line source
+ − % 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]