732642856
169 lines
7.4 KiB
Python
169 lines
7.4 KiB
Python
# NOTE: This script was sanitized for the public repository in v6.1.9.
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# It assumes it is run from the repository root unless JYOTISH_BENCHMARK_ROOT
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# or JYOTISH_SKILL_SCRIPT is provided. Raw output directories are generated locally
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# and are intentionally not committed.
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#!/usr/bin/env python3
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"""Ashtakavarga benchmark.
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Compares the local Jyotish skill BPHS Ashtakavarga implementation with PyJHora's
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get_ashtaka_varga() over fictional/public smoke samples.
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PyJHora is used only as an external benchmark; AGPL code is not copied into the skill.
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"""
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import csv
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import json
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import sys
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from pathlib import Path
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import swisseph as swe
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ROOT = Path(__file__).resolve().parents[1]
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DATA = ROOT / 'data/benchmark_samples.json'
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OUT = ROOT / 'outputs'
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CANON = OUT / 'canonical'
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REPORT = OUT / 'jyotish_benchmark_round6_ashtakavarga_compare.md'
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MATRIX = OUT / 'ashtakavarga_comparison_matrix.csv'
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SKILL_SCRIPTS = Path(__file__).resolve().parents[3] / 'scripts'
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PYJHORA_SITE = Path(__import__('os').environ.get('PYJHORA_SITE', ''))
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PYJHORA_COMPAT = ROOT / 'scripts/pyjhora_compat'
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SIGNS = ['Aries','Taurus','Gemini','Cancer','Leo','Virgo','Libra','Scorpio','Sagittarius','Capricorn','Aquarius','Pisces']
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PLANET_TO_ID = {'Sun': 0, 'Moon': 1, 'Mars': 2, 'Mercury': 3, 'Jupiter': 4, 'Venus': 5, 'Saturn': 6, 'Rahu': 7, 'Ketu': 8}
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LOCAL_PLANETS = ['Sun', 'Moon', 'Mars', 'Mercury', 'Jupiter', 'Venus', 'Saturn', 'Lagna']
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PYJHORA_ROW_LABELS = ['Sun', 'Moon', 'Mars', 'Mercury', 'Jupiter', 'Venus', 'Saturn', 'Lagna']
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def local_canonical(sample_id):
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return json.loads((CANON / f'{sample_id}.canonical.json').read_text())
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def local_ashtakavarga(canon):
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if str(SKILL_SCRIPTS) not in sys.path:
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sys.path.insert(0, str(SKILL_SCRIPTS))
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from ashtakavarga import calc_ashtakavarga
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planets = canon['planets']
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asc_idx = SIGNS.index(canon['ascendant']['sign'])
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return calc_ashtakavarga(planets, asc_idx)
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def patch_swisseph_for_pyjhora():
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for name in ['SIDM_KRISHNAMURTI_VP291', 'SIDM_TRUE_MULA', 'SIDM_TRUE_CITRA', 'SIDM_TRUE_REVATI']:
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if not hasattr(swe, name):
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setattr(swe, name, getattr(swe, 'SIDM_KRISHNAMURTI', 1))
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def pyjhora_ashtakavarga_from_canon(canon):
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if str(PYJHORA_COMPAT) not in sys.path:
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sys.path.insert(0, str(PYJHORA_COMPAT))
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if str(PYJHORA_SITE) not in sys.path:
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sys.path.insert(0, str(PYJHORA_SITE))
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patch_swisseph_for_pyjhora()
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from jhora import utils, const
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from jhora.horoscope.chart import ashtakavarga
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p_to_h = {}
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for pname, pid in PLANET_TO_ID.items():
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p_to_h[pid] = SIGNS.index(canon['planets'][pname]['sign'])
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p_to_h[const._ascendant_symbol] = SIGNS.index(canon['ascendant']['sign'])
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h_to_p = utils.get_house_to_planet_dict_from_planet_to_house_dict(p_to_h)
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bav, sav, pav = ashtakavarga.get_ashtaka_varga(h_to_p)
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return {'bav': bav, 'sav': sav, 'house_to_planet': h_to_p}
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def add_row(rows, sample_id, target, field, local_value, target_value):
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rows.append({
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'sample_id': sample_id,
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'target': target,
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'field': field,
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'local_skill': local_value,
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'target_value': target_value,
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'status': 'match' if local_value == target_value else 'mismatch',
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})
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def summarize(rows, target):
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subset = [r for r in rows if r['target'] == target]
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total = len(subset)
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match = sum(1 for r in subset if r['status'] == 'match')
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return total, match, total - match, match / total if total else 0.0
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def write_report(rows, per_sample):
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targets = ['pyjhora_sav', 'pyjhora_bav', 'invariants']
