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

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Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % INTERPMISSING interpolate missing samples in a time-series.
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % INTERPMISSING interpolate missing samples in a time-series. Missing samples
5 % are identified as being those where the time-span between one
6 % sample and the next is larger than d/fs where d is a
7 % tolerance value. Missing data is then placed in the gap in
8 % steps of 1/fs. Obviously this is only really correct for
9 % evenly sampled time-series.
10 %
11 % CALL: bs = interpmissing(as)
12 %
13 % INPUTS: as - array of analysis objects
14 % pl - parameter list (see below)
15 %
16 % OUTPUTS: bs - array of analysis objects, one for each input
17 %
18 %
19 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'interpmissing')">Parameters Description</a>
20 %
21 % VERSION: $Id: interpmissing.m,v 1.30 2011/04/08 08:56:16 hewitson Exp $
22 %
23 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
24
25 function varargout = interpmissing(varargin)
26
27 % Check if this is a call for parameters
28 if utils.helper.isinfocall(varargin{:})
29 varargout{1} = getInfo(varargin{3});
30 return
31 end
32
33 import utils.const.*
34 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
35
36 % Collect input variable names
37 in_names = cell(size(varargin));
38 for ii = 1:nargin,in_names{ii} = inputname(ii);end
39
40 % Collect all AOs and plists
41 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
42 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
43
44 % Decide on a deep copy or a modify
45 bs = copy(as, nargout);
46
47 % Combine plists
48 pl = parse(pl, getDefaultPlist);
49
50
51 % Get tolerance
52 dtol = find(pl, 'd');
53
54 % Get only tsdata AOs
55 for j=1:numel(bs)
56 if isa(bs(j).data, 'tsdata')
57
58 % capture input history
59 ih = bs(j).hist;
60
61 % find missing samples
62 t = [];
63 d = diff(bs(j).data.getX);
64 idxs = find(d>dtol/bs(j).data.fs);
65 utils.helper.msg(msg.PROC1, 'found %d data gaps', numel(idxs));
66
67 % create new time grid
68 count = 0;
69 fs = bs(j).data.fs;
70 for k=1:numel(idxs)
71 idx = idxs(k);
72 if isempty(t)
73 t = bs(j).data.getX(1:idxs(1));
74 end
75 % now add samples at 1/fs until we are within 1/fs of the next sample
76 gap = bs(j).data.getX(idx+1) - bs(j).data.getX(idx) - 1/fs;
77 tfill = [[1/fs:1/fs:gap] + bs(j).data.getX(idx)].';
78 count = count + numel(tfill);
79
80 if k==numel(idxs)
81 t = [t; tfill; bs(j).data.getX(idx+1:end)];
82 else
83 t = [t; tfill; bs(j).data.getX(idx+1:idxs(k+1))];
84 end
85 end
86 utils.helper.msg(msg.PROC1, 'filled with %d samples', count);
87
88 % now interpolate onto this new time-grid
89 if ~isempty(t)
90 bs(j).interp(plist('vertices', t, 'method', find(pl, 'method')));
91 bs(j).name = sprintf('interpmissing(%s)', ao_invars{j});
92 % Add history
93 bs(j).addHistory(getInfo('None'), pl, ao_invars(j), ih);
94 % clear errors
95 bs(j).clearErrors;
96 else
97 utils.helper.msg(msg.PROC1, 'no missing samples found in %s - no action performed.', ao_invars{j});
98 end
99 else
100 utils.helper.msg(msg.PROC1, 'skipping AO %s - it''s not a time-series AO.', ao_invars{j});
101 end
102 end
103
104 % Set output
105 if nargout == numel(bs)
106 % List of outputs
107 for ii = 1:numel(bs)
108 varargout{ii} = bs(ii);
109 end
110 else
111 % Single output
112 varargout{1} = bs;
113 end
114 end
115
116 %--------------------------------------------------------------------------
117 % Get Info Object
118 %--------------------------------------------------------------------------
119 function ii = getInfo(varargin)
120 if nargin == 1 && strcmpi(varargin{1}, 'None')
121 sets = {};
122 pl = [];
123 else
124 sets = {'Default'};
125 pl = getDefaultPlist;
126 end
127 % Build info object
128 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: interpmissing.m,v 1.30 2011/04/08 08:56:16 hewitson Exp $', sets, pl);
129 end
130
131 %--------------------------------------------------------------------------
132 % Get Default Plist
133 %--------------------------------------------------------------------------
134 function plout = getDefaultPlist()
135 persistent pl;
136 if exist('pl', 'var')==0 || isempty(pl)
137 pl = buildplist();
138 end
139 plout = pl;
140 end
141
142 function pl = buildplist()
143 pl = plist();
144
145 % d
146 p = param({'d','The time interval tolerance for finding missing samples.'}, {1, {1.5}, paramValue.OPTIONAL});
147 pl.append(p);
148
149 % Interpolation method
150 pli = ao.getInfo('interp').plists;
151 p = pli.params(pli.getIndexForKey('method'));
152 pl.append(p);
153
154 end