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
200 lines
8.6 KiB
Python
200 lines
8.6 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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"""Chara Dasha benchmark.
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Compares the local Jyotish skill's simplified Chara Dasha implementation with
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PyJHora's KN Rao Chara Dasha over fictional/public smoke samples. This script is
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intended to identify whether the local module is production-grade or only a
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placeholder workflow component.
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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_round7_chara_dasha_compare.md'
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MATRIX = OUT / 'chara_dasha_comparison_matrix.csv'
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SKILL_SCRIPTS = Path(__file__).resolve().parents[2] / '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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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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def canon(sample_id):
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return json.loads((CANON / f'{sample_id}.canonical.json').read_text())
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def local_chara(sample, c):
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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 jaimini import calc_chara_dasha
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asc_idx = SIGNS.index(c['ascendant']['sign'])
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planet_lons = {}
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for pname, pdata in c['planets'].items():
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if pname not in ('Sun','Moon','Mars','Mercury','Jupiter','Venus','Saturn','Rahu','Ketu'):
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continue
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sign_idx = SIGNS.index(pdata['sign'])
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deg = pdata.get('degree_in_sign_raw', pdata.get('degree_in_sign', pdata.get('degree', 0)))
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planet_lons[pname] = sign_idx * 30.0 + float(deg)
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b = sample['birth']
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d = calc_chara_dasha(asc_idx, planet_lons, b['year'], b['month'])
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return [{'order': x['order'], 'sign': x['sign'], 'duration_years': float(x['duration_years'])} for x in d['dasha_sequence']]
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def pyjhora_chara(sample):
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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 const
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from jhora.panchanga import drik
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from jhora.horoscope.dhasa.raasi import chara
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const._DEFAULT_AYANAMSA_MODE = 'LAHIRI'
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drik.set_ayanamsa_mode('LAHIRI')
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b = sample['birth']
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dob = (b['year'], b['month'], b['day'])
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tob = (b['hour'], b['minute'], 0)
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place = drik.Place(sample['label'], b['lat'], b['lon'], b['tz'])
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rows = chara.get_dhasa_antardhasa(
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dob,
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tob,
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place,
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chara_method=const.CHARA_TYPE.KN_RAO,
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dhasa_level_index=const.MAHA_DHASA_DEPTH.MAHA_DHASA_ONLY,
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round_duration=False,
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dhasa_duration_type=const.DHASA_YEAR_DURATION.MEAN_SIDEREAL_YEAR,
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)
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out = []
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for order, row in enumerate(rows[:12], 1):
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lord_tuple, _start, duration = row
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sign_idx = lord_tuple[0]
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out.append({'order': order, 'sign': SIGNS[sign_idx], 'duration_years': float(duration)})
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return out
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def add(rows, sample_id, field, local, target):
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rows.append({
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'sample_id': sample_id,
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'field': field,
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'local_skill': local,
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'pyjhora_kn_rao': target,
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'status': 'match' if local == target else 'mismatch',
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})
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def summarize(rows, prefix=None):
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subset = [r for r in rows if prefix is None or r['field'].startswith(prefix)]
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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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lines = []
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lines.append('# Jyotish benchmark 第七轮：Chara Dasha / Jaimini 时间线对标')
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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 `scripts/jaimini.py` 的 Chara Dasha 是否可作为正式计算模块使用。')
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lines.append('- 对标对象：PyJHora `raasi/chara.py` 的 KN Rao method（PyJHora 默认 `CHARA_TYPE_DEFAULT = KN_RAO`）。')
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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('| Field group | Total | Match | Mismatch | Match rate |')
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lines.append('|---|---:|---:|---:|---:|')
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for label, prefix in [('sequence_sign', 'md.sign'), ('duration_years', 'md.duration'), ('all', None)]:
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total, match, mismatch, rate = summarize(rows, prefix)
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lines.append(f'| {label} | {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 | Sign match | Duration match | Local first 3 | PyJHora first 3 |')
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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['sign_match']}/12 | {item['duration_match']}/12 | {item['local_first3']} | {item['pyjhora_first3']} |")
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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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sign_total, sign_match, _, sign_rate = summarize(rows, 'md.sign')
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dur_total, dur_match, _, dur_rate = summarize(rows, 'md.duration')
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if sign_rate < 0.9 or dur_rate < 0.9:
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lines.append('- 当前 skill 的 Chara Dasha 与 PyJHora KN Rao method 存在明显差异。')
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lines.append('- 根因从源码可见：当前 `calc_chara_dasha()` 仍是简化实现（上升顺/逆 + `12 - sign planet count`），并非 KN Rao / PVN Rao / Iranganti 的完整传统算法。')
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lines.append('- 决策：Chara Dasha 不应标记为 `covered` 的强计算模块；在可信度矩阵中应降级为 `partial-code`，除非后续直接实装 KN Rao/PVN Rao method 并回归通过。')
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lines.append('- 加速策略：可把 PyJHora KN Rao method 作为外部 oracle，重写本地 Chara Dasha；或者在 skill 中明确声明 Jaimini Chara Dasha 暂不可用于高置信度应期。')
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else:
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lines.append('- 当前 skill Chara Dasha 与 PyJHora KN Rao method 基本一致，可暂定通过。')
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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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c = canon(sid)
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local = local_chara(sample, c)
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pyj = pyjhora_chara(sample)
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sign_match = 0
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duration_match = 0
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for i in range(12):
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add(rows, sid, f'md.sign.{i+1}', local[i]['sign'], pyj[i]['sign'])
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sign_match += 1 if rows[-1]['status'] == 'match' else 0
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# durations are integers in both systems for maha periods; compare rounded to 4 places.
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lv = round(local[i]['duration_years'], 4)
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pv = round(pyj[i]['duration_years'], 4)
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add(rows, sid, f'md.duration.{i+1}', lv, pv)
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duration_match += 1 if rows[-1]['status'] == 'match' else 0
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per_sample.append({
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'sample_id': sid,
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'sign_match': sign_match,
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'duration_match': duration_match,
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'local_first3': ', '.join(f"{x['sign']}({x['duration_years']:.0f})" for x in local[:3]),
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'pyjhora_first3': ', '.join(f"{x['sign']}({x['duration_years']:.0f})" for x in pyj[:3]),
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})
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with MATRIX.open('w', newline='') as f:
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writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
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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), 'rows': len(rows)}, ensure_ascii=False, indent=2))
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if __name__ == '__main__':
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main()
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