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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):
632 632 ini = time.time()
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
640 642 if self.__profIndex == self.nCode-1:
641 643 self.__profIndex = 0
642 644 return ndatadec, datadec
643 645
644 646 self.__profIndex += 1
645 print time.time() - ini
647
646 648 return ndatadec, datadec
647 649
648 650 def convolutionInTime(self, data):
649 651
650 652 self.__nChannels, self.__nHeis = data.shape
651 653 self.__codeBuffer = self.code[self.__profIndex]
652 654 ndatadec = self.__nHeis - self.nBaud + 1
653 655
654 656 datadec = numpy.zeros((self.__nChannels, ndatadec))
655 657
656 658 for i in range(self.__nChannels):
657 659 datadec[i,:] = numpy.correlate(data[i,:], self.__codeBuffer)
658 660
659 661 if self.__profIndex == self.nCode-1:
660 662 self.__profIndex = 0
661 663 return ndatadec, datadec
662 664
663 665 self.__profIndex += 1
664 666
665 667 return ndatadec, datadec
666 668
667 669 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
668 670
669 671 if not self.__isConfig:
670 672
671 673 if code == None:
672 674 code = dataOut.code
673 675 else:
674 676 code = numpy.array(code).reshape(nCode,nBaud)
675 677 dataOut.code = code
676 678 dataOut.nCode = nCode
677 679 dataOut.nBaud = nBaud
678 680
679 681 if code == None:
680 682 return 1
681 683
682 684 self.setup(code, dataOut.data.shape)
683 685 self.__isConfig = True
684 686
685 687 if mode == 0:
686 688 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
687 689
688 690 if mode == 1:
689 691 print "This function is not implemented"
690 692 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
691 693
692 694 if mode == 2:
693 695 ndatadec, datadec = self.convolutionInFreqOpt(dataOut.data)
694 696
695 697 dataOut.data = datadec
696 698
697 699 dataOut.heightList = dataOut.heightList[0:ndatadec]
698 700
699 701 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
700 702
701 703 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
702 704
703 705
704 706 class SpectraProc(ProcessingUnit):
705 707
706 708 def __init__(self):
707 709
708 710 self.objectDict = {}
709 711 self.buffer = None
710 712 self.firstdatatime = None
711 713 self.profIndex = 0
712 714 self.dataOut = Spectra()
713 715
714 716 def __updateObjFromInput(self):
715 717
716 718 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
717 719 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
718 720 self.dataOut.channelList = self.dataIn.channelList
719 721 self.dataOut.heightList = self.dataIn.heightList
720 722 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
721 723 # self.dataOut.nHeights = self.dataIn.nHeights
722 724 # self.dataOut.nChannels = self.dataIn.nChannels
723 725 self.dataOut.nBaud = self.dataIn.nBaud
724 726 self.dataOut.nCode = self.dataIn.nCode
725 727 self.dataOut.code = self.dataIn.code
726 728 self.dataOut.nProfiles = self.dataOut.nFFTPoints
727 729 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
728 730 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
729 731 self.dataOut.utctime = self.firstdatatime
730 732 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
731 733 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
732 734 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
733 735 self.dataOut.nCohInt = self.dataIn.nCohInt
734 736 self.dataOut.nIncohInt = 1
735 737 self.dataOut.ippSeconds = self.dataIn.ippSeconds
736 738 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
737 739
738 740 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
739 741
740 742 def __getFft(self):
741 743 """
742 744 Convierte valores de Voltaje a Spectra
743 745
744 746 Affected:
745 747 self.dataOut.data_spc
746 748 self.dataOut.data_cspc
747 749 self.dataOut.data_dc
748 750 self.dataOut.heightList
749 751 self.profIndex
750 752 self.buffer
751 753 self.dataOut.flagNoData
752 754 """
753 755 fft_volt = numpy.fft.fft(self.buffer,axis=1)
754 756 fft_volt = fft_volt.astype(numpy.dtype('complex'))
755 757 dc = fft_volt[:,0,:]
756 758
