732642856
f83db2fac1
- add external validation reports and open-source comparison references - integrate Jaimini arudha/graha pada, enhanced argala, and additional synastry kutas - update skill docs and capability matrices - add smoke tests for open-source integrations
226 lines
10 KiB
Python
226 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Vedicka 咨询案例结构化验证脚本
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从 consultation-case-library.md 中提取有完整出生数据的案例，
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用引擎计算验证：Lagna、太阳/月亮星座、Dasha分析。
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"""
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import json
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import subprocess
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import sys
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import os
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from datetime import datetime
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SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
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SKILL_DIR = os.path.dirname(SCRIPT_DIR)
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ENGINE = os.path.join(SKILL_DIR, 'scripts', 'jyotish_engine.py')
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PYTHON = sys.executable
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def run_chart(year, month, day, hour, minute, lat, lon, tz):
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"""运行引擎 chart 命令"""
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cmd = [
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PYTHON, ENGINE, 'chart',
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'--year', str(year), '--month', str(month), '--day', str(day),
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'--hour', str(hour), '--minute', str(minute),
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'--lat', str(lat), '--lon', str(lon), '--tz', str(tz)
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]
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
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return json.loads(result.stdout)
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def sign_num(name):
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"""星座名称转数字"""
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signs = ['Aries', 'Taurus', 'Gemini', 'Cancer', 'Leo', 'Virgo',
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'Libra', 'Scorpio', 'Sagittarius', 'Capricorn', 'Aquarius', 'Pisces']
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return signs.index(name) + 1
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def run_dasha(year, month, day, hour, minute, lat, lon, tz):
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"""运行引擎 dasha 命令"""
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cmd = [
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PYTHON, ENGINE, 'dasha',
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'--year', str(year), '--month', str(month), '--day', str(day),
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'--hour', str(hour), '--minute', str(minute),
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'--lat', str(lat), '--lon', str(lon), '--tz', str(tz)
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]
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result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
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try:
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return json.loads(result.stdout)
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except:
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return {"error": result.stderr[:500]}
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def main():
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print("=" * 90)
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print("VEDICKA 咨询案例结构化验证报告")
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print(f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print("=" * 90)
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# ============================================================
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# 案例1: 阿南达莫依玛 (Anandamoyi Ma)
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# 来源: consultation-case-library.md 案例六
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# ============================================================
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print("\n## 案例1: 阿南达莫依玛 (Anandamoyi Ma)")
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print("- 出生: 1896-04-30 03:42")
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print("- 地点: Brahmanbaria (23.45N, 91.13E)")
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print("- 报告时区: 东6区 (UTC+6)")
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print("- 参考ASC: 白羊座 6°48'")
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print("- 参考MC: 摩羯座 4°38'")
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print('- 资料来源: 印度占星书案例，\u201c应为经过矫正的生时\u201d')
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print()
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# 测试多种时区 - 1896年 Bengal 使用 Calcutta Time (UTC+5:53:20)
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tz_options = [
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(5.883, "Calcutta Time (UTC+5:53)"),
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(6.0, "Bangladesh (UTC+6)"),
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(5.5, "IST (UTC+5:30)"),
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]
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for tz, tz_desc in tz_options:
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data = run_chart(1896, 4, 30, 3, 42, 23.45, 91.13, tz)
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a = data['ascendant']
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s = data['planets']['Sun']
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m = data['planets']['Moon']
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print(f" UTC{tz:+.3f} ({tz_desc}):")
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print(f" Lagna: {a['sign']} {a['degree']:.2f}° (参考: Aries 6°48')")
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print(f" Sun: {s['sign']} {s['degree']:.2f}°")
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print(f" Moon: {m['sign']} {m['degree']:.2f}°")
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if a['sign'] == 'Aries':
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diff = abs(a['degree'] - 6.8)
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print(f" ✓ Lagna匹配白羊座! 偏差={diff:.2f}°")
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print()
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# 使用参考ASC反推 - 如果ASC=Aries 6°48'，需要什么tz?
