dataIntersection
dataIntersection( int[] itE, int[] itB ) → int
return the values which occur in both rank 1 datasets. Each dataset is sorted.
Parameters
itE - a bunch of values.
itB - a bunch of values.
Returns:
the set of values found in both.
See Also:
eventsConjunction(QDataSet, QDataSet)
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
dataIntersection( QDataSet tE, QDataSet tB ) → QDataSet
dataset
dataset( Object arg0 ) → QDataSet
coerce Java objects like arrays Lists and scalars into a QDataSet.
This is introduced to mirror the useful Jython dataset command. This is a nasty business that
is surely going to cause all sorts of problems, so we should do it all in one place.
See http://jfaden.net/jenkins/job/autoplot-test029/
This supports:
- int, float, double, etc to Rank 0 datasets
- List<Number> to Rank 1 datasets.
- Java arrays of Number to Rank 1-4 qubes datasets
- Strings to rank 0 datasets with units ("5 s" or "2014-01-01T00:00")
- Datums to rank 0 datasets
- DatumRanges to rank 1 bins
- DatumVector to rank 1 datasets.
Parameters
arg0 - null,QDataSet,Number,Datum,DatumRange,String,List,or array.
Returns:
QDataSet
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
dataset( Object arg0, Units u ) → QDataSet
datum
datum( Object arg0 ) → Datum
coerce Java objects like numbers and strings into a Datum.
This is introduced to mirror the useful Jython dataset command. This is a nasty business that
is surely going to cause all sorts of problems, so we should do it all in one place.
See http://jfaden.net:8080/hudson/job/autoplot-test029/
This supports:
- int, float, double, etc to Rank 0 datasets
- Strings to rank 0 datasets with units ("5 s" or "2014-01-01T00:00")
- rank 0 datasets
Parameters
arg0 - null,QDataSet,Number,Datum, or String.
Returns:
Datum
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
datumRange
datumRange( Object arg0 ) → DatumRange
coerce Java objects like arrays and strings into a DatumRange.
This is introduced to mirror the useful Jython dataset command. This is a nasty business that
is surely going to cause all sorts of problems, so we should do it all in one place.
See http://jfaden.net:8080/hudson/job/autoplot-test029/
This supports:
- 2-element rank 1 QDataSet
- Strings like ("5 to 15 s" or "2014-01-01")
- 2-element arrays and lists
Parameters
arg0 - null, QDataSet, String, array or List.
Returns:
DatumRange
See Also:
boundsDataset(java.lang.String)
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
dblarr
dblarr( int len0 ) → QDataSet
create a rank 1 dataset filled with zeros, stored in 8-byte doubles.
Parameters
len0 - the length of the zeroth dimension.
Returns:
rank 1 dataset filled with zeros.
See Also:
zeros(int)
fltarr(int)
bytarr(int)
shortarr(int)
intarr(int)
lonarr(int)
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
dblarr( int len0, int len1 ) → QDataSet
dblarr( int len0, int len1, int len2 ) → QDataSet
decimate
decimate( QDataSet ds ) → QDataSet
reduce the size of the data by keeping every 10th measurement.
Parameters
ds - a qube dataset.
Returns:
a decimated qube dataset.
See Also:
decimate(QDataSet, int)
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
decimate( QDataSet ds, int m ) → QDataSet
decimate( QDataSet ds, int m, int n ) → QDataSet
dependsOn
dependsOn( QDataSet ds, int dim, QDataSet dep ) → MutablePropertyDataSet
declare that the dataset is a dependent parameter of an independent parameter.
This isolates the QDataSet semantics, and verifies correctness. See also link(x,y).
Parameters
ds - the dataset
dim - dimension to declare dependence: 0,1,2.
dep - the independent dataset.
Returns:
the dataset, which may be a copy if the data was not mutable.
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
detrend
detrend( QDataSet yy, int size ) → QDataSet
remove D/C and low-frequency components from the data by subtracting
out the smoothed data with a boxcar of the given size. Points on the
end are zero.
Parameters
yy - rank 1 dataset
size - size of the boxcar
Returns:
dataset
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
detrend( Object yy, int size ) → QDataSet
detrend1
detrend1( QDataSet yy, int size ) → QDataSet
remove D/C and low-frequency components from the data by subtracting
out the smoothed data with a boxcar of the given size, along each
record. Points on the end are zero.
Parameters
yy - rank 2 dataset
size - size of the boxcar
Returns:
dataset
See Also:
smooth1(QDataSet, int)
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
diff
diff( QDataSet ds ) → QDataSet
return array that is the differences between each successive pair in the dataset.
Result[i]= ds[i+1]-ds[i], so that for an array with N elements, an array with
N-1 elements is returned. When the data has a DEPEND_0, the result
will have a DEPEND_0 which contains the average of the corresponding points.
Parameters
ds - a rank 1 dataset with N elements.
Returns:
a rank 1 dataset with N-1 elements.
See Also:
accum(QDataSet)
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
diff( Object ds ) → QDataSet
dimensionCount
dimensionCount( QDataSet dss ) → int
returns the number of physical dimensions of a dataset.
- JOIN, BINS do not increase dataset dimensionality.
- DEPEND increases dimensionality by dimensionality of DEPEND ds.
- BUNDLE increases dimensionality by N, where N is the number of bundled datasets.
Note this includes implicit dimensions taken by the primary dataset:
- Z(time,freq)→3
- rand(20,20)→3
- B_gsm(20,[X,Y,Z])→4
Parameters
dss - the dataset
Returns:
the number of dimensions occupied by the data.
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
dimensionCount( Object dss ) → int
dindgen
dindgen( int len0 ) → QDataSet
returns rank 1 dataset with values [0.,1.,2.,...]
Parameters
len0 - an int
Returns:
a QDataSet
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
dindgen( int len0, int len1 ) → QDataSet
dindgen( int len0, int len1, int len2 ) → QDataSet
dindgen( int len0, int len1, int len2, int len3 ) → QDataSet
distance
distance( int len0, double c0, double r0 ) → QDataSet
return a table of distances d[len0] to the indices c0; in units of r0.
This is motivated by a need for more interesting datasets for testing.
Parameters
len0 - the length of the dataset
c0 - the center point 0
r0 - the units to normalize in the 0 direction
Returns:
rank 2 table
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
distance( int len0, int len1, double c0, double c1, double r0, double r1 ) → QDataSet
div
div( QDataSet ds1, QDataSet ds2 ) → QDataSet
element-wise div of two datasets with compatible geometry.
Parameters
ds1 - a QDataSet
ds2 - a QDataSet
Returns:
a QDataSet
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
div( Object ds1, Object ds2 ) → QDataSet
divide
divide( QDataSet ds1, QDataSet ds2 ) → QDataSet
element-wise divide of two datasets with compatible geometry. Either
ds1 or ds2 should be dimensionless, or the units be convertible.
TODO: units improvements.
Parameters
ds1 - the numerator
ds2 - the divisor
Returns:
the ds1/ds2
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
divide( Object ds1, Object ds2 ) → QDataSet
divp
divp( QDataSet ds1, QDataSet ds2 ) → QDataSet
This div goes with modp, where -18 divp 10 = -2 and -18 modp 10 = 8.
the div operator always goes towards zero, but divp always goes to
the more negative number so the remainder is positive.
Parameters
ds1 - a QDataSet
ds2 - a QDataSet
Returns:
a QDataSet
[search for examples]
[view on GitHub]
[view on old javadoc]
[view source]
divp( Object ds1, Object ds2 ) → QDataSet