Mercurial > hg > ltpda
view m-toolbox/classes/@ao/linedetect.m @ 14:6d43f39633b8 database-connection-manager
Remove unused functions from utils.jmysql
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
children |
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% LINEDETECT find spectral lines in the ao/fsdata objects. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: LINEDETECT find spectral lines in the ao/fsdata objects. % % CALL: b = linedetect(a, pl) % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'linedetect')">Parameters Description</a> % % VERSION: $Id: linedetect.m,v 1.15 2011/04/08 08:56:16 hewitson Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = linedetect(varargin) % Check if this is a call for parameters if utils.helper.isinfocall(varargin{:}) varargout{1} = getInfo(varargin{3}); return end if nargout == 0 error('### cat 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); [pli, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names); % Decide on a deep copy or a modify bs = copy(as, nargout); Na = numel(bs); if isempty(bs) error('### Please input at least one AO.'); end % Combine plists if ~isempty(pli) pl = parse(pli, getDefaultPlist()); else pl = getDefaultPlist(); end % Get parameters from plist N = find(pl, 'N'); fsearch = find(pl, 'fsearch'); thresh = find(pl, 'thresh'); % Loop over input AOs for jj = 1:Na if isa(bs(jj).data, 'fsdata') % Make noise-floor estimate nf = smoother(bs(jj), pl); % Make ratio r = bs(jj)./nf; % find lines lines = findLines(bs(jj).data.getY, r.data.getX, r.data.getY, thresh, N, fsearch); f = [lines(:).f]; y = [lines(:).a]; % Keep the data shpare of the input AO if size(bs(jj).data.y, 2) == 1 f = f.'; y = y.'; end % Make output data: copy the fsdata object so to inherit all the feautures fs = copy(bs(jj).data, 1); % Make output data: set the values fs.setX(f); fs.setY(y); else error('### I can only find lines in frequency-series AOs.'); end %------- Make output AO % make output analysis object bs(jj).data = fs; bs(jj).name = sprintf('lines(%s)', ao_invars{1}); bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), bs(jj).hist); 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 %-------------------------------------------------------------------------- % find spectral lines function lines = findLines(ay, f, nay, thresh, N, fsearch) % look for spectral lines l = 0; pmax = 0; pmaxi = 0; line = []; idx = find( f>=fsearch(1) & f<=fsearch(2) ); for jj = 1:length(idx) v = nay(idx(jj)); if v > thresh if v > pmax pmax = v; pmaxi = idx(jj); end else if pmax > 0 % find index when we have pmax fidx = pmaxi; %(find(nay(1:idx(jj))==pmax)); l = l+1; line(l).idx = fidx; line(l).f = f(fidx); line(l).a = ay(fidx); end pmax = 0; end end % Select largest peaks lines = []; if ~isempty(line) [bl, lidx] = sort([line.a], 'descend'); lidxs = lidx(1:min([N length(lidx)])); lines = line(lidxs); disp(sprintf(' + found %d lines.', length([lines.f]))); else disp(' + found 0 lines.'); end end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Local Functions % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % FUNCTION: getInfo % % DESCRIPTION: Get Info Object % % HISTORY: 11-07-07 M Hewitson % Creation. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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: linedetect.m,v 1.15 2011/04/08 08:56:16 hewitson Exp $', sets, pl); ii.setModifier(false); end %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % FUNCTION: getDefaultPlist % % DESCRIPTION: Get Default Plist % % HISTORY: 11-07-07 M Hewitson % Creation. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function plout = getDefaultPlist() persistent pl; if exist('pl', 'var')==0 || isempty(pl) pl = buildplist(); end plout = pl; end function pl = buildplist() pl = plist(); % N p = param({'N', 'The maximum number of lines to return.'}, {1, {10}, paramValue.OPTIONAL}); pl.append(p); % fsearch p = param({'fsearch', 'The frequency search interval.'}, {1, {[0 1e10]}, paramValue.OPTIONAL}); pl.append(p); % thresh p = param({'thresh', 'A threshold to test normalised amplitude against. (A sort-of SNR threshold.)'}, {1, {2}, paramValue.OPTIONAL}); pl.append(p); % BW p = param({'bw', ['The bandwidth of the running median filter used to<br>'... 'estimate the noise-floor.']}, {1, {20}, paramValue.OPTIONAL}); pl.append(p); % HC p = param({'hc', 'The cutoff used to reject outliers (0-1).'}, {1, {0.8}, paramValue.OPTIONAL}); pl.append(p); end % PARAMETERS: N - max number of lines to return [default: 10] % fsearch - freqeuncy search interval [default: all] % thresh - a threshold to test normalised amplitude spectrum against % [default: 2] % bw - bandwidth over which to compute median [default: 20 samples] % hc - percent of outliers to exclude from median estimation (0-1) % [default: 0.8]