org.das2.qds.DataSetOps

Useful operations for QDataSets, such as slice2, leafTrim. TODO: identify which functions appear here instead of Ops.java.

DS_LENGTH_LIMIT

absolute length limit for plots. This is used to limit the elements used in autoranging, etc.

applyIndexInSitu

applyIndexInSitu( org.das2.qds.WritableDataSet ds, QDataSet sort ) → void

apply the sort to the data on the zeroth dimension. The dataset must be mutable, and the dataset itself is modified. This was introduced to support AggregatingDataSource but should be generally useful.

Parameters

ds - a writable dataset that is still mutable.
sort - the new sort indeces.

Returns:

void (returns nothing)

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boundsContains

boundsContains( QDataSet bounds, Datum xValue, Datum yValue ) → boolean

return true of the bounds overlaps with the x and y values.

Parameters

bounds - bounding box
xValue - the x range
yValue - the y range

Returns:

true of the bounds overlap

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changesDimensions

changesDimensions( String p ) → boolean

indicate if this one operator changes the dimensions. For example, |smooth doesn't change the dimensions, but fftPower and slice do.

Parameters

p - the filter, e.g. "|smooth"

Returns:

true if the dimensions change.

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dbAboveBackgroundDim0

dbAboveBackgroundDim0( QDataSet ds, double level ) → QDataSet

normalize the level-th percentile from: rank 1: each element (same as removeBackground1) rank 2: each column of the dataset rank 3: each column of each rank 2 dataset slice. There must be at least 10 elements. If the data is already in dB, then the result is a difference. This is assuming the units are similar to voltage, not a power, we think, containing code like 20 * Math.log10( ds / background ).

Parameters

ds -
level - the percentile level, e.g. 10= 10%

Returns:

the result dataset, in dB above background.

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dbAboveBackgroundDim1

dbAboveBackgroundDim1( QDataSet ds, double level ) → QDataSet

normalize the nth-level percentile from:

If the data is already in dB, then the result is a difference. This is assuming the units are similar to voltage, not a power, we think, containing code like 20 * Math.log10( ds / background ).

Parameters

ds -
level - the percentile level, e.g. 10= 10%

Returns:

the result dataset, in dB above background.

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dependBounds

dependBounds( QDataSet ds ) → QDataSet

return a bounding qube of the independent dimensions containing the dataset. If r is the result of the function, then for

Parameters

ds - rank 1,2, or 3 dataset.

Returns:

a bounding qube of the independent dimensions

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dependBoundsSimple

dependBoundsSimple( QDataSet ds ) → QDataSet

return a bounding qube of the independent dimensions containing the dataset. If r is the result of the function, then for

This does not take DELTA_PLUS and DELTA_MINUS into account. When all the data is fill, ds[0,0] will be positive infinity.

Parameters

ds - a rank 1,2, or 3 dataset.

Returns:

a bounding qube of the independent dimensions

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flattenBundleDescriptor

flattenBundleDescriptor( QDataSet bundle1 ) → QDataSet

returns a bundle descriptor roughly equivalent to the BundleDescriptor passed in, but will describe each dataset as if it were rank 1. This is useful for when the client can't work with mixed rank bundles anyway (like display data).

Parameters

bundle1 -

Returns:

org.das2.qds.QDataSet

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flattenRank2

flattenRank2( QDataSet ds ) → QDataSet

flatten a rank 2 dataset. The result is a n,3 dataset of [x,y,f]. History:

Parameters

ds - rank 2 table dataset

Returns:

rank 2 dataset that is that is array of (x,y,f).

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flattenRank3

flattenRank3( QDataSet ds ) → QDataSet

flatten a rank 3 dataset. The result is a n,4 dataset of [x,y,z,f], or if there are no tags just rank 1 f.

Parameters

ds - rank 3 table dataset

Returns:

rank 2 dataset that is array of (x,y,z,f) or rank 1 f.

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getBackgroundLevel

getBackgroundLevel( QDataSet ds, double level ) → QDataSet

Get the background level by sorting the data. The result is rank one less than the input rank.

Parameters

ds - rank 1, 2, or rank 3 join.
level - the level between 0 and 100.

Returns:

org.das2.qds.QDataSet

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getNthPercentileSort

getNthPercentileSort( QDataSet ds, double n ) → QDataSet

returns the value from within a distribution that is the nth percentile division. This returns a fill dataset (Units.dimensionless.getFillDouble()) when the data is all fill.

Parameters

ds - the dataset
n - percent between 0 and 100.

