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se modifica del Decoder: si no existe codigo sale de funcion
Daniel Valdez -
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1 1 '''
2 2
3 3 $Author: dsuarez $
4 4 $Id: Processor.py 1 2012-11-12 18:56:07Z dsuarez $
5 5 '''
6 6 import os
7 7 import numpy
8 8 import datetime
9 9 import time
10 10
11 11 from jrodata import *
12 12 from jrodataIO import *
13 13 from jroplot import *
14 14
15 15 class ProcessingUnit:
16 16
17 17 """
18 18 Esta es la clase base para el procesamiento de datos.
19 19
20 20 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
21 21 - Metodos internos (callMethod)
22 22 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
23 23 tienen que ser agreagados con el metodo "add".
24 24
25 25 """
26 26 # objeto de datos de entrada (Voltage, Spectra o Correlation)
27 27 dataIn = None
28 28
29 29 # objeto de datos de entrada (Voltage, Spectra o Correlation)
30 30 dataOut = None
31 31
32 32
33 33 objectDict = None
34 34
35 35 def __init__(self):
36 36
37 37 self.objectDict = {}
38 38
39 39 def init(self):
40 40
41 41 raise ValueError, "Not implemented"
42 42
43 43 def addOperation(self, object, objId):
44 44
45 45 """
46 46 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
47 47 identificador asociado a este objeto.
48 48
49 49 Input:
50 50
51 51 object : objeto de la clase "Operation"
52 52
53 53 Return:
54 54
55 55 objId : identificador del objeto, necesario para ejecutar la operacion
56 56 """
57 57
58 58 self.objectDict[objId] = object
59 59
60 60 return objId
61 61
62 62 def operation(self, **kwargs):
63 63
64 64 """
65 65 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
66 66 atributos del objeto dataOut
67 67
68 68 Input:
69 69
70 70 **kwargs : Diccionario de argumentos de la funcion a ejecutar
71 71 """
72 72
73 73 raise ValueError, "ImplementedError"
74 74
75 75 def callMethod(self, name, **kwargs):
76 76
77 77 """
78 78 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
79 79
80 80 Input:
81 81 name : nombre del metodo a ejecutar
82 82
83 83 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
84 84
85 85 """
86 86 if name != 'run':
87 87
88 88 if name == 'init' and self.dataIn.isEmpty():
89 89 self.dataOut.flagNoData = True
90 90 return False
91 91
92 92 if name != 'init' and self.dataOut.isEmpty():
93 93 return False
94 94
95 95 methodToCall = getattr(self, name)
96 96
97 97 methodToCall(**kwargs)
98 98
99 99 if name != 'run':
100 100 return True
101 101
102 102 if self.dataOut.isEmpty():
103 103 return False
104 104
105 105 return True
106 106
107 107 def callObject(self, objId, **kwargs):
108 108
109 109 """
110 110 Ejecuta la operacion asociada al identificador del objeto "objId"
111 111
112 112 Input:
113 113
114 114 objId : identificador del objeto a ejecutar
115 115
116 116 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
117 117
118 118 Return:
119 119
120 120 None
121 121 """
122 122
123 123 if self.dataOut.isEmpty():
124 124 return False
125 125
126 126 object = self.objectDict[objId]
127 127
128 128 object.run(self.dataOut, **kwargs)
129 129
130 130 return True
131 131
132 132 def call(self, operationConf, **kwargs):
133 133
134 134 """
135 135 Return True si ejecuta la operacion "operationConf.name" con los
136 136 argumentos "**kwargs". False si la operacion no se ha ejecutado.
137 137 La operacion puede ser de dos tipos:
138 138
139 139 1. Un metodo propio de esta clase:
140 140
141 141 operation.type = "self"
142 142
143 143 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
144 144 operation.type = "other".
145 145
146 146 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
147 147 "addOperation" e identificado con el operation.id
148 148
149 149
150 150 con el id de la operacion.
151 151
152 152 Input:
153 153
154 154 Operation : Objeto del tipo operacion con los atributos: name, type y id.
155 155
156 156 """
157 157
158 158 if operationConf.type == 'self':
159 159 sts = self.callMethod(operationConf.name, **kwargs)
160 160
161 161 if operationConf.type == 'other':
162 162 sts = self.callObject(operationConf.id, **kwargs)
163 163
164 164 return sts
165 165
166 166 def setInput(self, dataIn):
167 167
168 168 self.dataIn = dataIn
169 169
170 170 def getOutput(self):
171 171
172 172 return self.dataOut
173 173
174 174 class Operation():
175 175
176 176 """
177 177 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
178 178 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
179 179 acumulacion dentro de esta clase
180 180
181 181 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
182 182
183 183 """
184 184
185 185 __buffer = None
186 186 __isConfig = False
187 187
188 188 def __init__(self):
189 189
190 190 pass
191 191
192 192 def run(self, dataIn, **kwargs):
193 193
194 194 """
195 195 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
196 196
197 197 Input:
198 198
199 199 dataIn : objeto del tipo JROData
200 200
201 201 Return:
202 202
203 203 None
204 204
205 205 Affected:
206 206 __buffer : buffer de recepcion de datos.
207 207
208 208 """
209 209
210 210 raise ValueError, "ImplementedError"
211 211
212 212 class VoltageProc(ProcessingUnit):
213 213
214 214
215 215 def __init__(self):
216 216
217 217 self.objectDict = {}
218 218 self.dataOut = Voltage()
219 219 self.flip = 1
220 220
221 221 def init(self):
222 222
223 223 self.dataOut.copy(self.dataIn)
224 224 # No necesita copiar en cada init() los atributos de dataIn
225 225 # la copia deberia hacerse por cada nuevo bloque de datos
226 226
227 227 def selectChannels(self, channelList):
228 228
229 229 channelIndexList = []
230 230
231 231 for channel in channelList:
232 232 index = self.dataOut.channelList.index(channel)
233 233 channelIndexList.append(index)
234 234
235 235 self.selectChannelsByIndex(channelIndexList)
236 236
237 237 def selectChannelsByIndex(self, channelIndexList):
238 238 """
239 239 Selecciona un bloque de datos en base a canales segun el channelIndexList
240 240
241 241 Input:
242 242 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
243 243
244 244 Affected:
245 245 self.dataOut.data
246 246 self.dataOut.channelIndexList
247 247 self.dataOut.nChannels
248 248 self.dataOut.m_ProcessingHeader.totalSpectra
249 249 self.dataOut.systemHeaderObj.numChannels
250 250 self.dataOut.m_ProcessingHeader.blockSize
251 251
252 252 Return:
253 253 None
254 254 """
255 255
256 256 for channelIndex in channelIndexList:
257 257 if channelIndex not in self.dataOut.channelIndexList:
258 258 print channelIndexList
259 259 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
260 260
261 261 nChannels = len(channelIndexList)
262 262
263 263 data = self.dataOut.data[channelIndexList,:]
264 264
265 265 self.dataOut.data = data
266 266 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
267 267 # self.dataOut.nChannels = nChannels
268 268
269 269 return 1
270 270
271 271 def selectHeights(self, minHei, maxHei):
272 272 """
273 273 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
274 274 minHei <= height <= maxHei
275 275
276 276 Input:
277 277 minHei : valor minimo de altura a considerar
278 278 maxHei : valor maximo de altura a considerar
279 279
280 280 Affected:
281 281 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
282 282
283 283 Return:
284 284 1 si el metodo se ejecuto con exito caso contrario devuelve 0
285 285 """
286 286 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
287 287 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
288 288
289 289 if (maxHei > self.dataOut.heightList[-1]):
290 290 maxHei = self.dataOut.heightList[-1]
291 291 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
292 292
293 293 minIndex = 0
294 294 maxIndex = 0
295 295 heights = self.dataOut.heightList
296 296
297 297 inda = numpy.where(heights >= minHei)
298 298 indb = numpy.where(heights <= maxHei)
299 299
300 300 try:
301 301 minIndex = inda[0][0]
302 302 except:
303 303 minIndex = 0
304 304
305 305 try:
306 306 maxIndex = indb[0][-1]
307 307 except:
308 308 maxIndex = len(heights)
309 309
310 310 self.selectHeightsByIndex(minIndex, maxIndex)
311 311
312 312 return 1
313 313
314 314
315 315 def selectHeightsByIndex(self, minIndex, maxIndex):
316 316 """
317 317 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
318 318 minIndex <= index <= maxIndex
319 319
320 320 Input:
321 321 minIndex : valor de indice minimo de altura a considerar
322 322 maxIndex : valor de indice maximo de altura a considerar
323 323
324 324 Affected:
325 325 self.dataOut.data
326 326 self.dataOut.heightList
327 327
328 328 Return:
329 329 1 si el metodo se ejecuto con exito caso contrario devuelve 0
330 330 """
331 331
332 332 if (minIndex < 0) or (minIndex > maxIndex):
333 333 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
334 334
335 335 if (maxIndex >= self.dataOut.nHeights):
336 336 maxIndex = self.dataOut.nHeights-1
337 337 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
338 338
339 339 nHeights = maxIndex - minIndex + 1
340 340
341 341 #voltage
342 342 data = self.dataOut.data[:,minIndex:maxIndex+1]
343 343
344 344 firstHeight = self.dataOut.heightList[minIndex]
345 345
346 346 self.dataOut.data = data
347 347 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
348 348
349 349 return 1
350 350
351 351
352 352 def filterByHeights(self, window):
353 353 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
354 354
355 355 if window == None:
356 356 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
357 357
358 358 newdelta = deltaHeight * window
359 359 r = self.dataOut.data.shape[1] % window
360 360 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
361 361 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
362 362 buffer = numpy.sum(buffer,2)
363 363 self.dataOut.data = buffer
364 364 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
365 365 self.dataOut.windowOfFilter = window
366 366
367 367 def deFlip(self):
368 368 self.dataOut.data *= self.flip
369 369 self.flip *= -1.
370 370
371 371
372 372 class CohInt(Operation):
373 373
374 374 __isConfig = False
375 375
376 376 __profIndex = 0
377 377 __withOverapping = False
378 378
379 379 __byTime = False
380 380 __initime = None
381 381 __lastdatatime = None
382 382 __integrationtime = None
383 383
384 384 __buffer = None
385 385
386 386 __dataReady = False
387 387
388 388 n = None
389 389
390 390
391 391 def __init__(self):
392 392
393 393 self.__isConfig = False
394 394
395 395 def setup(self, n=None, timeInterval=None, overlapping=False):
396 396 """
397 397 Set the parameters of the integration class.
398 398
399 399 Inputs:
400 400
401 401 n : Number of coherent integrations
402 402 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
403 403 overlapping :
404 404
405 405 """
406 406
407 407 self.__initime = None
408 408 self.__lastdatatime = 0
409 409 self.__buffer = None
410 410 self.__dataReady = False
411 411
412 412
413 413 if n == None and timeInterval == None:
414 414 raise ValueError, "n or timeInterval should be specified ..."
415 415
416 416 if n != None:
417 417 self.n = n
418 418 self.__byTime = False
419 419 else:
420 420 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
421 421 self.n = 9999
422 422 self.__byTime = True
423 423
424 424 if overlapping:
425 425 self.__withOverapping = True
426 426 self.__buffer = None
427 427 else:
428 428 self.__withOverapping = False
429 429 self.__buffer = 0
430 430
431 431 self.__profIndex = 0
432 432
433 433 def putData(self, data):
434 434
435 435 """
436 436 Add a profile to the __buffer and increase in one the __profileIndex
437 437
438 438 """
439 439
440 440 if not self.__withOverapping:
441 441 self.__buffer += data.copy()
442 442 self.__profIndex += 1
443 443 return
444 444
445 445 #Overlapping data
446 446 nChannels, nHeis = data.shape
447 447 data = numpy.reshape(data, (1, nChannels, nHeis))
448 448
449 449 #If the buffer is empty then it takes the data value
450 450 if self.__buffer == None:
451 451 self.__buffer = data
452 452 self.__profIndex += 1
453 453 return
454 454
455 455 #If the buffer length is lower than n then stakcing the data value
456 456 if self.__profIndex < self.n:
457 457 self.__buffer = numpy.vstack((self.__buffer, data))
458 458 self.__profIndex += 1
459 459 return
460 460
461 461 #If the buffer length is equal to n then replacing the last buffer value with the data value
462 462 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
463 463 self.__buffer[self.n-1] = data
464 464 self.__profIndex = self.n
465 465 return
466 466
467 467
468 468 def pushData(self):
469 469 """
470 470 Return the sum of the last profiles and the profiles used in the sum.
471 471
472 472 Affected:
473 473
474 474 self.__profileIndex
475 475
476 476 """
477 477
478 478 if not self.__withOverapping:
479 479 data = self.__buffer
480 480 n = self.__profIndex
481 481
482 482 self.__buffer = 0
483 483 self.__profIndex = 0
484 484
485 485 return data, n
486 486
487 487 #Integration with Overlapping
488 488 data = numpy.sum(self.__buffer, axis=0)
489 489 n = self.__profIndex
490 490
491 491 return data, n
492 492
493 493 def byProfiles(self, data):
494 494
495 495 self.__dataReady = False
496 496 avgdata = None
497 497 n = None
498 498
499 499 self.putData(data)
500 500
501 501 if self.__profIndex == self.n:
502 502
503 503 avgdata, n = self.pushData()
504 504 self.__dataReady = True
505 505
506 506 return avgdata
507 507
508 508 def byTime(self, data, datatime):
509 509
510 510 self.__dataReady = False
511 511 avgdata = None
512 512 n = None
513 513
514 514 self.putData(data)
515 515
516 516 if (datatime - self.__initime) >= self.__integrationtime:
517 517 avgdata, n = self.pushData()
518 518 self.n = n
519 519 self.__dataReady = True
520 520
521 521 return avgdata
522 522
523 523 def integrate(self, data, datatime=None):
524 524
525 525 if self.__initime == None:
526 526 self.__initime = datatime
527 527
528 528 if self.__byTime:
529 529 avgdata = self.byTime(data, datatime)
530 530 else:
531 531 avgdata = self.byProfiles(data)
532 532
533 533
534 534 self.__lastdatatime = datatime
535 535
536 536 if avgdata == None:
537 537 return None, None
538 538
539 539 avgdatatime = self.__initime
540 540
541 541 deltatime = datatime -self.__lastdatatime
542 542
543 543 if not self.__withOverapping:
544 544 self.__initime = datatime
545 545 else:
546 546 self.__initime += deltatime
547 547
548 548 return avgdata, avgdatatime
549 549
