--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/normdist.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,148 @@
+% 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
+
+