function [vecCl,vecClI,nCl,sCl] = findClu(hV,tStat,lenMin)
% IDENTIFY CLUSTERS IN OBSERVED DATA
% This function identifies clusters in the observed data, based on the
% given minimum length of consecutive points. It also counts the number
% of clusters and returns the size of each identified cluster

% create vector that contains all clusters
% code from forum: replace ones by number of consecutive repetitions
d = [1,diff(hV)~=0,1]; % TRUE if values change
n = diff(find(d)); % Number of repetitions
X = repelem(n,n);
vecClI = X.*hV;
vecClI(vecClI<lenMin) = 0; % replace numbers that are smaller than criterion (less than X consecutive repetitions) by 0
vecClI(vecClI>=lenMin) = 1; % replace numbers that are equal or larger than criterion (X consecutive repetitions or more) by 1

% index into t-value vector to extract clusters (based on
% defined criterion)
vecCl = tStat.*vecClI;

% count number of clusters in vector
vecClIs = [0,vecClI]; % add 0 at start to make sure possible cluster starting at point 1 is considered
vecClIe = [vecClI,0]; % add 0 at the end to make sure possible cluster lasting until the end is considered
startCl = find(diff(vecClIs)==1); % find all points where difference vector is 1 (starting points of clusters in original vector)
endCl = find(diff(vecClIe)==-1); % find all points where difference vector is -1 (ending points of clusters in original vector)
nCl = numel(startCl); % return number of cluster starting points = number of clusters



% calculate size of each cluster
sCl = zeros(nCl,1);
for c = 1:nCl
    sCl(c) = sum(vecCl(startCl(c):endCl(c))); % calulate size of each identified cluster
end
end