Uses of Interface
visad.Data

Packages that use Data

visad	The core VisAD package, providing support for VisAD's Data & MathType hierarchy, as well as for VisAD Displays and remote collaboration.
visad.bom
visad.cluster
visad.data	Provides for importing data to and exporting data from VisAD.
visad.data.amanda
visad.data.bio	Provides data forms for handling common microscopy formats.
visad.data.dods	Supports read-only access to datasets on DODS servers by importing such datasets as VisAD data objects.
visad.data.fits	Provides for importing a FITS dataset into VisAD.
visad.data.gif	Provides for importing GIF, JPEG and PNG files into VisAD.
visad.data.gis
visad.data.hdf5
visad.data.hdfeos	Provides for importing an HDF-EOS dataset into VisAD.
visad.data.hrit
visad.data.jai
visad.data.mcidas	Provides for importing McIDAS AREA files and McIDAS base map (OUTL) files into VisAD.
visad.data.netcdf	Provides for importing a netCDF dataset into VisAD and for exporting a VisAD data object to a netCDF dataset.
visad.data.netcdf.in	Provides for importing a netCDF dataset into VisAD.
visad.data.netcdf.out	Provides for exporting a VisAD data object to a netCDF dataset.
visad.data.text
visad.data.tiff
visad.data.vis5d	Provides for importing a Vis5D dataset into VisAD.
visad.data.visad	Provides for importing and exporting serialized Java object files into and out of VisAD.
visad.data.visad.object
visad.formula	Provides an interface for automatically evaluating formulas based on user-defined operators and functions.
visad.georef	Provides classes for geo-referencing.
visad.java2d	Provides support for two-dimensional VisAD Displays using Java2D.
visad.java3d	Provides support for two- and three-dimensional VisAD Displays using Java3D.
visad.jmet
visad.math
visad.matrix
visad.meteorology	Provides classes that are useful in the field of meteorology.
visad.python
visad.sounder
visad.ss	Provides a spreadsheet user interface for VisAD that can import data from any form VisAD supports and compute new data objects using formulas by utilizing the visad.formula package.
visad.util	Provides a collection of useful utilities, many of them GUI widgets, to aid in VisAD application design.

Uses of Data in visad

Subinterfaces of Data in visad

interface	Field Field is the VisAD interface for finite samplings of functions from R^n to a range type, where n>0.
interface	FlatFieldIface FlatField is the VisAD class for finite samplings of functions whose range type and range coordinate systems are simple enough to allow efficient representation.
interface	Function Function is the interface for approximate implmentations of mathematical function.
interface	Gridded1DSetIface Gridded1DSetIface is the interface to a finite set of samples of R.
interface	GriddedDoubleSet GriddedDoubleSet is an interface for GriddedSets that have double-precision samples rather than single-precision samples.
interface	GriddedSetIface GriddedSetIface is the interface to a finite set of samples of R^n.
interface	RealIface Interface to scalar data for real numbers represented as double precision floating point values.
interface	RealTupleIface Interface to the VisAD data class for vectors in R^n for n>0.
interface	RemoteData RemoteData is the interface for Remote VisAD data objects.
interface	RemoteField RemoteField is the interface for Remote VisAD Field-s.
interface	RemoteFlatField RemoteFlatField is the interface for Remote VisAD FlatField-s.
interface	RemoteFunction RemoteFunction is the interface for Remote VisAD Function-s.
interface	RemoteTupleIface RemoteTupleIface is the interface for Remote VisAD TupleIface-s.
interface	SampledSetIface Interface to the abstract superclass of GriddedSet-s and the like.
interface	ScalarIface Interface to the VisAD hierarchy of scalar data.
interface	SetIface Interface to the abstract superclass of the VisAD hierarchy of sets.
interface	SimpleSetIface Interface to the abstract superclass of Sets with a unique ManifoldDimension.
interface	TupleIface TupleIface is the VisAD data interface for vectors.

Classes in visad that implement Data

class	DataImpl DataImpl is the superclass for VisAD's data hierarchy, inheriting the Data interface.
class	DateTime DateTime is a class of objects for holding date and time information.
class	DoubleSet DoubleSet represents the finite (but large) set of samples of R^dimension made by vectors of IEEE double precision floating point numbers.
class	DoubleStringTuple This provides a LoCal Tuple that can hold numeric and string values without taking the hit that having slots and lots of Real and Text objects around.
class	DoubleTuple This provides a LoCal RealTuple that can hold numeric values without taking the hit that having lots and lots of Real objects around.
class	FieldImpl FieldImpl is the VisAD class for finite samplings of functions from R^n to a range type, where n>0.
class	FlatField FlatField is the VisAD class for finite samplings of functions whose range type and range coordinate systems are simple enough to allow efficient representation.
class	FloatSet FloatSet represents the finite (but large) set of samples of R^dimension made by vectors of IEEE single precision floating point numbers.
class	FunctionImpl FunctionImpl is the abstract superclass for approximate implmentations of mathematical functions.
class	Gridded1DDoubleSet Gridded1DDoubleSet is a Gridded1DSet with double-precision samples.
class	Gridded1DSet Gridded1DSet represents a finite set of samples of R.
class	Gridded2DDoubleSet Gridded2DDoubleSet is a Gridded2DSet with double-precision samples.
class	Gridded2DSet Gridded2DSet represents a finite set of samples of R^2.
class	Gridded3DDoubleSet Gridded3DDoubleSet is a Gridded3DSet with double-precision samples.
class	Gridded3DSet Gridded3DSet represents a finite set of samples of R^3.
class	GriddedSet GriddedSet is implemented by those Set sub-classes whose samples lie on a rectangular grid topology (but note the geometry need not be rectangular).
class	ImageFlatField ImageFlatField is a VisAD FlatField backed by a java.awt.image.BufferedImage object, instead of the usual float[][] or double[][] samples array.
class	Integer1DSet Integer1DSet represents a finite set of samples of R at an integer lattice based at the origin (i.e, 0, 1, 2, ..., length-1).
class	Integer2DSet Integer2DSet represents a finite set of samples of R^2 at an integer lattice based at the origin.
class	Integer3DSet Integer3DSet represents a finite set of samples of R^3 at an integer lattice based at the origin.
class	IntegerNDSet IntegerNDSet represents a finite set of samples of R^n at an integer lattice based at the origin.
class	Irregular1DSet Irregular1DSet represents a finite set of samples of R.
class	Irregular2DSet IrregularSet for a finite number of samples of R.
class	Irregular3DSet Irregular3DSet represents a finite set of samples of R^3.
class	IrregularSet IrregularSet is implemented by those Set sub-classes whose samples do not form any ordered pattern.
class	Linear1DSet Linear1DSet represents a finite set of samples of R in an arithmetic progression.
class	Linear2DSet Linear2DSet represents a finite set of samples of R^2 in a cross product of two arithmetic progressions.
class	Linear3DSet Linear3DSet represents a finite set of samples of R^3 in a cross product of three arithmetic progressions.
class	LinearLatLonSet LinearLatLonSet represents a finite set of samples of (Latitude, Longitude) in a cross product of two arithmetic progressions.
class	LinearNDSet LinearNDSet represents a finite set of samples of R^Dimension in a cross product of arithmetic progressions.
class	List1DDoubleSet List1DDoubleSet is the class for Set-s containing lists of 1-D double values with no topology.
class	List1DSet List1DSet is the class for Set-s containing lists of 1-D values with no topology.
class	ProductSet ProductSet is the cross-product of an array of SampledSets.
class	Real Real is the class of VisAD scalar data for real numbers represented as double precision floating point values.
class	RealTuple RealTuple is the VisAD data class for vectors in R^n for n>0.
class	RemoteDataImpl RemoteDataImpl is the VisAD remote adapter for DataImpl.
class	RemoteFieldImpl RemoteFieldImpl is the VisAD remote adapter for FieldImpl.
class	RemoteFlatFieldImpl RemoteFlatFieldImpl is the VisAD remote adapter for FlatField.
class	RemoteFunctionImpl RemoteFunctionImpl is the VisAD remote adapter for FieldImpl.
class	SampledSet SampledSet is the abstract superclass of GriddedSets, PolyCells and MultiCells.
class	Scalar Scalar is the superclass of the VisAD hierarchy of scalar data.
class	Set Set is the abstract superclass of the VisAD hierarchy of sets.
class	SimpleSet SimpleSet is the abstract superclass of Sets with a unique ManifoldDimension.
class	SingletonSet SingletonSet is the class for Set-s containing one member.
class	Text Text is the class of VisAD scalar data for text strings.
class	Tuple Tuple is the general VisAD data class for vectors.
class	UnionSet UnionSet is the union of an array of SampledSets.

