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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 try:
16 16 import cfunctions
17 17 except:
18 18 pass
19 19
20 20 class ProcessingUnit:
21 21
22 22 """
23 23 Esta es la clase base para el procesamiento de datos.
24 24
25 25 Contiene el metodo "call" para llamar operaciones. Las operaciones pueden ser:
26 26 - Metodos internos (callMethod)
27 27 - Objetos del tipo Operation (callObject). Antes de ser llamados, estos objetos
28 28 tienen que ser agreagados con el metodo "add".
29 29
30 30 """
31 31 # objeto de datos de entrada (Voltage, Spectra o Correlation)
32 32 dataIn = None
33 33
34 34 # objeto de datos de entrada (Voltage, Spectra o Correlation)
35 35 dataOut = None
36 36
37 37
38 38 objectDict = None
39 39
40 40 def __init__(self):
41 41
42 42 self.objectDict = {}
43 43
44 44 def init(self):
45 45
46 46 raise ValueError, "Not implemented"
47 47
48 48 def addOperation(self, object, objId):
49 49
50 50 """
51 51 Agrega el objeto "object" a la lista de objetos "self.objectList" y retorna el
52 52 identificador asociado a este objeto.
53 53
54 54 Input:
55 55
56 56 object : objeto de la clase "Operation"
57 57
58 58 Return:
59 59
60 60 objId : identificador del objeto, necesario para ejecutar la operacion
61 61 """
62 62
63 63 self.objectDict[objId] = object
64 64
65 65 return objId
66 66
67 67 def operation(self, **kwargs):
68 68
69 69 """
70 70 Operacion directa sobre la data (dataOut.data). Es necesario actualizar los valores de los
71 71 atributos del objeto dataOut
72 72
73 73 Input:
74 74
75 75 **kwargs : Diccionario de argumentos de la funcion a ejecutar
76 76 """
77 77
78 78 raise ValueError, "ImplementedError"
79 79
80 80 def callMethod(self, name, **kwargs):
81 81
82 82 """
83 83 Ejecuta el metodo con el nombre "name" y con argumentos **kwargs de la propia clase.
84 84
85 85 Input:
86 86 name : nombre del metodo a ejecutar
87 87
88 88 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
89 89
90 90 """
91 91 if name != 'run':
92 92
93 93 if name == 'init' and self.dataIn.isEmpty():
94 94 self.dataOut.flagNoData = True
95 95 return False
96 96
97 97 if name != 'init' and self.dataOut.isEmpty():
98 98 return False
99 99
100 100 methodToCall = getattr(self, name)
101 101
102 102 methodToCall(**kwargs)
103 103
104 104 if name != 'run':
105 105 return True
106 106
107 107 if self.dataOut.isEmpty():
108 108 return False
109 109
110 110 return True
111 111
112 112 def callObject(self, objId, **kwargs):
113 113
114 114 """
115 115 Ejecuta la operacion asociada al identificador del objeto "objId"
116 116
117 117 Input:
118 118
119 119 objId : identificador del objeto a ejecutar
120 120
121 121 **kwargs : diccionario con los nombres y valores de la funcion a ejecutar.
122 122
123 123 Return:
124 124
125 125 None
126 126 """
127 127
128 128 if self.dataOut.isEmpty():
129 129 return False
130 130
131 131 object = self.objectDict[objId]
132 132
133 133 object.run(self.dataOut, **kwargs)
134 134
135 135 return True
136 136
137 137 def call(self, operationConf, **kwargs):
138 138
139 139 """
140 140 Return True si ejecuta la operacion "operationConf.name" con los
141 141 argumentos "**kwargs". False si la operacion no se ha ejecutado.
142 142 La operacion puede ser de dos tipos:
143 143
144 144 1. Un metodo propio de esta clase:
145 145
146 146 operation.type = "self"
147 147
148 148 2. El metodo "run" de un objeto del tipo Operation o de un derivado de ella:
149 149 operation.type = "other".
150 150
151 151 Este objeto de tipo Operation debe de haber sido agregado antes con el metodo:
152 152 "addOperation" e identificado con el operation.id
153 153
154 154
155 155 con el id de la operacion.
156 156
157 157 Input:
158 158
159 159 Operation : Objeto del tipo operacion con los atributos: name, type y id.
160 160
161 161 """
162 162
163 163 if operationConf.type == 'self':
164 164 sts = self.callMethod(operationConf.name, **kwargs)
165 165
166 166 if operationConf.type == 'other':
167 167 sts = self.callObject(operationConf.id, **kwargs)
168 168
169 169 return sts
170 170
171 171 def setInput(self, dataIn):
172 172
173 173 self.dataIn = dataIn
174 174
175 175 def getOutput(self):
176 176
177 177 return self.dataOut
178 178
179 179 class Operation():
180 180
181 181 """
182 182 Clase base para definir las operaciones adicionales que se pueden agregar a la clase ProcessingUnit
183 183 y necesiten acumular informacion previa de los datos a procesar. De preferencia usar un buffer de
184 184 acumulacion dentro de esta clase
185 185
186 186 Ejemplo: Integraciones coherentes, necesita la informacion previa de los n perfiles anteriores (bufffer)
187 187
188 188 """
189 189
190 190 __buffer = None
191 191 __isConfig = False
192 192
193 193 def __init__(self):
194 194
195 195 pass
196 196
197 197 def run(self, dataIn, **kwargs):
198 198
199 199 """
200 200 Realiza las operaciones necesarias sobre la dataIn.data y actualiza los atributos del objeto dataIn.
