org.das2.dataset

Class VectorUtil

java.lang.Object

org.das2.dataset.VectorUtil

public class VectorUtil
extends java.lang.Object

Constructor Summary

Constructors

Constructor and Description
VectorUtil()

Method Summary

Modifier and Type	Method and Description
static int	closestXTag(DataSet ds, Datum datum)
static int	closestXTag(DataSet ds, double x, Units units)
static void	dumpToAsciiStream(VectorDataSet vds, Datum xmin, Datum xmax, java.io.OutputStream out)
static void	dumpToAsciiStream(VectorDataSet vds, java.io.OutputStream out)
static void	dumpToAsciiStream(VectorDataSet vds, java.nio.channels.WritableByteChannel out)
static void	dumpToBinaryStream(VectorDataSet vds, java.io.OutputStream out)
static void	dumpToDas2Stream(VectorDataSet vds, java.nio.channels.WritableByteChannel out, boolean asciiTransferTypes, boolean sendStreamDescriptor) write the data to a das2Stream
static VectorDataSet	finiteDerivative(VectorDataSet ds, int n) Return the finite difference derivative of the dataset, between elements that are n steps apart.
static double[]	getXTagArrayDouble(DataSet vds, Units units)
static Datum	median(VectorDataSet ds)
static QDataSet	reduce2D(QDataSet xds, QDataSet ds, int start, int finish, Datum xLimit, Datum yLimit) produce a simpler version of the dataset by averaging adjacent data.
static java.lang.String	toString(VectorDataSet ds)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

VectorUtil

public VectorUtil()

Method Detail

getXTagArrayDouble

public static double[] getXTagArrayDouble(DataSet vds,
                                          Units units)

closestXTag

public static int closestXTag(DataSet ds,
                              Datum datum)

closestXTag

public static int closestXTag(DataSet ds,
                              double x,
                              Units units)

median

public static Datum median(VectorDataSet ds)

dumpToAsciiStream

public static void dumpToAsciiStream(VectorDataSet vds,
                                     Datum xmin,
                                     Datum xmax,
                                     java.io.OutputStream out)

dumpToAsciiStream

public static void dumpToAsciiStream(VectorDataSet vds,
                                     java.io.OutputStream out)

dumpToAsciiStream

public static void dumpToAsciiStream(VectorDataSet vds,
                                     java.nio.channels.WritableByteChannel out)

dumpToBinaryStream

public static void dumpToBinaryStream(VectorDataSet vds,
                                      java.io.OutputStream out)

dumpToDas2Stream

public static void dumpToDas2Stream(VectorDataSet vds,
                                    java.nio.channels.WritableByteChannel out,
                                    boolean asciiTransferTypes,
                                    boolean sendStreamDescriptor)

write the data to a das2Stream

Parameters:

vds -

out -

asciiTransferTypes -

sendStreamDescriptor - if false, then don't send the stream and don't close

toString

public static java.lang.String toString(VectorDataSet ds)

finiteDerivative

public static VectorDataSet finiteDerivative(VectorDataSet ds,
                                             int n)

Return the finite difference derivative of the dataset, between elements that are n steps apart. Because we don't have a general-purpose way to divide units, the units returned are dimensionless.

reduce2D

public static QDataSet reduce2D(QDataSet xds,
                                QDataSet ds,
                                int start,
                                int finish,
                                Datum xLimit,
                                Datum yLimit)

produce a simpler version of the dataset by averaging adjacent data. code taken from org.das2.graph.GraphUtil.reducePath. Adjacent points are averaged together until a point is found that is not in the bin, and then a new bin is started. The bin's lower bounds are integer multiples of xLimit and yLimit. If yLimit is null, then averaging is done for all points in the x bin, regardless of how close they are in Y. This is similarly true when xLimit is null. xLimit and yLimit are rank 0 datasets, so that they can indicate that binning should be done in log space rather than linear. In this case, a SCALE_TYPE for the dataset should be "log" and its unit should be convertible to Units.logERatio (for example, Units.log10Ratio or Units.percentIncrease). Note when either is log, then averaging is done in the log space.

Parameters:

xds - the x tags

ds - the y tags

start - first index.

finish - last (non-inclusive) index.

xLimit - the size of the bins or null to indicate no limit.

yLimit - the size of the bins or null to indicate no limit.

Returns: