##// END OF EJS Templates
schain
RSS
modifacion en el Decoder
Daniel Valdez -
r289:8fb6ef0e57c7
parent child
Show More
Show file before | Show file after | Hide comments
@@ -1,1293 +1,1292
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
642 if not self.__isConfig:
643 if code == None:
641 if code == None:
644 642 code = dataOut.code
645 else:
646 code = numpy.array(code).reshape(nCode,nBaud)
647 dataOut.code = code
648 dataOut.nCode = nCode
649 dataOut.nBaud = nBaud
643 else:
644 code = numpy.array(code).reshape(nCode,nBaud)
645 dataOut.code = code
646 dataOut.nCode = nCode
647 dataOut.nBaud = nBaud
650 648
651 649 if code == None:
652 650 return 1
653 651
652 if not self.__isConfig:
654 653 self.setup(code)
655 654 self.__isConfig = True
656 655
657 656 if mode == 0:
658 657 ndatadec, datadec = self.convolutionInFreq(dataOut.data)
659 658
660 659 if mode == 1:
661 660 print "This function is not implemented"
662 661 # ndatadec, datadec = self.convolutionInTime(dataOut.data)
663 662
664 663 dataOut.data = datadec
665 664
666 665 dataOut.heightList = dataOut.heightList[0:ndatadec]
667 666
668 667 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
669 668
670 669 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
671 670
672 671
673 672 class SpectraProc(ProcessingUnit):
674 673
675 674 def __init__(self):
676 675
677 676 self.objectDict = {}
678 677 self.buffer = None
679 678 self.firstdatatime = None
680 679 self.profIndex = 0
681 680 self.dataOut = Spectra()
682 681
683 682 def __updateObjFromInput(self):
684 683
685 684 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
686 685 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
687 686 self.dataOut.channelList = self.dataIn.channelList
688 687 self.dataOut.heightList = self.dataIn.heightList
689 688 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
690 689 # self.dataOut.nHeights = self.dataIn.nHeights
691 690 # self.dataOut.nChannels = self.dataIn.nChannels
692 691 self.dataOut.nBaud = self.dataIn.nBaud
693 692 self.dataOut.nCode = self.dataIn.nCode
694 693 self.dataOut.code = self.dataIn.code
695 694 self.dataOut.nProfiles = self.dataOut.nFFTPoints
696 695 # self.dataOut.channelIndexList = self.dataIn.channelIndexList
697 696 self.dataOut.flagTimeBlock = self.dataIn.flagTimeBlock
698 697 self.dataOut.utctime = self.firstdatatime
699 698 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
700 699 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
701 700 self.dataOut.flagShiftFFT = self.dataIn.flagShiftFFT
702 701 self.dataOut.nCohInt = self.dataIn.nCohInt
703 702 self.dataOut.nIncohInt = 1
704 703 self.dataOut.ippSeconds = self.dataIn.ippSeconds
705 704 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
706 705
707 706 self.dataOut.timeInterval = self.dataIn.timeInterval*self.dataOut.nFFTPoints*self.dataOut.nIncohInt
708 707
709 708 def __getFft(self):
710 709 """
711 710 Convierte valores de Voltaje a Spectra
712 711
713 712 Affected:
714 713 self.dataOut.data_spc
715 714 self.dataOut.data_cspc
716 715 self.dataOut.data_dc
717 716 self.dataOut.heightList
718 717 self.profIndex
719 718 self.buffer
720 719 self.dataOut.flagNoData
721 720 """
722 721 fft_volt = numpy.fft.fft(self.buffer,axis=1)
723 722 fft_volt = fft_volt.astype(numpy.dtype('complex'))
724 723 dc = fft_volt[:,0,:]
725 724
726 725 #calculo de self-spectra
727 726 fft_volt = numpy.fft.fftshift(fft_volt,axes=(1,))
728 727 spc = fft_volt * numpy.conjugate(fft_volt)
729 728 spc = spc.real
730 729
731 730 blocksize = 0
732 731 blocksize += dc.size
733 732 blocksize += spc.size
734 733
735 734 cspc = None
736 735 pairIndex = 0
737 736 if self.dataOut.pairsList != None:
738 737 #calculo de cross-spectra
739 738 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
740 739 for pair in self.dataOut.pairsList:
741 740 cspc[pairIndex,:,:] = fft_volt[pair[0],:,:] * numpy.conjugate(fft_volt[pair[1],:,:])
742 741 pairIndex += 1
743 742 blocksize += cspc.size
744 743
745 744 self.dataOut.data_spc = spc
746 745 self.dataOut.data_cspc = cspc
747 746 self.dataOut.data_dc = dc
748 747 self.dataOut.blockSize = blocksize
749 748
750 749 def init(self, nFFTPoints=None, pairsList=None):
751 750
752 751 self.dataOut.flagNoData = True
753 752
754 753 if self.dataIn.type == "Spectra":
755 754 self.dataOut.copy(self.dataIn)
756 755 return
757 756
758 757 if self.dataIn.type == "Voltage":
759 758
760 759 if nFFTPoints == None:
761 760 raise ValueError, "This SpectraProc.init() need nFFTPoints input variable"
762 761
763 762 if pairsList == None:
764 763 nPairs = 0
765 764 else:
766 765 nPairs = len(pairsList)
767 766
768 767 self.dataOut.nFFTPoints = nFFTPoints
769 768 self.dataOut.pairsList = pairsList
770 769 self.dataOut.nPairs = nPairs
771 770
772 771 if self.buffer == None:
773 772 self.buffer = numpy.zeros((self.dataIn.nChannels,
774 773 self.dataOut.nFFTPoints,
775 774 self.dataIn.nHeights),
776 775 dtype='complex')
777 776
778 777
779 778 self.buffer[:,self.profIndex,:] = self.dataIn.data.copy()
780 779 self.profIndex += 1
781 780
782 781 if self.firstdatatime == None:
783 782 self.firstdatatime = self.dataIn.utctime
784 783
785 784 if self.profIndex == self.dataOut.nFFTPoints:
786 785 self.__updateObjFromInput()
787 786 self.__getFft()
788 787
789 788 self.dataOut.flagNoData = False
790 789
791 790 self.buffer = None
792 791 self.firstdatatime = None
793 792 self.profIndex = 0
794 793
795 794 return
796 795
797 796 raise ValuError, "The type object %s is not valid"%(self.dataIn.type)
798 797
799 798 def selectChannels(self, channelList):
800 799
801 800 channelIndexList = []
802 801
803 802 for channel in channelList:
804 803 index = self.dataOut.channelList.index(channel)
805 804 channelIndexList.append(index)
806 805
807 806 self.selectChannelsByIndex(channelIndexList)
808 807
809 808 def selectChannelsByIndex(self, channelIndexList):
810 809 """
811 810 Selecciona un bloque de datos en base a canales segun el channelIndexList
812 811
813 812 Input:
814 813 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
815 814
816 815 Affected:
817 816 self.dataOut.data_spc
818 817 self.dataOut.channelIndexList
819 818 self.dataOut.nChannels
820 819
821 820 Return:
822 821 None
823 822 """
824 823
825 824 for channelIndex in channelIndexList:
826 825 if channelIndex not in self.dataOut.channelIndexList:
827 826 print channelIndexList
828 827 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
829 828
830 829 nChannels = len(channelIndexList)
831 830
832 831 data_spc = self.dataOut.data_spc[channelIndexList,:]
833 832
834 833 self.dataOut.data_spc = data_spc
835 834 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
836 835 # self.dataOut.nChannels = nChannels
837 836
838 837 return 1
839 838
840 839 def selectHeights(self, minHei, maxHei):
841 840 """
842 841 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
843 842 minHei <= height <= maxHei
844 843
845 844 Input:
846 845 minHei : valor minimo de altura a considerar
847 846 maxHei : valor maximo de altura a considerar
848 847
849 848 Affected:
850 849 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
851 850
852 851 Return:
853 852 1 si el metodo se ejecuto con exito caso contrario devuelve 0
854 853 """
855 854 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
856 855 raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
857 856
