--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/interpmissing.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,154 @@
+% INTERPMISSING interpolate missing samples in a time-series.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% INTERPMISSING interpolate missing samples in a time-series. Missing samples
+%               are identified as being those where the time-span between one
+%               sample and the next is larger than d/fs where d is a
+%               tolerance value. Missing data is then placed in the gap in
+%               steps of 1/fs. Obviously this is only really correct for
+%               evenly sampled time-series.
+%
+% CALL:        bs = interpmissing(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', 'interpmissing')">Parameters Description</a>
+%
+% VERSION:     $Id: interpmissing.m,v 1.30 2011/04/08 08:56:16 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = interpmissing(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 and plists
+  [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
+  dtol = find(pl, 'd');
+
+  % Get only tsdata AOs
+  for j=1:numel(bs)
+    if isa(bs(j).data, 'tsdata')
+
+      % capture input history
+      ih = bs(j).hist;
+
+      % find missing samples
+      t    = [];
+      d    = diff(bs(j).data.getX);
+      idxs = find(d>dtol/bs(j).data.fs);
+      utils.helper.msg(msg.PROC1, 'found %d data gaps', numel(idxs));
+
+      % create new time grid
+      count = 0;
+      fs    = bs(j).data.fs;
+      for k=1:numel(idxs)
+        idx = idxs(k);
+        if isempty(t)
+          t   = bs(j).data.getX(1:idxs(1));
+        end
+        % now add samples at 1/fs until we are within 1/fs of the next sample
+        gap   = bs(j).data.getX(idx+1) - bs(j).data.getX(idx) - 1/fs;
+        tfill = [[1/fs:1/fs:gap] + bs(j).data.getX(idx)].';
+        count = count + numel(tfill);
+        
+        if k==numel(idxs)
+          t = [t; tfill; bs(j).data.getX(idx+1:end)];
+        else
+          t = [t; tfill; bs(j).data.getX(idx+1:idxs(k+1))];
+        end
+      end
+      utils.helper.msg(msg.PROC1, 'filled with %d samples', count);
+
+      % now interpolate onto this new time-grid
+      if ~isempty(t)
+        bs(j).interp(plist('vertices', t, 'method', find(pl, 'method')));
+        bs(j).name = sprintf('interpmissing(%s)', ao_invars{j});
+        % Add history
+        bs(j).addHistory(getInfo('None'), pl, ao_invars(j), ih);
+        % clear errors
+        bs(j).clearErrors;
+      else
+        utils.helper.msg(msg.PROC1, 'no missing samples found in %s - no action performed.', ao_invars{j});
+      end
+    else
+      utils.helper.msg(msg.PROC1, 'skipping AO %s - it''s not a time-series AO.', ao_invars{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: interpmissing.m,v 1.30 2011/04/08 08:56:16 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();
+  
+  % d
+  p = param({'d','The time interval tolerance for finding missing samples.'}, {1, {1.5}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+  % Interpolation method
+  pli = ao.getInfo('interp').plists;
+  p = pli.params(pli.getIndexForKey('method'));
+  pl.append(p);
+  
+end