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Cubic spline interpolation with end conditions


pp = csape(x,y)
pp = csape(x,y,conds)


pp = csape(x,y) is the ppform of a cubic spline s with knot sequence x that satisfies s(x(j)) = y(:,j) for all j, as well as an additional end condition at the ends (meaning the leftmost and at the rightmost data site), namely the default condition listed below. The data values y(:,j) may be scalars, vectors, matrices, even ND-arrays. Data values at the same data site are averaged.

pp = csape(x,y,conds) lets you choose the end conditions to be used, from a rather large and varied catalog, by proper choice of conds. If needed, you supply the corresponding end condition values as additional data values, with the first (last) data value taken as the end condition value at the left (right) end. In other words, in that case, s(x(j)) matches y(:,j+1) for all j, and the variable endcondvals used in the detailed description below is set to y(:,[1 end]). For some choices of conds, these end condition values need not be present and/or are ignored when present.

conds may be a character vector whose first character matches one of the following: 'complete' or 'clamped', 'not-a-knot', 'periodic', 'second', 'variational', with the following meanings.

'complete' or 'clamped'

Match endslopes (as given, with default as under “default”).


Make second and second-last sites inactive knots (ignoring end condition values if given).


Match first and second derivatives at left end with those at right end.


Match end second derivatives (as given, with default [0 0], i.e., as in 'variational').


Set end second derivatives equal to zero (ignoring end condition values if given).


Match endslopes to the slope of the cubic that matches the first four data at the respective end (i.e., Lagrange).

By giving conds as a 1-by-2 matrix instead, it is possible to specify different conditions at the two ends. Explicitly, the ith derivative, Dis, is given the value endcondvals(:,j) at the left (j is 1) respectively right (j is 2) end in case conds(j) is i,i = 1:2. There are default values for conds and/or endcondvals.  

Available conditions are:   


Ds(e) = endcondvals(:,j)

if conds(j) == 1


D2s(e) = endcondvals(:,j)

if conds(j) == 2


Ds(e) = Dp(e)



Drs(a) = Drs(b), r = 1,2

if conds == [0 0]


D2s(e) = 0

if conds(j) == 2 & endcondvals(:,j) == 0

Here, e is a (e is b), i.e., the left (right) end, in case j is 1 (j is 2), and (in the Lagrange condition) P is the cubic polynomial that interpolates to the given data at e and the three sites nearest e.

If conds(j) is not specified or is different from 0, 1, or 2, then it is taken to be 1 and the corresponding endcondvals(:,j) is taken to be the corresponding default value.

The default value for endcondvals(:,j) is the derivative of the cubic interpolant at the nearest four sites in case conds(j) is 1, and is 0 otherwise.

It is also possible to handle gridded data, by having x be a cell array containing m univariate meshes and, correspondingly, having y be an m-dimensional array (or an m+r-dimensional array if the function is to be r-valued). Correspondingly, conds is a cell array with m entries, and end condition values may be correspondingly supplied in each of the m variables. This, as the last example below, of bicubic spline interpolation, makes clear, may require you to supply end conditions for end conditions.

This command calls on a much expanded version of the Fortran routine CUBSPL in PGS.


csape(x,y) provides the cubic spline interpolant with the Lagrange end conditions, while csape(x,y,[2 2]) provides the variational, or natural cubic spline interpolant, as does csape(x,y,'v'). csape([-1 1],[3 -1 1 6],[1 2]) provides the cubic polynomial p for which Dp(–1) = 3, p(–1) = –1, p(1) = 1, D2p(1) = 6, i.e., p(x) = x3. Finally, csape([-1 1],[-1 1]) provides the straight line p for which p(±1) = ±1, i.e., p(x) = x.

