org.das2.qds.SemanticOps

Common expressions that apply semantics to QDataSet. Introduced to reduce a lot of repeated code, but also to make it clear where semantics are being applied.

bounds

return the bounds DS[ JOIN_0=x,y; BINS_1=min,maxInclusive ] of the datasets independent parameters. This is only implemented for:

rank 2 Tables	extent(X),extent(Y) and Z is not represented
rank 3 array of tables	extent(X),extent(Y) and Z is not represented
rank 1 Y(X)	extent(X),extent(Y)
not for rank 2 bundle dataset
not for rank 1 bundle dataset

The zeroth dimension will be the physical dimension of the DEPEND_0 values. Or said another way:

bounds[0,0]= X min	bounds[0,1] = X max	bounds.slice(0) is the extent of X
bounds[1,0]= Y min	bounds[1,1] = Y max	bounds.slice(1) is the extent of Y

Parameters

Returns:

cadenceCheck

return a dataset with 1's where the cadence preceeding this measurement is acceptable, and 0's where there should be a break in the data. For example, here's some pseudocode:

   findex= Ops.interpolate( xds, x )
   cadenceCheck= cadenceCheck(xds)
   r= where( cadenceCheck[floor(findex)] eq 0 )
   x[r]= fill

Parameters

Returns:

See Also:

checkPropertyType

verify property types. For example, that UNITS is a org.das2.datum.Units, etc. Returns true for unrecognized property names (future expansion) and null. If throwException is true, then an IllegalArgumentException is thrown.

Parameters

Returns:

doubleValue

returns the Double value of the number, preserving null and NaN.

Parameters

Returns:

getComponentLabels

return the labels for a dataset using BUNDLE_1 and DEPEND_1. If BUNDLE_1 is defined and contains a LABEL, then it is used, otherwise a string value of the data is used, supporting legacy bundles, and if no DEPEND_1 is found then use the NAME properties from the bundle, or "ch_i" for each channel.

Parameters

Returns:

getComponentNames

return the labels for a bundle dataset. For a rank 2 bundle, this will be found in BUNDLE_1, or legacy ones may have nominal data for DEPEND_1. For a rank 1 bundle this will be BUNDLE_0.

Parameters

Returns:

getDatum

returns the value as a datum. Note this should be used with reservation, this is not very efficient when the operation is done many times.

Parameters

Returns:

getDependentDataSet

return the dataset that is dependent on others. For a bundle, we use DataSetOps.unbundleDefaultDataSet

Parameters

Returns:

getLooseUnitsConverter

returns the UnitsConverter, or IDENTITY if the converter cannot be found and one of the two units is dimensionless.

Parameters

Returns:

getPlanarView

returns the plane requested by name, or null if it does not exist. If the name is PLANE_i, then return PLANE_i, otherwise return the dataset with this name. Note QDataSet has the rule that if PLANE_i is null, then all PLANE_(i+1) must also be null.

Parameters

Returns:

getPropertyType

return the property type expected for the property.

Parameters

Returns:

getSimpleTableContaining

return the first table of the bundle containing x and y

Parameters

Returns:

getUnits

returns the units found in the UNITS property of the dataset, or Units.dimensionless if it is not found.

Parameters

Returns:

getUnitsConverter

return the UnitsConverter that will convert data from src to the units of dst.

Parameters

Returns:

guessXTagWidth

return a reasonable tag width to use, or null if one cannot be guessed.

Parameters

Returns:

isBins

Test for bins scheme, where BINS_1 (or BINS_0) is set. This is where a two-element index is min, max. Note the BINS dimension must be the last index of the QDataSet.

Parameters

Returns:

isBundle

Test for bundle scheme. Returns true if the BUNDLE_1 is set.

Parameters

Returns:

isJoin

returns true if the dataset is rank 2 or greater with the first dimension a join dimension. Note this does not return true for implicit joins, where JOIN_0 is not set.

Parameters

Returns:

isLegacyBundle

See Ops.isLegacyBundle

Parameters

Returns:

isMonotonic

returns true if the dataset indicates that it is monotonically increasing. See DataSetUtil.isMonotonic.

Parameters

Returns:

isRank1Bundle

returns true if the rank 1 dataset is a bundle of rank 0 datasets, or where DEPEND_0 is a labels dataset.

Parameters

Returns:

isRank2Waveform

Test for rank 2 waveform dataset, where DEPEND_1 is offset from DEPEND_0, and the data is the waveform. Other rules include:

• DEPEND_1 must be at least 128 elements long.
• DEPEND_1 must not be dimensionless.
• if DEPEND_1 is in the same units as DEPEND_0, then DEPEND_0 can be ignored.

