wolffd@0: function varargout = mirrms(x,varargin)
wolffd@0: %   e = mirrms(x) calculates the root mean square energy.
wolffd@0: %   Optional arguments:
wolffd@0: %       mirrms(...,'Frame') computes the temporal evolution of the energy.
wolffd@0: %       mirrms(...,'Root',0) does not apply the root operation to the mean
wolffd@0: %           square energy.
wolffd@0: 
wolffd@0:         normal.key = 'Normal';
wolffd@0:         normal.type = 'Boolean';
wolffd@0:         normal.default = 1;
wolffd@0:     option.normal = normal;
wolffd@0:     
wolffd@0:         root.key = 'Root';
wolffd@0:         root.type = 'Boolean';
wolffd@0:         root.default = 1;
wolffd@0:     option.root = root;
wolffd@0:     
wolffd@0: specif.option = option;
wolffd@0: 
wolffd@0: specif.defaultframelength = 0.05;
wolffd@0: specif.defaultframehop = 0.5;
wolffd@0: 
wolffd@0: specif.eachchunk = @eachchunk;
wolffd@0: specif.combinechunk = @combinechunk;
wolffd@0: specif.afterchunk = @afterchunk;
wolffd@0: 
wolffd@0: varargout = mirfunction(@mirrms,x,varargin,nargout,specif,@init,@main);
wolffd@0: 
wolffd@0: 
wolffd@0: function [x type] = init(x,option)
wolffd@0: type = 'mirscalar';
wolffd@0: 
wolffd@0: 
wolffd@0: function e = 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: v = mircompute(@algo,d,option);
wolffd@0: e = mirscalar(x,'Data',v,'Title','RMS energy');
wolffd@0: 
wolffd@0: 
wolffd@0: function e = algo(d,option)
wolffd@0: nl = size(d,1);
wolffd@0: nc = size(d,2);
wolffd@0: nch = size(d,3);
wolffd@0: e = zeros(1,nc,nch);
wolffd@0: for i = 1:nch
wolffd@0:     for j = 1:nc
wolffd@0:         if option.root
wolffd@0:             e(1,j,i) = norm(d(:,j,i));
wolffd@0:         else
wolffd@0:             e(1,j,i) = d(:,j,i)'*d(:,j,i);
wolffd@0:         end
wolffd@0:     end
wolffd@0: end
wolffd@0: if option.normal
wolffd@0:     e = e/sqrt(nl);
wolffd@0: end
wolffd@0: 
wolffd@0: 
wolffd@0: function [y orig] = eachchunk(orig,option,missing,postchunk)
wolffd@0: option.normal = 0;
wolffd@0: y = mirrms(orig,option);
wolffd@0: 
wolffd@0: 
wolffd@0: function y = combinechunk(old,new)
wolffd@0: do = get(old,'Data');
wolffd@0: do = do{1}{1};
wolffd@0: dn = get(new,'Data');
wolffd@0: dn = dn{1}{1};
wolffd@0: y = set(old,'ChunkData',sqrt(do^2+dn^2));
wolffd@0: 
wolffd@0: 
wolffd@0: function y = afterchunk(orig,length,postoption)
wolffd@0: d = get(orig,'Data');
wolffd@0: v = mircompute(@afternorm,d,length);
wolffd@0: y = set(orig,'Data',v);
wolffd@0: 
wolffd@0: 
wolffd@0: function e = afternorm(d,length)
wolffd@0: e = d/sqrt(length);