Uses of Interface
vec_math.Multidimensional

Packages that use Multidimensional

astro
astro.fits
jview
stella.util
vec_math

Uses of Multidimensional in astro

Classes in astro that implement Multidimensional

private class

WcsMatch.Stereographic The multidimensional for stereographic projection.

Methods in astro with parameters of type Multidimensional

private nom.tam.fits.ImageHDU

WcsFit.sigmaImage(Multidimensional sigma, Dimension box, int compress, int blowup) We take the residuals to the solution and return an sigma-fits from them.

Uses of Multidimensional in astro.fits

Classes in astro.fits that implement Multidimensional

static class	FitsStatistic.Constant Multidimensional that returns one.
static class	FitsStatistic.Coordinate Multidimensional that returns the x or y of the input vector.
static class	FitsStatistic.Square Multidimensional that returns the x or y of the input vector squared.
static class	FitsStatistic.XY Multidimensional that returns x times y

Methods in astro.fits that return Multidimensional

static Multidimensional[]	AmplifierCrosstalk.lowestTwoFit() Creates the model of a planar plus an xy term fit through the correction matrix.
static Multidimensional[]	AmplifierCrosstalk.parabolicFit() Parabolic fit.
static Multidimensional[]	AmplifierCrosstalk.planeFit() Creates the model of a planar fit through the correction matrix.

Methods in astro.fits with parameters of type Multidimensional

static nom.tam.fits.ImageHDU	AmplifierCrosstalk.fitBackground(nom.tam.fits.ImageHDU amplifier, nom.tam.fits.ImageHDU crstlk, nom.tam.fits.ImageHDU sigma, double minillu, Multidimensional[] model) If the input images are too noise, we can subtract a fit here.
static nom.tam.fits.ImageHDU[]	AmplifierCrosstalk.fitCorrectionMatrix(nom.tam.fits.ImageHDU corr, nom.tam.fits.ImageHDU sig, Multidimensional[] model) Normally, measurements of the crosstalk cannot be obtained across an entire quadrant, meaning that normally only a portion of the quadrant will be illuminated.
static nom.tam.fits.ImageHDU	FitsStatistic.removeSky(nom.tam.fits.ImageHDU sky, Multidimensional[] model, int n, double lofac, double hifac) Models the background and removes it from the image.
static GeneralLinearRegression	FitsStatistic.shuffleForRegression(List<VectorG> valid, Multidimensional[] model, boolean sigma) Shuffels ADU 4-vectors with x, y, ADU, [sigma] into the format for a general linear regression and fuels them into one.

Uses of Multidimensional in jview

Classes in jview that implement Multidimensional

static class	UserDrivenFitting.PeriodError
static class	UserDrivenFitting.PeriodExtrema

Fields in jview declared as Multidimensional

private Multidimensional	UserDrivenFitting.error The function to calculate a model parameter error from the extrema.
private Multidimensional	UserDrivenFitting.func The function to calculate a model parameter from the extrema select.
private Multidimensional	JDataModelDisplay.xfunc Transfer function for x.

Uses of Multidimensional in stella.util

Subinterfaces of Multidimensional in stella.util

static interface

FocusSpindleFit.PositionModel

Classes in stella.util that implement Multidimensional

class	BeamSplitterFit This class tries to fit data from the guider ccd to a double-peaked gaussian to resemble the STELLA-I guiding setup.
private class	FocusSpindleFit.AbstractPosition
private class	FocusSpindleFit.CorkScrew Full model, including a cork-screw like dependency.
private class	FocusSpindleFit.Drift Simple model, center plus linear drift.
private class	GuiderParametersRaDe.SimpleGnomic
class	ImageAmoeba This class searches the parameter space to determine the best values to use on guider images for preparing a star detection.
class	SineError A class that models a time-dependend shift according to an overlay of sine functions.
class	StarAmoeba This class takes a good set of image-filtering parameters and uses actual guider images to train.
class	TelescopeError A class that models a time-dependend shift according to an overlay of sine functions.

