bml_annot_consolidate

 BML_ANNOT_CONSOLIDATE returns a consolidated annotation table

 Use as
   annot = bml_annot_consolidate(cfg, annot1);

 The first argument cfg is a configuration structure, which can contain
 the following field:
 cfg.criterion - function handle: consolidation criteria. This function should
             accept a table of candidate annotations to consolidate and 
             return a true or false.  
 cfg.description - string: description of the output annot table
 cfg.additive - cellstr with names of variables to be treated as additive 
             defaults to empty

 Returns a annotation table with the folloing variables:
   cons_duration - sum of the consolidated durations
   id_starts - first original id of the consolidated row
   id_ends - last original id of the consolidated row
   cons_n - number of consolidated rows
 
 EXAMPLES
 ========

 %detecting stretches of constant depth in neuroomega
 cfg=[];
 cfg.criterion = @(x) (length(unique(x.depth))==1) && (abs((max(x.ends)-min(x.starts))-sum(x.duration))<10e-3);
 neuro_cons_depth = bml_annot_consolidate(cfg,info_neuroomega);
 
 %grouping annotations in fours
 cfg=[];
 cfg.criterion = @(x) height(x)<=4
 grouped_annot = bml_annot_consolidate(cfg,annot);

0001 function annot = bml_annot_consolidate(cfg, x)
0002 
0003 % BML_ANNOT_CONSOLIDATE returns a consolidated annotation table
0004 %
0005 % Use as
0006 %   annot = bml_annot_consolidate(cfg, annot1);
0007 %
0008 % The first argument cfg is a configuration structure, which can contain
0009 % the following field:
0010 % cfg.criterion - function handle: consolidation criteria. This function should
0011 %             accept a table of candidate annotations to consolidate and
0012 %             return a true or false.
0013 % cfg.description - string: description of the output annot table
0014 % cfg.additive - cellstr with names of variables to be treated as additive
0015 %             defaults to empty
0016 %
0017 % Returns a annotation table with the folloing variables:
0018 %   cons_duration - sum of the consolidated durations
0019 %   id_starts - first original id of the consolidated row
0020 %   id_ends - last original id of the consolidated row
0021 %   cons_n - number of consolidated rows
0022 %
0023 % EXAMPLES
0024 % ========
0025 %
0026 % %detecting stretches of constant depth in neuroomega
0027 % cfg=[];
0028 % cfg.criterion = @(x) (length(unique(x.depth))==1) && (abs((max(x.ends)-min(x.starts))-sum(x.duration))<10e-3);
0029 % neuro_cons_depth = bml_annot_consolidate(cfg,info_neuroomega);
0030 %
0031 % %grouping annotations in fours
0032 % cfg=[];
0033 % cfg.criterion = @(x) height(x)<=4
0034 % grouped_annot = bml_annot_consolidate(cfg,annot);
0035 
0036 x = bml_annot_table(x,[],inputname(2));
0037 
0038 description = bml_getopt(cfg,'description', ['cons_' x.Properties.Description]);
0039 criterion   = bml_getopt(cfg,'criterion',[]);
0040 additive    = bml_getopt(cfg,'additive',{});
0041 
0042 if ~isa(criterion, 'function_handle')
0043   error('''criterion'' should be a function handle');
0044 end
0045 
0046 i=1; j=1;
0047 tmp=collapse_table_rows(x(1,:),additive);
0048 annot = cell2table(cell(0,width(tmp))); 
0049 annot.Properties.VariableNames = tmp.Properties.VariableNames;
0050 
0051 if height(x)<=1
0052   annot = collapse_table_rows(x,additive);
0053   annot.id=[];
0054   annot = bml_annot_table(annot,description);
0055   return
0056 end
0057 
0058 while i<=height(x)
0059   if j==1 
0060     curr_s=collapse_table_rows(x(i,:),additive);
0061   end
0062   
0063   merge_s = x(i:(i+j),:);
0064   
0065   if criterion(merge_s)
0066     curr_s = collapse_table_rows(merge_s,additive);
0067     j = j + 1;
0068     if i + j > height(x)
0069       annot = [annot; curr_s];
0070       i = height(x)+1;
0071     end
0072   else
0073       annot = [annot;curr_s];
0074     i = i + j;
0075     j = 1;
0076     if i == height(x) %adding last register
0077       curr_s = collapse_table_rows(x(i,:),additive);
0078       annot = [annot; curr_s];
0079       i = height(x)+1;
0080     end
0081   end
0082 end
0083 
0084 annot.id=[];
0085 annot = bml_annot_table(annot,description);
0086 
0087 
0088 
0089 function collapsed = collapse_table_rows(merge_s,additive)
0090 %
0091 % Private function. Collapses a table to a record
0092 
0093 collapsed = table(...
0094   min(merge_s.starts),...
0095   max(merge_s.ends),...
0096   sum(merge_s.duration),...
0097   min(merge_s.id),...
0098   max(merge_s.id),...
0099   length(merge_s.id),...
0100   'VariableNames',{'starts','ends','cons_duration','id_starts','id_ends','cons_n'});
0101   
0102 vars = setdiff(merge_s.Properties.VariableNames,[collapsed.Properties.VariableNames,additive]);
0103 row=[];
0104 for i=1:length(vars)
0105   uval = unique(merge_s.(vars{i}));
0106   if length(uval)==1
0107     row.(vars{i}) = uval;
0108   elseif iscell(uval)
0109     row.(vars{i}) = {[]};   
0110   elseif isa(uval,'datetime')
0111     row.(vars{i}) = NaT;
0112   else
0113     row.(vars{i}) = nan;
0114   end
0115 end
0116 
0117 %dealing with additive vars
0118 for i=1:length(additive)
0119   uval = sum(merge_s.(additive{i}));
0120   row.(vars{i}) = uval;
0121 end
0122 
0123 collapsed = [collapsed, struct2table(row)];
0124 
0125 
0126 
0127 
0128