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annotate m-toolbox/classes/@ao/normdist.m @ 38:3aef676a1b20 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % NORMDIST computes the equivalent normal distribution for the data.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 % DESCRIPTION: NORMDIST computes the equivalent normal distribution for the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % data. The mean and standard deviation are computed from the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % data. The method returns the normal distribution evaluated
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 % at the bin centers.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % CALL: b = normdist(a)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % b = normdist(a, pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % INPUTS: a - input analysis object(s)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 % pl - a parameter list
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % OUTPUTS: b - xydata type analysis object(s) containing the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % normal distribution pdf
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'normdist')">Parameters Description</a>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 % VERSION: $Id: normdist.m,v 1.11 2011/04/08 08:56:13 hewitson Exp $
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 function varargout = normdist(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 end
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30
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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33
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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37
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 [pli, pl_invars, rest] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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41
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 pl = parse(pli, getDefaultPlist('Number of bins'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 normalize = utils.prog.yes2true(find(pl, 'norm'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 % start looping
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 bs(numel(as),1) = ao();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 for jj=1:numel(bs)
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49
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 % compute histogram to get bin centers.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 h = hist(as(jj), pl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 % compute mean and standard deviation from the data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 mu = mean(as(jj).y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 sig = std(as(jj).y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 % Compute exponent
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 e = ((h.x-mu)./sig).^2;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 % compute PDF
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 y = (exp(-0.5.*e))./(sig*sqrt(2*pi));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 % Introduce normalization
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60 if normalize
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 yunits = (as(jj).data.yunits)^(-1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 nn = sum(y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 nd = sum(h.y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 y = y.*nd./nn;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 yunits = 'Count';
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 % construct new AO
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 % make a new xydata object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 xy = xydata(h.x, y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 xy.setXunits(as(jj).data.yunits);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 xy.setYunits(yunits);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 bs(jj) = ao(xy);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74 bs(jj).procinfo = plist('mu', mu, 'sig', sig);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 % name for this object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 bs(jj).name = sprintf('normdist(%s)', ao_invars{jj});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 % Add history
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 bs(jj).addHistory(getInfo('None'), pl, ao_invars(jj), bs(jj).hist);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 % Set output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 if nargout == numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 % List of outputs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 for ii = 1:numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 varargout{ii} = bs(ii);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 % Single output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 varargout{1} = bs;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 pls = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 elseif nargin == 1 && ~isempty(varargin{1}) && ischar(varargin{1})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 sets{1} = varargin{1};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 pls = getDefaultPlist(sets{1});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 sets = {'Number Of Bins'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 pls = [];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 for kk=1:numel(sets)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 pls = [pls getDefaultPlist(sets{kk})];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111 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);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 % Get Default Plist
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116 %--------------------------------------------------------------------------
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117 function plout = getDefaultPlist(set)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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119 persistent lastset;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 if exist('pl', 'var')==0 || isempty(pl) || ~strcmp(lastset, set)
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121 pl = buildplist(set);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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122 lastset = set;
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123 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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124 plout = pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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126
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 function plo = buildplist(set)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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128 switch lower(set)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129 case 'number of bins'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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130 plo = plist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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131
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Daniele Nicolodi <nicolodi@science.unitn.it>
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132 % N number of bins
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Daniele Nicolodi <nicolodi@science.unitn.it>
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133 p = param({'N', ['The number of bins to compute the histogram on. <br>' ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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134 'This defines the bin centers for the PDF.']}, {1, {10}, paramValue.OPTIONAL});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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135 plo.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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136
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Daniele Nicolodi <nicolodi@science.unitn.it>
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137 % normalized output
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Daniele Nicolodi <nicolodi@science.unitn.it>
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138 p = param({'norm', ['Normalized output. If set to true, it will give the normal distrubution PDF. <br>' ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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139 'Otherwise, it will give an output comparable to the ao/hist method']}, paramValue.TRUE_FALSE);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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140 p.val.setValIndex(2);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 plo.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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142
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Daniele Nicolodi <nicolodi@science.unitn.it>
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143 otherwise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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144 error('### Unknown default plist for the set [%s]', set);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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145 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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146 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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147
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Daniele Nicolodi <nicolodi@science.unitn.it>
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148