Jyotisha/scripts/career_analysis.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Career Analysis（事业分析）引擎 v1.0
超越 vedic-astro-skills 的 AI prompt，实现结构化引擎级分析

核心分析链：
D10(Dashamsa)事业盘 → 10宫主状态 → Saturn/Jupiter角色 → Dasha事业窗口 → Yogas
"""

from typing import Dict, List, Any
import math

try:
    from .functional_benefics import derive_functional_benefic_malefic, moon_paksha_from_planets, SIGN_LORDS
    from .affliction_evidence import collect_affliction_evidence
except ImportError:
    from functional_benefics import derive_functional_benefic_malefic, moon_paksha_from_planets, SIGN_LORDS
    from affliction_evidence import collect_affliction_evidence

SIGNS = ['Aries','Taurus','Gemini','Cancer','Leo','Virgo',
         'Libra','Scorpio','Sagittarius','Capricorn','Aquarius','Pisces']

CAREER_SIGN_INDICATORS = {
    'Aries': ['军事/执法','工程/技术','创业/先驱','外科医学'],
    'Taurus': ['金融/银行','艺术/设计','餐饮/食品','奢侈品'],
    'Gemini': ['媒体/传播','教育/培训','销售/贸易','IT/编程'],
    'Cancer': ['医疗/护理','房地产','酒店/旅游','心理学/咨询'],
    'Leo': ['管理/领导','娱乐/表演','政府/政治','教育/学术'],
    'Virgo': ['数据分析','会计/审计','编辑/校对','医疗技术'],
    'Libra': ['法律/司法','艺术/美学','公共关系','人力资源'],
    'Scorpio': ['投资/风控','研究/侦探','心理学/精神分析','保险/精算'],
    'Sagittarius': ['国际商务','教育/哲学','法律/宗教','出版/写作'],
    'Capricorn': ['管理/行政','土木工程','政府/政策','传统行业'],
    'Aquarius': ['科技/创新','NGO/公益','科学/研究','网络/电商'],
    'Pisces': ['艺术/影视','医疗/康复','慈善/NGO','创意/设计'],
}

PLANET_CAREER_ROLES = {
    'Sun': {'domain': '领导/管理','strength': '威权、决策、独立执业'},
    'Moon': {'domain': '公众/服务','strength': '人际、关怀、变动适应'},
    'Mars': {'domain': '执行/技术','strength': '行动力、工程、军事'},
    'Mercury': {'domain': '信息/沟通','strength': '智力、商业、IT'},
    'Jupiter': {'domain': '指导/教育','strength': '教导、咨询、法律'},
    'Venus': {'domain': '艺术/财富','strength': '审美、金融、奢侈'},
    'Saturn': {'domain': '管理/组织','strength': '纪律、行政、工业'},
    'Rahu': {'domain': '创新/涉外','strength': '科技、外企、非传统'},
    'Ketu': {'domain': '研究/灵性','strength': '分析、玄学、幕后'},
}

# Candidate symbolism, not a calibrated occupation classifier. Each candidate
# carries the actual tenth-house/lord witness; scores are not probabilities.
PLANET_INDUSTRIES = {
    'Sun': ['领导/管理', '权力/公职'],
    'Moon': ['公众/服务', '照护/餐旅'],
    'Mars': ['体育/竞技', '工程/技术', '军事/执法'],
    'Mercury': ['信息/沟通', '商业/贸易', '计算/分析'],
    'Jupiter': ['教育/咨询', '法律/学术'],
    'Venus': ['艺术/表演', '设计/审美'],
    'Saturn': ['工业/工程', '组织/行政'],
}

D10_HOUSE_MEANINGS = {
    1: '事业身份/职业形象', 2: '事业财富/收入来源', 3: '事业沟通/技能',
    4: '事业根基/安稳度', 5: '事业创意/子女相关', 6: '事业竞争/服务',
    7: '事业合作/客户', 8: '事业变革/危机管理', 9: '事业远见/高等教育',
    10: '事业成就/社会地位', 11: '事业收益/人脉', 12: '事业幕后/海外',
}


def analyze_career(planets: Dict, asc_sign: str = 'Aries',
                   d10_positions: Dict = None, dasha_info: Dict = None,
                   shadbala: Dict = None,
                   career_semantic_profile: Dict = None, a10: Dict = None,
                   user_industry: str | None = None) -> Dict:
    """
    完整事业分析引擎。

    Args:
        planets: D1行星位置 {planet: {'sign','house','degree',...}}
        asc_sign: 上升星座
        d10_positions: D10分盘行星位置（可选，提升精度）
        dasha_info: 当前大运/小运信息（可选）
        shadbala: Shadbala结果（可选）