End conditions other than the ones listed earlier can be handled along the following lines. Suppose that you want to enforce the condition


for given scalars a, b, and c, and with e equal to x(1). Then one could compute the cubic spline interpolant s1 to the given data using the default end condition as well as the cubic spline interpolant s0 to zero data and some (nontrivial) end condition at e, and then obtain the desired interpolant in the form


Here are the (not inconsiderable) details (in which the first polynomial piece of s1 and s0 is pulled out to avoid differentiating all of s1 and s0):

% Data: x and y
[x, y] = titanium();

% Scalars a, b, and c
a = -2;
b = -1;
c = 0;

% End condition at left
e = x(1);

% The cubic spline interpolant s1 to the
% given data using the default end
% condition
s1 = csape(x,y);

% The cubic spline interpolant s0 to 
% zero data and some (nontrivial) end
% condition at e
s0 = csape(x,[1,zeros(1,length(y)),0],[1,0]);

% Compute the derivatives of the first
%  polynomial piece of s1 and s0
ds1 = fnder(fnbrk(s1,1));
ds0 = fnder(fnbrk(s0,1));

% Compute interpolant with desired end conditions
lam1 = a*fnval(ds1,e) + b*fnval(fnder(ds1),e);
lam0 = a*fnval(ds0,e) + b*fnval(fnder(ds0),e);
pp = fncmb(s0,(c-lam1)/lam0,s1);

Plot to see the results:

fnplt( pp, [594, 632] )
hold on
fnplt( s1, 'b--', [594, 632] )
plot( x, y, 'ro', 'MarkerFaceColor', 'r' )
hold off
axis( [594, 632, 0.62, 0.655] )
legend 'Desired end-conditions' ...
'Default end-conditions' 'Data' ...
    Location SouthEast

As a multivariate vector-valued example, here is a sphere, done as a parametric bicubic spline, 3D-valued, using prescribed slopes in one direction and periodic end conditions in the other:

x = 0:4; y=-2:2; s2 = 1/sqrt(2);

v = zeros( 3, 7, 5 );
v(1,:,:) = [1 0 s2 1 s2 0 -1].'*[1 0 -1 0 1];
v(2,:,:) = [1 0 s2 1 s2 0 -1].'*[0 1 0 -1 0];
v(3,:,:) = [0 1 s2 0 -s2 -1 0].'*[1 1 1 1 1];

sph = csape({x,y},v,{'clamped','periodic'});
values = fnval(sph,{0:.1:4,-2:.1:2});

surf( squeeze(values(1,:,:)), ...
squeeze(values(2,:,:)), squeeze(values(3,:,:)) );

axis equal
axis off

The lines involving fnval and surf could have been replaced by the simple command: fnplt(sph). Note that v is a 3-dimensional array, with v(:,i+1,j) the 3-vector to be matched at (x(i),y(j)), i=1:5, j=1:5. Note further that, in accordance with conds{1} being 'clamped', size(v,2) is 7 (and not 5), with the first and last entry of v(r,:,j) specifying the end slopes to be matched.

Here is a bivariate example that shows the need for supplying end conditions of end conditions when supplying end conditions in both variables. You reproduce the bicubic polynomial g(x,y) = x^3y^3 by complete bicubic interpolation. You then derive the needed data, including end condition values, directly from g in order to make it easier for you to see just how the end condition values must be placed. Finally, you check the result.

sites = {[0 1],[0 2]}; coefs = zeros(4, 4); coefs(1,1) = 1;
g = ppmak(sites,coefs);
Dxg = fnval(fnder(g,[1 0]),sites);
Dyg = fnval(fnder(g,[0 1]),sites);
Dxyg = fnval(fnder(g,[1 1]),sites);

f = csape(sites,[Dxyg(1,1),   Dxg(1,:),    Dxyg(1,2); ...
                 Dyg(:,1), fnval(g,sites), Dyg(:,2) ; ...
                 Dxyg(2,1),   Dxg(2,:),    Dxyg(2,2)], ...
if any(squeeze(fnbrk(f,'c'))-coefs)
    disp( 'this is wrong' )

Cautionary Note

csape recognizes that you supplied explicit end condition values by the fact that you supplied exactly two more data values than data sites. In particular, even when using different end conditions at the two ends, if you wish to supply an end condition value at one end, you must also supply one for the other end.


The relevant tridiagonal linear system is constructed and solved using the sparse matrix capabilities of MATLAB®.

See Also

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