##// END OF EJS Templates
schain-jc
RSS
Feature added to jroproc_voltage.ProfileSelector(): rangeList replaces to profileRangeList. This parameter will be eliminated in future versions.
Miguel Valdez -
r756:ca0956eb2236
parent child
Show More
Show file before | Show file after | Hide comments
@@ -1,1157 +1,1161
1 1 import sys
2 2 import numpy
3 3
4 4 from jroproc_base import ProcessingUnit, Operation
5 5 from schainpy.model.data.jrodata import Voltage
6 6
7 7 class VoltageProc(ProcessingUnit):
8 8
9 9
10 10 def __init__(self):
11 11
12 12 ProcessingUnit.__init__(self)
13 13
14 14 # self.objectDict = {}
15 15 self.dataOut = Voltage()
16 16 self.flip = 1
17 17
18 18 def run(self):
19 19 if self.dataIn.type == 'AMISR':
20 20 self.__updateObjFromAmisrInput()
21 21
22 22 if self.dataIn.type == 'Voltage':
23 23 self.dataOut.copy(self.dataIn)
24 24
25 25 # self.dataOut.copy(self.dataIn)
26 26
27 27 def __updateObjFromAmisrInput(self):
28 28
29 29 self.dataOut.timeZone = self.dataIn.timeZone
30 30 self.dataOut.dstFlag = self.dataIn.dstFlag
31 31 self.dataOut.errorCount = self.dataIn.errorCount
32 32 self.dataOut.useLocalTime = self.dataIn.useLocalTime
33 33
34 34 self.dataOut.flagNoData = self.dataIn.flagNoData
35 35 self.dataOut.data = self.dataIn.data
36 36 self.dataOut.utctime = self.dataIn.utctime
37 37 self.dataOut.channelList = self.dataIn.channelList
38 38 # self.dataOut.timeInterval = self.dataIn.timeInterval
39 39 self.dataOut.heightList = self.dataIn.heightList
40 40 self.dataOut.nProfiles = self.dataIn.nProfiles
41 41
42 42 self.dataOut.nCohInt = self.dataIn.nCohInt
43 43 self.dataOut.ippSeconds = self.dataIn.ippSeconds
44 44 self.dataOut.frequency = self.dataIn.frequency
45 45
46 46 self.dataOut.azimuth = self.dataIn.azimuth
47 47 self.dataOut.zenith = self.dataIn.zenith
48 48
49 49 self.dataOut.beam.codeList = self.dataIn.beam.codeList
50 50 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
51 51 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
52 52 #
53 53 # pass#
54 54 #
55 55 # def init(self):
56 56 #
57 57 #
58 58 # if self.dataIn.type == 'AMISR':
59 59 # self.__updateObjFromAmisrInput()
60 60 #
61 61 # if self.dataIn.type == 'Voltage':
62 62 # self.dataOut.copy(self.dataIn)
63 63 # # No necesita copiar en cada init() los atributos de dataIn
64 64 # # la copia deberia hacerse por cada nuevo bloque de datos
65 65
66 66 def selectChannels(self, channelList):
67 67
68 68 channelIndexList = []
69 69
70 70 for channel in channelList:
71 71 if channel not in self.dataOut.channelList:
72 72 raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
73 73
74 74 index = self.dataOut.channelList.index(channel)
75 75 channelIndexList.append(index)
76 76
77 77 self.selectChannelsByIndex(channelIndexList)
78 78
79 79 def selectChannelsByIndex(self, channelIndexList):
80 80 """
81 81 Selecciona un bloque de datos en base a canales segun el channelIndexList
82 82
83 83 Input:
84 84 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
85 85
86 86 Affected:
87 87 self.dataOut.data
88 88 self.dataOut.channelIndexList
89 89 self.dataOut.nChannels
90 90 self.dataOut.m_ProcessingHeader.totalSpectra
91 91 self.dataOut.systemHeaderObj.numChannels
92 92 self.dataOut.m_ProcessingHeader.blockSize
93 93
94 94 Return:
95 95 None
96 96 """
97 97
98 98 for channelIndex in channelIndexList:
99 99 if channelIndex not in self.dataOut.channelIndexList:
100 100 print channelIndexList
101 101 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
102 102
103 103 if self.dataOut.flagDataAsBlock:
104 104 """
105 105 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
106 106 """
107 107 data = self.dataOut.data[channelIndexList,:,:]
108 108 else:
109 109 data = self.dataOut.data[channelIndexList,:]
110 110
111 111 self.dataOut.data = data
112 112 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
113 113 # self.dataOut.nChannels = nChannels
114 114
115 115 return 1
116 116
117 117 def selectHeights(self, minHei=None, maxHei=None):
118 118 """
119 119 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
120 120 minHei <= height <= maxHei
121 121
122 122 Input:
123 123 minHei : valor minimo de altura a considerar
124 124 maxHei : valor maximo de altura a considerar
125 125
126 126 Affected:
127 127 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
128 128
129 129 Return:
130 130 1 si el metodo se ejecuto con exito caso contrario devuelve 0
131 131 """
132 132
133 133 if minHei == None:
134 134 minHei = self.dataOut.heightList[0]
135 135