    Returns:
        结构化事业分析
    """
    analysis = {'indicators': [], 'strengths': [], 'challenges': [], 'timing': [], 'fields': []}
    if asc_sign not in SIGNS:
        raise ValueError('asc_sign must be a valid sign')
    # 占星师 2026-11-08 第五轮裁定 3：默认 PVR 表 30；月亮盈亏供动星座上升的月亮用。
    functional = derive_functional_benefic_malefic(asc_sign, moon_paksha=moon_paksha_from_planets(planets))
    analysis['functional_benefic_malefic'] = functional
    analysis['assertion_label'] = 'single_system_inference'
    analysis['career_claim_gate'] = {
        'industry_claim_allowed': bool(user_industry),
        'public_backstage_claim_allowed': bool(user_industry and a10),
        'user_industry': user_industry,
        'boundary': 'Without the user\'s actual industry, fields remain candidate symbolism; public/backstage is not inferred from 6/8/12 placement alone.',
    }

    # 1. 10宫分析
    asc_idx = SIGNS.index(asc_sign)
    h10_sign = SIGNS[(asc_idx + 9) % 12]
    h10_lord = {'Aries':'Mars','Taurus':'Venus','Gemini':'Mercury','Cancer':'Moon',
                'Leo':'Sun','Virgo':'Mercury','Libra':'Venus','Scorpio':'Mars',
                'Sagittarius':'Jupiter','Capricorn':'Saturn','Aquarius':'Saturn','Pisces':'Jupiter'}[h10_sign]

    # 10宫主状态
    h10_data = planets.get(h10_lord, {})
    h10_house = h10_data.get('house', 0)
    h10_dignity = h10_data.get('dignity', 'neutral')

    analysis['indicators'].append({
        'type': '10宫主分析',
        'lord': h10_lord,
        'sign': h10_sign,
        'house': h10_house,
        'dignity': h10_dignity,
        'potential_fields': CAREER_SIGN_INDICATORS.get(h10_sign, []),
    })
    analysis['affliction_evidence'] = collect_affliction_evidence(
        target_house=10,
        target_lord=h10_lord,
        planets=planets,
        functional_malefics=functional.get('functional_malefics', []),
        varga_pressure=bool(d10_positions),
    )

    # 2. 行星角色分配
    for planet in ['Sun','Moon','Mars','Mercury','Jupiter','Venus','Saturn']:
        pd = planets.get(planet, {})
        if not pd:
            continue
        house = pd.get('house', 0)
        if house in (1, 4, 7, 10):
            analysis['strengths'].append({
                'planet': planet,
                'role': PLANET_CAREER_ROLES.get(planet, {}).get('domain', ''),
                'detail': PLANET_CAREER_ROLES.get(planet, {}).get('strength', ''),
                'house': house,
                'note': f'{planet}在角宫({house}) — 事业核心驱动力',
            })

    # 3. Saturn角色（事业核心指示星）
    saturn = planets.get('Saturn', {})
    saturn_house = saturn.get('house', 0)
    if saturn_house in (10, 6):
        analysis['strengths'].append({'planet':'Saturn','role':'事业纪律候选','detail':'土星落事业/服务宫；组织、责任或持续训练是候选主题，需结合功能角色与强度。'})
    if saturn_house in (8, 12):
        analysis['challenges'].append({'planet':'Saturn','issue':'需核验工作环境与压力',
            'detail':f'Saturn在{saturn_house}宫；这一落位不能单独确定发展速度或后期回报。',
            'assertion_label':'user_history_verification_required', 'source_path':'planets.Saturn.house'})

    # 4. 事业领域推荐
    scored_fields = {}
    # 10宫星座
    for field in CAREER_SIGN_INDICATORS.get(h10_sign, []):
        scored_fields[field] = scored_fields.get(field, 0) + 3
    # 10宫主
    if h10_lord in PLANET_CAREER_ROLES:
        scored_fields[PLANET_CAREER_ROLES[h10_lord]['domain']] = scored_fields.get(PLANET_CAREER_ROLES[h10_lord]['domain'], 0) + 2
    # D10分析（如果有）
    d10_positions = d10_positions or {}
    d10_planets = d10_positions.get('planets', d10_positions)
    d10_asc = d10_positions.get('ascendant', d10_positions.get('Ascendant'))
    if isinstance(d10_asc, dict):
        d10_asc = d10_asc.get('sign')
    d10_h10_sign = SIGNS[(SIGNS.index(d10_asc) + 9) % 12] if d10_asc in SIGNS else None
    analysis['d10_evidence'] = {'ascendant': d10_asc, 'tenth_sign': d10_h10_sign,
        'status': 'executed' if d10_h10_sign else 'blocked',
        'missing_inputs': [] if d10_h10_sign else ['D10 ascendant']}
    if d10_h10_sign:
        for field in CAREER_SIGN_INDICATORS.get(d10_h10_sign, []):
            scored_fields[field] = scored_fields.get(field, 0) + 1

