Mercurial > hg > ltpda
view m-toolbox/classes/@ao/delayEstimate.m @ 43:bc767aaa99a8
CVS Update
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Tue, 06 Dec 2011 11:09:25 +0100 |
parents | f0afece42f48 |
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% DELAYESTIMATE estimates the delay between two AOs %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: DELAYESTIMATE returns an estimate of the delay between the two % input analysis objects. Different weights in frequency % domain can be used. % % CALL: bs = delayEstimate(a1,a2,pl) % % INPUTS: a1 - input analysis objects % a2 - delayed analysis objects % pl - input parameter list % % OUTPUTS: bs - analysis object with the delay (as cdata) % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'delayEstimate')">Parameters Description</a> % % VERSION: $Id: delayEstimate.m,v 1.6 2011/04/08 08:56:15 hewitson Exp $ % % HISTORY: 15-10-09 M Nofrarias % Creation % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = delayEstimate(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); % 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); pl = utils.helper.collect_objects(varargin(:), 'plist', in_names); % Decide on a deep copy or a modify bs = copy(as, nargout); % Combine plists pl = parse(pl, getDefaultPlist); % get frequency weight weight = find(pl, 'weight'); N = len(bs(1)); T = bs(1).x(end); fs = bs(1).fs; % zeropad to avoid circular convolution in ifft bs(1).zeropad; bs(2).zeropad; % compute spectral density, scale applied after to avoid round-off errors scale = fs/N; Gx11 = conj(fft(bs(1).y)).*fft(bs(1).y); Gx11 = Gx11*scale; Gx12 = conj(fft(bs(1).y)).*fft(bs(2).y); Gx12 = Gx12*scale; Gx22 = conj(fft(bs(2).y)).*fft(bs(2).y); Gx22 = Gx22*scale; % frequencies for two-sided spectrum f = linspace(-fs,fs,2*N); % select weight if strcmp(weight,'roth') weight = Gx11; elseif strcmp(weight,'scot') weight = sqrt(Gx11.* Gx22); elseif strcmp(weight,'scc') weight = 1; elseif strcmp(weight,'phat') weight = abs(Gx12); elseif strcmp(weight,'eckart') weight = 1; elseif strcmp(weight,'ML') weight = 1; else error('### Unknown value for ''weight'' parameter ' ) end % compute unscaled correlation function Ru = ifft(Gx12./weight); % lag= 0:\deltat*(N-1) % n= 1:N/2-1 r = linspace(0,T-1/fs,length(Ru)/2-1); % scaling to correct zeropad bias R = (N./(N-r))'.*Ru(1:length(Ru)/2-1); % get maximum [m,ind] = max(R); del = r(ind); % plot if required plt = find(pl, 'plot'); if plt Rxy = ao(xydata(r,R)); Rxy.setName(sprintf('Correlation(%s,%s)', ao_invars{1},ao_invars{2})) iplot(Rxy) end % create new output cdata cd = cdata(del); % add unitss cd.yunits = 's'; % update AO c = ao(cd); % add error % bs(jj).data.dy = dev; % Add name c.name = sprintf('delayEst(%s,%s)', ao_invars{1},ao_invars{2}); % Add history c.addHistory(getInfo('None'), pl, [ao_invars(:)], [bs(:).hist]); % set output varargout{1} = c; 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: delayEstimate.m,v 1.6 2011/04/08 08:56:15 hewitson Exp $', sets, pl); 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(); % method p = param({'weight',['scaling of output. Choose from:<ul>', ... '<li>scc - </li>', ... '<li>roth - </li>', ... '<li>scot - </li>', ... '<li>phat - </li>', ... '<li>eckart - </li>', ... '<li>ML - </li></ul>']}, {1, {'scc','roth', 'scot','phat','eckart','ML'}, paramValue.SINGLE}); pl.append(p); % Plot p = param({'Plot', 'Plot the correlation function'}, ... {2, {'true', 'false'}, paramValue.SINGLE}); pl.append(p); end % END