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lines = []
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lines.append('# Jyotish benchmark 第六轮：Ashtakavarga BAV/SAV 交叉验证')
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lines.append('')
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lines.append('生成时间：2026-06-03')
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lines.append('')
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lines.append('## 1. 本轮目的')
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lines.append('')
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lines.append('- 验证当前 skill 的 Ashtakavarga BAV/SAV 是否与 PyJHora `get_ashtaka_varga()` 对齐。')
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lines.append('- 同时检查内部不变量：7行星 SAV 总分=337；含 Lagna full SAV 总分=386；各行星 BAV 固定总分正确。')
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lines.append('- 样本仍为10个公开/虚构 smoke case，不包含用户个人资料。')
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lines.append('')
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lines.append('## 2. 总体结果')
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lines.append('')
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lines.append('| Target | Total | Match | Mismatch | Match rate |')
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lines.append('|---|---:|---:|---:|---:|')
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for target in targets:
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total, match, mismatch, rate = summarize(rows, target)
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lines.append(f'| {target} | {total} | {match} | {mismatch} | {rate:.2%} |')
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lines.append('')
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lines.append('## 3. 逐样本摘要')
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lines.append('')
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lines.append('| Sample | SAV match | BAV match | SAV total | Full SAV | Strongest signs | Weakest signs |')
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lines.append('|---|---:|---:|---:|---:|---|---|')
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for item in per_sample:
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lines.append(f"| {item['sample_id']} | {item['sav_match']}/12 | {item['bav_match']}/96 | {item['sav_total']} | {item['full_sav_total']} | {', '.join(item['strongest'])} | {', '.join(item['weakest'])} |")
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lines.append('')
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lines.append('## 4. 仲裁结论')
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lines.append('')
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lines.append('- 若 `pyjhora_sav` 与 `pyjhora_bav` 均为 100%，则 Ashtakavarga BAV/SAV 计算层可暂定通过。')
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lines.append('- Shodhya Pinda 不纳入本轮硬性通过；PyJHora 源码示例本身说明个别书例存在不一致，适合单独做弱口径验证。')
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return '\n'.join(lines)
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def main():
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samples = json.loads(DATA.read_text())
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rows = []
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per_sample = []
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for sample in samples:
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sid = sample['id']
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canon = local_canonical(sid)
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local = local_ashtakavarga(canon)
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pyj = pyjhora_ashtakavarga_from_canon(canon)
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local_sav = [local['sav']['scores'][sign] for sign in SIGNS]
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pyj_sav = pyj['sav']
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sav_match = 0
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for idx, sign in enumerate(SIGNS):
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before = len(rows)
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add_row(rows, sid, 'pyjhora_sav', f'sav.{sign}', local_sav[idx], pyj_sav[idx])
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sav_match += 1 if rows[-1]['status'] == 'match' else 0
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bav_match = 0
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for pidx, planet in enumerate(LOCAL_PLANETS):
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local_bav = local['bav'][planet]['bindus']
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pyj_bav = pyj['bav'][pidx]
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for sidx, sign in enumerate(SIGNS):
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add_row(rows, sid, 'pyjhora_bav', f'bav.{planet}.{sign}', local_bav[sidx], pyj_bav[sidx])
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bav_match += 1 if rows[-1]['status'] == 'match' else 0
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add_row(rows, sid, 'invariants', 'sav.total_337', local['sav']['total'], 337)
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add_row(rows, sid, 'invariants', 'full_sav.total_386', local['sav']['full_total_with_lagna'], 386)
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add_row(rows, sid, 'invariants', 'all_bav_valid', local['all_bav_valid'], True)
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per_sample.append({
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'sample_id': sid,
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'sav_match': sav_match,
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'bav_match': bav_match,
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'sav_total': local['sav']['total'],
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'full_sav_total': local['sav']['full_total_with_lagna'],
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'strongest': local['strongest_signs'],
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'weakest': local['weakest_signs'],
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})
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with MATRIX.open('w', newline='') as f:
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writer = csv.DictWriter(f, fieldnames=['sample_id', 'target', 'field', 'local_skill', 'target_value', 'status'])
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writer.writeheader()
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writer.writerows(rows)
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REPORT.write_text(write_report(rows, per_sample))
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print(json.dumps({'report': str(REPORT), 'matrix': str(MATRIX), 'samples': len(samples), 'fields': len(rows)}, ensure_ascii=False, indent=2))
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if __name__ == '__main__':
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main()
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