732642856
d2cd5a369f
- Add sanitized Jyotish benchmark suite with fictional/public smoke samples - Include benchmark scripts and markdown reports while excluding raw JSON/CSV outputs - Add open-source Jyotish project comparison research - Add complete technique coverage roadmap - Add privacy-safe PDF chart validation methodology - Update SKILL.md and CHANGELOG with v6.1.9 scope and privacy boundaries Validation: - py_compile benchmarks/jyotish/scripts/*.py passed - quality gate passed with 35 pytest tests and golden case
212 lines
7.9 KiB
Python
212 lines
7.9 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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import csv
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import json
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import math
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from datetime import datetime, timezone, timedelta
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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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SWISS_OUT = OUT / 'swiss_direct'
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CANON = OUT / 'canonical'
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PLANETS = {
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'Sun': swe.SUN,
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'Moon': swe.MOON,
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'Mars': swe.MARS,
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'Mercury': swe.MERCURY,
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'Jupiter': swe.JUPITER,
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'Venus': swe.VENUS,
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'Saturn': swe.SATURN,
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'Rahu': swe.MEAN_NODE,
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}
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SIGNS = ['Aries','Taurus','Gemini','Cancer','Leo','Virgo','Libra','Scorpio','Sagittarius','Capricorn','Aquarius','Pisces']
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NAKSHATRAS = [
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'Ashwini','Bharani','Krittika','Rohini','Mrigashira','Ardra','Punarvasu','Pushya','Ashlesha',
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'Magha','Purva Phalguni','Uttara Phalguni','Hasta','Chitra','Swati','Vishakha','Anuradha','Jyeshtha',
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'Mula','Purva Ashadha','Uttara Ashadha','Shravana','Dhanishta','Shatabhisha','Purva Bhadrapada','Uttara Bhadrapada','Revati'
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]
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def normalize(deg):
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return deg % 360.0
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def sign_of(lon):
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idx = int(normalize(lon) // 30)
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return SIGNS[idx]
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def degree_in_sign(lon):
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return normalize(lon) % 30
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def nakshatra_of(lon):
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unit = 360.0 / 27.0
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pos = normalize(lon) / unit
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idx = int(math.floor(pos))
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pada = int(math.floor((pos - idx) * 4)) + 1
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if pada > 4:
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pada = 4
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return NAKSHATRAS[idx], pada
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def julian_day_utc(birth):
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tz = float(birth['tz'])
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local = datetime(int(birth['year']), int(birth['month']), int(birth['day']), int(birth['hour']), int(birth['minute']))
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utc_dt = local - timedelta(hours=tz)
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hour = utc_dt.hour + utc_dt.minute / 60 + utc_dt.second / 3600
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return swe.julday(utc_dt.year, utc_dt.month, utc_dt.day, hour, swe.GREG_CAL)
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def calc_swiss(sample):
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birth = sample['birth']
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jd = julian_day_utc(birth)
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swe.set_sid_mode(swe.SIDM_LAHIRI, 0, 0)
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flags = swe.FLG_SWIEPH | swe.FLG_SIDEREAL | swe.FLG_SPEED
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planets = {}
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for name, pid in PLANETS.items():
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res, ret = swe.calc_ut(jd, pid, flags)
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lon = normalize(res[0])
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nak, pada = nakshatra_of(lon)
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planets[name] = {
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'longitude': round(lon, 6),
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'sign': sign_of(lon),
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'degree_in_sign': round(degree_in_sign(lon), 6),
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'nakshatra': nak,
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'nakshatra_pada': pada,
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'retrograde': bool(res[3] < 0),
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}
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rahu_lon = planets['Rahu']['longitude']
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ketu_lon = normalize(rahu_lon + 180)
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nak, pada = nakshatra_of(ketu_lon)
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planets['Ketu'] = {
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'longitude': round(ketu_lon, 6),
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'sign': sign_of(ketu_lon),
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'degree_in_sign': round(degree_in_sign(ketu_lon), 6),
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'nakshatra': nak,
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'nakshatra_pada': pada,
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'retrograde': planets['Rahu']['retrograde'],
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}
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return {
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'sample_id': sample['id'],
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'engine': 'swiss_direct_lahiri_mean_node',
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'julian_day_ut': jd,
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'parameters': {'ayanamsa': 'Lahiri', 'node': 'Mean Node', 'flags': int(flags)},
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'planets': planets,
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}
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def compare_sample(sample_id, swiss):
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local_path = CANON / f'{sample_id}.canonical.json'
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local = json.loads(local_path.read_text())
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rows = []
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for pname in ['Sun','Moon','Mars','Mercury','Jupiter','Venus','Saturn','Rahu','Ketu']:
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s = swiss['planets'][pname]
