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