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See the recommended documentation of this function

Справка Scilab >> Interpolation > interp


cubic spline evaluation function

Calling Sequence

[ yp [,yp1 [,yp2 [,yp3]]]] = interp(xp, x, y, d [, out_mode])



real vectors of same size n: Coordinates of data points on which the interpolation and the related cubic spline (called s(X) in the following) or sub-spline function is based and built.


real vector of size(x): The derivative s'(x). Most often, s'(x) will be priorly estimated through the function splin(x, y,..)


(optional) string defining s(X) for X outside . Possible values: "by_zero" | "by_nan" | "C0" | "natural" | "linear" | "periodic"


real vector or matrix: abscissae at which Y is unknown and must be estimated with s(xp)


vector or matrix of size(xp): yp(i) = s(xp(i)) or yp(i,j) = s(xp(i,j))

yp1, yp2, yp3

vectors (or matrices) of size(x): elementwise evaluation of the derivatives s'(xp), s''(xp) and s'''(xp).


The cubic spline function s(X) interpolating the (x,y) set of given points is a continuous and derivable piece-wise function defined over . It consists of a set of cubic polynomials, each one being defined on and connected in values and slopes to both its neighbours. Thus, we can state that for each , such that . Then, interp() evaluates s(X) (and s'(X), s''(X), s'''(X) if needed) at xp(i), such that

The out_mode parameter set the evaluation rule for extrapolation, i.e. for xp(i) outside :


an extrapolation by zero is done


extrapolation by Nan (%nan)


the extrapolation is defined as follows :


the extrapolation is defined as follows ( being the polynomial defining s(X) on )


the extrapolation is defined as follows :


s(X) is extended by periodicity:


// see the examples of splin and lsq_splin

// an example showing C2 and C1 continuity of spline and subspline
a = -8; b = 8;
x = linspace(a,b,20)';
y = sinc(x);
dk = splin(x,y);  // not_a_knot
df = splin(x,y, "fast");
xx = linspace(a,b,800)';
[yyk, yy1k, yy2k] = interp(xx, x, y, dk); 
[yyf, yy1f, yy2f] = interp(xx, x, y, df); 
plot2d(xx, [yyk yyf])
plot2d(x, y, style=-9)
legends(["not_a_knot spline","fast sub-spline","interpolation points"],...
        [1 2 -9], "ur",%f)
xtitle("spline interpolation")
plot2d(xx, [yy1k yy1f])
legends(["not_a_knot spline","fast sub-spline"], [1 2], "ur",%f)
xtitle("spline interpolation (derivatives)")
plot2d(xx, [yy2k yy2f])
legends(["not_a_knot spline","fast sub-spline"], [1 2], "lr",%f)
xtitle("spline interpolation (second derivatives)")
// here is an example showing the different extrapolation possibilities
x = linspace(0,1,11)';
y = cosh(x-0.5);
d = splin(x,y);
xx = linspace(-0.5,1.5,401)';
yy0 = interp(xx,x,y,d,"C0");
yy1 = interp(xx,x,y,d,"linear");
yy2 = interp(xx,x,y,d,"natural");
yy3 = interp(xx,x,y,d,"periodic");  
plot2d(xx,[yy0 yy1 yy2 yy3],style=2:5,frameflag=2,leg="C0@linear@natural@periodic")
xtitle(" different way to evaluate a spline outside its domain")

See Also

  • splin — cubic spline interpolation
  • lsq_splin — weighted least squares cubic spline fitting


5.4.0 previously, imaginary part of input arguments were implicitly ignored.
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Last updated:
Thu Oct 02 14:01:05 CEST 2014