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print(" ASC反推 (目标: Aries 6°48'):")
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# 引擎未直接支持，手动计算
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# Pisces 14.25° -> Aries 6.8°: need +22.55° in ascendant
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# 1° ascendant ≈ 4 min time difference, so need ~90 min earlier
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# or different tz
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# UTC+6 gives Pisces 14.25°, need Aries 6.8° = Pisces 36.8° = +22.55° from Pisces 14.25°
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# 22.55° / 15° per hour = 1.5 hours later UTC = UTC+4.5?
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# Actually, for later local time (same clock time but more UTC offset = later UTC)
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# UTC+4.5 means 3:42 local = 23:12 UTC (earlier) instead of 21:42 UTC (with UTC+6)
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# Earlier UTC = EARLIER ascendant
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# We need ascendant to be LATER, so we need UTC to be later = slighter offset
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# So let's try some values
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for tz in [4.0, 4.5, 5.0, 5.3, 5.5, 5.883, 6.0]:
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data = run_chart(1896, 4, 30, 3, 42, 23.45, 91.13, tz)
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a = data['ascendant']
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if a['sign'] == 'Aries':
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print(f" UTC{tz:+.1f}: Lagna={a['sign']} {a['degree']:.2f}° ✓ MATCH!")
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else:
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deg_in_aries = a['degree'] + (sign_num(a['sign']) * 30)
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target = 6.8
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diff = abs(deg_in_aries - target)
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print(f" UTC{tz:+.1f}: Lagna={a['sign']} {a['degree']:.2f}° (差{diff:.1f}°)")
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# ============================================================
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# Vedicka 案例研究验证
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# ============================================================
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print("\n" + "=" * 90)
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print("## Vedicka案例研究 — Dasha/星盘逻辑验证")
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print()
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vedicka_cases = [
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{
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"name": "学术卓越案例",
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"claim": "水瓶座上升, 4宫主金星强旺, 5宫主水星高度强化, 9宫主金星与木星强力会合",
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"notes": "化名'学者'，无具体出生数据，无法直接验证。但可验证逻辑：\n"
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"- Saraswati Yoga需要木星/金星/水星在角宫或三分宫\n"
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" - 如果水星在角宫入旺(处女座10宫?)，且木星对水星形成相位 → 成立\n"
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"- Raja Yoga: 9宫主与10宫主会合/互相位\n"
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" - 水瓶座9宫主=金星，10宫主=火星, 需金星+火星会合\n"
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"- 学术成功: 强化5宫/9宫 + 木星/9宫主大运激活 → 符合BPHS理论",
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"verdict": "理论逻辑合理，符合BPHS经典。Saraswati Yoga+Raja Yoga联合效应可信。"
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},
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{
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"name": "商业失败案例",
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"claim": "天蝎座上升, 第2宫主木星虚弱, 第11宫主水星落入第6宫, 金星大运激活第12宫导致破产",
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"notes": "化名'The Challenger'，无具体出生数据。\n"
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"- 2宫主木星虚弱 → 财富积累能力弱\n"
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"- 11宫主水星在6宫 → 收益渠道受阻于债务/竞争\n"
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"- 金星大运 + 12宫激活 → 大额支出/海外损失\n"
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"- BPHS: 当2/11宫主受克且大运激活dushtana时，财务危机可验证",
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"verdict": "Dasha分析与BPHS原理一致。金星作为12宫主激活支出损失，逻辑成立。"
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},
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{
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"name": "职业成功案例",
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"claim": "双鱼座上升, Malavya Mahapurusha Yoga(金星7宫), Neecha Bhanga Raja Yoga, 水星Dasha激活第10宫",
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"notes": "化名'The Dynamo'。\n"
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"- Malavya Yoga: 金星在Kendra宫(7宫) → 明星/演艺潜质\n"
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"- 金星落陷处女座 + 水星(处女座主星)也在7宫 → Neecha Bhanga成立\n"
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"- 2/11宫主火星在10宫射手座入庙 → Dhana Yoga\n"
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"- 水星Dasha: 水星是10宫主, 激活职业承诺\n"
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"- 时间线: 土星期(贫困)→水星期(突破)→罗喉期(财富巩固) → 精准的Dasha递进",
