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comparison m-toolbox/classes/@ao/consolidate.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 % CONSOLIDATE resamples all input AOs onto the same time grid.
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % CONSOLIDATE resamples all input AOs onto the same time grid and truncates all
5 % time-series to start at the maximum start time of the inputs and end
6 % at the minimum stop time of the inputs.
7 %
8 % ALGORITHM:
9 % 1) Drop duplicate samples (ao/dropduplicates)
10 % 2) Interpolate missing samples (ao/interpmissing)
11 % 3) Fix uneven sample rate using interpolate (ao/fixfs)
12 % 4) Resample to same fs, either max or specified (ao/resample
13 % or ao/interp depending on ratio of old and new sample
14 % rate)
15 % 5) Truncate all vectors to minimum overlap of time-series
16 % (ao/split)
17 % 6) Resample on to the same timing grid (ao/interp)
18 % 7) Truncate all vectors to same number of samples to correct for
19 % any rounding errors in previous steps (ao/select)
20 %
21 % CALL: >> bs = consolidate(as)
22 %
23 % INPUTS: as - array of at least two time-series analysis objects
24 % pl - parameter list (see below)
25 %
26 % OUTPUTS: bs - array of analysis objects, one for each input
27 %
28 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'consolidate')">Parameters Description</a>
29 %
30 % VERSION: $Id: consolidate.m,v 1.32 2011/04/08 08:56:13 hewitson Exp $
31 %
32 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
33
34 % 't' - specify a new time vector to resample on to. This
35 % will be truncated to fit within the maximum start
36 % time and minimum stop time of the inputs.
37 % or
38
39 function varargout = consolidate(varargin)
40
41 % Check if this is a call for parameters
42 if utils.helper.isinfocall(varargin{:})
43 varargout{1} = getInfo(varargin{3});
44 return
45 end
46
47 import utils.const.*
48 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
49
50 % Collect input variable names
51 in_names = cell(size(varargin));
52 for ii = 1:nargin,in_names{ii} = inputname(ii);end
53
54 % Collect all AOs and plists
55 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
56 [pl, pl_invars] = utils.helper.collect_objects(varargin(:), 'plist', in_names);
57
58 if numel(as) < 2
59 error('### Consolidate requires at least two time-series AOs to work.');
60 end
61
62 if nargout == 0
63 error('### Consolidate cannot be used as a modifier. Please give an output variable.');
64 end
65
66 % Decide on a deep copy or a modify
67 bs = copy(as, nargout);
68 na = numel(bs);
69
70 % Combine plists
71 pl = parse(pl, getDefaultPlist);
72
73 % Get only tsdata AOs
74 inhists = [];
75 for j=1:na
76 if ~isa(bs(j).data, 'tsdata')
77 bs(j) = [];
78 warning('!!! Skipping AO %s - it''s not a time-series AO.', bs(j).name);
79 else
80 % gather the input history objects
81 inhists = [inhists bs(j).hist];
82 end
83 end
84
85 % If fs is specified, use it. Otherwise, use max of all
86 % input AOs.
87 fs = find(pl, 'fs');
88 if isempty(fs)
89 % compute max fs
90 fs = 0;
91 for j=1:na
92 if bs(j).data.fs > fs
93 fs = bs(j).data.fs;
94 end
95 end
96 end
97 utils.helper.msg(msg.PROC2, 'resampling all time-series to an fs of %f', fs);
98
99 %----------------- Drop all repeated samples
100 utils.helper.msg(msg.PROC1, 'drop duplicates');
101 for j=1:na
102 utils.helper.msg(msg.PROC2, 'processing %s', bs(j).name);
103 dropduplicates(bs(j),pl);
104 end
105
106 %----------------- Interpolate all missing samples
107 utils.helper.msg(msg.PROC1, 'interpolate missing samples');
108 for j=1:na
109 utils.helper.msg(msg.PROC2, 'processing %s', bs(j).name);
110 interpmissing(bs(j),pl.pset('method', find(pl, 'interp_method')));
111 end
112
113
114 %----------------- Fix uneven sampling
115 utils.helper.msg(msg.PROC1, 'fixing uneven sample rates');
116 for j=1:na
117 utils.helper.msg(msg.PROC2, 'processing %s', bs(j).name);
118 fixfs(bs(j),pl.pset('method', find(pl, 'fixfs_method')));
