--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/smoother.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,164 @@
+% SMOOTHER smooths a given series of data points using the specified method.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: SMOOTHER smooths a given series of data points using
+%              the specified method.
+%
+% CALL:        b = smoother(a, pl)
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'smoother')">Parameters Description</a>
+%
+% VERSION:     $Id: smoother.m,v 1.30 2011/11/11 15:21:19 luigi Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = smoother(varargin)
+
+  callerIsMethod = utils.helper.callerIsMethod;
+
+  % 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 parameters from plist
+  bw      = find(pl, 'width');
+  hc      = find(pl, 'hc');
+  method  = find(pl, 'method');
+
+  % check the method
+  if  ~strcmp(method, 'median') && ...
+      ~strcmp(method, 'mean') && ...
+      ~strcmp(method, 'min') && ...
+      ~strcmp(method, 'max') && ...
+      ~strcmp(method, 'mode')
+    help(mfilename)
+    error('### Unknown smoothing method');
+  end
+
+  % Loop over input AOs
+  for j=1:numel(bs)
+    utils.helper.msg(msg.PROC1, 'smoothing %s', bs(j).name);
+    switch lower(method)
+      case {'median', 'mean', 'min', 'max'}
+        bs(j).data.setY(ltpda_smoother(bs(j).data.getY, bw, hc, method));
+      otherwise
+        bs(j).data.setY(smooth(bs(j).data.getY, bw, hc, method));
+    end
+    % set name
+    bs(j).name = sprintf('smoother(%s)', ao_invars{j});
+    % Add history
+    if ~callerIsMethod
+      bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist);
+    end
+  end
+
+  % clear errors
+  bs.clearErrors;
+
+  % 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: smoother.m,v 1.30 2011/11/11 15:21:19 luigi Exp $', sets, pl);
+end
+
+
+%--------------------------------------------------------------------------
+% smooth data
+function ys = smooth(y, bw, hc, method)
+  N = length(y);
+  ys = zeros(size(y));
+
+  % function to smooth with
+  mfcn = eval(['@(x) ' method '(x)' ]);
+
+  for kk=1:N
+    if mod(kk, 1000)==0
+      utils.helper.msg(utils.const.msg.PROC1, 'smoothed %06d samples', kk);
+    end
+    % Determine the interval we are looking in
+    interval = kk-bw/2:kk+bw/2;
+    interval(interval<=0)=1;
+    interval(interval>N)=N;
+    % calculate method(values) of interval
+    % after throwing away outliers
+    trial = sort(y(interval));
+    b = round(hc*length(trial));
+    ys(kk)  = mfcn(trial(1:b));
+  end
+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();
+  
+  % width
+  p = param({'width', 'The width of the smoothing filter.'}, paramValue.DOUBLE_VALUE(20));
+  pl.append(p);
+  
+  % hc
+  p = param({'hc', 'A cutoff to throw away outliers (0-1).'}, paramValue.DOUBLE_VALUE(0.8));
+  pl.append(p);
+  
+  % Method
+  p = param({'method', 'The smoothing method.'}, {1, {'median', 'mean', 'max', 'mode'}, paramValue.SINGLE});
+  pl.append(p);
+  
+end
+% END
+
+% PARAMETERS:  width  - the width of the smoothing filter [default: 20 samples]
+%              hc     - a cutoff to throw away outliers (0-1)  [default: 0.8]
+%              method - the smoothing method:
+%                       'median'  [default]
+%                       'mean', 'min', 'max', 'mode'
+%