public static void AddFittsSequence(FittsSequence sequence)
{
if (sequencesStats == null)
{
sequencesStats = new List <SequenceStatistic>();
}
sequencesStats.Add(new SequenceStatistic(sequence));
movementTimeMean = sequencesStats.Sum(x => x.timeMean.movementTime) / sequencesStats.Count;
errorMean = sequencesStats.Sum(x => x.errorMean) / sequencesStats.Count;
throughputMean = sequencesStats.Sum(x => x.throughput) / sequencesStats.Count;
}
public SequenceStatistic(FittsSequence _sequence)
{
trialsStats = new List <TrialStatistic>();
sequence = _sequence;
foreach (FittsTrial trial in sequence.fittsTrials)
{
trialsStats.Add(new TrialStatistic(trial));
}
// We
effectiveWidth = 4.133 * CalculateStandardDeviation(new List <double>(trialsStats.Select(x => x.dx)));
// Ae (mean)
effectiveAmplitudeMean = trialsStats.Sum(x => x.effectiveAmplitude) / trialsStats.Count;
// IDe
effectiveIndexDifficulty = Math.Log((effectiveAmplitudeMean / effectiveWidth) + 1, 2);
// TP
throughput = effectiveIndexDifficulty / (sequence.fittsTrials.Sum(x => x.time.movementTime) / sequence.fittsTrials.Count);
// MT
timeMean = new FittsTime(sequence.fittsTrials.Sum(x => x.time.pointingTime) / sequence.fittsTrials.Count, sequence.fittsTrials.Sum(x => x.time.selectionTime) / sequence.fittsTrials.Count);
// ER(%)
errorMean = (double)sequence.fittsTrials.Where(x => x.isError).Count() / (double)sequence.fittsTrials.Count;
errorMean *= 100;
// Eye Data
eyeData = TargetsCreator.GetEyeData();
}