--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/whiten1D.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,244 @@
+% WHITEN1D whitens the input time-series.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: WHITEN1D whitens the input time-series. The filter is built
+%              by fitting to the model provided. If no model is provided, a
+%              fit is made to a spectral-density estimate of the
+%              time-series (made using psd+bin_data or lpsd).
+%              Note: The function assumes that the input model corresponds
+%              to the one-sided psd of the data to be whitened.
+%
+% ALGORITHM:
+%            1) If no model provided, make psd+bin_data or lpsd
+%               of time-series and take it as a model
+%               for the data power spectral density
+%            2) Fit a set of partial fraction z-domain filters using
+%               utils.math.psd2wf. The fit is automatically stopped when
+%               the accuracy tolerance is reached.
+%            3) Convert to array of MIIR filters
+%            4) Assemble into a parallel filterbank object
+%            5) Filter time-series in parallel
+%
+%
+% CALL:         b = whiten1D(a, pl)
+%               [b1,b2,...,bn] = whiten1D(a1,a2,...,an, pl);
+%
+% INPUT:
+%               - as are time-series analysis objects or a vector of
+%               analysis objects
+%               - pl is a plist with the input parameters
+%
+% OUTPUT:
+%               - bs "whitened" time-series AOs. The whitening filters used
+%               are stored in the objects procinfo field under the
+%               parameter 'Filter'.
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'whiten1D')">Parameters Description</a>
+%
+% VERSION:     $Id: whiten1D.m,v 1.43 2011/04/08 08:56:12 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = whiten1D(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              = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+  
+  % Decide on a deep copy or a modify
+  bs = copy(as, nargout);
+  inhists = [as.hist];
+  
+  % combine plists
+  if isempty(pl)
+    model = 'psd';
+  else
+    model = find(pl, 'model');
+    if isempty(model)
+      model = 'psd';
+    end
+  end
+  
+  if ischar(model)
+    pl = parse(pl, getDefaultPlist(model));
+  else
+    pl = parse(pl, getDefaultPlist('Default'));
+  end
+  pl.getSetRandState();
+  
+  scale = find(pl, 'scaleOut');
+  flim = find(pl, 'flim');
+  
+  
+  % Loop over input AOs
+  for jj = 1:numel(as)
+    if ~isa(as(jj).data, 'tsdata')
+      utils.helper.msg(msg.IMPORTANT, '%s expects ao/tsdata objects. Skipping AO %s', mfilename, ao_invars{jj});
+    else
+      
+      %-------------- Whiten this AO
+      
+      % 1) Build whitening filterbank
+      switch class(model)
+        case 'char'
+          % Model is to be evaluated from data
+          in = as(jj);
+          pl.pset('model', model);
+        case 'ao'
+          % Model was provided as fsdata
+          in = model;
+          pl.pset('model', []);          
+      end
+      wf = buildWhitener1D(in, pl);
+            
+      % 1.5) Scale the date if demanded
+      if (scale)
+        spsd = lpsd(as(jj));
+        freqs = spsd.x;
+        if isempty(flim)
+          error('Please specify a flim field, to know the analysis band.');
+        elseif (flim(2) < flim(1))
+          error('flim should go from the smaller frequency to the bigger frequency. Please reverse them!')
+        else
+          index = find((freqs > flim(1)) & (freqs < flim(2)));
+        end
+        
+        v1 = spsd.y(index(1):index(end-1));
+        v2 = spsd.y(index(2):index(end));
+        m = (v1 + v2) /2;
+        p = sum(m.* diff(freqs(index(1):index(end)))); 
+      end
+      
+      % 2) Filter data and scale it if necessary
+      bs(jj).filter(wf);
+      if (scale)
+        bs(jj) = bs(jj) * sqrt(p);
+      end
+
+      
+      % 3) Output data
+      % name for this object
+      bs(jj).name = sprintf('whiten1D(%s)', ao_invars{jj});
+      % Collect the filters into procinfo
+      bs(jj).procinfo = combine(plist('Filter', wf.filters), as(jj).procinfo);
+      if(scale)
+        bs(jj).procinfo = combine(plist('ScaleFactor', p, 'Filter', wf.filters), as(jj).procinfo);
+      end
+      % Make sure that the output yunits are empty
+      if ~eq(bs(jj).yunits, unit(''))
+        utils.helper.msg(msg.PROC1, 'Resetting output yunits to empty');
+        bs(jj).setYunits(unit(''));
+      end
+      % add history
+      bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), inhists(jj));
+      % clear errors
+      bs(jj).clearErrors;
+      
+      
+    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   = [];
+  elseif nargin == 1 && ~isempty(varargin{1}) && ischar(varargin{1})
+    sets{1} = varargin{1};
+    pl = getDefaultPlist(sets{1});
+  else
+    sets = SETS();
+    % get plists
+    pl(size(sets)) = plist;
+    for kk = 1:numel(sets)
+      pl(kk) =  getDefaultPlist(sets{kk});
+    end
+  end
+  % Build info object
+  ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: whiten1D.m,v 1.43 2011/04/08 08:56:12 hewitson Exp $', sets, pl);
+end
+
+
+%--------------------------------------------------------------------------
+% Defintion of Sets
+%--------------------------------------------------------------------------
+
+function out = SETS()
+  out = ao.getInfo('buildWhitener1D').sets;
+end
+
+%--------------------------------------------------------------------------
+% Get Default Plist
+%--------------------------------------------------------------------------
+function plout = getDefaultPlist(set)
+  persistent pl;  
+  persistent lastset;
+  if ~exist('pl', 'var') || isempty(pl) || ~strcmp(lastset, set)
+    pl = buildplist(set);
+    lastset = set;
+  end
+  plout = pl;  
+end
+
+function pl = buildplist(set)
+  
+  pl = plist();
+  
+  % Append sets of parameters according to the chosen spectral estimator
+  if ~utils.helper.ismember(lower(SETS), lower(set))
+    error('### Unknown set [%s]', set);
+  else
+    pl = ao.getInfo('buildWhitener1D', lower(set)).plists;
+  end
+  
+   switch lower(set)
+    case 'default'
+      % Model
+      p = param({'model', ['A frequency-series AO describing the model<br>'...
+        'response to build the filter from. <br>' ...
+        'As an alternative, the user '...
+        'can choose a model estimation technique:<br>'...
+        '<li>PSD - using <tt>psd</tt> + <tt>bin_data</tt></li>'...
+        '<li>LPSD - using <tt>lpsd</tt></li>']}, paramValue.EMPTY_DOUBLE);
+      pl = combine(plist(p), pl);
+    otherwise
+   end
+  
+   p = param({'scaleOut', ['Scale your output by the inband power']},paramValue.FALSE_TRUE);
+   pl = combine(plist(p), pl);
+
+   p = param({'flim', ['Band to calculate the scaling power']},[1e-3 30e-3]);
+   pl = combine(plist(p), pl);
+
+   
+end
+