public TypedValue CreateTypedValue()
{
var buckets = _histogram
.Percentiles(3)
.Select(v => new
{
v.CountAddedInThisIterationStep,
v.ValueIteratedTo
})
.GroupBy(v => v.ValueIteratedTo)
.Select(v => v.First())
.ToList();
var bucketCounts = buckets.Select(v => v.CountAddedInThisIterationStep).ToList();
bucketCounts.Add(0);
var bucketBounds = buckets.Select(v => (double)v.ValueIteratedTo).ToList();
var hasZeroLowerBounds = bucketBounds[0] == 0;
if (!hasZeroLowerBounds)
{
bucketBounds.Insert(0, 0);
bucketCounts.Insert(0, 0);
}
return(new TypedValue
{
DistributionValue = new Distribution
{
BucketCounts = { bucketCounts },
BucketOptions = new BucketOptions
{
ExplicitBuckets = new Explicit {
Bounds = { bucketBounds }
}
},
Count = bucketCounts.Sum(),
Mean = _histogram.GetMean(),
SumOfSquaredDeviation = Math.Pow(_histogram.GetStdDeviation(), 2) * _histogram.TotalCount
}
});
}
public void WriteFooter(HistogramBase histogram)
{
// Calculate and output mean and std. deviation.
// Note: mean/std. deviation numbers are very often completely irrelevant when
// data is extremely non-normal in distribution (e.g. in cases of strong multi-modal
// response time distribution associated with GC pauses). However, reporting these numbers
// can be very useful for contrasting with the detailed percentile distribution
// reported by outputPercentileDistribution(). It is not at all surprising to find
// percentile distributions where results fall many tens or even hundreds of standard
// deviations away from the mean - such results simply indicate that the data sampled
// exhibits a very non-normal distribution, highlighting situations for which the std.
// deviation metric is a useless indicator.
var mean = histogram.GetMean() / _outputValueUnitScalingRatio;
var stdDeviation = histogram.GetStdDeviation() / _outputValueUnitScalingRatio;
_printStream.Write(_footerLine1FormatString, mean, stdDeviation);
_printStream.Write(_footerLine2FormatString, histogram.GetMaxValue() / _outputValueUnitScalingRatio, histogram.TotalCount);
_printStream.Write(_footerLine3FormatString, histogram.BucketCount, histogram.SubBucketCount);
}
public void WriteFooter(HistogramBase histogram)
{
// Calculate and output mean and std. deviation.
// Note: mean/std. deviation numbers are very often completely irrelevant when
// data is extremely non-normal in distribution (e.g. in cases of strong multi-modal
// response time distribution associated with GC pauses). However, reporting these numbers
// can be very useful for contrasting with the detailed percentile distribution
// reported by outputPercentileDistribution(). It is not at all surprising to find
// percentile distributions where results fall many tens or even hundreds of standard
// deviations away from the mean - such results simply indicate that the data sampled
// exhibits a very non-normal distribution, highlighting situations for which the std.
// deviation metric is a useless indicator.
var mean = histogram.GetMean() / _outputValueUnitScalingRatio;
var stdDeviation = histogram.GetStdDeviation() / _outputValueUnitScalingRatio;
_printStream.Write(_footerLine1FormatString, mean, stdDeviation);
_printStream.Write(_footerLine2FormatString, histogram.GetMaxValue() / _outputValueUnitScalingRatio, histogram.TotalCount);
_printStream.Write(_footerLine3FormatString, histogram.BucketCount, histogram.SubBucketCount);
}
