--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/linedetect.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,231 @@
+% LINEDETECT find spectral lines in the ao/fsdata objects.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: LINEDETECT find spectral lines in the ao/fsdata objects.
+%
+% CALL:        b = linedetect(a, pl)
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'linedetect')">Parameters Description</a>
+%
+% VERSION:    $Id: linedetect.m,v 1.15 2011/04/08 08:56:16 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = linedetect(varargin)
+
+  % Check if this is a call for parameters
+  if utils.helper.isinfocall(varargin{:})
+    varargout{1} = getInfo(varargin{3});
+    return
+  end
+
+  if nargout == 0
+    error('### cat cannot be used as a modifier. Please give an output variable.');
+  end
+
+  % 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);
+  [pli, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+
+  % Decide on a deep copy or a modify
+  bs = copy(as, nargout);
+
+  Na = numel(bs);
+  if isempty(bs)
+    error('### Please input at least one AO.');
+  end
+
+  % Combine plists
+  if ~isempty(pli)
+    pl = parse(pli, getDefaultPlist());
+  else
+    pl = getDefaultPlist();
+  end
+
+  % Get parameters from plist
+  N       = find(pl, 'N');
+  fsearch = find(pl, 'fsearch');
+  thresh  = find(pl, 'thresh');
+
+  % Loop over input AOs
+  for jj = 1:Na
+    if isa(bs(jj).data, 'fsdata')
+
+      % Make noise-floor estimate
+      nf = smoother(bs(jj), pl);
+
+      % Make ratio
+      r = bs(jj)./nf;
+
+      % find lines
+      lines = findLines(bs(jj).data.getY, r.data.getX, r.data.getY, thresh, N, fsearch);
+
+      f = [lines(:).f];
+      y = [lines(:).a];
+
+      % Keep the data shpare of the input AO
+      if size(bs(jj).data.y, 2) == 1
+        f = f.';
+        y = y.';
+      end
+
+      % Make output data: copy the fsdata object so to inherit all the feautures
+      fs = copy(bs(jj).data, 1);
+      
+      % Make output data: set the values
+      fs.setX(f);
+      fs.setY(y);
+
+    else
+      error('### I can only find lines in frequency-series AOs.');
+    end
+
+    %------- Make output AO
+
+    % make output analysis object
+    bs(jj).data = fs;
+
+    bs(jj).name = sprintf('lines(%s)', ao_invars{1});
+    bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), bs(jj).hist);
+  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
+
+%--------------------------------------------------------------------------
+% find spectral lines
+function lines = findLines(ay, f, nay, thresh, N, fsearch)
+
+  % look for spectral lines
+  l       = 0;
+  pmax    = 0;
+  pmaxi   = 0;
+  line    = [];
+  idx     = find( f>=fsearch(1) & f<=fsearch(2) );
+  for jj = 1:length(idx)
+    v = nay(idx(jj));
+    if v > thresh
+      if v > pmax
+        pmax  = v;
+        pmaxi = idx(jj);
+      end
+    else
+      if pmax > 0
+        % find index when we have pmax
+        fidx = pmaxi; %(find(nay(1:idx(jj))==pmax));
+        l = l+1;
+        line(l).idx = fidx;
+        line(l).f   = f(fidx);
+        line(l).a   = ay(fidx);
+      end
+      pmax = 0;
+    end
+  end
+
+  % Select largest peaks
+  lines = [];
+  if ~isempty(line)
+    [bl, lidx] = sort([line.a], 'descend');
+    lidxs = lidx(1:min([N length(lidx)]));
+    lines = line(lidxs);
+    disp(sprintf('   + found %d lines.', length([lines.f])));
+  else
+    disp('   + found 0 lines.');
+  end
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%                               Local Functions                               %
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% FUNCTION:    getInfo
+%
+% DESCRIPTION: Get Info Object
+%
+% HISTORY:     11-07-07 M Hewitson
+%                Creation.
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+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: linedetect.m,v 1.15 2011/04/08 08:56:16 hewitson Exp $', sets, pl);
+  ii.setModifier(false);
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% FUNCTION:    getDefaultPlist
+%
+% DESCRIPTION: Get Default Plist
+%
+% HISTORY:     11-07-07 M Hewitson
+%                Creation.
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function plout = getDefaultPlist()
+  persistent pl;  
+  if exist('pl', 'var')==0 || isempty(pl)
+    pl = buildplist();
+  end
+  plout = pl;  
+end
+
+function pl = buildplist()
+
+  pl = plist();
+  
+  % N
+  p = param({'N', 'The maximum number of lines to return.'}, {1, {10}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+  % fsearch
+  p = param({'fsearch', 'The frequency search interval.'}, {1, {[0 1e10]}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+  % thresh
+  p = param({'thresh', 'A threshold to test normalised amplitude against. (A sort-of SNR threshold.)'}, {1, {2}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+  % BW
+  p = param({'bw', ['The bandwidth of the running median filter used to<br>'...
+                    'estimate the noise-floor.']}, {1, {20}, paramValue.OPTIONAL});
+  pl.append(p);                
+                  
+  % HC
+  p = param({'hc', 'The cutoff used to reject outliers (0-1).'}, {1, {0.8}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+  
+end
+
+% PARAMETERS:  N        - max number of lines to return  [default: 10]
+%              fsearch  - freqeuncy search interval  [default: all]
+%              thresh   - a threshold to test normalised amplitude spectrum against
+%                         [default: 2]
+%              bw       - bandwidth over which to compute median [default: 20 samples]
+%              hc       - percent of outliers to exclude from median estimation (0-1)
+%                         [default: 0.8]