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view m-toolbox/classes/@ao/dropduplicates.m @ 4:e3c5468b1bfe database-connection-manager
Integrate with LTPDAPreferences
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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% DROPDUPLICATES drops all duplicate samples in time-series AOs. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DROPDUPLICATES drops all duplicate samples in time-series AOs. Duplicates % are identified by having a two consecutive time stamps % closer than a set tolerance. % % CALL: bs = dropduplicates(as) % % INPUTS: as - array of analysis objects % pl - parameter list (see below) % % OUTPUTS: bs - array of analysis objects, one for each input % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'dropduplicates')">Parameters Description</a> % % VERSION: $Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = dropduplicates(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, pl_invars] = 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 tolerance tol = find(pl, 'tol'); % Get only tsdata AOs for j=1:numel(bs) if isa(bs(j).data, 'tsdata') d = abs(diff(bs(j).data.getX)); idx = find(d<tol); utils.helper.msg(msg.PROC1, 'found %d duplicate samples', numel(idx)); % Wipe out x samples if ~isempty(bs(j).data.x) bs(j).data.x(idx) = []; end % Wipe out y samples bs(j).data.y(idx) = []; % Wipe out error if numel(bs(j).data.dx) > 1 bs(j).data.dx(idx) = []; end if numel(bs(j).data.dy) > 1 bs(j).data.dy(idx) = []; end % set name bs(j).name = sprintf('dropduplicates(%s)', ao_invars{j}); % Add history bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist); else warning('!!! Skipping AO %s - it''s not a time-series AO.', ao_invars{j}); bs(j) = []; end end % Set output if nargout == numel(bs) % List of outputs for ii = 1:numel(bs) varargout{ii} = bs(ii); end else % Single output varargout{1} = bs; end 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: dropduplicates.m,v 1.24 2011/04/08 08:56:13 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(); % tol p = param({'tol','The time interval tolerance to consider two consecutive samples as duplicates.'}, ... {1, {5e-3}, paramValue.OPTIONAL}); pl.append(p); end