##// END OF EJS Templates
schain
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jrodata se cambio los atrituos de pulsepair,jroplot_voltage se cambio el ploteo de la potencial y la senal, jroIO_simulator se anadio la modificacion de bloquesporarchivo y perfilesporbloque,jro_proc_voltage se corrigio el calculo de noise con remocion DC y se asignaron nuevos nombres al dataOut, jroproc_parameters se anadio los nuevos atributos del dataOut jroproc_voltage pulsepair,test_sim0009.py es el nuevo test de escritura
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r1311:816ce18b0db1 v3-devel-julio
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1 1 '''
2 2
3 3 $Author: murco $
4 4 $Id: JROData.py 173 2012-11-20 15:06:21Z murco $
5 5 '''
6 6
7 7 import copy
8 8 import numpy
9 9 import datetime
10 10 import json
11 11
12 12 import schainpy.admin
13 13 from schainpy.utils import log
14 14 from .jroheaderIO import SystemHeader, RadarControllerHeader
15 15 from schainpy.model.data import _noise
16 16
17 17
18 18 def getNumpyDtype(dataTypeCode):
19 19
20 20 if dataTypeCode == 0:
21 21 numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')])
22 22 elif dataTypeCode == 1:
23 23 numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')])
24 24 elif dataTypeCode == 2:
25 25 numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')])
26 26 elif dataTypeCode == 3:
27 27 numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')])
28 28 elif dataTypeCode == 4:
29 29 numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
30 30 elif dataTypeCode == 5:
31 31 numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')])
32 32 else:
33 33 raise ValueError('dataTypeCode was not defined')
34 34
35 35 return numpyDtype
36 36
37 37
38 38 def getDataTypeCode(numpyDtype):
39 39
40 40 if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]):
41 41 datatype = 0
42 42 elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]):
43 43 datatype = 1
44 44 elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]):
45 45 datatype = 2
46 46 elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]):
47 47 datatype = 3
48 48 elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]):
49 49 datatype = 4
50 50 elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]):
51 51 datatype = 5
52 52 else:
53 53 datatype = None
54 54
55 55 return datatype
56 56
57 57
58 58 def hildebrand_sekhon(data, navg):
59 59 """
60 60 This method is for the objective determination of the noise level in Doppler spectra. This
61 61 implementation technique is based on the fact that the standard deviation of the spectral
62 62 densities is equal to the mean spectral density for white Gaussian noise
63 63
64 64 Inputs:
65 65 Data : heights
66 66 navg : numbers of averages
67 67
68 68 Return:
69 69 mean : noise's level
70 70 """
71 71
72 72 sortdata = numpy.sort(data, axis=None)
73 73 '''
74 74 lenOfData = len(sortdata)
75 75 nums_min = lenOfData*0.2
76 76
77 77 if nums_min <= 5:
78 78
79 79 nums_min = 5
80 80
81 81 sump = 0.
82 82 sumq = 0.
83 83
84 84 j = 0
85 85 cont = 1
86 86
87 87 while((cont == 1)and(j < lenOfData)):
88 88
89 89 sump += sortdata[j]
90 90 sumq += sortdata[j]**2
91 91
92 92 if j > nums_min:
93 93 rtest = float(j)/(j-1) + 1.0/navg
94 94 if ((sumq*j) > (rtest*sump**2)):
95 95 j = j - 1
96 96 sump = sump - sortdata[j]
97 97 sumq = sumq - sortdata[j]**2
98 98 cont = 0
99 99
100 100 j += 1
101 101
102 102 lnoise = sump / j
103 103 '''
104 104 return _noise.hildebrand_sekhon(sortdata, navg)
105 105
106 106
107 107 class Beam:
108 108
109 109 def __init__(self):
110 110 self.codeList = []
111 111 self.azimuthList = []
112 112 self.zenithList = []
113 113
114 114
115 115 class GenericData(object):
116 116
117 117 flagNoData = True
118 118
119 119 def copy(self, inputObj=None):
120 120
121 121 if inputObj == None:
122 122 return copy.deepcopy(self)
123 123
124 124 for key in list(inputObj.__dict__.keys()):
125 125
126 126 attribute = inputObj.__dict__[key]
127 127
128 128 # If this attribute is a tuple or list
129 129 if type(inputObj.__dict__[key]) in (tuple, list):
130 130 self.__dict__[key] = attribute[:]
131 131 continue
132 132
133 133 # If this attribute is another object or instance
134 134 if hasattr(attribute, '__dict__'):
135 135 self.__dict__[key] = attribute.copy()
136 136 continue
137 137
138 138 self.__dict__[key] = inputObj.__dict__[key]
139 139
140 140 def deepcopy(self):
141 141
142 142 return copy.deepcopy(self)
143 143
144 144 def isEmpty(self):
145 145
146 146 return self.flagNoData
147 147
148 148 def isReady(self):
149 149
150 150 return not self.flagNoData
151 151
152 152
153 153 class JROData(GenericData):
154 154
155 155 # m_BasicHeader = BasicHeader()
156 156 # m_ProcessingHeader = ProcessingHeader()
157 157
158 158 systemHeaderObj = SystemHeader()
159 159 radarControllerHeaderObj = RadarControllerHeader()
160 160 # data = None
161 161 type = None
162 162 datatype = None # dtype but in string
163 163 # dtype = None
164 164 # nChannels = None
165 165 # nHeights = None
166 166 nProfiles = None
167 167 heightList = None
168 168 channelList = None
169 169 flagDiscontinuousBlock = False
170 170 useLocalTime = False
171 171 utctime = None
172 172 timeZone = None
173 173 dstFlag = None
174 174 errorCount = None
175 175 blocksize = None
176 176 # nCode = None
177 177 # nBaud = None
178 178 # code = None
179 179 flagDecodeData = False # asumo q la data no esta decodificada
180 180 flagDeflipData = False # asumo q la data no esta sin flip
181 181 flagShiftFFT = False
182 182 # ippSeconds = None
183 183 # timeInterval = None
184 184 nCohInt = None
185 185 # noise = None
186 186 windowOfFilter = 1
187 187 # Speed of ligth
188 188 C = 3e8
189 189 frequency = 49.92e6
190 190 realtime = False
191 191 beacon_heiIndexList = None
192 192 last_block = None
193 193 blocknow = None
194 194 azimuth = None
195 195 zenith = None
196 196 beam = Beam()
197 197 profileIndex = None
198 198 error = None
199 199 data = None
200 200 nmodes = None
201 201
202 202 def __str__(self):
203 203
204 204 return '{} - {}'.format(self.type, self.getDatatime())
205 205
206 206 def getNoise(self):
207 207
208 208 raise NotImplementedError
209 209
210 210 def getNChannels(self):
211 211
212 212 return len(self.channelList)
213 213
214 214 def getChannelIndexList(self):
215 215
216 216 return list(range(self.nChannels))
217 217
218 218 def getNHeights(self):
219 219
220 220 return len(self.heightList)
221 221
222 222 def getHeiRange(self, extrapoints=0):
223 223
224 224 heis = self.heightList
225 225 # deltah = self.heightList[1] - self.heightList[0]
226 226 #
227 227 # heis.append(self.heightList[-1])
228 228
229 229 return heis
230 230
231 231 def getDeltaH(self):
232 232
233 233 delta = self.heightList[1] - self.heightList[0]
234 234
235 235 return delta
236 236
237 237 def getltctime(self):
238 238
239 239 if self.useLocalTime:
240 240 return self.utctime - self.timeZone * 60
241 241
242 242 return self.utctime
243 243
244 244 def getDatatime(self):
245 245
246 246 datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime)
247 247 return datatimeValue
248 248
249 249 def getTimeRange(self):
250 250
251 251 datatime = []
252 252
253 253 datatime.append(self.ltctime)
254 254 datatime.append(self.ltctime + self.timeInterval + 1)
255 255
256 256 datatime = numpy.array(datatime)
257 257
258 258 return datatime
259 259
260 260 def getFmaxTimeResponse(self):
261 261
262 262 period = (10**-6) * self.getDeltaH() / (0.15)
263 263
264 264 PRF = 1. / (period * self.nCohInt)
265 265
266 266 fmax = PRF
267 267
268 268 return fmax
269 269
270 270 def getFmax(self):
271 271 PRF = 1. / (self.ippSeconds * self.nCohInt)
272 272
273 273 fmax = PRF
274 274 return fmax
275 275
276 276 def getVmax(self):
277 277
278 278 _lambda = self.C / self.frequency
279 279
280 280 vmax = self.getFmax() * _lambda / 2
281 281
282 282 return vmax
283 283
284 284 def get_ippSeconds(self):
285 285 '''
286 286 '''
287 287 return self.radarControllerHeaderObj.ippSeconds
288 288
289 289 def set_ippSeconds(self, ippSeconds):
290 290 '''
291 291 '''
292 292
293 293 self.radarControllerHeaderObj.ippSeconds = ippSeconds
294 294
295 295 return
296 296
297 297 def get_dtype(self):
298 298 '''
299 299 '''
300 300 return getNumpyDtype(self.datatype)
301 301
302 302 def set_dtype(self, numpyDtype):
303 303 '''
304 304 '''
305 305
306 306 self.datatype = getDataTypeCode(numpyDtype)
307 307
308 308 def get_code(self):
309 309 '''
310 310 '''
311 311 return self.radarControllerHeaderObj.code
312 312
313 313 def set_code(self, code):
314 314 '''
315 315 '''
316 316 self.radarControllerHeaderObj.code = code
317 317
318 318 return
319 319
320 320 def get_ncode(self):
321 321 '''
322 322 '''
323 323 return self.radarControllerHeaderObj.nCode
324 324
325 325 def set_ncode(self, nCode):
326 326 '''
327 327 '''
328 328 self.radarControllerHeaderObj.nCode = nCode
329 329
330 330 return
331 331
332 332 def get_nbaud(self):
333 333 '''
334 334 '''
335 335 return self.radarControllerHeaderObj.nBaud
336 336
337 337 def set_nbaud(self, nBaud):
338 338 '''
339 339 '''
340 340 self.radarControllerHeaderObj.nBaud = nBaud
341 341
342 342 return
343 343
344 344 nChannels = property(getNChannels, "I'm the 'nChannel' property.")