858 857 if (maxHei > self.dataOut.heightList[-1]):
859 858 maxHei = self.dataOut.heightList[-1]
860 859 # raise ValueError, "some value in (%d,%d) is not valid" % (minHei, maxHei)
861 860
862 861 minIndex = 0
863 862 maxIndex = 0
864 863 heights = self.dataOut.heightList
865 864
866 865 inda = numpy.where(heights >= minHei)
867 866 indb = numpy.where(heights <= maxHei)
868 867
869 868 try:
870 869 minIndex = inda[0][0]
871 870 except:
872 871 minIndex = 0
873 872
874 873 try:
875 874 maxIndex = indb[0][-1]
876 875 except:
877 876 maxIndex = len(heights)
878 877
879 878 self.selectHeightsByIndex(minIndex, maxIndex)
880 879
881 880 return 1
882 881
883 882
884 883 def selectHeightsByIndex(self, minIndex, maxIndex):
885 884 """
886 885 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
887 886 minIndex <= index <= maxIndex
888 887
889 888 Input:
890 889 minIndex : valor de indice minimo de altura a considerar
891 890 maxIndex : valor de indice maximo de altura a considerar
892 891
893 892 Affected:
894 893 self.dataOut.data_spc
895 894 self.dataOut.data_cspc
896 895 self.dataOut.data_dc
897 896 self.dataOut.heightList
898 897
899 898 Return:
900 899 1 si el metodo se ejecuto con exito caso contrario devuelve 0
901 900 """
902 901
903 902 if (minIndex < 0) or (minIndex > maxIndex):
904 903 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
905 904
906 905 if (maxIndex >= self.dataOut.nHeights):
907 906 maxIndex = self.dataOut.nHeights-1
908 907 # raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
909 908
910 909 nHeights = maxIndex - minIndex + 1
911 910
912 911 #Spectra
913 912 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
914 913
915 914 data_cspc = None
916 915 if self.dataOut.data_cspc != None:
917 916 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
918 917
919 918 data_dc = None
920 919 if self.dataOut.data_dc != None:
921 920 data_dc = self.dataOut.data_dc[:,:,minIndex:maxIndex+1]
922 921
923 922 self.dataOut.data_spc = data_spc
924 923 self.dataOut.data_cspc = data_cspc
925 924 self.dataOut.data_dc = data_dc
926 925
927 926 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
928 927
929 928 return 1
930 929
931 930 def removeDC(self, mode = 1):
932 931
933 932 dc_index = 0
934 933 freq_index = numpy.array([-2,-1,1,2])
935 934 data_spc = self.dataOut.data_spc
936 935 data_cspc = self.dataOut.data_cspc
937 936 data_dc = self.dataOut.data_dc
938 937
939 938 if self.dataOut.flagShiftFFT:
940 939 dc_index += self.dataOut.nFFTPoints/2
941 940 freq_index += self.dataOut.nFFTPoints/2
942 941
943 942 if mode == 1:
944 943 data_spc[dc_index] = (data_spc[:,freq_index[1],:] + data_spc[:,freq_index[2],:])/2
945 944 if data_cspc != None:
946 945 data_cspc[dc_index] = (data_cspc[:,freq_index[1],:] + data_cspc[:,freq_index[2],:])/2
947 946 return 1
948 947
949 948 if mode == 2:
950 949 pass
951 950
952 951 if mode == 3:
953 952 pass
954 953
955 954 raise ValueError, "mode parameter has to be 1, 2 or 3"
956 955
957 956 def removeInterference(self):
958 957
959 958 pass
960 959
961 960
962 961 class IncohInt(Operation):
963 962
964 963
965 964 __profIndex = 0
966 965 __withOverapping = False
967 966
968 967 __byTime = False
969 968 __initime = None
970 969 __lastdatatime = None
971 970 __integrationtime = None
972 971
973 972 __buffer_spc = None
974 973 __buffer_cspc = None
975 974 __buffer_dc = None
976 975
977 976 __dataReady = False
978 977
979 978 __timeInterval = None
980 979
981 980 n = None
982 981
983 982
984 983
985 984 def __init__(self):
986 985
987 986 self.__isConfig = False
988 987
989 988 def setup(self, n=None, timeInterval=None, overlapping=False):
990 989 """
991 990 Set the parameters of the integration class.
