基于matlab实现EOF分析的代码，气象水文海洋专用.rar

function [EOFs,expl,PCs]=eof(xdata); %A program to do EOF analysis, and scale the EOFs. % This is an example of scaling EOFs % usage: function [EOFs,expl,PCs]=eof(xdata) % xdata[m,n], m: spatial dimension, n:temporal dimension % output: EOFs, expl, PCs [M,N]=size(xdata); xm=xdata; % calculate covariance matrix C=xm*xm'/N; %C % Do eigenanalysis [EOFs,D]=eig(C); PCs=EOFs'*xm; %calculate autocorrelation for j=1:M alpha(j,:)=auto(xm(j,:),1); end % the temporal autocorrelation is different for each spatial grid point, % a problem. Let's kluge this by using the mean of the mean and the maximum % autocorrelation. We'll take the absolute value before averaging, so that % random negative correlations can't cancel out positive ones. This is % conservative. ralph=(mean(abs(alpha(:,2)))+max(alpha(:,2)))/2.; tau=-1.0/log(ralph); Nstar=N/(2*tau); factor=sqrt(2./Nstar); % Plot Eigenvalues L=diag(D,0); LE=L*factor; Lexp=L(:); wkp=100*Lexp/sum(Lexp); expla=flipud(wkp); [ntmp1,ntmp2]=size(expla); stmp=0.0; for ikp=1:ntmp1 stmp=stmp+expla(ikp); wkexpl(ikp)=stmp; end expl=[expla(:) wkexpl(:)]; % [m,2] KKa= flipud(L);KKb=flipud(LE); errorbar(1:10,KKa(1:10),KKb(1:10)); title(['Eigenvalue Spectrum, first 10']); xlabel('Number'); EOFs=fliplr(EOFs);% [m,m] now the most important five are EOFs[:,1:5] % Plot eigenvectors %plot(E(:,M-2:M)); %MP=M+1; %axis([0 MP -0.60 0.60]); %set(gca,'DataAspectRatio',[1 22 1]) %title(['First Three Eigenvectors: Norm= ' ans]); %xlabel('Structure dimension'); PCs=flipud(PCs);% ?[m,n] now the most important five are PCs[1:5,:] PCs=PCs';%[n,m] now the most important five are PCs[:,1:5] % Project eigenvectors onto original data to get PCs %Z=E'*x; %plot(Z(M-2:M,:)'); %title(['First Three Principle Components: Norm= ' ans]); %xlabel('Sampling dimension'); % clear stmp ikp ntmp1 ntmp2 wkexpl expla wkp %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5 %程序里的auto()没有呀 %把auto改成autocorr就可以了