wolffd@0: function varargout = mirmean(f,varargin)
wolffd@0: % m = mirmean(f) returns the mean along frames of the feature f
wolffd@0: %
wolffd@0: %   f can be a structure array composed of features. In this case,
wolffd@0: %       m will be structured the same way.
wolffd@0: 
wolffd@0: if isa(f,'mirstruct')
wolffd@0:     data = get(f,'Data');
wolffd@0:     for fi = 1:length(data)
wolffd@0:         data{fi} = mirmean(data{fi});
wolffd@0:     end
wolffd@0:     varargout = {set(f,'Data',data)};
wolffd@0: elseif isstruct(f)
wolffd@0:     fields = fieldnames(f);
wolffd@0:     for i = 1:length(fields)
wolffd@0:         field = fields{i};
wolffd@0:         stat.(field) = mirmean(f.(field));
wolffd@0:     end
wolffd@0:     varargout = {stat};
wolffd@0: else
wolffd@0:     specif.nochunk = 1;
wolffd@0:     varargout = mirfunction(@mirmean,f,varargin,nargout,specif,@init,@main);
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: function [x type] = init(x,option)
wolffd@0: type = '';
wolffd@0: 
wolffd@0: 
wolffd@0: function m = main(f,option,postoption)
wolffd@0: if iscell(f)
wolffd@0:     f = f{1};
wolffd@0: end
wolffd@0: if isa(f,'mirhisto')
wolffd@0:     warning('WARNING IN MIRMEAN: histograms are not taken into consideration yet.')
wolffd@0:     m = struct;
wolffd@0:     return
wolffd@0: end
wolffd@0: fp = get(f,'FramePos');
wolffd@0: ti = get(f,'Title');
wolffd@0: if 0 %get(f,'Peaks')
wolffd@0:     if not(isempty(get(f,'PeakPrecisePos')))
wolffd@0:         stat = addstat(struct,get(f,'PeakPrecisePos'),fp,'PeakPos');
wolffd@0:         stat = addstat(stat,get(f,'PeakPreciseVal'),fp,'PeakMag');
wolffd@0:     else
wolffd@0:         stat = addstat(struct,get(f,'PeakPosUnit'),fp,'PeakPos');
wolffd@0:         stat = addstat(stat,get(f,'PeakVal'),fp,'PeakMag');
wolffd@0:     end
wolffd@0: else
wolffd@0:     d = get(f,'Data');
wolffd@0: end
wolffd@0: l = length(d);
wolffd@0: for i = 1:l
wolffd@0:     if iscell(d{i})
wolffd@0:         if length(d{i}) > 1
wolffd@0:             error('ERROR IN MIRMEAN: segmented data not accepted yet.');
wolffd@0:         else
wolffd@0:             dd = d{i}{1};
wolffd@0:         end
wolffd@0:     else
wolffd@0:         dd = d{i};
wolffd@0:     end
wolffd@0:     %dd = uncell(dd);
wolffd@0:     if 0 %iscell(dd)
wolffd@0:         j = 0;
wolffd@0:         singul = 1;
wolffd@0:         ddd = [];
wolffd@0:         while j<length(dd) && singul
wolffd@0:             j = j+1;
wolffd@0:             if length(dd{j}) > 1
wolffd@0:                 singul = 0;
wolffd@0:             elseif length(dd{j}) == 1
wolffd@0:                 ddd(end+1) = dd{j};
wolffd@0:             end
wolffd@0:         end
wolffd@0:         if singul
wolffd@0:             dd = ddd;
wolffd@0:         end
wolffd@0:     end
wolffd@0:     if iscell(dd)
wolffd@0:         m{i} = {zeros(1,length(dd))};
wolffd@0:         for j = 1:length(dd)
wolffd@0:             m{i}{1}(j) = mean(dd{j});
wolffd@0:         end
wolffd@0:     elseif size(dd,2) < 2
wolffd@0:         nonan = find(not(isnan(dd)));
wolffd@0:         dn = dd(nonan);
wolffd@0:         m{i}{1} = mean(dn,2);
wolffd@0:     else
wolffd@0:         %diffp = fp{i}{1}(1,2:end) - fp{i}{1}(1,1:end-1);
wolffd@0:         %if round((diffp(2:end)-diffp(1:end-1))*1000)
wolffd@0:             % Not regular sampling (in mirattacktime for instance)
wolffd@0:         %    framesampling = NaN;
wolffd@0:         %else
wolffd@0:         %    framesampling = fp{i}{1}(1,2)-fp{i}{1}(1,1);
wolffd@0:         %end
wolffd@0:         dd = mean(dd,4);
wolffd@0:         m{i} = {NaN(size(dd,1),1,size(dd,3))};
wolffd@0:         for k = 1:size(dd,1)
wolffd@0:             for l = 1:size(dd,3)
wolffd@0:                 dk = dd(k,:,l);
wolffd@0:                 nonan = find(not(isnan(dk)));
wolffd@0:                 if not(isempty(nonan))
wolffd@0:                     dn = dk(nonan);
wolffd@0:                     m{i}{1}(k,1,l) = mean(dn,2);
wolffd@0:                 end
wolffd@0:             end
wolffd@0:         end
wolffd@0:     end
wolffd@0: end
wolffd@0: m = mirscalar(f,'Data',m,'Title',['Average of ',ti]);