"""Reproduce Mayden's published coding arithmetic, not the truth of source claims.
Save as reproduce_score.py. Run: python -B reproduce_score.py mayden-index-2026-10.json
Python 3.11+, standard library only; no network or output-file writes.
"""
import sys,json,math,hashlib
from fractions import Fraction
from pathlib import Path

def main(filename):
    body=Path(filename).read_bytes();d=json.loads(body);dims=[x['id'] for x in d['dimensions']]
    def vector(c,cap=3,minimum=1,omit=None):
        return {dim:max([min(e['level'],cap) for e in c['evidence'] if e['eligible'] and e['dimension']==dim and e['level']>=minimum and e['url']!=omit]+[0]) for dim in dims}
    def value(p,weights):
        w={k:Fraction(str(v)) for k,v in weights.items()}
        return 100*sum(p[k]*w[k] for k in p)/(3*sum(w.values()))
    equal={k:1 for k in dims};seen=set()
    for c in d['companies']:
        p=vector(c);assert all((c['profile'][k] or 0)==p[k] for k in dims)
        assert sum(p.values())==c['points'];assert math.floor(float(value(p,equal))+.5)==c['score']
        assert sum(v>0 for v in p.values())==c['coverage']
        rank=None if c['points']==0 else 1+sum(o['points']>c['points'] for o in d['companies'] if o['sector']==c['sector'])
        assert rank==c['rank']
        for e in c['evidence']:
            assert e['id'] not in seen;seen.add(e['id'])
            assert (isinstance(e['level'],int) and not isinstance(e['level'],bool) and 1<=e['level']<=3) if e['eligible'] else e['level'] is None
        if 'audit' in c:
            for row in c['audit']['lineage']:
                dim=row['dimension'];winning=sorted(e['id'] for e in c['evidence'] if e['eligible'] and e['dimension']==dim and e['level']==p[dim])
                assert sorted(row['winningClaimIds'])==winning
                assert row['contribution']==round(p[dim]*100/27,4)
            for row in c['audit']['singleSourceRemoval']:
                assert row['scoreWithoutSource']==round(float(value(vector(c,omit=row['url']),equal)),2)
    checked=0
    for scenario in d.get('sensitivity',{}).get('scenarios',[]):
        vals={c['id']:value(vector(c,scenario['cap'],scenario['minimum']),scenario['weights']) for c in d['companies']}
        for c in d['companies']:
            v=vals[c['id']];rank=None if v==0 else 1+sum(vals[o['id']]>v for o in d['companies'] if o['sector']==c['sector'])
            row=next(x for x in c['audit']['scenarioResults'] if x['scenario']==scenario['id'])
            assert row['rank']==rank and row['score']==round(float(v),2)
            checked+=1
    assert len(seen)==d['evidenceCount']
    print(json.dumps({'version':d['version'],'companies':len(d['companies']),'claims':len(seen),'scenarioCompanyChecks':checked,'sha256':hashlib.sha256(body).hexdigest(),'result':'passed'}))
if __name__=='__main__':main(sys.argv[1])