## org.das2.qds.examples.Schemes

For the various QDataSet schemes, show examples and documentation for each. This was motivated when trying to describe the output of org.das2.graph.ContoursRenderer.doAutorange() Note all QDataSets are "duck-typed," meaning if they happen to meet the requirements of an interface then they are an instance of the interface. All schemes can be executed using reflection, looking for methods starting with "is". This Jython code scans through the class looking for all the methods which make a dataset:

    {@code
     mm= dir( Schemes )
     i=0
     for m in mm:
     if ( m[0:2]=='is' ):
         continue
     else:
         i=i+1
     }

## Schemes( )

## angleDistribution

angleDistribution( ) → QDataSet

return example angle distribution.

### Returns:

example angle distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## angleDistribution

angleDistribution( int i ) → QDataSet

return example angle distribution.

### Parameters

i - the example number. 0..n-1.

### Returns:

example angle distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## arrayOfBoundingBox

arrayOfBoundingBox( ) → QDataSet

array of bounding boxes, joined by the zeroth dimension.

### Returns:

array of bounding boxes

### See Also:

boundingBox()
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## boundingBox

boundingBox( ) → QDataSet

return a bounding box for the data. This is a rank 2 dataset where ds[0,:] shows the bounds for the first dimension and ds[1,:] shows the bounds for the second dimension. Therefor ds[0,0] is the minumum extent of the first dimension, and ds[0,1] is the maximum. Note this can be extended to any number of dimensions (cube or hypercube). Note,

>     from org.das2.qds.examples import Schemes
>     ds= Schemes.boundingBox()
>     print asDatumRange(ds.slice(0))

### Returns:

a bounding box for the data.

### See Also:

org.das2.qds.DataSetUtil#asDatumRange(QDataSet)
arrayOfBoundingBox()
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## bundleDataSet

bundleDataSet( ) → QDataSet

return an example bundle dataset that bundles timetags, a rank 1 dataset and a rank 1 dataset.

### Returns:

an example bundle dataset

### See Also:

complexBundleDataSet() which has differing rank.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## bundleDescriptor

bundleDescriptor( ) → QDataSet

return data that describes the columns of another dataset. Note these are typically not found in APIs. The bundle descriptor will be

- rank 2,
- length() gives the number of columns.
- length(i) gives the number of indices of the ith column, or the ith column's rank.
- property(i,NAME) gives the name of the column
- property(i,START_INDEX) gives the column which is the first column of the rank 2 or higher bundled data set.
- property(i,xxx) gives property xxx of the column
- property(i,DEPEND0_NAME) gives the name of the column containing the depend 0 values.

### Returns:

data that describes the columns of another dataset.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## canonicalEvent

canonicalEvent( ) → QDataSet

return a canonical event

### Returns:

a canonical event
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## complexBundleDataSet

complexBundleDataSet( ) → QDataSet

return bundle with Time, Density, Speed, and Flux, to demonstrate a bundle of datasets with differing rank.

### Returns:

bundle with Time, Density, Speed, and Flux

### See Also:

complexBundleDataSet2()
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## complexBundleDataSet2

complexBundleDataSet2( ) → QDataSet

A complexBundleDataSet is a set of datasets which have different rank. The rank 2 datasets take multiple columns of the dataset, and rank 3 and 4 datasets are unrolled to make them rank 2 and the property ELEMENT_DIMENSIONS is used to re-form them. This returns an example bundle dataset that bundles timetags, density, velocity, and flux. Yes, this was coded this twice, not realizing it was already done. This code is probably easier to read, so it is left in.

### Returns:

an example bundle dataset

### See Also:

complexBundleDataSet()
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## complexCoordinateSystemDepend

complexCoordinateSystemDepend( ) → QDataSet

returns the QDataSet used to identify the columns of a complex coordinate frame.

### Returns:

the QDataSet used to identify the columns of a complex coordinate frame.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## complexNumbers

complexNumbers( ) → QDataSet

return an array of complex numbers

### Returns:

an array of complex numbers
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## compositeImage

compositeImage( ) → QDataSet

return an example of a compositeImage.

### Returns:

image[320,240,3]
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## datum

datum( ) → QDataSet

return a rank 0 atom of data.

### Returns:

a rank 0 atom of data.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## datumRange

datumRange( ) → QDataSet

return a rank 0 atom of data.

### Returns:

a rank 0 atom of data.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## eventsList

eventsList( ) → QDataSet

return example events list. This is a four-column rank 2 dataset with start time, end time, RGB color, and ordinal data for the message.

### Returns:

example events list.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## irregularJoin

irregularJoin( ) → QDataSet

return a rank 3 irregular join of three datasets, the first is 13 records of 27 energies, the second is 13 records of 20 energies, and the third is 14 records of 24 energies.

### Returns:

a rank 3 irregular join.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isAngleDistribution

isAngleDistribution( QDataSet ds ) → boolean

return true if the data is an angle distribution.

### Parameters

ds - a dataset

### Returns:

true if the data is an angle distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isArrayOfBoundingBox

isArrayOfBoundingBox( QDataSet ds ) → boolean

return true if the data is a rank 3 array of bounding boxes. Presently only 2-D bounding boxes are allowed. The zeroth dimension is the number of boxes, and each slice is a bounding box.

### Parameters

ds - a QDataSet

### Returns:

true if the data is a rank 3 array of bounding boxes.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isBoundingBox

isBoundingBox( QDataSet ds ) → boolean

return true if the data is a boundingBox. A bounding box is rank 2 and has length 2. ds[0,:] is typically the horizontal direction, and ds[1,:] is the vertical.

### Parameters

ds - a dataset

### Returns:

true if the dataset is a bounding box.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isBundleDataSet

isBundleDataSet( QDataSet ds ) → boolean

return true if the data is a bundle dataset

### Parameters

ds - a dataset

### Returns:

true if the data is a bundle dataset
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isBundleDescriptor

isBundleDescriptor( QDataSet bds ) → boolean

return true if the data describes the columns of another dataset.

### Parameters

bds - a QDataSet

### Returns:

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

## isCanonicalEvent

isCanonicalEvent( QDataSet ds ) → boolean

returns true if the dataset is a rank 1 canonical event, having three or four elements, the first two are times or the same units and the last is a nominal datum describing the event.

### Parameters

ds - dataset

### Returns:

true if is a canonical event.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isComplexCoordinateSystemDepend

isComplexCoordinateSystemDepend( QDataSet dep ) → boolean

return true if the data is length 2, rank 1, and has "ComplexNumber" as the COORDINATE_FRAME.

### Parameters

dep - a QDataSet

### Returns:

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

## isComplexNumbers

isComplexNumbers( QDataSet ds1 ) → boolean

return true if the data represents an array of complex numbers, containing the property COORDINATE_FRAME on the last DEPEND, which is equal to "ComplexNumber"

### Parameters

ds1 - a dataset

### Returns:

true if the data represents an array of complex numbers.

### See Also:

Ops#checkComplexArgument(QDataSet)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isCompositeImage

isCompositeImage( QDataSet ds ) → boolean

return true if the dataset is a composite image, and is plottable with the RGBImageRenderer

### Parameters

ds - a dataset

### Returns:

true if the dataset is a composite image.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isDatum

isDatum( QDataSet ds ) → boolean

true if its a rank 0 dataset

### Parameters

ds - a QDataSet

### Returns:

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

## isDatumRange

isDatumRange( QDataSet ds ) → boolean

true if it's a rank 1 datum range

### Parameters

ds - a QDataSet

### Returns:

true if it's a rank 1 datum range
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isEventsList

isEventsList( QDataSet ds ) → boolean

return true if the data is an events list.

### Parameters

ds - a dataset

### Returns:

true if the data is an events list.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isIrregularJoin

isIrregularJoin( QDataSet ds ) → boolean

return true if the data is a join of datasets of different cadences or lengths.

### Parameters

ds - a QDataSet

### Returns:

return true if the data is a join of datasets of different cadences or lengths.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isLegacyXYZScatter

isLegacyXYZScatter( QDataSet zds ) → boolean

Many of Autoplot's codes use the "legacyXYZScatter" QDataSet scheme, where the X tags are in DEPEND_0, the Y tags are the QDataSet, and PLANE_0 contains the Z values.

### Parameters

zds - a QDataSet

### Returns:

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

## isNominalAtScatter

isNominalAtScatter( QDataSet fillds ) → boolean

true if it is a rank 2 nominal values at location

### Parameters

fillds - a QDataSet

### Returns:

true if it is a rank 2 nominal values at location
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isPolyMesh

isPolyMesh( QDataSet ds ) → boolean

return true if the data can be used as a triangulation or tessalation of 4-point rectangles.

### Parameters

ds - a QDataSet

### Returns:

a boolean

### See Also:

Ops#polyCenters(QDataSet)
https://sourceforge.net/p/autoplot/feature-requests/819/
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank1AlongTrajectory

isRank1AlongTrajectory( QDataSet ds ) → boolean

return true if the data is rank 1 along a trajectory

### Parameters

ds - a dataset

### Returns:

true if the data is rank 1 along a trajectory
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank1AtXYScatter

isRank1AtXYScatter( QDataSet ds ) → boolean

is a Z that is a function of X and Y of a xyScatter.