Methods in visad that return Data

Data	DataImpl.__add__(Data data) A wrapper around add for JPython
Data	DataImpl.__add__(double data) A wrapper around __add__ for JPython
Data	DataImpl.__div__(Data data) A wrapper around divide for JPython
Data	DataImpl.__div__(double data) A wrapper around __div__ for JPython
Data	Tuple.__getitem__(int index) A wrapper around getComponent for JPython.
Data	Set.__getitem__(int index) for JPython
Data	FieldImpl.__getitem__(int index) A wrapper around getSample for JPython.
Data	FunctionImpl.__getitem__(Real domain) A wrapper around evaluate for JPython.
Data	FunctionImpl.__getitem__(RealTuple domain) A wrapper around evaluate for JPython.
Data	DataImpl.__mod__(Data data) A wrapper around remainder for JPython
Data	DataImpl.__mod__(double data) A wrapper around __mod__ for JPython
Data	DataImpl.__mul__(Data data) A wrapper around multiply for JPython
Data	DataImpl.__mul__(double data) A wrapper around __mul__ for JPython
Data	DataImpl.__neg__() A wrapper around negate for JPython
Data	DataImpl.__pow__(Data data) A wrapper around pow for JPython
Data	DataImpl.__pow__(double data) A wrapper around __pow__ for JPython For low powers, do the multiply directly to preserve units.
Data	DataImpl.__radd__(double data) A wrapper around __add__ for JPython
Data	DataImpl.__rdiv__(double data) A wrapper around __div__ for JPython
Data	DataImpl.__rmod__(double data) A wrapper around __mod__ for JPython
Data	DataImpl.__rmul__(double data) A wrapper around __mul__ for JPython
Data	DataImpl.__rpow__(double data) A wrapper around __pow__ for JPython
Data	DataImpl.__rsub__(double data) A wrapper around __sub__ for JPython
Data	DataImpl.__sub__(Data data) A wrapper around subtract for JPython
Data	DataImpl.__sub__(double data) A wrapper around __sub__ for JPython
Data	RemoteDataImpl.abs() call unary() to take the absolute value of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.abs() call unary() to take the absolute value of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.abs() call unary() to take the absolute value of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.abs(int sampling_mode, int error_mode) call unary() to take the absolute value of this
Data	DataImpl.abs(int sampling_mode, int error_mode) call unary() to take the absolute value of this
Data	Data.abs(int sampling_mode, int error_mode) call unary() to take the absolute value of this
Data	RemoteDataImpl.acos() call unary() to take the arccos of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.acos() call unary() to take the arccos of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.acos() call unary() to take the arccos of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.acos(int sampling_mode, int error_mode) call unary() to take the arccos of this producing radian Units
Data	DataImpl.acos(int sampling_mode, int error_mode) call unary() to take the arccos of this producing radian Units
Data	Data.acos(int sampling_mode, int error_mode) call unary() to take the arccos of this producing radian Units
Data	RemoteDataImpl.acosDegrees() call unary() to take the arccos of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.acosDegrees() call unary() to take the arccos of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.acosDegrees() call unary() to take the arccos of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.acosDegrees(int sampling_mode, int error_mode) call unary() to take the arccos of this producing degree Units
Data	DataImpl.acosDegrees(int sampling_mode, int error_mode) call unary() to take the arccos of this producing degree Units
Data	Data.acosDegrees(int sampling_mode, int error_mode) call unary() to take the arccos of this producing degree Units
Data	RemoteDataImpl.add(Data data) call binary() to add data to this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.add(Data data) call binary() to add data to this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.add(Data data) call binary() to add data to this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.add(Data data, int sampling_mode, int error_mode) call binary() to add data to this
Data	DataImpl.add(Data data, int sampling_mode, int error_mode) call binary() to add data to this
Data	Data.add(Data data, int sampling_mode, int error_mode) call binary() to add data to this
Data	TupleIface.adjustSamplingError(Data error, int error_mode) return a Tuple that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	Tuple.adjustSamplingError(Data error, int error_mode) return a Tuple that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	RemoteDataImpl.adjustSamplingError(Data error, int error_mode) return a clone of this, except with ErrorEstimates combined with values in error, according to error_mode
Data	RealIface.adjustSamplingError(Data error, int error_mode) Returns a clone, except that the ErrorEstimate of the clone is adjusted for a given error mode and uncertainty.
Data	Real.adjustSamplingError(Data error, int error_mode) return a Real that clones this, except its ErrorEstimate is adjusted for the sampling error in error
Data	FlatField.adjustSamplingError(Data error, int error_mode) return a FlatField that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	FieldImpl.adjustSamplingError(Data error, int error_mode) return a Field that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	DataImpl.adjustSamplingError(Data error, int error_mode) return a clone of this, except with ErrorEstimates combined with values in error, according to error_mode
Data	Data.adjustSamplingError(Data error, int error_mode) return a clone of this, except with ErrorEstimates combined with values in error, according to error_mode
Data	RemoteDataImpl.asin() call unary() to take the arcsin of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.asin() call unary() to take the arcsin of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.asin() call unary() to take the arcsin of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.asin(int sampling_mode, int error_mode) call unary() to take the arcsin of this producing radian Units
Data	DataImpl.asin(int sampling_mode, int error_mode) call unary() to take the arcsin of this producing radian Units
Data	Data.asin(int sampling_mode, int error_mode) call unary() to take the arcsin of this producing radian Units
Data	RemoteDataImpl.asinDegrees() call unary() to take the arcsin of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.asinDegrees() call unary() to take the arcsin of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.asinDegrees() call unary() to take the arcsin of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.asinDegrees(int sampling_mode, int error_mode) call unary() to take the arcsin of this producing degree Units
Data	DataImpl.asinDegrees(int sampling_mode, int error_mode) call unary() to take the arcsin of this producing degree Units
Data	Data.asinDegrees(int sampling_mode, int error_mode) call unary() to take the arcsin of this producing degree Units
Data	RemoteDataImpl.atan() call unary() to take the arctan of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.atan() call unary() to take the arctan of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.atan() call unary() to take the arctan of this producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.atan(int sampling_mode, int error_mode) call unary() to take the arctan of this producing radian Units
Data	DataImpl.atan(int sampling_mode, int error_mode) call unary() to take the arctan of this producing radian Units
Data	Data.atan(int sampling_mode, int error_mode) call unary() to take the arctan of this producing radian Units
Data	RemoteDataImpl.atan2(Data data) call binary() to take the atan of this by data producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.atan2(Data data) call binary() to take the atan of this by data producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.atan2(Data data) call binary() to take the atan of this by data producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.atan2(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing radian Units
Data	DataImpl.atan2(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing radian Units
Data	Data.atan2(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing radian Units
Data	RemoteDataImpl.atan2Degrees(Data data) call binary() to take the atan of this by data producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.atan2Degrees(Data data) call binary() to take the atan of this by data producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.atan2Degrees(Data data) call binary() to take the atan of this by data producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.atan2Degrees(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing degree Units
Data	DataImpl.atan2Degrees(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing degree Units
Data	Data.atan2Degrees(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing degree Units
Data	RemoteDataImpl.atanDegrees() call unary() to take the arctan of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.atanDegrees() call unary() to take the arctan of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.atanDegrees() call unary() to take the arctan of this producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.atanDegrees(int sampling_mode, int error_mode) call unary() to take the arctan of this producing degree Units
Data	DataImpl.atanDegrees(int sampling_mode, int error_mode) call unary() to take the arctan of this producing degree Units
Data	Data.atanDegrees(int sampling_mode, int error_mode) call unary() to take the arctan of this producing degree Units
Data	Text.binary(Data data, int op, int sampling_mode, int error_mode)
Data	RemoteDataImpl.binary(Data data, int op, int sampling_mode, int error_mode) Pointwise binary operation between this (AdaptedData) and data.
Data	DataImpl.binary(Data data, int op, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	Data.binary(Data data, int op, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	TupleIface.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	Tuple.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteDataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Pointwise binary operation between this (AdaptedData) and data.
Data	RealTuple.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	Real.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	FlatField.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Return new Field with value 'this op data'.
Data	FieldImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) return new Field with value 'this op data'; test for various relations between types of this and data
Data	DataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	Data.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	RemoteDataImpl.ceil() call unary() to take the ceiling of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.ceil() call unary() to take the ceiling of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.ceil() call unary() to take the ceiling of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.ceil(int sampling_mode, int error_mode) call unary() to take the ceiling of this
Data	DataImpl.ceil(int sampling_mode, int error_mode) call unary() to take the ceiling of this
Data	Data.ceil(int sampling_mode, int error_mode) call unary() to take the ceiling of this
Data	RemoteDataImpl.changeMathType(MathType new_type) call unary() to clone this except with a new MathType
Data	DataImpl.changeMathType(MathType new_type) call unary() to clone this except with a new MathType
Data	Data.changeMathType(MathType new_type) call unary() to clone this except with a new MathType
Data	RemoteDataImpl.cos() call unary() to take the cos of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.cos() call unary() to take the cos of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.cos() call unary() to take the cos of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.cos(int sampling_mode, int error_mode) call unary() to take the cos of this assuming radian Units unless this actual Units are degrees
Data	DataImpl.cos(int sampling_mode, int error_mode) call unary() to take the cos of this assuming radian Units unless this actual Units are degrees
Data	Data.cos(int sampling_mode, int error_mode) call unary() to take the cos of this assuming radian Units unless this actual Units are degrees
Data	RemoteDataImpl.cosDegrees() call unary() to take the cos of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.cosDegrees() call unary() to take the cos of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.cosDegrees() call unary() to take the cos of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.cosDegrees(int sampling_mode, int error_mode) call unary() to take the cos of this assuming degree Units unless this actual Units are radians
Data	DataImpl.cosDegrees(int sampling_mode, int error_mode) call unary() to take the cos of this assuming degree Units unless this actual Units are radians
Data	Data.cosDegrees(int sampling_mode, int error_mode) call unary() to take the cos of this assuming degree Units unless this actual Units are radians
Data	RemoteFunctionImpl.derivative(int error_mode)
abstract Data	FunctionImpl.derivative(int error_mode)
Data	Function.derivative(int error_mode) return the tuple of derivatives of this Function with respect to all RealType components of its domain RealTuple; propogate errors according to error_mode
Data	FlatField.derivative(int error_mode)
Data	FieldImpl.derivative(int error_mode)
Data	RemoteFunctionImpl.derivative(MathType[] derivType_s, int error_mode)
abstract Data	FunctionImpl.derivative(MathType[] derivType_s, int error_mode)
Data	Function.derivative(MathType[] derivType_s, int error_mode) return the tuple of derivatives of this Function with respect to all RealType components of its domain RealTuple; set result MathTypes of tuple components to derivType_s; propogate errors according to error_mode
Data	FlatField.derivative(MathType[] derivType_s, int error_mode)
Data	FieldImpl.derivative(MathType[] derivType_s, int error_mode)
Data	RemoteFunctionImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
abstract Data	FunctionImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	Function.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode) return the tuple of derivatives of this Function with respect to the RealTypes in d_partial_s; the RealTypes in d_partial_s may occur in this Function's domain RealTupleType, or, if the domain has a CoordinateSystem, in its Reference RealTupleType; set result MathTypes of tuple components to derivType_s; propogate errors according to error_mode
Data	FlatField.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	FieldImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	RemoteDataImpl.divide(Data data) call binary() to divide this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.divide(Data data) call binary() to divide this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.divide(Data data) call binary() to divide this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.divide(Data data, int sampling_mode, int error_mode) call binary() to divide this by data
Data	DataImpl.divide(Data data, int sampling_mode, int error_mode) call binary() to divide this by data
Data	Data.divide(Data data, int sampling_mode, int error_mode) call binary() to divide this by data
Data	RemoteFunctionImpl.evaluate(Real domain)
Data	FunctionImpl.evaluate(Real domain) Evaluate this Function at domain; use default modes for resampling (Data.WEIGHTED_AVERAGE) and errors (Data.NO_ERRORS)
