wolffd@0: function varargout = mirhisto(x,varargin)
wolffd@0: %   h = mirhisto(x) constructs the histogram from x. The elements of x are
wolffd@0: %       binned into equally spaced containers.
wolffd@0: %   Optional argument:
wolffd@0: %       mirhisto(...,'Number',n): specifies the number of containers.
wolffd@0: %           Default value : n = 10.
wolffd@0: %       mirhisto(...,'Ampli'): adds the amplitude of the elements,instead of
wolffd@0: %           simply counting then.
wolffd@0: 
wolffd@0: 
wolffd@0:         n.key = 'Number';
wolffd@0:         n.type = 'Integer';
wolffd@0:         n.default = 10;
wolffd@0:     option.n = n;
wolffd@0:     
wolffd@0:         a.key = 'Ampli';
wolffd@0:         a.type = 'Boolean';
wolffd@0:         a.default = 0;
wolffd@0:     option.a = a;
wolffd@0:     
wolffd@0: specif.option = option;
wolffd@0: 
wolffd@0: 
wolffd@0: varargout = mirfunction(@mirhisto,x,varargin,nargout,specif,@init,@main);
wolffd@0: 
wolffd@0: 
wolffd@0: function [x type] = init(x,option)
wolffd@0: type = 'mirhisto';
wolffd@0: 
wolffd@0: 
wolffd@0: function h = main(x,option,postoption)
wolffd@0: if iscell(x)
wolffd@0:     x = x{1};
wolffd@0: end
wolffd@0: d = get(x,'Data');
wolffd@0: %disp('Computing histogram...')
wolffd@0: ddd = cell(1,length(d));
wolffd@0: bbb = cell(1,length(d));
wolffd@0: for i = 1:length(d)
wolffd@0:     di = d{i}{1};  % To be generalized for segmented data
wolffd@0:     if iscell(di)
wolffd@0:         mx = -Inf;
wolffd@0:         mn = Inf;
wolffd@0:         nc = size(di,2);
wolffd@0:         for k = 1:nc
wolffd@0:             dk = di{k};
wolffd@0:             if size(dk,4) == 2
wolffd@0:                 dk(end+1:end*2,:,:,1) = dk(:,:,:,2);
wolffd@0:                 dk(:,:,:,2) = [];
wolffd@0:             end
wolffd@0:             mxk = max(dk);
wolffd@0:             mnk = min(dk);
wolffd@0:             if mxk > mx
wolffd@0:                 mx = mxk;
wolffd@0:             end
wolffd@0:             if mnk < mn
wolffd@0:                 mn = mnk;
wolffd@0:             end
wolffd@0:         end
wolffd@0:         if isinf(mx) || isinf(mx)
wolffd@0:             b = [];
wolffd@0:             dd = [];
wolffd@0:         else
wolffd@0:             dd = zeros(1,option.n);
wolffd@0:             if mn == mx
wolffd@0:                 b(1,:) = mn-ceil(option.n/2) : mn+floor(option.n/2);
wolffd@0:             else
wolffd@0:                 b(1,:) = mn : (mx-mn)/option.n : mx;
wolffd@0:             end
wolffd@0:             for k = 1:nc
wolffd@0:                 dk = di{k};
wolffd@0:                 for j = 1:option.n
wolffd@0:                     found = find(and(dk>=b(1,j),dk<=b(1,j+1)));
wolffd@0:                     if option.a
wolffd@0:                         dd(1,j) = dd(1,j) + sum(dk(found));
wolffd@0:                     else
wolffd@0:                         dd(1,j) = dd(1,j) + length(found);
wolffd@0:                     end
wolffd@0:                 end
wolffd@0:             end
wolffd@0:         end
wolffd@0:     else
wolffd@0:         if isa(x,'mirscalar')
wolffd@0:             di = permute(di,[3 2 1]);
wolffd@0:         end
wolffd@0:         if size(di,4) == 2
wolffd@0:             di(end+1:end*2,:,:,1) = di(:,:,:,2);
wolffd@0:             di(:,:,:,2) = [];
wolffd@0:         end
wolffd@0:         nl = size(di,1);
wolffd@0:         nc = size(di,2);
wolffd@0:         np = size(di,3);
wolffd@0:         dd = zeros(1,option.n,np);
wolffd@0:         for l = 1:np
wolffd@0:             mx = max(max(di(:,:,l),[],1),[],2);
wolffd@0:             mn = min(min(di(:,:,l),[],1),[],2);
wolffd@0:             b(l,:) = mn:(mx-mn)/option.n:mx;
wolffd@0:             for k = 1:nc
wolffd@0:                 dk = di(:,k,l);
wolffd@0:                 for j = 1:option.n
wolffd@0:                     found = (find(and(dk>=b(l,j),dk<=b(l,j+1))));
wolffd@0:                     if option.a
wolffd@0:                         dd(1,j,l) = dd(1,j,l) + sum(dk(found));
wolffd@0:                     else
wolffd@0:                         dd(1,j,l) = dd(1,j,l) + length(found);
wolffd@0:                     end
wolffd@0:                 end
wolffd@0:             end
wolffd@0:         end
wolffd@0:     end
wolffd@0:     ddd{i} = ipermute(dd,[3 2 1]);
wolffd@0:     bbb{i}(:,:,1) = b(:,1:end-1);
wolffd@0:     bbb{i}(:,:,2) = b(:,2:end);
wolffd@0: end
wolffd@0: h = class(struct,'mirhisto',mirdata(x));
wolffd@0: h = purgedata(h);
wolffd@0: h = set(h,'Bins',bbb,'Weight',ddd);