03 写：目录和文件名、列名、问题清单的「环节」与「问题」原文、关键帧文件名的秒数
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（`video_time_s` 保留一位小数）都按任务书的文字手写，不从实现里推。
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假数据（6 例）：
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- P001：进库；定位到 D1、D2、F1，其余 4 个槽位未取到；病灶去暗区曲线拟合失败
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（parameter_on_boundary）；紫圈不可用。
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- P002：CEUS 第 2 步失败（C15 跳过）。P003：CEUS 运行里没有结果目录（C15 跳过）。
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- P004：进库；7 个槽位都取到，D2、F1 的时间改成 5.04、12.46 秒（测一位小数）；拟合都成功；紫圈可用。
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- P005：MR 表诊断与另两张表不同（C10 跳过）；CEUS 运行里却有它完整的结果目录（被跳过但定量成功）。
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- P006：进库；7 个槽位都取到；病灶整体曲线拟合失败（credible_peak_not_found）；紫圈可用。
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"""
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import csv
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import hashlib
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import json
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import re
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import shutil
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import subprocess
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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import pytest
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import yaml
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from cadx.answers import read_aliases
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from cadx.build import BuildSummary, run_build
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from cadx.library import read_ultrasound
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from cadx.review import run_review_pack
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from cadx.review._pdf import markdown_to_pdf, report_markdown
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from fake_ceus_run import UNLOCATED_REASON, summary, write_run
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from fake_delivery import DIAGNOSIS_COLUMN, REPO_ROOT, make_delivery, write_config
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BUILD_NOW = datetime(2026, 10, 17, 9, 0, 0).astimezone()
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PACK_NOW = datetime(2026, 10, 17, 10, 0, 0).astimezone()
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RUN_ID = "20261017-080000-abcdef0"
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ALL_SLOTS = ("B0", "D1", "D2", "D3", "D4", "F1", "F2")
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SUMMARY_HEADER = [
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"病例号",
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"视频号",
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"化名",
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"是否进入资料库",
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"没进的原因",
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"CEUS 定量是否成功",
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"CEUS 失败在第几步",
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*ALL_SLOTS,
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]
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PROBLEM_HEADER = ["病例号", "视频号", "环节", "问题"]
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ANSWER_HEADERS = {
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"化名对照.csv": ["化名", "病例号", "视频号"],
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"诊断.csv": ["化名", "病例号", "临床表诊断编码", "超声表诊断编码", "MR表诊断编码", "诊断名"],
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"跳过病例.csv": ["病例", "检查编号", "检查说明", "文件", "列", "涉及值"],
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"报告复核句.csv": ["化名", "路", "命中的词", "所在句子"],
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}
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CASE_FILES = {"临床信息.csv", "超声报告.pdf", "MR报告.pdf", "关键图.png"}
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CEUS_FILES = {"关键帧", "TIC曲线.png", "TIC结果.csv", "回放视频.mp4"}
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def _rewrite_summary(run: Path, video_id: str, located: tuple[str, ...], edit: Any) -> None:
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content = summary(video_id, located)
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edit(content)
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(run / video_id / "summary.json").write_text(json.dumps(content), encoding="utf-8")
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def _fail_fit(curve: str, code: str) -> Any:
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def edit(content: dict[str, Any]) -> None:
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content["fits"][curve].update(success=False, failure_reason=code)
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return edit
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def _p004(content: dict[str, Any]) -> None:
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content["reference_valid"] = True
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content["anchor_frames"]["D2"]["frame_time_video_s"] = 5.04
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content["anchor_frames"]["F1"]["frame_time_video_s"] = 12.46
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def _p006(content: dict[str, Any]) -> None:
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_fail_fit("roi_all", "credible_peak_not_found")(content)
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content["reference_valid"] = True
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def _build_with_ceus(root: Path) -> tuple[BuildSummary, Path]:
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delivery = make_delivery(6)
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delivery.case(5).mr[DIAGNOSIS_COLUMN] = 2
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delivery_dir = delivery.write(root / "delivery")
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run = write_run(
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root / "runs" / RUN_ID,