345 345 channelIndexList = property(
346 346 getChannelIndexList, "I'm the 'channelIndexList' property.")
347 347 nHeights = property(getNHeights, "I'm the 'nHeights' property.")
348 348 #noise = property(getNoise, "I'm the 'nHeights' property.")
349 349 datatime = property(getDatatime, "I'm the 'datatime' property")
350 350 ltctime = property(getltctime, "I'm the 'ltctime' property")
351 351 ippSeconds = property(get_ippSeconds, set_ippSeconds)
352 352 dtype = property(get_dtype, set_dtype)
353 353 # timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
354 354 code = property(get_code, set_code)
355 355 nCode = property(get_ncode, set_ncode)
356 356 nBaud = property(get_nbaud, set_nbaud)
357 357
358 358
359 359 class Voltage(JROData):
360 360
361 361 # data es un numpy array de 2 dmensiones (canales, alturas)
362 362 data = None
363 data_intensity = None
364 data_velocity = None
365 data_specwidth = None
363 dataPP_POW = None
364 dataPP_DOP = None
365 dataPP_WIDTH = None
366 dataPP_SNR = None
367
366 368 def __init__(self):
367 369 '''
368 370 Constructor
369 371 '''
370 372
371 373 self.useLocalTime = True
372 374 self.radarControllerHeaderObj = RadarControllerHeader()
373 375 self.systemHeaderObj = SystemHeader()
374 376 self.type = "Voltage"
375 377 self.data = None
376 378 # self.dtype = None
377 379 # self.nChannels = 0
378 380 # self.nHeights = 0
379 381 self.nProfiles = None
380 382 self.heightList = None
381 383 self.channelList = None
382 384 # self.channelIndexList = None
383 385 self.flagNoData = True
384 386 self.flagDiscontinuousBlock = False
385 387 self.utctime = None
386 388 self.timeZone = None
387 389 self.dstFlag = None
388 390 self.errorCount = None
389 391 self.nCohInt = None
390 392 self.blocksize = None
391 393 self.flagDecodeData = False # asumo q la data no esta decodificada
392 394 self.flagDeflipData = False # asumo q la data no esta sin flip
393 395 self.flagShiftFFT = False
394 396 self.flagDataAsBlock = False # Asumo que la data es leida perfil a perfil
395 397 self.profileIndex = 0
396 398
397 399 def getNoisebyHildebrand(self, channel=None):
398 400 """
399 401 Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
400 402
401 403 Return:
402 404 noiselevel
403 405 """
404 406
405 407 if channel != None:
406 408 data = self.data[channel]
407 409 nChannels = 1
408 410 else:
409 411 data = self.data
410 412 nChannels = self.nChannels
411 413
412 414 noise = numpy.zeros(nChannels)
413 415 power = data * numpy.conjugate(data)
414 416
415 417 for thisChannel in range(nChannels):
416 418 if nChannels == 1:
417 419 daux = power[:].real
418 420 else:
419 421 daux = power[thisChannel, :].real
420 422 noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt)
421 423
422 424 return noise
423 425
426 def getNoisebyHildebrandDC(self, channel=None,DC=0):
427 """
428 Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
429
430 Return:
431 noiselevel
432 """
433
434 if channel != None:
435 data = self.data[channel]-DC
436 nChannels = 1
437 else:
438 data = self.data
439 nChannels = self.nChannels
440
441 noise = numpy.zeros(nChannels)
442 power = data * numpy.conjugate(data)
443
444 for thisChannel in range(nChannels):
445 if nChannels == 1:
446 daux = power[:].real
447 else:
448 daux = power[thisChannel, :].real
449 noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt)
450
451 return noise
452
453
454
424 455 def getNoise(self, type=1, channel=None):
425 456
426 457 if type == 1:
427 458 noise = self.getNoisebyHildebrand(channel)
428 459
429 460 return noise
430 461
431 462 def getPower(self, channel=None):
432 463
433 464 if channel != None:
434 465 data = self.data[channel]
435 466 else:
436 467 data = self.data
437 468
438 469 power = data * numpy.conjugate(data)
439 470 powerdB = 10 * numpy.log10(power.real)
440 471 powerdB = numpy.squeeze(powerdB)
441 472
442 473 return powerdB
443 474
444 475 def getTimeInterval(self):
445 476
446 477 timeInterval = self.ippSeconds * self.nCohInt
447 478
448 479 return timeInterval
449 480
450 481 noise = property(getNoise, "I'm the 'nHeights' property.")
451 482 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
452 483
453 484
454 485 class Spectra(JROData):
455 486
456 487 # data spc es un numpy array de 2 dmensiones (canales, perfiles, alturas)
457 488 data_spc = None
458 489 # data cspc es un numpy array de 2 dmensiones (canales, pares, alturas)
459 490 data_cspc = None
460 491 # data dc es un numpy array de 2 dmensiones (canales, alturas)
461 492 data_dc = None
462 493 # data power
463 494 data_pwr = None
464 495 nFFTPoints = None
465 496 # nPairs = None
466 497 pairsList = None
467 498 nIncohInt = None
468 499 wavelength = None # Necesario para cacular el rango de velocidad desde la frecuencia
469 500 nCohInt = None # se requiere para determinar el valor de timeInterval
470 501 ippFactor = None
471 502 profileIndex = 0
472 503 plotting = "spectra"
473 504
474 505 def __init__(self):
475 506 '''
476 507 Constructor
477 508 '''
478 509
479 510 self.useLocalTime = True
480 511 self.radarControllerHeaderObj = RadarControllerHeader()
481 512 self.systemHeaderObj = SystemHeader()
482 513 self.type = "Spectra"
483 514 # self.data = None
484 515 # self.dtype = None
485 516 # self.nChannels = 0
486 517 # self.nHeights = 0
487 518 self.nProfiles = None
488 519 self.heightList = None
489 520 self.channelList = None
490 521 # self.channelIndexList = None
491 522 self.pairsList = None
492 523 self.flagNoData = True
493 524 self.flagDiscontinuousBlock = False
494 525 self.utctime = None
495 526 self.nCohInt = None
496 527 self.nIncohInt = None
497 528 self.blocksize = None
498 529 self.nFFTPoints = None
499 530 self.wavelength = None
500 531 self.flagDecodeData = False # asumo q la data no esta decodificada
501 532 self.flagDeflipData = False # asumo q la data no esta sin flip
502 533 self.flagShiftFFT = False
503 534 self.ippFactor = 1
504 535 #self.noise = None
505 536 self.beacon_heiIndexList = []
506 537 self.noise_estimation = None
507 538
508 539 def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
509 540 """
510 541 Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
511 542
512 543 Return:
513 544 noiselevel
514 545 """
515 546
516 547 noise = numpy.zeros(self.nChannels)
517 548
518 549 for channel in range(self.nChannels):
519 550 daux = self.data_spc[channel,
520 551 xmin_index:xmax_index, ymin_index:ymax_index]
521 552 noise[channel] = hildebrand_sekhon(daux, self.nIncohInt)
522 553
523 554 return noise
524 555
525 556 def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
526 557
527 558 if self.noise_estimation is not None:
528 559 # this was estimated by getNoise Operation defined in jroproc_spectra.py
529 560 return self.noise_estimation
530 561 else:
531 562 noise = self.getNoisebyHildebrand(
532 563 xmin_index, xmax_index, ymin_index, ymax_index)
533 564 return noise
534 565
535 566 def getFreqRangeTimeResponse(self, extrapoints=0):
536 567
537 568 deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor)
538 569 freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2
539 570
540 571 return freqrange
541 572
542 573 def getAcfRange(self, extrapoints=0):
543 574
544 575 deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor))
545 576 freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
546 577
547 578 return freqrange
548 579
549 580 def getFreqRange(self, extrapoints=0):
550 581
551 582 deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor)
552 583 freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
553 584
554 585 return freqrange
555 586
556 587 def getVelRange(self, extrapoints=0):
557 588
558 589 deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor)
559 590 velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.)
