--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/lpsd.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,237 @@
+% LPSD implements the LPSD algorithm for analysis objects.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: LPSD implements the LPSD algorithm for analysis objects.
+%
+% CALL:        bs = lpsd(a1,a2,a3,...,pl)
+%              bs = lpsd(as,pl)
+%              bs = as.lpsd(pl)
+%
+% INPUTS:      aN   - input analysis objects
+%              as   - input analysis objects array
+%              pl   - input parameter list
+%
+% OUTPUTS:     bs   - array of analysis objects, one for each input
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'lpsd')">Parameters Description</a>
+%
+% VERSION:     $Id: lpsd.m,v 1.55 2011/05/22 21:22:09 mauro Exp $
+%
+% References:  "Improved spectrum estimation from digitized time series
+%               on a logarithmic frequency axis", Michael Troebs, Gerhard Heinzel,
+%               Measurement 39 (2006) 120-129.
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = lpsd(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);
+  
+  % Decide on a deep copy or a modify
+  bs = copy(as, nargout);
+  
+  % Apply defaults to plist
+  pl = applyDefaults(getDefaultPlist, varargin{:});
+  
+  inhists = [];
+  
+  % Loop over input AOs
+  for jj = 1 : numel(bs)
+    % gather the input history objects
+    inhists = [inhists bs(jj).hist];
+    
+    % check this is a time-series object
+    if ~isa(bs(jj).data, 'tsdata')
+      warning('!!! lpsd requires tsdata (time-series) inputs. Skipping AO %s', ao_invars{jj});
+    else
+      
+      % Check the time range.
+      time_range = mfind(pl, 'split', 'times');
+      if ~isempty(time_range)
+        switch class(time_range)
+          case 'double'
+            bs(jj) = split(bs(jj), plist(...
+              'times', time_range));
+          case 'timespan'
+            bs(jj) = split(bs(jj), plist(...
+              'timespan', time_range));
+          case 'time'
+            bs(jj) = split(bs(jj), plist(...
+              'start_time', time_range(1), ...
+              'end_time', time_range(2)));
+          case 'cell'
+            bs(jj) = split(bs(jj), plist(...
+              'start_time', time_range{1}, ...
+              'end_time', time_range{2}));
+          otherwise
+        end
+      end
+      
+      % Check the length of the object
+      if bs(jj).len <= 0
+        error('### The object is empty! Please revise your settings ...');
+      end
+      
+      pl = utils.helper.process_spectral_options(pl, 'log');
+      
+      % Desired number of averages
+      Kdes = find(pl, 'Kdes');
+      % num desired spectral frequencies
+      Jdes = find(pl, 'Jdes');
+      % Minimum segment length
+      Lmin = find(pl, 'Lmin');
+      % Window function
+      Win = find(pl, 'Win');
+      % Overlap
+      Nolap = find(pl, 'Olap')/100;
+      % Order of detrending
+      Order = find(pl, 'Order');      
+
+      % Get frequency vector
+      [f, r, m, L, K] = ao.ltf_plan(length(bs(jj).data.y), bs(jj).data.fs, Nolap, 1, Lmin, Jdes, Kdes);
+      
+      % compute LPSD
+      try
+        if find(pl, 'M-FILE ONLY')
+          % Using pure m-file version
+          [P, Pxx, ENBW] = ao.mlpsd_m(bs(jj).data.y, f, r, m, L, bs(jj).data.fs, Win, Order, Nolap);
+        else
+          [P, Pxx, dev, devxx, ENBW] = ao.mlpsd_mex(bs(jj).data.y, f, r, m, L, bs(jj).data.fs, Win, Order, Nolap*100, Lmin);
+        end
+      catch ME
+        warning('!!! mex file dft failed. Using m-file version of lpsd.');
+        % Using pure m-file version
+        [P, Pxx, ENBW] = ao.mlpsd_m(bs(jj).data.y, f, r, m, L, bs(jj).data.fs, Win, Order, Nolap);
+      end
+      
+      % Keep the data shape of the input AO
