--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/lcohere.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,139 @@
+% LCOHERE implement magnitude-squadred coherence estimation on a log frequency axis.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: LCOHERE implement coherence estimation on a log frequency axis.
+%              The estimate is done by taking 
+%              the ratio of the CPSD between the two inputs, Sxy, divided by 
+%              the product of the PSDs of the inputs, Sxx and Syy,              
+%              and is either magnitude-squared: (abs(Sxy))^2 / (Sxx * Syy) 
+%              or complex value: Sxy / sqrt(Sxx * Syy)
+%              Here x is the first input, y is the second input
+%
+% CALL:        b = lcohere(a1,a2,pl)
+%
+% INPUTS:      a1   - input analysis object
+%              a2   - input analysis object
+%              pl   - input parameter list
+%
+% OUTPUTS:     b    - output analysis object
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'lcohere')">Parameters Description</a>
+%
+% VERSION:     $Id: lcohere.m,v 1.30 2011/04/08 08:56:18 hewitson 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 = lcohere(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('### lcohere 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);
+
+  % Apply defaults to plist
+  pl = applyDefaults(getDefaultPlist, varargin{:});
+
+  % Throw an error if input is not two AOs
+  if numel(as) ~= 2
+    error('### lcohere only accepts two inputs AOs.');
+  end
+  
+  % Compute coherence with lxspec
+  scale_type = find(pl, 'Type');
+  switch lower(scale_type)
+    case 'c'
+      bs = ao.lxspec(as, pl, 'cohere', getInfo, ao_invars);
+    case 'ms'
+      bs = ao.lxspec(as, pl, 'mscohere', getInfo, ao_invars);
+    otherwise
+      error(['### Unknown coherence type: [' scale_type ']']);
+  end
+
+  % Set output
+  varargout{1} = 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: lcohere.m,v 1.30 2011/04/08 08:56:18 hewitson Exp $', sets, pl);
+  ii.setModifier(false);
+  ii.setArgsmin(2);
+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;
+  
+  % Type
+  p = param({'Type',['type of output scaling. Choose from:<ul>', ...
+    '<li>MS - Magnitude-Squared Coherence:<br><tt>(abs(Sxy))^2 / (Sxx * Syy)</tt></li>', ...
+    '<li>C  - Complex Coherence:<br><tt>Sxy / sqrt(Sxx * Syy)</tt></li></ul>']}, {1, {'C', 'MS'}, paramValue.SINGLE});
+  pl.append(p);
+  
+end
+
+% PARAMETERS:
+%
+%     'Kdes'  - desired number of averages   [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)]  
+%     'Type'  - type of output scaling. Choose from:
+%                MS - Magnitude-Squared Coherence (abs(Sxy))^2 / (Sxx * Syy)  
+%                C  - Complex Coherence Sxy / sqrt(Sxx * Syy) [default] 
+%     'Order' - order of segment detrending
+%                -1 - no detrending
+%                0 - subtract mean [default]
+%                1 - subtract linear fit
+%                N - subtract fit of polynomial, order N