ivan@77: function [minIntrVec,stat,actpctg] = genSampling(pdf,iter,tol)
ivan@77: 
ivan@77: %[mask,stat,N] = genSampling(pdf,iter,tol)
ivan@77: %
ivan@77: % a monte-carlo algorithm to generate a sampling pattern with 
ivan@77: % minimum peak interference. The number of samples will be
ivan@77: % sum(pdf) +- tol
ivan@77: %
ivan@77: %	pdf - probability density function to choose samples from
ivan@77: %	iter - number of tries
ivan@77: %	tol  - the deviation from the desired number of samples in samples
ivan@77: %
ivan@77: % returns:
ivan@77: %	mask - sampling pattern
ivan@77: %	stat - vector of min interferences measured each try
ivan@77: %	actpctg    - actual undersampling factor
ivan@77: %
ivan@77: %	(c) Michael Lustig 2007
ivan@77: 
ivan@77: %   This file is used with the kind permission of Michael Lustig
ivan@77: %   (mlustig@stanford.edu), and originally appeared in the
ivan@77: %   SparseMRI toolbox, http://www.stanford.edu/~mlustig/ .
ivan@77: %
ivan@77: % $Id: genSampling.m 1040 2008-06-26 20:29:02Z ewout78 $
ivan@77: 
ivan@77: % h = waitbar(0);
ivan@77: 
ivan@77: pdf(find(pdf>1)) = 1;
ivan@77: K = sum(pdf(:));
ivan@77: 
ivan@77: minIntr = 1e99;
ivan@77: minIntrVec = zeros(size(pdf));
ivan@77: 
ivan@77: for n=1:iter
ivan@77: 	tmp = zeros(size(pdf));
ivan@77: 	while abs(sum(tmp(:)) - K) > tol
ivan@77: 		tmp = rand(size(pdf))<pdf;
ivan@77: 	end
ivan@77: 	
ivan@77: 	TMP = ifft2(tmp./pdf);
ivan@77: 	if max(abs(TMP(2:end))) < minIntr
ivan@77: 		minIntr = max(abs(TMP(2:end)));
ivan@77: 		minIntrVec = tmp;
ivan@77: 	end
ivan@77: 	stat(n) = max(abs(TMP(2:end)));
ivan@77:       % waitbar(n/iter,h);
ivan@77: end
ivan@77: 
ivan@77: actpctg = sum(minIntrVec(:))/prod(size(minIntrVec));
ivan@77: 
ivan@77: % close(h);
ivan@77: 
ivan@77: