##// END OF EJS Templates
schain-jc
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sumado de stride en stride. ej 96 -> 24x4 -> 1
José Chávez -
r1116:277434aacbd1
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@@ -1,1281 +1,1321
1 1 import sys
2 2 import numpy
3 3 from scipy import interpolate
4 4 from schainpy import cSchain
5 5 from jroproc_base import ProcessingUnit, Operation
6 6 from schainpy.model.data.jrodata import Voltage
7 7 from time import time
8 8
9 9 class VoltageProc(ProcessingUnit):
10 10
11 11
12 12 def __init__(self, **kwargs):
13 13
14 14 ProcessingUnit.__init__(self, **kwargs)
15 15
16 16 # self.objectDict = {}
17 17 self.dataOut = Voltage()
18 18 self.flip = 1
19 19
20 20 def run(self):
21 21 if self.dataIn.type == 'AMISR':
22 22 self.__updateObjFromAmisrInput()
23 23
24 24 if self.dataIn.type == 'Voltage':
25 25 self.dataOut.copy(self.dataIn)
26 26
27 27 # self.dataOut.copy(self.dataIn)
28 28
29 29 def __updateObjFromAmisrInput(self):
30 30
31 31 self.dataOut.timeZone = self.dataIn.timeZone
32 32 self.dataOut.dstFlag = self.dataIn.dstFlag
33 33 self.dataOut.errorCount = self.dataIn.errorCount
34 34 self.dataOut.useLocalTime = self.dataIn.useLocalTime
35 35
36 36 self.dataOut.flagNoData = self.dataIn.flagNoData
37 37 self.dataOut.data = self.dataIn.data
38 38 self.dataOut.utctime = self.dataIn.utctime
39 39 self.dataOut.channelList = self.dataIn.channelList
40 40 # self.dataOut.timeInterval = self.dataIn.timeInterval
41 41 self.dataOut.heightList = self.dataIn.heightList
42 42 self.dataOut.nProfiles = self.dataIn.nProfiles
43 43
44 44 self.dataOut.nCohInt = self.dataIn.nCohInt
45 45 self.dataOut.ippSeconds = self.dataIn.ippSeconds
46 46 self.dataOut.frequency = self.dataIn.frequency
47 47
48 48 self.dataOut.azimuth = self.dataIn.azimuth
49 49 self.dataOut.zenith = self.dataIn.zenith
50 50
51 51 self.dataOut.beam.codeList = self.dataIn.beam.codeList
52 52 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
53 53 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
54 54 #
55 55 # pass#
56 56 #
57 57 # def init(self):
58 58 #
59 59 #
60 60 # if self.dataIn.type == 'AMISR':
61 61 # self.__updateObjFromAmisrInput()
62 62 #
63 63 # if self.dataIn.type == 'Voltage':
64 64 # self.dataOut.copy(self.dataIn)
65 65 # # No necesita copiar en cada init() los atributos de dataIn
66 66 # # la copia deberia hacerse por cada nuevo bloque de datos
67 67
68 68 def selectChannels(self, channelList):
69 69
70 70 channelIndexList = []
71 71
72 72 for channel in channelList:
73 73 if channel not in self.dataOut.channelList:
74 74 raise ValueError, "Channel %d is not in %s" %(channel, str(self.dataOut.channelList))
75 75
76 76 index = self.dataOut.channelList.index(channel)
77 77 channelIndexList.append(index)
78 78
79 79 self.selectChannelsByIndex(channelIndexList)
80 80
81 81 def selectChannelsByIndex(self, channelIndexList):
82 82 """
83 83 Selecciona un bloque de datos en base a canales segun el channelIndexList
84 84
85 85 Input:
86 86 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
87 87
88 88 Affected:
89 89 self.dataOut.data
90 90 self.dataOut.channelIndexList
91 91 self.dataOut.nChannels
92 92 self.dataOut.m_ProcessingHeader.totalSpectra
93 93 self.dataOut.systemHeaderObj.numChannels
94 94 self.dataOut.m_ProcessingHeader.blockSize
95 95
96 96 Return:
97 97 None
98 98 """
99 99
100 100 for channelIndex in channelIndexList:
101 101 if channelIndex not in self.dataOut.channelIndexList:
102 102 print channelIndexList
103 103 raise ValueError, "The value %d in channelIndexList is not valid" %channelIndex
104 104
105 105 if self.dataOut.flagDataAsBlock:
106 106 """
107 107 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
108 108 """
109 109 data = self.dataOut.data[channelIndexList,:,:]
110 110 else:
111 111 data = self.dataOut.data[channelIndexList,:]
112 112
113 113 self.dataOut.data = data
114 114 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
115 115 # self.dataOut.nChannels = nChannels
116 116
117 117 return 1
118 118
119 119 def selectHeights(self, minHei=None, maxHei=None):
120 120 """
121 121 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
122 122 minHei <= height <= maxHei
123 123
124 124 Input:
125 125 minHei : valor minimo de altura a considerar
126 126 maxHei : valor maximo de altura a considerar
127 127
128 128 Affected:
129 129 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
130 130
131 131 Return:
132 132 1 si el metodo se ejecuto con exito caso contrario devuelve 0
133 133 """
134 134
135 135 if minHei == None:
136 136 minHei = self.dataOut.heightList[0]
137 137
138 138 if maxHei == None:
139 139 maxHei = self.dataOut.heightList[-1]
140 140
141 141 if (minHei < self.dataOut.heightList[0]):
142 142 minHei = self.dataOut.heightList[0]
143 143
144 144 if (maxHei > self.dataOut.heightList[-1]):
145 145 maxHei = self.dataOut.heightList[-1]
146 146
147 147 minIndex = 0
148 148 maxIndex = 0
149 149 heights = self.dataOut.heightList
150 150
151 151 inda = numpy.where(heights >= minHei)
152 152 indb = numpy.where(heights <= maxHei)
153 153
154 154 try:
155 155 minIndex = inda[0][0]
156 156 except:
157 157 minIndex = 0
158 158
159 159 try:
160 160 maxIndex = indb[0][-1]
161 161 except:
162 162 maxIndex = len(heights)
163 163
164 164 self.selectHeightsByIndex(minIndex, maxIndex)
165 165
166 166 return 1
167 167
168 168
169 169 def selectHeightsByIndex(self, minIndex, maxIndex):
170 170 """
171 171 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
172 172 minIndex <= index <= maxIndex
173 173
174 174 Input:
175 175 minIndex : valor de indice minimo de altura a considerar
176 176 maxIndex : valor de indice maximo de altura a considerar
177 177
178 178 Affected:
179 179 self.dataOut.data
180 180 self.dataOut.heightList
181 181
182 182 Return:
183 183 1 si el metodo se ejecuto con exito caso contrario devuelve 0
184 184 """
185 185
186 186 if (minIndex < 0) or (minIndex > maxIndex):
187 187 raise ValueError, "Height index range (%d,%d) is not valid" % (minIndex, maxIndex)
188 188
189 189 if (maxIndex >= self.dataOut.nHeights):
190 190 maxIndex = self.dataOut.nHeights
191 191
192 192 #voltage
193 193 if self.dataOut.flagDataAsBlock:
194 194 """
195 195 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
196 196 """
197 197 data = self.dataOut.data[:,:, minIndex:maxIndex]
198 198 else:
199 199 data = self.dataOut.data[:, minIndex:maxIndex]
200 200
201 201 # firstHeight = self.dataOut.heightList[minIndex]
202 202
203 203 self.dataOut.data = data
204 204 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
205 205
206 206 if self.dataOut.nHeights <= 1:
207 207 raise ValueError, "selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights)
208 208
209 209 return 1
210 210
211 211
212 212 def filterByHeights(self, window):
213 213
214 214 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
215 215
216 216 if window == None:
217 217 window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
218 218
219 219 newdelta = deltaHeight * window
220 220 r = self.dataOut.nHeights % window
221 221 newheights = (self.dataOut.nHeights-r)/window
222 222
223 223 if newheights <= 1:
224 224 raise ValueError, "filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window)
225 225
226 226 if self.dataOut.flagDataAsBlock:
227 227 """
228 228 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
229 229 """
230 230 buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
231 231 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
232 232 buffer = numpy.sum(buffer,3)
233 233
234 234 else:
235 235 buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
236 236 buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
237 237 buffer = numpy.sum(buffer,2)
238 238
239 239 self.dataOut.data = buffer
240 240 self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
241 241 self.dataOut.windowOfFilter = window
242 242
243 243 def setH0(self, h0, deltaHeight = None):
244 244
245 245 if not deltaHeight:
246 246 deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
247 247
248 248 nHeights = self.dataOut.nHeights
249 249
250 250 newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
251 251
252 252 self.dataOut.heightList = newHeiRange
253 253
254 254 def deFlip(self, channelList = []):
255 255
256 256 data = self.dataOut.data.copy()
257 257
258 258 if self.dataOut.flagDataAsBlock:
259 259 flip = self.flip
260 260 profileList = range(self.dataOut.nProfiles)
261 261
262 262 if not channelList:
263 263 for thisProfile in profileList:
264 264 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
265 265 flip *= -1.0
266 266 else:
267 267 for thisChannel in channelList:
268 268 if thisChannel not in self.dataOut.channelList:
269 269 continue
270 270
271 271 for thisProfile in profileList:
272 272 data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
273 273 flip *= -1.0
274 274
275 275 self.flip = flip
276 276
277 277 else:
278 278 if not channelList:
279 279 data[:,:] = data[:,:]*self.flip
280 280 else:
281 281 for thisChannel in channelList:
282 282 if thisChannel not in self.dataOut.channelList:
283 283 continue
284 284
285 285 data[thisChannel,:] = data[thisChannel,:]*self.flip
286 286
287 287 self.flip *= -1.
288 288
289 289 self.dataOut.data = data
290 290
291 291 def setRadarFrequency(self, frequency=None):
292 292
293 293 if frequency != None:
294 294 self.dataOut.frequency = frequency
295 295
296 296 return 1
297 297
298 298 def interpolateHeights(self, topLim, botLim):
299 299 #69 al 72 para julia
300 300 #82-84 para meteoros
301 301 if len(numpy.shape(self.dataOut.data))==2:
302 302 sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
303 303 sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
304 304 #self.dataOut.data[:,botLim:limSup+1] = sampInterp
305 305 self.dataOut.data[:,botLim:topLim+1] = sampInterp
306 306 else:
307 307 nHeights = self.dataOut.data.shape[2]
308 308 x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
309 309 y = self.dataOut.data[:,:,range(botLim)+range(topLim+1,nHeights)]
310 310 f = interpolate.interp1d(x, y, axis = 2)
311 311 xnew = numpy.arange(botLim,topLim+1)
312 312 ynew = f(xnew)
313 313
314 314 self.dataOut.data[:,:,botLim:topLim+1] = ynew
315 315
316 316 # import collections
317 317
318 318 class CohInt(Operation):
319 319
320 320 isConfig = False
321
322 321 __profIndex = 0
323 __withOverapping = False
324
325 322 __byTime = False
326 323 __initime = None
327 324 __lastdatatime = None
328 325 __integrationtime = None
329
330 326 __buffer = None
331
327 __bufferStride = []
332 328 __dataReady = False
333
329 __profIndexStride = 0
330 __dataToPutStride = False
334 331 n = None
335 332
336 333 def __init__(self, **kwargs):
337 334
338 335 Operation.__init__(self, **kwargs)
339 336
340 337 # self.isConfig = False
341 338
342 def setup(self, n=None, timeInterval=None, overlapping=False, byblock=False):
339 def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
343 340 """
344 341 Set the parameters of the integration class.
345 342
346 343 Inputs:
347 344
348 345 n : Number of coherent integrations
349 346 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
350 347 overlapping :
351 348 """
352 349
353 350 self.__initime = None
354 351 self.__lastdatatime = 0
355 352 self.__buffer = None
356 353 self.__dataReady = False
357 354 self.byblock = byblock
355 self.stride = stride
358 356
359 357 if n == None and timeInterval == None:
360 358 raise ValueError, "n or timeInterval should be specified ..."
361 359
362 360 if n != None:
363 361 self.n = n
364 362 self.__byTime = False
365 363 else:
366 364 self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
367 365 self.n = 9999
368 366 self.__byTime = True
369 367
370 368 if overlapping:
371 self.__withOverapping = True
369 self.__withOverlapping = True
372 370 self.__buffer = None
373 371 else:
374 self.__withOverapping = False
372 self.__withOverlapping = False
375 373 self.__buffer = 0
376 374
377 375 self.__profIndex = 0
378 376
379 377 def putData(self, data):
380 378
381 379 """
382 380 Add a profile to the __buffer and increase in one the __profileIndex
383 381
384 382 """
385 383
386 if not self.__withOverapping:
384 if not self.__withOverlapping:
387 385 self.__buffer += data.copy()
388 386 self.__profIndex += 1
389 387 return
390 388
391 389 #Overlapping data
392 390 nChannels, nHeis = data.shape
393 391 data = numpy.reshape(data, (1, nChannels, nHeis))
394 392
395 393 #If the buffer is empty then it takes the data value
396 394 if self.__buffer is None:
397 395 self.__buffer = data
398 396 self.__profIndex += 1
399 397 return
400 398
401 399 #If the buffer length is lower than n then stakcing the data value
402 400 if self.__profIndex < self.n:
403 401 self.__buffer = numpy.vstack((self.__buffer, data))
404 402 self.__profIndex += 1
405 403 return
406 404
407 405 #If the buffer length is equal to n then replacing the last buffer value with the data value
408 406 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
409 407 self.__buffer[self.n-1] = data
410 408 self.__profIndex = self.n
411 409 return
412 410
413 411
414 412 def pushData(self):
415 413 """
416 414 Return the sum of the last profiles and the profiles used in the sum.
417 415
418 416 Affected:
419 417
420 418 self.__profileIndex
421 419
422 420 """
423 421
424 if not self.__withOverapping:
422 if not self.__withOverlapping:
425 423 data = self.__buffer
426 424 n = self.__profIndex
427 425
428 426 self.__buffer = 0
429 427 self.__profIndex = 0
430 428
431 429 return data, n
432 430
433 431 #Integration with Overlapping
434 432 data = numpy.sum(self.__buffer, axis=0)
433 # print data
434 # raise
435 435 n = self.__profIndex
436 436
437 437 return data, n
438 438
439 439 def byProfiles(self, data):
440 440
441 441 self.__dataReady = False
442 442 avgdata = None
443 443 # n = None
444
444 # print data
445 # raise
445 446 self.putData(data)
446 447
447 448 if self.__profIndex == self.n:
448
449 449 avgdata, n = self.pushData()
450 450 self.__dataReady = True
451 451
452 452 return avgdata
453 453
454 454 def byTime(self, data, datatime):
455 455
456 456 self.__dataReady = False
457 457 avgdata = None
458 458 n = None
459 459
460 460 self.putData(data)
461 461
462 462 if (datatime - self.__initime) >= self.__integrationtime:
463 463 avgdata, n = self.pushData()
464 464 self.n = n
465 465 self.__dataReady = True
466 466
467 467 return avgdata
468 468
469 def integrateByStride(self, data, datatime):
470 # print data
471 if self.__profIndex == 0:
472 self.__buffer = [[data.copy(), datatime]]
473 else:
474 self.__buffer.append([data.copy(), datatime])
475 self.__profIndex += 1
476 self.__dataReady = False
477
478 if self.__profIndex == self.n * self.stride :
479 self.__dataToPutStride = True
480 self.__profIndexStride = 0
481 self.__profIndex = 0
482 self.__bufferStride = []
483 for i in range(self.stride):
484 current = self.__buffer[i::self.stride]
485 data = numpy.sum([t[0] for t in current], axis=0)
486 avgdatatime = numpy.average([t[1] for t in current])
487 # print data
488 self.__bufferStride.append((data, avgdatatime))
489
490 if self.__dataToPutStride:
491 self.__dataReady = False
492 self.__profIndexStride += 1
493 if self.__profIndexStride == self.stride:
494 self.__dataReady = True
495 self.__dataToPutStride = False
496 self.__profIndexStride = 0
497 # print self.__bufferStride[self.__profIndexStride - 1]
498 # raise
499 return (numpy.sum([t[0] for t in self.__bufferStride], axis=0), numpy.average([t[1] for t in self.__bufferStride]))
500
501
502 return None, None
503
469 504 def integrate(self, data, datatime=None):
470 505
471 506 if self.__initime == None:
472 507 self.__initime = datatime
473 508
474 509 if self.__byTime:
475 510 avgdata = self.byTime(data, datatime)
476 511 else:
477 512 avgdata = self.byProfiles(data)
478 513
479 514
480 515 self.__lastdatatime = datatime
481 516
482 517 if avgdata is None:
483 518 return None, None
484 519
485 520 avgdatatime = self.__initime
486 521
487 522 deltatime = datatime -self.__lastdatatime
488 523
489 if not self.__withOverapping:
524 if not self.__withOverlapping:
490 525 self.__initime = datatime
491 526 else:
492 527 self.__initime += deltatime
493 528
494 529 return avgdata, avgdatatime
495 530
496 531 def integrateByBlock(self, dataOut):
497 532
498 533 times = int(dataOut.data.shape[1]/self.n)
499 534 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
500 535
501 536 id_min = 0
502 537 id_max = self.n
503 538
504 539 for i in range(times):
505 540 junk = dataOut.data[:,id_min:id_max,:]
506 541 avgdata[:,i,:] = junk.sum(axis=1)
507 542 id_min += self.n
508 543 id_max += self.n
509 544
510 545 timeInterval = dataOut.ippSeconds*self.n
511 546 avgdatatime = (times - 1) * timeInterval + dataOut.utctime
512 547 self.__dataReady = True
513 548 return avgdata, avgdatatime
514 549
515
516 def run(self, dataOut, n=None, timeInterval=None, overlapping=False, byblock=False, **kwargs):
550 def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
517 551 if not self.isConfig:
518 self.setup(n=n, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
552 self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
519 553 self.isConfig = True
520 554
521 555 if dataOut.flagDataAsBlock:
522 556 """
523 557 Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
524 558 """
525 559 avgdata, avgdatatime = self.integrateByBlock(dataOut)
526 560 dataOut.nProfiles /= self.n
527 561 else:
562 if stride is None:
528 563 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
564 else:
565 avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
566
529 567
530 568 # dataOut.timeInterval *= n
531 569 dataOut.flagNoData = True
532 570
533 571 if self.__dataReady:
534 572 dataOut.data = avgdata
535 573 dataOut.nCohInt *= self.n
536 574 dataOut.utctime = avgdatatime
575 # print avgdata, avgdatatime
576 # raise
537 577 # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
538 578 dataOut.flagNoData = False
539 579
540 580 class Decoder(Operation):
541 581
542 582 isConfig = False
543 583 __profIndex = 0
544 584
545 585 code = None
546 586
547 587 nCode = None
548 588 nBaud = None
549 589
550 590 def __init__(self, **kwargs):
551 591
552 592 Operation.__init__(self, **kwargs)
553 593
554 594 self.times = None
555 595 self.osamp = None
556 596 # self.__setValues = False
557 597 self.isConfig = False
558 598
559 599 def setup(self, code, osamp, dataOut):
560 600
561 601 self.__profIndex = 0
562 602
563 603 self.code = code
564 604
565 605 self.nCode = len(code)
566 606 self.nBaud = len(code[0])
567 607
568 608 if (osamp != None) and (osamp >1):
569 609 self.osamp = osamp
570 610 self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
571 611 self.nBaud = self.nBaud*self.osamp
572 612
573 613 self.__nChannels = dataOut.nChannels
574 614 self.__nProfiles = dataOut.nProfiles
575 615 self.__nHeis = dataOut.nHeights
576 616
577 617 if self.__nHeis < self.nBaud:
578 618 raise ValueError, 'Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud)
579 619
580 620 #Frequency
581 621 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
582 622
583 623 __codeBuffer[:,0:self.nBaud] = self.code
584 624
585 625 self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
586 626
587 627 if dataOut.flagDataAsBlock:
588 628
589 629 self.ndatadec = self.__nHeis #- self.nBaud + 1
590 630
591 631 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
592 632
593 633 else:
594 634
595 635 #Time
596 636 self.ndatadec = self.__nHeis #- self.nBaud + 1
597 637
598 638 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
599 639
600 640 def __convolutionInFreq(self, data):
601 641
602 642 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
603 643
604 644 fft_data = numpy.fft.fft(data, axis=1)
605 645
606 646 conv = fft_data*fft_code
607 647
608 648 data = numpy.fft.ifft(conv,axis=1)
609 649
610 650 return data
611 651
612 652 def __convolutionInFreqOpt(self, data):
613 653
614 654 raise NotImplementedError
615 655
616 656 def __convolutionInTime(self, data):
617 657
618 658 code = self.code[self.__profIndex]
619 659 for i in range(self.__nChannels):
620 660 self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
621 661
622 662 return self.datadecTime
623 663
624 664 def __convolutionByBlockInTime(self, data):
625 665
626 666 repetitions = self.__nProfiles / self.nCode
627 667
628 668 junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
629 669 junk = junk.flatten()
630 670 code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
631 671 profilesList = xrange(self.__nProfiles)
632 672
633 673 for i in range(self.__nChannels):
634 674 for j in profilesList:
635 675 self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
636 676 return self.datadecTime
637 677
638 678 def __convolutionByBlockInFreq(self, data):
639 679
640 680 raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
641 681
642 682
643 683 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
644 684
645 685 fft_data = numpy.fft.fft(data, axis=2)
646 686
647 687 conv = fft_data*fft_code
648 688
649 689 data = numpy.fft.ifft(conv,axis=2)
650 690
651 691 return data
652 692
653 693
654 694 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
655 695
656 696 if dataOut.flagDecodeData:
657 697 print "This data is already decoded, recoding again ..."
658 698
659 699 if not self.isConfig:
660 700
661 701 if code is None:
662 702 if dataOut.code is None:
663 703 raise ValueError, "Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type
664 704
665 705 code = dataOut.code
666 706 else:
667 707 code = numpy.array(code).reshape(nCode,nBaud)
668 708 self.setup(code, osamp, dataOut)
669 709
670 710 self.isConfig = True
671 711
672 712 if mode == 3:
673 713 sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
674 714
675 715 if times != None:
676 716 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
677 717
678 718 if self.code is None:
679 719 print "Fail decoding: Code is not defined."
680 720 return
681 721
682 722 self.__nProfiles = dataOut.nProfiles
683 723 datadec = None
684 724
685 725 if mode == 3:
686 726 mode = 0
687 727
688 728 if dataOut.flagDataAsBlock:
689 729 """
690 730 Decoding when data have been read as block,
691 731 """
692 732
693 733 if mode == 0:
694 734 datadec = self.__convolutionByBlockInTime(dataOut.data)
695 735 if mode == 1:
696 736 datadec = self.__convolutionByBlockInFreq(dataOut.data)
697 737 else:
698 738 """
699 739 Decoding when data have been read profile by profile
700 740 """
701 741 if mode == 0:
702 742 datadec = self.__convolutionInTime(dataOut.data)
703 743
704 744 if mode == 1:
705 745 datadec = self.__convolutionInFreq(dataOut.data)
706 746
707 747 if mode == 2:
708 748 datadec = self.__convolutionInFreqOpt(dataOut.data)
709 749
710 750 if datadec is None:
711 751 raise ValueError, "Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode
712 752
713 753 dataOut.code = self.code
714 754 dataOut.nCode = self.nCode
715 755 dataOut.nBaud = self.nBaud
716 756
717 757 dataOut.data = datadec
718 758
719 759 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
720 760
721 761 dataOut.flagDecodeData = True #asumo q la data esta decodificada
722 762
723 763 if self.__profIndex == self.nCode-1:
724 764 self.__profIndex = 0
725 765 return 1
726 766
727 767 self.__profIndex += 1
728 768
729 769 return 1
730 770 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
731 771
732 772
733 773 class ProfileConcat(Operation):
734 774
735 775 isConfig = False
736 776 buffer = None
737 777
738 778 def __init__(self, **kwargs):
739 779
740 780 Operation.__init__(self, **kwargs)
741 781 self.profileIndex = 0
742 782
743 783 def reset(self):
744 784 self.buffer = numpy.zeros_like(self.buffer)
745 785 self.start_index = 0
746 786 self.times = 1
747 787
748 788 def setup(self, data, m, n=1):
749 789 self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
750 790 self.nHeights = data.shape[1]#.nHeights
751 791 self.start_index = 0
752 792 self.times = 1
753 793
754 794 def concat(self, data):
755 795
756 796 self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
757 797 self.start_index = self.start_index + self.nHeights
758 798
759 799 def run(self, dataOut, m):
760 800
761 801 dataOut.flagNoData = True
762 802
763 803 if not self.isConfig:
764 804 self.setup(dataOut.data, m, 1)
765 805 self.isConfig = True
766 806
767 807 if dataOut.flagDataAsBlock:
768 808 raise ValueError, "ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False"
769 809
770 810 else:
771 811 self.concat(dataOut.data)
772 812 self.times += 1
773 813 if self.times > m:
774 814 dataOut.data = self.buffer
775 815 self.reset()
776 816 dataOut.flagNoData = False
777 817 # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
778 818 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
779 819 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
780 820 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
781 821 dataOut.ippSeconds *= m
782 822
783 823 class ProfileSelector(Operation):
784 824
785 825 profileIndex = None
786 826 # Tamanho total de los perfiles
787 827 nProfiles = None
788 828
789 829 def __init__(self, **kwargs):
790 830
791 831 Operation.__init__(self, **kwargs)
792 832 self.profileIndex = 0
793 833
794 834 def incProfileIndex(self):
795 835
796 836 self.profileIndex += 1
797 837
798 838 if self.profileIndex >= self.nProfiles:
799 839 self.profileIndex = 0
800 840
801 841 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
802 842
803 843 if profileIndex < minIndex:
804 844 return False
805 845
806 846 if profileIndex > maxIndex:
807 847 return False
808 848
809 849 return True
810 850
811 851 def isThisProfileInList(self, profileIndex, profileList):
812 852
813 853 if profileIndex not in profileList:
814 854 return False
815 855
816 856 return True
817 857
818 858 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
819 859
820 860 """
821 861 ProfileSelector:
822 862
823 863 Inputs:
824 864 profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
825 865
826 866 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
827 867
828 868 rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
829 869
830 870 """
831 871
832 872 if rangeList is not None:
833 873 if type(rangeList[0]) not in (tuple, list):
834 874 rangeList = [rangeList]
835 875
836 876 dataOut.flagNoData = True
837 877
838 878 if dataOut.flagDataAsBlock:
839 879 """
840 880 data dimension = [nChannels, nProfiles, nHeis]
841 881 """
842 882 if profileList != None:
843 883 dataOut.data = dataOut.data[:,profileList,:]
844 884
845 885 if profileRangeList != None:
846 886 minIndex = profileRangeList[0]
847 887 maxIndex = profileRangeList[1]
848 888 profileList = range(minIndex, maxIndex+1)
849 889
850 890 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
851 891
852 892 if rangeList != None:
853 893
854 894 profileList = []
855 895
856 896 for thisRange in rangeList:
857 897 minIndex = thisRange[0]
858 898 maxIndex = thisRange[1]
859 899
860 900 profileList.extend(range(minIndex, maxIndex+1))
861 901
862 902 dataOut.data = dataOut.data[:,profileList,:]
863 903
864 904 dataOut.nProfiles = len(profileList)
865 905 dataOut.profileIndex = dataOut.nProfiles - 1
866 906 dataOut.flagNoData = False
867 907
868 908 return True
869 909
870 910 """
871 911 data dimension = [nChannels, nHeis]
872 912 """
873 913
874 914 if profileList != None:
875 915
876 916 if self.isThisProfileInList(dataOut.profileIndex, profileList):
877 917
878 918 self.nProfiles = len(profileList)
879 919 dataOut.nProfiles = self.nProfiles
880 920 dataOut.profileIndex = self.profileIndex
881 921 dataOut.flagNoData = False
882 922
883 923 self.incProfileIndex()
884 924 return True
885 925
886 926 if profileRangeList != None:
887 927
888 928 minIndex = profileRangeList[0]
889 929 maxIndex = profileRangeList[1]
890 930
891 931 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
892 932
893 933 self.nProfiles = maxIndex - minIndex + 1
894 934 dataOut.nProfiles = self.nProfiles
895 935 dataOut.profileIndex = self.profileIndex
896 936 dataOut.flagNoData = False
897 937
898 938 self.incProfileIndex()
899 939 return True
900 940
901 941 if rangeList != None:
902 942
903 943 nProfiles = 0
904 944
905 945 for thisRange in rangeList:
906 946 minIndex = thisRange[0]
907 947 maxIndex = thisRange[1]
908 948
909 949 nProfiles += maxIndex - minIndex + 1
910 950
911 951 for thisRange in rangeList:
912 952
913 953 minIndex = thisRange[0]
914 954 maxIndex = thisRange[1]
915 955
916 956 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
917 957
918 958 self.nProfiles = nProfiles
919 959 dataOut.nProfiles = self.nProfiles
920 960 dataOut.profileIndex = self.profileIndex
921 961 dataOut.flagNoData = False
922 962
923 963 self.incProfileIndex()
924 964
925 965 break
926 966
927 967 return True
928 968
929 969
930 970 if beam != None: #beam is only for AMISR data
931 971 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
932 972 dataOut.flagNoData = False
933 973 dataOut.profileIndex = self.profileIndex
934 974
935 975 self.incProfileIndex()
936 976
937 977 return True
938 978
939 979 raise ValueError, "ProfileSelector needs profileList, profileRangeList or rangeList parameter"
940 980
941 981 return False
942 982
943 983 class Reshaper(Operation):
944 984
945 985 def __init__(self, **kwargs):
946 986
947 987 Operation.__init__(self, **kwargs)
948 988
949 989 self.__buffer = None
950 990 self.__nitems = 0
951 991
952 992 def __appendProfile(self, dataOut, nTxs):
953 993
954 994 if self.__buffer is None:
955 995 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
956 996 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
957 997
958 998 ini = dataOut.nHeights * self.__nitems
959 999 end = ini + dataOut.nHeights
960 1000
961 1001 self.__buffer[:, ini:end] = dataOut.data
962 1002
963 1003 self.__nitems += 1
964 1004
965 1005 return int(self.__nitems*nTxs)
966 1006
967 1007 def __getBuffer(self):
968 1008
969 1009 if self.__nitems == int(1./self.__nTxs):
970 1010
971 1011 self.__nitems = 0
972 1012
973 1013 return self.__buffer.copy()
974 1014
975 1015 return None
976 1016
977 1017 def __checkInputs(self, dataOut, shape, nTxs):
978 1018
979 1019 if shape is None and nTxs is None:
980 1020 raise ValueError, "Reshaper: shape of factor should be defined"
981 1021
982 1022 if nTxs:
983 1023 if nTxs < 0:
984 1024 raise ValueError, "nTxs should be greater than 0"
985 1025
986 1026 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
987 1027 raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
988 1028
989 1029 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
990 1030
991 1031 return shape, nTxs
992 1032
993 1033 if len(shape) != 2 and len(shape) != 3:
994 1034 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)
995 1035
996 1036 if len(shape) == 2:
997 1037 shape_tuple = [dataOut.nChannels]
998 1038 shape_tuple.extend(shape)
999 1039 else:
1000 1040 shape_tuple = list(shape)
1001 1041
1002 1042 nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
1003 1043
1004 1044 return shape_tuple, nTxs
1005 1045
1006 1046 def run(self, dataOut, shape=None, nTxs=None):
1007 1047
1008 1048 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
1009 1049
1010 1050 dataOut.flagNoData = True
1011 1051 profileIndex = None
1012 1052
1013 1053 if dataOut.flagDataAsBlock:
1014 1054
1015 1055 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
1016 1056 dataOut.flagNoData = False
1017 1057
1018 1058 profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
1019 1059
1020 1060 else:
1021 1061
1022 1062 if self.__nTxs < 1:
1023 1063
1024 1064 self.__appendProfile(dataOut, self.__nTxs)
1025 1065 new_data = self.__getBuffer()
1026 1066
1027 1067 if new_data is not None:
1028 1068 dataOut.data = new_data
1029 1069 dataOut.flagNoData = False
1030 1070
1031 1071 profileIndex = dataOut.profileIndex*nTxs
1032 1072
1033 1073 else:
1034 1074 raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
1035 1075
1036 1076 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1037 1077
1038 1078 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
1039 1079
1040 1080 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
1041 1081
1042 1082 dataOut.profileIndex = profileIndex
1043 1083
1044 1084 dataOut.ippSeconds /= self.__nTxs
1045 1085
1046 1086 class SplitProfiles(Operation):
1047 1087
1048 1088 def __init__(self, **kwargs):
1049 1089
1050 1090 Operation.__init__(self, **kwargs)
1051 1091
1052 1092 def run(self, dataOut, n):
1053 1093
1054 1094 dataOut.flagNoData = True
1055 1095 profileIndex = None
1056 1096
1057 1097 if dataOut.flagDataAsBlock:
1058 1098
1059 1099 #nchannels, nprofiles, nsamples
1060 1100 shape = dataOut.data.shape
1061 1101
1062 1102 if shape[2] % n != 0:
1063 1103 raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[2])
1064 1104
1065 1105 new_shape = shape[0], shape[1]*n, shape[2]/n
1066 1106
1067 1107 dataOut.data = numpy.reshape(dataOut.data, new_shape)
1068 1108 dataOut.flagNoData = False
1069 1109
1070 1110 profileIndex = int(dataOut.nProfiles/n) - 1
1071 1111
1072 1112 else:
1073 1113
1074 1114 raise ValueError, "Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)"
1075 1115
1076 1116 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1077 1117
1078 1118 dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
1079 1119
1080 1120 dataOut.nProfiles = int(dataOut.nProfiles*n)
1081 1121
1082 1122 dataOut.profileIndex = profileIndex
1083 1123
1084 1124 dataOut.ippSeconds /= n
1085 1125
1086 1126 class CombineProfiles(Operation):
1087 1127 def __init__(self, **kwargs):
1088 1128
1089 1129 Operation.__init__(self, **kwargs)
1090 1130
1091 1131 self.__remData = None
1092 1132 self.__profileIndex = 0
1093 1133
1094 1134 def run(self, dataOut, n):
1095 1135
1096 1136 dataOut.flagNoData = True
1097 1137 profileIndex = None
1098 1138
1099 1139 if dataOut.flagDataAsBlock:
1100 1140
1101 1141 #nchannels, nprofiles, nsamples
1102 1142 shape = dataOut.data.shape
1103 1143 new_shape = shape[0], shape[1]/n, shape[2]*n
1104 1144
1105 1145 if shape[1] % n != 0:
1106 1146 raise ValueError, "Could not split the data, n=%d has to be multiple of %d" %(n, shape[1])
1107 1147
1108 1148 dataOut.data = numpy.reshape(dataOut.data, new_shape)
1109 1149 dataOut.flagNoData = False
1110 1150
1111 1151 profileIndex = int(dataOut.nProfiles*n) - 1
1112 1152
1113 1153 else:
1114 1154
1115 1155 #nchannels, nsamples
1116 1156 if self.__remData is None:
1117 1157 newData = dataOut.data
1118 1158 else:
1119 1159 newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
1120 1160
1121 1161 self.__profileIndex += 1
1122 1162
1123 1163 if self.__profileIndex < n:
1124 1164 self.__remData = newData
1125 1165 #continue
1126 1166 return
1127 1167
1128 1168 self.__profileIndex = 0
1129 1169 self.__remData = None
1130 1170
1131 1171 dataOut.data = newData
1132 1172 dataOut.flagNoData = False
1133 1173
1134 1174 profileIndex = dataOut.profileIndex/n
1135 1175
1136 1176
1137 1177 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1138 1178
1139 1179 dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
1140 1180
1141 1181 dataOut.nProfiles = int(dataOut.nProfiles/n)
1142 1182
1143 1183 dataOut.profileIndex = profileIndex
1144 1184
1145 1185 dataOut.ippSeconds *= n
1146 1186
1147 1187 # import collections
1148 1188 # from scipy.stats import mode
1149 1189 #
1150 1190 # class Synchronize(Operation):
