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view m-toolbox/classes/@ao/normdist.m @ 5:5a49956df427 database-connection-manager
LTPDAPreferences panel for new LTPDADatabaseConnectionManager
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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% NORMDIST computes the equivalent normal distribution for the data. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: NORMDIST computes the equivalent normal distribution for the % data. The mean and standard deviation are computed from the % data. The method returns the normal distribution evaluated % at the bin centers. % % CALL: b = normdist(a) % b = normdist(a, pl) % % INPUTS: a - input analysis object(s) % pl - a parameter list % % OUTPUTS: b - xydata type analysis object(s) containing the % normal distribution pdf % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'normdist')">Parameters Description</a> % % VERSION: $Id: normdist.m,v 1.11 2011/04/08 08:56:13 hewitson Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = normdist(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 and plists [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names); [pli, pl_invars, rest] = utils.helper.collect_objects(varargin(:), 'plist', in_names); pl = parse(pli, getDefaultPlist('Number of bins')); normalize = utils.prog.yes2true(find(pl, 'norm')); % start looping bs(numel(as),1) = ao(); for jj=1:numel(bs) % compute histogram to get bin centers. h = hist(as(jj), pl); % compute mean and standard deviation from the data mu = mean(as(jj).y); sig = std(as(jj).y); % Compute exponent e = ((h.x-mu)./sig).^2; % compute PDF y = (exp(-0.5.*e))./(sig*sqrt(2*pi)); % Introduce normalization if normalize yunits = (as(jj).data.yunits)^(-1); else nn = sum(y); nd = sum(h.y); y = y.*nd./nn; yunits = 'Count'; end % construct new AO % make a new xydata object xy = xydata(h.x, y); xy.setXunits(as(jj).data.yunits); xy.setYunits(yunits); bs(jj) = ao(xy); bs(jj).procinfo = plist('mu', mu, 'sig', sig); % name for this object bs(jj).name = sprintf('normdist(%s)', ao_invars{jj}); % Add history 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 %-------------------------------------------------------------------------- % Get Info Object %-------------------------------------------------------------------------- function ii = getInfo(varargin) if nargin == 1 && strcmpi(varargin{1}, 'None') sets = {}; pls = []; elseif nargin == 1 && ~isempty(varargin{1}) && ischar(varargin{1}) sets{1} = varargin{1}; pls = getDefaultPlist(sets{1}); else sets = {'Number Of Bins'}; pls = []; for kk=1:numel(sets) pls = [pls getDefaultPlist(sets{kk})]; end end % Build info object ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: normdist.m,v 1.11 2011/04/08 08:56:13 hewitson Exp $', sets, pls); end %-------------------------------------------------------------------------- % Get Default Plist %-------------------------------------------------------------------------- function plout = getDefaultPlist(set) persistent pl; persistent lastset; if exist('pl', 'var')==0 || isempty(pl) || ~strcmp(lastset, set) pl = buildplist(set); lastset = set; end plout = pl; end function plo = buildplist(set) switch lower(set) case 'number of bins' plo = plist(); % N number of bins p = param({'N', ['The number of bins to compute the histogram on. <br>' ... 'This defines the bin centers for the PDF.']}, {1, {10}, paramValue.OPTIONAL}); plo.append(p); % normalized output p = param({'norm', ['Normalized output. If set to true, it will give the normal distrubution PDF. <br>' ... 'Otherwise, it will give an output comparable to the ao/hist method']}, paramValue.TRUE_FALSE); p.val.setValIndex(2); plo.append(p); otherwise error('### Unknown default plist for the set [%s]', set); end end