992 991
993 992 Inputs:
994 993
995 994 n : Number of coherent integrations
996 995 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
997 996 overlapping :
998 997
999 998 """
1000 999
1001 1000 self.__initime = None
1002 1001 self.__lastdatatime = 0
1003 1002 self.__buffer_spc = None
1004 1003 self.__buffer_cspc = None
1005 1004 self.__buffer_dc = None
1006 1005 self.__dataReady = False
1007 1006
1008 1007
1009 1008 if n == None and timeInterval == None:
1010 1009 raise ValueError, "n or timeInterval should be specified ..."
1011 1010
1012 1011 if n != None:
1013 1012 self.n = n
1014 1013 self.__byTime = False
1015 1014 else:
1016 1015 self.__integrationtime = timeInterval * 60. #if (type(timeInterval)!=integer) -> change this line
1017 1016 self.n = 9999
1018 1017 self.__byTime = True
1019 1018
1020 1019 if overlapping:
1021 1020 self.__withOverapping = True
1022 1021 else:
1023 1022 self.__withOverapping = False
1024 1023 self.__buffer_spc = 0
1025 1024 self.__buffer_cspc = 0
1026 1025 self.__buffer_dc = 0
1027 1026
1028 1027 self.__profIndex = 0
1029 1028
1030 1029 def putData(self, data_spc, data_cspc, data_dc):
1031 1030
1032 1031 """
1033 1032 Add a profile to the __buffer_spc and increase in one the __profileIndex
1034 1033
1035 1034 """
1036 1035
1037 1036 if not self.__withOverapping:
1038 1037 self.__buffer_spc += data_spc
1039 1038
1040 1039 if data_cspc == None:
1041 1040 self.__buffer_cspc = None
1042 1041 else:
1043 1042 self.__buffer_cspc += data_cspc
1044 1043
1045 1044 if data_dc == None:
1046 1045 self.__buffer_dc = None
1047 1046 else:
1048 1047 self.__buffer_dc += data_dc
1049 1048
1050 1049 self.__profIndex += 1
1051 1050 return
1052 1051
1053 1052 #Overlapping data
1054 1053 nChannels, nFFTPoints, nHeis = data_spc.shape
1055 1054 data_spc = numpy.reshape(data_spc, (1, nChannels, nFFTPoints, nHeis))
1056 1055 if data_cspc != None:
1057 1056 data_cspc = numpy.reshape(data_cspc, (1, -1, nFFTPoints, nHeis))
1058 1057 if data_dc != None:
1059 1058 data_dc = numpy.reshape(data_dc, (1, -1, nHeis))
1060 1059
1061 1060 #If the buffer is empty then it takes the data value
1062 1061 if self.__buffer_spc == None:
1063 1062 self.__buffer_spc = data_spc
1064 1063
1065 1064 if data_cspc == None:
1066 1065 self.__buffer_cspc = None
1067 1066 else:
1068 1067 self.__buffer_cspc += data_cspc
1069 1068
1070 1069 if data_dc == None:
1071 1070 self.__buffer_dc = None
1072 1071 else:
1073 1072 self.__buffer_dc += data_dc
1074 1073
1075 1074 self.__profIndex += 1
1076 1075 return
1077 1076
1078 1077 #If the buffer length is lower than n then stakcing the data value
1079 1078 if self.__profIndex < self.n:
1080 1079 self.__buffer_spc = numpy.vstack((self.__buffer_spc, data_spc))
1081 1080
1082 1081 if data_cspc != None:
1083 1082 self.__buffer_cspc = numpy.vstack((self.__buffer_cspc, data_cspc))
1084 1083
1085 1084 if data_dc != None:
1086 1085 self.__buffer_dc = numpy.vstack((self.__buffer_dc, data_dc))
1087 1086
1088 1087 self.__profIndex += 1
1089 1088 return
1090 1089
1091 1090 #If the buffer length is equal to n then replacing the last buffer value with the data value
