spline, a C code which constructs and evaluates spline functions.

These spline functions are typically used to

The most common use of this software is for situations where a set of (X,Y) data points is known, and it is desired to determine a smooth function which passes exactly through those points, and which can be evaluated everywhere. Thus, it is possible to get a formula that allows you to "connect the dots".

Of course, you could could just connect the dots with straight lines, but that would look ugly, and if there really is some function that explains your data, you'd expect it to curve around rather than make sudden angular turns. The functions in SPLINE offer a variety of choices for slinky curves that will make pleasing interpolants of your data.

There are a variety of types of approximation curves available, including:

Also included are a set of routines that return the local "basis matrix", which allows the evaluation of the spline in terms of local function data.


The computer code and data files described and made available on this web page are distributed under the MIT license


spline is available in a C version and a C++ version and a FORTRAN90 version and a MATLAB version.

Related Data and Programs:

bernstein_polynomial, a C code which evaluates the Bernstein polynomials, useful for uniform approximation of functions;

CHEBYSHEV, a C code which computes the Chebyshev interpolant/approximant to a given function over an interval.

DIVDIF, a C code which uses divided differences to interpolate data.

HERMITE_CUBIC, a C code which can compute the value, derivatives or integral of a Hermite cubic polynomial, or manipulate an interpolating function made up of piecewise Hermite cubic polynomials.

LAGRANGE_INTERP_1D, a C code which defines and evaluates the Lagrange polynomial p(x) which interpolates a set of data, so that p(x(i)) = y(i).


TEST_APPROX, a C code which defines test problems for approximation, provided as a set of (x,y) data.

TEST_INTERP_1D, a C code which defines test problems for interpolation of data y(x), depending on a 1D argument.


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Source Code:

Last revised on 08 August 2019.