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+ − % 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