201 201
202 202 Input:
203 203
204 204 dataIn : objeto del tipo JROData
205 205
206 206 Return:
207 207
208 208 None
209 209
210 210 Affected:
211 211 __buffer : buffer de recepcion de datos.
212 212
213 213 """
214 214
215 215 raise ValueError, "ImplementedError"
216 216
217 217 class VoltageProc(ProcessingUnit):
218 218
219 219
220 220 def __init__(self):
221 221
222 222 self.objectDict = {}
223 223 self.dataOut = Voltage()
224 224 self.flip = 1
225 225
226 226 def init(self):
227 227
228 228 self.dataOut.copy(self.dataIn)
229 229 # No necesita copiar en cada init() los atributos de dataIn
230 230 # la copia deberia hacerse por cada nuevo bloque de datos
231 231
232 232 def selectChannels(self, channelList):
233 233
234 234 channelIndexList = []
235 235
236 236 for channel in channelList:
237 237 index = self.dataOut.channelList.index(channel)
238 238 channelIndexList.append(index)
239 239
240 240 self.selectChannelsByIndex(channelIndexList)
241 241
242 242 def selectChannelsByIndex(self, channelIndexList):
243 243 """
244 244 Selecciona un bloque de datos en base a canales segun el channelIndexList
245 245
246 246 Input:
247 247 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
248 248
249 249 Affected:
250 250 self.dataOut.data
251 251 self.dataOut.channelIndexList
252 252 self.dataOut.nChannels
253 253 self.dataOut.m_ProcessingHeader.totalSpectra
254 254 self.dataOut.systemHeaderObj.numChannels
255 255 self.dataOut.m_ProcessingHeader.blockSize
256 256
257 257 Return:
258 258 None
259 259 """
260 260
261 261 for channelIndex in channelIndexList:
262 262 if channelIndex not in self.dataOut.channelIndexList:
263 263 print channelIndexList
264 264 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
265 265
266 266 nChannels = len(channelIndexList)
267 267
268 268 data = self.dataOut.data[channelIndexList,:]
269 269
270 270 self.dataOut.data = data
271 271 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
272 272 # self.dataOut.nChannels = nChannels
273 273
274 274 return 1
275 275
276 276 def selectHeights(self, minHei, maxHei):
277 277 """
278 278 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
279 279 minHei <= height <= maxHei
280 280
281 281 Input:
282 282 minHei : valor minimo de altura a considerar
283 283 maxHei : valor maximo de altura a considerar
284 284
285 285 Affected:
286 286 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
287 287
288 288 Return:
289 289 1 si el metodo se ejecuto con exito caso contrario devuelve 0
290 290 """
291 291 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
292 292 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
293 293
294 294 if (maxHei > self.dataOut.heightList[-1]):
295 295 maxHei = self.dataOut.heightList[-1]
296 296 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
297 297
298 298 minIndex = 0
299 299 maxIndex = 0
300 300 heights = self.dataOut.heightList
301 301
302 302 inda = numpy.where(heights >= minHei)
303 303 indb = numpy.where(heights <= maxHei)
304 304
305 305 try:
306 306 minIndex = inda[0][0]
307 307 except:
308 308 minIndex = 0
309 309
310 310 try:
311 311 maxIndex = indb[0][-1]
312 312 except:
313 313 maxIndex = len(heights)
314 314
315 315 self.selectHeightsByIndex(minIndex, maxIndex)
316 316
317 317 return 1
318 318
319 319
320 320 def selectHeightsByIndex(self, minIndex, maxIndex):
321 321 """
322 322 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
323 323 minIndex <= index <= maxIndex
324 324
325 325 Input:
326 326 minIndex : valor de indice minimo de altura a considerar
327 327 maxIndex : valor de indice maximo de altura a considerar
328 328
329 329 Affected:
330 330 self.dataOut.data
331 331 self.dataOut.heightList
332 332
333 333 Return:
334 334 1 si el metodo se ejecuto con exito caso contrario devuelve 0
335 335 """
336 336
337 337 if (minIndex < 0) or (minIndex > maxIndex):
338 338 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
339 339
340 340 if (maxIndex >= self.dataOut.nHeights):
341 341 maxIndex = self.dataOut.nHeights-1
342 342 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
343 343
344 344 nHeights = maxIndex - minIndex + 1
345 345
346 346 #voltage
347 347 data = self.dataOut.data[:,minIndex:maxIndex+1]
348 348
349 349 firstHeight = self.dataOut.heightList[minIndex]
350 350
351 351 self.dataOut.data = data
352 352 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
353 353
354 354 return 1
355 355
356 356
357 357 def filterByHeights(self, window):
358 358 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
359 359
360 360 if window == None:
361 361 window = self.dataOut.radarControllerHeaderObj.txA / deltaHeight
362 362
363 363 newdelta = deltaHeight * window
364 364 r = self.dataOut.data.shape[1] % window
365 365 buffer = self.dataOut.data[:,0:self.dataOut.data.shape[1]-r]
366 366 buffer = buffer.reshape(self.dataOut.data.shape[0],self.dataOut.data.shape[1]/window,window)
367 367 buffer = numpy.sum(buffer,2)
368 368 self.dataOut.data = buffer
369 369 self.dataOut.heightList = numpy.arange(self.dataOut.heightList[0],newdelta*self.dataOut.nHeights/window-newdelta,newdelta)
370 370 self.dataOut.windowOfFilter = window
371 371
372 372 def deFlip(self):
373 373 self.dataOut.data *= self.flip
374 374 self.flip *= -1.
