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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 % JOIN multiple AOs into a single AO.
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % DESCRIPTION: JOIN multiple AOs into a single AO.
5 % If any two AOs overlap, then the values from the first appear
6 % in the output AO.
7 %
8 % Note: If the input AOs are of type 'tsdata', then they will
9 % be sorted in ascending order according the t0 of each
10 % object.
11 %
12 %
13 % CALL: bs = join(a1,a2,a3,...,pl)
14 % bs = join(as,pl)
15 % bs = as.join(pl)
16 %
17 % INPUTS: aN - input analysis objects
18 % as - input analysis objects array
19 % pl - input parameter list
20 %
21 % OUTPUTS: b - output analysis object
22 %
23 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'join')">Parameters Description</a>
24 %
25 % REMARK: Input AOs should be of the same type; if not, only AOs of the
26 % type of the first input AO will be joined together to produce
27 % the output.
28 %
29 % VERSION: $Id: join.m,v 1.60 2011/09/02 11:11:51 ingo Exp $
30 %
31 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
32
33 % PARAMETERS: 'zerofill' - Fills with zeros the gaps between the data
34 % points of the subsequent aos. [Default: 'no']
35 % 'sameT0' - Does not recalculate t0 but uses the common
36 % one. [Default: 'no']
37 % Note: the t0 among different objects must be the same!
38 %
39
40 function varargout = join(varargin)
41
42 % Check if this is a call for parameters
43 if utils.helper.isinfocall(varargin{:})
44 varargout{1} = getInfo(varargin{3});
45 return
46 end
47
48 import utils.const.*
49 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
50
51 % Collect input variable names
52 in_names = cell(size(varargin));
53 for ii = 1:nargin,in_names{ii} = inputname(ii);end
54
55 % Collect all AOs
56 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
57 pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
58
59 % Combine plists
60 pl = parse(pl, getDefaultPlist);
61
62
63
64 %----------------------------------------------
65 % Get data type from the first AO
66 dtype = class(as(1).data);
67
68 % Sort the input AOs by t0, if applicable
69 if strcmp(dtype, 'tsdata')
70 times = as.t0.double;
71 [~, idx] = sort(times);
72 as = as(idx);
73 end
74
75 %----------------------------------------------
76 % Go through each AO and collect the data of type 'dtype'
77
78 histin = [];
79 xo = [];
80 yo = [];
81 dxo = [];
82 dyo = [];
83 enbw0 = [];
84 fs = -1;
85 aname = '';
86 adescr = '';
87 plotinfo = [];
88 if as(1).data.isprop('xunits')
89 xunitsSimple = simplify(as(1).data.xunits);
90 xunits = as(1).data.xunits;
91 end
92 yunitsSimple = simplify(as(1).data.yunits);
93 yunits = as(1).data.yunits;
94
95 % Get the tolerance for considering fs equal
96 fstol = find(pl, 'fstol');
97
98 % Compute time offset for tsdata objects to avoid rounding errors later
99 minT0milli = getMinT0(as);
100
101 % loop over AOs
102 for jj=1:numel(as)
103 % Only get the data type we want
104 if isa(as(jj).data, dtype)
105 switch lower(dtype)
106 case 'tsdata'
107 % here we concatonate time-series
108 t0 = (as(jj).data.t0.utc_epoch_milli - minT0milli)/1000;
109 % make proper time vector
110 x = as(jj).x + t0;
111
112 % only add samples past the end of existing (first loop)
113 if isempty(xo)
114 yo = as(jj).y;
115 xo = x;
116 if numel(as(jj).dx) == 0
117 dxo = zeros(numel(as(jj).x),1);
118 elseif numel(as(jj).dx) == 1
119 dxo = ones(numel(as(jj).x),1) .* as(jj).dx;
120 else
121 dxo = as(jj).dx;
122 end
123 if numel(as(jj).dy) == 0
124 dyo = zeros(numel(as(jj).y),1);
125 elseif numel(as(jj).dy) == 1
126 dyo = ones(numel(as(jj).y),1) .* as(jj).dy;
127 else
128 dyo = as(jj).dy;
129 end
130 else
131 idxPost = find(x > max(xo));
132 idxPre = find(x < min(xo));
133
134 %%%%%%%%%% Fill the gaps with zeros %%%%%%%%%%
135 zerofill = utils.prog.yes2true(find(pl, 'zerofill'));
136
137 if zerofill
138 % Check if there is a gap between the x-values and the pre-values.
139 if ~isempty(idxPre)
140 interStart = x(idxPre(end));
141 interEnd = xo(1);
142 nsecsPre2no = interEnd - interStart;
143
144 % The gap must be larger than 1/fs in order to
145 % fill the gap with zeros
146 if nsecsPre2no > 1/fs
147 x_interPre = linspace(interStart+1/fs, interEnd-1/fs, nsecsPre2no*fs-2*1/fs).';
148 y_interPre = zeros(length(x_interPre), 1);
149 else
150 x_interPre = [];
151 y_interPre = [];
152 end
153 else
154 x_interPre = [];
155 y_interPre = [];
156 end
157
158 % Check if there is a gap between the x-values and the post-values.
159 if ~isempty(idxPost)
160 interStart = xo(end);
161 interEnd = x(idxPost(1));
162 nsecsPost2no = interEnd - interStart;
163
164 % The gap must be larger than 1/fs in order to
165 % fill the gap with zeros
166 if nsecsPost2no > 1/fs
167 x_interPost = linspace(interStart+1/fs, interEnd-1/fs, nsecsPost2no*fs-1/fs).';
168 y_interPost = zeros(length(x_interPost), 1);
169 else
170 x_interPost = [];
171 y_interPost = [];
172 end
173 else
174 x_interPost = [];
175 y_interPost = [];
176 end
177
178 else
179 %%%%%%%%%% Don't fill the gaps with zeros %%%%%%%%%%
180 x_interPre = [];
181 y_interPre = [];
182 x_interPost = [];
183 y_interPost = [];
184 end
185 xo = [x(idxPre); x_interPre; xo; x_interPost; x(idxPost)];
186 yo = [as(jj).data.getY(idxPre); y_interPre; yo; y_interPost; as(jj).data.getY(idxPost)];
187
188 %%% Collect errors
189 if numel(as(jj).dx) == 0
190 dx = zeros(numel(as(jj).x),1);
191 elseif numel(as(jj).dx) == 1
192 dx = ones(numel(as(jj).x),1) .* as(jj).dx;
193 else
194 dx = as(jj).dx;
195 end
196
197 if numel(as(jj).dy) == 0
198 dy = zeros(numel(as(jj).y),1);
199 elseif numel(as(jj).dy) == 1
200 dy = ones(numel(as(jj).y),1) .* as(jj).dy;
201 else
202 dy = [dyo; as(jj).dy];
203 end
204
205 x_interPre = zeros(numel(x_interPre),1);
206 y_interPre = zeros(numel(y_interPre),1);
207 x_interPost = zeros(numel(x_interPost),1);
208 y_interPost = zeros(numel(y_interPost),1);
209 dxo = [dx(idxPre); x_interPre; dxo; x_interPost; dx(idxPost)];
210 dyo = [dy(idxPre); y_interPre; dyo; y_interPost; dy(idxPost)];
211
212 end
213
214 % check fs
215 if (fs > 0) && (abs(as(jj).fs - fs) > fstol*fs)
216 error('### Data has different sample rates');
217 end
218 % store fs
219 fs = as(jj).fs;
220
221 % check xunits
222 if ~eq(xunitsSimple, simplify(as(jj).xunits))
223 error('### The x-units of the analysis objects are not the same %s <-> %s', char(xunits), char(as(jj).xunits));
224 end
225 % check yunits
226 if ~eq(yunitsSimple, simplify(as(jj).yunits))
227 error('### The y-units of the analysis objects are not the same %s <-> %s', char(yunits), char(as(jj).yunits));
228 end
229
230 % store T0
231 T0s(jj) = as(jj).t0;
232
233 case 'fsdata'
234
235 %%% Collect all fsdata samples
236 if isempty(xo)
237 idxBefore = 1:numel(as(jj).x);
238 idxAfter = [];
239 else
240 idxBefore = find(as(jj).x < xo(1));
241 idxAfter = find(as(jj).x > xo(end));
242 end
243 xo = [as(jj).x(idxBefore); xo; as(jj).x(idxAfter)];
244 yo = [as(jj).y(idxBefore); yo; as(jj).y(idxAfter)];
245
246 %%% Collect all errors
247 % dx
248 if numel(as(jj).dx) == 0
249 dx = zeros(numel(as(jj).x),1);
250 elseif numel(as(jj).dx) == 1
251 dx = ones(numel(as(jj).x),1) .* as(jj).dx;
252 else
253 dx = as(jj).dx;
254 end
255 dxo = [dx(idxBefore); dxo; dx(idxAfter)];
256 % dy
257 if numel(as(jj).dy) == 0
258 dy = zeros(numel(as(jj).y),1);
259 elseif numel(as(jj).dy) == 1
260 dy = ones(numel(as(jj).y),1) .* as(jj).dy;
261 else
262 dy = as(jj).dy;
263 end
264 dyo = [dy(idxBefore); dyo; dy(idxAfter)];
265 % enbw
266 if numel(as(jj).data.enbw) == 0
