| | Class | Description |
|---|
 | AkimaCubicSpline |
Akima cubic spline interpolation for the given abscissa
vector x and ordinate vector y.
All vectors must have conformant dimenions.
The abscissa vector must be strictly increasing.
|
| AkimaCubicSplineOptions |
Options for an Akima cubic spline (AkimaCubicSpline).
|
| AkimaCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| Barycentric |
Barycentric Interpolation Algorithm.
|
| BezierCubicSpline |
Calculate the Bezier cubic spline interpolation for the
given abscissa vector x and ordinate vector y.
All vectors must have conformant dimensions.
|
| BezierCubicSplineOptions |
Options for a Bezier cubic spline (BezierCubicSpline).
|
| BezierCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| BivariateAkimaSpline |
Class to spline bivariate function data (in gridded form).
|
| BivariateLinearSpline | |
| BSpline1D |
Implements a BSpline in one dimension.
|
| BSpline1DOptions |
Options for a cross validated cubic spline (CrossValidatedCubicSpline).
|
| BSpline1DOptionsSerializationSurrogate0 |
2023-03-07 initial version
|
| BulirschStoerRationalInterpolation |
Rational Interpolation (with poles) using Roland Bulirsch and Josef Stoer's Algorithm.
|
| CardinalCubicSpline |
Calculate the Cardinal cubic spline interpolation for the
given abscissa vector x and ordinate vector y.
All vectors must have conformant dimensions.
|
| CardinalCubicSplineOptions |
Options for a cardinal cubic spline (CardinalCubicSpline).
|
| CardinalCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| CrossValidatedCubicSpline |
Calculates a natural cubic spline curve which smoothes a given set
of data points, using statistical considerations to determine the amount
of smoothing required as described in reference 2.
|
| CrossValidatedCubicSplineOptions |
Options for a cross validated cubic spline (CrossValidatedCubicSpline).
|
| CrossValidatedCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| CubicSpline |
Cubic Spline Interpolation.
|
| CurveBase |
Base for most interpolations.
|
| ExponentialSpline |
Exponential Splines.
|
| ExponentialSplineOptions |
Options for an exponential spline (ExponentialSpline).
|
| ExponentialSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| FritschCarlsonCubicSpline |
Calculate the Fritsch-Carlson monotone cubic spline interpolation for the
given abscissa vector x and ordinate vector y.
All vectors must have conformant dimenions.
The abscissa vector must be strictly increasing.
The Fritsch-Carlson interpolation produces a neat monotone
piecewise cubic curve, which is especially suited for the
presentation of scientific data.
This is the state of the art to create curves that preserve
monotonicity, although it is not so well known as Akima's
interpolation. The commonly used Akima interpolation doesn't
produce so pleasant results.
Reference:
F.N.Fritsch,R.E.Carlson: Monotone Piecewise Cubic
Interpolation, SIAM J. Numer. Anal. Vol 17, No. 2,
April 1980
Copyright (C) 1991-1998 by Berndt M. Gammel
Translated to C# by Dirk Lellinger. |
| FritschCarlsonCubicSplineOptions |
Options for an Fritsch-Carlson cubic spline (FritschCarlsonCubicSpline).
|
| FritschCarlsonCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| LinearInterpolation |
Contains static methods for linear interpolation of data.
|
| LinearInterpolationOptions |
Options for a linear interpolation (LinearInterpolation).
|
| LinearInterpolationOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| LinearSpline |
Piece-wise Linear Interpolation.
|
| LogLinear |
Piece-wise Log-Linear Interpolation
|
| NevillePolynomialInterpolation |
Lagrange Polynomial Interpolation using Neville's Algorithm.
|
| NonlinearFitAsInterpolation |
Uses a non-linear fit as a interpolation. Initial parameters for the fit must be provided beforehand, thus this interpolation
is limited to use cases for which approximate parameters are already known.
|
| NonlinearFitAsInterpolationSerializationSurrogate0 |
2024-02-27 V0
|
| PolyharmonicSpline |
Interpolation method for scattered data in any dimension based on radial basis functions.
In 2D this is the so called Thin Plate Spline, which is an interpolation method that finds a "minimally bended"
smooth surface that passes through all given points.
The polyharmonic spline has an arbitrary number of dimensions and arbitrary derivative order.