550 550 def run(self, dataOut, **kwargs):
551 551
552 552 if not self.__isConfig:
553 553 self.setup(**kwargs)
554 554 self.__isConfig = True
555 555
556 556 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
557 557
558 558 # dataOut.timeInterval *= n
559 559 dataOut.flagNoData = True
560 560
561 561 if self.__dataReady:
562 562 dataOut.data = avgdata
563 563 dataOut.nCohInt *= self.n
564 564 dataOut.utctime = avgdatatime
565 565 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
566 566 dataOut.flagNoData = False
567 567
568 568
569 569 class Decoder(Operation):
570 570
571 571 __isConfig = False
572 572 __profIndex = 0
573 573
574 574 code = None
575 575
576 576 nCode = None
577 577 nBaud = None
578 578
579 579 def __init__(self):
580 580
581 581 self.__isConfig = False
582 582
583 583 def setup(self, code):
584 584
585 585 self.__profIndex = 0
586 586
587 587 self.code = code
588 588
589 589 self.nCode = len(code)
590 590 self.nBaud = len(code[0])
591 591
592 592 def convolutionInFreq(self, data):
593 593
594 594 nchannel, ndata = data.shape
595 595 newcode = numpy.zeros(ndata)
596 596 newcode[0:self.nBaud] = self.code[self.__profIndex]
597 597
598 598 fft_data = numpy.fft.fft(data, axis=1)
599 599 fft_code = numpy.conj(numpy.fft.fft(newcode))
600 600 fft_code = fft_code.reshape(1,len(fft_code))
601 601
602 602 # conv = fft_data.copy()
603 603 # conv.fill(0)
604 604
605 605 conv = fft_data*fft_code
606 606
607 607 data = numpy.fft.ifft(conv,axis=1)
608 608
609 609 datadec = data[:,:-self.nBaud+1]
610 610 ndatadec = ndata - self.nBaud + 1
611 611
612 612 if self.__profIndex == self.nCode-1:
613 613 self.__profIndex = 0
614 614 return ndatadec, datadec
615 615
616 616 self.__profIndex += 1
617 617
618 618 return ndatadec, datadec
619 619
620 620
621 621 def convolutionInTime(self, data):
622 622
623 623 nchannel, ndata = data.shape
624 624 newcode = self.code[self.__profIndex]
625 625 ndatadec = ndata - self.nBaud + 1
626 626
627 627 datadec = numpy.zeros((nchannel, ndatadec))
628 628
629 629 for i in range(nchannel):
630 630 datadec[i,:] = numpy.correlate(data[i,:], newcode)
631 631
632 632 if self.__profIndex == self.nCode-1:
633 633 self.__profIndex = 0
634 634 return ndatadec, datadec
635 635
636 636 self.__profIndex += 1
637 637
638 638 return ndatadec, datadec
639 639
640 640 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
641 641
642 642 if not self.__isConfig:
643 if code != None:
643 if code == None:
644 code = dataOut.code
645 else:
644 646 code = numpy.array(code).reshape(nCode,nBaud)
645 647 dataOut.code = code
646 648 dataOut.nCode = nCode
647 649 dataOut.nBaud = nBaud
650
648 651 if code == None:
649 code = dataOut.code
650
652 return 1
653
651 654 self.setup(code)
652 655 self.__isConfig = True
653 656
654 657 if mode == 0:
655 658 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
656 659
657 660 if mode == 1:
658 661 print "This function is not implemented"
659 662 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
660 663
661 664 dataOut.data = datadec
662 665
663 666 dataOut.heightList = dataOut.heightList[0:ndatadec]
664 667
665 668 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
666 669
667 670 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
668 671
669 672
670 673 class SpectraProc(ProcessingUnit):
671 674
672 675 def __init__(self):
673 676
674 677 self.objectDict = {}
675 678 self.buffer = None
676 679 self.firstdatatime = None
677 680 self.profIndex = 0
678 681 self.dataOut = Spectra()
679 682
680 683 def __updateObjFromInput(self):
681 684
682 685 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
683 686 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
684 687 self.dataOut.channelList = self.dataIn.channelList
685 688 self.dataOut.heightList = self.dataIn.heightList
686 689 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
687 690 # self.dataOut.nHeights = self.dataIn.nHeights
688 691 # self.dataOut.nChannels = self.dataIn.nChannels
689 692 self.dataOut.nBaud = self.dataIn.nBaud
690 693 self.dataOut.nCode = self.dataIn.nCode
691 694 self.dataOut.code = self.dataIn.code
692 695 self.dataOut.nProfiles = self.dataOut.nFFTPoints
693 696 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
694 697 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
695 698 self.dataOut.utctime = self.firstdatatime
696 699 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
697 700 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