375 375
376 376
377 377 class CohInt(Operation):
378 378
379 379 __isConfig = False
380 380
381 381 __profIndex = 0
382 382 __withOverapping = False
383 383
384 384 __byTime = False
385 385 __initime = None
386 386 __lastdatatime = None
387 387 __integrationtime = None
388 388
389 389 __buffer = None
390 390
391 391 __dataReady = False
392 392
393 393 n = None
394 394
395 395
396 396 def __init__(self):
397 397
398 398 self.__isConfig = False
399 399
400 400 def setup(self, n=None, timeInterval=None, overlapping=False):
401 401 """
402 402 Set the parameters of the integration class.
403 403
404 404 Inputs:
405 405
406 406 n : Number of coherent integrations
407 407 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
408 408 overlapping :
409 409
410 410 """
411 411
412 412 self.__initime = None
413 413 self.__lastdatatime = 0
414 414 self.__buffer = None
415 415 self.__dataReady = False
416 416
417 417
418 418 if n == None and timeInterval == None:
419 419 raise ValueError, "n or timeInterval should be specified ..."
420 420
421 421 if n != None:
422 422 self.n = n
423 423 self.__byTime = False
424 424 else:
425 425 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
426 426 self.n = 9999
427 427 self.__byTime = True
428 428
429 429 if overlapping:
430 430 self.__withOverapping = True
431 431 self.__buffer = None
432 432 else:
433 433 self.__withOverapping = False
434 434 self.__buffer = 0
435 435
436 436 self.__profIndex = 0
437 437
438 438 def putData(self, data):
439 439
440 440 """
441 441 Add a profile to the __buffer and increase in one the __profileIndex
442 442
443 443 """
444 444
445 445 if not self.__withOverapping:
446 446 self.__buffer += data.copy()
447 447 self.__profIndex += 1
448 448 return
449 449
450 450 #Overlapping data
451 451 nChannels, nHeis = data.shape
452 452 data = numpy.reshape(data, (1, nChannels, nHeis))
453 453
454 454 #If the buffer is empty then it takes the data value
455 455 if self.__buffer == None:
456 456 self.__buffer = data
457 457 self.__profIndex += 1
458 458 return
459 459
460 460 #If the buffer length is lower than n then stakcing the data value
461 461 if self.__profIndex < self.n:
462 462 self.__buffer = numpy.vstack((self.__buffer, data))
463 463 self.__profIndex += 1
464 464 return
465 465
466 466 #If the buffer length is equal to n then replacing the last buffer value with the data value
467 467 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
468 468 self.__buffer[self.n-1] = data
469 469 self.__profIndex = self.n
470 470 return
471 471
472 472
473 473 def pushData(self):
474 474 """
475 475 Return the sum of the last profiles and the profiles used in the sum.
476 476
477 477 Affected:
478 478
479 479 self.__profileIndex
480 480
481 481 """
482 482
483 483 if not self.__withOverapping:
484 484 data = self.__buffer
485 485 n = self.__profIndex
486 486
487 487 self.__buffer = 0
488 488 self.__profIndex = 0
489 489
490 490 return data, n
491 491
492 492 #Integration with Overlapping
493 493 data = numpy.sum(self.__buffer, axis=0)
494 494 n = self.__profIndex
495 495
496 496 return data, n
497 497
498 498 def byProfiles(self, data):
499 499
500 500 self.__dataReady = False
501 501 avgdata = None
502 502 n = None
503 503
504 504 self.putData(data)
505 505
506 506 if self.__profIndex == self.n:
507 507
508 508 avgdata, n = self.pushData()
509 509 self.__dataReady = True
510 510
511 511 return avgdata
512 512
513 513 def byTime(self, data, datatime):
514 514
515 515 self.__dataReady = False
516 516 avgdata = None
517 517 n = None
518 518
519 519 self.putData(data)
520 520
521 521 if (datatime - self.__initime) >= self.__integrationtime:
522 522 avgdata, n = self.pushData()
523 523 self.n = n
524 524 self.__dataReady = True
525 525
526 526 return avgdata
527 527
528 528 def integrate(self, data, datatime=None):
529 529
530 530 if self.__initime == None:
531 531 self.__initime = datatime
532 532
533 533 if self.__byTime:
534 534 avgdata = self.byTime(data, datatime)
535 535 else:
536 536 avgdata = self.byProfiles(data)
537 537
538 538
539 539 self.__lastdatatime = datatime
540 540
541 541 if avgdata == None:
542 542 return None, None
543 543
544 544 avgdatatime = self.__initime
545 545
546 546 deltatime = datatime -self.__lastdatatime
547 547
548 548 if not self.__withOverapping:
549 549 self.__initime = datatime
550 550 else:
551 551 self.__initime += deltatime
552 552
553 553 return avgdata, avgdatatime
554 554
555 555 def run(self, dataOut, **kwargs):
556 556
557 557 if not self.__isConfig:
558 558 self.setup(**kwargs)
559 559 self.__isConfig = True
560 560
561 561 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
562 562
563 563 # dataOut.timeInterval *= n
564 564 dataOut.flagNoData = True
565 565
566 566 if self.__dataReady:
567 567 dataOut.data = avgdata
568 568 dataOut.nCohInt *= self.n
569 569 dataOut.utctime = avgdatatime
570 570 dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
571 571 dataOut.flagNoData = False
572 572
573 573
574 574 class Decoder(Operation):
575 575
576 576 __isConfig = False
577 577 __profIndex = 0
578 578
579 579 code = None
580 580
581 581 nCode = None
582 582 nBaud = None
583 583
584 584 def __init__(self):
585 585
586 586 self.__isConfig = False
587 587
588 588 def setup(self, code, shape):
589 589
590 590 self.__profIndex = 0
591 591
592 592 self.code = code
593 593
594 594 self.nCode = len(code)
595 595 self.nBaud = len(code[0])
596 596
597 597 self.__nChannels, self.__nHeis = shape
598 598
599 599 self.__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.float32)
600 600
601 601 self.__codeBuffer[:,0:self.nBaud] = self.code
602 602
603 603 self.fft_code = numpy.conj(numpy.fft.fft(self.__codeBuffer, axis=1))
604 604
605 605
606 606 def convolutionInFreq(self, data):
607 607
608 608 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
609 609
610 610 fft_data = numpy.fft.fft(data, axis=1)
611 611
612 612
613 613 # conv = fft_data.copy()
614 614 # conv.fill(0)
615 615
616 616 conv = fft_data*fft_code
617 617
618 618 data = numpy.fft.ifft(conv,axis=1)
619 619
620 620 datadec = data[:,:-self.nBaud+1]
621 621 ndatadec = self.__nHeis - self.nBaud + 1
622 622
623 623 if self.__profIndex == self.nCode-1:
624 624 self.__profIndex = 0
625 625 return ndatadec, datadec
626 626
627 627 self.__profIndex += 1
628 628
629 629 return ndatadec, datadec
630 630
631 631 def convolutionInFreqOpt(self, data):
632 ini = time.time()
632
633 633 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
634 634
635 635 data = cfunctions.decoder(fft_code, data)
636 636
637 637 datadec = data[:,:-self.nBaud+1]
638 638 ndatadec = self.__nHeis - self.nBaud + 1
639 639
640 print time.time() - ini, "prof = %d, nCode=%d" %(self.__profIndex, self.nCode)
641
642 640 if self.__profIndex == self.nCode-1:
643 641 self.__profIndex = 0
644 642 return ndatadec, datadec
645 643
646 644 self.__profIndex += 1
647 645
648 646 return ndatadec, datadec
649 647
650 648 def convolutionInTime(self, data):
651 649
652 650 self.__nChannels, self.__nHeis = data.shape
653 651 self.__codeBuffer = self.code[self.__profIndex]
654 652 ndatadec = self.__nHeis - self.nBaud + 1
655 653
656 654 datadec = numpy.zeros((self.__nChannels, ndatadec))
657 655
658 656 for i in range(self.__nChannels):
659 657 datadec[i,:] = numpy.correlate(data[i,:], self.__codeBuffer)
660 658
661 659 if self.__profIndex == self.nCode-1:
662 660 self.__profIndex = 0
663 661 return ndatadec, datadec
664 662
665 663 self.__profIndex += 1
666 664
667 665 return ndatadec, datadec
668 666
669 667 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
670
668 ini = time.time()
671 669 if not self.__isConfig:
672 670
673 671 if code == None:
674 672 code = dataOut.code
675 673 else:
676 674 code = numpy.array(code).reshape(nCode,nBaud)
677 675 dataOut.code = code
678 676 dataOut.nCode = nCode
679 677 dataOut.nBaud = nBaud
680 678
681 679 if code == None:
682 680 return 1
683 681
684 682 self.setup(code, dataOut.data.shape)
685 683 self.__isConfig = True
686 684
687 685 if mode == 0:
688 686 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
689 687
690 688 if mode == 1:
691 689 print "This function is not implemented"
692 690 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
693 691
694 692 if mode == 2:
695 693 ndatadec, datadec = self.convolutionInFreqOpt(dataOut.data)
696 694
697 695 dataOut.data = datadec
698 696
699 697 dataOut.heightList = dataOut.heightList[0:ndatadec]
700 698
701 699 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
702
700
701 print time.time() - ini, "prof = %d, nCode=%d" %(self.__profIndex, self.nCode)
703 702 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
704 703
705 704
706 705 class SpectraProc(ProcessingUnit):
707 706
708 707 def __init__(self):
709 708
710 709 self.objectDict = {}
711 710 self.buffer = None
712 711 self.firstdatatime = None
713 712 self.profIndex = 0
714 713 self.dataOut = Spectra()
715 714
716 715 def __updateObjFromInput(self):
717 716
718 717 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
719 718 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
720 719 self.dataOut.channelList = self.dataIn.channelList
721 720 self.dataOut.heightList = self.dataIn.heightList
722 721 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
723 722 # self.dataOut.nHeights = self.dataIn.nHeights
724 723 # self.dataOut.nChannels = self.dataIn.nChannels
725 724 self.dataOut.nBaud = self.dataIn.nBaud
726 725 self.dataOut.nCode = self.dataIn.nCode
727 726 self.dataOut.code = self.dataIn.code
728 727 self.dataOut.nProfiles = self.dataOut.nFFTPoints
729 728 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
730 729 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
731 730 self.dataOut.utctime = self.firstdatatime
732 731 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
733 732 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
734 733 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
735 734 self.dataOut.nCohInt = self.dataIn.nCohInt
736 735 self.dataOut.nIncohInt = 1
737 736 self.dataOut.ippSeconds = self.dataIn.ippSeconds
738 737 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
739 738
740 739 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
741 740
742 741 def __getFft(self):
743 742 """
744 743 Convierte valores de Voltaje a Spectra
745 744
746 745 Affected:
747 746 self.dataOut.data_spc
748 747 self.dataOut.data_cspc
749 748 self.dataOut.data_dc
750 749 self.dataOut.heightList
751 750 self.profIndex
752 751 self.buffer
753 752 self.dataOut.flagNoData
754 753 """
755 754 fft_volt = numpy.fft.fft(self.buffer,axis=1)
756 755 fft_volt = fft_volt.astype(numpy.dtype('complex'))
757 756 dc = fft_volt[:,0,:]
758 757
759 758 #calculo de self-spectra
760 759 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
761 760 spc = fft_volt * numpy.conjugate(fft_volt)
762 761 spc = spc.real
763 762
764 763 blocksize = 0
765 764 blocksize += dc.size
766 765 blocksize += spc.size
767 766
768 767 cspc = None
769 768 pairIndex = 0
770 769 if self.dataOut.pairsList != None:
771 770 #calculo de cross-spectra
772 771 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
773 772 for pair in self.dataOut.pairsList:
774 773 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
775 774 pairIndex += 1
776 775 blocksize += cspc.size
777 776
778 777 self.dataOut.data_spc = spc
779 778 self.dataOut.data_cspc = cspc
780 779 self.dataOut.data_dc = dc
781 780 self.dataOut.blockSize = blocksize
782 781
783 782 def init(self, nFFTPoints=None, pairsList=None):
784 783
785 784 self.dataOut.flagNoData = True
786 785
787 786 if self.dataIn.type == "Spectra":
788 787 self.dataOut.copy(self.dataIn)
789 788 return
790 789
791 790 if self.dataIn.type == "Voltage":
792 791
793 792 if nFFTPoints == None:
794 793 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
795 794
796 795 if pairsList == None:
797 796 nPairs = 0
798 797 else:
799 798 nPairs = len(pairsList)
800 799
801 800 self.dataOut.nFFTPoints = nFFTPoints
802 801 self.dataOut.pairsList = pairsList
803 802 self.dataOut.nPairs = nPairs
804 803
805 804 if self.buffer == None:
806 805 self.buffer = numpy.zeros((self.dataIn.nChannels,
807 806 self.dataOut.nFFTPoints,
808 807 self.dataIn.nHeights),
809 808 dtype='complex')
810 809
811 810
812 811 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
813 812 self.profIndex += 1
814 813
815 814 if self.firstdatatime == None:
816 815 self.firstdatatime = self.dataIn.utctime
817 816
818 817 if self.profIndex == self.dataOut.nFFTPoints:
819 818 self.__updateObjFromInput()
820 819 self.__getFft()
821 820
822 821 self.dataOut.flagNoData = False
823 822
824 823 self.buffer = None
825 824 self.firstdatatime = None
826 825 self.profIndex = 0
827 826
828 827 return
829 828
830 829 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
831 830
832 831 def selectChannels(self, channelList):
833 832
834 833 channelIndexList = []
835 834
836 835 for channel in channelList:
837 836 index = self.dataOut.channelList.index(channel)
838 837 channelIndexList.append(index)
839 838
840 839 self.selectChannelsByIndex(channelIndexList)
