拆分报告渲染函数的第一阶段，把 34 个无外层依赖的辅助函数搬到 report_render。
同一份真实盘包在屏蔽与不屏蔽 PyJHora 下新旧渲染 sha 都一致，各 24 项且退出码 0。指定 pytest 233 通过、3 跳过。 Co-Authored-By: Grok (agent loop)
This commit is contained in:
jesse-ux
co-authored by
Grok (agent loop)
Grok (agent loop)
parent
32b238cf86
commit
dbba127e71
@@ -0,0 +1,255 @@
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"""Helpers moved unchanged out of render_pl9_markdown (phase 1)."""
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import re
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try:
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from scripts.report_render.envelope import _unwrap
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except ModuleNotFoundError: # pragma: no cover - direct scripts/ execution
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from report_render.envelope import _unwrap
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SIGNS = ['Aries', 'Taurus', 'Gemini', 'Cancer', 'Leo', 'Virgo', 'Libra', 'Scorpio', 'Sagittarius', 'Capricorn', 'Aquarius', 'Pisces']
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SIGNS_CN = {'Aries': '白羊座', 'Taurus': '金牛座', 'Gemini': '双子座', 'Cancer': '巨蟹座', 'Leo': '狮子座', 'Virgo': '处女座', 'Libra': '天秤座', 'Scorpio': '天蝎座', 'Sagittarius': '射手座', 'Capricorn': '摩羯座', 'Aquarius': '水瓶座', 'Pisces': '双鱼座'}
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PLANET_LABEL_CN = {
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'Sun': '太阳',
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'Moon': '月亮',
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'Mars': '火星',
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'Mercury': '水星',
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'Jupiter': '木星',
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'Venus': '金星',
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'Saturn': '土星',
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'Rahu': '北交点',
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'Ketu': '南交点',
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}
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def _humanize_reader_token(token: str) -> str:
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value = str(token or '').strip()
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if not value:
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return value
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if value in SIGNS_CN:
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return SIGNS_CN[value]
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if value in PLANET_LABEL_CN:
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return PLANET_LABEL_CN[value]
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return value
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def _humanize_reader_group(raw_group: str) -> str:
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seen = set()
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parts = []
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for item in str(raw_group or '').split(','):
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humanized = _humanize_reader_token(item)
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if humanized and humanized not in seen:
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seen.add(humanized)
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parts.append(humanized)
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return ' / '.join(parts)
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def _humanize_tajika_candidate(name: str) -> str:
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return {
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'Ithasala_candidate': '趋近相位候选',
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'Easarapha_candidate': '分离相位候选',
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}.get(str(name or ''), '年度相位候选')
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def _humanize_tajika_motion(motion: str) -> str:
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return {'applying': '趋近', 'separating': '分离'}.get(str(motion or ''), str(motion or '-'))
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def _display_rashi_from_value(raw_value):
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if isinstance(raw_value, int) and 0 <= raw_value < len(SIGNS):
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return _humanize_reader_token(SIGNS[raw_value])
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return _humanize_reader_token(raw_value)
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def _humanize_reader_summary_line(summary_line: str) -> str:
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summary_line = _unwrap(summary_line)
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if isinstance(summary_line, dict):
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summary_line = (
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summary_line.get('brief')
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or summary_line.get('text')
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or summary_line.get('summary')
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or ''
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)
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text = str(summary_line or '').strip()
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if not text:
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return text
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if text == "年度层已经有可读壳层，但仍需要带着冲突标签阅读。":
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return "这一年的年度主线已经形成。"
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if text == "This pack converts Dasha trigger facts into report-ready interpretation seeds without final event claims.":
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return "这层已经把 Dasha 触发事实压成可读提示，但仍不直接升级为具体事件断语。"
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if text == "Source: dasha_master_pack.interpretation_triggers.":
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return "当前解释仍然直接来自 dasha_master_pack.interpretation_triggers 的证据层。"
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if text == "All claims remain evidence-labeled and parameter-sensitive where the source pack is unclosed.":
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return "只要 source pack 还没闭环，相关结论就继续保持 evidence-labeled 与 parameter_sensitive。"
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if text == "Tajika Yogas: annual candidate structures visible":
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return "Tajika Yogas 当前已经能读到年度候选交互结构。"
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if text == "Sahams: annual sensitive points visible":
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return "Sahams 当前已经能读到年度敏感点。"
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match = re.match(r'^([^:]+):\s*conflict between\s+(.+?)\s+vs\s+(.+)$', text)