698 701 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
699 702 self.dataOut.nCohInt = self.dataIn.nCohInt
700 703 self.dataOut.nIncohInt = 1
701 704 self.dataOut.ippSeconds = self.dataIn.ippSeconds
702 705 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
703 706
704 707 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
705 708
706 709 def __getFft(self):
707 710 """
708 711 Convierte valores de Voltaje a Spectra
709 712
710 713 Affected:
711 714 self.dataOut.data_spc
712 715 self.dataOut.data_cspc
713 716 self.dataOut.data_dc
714 717 self.dataOut.heightList
715 718 self.profIndex
716 719 self.buffer
717 720 self.dataOut.flagNoData
718 721 """
719 722 fft_volt = numpy.fft.fft(self.buffer,axis=1)
720 723 fft_volt = fft_volt.astype(numpy.dtype('complex'))
721 724 dc = fft_volt[:,0,:]
722 725
723 726 #calculo de self-spectra
724 727 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
725 728 spc = fft_volt * numpy.conjugate(fft_volt)
726 729 spc = spc.real
727 730
728 731 blocksize = 0
729 732 blocksize += dc.size
730 733 blocksize += spc.size
731 734
732 735 cspc = None
733 736 pairIndex = 0
734 737 if self.dataOut.pairsList != None:
735 738 #calculo de cross-spectra
736 739 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
737 740 for pair in self.dataOut.pairsList:
738 741 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
739 742 pairIndex += 1
740 743 blocksize += cspc.size
741 744
742 745 self.dataOut.data_spc = spc
743 746 self.dataOut.data_cspc = cspc
744 747 self.dataOut.data_dc = dc
745 748 self.dataOut.blockSize = blocksize
746 749
747 750 def init(self, nFFTPoints=None, pairsList=None):
748 751
749 752 self.dataOut.flagNoData = True
750 753
751 754 if self.dataIn.type == "Spectra":
752 755 self.dataOut.copy(self.dataIn)
753 756 return
754 757
755 758 if self.dataIn.type == "Voltage":
756 759
757 760 if nFFTPoints == None:
758 761 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
759 762
760 763 if pairsList == None:
761 764 nPairs = 0
762 765 else:
763 766 nPairs = len(pairsList)
764 767
765 768 self.dataOut.nFFTPoints = nFFTPoints
766 769 self.dataOut.pairsList = pairsList
767 770 self.dataOut.nPairs = nPairs
768 771
769 772 if self.buffer == None:
770 773 self.buffer = numpy.zeros((self.dataIn.nChannels,
771 774 self.dataOut.nFFTPoints,
772 775 self.dataIn.nHeights),
773 776 dtype='complex')
774 777
775 778
776 779 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
777 780 self.profIndex += 1
778 781
779 782 if self.firstdatatime == None:
780 783 self.firstdatatime = self.dataIn.utctime
781 784
782 785 if self.profIndex == self.dataOut.nFFTPoints:
783 786 self.__updateObjFromInput()
784 787 self.__getFft()
785 788
786 789 self.dataOut.flagNoData = False
787 790
788 791 self.buffer = None
789 792 self.firstdatatime = None
790 793 self.profIndex = 0
791 794
792 795 return
793 796
794 797 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
795 798
796 799 def selectChannels(self, channelList):
797 800
798 801 channelIndexList = []
799 802
800 803 for channel in channelList:
801 804 index = self.dataOut.channelList.index(channel)
802 805 channelIndexList.append(index)
803 806
804 807 self.selectChannelsByIndex(channelIndexList)
805 808
806 809 def selectChannelsByIndex(self, channelIndexList):
807 810 """
808 811 Selecciona un bloque de datos en base a canales segun el channelIndexList
809 812
810 813 Input:
811 814 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
812 815
813 816 Affected:
814 817 self.dataOut.data_spc
815 818 self.dataOut.channelIndexList
816 819 self.dataOut.nChannels
817 820
818 821 Return:
819 822 None
820 823 """
821 824
822 825 for channelIndex in channelIndexList:
823 826 if channelIndex not in self.dataOut.channelIndexList:
824 827 print channelIndexList
825 828 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
826 829
827 830 nChannels = len(channelIndexList)
828 831
829 832 data_spc = self.dataOut.data_spc[channelIndexList,:]
830 833
831 834 self.dataOut.data_spc = data_spc
832 835 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
833 836 # self.dataOut.nChannels = nChannels
834 837
835 838 return 1
836 839
837 840 def selectHeights(self, minHei, maxHei):
838 841 """
839 842 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
840 843 minHei <= height <= maxHei
841 844
842 845 Input:
843 846 minHei : valor minimo de altura a considerar
844 847 maxHei : valor maximo de altura a considerar
845 848
846 849 Affected:
847 850 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
848 851
849 852 Return:
850 853 1 si el metodo se ejecuto con exito caso contrario devuelve 0
851 854 """
852 855 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
853 856 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
854 857
855 858 if (maxHei > self.dataOut.heightList[-1]):
856 859 maxHei = self.dataOut.heightList[-1]
857 860 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
858 861
859 862 minIndex = 0
860 863 maxIndex = 0
861 864 heights = self.dataOut.heightList
862 865
863 866 inda = numpy.where(heights >= minHei)
864 867 indb = numpy.where(heights <= maxHei)
865 868
866 869 try:
867 870 minIndex = inda[0][0]
868 871 except:
869 872 minIndex = 0
870 873
871 874 try:
872 875 maxIndex = indb[0][-1]
873 876 except:
874 877 maxIndex = len(heights)
875 878
876 879 self.selectHeightsByIndex(minIndex, maxIndex)
877 880
878 881 return 1
879 882
880 883
881 884 def selectHeightsByIndex(self, minIndex, maxIndex):
882 885 """
883 886 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
884 887 minIndex <= index <= maxIndex
885 888
886 889 Input:
887 890 minIndex : valor de indice minimo de altura a considerar
888 891 maxIndex : valor de indice maximo de altura a considerar
889 892
890 893 Affected:
891 894 self.dataOut.data_spc
892 895 self.dataOut.data_cspc
893 896 self.dataOut.data_dc
894 897 self.dataOut.heightList
895 898
896 899 Return:
897 900 1 si el metodo se ejecuto con exito caso contrario devuelve 0
898 901 """
899 902
900 903 if (minIndex < 0) or (minIndex > maxIndex):
901 904 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
902 905
903 906 if (maxIndex >= self.dataOut.nHeights):
904 907 maxIndex = self.dataOut.nHeights-1
905 908 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
906 909
907 910 nHeights = maxIndex - minIndex + 1
908 911
909 912 #Spectra
910 913 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
911 914
912 915 data_cspc = None
913 916 if self.dataOut.data_cspc != None:
914 917 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
915 918
916 919 data_dc = None
917 920 if self.dataOut.data_dc != None:
918 921 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
919 922
920 923 self.dataOut.data_spc = data_spc
921 924 self.dataOut.data_cspc = data_cspc
922 925 self.dataOut.data_dc = data_dc
923 926
924 927 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
925 928
926 929 return 1
927 930
928 931 def removeDC(self, mode = 1):
929 932
930 933 dc_index = 0
931 934 freq_index = numpy.array([-2,-1,1,2])
932 935 data_spc = self.dataOut.data_spc
933 936 data_cspc = self.dataOut.data_cspc
934 937 data_dc = self.dataOut.data_dc
935 938
936 939 if self.dataOut.flagShiftFFT:
937 940 dc_index += self.dataOut.nFFTPoints/2
938 941 freq_index += self.dataOut.nFFTPoints/2
939 942
940 943 if mode == 1:
941 944 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
942 945 if data_cspc != None:
943 946 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
944 947 return 1
945 948
946 949 if mode == 2:
947 950 pass
948 951
949 952 if mode == 3:
950 953 pass
951 954
952 955 raise ValueError, "mode parameter has to be 1, 2 or 3"
953 956
954 957 def removeInterference(self):
955 958
956 959 pass
957 960
958 961
959 962 class IncohInt(Operation):
960 963
961 964
962 965 __profIndex = 0
963 966 __withOverapping = False
964 967
965 968 __byTime = False
966 969 __initime = None
967 970 __lastdatatime = None
968 971 __integrationtime = None
969 972
970 973 __buffer_spc = None
971 974 __buffer_cspc = None
972 975 __buffer_dc = None
973 976
974 977 __dataReady = False
975 978
976 979 __timeInterval = None
977 980
978 981 n = None
979 982
980 983
981 984
982 985 def __init__(self):
983 986
984 987 self.__isConfig = False
985 988
986 989 def setup(self, n=None, timeInterval=None, overlapping=False):
987 990 """
988 991 Set the parameters of the integration class.
989 992
990 993 Inputs:
991 994
992 995 n : Number of coherent integrations
993 996 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
994 997 overlapping :
995 998
996 999 """
997 1000
998 1001 self.__initime = None
999 1002 self.__lastdatatime = 0
1000 1003 self.__buffer_spc = None
1001 1004 self.__buffer_cspc = None
1002 1005 self.__buffer_dc = None
1003 1006 self.__dataReady = False
1004 1007
1005 1008
1006 1009 if n == None and timeInterval == None:
1007 1010 raise ValueError, "n or timeInterval should be specified ..."