1151 1191 #
1152 1192 # isConfig = False
1153 1193 # __profIndex = 0
1154 1194 #
1155 1195 # def __init__(self, **kwargs):
1156 1196 #
1157 1197 # Operation.__init__(self, **kwargs)
1158 1198 # # self.isConfig = False
1159 1199 # self.__powBuffer = None
1160 1200 # self.__startIndex = 0
1161 1201 # self.__pulseFound = False
1162 1202 #
1163 1203 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
1164 1204 #
1165 1205 # #Read data
1166 1206 #
1167 1207 # powerdB = dataOut.getPower(channel = channel)
1168 1208 # noisedB = dataOut.getNoise(channel = channel)[0]
1169 1209 #
1170 1210 # self.__powBuffer.extend(powerdB.flatten())
1171 1211 #
1172 1212 # dataArray = numpy.array(self.__powBuffer)
1173 1213 #
1174 1214 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
1175 1215 #
1176 1216 # maxValue = numpy.nanmax(filteredPower)
1177 1217 #
1178 1218 # if maxValue < noisedB + 10:
1179 1219 # #No se encuentra ningun pulso de transmision
1180 1220 # return None
1181 1221 #
1182 1222 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
1183 1223 #
1184 1224 # if len(maxValuesIndex) < 2:
1185 1225 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
1186 1226 # return None
1187 1227 #
1188 1228 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
1189 1229 #
1190 1230 # #Seleccionar solo valores con un espaciamiento de nSamples
1191 1231 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
1192 1232 #
1193 1233 # if len(pulseIndex) < 2:
1194 1234 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1195 1235 # return None
1196 1236 #
1197 1237 # spacing = pulseIndex[1:] - pulseIndex[:-1]
1198 1238 #
1199 1239 # #remover senales que se distancien menos de 10 unidades o muestras
1200 1240 # #(No deberian existir IPP menor a 10 unidades)
1201 1241 #
1202 1242 # realIndex = numpy.where(spacing > 10 )[0]
1203 1243 #
1204 1244 # if len(realIndex) < 2:
1205 1245 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1206 1246 # return None
1207 1247 #
1208 1248 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1209 1249 # realPulseIndex = pulseIndex[realIndex]
1210 1250 #
1211 1251 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1212 1252 #
1213 1253 # print "IPP = %d samples" %period
1214 1254 #
1215 1255 # self.__newNSamples = dataOut.nHeights #int(period)
1216 1256 # self.__startIndex = int(realPulseIndex[0])
1217 1257 #
1218 1258 # return 1
1219 1259 #
1220 1260 #
1221 1261 # def setup(self, nSamples, nChannels, buffer_size = 4):
1222 1262 #
1223 1263 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1224 1264 # maxlen = buffer_size*nSamples)
1225 1265 #
1226 1266 # bufferList = []
1227 1267 #
1228 1268 # for i in range(nChannels):
1229 1269 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1230 1270 # maxlen = buffer_size*nSamples)
1231 1271 #
1232 1272 # bufferList.append(bufferByChannel)
1233 1273 #
1234 1274 # self.__nSamples = nSamples
1235 1275 # self.__nChannels = nChannels
1236 1276 # self.__bufferList = bufferList
1237 1277 #
1238 1278 # def run(self, dataOut, channel = 0):
1239 1279 #
1240 1280 # if not self.isConfig:
1241 1281 # nSamples = dataOut.nHeights
1242 1282 # nChannels = dataOut.nChannels
1243 1283 # self.setup(nSamples, nChannels)
1244 1284 # self.isConfig = True
1245 1285 #
1246 1286 # #Append new data to internal buffer
1247 1287 # for thisChannel in range(self.__nChannels):
1248 1288 # bufferByChannel = self.__bufferList[thisChannel]
1249 1289 # bufferByChannel.extend(dataOut.data[thisChannel])
1250 1290 #
1251 1291 # if self.__pulseFound:
1252 1292 # self.__startIndex -= self.__nSamples
1253 1293 #
1254 1294 # #Finding Tx Pulse
1255 1295 # if not self.__pulseFound:
1256 1296 # indexFound = self.__findTxPulse(dataOut, channel)
1257 1297 #
1258 1298 # if indexFound == None:
1259 1299 # dataOut.flagNoData = True
1260 1300 # return
1261 1301 #
1262 1302 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1263 1303 # self.__pulseFound = True
1264 1304 # self.__startIndex = indexFound
1265 1305 #
1266 1306 # #If pulse was found ...
1267 1307 # for thisChannel in range(self.__nChannels):
1268 1308 # bufferByChannel = self.__bufferList[thisChannel]
1269 1309 # #print self.__startIndex
1270 1310 # x = numpy.array(bufferByChannel)
1271 1311 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1272 1312 #
1273 1313 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1274 1314 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1275 1315 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1276 1316 #
1277 1317 # dataOut.data = self.__arrayBuffer
1278 1318 #
1279 1319 # self.__startIndex += self.__newNSamples
1280 1320 #
1281 1321 # return
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