757 759 #calculo de self-spectra
758 760 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
759 761 spc = fft_volt * numpy.conjugate(fft_volt)
760 762 spc = spc.real
761 763
762 764 blocksize = 0
763 765 blocksize += dc.size
764 766 blocksize += spc.size
765 767
766 768 cspc = None
767 769 pairIndex = 0
768 770 if self.dataOut.pairsList != None:
769 771 #calculo de cross-spectra
770 772 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
771 773 for pair in self.dataOut.pairsList:
772 774 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
773 775 pairIndex += 1
774 776 blocksize += cspc.size
775 777
776 778 self.dataOut.data_spc = spc
777 779 self.dataOut.data_cspc = cspc
778 780 self.dataOut.data_dc = dc
779 781 self.dataOut.blockSize = blocksize
780 782
781 783 def init(self, nFFTPoints=None, pairsList=None):
782 784
783 785 self.dataOut.flagNoData = True
784 786
785 787 if self.dataIn.type == "Spectra":
786 788 self.dataOut.copy(self.dataIn)
787 789 return
788 790
789 791 if self.dataIn.type == "Voltage":
790 792
791 793 if nFFTPoints == None:
792 794 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
793 795
794 796 if pairsList == None:
795 797 nPairs = 0
796 798 else:
797 799 nPairs = len(pairsList)
798 800
799 801 self.dataOut.nFFTPoints = nFFTPoints
800 802 self.dataOut.pairsList = pairsList
801 803 self.dataOut.nPairs = nPairs
802 804
803 805 if self.buffer == None:
804 806 self.buffer = numpy.zeros((self.dataIn.nChannels,
805 807 self.dataOut.nFFTPoints,
806 808 self.dataIn.nHeights),
807 809 dtype='complex')
808 810
809 811
810 812 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
811 813 self.profIndex += 1
812 814
813 815 if self.firstdatatime == None:
814 816 self.firstdatatime = self.dataIn.utctime
815 817
816 818 if self.profIndex == self.dataOut.nFFTPoints:
817 819 self.__updateObjFromInput()
818 820 self.__getFft()
819 821
820 822 self.dataOut.flagNoData = False
821 823
822 824 self.buffer = None
823 825 self.firstdatatime = None
824 826 self.profIndex = 0
825 827
826 828 return
827 829
828 830 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
829 831
830 832 def selectChannels(self, channelList):
831 833
832 834 channelIndexList = []
833 835
834 836 for channel in channelList:
835 837 index = self.dataOut.channelList.index(channel)
836 838 channelIndexList.append(index)
837 839
838 840 self.selectChannelsByIndex(channelIndexList)
839 841
840 842 def selectChannelsByIndex(self, channelIndexList):
841 843 """
842 844 Selecciona un bloque de datos en base a canales segun el channelIndexList
843 845
844 846 Input:
845 847 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
846 848
847 849 Affected:
848 850 self.dataOut.data_spc
849 851 self.dataOut.channelIndexList
850 852 self.dataOut.nChannels
851 853
852 854 Return:
853 855 None
854 856 """
855 857
856 858 for channelIndex in channelIndexList:
857 859 if channelIndex not in self.dataOut.channelIndexList:
858 860 print channelIndexList
859 861 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
860 862
861 863 nChannels = len(channelIndexList)
862 864
863 865 data_spc = self.dataOut.data_spc[channelIndexList,:]
864 866
865 867 self.dataOut.data_spc = data_spc
866 868 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
867 869 # self.dataOut.nChannels = nChannels
868 870
869 871 return 1
870 872
871 873 def selectHeights(self, minHei, maxHei):
872 874 """
873 875 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
874 876 minHei <= height <= maxHei
875 877
876 878 Input:
877 879 minHei : valor minimo de altura a considerar
878 880 maxHei : valor maximo de altura a considerar
879 881
880 882 Affected:
881 883 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
882 884
883 885 Return:
884 886 1 si el metodo se ejecuto con exito caso contrario devuelve 0
885 887 """
886 888 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
887 889 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
888 890
889 891 if (maxHei > self.dataOut.heightList[-1]):
890 892 maxHei = self.dataOut.heightList[-1]
891 893 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
