org.das2.qds.util.BinAverage

utility class providing methods for bin averaging.

binAverage

binAverage( QDataSet ds, QDataSet newTags0 ) → org.das2.qds.DDataSet

returns a dataset with tags specified by newTags0. Data from ds are averaged together when they fall into the same bin. Note the result will have the property WEIGHTS.

Parameters

ds - a rank 1 dataset, no fill
newTags0 - a rank 1 tags dataset, that must be MONOTONIC.

Returns:

rank 1 dataset with DEPEND_0 = newTags.

binAverage

binAverage( QDataSet ds, QDataSet newTags0, QDataSet newTags1 ) → org.das2.qds.DDataSet

returns a dataset with tags specified by newTags, where linear averages of the points in each bin are returned.

Parameters

ds - a rank 2 dataset. If it's a bundle, then rebinBundle is called.
newTags0 - rank 1 monotonic dataset
newTags1 - rank 1 monotonic dataset

Returns:

rank 2 dataset with newTags0 for the DEPEND_0 tags, newTags1 for the DEPEND_1 tags. WEIGHTS property contains the weights.

binAverageBundle

binAverageBundle( QDataSet ds, QDataSet dep0, QDataSet dep1, QDataSet dep2 ) → org.das2.qds.DDataSet

takes rank 2 bundle (x,y,z,f) and averages it into rank 3 qube f(x,y,z). This is similar to what happens in the spectrogram routine.

Parameters

ds - rank 2 bundle(x,y,z,f)
dep0 - the rank 1 depend0 for the result, which must be uniformly spaced.
dep1 - the rank 1 depend1 for the result, which must be uniformly spaced.
dep2 - the rank 1 depend2 for the result, which must be uniformly spaced.

Returns:

rank 3 dataset of z averages with depend_0, depend_1, and depend_2. WEIGHTS contains the total weight for each bin.

binAverageBundle

binAverageBundle( QDataSet ds, QDataSet dep0, QDataSet dep1 ) → org.das2.qds.DDataSet

takes rank 2 bundle (x,y,z) and averages it into table z(x,y). This is similar to what happens in the spectrogram routine.

Parameters

ds - rank 2 bundle(x,y,z)
dep0 - the rank 1 depend0 for the result, which must be uniform in log or linear space.
dep1 - the rank 1 depend1 for the result, which must be uniform in log or linear space.

Returns:

rank 2 dataset of z averages with depend_0 and depend_1. WEIGHTS contains the total weight for each bin.

binMeanAverageDeviation

binMeanAverageDeviation( QDataSet ads, QDataSet ds ) → org.das2.qds.DDataSet

takes rank 2 bundle (x,y,z) and averages in table z(x,y) and computes the mean average deviation in each bin.

Parameters

ads - rank 2 grid of averages
ds - rank 2 bundle(x,y,z)

Returns:

rank 2 dataset of z averages with depend_0 and depend_1. WEIGHTS contains the total weight for each bin.

boxcar

boxcar( QDataSet ds, int size ) → org.das2.qds.DDataSet

run boxcar average over the dataset, returning a dataset of same geometry. Points near the edge are simply copied from the source dataset. The result dataset contains a property "weights" that is the weights for each point.

Parameters

ds - a rank 1 dataset of size N
size - the number of adjacent bins to average

Returns:

rank 1 dataset of size N

[search for examples] [view on GitHub] [view on old javadoc] [view source]

rebin

rebin( QDataSet ds, QDataSet newTags0 ) → org.das2.qds.DDataSet

returns a dataset with tags specified by newTags0. Data from ds are averaged together when they fall into the same bin. Note the result will have the property WEIGHTS.

Parameters

ds - a rank 1 dataset, no fill
newTags0 - a rank 1 tags dataset, that must be MONOTONIC.

Returns:

rank 1 dataset with DEPEND_0 = newTags.

rebin

Deprecated: see binAverage

rebin

rebin( QDataSet ds, int n0 ) → QDataSet

reduce the rank 1 dataset by averaging blocks of bins together

Parameters

ds - rank 1 dataset with N points
n0 - number of bins in the result.

Returns:

rank 1 dataset with n0 points. Weights plane added.

rebin

rebin( QDataSet ds, int n0, int n1 ) → QDataSet

reduce the rank 2 dataset by averaging blocks of bins together. depend datasets reduced as well.

Parameters

ds - rank 2 dataset with M by N points
n0 - the number of bins in the result. Note this changed in v2013a_6 from earlier versions of this routine.
n1 - the number of bins in the result.

Returns:

rank 2 dataset with n0 by n1 points, with a weights plane.

rebin

rebin( QDataSet ds, int n0, int n1, int n2 ) → QDataSet

reduce the rank 3 dataset by averaging blocks of bins together. depend datasets reduced as well.

Parameters

ds - rank 3 dataset
n0 - the number of bins in the result.
n1 - the number of bins in the result.
n2 - the number of bins in the result.

Returns:

rank 3 dataset ds[n0,n1,n2]

rebinBundle

Deprecated: see binAverageBundle

rebinBundle

Deprecated: see binAverageBundle

residuals

residuals( QDataSet ds, int boxcarSize ) → QDataSet

returns number of stddev from adjacent data.

Parameters

ds - a QDataSet
boxcarSize - an int

Returns:

QDataSet

[search for examples] [view on GitHub] [view on old javadoc] [view source]

org.das2.qds.util.BinAverage

binAverage

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binAverage

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binAverageBundle

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binAverageBundle

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binMeanAverageDeviation

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boxcar

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rebin

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rebin

rebin

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rebin

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rebin

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rebinBundle

rebinBundle

residuals

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