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delivery_dir,
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{"U001": ("D1", "D2", "F1"), "U004": ALL_SLOTS, "U005": ALL_SLOTS, "U006": ALL_SLOTS},
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[("U002", 2, "两半都没有绿框")],
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)
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_rewrite_summary(
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run, "U001", ("D1", "D2", "F1"), _fail_fit("roi_perf", "parameter_on_boundary")
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)
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_rewrite_summary(run, "U004", ALL_SLOTS, _p004)
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_rewrite_summary(run, "U006", ALL_SLOTS, _p006)
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return run_build(write_config(root, delivery_dir, 1, None, run), REPO_ROOT, BUILD_NOW), run
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def _pack_config(root: Path, library_dir: Path, answers_dir: Path) -> Path:
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config = {
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"library_dir": str(library_dir),
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"answers_dir": str(answers_dir),
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"output_root": str(root / "review_pack"),
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}
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path = root / "review_pack.yaml"
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path.write_text(yaml.safe_dump(config, allow_unicode=True), encoding="utf-8")
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return path
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def _tree_hashes(folder: Path) -> dict[str, str]:
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return {
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p.relative_to(folder).as_posix(): hashlib.sha256(p.read_bytes()).hexdigest()
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for p in sorted(folder.rglob("*"))
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if p.is_file()
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}
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class Packed:
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"""构建一次、出一次包，各条测试共用。"""
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def __init__(self, root: Path) -> None:
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self.build, self.run = _build_with_ceus(root)
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self.library_before = _tree_hashes(self.build.library_dir)
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self.answers_before = _tree_hashes(self.build.answers_dir)
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config = _pack_config(root, self.build.library_dir, self.build.answers_dir)
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self.config = config
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self.result = run_review_pack(config, REPO_ROOT, PACK_NOW)
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self.pack = self.result.pack_dir
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self.alias = {r.case_no: r.alias for r in read_aliases(self.build.answers_dir)}
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@pytest.fixture(scope="module")
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def packed(tmp_path_factory: pytest.TempPathFactory) -> Packed:
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return Packed(tmp_path_factory.mktemp("review"))
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def _csv(path: Path) -> list[list[str]]:
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raw = path.read_bytes()
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assert raw.startswith(b"\xef\xbb\xbf"), path
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with path.open(encoding="utf-8-sig", newline="") as handle:
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return list(csv.reader(handle))
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def test_directory_layout(packed: Packed) -> None:
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pack = packed.pack
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assert pack.name == f"医生核对包_{packed.build.build_id}"
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assert sorted(p.name for p in packed.result.run_dir.iterdir()) == sorted(
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["snapshot", pack.name]
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)
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assert {p.name for p in pack.iterdir()} == {
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"说明.pdf",
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"问题清单.csv",
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"病例总表.csv",
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"标准答案",
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"病例",
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}
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assert {p.name for p in (pack / "标准答案").iterdir()} == set(ANSWER_HEADERS)
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assert {p.name for p in (pack / "病例").iterdir()} == {"P001", "P004", "P006"}
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for case in ("P001", "P004", "P006"):
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assert {p.name for p in (pack / "病例" / case).iterdir()} == CASE_FILES | CEUS_FILES
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assert {p.name for p in (pack / "病例" / "P001" / "关键帧").iterdir()} == {
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"D1_第2.5秒.png",
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"D2_第5.0秒.png",
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"F1_第12.5秒.png",
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}
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assert {p.name for p in (pack / "病例" / "P004" / "关键帧").iterdir()} == {
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"B0_第0.0秒.png",
