--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/xcorr.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,240 @@
+% XCORR makes cross-correlation estimates of the time-series
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: XCORR makes cross-correlation estimates of the time-series
+%              objects in the input analysis objects. The cross-correlation is
+%              computed using MATLAB's xcorr (>> help xcorr).
+%
+% CALL:        b = xcorr(a1,a2,pl)
+%
+% INPUTS:      b     - output analysis objects
+%              a1,a2 - input analysis objects (only two)
+%              pl    - input parameter list
+%
+%              The function makes correlation estimates between a1 and a2.
+%
+%              If only on AO is input, the auto-correlation is computed.
+%
+%              If the last input argument is a parameter list (plist) it is used.
+%              The following parameters are recognised.
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'xcorr')">Parameters Description</a>
+%
+% VERSION:     $Id: xcorr.m,v 1.23 2011/04/08 08:56:15 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = xcorr(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);
+  
+  if nargout == 0
+    error('### xcorr 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 and plists
+  [as, invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
+  pl              = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+  
+  % combine plists
+  pl = parse(pl, getDefaultPlist());
+  
+  na = numel(as);
+  if na > 2
+    error('### XCORR accepts only two AOs to cross-correlate.');
+  end
+  
+  %----------------- Keep the history to suppress the history of the
+  %----------------- intermediate steps
+  inhists = [as(:).hist];
+  
+  %----------------- Resample all AOs
+  copies = zeros(size(as));
+ 
+  fsmax = findFsMax(as);
+  fspl  = plist('fsout', fsmax);
+  for jj=1:na
+    % check this is a time-series object
+    if ~isa(as(jj).data, 'tsdata')
+      error('### ltpda_xspec requires tsdata (time-series) inputs.');
+    end
+    % Check Fs
+    if as(jj).fs ~= fsmax
+      utils.helper.msg(msg.PROC1, 'resampling AO %s to %f Hz', as(jj).name, fsmax);
+      % Make a deep copy so we don't
+      % affect the original input data
+      as(jj) = copy(as(jj), 1);
+      copies(jj) = 1;
+      as(jj).resample(fspl);
+    end
+  end
+  
+  %----------------- Truncate all vectors
+  
+  % Get shortest vector
+  utils.helper.msg(msg.PROC1, '*** Truncating all vectors...');
+  lmin = findShortestVector(as);
+  nsecs = lmin / fsmax;
+  for jj=1:na
+    if len(as(jj)) ~= lmin
+      utils.helper.msg(msg.PROC2, 'truncating AO %s to %d secs', as(jj).name, nsecs);
+      % do we already have a copy?
+      if ~copies(jj)
+        % Make a deep copy so we don't
+        % affect the original input data
+        as(jj) = copy(as(jj), 1);
+        copies(jj) = 1;
+      end
+      as(jj).select(1:lmin);
+    end
+  end
+  
+  %----------------- check input parameters
+  
+  % Maximum lag for Xcorr
+  MaxLag = find(pl, 'MaxLag');
+  
+  % Scale for Xcorr
+  scale = find(pl, 'Scale');
+  
+  % Loop over input AOs
+  bs = ao;
+  
+  % -------- Make Xspec estimate
+  
+  % Compute cross-correlation estimates using XCORR
+  if MaxLag == -1
+    MaxLag = len(as(1));
+  end
+  % Use .data.y syntax (rather than .y) to preserve y vector shape
+  [c,lags] = xcorr(as(1).data.y, as(2).data.y, MaxLag, scale);
+  
+  % Keep the data shape of the first input AO
+  if size(as(1).y,1) == 1
+    c = c.';
+  end
+  
+  % create new output xydata
+  xy = xydata(lags./fsmax, c);
+  xy.setXunits('s');
+  switch scale
+    case {'none', 'biased', 'unbiased'}      
+      xy.setYunits(as(1).yunits *  as(2).yunits);
+    case 'coeff'
+      xy.setYunits('');
+    otherwise
+      error(['Unsupported scaling option ' scale]);
+  end
+  
+  
+  %----------- create new output history
+  
+  % make output analysis object
+  bs.data = xy;
+  % set name
+  bs.name = sprintf('xcorr(%s->%s)', invars{1}, invars{2});
+  % Propagate 'plotinfo'
+  plotinfo = [as(:).plotinfo];
+  if ~isempty(plotinfo)
+    bs.plotinfo = combine(plotinfo);
+  end
+  % we need to get the input histories in the same order as the inputs
+  % to this function call, not in the order of the input to tfestimate;
+  % otherwise the resulting matrix on a 'create from history' will be
+  % mirrored.
+  bs.addHistory(getInfo('None'), pl, [invars(:)], inhists);
+  
+  % Set output
+  varargout{1} = bs;
+  %   end
+end
+
+%--------------------------------------------------------------------------
+% Returns the length of the shortest vector in samples
+function lmin = findShortestVector(as)
+  lmin = 1e20;
+  for jj=1:numel(as)
+    if len(as(jj)) < lmin
+      lmin = len(as(jj));
+    end
+  end
+end
+%--------------------------------------------------------------------------
+% Returns the max Fs of a set of AOs
+function fs = findFsMax(as)
+  fs = 0;
+  for jj=1:numel(as)
+    a = as(jj);
+    if a.fs > fs
+      fs = a.fs;
+    end
+  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: xcorr.m,v 1.23 2011/04/08 08:56:15 hewitson Exp $', sets, pl);
+  ii.setModifier(false);
+  ii.setArgsmin(2);
+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();
+  
+  % MaxLag
+  p = param({'MaxLag', 'Compute over a range of lags -MaxLag to MaxLag  [default: M-1]'}, {1, {-1}, paramValue.OPTIONAL});
+  pl.append(p);
+  
+  % Scale
+  p = param({'Scale', ['normalisation of the correlation. Choose from:<ul>'...
+    '<li>''biased''   - scales the raw cross-correlation by 1/M</li>'...
+    '<li>''unbiased'' - scales the raw correlation by 1/(M-abs(lags))</li>'...
+    '<li>''coeff''    - normalizes the sequence so that the auto-correlations<br>'...
+    'at zero lag are identically 1.0.</li>'...
+    '<li>''none''     - no scaling</li></ul>']}, {1, {'none', 'biased', 'unbiased', 'coeff'}, paramValue.SINGLE});
+  pl.append(p);
+  
+end
+% PARAMETERS:  'MaxLag'   - compute over range of lags -MaxLag to MaxLag  [default: M-1]
+%              'Scale'    - normalisation of the correlation. Choose from:
+%                   'biased'   - scales the raw cross-correlation by 1/M.
+%                   'unbiased' - scales the raw correlation by 1/(M-abs(lags)).
+%                   'coeff'    - normalizes the sequence so that the auto-correlations
+%                           at zero lag are identically 1.0.
+%                   'none'     - no scaling (this is the default).
+%
+%              M is the length of longest input vector. If one vector is shorted,
+%              it is zero padded.