136 136 if maxHei == None:
137 137 maxHei = self.dataOut.heightList[-1]
138 138
139 139 if (minHei < self.dataOut.heightList[0]):
140 140 minHei = self.dataOut.heightList[0]
141 141
142 142 if (maxHei > self.dataOut.heightList[-1]):
143 143 maxHei = self.dataOut.heightList[-1]
144 144
145 145 minIndex = 0
146 146 maxIndex = 0
147 147 heights = self.dataOut.heightList
148 148
149 149 inda = numpy.where(heights >= minHei)
150 150 indb = numpy.where(heights <= maxHei)
151 151
152 152 try:
153 153 minIndex = inda[0][0]
154 154 except:
155 155 minIndex = 0
156 156
157 157 try:
158 158 maxIndex = indb[0][-1]
159 159 except:
160 160 maxIndex = len(heights)
161 161
162 162 self.selectHeightsByIndex(minIndex, maxIndex)
163 163
164 164 return 1
165 165
166 166
167 167 def selectHeightsByIndex(self, minIndex, maxIndex):
168 168 """
169 169 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
170 170 minIndex <= index <= maxIndex
171 171
172 172 Input:
173 173 minIndex : valor de indice minimo de altura a considerar
174 174 maxIndex : valor de indice maximo de altura a considerar
175 175
176 176 Affected:
177 177 self.dataOut.data
178 178 self.dataOut.heightList
179 179
180 180 Return:
181 181 1 si el metodo se ejecuto con exito caso contrario devuelve 0
182 182 """
183 183
184 184 if (minIndex < 0) or (minIndex > maxIndex):
185 185 raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
186 186
187 187 if (maxIndex >= self.dataOut.nHeights):
188 188 maxIndex = self.dataOut.nHeights
189 189
190 190 #voltage
191 191 if self.dataOut.flagDataAsBlock:
192 192 """
193 193 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
194 194 """
195 195 data = self.dataOut.data[:,:, minIndex:maxIndex]
196 196 else:
197 197 data = self.dataOut.data[:, minIndex:maxIndex]
198 198
199 199 # firstHeight = self.dataOut.heightList[minIndex]
200 200
201 201 self.dataOut.data = data
202 202 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
203 203
204 204 if self.dataOut.nHeights <= 1:
205 205 raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
206 206
207 207 return 1
208 208
209 209
210 210 def filterByHeights(self, window):
211 211
212 212 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
213 213
214 214 if window == None:
215 215 window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
216 216
217 217 newdelta = deltaHeight * window
218 218 r = self.dataOut.nHeights % window
219 219 newheights = (self.dataOut.nHeights-r)/window
220 220
221 221 if newheights <= 1:
222 222 raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
223 223
224 224 if self.dataOut.flagDataAsBlock:
225 225 """
226 226 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
227 227 """
228 228 buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
229 229 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
230 230 buffer = numpy.sum(buffer,3)
231 231
232 232 else:
233 233 buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
234 234 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
235 235 buffer = numpy.sum(buffer,2)
236 236
237 237 self.dataOut.data = buffer
238 238 self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
239 239 self.dataOut.windowOfFilter = window
240 240
241 241 def setH0(self, h0, deltaHeight = None):
242 242
243 243 if not deltaHeight:
244 244 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
245 245
246 246 nHeights = self.dataOut.nHeights
247 247
248 248 newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
249 249
250 250 self.dataOut.heightList = newHeiRange
251 251
252 252 def deFlip(self, channelList = []):
253 253
254 254 data = self.dataOut.data.copy()
255 255
256 256 if self.dataOut.flagDataAsBlock:
257 257 flip = self.flip
258 258 profileList = range(self.dataOut.nProfiles)
259 259
260 260 if not channelList:
261 261 for thisProfile in profileList:
262 262 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
263 263 flip *= -1.0
264 264 else:
265 265 for thisChannel in channelList:
266 266 if thisChannel not in self.dataOut.channelList:
267 267 continue
268 268
269 269 for thisProfile in profileList:
270 270 data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
271 271 flip *= -1.0
272 272
273 273 self.flip = flip
274 274
275 275 else:
276 276 if not channelList:
277 277 data[:,:] = data[:,:]*self.flip
278 278 else:
279 279 for thisChannel in channelList:
280 280 if thisChannel not in self.dataOut.channelList:
281 281 continue
282 282
283 283 data[thisChannel,:] = data[thisChannel,:]*self.flip
284 284
285 285 self.flip *= -1.
286 286
287 287 self.dataOut.data = data
288 288
289 289 def setRadarFrequency(self, frequency=None):
290 290
291 291 if frequency != None:
292 292 self.dataOut.frequency = frequency
293 293
294 294 return 1
295 295
296 296 class CohInt(Operation):
297 297
298 298 isConfig = False
299 299
300 300 __profIndex = 0
301 301 __withOverapping = False
302 302
303 303 __byTime = False
304 304 __initime = None
305 305 __lastdatatime = None
306 306 __integrationtime = None
307 307
308 308 __buffer = None
309 309
310 310 __dataReady = False
311 311
312 312 n = None
313 313
314 314
315 315 def __init__(self):
316 316
317 317 Operation.__init__(self)
318 318
319 319 # self.isConfig = False
320 320
321 321 def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False):
322 322 """
323 323 Set the parameters of the integration class.
324 324
325 325 Inputs:
326 326
327 327 n : Number of coherent integrations
328 328 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
329 329 overlapping :
330 330
331 331 """
332 332
333 333 self.__initime = None
334 334 self.__lastdatatime = 0
335 335 self.__buffer = None
336 336 self.__dataReady = False
337 337 self.byblock = byblock
338 338
339 339 if n == None and timeInterval == None:
340 340 raise ValueError, "n or timeInterval should be specified ..."
341 341
342 342 if n != None:
343 343 self.n = n
344 344 self.__byTime = False
345 345 else:
346 346 self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
347 347 self.n = 9999
348 348 self.__byTime = True
349 349
350 350 if overlapping:
351 351 self.__withOverapping = True
352 352 self.__buffer = None
353 353 else:
354 354 self.__withOverapping = False
355 355 self.__buffer = 0
356 356
357 357 self.__profIndex = 0
358 358
359 359 def putData(self, data):
360 360
361 361 """
362 362 Add a profile to the __buffer and increase in one the __profileIndex
363 363
364 364 """
365 365
366 366 if not self.__withOverapping:
367 367 self.__buffer += data.copy()
368 368 self.__profIndex += 1
369 369 return
370 370
371 371 #Overlapping data
372 372 nChannels, nHeis = data.shape
373 373 data = numpy.reshape(data, (1, nChannels, nHeis))
374 374
375 375 #If the buffer is empty then it takes the data value
376 376 if self.__buffer is None:
377 377 self.__buffer = data
378 378 self.__profIndex += 1
379 379 return
380 380
381 381 #If the buffer length is lower than n then stakcing the data value
382 382 if self.__profIndex < self.n:
383 383 self.__buffer = numpy.vstack((self.__buffer, data))
384 384 self.__profIndex += 1
385 385 return
386 386
387 387 #If the buffer length is equal to n then replacing the last buffer value with the data value
388 388 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
389 389 self.__buffer[self.n-1] = data
390 390 self.__profIndex = self.n
391 391 return
392 392
393 393
394 394 def pushData(self):
395 395 """
396 396 Return the sum of the last profiles and the profiles used in the sum.
397 397
398 398 Affected:
399 399
400 400 self.__profileIndex
401 401
402 402 """
403 403
404 404 if not self.__withOverapping:
405 405 data = self.__buffer
406 406 n = self.__profIndex
407 407
408 408 self.__buffer = 0
409 409 self.__profIndex = 0
410 410
411 411 return data, n
412 412
413 413 #Integration with Overlapping
414 414 data = numpy.sum(self.__buffer, axis=0)
415 415 n = self.__profIndex
416 416
417 417 return data, n
418 418
419 419 def byProfiles(self, data):
420 420
421 421 self.__dataReady = False
422 422 avgdata = None
423 423 # n = None
424 424
425 425 self.putData(data)
426 426
427 427 if self.__profIndex == self.n:
428 428
429 429 avgdata, n = self.pushData()
430 430 self.__dataReady = True
431 431
432 432 return avgdata
433 433
434 434 def byTime(self, data, datatime):
435 435
436 436 self.__dataReady = False
437 437 avgdata = None
438 438 n = None
439 439
440 440 self.putData(data)
441 441
442 442 if (datatime - self.__initime) >= self.__integrationtime:
443 443 avgdata, n = self.pushData()
444 444 self.n = n
445 445 self.__dataReady = True
446 446
447 447 return avgdata
448 448
449 449 def integrate(self, data, datatime=None):
450 450
451 451 if self.__initime == None:
452 452 self.__initime = datatime
453 453
454 454 if self.__byTime:
455 455 avgdata = self.byTime(data, datatime)
456 456 else:
457 457 avgdata = self.byProfiles(data)
458 458
459 459
460 460 self.__lastdatatime = datatime
461 461
462 462 if avgdata is None:
463 463 return None, None
464 464
465 465 avgdatatime = self.__initime
466 466
467 467 deltatime = datatime -self.__lastdatatime
468 468
469 469 if not self.__withOverapping:
470 470 self.__initime = datatime
471 471 else:
472 472 self.__initime += deltatime
473 473
474 474 return avgdata, avgdatatime
475 475
476 476 def integrateByBlock(self, dataOut):
477 477
478 478 times = int(dataOut.data.shape[1]/self.n)
479 479 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
480 480
481 481 id_min = 0
482 482 id_max = self.n
483 483
484 484 for i in range(times):
485 485 junk = dataOut.data[:,id_min:id_max,:]
486 486 avgdata[:,i,:] = junk.sum(axis=1)
487 487 id_min += self.n
488 488 id_max += self.n
489 489
490 490 timeInterval = dataOut.ippSeconds*self.n
491 491 avgdatatime = (times - 1) * timeInterval + dataOut.utctime
492 492 self.__dataReady = True
493 493 return avgdata, avgdatatime
494 494
495 495 def run(self, dataOut, **kwargs):
496 496
497 497 if not self.isConfig:
498 498 self.setup(**kwargs)
499 499 self.isConfig = True
500 500
501 501 if dataOut.flagDataAsBlock:
502 502 """
503 503 Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
504 504 """
505 505 avgdata, avgdatatime = self.integrateByBlock(dataOut)
506 506 dataOut.nProfiles /= self.n
507 507 else:
508 508 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
509 509
510 510 # dataOut.timeInterval *= n
511 511 dataOut.flagNoData = True
512 512
513 513 if self.__dataReady:
514 514 dataOut.data = avgdata
515 515 dataOut.nCohInt *= self.n
516 516 dataOut.utctime = avgdatatime
517 517 # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
518 518 dataOut.flagNoData = False
519 519
520 520 class Decoder(Operation):
521 521
522 522 isConfig = False
523 523 __profIndex = 0
524 524
525 525 code = None
526 526
527 527 nCode = None
528 528 nBaud = None
529 529
530 530
531 531 def __init__(self):
532 532
533 533 Operation.__init__(self)
534 534
535 535 self.times = None
536 536 self.osamp = None
537 537 # self.__setValues = False
538 538 self.isConfig = False
539 539
540 540 def setup(self, code, osamp, dataOut):
541 541
542 542 self.__profIndex = 0
543 543
544 544 self.code = code
545 545
546 546 self.nCode = len(code)
547 547 self.nBaud = len(code[0])
548 548
549 549 if (osamp != None) and (osamp >1):
550 550 self.osamp = osamp
551 551 self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
552 552 self.nBaud = self.nBaud*self.osamp
553 553
554 554 self.__nChannels = dataOut.nChannels
555 555 self.__nProfiles = dataOut.nProfiles
556 556 self.__nHeis = dataOut.nHeights
557 557
558 558 if self.__nHeis < self.nBaud:
559 559 raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
560 560
561 561 #Frequency
562 562 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
563 563
564 564 __codeBuffer[:,0:self.nBaud] = self.code
565 565
566 566 self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
567 567
568 568 if dataOut.flagDataAsBlock:
569 569
570 570 self.ndatadec = self.__nHeis #- self.nBaud + 1
571 571
572 572 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
573 573
574 574 else:
575 575
576 576 #Time
577 577 self.ndatadec = self.__nHeis #- self.nBaud + 1
578 578
579 579 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
580 580
581 581 def __convolutionInFreq(self, data):
582 582
583 583 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
584 584
585 585 fft_data = numpy.fft.fft(data, axis=1)
586 586
587 587 conv = fft_data*fft_code
588 588
589 589 data = numpy.fft.ifft(conv,axis=1)
590 590
591 591 return data
592 592
593 593 def __convolutionInFreqOpt(self, data):
594 594
595 595 raise NotImplementedError
596 596
597 597 def __convolutionInTime(self, data):
598 598
599 599 code = self.code[self.__profIndex]
600 600
601 601 for i in range(self.__nChannels):
602 602 self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
603 603
604 604 return self.datadecTime
605 605
606 606 def __convolutionByBlockInTime(self, data):
607 607
608 608 repetitions = self.__nProfiles / self.nCode
609 609
610 610 junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
611 611 junk = junk.flatten()
612 612 code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
613 613
614 614 for i in range(self.__nChannels):
615 615 for j in range(self.__nProfiles):
616 616 self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
617 617
618 618 return self.datadecTime
619 619
620 620 def __convolutionByBlockInFreq(self, data):
621 621
622 622 raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
623 623
624 624
625 625 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
626 626
627 627 fft_data = numpy.fft.fft(data, axis=2)
628 628
629 629 conv = fft_data*fft_code
630 630
631 631 data = numpy.fft.ifft(conv,axis=2)
632 632
633 633 return data
634 634
635 635 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
636 636
637 637 if dataOut.flagDecodeData:
638 638 print "This data is already decoded, recoding again ..."
639 639
640 640 if not self.isConfig:
641 641
642 642 if code is None:
643 643 if dataOut.code is None:
644 644 raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
645 645
646 646 code = dataOut.code
647 647 else:
648 648 code = numpy.array(code).reshape(nCode,nBaud)
649 649
650 650 self.setup(code, osamp, dataOut)
651 651
652 652 self.isConfig = True
653 653
654 654 if mode == 3:
655 655 sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
656 656
657 657 if times != None:
658 658 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
659 659
660 660 if self.code is None:
661 661 print "Fail decoding: Code is not defined."
662 662 return
663 663
664 664 datadec = None
665 665 if mode == 3:
666 666 mode = 0
667 667
668 668 if dataOut.flagDataAsBlock:
669 669 """
670 670 Decoding when data have been read as block,
671 671 """
672 672
673 673 if mode == 0:
674 674 datadec = self.__convolutionByBlockInTime(dataOut.data)
675 675 if mode == 1:
676 676 datadec = self.__convolutionByBlockInFreq(dataOut.data)
677 677 else:
678 678 """
679 679 Decoding when data have been read profile by profile
680 680 """
681 681 if mode == 0:
682 682 datadec = self.__convolutionInTime(dataOut.data)
683 683
684 684 if mode == 1:
685 685 datadec = self.__convolutionInFreq(dataOut.data)
686 686
687 687 if mode == 2:
688 688 datadec = self.__convolutionInFreqOpt(dataOut.data)
689 689
690 690 if datadec is None:
691 691 raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
692 692
693 693 dataOut.code = self.code
694 694 dataOut.nCode = self.nCode
695 695 dataOut.nBaud = self.nBaud
696 696
697 697 dataOut.data = datadec
698 698
699 699 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
700 700
701 701 dataOut.flagDecodeData = True #asumo q la data esta decodificada
702 702
703 703 if self.__profIndex == self.nCode-1:
704 704 self.__profIndex = 0
705 705 return 1
706 706
707 707 self.__profIndex += 1
708 708
709 709 return 1
710 710 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
711 711
712 712
713 713 class ProfileConcat(Operation):
714 714
715 715 isConfig = False
716 716 buffer = None
717 717
718 718 def __init__(self):
719 719
720 720 Operation.__init__(self)
721 721 self.profileIndex = 0
722 722
723 723 def reset(self):
724 724 self.buffer = numpy.zeros_like(self.buffer)
725 725 self.start_index = 0
726 726 self.times = 1