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"D1_第2.5秒.png",
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"D2_第5.0秒.png",
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"D3_第7.5秒.png",
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"D4_第10.0秒.png",
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"F1_第12.5秒.png",
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"F2_第15.0秒.png",
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}
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def test_every_csv_has_bom_and_no_placeholder_for_missing(packed: Packed) -> None:
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csvs = sorted(packed.pack.rglob("*.csv"))
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assert len(csvs) == 2 + 4 + 3 * 2
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for path in csvs:
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for row in _csv(path):
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assert not {"null", "None", "NaN", "nan"} & set(row), path
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assert _csv(packed.pack / "病例总表.csv")[0] == SUMMARY_HEADER
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assert _csv(packed.pack / "问题清单.csv")[0] == PROBLEM_HEADER
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for name, header in ANSWER_HEADERS.items():
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assert _csv(packed.pack / "标准答案" / name)[0] == header
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clinical = _csv(packed.pack / "病例" / "P001" / "临床信息.csv")
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assert len(clinical) == 2 and "Case_ID" not in clinical[0] and "Diagnosis" not in clinical[0]
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assert clinical[0][:3] == ["Sex", "Age", "Symptom_Pain"]
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# 假交付包里没测的化验是 None：留空。
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assert clinical[1][clinical[0].index("Amylase")] == ""
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def test_case_summary(packed: Packed) -> None:
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rows = _csv(packed.pack / "病例总表.csv")[1:]
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blank = [""] * 7
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a = packed.alias
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assert rows == [
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[
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"P001",
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"U001",
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a["001"],
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"是",
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"",
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"是",
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"",
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"未取到",
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"2.5",
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"5.0",
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"未取到",
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"未取到",
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"12.5",
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"未取到",
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],
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["P002", "U002", "", "否", "C15 CEUS 定量结果齐全", "否", "2", *blank],
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["P003", "U003", "", "否", "C15 CEUS 定量结果齐全", "", "", *blank],
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[
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"P004",
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"U004",
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a["004"],
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"是",
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"",
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"是",
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"",
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"0.0",
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"2.5",
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"5.04",
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"7.5",
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"10.0",
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"12.46",
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"15.0",
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],
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["P005", "U005", "", "否", "C10 跨表一致（诊断、年龄、性别）", "是", "", *blank],
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[
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"P006",
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"U006",
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a["006"],
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"是",
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"",
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"是",
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"",
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"0.0",
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"2.5",
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"5.0",
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"7.5",
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"10.0",
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"12.5",
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"15.0",
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],
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]
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def test_problem_list_has_every_source(packed: Packed) -> None:
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rows = _csv(packed.pack / "问题清单.csv")[1:]
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skipped = {row[0]: row for row in _csv(packed.pack / "标准答案" / "跳过病例.csv")[1:]}
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unlocated = [
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["P001", "U001", "关键帧", f"{slot} 未取到：{UNLOCATED_REASON}"]