892 894
893 895 minIndex = 0
894 896 maxIndex = 0
895 897 heights = self.dataOut.heightList
896 898
897 899 inda = numpy.where(heights >= minHei)
898 900 indb = numpy.where(heights <= maxHei)
899 901
900 902 try:
901 903 minIndex = inda[0][0]
902 904 except:
903 905 minIndex = 0
904 906
905 907 try:
906 908 maxIndex = indb[0][-1]
907 909 except:
908 910 maxIndex = len(heights)
909 911
910 912 self.selectHeightsByIndex(minIndex, maxIndex)
911 913
912 914 return 1
913 915
914 916
915 917 def selectHeightsByIndex(self, minIndex, maxIndex):
916 918 """
917 919 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
918 920 minIndex <= index <= maxIndex
919 921
920 922 Input:
921 923 minIndex : valor de indice minimo de altura a considerar
922 924 maxIndex : valor de indice maximo de altura a considerar
923 925
924 926 Affected:
925 927 self.dataOut.data_spc
926 928 self.dataOut.data_cspc
927 929 self.dataOut.data_dc
928 930 self.dataOut.heightList
929 931
930 932 Return:
931 933 1 si el metodo se ejecuto con exito caso contrario devuelve 0
932 934 """
933 935
934 936 if (minIndex < 0) or (minIndex > maxIndex):
935 937 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
936 938
937 939 if (maxIndex >= self.dataOut.nHeights):
938 940 maxIndex = self.dataOut.nHeights-1
939 941 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
940 942
941 943 nHeights = maxIndex - minIndex + 1
942 944
943 945 #Spectra
944 946 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
945 947
946 948 data_cspc = None
947 949 if self.dataOut.data_cspc != None:
948 950 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
949 951
950 952 data_dc = None
951 953 if self.dataOut.data_dc != None:
952 954 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
953 955
954 956 self.dataOut.data_spc = data_spc
955 957 self.dataOut.data_cspc = data_cspc
956 958 self.dataOut.data_dc = data_dc
957 959
958 960 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
959 961
960 962 return 1
961 963
962 964 def removeDC(self, mode = 1):
963 965
964 966 dc_index = 0
965 967 freq_index = numpy.array([-2,-1,1,2])
966 968 data_spc = self.dataOut.data_spc
967 969 data_cspc = self.dataOut.data_cspc
968 970 data_dc = self.dataOut.data_dc
969 971
970 972 if self.dataOut.flagShiftFFT:
971 973 dc_index += self.dataOut.nFFTPoints/2
972 974 freq_index += self.dataOut.nFFTPoints/2
973 975
974 976 if mode == 1:
975 977 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
976 978 if data_cspc != None:
977 979 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
978 980 return 1
979 981
980 982 if mode == 2:
981 983 pass
982 984
983 985 if mode == 3:
984 986 pass
985 987
986 988 raise ValueError, "mode parameter has to be 1, 2 or 3"
987 989
988 990 def removeInterference(self):
989 991
990 992 pass
991 993
992 994
993 995 class IncohInt(Operation):
994 996
995 997
996 998 __profIndex = 0
997 999 __withOverapping = False
998 1000
999 1001 __byTime = False
1000 1002 __initime = None
1001 1003 __lastdatatime = None
1002 1004 __integrationtime = None
1003 1005
1004 1006 __buffer_spc = None
1005 1007 __buffer_cspc = None
1006 1008 __buffer_dc = None
1007 1009
1008 1010 __dataReady = False
1009 1011
1010 1012 __timeInterval = None
1011 1013
1012 1014 n = None
1013 1015
1014 1016
1015 1017
1016 1018 def __init__(self):
1017 1019
1018 1020 self.__isConfig = False
1019 1021
1020 1022 def setup(self, n=None, timeInterval=None, overlapping=False):
1021 1023 """
1022 1024 Set the parameters of the integration class.
1023 1025
1024 1026 Inputs:
1025 1027
1026 1028 n : Number of coherent integrations
1027 1029 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
1028 1030 overlapping :
1029 1031
1030 1032 """
1031 1033
1032 1034 self.__initime = None
1033 1035 self.__lastdatatime = 0
1034 1036 self.__buffer_spc = None
1035 1037 self.__buffer_cspc = None
1036 1038 self.__buffer_dc = None
1037 1039 self.__dataReady = False
1038 1040
1039 1041
1040 1042 if n == None and timeInterval == None:
1041 1043 raise ValueError, "n or timeInterval should be specified ..."