727 727
728 728 def setup(self, data, m, n=1):
729 729 self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
730 730 self.nHeights = data.nHeights
731 731 self.start_index = 0
732 732 self.times = 1
733 733
734 734 def concat(self, data):
735 735
736 736 self.buffer[:,self.start_index:self.profiles*self.times] = data.copy()
737 737 self.start_index = self.start_index + self.nHeights
738 738
739 739 def run(self, dataOut, m):
740 740
741 741 dataOut.flagNoData = True
742 742
743 743 if not self.isConfig:
744 744 self.setup(dataOut.data, m, 1)
745 745 self.isConfig = True
746 746
747 747 if dataOut.flagDataAsBlock:
748 748 raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
749 749
750 750 else:
751 751 self.concat(dataOut.data)
752 752 self.times += 1
753 753 if self.times > m:
754 754 dataOut.data = self.buffer
755 755 self.reset()
756 756 dataOut.flagNoData = False
757 757 # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
758 758 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
759 759 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
760 760 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
761 761 dataOut.ippSeconds *= m
762 762
763 763 class ProfileSelector(Operation):
764 764
765 765 profileIndex = None
766 766 # Tamanho total de los perfiles
767 767 nProfiles = None
768 768
769 769 def __init__(self):
770 770
771 771 Operation.__init__(self)
772 772 self.profileIndex = 0
773 773
774 774 def incProfileIndex(self):
775 775
776 776 self.profileIndex += 1
777 777
778 778 if self.profileIndex >= self.nProfiles:
779 779 self.profileIndex = 0
780 780
781 781 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
782 782
783 783 if profileIndex < minIndex:
784 784 return False
785 785
786 786 if profileIndex > maxIndex:
787 787 return False
788 788
789 789 return True
790 790
791 791 def isThisProfileInList(self, profileIndex, profileList):
792 792
793 793 if profileIndex not in profileList:
794 794 return False
795 795
796 796 return True
797 797
798 798 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
799 799
800 800 """
801 801 ProfileSelector:
802 802
803 803 Inputs:
804 804 profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
805 805
806 806 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
807 807
808 808 rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
809 809
810 810 """
811
811
812 if rangeList is not None:
813 if type(rangeList[0]) not in (tuple, list):
814 rangeList = [rangeList]
815
812 816 dataOut.flagNoData = True
813 817
814 818 if dataOut.flagDataAsBlock:
815 819 """
816 820 data dimension = [nChannels, nProfiles, nHeis]
817 821 """
818 822 if profileList != None:
819 823 dataOut.data = dataOut.data[:,profileList,:]
820 824
821 825 if profileRangeList != None:
822 826 minIndex = profileRangeList[0]
823 827 maxIndex = profileRangeList[1]
824 828 profileList = range(minIndex, maxIndex+1)
825 829
826 830 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
827 831
828 832 if rangeList != None:
829 833
830 834 profileList = []
831 835
832 836 for thisRange in rangeList:
833 837 minIndex = thisRange[0]
834 838 maxIndex = thisRange[1]
835 839
836 840 profileList.extend(range(minIndex, maxIndex+1))
837 841
838 842 dataOut.data = dataOut.data[:,profileList,:]
839 843
840 844 dataOut.nProfiles = len(profileList)
841 845 dataOut.profileIndex = dataOut.nProfiles - 1
842 846 dataOut.flagNoData = False
843 847
844 848 return True
845 849
846 850 """
847 851 data dimension = [nChannels, nHeis]
848 852 """
849 853
850 854 if profileList != None:
851 855
852 856 if self.isThisProfileInList(dataOut.profileIndex, profileList):
853 857
854 858 self.nProfiles = len(profileList)
855 859 dataOut.nProfiles = self.nProfiles
856 860 dataOut.profileIndex = self.profileIndex
857 861 dataOut.flagNoData = False
858 862
859 863 self.incProfileIndex()
860 864 return True
861 865
862 866 if profileRangeList != None:
863 867
864 868 minIndex = profileRangeList[0]
865 869 maxIndex = profileRangeList[1]
866 870
867 871 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
868 872
869 873 self.nProfiles = maxIndex - minIndex + 1
870 874 dataOut.nProfiles = self.nProfiles
871 875 dataOut.profileIndex = self.profileIndex
872 876 dataOut.flagNoData = False
873 877
874 878 self.incProfileIndex()
875 879 return True
876 880
877 881 if rangeList != None:
878 882
879 883 nProfiles = 0
880 884
