Mouse-threat-and-escape-Han-et-al-PNAS/subfunc/deleteOutliers.m

function [ dat_clean, nOutliers, idx ] = deleteOutliers( dat, sdCrit )
% function dat = _deleteOutliers(dat, sdCrit)
% this function returns data vector without outliers.
% It automatically detect outliers by applying M+SD or M-SD threshold,
% making outliers with NaN data format. 

if nargin < 2
    sdCrit = 3;
end

l = length(dat);
m = nanmean( dat );
sd = nanstd( dat );
dat_clean = dat;

[ idx_upper ] = find( dat > m+ sd*sdCrit );
[ idx_lower ] = find( dat < m- sd*sdCrit );

idx = [ idx_lower; idx_upper ]';
if min(size(idx)) > 1 % Matrix orientation
    idx = [idx_lower, idx_upper ];
end

nOutliers = length(idx);
% disp([ 'hb_deleteOutliers.m >> Outlier N : ' num2str(nOutliers) ...
%     ' out of ' num2str( l )...
%     ' (' num2str( nOutliers/l*100 ) ' %) ']);

if nOutliers > 0
    for oIdx = idx
        dat_clean(oIdx) = nan;
    end
end