+      if size(bs(jj).data.y,1) == 1
+        P   = P.';
+        Pxx = Pxx.';
+        dev   = dev.';
+        devxx = devxx.';
+        f   = f.';
+      end
+      
+      % create new output fsdata
+      scale = find(pl, 'Scale');
+      switch lower(scale)
+        case 'as'
+          fsd = fsdata(f, sqrt(P), bs(jj).data.fs);
+          fsd.setYunits(bs(jj).data.yunits);
+          std = sqrt(dev);
+        case 'asd'
+          fsd = fsdata(f, sqrt(Pxx), bs(jj).data.fs);
+          fsd.setYunits(bs(jj).data.yunits / unit('Hz^0.5'));
+          std = sqrt(devxx);
+        case 'ps'
+          fsd = fsdata(f, P, bs(jj).data.fs);
+          fsd.setYunits(bs(jj).data.yunits.^2);
+          std = dev;
+        case 'psd'
+          fsd = fsdata(f, Pxx, bs(jj).data.fs);
+          fsd.setYunits(bs(jj).data.yunits.^2/unit('Hz'));
+          std = devxx;
+        otherwise
+          error(['### Unknown scaling:' scale]);
+      end
+      fsd.setXunits('Hz');
+      fsd.setEnbw(ENBW);
+      fsd.setT0(bs(jj).data.t0);
+      % make output analysis object
+      bs(jj).data = fsd;
+      % set name
+      bs(jj).name = sprintf('L%s(%s)', upper(scale), ao_invars{jj});
+      % Add processing info
+      bs(jj).procinfo = plist('r', r, 'm', m, 'l', L, 'k', K);
+      % Add standard deviation
+      bs(jj).data.dy = std;
+      % Add history
+      bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), inhists(jj));
+      
+    end % End tsdata if/else
+  end % loop over analysis objects
+  
+  % Set output
+  varargout = utils.helper.setoutputs(nargout, bs);
+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: lpsd.m,v 1.55 2011/05/22 21:22:09 mauro Exp $', sets, pl);
+end
+
+%--------------------------------------------------------------------------
+% Get Default Plist
+%--------------------------------------------------------------------------
+function plout = getDefaultPlist()
+  persistent pl;  
+  if ~exist('pl', 'var') || isempty(pl)
+    pl = buildplist();
+  end
+  plout = pl;  
+end
+
+function pl = buildplist()
+  
+  % General plist for Welch-based, log-scale spaced spectral estimators
+  pl = plist.LPSD_PLIST;
+  
+  % Scale
+  p = param({'Scale',['The scaling of output. Choose from:<ul>', ...
+    '<li>PSD - Power Spectral Density</li>', ...
+    '<li>ASD - Amplitude (linear) Spectral Density</li>', ...
+    '<li>PS  - Power Spectrum</li>', ...
+    '<li>AS  - Amplitude (linear) Spectrum</li></ul>']}, {1, {'PSD', 'ASD', 'PS', 'AS'}, paramValue.SINGLE});
+  pl.append(p);
+  
+end
+
+% PARAMETERS:
+%
+%     'Kdes'  - desired number of averages to perform  [default: 100]
+%     'Jdes'  - number of spectral frequencies to compute [default: 1000]
+%     'Lmin'  - minimum segment length   [default: 0]
+%     'Win'   - the window to be applied to the data to remove the
+%               discontinuities at edges of segments. [default: taken from
+%               user prefs]
+%               Only the design parameters of the window object are
+%               used. Enter either:
+%                - a specwin window object OR
+%                - a string value containing the window name
+%                  e.g., plist('Win', 'Kaiser', 'psll', 200)
+%     'Olap'  - segment percent overlap [default: -1, (taken from window function)]
+%     'Scale' - scaling of output. Choose from:
+%                PSD - Power Spectral Density [default]
+%                ASD - Amplitude (linear) Spectral Density
+%                PS  - Power Spectrum
+%                AS  - Amplitude (linear) Spectrum
+%     'Order' - order of segment detrending
+%                -1 - no detrending
+%                0 - subtract mean [default]
+%                1 - subtract linear fit
+%                N - subtract fit of polynomial, order N