Note: The allocation space requirement is in the order (N+3)*(N+3), where N is the number of control points. Thus it is not applicable for too many points.
|
| PolyharmonicSpline1DOptions | |
| PolyharmonicSpline1DOptionsSerializationSurrogate0 |
2022-08-18 initial version
|
| PolynomialInterpolation | |
| PolynomialInterpolationOptions |
Options for a polynomial interpolation (PolynomialInterpolationOptions).
|
| PolynomialInterpolationOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| PolynomialRegressionAsInterpolation | |
| PolynomialRegressionAsInterpolationOptions |
Options for a polynomial regression used as interpolation method (PolynomialRegressionAsInterpolation).
|
| PolynomialRegressionAsInterpolationOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| PronySeriesFrequencyDomainComplexInterpolation |
Interpolation with a sum of Prony terms of a complex relaxation or retardation function in frequency domain.
Note that for a relaxation the real part is increasing with frequency (e.g. complex mechanical modulus), whereas for a retardation the real part is decreasing with frequency (e.g. complex electrical permittivity).
We assume here that even for a retardation the imaginary part is positive: eps* = eps' - i eps''.
|
| PronySeriesFrequencyDomainComplexInterpolationSerializationSurrogate0 |
2024-02-18 V0: initial version
|
| PronySeriesFrequencyDomainImaginaryPartInterpolation |
Interpolation with a sum of Prony terms of the real part of a relaxation or retardation function in frequency domain.
Note that for a relaxation the real part is increasing with frequency (e.g. real part of mechanical modulus), whereas for a retardation the real part is decreasing with frequency (e.g. real part of electrical permittivity)
|
| PronySeriesFrequencyDomainImaginaryPartInterpolationSerializationSurrogate0 |
2024-02-18 V0: initial version
|
| PronySeriesFrequencyDomainMagnitudeInterpolation |
Interpolation with a sum of Prony terms of the magnitude of a relaxation or retardation function in frequency domain.
Note that for a relaxation the magnitude is increasing with frequency (e.g. the magnitude of the complex mechanical modulus),
whereas for a retardation the magnitude is decreasing with frequency (e.g. the magnitude of the complex electrical permittivity).
|
| PronySeriesFrequencyDomainMagnitudeInterpolationSerializationSurrogate0 |
2024-02-18 V0: initial version
|
| PronySeriesFrequencyDomainRealPartInterpolation |
Interpolation with a sum of Prony terms of the real part of a relaxation or retardation function in frequency domain.
Note that for a relaxation the real part is increasing with frequency (e.g. real part of mechanical modulus), whereas for a retardation the real part is decreasing with frequency (e.g. real part of electrical permittivity)
|
| PronySeriesFrequencyDomainRealPartInterpolationSerializationSurrogate0 |
2024-02-18 V0: initial version
|
| PronySeriesInterpolationBase |
Base class of the options for Prony series interpolation, both in the time domain as well as in the frequency domain.
|
 | PronySeriesInterpolationBaseInterpolationResultComplexWrapper | |
| PronySeriesInterpolationBaseInterpolationResultDoubleWrapper | |
| PronySeriesTimeDomainInterpolation |
Interpolation with a sum of Prony terms in time domain, either a relaxation (a time-decreasing function, e.g. a time dependent modulus), or a retardation (a time-increasing function, e.g. a time-dependent compliance).
|
| PronySeriesTimeDomainInterpolationSerializationSurrogate0 |
2023-06-16 V0 initial version was Altaxo.Calc.Interpolation.PronySeriesAsInterpolationOptions (AltaxoCore)
2024-02-01 V1
|
| QuadraticSpline |
Quadratic Spline Interpolation.
|
| RationalCubicSpline |
This kind of generalized splines give much more pleasent results
than cubic splines when interpolating, e.g., experimental data.
A control parameter p can be used to tune the interpolation smoothly
between cubic splines and a linear interpolation.
But this doesn't mean smoothing of the data - the rational spline curve
will still go through all data points.
|
| RationalCubicSplineOptions |
Options for a rational cubic spline (RationalCubicSpline).
|
| RationalCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| RationalInterpolation | |
| RationalInterpolationOptions |
Options for a rational interpolation (RationalInterpolation).
|
| RationalInterpolationOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| SmoothingCubicSpline |
Calculates a smoothing cubic spline, whose smoothness is determined by the property Smoothness.
|
| SmoothingCubicSplineBase |
Calculates a natural cubic spline curve which smoothes a given set
of data points, using statistical considerations to determine the amount
of smoothing required as described in reference 2.
|
| SmoothingCubicSplineOptions |
Options for a smoothing cubic spline (SmoothingCubicSpline).
|
| SmoothingCubicSplineOptionsSerializationSurrogate0 |
2022-08-14 initial version
|
| StepInterpolation |
A step function where the start of each segment is included, and the last segment is open-ended.
Segment i is [x_i, x_i+1) for i < N, or [x_i, infinity] for i = N.
The domain of the function is all real numbers, such that y = 0 where x <.
|
| TransformedInterpolation |
Wraps an interpolation with a transformation of the interpolated values.
|
Classes