560 591
561 592 if self.nmodes:
562 593 return velrange/self.nmodes
563 594 else:
564 595 return velrange
565 596
566 597 def getNPairs(self):
567 598
568 599 return len(self.pairsList)
569 600
570 601 def getPairsIndexList(self):
571 602
572 603 return list(range(self.nPairs))
573 604
574 605 def getNormFactor(self):
575 606
576 607 pwcode = 1
577 608
578 609 if self.flagDecodeData:
579 610 pwcode = numpy.sum(self.code[0]**2)
580 611 #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter
581 612 normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter
582 613
583 614 return normFactor
584 615
585 616 def getFlagCspc(self):
586 617
587 618 if self.data_cspc is None:
588 619 return True
589 620
590 621 return False
591 622
592 623 def getFlagDc(self):
593 624
594 625 if self.data_dc is None:
595 626 return True
596 627
597 628 return False
598 629
599 630 def getTimeInterval(self):
600 631
601 632 timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor
602 633 if self.nmodes:
603 634 return self.nmodes*timeInterval
604 635 else:
605 636 return timeInterval
606 637
607 638 def getPower(self):
608 639
609 640 factor = self.normFactor
610 641 z = self.data_spc / factor
611 642 z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
612 643 avg = numpy.average(z, axis=1)
613 644
614 645 return 10 * numpy.log10(avg)
615 646
616 647 def getCoherence(self, pairsList=None, phase=False):
617 648
618 649 z = []
619 650 if pairsList is None:
620 651 pairsIndexList = self.pairsIndexList
621 652 else:
622 653 pairsIndexList = []
623 654 for pair in pairsList:
624 655 if pair not in self.pairsList:
625 656 raise ValueError("Pair %s is not in dataOut.pairsList" % (
626 657 pair))
627 658 pairsIndexList.append(self.pairsList.index(pair))
628 659 for i in range(len(pairsIndexList)):
629 660 pair = self.pairsList[pairsIndexList[i]]
630 661 ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0)
631 662 powa = numpy.average(self.data_spc[pair[0], :, :], axis=0)
632 663 powb = numpy.average(self.data_spc[pair[1], :, :], axis=0)
633 664 avgcoherenceComplex = ccf / numpy.sqrt(powa * powb)
634 665 if phase:
635 666 data = numpy.arctan2(avgcoherenceComplex.imag,
636 667 avgcoherenceComplex.real) * 180 / numpy.pi
637 668 else:
638 669 data = numpy.abs(avgcoherenceComplex)
639 670
640 671 z.append(data)
641 672
642 673 return numpy.array(z)
643 674
644 675 def setValue(self, value):
645 676
646 677 print("This property should not be initialized")
647 678
648 679 return
649 680
650 681 nPairs = property(getNPairs, setValue, "I'm the 'nPairs' property.")
651 682 pairsIndexList = property(
652 683 getPairsIndexList, setValue, "I'm the 'pairsIndexList' property.")
653 684 normFactor = property(getNormFactor, setValue,
654 685 "I'm the 'getNormFactor' property.")
655 686 flag_cspc = property(getFlagCspc, setValue)
656 687 flag_dc = property(getFlagDc, setValue)
657 688 noise = property(getNoise, setValue, "I'm the 'nHeights' property.")
658 689 timeInterval = property(getTimeInterval, setValue,
659 690 "I'm the 'timeInterval' property")
660 691
661 692
662 693 class SpectraHeis(Spectra):
663 694
664 695 data_spc = None
665 696 data_cspc = None
666 697 data_dc = None
667 698 nFFTPoints = None
668 699 # nPairs = None
669 700 pairsList = None
670 701 nCohInt = None
671 702 nIncohInt = None
672 703
673 704 def __init__(self):
674 705
675 706 self.radarControllerHeaderObj = RadarControllerHeader()
676 707
677 708 self.systemHeaderObj = SystemHeader()
678 709
679 710 self.type = "SpectraHeis"
680 711
681 712 # self.dtype = None
682 713
683 714 # self.nChannels = 0
684 715
685 716 # self.nHeights = 0
686 717
687 718 self.nProfiles = None
688 719
689 720 self.heightList = None
690 721
691 722 self.channelList = None
692 723
693 724 # self.channelIndexList = None
694 725
695 726 self.flagNoData = True
696 727
697 728 self.flagDiscontinuousBlock = False
698 729
699 730 # self.nPairs = 0
700 731
701 732 self.utctime = None
702 733
703 734 self.blocksize = None
704 735
705 736 self.profileIndex = 0
706 737
707 738 self.nCohInt = 1
708 739
709 740 self.nIncohInt = 1
710 741
711 742 def getNormFactor(self):
712 743 pwcode = 1
713 744 if self.flagDecodeData:
714 745 pwcode = numpy.sum(self.code[0]**2)
715 746
716 747 normFactor = self.nIncohInt * self.nCohInt * pwcode
717 748
718 749 return normFactor
719 750
720 751 def getTimeInterval(self):
721 752
722 753 timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt
723 754
724 755 return timeInterval
725 756
726 757 normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.")
727 758 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
728 759
729 760
730 761 class Fits(JROData):
731 762
732 763 heightList = None
733 764 channelList = None
734 765 flagNoData = True
735 766 flagDiscontinuousBlock = False
736 767 useLocalTime = False
737 768 utctime = None
738 769 timeZone = None
739 770 # ippSeconds = None
740 771 # timeInterval = None
741 772 nCohInt = None
742 773 nIncohInt = None
743 774 noise = None
744 775 windowOfFilter = 1
745 776 # Speed of ligth
746 777 C = 3e8
747 778 frequency = 49.92e6
748 779 realtime = False
749 780
750 781 def __init__(self):
751 782
752 783 self.type = "Fits"
753 784
754 785 self.nProfiles = None
755 786
756 787 self.heightList = None
757 788
758 789 self.channelList = None
759 790
760 791 # self.channelIndexList = None
761 792
762 793 self.flagNoData = True
763 794
764 795 self.utctime = None
765 796
766 797 self.nCohInt = 1
767 798
768 799 self.nIncohInt = 1
769 800
770 801 self.useLocalTime = True
771 802
772 803 self.profileIndex = 0
773 804
774 805 # self.utctime = None
775 806 # self.timeZone = None
776 807 # self.ltctime = None
777 808 # self.timeInterval = None
778 809 # self.header = None
779 810 # self.data_header = None
780 811 # self.data = None
781 812 # self.datatime = None
782 813 # self.flagNoData = False
783 814 # self.expName = ''
784 815 # self.nChannels = None
785 816 # self.nSamples = None
786 817 # self.dataBlocksPerFile = None
787 818 # self.comments = ''
788 819 #
789 820
790 821 def getltctime(self):
791 822
792 823 if self.useLocalTime:
793 824 return self.utctime - self.timeZone * 60
794 825
795 826 return self.utctime
796 827
797 828 def getDatatime(self):
798 829
799 830 datatime = datetime.datetime.utcfromtimestamp(self.ltctime)
800 831 return datatime
801 832
802 833 def getTimeRange(self):
803 834
804 835 datatime = []
805 836
806 837 datatime.append(self.ltctime)
807 838 datatime.append(self.ltctime + self.timeInterval)
808 839
809 840 datatime = numpy.array(datatime)
810 841
811 842 return datatime
812 843
813 844 def getHeiRange(self):
814 845
815 846 heis = self.heightList
816 847
817 848 return heis
818 849
819 850 def getNHeights(self):
820 851
821 852 return len(self.heightList)
822 853
823 854 def getNChannels(self):
824 855
825 856 return len(self.channelList)
826 857
827 858 def getChannelIndexList(self):
828 859
829 860 return list(range(self.nChannels))
830 861
831 862 def getNoise(self, type=1):
832 863
833 864 #noise = numpy.zeros(self.nChannels)
834 865
835 866 if type == 1:
836 867 noise = self.getNoisebyHildebrand()
837 868
838 869 if type == 2:
839 870 noise = self.getNoisebySort()
840 871
841 872 if type == 3:
842 873 noise = self.getNoisebyWindow()
843 874
844 875 return noise
845 876
846 877 def getTimeInterval(self):
847 878
848 879 timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt
849 880
850 881 return timeInterval
851 882
852 883 def get_ippSeconds(self):
853 884 '''
854 885 '''
855 886 return self.ipp_sec
856 887
857 888
858 889 datatime = property(getDatatime, "I'm the 'datatime' property")
859 890 nHeights = property(getNHeights, "I'm the 'nHeights' property.")
860 891 nChannels = property(getNChannels, "I'm the 'nChannel' property.")
861 892 channelIndexList = property(
862 893 getChannelIndexList, "I'm the 'channelIndexList' property.")
863 894 noise = property(getNoise, "I'm the 'nHeights' property.")