  Parameters

  fillDs - a QDataSet

  Returns:

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

  ---

  isRank3JoinOfRank2Waveform

  isRank3JoinOfRank2Waveform( QDataSet ds ) → boolean

  Test for rank 3 dataset that is a join of rank 2 waveform datasets.

  Parameters

  ds - a QDataSet

  Returns:

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

  ---

  isSimpleBundleDataSet

  isSimpleBundleDataSet( QDataSet ds ) → boolean

  returns true if the dataset is a bundle of rank 1 datasets. If no dependence is declared, it is assumed that the first one or two datasets are the independent datasets, and the last is the dependent.

      X,Y   -->  Y(X)
      X,Y,Z -->  Z(X,Y)
  

  Parameters

  ds - a QDataSet

  Returns:

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

  ---

  isSimpleTableDataSet

  isSimpleTableDataSet( QDataSet ds ) → boolean

  returns true if the dataset is the scheme of a legacy TableDataSet with only one table. Note "Tables" have just one X unit and one Y unit, no bundles. Consider: rule about "duck typing": rules like !Ops.isBundle break this because it requires to be a simpleTable, you cannot be a bundle. LANL rich ASCII allows datasets to be both bundles and simple tables.

  Parameters

  ds - a QDataSet

  Returns:

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

  ---

  isTableDataSet

  isTableDataSet( QDataSet ds ) → boolean

  returns true if the dataset is the scheme of a legacy TableDataSet

  Parameters

  ds - a QDataSet

  Returns:

  a boolean
  
  [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 QDataSet

  Returns:

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

  ---

  isYXT

  isYXT( QDataSet ds ) → boolean

  YXT is a dataset where T → X → Y. This pattern comes up all the time and we need to identify it. Ideally the data would be represented as T &rarr (X;Y).

  Parameters

  ds - a QDataSet

  Returns:

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

  ---

  lookupTimeLengthUnit

  Deprecated: use Units.lookupTimeLengthUnit

  ---

  lookupTimeUnits

  Deprecated: use Units.lookupTimeUnits lookupTimeUnits( Datum base, Units offsetUnits ) → Units
  

  ---

  lookupUnits

  Deprecated: use Units.lookupUnits

  ---

  trim

  trim( QDataSet ds, DatumRange xrange, DatumRange yrange ) → QDataSet

  return the parts of the dataset within the bounds. This assumes how the data is visualized, so for example see SemanticOps.xtagsDataSet for which dimensions correspond to x and y.

  Parameters

  ds - the rank 1 or more dataset, including joins.
  xrange - the range or null if no trimming should be done
  yrange - the range or null if no trimming should be done

  Returns:

  the trimmed dataset.
  
  [search for examples] [view on GitHub] [view on old javadoc] [view source]
  
  

  ---

  weightsDataSet

  weightsDataSet( QDataSet ds ) → QDataSet

  return the weights dataset, possibly creating one based on VALID_MIN VALID_MAX and FILL_VALUE properties. The weights dataset will have value zero where the data is invalid, and a non-zero weight where it is valid. DataSets may also contain a weights table with relative weights, but this is not uniformly supported. Note: this uses QDataSet.WEIGHTS_PLANE Note: calls org.das2.qds.DataSetUtil.weightsDataSet.

  Parameters

  ds - a QDataSet

  Returns:

  QDataSet with same geometry containing zeros and non-zeros.

  See Also:

  org.das2.qds.ops.Ops#valid which is equivalent which is equivalent
  cadenceCheck which detects for gaps in cadence. which detects for gaps in cadence.
  
  
  [search for examples] [view on GitHub] [view on old javadoc] [view source]
  
  

  ---

  xtagsDataSet

  xtagsDataSet( QDataSet ds ) → QDataSet

  return the dataset containing the x tags for the dataset. This is QDataSet.DEPEND_0, or if that's null then IndexGenDataSet(ds.length). For a bundle, this is just the 0th dataset. For a join, this is a join of the xtags datasets of each dataset.

  Parameters

  ds - a QDataSet

  Returns:

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

  ---

  ytagsDataSet

  ytagsDataSet( QDataSet ds ) → QDataSet

  return the ytags for the simple table that is ds.

  • rank 2 spectrogram: Z[X,Y] → Y
  • bundle_1: ds[ :, [x,y,z] ] → y
  • [x,y,z] → y
  TODO: consider that these break duck typing goal, and really need a scheme to tell it how to get the dataset.

  Parameters

  ds - the dataset

  Returns:

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

  ---

  yxt

  yxt( ) → QDataSet

  YXT is a dataset where T → X → Y. This pattern comes up all the time and we need to identify it. Ideally the data would be represented as T &rarr (X;Y).

  Returns:

  a dataset with this form.
  
  [search for examples] [view on GitHub] [view on old javadoc] [view source]