subroutine surfit(iopt,m,x,y,z,w,xb,xe,yb,ye,kx,ky,s,nxest,nyest, * nmax,eps,nx,tx,ny,ty,c,fp,wrk1,lwrk1,wrk2,lwrk2,iwrk,kwrk,ier) c given the set of data points (x(i),y(i),z(i)) and the set of positive c numbers w(i),i=1,...,m, subroutine surfit determines a smooth bivar- c iate spline approximation s(x,y) of degrees kx and ky on the rect- c angle xb <= x <= xe, yb <= y <= ye. c if iopt = -1 surfit calculates the weighted least-squares spline c according to a given set of knots. c if iopt >= 0 the total numbers nx and ny of these knots and their c position tx(j),j=1,...,nx and ty(j),j=1,...,ny are chosen automatic- c ally by the routine. the smoothness of s(x,y) is then achieved by c minimalizing the discontinuity jumps in the derivatives of s(x,y) c across the boundaries of the subpanels (tx(i),tx(i+1))*(ty(j),ty(j+1). c the amounth of smoothness is determined by the condition that f(p) = c sum ((w(i)*(z(i)-s(x(i),y(i))))**2) be <= s, with s a given non-neg- c ative constant, called the smoothing factor. c the fit is given in the b-spline representation (b-spline coefficients c c((ny-ky-1)*(i-1)+j),i=1,...,nx-kx-1;j=1,...,ny-ky-1) and can be eval- c uated by means of subroutine bispev. c c calling sequence: c call surfit(iopt,m,x,y,z,w,xb,xe,yb,ye,kx,ky,s,nxest,nyest, c * nmax,eps,nx,tx,ny,ty,c,fp,wrk1,lwrk1,wrk2,lwrk2,iwrk,kwrk,ier) c c parameters: c iopt : integer flag. on entry iopt must specify whether a weighted c least-squares spline (iopt=-1) or a smoothing spline (iopt=0 c or 1) must be determined. c if iopt=0 the routine will start with an initial set of knots c tx(i)=xb,tx(i+kx+1)=xe,i=1,...,kx+1;ty(i)=yb,ty(i+ky+1)=ye,i= c 1,...,ky+1. if iopt=1 the routine will continue with the set c of knots found at the last call of the routine. c attention: a call with iopt=1 must always be immediately pre- c ceded by another call with iopt=1 or iopt=0. c unchanged on exit. c m : integer. on entry m must specify the number of data points. c m >= (kx+1)*(ky+1). unchanged on exit. c x : real array of dimension at least (m). c y : real array of dimension at least (m). c z : real array of dimension at least (m). c before entry, x(i),y(i),z(i) must be set to the co-ordinates c of the i-th data point, for i=1,...,m. the order of the data c points is immaterial. unchanged on exit. c w : real array of dimension at least (m). before entry, w(i) must c be set to the i-th value in the set of weights. the w(i) must c be strictly positive. unchanged on exit. c xb,xe : real values. on entry xb,xe,yb and ye must specify the bound- c yb,ye aries of the rectangular approximation domain. c xb<=x(i)<=xe,yb<=y(i)<=ye,i=1,...,m. unchanged on exit. c kx,ky : integer values. on entry kx and ky must specify the degrees c of the spline. 1<=kx,ky<=5. it is recommended to use bicubic c (kx=ky=3) splines. unchanged on exit. c s : real. on entry (in case iopt>=0) s must specify the smoothing c factor. s >=0. unchanged on exit. c for advice on the choice of s see further comments c nxest : integer. unchanged on exit. c nyest : integer. unchanged on exit. c on entry, nxest and nyest must specify an upper bound for the c number of knots required in the x- and y-directions respect. c these numbers will also determine the storage space needed by c the routine. nxest >= 2*(kx+1), nyest >= 2*(ky+1). c in most practical situation nxest = kx+1+sqrt(m/2), nyest = c ky+1+sqrt(m/2) will be sufficient. see also further comments. c nmax : integer. on entry nmax must specify the actual dimension of c the arrays tx and ty. nmax >= nxest, nmax >=nyest. c unchanged on exit. c eps : real. c on entry, eps must specify a threshold for determining the c effective rank of an over-determined linear system of equat- c ions. 0 < eps < 1. if the number of decimal digits in the c computer representation of a real number is q, then 10**(-q) c is a suitable value for eps in most practical applications. c unchanged on exit. c nx : integer. c unless ier=10 (in case iopt >=0), nx will contain the total c number of knots with respect to the x-variable, of the spline c approximation returned. if the computation mode iopt=1 is c used, the value of nx should be left unchanged between sub- c sequent calls. c in case iopt=-1, the value of nx should be specified on entry c tx : real array of dimension nmax. c on succesful exit, this array will contain the knots of the c spline with respect to the x-variable, i.e. the position of c the interior knots tx(kx+2),...,tx(nx-kx-1) as well as the c position of the additional knots tx(1)=...=tx(kx+1)=xb and c tx(nx-kx)=...=tx(nx)=xe needed for the b-spline representat. c if the computation mode iopt=1 is used, the values of tx(1), c ...,tx(nx) should be left unchanged between subsequent calls. c if the computation mode iopt=-1 is used, the values tx(kx+2), c ...tx(nx-kx-1) must be supplied by the user, before entry. c see also the restrictions (ier=10). c ny : integer. c unless ier=10 (in case iopt >=0), ny will contain the total c number of knots with respect to the y-variable, of the spline c approximation returned. if the computation mode iopt=1 is c used, the value of ny should be left unchanged between sub- c sequent calls. c in case iopt=-1, the value of ny should be specified on entry c ty : real array of dimension nmax. c on succesful exit, this array will contain the knots of the c spline with respect to the y-variable, i.e. the position of c the interior knots ty(ky+2),...,ty(ny-ky-1) as well as the c position of the additional knots ty(1)=...=ty(ky+1)=yb and c ty(ny-ky)=...=ty(ny)=ye needed for the b-spline representat. c if the computation mode iopt=1 is used, the values of ty(1), c ...,ty(ny) should be left unchanged between subsequent calls. c if the computation mode iopt=-1 is used, the values ty(ky+2), c ...ty(ny-ky-1) must be supplied by the user, before entry. c see also the restrictions (ier=10). c c : real array of dimension at least (nxest-kx-1)*(nyest-ky-1). c on succesful exit, c contains the coefficients of the spline c approximation s(x,y) c fp : real. unless ier=10, fp contains the weighted sum of c squared residuals of the spline approximation returned. c wrk1 : real array of dimension (lwrk1). used as workspace. c if the computation mode iopt=1 is used the value of wrk1(1) c should be left unchanged between subsequent calls. c on exit wrk1(2),wrk1(3),...,wrk1(1+(nx-kx-1)*(ny-ky-1)) will c contain the values d(i)/max(d(i)),i=1,...,(nx-kx-1)*(ny-ky-1) c with d(i) the i-th diagonal element of the reduced triangular c matrix for calculating the b-spline coefficients. it includes c those elements whose square is less than eps,which are treat- c ed as 0 in the case of presumed rank deficiency (ier<-2). c lwrk1 : integer. on entry lwrk1 must specify the actual dimension of c the array wrk1 as declared in the calling (sub)program. c lwrk1 must not be too small. let c u = nxest-kx-1, v = nyest-ky-1, km = max(kx,ky)+1, c ne = max(nxest,nyest), bx = kx*v+ky+1, by = ky*u+kx+1, c if(bx.le.by) b1 = bx, b2 = b1+v-ky c if(bx.gt.by) b1 = by, b2 = b1+u-kx then c lwrk1 >= u*v*(2+b1+b2)+2*(u+v+km*(m+ne)+ne-kx-ky)+b2+1 c wrk2 : real array of dimension (lwrk2). used as workspace, but c only in the case a rank deficient system is encountered. c lwrk2 : integer. on entry lwrk2 must specify the actual dimension of c the array wrk2 as declared in the calling (sub)program. c lwrk2 > 0 . a save upper boundfor lwrk2 = u*v*(b2+1)+b2 c where u,v and b2 are as above. if there are enough data c points, scattered uniformly over the approximation domain c and if the smoothing factor s is not too small, there is a c good chance that this extra workspace is not needed. a lot c of memory might therefore be saved by setting lwrk2=1. c (see also ier > 10) c iwrk : integer array of dimension (kwrk). used as workspace. c kwrk : integer. on entry kwrk must specify the actual dimension of c the array iwrk as declared in the calling (sub)program. c kwrk >= m+(nxest-2*kx-1)*(nyest-2*ky-1). c ier : integer. unless the routine detects an error, ier contains a c non-positive value on exit, i.e. c ier=0 : normal return. the spline returned has a residual sum of c squares fp such that abs(fp-s)/s <= tol with tol a relat- c ive tolerance set to 0.001 by the program. c ier=-1 : normal return. the spline returned is an interpolating c spline (fp=0). c ier=-2 : normal return. the spline returned is the weighted least- c squares polynomial of degrees kx and ky. in this extreme c case fp gives the upper bound for the smoothing factor s. c ier<-2 : warning. the coefficients of the spline returned have been c computed as the minimal norm least-squares solution of a c (numerically) rank deficient system. (-ier) gives the rank. c especially if the rank deficiency which can be computed as c (nx-kx-1)*(ny-ky-1)+ier, is large the results may be inac- c curate. they could also seriously depend on the value of c eps. c ier=1 : error. the required storage space exceeds the available c storage space, as specified by the parameters nxest and c nyest. c probably causes : nxest or nyest too small. if these param- c eters are already large, it may also indicate that s is c too small c the approximation returned is the weighted least-squares c spline according to the current set of knots. c the parameter fp gives the corresponding weighted sum of c squared residuals (fp>s). c ier=2 : error. a theoretically impossible result was found during c the iteration proces for finding a smoothing spline with c fp = s. probably causes : s too small or badly chosen eps. c there is an approximation returned but the corresponding c weighted sum of squared residuals does not satisfy the c condition abs(fp-s)/s < tol. c ier=3 : error. the maximal number of iterations maxit (set to 20 c by the program) allowed for finding a smoothing spline c with fp=s has been reached. probably causes : s too small c there is an approximation returned but the corresponding c weighted sum of squared residuals does not satisfy the c condition abs(fp-s)/s < tol. c ier=4 : error. no more knots can be added because the number of c b-spline coefficients (nx-kx-1)*(ny-ky-1) already exceeds c the number of data points m. c probably causes : either s or m too small. c the approximation returned is the weighted least-squares c spline according to the current set of knots. c the parameter fp gives the corresponding weighted sum of c squared residuals (fp>s). c ier=5 : error. no more knots can be added because the additional c knot would (quasi) coincide with an old one. c probably causes : s too small or too large a weight to an c inaccurate data point. c the approximation returned is the weighted least-squares c spline according to the current set of knots. c the parameter fp gives the corresponding weighted sum of c squared residuals (fp>s). c ier=10 : error. on entry, the input data are controlled on validity c the following restrictions must be satisfied. c -1<=iopt<=1, 1<=kx,ky<=5, m>=(kx+1)*(ky+1), nxest>=2*kx+2, c nyest>=2*ky+2, 0=nxest, nmax>=nyest, c xb<=x(i)<=xe, yb<=y(i)<=ye, w(i)>0, i=1,...,m c lwrk1 >= u*v*(2+b1+b2)+2*(u+v+km*(m+ne)+ne-kx-ky)+b2+1 c kwrk >= m+(nxest-2*kx-1)*(nyest-2*ky-1) c if iopt=-1: 2*kx+2<=nx<=nxest c xb=0: s>=0 c if one of these conditions is found to be violated,control c is immediately repassed to the calling program. in that c case there is no approximation returned. c ier>10 : error. lwrk2 is too small, i.e. there is not enough work- c space for computing the minimal least-squares solution of c a rank deficient system of linear equations. ier gives the c requested value for lwrk2. there is no approximation re- c turned but, having saved the information contained in nx, c ny,tx,ty,wrk1, and having adjusted the value of lwrk2 and c the dimension of the array wrk2 accordingly, the user can c continue at the point the program was left, by calling c surfit with iopt=1. c c further comments: c by means of the parameter s, the user can control the tradeoff c between closeness of fit and smoothness of fit of the approximation. c if s is too large, the spline will be too smooth and signal will be c lost ; if s is too small the spline will pick up too much noise. in c the extreme cases the program will return an interpolating spline if c s=0 and the weighted least-squares polynomial (degrees kx,ky)if s is c very large. between these extremes, a properly chosen s will result c in a good compromise between closeness of fit and smoothness of fit. c to decide whether an approximation, corresponding to a certain s is c satisfactory the user is highly recommended to inspect the fits c graphically. c recommended values for s depend on the weights w(i). if these are c taken as 1/d(i) with d(i) an estimate of the standard deviation of c z(i), a good s-value should be found in the range (m-sqrt(2*m),m+ c sqrt(2*m)). if nothing is known about the statistical error in z(i) c each w(i) can be set equal to one and s determined by trial and c error, taking account of the comments above. the best is then to c start with a very large value of s ( to determine the least-squares c polynomial and the corresponding upper bound fp0 for s) and then to c progressively decrease the value of s ( say by a factor 10 in the c beginning, i.e. s=fp0/10, fp0/100,...and more carefully as the c approximation shows more detail) to obtain closer fits. c to choose s very small is strongly discouraged. this considerably c increases computation time and memory requirements. it may also c cause rank-deficiency (ier<-2) and endager numerical stability. c to economize the search for a good s-value the program provides with c different modes of computation. at the first call of the routine, or c whenever he wants to restart with the initial set of knots the user c must set iopt=0. c if iopt=1 the program will continue with the set of knots found at c the last call of the routine. this will save a lot of computation c time if surfit is called repeatedly for different values of s. c the number of knots of the spline returned and their location will c depend on the value of s and on the complexity of the shape of the c function underlying the data. if the computation mode iopt=1 c is used, the knots returned may also depend on the s-values at c previous calls (if these were smaller). therefore, if after a number c of trials with different s-values and iopt=1, the user can finally c accept a fit as satisfactory, it may be worthwhile for him to call c surfit once more with the selected value for s but now with iopt=0. c indeed, surfit may then return an approximation of the same quality c of fit but with fewer knots and therefore better if data reduction c is also an important objective for the user. c the number of knots may also depend on the upper bounds nxest and c nyest. indeed, if at a certain stage in surfit the number of knots c in one direction (say nx) has reached the value of its upper bound c (nxest), then from that moment on all subsequent knots are added c in the other (y) direction. this may indicate that the value of c nxest is too small. on the other hand, it