1042 1044
1043 1045 if n != None:
1044 1046 self.n = n
1045 1047 self.__byTime = False
1046 1048 else:
1047 1049 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1048 1050 self.n = 9999
1049 1051 self.__byTime = True
1050 1052
1051 1053 if overlapping:
1052 1054 self.__withOverapping = True
1053 1055 else:
1054 1056 self.__withOverapping = False
1055 1057 self.__buffer_spc = 0
1056 1058 self.__buffer_cspc = 0
1057 1059 self.__buffer_dc = 0
1058 1060
1059 1061 self.__profIndex = 0
1060 1062
1061 1063 def putData(self, data_spc, data_cspc, data_dc):
1062 1064
1063 1065 """
1064 1066 Add a profile to the __buffer_spc and increase in one the __profileIndex
1065 1067
1066 1068 """
1067 1069
1068 1070 if not self.__withOverapping:
1069 1071 self.__buffer_spc += data_spc
1070 1072
1071 1073 if data_cspc == None:
1072 1074 self.__buffer_cspc = None
1073 1075 else:
1074 1076 self.__buffer_cspc += data_cspc
1075 1077
1076 1078 if data_dc == None:
1077 1079 self.__buffer_dc = None
1078 1080 else:
1079 1081 self.__buffer_dc += data_dc
1080 1082
1081 1083 self.__profIndex += 1
1082 1084 return
1083 1085
1084 1086 #Overlapping data
1085 1087 nChannels, nFFTPoints, nHeis = data_spc.shape
1086 1088 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1087 1089 if data_cspc != None:
1088 1090 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1089 1091 if data_dc != None:
1090 1092 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1091 1093
1092 1094 #If the buffer is empty then it takes the data value
1093 1095 if self.__buffer_spc == None:
1094 1096 self.__buffer_spc = data_spc
1095 1097
1096 1098 if data_cspc == None:
1097 1099 self.__buffer_cspc = None
1098 1100 else:
1099 1101 self.__buffer_cspc += data_cspc
1100 1102
1101 1103 if data_dc == None:
1102 1104 self.__buffer_dc = None
1103 1105 else:
1104 1106 self.__buffer_dc += data_dc
1105 1107
1106 1108 self.__profIndex += 1
1107 1109 return
1108 1110
1109 1111 #If the buffer length is lower than n then stakcing the data value
1110 1112 if self.__profIndex < self.n:
1111 1113 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1112 1114
1113 1115 if data_cspc != None:
1114 1116 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1115 1117
1116 1118 if data_dc != None:
1117 1119 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1118 1120
1119 1121 self.__profIndex += 1
1120 1122 return
1121 1123
1122 1124 #If the buffer length is equal to n then replacing the last buffer value with the data value
1123 1125 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1124 1126 self.__buffer_spc[self.n-1] = data_spc
1125 1127
1126 1128 if data_cspc != None:
1127 1129 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1128 1130 self.__buffer_cspc[self.n-1] = data_cspc
1129 1131
1130 1132 if data_dc != None:
1131 1133 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1132 1134 self.__buffer_dc[self.n-1] = data_dc
1133 1135
1134 1136 self.__profIndex = self.n
1135 1137 return
1136 1138
1137 1139
1138 1140 def pushData(self):
1139 1141 """
1140 1142 Return the sum of the last profiles and the profiles used in the sum.
1141 1143
1142 1144 Affected:
1143 1145
1144 1146 self.__profileIndex
1145 1147
1146 1148 """
1147 1149 data_spc = None
1148 1150 data_cspc = None
1149 1151 data_dc = None
1150 1152
1151 1153 if not self.__withOverapping:
1152 1154 data_spc = self.__buffer_spc
1153 1155 data_cspc = self.__buffer_cspc
1154 1156 data_dc = self.__buffer_dc
1155 1157
1156 1158 n = self.__profIndex
1157 1159
1158 1160 self.__buffer_spc = 0
1159 1161 self.__buffer_cspc = 0
1160 1162 self.__buffer_dc = 0
1161 1163 self.__profIndex = 0
1162 1164
1163 1165 return data_spc, data_cspc, data_dc, n
1164 1166
1165 1167 #Integration with Overlapping
1166 1168 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1167 1169
1168 1170 if self.__buffer_cspc != None:
1169 1171 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1170 1172
1171 1173 if self.__buffer_dc != None:
1172 1174 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1173 1175
1174 1176 n = self.__profIndex
1175 1177
1176 1178 return data_spc, data_cspc, data_dc, n
1177 1179
1178 1180 def byProfiles(self, *args):
1179 1181
1180 1182 self.__dataReady = False