864 895
865 896 ltctime = property(getltctime, "I'm the 'ltctime' property")
866 897 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
867 898 ippSeconds = property(get_ippSeconds, '')
868 899
869 900 class Correlation(JROData):
870 901
871 902 noise = None
872 903 SNR = None
873 904 #--------------------------------------------------
874 905 mode = None
875 906 split = False
876 907 data_cf = None
877 908 lags = None
878 909 lagRange = None
879 910 pairsList = None
880 911 normFactor = None
881 912 #--------------------------------------------------
882 913 # calculateVelocity = None
883 914 nLags = None
884 915 nPairs = None
885 916 nAvg = None
886 917
887 918 def __init__(self):
888 919 '''
889 920 Constructor
890 921 '''
891 922 self.radarControllerHeaderObj = RadarControllerHeader()
892 923
893 924 self.systemHeaderObj = SystemHeader()
894 925
895 926 self.type = "Correlation"
896 927
897 928 self.data = None
898 929
899 930 self.dtype = None
900 931
901 932 self.nProfiles = None
902 933
903 934 self.heightList = None
904 935
905 936 self.channelList = None
906 937
907 938 self.flagNoData = True
908 939
909 940 self.flagDiscontinuousBlock = False
910 941
911 942 self.utctime = None
912 943
913 944 self.timeZone = None
914 945
915 946 self.dstFlag = None
916 947
917 948 self.errorCount = None
918 949
919 950 self.blocksize = None
920 951
921 952 self.flagDecodeData = False # asumo q la data no esta decodificada
922 953
923 954 self.flagDeflipData = False # asumo q la data no esta sin flip
924 955
925 956 self.pairsList = None
926 957
927 958 self.nPoints = None
928 959
929 960 def getPairsList(self):
930 961
931 962 return self.pairsList
932 963
933 964 def getNoise(self, mode=2):
934 965
935 966 indR = numpy.where(self.lagR == 0)[0][0]
936 967 indT = numpy.where(self.lagT == 0)[0][0]
937 968
938 969 jspectra0 = self.data_corr[:, :, indR, :]
939 970 jspectra = copy.copy(jspectra0)
940 971
941 972 num_chan = jspectra.shape[0]
942 973 num_hei = jspectra.shape[2]
943 974
944 975 freq_dc = jspectra.shape[1] / 2
945 976 ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
946 977
947 978 if ind_vel[0] < 0:
948 979 ind_vel[list(range(0, 1))] = ind_vel[list(
949 980 range(0, 1))] + self.num_prof
950 981
951 982 if mode == 1:
952 983 jspectra[:, freq_dc, :] = (
953 984 jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2 # CORRECCION
954 985
955 986 if mode == 2:
956 987
957 988 vel = numpy.array([-2, -1, 1, 2])
958 989 xx = numpy.zeros([4, 4])
959 990
960 991 for fil in range(4):
961 992 xx[fil, :] = vel[fil]**numpy.asarray(list(range(4)))
962 993
963 994 xx_inv = numpy.linalg.inv(xx)
964 995 xx_aux = xx_inv[0, :]
965 996
966 997 for ich in range(num_chan):
967 998 yy = jspectra[ich, ind_vel, :]
968 999 jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy)
969 1000
970 1001 junkid = jspectra[ich, freq_dc, :] <= 0
971 1002 cjunkid = sum(junkid)
972 1003
973 1004 if cjunkid.any():
974 1005 jspectra[ich, freq_dc, junkid.nonzero()] = (
975 1006 jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2
976 1007
977 1008 noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :]
978 1009
979 1010 return noise
980 1011
981 1012 def getTimeInterval(self):
982 1013
983 1014 timeInterval = self.ippSeconds * self.nCohInt * self.nProfiles
984 1015
985 1016 return timeInterval
986 1017
987 1018 def splitFunctions(self):
988 1019
989 1020 pairsList = self.pairsList
990 1021 ccf_pairs = []
991 1022 acf_pairs = []
992 1023 ccf_ind = []
993 1024 acf_ind = []
994 1025 for l in range(len(pairsList)):
995 1026 chan0 = pairsList[l][0]
996 1027 chan1 = pairsList[l][1]
997 1028
998 1029 # Obteniendo pares de Autocorrelacion
999 1030 if chan0 == chan1:
1000 1031 acf_pairs.append(chan0)
1001 1032 acf_ind.append(l)
1002 1033 else:
1003 1034 ccf_pairs.append(pairsList[l])
1004 1035 ccf_ind.append(l)
1005 1036
1006 1037 data_acf = self.data_cf[acf_ind]
1007 1038 data_ccf = self.data_cf[ccf_ind]
1008 1039
1009 1040 return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf
1010 1041
1011 1042 def getNormFactor(self):
1012 1043 acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions()
1013 1044 acf_pairs = numpy.array(acf_pairs)
1014 1045 normFactor = numpy.zeros((self.nPairs, self.nHeights))
1015 1046
1016 1047 for p in range(self.nPairs):
1017 1048 pair = self.pairsList[p]
1018 1049
1019 1050 ch0 = pair[0]
1020 1051 ch1 = pair[1]
1021 1052
1022 1053 ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1)
1023 1054 ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1)
1024 1055 normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max)
1025 1056
1026 1057 return normFactor
1027 1058
1028 1059 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
1029 1060 normFactor = property(getNormFactor, "I'm the 'normFactor property'")
1030 1061
1031 1062
1032 1063 class Parameters(Spectra):
1033 1064
1034 1065 experimentInfo = None # Information about the experiment
1035 1066 # Information from previous data
1036 1067 inputUnit = None # Type of data to be processed
1037 1068 operation = None # Type of operation to parametrize
1038 1069 # normFactor = None #Normalization Factor
1039 1070 groupList = None # List of Pairs, Groups, etc
1040 1071 # Parameters
1041 1072 data_param = None # Parameters obtained
1042 1073 data_pre = None # Data Pre Parametrization
1043 1074 data_SNR = None # Signal to Noise Ratio
1044 1075 # heightRange = None #Heights
1045 1076 abscissaList = None # Abscissa, can be velocities, lags or time
1046 1077 # noise = None #Noise Potency
1047 1078 utctimeInit = None # Initial UTC time
1048 1079 paramInterval = None # Time interval to calculate Parameters in seconds
1049 1080 useLocalTime = True
1050 1081 # Fitting
1051 1082 data_error = None # Error of the estimation
1052 1083 constants = None
1053 1084 library = None
1054 1085 # Output signal
1055 1086 outputInterval = None # Time interval to calculate output signal in seconds
1056 1087 data_output = None # Out signal
1057 1088 nAvg = None
1058 1089 noise_estimation = None
1059 1090 GauSPC = None # Fit gaussian SPC
1060 1091
1061 1092 def __init__(self):
1062 1093 '''
1063 1094 Constructor
1064 1095 '''
1065 1096 self.radarControllerHeaderObj = RadarControllerHeader()
1066 1097
1067 1098 self.systemHeaderObj = SystemHeader()
1068 1099
1069 1100 self.type = "Parameters"
1070 1101
1071 1102 def getTimeRange1(self, interval):
1072 1103
1073 1104 datatime = []
1074 1105
1075 1106 if self.useLocalTime:
1076 1107 time1 = self.utctimeInit - self.timeZone * 60
1077 1108 else:
1078 1109 time1 = self.utctimeInit
1079 1110
1080 1111 datatime.append(time1)
1081 1112 datatime.append(time1 + interval)
1082 1113 datatime = numpy.array(datatime)
1083 1114
1084 1115 return datatime
1085 1116
1086 1117 def getTimeInterval(self):
1087 1118
1088 1119 if hasattr(self, 'timeInterval1'):
1089 1120 return self.timeInterval1
1090 1121 else:
1091 1122 return self.paramInterval
1092 1123
1093 1124 def setValue(self, value):
1094 1125
1095 1126 print("This property should not be initialized")
1096 1127
1097 1128 return
1098 1129
1099 1130 def getNoise(self):
1100 1131
1101 1132 return self.spc_noise
1102 1133
1103 1134 timeInterval = property(getTimeInterval)
1104 1135 noise = property(getNoise, setValue, "I'm the 'Noise' property.")