Data	Function.evaluate(Real domain) Evaluate this Function at domain; for 1-D domains use default modes for resampling (Data.WEIGHTED_AVERAGE) and errors (Data.NO_ERRORS)
Data	RemoteFunctionImpl.evaluate(Real domain, int sampling_mode, int error_mode)
Data	FunctionImpl.evaluate(Real domain, int sampling_mode, int error_mode) Evaluate this Function with non-default modes for resampling and errors
Data	Function.evaluate(Real domain, int sampling_mode, int error_mode) Evaluate this Function, for 1-D domains, with non-default modes for resampling and errors
Data	RemoteFunctionImpl.evaluate(RealTuple domain)
Data	FunctionImpl.evaluate(RealTuple domain) Evaluate this Function at domain; use default modes for resampling (Data.WEIGHTED_AVERAGE) and errors (Data.NO_ERRORS)
Data	Function.evaluate(RealTuple domain) Evaluate this Function at domain; use default modes for resampling (Data.WEIGHTED_AVERAGE) and errors (Data.NO_ERRORS)
Data	RemoteFunctionImpl.evaluate(RealTuple domain, int sampling_mode, int error_mode)
Data	FunctionImpl.evaluate(RealTuple domain, int sampling_mode, int error_mode) Evaluate this Function with non-default modes for resampling and errors
Data	Function.evaluate(RealTuple domain, int sampling_mode, int error_mode) Evaluate this Function with non-default modes for resampling and errors
Data	RemoteDataImpl.exp() call unary() to take the exponent of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.exp() call unary() to take the exponent of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.exp() call unary() to take the exponent of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.exp(int sampling_mode, int error_mode) call unary() to take the exponent of this
Data	DataImpl.exp(int sampling_mode, int error_mode) call unary() to take the exponent of this
Data	Data.exp(int sampling_mode, int error_mode) call unary() to take the exponent of this
Data	RemoteDataImpl.floor() call unary() to take the floor of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.floor() call unary() to take the floor of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.floor() call unary() to take the floor of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.floor(int sampling_mode, int error_mode) call unary() to take the floor of this
Data	DataImpl.floor(int sampling_mode, int error_mode) call unary() to take the floor of this
Data	Data.floor(int sampling_mode, int error_mode) call unary() to take the floor of this
Data	TupleIface.getComponent(int i) return component for i between 0 and getDimension() - 1
Data	Tuple.getComponent(int i) Returns a component of this instance.
Data	DoubleTuple.getComponent(int i) Get the i'th component.
Data	DoubleStringTuple.getComponent(int i) Get the i'th component.
Data[]	Tuple.getComponents() Returns the components that constitute this instance.
Data[]	Tuple.getComponents(boolean copy) Returns the components that constitute this instance.
Data[]	DoubleTuple.getComponents(boolean copy) Create, if needed, and return the component array.
Data[]	DoubleStringTuple.getComponents(boolean copy) Create, if needed, and return the component array.
Data	ShadowType.getData()
Data	RemoteDataReferenceImpl.getData() return referenced Data object, but if Data is a FieldImpl return a RemoteFieldImpl referencing Data to avoid copying entire FieldImpl between JVMs
Data	DataReferenceImpl.getData()
Data	DataReference.getData()
Data	DataDisplayLink.getData()
Data	RemoteFieldImpl.getSample(int index)
Data	ImageFlatField.getSample(int index)
Data	FlatField.getSample(int index) Get the range value at the index-th sample.
Data	FieldImpl.getSample(int index) Get the range value at the index-th sample.
Data	Field.getSample(int index) get the range value at the index-th sample
Data	FlatField.getSample(int index, boolean metadataOnly) A stub routine which simply invokes getSample to override FieldImpl.getSample
Data	FieldImpl.getSample(int index, boolean metadataOnly) Get the metadata for the range value at the index-th sample.
Data	RemoteDataImpl.log() call unary() to take the log of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.log() call unary() to take the log of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.log() call unary() to take the log of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.log(int sampling_mode, int error_mode) call unary() to take the log of this
Data	DataImpl.log(int sampling_mode, int error_mode) call unary() to take the log of this
Data	Data.log(int sampling_mode, int error_mode) call unary() to take the log of this
Data	RemoteDataImpl.max(Data data) call binary() to take the max of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.max(Data data) call binary() to take the max of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.max(Data data) call binary() to take the max of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.max(Data data, int sampling_mode, int error_mode) call binary() to take the max of this and data
Data	DataImpl.max(Data data, int sampling_mode, int error_mode) call binary() to take the max of this and data
Data	Data.max(Data data, int sampling_mode, int error_mode) call binary() to take the max of this and data
Data	RemoteDataImpl.min(Data data) call binary() to take the min of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.min(Data data) call binary() to take the min of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.min(Data data) call binary() to take the min of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.min(Data data, int sampling_mode, int error_mode) call binary() to take the min of this and data
Data	DataImpl.min(Data data, int sampling_mode, int error_mode) call binary() to take the min of this and data
Data	Data.min(Data data, int sampling_mode, int error_mode) call binary() to take the min of this and data
Data	TupleType.missingData()
Data	TextType.missingData()
Data	SetType.missingData()
Data	RealType.missingData()
Data	RealTupleType.missingData()
abstract Data	MathType.missingData() returns a missing Data object for any MathType
Data	FunctionType.missingData()
Data	RemoteDataImpl.multiply(Data data) call binary() to multiply this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.multiply(Data data) call binary() to multiply this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.multiply(Data data) call binary() to multiply this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.multiply(Data data, int sampling_mode, int error_mode) call binary() to multiply this by data
Data	DataImpl.multiply(Data data, int sampling_mode, int error_mode) call binary() to multiply this by data
Data	Data.multiply(Data data, int sampling_mode, int error_mode) call binary() to multiply this by data
Data	RemoteDataImpl.negate() call unary() to negate this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.negate() call unary() to negate this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.negate() call unary() to negate this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.negate(int sampling_mode, int error_mode) call unary() to negate this
Data	DataImpl.negate(int sampling_mode, int error_mode) call unary() to negate this
Data	Data.negate(int sampling_mode, int error_mode) call unary() to negate this
Data	RemoteDataImpl.pow(Data data) call binary() to raise this to data power, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.pow(Data data) call binary() to raise this to data power, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.pow(Data data) call binary() to raise this to data power, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.pow(Data data, int sampling_mode, int error_mode) call binary() to raise this to data power
Data	DataImpl.pow(Data data, int sampling_mode, int error_mode) call binary() to raise this to data power
Data	Data.pow(Data data, int sampling_mode, int error_mode) call binary() to raise this to data power
Data	RemoteDataImpl.remainder(Data data) call binary() to take the remainder of this divided by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.remainder(Data data) call binary() to take the remainder of this divided by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.remainder(Data data) call binary() to take the remainder of this divided by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.remainder(Data data, int sampling_mode, int error_mode) call binary() to take the remainder of this divided by data
Data	DataImpl.remainder(Data data, int sampling_mode, int error_mode) call binary() to take the remainder of this divided by data
Data	Data.remainder(Data data, int sampling_mode, int error_mode) call binary() to take the remainder of this divided by data
Data	RemoteDataImpl.rint() call unary() to take the rint (essentially round) of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.rint() call unary() to take the rint (essentially round) of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.rint() call unary() to take the rint (essentially round) of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.rint(int sampling_mode, int error_mode) call unary() to take the rint (essentially round) of this
Data	DataImpl.rint(int sampling_mode, int error_mode) call unary() to take the rint (essentially round) of this
Data	Data.rint(int sampling_mode, int error_mode) call unary() to take the rint (essentially round) of this
Data	RemoteDataImpl.round() call unary() to take the round of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.round() call unary() to take the round of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.round() call unary() to take the round of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.round(int sampling_mode, int error_mode) call unary() to take the round of this
Data	DataImpl.round(int sampling_mode, int error_mode) call unary() to take the round of this
Data	Data.round(int sampling_mode, int error_mode) call unary() to take the round of this
Data	RemoteDataImpl.sin() call unary() to take the sin of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.sin() call unary() to take the sin of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.sin() call unary() to take the sin of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.sin(int sampling_mode, int error_mode) call unary() to take the sin of this assuming radian Units unless this actual Units are degrees
Data	DataImpl.sin(int sampling_mode, int error_mode) call unary() to take the sin of this assuming radian Units unless this actual Units are degrees
Data	Data.sin(int sampling_mode, int error_mode) call unary() to take the sin of this assuming radian Units unless this actual Units are degrees
Data	RemoteDataImpl.sinDegrees() call unary() to take the sin of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.sinDegrees() call unary() to take the sin of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.sinDegrees() call unary() to take the sin of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.sinDegrees(int sampling_mode, int error_mode) call unary() to take the sin of this assuming degree Units unless this actual Units are radians
Data	DataImpl.sinDegrees(int sampling_mode, int error_mode) call unary() to take the sin of this assuming degree Units unless this actual Units are radians
Data	Data.sinDegrees(int sampling_mode, int error_mode) call unary() to take the sin of this assuming degree Units unless this actual Units are radians
Data	RemoteDataImpl.sqrt() call unary() to take the square root of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.sqrt() call unary() to take the square root of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.sqrt() call unary() to take the square root of this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.sqrt(int sampling_mode, int error_mode) call unary() to take the square root of this
Data	DataImpl.sqrt(int sampling_mode, int error_mode) call unary() to take the square root of this
Data	Data.sqrt(int sampling_mode, int error_mode) call unary() to take the square root of this
Data	RemoteDataImpl.subtract(Data data) call binary() to subtract data from this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.subtract(Data data) call binary() to subtract data from this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.subtract(Data data) call binary() to subtract data from this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.subtract(Data data, int sampling_mode, int error_mode) call binary() to subtract data from this
Data	DataImpl.subtract(Data data, int sampling_mode, int error_mode) call binary() to subtract data from this
Data	Data.subtract(Data data, int sampling_mode, int error_mode) call binary() to subtract data from this
Data	RemoteDataImpl.tan() call unary() to take the tan of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.tan() call unary() to take the tan of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.tan() call unary() to take the tan of this assuming radian Units unless this actual Units are degrees, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.tan(int sampling_mode, int error_mode) call unary() to take the tan of this assuming radian Units unless this actual Units are degrees
Data	DataImpl.tan(int sampling_mode, int error_mode) call unary() to take the tan of this assuming radian Units unless this actual Units are degrees
Data	Data.tan(int sampling_mode, int error_mode) call unary() to take the tan of this assuming radian Units unless this actual Units are degrees
Data	RemoteDataImpl.tanDegrees() call unary() to take the tan of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.tanDegrees() call unary() to take the tan of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.tanDegrees() call unary() to take the tan of this assuming degree Units unless this actual Units are radians, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.tanDegrees(int sampling_mode, int error_mode) call unary() to take the tan of this assuming degree Units unless this actual Units are radians
Data	DataImpl.tanDegrees(int sampling_mode, int error_mode) call unary() to take the tan of this assuming degree Units unless this actual Units are radians
Data	Data.tanDegrees(int sampling_mode, int error_mode) call unary() to take the tan of this assuming degree Units unless this actual Units are radians
Data	Text.unary(int op, int sampling_mode, int error_mode)
Data	RemoteDataImpl.unary(int op, int sampling_mode, int error_mode) Pointwise unary operation applied to this (AdaptedData).
Data	DataImpl.unary(int op, int sampling_mode, int error_mode) Pointwise unary operation applied to this.
Data	Data.unary(int op, int sampling_mode, int error_mode) Pointwise unary operation applied to this.
Data	TupleIface.unary(int op, MathType new_type, int sampling_mode, int error_mode)
Data	Tuple.unary(int op, MathType new_type, int sampling_mode, int error_mode)
Data	Set.unary(int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteDataImpl.unary(int op, MathType new_type, int sampling_mode, int error_mode) Pointwise unary operation applied to this (AdaptedData).
Data	RealTuple.unary(int op, MathType new_type, int sampling_mode, int error_mode)
Data	Real.unary(int op, MathType new_type, int sampling_mode, int error_mode) unary function on a Real; override some trig functions based on Unit; transcental functions destroy dimensionfull Unit
Data	FlatField.unary(int op, MathType new_type, int sampling_mode, int error_mode) Return new FlatField with value 'this op'.
Data	FieldImpl.unary(int op, MathType new_type, int sampling_mode, int error_mode) return new Field with value 'op this'
Data	DataImpl.unary(int op, MathType new_type, int sampling_mode, int error_mode) Pointwise unary operation applied to this.
Data	Data.unary(int op, MathType new_type, int sampling_mode, int error_mode) Pointwise unary operation applied to this.

