Daniel@0: function varargout = mirstd(f,varargin)
Daniel@0: % m = mirstd(f) returns the standard deviation along frames of the feature f
Daniel@0: %
Daniel@0: %   f can be a structure array composed of features. In this case,
Daniel@0: %       m will be structured the same way.
Daniel@0: 
Daniel@0: if isa(f,'mirstruct')
Daniel@0:     data = get(f,'Data');
Daniel@0:     for fi = 1:length(data)
Daniel@0:         data{fi} = mirstd(data{fi});
Daniel@0:     end
Daniel@0:     varargout = {set(f,'Data',data)};
Daniel@0: elseif isstruct(f)
Daniel@0:     fields = fieldnames(f);
Daniel@0:     for i = 1:length(fields)
Daniel@0:         field = fields{i};
Daniel@0:         stat.(field) = mirstd(f.(field));
Daniel@0:     end
Daniel@0:     varargout = {stat};
Daniel@0: else
Daniel@0:     
Daniel@0:         normdiff.key = 'NormDiff';
Daniel@0:         normdiff.type = 'Boolean';
Daniel@0:         normdiff.default = 0;
Daniel@0:     specif.option.normdiff = normdiff;
Daniel@0: 
Daniel@0:     specif.nochunk = 1;
Daniel@0:     
Daniel@0:     varargout = mirfunction(@mirstd,f,varargin,nargout,specif,@init,@main);
Daniel@0: end
Daniel@0: 
Daniel@0: 
Daniel@0: function [x type] = init(x,option)
Daniel@0: type = '';
Daniel@0: 
Daniel@0: 
Daniel@0: function m = main(f,option,postoption)
Daniel@0: if iscell(f)
Daniel@0:     f = f{1};
Daniel@0: end
Daniel@0: if isa(f,'mirhisto')
Daniel@0:     warning('WARNING IN MIRSTD: histograms are not taken into consideration yet.')
Daniel@0:     m = struct;
Daniel@0:     return
Daniel@0: end
Daniel@0: fp = get(f,'FramePos');
Daniel@0: ti = get(f,'Title');
Daniel@0: d = get(f,'Data');
Daniel@0: l = length(d);
Daniel@0: for i = 1:l
Daniel@0:     if iscell(d{i})
Daniel@0:         if length(d{i}) > 1
Daniel@0:             error('ERROR IN MIRSTD: segmented data not accepted yet.');
Daniel@0:         else
Daniel@0:             dd = d{i}{1};
Daniel@0:         end
Daniel@0:     else
Daniel@0:         dd = d{i};
Daniel@0:     end
Daniel@0:     if iscell(dd)
Daniel@0:         m{i} = {zeros(1,length(dd))};
Daniel@0:         for j = 1:length(dd)
Daniel@0:             m{i}{1}(j) = std(dd{j});
Daniel@0:         end
Daniel@0:     elseif size(dd,2) < 2
Daniel@0:         nonan = find(not(isnan(dd)));
Daniel@0:         dn = dd(nonan);
Daniel@0:         if option.normdiff
Daniel@0:             m{i}{1} = norm(diff(dn,2));
Daniel@0:         else
Daniel@0:             m{i}{1} = std(dn,0,2);
Daniel@0:         end
Daniel@0:     else
Daniel@0:         dd = mean(dd,4);
Daniel@0:         m{i} = {NaN(size(dd,1),1,size(dd,3))};
Daniel@0:         for k = 1:size(dd,1)
Daniel@0:             for l = 1:size(dd,3)
Daniel@0:                 dk = dd(k,:,l);
Daniel@0:                 nonan = find(not(isnan(dk)));
Daniel@0:                 if not(isempty(nonan))
Daniel@0:                     dn = dk(nonan);
Daniel@0:                     if option.normdiff
Daniel@0:                         m{i}{1}(k,1,l) = norm(diff(dn,2));
Daniel@0:                     else
Daniel@0:                         m{i}{1}(k,1,l) = std(dn,0,2);
Daniel@0:                     end
Daniel@0:                 end
Daniel@0:             end
Daniel@0:         end
Daniel@0:     end
Daniel@0: end
Daniel@0: m = mirscalar(f,'Data',m,'Title',['Standard deviation of ',ti]);