841 840
842 841 def selectChannelsByIndex(self, channelIndexList):
843 842 """
844 843 Selecciona un bloque de datos en base a canales segun el channelIndexList
845 844
846 845 Input:
847 846 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
848 847
849 848 Affected:
850 849 self.dataOut.data_spc
851 850 self.dataOut.channelIndexList
852 851 self.dataOut.nChannels
853 852
854 853 Return:
855 854 None
856 855 """
857 856
858 857 for channelIndex in channelIndexList:
859 858 if channelIndex not in self.dataOut.channelIndexList:
860 859 print channelIndexList
861 860 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
862 861
863 862 nChannels = len(channelIndexList)
864 863
865 864 data_spc = self.dataOut.data_spc[channelIndexList,:]
866 865
867 866 self.dataOut.data_spc = data_spc
868 867 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
869 868 # self.dataOut.nChannels = nChannels
870 869
871 870 return 1
872 871
873 872 def selectHeights(self, minHei, maxHei):
874 873 """
875 874 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
876 875 minHei <= height <= maxHei
877 876
878 877 Input:
879 878 minHei : valor minimo de altura a considerar
880 879 maxHei : valor maximo de altura a considerar
881 880
882 881 Affected:
883 882 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
884 883
885 884 Return:
886 885 1 si el metodo se ejecuto con exito caso contrario devuelve 0
887 886 """
888 887 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
889 888 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
890 889
891 890 if (maxHei > self.dataOut.heightList[-1]):
892 891 maxHei = self.dataOut.heightList[-1]
893 892 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
894 893
895 894 minIndex = 0
896 895 maxIndex = 0
897 896 heights = self.dataOut.heightList
898 897
899 898 inda = numpy.where(heights >= minHei)
900 899 indb = numpy.where(heights <= maxHei)
901 900
902 901 try:
903 902 minIndex = inda[0][0]
904 903 except:
905 904 minIndex = 0
906 905
907 906 try:
908 907 maxIndex = indb[0][-1]
909 908 except:
910 909 maxIndex = len(heights)
911 910
912 911 self.selectHeightsByIndex(minIndex, maxIndex)
913 912
914 913 return 1
915 914
916 915
917 916 def selectHeightsByIndex(self, minIndex, maxIndex):
918 917 """
919 918 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
920 919 minIndex <= index <= maxIndex
921 920
922 921 Input:
923 922 minIndex : valor de indice minimo de altura a considerar
924 923 maxIndex : valor de indice maximo de altura a considerar
925 924
926 925 Affected:
927 926 self.dataOut.data_spc
928 927 self.dataOut.data_cspc
929 928 self.dataOut.data_dc
930 929 self.dataOut.heightList
931 930
932 931 Return:
933 932 1 si el metodo se ejecuto con exito caso contrario devuelve 0
934 933 """
935 934
936 935 if (minIndex < 0) or (minIndex > maxIndex):
937 936 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
938 937
939 938 if (maxIndex >= self.dataOut.nHeights):
940 939 maxIndex = self.dataOut.nHeights-1
941 940 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
942 941
943 942 nHeights = maxIndex - minIndex + 1
944 943
945 944 #Spectra
946 945 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
947 946
948 947 data_cspc = None
949 948 if self.dataOut.data_cspc != None:
950 949 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
951 950
952 951 data_dc = None
953 952 if self.dataOut.data_dc != None:
954 953 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
955 954
956 955 self.dataOut.data_spc = data_spc
957 956 self.dataOut.data_cspc = data_cspc
958 957 self.dataOut.data_dc = data_dc
959 958
960 959 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
961 960
962 961 return 1
963 962
964 963 def removeDC(self, mode = 1):
965 964
966 965 dc_index = 0
967 966 freq_index = numpy.array([-2,-1,1,2])
968 967 data_spc = self.dataOut.data_spc
969 968 data_cspc = self.dataOut.data_cspc
970 969 data_dc = self.dataOut.data_dc
971 970
972 971 if self.dataOut.flagShiftFFT:
973 972 dc_index += self.dataOut.nFFTPoints/2
974 973 freq_index += self.dataOut.nFFTPoints/2
975 974
976 975 if mode == 1:
977 976 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
978 977 if data_cspc != None:
979 978 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
980 979 return 1
981 980
982 981 if mode == 2:
983 982 pass
984 983
985 984 if mode == 3:
986 985 pass
987 986
988 987 raise ValueError, "mode parameter has to be 1, 2 or 3"
989 988
990 989 def removeInterference(self):
991 990
992 991 pass
993 992
994 993
995 994 class IncohInt(Operation):
996 995
997 996
998 997 __profIndex = 0
999 998 __withOverapping = False
1000 999
1001 1000 __byTime = False
1002 1001 __initime = None
1003 1002 __lastdatatime = None
1004 1003 __integrationtime = None
1005 1004
1006 1005 __buffer_spc = None
1007 1006 __buffer_cspc = None
1008 1007 __buffer_dc = None
1009 1008
1010 1009 __dataReady = False
1011 1010
1012 1011 __timeInterval = None
1013 1012
1014 1013 n = None
1015 1014
1016 1015
1017 1016
1018 1017 def __init__(self):
1019 1018
1020 1019 self.__isConfig = False
1021 1020
1022 1021 def setup(self, n=None, timeInterval=None, overlapping=False):
1023 1022 """
1024 1023 Set the parameters of the integration class.
