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

return example angle distribution.

Returns:

arrayOfBoundingBox

array of bounding boxes, joined by the zeroth dimension.

Returns:

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boundingBox

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:

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bundleDataSet

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

Returns:

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bundleDescriptor

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:

complexBundleDataSet

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

Returns:

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complexBundleDataSet2

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.

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complexCoordinateSystemDepend

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

Returns:

compositeImage

return an example of a compositeImage.

Returns:

eventsList

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:

irregularJoin

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:

isAngleDistribution

return true if the data is an angle distribution.

Parameters

Returns:

isArrayOfBoundingBox

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

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isBoundingBox

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

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isBundleDataSet

return true if the data is a bundle dataset

Parameters

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isBundleDescriptor

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

Parameters

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isComplexCoordinateSystemDepend

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

Parameters

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isComplexNumbers

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"

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isCompositeImage

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

Parameters

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isEventsList

return true if the data is an events list.

Parameters

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isIrregularJoin

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

Parameters

Returns:

isLegacyXYZScatter

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

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isPolyMesh

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

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isRank1AlongTrajectory

return true if the data is rank 1 along a trajectory

Parameters

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isRank1AtXYScatter

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

Parameters

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isRank2Bins

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

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isRank2Waveform

return true if the data is a rank 2 waveform.

Parameters

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isRank2WaveformRank2Offsets

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

Parameters

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isRank3Waveform

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

Parameters

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isScalarSeriesWithErrors

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

Parameters

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isScalarTimeSeries

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

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isSimpleSpectrogram

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

Parameters

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isSimpleSpectrogramTimeSeries

return true if the data is a simple spectrogram.

Parameters

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isTimeSeries

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.

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isTrajectory

return true is the data is a trajectory

Parameters

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isUniformCadence

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

Parameters

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isUniformRatiometricCadence

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

Parameters

Returns:

isVectorTimeSeries

return true if the data is a vector time series.

Parameters

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isXYScatter

is a xyScatter data set.

Parameters

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isXYZScatter

Parameters

Returns:

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legacyXYZScatter

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.

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polyMesh

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:

rank1AlongTrajectory

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

Returns:

rank1AtXYScatter

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

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rank2Bins

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

Returns:

rank2ComplexNumbers

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

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rank2Waveform

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:

rank2WaveformRank2Offsets

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:

rank3Waveform

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

Returns:

scalarSeriesWithErrors

return a rank 1 scalar series with errors.

Returns:

scalarTimeSeries

return a rank 1 scalar time series.

Returns:

simpleSpectrogram

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

Returns:

simpleSpectrogramTimeSeries

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

Returns:

trajectory

return a trajectory through a space

Returns:

uniformCadence

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

Returns:

uniformRatiometricCadence

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

Returns:

vectorTimeSeries

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 ) )

Returns:

xyScatter

"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:

xyzScatter

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.

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