comparison toolboxes/MIRtoolbox1.3.2/MIRToolbox/mirstd.m @ 0:e9a9cd732c1e tip

first hg version after svn

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