"""Replay diagnostics from two downloaded JSON files; standard library, no network/writes.
Usage: python diagnostic-method.txt mayden-index-2026-10.json research-diagnostics.json
Validates declared calculations, not source truth or the appropriateness of the date rule.
"""
import json,sys,hashlib
from pathlib import Path

def main(release_path,diagnostic_path):
    raw=Path(release_path).read_bytes(); d=json.loads(raw); a=json.loads(Path(diagnostic_path).read_text(encoding='utf-8')); dims=[x['id'] for x in d['dimensions']]
    assert a['releaseSha256']==hashlib.sha256(raw).hexdigest() and a['edition']==d['version']
    cs={c['id']:c for c in d['companies']}; rows={x['companyId']:x for x in a['companies']}; assert cs.keys()==rows.keys()
    scores={}
    def vector(c,predicate):
        return {dim:max([e['level'] for e in c['evidence'] if e['eligible'] and e['dimension']==dim and predicate(e)]+[0]) or None for dim in dims}
    for cid,c in cs.items():
        row=rows[cid]; es=[e for e in c['evidence'] if e['eligible']]; dates=sorted(e['publicationDate'] for e in es if e.get('publicationDate'))
        assert row['eligibleClaims']==len(es) and row['undatedClaims']==len(es)-len(dates)
        assert row['datedBefore2025']==sum(x<'2025' for x in dates)
        assert row['earliestSourceDate']==(dates[0] if dates else None) and row['latestSourceDate']==(dates[-1] if dates else None)
        assert (row['baselinePoints'],row['baselineScore'],row['baselineRank'])==(c['points'],round(c['points']*100/27,2),c['rank'])
        v=vector(c,lambda e:bool(e.get('publicationDate')) and '2025'<=e['publicationDate']<=d['date']); points=sum(x or 0 for x in v.values()); scores[cid]=points
        assert row['dated2025Onwards']['profile']==v and row['dated2025Onwards']['points']==points and row['dated2025Onwards']['score']==round(points*100/27,2)
    for cid,c in cs.items():
        expected=None if scores[cid]==0 else 1+sum(scores[x['id']]>scores[cid] for x in cs.values() if x['sector']==c['sector'])
        assert rows[cid]['dated2025Onwards']['rank']==expected
    for key in ('eligibleClaims','undatedClaims','datedBefore2025'): assert a['counts'][key]==sum(x[key] for x in rows.values())
    mapping=a['dependencyPilot']['mapping']; claims={e['id']:e for c in cs.values() for e in c['evidence']}
    for m in mapping: assert claims[m['claimId']]['claimSha256']==m['claimSha256']
    for removal in a['dependencyPilot']['removalResults']:
        ids={m['claimId'] for m in mapping if m.get(removal['kind'])==removal['id']}; c=cs[removal['companyId']]
        assert sorted(ids)==removal['removedClaimIds']
        v=vector(c,lambda e:e['id'] not in ids); assert v==removal['remainingProfile']
        assert removal['scoreAfterRemoval']==round(sum(x or 0 for x in v.values())*100/27,2)
    print(json.dumps({'result':'passed','companies':len(cs),'dependencyPilotChecks':len(a['dependencyPilot']['removalResults']),'counts':a['counts']}))
if __name__=='__main__':main(sys.argv[1],sys.argv[2])