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for slot in ("B0", "D3", "D4", "F2")
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]
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assert rows == [
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["P001", "U001", "TIC 拟合", "病灶去暗区曲线未完成拟合（parameter_on_boundary）"],
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*unlocated,
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["P001", "U001", "紫圈", "紫圈曲线不可用"],
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[
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"P002",
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"U002",
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"装配资料库",
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f"C15 CEUS 定量结果齐全；文件：{RUN_ID}/failed.csv；"
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'涉及值："第 2 步失败：两半都没有绿框"',
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],
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["P002", "U002", "CEUS 第 2 步", "第 2 步失败：两半都没有绿框"],
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[
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"P003",
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"U003",
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"装配资料库",
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f"C15 CEUS 定量结果齐全；文件：{RUN_ID}/U003；"
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'涉及值："CEUS 运行目录里没有这一例的结果目录"',
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],
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[
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"P005",
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"U005",
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"装配资料库",
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f"C10 跨表一致（诊断、年龄、性别）；文件：{skipped['005'][3]}；"
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f"列：{skipped['005'][4]}；涉及值：{skipped['005'][5]}",
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],
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["P006", "U006", "TIC 拟合", "病灶整体曲线未完成拟合（credible_peak_not_found）"],
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]
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counts = {"装配资料库": 3, "CEUS 第 2 步": 1, "TIC 拟合": 2, "关键帧": 4, "紫圈": 1}
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assert packed.result.problems_by_stage == dict(sorted(counts.items()))
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def test_copies_are_byte_identical(packed: Packed) -> None:
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for case_no, video_id in (("001", "U001"), ("004", "U004")):
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case_dir = packed.pack / "病例" / f"P{case_no}"
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route = read_ultrasound(packed.build.library_dir, packed.alias[case_no])
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assert (case_dir / "关键图.png").read_bytes() == route.key_image.read_bytes()
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assert route.tic is not None
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copied = {p.name.split("_")[0]: p for p in (case_dir / "关键帧").iterdir()}
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assert sorted(copied) == sorted(route.keyframes)
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for slot, source in route.keyframes.items():
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assert copied[slot].read_bytes() == source.read_bytes()
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seconds = route.tic["keyframes"][slot]["video_time_s"]
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assert copied[slot].name == f"{slot}_第{round(seconds, 1)}秒.png"
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run_case = packed.run / video_id
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assert (case_dir / "TIC曲线.png").read_bytes() == (run_case / "tic.png").read_bytes()
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assert (case_dir / "回放视频.mp4").read_bytes() == (run_case / "overlay.mp4").read_bytes()
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answers = packed.build.answers_dir
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for source, name in (
|
||||
("aliases.csv", "化名对照.csv"),
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||||
("diagnoses.csv", "诊断.csv"),
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("skipped.csv", "跳过病例.csv"),
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||||
("review.csv", "报告复核句.csv"),
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):
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assert (packed.pack / "标准答案" / name).read_bytes() == (answers / source).read_bytes()
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||||
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||||
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||||
def _tool_output(tool: str, *args: str) -> str:
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path = shutil.which(tool)
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assert path is not None, f"找不到 {tool}"
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return subprocess.run([path, *args], check=True, capture_output=True, text=True).stdout # noqa: S603
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||||
|
||||
|
||||
def _expected_cell(value: object) -> str:
|
||||
if value is None:
|
||||
return ""
|
||||
if isinstance(value, bool):
|
||||
return "true" if value else "false"
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||||
return value if isinstance(value, str) else repr(value)
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||||
|
||||
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||||
def test_tic_results_match_tic_json(packed: Packed) -> None:
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||||
for case_no in ("001", "004", "006"):
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||||
tic = read_ultrasound(packed.build.library_dir, packed.alias[case_no]).tic
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||||
assert tic is not None
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||||