Methods in visad with parameters of type Data

Data	DataImpl.__add__(Data data) A wrapper around add for JPython
Data	DataImpl.__div__(Data data) A wrapper around divide for JPython
Data	DataImpl.__mod__(Data data) A wrapper around remainder for JPython
Data	DataImpl.__mul__(Data data) A wrapper around multiply for JPython
Data	DataImpl.__pow__(Data data) A wrapper around pow for JPython
void	FieldImpl.__setitem__(int index, Data data) A wrapper around setSample for JPython.
Data	DataImpl.__sub__(Data data) A wrapper around subtract for JPython
Data	RemoteDataImpl.add(Data data) call binary() to add data to this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.add(Data data) call binary() to add data to this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.add(Data data) call binary() to add data to this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.add(Data data, int sampling_mode, int error_mode) call binary() to add data to this
Data	DataImpl.add(Data data, int sampling_mode, int error_mode) call binary() to add data to this
Data	Data.add(Data data, int sampling_mode, int error_mode) call binary() to add data to this
Data	TupleIface.adjustSamplingError(Data error, int error_mode) return a Tuple that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	Tuple.adjustSamplingError(Data error, int error_mode) return a Tuple that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	RemoteDataImpl.adjustSamplingError(Data error, int error_mode) return a clone of this, except with ErrorEstimates combined with values in error, according to error_mode
Data	RealIface.adjustSamplingError(Data error, int error_mode) Returns a clone, except that the ErrorEstimate of the clone is adjusted for a given error mode and uncertainty.
Data	Real.adjustSamplingError(Data error, int error_mode) return a Real that clones this, except its ErrorEstimate is adjusted for the sampling error in error
Data	FlatField.adjustSamplingError(Data error, int error_mode) return a FlatField that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	FieldImpl.adjustSamplingError(Data error, int error_mode) return a Field that clones this, except its ErrorEstimate-s are adjusted for sampling errors in error
Data	DataImpl.adjustSamplingError(Data error, int error_mode) return a clone of this, except with ErrorEstimates combined with values in error, according to error_mode
Data	Data.adjustSamplingError(Data error, int error_mode) return a clone of this, except with ErrorEstimates combined with values in error, according to error_mode
Data	RemoteDataImpl.atan2(Data data) call binary() to take the atan of this by data producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.atan2(Data data) call binary() to take the atan of this by data producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.atan2(Data data) call binary() to take the atan of this by data producing radian Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.atan2(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing radian Units
Data	DataImpl.atan2(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing radian Units
Data	Data.atan2(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing radian Units
Data	RemoteDataImpl.atan2Degrees(Data data) call binary() to take the atan of this by data producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.atan2Degrees(Data data) call binary() to take the atan of this by data producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.atan2Degrees(Data data) call binary() to take the atan of this by data producing degree Units, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.atan2Degrees(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing degree Units
Data	DataImpl.atan2Degrees(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing degree Units
Data	Data.atan2Degrees(Data data, int sampling_mode, int error_mode) call binary() to take the atan of this by data producing degree Units
Data	Text.binary(Data data, int op, int sampling_mode, int error_mode)
Data	RemoteDataImpl.binary(Data data, int op, int sampling_mode, int error_mode) Pointwise binary operation between this (AdaptedData) and data.
Data	DataImpl.binary(Data data, int op, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	Data.binary(Data data, int op, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	TupleIface.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	Tuple.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteDataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Pointwise binary operation between this (AdaptedData) and data.
Data	RealTuple.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	Real.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	FlatField.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Return new Field with value 'this op data'.
Data	FieldImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) return new Field with value 'this op data'; test for various relations between types of this and data
Data	DataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
Data	Data.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode) Pointwise binary operation between this and data.
static TupleType	Tuple.buildTupleType(Data[] datums) Make a TupleType for an array of Data
DataShadow	DataRenderer.computeRanges(Data data, ShadowType type, DataShadow shadow) Compute ranges of values for each RealType in DisplayImpl.RealTypeVector.
Data	RemoteDataImpl.divide(Data data) call binary() to divide this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.divide(Data data) call binary() to divide this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.divide(Data data) call binary() to divide this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.divide(Data data, int sampling_mode, int error_mode) call binary() to divide this by data
Data	DataImpl.divide(Data data, int sampling_mode, int error_mode) call binary() to divide this by data
Data	Data.divide(Data data, int sampling_mode, int error_mode) call binary() to divide this by data
boolean	ShadowTupleType.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer, ShadowType shadow_api) transform data into a (Java3D or Java2D) scene graph; add generated scene graph components as children of group; group is Group (Java3D) or VisADGroup (Java2D); value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process
boolean	ShadowTextType.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer, ShadowType shadow_api) transform data into a (Java3D or Java2D) scene graph; add generated scene graph components as children of group; group is Group (Java3D) or VisADGroup (Java2D); value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process
boolean	ShadowRealType.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer, ShadowType shadow_api) transform data into a (Java3D or Java2D) scene graph; add generated scene graph components as children of group; group is Group (Java3D) or VisADGroup (Java2D); value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process
boolean	ShadowFunctionOrSetType.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer, ShadowType shadow_api) transform data into a (Java3D or Java2D) scene graph; add generated scene graph components as children of group; group is Group (Java3D) or VisADGroup (Java2D); value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process
static Tuple	Tuple.makeTuple(Data[] datums) Create a Tuple from an array of Data objects.
Data	RemoteDataImpl.max(Data data) call binary() to take the max of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.max(Data data) call binary() to take the max of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.max(Data data) call binary() to take the max of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.max(Data data, int sampling_mode, int error_mode) call binary() to take the max of this and data
Data	DataImpl.max(Data data, int sampling_mode, int error_mode) call binary() to take the max of this and data
Data	Data.max(Data data, int sampling_mode, int error_mode) call binary() to take the max of this and data
Data	RemoteDataImpl.min(Data data) call binary() to take the min of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.min(Data data) call binary() to take the min of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.min(Data data) call binary() to take the min of this and data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.min(Data data, int sampling_mode, int error_mode) call binary() to take the min of this and data
Data	DataImpl.min(Data data, int sampling_mode, int error_mode) call binary() to take the min of this and data
Data	Data.min(Data data, int sampling_mode, int error_mode) call binary() to take the min of this and data
Data	RemoteDataImpl.multiply(Data data) call binary() to multiply this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.multiply(Data data) call binary() to multiply this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.multiply(Data data) call binary() to multiply this by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.multiply(Data data, int sampling_mode, int error_mode) call binary() to multiply this by data
Data	DataImpl.multiply(Data data, int sampling_mode, int error_mode) call binary() to multiply this by data
Data	Data.multiply(Data data, int sampling_mode, int error_mode) call binary() to multiply this by data
Data	RemoteDataImpl.pow(Data data) call binary() to raise this to data power, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.pow(Data data) call binary() to raise this to data power, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.pow(Data data) call binary() to raise this to data power, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.pow(Data data, int sampling_mode, int error_mode) call binary() to raise this to data power
Data	DataImpl.pow(Data data, int sampling_mode, int error_mode) call binary() to raise this to data power
Data	Data.pow(Data data, int sampling_mode, int error_mode) call binary() to raise this to data power
boolean	ShadowType.recurseComponent(int i, Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowType.recurseRange(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
Data	RemoteDataImpl.remainder(Data data) call binary() to take the remainder of this divided by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.remainder(Data data) call binary() to take the remainder of this divided by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.remainder(Data data) call binary() to take the remainder of this divided by data, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.remainder(Data data, int sampling_mode, int error_mode) call binary() to take the remainder of this divided by data
Data	DataImpl.remainder(Data data, int sampling_mode, int error_mode) call binary() to take the remainder of this divided by data
Data	Data.remainder(Data data, int sampling_mode, int error_mode) call binary() to take the remainder of this divided by data
void	RemoteDataReferenceImpl.setData(Data d) set this RemoteDataReferenceImpl to refer to given Data
void	DataReferenceImpl.setData(Data d) Sets the Data object to which this instance refers.
void	DataReference.setData(Data d) set reference to data, replacing any currently referenced Data object; if this is local (i.e., an instance of DataReferenceImpl) then the Data argument must also be local (i.e., an instance of DataImpl); if this is Remote (i.e., an instance of RemoteDataReference) then a local Data argument (i.e., an instance of DataImpl) will be passed by copy and a remote Data argument (i.e., an instance of RemoteData) will be passed by remote reference; invokes d.addReference(DataReference r)
void	RemoteFieldImpl.setSample(int index, Data range)
void	FlatField.setSample(int index, Data range) Set the range value at the index-th sample
void	FieldImpl.setSample(int index, Data range) Set the range value at the index-th sample; makes a local copy
void	Field.setSample(int index, Data range) set the range value at the index-th sample
void	RemoteFieldImpl.setSample(int index, Data range, boolean copy)
void	FlatField.setSample(int index, Data range, boolean copy) Set the range value at the index-th sample
void	FieldImpl.setSample(int index, Data range, boolean copy) Set the range value at the index-th sample
void	Field.setSample(int index, Data range, boolean copy) set the range value at the index-th sample
void	FieldImpl.setSample(int index, Data range, boolean copy, boolean checkRangeType) Set the range value at the index-th sample
void	RemoteFieldImpl.setSample(RealTuple domain, Data range)
void	FieldImpl.setSample(RealTuple domain, Data range)
void	Field.setSample(RealTuple domain, Data range) set the range value at the sample nearest to domain
void	RemoteFieldImpl.setSample(RealTuple domain, Data range, boolean copy)
void	FieldImpl.setSample(RealTuple domain, Data range, boolean copy) set the range value at the sample nearest to domain
void	Field.setSample(RealTuple domain, Data range, boolean copy) set the range value at the sample nearest to domain
void	RemoteFieldImpl.setSamples(Data[] range, boolean copy) methods adapted from Field
void	ImageFlatField.setSamples(Data[] range, boolean copy)
void	FlatField.setSamples(Data[] range, boolean copy) set the range values of the function; the order of range values must be the same as the order of domain indices in the DomainSet; copy argument included for consistency with Field, but ignored
void	FieldImpl.setSamples(Data[] range, boolean copy) Set the range samples of the function; the order of range samples must be the same as the order of domain indices in the DomainSet; copy range objects if copy is true;
void	Field.setSamples(Data[] range, boolean copy) set the range samples of the function; the order of range samples must be the same as the order of domain indices in the DomainSet; copy range objects if copy is true; should use same MathType object in each Data object in range array
void	FieldImpl.setSamples(Data[] range, boolean copy, boolean checkAllRangeTypes) Set the range samples of the function; the order of range samples must be the same as the order of domain indices in the DomainSet; copy range objects if copy is true; should use same MathType object in each Data object in range array
Data	RemoteDataImpl.subtract(Data data) call binary() to subtract data from this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	DataImpl.subtract(Data data) call binary() to subtract data from this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	Data.subtract(Data data) call binary() to subtract data from this, using default modes for sampling (Data.NEAREST_NEIGHBOR) and error estimation (Data.NO_ERRORS)
Data	RemoteDataImpl.subtract(Data data, int sampling_mode, int error_mode) call binary() to subtract data from this
Data	DataImpl.subtract(Data data, int sampling_mode, int error_mode) call binary() to subtract data from this
Data	Data.subtract(Data data, int sampling_mode, int error_mode) call binary() to subtract data from this

Constructors in visad with parameters of type Data

DoubleStringTuple(TupleType type, Data[] prototypes, double[] doubles, String[] strings, Unit[] units)

DoubleTuple(RealTupleType type, Data[] prototypes, double[] doubles, Unit[] units)

Tuple(Data[] datums)
Construct a Tuple object from an array of Data objects; this constructs its MathType from the MathTypes of the data array

Tuple(Data[] datums, boolean copy)
Construct a Tuple object from an array of Data objects; this constructs its MathType from the MathTypes of the data array

Tuple(TupleType type, Data[] datums)
Construct a Tuple object from a type and an array of Data objects

Tuple(TupleType type, Data[] datums, boolean copy)
Construct a Tuple object from a type and an array of Data objects

Tuple(TupleType type, Data[] datums, boolean copy, boolean checkType)
Construct a Tuple object from a type and an array of Data objects

Uses of Data in visad.bom

Methods in visad.bom with parameters of type Data

boolean	ShadowTextureFillSetTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowImageFunctionTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowImageByRefFunctionTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowCurveSetTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) Transform data into a Java3D scene graph.
boolean	ShadowCurveSetTypeJ2D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) Transform data into a Java2D scene graph.
void	ShadowImageByRefFunctionTypeJ3D.makeColorBytes(Data data, ScalarMap cmap, ScalarMap[] cmaps, float constant_alpha, ShadowRealType[] RangeComponents, int color_length, int domain_length, int[] permute, byte[] byteData, int data_width, int data_height, int tile_width, int tile_height, int xStart, int yStart, int texture_width, int texture_height)
void	ShadowImageByRefFunctionTypeJ3D.makeColorBytesDriver(Data imgFlatField, ScalarMap cmap, ScalarMap[] cmaps, float constant_alpha, ShadowRealType[] RangeComponents, int color_length, int domain_length, int[] permute, int data_width, int data_height, int imageType, VisADImageTile tile, int image_index)

Uses of Data in visad.cluster

Subinterfaces of Data in visad.cluster

interface	RemoteClientData RemoteClientData is the interface for cluster client VisAD data objects.
interface	RemoteClientField RemoteClientField is the interface for cluster client VisAD Field data objects that are not partitioned over nodes.
interface	RemoteClientPartitionedField RemoteClientPartitionedField is the interface for cluster client VisAD Field data objects that are partitioned over nodes.
interface	RemoteClientTuple RemoteClientTuple is the interface for cluster client VisAD Tuple data objects.
interface	RemoteClusterData RemoteClusterData is the interface for cluster client and node Data.
interface	RemoteNodeData RemoteNodeData is the interface for cluster node VisAD data objects.
interface	RemoteNodeField RemoteNodeField is the interface for cluster node VisAD Field data objects that are not paritioned.
interface	RemoteNodePartitionedField RemoteNodePartitionedField is the interface for cluster node VisAD Field data objects that are paritioned.
interface	RemoteNodeTuple RemoteNodeTuple is the interface for cluster node VisAD Tuple data objects.

Classes in visad.cluster that implement Data

class	RemoteClientDataImpl RemoteClientData is the class for cluster client VisAD data objects.
class	RemoteClientFieldImpl RemoteClientFieldImpl is the class for cluster client VisAD Field data objects.
class	RemoteClientPartitionedFieldImpl RemoteClientPartitionedFieldImpl is the class for cluster client VisAD Field data objects.
class	RemoteClientTupleImpl RemoteClientTupleImpl is the class for cluster client VisAD Tuple data objects.
class	RemoteClusterDataImpl RemoteClusterDataImpl is the super class for cluster client and node Data.
class	RemoteNodeDataImpl RemoteNodeData is the class for cluster node VisAD data objects.
class	RemoteNodeFieldImpl RemoteNodeFieldImpl is the class for cluster node VisAD Field data objects.
class	RemoteNodePartitionedFieldImpl RemoteNodePartitionedFieldImpl is the class for cluster node VisAD Field data objects that are paritioned.
class	RemoteNodeTupleImpl RemoteNodeTupleImpl is the class for cluster client VisAD Tuple data objects.
class	UserDummyDataImpl RemoteClientData is the class for cluster client VisAD data objects.

Methods in visad.cluster that return Data

Data	RemoteNodeTupleImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteNodePartitionedFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteNodeFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteClientTupleImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteClientPartitionedFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteClientFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	UserDummyDataImpl.binary(Data data, int op, int sampling_mode, int error_mode)
Data	RemoteClientDataImpl.binary(Data data, int op, int sampling_mode, int error_mode)
Data	UserDummyDataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteClientDataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteNodePartitionedFieldImpl.derivative(int error_mode)
Data	RemoteNodeFieldImpl.derivative(int error_mode)
Data	RemoteClientPartitionedFieldImpl.derivative(int error_mode)
Data	RemoteClientFieldImpl.derivative(int error_mode)
Data	RemoteNodePartitionedFieldImpl.derivative(MathType[] derivType_s, int error_mode)
Data	RemoteNodeFieldImpl.derivative(MathType[] derivType_s, int error_mode)
Data	RemoteClientPartitionedFieldImpl.derivative(MathType[] derivType_s, int error_mode)
Data	RemoteClientFieldImpl.derivative(MathType[] derivType_s, int error_mode)
Data	RemoteNodePartitionedFieldImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	RemoteNodeFieldImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	RemoteClientPartitionedFieldImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	RemoteClientFieldImpl.derivative(RealTuple location, RealType[] d_partial_s, MathType[] derivType_s, int error_mode)
Data	RemoteNodePartitionedFieldImpl.evaluate(Real domain)
Data	RemoteNodeFieldImpl.evaluate(Real domain)
Data	RemoteClientPartitionedFieldImpl.evaluate(Real domain)
Data	RemoteClientFieldImpl.evaluate(Real domain)
Data	RemoteNodePartitionedFieldImpl.evaluate(Real domain, int sampling_mode, int error_mode)
Data	RemoteNodeFieldImpl.evaluate(Real domain, int sampling_mode, int error_mode)
Data	RemoteClientPartitionedFieldImpl.evaluate(Real domain, int sampling_mode, int error_mode)
Data	RemoteClientFieldImpl.evaluate(Real domain, int sampling_mode, int error_mode)
Data	RemoteNodePartitionedFieldImpl.evaluate(RealTuple domain)
Data	RemoteNodeFieldImpl.evaluate(RealTuple domain)
Data	RemoteClientPartitionedFieldImpl.evaluate(RealTuple domain)
Data	RemoteClientFieldImpl.evaluate(RealTuple domain)
Data	RemoteNodePartitionedFieldImpl.evaluate(RealTuple domain, int sampling_mode, int error_mode)
Data	RemoteNodeFieldImpl.evaluate(RealTuple domain, int sampling_mode, int error_mode)
Data	RemoteClientPartitionedFieldImpl.evaluate(RealTuple domain, int sampling_mode, int error_mode)
Data	RemoteClientFieldImpl.evaluate(RealTuple domain, int sampling_mode, int error_mode)
Data	RemoteNodeTupleImpl.getComponent(int i)
Data	RemoteClientTupleImpl.getComponent(int i)
Data	RemoteNodePartitionedFieldImpl.getSample(int index)
Data	RemoteNodeFieldImpl.getSample(int index)
Data	RemoteClientPartitionedFieldImpl.getSample(int index)
Data	RemoteClientFieldImpl.getSample(int index)
Data	UserDummyDataImpl.unary(int op, int sampling_mode, int error_mode)
Data	RemoteClientDataImpl.unary(int op, int sampling_mode, int error_mode)
Data	UserDummyDataImpl.unary(int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteClientDataImpl.unary(int op, MathType new_type, int sampling_mode, int error_mode)

Methods in visad.cluster with parameters of type Data

protected String	TestWRFCluster.addData(int id, Data data, ConstantMap[] cmaps, String source, int type, boolean notify) override method from BasicSSCell
protected String	TestSSCluster.addData(int id, Data data, ConstantMap[] cmaps, String source, int type, boolean notify) override method from BasicSSCell
protected String	TestProxyCluster.addData(int id, Data data, ConstantMap[] cmaps, String source, int type, boolean notify) override method from BasicSSCell
Data	RemoteNodeTupleImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteNodePartitionedFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteNodeFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteClientTupleImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteClientPartitionedFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	RemoteClientFieldImpl.adjustSamplingError(Data error, int error_mode)
Data	UserDummyDataImpl.binary(Data data, int op, int sampling_mode, int error_mode)
Data	RemoteClientDataImpl.binary(Data data, int op, int sampling_mode, int error_mode)
Data	UserDummyDataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	RemoteClientDataImpl.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
DataShadow	UserRendererJ3D.computeRanges(Data data, ShadowType type, DataShadow shadow)
DataShadow	ClientRendererJ3D.computeRanges(Data data, ShadowType type, DataShadow shadow)
boolean	ShadowNodeFunctionTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
void	RemoteNodePartitionedFieldImpl.setSample(int index, Data range)
void	RemoteNodeFieldImpl.setSample(int index, Data range)
void	RemoteClientPartitionedFieldImpl.setSample(int index, Data range)
void	RemoteClientFieldImpl.setSample(int index, Data range)
void	RemoteNodePartitionedFieldImpl.setSample(int index, Data range, boolean copy)
void	RemoteNodeFieldImpl.setSample(int index, Data range, boolean copy)
void	RemoteClientPartitionedFieldImpl.setSample(int index, Data range, boolean copy)
void	RemoteClientFieldImpl.setSample(int index, Data range, boolean copy)
void	RemoteNodePartitionedFieldImpl.setSample(RealTuple domain, Data range)
void	RemoteNodeFieldImpl.setSample(RealTuple domain, Data range)
void	RemoteClientPartitionedFieldImpl.setSample(RealTuple domain, Data range)
void	RemoteClientFieldImpl.setSample(RealTuple domain, Data range)
void	RemoteNodePartitionedFieldImpl.setSample(RealTuple domain, Data range, boolean copy)
void	RemoteNodeFieldImpl.setSample(RealTuple domain, Data range, boolean copy)
void	RemoteClientPartitionedFieldImpl.setSample(RealTuple domain, Data range, boolean copy)
void	RemoteClientFieldImpl.setSample(RealTuple domain, Data range, boolean copy)
void	RemoteNodePartitionedFieldImpl.setSamples(Data[] range, boolean copy)
void	RemoteNodeFieldImpl.setSamples(Data[] range, boolean copy)
void	RemoteClientPartitionedFieldImpl.setSamples(Data[] range, boolean copy)
void	RemoteClientFieldImpl.setSamples(Data[] range, boolean copy)

Constructors in visad.cluster with parameters of type Data

RemoteClientTupleImpl(Data[] datums)
must call setupClusterData after constructor to finish the "construction"

RemoteNodeTupleImpl(Data[] datums)
must call setupClusterData after constructor to finish the "construction"

Uses of Data in visad.data

Classes in visad.data that implement Data

class	AreaImageCacheAdapter Adapts a FlatField backed by a AreaImageAccessor to work with a FlatFieldCache.
class	CachedFlatField This is a FloatField that caches to disk its float array.
class	FileField
class	FileFlatField

