tseriesinterp

 function m = tseriesinterp(m,trorig,trnew,dim,numsamples)

 <m> is a matrix with time-series data along some dimension.
   can also be a cell vector of things like that.
 <trorig> is the sampling time of <m> (e.g. 1 second)
 <trnew> is the new desired sampling time
 <dim> (optional) is the dimension of <m> with time-series data.
   default to 2 if <m> is a row vector and to 1 otherwise.
 <numsamples> (optional) is the number of desired samples.
   default to the number of samples that makes the duration of the new
   data match or minimally exceed the duration of the original data.

 use interp1 to cubic-interpolate <m> (with extrapolation) such that
 the new version of <m> coincides with the original version of <m>
 at the first time point.

 example:
 x0 = 0:.1:10;
 y0 = sin(x0);
 y1 = tseriesinterp(y0,.1,.23);
 figure; hold on;
 plot(x0,y0,'r.-');
 plot(0:.23:.23*(length(y1)-1),y1,'go');

0001 function m = tseriesinterp(m,trorig,trnew,dim,numsamples)
0002 
0003 % function m = tseriesinterp(m,trorig,trnew,dim,numsamples)
0004 %
0005 % <m> is a matrix with time-series data along some dimension.
0006 %   can also be a cell vector of things like that.
0007 % <trorig> is the sampling time of <m> (e.g. 1 second)
0008 % <trnew> is the new desired sampling time
0009 % <dim> (optional) is the dimension of <m> with time-series data.
0010 %   default to 2 if <m> is a row vector and to 1 otherwise.
0011 % <numsamples> (optional) is the number of desired samples.
0012 %   default to the number of samples that makes the duration of the new
0013 %   data match or minimally exceed the duration of the original data.
0014 %
0015 % use interp1 to cubic-interpolate <m> (with extrapolation) such that
0016 % the new version of <m> coincides with the original version of <m>
0017 % at the first time point.
0018 %
0019 % example:
0020 % x0 = 0:.1:10;
0021 % y0 = sin(x0);
0022 % y1 = tseriesinterp(y0,.1,.23);
0023 % figure; hold on;
0024 % plot(x0,y0,'r.-');
0025 % plot(0:.23:.23*(length(y1)-1),y1,'go');
0026 
0027 % internal constants
0028 numchunks = 20;
0029 
0030 % input
0031 if ~exist('dim','var') || isempty(dim)
0032   dim = choose(isrowvector(m),2,1);
0033 end
0034 if ~exist('numsamples','var') || isempty(numsamples)
0035   numsamples = [];
0036 end
0037 
0038 % prep
0039 if iscell(m)
0040   leaveascell = 1;
0041 else
0042   leaveascell = 0;
0043   m = {m};
0044 end
0045 
0046 % do it
0047 for p=1:length(m)
0048 
0049   % prep 2D
0050   msize = size(m{p});
0051   m{p} = reshape2D(m{p},dim);
0052 
0053   % calc
0054   if isempty(numsamples)
0055     numsamples = ceil((size(m{p},1)*trorig)/trnew);
0056   end
0057 
0058   % do it
0059   timeorig = linspacefixeddiff(0,trorig,size(m{p},1));
0060   timenew  = linspacefixeddiff(0,trnew,numsamples);
0061 
0062   % do in chunks
0063   chunks = chunking(1:size(m{p},2),ceil(size(m{p},2)/numchunks));
0064   temp = {};
0065   parfor q=1:length(chunks)
0066     temp{q} = interp1(timeorig,m{p}(:,chunks{q}),timenew,'cubic','extrap');
0067   end
0068   m{p} = catcell(2,temp);
0069   clear temp;
0070 
0071   % prepare output
0072   msize(dim) = numsamples;
0073   m{p} = reshape2D_undo(m{p},dim,msize);
0074 
0075 end
0076 
0077 % prepare output
0078 if ~leaveascell
0079   m = m{1};
0080 end