comparison Problems/private/genSampling.m @ 61:42fcbcfca132

(none)

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