Methods in visad.data that return Data

Data	FileFlatField.adjustSamplingError(Data error, int error_mode)
Data	AreaImageCacheAdapter.adjustSamplingError(Data error, int error_mode)
Data	FileFlatField.binary(Data data, int op, int sampling_mode, int error_mode)
Data	AreaImageCacheAdapter.binary(Data data, int op, int sampling_mode, int error_mode)
Data	FileFlatField.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	AreaImageCacheAdapter.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	FileFlatField.getSample(int index)
Data	FileField.getSample(int index)
Data	CachedFlatField.getSample(int index) Get the range value at the index-th sample.
Data	AreaImageCacheAdapter.getSample(int index)
Data	FileFlatField.unary(int op, int sampling_mode, int error_mode)
Data	AreaImageCacheAdapter.unary(int op, int sampling_mode, int error_mode)
Data	FileFlatField.unary(int op, MathType new_type, int sampling_mode, int error_mode)
Data	AreaImageCacheAdapter.unary(int op, MathType new_type, int sampling_mode, int error_mode)

Methods in visad.data with parameters of type Data

void	Repository.add(String id, Data data, boolean replace) Add a data object to an existing data object in the repository.
void	FunctionFormFamily.add(String id, Data data, boolean replace) Add data to an existing data object using the first appropriate Form.
abstract void	FormNode.add(String id, Data data, boolean replace) Add data to an existing data object.
void	FormFamily.add(String id, Data data, boolean replace) Add data to an existing data object.
Data	FileFlatField.adjustSamplingError(Data error, int error_mode)
Data	AreaImageCacheAdapter.adjustSamplingError(Data error, int error_mode)
Data	FileFlatField.binary(Data data, int op, int sampling_mode, int error_mode)
Data	AreaImageCacheAdapter.binary(Data data, int op, int sampling_mode, int error_mode)
Data	FileFlatField.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
Data	AreaImageCacheAdapter.binary(Data data, int op, MathType new_type, int sampling_mode, int error_mode)
static DataNode	DataNode.create(Data data) Factory method for creating an instance of the appropriate type.
void	LinkedDataSource.dataChanged(Data data) Update the data to which this LinkedDataSource is linked.
FormNode	Repository.getForms(Data data) Return the forms of data that are both supported by this repository and compatible with a data object.
abstract FormNode	FormNode.getForms(Data data) Return the data forms that are compatible with a data object.
FormNode	FormFamily.getForms(Data data) Return the data forms that are compatible with a data object.
void	EmptyDataProcessor.processTuple(TupleType type, Data[] components, Tuple t, Object token)
void	DataProcessor.processTuple(TupleType type, Data[] components, Tuple t, Object token)
abstract void	BaseDataProcessor.processTuple(TupleType type, Data[] components, Tuple t, Object token)
void	Repository.save(String id, Data data, boolean replace) Save a data object in the first compatible data form.
void	FunctionFormFamily.save(String id, Data data, boolean replace) Save a Data object using the first appropriate Form.
abstract void	FormNode.save(String id, Data data, boolean replace) Save a VisAD data object in this form.
void	FormFamily.save(String id, Data data, boolean replace) Save a VisAD data object.
void	Repository.save(String id, Data data, FormNode form, boolean replace) Save a data object in a particular form.
void	FileFlatField.setSample(int index, Data range)
void	FileField.setSample(int index, Data range)
void	AreaImageCacheAdapter.setSample(int index, Data range)
void	FileFlatField.setSample(int index, Data range, boolean copy)
void	AreaImageCacheAdapter.setSample(int index, Data range, boolean copy)
void	FileFlatField.setSample(RealTuple domain, Data range)
void	AreaImageCacheAdapter.setSample(RealTuple domain, Data range)
abstract void	FileAccessor.writeFile(int[] fileLocations, Data range)

Uses of Data in visad.data.amanda

Fields in visad.data.amanda declared as Data

static Data

Event.missing

Methods in visad.data.amanda that return Data

Data	Tracks.makeData()

Methods in visad.data.amanda with parameters of type Data

void	F2000Form.add(String id, Data data, boolean replace)
FormNode	F2000Form.getForms(Data data)
void	F2000Form.save(String id, Data data, boolean replace)

Uses of Data in visad.data.bio

Methods in visad.data.bio with parameters of type Data

void	LociForm.add(String id, Data data, boolean replace) Adds data to an existing image file.
FormNode	LociForm.getForms(Data data) Returns the data forms that are compatible with a data object.
void	LociForm.save(String id, Data data, boolean replace) Saves a VisAD Data object at the given location.

Uses of Data in visad.data.dods

Methods in visad.data.dods with parameters of type Data

void	DODSForm.add(String id, Data data, boolean replace) Throws an exception.
FormNode	DODSForm.getForms(Data data) Returns null.
void	DODSForm.save(String id, Data data, boolean replace) Throws an exception.
void	VectorAccessor.writeFile(int[] fileLocation, Data range) Throws a VisADError.
void	SequenceVariableAdapter.SequenceAccessor.writeFile(int[] fileLocation, Data range) Throws a VisADError.
void	GridVariableAdapter.GridAccessor.writeFile(int[] fileLocation, Data range) Throws a VisADError.

Uses of Data in visad.data.fits

Methods in visad.data.fits with parameters of type Data

void	FitsForm.add(String id, Data data, boolean replace)
FormNode	FitsForm.getForms(Data data)
void	FitsForm.save(String id, Data data, boolean replace)
void	FitsAdapter.save(String name, Data data, boolean replace)
boolean	TourGuide.show(Data data, Tourist tourist, int depth)

Constructors in visad.data.fits with parameters of type Data

FitsTourGuide(Data data, Tourist tourist)

Uses of Data in visad.data.gif

Methods in visad.data.gif with parameters of type Data

void	GIFForm.add(String id, Data data, boolean replace)
FormNode	GIFForm.getForms(Data data)
void	GIFForm.save(String id, Data data, boolean replace)

Uses of Data in visad.data.gis

Methods in visad.data.gis with parameters of type Data

void	UsgsDemForm.add(String id, Data data, boolean replace) Add data to an existing data object
void	ArcAsciiGridForm.add(String id, Data data, boolean replace) Add data to an existing data object
FormNode	UsgsDemForm.getForms(Data data) Return the data forms that are compatible with a data object
FormNode	ArcAsciiGridForm.getForms(Data data) Return the data forms that are compatible with a data object
void	UsgsDemForm.save(String id, Data data, boolean replace) Save a VisAD data object in this form
void	ArcAsciiGridForm.save(String id, Data data, boolean replace) Save a VisAD data object in this form

Uses of Data in visad.data.hdf5

Methods in visad.data.hdf5 with parameters of type Data

void	HDF5Form.add(String id, Data data, boolean replace)
FormNode	HDF5Form.getForms(Data data)
void	HDF5Form.save(String filename, Data data, boolean replace)

Uses of Data in visad.data.hdfeos

Methods in visad.data.hdfeos with parameters of type Data

void	HdfeosForm.add(String id, Data data, boolean replace)
FormNode	HdfeosForm.getForms(Data data)
void	HdfeosForm.save(String id, Data data, boolean replace)
void	HdfeosAccessor.writeFile(int[] fileLocations, Data range)

Uses of Data in visad.data.hrit

Methods in visad.data.hrit with parameters of type Data

void	HRITForm.add(String id, Data data, boolean replace) This has not been implemented
FormNode	HRITForm.getForms(Data data) not implemented yet
void	HRITForm.save(String id, Data data, boolean replace) save the file back to disk This has not been implemented yet

Uses of Data in visad.data.jai

Methods in visad.data.jai with parameters of type Data

void	JAIForm.add(String id, Data data, boolean replace) Adds data to an existing JAI image file.
FormNode	JAIForm.getForms(Data data)
void	JAIForm.save(String id, Data data, boolean replace) Saves a VisAD Data object to a JAI image format at the given location.

Uses of Data in visad.data.mcidas

Methods in visad.data.mcidas with parameters of type Data

void	PointForm.add(String id, Data data, boolean replace) This has not been implemented
void	MapForm.add(String id, Data data, boolean replace) Add data to an existing data object
void	AreaForm.add(String id, Data data, boolean replace) This has not been implemented
FormNode	PointForm.getForms(Data data) not implemented yet
FormNode	MapForm.getForms(Data data) Return the data forms that are compatible with a data object
FormNode	AreaForm.getForms(Data data) not implemented yet
void	PointForm.save(String id, Data data, boolean replace) save the file back to disk This has not been implemented yet
void	MapForm.save(String id, Data data, boolean replace) Save a VisAD data object in this form
void	AreaForm.save(String id, Data data, boolean replace) save the file back to disk This has not been implemented yet

Uses of Data in visad.data.netcdf

Methods in visad.data.netcdf with parameters of type Data

void	Plain.add(String id, Data data, boolean replace) Add data to an existing data object.
FormNode	Plain.getForms(Data data) Return the data forms that are compatible with a data object.
void	Plain.save(String path, Data data, boolean replace) Save a VisAD data object in this form.

Uses of Data in visad.data.netcdf.in

Methods in visad.data.netcdf.in with parameters of type Data

void	FileDataFactory.netCDFFlatFieldAccessor.writeFile(int[] fileLocation, Data range) Does nothing.

Uses of Data in visad.data.netcdf.out

Methods in visad.data.netcdf.out with parameters of type Data

protected void

VisADAdapter.visit(Data data, visad.data.netcdf.out.VisADAccessor outerAccessor) Visit a VisAD data object.

Constructors in visad.data.netcdf.out with parameters of type Data

VisADAdapter(Data data)
Construct from a generic VisAD data object.

Uses of Data in visad.data.text

Methods in visad.data.text that return Data

static Data

TextAdapter.processFile(String file) Read in the given file and return the processed data

Methods in visad.data.text with parameters of type Data

void	TextForm.add(String id, Data data, boolean replace)
FormNode	TextForm.getForms(Data data)
void	TextAdapter.StreamProcessor.processValues(Data[] tuple)
void	TextForm.save(String id, Data data, boolean replace)

Uses of Data in visad.data.tiff

Methods in visad.data.tiff with parameters of type Data

void	LegacyTiffForm.add(String id, Data data, boolean replace) Deprecated. Adds data to an existing TIFF file.
FormNode	LegacyTiffForm.getForms(Data data) Deprecated.
void	LegacyTiffForm.save(String id, Data data, boolean replace) Deprecated. Saves a VisAD Data object to an uncompressed TIFF file.

Uses of Data in visad.data.vis5d

Methods in visad.data.vis5d with parameters of type Data

void	Vis5DTopoForm.add(String id, Data data, boolean replace) Add data to an existing data object.
void	Vis5DForm.add(String id, Data data, boolean replace)
FormNode	Vis5DTopoForm.getForms(Data data) Return the data forms that are compatible with a data object.
FormNode	Vis5DForm.getForms(Data data)
void	Vis5DTopoForm.save(String id, Data data, boolean replace) Save a VisAD data object in this form.
void	Vis5DForm.save(String id, Data data, boolean replace)
void	Vis5DFileAccessor.writeFile(int[] fileLocations, Data range)

Uses of Data in visad.data.visad

Methods in visad.data.visad with parameters of type Data

void	VisADForm.add(String id, Data data, boolean replace)
FormNode	VisADForm.getForms(Data data)
void	BinaryWriter.processTuple(TupleType type, Data[] components, Tuple t, Object token)
void	BinarySizer.processTuple(TupleType type, Data[] components, Tuple t, Object token)
void	VisADForm.save(String id, Data data, boolean replace) Save a Data object.
void	VisADForm.save(String id, Data data, boolean replace, boolean bigObject) Save a Data object.