1025 1024
1026 1025 Inputs:
1027 1026
1028 1027 n : Number of coherent integrations
1029 1028 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
1030 1029 overlapping :
1031 1030
1032 1031 """
1033 1032
1034 1033 self.__initime = None
1035 1034 self.__lastdatatime = 0
1036 1035 self.__buffer_spc = None
1037 1036 self.__buffer_cspc = None
1038 1037 self.__buffer_dc = None
1039 1038 self.__dataReady = False
1040 1039
1041 1040
1042 1041 if n == None and timeInterval == None:
1043 1042 raise ValueError, "n or timeInterval should be specified ..."
1044 1043
1045 1044 if n != None:
1046 1045 self.n = n
1047 1046 self.__byTime = False
1048 1047 else:
1049 1048 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1050 1049 self.n = 9999
1051 1050 self.__byTime = True
1052 1051
1053 1052 if overlapping:
1054 1053 self.__withOverapping = True
1055 1054 else:
1056 1055 self.__withOverapping = False
1057 1056 self.__buffer_spc = 0
1058 1057 self.__buffer_cspc = 0
1059 1058 self.__buffer_dc = 0
1060 1059
1061 1060 self.__profIndex = 0
1062 1061
1063 1062 def putData(self, data_spc, data_cspc, data_dc):
1064 1063
1065 1064 """
1066 1065 Add a profile to the __buffer_spc and increase in one the __profileIndex
1067 1066
1068 1067 """
1069 1068
1070 1069 if not self.__withOverapping:
1071 1070 self.__buffer_spc += data_spc
1072 1071
1073 1072 if data_cspc == None:
1074 1073 self.__buffer_cspc = None
1075 1074 else:
1076 1075 self.__buffer_cspc += data_cspc
1077 1076
1078 1077 if data_dc == None:
1079 1078 self.__buffer_dc = None
1080 1079 else:
1081 1080 self.__buffer_dc += data_dc
1082 1081
1083 1082 self.__profIndex += 1
1084 1083 return
1085 1084
1086 1085 #Overlapping data
1087 1086 nChannels, nFFTPoints, nHeis = data_spc.shape
1088 1087 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1089 1088 if data_cspc != None:
1090 1089 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1091 1090 if data_dc != None:
1092 1091 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1093 1092
1094 1093 #If the buffer is empty then it takes the data value
1095 1094 if self.__buffer_spc == None:
1096 1095 self.__buffer_spc = data_spc
1097 1096
1098 1097 if data_cspc == None:
1099 1098 self.__buffer_cspc = None
1100 1099 else:
1101 1100 self.__buffer_cspc += data_cspc
1102 1101
1103 1102 if data_dc == None:
1104 1103 self.__buffer_dc = None
1105 1104 else:
1106 1105 self.__buffer_dc += data_dc
1107 1106
1108 1107 self.__profIndex += 1
1109 1108 return
1110 1109
1111 1110 #If the buffer length is lower than n then stakcing the data value
1112 1111 if self.__profIndex < self.n:
1113 1112 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1114 1113
1115 1114 if data_cspc != None:
1116 1115 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1117 1116
1118 1117 if data_dc != None:
1119 1118 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1120 1119
1121 1120 self.__profIndex += 1
1122 1121 return
1123 1122
1124 1123 #If the buffer length is equal to n then replacing the last buffer value with the data value
1125 1124 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1126 1125 self.__buffer_spc[self.n-1] = data_spc
1127 1126
1128 1127 if data_cspc != None:
1129 1128 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1130 1129 self.__buffer_cspc[self.n-1] = data_cspc
1131 1130
1132 1131 if data_dc != None:
1133 1132 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1134 1133 self.__buffer_dc[self.n-1] = data_dc
1135 1134
1136 1135 self.__profIndex = self.n
1137 1136 return
1138 1137
1139 1138
1140 1139 def pushData(self):
1141 1140 """
1142 1141 Return the sum of the last profiles and the profiles used in the sum.
