Sparse_logistic_regression/RSCHMM_PlotTP_Experimental.m
bb18ee5f9eddmaster

RSCHMM_PlotTP_Experimental.m
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			%% This function plots, nicely, the information from my TP matrix
			% A is the transition probability matrix: it should have size K + 2, with
			% the first row/column the baseline state and the last one the unassigned
			% state
			function [] = RSCHMM_PlotTP_Experimental(A)

			%T = prctile(abs(A(:)),alpha);

			%A(abs(A) < T) = 0;

			% Constructs my directional graph
			G2 = digraph(A);

			n_ROIs = size(A,1);

			% Plots the initial version of my graph
			figure;
			p2 = plot(G2);

			% How much do I increase the angle by every time
			Phi = 2*pi/n_ROIs;

			for c = 1:n_ROIs
			p2.XData(c) = 2cos(Phic);
			p2.YData(c) = 2sin(Phic);
			end

			% I want marker sizes proportional to the Resilience
			%Res_OI = Resilience;
			%NodeSize = 10+20*Res_OI/max(Res_OI);
			p2.MarkerSize = 30;

			% Betweenness in green
			CM = cbrewer('qual','Dark2',25);

			for c = 1:n_ROIs
			tmp_col(c,:) = CM(c,:);
			end

			p2.NodeColor = tmp_col;

			% I want my edges to have their color and thickness proportional to
			% their weight
			n_edges = size(G2.Edges.EndNodes,1);

			CM2 = cbrewer('seq','Greys',2000);
			CM2 = CM2(1001:end,:);

			Weights = G2.Edges.Weight;

			Range2 = linspace(min(abs(Weights)),max(abs(Weights)),1000);

			for e = 1:n_edges
			if Weights(e) >= 0
			tmp_col2(e,:) = [193,45,52]/255;
			elseif Weights(e) < 0
			tmp_col2(e,:) = [40,114,180]/255;
			end
			end

			% I want edge color proportional to the weight
			p2.EdgeColor = tmp_col2;

			Weights = abs(Weights);

			% Weights between 0 and 1
			Weights = (Weights)/max(Weights);
			p2.LineWidth = realmin+11Weights;%2 + 10Weights;

			p2.NodeLabel = [];

			%axis square
			axis off
			set(gcf,'color','w');
			set(gca,'Box','off');
			end