1181 1183 avgdata_spc = None
1182 1184 avgdata_cspc = None
1183 1185 avgdata_dc = None
1184 1186 n = None
1185 1187
1186 1188 self.putData(*args)
1187 1189
1188 1190 if self.__profIndex == self.n:
1189 1191
1190 1192 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1191 1193 self.__dataReady = True
1192 1194
1193 1195 return avgdata_spc, avgdata_cspc, avgdata_dc
1194 1196
1195 1197 def byTime(self, datatime, *args):
1196 1198
1197 1199 self.__dataReady = False
1198 1200 avgdata_spc = None
1199 1201 avgdata_cspc = None
1200 1202 avgdata_dc = None
1201 1203 n = None
1202 1204
1203 1205 self.putData(*args)
1204 1206
1205 1207 if (datatime - self.__initime) >= self.__integrationtime:
1206 1208 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1207 1209 self.n = n
1208 1210 self.__dataReady = True
1209 1211
1210 1212 return avgdata_spc, avgdata_cspc, avgdata_dc
1211 1213
1212 1214 def integrate(self, datatime, *args):
1213 1215
1214 1216 if self.__initime == None:
1215 1217 self.__initime = datatime
1216 1218
1217 1219 if self.__byTime:
1218 1220 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1219 1221 else:
1220 1222 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1221 1223
1222 1224 self.__lastdatatime = datatime
1223 1225
1224 1226 if avgdata_spc == None:
1225 1227 return None, None, None, None
1226 1228
1227 1229 avgdatatime = self.__initime
1228 1230 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1229 1231
1230 1232 deltatime = datatime -self.__lastdatatime
1231 1233
1232 1234 if not self.__withOverapping:
1233 1235 self.__initime = datatime
1234 1236 else:
1235 1237 self.__initime += deltatime
1236 1238
1237 1239 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1238 1240
1239 1241 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1240 1242
1241 1243 if not self.__isConfig:
1242 1244 self.setup(n, timeInterval, overlapping)
1243 1245 self.__isConfig = True
1244 1246
1245 1247 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1246 1248 dataOut.data_spc,
1247 1249 dataOut.data_cspc,
1248 1250 dataOut.data_dc)
1249 1251
1250 1252 # dataOut.timeInterval *= n
1251 1253 dataOut.flagNoData = True
1252 1254
1253 1255 if self.__dataReady:
1254 1256
1255 1257 dataOut.data_spc = avgdata_spc
1256 1258 dataOut.data_cspc = avgdata_cspc
1257 1259 dataOut.data_dc = avgdata_dc
1258 1260
1259 1261 dataOut.nIncohInt *= self.n
1260 1262 dataOut.utctime = avgdatatime
1261 1263 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1262 1264 dataOut.timeInterval = self.__timeInterval*self.n
1263 1265 dataOut.flagNoData = False
1264 1266
1265 1267 class ProfileSelector(Operation):
1266 1268
1267 1269 profileIndex = None
1268 1270 # Tamanho total de los perfiles
1269 1271 nProfiles = None
1270 1272
1271 1273 def __init__(self):
1272 1274
1273 1275 self.profileIndex = 0
1274 1276
1275 1277 def incIndex(self):
1276 1278 self.profileIndex += 1
1277 1279
1278 1280 if self.profileIndex >= self.nProfiles:
1279 1281 self.profileIndex = 0
1280 1282
1281 1283 def isProfileInRange(self, minIndex, maxIndex):
1282 1284
1283 1285 if self.profileIndex < minIndex:
1284 1286 return False
1285 1287
1286 1288 if self.profileIndex > maxIndex:
1287 1289 return False
1288 1290
1289 1291 return True
1290 1292
1291 1293 def isProfileInList(self, profileList):
1292 1294
1293 1295 if self.profileIndex not in profileList:
1294 1296 return False
1295 1297
1296 1298 return True
1297 1299
1298 1300 def run(self, dataOut, profileList=None, profileRangeList=None):
1299 1301
1300 1302 dataOut.flagNoData = True
1301 1303 self.nProfiles = dataOut.nProfiles
1302 1304
1303 1305 if profileList != None:
1304 1306 if self.isProfileInList(profileList):
1305 1307 dataOut.flagNoData = False
1306 1308
1307 1309 self.incIndex()
1308 1310 return 1
1309 1311
1310 1312
1311 1313 elif profileRangeList != None:
1312 1314 minIndex = profileRangeList[0]
1313 1315 maxIndex = profileRangeList[1]
1314 1316 if self.isProfileInRange(minIndex, maxIndex):
1315 1317 dataOut.flagNoData = False
1316 1318
1317 1319 self.incIndex()
1318 1320 return 1
1319 1321
1320 1322 else:
1321 1323 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1322 1324
1323 1325 return 0
1324 1326
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