### Parameters

ds - a QDataSet

### Returns:

true if is a Z that is a function of X and Y of a xyScatter.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank2Bins

isRank2Bins( QDataSet dep ) → boolean

return true if the data is a rank 2 list of M bins. The data will have rank=2 and the property BINS_1='min,max'

### Parameters

dep - a QDataSet

### Returns:

true if the data is a rank 2 list of M bins
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank2Waveform

isRank2Waveform( QDataSet ds ) → boolean

return true if the data is a rank 2 waveform.

### Parameters

ds - a dataset

### Returns:

true if the data is a rank 2 waveform.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank2WaveformRank2Offsets

isRank2WaveformRank2Offsets( QDataSet ds ) → boolean

return true if the data is a rank 2 waveform with rank 2 offsets.

### Parameters

ds - a dataset

### Returns:

true if the data is a rank 2 waveform.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank3Waveform

isRank3Waveform( QDataSet ds ) → boolean

return true if the data is a rank 3 join of rank 2 waveforms.

### Parameters

ds - a dataset

### Returns:

true if the data is a rank 3 waveform.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isRank3WaveformXYZ

isRank3WaveformXYZ( QDataSet ds ) → boolean

### Parameters

ds - a QDataSet

### Returns:

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

## isScalarSeriesWithErrors

isScalarSeriesWithErrors( QDataSet ds ) → boolean

return true is the data is a simple series of scalars with errors.

### Parameters

ds - dataset

### Returns:

true is the data is a simple series of scalars with errors.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isScalarTimeSeries

isScalarTimeSeries( QDataSet ds ) → boolean

return true if the data is a simple time series of scalars.

### Parameters

ds - a dataset

### Returns:

true if the data is a simple spectrogram.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isSimpleSpectrogram

isSimpleSpectrogram( QDataSet ds ) → boolean

return true if the data is a simple spectrogram, which is rank 2, and not a small bundle.

### Parameters

ds - a dataset

### Returns:

true if the data is a simple spectrogram.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isSimpleSpectrogramTimeSeries

isSimpleSpectrogramTimeSeries( QDataSet ds ) → boolean

return true if the data is a simple spectrogram.

### Parameters

ds - a dataset

### Returns:

true if the data is a simple spectrogram.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isTimeSeries

isTimeSeries( QDataSet ds ) → boolean

returns true if the dataset is a time series. This is either something that has DEPEND_0 as a dataset with time location units, or a join of other datasets that are time series.

### Parameters

ds - a dataset

### Returns:

true if the dataset is a time series.

### See Also:

SemanticOps#isTimeSeries(QDataSet)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isTrajectory

isTrajectory( QDataSet ds ) → boolean

return true is the data is a trajectory

### Parameters

ds - a dataset

### Returns:

true if the data is a trajectory
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isUniformCadence

isUniformCadence( QDataSet ds ) → boolean

return true of the data has a uniform cadence. Note that the CADENCE property is ignored.

### Parameters

ds - a rank 1 dataset

### Returns:

true if the data has uniform cadence.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isUniformRatiometricCadence

isUniformRatiometricCadence( QDataSet ds ) → boolean

return true of the data has a uniform cadence in log space. Note that the CADENCE property is ignored.

### Parameters

ds - a rank 1 dataset

### Returns:

true if the data has uniform cadence.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isVectorTimeSeries

isVectorTimeSeries( QDataSet ds ) → boolean

return true if the data is a vector time series.

### Parameters

ds - a dataset

### Returns:

true if the data is a vector time series.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isXYScatter

isXYScatter( QDataSet ds ) → boolean

is a xyScatter data set.

### Parameters

ds - a QDataSet

### Returns:

true if is a xyScatter data set.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## isXYZScatter

isXYZScatter( QDataSet zds ) → boolean

### Parameters

zds - a QDataSet

### Returns:

true is it is an xyzScatter scheme.

### See Also:

xyzScatter()
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## legacyXYZScatter

legacyXYZScatter( ) → QDataSet

Many code use this form of data to represent Z(X,Y). This is not preferred, and ds[n;x,y,z] should be used instead. This is available for testing.

### Returns:

rank 1 dataset with DEPEND_0 and PLANE_0 properties.

### See Also:

rank1AtXYScatter
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## nominalAtScatter

nominalAtScatter( ) → QDataSet

return nominal data at X,Y positions

### Returns:

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

## polyMesh

polyMesh( ) → QDataSet

set of 2-d or 3-d points and the triangles connecting them. Note that Ops.triangulate does not return this schema. This is introduced to experiment with an oddly-structured dataset. For example, this can be a DEPEND_0 of a rank 1 rgb color dataset.

### Returns:

a triangular mesh.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank1AlongTrajectory

rank1AlongTrajectory( ) → QDataSet

return a rank 1 dataset that depends on a trajectory through a space, which is not supported currently.

### Returns:

rank 1 dataset with DEPEND_0 a trajectory.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank1AtXYScatter

rank1AtXYScatter( ) → QDataSet

Here there is a Z that is a function of X and Y of a xyScatter.

### Returns:

rank 1 dataset that has bundle for DEPEND_0.

### See Also:

xyzScatter()
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank2Bins

rank2Bins( ) → QDataSet

return a rank 2 dataset that is a list of bins.

### Returns:

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

## rank2ComplexNumbers

rank2ComplexNumbers( ) → QDataSet

return a complex rank 2 dataset, N by 2, which can be thought of as a 1-D array of N complex numbers

### Returns:

a complex rank 2 dataset ds[N,2]

### See Also:

isComplexNumbers(QDataSet)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank2Waveform

rank2Waveform( ) → QDataSet

return a rank 2 waveform, where the waveform is stored in packets. DEPEND_0 is the time for each packet, and DEPEND_1 is the difference in time for each measurement to the packet time. Note the additional requirement that the offsets be uniform, e.g.:

>     from org.das2.qds.examples import Schemes
>     ds= Schemes.rank2Waveform()
>     deltaT= ds.property( QDataSet.DEPEND_1 )
>     ddeltaT= diffs(dep1)
>     print ddeltaT[0], ddeltT[-1] # should be the same

### Returns:

rank 2 waveform.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank2WaveformRank2Offsets

rank2WaveformRank2Offsets( ) → QDataSet

return a rank 2 waveform, but DEPEND_1 which contains the offsets is also rank 2. This was introduced to support study where short waveform-like structures were identified.

### Returns:

a rank 2 waveform, but with rank 2 time-varying DEPEND_1 offsets.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank3Waveform

rank3Waveform( ) → QDataSet

return a join of rank 2 waveforms, also called a rank 3 waveform.

### Returns:

rank 3 waveform
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rank3WaveformXYZ

rank3WaveformXYZ( ) → QDataSet

### Returns:

org.das2.qds.QDataSet
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## scalarSeriesWithErrors

scalarSeriesWithErrors( ) → QDataSet

return a rank 1 scalar series with errors.

### Returns:

a rank 1 scalar series with errors.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## scalarTimeSeries

scalarTimeSeries( ) → QDataSet

return a rank 1 scalar time series.

### Returns:

a rank 1 scalar time series.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## simpleSpectrogram

simpleSpectrogram( ) → QDataSet

return a rank 2 simple spectrogram, which has two indeces.

### Returns:

rank 2 simple spectrogram
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## simpleSpectrogramTimeSeries

simpleSpectrogramTimeSeries( ) → QDataSet

return a rank 2 simple spectrogram, which has two indeces and is a TimeSeries.

### Returns:

simple spectrogram time series
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## trajectory

trajectory( ) → QDataSet

return a trajectory through a space

### Returns:

rank 2 dataset
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## uniformCadence

uniformCadence( ) → QDataSet

uniform cadence is when each tag is the same distance apart, within a reasonable threshold.

### Returns:

dataset with uniform cadence
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## uniformRatiometricCadence

uniformRatiometricCadence( ) → QDataSet

uniform ratiometric cadence is when the tags are uniform in log space.

### Returns:

dataset with uniform ratiometric cadence.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## vectorTimeSeries

vectorTimeSeries( ) → QDataSet

return a rank 2 vectorTimeSeries, which is a bundle of m rank 1 measurements. This tacitly asserts orthogonality, but the bundled data should at least all be rank 1 and in the same units.

>     from org.das2.qds.examples import Schemes
>     ds= Schemes.vectorTimeSeries()
>     plot( magnitude( ds ) )
>     plot( unbundle( ds, 0 ) )

dataset→rank2bundle→vectorTimeSeries.

### Returns:

rank 2 vector time series.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## xyScatter

xyScatter( ) → QDataSet

"scatter" is meant to indicate there is no connection between successive points, and that there is no dependence indicates no clean relation between the bundled datasets.

### Returns:

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

## xyzScatter

xyzScatter( ) → QDataSet

This will be the preferred way to represent X,Y → Z. This shows a problem, however, where there is no way to indicate the dependencies for the columns. The Z column can have DEPENDNAME_0, but how does one declare the dependence on Y as well? In https://sourceforge.net/p/autoplot/bugs/1710/, I propose CONTEXT_0=field0,field2, which seems like it would work nicely.

### Returns:

a QDataSet

### See Also:

rank1AlongTrajectory()
rank1AtXYScatter()
[search for examples] [view on GitHub] [view on old javadoc] [view source]