File size: 4,810 Bytes
97b0f0e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | """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
# Rounded period columns do not redefine the source's full-precision criterion.
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()
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