splin2d - bicubic spline gridded 2d interpolation
This function computes a bicubic spline or sub-spline s which interpolates the (xi,yj,zij) points, ie, we have s(xi,yj)=zij for all i=1,..,nx and j=1,..,ny. The resulting spline s is defined by the triplet (x,y,C) where C is the vector (of length 16(nx-1)(ny-1)) with the coefficients of each of the (nx-1)(ny-1) bicubic patches : on [x(i) x(i+1)]x[y(j) y(j+1)], s is defined by :
_3_ _3_ \ \ k l s(x,y) = / / C (k,l) (x - xi) (y - yj) --- --- ij k=0 l=0
The evaluation of s at some points must be done by the interp2d function. Several kind of splines may be computed by selecting the appropriate spline_type parameter. The method used to compute the bicubic spline (or sub-spline) is the old fashionned one 's, i.e. to compute on each grid point (xi,yj) an approximation of the first derivatives ds/dx(xi,yj) and ds/dy(xi,yj) and of the cross derivative d2s/dxdy(xi,yj). Those derivatives are computed by the mean of 1d spline schemes leading to a C2 function (s is twice continuously differentiable) or by the mean of a local approximation scheme leading to a C1 function only. This scheme is selected with the spline_type parameter (see splin for details) :
From an accuracy point of view use essentially the not_a_knot type or periodic type if the underlying interpolated function is periodic.
The natural, monotone, fast (or fast_periodic) type may be useful in some cases, for instance to limit oscillations (monotone being the most powerfull for that).
// example 1 : interpolation of cos(x)cos(y) n = 7; // a regular grid with n x n interpolation points // will be used x = linspace(0,2*%pi,n); y = x; z = cos(x')*cos(y); C = splin2d(x, y, z, "periodic"); m = 50; // discretisation parameter of the evaluation grid xx = linspace(0,2*%pi,m); yy = xx; [XX,YY] = ndgrid(xx,yy); zz = interp2d(XX,YY, x, y, C); emax = max(abs(zz - cos(xx')*cos(yy))); xbasc() plot3d(xx, yy, zz, flag=[2 4 4]) [X,Y] = ndgrid(x,y); param3d1(X,Y,list(z,-9*ones(1,n)), flag=[0 0]) str = msprintf(" with %d x %d interpolation points. ermax = %g",n,n,emax) xtitle("spline interpolation of cos(x)cos(y)"+str) // example 2 : different interpolation functions on random datas n = 6; x = linspace(0,1,n); y = x; z = rand(n,n); np = 50; xp = linspace(0,1,np); yp = xp; [XP, YP] = ndgrid(xp,yp); ZP1 = interp2d(XP, YP, x, y, splin2d(x, y, z, "not_a_knot")); ZP2 = linear_interpn(XP, YP, x, y, z); ZP3 = interp2d(XP, YP, x, y, splin2d(x, y, z, "natural")); ZP4 = interp2d(XP, YP, x, y, splin2d(x, y, z, "monotone")); xset("colormap", jetcolormap(64)) xbasc() subplot(2,2,1) plot3d1(xp, yp, ZP1, flag=[2 2 4]) xtitle("not_a_knot") subplot(2,2,2) plot3d1(xp, yp, ZP2, flag=[2 2 4]) xtitle("bilinear interpolation") subplot(2,2,3) plot3d1(xp, yp, ZP3, flag=[2 2 4]) xtitle("natural") subplot(2,2,4) plot3d1(xp, yp, ZP4, flag=[2 2 4]) xtitle("monotone") xselect() // example 3 : not_a_knot spline and monotone sub-spline // on a step function a = 0; b = 1; c = 0.25; d = 0.75; // create interpolation grid n = 11; x = linspace(a,b,n); ind = find(c <= x & x <= d); z = zeros(n,n); z(ind,ind) = 1; // a step inside a square // create evaluation grid np = 220; xp = linspace(a,b, np); [XP, YP] = ndgrid(xp, xp); zp1 = interp2d(XP, YP, x, x, splin2d(x,x,z)); zp2 = interp2d(XP, YP, x, x, splin2d(x,x,z,"monotone")); // plot xbasc() xset("colormap",jetcolormap(128)) subplot(1,2,1) plot3d1(xp, xp, zp1, flag=[-2 6 4]) xtitle("spline (not_a_knot)") subplot(1,2,2) plot3d1(xp, xp, zp2, flag=[-2 6 4]) xtitle("subspline (monotone)")
cshep2d , linear_interpn , interp2d ,
B. Pincon