| """Recompute published numerical comparisons from non-media derivative tables. |
| |
| Python 3.9+, standard library only. This verifies arithmetic and event pairing; |
| it cannot re-establish video semantics or reproduce sensor analyses without raw IMU. |
| """ |
| import csv |
| import json |
| import math |
| from pathlib import Path |
| from statistics import mean, median, pstdev |
|
|
| DATA = Path(__file__).resolve().parents[1] / 'data' |
| def rows(name): |
| with (DATA / name).open(newline='') as handle: |
| return list(csv.DictReader(handle)) |
|
|
| def compare(a, b): |
| a, b = sorted(a), sorted(b) |
| gaps = [y-x for series in (a,b) for x,y in zip(series,series[1:])] |
| limit = min(1., .45*median(gaps)) if gaps else .5 |
| n,m = len(a),len(b) |
| dp = [[0.]*(m+1) for _ in range(n+1)] |
| move = [[0]*(m+1) for _ in range(n+1)] |
| for i in range(1,n+1): dp[i][0],move[i][0] = i,1 |
| for j in range(1,m+1): dp[0][j],move[0][j] = j,2 |
| for i in range(1,n+1): |
| for j in range(1,m+1): |
| delta = abs(a[i-1]-b[j-1]) |
| pair = dp[i-1][j-1]+delta/limit if delta<=limit else math.inf |
| value = min(pair,dp[i-1][j]+1,dp[i][j-1]+1) |
| dp[i][j] = value |
| move[i][j] = 3 if value==pair else 1 if value==dp[i-1][j]+1 else 2 |
| i,j = n,m |
| differences=[] |
| while i or j: |
| if move[i][j]==3: |
| differences.append(b[j-1]-a[i-1]);i-=1;j-=1 |
| elif move[i][j]==1:i-=1 |
| else:j-=1 |
| absolute = [abs(v) for v in differences] |
| return dict(matched=len(absolute),unmatched_first=n-len(absolute),unmatched_second=m-len(absolute), |
| pairing_limit_seconds=limit, |
| median_absolute_seconds=median(absolute) if absolute else None, |
| maximum_absolute_seconds=max(absolute) if absolute else None) |
|
|
| def main(): |
| events = rows('review_events.csv') |
| series = {name:[float(r['video_seconds']) for r in events if r['source']==name] |
| for name in ('human_A','human_B','AI')} |
| comparisons = {'human_A_vs_B':compare(series['human_A'],series['human_B']), |
| 'AI_candidates_vs_A':compare(series['AI'],series['human_A']), |
| 'AI_candidates_vs_B':compare(series['AI'],series['human_B'])} |
| expected = [(10,.101950,.933567),(9,.263633,.499767),(10,.301250,.365067)] |
| for result,(count,med,max_gap) in zip(comparisons.values(),expected): |
| assert result['matched']==count |
| assert math.isclose(result['median_absolute_seconds'],med,abs_tol=1e-9) |
| assert math.isclose(result['maximum_absolute_seconds'],max_gap,abs_tol=1e-9) |
| assert len(events)==30 |
| assert all(e['status']=='uncertain' for e in events if e['source']=='AI') |
| anchors=rows('sync_anchors.csv') |
| x=[float(r['video_seconds']) for r in anchors];y=[float(r['watch_seconds']) for r in anchors] |
| mx,my=mean(x),mean(y) |
| scale=sum((a-mx)*(b-my) for a,b in zip(x,y))/sum((a-mx)**2 for a in x) |
| offset=my-scale*mx |
| residual=[b-offset-scale*a for a,b in zip(x,y)] |
| rms=1000*math.sqrt(mean(v*v for v in residual)) |
| maximum=1000*max(abs(v) for v in residual) |
| assert len(anchors)==6 and math.isclose(rms,20.368108909930335,abs_tol=1e-9) |
| assert math.isclose(maximum,40.81906035135319,abs_tol=1e-9) |
| control=rows('randomized_control.csv') |
| actual=[float(r['mean_correlation']) for r in control if r['condition']=='actual'] |
| null=[float(r['mean_correlation']) for r in control if r['condition']=='randomized'] |
| assert len(actual)==1 and len(null)==20 |
| assert (round(actual[0],3),round(mean(null),3),round(pstdev(null),3))==(.526,.525,.024) |
| acf=rows('autocorrelation_summary.csv') |
| assert len(acf)==7 and sum(int(r['meets_source_criterion']) for r in acf)==4 |
| |
| timing=rows('session_timing.csv');legacy=timing[:6] |
| assert [r['session_id'] for r in timing]==['H01','H02','H03','H04','H05','H06','H07','V01'] |
| assert sum(int(r['samples']) for r in legacy)==43563 |
| assert sum(int(r['shortIntervalsBelowHalfExpected']) for r in legacy)==2383 |
| assert sum(int(r['longIntervalsAboveOnePointFiveExpected']) for r in legacy)==2087 |
| assert math.isclose(float(timing[6]['secondsFromLastCaptureMarkToLastSample']),-2.238,abs_tol=1e-12) |
| print(json.dumps(dict(comparisons=comparisons, |
| synchronization=dict(scale=scale,offset_seconds=offset,rms_ms=rms,max_ms=maximum), |
| randomized_control=dict(actual=actual[0],randomized_mean=mean(null),randomized_population_sd=pstdev(null),trials=len(null)), |
| autocorrelation_direct_matches=4,legacy_median_interval_cv_percent=median(float(r['intervalJitterCvPercent']) for r in legacy), |
| checked='five derivative CSV tables; no media or raw-sensor replication'),indent=2)) |
|
|
| if __name__=='__main__':main() |
|
|