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if match:
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topic = match.group(1).strip()
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left = _humanize_reader_group(match.group(2))
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right = _humanize_reader_group(match.group(3))
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topic_label = {
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'Muntha': 'Muntha',
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'Year Lord': 'Year Lord',
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'Annual Asc': '返照上升',
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}.get(topic, topic)
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if left == right:
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return f"{topic_label} 当前结果指向{left}。"
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return f"{topic_label} 当前存在候选分歧：一组结果指向{left}，另一组结果指向{right}。"
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simple_match = re.match(r'^([^:]+):\s*(.+)$', text)
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if simple_match:
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topic = simple_match.group(1).strip()
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value = _humanize_reader_group(simple_match.group(2))
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if topic == 'Year Lord' and value:
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return f"Year Lord 当前优先候选指向{value}。"
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if topic == 'Muntha' and value:
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return f"Muntha 当前优先候选指向{value}。"
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if topic == 'Tajika Yogas' and 'annual candidate structures visible' in str(simple_match.group(2)).lower():
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return "Tajika Yogas 当前已经能读到年度候选交互结构。"
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if topic == 'Sahams' and 'annual sensitive points visible' in str(simple_match.group(2)).lower():
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return "Sahams 当前已经能读到年度敏感点。"
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return text
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def _humanize_annual_highlight_line(highlight: str) -> str:
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text = str(highlight or '').strip()
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if not text:
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return text
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if text == "Solar Return / Tajika annual shell is available.":
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return "本年度返照与 Tajika 时间结构已经生成。"
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if text == "Muntha and Year Lord are visible, but Year Lord remains producer-disagree sensitive.":
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return "Muntha 和 Year Lord 现在都已经可见，但 Year Lord 仍要带着分歧标签阅读。"
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if text == "The report should read annual structure first, then the evidence appendix.":
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return "这一层适合从年度结构展开，再回头核对证据附录。"
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return text
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def _humanize_annual_narrative_highlight(highlight: str) -> str:
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return _humanize_annual_highlight_line(str(highlight)).rstrip('。')
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def _humanize_domain_title(title: str) -> str:
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text = str(title or '').strip()
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if not text:
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return text
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mapping = {
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"Career and public direction": "事业与公众方向",
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"Wealth and growth opportunities": "财富与增长机会",
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"Relationship and alliance themes": "关系与合作主题",
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"Health and vitality caution": "健康与精力提醒",
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"Risk, delay, and friction markers": "阻力、延迟与摩擦提示",
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"Timing windows and cross-check needs": "时间窗口与交叉核对重点",
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}
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return mapping.get(text, text)
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def _workbook_readable_label(worksheet_id: str) -> str:
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if worksheet_id == 'birth_and_parameters':
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return '出生与计算口径'
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if worksheet_id == 'd1_rasi_bhava':
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return '本命盘主结构'
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if worksheet_id == 'strengths_and_scores':
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return '力量与技术表'
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if worksheet_id == 'divisional_and_special_charts':
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return '分盘与特殊点位'
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if worksheet_id == 'timing_and_predictive_systems':
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return '时间系统与流年层'
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if worksheet_id == 'advanced_systems':
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return '进阶体系'
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if worksheet_id == 'ai_and_audit':
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return '审计与证据附录'
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return '补充工作簿'
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def _workbook_readable_sentence(worksheet_id: str) -> str:
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if worksheet_id == 'birth_and_parameters':
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return '出生参数、岁差、交点模式与计算口径已经固定，是整份报告的可信底座。'
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if worksheet_id == 'd1_rasi_bhava':
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return '本命盘主结构已经就位，适合先看性格主轴、房屋结构和基础趋势。'
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if worksheet_id == 'strengths_and_scores':
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return '力量、功能性吉凶与技术表可以直接用来判断支撑与压力来源。'
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if worksheet_id == 'divisional_and_special_charts':
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return '分盘与特殊点位可作为扩展阅读，适合补充看细节。'