267 enbw = NaN(numel(as(jj).y),1);
268 elseif numel(as(jj).data.enbw) == 1
269 enbw = ones(numel(as(jj).y),1) .* as(jj).data.enbw;
270 else
271 enbw = as(jj).data.enbw;
272 end
273 enbw0 = [enbw(idxBefore); enbw0; enbw(idxAfter)];
274
275 % check fs
276 if (fs > 0) && (abs(as(jj).fs - fs) > fstol*fs)
277 error('### Data has different sample rates');
278 end
279 % store fs
280 fs = as(jj).fs;
281
282 % check xunits
283 if ~eq(xunitsSimple, simplify(as(jj).xunits))
284 error('### The x-units of the analysis objects are not the same %s <-> %s', char(xunits), char(as(jj).xunits));
285 end
286 % check yunits
287 if ~eq(yunitsSimple, simplify(as(jj).yunits))
288 error('### The y-units of the analysis objects are not the same %s <-> %s', char(yunits), char(as(jj).yunits));
289 end
290
291 case 'xydata'
292 xo = [xo; as(jj).x];
293 yo = [yo; as(jj).y];
294 if numel(as(jj).dx) == 0
295 dx = zeros(numel(as(jj).x),1);
296 elseif numel(as(jj).dx) == 1
297 dx = ones(numel(as(jj).x),1) .* as(jj).dx;
298 else
299 dx = as(jj).dx;
300 end
301 dxo = [dxo; dx];
302 if numel(as(jj).dy) == 0
303 dy = zeros(numel(as(jj).y),1);
304 elseif numel(as(jj).dy) == 1
305 dy = ones(numel(as(jj).y),1) .* as(jj).dy;
306 else
307 dy = as(jj).dy;
308 end
309 dyo = [dyo; dy];
310
311 % check xunits
312 if ~eq(xunitsSimple, simplify(as(jj).xunits))
313 error('### The x-units of the analysis objects are not the same %s <-> %s', char(xunits), char(as(jj).xunits));
314 end
315 % check yunits
316 if ~eq(yunitsSimple, simplify(as(jj).yunits))
317 error('### The y-units of the analysis objects are not the same %s <-> %s', char(yunits), char(as(jj).yunits));
318 end
319
320 case 'cdata'
321 try
322 yo = [yo; as(jj).y];
323 if numel(as(jj).dy) == 0
324 dy = zeros(numel(as(jj).y),1);
325 elseif numel(as(jj).dy) == 1
326 dy = ones(numel(as(jj).y),1) .* as(jj).dy;
327 else
328 dy = as(jj).dy;
329 end
330 dyo = [dyo; dy];
331 catch E
332 disp(E.message)
333 error('### It is not possible to join the data or error because they have different dimensions.');
334 end
335
336 % check yunits
337 if ~eq(yunitsSimple, simplify(as(jj).yunits))
338 error('### The y-units of the analysis objects are not the same %s <-> %s', char(yunits), char(as(jj).yunits));
339 end
340
341 otherwise
342 error('### Unknown data type');
343 end
344 % Collect this input history
345 histin = [histin as(jj).hist];
346 % Collect the 'plotinfo'
347 if ~isempty(as(jj).plotinfo)
348 plotinfo = combine(plotinfo, as(jj).plotinfo);
349 end
350 % Collect the descriptions
351 adescr = strcat(adescr, as(jj).description);
352 % Collect names, invars
353 if ~isempty(aname)
354 if ~strcmp(aname, as(jj).name)
355 aname = [aname ',' as(jj).name];
356 end
357 else
358 aname = as(jj).name;
359 end
360 else
361 warning('!!! Ignoring AO input with data type %s', dtype);
362 end
363 end
364
365 %----------------------------------------------
366 % Now sort output vectors
367 if ~isempty(xo)
368 [xos, idx] = sort(xo);
369 yos = yo(idx);
370 dxos = dxo(idx);
371 dyos = dyo(idx);
372 else
373 xos = xo;
374 yos = yo;
375 dxos = dxo;
376 dyos = dyo;
377 end
378
379 if all(dxos == 0)
380 dxos = [];
381 elseif all(diff(dxos) == 0)
382 dxos = dxos(1);
383 end
384 if all(dyos == 0)
385 dyos = [];
386 elseif all(diff(dyos) == 0)
387 dyos = dyos(1);
388 end
389
390 % Keep the data shape if the input AO
391 if size(as(1).data.y,1) == 1
392 xos = xos.';
393 yos = yos.';
394 end
395
396 %%% Build output data object
397 switch lower(dtype)
398 case 'tsdata'
399 sameT0 = utils.prog.yes2true(find(pl, 'sameT0'));
