nt_smooth

y=nt_smooth(x,T,nIterations,nodelayflag) - smooth by convolution with square window

  y: smoothed data
 
  x: data to smooth
  T: samples, size of window (can be fractionary)
  nIterations: number of iterations of smoothing operation (large --> gaussian kernel)
  nodelayflag: if true, compensate for delay [default:false]

0001 function x=nt_smooth(x,T,nIterations,nodelayflag)
0002 %y=nt_smooth(x,T,nIterations,nodelayflag) - smooth by convolution with square window
0003 %
0004 %  y: smoothed data
0005 %
0006 %  x: data to smooth
0007 %  T: samples, size of window (can be fractionary)
0008 %  nIterations: number of iterations of smoothing operation (large --> gaussian kernel)
0009 %  nodelayflag: if true, compensate for delay [default:false]
0010 %
0011 nt_greetings;
0012 
0013 if nargin<4||isempty(nodelayflag); nodelayflag=0; end
0014 if nargin<3||isempty(nIterations); nIterations=1; end
0015 if nargin<2; help nt_smooth ; error('!'); end
0016 
0017 if ndims(x)>4; error('!'); end
0018 
0019 integ=floor(T);
0020 frac=T-integ;
0021 
0022 if integ>=size(x,1)
0023     x=repmat(mean(x),[size(x,1),1,1,1]);
0024     return;
0025 end
0026 
0027 % remove onset step
0028 mn=mean(x(1:(integ+1),:,:),1);
0029 x=bsxfun(@minus,x,mn);
0030 
0031 if nIterations==1 && frac==0;
0032     % faster
0033     x=cumsum(x);
0034     x(T+1:end,:)=x(T+1:end,:)-x(1:end-T,:);
0035     x=x/T;
0036 else
0037     % filter kernel
0038     B=[ones(integ,1);frac]/T;
0039     for k=1:nIterations-1
0040         B=conv(B,[ones(integ,1);frac]/T);
0041     end
0042     x=filter(B,1,x);    
0043 end
0044 
0045 if nodelayflag
0046     shift=round(T/2*nIterations); %[shift n*T]
0047     x=[x(shift+1:end,:,:,:); zeros(shift,size(x,2),size(x,3),size(x,4))];
0048 end
0049 
0050 
0051 % restore DC
0052 x=bsxfun(@plus,x,mn);