## rand

Deprecated: use randu instead

## rand

Deprecated: use randu instead. This is used in many test programs and Jython codes, and will not be removed.

## rand

Deprecated: use randu instead

## rand

Deprecated: use randu instead

## randn

randn( ) → QDataSet

return a rank 0 dataset of random numbers of a Gaussian (normal) distribution.

### Returns:

a rank 0 dataset of random numbers of a Gaussian (normal) distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randn

randn( int len0 ) → QDataSet

return a rank 1 dataset of random numbers of a Gaussian (normal) distribution.

### Parameters

len0 - the number of elements in the first index.

### Returns:

a rank 1 dataset of random numbers of a Gaussian (normal) distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randn

randn( int len0, int len1 ) → QDataSet

return a rank 2 dataset of random numbers of a Gaussian (normal) distribution.

### Parameters

len0 - the number of elements in the first index.
len1 - the number of elements in the second index.

### Returns:

a rank 2 dataset of random numbers of a Gaussian (normal) distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randn

randn( int len0, int len1, int len2 ) → QDataSet

return a rank 3 dataset of random numbers of a Gaussian (normal) distribution.

### Parameters

len0 - the number of elements in the first index.
len1 - the number of elements in the second index.
len2 - the number of elements in the third index.

### Returns:

a rank 3 dataset of random numbers of a Gaussian (normal) distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randn

randn( int len0, int len1, int len2, int len3 ) → QDataSet

return a rank 4 dataset of random numbers of a Gaussian (normal) distribution.

### Parameters

len0 - the number of elements in the first index.
len1 - the number of elements in the second index.
len2 - the number of elements in the third index.
len3 - the number of elements in the fourth index.

### Returns:

a rank 4 dataset of random numbers of a Gaussian (normal) distribution.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomSeed

randomSeed( ) → long

restart the random sequence used by randu and randn. Note if there if there are multiple threads using random functions, this becomes unpredictable.

### Returns:

the seed is returned.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomSeed

randomSeed( long seed ) → long

reset the random sequence used by randu and randn to the given seed.

### Parameters

seed - the new seed for the sequence.

### Returns:

the seed (which will be the same as the input).
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomn

randomn( long seed ) → QDataSet

returns a rank 0 dataset of random numbers of a Gaussian (normal) distribution. System.currentTimeMillis() may be used for the seed. Note this is unlike the IDL randomn function because the seed is not modified. (Any long parameter in Jython and Java is read-only.) System.currentTimeMillis() may be used for the seed.

### Parameters

seed - basis for the random number (which will not be modified).

### Returns:

rank 0 dataset
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomn

randomn( long seed, int len0 ) → QDataSet

returns a rank 1 dataset of random numbers of a Gaussian (normal) distribution. System.currentTimeMillis() may be used for the seed.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index

### Returns:

rank 1 dataset of normal distribution
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomn

randomn( long seed, int len0, int len1 ) → QDataSet

returns a rank 2 dataset of random numbers of a Gaussian (normal) distribution.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index
len1 - number of elements in the second index

### Returns:

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

## randomn

randomn( long seed, int len0, int len1, int len2 ) → QDataSet

returns a rank 3 dataset of random numbers of a gaussian (normal) distribution.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index
len1 - number of elements in the second index
len2 - number of elements in the third index

### Returns:

rank 3 dataset of normal distribution
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomn

randomn( long seed, int len0, int len1, int len2, int len3 ) → QDataSet

returns a rank 3 dataset of random numbers of a gaussian (normal) distribution.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index
len1 - number of elements in the second index
len2 - number of elements in the third index
len3 - number of elements in the fourth index

### Returns:

rank 4 dataset of normal distribution
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomu

randomu( long seed ) → QDataSet

returns a rank 0 dataset of random numbers of a uniform distribution. System.currentTimeMillis() may be used for the seed. Note this is unlike the IDL randomn function because the seed is not modified. (Any long parameter in Jython and Java is read-only.)

### Parameters

seed - basis for the random number (which will not be modified).