1143 1142
1144 1143 Affected:
1145 1144
1146 1145 self.__profileIndex
1147 1146
1148 1147 """
1149 1148 data_spc = None
1150 1149 data_cspc = None
1151 1150 data_dc = None
1152 1151
1153 1152 if not self.__withOverapping:
1154 1153 data_spc = self.__buffer_spc
1155 1154 data_cspc = self.__buffer_cspc
1156 1155 data_dc = self.__buffer_dc
1157 1156
1158 1157 n = self.__profIndex
1159 1158
1160 1159 self.__buffer_spc = 0
1161 1160 self.__buffer_cspc = 0
1162 1161 self.__buffer_dc = 0
1163 1162 self.__profIndex = 0
1164 1163
1165 1164 return data_spc, data_cspc, data_dc, n
1166 1165
1167 1166 #Integration with Overlapping
1168 1167 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1169 1168
1170 1169 if self.__buffer_cspc != None:
1171 1170 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1172 1171
1173 1172 if self.__buffer_dc != None:
1174 1173 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1175 1174
1176 1175 n = self.__profIndex
1177 1176
1178 1177 return data_spc, data_cspc, data_dc, n
1179 1178
1180 1179 def byProfiles(self, *args):
1181 1180
1182 1181 self.__dataReady = False
1183 1182 avgdata_spc = None
1184 1183 avgdata_cspc = None
1185 1184 avgdata_dc = None
1186 1185 n = None
1187 1186
1188 1187 self.putData(*args)
1189 1188
1190 1189 if self.__profIndex == self.n:
1191 1190
1192 1191 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1193 1192 self.__dataReady = True
1194 1193
1195 1194 return avgdata_spc, avgdata_cspc, avgdata_dc
1196 1195
1197 1196 def byTime(self, datatime, *args):
1198 1197
1199 1198 self.__dataReady = False
1200 1199 avgdata_spc = None
1201 1200 avgdata_cspc = None
1202 1201 avgdata_dc = None
1203 1202 n = None
1204 1203
1205 1204 self.putData(*args)
1206 1205
1207 1206 if (datatime - self.__initime) >= self.__integrationtime:
1208 1207 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1209 1208 self.n = n
1210 1209 self.__dataReady = True
1211 1210
1212 1211 return avgdata_spc, avgdata_cspc, avgdata_dc
1213 1212
1214 1213 def integrate(self, datatime, *args):
1215 1214
1216 1215 if self.__initime == None:
1217 1216 self.__initime = datatime
1218 1217
1219 1218 if self.__byTime:
1220 1219 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1221 1220 else:
1222 1221 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1223 1222
1224 1223 self.__lastdatatime = datatime
1225 1224
1226 1225 if avgdata_spc == None:
1227 1226 return None, None, None, None
1228 1227
1229 1228 avgdatatime = self.__initime
1230 1229 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1231 1230
1232 1231 deltatime = datatime -self.__lastdatatime
1233 1232
1234 1233 if not self.__withOverapping:
1235 1234 self.__initime = datatime
1236 1235 else:
1237 1236 self.__initime += deltatime
1238 1237
1239 1238 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1240 1239
1241 1240 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1242 1241
1243 1242 if not self.__isConfig:
1244 1243 self.setup(n, timeInterval, overlapping)
1245 1244 self.__isConfig = True
1246 1245
1247 1246 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1248 1247 dataOut.data_spc,
1249 1248 dataOut.data_cspc,
1250 1249 dataOut.data_dc)
1251 1250
1252 1251 # dataOut.timeInterval *= n
1253 1252 dataOut.flagNoData = True
1254 1253
1255 1254 if self.__dataReady:
1256 1255
1257 1256 dataOut.data_spc = avgdata_spc
1258 1257 dataOut.data_cspc = avgdata_cspc
1259 1258 dataOut.data_dc = avgdata_dc
1260 1259
1261 1260 dataOut.nIncohInt *= self.n
1262 1261 dataOut.utctime = avgdatatime
1263 1262 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1264 1263 dataOut.timeInterval = self.__timeInterval*self.n
1265 1264 dataOut.flagNoData = False
1266 1265
1267 1266 class ProfileSelector(Operation):
1268 1267
1269 1268 profileIndex = None
1270 1269 # Tamanho total de los perfiles
1271 1270 nProfiles = None
1272 1271
1273 1272 def __init__(self):
1274 1273
1275 1274 self.profileIndex = 0
1276 1275
1277 1276 def incIndex(self):
1278 1277 self.profileIndex += 1
1279 1278
1280 1279 if self.profileIndex >= self.nProfiles:
1281 1280 self.profileIndex = 0
1282 1281
1283 1282 def isProfileInRange(self, minIndex, maxIndex):
1284 1283
1285 1284 if self.profileIndex < minIndex:
1286 1285 return False
1287 1286
1288 1287 if self.profileIndex > maxIndex:
1289 1288 return False
1290 1289
1291 1290 return True
1292 1291
1293 1292 def isProfileInList(self, profileList):
1294 1293
1295 1294 if self.profileIndex not in profileList:
1296 1295 return False
1297 1296
1298 1297 return True
1299 1298
1300 1299 def run(self, dataOut, profileList=None, profileRangeList=None):
1301 1300
1302 1301 dataOut.flagNoData = True
1303 1302 self.nProfiles = dataOut.nProfiles
1304 1303
1305 1304 if profileList != None:
1306 1305 if self.isProfileInList(profileList):
1307 1306 dataOut.flagNoData = False
1308 1307
1309 1308 self.incIndex()
1310 1309 return 1
1311 1310
1312 1311
1313 1312 elif profileRangeList != None:
1314 1313 minIndex = profileRangeList[0]
1315 1314 maxIndex = profileRangeList[1]
1316 1315 if self.isProfileInRange(minIndex, maxIndex):
1317 1316 dataOut.flagNoData = False
1318 1317
1319 1318 self.incIndex()
1320 1319 return 1
1321 1320
1322 1321 else:
1323 1322 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1324 1323
1325 1324 return 0
1326 1325
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