400
401 % % Check that all objects share the t0
402 % for kk = 1:numel(T0s)
403 % if ne(T0s(kk), T0s(1))
404 % sameT0 = false;
405 % end
406 % end
407 % if sameT0
408 % data = tsdata(xos, yos, fs);
409 % data.setT0(minT0milli/1000);
410 % else
411 % get t0
412 % toffset = xos(1);
413 % xos = xos - toffset;
414 data = tsdata(xos, yos, fs);
415 data.setT0((minT0milli/1000));
416 % data.setToffset(toffset*1000);
417 % end
418 data.setDx(dxos);
419 data.setDy(dyos);
420 data.setXunits(xunits);
421 data.setYunits(yunits);
422 data.collapseX;
423 case 'fsdata'
424 if all(isnan(enbw0))
425 enbw0 = [];
426 elseif all(diff(enbw0) == 0)
427 enbw0 = enbw0(1);
428 end
429 data = fsdata(xos, yos);
430 data.setDx(dxos);
431 data.setDy(dyos);
432 data.setEnbw(enbw0);
433 data.setFs(fs);
434 data.setXunits(xunits);
435 data.setYunits(yunits);
436 case 'xydata'
437 data = xydata(xos, yos);
438 data.setDx(dxos);
439 data.setDy(dyos);
440 data.setXunits(xunits);
441 data.setYunits(yunits);
442 case 'cdata'
443 data = cdata(yos);
444 data.setDy(dyos);
445 data.setYunits(yunits);
446 end
447
448 %----------------------------------------------
449 % Build output AO
450 if nargout == 0
451 a = as(1);
452 a.data = data;
453 else
454 a = ao(data);
455 end
456 % Set name
457 a.name = aname;
458 % Set description
459 a.description = adescr;
460 % Set plotinfo
461 a.plotinfo = plotinfo;
462 % Add history
463 a.addHistory(getInfo('None'), pl, ao_invars, histin);
464
465 %----------------------------------------------
466 % set output
467 varargout{1} = a;
468 end
469
470 %--------------------------------------------------------------------------
471 % Get Info Object
472 %--------------------------------------------------------------------------
473 function ii = getInfo(varargin)
474 if nargin == 1 && strcmpi(varargin{1}, 'None')
475 sets = {};
476 pl = [];
477 else
478 sets = {'Default'};
479 pl = getDefaultPlist;
480 end
481 % Build info object
482 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.helper, '$Id: join.m,v 1.60 2011/09/02 11:11:51 ingo Exp $', sets, pl);
483 ii.setArgsmin(2);
484 end
485
486 %--------------------------------------------------------------------------
487 % Get Default Plist
488 %--------------------------------------------------------------------------
489 function plout = getDefaultPlist()
490 persistent pl;
491 if exist('pl', 'var')==0 || isempty(pl)
492 pl = buildplist();
493 end
494 plout = pl;
495 end
496
497 function pl = buildplist()
498
499 pl = plist();
500
501 % Zero fill
502 p = param({'zerofill','Fills with zeros the gaps between the data points of the subsequent aos.'}, paramValue.YES_NO);
503 p.val.setValIndex(2);
504 pl.append(p);
505
506 % Same T0
507 p = param({'sameT0', ['Does not recalculate t0 but uses the common one.<br>', ...
508 'Note: the t0 among different objects must be the same!']}, paramValue.YES_NO);
509 p.val.setValIndex(2);
510 pl.append(p);
511
512 % fstol
513 p = param({'fstol', ['Relative tolerance between sampling frequency of different objects.<br>', ...
514 'Jitter in the sampling frequency by less than this amount will be neglected.<br>', ...
515 'If the difference is more than the set value, an error will occur.']}, paramValue.DOUBLE_VALUE(1e-6));
516 pl.append(p);
517 end
518
519
520 %--------------------------------------------------------------------------
521 % Get Offset of this set of time-vectors
522 %--------------------------------------------------------------------------
523 function Toff = getMinT0(as)
524 Toff = min(double(as.t0))*1e3;
525 end
526