Uses of Data in visad.data.visad.object

Methods in visad.data.visad.object that return Data

static Data[]

BinaryDataArray.read(BinaryReader reader)

Methods in visad.data.visad.object with parameters of type Data

static int	BinaryTuple.computeBytes(Data[] components)
static int	BinaryDataArray.computeBytes(Data[] array)
static void	BinaryDataArray.write(BinaryWriter writer, Data[] array, Object token)
static void	BinaryTuple.write(BinaryWriter writer, TupleType type, Data[] components, Tuple t, Object token)

Uses of Data in visad.formula

Methods in visad.formula that return Data

static Data	FormulaUtil.brackets(Field f, Real r) evaluate the bracket function; e.g., A1[5] or A1[A2]
static Data	FormulaUtil.derive(Function f, VRealType rt) evaluate the derive function
static Data	FormulaUtil.dot(TupleIface t, Real r) evaluate the dot operator
static Data	FormulaUtil.extract(Field f, Real r) evaluate the extract function
static Data	FormulaUtil.implicit(Function f, Real r) evaluate implicit function syntax; e.g., A1(5) or A1(A2)
static Data	FormulaUtil.link(VMethod m, Object[] o) evaluate the link function

Uses of Data in visad.georef

Subinterfaces of Data in visad.georef

interface	EarthLocation Interface for specifying a point on the earth's surface in terms of latitude, longitude and altitude above sea level.
interface	LatLonPoint Interface for supporting latitude/longitude points.
interface	NamedLocation An interface for a named earth location.
interface	NavigatedField A particular type of Field whose Domain Set contains values of Latitude and Longitude, or whose CoordinateSystem can transform to RealType.Latitude and RealType.Longitude

Classes in visad.georef that implement Data

class	EarthLocationLite This provides a LoCal EarthLocation that is much faster to create than the EarthLocationTuple.
class	EarthLocationTuple RealTuple implementation of EarthLocation for representing a location on the earth's surface in terms of latitude, longitude and altitude above sea level.
class	LatLonTuple RealTuple implementation of LatLonPoint for defining lat/lon points
class	NamedLocationTuple Tuple implementation of NamedLocation for representing a location on the earth's surface in terms of latitude, longitude and altitude above sea level and some sort of identifier.
class	UTMCoordinate RealTuple implementation of a Universal Transverse Mercator (UTM) coordinate

Methods in visad.georef that return Data

Data	EarthLocationLite.getComponent(int i) Get the i'th component.
Data[]	EarthLocationLite.getComponents(boolean copy) Create, if needed, and return the component array.

Uses of Data in visad.java2d

Methods in visad.java2d that return Data

Data	ShadowTypeJ2D.getData()

Methods in visad.java2d with parameters of type Data

boolean	ShadowTupleTypeJ2D.doTransform(VisADGroup group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a VisADSceneGraphObject; return true if need post-process
boolean	ShadowTextTypeJ2D.doTransform(VisADGroup group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java2D VisADSceneGraphObject; return true if need post-process
boolean	ShadowRealTypeJ2D.doTransform(VisADGroup group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java2D VisADSceneGraphObject; return true if need post-process
boolean	ShadowFunctionOrSetTypeJ2D.doTransform(VisADGroup group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a VisADSceneGraphObject; add generated scene graph components as children of group; value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process
boolean	ShadowTupleTypeJ2D.recurseComponent(int i, Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowFunctionOrSetTypeJ2D.recurseRange(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)

Uses of Data in visad.java3d

Methods in visad.java3d that return Data

Data	ShadowTypeJ3D.getData() Get the data

Methods in visad.java3d with parameters of type Data

boolean	ShadowTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java3D scene graph; add generated scene graph components as children of group; value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process; this is default (for ShadowTextType)
boolean	ShadowTupleTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java3D scene graph; return true if need post-process
boolean	ShadowTextTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java3D scene graph; return true if need post-process
boolean	ShadowRealTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java3D scene graph; return true if need post-process
boolean	ShadowFunctionOrSetTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer) transform data into a Java3D scene graph; add generated scene graph components as children of group; value_array are inherited valueArray values; default_values are defaults for each display.DisplayRealTypeVector; return true if need post-process
boolean	ShadowAnimationFunctionTypeJ3D.doTransform(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowTupleTypeJ3D.recurseComponent(int i, Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)
boolean	ShadowFunctionOrSetTypeJ3D.recurseRange(Object group, Data data, float[] value_array, float[] default_values, DataRenderer renderer)

Uses of Data in visad.jmet

Methods in visad.jmet with parameters of type Data

static void	DumpType.dumpDataType(Data d) Decomposes a VisAD Data object and lists out information about its components.
static void	DumpType.dumpDataType(Data d, OutputStream uos) Decomposes a VisAD Data object and lists out information about its components.

Uses of Data in visad.math

Methods in visad.math with parameters of type Data

static FlatField	FFT.backwardFT(Data[] datums) for use by SpreadSheet only - ordinary applications should use other method signatures; invoke in SpreadSheet by: link(visad.math.FFT.backwardFT(A1))
static FlatField	FFT.forwardFT(Data[] datums) for use by SpreadSheet only - ordinary applications should use other method signatures; invoke in SpreadSheet by: link(visad.math.FFT.forwardFT(A1))
static FlatField	Histogram.makeHistogram(Data[] datums) invoke in SpreadSheet by: link(visad.math.Histogram.makeHistogram(A1, A2))

Uses of Data in visad.matrix

Classes in visad.matrix that implement Data

class	JamaCholeskyDecomposition JamaCholeskyDecomposition is a VisAD wrapper for JAMA CholeskyDecompositions.
class	JamaEigenvalueDecomposition JamaEigenvalueDecomposition is a VisAD wrapper for JAMA EigenvalueDecompositions.
class	JamaLUDecomposition JamaLUDecomposition is a VisAD wrapper for JAMA LUDecompositions.
class	JamaMatrix JamaMatrix is a VisAD wrapper for JAMA matrices.
class	JamaQRDecomposition JamaQRDecomposition is a VisAD wrapper for JAMA QRDecompositions.
class	JamaSingularValueDecomposition JamaSingularValueDecomposition is a VisAD wrapper for JAMA SingularValueDecompositions.

Uses of Data in visad.meteorology

Subinterfaces of Data in visad.meteorology

interface	ImageSequence Interface for representing a time sequence of single-banded images.
interface	SatelliteData An interface for defining properties associated with satellite data.
interface	SingleBandedImage An interface for representing single--banded planar satellite or radar imagery.

Classes in visad.meteorology that implement Data

class	ImageSequenceImpl Implementation of an ImageSequence.
class	NavigatedImage An implementation for representing single-banded planar satellite or radar imagery.
class	SatelliteImage An implementation for representing single-banded planar satellite that has navigation.
class	SingleBandedImageImpl An implementation for representing single-banded planar satellite or radar imagery.

Methods in visad.meteorology that return Data

Data	SingleBandedImageImpl.binary(Data data, int op, int samplingMode, int errorMode) Return the result of a binary operation between this instance and another operand.
Data	SingleBandedImageImpl.unary(int op, int samplingMode, int errorMode) Return the result of a unary operation on this instance.
Data	SingleBandedImageImpl.unary(int op, MathType new_type, int sampling_mode, int error_mode) return new SingleBandedImageImpl with value 'op this'
Data	SatelliteImage.unary(int op, MathType new_type, int sampling_mode, int error_mode) return new SatelliteImage with value 'op this'
Data	NavigatedImage.unary(int op, MathType new_type, int sampling_mode, int error_mode) return new NavigatedImage with value 'op this'

Methods in visad.meteorology with parameters of type Data

Data	SingleBandedImageImpl.binary(Data data, int op, int samplingMode, int errorMode) Return the result of a binary operation between this instance and another operand.

Uses of Data in visad.python

Methods in visad.python that return Data

static Data	JPythonMethods.abs_data(Data data) Return point-wise absolute value of data name changed 1/11/02 to avoid conflicts with Jython built-in
static Data	JPythonMethods.abs(Data data) Deprecated. Consider using JPythonMethods.abs_data(Data) instead.
static Data	JPythonMethods.acos(Data data) Return point-wise arccosine value of data, in radians.
static Data	JPythonMethods.acosDegrees(Data data) return point-wise arccosine value of data, in degrees.
static Data	JPythonMethods.asin(Data data) return point-wise arcsine value of data, in radians
static Data	JPythonMethods.asinDegrees(Data data) return point-wise arcsine value of data, in degrees.
static Data	JPythonMethods.atan(Data data) return point-wise arctangent value of data, in radians.
static Data	JPythonMethods.atan2(Data data1, Data data2) return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in radians.
static Data	JPythonMethods.atan2(Data data1, double data2) Return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in radians.
static Data	JPythonMethods.atan2(double data1, Data data2) Return point-wise arctangent value of data1 / data2 over full (-pi, pi) range, returned in radians.
static Data	JPythonMethods.atan2Degrees(Data data1, Data data2) return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in degrees.
static Data	JPythonMethods.atan2Degrees(Data data1, double data2) Return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in degrees.
static Data	JPythonMethods.atan2Degrees(double data1, Data data2) Return point-wise arctangent value of data1 / data2 over full (-pi, pi) range, returned in degrees.
static Data	JPythonMethods.atanDegrees(Data data) return point-wise arctangent value of data, in degrees.
static Data	JPythonMethods.ceil(Data data) return point-wise ceil value of data (smallest integer not less than).
static Data	JPythonMethods.cos(Data data) return point-wise cosine value of data, assuming input values are in radians unless they have units convertable with radians, in which case those units are converted to radians
static Data	JPythonMethods.cosDegrees(Data data) return point-wise cosine value of data, assuming input values are in degrees unless they have units convertable with degrees, in which case those units are converted to degrees
static Data	JPythonMethods.evaluate(Field data, double domain)
static Data	JPythonMethods.evaluate(Field data, Real domain) Creates a VisAD Data by evaluating the Field at the point given in the domain.
static Data	JPythonMethods.exp(Data data) return point-wise exp value of data.
static Data	JPythonMethods.floor(Data data) return point-wise floor value of data (largest integer not greater than)
static Data	JPythonMethods.getNetcdfData(String filename) Helper method to read netcdf files with possible factor
static Data	JPythonMethods.getNetcdfData(String filename, String factor) Try to create a Data object from a NetCDF file
static Data	JPythonMethods.log(Data data) return point-wise log value of data
static Data	JPythonMethods.max_data(Data data1, Data data2) Return point-wise maximum value of data1 and data2.
static Data	JPythonMethods.max_data(Data data1, double data2) Return point-wise maximum value of data1 and data2.
static Data	JPythonMethods.max_data(double data1, Data data2) Return point-wise maximum value of data1 and data2.
static Data	JPythonMethods.min_data(Data data1, Data data2) return point-wise minimum value of data1 and data2 name changed 1/11/02 to avoid conflicts with Jython built-in
static Data	JPythonMethods.min_data(Data data1, double data2) return point-wise minimum value of data1 and data2 name changed 1/11/02 to avoid conflicts with Jython built-in
static Data	JPythonMethods.min_data(double data1, Data data2) Return point-wise minimum value of data1 and data2.
static Data	JPythonMethods.rint(Data data) return point-wise rint value of data (closest integer)
static Data	JPythonMethods.round(Data data) return point-wise round value of data (closest integer).
static Data	JPythonMethods.sin(Data data) return point-wise sine value of data, assuming input values are in radians unless they have units convertable with radians, in which case those units are converted to radians
static Data	JPythonMethods.sinDegrees(Data data) return point-wise sine value of data, assuming input values are in degrees unless they have units convertable with degrees, in which case those units are converted to degrees
static Data	JPythonMethods.sqrt(Data data) return point-wise square root value of data
static Data	JPythonMethods.tan(Data data) return point-wise tan value of data, assuming input values are in radians unless they have units convertable with radians, in which case those units are converted to radians
static Data	JPythonMethods.tanDegrees(Data data) return point-wise tangent value of data, assuming input values are in degrees unless they have units convertable with degrees, in which case those units are converted to degrees

