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#!/usr/bin/env python3
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"""Generate active-choice birth-time rectification questions."""
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from __future__ import annotations
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import argparse
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import json
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from datetime import datetime, timedelta
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from typing import Any
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OPTIONS = [
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{"key": "A", "label": "明确有，且时间大致吻合", "score": 2},
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{"key": "B", "label": "有类似，但时间略偏或不够重大", "score": 1},
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{"key": "C", "label": "没有明显发生", "score": -2},
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{"key": "D", "label": "不确定 / 不记得", "score": 0},
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]
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QUESTION_TEMPLATES = [
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("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"),
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("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"),
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("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"),
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("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"),
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("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"),
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("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"),
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("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"),
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("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"),
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]
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def _parse_time(value: str) -> datetime:
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return datetime.strptime(value, "%Y-%m-%d %H:%M")
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def _candidate_scan(center: datetime, uncertainty_minutes: int, step_minutes: int) -> dict[str, Any]:
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start = center - timedelta(minutes=uncertainty_minutes)
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end = center + timedelta(minutes=uncertainty_minutes)
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return {
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"start": start.strftime("%Y-%m-%d %H:%M"),
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"end": end.strftime("%Y-%m-%d %H:%M"),
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"step_minutes": step_minutes,
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"candidate_count": int((end - start).total_seconds() // 60 // step_minutes) + 1,
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"cluster_labels": ["early_candidate_cluster", "middle_candidate_cluster", "late_candidate_cluster"],
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}
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def build_questionnaire(birth_time: str, uncertainty_minutes: int = 30, step_minutes: int = 1) -> dict[str, Any]:
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questions = []
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for qid, round_id, domain, sensitivity, window, prompt, yes_bias, no_bias in QUESTION_TEMPLATES:
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questions.append({
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"id": qid,
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"round": round_id,
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"domain": domain,
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"sensitivity": sensitivity,
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"window": window,
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"prompt": prompt,
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"options": OPTIONS,
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"scoring_map": {
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"A": {"effect": "support", "cluster": yes_bias, "points": 2},
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"B": {"effect": "weak_support", "cluster": yes_bias, "points": 1},
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"C": {"effect": "exclude_or_penalize", "cluster": no_bias, "points": -2},
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"D": {"effect": "neutral", "cluster": "neutral", "points": 0},
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},
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})
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return {
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"scope": "active_birth_time_rectification_questionnaire",
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"schema_version": 1,
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"candidate_scan": _candidate_scan(_parse_time(birth_time), uncertainty_minutes, step_minutes),
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"workflow": [
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"candidate_time_scan",
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"varga_arudha_kp_sensitivity_diff",
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"high_information_question_generation",
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"multiple_choice_user_answers",
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"dynamic_candidate_cluster_scoring",
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"next_round_question_selection",
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],
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"rounds": {
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"1": "coarse screen",
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"2": "domain follow-up",
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"3": "fine confirmation",
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},
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"sensitivity_layers": ["D9", "D10", "D24", "D30", "D60", "D4", "UL", "A7", "A10", "KP_cusp", "Vimshottari", "Narayana", "Chara"],
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"questions": questions,
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"boundary": "Question generation only; final rectification requires scoring answers against actual candidate chart differences.",
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}
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--birth-time", required=True, help="Approximate local birth time, YYYY-MM-DD HH:MM")
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parser.add_argument("--uncertainty-minutes", type=int, default=30)
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parser.add_argument("--step-minutes", type=int, default=1)
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parser.add_argument("--pretty", action="store_true")
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args = parser.parse_args()
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print(json.dumps(build_questionnaire(args.birth_time, args.uncertainty_minutes, args.step_minutes), ensure_ascii=False, indent=2 if args.pretty else None))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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