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Improve look of LTPDAPreferences diaolog
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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% LINSUBTRACT subtracts a linear contribution from an input ao. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: LINSUBTRACT subtracts a linear contribution from an input ao. % The methods assumes the input data to be synchronous. The % user selects a filter to be applied to the data before % fitting and a time segment where the fit is performed. % % CALL: c = linSubtract(a,b1,b2,b3,...,bN, pl) % % INPUTS: a - AO from where subtract linear contributions % b - AOs with noise contributions % pl - parameter list (see below) % % OUTPUTs: c - output AO with contributions subtracted (tsdata) % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'linSubtract')">Parameters Description</a> % % EXAMPLES: % % 1) Given the data (d): % % d = a + c1*b1 + c2*(b2+b3)^2 % % where (bs) are noisy contributions added to a signal (a). To recover (a) % in the [1 1e3] segment applying a [5e-2 0.1] 2nd order bandpass % filter to the data, the call to the function would be % % pl = plist('type','bandpass',... % 'fc',[5e-2 0.1],... % 'order',2,... % 'times',[1 1e3],... % 'coupling',{{'n(1)'},{'(n(2) + n(3)).^2'}}); % % a = linSubtract(d,b1,b2,b3, pl) % % % VERSION: $Id: linSubtract.m,v 1.13 2011/04/08 08:56:15 hewitson Exp $ % % TODO: option for parallel and serial subtraction % handling errors % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = linSubtract(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); % Method can not be used as a modifier if nargout == 0 error('### tfe 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, 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 parameters fc = find(pl,'fc'); if isempty(fc) error('### Please define a cut-off frequency ''fc'''); end times = find(pl,'times'); cp = find(pl,'coupling'); if isempty(cp) error('### Please define a coupling model ''coupling''') end order = find(pl,'order'); type = find(pl,'type'); % split in time if ~isempty(times) cs = split(bs,plist('times',times)); else cs = bs; end s = cs(1); for i = 2:length(bs) n(i-1) = cs(i); end subt = ao(); % Loop noise sources for i=1:length(cp) % coupling nterm = ao(); if numel(cp{i}) == 1 nterm = eval([char(cp{i}) ';']); else nn = numel(cp{i}); for j =1:nn nterm(j) = eval([char(cp{i}{j}) ';']); end end % bandpass filter fbp = miir(plist('type',type,'fc',fc,'order',order,'fs',s.fs)); sbp = filtfilt(s,fbp); nterm_bp = filtfilt(nterm,fbp); % linear fit c = lscov(nterm_bp,sbp); sn = lincom(nterm,c); % subtract s = s - sn; end % new tsdata fsd = tsdata(s.x,s.y,s.fs); % make output analysis object cs = ao(fsd); % set name cs.name = sprintf('linSubtract(%s)', ao_invars{1}); % t0 if ~isempty(times) cs.setT0(times(1)); else cs.setT0(bs(1).t0); end % Propagate 'plotinfo' plotinfo = [as(:).plotinfo]; if ~isempty(plotinfo) cs.plotinfo = combine(plotinfo); end % Add history cs.addHistory(getInfo('None'), pl, [ao_invars(:)], [bs(:).hist]); % Set output varargout{1} = cs; 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: linSubtract.m,v 1.13 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(); % Type p = param({'type', 'Sets the type of filter used to fit the data.'}, {1, {'bandpass', 'bandreject', 'highpass', 'lowpass'}, paramValue.SINGLE}); pl.append(p); % fc p = param({'fc', 'Frequency cut-off of the filter.'}, paramValue.EMPTY_DOUBLE); pl.append(p); % Order p = param({'order', 'Order of the filter.'}, {1, {2}, paramValue.OPTIONAL}); pl.append(p); % Times p = param({'times', 'A set of times where the fit+subtraction is applied.'}, paramValue.EMPTY_DOUBLE); pl.append(p); % Coupling p = param({'coupling', ['A cell-array defining the model of the noise<br>'... 'terms to be subtracted. In the cell expression<br>'... '''s'' stands for the input ao and ''n(i)'' for the N<br>' ... 'N noise contributions.']}, {1, {'{}'}, paramValue.OPTIONAL}); pl.append(p); end % PARAMETERS: % 'type' - Sets the type of filter used to fit the data % (help miir). % 'fc' - Frequency cut-off of the filter (help miir)' % 'order' - Order of the filter (help miir). % 'times' - Sets split times where the subtraction applies % (help split). % 'coupling' - A cell-array defining the model of the noise % terms to be subtracted. In the cell expression % 's' stands for the input ao and 'n(i)' for the N % N noise contributions.