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if worksheet_id == 'timing_and_predictive_systems':
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return '大运、行运与年度层已经可以形成时间主线。'
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if worksheet_id == 'advanced_systems':
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return 'Jaimini、KP、Yoga 等进阶体系可作为补充主轴。'
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if worksheet_id == 'ai_and_audit':
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return 'AI 读写、审计与证据层用于核对来源与边界，不直接替代正文。'
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return f'{worksheet_id} 已纳入当前报告。'
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def _workbook_reading_paragraph(worksheet_ids: list[str]) -> str:
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parts = []
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for worksheet_id in worksheet_ids:
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sentence = _workbook_readable_sentence(worksheet_id).rstrip('。')
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parts.append(f"先看{_workbook_readable_label(worksheet_id)}，这里会告诉你{sentence}")
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if not parts:
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return '当前暂无可用工作簿阅读顺序。'
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return '；'.join(parts) + '。'
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def _md_cell(value) -> str:
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text = str(value if value not in (None, '') else '-').replace('\n', ' ').strip()
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return text.replace('|', '/')
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def _render_compact_payload(value, depth: int = 0) -> str:
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if value in (None, '', [], {}):
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return '-'
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if depth >= 2:
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return _md_cell(type(value).__name__)
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if isinstance(value, dict):
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parts = []
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for key in sorted(value, key=str):
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if key in {
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'narrative_items',
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'domain_paragraphs',
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'response_envelope',
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'input_hash',
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'profile_hash',
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'raw_calculation',
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'shared_full_report_authority',
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}:
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continue
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parts.append(f"{key}={_render_compact_payload(value.get(key), depth + 1)}")
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return '; '.join(parts) if parts else '-'
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if isinstance(value, (list, tuple)):
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items = [_render_compact_payload(item, depth + 1) for item in list(value)[:8]]
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suffix = ' …' if len(value) > 8 else ''
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return ', '.join(items) + suffix if items else '-'
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return _md_cell(value)
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def _sign_text(row: dict, sign_key: str = 'sign', cn_key: str = 'sign_cn') -> str:
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if not isinstance(row, dict):
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return '-'
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sign = row.get(cn_key) or _humanize_reader_token(row.get(sign_key)) or row.get(sign_key)
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return _md_cell(sign)
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def _value_text(*values):
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for value in values:
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if value not in (None, '', [], {}):
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return str(value)
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return None
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def _compose_narrative_paragraph(*parts: str | None) -> str | None:
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cleaned = []
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for part in parts:
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text = str(part or '').strip()
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if not text:
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continue
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text = text.rstrip()
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if text.endswith('。'):
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text = text[:-1]
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cleaned.append(text)
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if not cleaned:
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return None
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return '；'.join(cleaned) + '。'
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def _planet_list_text(items: list[str], limit: int = 3) -> str | None:
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names = []
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seen = set()
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for item in items:
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humanized = _humanize_reader_token(item)
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if humanized and humanized not in seen:
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seen.add(humanized)
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names.append(humanized)
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if len(names) >= limit:
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break
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if not names:
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return None
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return '、'.join(names)
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def _append_unique_text(items: list[str], value: str) -> None:
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text = str(value or '').strip().rstrip('。')
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if text and text not in items:
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items.append(text)
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