Fields in stella.util declared as Multidimensional

private Multidimensional[]	PointingModel.altmodel The altitude model, parsed from PointingModel.KEY_ALTMODEL.
private Multidimensional[]	PointingModel.azmodel The azimuth model, parsed from PointingModel.KEY_AZMODEL.
private Multidimensional	MirrorCenter.center The function to minimize.
private Multidimensional	GuiderParameters.dist The multidimensional function that is to be minimized with amoeba.

Uses of Multidimensional in vec_math

Subinterfaces of Multidimensional in vec_math

interface	Gradient Multidimensionals that implement this interface can calculate their gradient on a given point P.
interface	MultidimensionalInverse Inverts a function defined in vector space.
interface	PrintMultidimensional This interface describes a multi-dimensional functions.

Classes in vec_math that implement Multidimensional

(package private) static class	AbstractDataModel.LocalM A localM-estimate model for the data.
(package private) static class	AbstractMultidimensionalDataModel.LocalMM A localM-estimate model for the data.
class	ExpressionMultidimensional A representation of a Multidimensional function using a parsable expression.
static class	Fourier.Series A Fourier series multidimensional is the representation of a discrete Fouriere series up to a specified order M.
static class	Phasing.PhaseMultidimensional The multidimensional version of it, only implemented to work for one-dimensional vectors.

Fields in vec_math declared as Multidimensional

private Multidimensional	Amoeba.f An n-dimensional function that is evaluated with an VectorG.
(package private) Multidimensional	AbstractMultidimensionalDataModel.LocalMM.rho

Methods in vec_math that return Multidimensional

Multidimensional	AbstractMultidimensionalDataModel.getChiSquareModel() We return a multidimensional that calculated the chi-square of the model to the data given.
Multidimensional	AbstractDataModel.getChiSquareModel() We return a multidimensional that calculated the chi-square of the model to the data given.
static Multidimensional	AbstractDataModel.getChiSquareModel(DataModel fit) We return a multidimensional that calculate the chi-square of the model to the data model given.
static Multidimensional	AbstractMultidimensionalDataModel.getChiSquareModel(MultidimensionalDataModel fit) We return a multidimensional that calculate the chi-square of the model to the data model given.
Multidimensional	Amoeba.getFunction() Returns the function set.
static Multidimensional	AbstractDataModel.getLorentzianModel(DataModel fit) We return a multidimensional that calculate the model parameters with errors that are Lorentzian.
static Multidimensional	AbstractMultidimensionalDataModel.getLorentzianModel(MultidimensionalDataModel fit) We return a multidimensional that calculate the model parameters with errors that are Lorentzian.
static Multidimensional	AbstractDataModel.getRobustModel(DataModel fit) We return a multidimensional that calculate the model parameters with errors that are double-sided exponential, which gives a minimization to absolut divergence instead of least-squares.
static Multidimensional	AbstractMultidimensionalDataModel.getRobustModel(MultidimensionalDataModel fit) We return a multidimensional that calculate the model parameters with errors that are double-sided exponential, which gives a minimization to absolut divergence instead of least-squares.

Methods in vec_math with parameters of type Multidimensional

protected Matrix	GeneralLinearRegression.deriveDesignMatrix(Multidimensional[] base, VectorG[] xi) Calculates the design matrix from the basic functions and the measurement dependables.
boolean	GeneralLinearRegression.setBasicFunctions(Multidimensional[] fitting, VectorG[] t) Daugther classes must interfere here.
void	Amoeba.setFunction(Multidimensional fmult) Sets the multidimensional function that should be minimized.

Constructors in vec_math with parameters of type Multidimensional

AbstractMultidimensionalDataModel.LocalMM(Multidimensional f, MultidimensionalDataModel fit, VectorG[] yvec, VectorG[] sigvec)

Bootstrap(Multidimensional[] fitting, VectorG[] x, VectorG y, VectorG sigma)
Constructs a new bootstrap object from the supplied basic functions and the measurement with their errors.

GeneralLinearRegression(Multidimensional[] fitting, VectorG[] x, VectorG y, VectorG sigma)
Constructs a new general linear regression.