Compute_MetaMetrics.m
854ba9392397master

Compute_MetaMetrics.m
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			function [Counts,Counts_total,Label,META] = Compute_MetaMetrics(TParams,MaskXYZABG,n_subjects)

			% Creation of a counts vector for population-level data (Counts_total) and
			% separately across subjects
			Counts = zeros(3^6,n_subjects);
			Counts_total = zeros(3^6,1);

			% Factorial design
			META = fullfact([3 3 3 3 3 3]) -2;

			for i = 1:size(META,1)

			% Total counts
			Counts_total(i) = sum(ismember(TParams,META(i,:),'rows'));

			% Subject-specific counts
			for s = 1:n_subjects
			Counts(i,s) = sum(ismember(MaskXYZABG(:,:,s),META(i,:),'rows'));
			end

			% I want to know what each of my meta-states corresponds to, hence the
			% creation of Label
			Label{i} = num2str(META(i,:));

			end

			% In addition, I also want to compute transition probabilities across all
			% my states
			% Transitions = zeros(3^6,3^6,n_subjects);
			%
			% for i = 1:size(META,1)
			% for j = 1:size(META,1)
			%
			% for s = 1:n_subjects
			%
			% % Samples the scrubbed frames of the subject of interest
			% tmp_frames = TParams(s_index == s,:);
			%
			% % How many times do we observe a given transition
			% Transitions(i,j,s) = sum(ismember(tmp_frames,META(i,:),'rows') & ismember(circshift(tmp_frames,-1),META(j,:),'rows'));
			% end
			%
			% Transitions_total = sum(Transitions,3);
			% end
			% end

			end