### Returns:

a rank 0 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomu

randomu( long seed, int len0 ) → QDataSet

returns a rank 1 dataset of random numbers of a uniform distribution. System.currentTimeMillis() may be used for the seed.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index

### Returns:

a rank 0 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomu

randomu( long seed, int len0, int len1 ) → QDataSet

returns a rank 2 dataset of random numbers of a uniform distribution.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index
len1 - number of elements in the second index

### Returns:

a rank 0 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomu

randomu( long seed, int len0, int len1, int len2 ) → QDataSet

returns a rank 3 dataset of random numbers of a uniform distribution.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index
len1 - number of elements in the second index
len2 - number of elements in the third index

### Returns:

a rank 0 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randomu

randomu( long seed, int len0, int len1, int len2, int len3 ) → QDataSet

returns a rank 3 dataset of random numbers of a uniform distribution.

### Parameters

seed - basis for the random number (which will not be modified).
len0 - number of elements in the first index
len1 - number of elements in the second index
len2 - number of elements in the third index
len3 - number of elements in the fourth index

### Returns:

rank 4 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randu

randu( ) → QDataSet

returns a rank 0 dataset of random uniform numbers from 0 to 1 but not including 1.

### Returns:

a rank 0 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randu

randu( int len0 ) → QDataSet

returns a rank 1 dataset of random uniform numbers from 0 to 1 but not including 1.

### Parameters

len0 - the number of elements in the result.

### Returns:

a rank 1 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randu

randu( int len0, int len1 ) → QDataSet

returns a rank 2 dataset of random uniform numbers from 0 to 1 but not including 1.

### Parameters

len0 - the number of elements in the first index.
len1 - the number of elements in the second index.

### Returns:

a rank 2 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randu

randu( int len0, int len1, int len2 ) → QDataSet

returns a rank 3 dataset of random uniform numbers from 0 to 1 but not including 1.

### Parameters

len0 - the number of elements in the first index.
len1 - the number of elements in the second index.
len2 - the number of elements in the third index.

### Returns:

a rank 3 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## randu

randu( int len0, int len1, int len2, int len3 ) → QDataSet

return a rank 4 dataset of random uniform numbers from 0 to 1 but not including 1.

### Parameters

len0 - the number of elements in the first index.
len1 - the number of elements in the second index.
len2 - the number of elements in the third index.
len3 - the number of elements in the fourth index.

### Returns:

a rank 4 dataset of random uniform numbers from 0 to 1 but not including 1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rebundle

rebundle( QDataSet bundle1, String[] names ) → QDataSet

unbundle the names from the bundle, and rebundle them in the order specified.

### Parameters

bundle1 - a bundle of datasets
names - the bundled dataset names

### Returns:

the new bundle

### See Also:

unbundle(QDataSet, java.lang.String)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rebundle

rebundle( QDataSet bundle1, int[] ii ) → QDataSet

unbundle datasets by index from the bundle, and rebundle them in the order specified.

### Parameters

bundle1 - a bundle of datasets
ii - the index of each dataset

### Returns:

the new bundle

### See Also:

unbundle(QDataSet, java.lang.String)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceBins

reduceBins( QDataSet dep1 ) → QDataSet

reduce each bin to its center. If the spacing is log, then geometric centers are used.

### Parameters

dep1 - rank 2 [N,2] bins dataset, where bins are min,max boundaries.

### Returns:

rank 1 N element dataset
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMax

reduceMax( QDataSet ds, int dim ) → QDataSet

reduce the dataset's rank by reporting the max of all the elements along a dimension. Only QUBEs are supported presently.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.

### Returns:

rank N-1 dataset.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMax

reduceMax( QDataSet ds, int dim, ProgressMonitor mon ) → QDataSet

reduce the dataset's rank by reporting the max of all the elements along a dimension. Only QUBEs are supported presently.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.
mon - progress monitor

### Returns:

rank N-1 dataset.

### See Also:

reduceMin(QDataSet, int, org.das2.util.monitor.ProgressMonitor)
reduceMean(QDataSet, int, org.das2.util.monitor.ProgressMonitor)
extent(QDataSet)
extent(QDataSet, QDataSet)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMean

reduceMean( QDataSet ds, int dim ) → QDataSet

reduce the dataset's rank by reporting the mean of all the elements along a dimension. Only QUBEs are supported presently. Note this does not contain code that would remove large offsets from zero when making the average, so the number of points is limited.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.

### Returns:

rank N-1 qube dataset.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMean

reduceMean( QDataSet ds, int dim, ProgressMonitor mon ) → QDataSet

reduce the dataset's rank by reporting the mean of all the elements along a dimension. Only QUBEs are supported presently. Note this does not contain code that would remove large offsets from zero when making the average, so the number of points is limited.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.
mon - progress monitor.

### Returns:

rank N-1 qube dataset
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMedian

reduceMedian( QDataSet ds, int dim, ProgressMonitor mon ) → QDataSet

reduce the dataset's rank by reporting the median of all the elements along a dimension. Only QUBEs are supported presently. Note the weights reported are the totals of the data going in to each median, typically the number of measurements compared (when all weights are 0 or 1).

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.
mon - progress monitor.

### Returns:

rank N-1 qube dataset
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMin

reduceMin( QDataSet ds, int dim ) → QDataSet

reduce the dataset's rank by reporting the min of all the elements along a dimension. Only QUBEs are supported presently.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.

### Returns:

rank N-1 dataset.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceMin

reduceMin( QDataSet ds, int dim, ProgressMonitor mon ) → QDataSet

reduce the dataset's rank by reporting the min of all the elements along a dimension. Only QUBEs are supported presently.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.
mon - progress monitor

### Returns:

rank N-1 dataset.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reduceSum

reduceSum( QDataSet ds, int dim ) → QDataSet

reduce the dataset's rank by reporting the sum of all the valid elements along a dimension. The property "WEIGHTS" will contain the sum of the weights. Only QUBEs are supported presently. This is like the function "total," but skips invalid values.

### Parameters

ds - rank N qube dataset.
dim - zero-based index number.

### Returns:

rank N-1 dataset.

### See Also:

total(QDataSet, int)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reform

reform( QDataSet ds ) → QDataSet

Reshape the dataset to remove the first dimension with length 1, reducing its rank by 1. Dependencies are also preserved. If no indices are found, then the dataset is returned.

### Parameters

ds - rank N dataset

### Returns:

the dataset, or rank N-1 dataset with the first 1-element dimension removed.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reform

reform( QDataSet ds, int nrec, int[] qube ) → QDataSet

allow reform record-by-record, which comes up often.

### Parameters

ds - rank 2 or greater dataset of length nrec.
nrec - number of records in ds
qube - length nn array with the new geometry of each record.

### Returns:

ds of rank nn+1.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## reform

reform( QDataSet ds, int[] qube ) → QDataSet

change the dimensionality of the elements of the QUBE dataset. For example, convert [1,2,3,4,5,6] to [[1,2],[3,4],[5,6]].

### Parameters

ds - dataset
qube - the dimensions of the result dataset.

### Returns:

a new dataset with the specified dimensions, and the properties (e.g. UNITS) of the input dataset.
[search for examples] [view on GitHub] [view on old javadoc] [view source]
reform( Object ds, int[] qube ) → QDataSet [view source]

## removeFill

removeFill( QDataSet ds ) → org.das2.qds.WritableDataSet

remove the fill values from the rank 1 dataset, returning a smaller dataset. This was introduced to support the mash-up dialog.

### Parameters

ds - the dataset, with VALID_MIN, VALID_MAX, or FILL_VALUE indicating the invalid data points.

### Returns:

dataset with the values removed.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## removeIndeces

removeIndeces( QDataSet vv, QDataSet indices ) → QDataSet

remove the data at the indices from the rank 1 dataset. This can be used for example like so:

     ds= ripples(20)
     ds= removeIndeces( ds, where( valid(ds).eq(0) ) )
     print ds.length()

### Parameters

vv - a rank 1 dataset
indices - the indices to remove.

### Returns:

a dataset with the values removed.

### See Also:

https://github.com/autoplot/dev/blob/master/rfe/20190208/demoRemoveIndeces.jy
removeValues(QDataSet, QDataSet) which inserts fill.
where(QDataSet)
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## removeValues

removeValues( QDataSet ds, QDataSet indices ) → org.das2.qds.WritableDataSet

put fill data for these indices

### Parameters

ds - the rank 1 or greater dataset
indices - rank 1 indices when ds is rank 1, or rank 2 [:,m] indices for a rank m dataset.

### Returns:

the dataset with the data at the indices made invalid.

### See Also:

putValues(QDataSet, QDataSet, QDataSet)
where(QDataSet)
removeIndeces(QDataSet, QDataSet) which copies the data to remove the indices.
[search for examples] [view on GitHub] [view on old javadoc] [view source]
removeValues( Object ds, Object indices ) → org.das2.qds.WritableDataSet [view source]

## removeValuesGreaterThan

removeValuesGreaterThan( QDataSet ds, QDataSet v ) → org.das2.qds.WritableDataSet

remove values in the dataset which are greater than the value. This is a convenient method for the common case where we want to filter data by values within the data, introduced to support the data mash up dialog.

### Parameters

ds - rank N dataset
v - the value, a rank 0 scalar or dataset with compatible geometry

### Returns:

the dataset with these
[search for examples] [view on GitHub] [view on old javadoc] [view source]
removeValuesGreaterThan( Object ds, Object v ) → org.das2.qds.WritableDataSet [view source]

## removeValuesLessThan

removeValuesLessThan( QDataSet ds, QDataSet v ) → org.das2.qds.WritableDataSet

remove values in the dataset which are less than the value. This is a convenient method for the common case where we want to filter data by values within the data, introduced to support the data mash up dialog. Note that this inserts fill where data is to be removed.

### Parameters

ds - rank N dataset
v - the value, a rank 0 scalar or dataset with compatible geometry

### Returns:

the dataset with these
[search for examples] [view on GitHub] [view on old javadoc] [view source]
removeValuesLessThan( Object ds, Object v ) → org.das2.qds.WritableDataSet [view source]

## replicate

replicate( short val, int len0 ) → org.das2.qds.WritableDataSet

returns rank 1 dataset with value

### Parameters

val - fill the dataset with this value.
len0 - an int

### Returns:

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

## replicate

replicate( short val, int len0, int len1 ) → org.das2.qds.WritableDataSet

returns rank 2 dataset filled with value

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int

### Returns:

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

## replicate

replicate( short val, int len0, int len1, int len2 ) → org.das2.qds.WritableDataSet

returns rank 3 dataset with filled with value.

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int
len2 - an int

### Returns:

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

## replicate

replicate( int val, int len0 ) → org.das2.qds.WritableDataSet

returns rank 1 dataset with value

### Parameters

val - fill the dataset with this value.
len0 - an int

### Returns:

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

## replicate

replicate( int val, int len0, int len1 ) → org.das2.qds.WritableDataSet

returns rank 2 dataset filled with value

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int

### Returns:

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

## replicate

replicate( int val, int len0, int len1, int len2 ) → org.das2.qds.WritableDataSet

returns rank 3 dataset with filled with value.

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int
len2 - an int

### Returns:

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

## replicate

replicate( long val, int len0 ) → org.das2.qds.WritableDataSet

returns rank 1 dataset with value

### Parameters

val - fill the dataset with this value.
len0 - an int

### Returns:

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

## replicate

replicate( long val, int len0, int len1 ) → org.das2.qds.WritableDataSet

returns rank 2 dataset filled with value

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int

### Returns:

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

## replicate

replicate( long val, int len0, int len1, int len2 ) → org.das2.qds.WritableDataSet

returns rank 3 dataset with filled with value.

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int
len2 - an int

### Returns:

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

## replicate

replicate( double val, int len0 ) → org.das2.qds.WritableDataSet

returns rank 1 dataset with value

### Parameters

val - fill the dataset with this value.
len0 - an int

### Returns:

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

## replicate

replicate( double val, int len0, int len1 ) → org.das2.qds.WritableDataSet

returns rank 2 dataset filled with value

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int

### Returns:

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

## replicate

replicate( double val, int len0, int len1, int len2 ) → org.das2.qds.WritableDataSet

returns rank 3 dataset with filled with value.

