feat: add active rectification questionnaire

references/birth-time-rectification-decision-tree.md

@@ -31,6 +31,34 @@
 - [jyotish-app/rectification-engine.js](<repo>/jyotish-app/rectification-engine.js)
 - [references/varga-system-quick-reference.md](<repo>/references/varga-system-quick-reference.md)
 
+## 1.1 产品化问询原则
+
+生时校正不应默认要求用户先列出 8-15 个事件。普通用户路径优先采用主动问询：
+
+```text
+候选时间扫描
+→ 分盘 / Arudha / KP cusp / Dasha 敏感差异提取
+→ 生成高信息量选择题
+→ 用户回答 A/B/C/D
+→ 动态缩小候选时间簇
+→ 进入下一轮追问
+```
+
+用户只需回答：
+
+```text
+A. 明确有，且时间大致吻合
+B. 有类似，但时间略偏或不够重大
+C. 没有明显发生
+D. 不确定 / 不记得
+```
+
+机器可审计入口：
+
+```bash
+python3 scripts/active_rectification_questions.py --birth-time "1955-02-24 19:15" --uncertainty-minutes 30 --pretty
+```
+
 ## 2. 第一层：主引擎
 
 ### Dasha + dated events

scripts/active_rectification_questions.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+"""Generate active-choice birth-time rectification questions."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from datetime import datetime, timedelta
+from typing import Any
+
+OPTIONS = [
+    {"key": "A", "label": "明确有，且时间大致吻合", "score": 2},
+    {"key": "B", "label": "有类似，但时间略偏或不够重大", "score": 1},
+    {"key": "C", "label": "没有明显发生", "score": -2},
+    {"key": "D", "label": "不确定 / 不记得", "score": 0},
+]
+
+QUESTION_TEMPLATES = [
+    ("education_environment_shift", 1, "education", ["D24", "D4", "Dasha"], "age_16_to_18", "16-18岁附近，是否有明显学业、学校、专业方向或学习环境变化？", "middle_candidate_cluster", "against_D24_sensitive_cluster"),
+    ("residence_relocation_shift", 1, "residence", ["D4", "12H", "Rahu/Ketu", "Transit"], "age_20_to_24", "20-24岁附近，是否有搬家、离乡、长期异地、住宿或居住结构变化？", "D4_relocation_cluster", "against_D4_relocation_cluster"),
+    ("relationship_or_partner_entry", 1, "relationship", ["D9", "UL", "A7", "7H"], "age_21_to_26", "21-26岁附近，是否有关系对象进入、关系断裂、暧昧升级或关系观明显转变？", "D9_UL_A7_cluster", "against_relationship_cluster"),
+    ("career_responsibility_pressure", 1, "career", ["D10", "A10", "Saturn", "10H"], "age_26_to_30", "26-30岁附近，是否有责任增加、合作压力、工作结构变化或长期压力阶段？", "D10_A10_saturn_cluster", "against_career_pressure_cluster"),
+    ("research_tool_expression_shift", 1, "career_learning", ["D10", "D24", "Mercury", "A10"], "recent_three_years", "近三年是否明显进入写作、技术、系统化学习、工具搭建、内容表达、AI/研究类方向？", "Mercury_D24_A10_cluster", "against_learning_expression_cluster"),
+    ("health_crisis_or_low_period", 2, "health_pressure", ["D30", "6H", "8H", "Saturn/Mars"], "largest_pressure_window", "某个压力窗口附近，是否有健康、事故、低谷、睡眠/精神压力或身体负担明显阶段？", "D30_crisis_cluster", "against_D30_crisis_cluster"),
+    ("public_role_or_project_visibility", 2, "public_work", ["A10", "D10", "AmK", "Karakamsha"], "career_visibility_window", "某个事业窗口附近，是否有项目公开、作品产出、职位/身份变化或被他人看见的机会？", "A10_public_visibility_cluster", "against_A10_cluster"),
+    ("sequence_inner_vs_outer", 3, "fine_timing", ["KP_cusp", "Pratyantar", "Dasha_boundary"], "top_candidate_window", "关键变化更像先有内在转向、后有外部结果，还是几乎同时发生？", "fine_boundary_cluster", "neutral"),
+]
+
+
+def _parse_time(value: str) -> datetime:
+    return datetime.strptime(value, "%Y-%m-%d %H:%M")
+
+
+def _candidate_scan(center: datetime, uncertainty_minutes: int, step_minutes: int) -> dict[str, Any]:
+    start = center - timedelta(minutes=uncertainty_minutes)
+    end = center + timedelta(minutes=uncertainty_minutes)
+    return {
+        "start": start.strftime("%Y-%m-%d %H:%M"),
+        "end": end.strftime("%Y-%m-%d %H:%M"),
+        "step_minutes": step_minutes,
+        "candidate_count": int((end - start).total_seconds() // 60 // step_minutes) + 1,
+        "cluster_labels": ["early_candidate_cluster", "middle_candidate_cluster", "late_candidate_cluster"],
+    }
+
+
+def build_questionnaire(birth_time: str, uncertainty_minutes: int = 30, step_minutes: int = 1) -> dict[str, Any]:
+    questions = []
+    for qid, round_id, domain, sensitivity, window, prompt, yes_bias, no_bias in QUESTION_TEMPLATES:
+        questions.append({
+            "id": qid,
+            "round": round_id,
+            "domain": domain,
+            "sensitivity": sensitivity,
+            "window": window,
+            "prompt": prompt,
+            "options": OPTIONS,
+            "scoring_map": {
+                "A": {"effect": "support", "cluster": yes_bias, "points": 2},
+                "B": {"effect": "weak_support", "cluster": yes_bias, "points": 1},
+                "C": {"effect": "exclude_or_penalize", "cluster": no_bias, "points": -2},
+                "D": {"effect": "neutral", "cluster": "neutral", "points": 0},
+            },
+        })
+    return {
+        "scope": "active_birth_time_rectification_questionnaire",
+        "schema_version": 1,
+        "candidate_scan": _candidate_scan(_parse_time(birth_time), uncertainty_minutes, step_minutes),
+        "workflow": [
+            "candidate_time_scan",
+            "varga_arudha_kp_sensitivity_diff",
+            "high_information_question_generation",
+            "multiple_choice_user_answers",
+            "dynamic_candidate_cluster_scoring",
+            "next_round_question_selection",
+        ],
+        "rounds": {
+            "1": "coarse screen",
+            "2": "domain follow-up",
+            "3": "fine confirmation",
+        },
+        "sensitivity_layers": ["D9", "D10", "D24", "D30", "D60", "D4", "UL", "A7", "A10", "KP_cusp", "Vimshottari", "Narayana", "Chara"],
+        "questions": questions,
+        "boundary": "Question generation only; final rectification requires scoring answers against actual candidate chart differences.",
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--birth-time", required=True, help="Approximate local birth time, YYYY-MM-DD HH:MM")
+    parser.add_argument("--uncertainty-minutes", type=int, default=30)
+    parser.add_argument("--step-minutes", type=int, default=1)
+    parser.add_argument("--pretty", action="store_true")
+    args = parser.parse_args()
+    print(json.dumps(build_questionnaire(args.birth_time, args.uncertainty_minutes, args.step_minutes), ensure_ascii=False, indent=2 if args.pretty else None))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

tests/test_active_rectification_questions.py

@@ -0,0 +1,18 @@
+from __future__ import annotations
+
+from scripts.active_rectification_questions import build_questionnaire
+
+
+def test_active_rectification_questions_generate_choice_based_workflow() -> None:
+    report = build_questionnaire("1955-02-24 19:15", uncertainty_minutes=30)
+
+    assert report["scope"] == "active_birth_time_rectification_questionnaire"
+    assert report["candidate_scan"]["start"] == "1955-02-24 18:45"
+    assert report["candidate_scan"]["end"] == "1955-02-24 19:45"
+    assert report["candidate_scan"]["candidate_count"] == 61
+    assert "high_information_question_generation" in report["workflow"]
+    assert {"D9", "D10", "D24", "D30", "UL", "A10", "KP_cusp"} <= set(report["sensitivity_layers"])
+    assert len(report["questions"]) >= 8
+    assert {q["round"] for q in report["questions"]} == {1, 2, 3}
+    assert all({option["key"] for option in q["options"]} == {"A", "B", "C", "D"} for q in report["questions"])
+    assert all("scoring_map" in q for q in report["questions"])

tests/test_rectification_decision_tree_doc.py

@@ -21,6 +21,8 @@ def test_rectification_decision_tree_doc_exists_and_covers_priority_layers() ->
     assert "D60" in text
     assert "不是所有分盘一股脑上" in text
     assert "Dasha 定框，D9/D10 定核心" in text
+    assert "主动问询" in text
+    assert "scripts/active_rectification_questions.py" in text
 
 
 def test_rectification_decision_tree_doc_maps_event_groups_to_vargas() -> None: