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view m-toolbox/classes/@ao/smoother.m @ 50:7d2e2e065cf1 database-connection-manager
Update unit tests
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Wed, 07 Dec 2011 17:24:37 +0100 |
parents | f0afece42f48 |
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% SMOOTHER smooths a given series of data points using the specified method. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: SMOOTHER smooths a given series of data points using % the specified method. % % CALL: b = smoother(a, pl) % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'smoother')">Parameters Description</a> % % VERSION: $Id: smoother.m,v 1.30 2011/11/11 15:21:19 luigi Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = smoother(varargin) callerIsMethod = utils.helper.callerIsMethod; % 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 and plists [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 parameters from plist bw = find(pl, 'width'); hc = find(pl, 'hc'); method = find(pl, 'method'); % check the method if ~strcmp(method, 'median') && ... ~strcmp(method, 'mean') && ... ~strcmp(method, 'min') && ... ~strcmp(method, 'max') && ... ~strcmp(method, 'mode') help(mfilename) error('### Unknown smoothing method'); end % Loop over input AOs for j=1:numel(bs) utils.helper.msg(msg.PROC1, 'smoothing %s', bs(j).name); switch lower(method) case {'median', 'mean', 'min', 'max'} bs(j).data.setY(ltpda_smoother(bs(j).data.getY, bw, hc, method)); otherwise bs(j).data.setY(smooth(bs(j).data.getY, bw, hc, method)); end % set name bs(j).name = sprintf('smoother(%s)', ao_invars{j}); % Add history if ~callerIsMethod bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist); end end % clear errors bs.clearErrors; % 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: smoother.m,v 1.30 2011/11/11 15:21:19 luigi Exp $', sets, pl); end %-------------------------------------------------------------------------- % smooth data function ys = smooth(y, bw, hc, method) N = length(y); ys = zeros(size(y)); % function to smooth with mfcn = eval(['@(x) ' method '(x)' ]); for kk=1:N if mod(kk, 1000)==0 utils.helper.msg(utils.const.msg.PROC1, 'smoothed %06d samples', kk); end % Determine the interval we are looking in interval = kk-bw/2:kk+bw/2; interval(interval<=0)=1; interval(interval>N)=N; % calculate method(values) of interval % after throwing away outliers trial = sort(y(interval)); b = round(hc*length(trial)); ys(kk) = mfcn(trial(1:b)); end 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(); % width p = param({'width', 'The width of the smoothing filter.'}, paramValue.DOUBLE_VALUE(20)); pl.append(p); % hc p = param({'hc', 'A cutoff to throw away outliers (0-1).'}, paramValue.DOUBLE_VALUE(0.8)); pl.append(p); % Method p = param({'method', 'The smoothing method.'}, {1, {'median', 'mean', 'max', 'mode'}, paramValue.SINGLE}); pl.append(p); end % END % PARAMETERS: width - the width of the smoothing filter [default: 20 samples] % hc - a cutoff to throw away outliers (0-1) [default: 0.8] % method - the smoothing method: % 'median' [default] % 'mean', 'min', 'max', 'mode' %