881 885 for thisRange in rangeList:
882 886 minIndex = thisRange[0]
883 887 maxIndex = thisRange[1]
884 888
885 889 nProfiles += maxIndex - minIndex + 1
886 890
887 891 for thisRange in rangeList:
888 892
889 893 minIndex = thisRange[0]
890 894 maxIndex = thisRange[1]
891 895
892 896 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
893 897
894 898 self.nProfiles = nProfiles
895 899 dataOut.nProfiles = self.nProfiles
896 900 dataOut.profileIndex = self.profileIndex
897 901 dataOut.flagNoData = False
898 902
899 903 self.incProfileIndex()
900 904
901 905 break
902 906
903 907 return True
904 908
905 909
906 910 if beam != None: #beam is only for AMISR data
907 911 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
908 912 dataOut.flagNoData = False
909 913 dataOut.profileIndex = self.profileIndex
910 914
911 915 self.incProfileIndex()
912 916
913 917 return True
914 918
915 919 raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
916 920
917 921 return False
918 922
919 923
920 924
921 925 class Reshaper(Operation):
922 926
923 927 def __init__(self):
924 928
925 929 Operation.__init__(self)
926 930
927 931 self.__buffer = None
928 932 self.__nitems = 0
929 933
930 934 def __appendProfile(self, dataOut, nTxs):
931 935
932 936 if self.__buffer is None:
933 937 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
934 938 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
935 939
936 940 ini = dataOut.nHeights * self.__nitems
937 941 end = ini + dataOut.nHeights
938 942
939 943 self.__buffer[:, ini:end] = dataOut.data
940 944
941 945 self.__nitems += 1
942 946
943 947 return int(self.__nitems*nTxs)
944 948
945 949 def __getBuffer(self):
946 950
947 951 if self.__nitems == int(1./self.__nTxs):
948 952
949 953 self.__nitems = 0
950 954
951 955 return self.__buffer.copy()
952 956
953 957 return None
954 958
955 959 def __checkInputs(self, dataOut, shape, nTxs):
956 960
957 961 if shape is None and nTxs is None:
958 962 raise ValueError, "Reshaper: shape of factor should be defined"
959 963
960 964 if nTxs:
961 965 if nTxs < 0:
962 966 raise ValueError, "nTxs should be greater than 0"
963 967
964 968 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
965 969 raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
966 970
967 971 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
968 972
969 973 if len(shape) != 2 and len(shape) != 3:
970 974 raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
971 975
972 976 if len(shape) == 2:
973 977 shape_tuple = [dataOut.nChannels]
974 978 shape_tuple.extend(shape)
975 979 else:
976 980 shape_tuple = list(shape)
977 981
978 982 if not nTxs:
979 983 nTxs = int(shape_tuple[1]/dataOut.nProfiles)
980 984
981 985 return shape_tuple, nTxs
982 986
983 987 def run(self, dataOut, shape=None, nTxs=None):
984 988
985 989 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
986 990
987 991 dataOut.flagNoData = True
988 992 profileIndex = None
989 993
990 994 if dataOut.flagDataAsBlock:
991 995
992 996 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
993 997 dataOut.flagNoData = False
994 998
995 999 profileIndex = int(dataOut.nProfiles*nTxs) - 1
996 1000
997 1001 else:
998 1002
999 1003 if self.__nTxs < 1:
1000 1004
1001 1005 self.__appendProfile(dataOut, self.__nTxs)
1002 1006 new_data = self.__getBuffer()
1003 1007
1004 1008 if new_data is not None:
1005 1009 dataOut.data = new_data
1006 1010 dataOut.flagNoData = False
1007 1011
1008 1012 profileIndex = dataOut.profileIndex*nTxs
1009 1013
1010 1014 else:
1011 1015 raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
1012 1016
1013 1017 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1014 1018
1015 1019 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
1016 1020
1017 1021 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
1018 1022
1019 1023 dataOut.profileIndex = profileIndex
1020 1024
1021 1025 dataOut.ippSeconds /= self.__nTxs
1022 1026 #
1023 1027 # import collections
1024 1028 # from scipy.stats import mode
1025 1029 #
1026 1030 # class Synchronize(Operation):
1027 1031 #
1028 1032 # isConfig = False
1029 1033 # __profIndex = 0
1030 1034 #
1031 1035 # def __init__(self):
1032 1036 #
1033 1037 # Operation.__init__(self)
1034 1038 # # self.isConfig = False
1035 1039 # self.__powBuffer = None
1036 1040 # self.__startIndex = 0
1037 1041 # self.__pulseFound = False
1038 1042 #