1008 1011
1009 1012 if n != None:
1010 1013 self.n = n
1011 1014 self.__byTime = False
1012 1015 else:
1013 1016 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1014 1017 self.n = 9999
1015 1018 self.__byTime = True
1016 1019
1017 1020 if overlapping:
1018 1021 self.__withOverapping = True
1019 1022 else:
1020 1023 self.__withOverapping = False
1021 1024 self.__buffer_spc = 0
1022 1025 self.__buffer_cspc = 0
1023 1026 self.__buffer_dc = 0
1024 1027
1025 1028 self.__profIndex = 0
1026 1029
1027 1030 def putData(self, data_spc, data_cspc, data_dc):
1028 1031
1029 1032 """
1030 1033 Add a profile to the __buffer_spc and increase in one the __profileIndex
1031 1034
1032 1035 """
1033 1036
1034 1037 if not self.__withOverapping:
1035 1038 self.__buffer_spc += data_spc
1036 1039
1037 1040 if data_cspc == None:
1038 1041 self.__buffer_cspc = None
1039 1042 else:
1040 1043 self.__buffer_cspc += data_cspc
1041 1044
1042 1045 if data_dc == None:
1043 1046 self.__buffer_dc = None
1044 1047 else:
1045 1048 self.__buffer_dc += data_dc
1046 1049
1047 1050 self.__profIndex += 1
1048 1051 return
1049 1052
1050 1053 #Overlapping data
1051 1054 nChannels, nFFTPoints, nHeis = data_spc.shape
1052 1055 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1053 1056 if data_cspc != None:
1054 1057 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1055 1058 if data_dc != None:
1056 1059 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1057 1060
1058 1061 #If the buffer is empty then it takes the data value
1059 1062 if self.__buffer_spc == None:
1060 1063 self.__buffer_spc = data_spc
1061 1064
1062 1065 if data_cspc == None:
1063 1066 self.__buffer_cspc = None
1064 1067 else:
1065 1068 self.__buffer_cspc += data_cspc
1066 1069
1067 1070 if data_dc == None:
1068 1071 self.__buffer_dc = None
1069 1072 else:
1070 1073 self.__buffer_dc += data_dc
1071 1074
1072 1075 self.__profIndex += 1
1073 1076 return
1074 1077
1075 1078 #If the buffer length is lower than n then stakcing the data value
1076 1079 if self.__profIndex < self.n:
1077 1080 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1078 1081
1079 1082 if data_cspc != None:
1080 1083 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1081 1084
1082 1085 if data_dc != None:
1083 1086 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1084 1087
1085 1088 self.__profIndex += 1
1086 1089 return
1087 1090
1088 1091 #If the buffer length is equal to n then replacing the last buffer value with the data value
1089 1092 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1090 1093 self.__buffer_spc[self.n-1] = data_spc
1091 1094
1092 1095 if data_cspc != None:
1093 1096 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1094 1097 self.__buffer_cspc[self.n-1] = data_cspc
1095 1098
1096 1099 if data_dc != None:
1097 1100 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1098 1101 self.__buffer_dc[self.n-1] = data_dc
1099 1102
1100 1103 self.__profIndex = self.n
1101 1104 return
1102 1105
1103 1106
1104 1107 def pushData(self):
1105 1108 """
1106 1109 Return the sum of the last profiles and the profiles used in the sum.
1107 1110
1108 1111 Affected:
1109 1112
1110 1113 self.__profileIndex
1111 1114
1112 1115 """
1113 1116 data_spc = None
1114 1117 data_cspc = None
1115 1118 data_dc = None
1116 1119
1117 1120 if not self.__withOverapping:
1118 1121 data_spc = self.__buffer_spc
1119 1122 data_cspc = self.__buffer_cspc
1120 1123 data_dc = self.__buffer_dc
1121 1124
1122 1125 n = self.__profIndex
1123 1126
1124 1127 self.__buffer_spc = 0
1125 1128 self.__buffer_cspc = 0
1126 1129 self.__buffer_dc = 0
1127 1130 self.__profIndex = 0
1128 1131
1129 1132 return data_spc, data_cspc, data_dc, n
1130 1133
1131 1134 #Integration with Overlapping
1132 1135 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1133 1136
1134 1137 if self.__buffer_cspc != None:
1135 1138 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1136 1139
1137 1140 if self.__buffer_dc != None:
1138 1141 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1139 1142
1140 1143 n = self.__profIndex
1141 1144
1142 1145 return data_spc, data_cspc, data_dc, n
1143 1146
1144 1147 def byProfiles(self, *args):
1145 1148
1146 1149 self.__dataReady = False
1147 1150 avgdata_spc = None
1148 1151 avgdata_cspc = None
1149 1152 avgdata_dc = None
1150 1153 n = None
1151 1154
1152 1155 self.putData(*args)
1153 1156
1154 1157 if self.__profIndex == self.n:
1155 1158
1156 1159 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1157 1160 self.__dataReady = True
1158 1161
1159 1162 return avgdata_spc, avgdata_cspc, avgdata_dc
1160 1163
1161 1164 def byTime(self, datatime, *args):
1162 1165
1163 1166 self.__dataReady = False
1164 1167 avgdata_spc = None
1165 1168 avgdata_cspc = None
1166 1169 avgdata_dc = None
1167 1170 n = None
1168 1171
1169 1172 self.putData(*args)
1170 1173
1171 1174 if (datatime - self.__initime) >= self.__integrationtime:
1172 1175 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1173 1176 self.n = n
1174 1177 self.__dataReady = True
1175 1178
1176 1179 return avgdata_spc, avgdata_cspc, avgdata_dc
1177 1180
1178 1181 def integrate(self, datatime, *args):
1179 1182
1180 1183 if self.__initime == None:
1181 1184 self.__initime = datatime
1182 1185
1183 1186 if self.__byTime:
1184 1187 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1185 1188 else:
1186 1189 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1187 1190
1188 1191 self.__lastdatatime = datatime
1189 1192
1190 1193 if avgdata_spc == None:
1191 1194 return None, None, None, None
1192 1195
1193 1196 avgdatatime = self.__initime
1194 1197 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1195 1198
1196 1199 deltatime = datatime -self.__lastdatatime
1197 1200
1198 1201 if not self.__withOverapping:
1199 1202 self.__initime = datatime
1200 1203 else:
1201 1204 self.__initime += deltatime
1202 1205
1203 1206 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1204 1207
1205 1208 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1206 1209
1207 1210 if not self.__isConfig:
1208 1211 self.setup(n, timeInterval, overlapping)
1209 1212 self.__isConfig = True
1210 1213
1211 1214 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1212 1215 dataOut.data_spc,
1213 1216 dataOut.data_cspc,
1214 1217 dataOut.data_dc)
1215 1218
1216 1219 # dataOut.timeInterval *= n
1217 1220 dataOut.flagNoData = True
1218 1221
1219 1222 if self.__dataReady:
1220 1223
1221 1224 dataOut.data_spc = avgdata_spc
1222 1225 dataOut.data_cspc = avgdata_cspc
1223 1226 dataOut.data_dc = avgdata_dc
1224 1227
1225 1228 dataOut.nIncohInt *= self.n
1226 1229 dataOut.utctime = avgdatatime
1227 1230 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1228 1231 dataOut.timeInterval = self.__timeInterval*self.n
1229 1232 dataOut.flagNoData = False
1230 1233
1231 1234 class ProfileSelector(Operation):
1232 1235
1233 1236 profileIndex = None
1234 1237 # Tamanho total de los perfiles
1235 1238 nProfiles = None
1236 1239
1237 1240 def __init__(self):
1238 1241
1239 1242 self.profileIndex = 0
1240 1243
1241 1244 def incIndex(self):
1242 1245 self.profileIndex += 1
1243 1246
1244 1247 if self.profileIndex >= self.nProfiles:
1245 1248 self.profileIndex = 0
1246 1249
1247 1250 def isProfileInRange(self, minIndex, maxIndex):
1248 1251
1249 1252 if self.profileIndex < minIndex:
1250 1253 return False
1251 1254
1252 1255 if self.profileIndex > maxIndex:
1253 1256 return False
1254 1257
1255 1258 return True
1256 1259
1257 1260 def isProfileInList(self, profileList):
1258 1261
1259 1262 if self.profileIndex not in profileList:
1260 1263 return False
1261 1264
1262 1265 return True
1263 1266
1264 1267 def run(self, dataOut, profileList=None, profileRangeList=None):
1265 1268
1266 1269 dataOut.flagNoData = True
1267 1270 self.nProfiles = dataOut.nProfiles
1268 1271
1269 1272 if profileList != None:
1270 1273 if self.isProfileInList(profileList):
1271 1274 dataOut.flagNoData = False
1272 1275
1273 1276 self.incIndex()
1274 1277 return 1
1275 1278
1276 1279
1277 1280 elif profileRangeList != None:
1278 1281 minIndex = profileRangeList[0]
1279 1282 maxIndex = profileRangeList[1]
1280 1283 if self.isProfileInRange(minIndex, maxIndex):
1281 1284 dataOut.flagNoData = False
1282 1285
1283 1286 self.incIndex()
1284 1287 return 1
1285 1288
1286 1289 else:
1287 1290 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1288 1291
1289 1292 return 0
1290 1293
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