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−% LCOHERE implement magnitude-squadred coherence estimation on a log frequency axis.
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−%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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−%
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−% DESCRIPTION: LCOHERE implement coherence estimation on a log frequency axis.
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−%              The estimate is done by taking 
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−%              the ratio of the CPSD between the two inputs, Sxy, divided by 
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−%              the product of the PSDs of the inputs, Sxx and Syy,              
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−%              and is either magnitude-squared: (abs(Sxy))^2 / (Sxx * Syy) 
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−%              or complex value: Sxy / sqrt(Sxx * Syy)
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−%              Here x is the first input, y is the second input
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−%
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−% CALL:        b = lcohere(a1,a2,pl)
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−%
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−% INPUTS:      a1   - input analysis object
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−%              a2   - input analysis object
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−%              pl   - input parameter list
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−%
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−% OUTPUTS:     b    - output analysis object
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−%
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−% <a href="matlab:utils.helper.displayMethodInfo('ao', 'lcohere')">Parameters Description</a>
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−%
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−% VERSION:     $Id: lcohere.m,v 1.30 2011/04/08 08:56:18 hewitson Exp $
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−%
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−% References:  "Improved spectrum estimation from digitized time series
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−%               on a logarithmic frequency axis", Michael Troebs, Gerhard Heinzel,
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−%               Measurement 39 (2006) 120-129.
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−%
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−%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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−
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−function varargout = lcohere(varargin)
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−
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−  % Check if this is a call for parameters
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−  if utils.helper.isinfocall(varargin{:})
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−    varargout{1} = getInfo(varargin{3});
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−    return
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−  end
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−
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−  import utils.const.*
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−  utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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−
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−  if nargout == 0
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−    error('### lcohere cannot be used as a modifier. Please give an output variable.');
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−  end
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−
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−  % Collect input variable names
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−  in_names = cell(size(varargin));
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−  for ii = 1:nargin,in_names{ii} = inputname(ii);end
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−
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−  % Collect all AOs
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−  [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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−
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−  % Apply defaults to plist
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−  pl = applyDefaults(getDefaultPlist, varargin{:});
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−
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−  % Throw an error if input is not two AOs
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−  if numel(as) ~= 2
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−    error('### lcohere only accepts two inputs AOs.');
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−  end
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−  
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−  % Compute coherence with lxspec
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−  scale_type = find(pl, 'Type');
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−  switch lower(scale_type)
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−    case 'c'
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−      bs = ao.lxspec(as, pl, 'cohere', getInfo, ao_invars);
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−    case 'ms'
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−      bs = ao.lxspec(as, pl, 'mscohere', getInfo, ao_invars);
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−    otherwise
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−      error(['### Unknown coherence type: [' scale_type ']']);
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−  end
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−
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−  % Set output
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−  varargout{1} = bs;
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−
+
−end
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−
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−%--------------------------------------------------------------------------
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−% Get Info Object
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−%--------------------------------------------------------------------------
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−function ii = getInfo(varargin)
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−  if nargin == 1 && strcmpi(varargin{1}, 'None')
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−    sets = {};
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−    pl   = [];
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−  else
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−    sets = {'Default'};
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−    pl   = getDefaultPlist();
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−  end
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−  % Build info object
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−  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);
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−  ii.setModifier(false);
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−  ii.setArgsmin(2);
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−end
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−
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−%--------------------------------------------------------------------------
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−% Get Default Plist
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−%--------------------------------------------------------------------------
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−
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−function plout = getDefaultPlist()
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−  persistent pl;  
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−  if ~exist('pl', 'var') || isempty(pl)
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−    pl = buildplist();
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−  end
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−  plout = pl;  
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−end
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−
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−function pl = buildplist()
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−  
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−  % General plist for Welch-based, log-scale spaced spectral estimators
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−  pl = plist.LPSD_PLIST;
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−  
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−  % Type
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−  p = param({'Type',['type of output scaling. Choose from:<ul>', ...
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−    '<li>MS - Magnitude-Squared Coherence:<br><tt>(abs(Sxy))^2 / (Sxx * Syy)</tt></li>', ...
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−    '<li>C  - Complex Coherence:<br><tt>Sxy / sqrt(Sxx * Syy)</tt></li></ul>']}, {1, {'C', 'MS'}, paramValue.SINGLE});
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−  pl.append(p);
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−  
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−end
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−
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−% PARAMETERS:
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−%
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−%     'Kdes'  - desired number of averages   [default: 100]
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−%     'Jdes'  - number of spectral frequencies to compute [default: 1000]
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−%     'Lmin'  - minimum segment length   [default: 0]
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−%     'Win'   - the window to be applied to the data to remove the 
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−%               discontinuities at edges of segments. [default: taken from
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−%               user prefs]
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−%               Only the design parameters of the window object are
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−%               used. Enter either:
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−%                - a specwin window object OR
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−%                - a string value containing the window name 
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−%                  e.g., plist('Win', 'Kaiser', 'psll', 200)
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−%     'Olap'  - segment percent overlap [default: -1, (taken from window function)]  
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−%     'Type'  - type of output scaling. Choose from:
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−%                MS - Magnitude-Squared Coherence (abs(Sxy))^2 / (Sxx * Syy)  
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−%                C  - Complex Coherence Sxy / sqrt(Sxx * Syy) [default] 
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−%     'Order' - order of segment detrending
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−%                -1 - no detrending
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−%                0 - subtract mean [default]
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−%                1 - subtract linear fit
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−%                N - subtract fit of polynomial, order N