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
252 lines
10 KiB
Python
252 lines
10 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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"""Rahu/Ketu node-mode arbitration benchmark.
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Compares local Jyotish skill canonical output against Swiss Ephemeris Mean/True Node
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and PyJHora's default rasi_chart node mode. Samples are fictional/public smoke cases.
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"""
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import csv
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import json
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import math
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import sys
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from datetime import datetime, 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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CANON = OUT / 'canonical'
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REPORT = OUT / 'jyotish_benchmark_round4_node_mode_compare.md'
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MATRIX = OUT / 'node_mode_comparison_matrix.csv'
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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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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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PYJHORA_PLANET_ID = {7: 'Rahu', 8: 'Ketu'}
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def norm(deg):
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return deg % 360.0
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def sign_of(lon):
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return SIGNS[int(norm(lon) // 30)]
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def degree_in_sign(lon):
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return norm(lon) % 30.0
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def nakshatra_of(lon):
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unit = 360.0 / 27.0
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idx = int(math.floor(norm(lon) / unit))
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pada = int(math.floor((norm(lon) % unit) / (unit / 4.0))) + 1
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return NAKSHATRAS[idx], min(pada, 4)
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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 / 60.0 + utc_dt.second / 3600.0
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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 point_from_lon(lon):
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nak, pada = nakshatra_of(lon)
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return {
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'longitude': round(norm(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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}
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def swiss_nodes(sample, node_pid):
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jd = julian_day_utc(sample['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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res, _ = swe.calc_ut(jd, node_pid, flags)
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rahu = point_from_lon(res[0])
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ketu = point_from_lon(res[0] + 180.0)
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return {'Rahu': rahu, 'Ketu': ketu}
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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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orig_calc_ut = swe.calc_ut
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def calc_ut(jd, body, flags=0, *args, **kwargs):
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if 'flags' in kwargs:
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flags = kwargs.pop('flags')
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return orig_calc_ut(jd, body, flags)
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swe.calc_ut = calc_ut
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orig_houses_ex = swe.houses_ex
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def houses_ex(tjdut, lat, lon, hsys=b'P', flags=0, *args, **kwargs):
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if 'flags' in kwargs:
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flags = kwargs.pop('flags')
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if 'hsys' in kwargs:
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hsys = kwargs.pop('hsys')
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return orig_houses_ex(tjdut, lat, lon, hsys, flags)
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swe.houses_ex = houses_ex
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return swe
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def pyjhora_default_nodes(sample):
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# PyJHora is used only as an external benchmark. Do not vendor/copy its code into the skill.
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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.panchanga import drik
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from jhora.horoscope.chart import charts
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const._DEFAULT_AYANAMSA_MODE = 'LAHIRI'
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drik.set_ayanamsa_mode('LAHIRI')
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# Note: charts.rasi_chart -> drik.dhasavarga() defaults to set_rahu_ketu_as_true_nodes=True.
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b = sample['birth']
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jd = utils.julian_day_number((b['year'], b['month'], b['day']), (b['hour'], b['minute'], 0))
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place = drik.Place(sample['label'], b['lat'], b['lon'], b['tz'])
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nodes = {}
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for key, value in charts.rasi_chart(jd, place):
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body = PYJHORA_PLANET_ID.get(key)
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if not body:
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continue
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sign_idx, deg = value
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abs_lon = int(sign_idx) * 30.0 + float(deg)
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nodes[body] = point_from_lon(abs_lon)
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return nodes
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def local_nodes(sample_id):
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local = json.loads((CANON / f'{sample_id}.canonical.json').read_text())
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out = {}
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for body in ['Rahu', 'Ketu']:
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p = local['planets'][body]
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lon = SIGNS.index(p['sign']) * 30.0 + float(p['degree_in_sign'])
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out[body] = {
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'longitude': round(lon, 6),
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'sign': p['sign'],
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'degree_in_sign': round(float(p['degree_in_sign']), 6),
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'nakshatra': p['nakshatra'],
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'nakshatra_pada': p['nakshatra_pada'],