1092 1091 self.__buffer_spc = numpy.roll(self.__buffer_spc, -1, axis=0)
1093 1092 self.__buffer_spc[self.n-1] = data_spc
1094 1093
1095 1094 if data_cspc != None:
1096 1095 self.__buffer_cspc = numpy.roll(self.__buffer_cspc, -1, axis=0)
1097 1096 self.__buffer_cspc[self.n-1] = data_cspc
1098 1097
1099 1098 if data_dc != None:
1100 1099 self.__buffer_dc = numpy.roll(self.__buffer_dc, -1, axis=0)
1101 1100 self.__buffer_dc[self.n-1] = data_dc
1102 1101
1103 1102 self.__profIndex = self.n
1104 1103 return
1105 1104
1106 1105
1107 1106 def pushData(self):
1108 1107 """
1109 1108 Return the sum of the last profiles and the profiles used in the sum.
1110 1109
1111 1110 Affected:
1112 1111
1113 1112 self.__profileIndex
1114 1113
1115 1114 """
1116 1115 data_spc = None
1117 1116 data_cspc = None
1118 1117 data_dc = None
1119 1118
1120 1119 if not self.__withOverapping:
1121 1120 data_spc = self.__buffer_spc
1122 1121 data_cspc = self.__buffer_cspc
1123 1122 data_dc = self.__buffer_dc
1124 1123
1125 1124 n = self.__profIndex
1126 1125
1127 1126 self.__buffer_spc = 0
1128 1127 self.__buffer_cspc = 0
1129 1128 self.__buffer_dc = 0
1130 1129 self.__profIndex = 0
1131 1130
1132 1131 return data_spc, data_cspc, data_dc, n
1133 1132
1134 1133 #Integration with Overlapping
1135 1134 data_spc = numpy.sum(self.__buffer_spc, axis=0)
1136 1135
1137 1136 if self.__buffer_cspc != None:
1138 1137 data_cspc = numpy.sum(self.__buffer_cspc, axis=0)
1139 1138
1140 1139 if self.__buffer_dc != None:
1141 1140 data_dc = numpy.sum(self.__buffer_dc, axis=0)
1142 1141
1143 1142 n = self.__profIndex
1144 1143
1145 1144 return data_spc, data_cspc, data_dc, n
1146 1145
1147 1146 def byProfiles(self, *args):
1148 1147
1149 1148 self.__dataReady = False
1150 1149 avgdata_spc = None
1151 1150 avgdata_cspc = None
1152 1151 avgdata_dc = None
1153 1152 n = None
1154 1153
1155 1154 self.putData(*args)
1156 1155
1157 1156 if self.__profIndex == self.n:
1158 1157
1159 1158 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1160 1159 self.__dataReady = True
1161 1160
1162 1161 return avgdata_spc, avgdata_cspc, avgdata_dc
1163 1162
1164 1163 def byTime(self, datatime, *args):
1165 1164
1166 1165 self.__dataReady = False
1167 1166 avgdata_spc = None
1168 1167 avgdata_cspc = None
1169 1168 avgdata_dc = None
1170 1169 n = None
1171 1170
1172 1171 self.putData(*args)
1173 1172
1174 1173 if (datatime - self.__initime) >= self.__integrationtime:
1175 1174 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1176 1175 self.n = n
1177 1176 self.__dataReady = True
1178 1177
1179 1178 return avgdata_spc, avgdata_cspc, avgdata_dc
1180 1179
1181 1180 def integrate(self, datatime, *args):
1182 1181
1183 1182 if self.__initime == None:
1184 1183 self.__initime = datatime
1185 1184
1186 1185 if self.__byTime:
1187 1186 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
1188 1187 else:
1189 1188 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
1190 1189
1191 1190 self.__lastdatatime = datatime
1192 1191
1193 1192 if avgdata_spc == None:
1194 1193 return None, None, None, None
1195 1194
1196 1195 avgdatatime = self.__initime
1197 1196 self.__timeInterval = (self.__lastdatatime - self.__initime)/(self.n - 1)
1198 1197
1199 1198 deltatime = datatime -self.__lastdatatime
1200 1199
1201 1200 if not self.__withOverapping:
1202 1201 self.__initime = datatime
1203 1202 else:
1204 1203 self.__initime += deltatime
1205 1204
1206 1205 return avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc
1207 1206
1208 1207 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
1209 1208
1210 1209 if not self.__isConfig:
1211 1210 self.setup(n, timeInterval, overlapping)
1212 1211 self.__isConfig = True
1213 1212
1214 1213 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
1215 1214 dataOut.data_spc,
1216 1215 dataOut.data_cspc,
1217 1216 dataOut.data_dc)
1218 1217
1219 1218 # dataOut.timeInterval *= n
1220 1219 dataOut.flagNoData = True
1221 1220
1222 1221 if self.__dataReady:
1223 1222
1224 1223 dataOut.data_spc = avgdata_spc
1225 1224 dataOut.data_cspc = avgdata_cspc
1226 1225 dataOut.data_dc = avgdata_dc
1227 1226
1228 1227 dataOut.nIncohInt *= self.n
1229 1228 dataOut.utctime = avgdatatime
1230 1229 #dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt * dataOut.nIncohInt * dataOut.nFFTPoints
1231 1230 dataOut.timeInterval = self.__timeInterval*self.n
1232 1231 dataOut.flagNoData = False
1233 1232
1234 1233 class ProfileSelector(Operation):
1235 1234
1236 1235 profileIndex = None
1237 1236 # Tamanho total de los perfiles
1238 1237 nProfiles = None
1239 1238
1240 1239 def __init__(self):
1241 1240
1242 1241 self.profileIndex = 0
1243 1242
1244 1243 def incIndex(self):
1245 1244 self.profileIndex += 1
1246 1245
1247 1246 if self.profileIndex >= self.nProfiles:
1248 1247 self.profileIndex = 0
1249 1248
1250 1249 def isProfileInRange(self, minIndex, maxIndex):
1251 1250
1252 1251 if self.profileIndex < minIndex:
1253 1252 return False
1254 1253
1255 1254 if self.profileIndex > maxIndex:
1256 1255 return False
1257 1256
1258 1257 return True
1259 1258
1260 1259 def isProfileInList(self, profileList):
1261 1260
1262 1261 if self.profileIndex not in profileList:
1263 1262 return False
1264 1263
1265 1264 return True
1266 1265
1267 1266 def run(self, dataOut, profileList=None, profileRangeList=None):
1268 1267
1269 1268 dataOut.flagNoData = True
1270 1269 self.nProfiles = dataOut.nProfiles
1271 1270
1272 1271 if profileList != None:
1273 1272 if self.isProfileInList(profileList):
1274 1273 dataOut.flagNoData = False
1275 1274
1276 1275 self.incIndex()
1277 1276 return 1
1278 1277
1279 1278
1280 1279 elif profileRangeList != None:
1281 1280 minIndex = profileRangeList[0]
1282 1281 maxIndex = profileRangeList[1]
1283 1282 if self.isProfileInRange(minIndex, maxIndex):
1284 1283 dataOut.flagNoData = False
1285 1284
1286 1285 self.incIndex()
1287 1286 return 1
1288 1287
1289 1288 else:
1290 1289 raise ValueError, "ProfileSelector needs profileList or profileRangeList"
1291 1290
1292 1291 return 0
1293 1292
General Comments 0
You need to be logged in to leave comments. Login now