Methods in visad.python with parameters of type Data

static Data	JPythonMethods.abs_data(Data data) Return point-wise absolute value of data name changed 1/11/02 to avoid conflicts with Jython built-in
static Data	JPythonMethods.abs(Data data) Deprecated. Consider using JPythonMethods.abs_data(Data) instead.
static Data	JPythonMethods.acos(Data data) Return point-wise arccosine value of data, in radians.
static Data	JPythonMethods.acosDegrees(Data data) return point-wise arccosine value of data, in degrees.
static Data	JPythonMethods.asin(Data data) return point-wise arcsine value of data, in radians
static Data	JPythonMethods.asinDegrees(Data data) return point-wise arcsine value of data, in degrees.
static Data	JPythonMethods.atan(Data data) return point-wise arctangent value of data, in radians.
static Data	JPythonMethods.atan2(Data data1, Data data2) return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in radians.
static Data	JPythonMethods.atan2(Data data1, double data2) Return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in radians.
static Data	JPythonMethods.atan2(double data1, Data data2) Return point-wise arctangent value of data1 / data2 over full (-pi, pi) range, returned in radians.
static Data	JPythonMethods.atan2Degrees(Data data1, Data data2) return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in degrees.
static Data	JPythonMethods.atan2Degrees(Data data1, double data2) Return point-wise arc tangent value of data1 / data2 over full (-pi, pi) range, returned in degrees.
static Data	JPythonMethods.atan2Degrees(double data1, Data data2) Return point-wise arctangent value of data1 / data2 over full (-pi, pi) range, returned in degrees.
static Data	JPythonMethods.atanDegrees(Data data) return point-wise arctangent value of data, in degrees.
static Data	JPythonMethods.ceil(Data data) return point-wise ceil value of data (smallest integer not less than).
static Data	JPythonMethods.cos(Data data) return point-wise cosine value of data, assuming input values are in radians unless they have units convertable with radians, in which case those units are converted to radians
static Data	JPythonMethods.cosDegrees(Data data) return point-wise cosine value of data, assuming input values are in degrees unless they have units convertable with degrees, in which case those units are converted to degrees
static int	JPythonMethods.domainDimension(Data data) Get the domain dimension of the Data object
static RealTupleType	JPythonMethods.domainType(Data data) Get the domain Type for the Data object
static String	JPythonMethods.domainType(Data data, int comp) Get the name of the given component of the domain RealType.
static void	JPythonMethods.dumpType(Data d) helper method for dumpMathType() only This just dumps out the MathType of the Data object.
static void	JPythonMethods.dumpTypes(Data d) Helper method for the dump(Data|Math)Type() methods.
static Data	JPythonMethods.exp(Data data) return point-wise exp value of data.
static int[]	JPythonMethods.find(Data f, String op, Data v) Get a list of points where a comparison is true.
static Data	JPythonMethods.floor(Data data) return point-wise floor value of data (largest integer not greater than)
static Set	JPythonMethods.getDomain(Data data) Return the sampling set for the domain of the Data object
static int	JPythonMethods.getDomainDimension(Data data) Get the number of domain components from a given Data object.
static Set	JPythonMethods.getDomainSet(Data data) Return the sampling set for the domain of the Data object
static int[]	JPythonMethods.getDomainSizes(Data data) Return the lengths of the components of the sampling set
static RealTupleType	JPythonMethods.getDomainType(Data data) Get the domain Type for the Data object
static int	JPythonMethods.getRangeDimension(Data data) Get the number of range components from a given Data object.
static MathType	JPythonMethods.getRangeType(Data data) Get the range Type for the field
static MathType	JPythonMethods.getType(Data data) Get the MathType of the named VisAD data object
static Data	JPythonMethods.log(Data data) return point-wise log value of data
static FieldImpl	JPythonMethods.mask(Data f, String op, Data v) Mask out values outside testing limits...
static FieldImpl	JPythonMethods.mask(FieldImpl f, String op, Data v) Mask out values outside testing limits in a FieldImpl
static FieldImpl	JPythonMethods.mask(FieldImpl f, String op, Data v, boolean useNaN) Mask out values outside testing limits in a FieldImpl
static Data	JPythonMethods.max_data(Data data1, Data data2) Return point-wise maximum value of data1 and data2.
static Data	JPythonMethods.max_data(Data data1, double data2) Return point-wise maximum value of data1 and data2.
static Data	JPythonMethods.max_data(double data1, Data data2) Return point-wise maximum value of data1 and data2.
static Data	JPythonMethods.min_data(Data data1, Data data2) return point-wise minimum value of data1 and data2 name changed 1/11/02 to avoid conflicts with Jython built-in
static Data	JPythonMethods.min_data(Data data1, double data2) return point-wise minimum value of data1 and data2 name changed 1/11/02 to avoid conflicts with Jython built-in
static Data	JPythonMethods.min_data(double data1, Data data2) Return point-wise minimum value of data1 and data2.
static int	JPythonMethods.rangeDimension(Data data) get the number of range components of the Data object
static MathType	JPythonMethods.rangeType(Data data) get the range Type for the field
static String	JPythonMethods.rangeType(Data data, int comp) Get the name of the given component of the range RealType.
static Data	JPythonMethods.rint(Data data) return point-wise rint value of data (closest integer)
static Data	JPythonMethods.round(Data data) return point-wise round value of data (closest integer).
static void	JPythonMethods.saveNetcdf(String fn, Data d) Save the Data in a netcdf file
static Data	JPythonMethods.sin(Data data) return point-wise sine value of data, assuming input values are in radians unless they have units convertable with radians, in which case those units are converted to radians
static Data	JPythonMethods.sinDegrees(Data data) return point-wise sine value of data, assuming input values are in degrees unless they have units convertable with degrees, in which case those units are converted to degrees
static Data	JPythonMethods.sqrt(Data data) return point-wise square root value of data
static Data	JPythonMethods.tan(Data data) return point-wise tan value of data, assuming input values are in radians unless they have units convertable with radians, in which case those units are converted to radians
static Data	JPythonMethods.tanDegrees(Data data) return point-wise tangent value of data, assuming input values are in degrees unless they have units convertable with degrees, in which case those units are converted to degrees
static ByteArrayOutputStream	JPythonMethods.whatType(Data d) helper method for dumpMathType() only This just dumps out the MathType of the Data object into a ByteArrayOutputStream which is returned.
static ByteArrayOutputStream	JPythonMethods.whatTypes(Data d) helper method for the dump(Data|Math)Type() methods this will list both the MathType and DataType information to a ByteArrayOutputStream which is returned.

Uses of Data in visad.sounder

Classes in visad.sounder that implement Data

class	Sounding Sounding is the VisAD class for atmospheric soundings.
class	Spectrum Sounding is the VisAD class for atmospheric soundings.

Uses of Data in visad.ss

Methods in visad.ss that return Data

Data	SSCellData.getData() Gets the Data object.
Data[]	BasicSSCell.getData() Gets this cell's Data objects.
Data	BasicSSCell.getData(String varName) Gets this cell's Data object with the specified variable name.

Methods in visad.ss with parameters of type Data

String	BasicSSCell.addData(Data data) Adds a Data object to this cell, creating an associated DataReference for it.
String	BasicSSCell.addData(Data data, ConstantMap[] cmaps) Adds a Data object to this cell, creating an associated DataReference with the specified ConstantMaps for it.
protected String	BasicSSCell.addData(int id, Data data, ConstantMap[] cmaps, String source, int type, boolean notify) Adds a Data object to this cell from the given source of the specified type, creating an associated DataReference for it.
static DataImpl	BasicSSCell.makeLocal(Data data) Deprecated. Use visad.DataUtility.makeLocal(data) instead.
void	BasicSSCell.removeData(Data data) Removes the given Data object from this cell.
void	SSCellData.setData(Data data) Sets the data.
void	BasicSSCell.setData(Data data) Deprecated. Use addData(Data) instead.

Constructors in visad.ss with parameters of type Data

MappingDialog(Frame parent, Data[] data, ScalarMap[] startMaps, boolean allowAlpha, boolean allow3D)
Constructs a MappingDialog from multiple Data objects.

MappingDialog(Frame parent, Data data, ScalarMap[] startMaps, boolean allowAlpha, boolean allow3D)
Constructs a MappingDialog from a single Data object.

Uses of Data in visad.util

Methods in visad.util with parameters of type Data

static ScalarMap[]	DataUtility.convertStringToMaps(String mapString, Data[] data, boolean showErrors) Converts the given map string to its corresponding array of mappings.
static ScalarMap[]	DataUtility.convertStringToMaps(String mapString, Data data, boolean showErrors) Converts the given map string to its corresponding array of mappings.
static TupleIface	DataUtility.ensureTuple(Data datum) Ensures that a Data is a Tuple.
static FlatField[]	DataUtility.getImageFields(Data data)
static int	DataUtility.getRealTypes(Data[] data, Vector v, boolean keepDupl, boolean doCoordSys) Deprecated. Use getScalarTypes(Data[], Vector, boolean, boolean) instead.
static int	DataUtility.getRealTypes(Data data, Vector v) Deprecated. Use getScalarTypes(Data, Vector) instead.
static int	DataUtility.getScalarTypes(Data[] data, Vector v, boolean keepDupl, boolean doCoordSys) Obtains a Vector consisting of all ScalarTypes present in an array of Data objects' MathTypes.
static int	DataUtility.getScalarTypes(Data data, Vector v) Obtains a Vector consisting of all ScalarTypes present in a Data object's MathType.
static DataImpl	DataUtility.makeLocal(Data data) Converts a remote Data object to a local Data object.
static DataImpl	DataUtility.makeLocal(Data data, boolean printStackTraces) Converts a remote Data object to a local Data object.