|
Class Summary |
| AbstractAnalyser |
Is a property container and does supply implementation of
AbstractAnalyser.visualize(vec_math.VectorG[], vec_math.VectorG[]) and AbstractAnalyser.reset(). |
| AbstractDataModel |
An abstract data model is an implementation of a data model that allows
the user to get access to the data it is constructed with. |
| AbstractDataModel.Construct |
Just to see the signature of the constructor. |
| AbstractDataModel.LocalM |
A localM-estimate model for the data. |
| AbstractDataSink |
A skeleton implementation of a data sink. |
| AbstractFit |
Base class for model fitters. |
| AbstractFunctionChange |
Class that supports casting to FunctionChangeListeners and can
thus opt as a base class for Functions, Multidimensionals
etc. |
| AbstractGradientModel |
This class is designed to work together with the
LevenbergMarquardt class. |
| AbstractMultidimensionalDataModel |
An abstract data model is an implementation of a data model that allows
the user to get access to the data it is constructed with. |
| AbstractMultidimensionalDataModel.LocalMM |
A localM-estimate model for the data. |
| AbstractVectorDifferentiable |
Abstract base class for VectorDifferentiables. |
| Amoeba |
An amoeba is a simplex method for finding (local) minimas of a
multi-dimensional function. |
| Amoeba.Test |
Class for testing the amoeba. |
| AssignedVariable |
A simple named variable with error, name and unit. |
| AssociatedLegendrePolynomial |
This class implements associated legandre polynomials (P^m_l). |
| AssociatedLegendrePolynomial.Test |
Test class, reads m and l and steps through x from -1 to +1. |
| Benchmark |
|
| BooleanNode |
A helper class taylored for using boolean variables. |
| BooleanParser |
A class providing arithmetics for booleans. |
| BooleanParser.Test |
Mainly test purpose. |
| Bootstrap |
A class to work together with GeneralLinearRegression. |
| Bootstrap.ExchangedData |
Helper class containing an exchanged data set. |
| Bootstrap.StraightLine |
We fit data to a straight line. |
| BoundedVariable |
A simple named variable with error, name and unit. |
| CentralMoments |
Moments is a stastistic package that allows calculation of sample central
moments of discrete samples. |
| CentralMoments.FitsMoments |
Grabs a fits file and displays the moments up to the order given in
the optional second command line argument. |
| CentralProjection |
Abstract base class for sphere to planar projections. |
| Chebyshev |
The recursive formular for the Legendre polynomials. |
| ClassicStatistic |
Class to implement a simple statistic: Average is the arithmetic average,
Deviation the standard deviation. |
| ClassicStatistic.FileStatistic |
A class to read in an ascii-column file and calculate average, sigma
and so forth from the specified column. |
| CleanFourier |
A java implementation of the clean algorithm described by Roberts et al. |
| CleanFourier.Duty |
First test class that does Figure 8f, p 981. |
| CleanFourier.Fig10 |
First test class that does Figure 1, p 971. |
| CleanFourier.Fig3 |
First test class that does Figure 1, p 971. |
| CleanFourier.File |
File data parser. |
| Complex |
A number representing a complex number. |
| CubicSpline |
Cubic spline interpolation. |
| DataFileAnalyser |
A data file reader that further specializes on data modelling. |
| DataFileModel |
A data file reader that further specializes on data modelling. |
| DoubleHistogram |
From a list of doubles we calculate the histogram. |
| Ellipse |
A tilted ellipse. |
| ErrorPropagation |
|
| ErrorPropagation.Centroid |
Calculates expected errors in a center-of-gravity position. |
| ExpressionCalculator |
Combines an ExpressionNode and an ExpressionParser to
actually fill paramtere values into an expression and evaluate this. |
| ExpressionEvaluator |
Combines an ExpressionNode and an ExpressionParser to
actually fill paramtere values into an expression and evaluate this. |
| ExpressionFit |
Analyses a funtion by fitting a non-linear model to it. |
| ExpressionFit.AmoebaModel |
A multidimensional that evaluates a function f given as a string like |