1105 1136
1106 1137
1107 1138 class PlotterData(object):
1108 1139 '''
1109 1140 Object to hold data to be plotted
1110 1141 '''
1111 1142
1112 1143 MAXNUMX = 200
1113 1144 MAXNUMY = 200
1114 1145
1115 1146 def __init__(self, code, throttle_value, exp_code, buffering=True, snr=False):
1116 1147
1117 1148 self.key = code
1118 1149 self.throttle = throttle_value
1119 1150 self.exp_code = exp_code
1120 1151 self.buffering = buffering
1121 1152 self.ready = False
1122 1153 self.flagNoData = False
1123 1154 self.localtime = False
1124 1155 self.data = {}
1125 1156 self.meta = {}
1126 1157 self.__times = []
1127 1158 self.__heights = []
1128 1159
1129 1160 if 'snr' in code:
1130 1161 self.plottypes = ['snr']
1131 1162 elif code == 'spc':
1132 1163 self.plottypes = ['spc', 'noise', 'rti']
1133 1164 elif code == 'rti':
1134 1165 self.plottypes = ['noise', 'rti']
1135 1166 else:
1136 1167 self.plottypes = [code]
1137 1168
1138 1169 if 'snr' not in self.plottypes and snr:
1139 1170 self.plottypes.append('snr')
1140 1171
1141 1172 for plot in self.plottypes:
1142 1173 self.data[plot] = {}
1143 1174
1144 1175
1145 1176 def __str__(self):
1146 1177 dum = ['{}{}'.format(key, self.shape(key)) for key in self.data]
1147 1178 return 'Data[{}][{}]'.format(';'.join(dum), len(self.__times))
1148 1179
1149 1180 def __len__(self):
1150 1181 return len(self.__times)
1151 1182
1152 1183 def __getitem__(self, key):
1153 1184
1154 1185 if key not in self.data:
1155 1186 raise KeyError(log.error('Missing key: {}'.format(key)))
1156 1187 if 'spc' in key or not self.buffering:
1157 1188 ret = self.data[key]
1158 1189 elif 'scope' in key:
1159 1190 ret = numpy.array(self.data[key][float(self.tm)])
1160 1191 else:
1161 1192 ret = numpy.array([self.data[key][x] for x in self.times])
1162 1193 if ret.ndim > 1:
1163 1194 ret = numpy.swapaxes(ret, 0, 1)
1164 1195 return ret
1165 1196
1166 1197 def __contains__(self, key):
1167 1198 return key in self.data
1168 1199
1169 1200 def setup(self):
1170 1201 '''
1171 1202 Configure object
1172 1203 '''
1173 1204 self.type = ''
1174 1205 self.ready = False
1175 1206 self.data = {}
1176 1207 self.__times = []
1177 1208 self.__heights = []
1178 1209 self.__all_heights = set()
1179 1210 for plot in self.plottypes:
1180 1211 if 'snr' in plot:
1181 1212 plot = 'snr'
1182 1213 elif 'spc_moments' == plot:
1183 1214 plot = 'moments'
1184 1215 self.data[plot] = {}
1185 1216
1186 1217 if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data or 'moments' in self.data:
1187 1218 self.data['noise'] = {}
1188 1219 self.data['rti'] = {}
1189 1220 if 'noise' not in self.plottypes:
1190 1221 self.plottypes.append('noise')
1191 1222 if 'rti' not in self.plottypes:
1192 1223 self.plottypes.append('rti')
1193 1224
1194 1225 def shape(self, key):
1195 1226 '''
1196 1227 Get the shape of the one-element data for the given key
1197 1228 '''
1198 1229
1199 1230 if len(self.data[key]):
1200 1231 if 'spc' in key or not self.buffering:
1201 1232 return self.data[key].shape
1202 1233 return self.data[key][self.__times[0]].shape
1203 1234 return (0,)
1204 1235
1205 1236 def update(self, dataOut, tm):
1206 1237 '''
1207 1238 Update data object with new dataOut
1208 1239 '''
1209 1240 if tm in self.__times:
1210 1241 return
1211 1242 self.profileIndex = dataOut.profileIndex
1212 1243 self.tm = tm
1213 1244 self.type = dataOut.type
1214 1245 self.parameters = getattr(dataOut, 'parameters', [])
1215 1246
1216 1247 if hasattr(dataOut, 'meta'):
1217 1248 self.meta.update(dataOut.meta)
1218 1249
1219 1250 if hasattr(dataOut, 'pairsList'):
1220 1251 self.pairs = dataOut.pairsList
1221 1252
1222 1253 self.interval = dataOut.getTimeInterval()
1223 1254 self.localtime = dataOut.useLocalTime
1224 1255 if True in ['spc' in ptype for ptype in self.plottypes]:
1225 1256 self.xrange = (dataOut.getFreqRange(1)/1000.,
1226 1257 dataOut.getAcfRange(1), dataOut.getVelRange(1))
1227 1258 self.factor = dataOut.normFactor
1228 1259 self.__heights.append(dataOut.heightList)
1229 1260 self.__all_heights.update(dataOut.heightList)
1230 1261 self.__times.append(tm)
1231 1262 for plot in self.plottypes:
1232 1263 if plot in ('spc', 'spc_moments', 'spc_cut'):
1233 1264 z = dataOut.data_spc/dataOut.normFactor
1234 1265 buffer = 10*numpy.log10(z)
1235 1266 if plot == 'cspc':
1236 1267 z = dataOut.data_spc/dataOut.normFactor
1237 1268 buffer = (dataOut.data_spc, dataOut.data_cspc)
1238 1269 if plot == 'noise':
1239 1270 buffer = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
1240 1271 if plot in ('rti', 'spcprofile'):
1241 1272 buffer = dataOut.getPower()
1242 1273 if plot == 'snr_db':
1243 1274 buffer = dataOut.data_SNR
1244 1275 if plot == 'snr':
1245 1276 buffer = 10*numpy.log10(dataOut.data_SNR)
1246 1277 if plot == 'dop':
1247 1278 buffer = dataOut.data_DOP
1248 1279 if plot == 'pow':
1249 1280 buffer = 10*numpy.log10(dataOut.data_POW)
1250 1281 if plot == 'width':
1251 1282 buffer = dataOut.data_WIDTH
1252 1283 if plot == 'coh':
1253 1284 buffer = dataOut.getCoherence()
1254 1285 if plot == 'phase':
1255 1286 buffer = dataOut.getCoherence(phase=True)
1256 1287 if plot == 'output':
1257 1288 buffer = dataOut.data_output
1258 1289 if plot == 'param':
1259 1290 buffer = dataOut.data_param
1260 1291 if plot == 'scope':
1261 1292 buffer = dataOut.data
1262 1293 self.flagDataAsBlock = dataOut.flagDataAsBlock
1263 1294 self.nProfiles = dataOut.nProfiles
1264 1295 if plot == 'pp_power':
1265 buffer = dataOut.data_intensity
1296 buffer = dataOut.dataPP_POWER
1297 self.flagDataAsBlock = dataOut.flagDataAsBlock
1298 self.nProfiles = dataOut.nProfiles
1299 if plot == 'pp_signal':
1300 buffer = dataOut.dataPP_POW
1266 1301 self.flagDataAsBlock = dataOut.flagDataAsBlock
1267 1302 self.nProfiles = dataOut.nProfiles
1268 1303 if plot == 'pp_velocity':
1269 buffer = dataOut.data_velocity
1304 buffer = dataOut.dataPP_DOP
1270 1305 self.flagDataAsBlock = dataOut.flagDataAsBlock
1271 1306 self.nProfiles = dataOut.nProfiles
1272 1307 if plot == 'pp_specwidth':
1273 buffer = dataOut.data_specwidth
1308 buffer = dataOut.dataPP_WIDTH
1274 1309 self.flagDataAsBlock = dataOut.flagDataAsBlock
1275 1310 self.nProfiles = dataOut.nProfiles
1276 1311
1277 1312 if plot == 'spc':
1278 1313 self.data['spc'] = buffer
1279 1314 elif plot == 'cspc':
1280 1315 self.data['spc'] = buffer[0]
1281 1316 self.data['cspc'] = buffer[1]
1282 1317 elif plot == 'spc_moments':
1283 1318 self.data['spc'] = buffer
1284 1319 self.data['moments'][tm] = dataOut.moments
1285 1320 else:
1286 1321 if self.buffering:
1287 1322 self.data[plot][tm] = buffer
1288 1323 else:
1289 1324 self.data[plot] = buffer
1290 1325
1291 1326 if dataOut.channelList is None:
1292 1327 self.channels = range(buffer.shape[0])
1293 1328 else:
1294 1329 self.channels = dataOut.channelList
1295 1330
1296 1331 if buffer is None:
1297 1332 self.flagNoData = True
1298 1333 raise schainpy.admin.SchainWarning('Attribute data_{} is empty'.format(self.key))
1299 1334
1300 1335 def normalize_heights(self):
1301 1336 '''
1302 1337 Ensure same-dimension of the data for different heighList
1303 1338 '''
1304 1339
1305 1340 H = numpy.array(list(self.__all_heights))
1306 1341 H.sort()
1307 1342 for key in self.data:
1308 1343 shape = self.shape(key)[:-1] + H.shape
1309 1344 for tm, obj in list(self.data[key].items()):
1310 1345 h = self.__heights[self.__times.index(tm)]
1311 1346 if H.size == h.size:
1312 1347 continue
1313 1348 index = numpy.where(numpy.in1d(H, h))[0]
1314 1349 dummy = numpy.zeros(shape) + numpy.nan
1315 1350 if len(shape) == 2:
1316 1351 dummy[:, index] = obj
1317 1352 else:
1318 1353 dummy[index] = obj
1319 1354 self.data[key][tm] = dummy
1320 1355
1321 1356 self.__heights = [H for tm in self.__times]
1322 1357
1323 1358 def jsonify(self, plot_name, plot_type, decimate=False):
1324 1359 '''
1325 1360 Convert data to json
1326 1361 '''
1327 1362
1328 1363 tm = self.times[-1]
1329 1364 dy = int(self.heights.size/self.MAXNUMY) + 1
1330 1365 if self.key in ('spc', 'cspc') or not self.buffering:
1331 1366 dx = int(self.data[self.key].shape[1]/self.MAXNUMX) + 1
1332 1367 data = self.roundFloats(
1333 1368 self.data[self.key][::, ::dx, ::dy].tolist())
1334 1369 else:
1335 1370 if self.key is 'noise':
1336 1371 data = [[x] for x in self.roundFloats(self.data[self.key][tm].tolist())]
1337 1372 else:
1338 1373 data = self.roundFloats(self.data[self.key][tm][::, ::dy].tolist())
1339 1374
1340 1375 meta = {}
1341 1376 ret = {
1342 1377 'plot': plot_name,
1343 1378 'code': self.exp_code,
1344 1379 'time': float(tm),
1345 1380 'data': data,
1346 1381 }
1347 1382 meta['type'] = plot_type
1348 1383 meta['interval'] = float(self.interval)
1349 1384 meta['localtime'] = self.localtime
1350 1385 meta['yrange'] = self.roundFloats(self.heights[::dy].tolist())
1351 1386 if 'spc' in self.data or 'cspc' in self.data:
1352 1387 meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist())
1353 1388 else:
1354 1389 meta['xrange'] = []
1355 1390
1356 1391 meta.update(self.meta)
1357 1392 ret['metadata'] = meta
1358 1393 return json.dumps(ret)
1359 1394
1360 1395 @property
1361 1396 def times(self):
1362 1397 '''
1363 1398 Return the list of times of the current data
1364 1399 '''
1365 1400
1366 1401 ret = numpy.array(self.__times)
1367 1402 ret.sort()
1368 1403 return ret
1369 1404
1370 1405 @property
1371 1406 def min_time(self):
1372 1407 '''
1373 1408 Return the minimun time value
1374 1409 '''
1375 1410
1376 1411 return self.times[0]
1377 1412
1378 1413 @property
1379 1414 def max_time(self):
1380 1415 '''
1381 1416 Return the maximun time value
1382 1417 '''
1383 1418
1384 1419 return self.times[-1]
1385 1420
1386 1421 @property
1387 1422 def heights(self):
1388 1423 '''
1389 1424 Return the list of heights of the current data
1390 1425 '''
1391 1426
1392 1427 return numpy.array(self.__heights[-1])
1393 1428
1394 1429 @staticmethod
1395 1430 def roundFloats(obj):
1396 1431 if isinstance(obj, list):
1397 1432 return list(map(PlotterData.roundFloats, obj))
1398 1433 elif isinstance(obj, float):
1399 1434 return round(obj, 2)
Show file before | Show file after | Hide comments
@@ -1,276 +1,302
1 1 '''
2 2 Created on Jul 9, 2014
3 3
4 4 @author: roj-idl71
5 5 '''
6 6 import os
7 7 import datetime
8 8 import numpy
9 9
10 10 from schainpy.model.graphics.jroplot_base import Plot, plt
11 11
12 12
13 13 class ScopePlot(Plot):
14 14
15 15 '''
16 16 Plot for Scope
17 17 '''
18 18
19 19 CODE = 'scope'
20 20 plot_name = 'Scope'
21 21 plot_type = 'scatter'
22 22
23 23 def setup(self):
24 24
25 25 self.xaxis = 'Range (Km)'
26 26 self.ncols = 1
27 27 self.nrows = 1
28 28 self.nplots = 1
29 29 self.ylabel = 'Intensity [dB]'
30 30 self.titles = ['Scope']
31 31 self.colorbar = False
32 32 self.width = 6
33 33 self.height = 4
34 34
35 35 def plot_iq(self, x, y, channelIndexList, thisDatetime, wintitle):
36 36
37 37 yreal = y[channelIndexList,:].real
38 38 yimag = y[channelIndexList,:].imag
39 39 title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y"))
40 40 self.xlabel = "Range (Km)"
41 41 self.ylabel = "Intensity - IQ"
42 42
43 43 self.y = yreal
44 44 self.x = x
45 45 self.xmin = min(x)
46 46 self.xmax = max(x)
47 47
48 48
49 49 self.titles[0] = title
50 50
51 51 for i,ax in enumerate(self.axes):
52 52 title = "Channel %d" %(i)
53 53 if ax.firsttime:
54 54 ax.plt_r = ax.plot(x, yreal[i,:], color='b')[0]
55 55 ax.plt_i = ax.plot(x, yimag[i,:], color='r')[0]
56 56 else:
57 57 ax.plt_r.set_data(x, yreal[i,:])
58 58 ax.plt_i.set_data(x, yimag[i,:])
59 59
60 60 def plot_power(self, x, y, channelIndexList, thisDatetime, wintitle):
61 61 y = y[channelIndexList,:] * numpy.conjugate(y[channelIndexList,:])
62 62 yreal = y.real
63 63 yreal = 10*numpy.log10(yreal)
64 64 self.y = yreal
65 65 title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y"))
66 66 self.xlabel = "Range (Km)"
67 67 self.ylabel = "Intensity"
68 68 self.xmin = min(x)
69 69 self.xmax = max(x)
70 70
71 71
72 72 self.titles[0] = title
73 73
74 74 for i,ax in enumerate(self.axes):
75 75 title = "Channel %d" %(i)
76 76
77 77 ychannel = yreal[i,:]
78 78
79 79 if ax.firsttime:
80 80 ax.plt_r = ax.plot(x, ychannel)[0]
81 81 else:
82 82 #pass
83 83 ax.plt_r.set_data(x, ychannel)
84 84
85 85 def plot_weatherpower(self, x, y, channelIndexList, thisDatetime, wintitle):
86 86
87 87
88 88 y = y[channelIndexList,:]
89 89 yreal = y.real
90 90 yreal = 10*numpy.log10(yreal)
91 91 self.y = yreal
92 92 title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
93 93 self.xlabel = "Range (Km)"
94 94 self.ylabel = "Intensity"
95 95 self.xmin = min(x)
96 96 self.xmax = max(x)
97 97
98 98 self.titles[0] =title
99 99 for i,ax in enumerate(self.axes):
100 100 title = "Channel %d" %(i)
101 101
102 102 ychannel = yreal[i,:]
103 103
104 104 if ax.firsttime:
105 105 ax.plt_r = ax.plot(x, ychannel)[0]
106 106 else:
107 107 #pass
108 108 ax.plt_r.set_data(x, ychannel)
109 109
110 110 def plot_weathervelocity(self, x, y, channelIndexList, thisDatetime, wintitle):
111 111
112 112 x = x[channelIndexList,:]
113 113 yreal = y
114 114 self.y = yreal
115 115 title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
116 116 self.xlabel = "Velocity (m/s)"
117 117 self.ylabel = "Range (Km)"
118 118 self.xmin = numpy.min(x)
119 119 self.xmax = numpy.max(x)
120 120 self.titles[0] =title
121 121 for i,ax in enumerate(self.axes):
122 122 title = "Channel %d" %(i)
123 123 xchannel = x[i,:]
124 124 if ax.firsttime:
125 125 ax.plt_r = ax.plot(xchannel, yreal)[0]
126 126 else:
127 127 #pass
128 128 ax.plt_r.set_data(xchannel, yreal)
129 129
130 130 def plot_weatherspecwidth(self, x, y, channelIndexList, thisDatetime, wintitle):
131 131
132 132 x = x[channelIndexList,:]
133 133 yreal = y
134 134 self.y = yreal
135 135 title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
136 136 self.xlabel = "width "
137 137 self.ylabel = "Range (Km)"
138 138 self.xmin = numpy.min(x)
139 139 self.xmax = numpy.max(x)
140 140 self.titles[0] =title
141 141 for i,ax in enumerate(self.axes):
142 142 title = "Channel %d" %(i)
143 143 xchannel = x[i,:]
144 144 if ax.firsttime:
145 145 ax.plt_r = ax.plot(xchannel, yreal)[0]
146 146 else:
147 147 #pass
148 148 ax.plt_r.set_data(xchannel, yreal)
149 149
150 150 def plot(self):
151 151 if self.channels:
152 152 channels = self.channels
153 153 else:
154 154 channels = self.data.channels
155 155
156 156 thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1])
157 157 if self.CODE == "pp_power":
158 158 scope = self.data['pp_power']
159 elif self.CODE == "pp_signal":
160 scope = self.data["pp_signal"]
159 161 elif self.CODE == "pp_velocity":
160 162 scope = self.data["pp_velocity"]
161 163 elif self.CODE == "pp_specwidth":
162 164 scope = self.data["pp_specwidth"]
163 165 else:
164 166 scope =self.data["scope"]
165 167
166 168 if self.data.flagDataAsBlock:
167 169
168 170 for i in range(self.data.nProfiles):
169 171
170 172 wintitle1 = " [Profile = %d] " %i
171 173 if self.CODE =="scope":
172 174 if self.type == "power":
173 175 self.plot_power(self.data.heights,
174 176 scope[:,i,:],
175 177 channels,
176 178 thisDatetime,
177 179 wintitle1
178 180 )
179 181
180 182 if self.type == "iq":
181 183 self.plot_iq(self.data.heights,