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"verdict": "Yoga组合+Dasha时间线完全符合BPHS。Neecha Bhanga转化机制明确。"
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},
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{
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"name": "婚姻离婚案例",
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"claim": "天秤座上升, 7宫主火星在8宫, Rahu在7宫/Ketu在1宫, 火星大运激活离婚",
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"notes": "- 7宫主(火星)在8宫 → 婚姻不稳定的首要指标(BPHS经典)\n"
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"- Rahu 7宫: 对伴侣关系执着但无法稳定\n"
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"- Ketu 1宫: 自我身份困惑, 前世业力\n"
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"- 火星大运 + 火星-土星小运 → 冲突爆发+正式分离\n"
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"- Trika宫(6/8/12)干扰 → 8宫婚姻主星被Trika影响",
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"verdict": "婚姻危机的经典BPHS配置。7宫主在dushtana + Rahu-Ketu轴线 + Dasha触发完全符合经典。"
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},
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]
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for vc in vedicka_cases:
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print(f"### {vc['name']}")
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print(f"**论断**: {vc['claim']}")
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print(f"**验证说明**: {vc['notes']}")
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print(f"**结论**: {vc['verdict']}")
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print()
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# ============================================================
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# 总结
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# ============================================================
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print("=" * 90)
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print("## 验证总结")
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print()
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print("### 可精确验证的案例")
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print("| 案例 | Lagna验证 | Sun验证 | Moon验证 | 结论 |")
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print("|------|-----------|---------|----------|------|")
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# Anandamoyi Ma with best-matching tz
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for tz_name, tz_val in [("UTC+5.883", 5.883), ("UTC+6.0", 6.0), ("UTC+5.5", 5.5)]:
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data = run_chart(1896, 4, 30, 3, 42, 23.45, 91.13, tz_val)
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a = data['ascendant']
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ref_asc = "Aries 6°48'"
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aries_target = 6.8
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actual = a['degree'] + (sign_num(a['sign']) * 30) if a['sign'] != 'Aries' else a['degree']
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diff_asc = abs(actual - aries_target)
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if a['sign'] == 'Aries' and diff_asc < 2:
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status = "✓ Lagna精确匹配"
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elif a['sign'] == 'Aries':
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status = f"~ Lagna星座匹配(差{diff_asc:.1f}°)"
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else:
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status = f"✗ Lagna不匹配({a['sign']}≠Aries)"
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if tz_name == "UTC+5.883":
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print(f"| Anandamoyi Ma ({tz_name}) | {status} | 待查 | 待查 | 需矫正时区 |")
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print()
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print("### Vedicka案例研究 (无法精确验证, 无出生数据)")
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print("| 案例 | 理论一致性 | 说明 |")
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print("|------|-----------|------|")
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for vc in vedicka_cases:
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print(f"| {vc['name']} | ✓ 一致 | {vc['verdict']} |")
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print()
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print("### 关键发现")
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print("1. 多数Vedicka案例研究使用化名且无精确出生数据，无法进行数学验证")
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print("2. 阿南达莫依玛案例：使用UTC+6时Lagna不匹配（引擎=Pisces, 参考=Aries）")
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print(" - 可能原因1: 时区不使用UTC+6（1896年Bengal使用Calcutta Time UTC+5:53）")
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print(" - 可能原因2: 出生时间经过矫正（原文注明「经过矫正的生时」）")
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print(" - 可能原因3: 参考ASC本身基于西洋占星/热带黄道计算")
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print("3. Vedicka的Dasha分析和Yoga识别在理论上符合BPHS经典原理")
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print("4. 建议后续任务：为Vedicka案例获取更精确的出生数据，进行定量验证")
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print("=" * 90)
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if __name__ == '__main__':
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main()
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