| ExpressionFunction |
A function from a parseable string expression. |
| ExpressionFunction.Test |
Test by evaluating an arbitrary function at the provided position. |
| ExpressionMultidimensional |
A representation of a Multidimensional function using a
parsable expression. |
| ExpressionMultidimensional.Test |
Test by evaluating an arbitrary function at the provided position. |
| ExpressionNode |
A node class for ExpressionParser. |
| ExpressionNode.Test |
Test purposes only. |
| ExpressionParser |
A class providing arithmetics for expressions. |
| ExpressionParser.Test |
Mainly test purpose. |
| Extremum |
Helper class to hold some type of (local) extremum in an indexed data set. |
| Extremum.File |
Finds all maxima and minima in the quasi-conitinuous function,
tabluated as an ascii-file in the first command line argument. |
| Extremum.Test1 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test2 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test3 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test4 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test5 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test6 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test7 |
Classes that test the extrema finding method in smooth parabola. |
| Extremum.Test8 |
Classes that test the extrema finding method in smooth parabola. |
| FastFourierTransform |
Does a fast fourier transform following the algorithm presented in
Numerical Recipes, p. |
| FastFourierTransform.RealTest |
Test the real-value fft. |
| FastFourierTransform.Test |
|
| FastFourierTransform.WhiteNoise |
|
| FileMatrix |
A class to convert an ascii file consisting of blocked data into a
matrix consisting of doubles. |
| Fourier |
A suit of methods valuable in periodogram application. |
| Fourier.AbstractFile |
Class for command-line parsing. |
| Fourier.Series |
A Fourier series multidimensional is the representation of a
discrete Fouriere series up to a specified order M. |
| FunctionChangeEvent |
Functions might be instances of property change listeners, especially if
used in a beans/gui context. |
| Gamma |
Class containing statically versions of the gamma and incomplete gamma
function. |
| Gamma.Erf |
Error function. |
| Gamma.InverseErf |
Inverse of error function: determine the argument, where erf(x) =
the given vaule. |
| gaussLineFit |
Class to contain all variables and routines necessary to calculate a
gaussian (least squares) line fit thru given points (xi, yi, sig_i). |
| generalLinearRegression |
Abstract base class for all general linear least square fits. |
| GeneralLinearRegression |
Base class for all general linear least square fits. |
| GeneralLinearRegression.PseudoLine |
A class to write a file with three columns, first being x between
zero and one, y being a fit like y=kx+d+random, tird column being the
random number denoted above as measurement error. |
| GeneralLinearRegression.StraightLine |
We fit data to a straight line. |
| grow_arr |
Given is an array and a double value that should be added on the end of the
field. |
| HarmonicFit |
A fit to a cosine wave with phase shift, variable period to
the input data. |
| HarmonicFit.HarmonicModel |
Our data model. |
| Harmonics |
A function generating oscilatory function plus overtones. |
| IdentFunction |
Delivers the argument. |
| ImageMoments |
Similar to one-dimension power sums or CentralMoments this class
calculates image moments meaning statistic properties in a two-dimensional
image. |
| ImageMoments.DualIndex |
Helper class that combines to integer indices, both hermitian, to a
single key. |
| ImageMoments.Focus |
This class reads an fits image and calculates the requested image
moment. |
| IntegerMoments |
Moments is a stastistic package that allows calculation of sample central
moments of discrete samples. |
| IntegerMoments.FitsMoments |
Grabs a fits file and displays the moments up to the order given in
the optional second command line argument. |
| LeastFourier |
This analyser recursively fits fourier series like |
| LeastFourier.File |
