fix: 8个严重bug修复 - 预测引擎从空壳恢复为全功能
修复清单: 1. jyotish_engine.py: 缺少 List 类型导入 (NameError崩溃) 2. cmd_predict: 传给EventPredictionModel的ascendant是str不是dict 3. cmd_predict: 未传入dasha/congregation/vivah_saham/chara_dasha数据 4. Dasha timeline: MD的is_current字段未设置 5. event_prediction_model: status检查不兼容中英混合格式 6. event_prediction_model: Dasha数据格式不匹配(current_dasha vs current_mahadasha) 7. event_prediction_model: Chara Dasha key名不匹配(dasha_sequence vs dasha_list) 8. cmd_predict: 序列化缺少confidence/dasha_signals/transit_signals字段 修复前: predict命令直接fallback到空壳,所有事件概率30%,0信号 修复后: marriage=43%(1静态+1Dasha), career=54%(3Dasha)
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732642856
@@ -252,11 +252,13 @@ class EventPredictionModel:
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if ph == h and pn in target_karakas:
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pd = self.chart.get('planets', {}).get(pn, {})
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status = pd.get('status', '')
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if status == 'exalted':
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# 支持"擢升(Exalted)"、"落陷(Debilitated)"、"入庙(Own Sign)"等中英混合格式
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status_lower = status.lower() if status else ''
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if 'exalted' in status_lower or '擢升' in status:
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result['signals'].append(f'Karaka {pn}在{h}宫擢升')
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elif status == 'own_sign':
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elif 'own' in status_lower or '入庙' in status:
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result['signals'].append(f'Karaka {pn}在{h}宫入庙')
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elif status == 'debilitated':
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elif 'debilitated' in status_lower or '落陷' in status:
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result['signals'].append(f'Karaka {pn}在{h}宫落陷(负面)')
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# 2. 行星聚集分析（使用 congregation_data）
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@@ -309,8 +311,24 @@ class EventPredictionModel:
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# ── 2a. Vimshottari Dasha ──
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if self.dasha:
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# 当前 Mahadasha
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md_lord = self.dasha.get('current_mahadasha', {}).get('lord', '')
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# 适配 full-reading 输出格式：current_dasha.lord + current_dasha.antardasha[]
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md_lord = ''
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ad_lord = ''
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# 方式1：current_dasha 格式（full-reading输出）
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current_md = self.dasha.get('current_dasha')
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if current_md and isinstance(current_md, dict):
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md_lord = current_md.get('lord', '')
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# 从 antardasha 列表中找 is_current=True 的
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for ad in current_md.get('antardasha', []):
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if ad.get('is_current'):
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ad_lord = ad.get('lord', '')
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break
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else:
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# 方式2：current_mahadasha / current_antardasha 格式
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md_lord = self.dasha.get('current_mahadasha', {}).get('lord', '')
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ad_lord = self.dasha.get('current_antardasha', {}).get('lord', '')
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if md_lord:
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md_house = self.planet_houses.get(md_lord, 0)
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# MD 主星是否关联目标宫位
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@@ -326,8 +344,6 @@ class EventPredictionModel:
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if self.house_lords.get(h) == md_lord:
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signals.append(f'当前MD {md_lord}是{h}宫主(目标宫)')
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# 当前 Antardasha
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ad_lord = self.dasha.get('current_antardasha', {}).get('lord', '')
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if ad_lord:
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ad_house = self.planet_houses.get(ad_lord, 0)
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if ad_house in target_houses:
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@@ -339,28 +355,30 @@ class EventPredictionModel:
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# MD+AD 组合信号（高权重）
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if md_lord and ad_lord:
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if ad_house in target_houses and md_house in target_houses:
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md_house2 = self.planet_houses.get(md_lord, 0)
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if ad_house in target_houses and md_house2 in target_houses:
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signals.append(f'★ MD+AD双激活目标宫位({md_lord}+{ad_lord})')
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# ── 2b. Chara Dasha (Jaimini) ──
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if self.chara_dasha:
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cd_list = self.chara_dasha.get('dasha_list', [])
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# 适配实际格式：dasha_sequence[] 或 dasha_list[]
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cd_list = self.chara_dasha.get('dasha_sequence') or self.chara_dasha.get('dasha_list', [])
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if cd_list:
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# 当前 Chara Mahadasha
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# 当前 Chara Mahadasha（第一个条目）
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current_cd = cd_list[0] if cd_list else {}
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cd_sign = current_cd.get('sign', '')
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cd_lord = SIGN_LORDS.get(cd_sign, '')
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cd_lord = current_cd.get('lord', '') or SIGN_LORDS.get(cd_sign, '')
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cd_house = self.planet_houses.get(cd_lord, 0)
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if cd_house in target_houses:
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signals.append(f'当前Chara Dasha {cd_sign}({cd_lord})在{cd_house}宫(目标宫)')
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# Chara Antardasha
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antardashas = current_cd.get('antardashas', [])
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antardashas = current_cd.get('antardashas') or current_cd.get('antardasha', [])
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if antardashas:
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current_ad = antardashas[0] if antardashas else {}
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ad_sign = current_ad.get('sign', '')
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ad_lord_name = SIGN_LORDS.get(ad_sign, '')
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ad_lord_name = current_ad.get('lord', '') or SIGN_LORDS.get(ad_sign, '')
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ad_h = self.planet_houses.get(ad_lord_name, 0)
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if ad_h in target_houses:
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signals.append(f'Chara AD {ad_sign}({ad_lord_name})在{ad_h}宫(目标宫)')
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+21
-11
@@ -43,7 +43,7 @@ import csv
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import math
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import sqlite3
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from datetime import datetime, timedelta
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from typing import Dict
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from typing import Dict, List
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# ============================================================================
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# 路径常量
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@@ -219,7 +219,7 @@ def cmd_dasha(args):
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for i in range(9):
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lord = DASHA_ORDER[(si + i) % 9]; years = DASHA_YEARS[lord]
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end_dt = dt + timedelta(days=years * 365.25)
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timeline.append({"lord": lord, "lord_cn": PLANET_CN[lord], "start": dt.strftime("%Y-%m-%d"), "end": end_dt.strftime("%Y-%m-%d"), "years": years})
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timeline.append({"lord": lord, "lord_cn": PLANET_CN[lord], "start": dt.strftime("%Y-%m-%d"), "end": end_dt.strftime("%Y-%m-%d"), "years": years, "is_current": False})
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dt = end_dt
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today = datetime.strptime(args.today, "%Y-%m-%d") if args.today else datetime.now()
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@@ -227,6 +227,7 @@ def cmd_dasha(args):
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for d in timeline:
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ds = datetime.strptime(d["start"], "%Y-%m-%d"); de = datetime.strptime(d["end"], "%Y-%m-%d")
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if ds <= today < de:
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d["is_current"] = True
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total_days = (de - ds).days; li = DASHA_ORDER.index(d["lord"])
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sub = []; sdt = ds
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for j in range(9):
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@@ -313,16 +314,21 @@ def cmd_predict(args):
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try:
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sys.path.insert(0, SCRIPT_DIR)
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from event_prediction_model import EventPredictionModel
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asc_sign = chart.get("ascendant", {}).get("sign", "Unknown")
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planets = chart.get("planets", {})
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# 构建模型需要的行星简化数据
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planet_positions = {}
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for pn, pd in planets.items():
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if isinstance(pd, dict) and 'house' in pd:
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planet_positions[pn] = {'sign': pd.get('sign', ''), 'house': pd.get('house', 0)}
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model = EventPredictionModel(chart_data={"ascendant": asc_sign, "planets": planet_positions})
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# 直接传完整chart数据给EventPredictionModel（v5.0需要ascendant dict和planets dict）
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# 同时从 full-reading 输出中提取所有模块数据传入（v5.1修复：之前丢失dasha/congregation等）
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modules = chart.get("modules", {})
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model = EventPredictionModel(
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chart_data={
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"ascendant": chart.get("ascendant", {}),
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"planets": chart.get("planets", {}),
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},
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dasha_data=modules.get("dasha"),
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congregation_data=modules.get("congregation"),
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vivah_saham_data=modules.get("vivah_saham"),
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chara_dasha_data=modules.get("jaimini", {}).get("chara_dasha"),
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)
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raw_preds = model.predict_all_events()
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# 将 Prediction dataclass 转为可序列化 dict
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# 将 Prediction dataclass 转为可序列化 dict（v5.1补充缺失字段）
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predictions = []
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for p in raw_preds:
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predictions.append({
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@@ -330,9 +336,13 @@ def cmd_predict(args):
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"description": p.description,
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"probability": p.probability,
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"risk_level": str(p.risk_level.value) if hasattr(p.risk_level, 'value') else str(p.risk_level),
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"confidence": str(p.confidence.value) if hasattr(p.confidence, 'value') else str(p.confidence),
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"timing": p.timing,
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"key_factors": p.key_factors,
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"recommendations": p.recommendations,
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"dasha_signals": p.dasha_signals,
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"transit_signals": p.transit_signals,
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"timing_windows": p.timing_windows,
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})
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return {
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"method": "三层验证法（EventPredictionModel规则引擎）",
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