gives the user the option c of limiting the number of knots the routine locates in any direction c for example, by setting nxest=2*kx+2 (the lowest allowable value for c nxest), the user can indicate that he wants an approximation which c is a simple polynomial of degree kx in the variable x. c c other subroutines required: c fpback,fpbspl,fpsurf,fpdisc,fpgivs,fprank,fprati,fprota,fporde c c references: c dierckx p. : an algorithm for surface fitting with spline functions c ima j. numer. anal. 1 (1981) 267-283. c dierckx p. : an algorithm for surface fitting with spline functions c report tw50, dept. computer science,k.u.leuven, 1980. c dierckx p. : curve and surface fitting with splines, monographs on c numerical analysis, oxford university press, 1993. c c author: c p.dierckx c dept. computer science, k.u. leuven c celestijnenlaan 200a, b-3001 heverlee, belgium. c e-mail : Paul.Dierckx@cs.kuleuven.ac.be c c creation date : may 1979 c latest update : march 1987 c c .. c ..scalar arguments.. real*8 xb,xe,yb,ye,s,eps,fp integer iopt,m,kx,ky,nxest,nyest,nmax,nx,ny,lwrk1,lwrk2,kwrk,ier c ..array arguments.. real*8 x(m),y(m),z(m),w(m),tx(nmax),ty(nmax), * c((nxest-kx-1)*(nyest-ky-1)),wrk1(lwrk1),wrk2(lwrk2) integer iwrk(kwrk) c ..local scalars.. real*8 tol integer i,ib1,ib3,jb1,ki,kmax,km1,km2,kn,kwest,kx1,ky1,la,lbx, * lby,lco,lf,lff,lfp,lh,lq,lsx,lsy,lwest,maxit,ncest,nest,nek, * nminx,nminy,nmx,nmy,nreg,nrint,nxk,nyk c ..function references.. integer max0 c ..subroutine references.. c fpsurf c .. c we set up the parameters tol and maxit. maxit = 20 tol = 0.1e-02 c before starting computations a data check is made. if the input data c are invalid,control is immediately repassed to the calling program. ier = 10 if(eps.le.0. .or. eps.ge.1.) go to 71 if(kx.le.0 .or. kx.gt.5) go to 71 kx1 = kx+1 if(ky.le.0 .or. ky.gt.5) go to 71 ky1 = ky+1 kmax = max0(kx,ky) km1 = kmax+1 km2 = km1+1 if(iopt.lt.(-1) .or. iopt.gt.1) go to 71 if(m.lt.(kx1*ky1)) go to 71 nminx = 2*kx1 if(nxest.lt.nminx .or. nxest.gt.nmax) go to 71 nminy = 2*ky1 if(nyest.lt.nminy .or. nyest.gt.nmax) go to 71 nest = max0(nxest,nyest) nxk = nxest-kx1 nyk = nyest-ky1 ncest = nxk*nyk nmx = nxest-nminx+1 nmy = nyest-nminy+1 nrint = nmx+nmy nreg = nmx*nmy ib1 = kx*nyk+ky1 jb1 = ky*nxk+kx1 ib3 = kx1*nyk+1 if(ib1.le.jb1) go to 10 ib1 = jb1 ib3 = ky1*nxk+1 10 lwest = ncest*(2+ib1+ib3)+2*(nrint+nest*km2+m*km1)+ib3 kwest = m+nreg if(lwrk1.lt.lwest .or. kwrk.lt.kwest) go to 71 if(xb.ge.xe .or. yb.ge.ye) go to 71 do 20 i=1,m if(w(i).le.0.) go to 70 if(x(i).lt.xb .or. x(i).gt.xe) go to 71 if(y(i).lt.yb .or. y(i).gt.ye) go to 71 20 continue if(iopt.ge.0) go to 50 if(nx.lt.nminx .or. nx.gt.nxest) go to 71 nxk = nx-kx1 tx(kx1) = xb tx(nxk+1) = xe do 30 i=kx1,nxk if(tx(i+1).le.tx(i)) go to 72 30 continue if(ny.lt.nminy .or. ny.gt.nyest) go to 71 nyk = ny-ky1 ty(ky1) = yb ty(nyk+1) = ye do 40 i=ky1,nyk if(ty(i+1).le.ty(i)) go to 73 40 continue go to 60 50 if(s.lt.0.) go to 71 60 ier = 0 c we partition the working space and determine the spline approximation kn = 1 ki = kn+m lq = 2 la = lq+ncest*ib3 lf = la+ncest*ib1 lff = lf+ncest lfp = lff+ncest lco = lfp+nrint lh = lco+nrint lbx = lh+ib3 nek = nest*km2 lby = lbx+nek lsx = lby+nek lsy = lsx+m*km1 call fpsurf(iopt,m,x,y,z,w,xb,xe,yb,ye,kx,ky,s,nxest,nyest, * eps,tol,maxit,nest,km1,km2,ib1,ib3,ncest,nrint,nreg,nx,tx, * ny,ty,c,fp,wrk1(1),wrk1(lfp),wrk1(lco),wrk1(lf),wrk1(lff), * wrk1(la),wrk1(lq),wrk1(lbx),wrk1(lby),wrk1(lsx),wrk1(lsy), * wrk1(lh),iwrk(ki),iwrk(kn),wrk2,lwrk2,ier) 70 return 71 print*,"iopt,kx,ky,m=",iopt,kx,ky,m print*,"nxest,nyest,nmax=",nxest,nyest,nmax print*,"lwrk1,lwrk2,kwrk=",lwrk1,lwrk2,kwrk print*,"xb,xe,yb,ye=",xb,xe,yb,ye print*,"eps,s",eps,s return 72 print*,"tx=",tx return 73 print*,"ty=",ty return end