#!/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 score_answers(questionnaire: dict[str, Any], answers: dict[str, str]) -> dict[str, Any]: questions = questionnaire.get("questions") if isinstance(questionnaire.get("questions"), list) else [] by_id = {question["id"]: question for question in questions if isinstance(question, dict) and question.get("id")} cluster_scores: dict[str, int] = {} applied = [] unknown_ids = [] invalid_answers = [] for question_id, raw_choice in (answers or {}).items(): question = by_id.get(question_id) if not question: unknown_ids.append(question_id) continue choice = str(raw_choice or "").strip().upper() scoring = (question.get("scoring_map") or {}).get(choice) if not isinstance(scoring, dict): invalid_answers.append({"id": question_id, "answer": raw_choice}) continue cluster = str(scoring.get("cluster") or "neutral") points = int(scoring.get("points") or 0) if cluster != "neutral": cluster_scores[cluster] = cluster_scores.get(cluster, 0) + points applied.append({"id": question_id, "answer": choice, "cluster": cluster, "points": points}) answered_ids = {item["id"] for item in applied} unanswered = [question for question in questions if question.get("id") not in answered_ids] next_round = min((int(question.get("round") or 0) for question in unanswered), default=None) rankings = [ {"cluster": cluster, "score": score} for cluster, score in sorted(cluster_scores.items(), key=lambda item: (-item[1], item[0])) ] return { "scope": "active_birth_time_rectification_scoring", "schema_version": 1, "answered_count": len(applied), "candidate_cluster_rankings": rankings, "next_round": next_round, "next_round_questions": [question for question in unanswered if question.get("round") == next_round], "applied_scoring": applied, "unknown_question_ids": unknown_ids, "invalid_answers": invalid_answers, "boundary": "This narrows candidate clusters 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("--answers-json", default="", help="Optional JSON object mapping question id to A/B/C/D") parser.add_argument("--pretty", action="store_true") args = parser.parse_args() questionnaire = build_questionnaire(args.birth_time, args.uncertainty_minutes, args.step_minutes) report = score_answers(questionnaire, json.loads(args.answers_json)) if args.answers_json else questionnaire print(json.dumps(report, ensure_ascii=False, indent=2 if args.pretty else None)) return 0 if __name__ == "__main__": raise SystemExit(main())