182 184 scope[:,i,:],
183 185 channels,
184 186 thisDatetime,
185 187 wintitle1
186 188 )
187 189 if self.CODE=="pp_power":
188 190 self.plot_weatherpower(self.data.heights,
189 191 scope[:,i,:],
190 192 channels,
191 193 thisDatetime,
192 194 wintitle
193 195 )
196 if self.CODE=="pp_signal":
197 self.plot_weatherpower(self.data.heights,
198 scope[:,i,:],
199 channels,
200 thisDatetime,
201 wintitle
202 )
194 203 if self.CODE=="pp_velocity":
195 204 self.plot_weathervelocity(scope[:,i,:],
196 205 self.data.heights,
197 206 channels,
198 207 thisDatetime,
199 208 wintitle
200 209 )
201 210 if self.CODE=="pp_spcwidth":
202 211 self.plot_weatherspecwidth(scope[:,i,:],
203 212 self.data.heights,
204 213 channels,
205 214 thisDatetime,
206 215 wintitle
207 216 )
208 217 else:
209 218 wintitle = " [Profile = %d] " %self.data.profileIndex
210 219 if self.CODE== "scope":
211 220 if self.type == "power":
212 221 self.plot_power(self.data.heights,
213 222 scope,
214 223 channels,
215 224 thisDatetime,
216 225 wintitle
217 226 )
218 227
219 228 if self.type == "iq":
220 229 self.plot_iq(self.data.heights,
221 230 scope,
222 231 channels,
223 232 thisDatetime,
224 233 wintitle
225 234 )
226 235 if self.CODE=="pp_power":
227 236 self.plot_weatherpower(self.data.heights,
228 237 scope,
229 238 channels,
230 239 thisDatetime,
231 240 wintitle
232 241 )
242 if self.CODE=="pp_signal":
243 self.plot_weatherpower(self.data.heights,
244 scope,
245 channels,
246 thisDatetime,
247 wintitle
248 )
233 249 if self.CODE=="pp_velocity":
234 250 self.plot_weathervelocity(scope,
235 251 self.data.heights,
236 252 channels,
237 253 thisDatetime,
238 254 wintitle
239 255 )
240 256 if self.CODE=="pp_specwidth":
241 257 self.plot_weatherspecwidth(scope,
242 258 self.data.heights,
243 259 channels,
244 260 thisDatetime,
245 261 wintitle
246 262 )
247 263
248 264
249 265
250 266 class PulsepairPowerPlot(ScopePlot):
251 267 '''
252 Plot for
268 Plot for P= S+N
253 269 '''
254 270
255 271 CODE = 'pp_power'
256 272 plot_name = 'PulsepairPower'
257 273 plot_type = 'scatter'
258 274 buffering = False
259 275
260 276 class PulsepairVelocityPlot(ScopePlot):
261 277 '''
262 Plot for
278 Plot for VELOCITY
263 279 '''
264 280 CODE = 'pp_velocity'
265 281 plot_name = 'PulsepairVelocity'
266 282 plot_type = 'scatter'
267 283 buffering = False
268 284
269 285 class PulsepairSpecwidthPlot(ScopePlot):
270 286 '''
271 Plot for
287 Plot for WIDTH
272 288 '''
273 289 CODE = 'pp_specwidth'
274 290 plot_name = 'PulsepairSpecwidth'
275 291 plot_type = 'scatter'
276 292 buffering = False
293
294 class PulsepairSignalPlot(ScopePlot):
295 '''
296 Plot for S
297 '''
298
299 CODE = 'pp_signal'
300 plot_name = 'PulsepairSignal'
301 plot_type = 'scatter'
302 buffering = False
Show file before | Show file after | Hide comments
@@ -1,512 +1,519
1 1 import numpy,math,random,time
2 2 #---------------1 Heredamos JRODatareader
3 3 from schainpy.model.io.jroIO_base import *
4 4 #---------------2 Heredamos las propiedades de ProcessingUnit
5 5 from schainpy.model.proc.jroproc_base import ProcessingUnit,Operation,MPDecorator
6 6 #---------------3 Importaremos las clases BascicHeader, SystemHeader, RadarControlHeader, ProcessingHeader
7 7 from schainpy.model.data.jroheaderIO import PROCFLAG, BasicHeader,SystemHeader,RadarControllerHeader, ProcessingHeader
8 8 #---------------4 Importaremos el objeto Voltge
9 9 from schainpy.model.data.jrodata import Voltage
10 10
11 11 class SimulatorReader(JRODataReader, ProcessingUnit):
12 12 incIntFactor = 1
13 13 nFFTPoints = 0
14 14 FixPP_IncInt = 1
15 15 FixRCP_IPP = 1000
16 16 FixPP_CohInt = 1
17 17 Tau_0 = 250
18 18 AcqH0_0 = 70
19 19 H0 = AcqH0_0
20 20 AcqDH_0 = 1.25
21 21 DH0 = AcqDH_0
22 22 Bauds = 32
23 23 BaudWidth = None
24 24 FixRCP_TXA = 40
25 25 FixRCP_TXB = 70
26 26 fAngle = 2.0*math.pi*(1/16)
27 27 DC_level = 500
28 28 stdev = 8
29 29 Num_Codes = 2
30 30 #code0 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1])
31 31 #code1 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0])
32 32 #Dyn_snCode = numpy.array([Num_Codes,Bauds])
33 33 Dyn_snCode = None
34 34 Samples = 200
35 35 channels = 2
36 36 pulses = None
37 37 Reference = None
38 38 pulse_size = None
39 39 prof_gen = None
40 40 Fdoppler = 100
41 41 Hdoppler = 36
42 42 Adoppler = 300
43 43 frequency = 9345
44 44 nTotalReadFiles = 1000
45 45
46 46 def __init__(self):
47 47 """
48 48 Inicializador de la clases SimulatorReader para
49 49 generar datos de voltage simulados.
50 50 Input:
51 51 dataOut: Objeto de la clase Voltage.
52 52 Este Objeto sera utilizado apra almacenar
53 53 un perfil de datos cada vez qe se haga
54 54 un requerimiento (getData)
55 55 """
56 56 ProcessingUnit.__init__(self)
57 57 print(" [ START ] init - Metodo Simulator Reader")
58 58
59 59 self.isConfig = False
60 60 self.basicHeaderObj = BasicHeader(LOCALTIME)
61 61 self.systemHeaderObj = SystemHeader()
62 62 self.radarControllerHeaderObj = RadarControllerHeader()
63 63 self.processingHeaderObj = ProcessingHeader()
64 64 self.profileIndex = 2**32-1
65 65 self.dataOut = Voltage()
66 66 #code0 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1])
67 67 code0 = numpy.array([1,1,1,-1,1,1,-1,1,1,1,1,-1,-1,-1,1,-1,1,1,1,-1,1,1,-1,1,-1,-1,-1,1,1,1,-1,1])
68 68 #code1 = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0])
69 69 code1 = numpy.array([1,1,1,-1,1,1,-1,1,1,1,1,-1,-1,-1,1,-1,-1,-1,-1,1,-1,-1,1,-1,1,1,1,-1,-1,-1,1,-1])
70 70 #self.Dyn_snCode = numpy.array([code0,code1])
71 71 self.Dyn_snCode = None
72 72
73 73 def set_kwargs(self, **kwargs):
74 74 for key, value in kwargs.items():
75 75 setattr(self, key, value)
76 76
77 77 def __hasNotDataInBuffer(self):
78 78
79 79 if self.profileIndex >= self.processingHeaderObj.profilesPerBlock* self.nTxs:
80 80 if self.nReadBlocks>0:
81 81 tmp = self.dataOut.utctime
82 82 tmp_utc = int(self.dataOut.utctime)
83 83 tmp_milisecond = int((tmp-tmp_utc)*1000)
84 84 self.basicHeaderObj.utc = tmp_utc
85 85 self.basicHeaderObj.miliSecond= tmp_milisecond
86 86 return 1
87 87 return 0
88 88
89 89 def setNextFile(self):
90 90 """Set the next file to be readed open it and parse de file header"""
91 91
92 92 if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile):
93 93 self.nReadFiles=self.nReadFiles+1
94 94 if self.nReadFiles > self.nTotalReadFiles:
95 95 self.flagNoMoreFiles=1
96 96 raise schainpy.admin.SchainWarning('No more files to read')
97 97
98 98 print('------------------- [Opening file] ------------------------------',self.nReadFiles)
99 99 self.nReadBlocks = 0
100 100 #if self.nReadBlocks==0:
101 101 # self.readFirstHeader()
102 102
103 103 def __setNewBlock(self):
104 104 self.setNextFile()
105 105 if self.flagIsNewFile:
106 106 return 1
107 107
108 108 def readNextBlock(self):
109 109 while True:
110 110 self.__setNewBlock()
111 111 if not(self.readBlock()):
112 112 return 0
113 113 self.getBasicHeader()
114 114 break
115 115 if self.verbose:
116 116 print("[Reading] Block No. %d/%d -> %s" %(self.nReadBlocks,
117 117 self.processingHeaderObj.dataBlocksPerFile,
118 118 self.dataOut.datatime.ctime()) )
119 119 return 1
120 120
121 121 def getFirstHeader(self):
122 122 self.getBasicHeader()
123 123 self.dataOut.processingHeaderObj = self.processingHeaderObj.copy()
124 124 self.dataOut.systemHeaderObj = self.systemHeaderObj.copy()
125 125 self.dataOut.radarControllerHeaderObj = self.radarControllerHeaderObj.copy()
126 126 self.dataOut.dtype = self.dtype
127 127
128 128 self.dataOut.nProfiles = self.processingHeaderObj.profilesPerBlock