119 end
120 %----------------- Resample all vectors to same fs
121 utils.helper.msg(msg.PROC1, 'resample to same fs');
122
123 for j=1:na
124 % Check the resampling factor
125 [P,Q] = utils.math.intfact(fs,bs(j).data.fs);
126 if P > 100 || Q > 100
127 utils.helper.msg(msg.PROC2, 'resampling factor too high [%g/%g]. Trying interpolation', P, Q);
128 N = length(bs(j).data.getX);
129 t = linspace(0, (P*N/Q-1)/fs, P*N/Q);
130 interp(bs(j), plist('vertices', t));
131 else
132 resample(bs(j), plist('fsout', fs));
133 end
134 end
135
136 %---------------- Time properties of AOs
137 % Find max start time
138 start = 0;
139 for j=1:na
140 dstart = bs(j).data.t0.utc_epoch_milli/1000 + bs(j).data.getX(1);
141 if dstart > start
142 start = dstart;
143 end
144 end
145
146 % Find min stop time
147 stop = 1e20;
148 for j=1:na
149 dstop = floor(bs(j).data.t0.utc_epoch_milli/1000 + bs(j).data.getX(end));
150 if dstop < stop
151 stop = dstop;
152 end
153 end
154
155 %----------------- Truncate all vectors
156 utils.helper.msg(msg.PROC1, 'truncate all vectors');
157 utils.helper.msg(msg.PROC2, 'truncating vectors on interval [%.4f,%.4f]', start, stop);
158
159 % split each ao
160 bs = split(bs, plist('timespan', timespan(start, stop)));
161
162 %----------------- Resample all vectors on to the same grid
163 utils.helper.msg(msg.PROC1, 'resample to same grid');
164 % compute new time grid
165
166 % get the grid from the first AO
167 for j=1:na
168 toff = start - bs(j).t0.utc_epoch_milli/1000;
169 N = length(bs(j).data.getX);
170 t = linspace(toff, toff+(N-1)/fs, N);
171 interp(bs(j), plist('vertices', t));
172 end
173
174 % Now ensure that we have the same data length
175 ns = realmax;
176 for jj=1:na
177 if len(bs(jj)) < ns
178 ns = len(bs(jj));
179 end
180 end
181
182 bs = select(bs, 1:ns);
183
184 nsecs = [];
185 for j=1:na
186 if isempty(nsecs)
187 nsecs = bs(j).data.nsecs;
188 end
189 if nsecs ~= bs(j).data.nsecs
190 error('### Something went wrong with the truncation. Vectors don''t span the same time period.');
191 end
192 end
193
194 %----------------- Set history on output AOs
195
196 for j=1:na
197 bs(j).name = sprintf('%s(%s)', mfilename, ao_invars{j});
198 bs(j).addHistory(getInfo('None'), pl, ao_invars(j), inhists(j));
199 end
200
201 % Set output
202 if nargout == numel(bs)
203 % List of outputs
204 for ii = 1:numel(bs)
205 varargout{ii} = bs(ii);
206 end
207 else
208 % Single output
209 varargout{1} = bs;
210 end
211 end
212
213 %--------------------------------------------------------------------------
214 % Get Info Object
215 %--------------------------------------------------------------------------
216 function ii = getInfo(varargin)
217 if nargin == 1 && strcmpi(varargin{1}, 'None')
218 sets = {};
219 pl = [];
220 else
221 sets = {'Default'};
222 pl = getDefaultPlist;
223 end
224 % Build info object
225 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: consolidate.m,v 1.32 2011/04/08 08:56:13 hewitson Exp $', sets, pl);
226 ii.setModifier(false);
227 ii.setArgsmin(2);
228 end
229
230 %--------------------------------------------------------------------------
231 % Get Default Plist
232 %--------------------------------------------------------------------------
233
234 function plout = getDefaultPlist()
235 persistent pl;
236 if exist('pl', 'var')==0 || isempty(pl)
237 pl = buildplist();
238 end
239 plout = pl;
240 end
241
242 function pl_default = buildplist()
243 pl_default = combine(...
244 plist({'fs','The target sampling frequency for consolidate'}, paramValue.EMPTY_DOUBLE),...
245 plist({'interp_method', 'The method for the interpolation step'}, {2, {'nearest', 'linear', 'spline', 'cubic'}, paramValue.SINGLE}), ...
246 plist({'fixfs_method', 'The method for the fixfs step'}, {1, {'Time', 'Samples'}, paramValue.SINGLE}), ...
247 ao.getInfo('dropduplicates').plists,...
248 ao.getInfo('interpmissing').plists,...
249 ao.getInfo('fixfs').plists);
250 end
251