rows = _csv(packed.pack / "病例" / f"P{case_no}" / "TIC结果.csv")
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||||
assert rows[0] == ["项目", "值"]
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||||
# 4 条曲线 × 2 + 病灶 2 条 ×（3 个口径 + 11 个指标 + 3 个辅助时刻）+ 紫圈 1 + 7 槽位 × 3。
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||||
assert len(rows) - 1 == 8 + 34 + 1 + 21
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||||
for item, value in rows[1:]:
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||||
path = item[item.rindex("（") + 1 : -1]
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||||
node: Any = tic
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||||
for key in path.split("."):
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||||
node = None if node is None else node[key]
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||||
assert value == _expected_cell(node), (case_no, item)
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||||
rows = dict(_csv(packed.pack / "病例" / "P001" / "TIC结果.csv")[1:])
|
||||
assert rows["病灶整体曲线 拟合成功（fits.roi_all.success）"] == "true"
|
||||
assert rows["病灶去暗区曲线 拟合成功（fits.roi_perf.success）"] == "false"
|
||||
assert rows["紫圈曲线可用（pan_curve_usable）"] == "false"
|
||||
assert rows["关键帧 D4 未取到的原因（keyframes.D4.reason）"] == UNLOCATED_REASON
|
||||
|
||||
|
||||
def test_pdfs_use_noto_cjk_and_carry_ids(packed: Packed) -> None:
|
||||
pdfs = sorted(packed.pack.rglob("*.pdf"))
|
||||
assert len(pdfs) == packed.result.pdfs == 1 + 3 * 2
|
||||
for pdf in pdfs:
|
||||
assert pdf.read_bytes().startswith(b"%PDF") and pdf.stat().st_size > 1000
|
||||
readme = packed.pack / "说明.pdf"
|
||||
fonts = _tool_output("pdffonts", str(readme))
|
||||
assert "NotoSerifCJK" in fonts and "NotoSansCJK" in fonts
|
||||
text = _tool_output("pdftotext", str(readme), "-")
|
||||
assert packed.build.build_id in text and RUN_ID in text and "parameter_on_boundary" in text
|
||||
report = _tool_output("pdftotext", str(packed.pack / "病例" / "P001" / "超声报告.pdf"), "-")
|
||||
assert report.startswith("病例号 P001 超声描述")
|
||||
|
||||
|
||||
def test_snapshot_and_no_writes_to_libraries(packed: Packed) -> None:
|
||||
snapshot = packed.result.run_dir / "snapshot"
|
||||
assert (snapshot / "review_pack.yaml").read_bytes() == packed.config.read_bytes()
|
||||
record = json.loads((snapshot / "review.json").read_text(encoding="utf-8"))
|
||||
assert record["build_id"] == packed.build.build_id and record["ceus_run_id"] == RUN_ID
|
||||
assert record["pandoc"].startswith("pandoc ") and "LibreOffice" in record["soffice"]
|
||||
assert (snapshot / "finished.json").is_file()
|
||||
assert _tree_hashes(packed.build.library_dir) == packed.library_before
|
||||
assert _tree_hashes(packed.build.answers_dir) == packed.answers_before
|
||||
|
||||
|
||||
def test_existing_run_dir_stops(packed: Packed) -> None:
|
||||
with pytest.raises(FileExistsError):
|
||||
run_review_pack(packed.config, REPO_ROOT, PACK_NOW)
|
||||
|
||||
|
||||
def test_build_without_ceus(tmp_path: Path) -> None:
|
||||
delivery_dir = make_delivery(2).write(tmp_path / "delivery")
|
||||
build = run_build(write_config(tmp_path, delivery_dir, 1, None, None), REPO_ROOT, BUILD_NOW)
|
||||
result = run_review_pack(
|
||||
_pack_config(tmp_path, build.library_dir, build.answers_dir), REPO_ROOT, PACK_NOW
|
||||
)
|
||||
for case in ("P001", "P002"):
|
||||
assert {p.name for p in (result.pack_dir / "病例" / case).iterdir()} == CASE_FILES
|
||||
alias = {r.case_no: r.alias for r in read_aliases(build.answers_dir)}
|
||||
rows = _csv(result.pack_dir / "病例总表.csv")[1:]
|
||||
assert [row[:5] for row in rows] == [
|
||||
["P001", "U001", alias["001"], "是", ""],
|
||||
["P002", "U002", alias["002"], "是", ""],
|
||||
]
|
||||
assert all(row[5:] == [""] * 9 for row in rows)
|
||||
assert _csv(result.pack_dir / "问题清单.csv") == [PROBLEM_HEADER]
|
||||
assert result.problems_by_stage == {} and result.video_bytes == 0
|
||||
|
||||
|
||||
def test_build_ids_differ_stops(tmp_path: Path) -> None:
|
||||
config = _pack_config(tmp_path, tmp_path / "library" / "a", tmp_path / "answers" / "b")
|
||||
with pytest.raises(ValueError, match="不是同一次构建"):
|
||||
run_review_pack(config, REPO_ROOT, PACK_NOW)
|
||||
assert not (tmp_path / "review_pack").exists()
|
||||
|
||||
|
||||
def test_report_pdf_keeps_markdown_characters(tmp_path: Path) -> None:
|
||||
"""报告正文里的 Markdown 记号、引号、连字符、换行照抄，不被 pandoc 当成格式。
|
||||
|
||||
行首空格抽回文字时只剩一个（pdftotext 按版面估空格），所以每行比较去掉首尾空白后的内容。
|
||||
"""
|
||||
text = (
|
||||
'1. 囊壁*增厚* #2 a_b_c <b>x</b> [链接](y) -- "引号" ... `码` | 竖线\n 第二行行首两个空格'
|
||||
)
|
||||
target = tmp_path / "报告.pdf"
|
||||
markdown_to_pdf({target: report_markdown("病例号 P001 超声描述", text)})
|
||||
lines = _tool_output("pdftotext", "-layout", str(target), "-").splitlines()
|
||||
assert [line.strip() for line in lines if line.strip()][1:] == [
|
||||
line.strip() for line in text.splitlines()
|
||||
]
|
||||
|
||||
|
||||
def _gap_after_ab(pdf: Path) -> float:
|
||||
"""pdftotext -bbox 里词「ab」右边界到下一个词左边界的距离（点）。"""
|
||||
boxes = re.findall(
|
||||
r'xMin="([0-9.]+)"[^>]*xMax="([0-9.]+)"[^>]*>([^<]*)</word>',
|
||||
_tool_output("pdftotext", "-bbox", str(pdf), "-"),
|
||||
)
|
||||
words = [(float(x0), float(x1), w) for x0, x1, w in boxes]
|
||||
index = next(i for i, (_, _, w) in enumerate(words) if w == "ab")
|
||||
return words[index + 1][0] - words[index][1]
|
||||
|
||||
|
||||
def test_report_pdf_keeps_repeated_spaces(tmp_path: Path) -> None:
|
||||
"""一行里连续三个空格不被 pandoc 并成一个。
|
||||
|
||||
pdftotext 抽文字时按间距估空格，三个空格也只吐出一个，所以量版面：「ab」与「cd」之间的间距是
|
||||
单个空格那份 PDF 的约三倍。
|
||||
"""
|
||||
one, three = tmp_path / "一个空格.pdf", tmp_path / "三个空格.pdf"
|
||||
markdown_to_pdf(
|
||||
{
|
||||
one: report_markdown("病例号 P001 超声描述", "ab cd"),
|
||||
three: report_markdown("病例号 P001 超声描述", "ab cd"),
|
||||
}
|
||||
)
|
||||
assert _gap_after_ab(three) / _gap_after_ab(one) == pytest.approx(3.0, abs=0.3)
|
||||
|
||||
|
||||
def test_report_with_tab_stops() -> None:
|
||||
with pytest.raises(ValueError, match="病例号 P001 超声描述：报告原文里有 Tab"):
|
||||
report_markdown("病例号 P001 超声描述", "囊壁厚\t约2mm")
|
||||
Reference in New Issue
Block a user