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}
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return out
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def compare_point(rows, sample_id, body, field, local_value, target_name, target_value, tolerance=None):
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status = 'match'
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delta = ''
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if tolerance is not None:
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delta_val = abs(float(local_value) - float(target_value))
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delta = round(delta_val, 6)
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status = 'match' if delta_val <= tolerance else 'mismatch'
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else:
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status = 'match' if local_value == target_value else 'mismatch'
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rows.append({
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'sample_id': sample_id,
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'body': body,
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'field': field,
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'target': target_name,
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'local_skill': local_value,
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'target_value': target_value,
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'delta': delta,
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'status': status,
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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 {'target': target, 'total': total, 'match': match, 'mismatch': total - match, 'rate': match / total if total else 0.0}
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def write_report(rows):
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targets = ['swiss_mean_node', 'swiss_true_node', 'pyjhora_default_rasi']
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lines = []
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lines.append('# Jyotish benchmark 第四轮：Rahu/Ketu 节点口径仲裁')
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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('- 解释第三轮 PyJHora 对比中 Rahu/Ketu 大量差异的根因。')
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lines.append('- 对比当前 skill canonical baseline 与 Swiss Ephemeris Mean Node、Swiss Ephemeris True Node、PyJHora rasi_chart 默认输出。')
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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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summaries = [summarize(rows, t) for t in targets]
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for s in summaries:
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lines.append(f"| {s['target']} | {s['total']} | {s['match']} | {s['mismatch']} | {s['rate']:.2%} |")
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lines.append('')
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lines.append('## 3. 分字段统计')
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lines.append('')
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lines.append('| Target | Field | Total | Match | Mismatch |')
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lines.append('|---|---|---:|---:|---:|')
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for target in targets:
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for field in ['sign', 'degree_in_sign', 'nakshatra', 'nakshatra_pada']:
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subset = [r for r in rows if r['target'] == target and r['field'] == field]
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match = sum(1 for r in subset if r['status'] == 'match')
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lines.append(f'| {target} | {field} | {len(subset)} | {match} | {len(subset)-match} |')
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lines.append('')
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mismatches = [r for r in rows if r['status'] == 'mismatch']
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lines.append('## 4. 关键不匹配样例')
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lines.append('')
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lines.append('| Sample | Target | Body | Field | Local skill | Target value | Delta |')
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lines.append('|---|---|---|---|---|---|---:|')
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for r in mismatches[:120]:
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lines.append(f"| {r['sample_id']} | {r['target']} | {r['body']} | {r['field']} | {r['local_skill']} | {r['target_value']} | {r['delta']} |")
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lines.append('')
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lines.append('## 5. 仲裁结论')
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lines.append('')
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lines.append('- 当前 skill 的 Rahu/Ketu 与 Swiss Ephemeris **Mean Node** 口径完全一致；这解释了第一轮 Swiss direct 450/450 匹配。')
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lines.append('- PyJHora 4.8.6 的 `rasi_chart()` 默认走 `drik.dhasavarga(... set_rahu_ketu_as_true_nodes=True)`，即默认使用 **True Node**。')
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lines.append('- 因此第三轮 PyJHora 中 Rahu/Ketu 的 degree/nakshatra/D9/D10 差异，主要不是当前 skill 的计算 bug，而是 **Mean Node vs True Node 口径差异**。')
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lines.append('- 工程建议：当前 skill 应显式声明默认 `node_mode=mean`，后续可新增 `--node-mode mean|true` 参数；benchmark 报告中也应把节点口径列为冻结参数。')
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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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for sample in samples:
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sample_id = sample['id']
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local = local_nodes(sample_id)
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targets = {
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'swiss_mean_node': swiss_nodes(sample, swe.MEAN_NODE),
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'swiss_true_node': swiss_nodes(sample, swe.TRUE_NODE),
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'pyjhora_default_rasi': pyjhora_default_nodes(sample),
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}
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for target_name, target_nodes in targets.items():
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for body in ['Rahu', 'Ketu']:
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for field in ['sign', 'nakshatra', 'nakshatra_pada']:
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compare_point(rows, sample_id, body, field, local[body][field], target_name, target_nodes[body][field])
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compare_point(rows, sample_id, body, 'degree_in_sign', local[body]['degree_in_sign'], target_name, target_nodes[body]['degree_in_sign'], tolerance=0.1)
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with MATRIX.open('w', newline='') as f:
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writer = csv.DictWriter(f, fieldnames=['sample_id', 'body', 'field', 'target', 'local_skill', 'target_value', 'delta', 'status'])
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writer.writeheader()
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writer.writerows(rows)
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REPORT.write_text(write_report(rows))
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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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