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l = local['planets'][pname]
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for field in ['sign','degree_in_sign','nakshatra','nakshatra_pada','retrograde']:
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sv = s.get(field)
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lv = l.get(field)
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status = 'match'
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delta = ''
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if field == 'degree_in_sign':
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try:
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delta_val = abs(float(sv) - float(lv))
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delta = round(delta_val, 6)
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status = 'match' if delta_val <= 0.1 else 'mismatch'
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except Exception:
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status = 'not_comparable'
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else:
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status = 'match' if sv == lv else 'mismatch'
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rows.append({
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'sample_id': sample_id,
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'body': pname,
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'field': field,
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'local_skill': lv,
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'swiss_direct': sv,
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'delta': delta,
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'status': status,
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})
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return rows
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def write_report(rows):
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total = len(rows)
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matches = sum(1 for r in rows if r['status'] == 'match')
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mismatches = [r for r in rows if r['status'] == 'mismatch']
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by_field = {}
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for r in rows:
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by_field.setdefault(r['field'], {'total':0, 'match':0, 'mismatch':0})
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by_field[r['field']]['total'] += 1
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by_field[r['field']][r['status']] = by_field[r['field']].get(r['status'], 0) + 1
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lines = []
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lines.append('# Jyotish benchmark 第一轮 Swiss direct 对比报告')
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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. 范围')
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lines.append('')
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lines.append('- 对比对象：当前 skill canonical baseline vs 直接调用 Swiss Ephemeris。')
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lines.append('- 配置：Sidereal Lahiri，Mean Node，行星黄经与 Nakshatra 字段。')
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lines.append('- 本轮不比较上升、宫位、D9/D10、大运；这些留给下一轮多引擎/参数冻结测试。')
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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(f'- 字段总数：{total}')
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lines.append(f'- 匹配：{matches}')
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lines.append(f'- 不匹配：{len(mismatches)}')
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lines.append(f'- 匹配率：{matches / total:.2%}' if total else '- 匹配率：N/A')
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lines.append('')
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lines.append('## 3. 分字段结果')
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lines.append('')
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lines.append('| Field | Total | Match | Mismatch |')
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lines.append('|---|---:|---:|---:|')
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for field, stat in sorted(by_field.items()):
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lines.append(f"| {field} | {stat['total']} | {stat.get('match',0)} | {stat.get('mismatch',0)} |")
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lines.append('')
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if mismatches:
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lines.append('## 4. 不匹配样例')
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lines.append('')
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lines.append('| Sample | Body | Field | Local skill | Swiss direct | Delta |')
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lines.append('|---|---|---|---|---|---:|')
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for r in mismatches[:80]:
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lines.append(f"| {r['sample_id']} | {r['body']} | {r['field']} | {r['local_skill']} | {r['swiss_direct']} | {r['delta']} |")
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lines.append('')
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lines.append('## 5. 解释')
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lines.append('')
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lines.append('- 若 sign/nakshatra 大量一致，说明当前 skill 的核心 Lahiri 行星计算大方向可信。')
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lines.append('- 若 degree_in_sign 出现系统性差异，优先检查 ayanamsa、True/Mean Node、UTC换算、Swiss flags。')
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lines.append('- 本轮发现的问题只约束计算层，不直接评价解释和预测能力。')
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report = OUT / 'jyotish_benchmark_round1_swiss_direct_compare.md'
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report.write_text('\n'.join(lines))
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return report
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def main():
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OUT.mkdir(parents=True, exist_ok=True)
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SWISS_OUT.mkdir(parents=True, exist_ok=True)
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samples = json.loads(DATA.read_text())
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all_rows = []
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for sample in samples:
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swiss = calc_swiss(sample)
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(SWISS_OUT / f"{sample['id']}.swiss_direct.json").write_text(json.dumps(swiss, ensure_ascii=False, indent=2))
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all_rows.extend(compare_sample(sample['id'], swiss))
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csv_path = OUT / 'swiss_direct_comparison_matrix.csv'
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with csv_path.open('w', newline='') as f:
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writer = csv.DictWriter(f, fieldnames=['sample_id','body','field','local_skill','swiss_direct','delta','status'])
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writer.writeheader()
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writer.writerows(all_rows)
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report = write_report(all_rows)
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print(json.dumps({'matrix': str(csv_path), 'report': str(report), 'rows': len(all_rows)}, ensure_ascii=False, indent=2))
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if __name__ == '__main__':
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main()
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