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comparison m-toolbox/classes/@ao/normdist.m @ 0:f0afece42f48

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