Returns:

org.das2.qds.QDataSet

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indexOfBundledDataSet

indexOfBundledDataSet( QDataSet bundleDs, String name ) → int

return the index of the named bundled dataset. This cleans up the name so that is contains just a Java-style identifier. Also, ch_1 is always implicitly index 1. Last, if safe names created from labels match that this is used. For example, 

bds=ripplesVectorTimeSeries(100)
2==indexOfBundledDataSet( bds, "Z" )
demonstrates its use. Last, extraneous spaces and underscores are removed to see if this will result in a match.

Parameters

bundleDs - a bundle dataset with the property BUNDLE_1 or DEPEND_1 having EnumerationUnits, (or BUNDLE_0 for a rank 1 dataset).
name - the named dataset.

Returns:

the index or -1 if the name is not found.

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isProcessAsync

isProcessAsync( String c ) → boolean

return true if the process described in c is probably a slow process that should be done asynchronously. For example, do a long fft on a different thread and use a progress monitor. Processes that take a trivial, constant amount of time should return false, and may be completed on the event thread,etc.

Parameters

c - process string, as in sprocess.

Returns:

true if the process described in c is probably a slow process

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leafTrim

leafTrim( QDataSet ds, int start, int end ) → MutablePropertyDataSet

pull out a subset of the dataset by reducing the number of columns in the last dimension. This does not reduce rank. This assumes the dataset has no row with length>end. This is extended to support rank 4 datasets. TODO: This probably doesn't handle bundles property. TODO: slice and trim should probably be implemented here for efficiently.

Parameters

ds - rank 1 or more dataset
start - first index to include.
end - last index, exclusive

Returns:

dataset of the same rank.

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makePropertiesMutable

makePropertiesMutable( QDataSet dataset ) → MutablePropertyDataSet

return a dataset that has mutable properties. If the dataset parameter already has, then the dataset is returned. If the dataset is a MutablePropertyDataSet but the immutable flag is set, then the dataset is wrapped to make the properties mutable.

Parameters

dataset - dataset

Returns:

a MutablePropertyDataSet that is has a wrapper around the dataset, or the dataset.

See Also:

DataSetWrapper


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makeWritable

makeWritable( QDataSet dataset ) → WritableDataSet

return a dataset that is writable. If the dataset parameter of this idempotent function is already writable, then the dataset is returned. If the dataset is a WritableDataSet but the immutable flag is set, then the a copy is returned.

Parameters

dataset -

Returns:

a WritableDataSet that is either a copy of the read-only dataset provided, or the parameter writable dataset provided.

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moment

moment( QDataSet ds ) → RankZeroDataSet

performs the moment (mean,variance,etc) on the dataset.

Parameters

ds - rank N QDataSet.

Returns:

rank 0 dataset of the mean. Properties contain other stats: stddev, RankZeroDataSet validCount, Integer, the number valid measurements invalidCount, Integer, the number of invalid measurements

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processDataSet

processDataSet( String c, QDataSet fillDs, ProgressMonitor mon ) → QDataSet

apply process to the data. This is like sprocess, except that the component can be extracted as the first step. In general these can be done on the same thread (like slice1), but some are slow (like fftPower). This is a copy of PlotElementController.processDataSet.

Parameters

c - the process string, like "bgsmx|slice0(9)|histogram()"
fillDs - the input dataset.
mon - a monitor for the processing

Returns:

dataset resulting form filters.

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removeElement

removeElement( int[] array, int index ) → int

removes the index-th element from the array.

Parameters

array - length N array
index - the index to remove

Returns:

array without the element, length N-1.

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sliceProperties0

sliceProperties0( int index, java.util.Map props ) → Map

method to help dataset implementations implement slice. 2010-09-23: support rank 2 DEPEND_2 and DEPEND_3 2010-09-23: add BINS_1 and BUNDLE_1, Slice0DataSet calls this. 2010-02-24: BUNDLE_0 handled. 2011-03-25: add WEIGHTS_PLANE

Parameters

index - the index to slice at in the zeroth index.
props - the properties to slice.

Returns:

the properties after the slice.

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suggestFillForComponentType

suggestFillForComponentType( java.lang.Class c ) → double

return a fill value that is representable by the type.

Parameters

c - the class type, including double.class, float.class, etc.

Returns:

a fill value that is representable by the type.

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transpose2

transpose2( QDataSet ds ) → QDataSet

transpose the rank 2 qube dataset so the rows are columns and the columns are rows.

Parameters

ds - rank 2 Qube DataSet.

Returns:

rank 2 Qube DataSet

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unbundleDefaultDataSet

unbundleDefaultDataSet( QDataSet bundleDs ) → QDataSet

extract the dataset that is dependent on others, or the last one. For example, the dataset ds[:,"x,y"] → y[:]

Parameters

bundleDs - a bundle of datasets

Returns:

the default dataset

See Also:

Schemes#bundleDataSet()


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