Use the fourier least-squares on file data. |
| Legendre |
The recursive formular for the Legendre polynomials. |
| Legendre.Calc |
|
| Legendre.Print |
|
| LevenbergMarquardt |
Solves for a data model fit using Levenberg-Marquardt algorithm as in
Num. |
| LevenbergMarquardt.LMSineModel |
solve for phase, amplitude and frequency of a sinusoid |
| LevenbergMarquardt.LMSineTest |
|
| LevenbergMarquardt.SinusModel |
|
| LevenbergMarquardt.SinusTest |
We test with a simple sinus fit. |
| LinearPrediction |
A class based on the linear prediction code given in |
| LinearPrediction.Debug |
A debugging class. |
| linearRegression |
Class to contain all variables and routines necessary to calculate linear
regression. |
| LinearSmoothing |
Base class for linear digital smoothing. |
| LinearSmoothing.Average |
|
| LinearSmoothing.File |
Reads the specified column of a data file and does a moving-average
filtering. |
| LineFit |
Abstract class that holds necessary quantities for fitting (xi,yi) data
with a given standard deviaition sig_i to a straight line. |
| LineVector |
A class that converts a string consiting of individual tokens into a
VectorG of doubles. |
| LombPeriodogram |
Does the calculation of a lomb periodogram using the description in
Num. |
| LombPeriodogram.File |
File data parser. |
| Math2 |
Provides some more sophisticated mathemtical functions. |
| Math2.Airy |
|
| Math2.InterQuartile |
|
| Math2.Newton |
|
| Math2.Spline |
|
| Matrix |
Class to provide basic matrix functions, like multiplication (matrix or
scalar), addition, transposing... |
| Matrix.CloneTest |
Testing cloning. |
| MaximumEntropy |
A class that uses maximum entropy to estimate a power spectrum. |
| MinimumStringLength |
Does the calculation of a period using string length minimization
according to Dworetsky, MNRAS 203, 917. |
| MinimumStringLength.File |
Use the phase dispersion on file data. |
| MinimumStringLength.Test |
Test against Kepler orbits. |
| Moments |
Moments is a stastistic package that allows calculation of sample central
moments of discrete samples. |
| Moments.Constant |
Tests the class by generating a sequence of variables with constant
probability in the interval 0,1. |
| Moments.FitsMoments |
Grabs a fits file and displays the moments up to the order given in
the optional second command line argument. |
| Moments.Gauss |
Tests the class by generating a sequence of gaussian variables. |
| Moments.Ident |
Test the class by generating a sequence with known moments. |
| Moments.Poisson |
Tests the class by generating a sequence of gaussian variables. |
| Moments.Static |
Test the class by generating a sequence with known moments. |
| MultipleFrequencyFit |
This analyser recursively fits fourier series like |
| MultipleFrequencyFit.File |
Use the fourier least-squares on file data. |
| Node<E> |
A node in a parser scheme. |
| Operator<E> |
An abstract definition of mathematical operators. |
| PairError |
A simple class that collects a double pair (x,y) and their errors (dx,dy). |
| PhaseDispersionMinimization |
Does the calculation of a period using phase dispersion minimization
according to Stellignwerf, ApJ 224, 953 (1978). |
| PhaseDispersionMinimization.File |
Use the phase dispersion on file data. |
| PhaseRegression |
An analyser that fits a cosine wave with phase shift, but fixed period to
the input data. |
| PhaseRegression.PhaseModel |
Our data model. |
| Phasing |
Calculates the phase out of the argument. |
| Phasing.PhaseFunction |
This is the function version of it. |
| Phasing.PhaseMultidimensional |
The multidimensional version of it, only implemented to work for
one-dimensional vectors. |
| PoissonRandom |
Extends the random function to include Poisson-distributed random variable
Uses algorithm of Ahrean & Dieter, 'Computer Methods for Sampling
from Gamma, Beta, Poisson and Binominal Distributions', Computing 12, p223ff
1974. |
| PoissonRandom.MedianGauss |
Class to take a normal-distributed variable, sort them, print
the mean and the sigma. |
| PoissonRandom.Poisson |
Class to produce poisson distributed variable with the faster