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int
len2 - an int

### Returns:

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

## replicate

replicate( double val, int len0, int len1, int len2, int len3 ) → org.das2.qds.WritableDataSet

returns rank 4 dataset with filled with value.

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int
len2 - an int
len3 - an int

### Returns:

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

## replicate

replicate( float val, int len0 ) → org.das2.qds.WritableDataSet

returns rank 1 dataset with value

### Parameters

val - fill the dataset with this value.
len0 - an int

### Returns:

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

## replicate

replicate( float val, int len0, int len1 ) → org.das2.qds.WritableDataSet

returns rank 2 dataset filled with value

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int

### Returns:

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

## replicate

replicate( float val, int len0, int len1, int len2 ) → org.das2.qds.WritableDataSet

returns rank 3 dataset with filled with value.

### Parameters

val - fill the dataset with this value.
len0 - an int
len1 - an int
len2 - an int

### Returns:

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

## replicate

replicate( QDataSet val, int len0 ) → org.das2.qds.MutablePropertyDataSet

returns a rank N+1 dataset by repeating the rank N dataset, so all records will have the same value. E.g. result.value(i,j)= val.value(j)

### Parameters

val - the rank N dataset
len0 - the number of times to repeat

### Returns:

rank N+1 dataset. TODO: support rank N
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## replicate

replicate( QDataSet val, int len0, int len1 ) → org.das2.qds.MutablePropertyDataSet

returns a rank N+2 dataset by repeating the rank N dataset, so all records will have the same value. E.g. result.value(i,j)= val.value()

### Parameters

val - the rank N dataset
len0 - the number of times to repeat
len1 - the length of the second index.

### Returns:

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

## rescale

rescale( QDataSet data, QDataSet min, QDataSet max ) → QDataSet

calculate the range of data, then rescale it so that the smallest values becomes min and the largest values becomes max.

### Parameters

data - rank N dataset
min - rank 0 min
max - rank 0 max

### Returns:

rescaled data.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rescaleRange

rescaleRange( QDataSet dr, double min, double max ) → QDataSet

returns rank 1 QDataSet range relative to range "dr", where 0. is the minimum, and 1. is the maximum. For example rescaleRange(ds,1,2) is scanNext, rescaleRange(ds,0.5,1.5) is zoomOut. This is similar to the DatumRange rescale functions.

### Parameters

dr - a QDataSet with bins and with nonzero width.
min - the new min normalized with respect to this range. 0. is this range's min, 1 is this range's max, -1 is min-width.
max - the new max normalized with respect to this range. 0. is this range's min, 1 is this range's max, -1 is min-width.

### Returns:

new rank 1 QDataSet range.
[search for examples] [view on GitHub] [view on old javadoc] [view source]

## rescaleRangeLogLin

rescaleRangeLogLin( QDataSet dr, double min, double max ) → QDataSet

like rescaleRange, but look at log/lin flag.

### Parameters

dr - a QDataSet
min - a double
max - a double

### Returns:

two-element rank 1 QDataSet

### See Also:

QDataSet#SCALE_TYPE
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## reverse

reverse( QDataSet ds ) → QDataSet

returns the reverse of the rank 1 dataset.

### Parameters

ds - a QDataSet

### Returns:

a QDataSet
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reverse( Object ds ) → QDataSet [view source]

## rgbColorDataset

rgbColorDataset( QDataSet red, QDataSet green, QDataSet blue ) → QDataSet

create a dataset of RGB colors. The output is int(red)*256*256 + int(green)*256 + int(blue) with the units of Units.rgbColor

### Parameters

red - the red component, from 0 to 255
green - the green component, from 0 to 255
blue - the blue component, from 0 to 255

### Returns:

the rgb encoded colors.