1039 1043 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
1040 1044 #
1041 1045 # #Read data
1042 1046 #
1043 1047 # powerdB = dataOut.getPower(channel = channel)
1044 1048 # noisedB = dataOut.getNoise(channel = channel)[0]
1045 1049 #
1046 1050 # self.__powBuffer.extend(powerdB.flatten())
1047 1051 #
1048 1052 # dataArray = numpy.array(self.__powBuffer)
1049 1053 #
1050 1054 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
1051 1055 #
1052 1056 # maxValue = numpy.nanmax(filteredPower)
1053 1057 #
1054 1058 # if maxValue < noisedB + 10:
1055 1059 # #No se encuentra ningun pulso de transmision
1056 1060 # return None
1057 1061 #
1058 1062 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
1059 1063 #
1060 1064 # if len(maxValuesIndex) < 2:
1061 1065 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
1062 1066 # return None
1063 1067 #
1064 1068 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
1065 1069 #
1066 1070 # #Seleccionar solo valores con un espaciamiento de nSamples
1067 1071 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
1068 1072 #
1069 1073 # if len(pulseIndex) < 2:
1070 1074 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1071 1075 # return None
1072 1076 #
1073 1077 # spacing = pulseIndex[1:] - pulseIndex[:-1]
1074 1078 #
1075 1079 # #remover senales que se distancien menos de 10 unidades o muestras
1076 1080 # #(No deberian existir IPP menor a 10 unidades)
1077 1081 #
1078 1082 # realIndex = numpy.where(spacing > 10 )[0]
1079 1083 #
1080 1084 # if len(realIndex) < 2:
1081 1085 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1082 1086 # return None
1083 1087 #
1084 1088 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1085 1089 # realPulseIndex = pulseIndex[realIndex]
1086 1090 #
1087 1091 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1088 1092 #
1089 1093 # print "IPP = %d samples" %period
1090 1094 #
1091 1095 # self.__newNSamples = dataOut.nHeights #int(period)
1092 1096 # self.__startIndex = int(realPulseIndex[0])
1093 1097 #
1094 1098 # return 1
1095 1099 #
1096 1100 #
1097 1101 # def setup(self, nSamples, nChannels, buffer_size = 4):
1098 1102 #
1099 1103 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1100 1104 # maxlen = buffer_size*nSamples)
1101 1105 #
1102 1106 # bufferList = []
1103 1107 #
1104 1108 # for i in range(nChannels):
1105 1109 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1106 1110 # maxlen = buffer_size*nSamples)
1107 1111 #
1108 1112 # bufferList.append(bufferByChannel)
1109 1113 #
1110 1114 # self.__nSamples = nSamples
1111 1115 # self.__nChannels = nChannels
1112 1116 # self.__bufferList = bufferList
1113 1117 #
1114 1118 # def run(self, dataOut, channel = 0):
1115 1119 #
1116 1120 # if not self.isConfig:
1117 1121 # nSamples = dataOut.nHeights
1118 1122 # nChannels = dataOut.nChannels
1119 1123 # self.setup(nSamples, nChannels)
1120 1124 # self.isConfig = True
1121 1125 #
1122 1126 # #Append new data to internal buffer
1123 1127 # for thisChannel in range(self.__nChannels):
1124 1128 # bufferByChannel = self.__bufferList[thisChannel]
1125 1129 # bufferByChannel.extend(dataOut.data[thisChannel])
1126 1130 #
1127 1131 # if self.__pulseFound:
1128 1132 # self.__startIndex -= self.__nSamples
1129 1133 #
1130 1134 # #Finding Tx Pulse
1131 1135 # if not self.__pulseFound:
1132 1136 # indexFound = self.__findTxPulse(dataOut, channel)
1133 1137 #
1134 1138 # if indexFound == None:
1135 1139 # dataOut.flagNoData = True
1136 1140 # return
1137 1141 #
1138 1142 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1139 1143 # self.__pulseFound = True
1140 1144 # self.__startIndex = indexFound
1141 1145 #
1142 1146 # #If pulse was found ...
1143 1147 # for thisChannel in range(self.__nChannels):
1144 1148 # bufferByChannel = self.__bufferList[thisChannel]
1145 1149 # #print self.__startIndex
1146 1150 # x = numpy.array(bufferByChannel)
1147 1151 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1148 1152 #
1149 1153 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1150 1154 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1151 1155 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1152 1156 #
1153 1157 # dataOut.data = self.__arrayBuffer
1154 1158 #
1155 1159 # self.__startIndex += self.__newNSamples
1156 1160 #
1157 1161 # return No newline at end of file
General Comments 0
You need to be logged in to leave comments. Login now