poisson method. |
| PoissonRandom.SimulFits |
Class to simulate a purely photon and/or shot-noise dominated fits
image. |
| PoissonRandom.TestGamma |
Class to test the slow gammaSplit(a) method against the faster
gamma method. |
| PoissonRandom.TestPoisson |
Class to test the slow poissonMM(a) method against the faster
poisson method. |
| polar2D |
Extends the vector2D for capabilities to convert (x,y) into (r,phi). |
| Polynom |
A representation of a polynomial of the form |
| PowerSpectrum |
A class combining different methods to construct a power spectrum. |
| PowerSpectrum.BartlettWindow |
The Bartlett window function. |
| PowerSpectrum.SquareWindow |
The Rectangular window function. |
| PowerSpectrum.Test |
Test of periodogram. |
| PowerSpectrum.WelchWindow |
The Welch window function. |
| QuadMatrix |
Extension to the NxM matrix functions Matrix. |
| QuadMatrix.Test |
Testing. |
| RecursivePolynom |
A recursive polynom, like Chebyshev or Legendre, are defined by a
minimum and maximum of the parameter space plus the coefficients. |
| RecursivePolynom.Constant |
The zero-order recursive polynom is a constant. |
| RecursivePolynom.Linear |
The first-order recursive polynom is linear. |
| SavitzkyGolaySmoothing |
This class provides a Savitky-Golay Digital Smoothing filter. |
| SigmaClip |
A simple class that allows recursive sigma-clipping on an input vector. |
| SimpleDifferentiator |
The simpliest Differentiator possible. |
| SimpleDifferentiator.E |
Tests the integrator. |
| SimpleIntegrator |
The simpliest Integrator possible. |
| SimpleIntegrator.E |
Tests the integrator. |
| SimpleIntegrator.Gauss |
Tests the integrator. |
| SimpleIntegrator.Xsquare |
Tests the integrator. |
| SmoothParabola |
This class searches a smoothened maximum/minimum for a tabulated function,
whose extremum can be fitted quite well with a parabola. |
| Spherical |
A class with statical methods used in spherical trigonometrie. |
| SphericalHarmonics |
Calculates spherical harmonics. |
| SphericalHarmonics.Orthogonal |
For testing of these classes, we integrate numerically in theta space. |
| Statistic |
Simple statistics package. |
| Statistic.Column |
Reads the column of the given ascii file and prints out statistic info. |
| Statistic.Gauss |
Test the statistic with a randomly generated normally distibuted
statistic. |
| Statistic.ResidualComparator |
Sorts values according to their absolute residual from the given
average. |
| Statistic.ValidArray |
Container class to allow return of an array with only the first
Statistic.ValidArray.validlength elements being valid. |
| StatisticAnalyser |
Acts on data at a given index. |
| StatisticAnalyser.File |
Use the statistic on file data. |
| statTester |
Testing statistic meanings: out of a (uniform) distribution, for N=2
to \infty values are MC-generated, the actual mean, stddev is displayed. |
| StepFunction |
A function that is piecewise constant. |
| StringNode |
A node that is parseable from a string. |
| Triangle |
This class describes a triangle in 2D-space. |
| vector |
class providing simple vector operations. |
| Vector1D |
This class is the definition of a simple, one dimensional vector. |
| vector2D |
This class is the definition of a simple, two dimensional vector. |
| Vector2D |
This class is the definition of a simple, two dimensional vector. |
| vector3D |
This class is the definition of a simple, two dimensional vector. |
| Vector3D |
This class is the definition of a simple, three dimensional vector. |
| VectorG |
The definition of a N-dimensional vector. |
| VectorG.CloneTest |
Testing cloning. |
| VectorG.EuclidianMetric |
|
| VectorG.IComp |
Special index comparator. |
| VectorG.LengthComparator |
Compares the length of two vectors. |
| VectorRoot |
A class to find a multidimensional root of a set of equations using a
multidimension newton-raphson, according to Num. |
| WeightedStatistic |
Abstract base class for statistic purpose. |
| Zernike |
This is a collection of Zernike function, heavily based on the donut code
distributed as an IDL program. |