--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/dropduplicates.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,127 @@
+% DROPDUPLICATES drops all duplicate samples in time-series AOs.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DROPDUPLICATES drops all duplicate samples in time-series AOs. Duplicates
+%                are identified by having a two consecutive time stamps
+%                closer than a set tolerance.
+%
+% CALL:        bs = dropduplicates(as)
+%
+% INPUTS:      as  - array of analysis objects
+%              pl  - parameter list (see below)
+%
+% OUTPUTS:     bs  - array of analysis objects, one for each input
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'dropduplicates')">Parameters Description</a>
+%
+% VERSION:     $Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = dropduplicates(varargin)
+
+  % Check if this is a call for parameters
+  if utils.helper.isinfocall(varargin{:})
+    varargout{1} = getInfo(varargin{3});
+    return
+  end
+
+  import utils.const.*
+  utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
+  
+  % Collect input variable names
+  in_names = cell(size(varargin));
+  for ii = 1:nargin,in_names{ii} = inputname(ii);end
+
+  % Collect all AOs
+  [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
+  [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+
+  % Decide on a deep copy or a modify
+  bs = copy(as, nargout);
+
+  % Combine plists
+  pl = parse(pl, getDefaultPlist);
+
+  % Get tolerance
+  tol = find(pl, 'tol');
+
+  % Get only tsdata AOs
+  for j=1:numel(bs)
+    if isa(bs(j).data, 'tsdata')
+      d = abs(diff(bs(j).data.getX));
+      idx = find(d<tol);
+      utils.helper.msg(msg.PROC1, 'found %d duplicate samples', numel(idx));
+      % Wipe out x samples
+      if ~isempty(bs(j).data.x)
+        bs(j).data.x(idx) = [];
+      end
+      % Wipe out y samples
+      bs(j).data.y(idx) = [];
+      % Wipe out error
+      if numel(bs(j).data.dx) > 1
+        bs(j).data.dx(idx) = [];
+      end
+      if numel(bs(j).data.dy) > 1
+        bs(j).data.dy(idx) = [];
+      end
+      % set name
+      bs(j).name = sprintf('dropduplicates(%s)', ao_invars{j});
+      % Add history
+      bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist);
+    else
+      warning('!!! Skipping AO %s - it''s not a time-series AO.', ao_invars{j});
+      bs(j) = [];
+    end
+  end
+
+  % Set output
+  if nargout == numel(bs)
+    % List of outputs
+    for ii = 1:numel(bs)
+      varargout{ii} = bs(ii);
+    end
+  else
+    % Single output
+    varargout{1} = bs;
+  end
+end
+
+%--------------------------------------------------------------------------
+% Get Info Object
+%--------------------------------------------------------------------------
+function ii = getInfo(varargin)
+  if nargin == 1 && strcmpi(varargin{1}, 'None')
+    sets = {};
+    pl   = [];
+  else
+    sets = {'Default'};
+    pl   = getDefaultPlist;
+  end
+  % Build info object
+  ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $', sets, pl);
+end
+
+%--------------------------------------------------------------------------
+% Get Default Plist
+%--------------------------------------------------------------------------
+
+function plout = getDefaultPlist()
+  persistent pl;  
+  if exist('pl', 'var')==0 || isempty(pl)
+    pl = buildplist();
+  end
+  plout = pl;  
+end
+
+function pl = buildplist()
+  pl = plist();
+  
+  % tol
+  p = param({'tol','The time interval tolerance to consider two consecutive samples as duplicates.'}, ...
+    {1, {5e-3}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+end
+
+