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.

Calculate the Bezier cubic spline interpolation for the given abscissa vector x and ordinate vector y. All vectors must have conformant dimensions.

Class to spline bivariate function data (in gridded form).

Condition how to manage the left and right boundary of a spline.

Calculate the Cardinal cubic spline interpolation for the given abscissa vector x and ordinate vector y. All vectors must have conformant dimensions.

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.

Base for most interpolations.

Exponential Splines.

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.

Represents an interpolation curve. The curve is parametrized using a parameter u. Because of the parametrization, it is possible that for some x values there can exist more than one corresponding y values.

Gives an interpolation function, i.e. for every given x, there is exactly one corresponding y value.

Contains static methods for linear interpolation of data.

curve parametrization methods

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.

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.

Calculates a smoothing cubic spline, whose smoothness is determined by the property SmoothingCubicSpline.Smoothness.

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.