class 
CAPlotBinning 
Approx implementation of ChannelArchiver plotbinning for Michael Davidsaver
From the doc
If there is no sample for the time span of a bin, the bin remains empty.
If there is one sample, it is placed in the bin.
If there are two samples, they are placed in the bin
If there are more than two samples, the first and last one are placed in the bin.

class 
Count 
Counts the number of items in an interval.

class 
ErrorBars 
Similar to the mean operator; in additon, the stdz is passed in as an extra column

class 
FirstFill 
Similar to the firstSample operator with the exception that we alter the timestamp to the middle of the bin and copy over the previous bin's value if a bin does not have any samples.

class 
Jitter 
Implements SummaryStatistics.getStandardDeviation()/SummaryStatistics.getMean()

class 
Kurtosis 
Implements the kurtosis over a bin.

class 
LastFill 
Similar to the firstFill operator with the exception that we use the last sample in the bin.

class 
LastSample 
Similar to the firstSample operator with the exception that we use the last sample in the bin.\

class 
LinearInterpolation 
Implements the arithmetic mean across an interval

class 
LoessInterpolation 
Implements the arithmetic mean across an interval

class 
Max 
Implements the max item in an interval

class 
Mean 
Implements the arithmetic mean across an interval

class 
Median 
Implements the median over a bin.

class 
Min 
Implements the min item in an interval

class 
NCount 
NCount is a post processor which returns number of samples in
a selected time span.

class 
Nth 
Nth is a post processor which returns every nth value.

class 
Optimized 
Optimized expects one parameter at initialization, which is the number of requested points.

class 
OptimizedWithLastSample 
OptimizedWithLastSample expects one parameter at initialization, which is the number of requested points.

class 
PopulationVariance 
Implements the SummaryStatistics.getPopulationVariance

class 
RMS 
Implements the RMS across an interval

class 
Skewness 
Implements the skewness over a bin.

class 
StandardDeviation 
Implements the RMS across an interval

class 
Statistics 
Statistics is a post processor which provides a set of statistical numbers for a specific bin.

class 
SummaryStatsPostProcessor 
Abstract class for various operators that operate on a SummaryStatistics
Child classes implement the getIdentity and the getStats method.

class 
Variance 
Implements the SummaryStatistics.getVariance