### See Also:

toTimeDataSet(QDataSet, QDataSet, QDataSet, QDataSet, QDataSet, QDataSet, QDataSet)
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## ripples

ripples( int len0 ) → QDataSet

rank 1 dataset for demos and testing.

### Parameters

len0 - number of elements in the first index

### Returns:

rank 1 dataset for demos and testing.
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## ripples

ripples( int len0, int len1 ) → QDataSet

rank 2 dataset for demos and testing.

### Parameters

len0 - number of elements in the first index
len1 - number of elements in the second index

### Returns:

rank 2 dataset for demos and testing.
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## ripples

ripples( int len0, int len1, int len2 ) → QDataSet

rank 3 dataset for demos and testing.

### Parameters

len0 - number of elements in the first index
len1 - number of elements in the second index
len2 - number of elements in the third index

### Returns:

rank 3 dataset for demos and testing.
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## ripples

ripples( int len0, int len1, int len2, int len3 ) → QDataSet

rank 4 dataset for demos and testing.

### Parameters

len0 - number of elements in the first index
len1 - number of elements in the second index
len2 - number of elements in the third index
len3 - number of elements in the fourth index

### Returns:

rank 4 dataset for demos and testing.
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## ripplesJoinSpectrogramTimeSeries

ripplesJoinSpectrogramTimeSeries( int len ) → QDataSet

return fake position data for testing result is rank 3 bundle [3,len/3,27*]

### Parameters

len - the total number of records.

### Returns:

an example join spectrogram time series.

### See Also:

Schemes#irregularJoin()
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## ripplesPitchAngleDistribution

ripplesPitchAngleDistribution( ) → QDataSet

return an example of a QDataSet containing a pitch angle distribution. This is a rank 2 dataset with angle in radians for DEPEND_0 and radius for DEPEND_1.

### Returns:

an example pitch angle distribution.
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## ripplesSpectrogramTimeSeries

ripplesSpectrogramTimeSeries( int len ) → QDataSet

return fake position data for testing result is rank 2 bundle [len,27]

### Parameters

len - the number of records

### Returns:

fake position data for testing
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## ripplesTimeSeries

ripplesTimeSeries( int len ) → QDataSet

return fake rank 1 data timeseries for testing

### Parameters

len - number of records

### Returns:

fake rank 1 data timeseries for testing
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## ripplesVectorTimeSeries

ripplesVectorTimeSeries( int len ) → QDataSet

return fake position data for testing. result is rank 2 bundle [len,3]

### Parameters

len - number of records

### Returns:

vector time series.
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## ripplesWaveformTimeSeries

ripplesWaveformTimeSeries( int len ) → QDataSet

return fake waveform data for testing result is rank 2 bundle [len,512]

### Parameters

len - number of 512-element waveforms.

### Returns:

rank 2 waveform
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## round

round( QDataSet ds1 ) → QDataSet

element-wise round function. 0.5 is round up.

### Parameters

ds1 - a QDataSet

### Returns:

a QDataSet
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## round

round( double x ) → double

for Jython, we handle this because the double isn't coerced.

### Parameters

x - a double

### Returns:

a double
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round( Object x ) → QDataSet [view source]

## round

round( QDataSet ds1, int ndigits ) → QDataSet

element-wise round function, which rounds to i decimal places. 0.5 is round up.

### Parameters

ds1 - the dataset.
ndigits - round to this number of digits after the decimal point (e.g. 1=0.1 2=0.01 -1=10)

### Returns:

dataset with the same geometry with each value rounded.
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## round

round( double x, int ndigits ) → double

for Jython, we handle this because the double isn't coerced.

### Parameters

x - the double
ndigits - round to this number of digits after the decimal point (e.g. 1=0.1 2=0.01 -1=10)

### Returns:

the rounded double.
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## round

round( Object ds1, int ndigits ) → QDataSet

element-wise round function, which rounds to i decimal places. 0.5 is round up.

### Parameters

ds1 - the dataset.
ndigits - round to this number of digits after the decimal point (e.g. 1=0.1 2=0.01 -1=10)

### Returns:

dataset with the same geometry with each value rounded.
[search for examples] [view on GitHub] [view on old javadoc] [view source]