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comparison m-toolbox/classes/@ao/dropduplicates.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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-1:000000000000 0:f0afece42f48
1 % DROPDUPLICATES drops all duplicate samples in time-series AOs.
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % DROPDUPLICATES drops all duplicate samples in time-series AOs. Duplicates
5 % are identified by having a two consecutive time stamps
6 % closer than a set tolerance.
7 %
8 % CALL: bs = dropduplicates(as)
9 %
10 % INPUTS: as - array of analysis objects
11 % pl - parameter list (see below)
12 %
13 % OUTPUTS: bs - array of analysis objects, one for each input
14 %
15 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'dropduplicates')">Parameters Description</a>
16 %
17 % VERSION: $Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $
18 %
19 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
20
21 function varargout = dropduplicates(varargin)
22
23 % Check if this is a call for parameters
24 if utils.helper.isinfocall(varargin{:})
25 varargout{1} = getInfo(varargin{3});
26 return
27 end
28
29 import utils.const.*
30 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
31
32 % Collect input variable names
33 in_names = cell(size(varargin));
34 for ii = 1:nargin,in_names{ii} = inputname(ii);end
35
36 % Collect all AOs
37 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
38 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
39
40 % Decide on a deep copy or a modify
41 bs = copy(as, nargout);
42
43 % Combine plists
44 pl = parse(pl, getDefaultPlist);
45
46 % Get tolerance
47 tol = find(pl, 'tol');
48
49 % Get only tsdata AOs
50 for j=1:numel(bs)
51 if isa(bs(j).data, 'tsdata')
52 d = abs(diff(bs(j).data.getX));
53 idx = find(d<tol);
54 utils.helper.msg(msg.PROC1, 'found %d duplicate samples', numel(idx));
55 % Wipe out x samples
56 if ~isempty(bs(j).data.x)
57 bs(j).data.x(idx) = [];
58 end
59 % Wipe out y samples
60 bs(j).data.y(idx) = [];
61 % Wipe out error
62 if numel(bs(j).data.dx) > 1
63 bs(j).data.dx(idx) = [];
64 end
65 if numel(bs(j).data.dy) > 1
66 bs(j).data.dy(idx) = [];
67 end
68 % set name
69 bs(j).name = sprintf('dropduplicates(%s)', ao_invars{j});
70 % Add history
71 bs(j).addHistory(getInfo('None'), pl, ao_invars(j), bs(j).hist);
72 else
73 warning('!!! Skipping AO %s - it''s not a time-series AO.', ao_invars{j});
74 bs(j) = [];
75 end
76 end
77
78 % Set output
79 if nargout == numel(bs)
80 % List of outputs
81 for ii = 1:numel(bs)
82 varargout{ii} = bs(ii);
83 end
84 else
85 % Single output
86 varargout{1} = bs;
87 end
88 end
89
90 %--------------------------------------------------------------------------
91 % Get Info Object
92 %--------------------------------------------------------------------------
93 function ii = getInfo(varargin)
94 if nargin == 1 && strcmpi(varargin{1}, 'None')
95 sets = {};
96 pl = [];
97 else
98 sets = {'Default'};
99 pl = getDefaultPlist;
100 end
101 % Build info object
102 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: dropduplicates.m,v 1.24 2011/04/08 08:56:13 hewitson Exp $', sets, pl);
103 end
104
105 %--------------------------------------------------------------------------
106 % Get Default Plist
107 %--------------------------------------------------------------------------
108
109 function plout = getDefaultPlist()
110 persistent pl;
111 if exist('pl', 'var')==0 || isempty(pl)
112 pl = buildplist();
113 end
114 plout = pl;
115 end
116
117 function pl = buildplist()
118 pl = plist();
119
120 % tol
121 p = param({'tol','The time interval tolerance to consider two consecutive samples as duplicates.'}, ...
122 {1, {5e-3}, paramValue.OPTIONAL});
123 pl.append(p);
124
125 end
126
127