    candidates = {}
    for layer, positions, lord in (
        ('D1', planets, h10_lord),
        ('D10', d10_planets, SIGN_LORDS.get(d10_h10_sign)),
    ):
        for planet, fields in PLANET_INDUSTRIES.items():
            pd = positions.get(planet)
            if not isinstance(pd, dict) or not pd:
                continue
            witnesses = []
            if pd.get('house') == 10:
                witnesses.append('tenth_house_occupant')
            if planet == lord:
                witnesses.append('tenth_house_lord')
            for field in fields:
                if not witnesses:
                    continue
                candidate = candidates.setdefault(field, {'field': field,
                    'assertion_label': 'single_system_inference', 'supporting_evidence': [],
                    'verification_question': '实际训练、作品或职业经历是否支持这一候选方向？'})
                candidate['supporting_evidence'].append({'layer': layer, 'planet': planet,
                    'roles': witnesses, 'source_path': f'{layer}.planets.{planet}',
                    'functional_role': next(k for k in ('functional_benefics', 'functional_malefics', 'functional_neutrals') if planet in functional[k])})
    analysis['industry_candidates'] = sorted(candidates.values(),
        key=lambda row: (-len(row['supporting_evidence']), row['field']))

    top_fields = sorted(scored_fields.items(), key=lambda x: x[1], reverse=True)[:5]
    ranked_clusters = (
        career_semantic_profile.get('ranked_role_clusters')
        if isinstance(career_semantic_profile, dict)
        and career_semantic_profile.get('status') == 'used'
        else None
    )
    if isinstance(ranked_clusters, list) and ranked_clusters:
        analysis['fields'] = ranked_clusters
        analysis['field_recommendation_mode'] = 'evidence_ranked_clusters'
    else:
        analysis['fields'] = analysis['industry_candidates'] or [
            {'field': f, 'score': s, 'assertion_label': 'parameter_sensitive',
             'source_path': 'D1.tenth_sign', 'boundary': 'sign-only candidate, not a career verdict'}
            for f, s in top_fields]
        analysis['field_recommendation_mode'] = 'legacy_sign_indicator'

    # 5. 时间窗口
    if dasha_info:
        current = dasha_info.get('current_dasha') or {}
        current_ad = current.get('antardasha') or {}
        md = current.get('lord') or dasha_info.get('maha_dasha') or dasha_info.get('current_md')
        ad = current_ad.get('lord') if isinstance(current_ad, dict) else None
        ad = ad or dasha_info.get('antar_dasha') or dasha_info.get('current_ad')
        if md:
            analysis['timing'].append({'current': '-'.join(p for p in (md, ad) if p),
                'mahadasha': md, 'antardasha': ad, 'start': current.get('start'), 'end': current.get('end'),
                'note': '保留引擎已计算的当前周期；具体事业应期还需领域激活与交叉验证。',
                'assertion_label': 'single_system_inference'})

    # 6. 综合评估
    sb = shadbala or {}
    sb_planets = sb.get('planets', sb)
    strength_row = sb_planets.get(h10_lord) or {}
    h10_strength = strength_row.get('total_rupas', strength_row.get('rupas'))
    strength_present = isinstance(h10_strength, (int, float)) and not isinstance(h10_strength, bool) and math.isfinite(h10_strength) and h10_strength >= 0
    analysis['strength_evidence'] = {'planet': h10_lord,
        'rupas': h10_strength if strength_present else None,
        'min_required': strength_row.get('min_required'),
        'source_path': f'shadbala.{"planets." if "planets" in sb else ""}{h10_lord}'}
    analysis['assessment_status'] = 'single_system_inference' if strength_present else 'blocked'
    level = (f'10宫主{h10_lord}的Shadbala为{h10_strength:g} rupas；强度不是职业成功或公众知名度的保证。'
             if strength_present else '缺少10宫主强度，不能确定事业承载力；已有职业候选与盘面依据仍保留。')
    visibility = []
    if planets.get('Sun', {}).get('house') == 10:
        visibility.append({'source_path': 'D1.planets.Sun.house', 'theme': 'public_responsibility_candidate'})
    if h10_house in (1, 4, 7, 10):
        visibility.append({'source_path': f'D1.planets.{h10_lord}.house', 'theme': 'angular_career_lord'})
    if a10:
        visibility.append({'source_path': 'special_lagnas.A10_Karma_Pada', 'value': a10,
                           'theme': 'public_image_point_requires_context'})
    analysis['visibility'] = {'status': 'single_system_inference' if visibility else 'blocked',
        'supporting_evidence': visibility,
        'boundary': '缺少公众信号不等于幕后；可见度与行业、工作方式分别判断。'}
    analysis['technique_audit'] = [
        {'technique': name, 'status': 'executed' if present else 'blocked'} for name, present in (
            ('D1', bool(planets)), ('Functional Benefic/Malefic', True),
            ('D10', bool(d10_h10_sign)), ('A10', bool(a10)),
            ('Shadbala', strength_present), ('Vimshottari', bool(analysis['timing'])),
            ('Narayana', False), ('MEVG / Global Web Evidence', False), ('Real Case Calibration', False))]
    analysis['workflow_complete'] = False

    analysis['assessment'] = level

    return analysis