129 129 self.dataOut.heightList = numpy.arange(self.processingHeaderObj.nHeights) * self.processingHeaderObj.deltaHeight + self.processingHeaderObj.firstHeight
130 130 self.dataOut.channelList = list(range(self.systemHeaderObj.nChannels))
131 131 self.dataOut.nCohInt = self.processingHeaderObj.nCohInt
132 132 # asumo q la data no esta decodificada
133 133 self.dataOut.flagDecodeData = self.processingHeaderObj.flag_decode
134 134 # asumo q la data no esta sin flip
135 135 self.dataOut.flagDeflipData = self.processingHeaderObj.flag_deflip
136 136 self.dataOut.flagShiftFFT = self.processingHeaderObj.shif_fft
137 137 self.dataOut.frequency = self.frequency
138 138
139 139 def getBasicHeader(self):
140 140 self.dataOut.utctime = self.basicHeaderObj.utc + self.basicHeaderObj.miliSecond / \
141 141 1000. + self.profileIndex * self.radarControllerHeaderObj.ippSeconds
142 142
143 143 self.dataOut.flagDiscontinuousBlock = self.flagDiscontinuousBlock
144 144 self.dataOut.timeZone = self.basicHeaderObj.timeZone
145 145 self.dataOut.dstFlag = self.basicHeaderObj.dstFlag
146 146 self.dataOut.errorCount = self.basicHeaderObj.errorCount
147 147 self.dataOut.useLocalTime = self.basicHeaderObj.useLocalTime
148 148 self.dataOut.ippSeconds = self.radarControllerHeaderObj.ippSeconds / self.nTxs
149 149
150 150 def readFirstHeader(self):
151 151
152 152 datatype = int(numpy.log2((self.processingHeaderObj.processFlags &
153 153 PROCFLAG.DATATYPE_MASK)) - numpy.log2(PROCFLAG.DATATYPE_CHAR))
154 154 if datatype == 0:
155 155 datatype_str = numpy.dtype([('real', '<i1'), ('imag', '<i1')])
156 156 elif datatype == 1:
157 157 datatype_str = numpy.dtype([('real', '<i2'), ('imag', '<i2')])
158 158 elif datatype == 2:
159 159 datatype_str = numpy.dtype([('real', '<i4'), ('imag', '<i4')])
160 160 elif datatype == 3:
161 161 datatype_str = numpy.dtype([('real', '<i8'), ('imag', '<i8')])
162 162 elif datatype == 4:
163 163 datatype_str = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
164 164 elif datatype == 5:
165 165 datatype_str = numpy.dtype([('real', '<f8'), ('imag', '<f8')])
166 166 else:
167 167 raise ValueError('Data type was not defined')
168 168
169 169 self.dtype = datatype_str
170 170
171 171
172 172 def set_RCH(self, expType=2, nTx=1,ipp=None, txA=0, txB=0,
173 173 nWindows=None, nHeights=None, firstHeight=None, deltaHeight=None,
174 174 numTaus=0, line6Function=0, line5Function=0, fClock=None,
175 175 prePulseBefore=0, prePulseAfter=0,
176 176 codeType=0, nCode=0, nBaud=0, code=None,
177 177 flip1=0, flip2=0,Taus=0):
178 178 self.radarControllerHeaderObj.expType = expType
179 179 self.radarControllerHeaderObj.nTx = nTx
180 180 self.radarControllerHeaderObj.ipp = float(ipp)
181 181 self.radarControllerHeaderObj.txA = float(txA)
182 182 self.radarControllerHeaderObj.txB = float(txB)
183 183 self.radarControllerHeaderObj.rangeIpp = b'A\n'#ipp
184 184 self.radarControllerHeaderObj.rangeTxA = b''
185 185 self.radarControllerHeaderObj.rangeTxB = b''
186 186
187 187 self.radarControllerHeaderObj.nHeights = int(nHeights)
188 188 self.radarControllerHeaderObj.firstHeight = numpy.array([firstHeight])
189 189 self.radarControllerHeaderObj.deltaHeight = numpy.array([deltaHeight])
190 190 self.radarControllerHeaderObj.samplesWin = numpy.array([nHeights])
191 191
192 192
193 193 self.radarControllerHeaderObj.nWindows = nWindows
194 194 self.radarControllerHeaderObj.numTaus = numTaus
195 195 self.radarControllerHeaderObj.codeType = codeType
196 196 self.radarControllerHeaderObj.line6Function = line6Function
197 197 self.radarControllerHeaderObj.line5Function = line5Function
198 198 #self.radarControllerHeaderObj.fClock = fClock
199 199 self.radarControllerHeaderObj.prePulseBefore= prePulseBefore
200 200 self.radarControllerHeaderObj.prePulseAfter = prePulseAfter
201 201
202 202 self.radarControllerHeaderObj.flip1 = flip1
203 203 self.radarControllerHeaderObj.flip2 = flip2
204 204
205 205 self.radarControllerHeaderObj.code_size = 0
206 206 if self.radarControllerHeaderObj.codeType != 0:
207 207 self.radarControllerHeaderObj.nCode = nCode
208 208 self.radarControllerHeaderObj.nBaud = nBaud
209 209 self.radarControllerHeaderObj.code = code
210 210 self.radarControllerHeaderObj.code_size = int(numpy.ceil(nBaud / 32.)) * nCode * 4
211 211
212 212 if fClock is None and deltaHeight is not None:
213 213 self.fClock = 0.15 / (deltaHeight * 1e-6)
214 214 self.radarControllerHeaderObj.fClock = self.fClock
215 215 if numTaus==0:
216 216 self.radarControllerHeaderObj.Taus = numpy.array(0,'<f4')
217 217 else:
218 218 self.radarControllerHeaderObj.Taus = numpy.array(Taus,'<f4')
219 219
220 220 def set_PH(self, dtype=0, blockSize=0, profilesPerBlock=0,
221 221 dataBlocksPerFile=0, nWindows=0, processFlags=0, nCohInt=0,
222 222 nIncohInt=0, totalSpectra=0, nHeights=0, firstHeight=0,
223 223 deltaHeight=0, samplesWin=0, spectraComb=0, nCode=0,
224 224 code=0, nBaud=None, shif_fft=False, flag_dc=False,
225 225 flag_cspc=False, flag_decode=False, flag_deflip=False):
226 226
227 227 self.processingHeaderObj.dtype = dtype
228 228 self.processingHeaderObj.profilesPerBlock = profilesPerBlock
229 229 self.processingHeaderObj.dataBlocksPerFile = dataBlocksPerFile
230 230 self.processingHeaderObj.nWindows = nWindows
231 231 self.processingHeaderObj.processFlags = processFlags
232 232 self.processingHeaderObj.nCohInt = nCohInt
233 233 self.processingHeaderObj.nIncohInt = nIncohInt
234 234 self.processingHeaderObj.totalSpectra = totalSpectra
235 235
236 236 self.processingHeaderObj.nHeights = int(nHeights)
237 237 self.processingHeaderObj.firstHeight = firstHeight#numpy.array([firstHeight])#firstHeight
238 238 self.processingHeaderObj.deltaHeight = deltaHeight#numpy.array([deltaHeight])#deltaHeight
239 239 self.processingHeaderObj.samplesWin = nHeights#numpy.array([nHeights])#nHeights
240 240
241 241 def set_BH(self, utc = 0, miliSecond = 0, timeZone = 0):
242 242 self.basicHeaderObj.utc = utc
243 243 self.basicHeaderObj.miliSecond = miliSecond
244 244 self.basicHeaderObj.timeZone = timeZone
245 245
246 246 def set_SH(self, nSamples=0, nProfiles=0, nChannels=0, adcResolution=14, pciDioBusWidth=32):
247 247 #self.systemHeaderObj.size = size
248 248 self.systemHeaderObj.nSamples = nSamples
249 249 self.systemHeaderObj.nProfiles = nProfiles
250 250 self.systemHeaderObj.nChannels = nChannels
251 251 self.systemHeaderObj.adcResolution = adcResolution
252 252 self.systemHeaderObj.pciDioBusWidth = pciDioBusWidth
253 253
254 254 def init_acquisition(self):
255 255
256 256 if self.nFFTPoints != 0:
257 257 self.incIntFactor = m_nProfilesperBlock/self.nFFTPoints
258 258 if (self.FixPP_IncInt > self.incIntFactor):
259 259 self.incIntFactor = self.FixPP_IncInt/ self.incIntFactor
260 260 elif(self.FixPP_IncInt< self.incIntFactor):
261 261 print("False alert...")
262 262
263 263 ProfilesperBlock = self.processingHeaderObj.profilesPerBlock
264 264
265 265 self.timeperblock =int(((self.FixRCP_IPP
266 266 *ProfilesperBlock
267 267 *self.FixPP_CohInt
268 268 *self.incIntFactor)
269 269 /150.0)
270 270 *0.9
271 271 +0.5)
272 272 # para cada canal
273 273 self.profiles = ProfilesperBlock*self.FixPP_CohInt
274 274 self.profiles = ProfilesperBlock
275 275 self.Reference = int((self.Tau_0-self.AcqH0_0)/(self.AcqDH_0)+0.5)
276 276 self.BaudWidth = int((self.FixRCP_TXA/self.AcqDH_0)/self.Bauds + 0.5 )
277 277
278 278 if (self.BaudWidth==0):
279 279 self.BaudWidth=1
280 280
281 281 def init_pulse(self,Num_Codes=Num_Codes,Bauds=Bauds,BaudWidth=BaudWidth,Dyn_snCode=Dyn_snCode):
282 282
283 283 Num_Codes = Num_Codes
284 284 Bauds = Bauds
285 285 BaudWidth = BaudWidth
286 286 Dyn_snCode = Dyn_snCode
287 287
288 288 if Dyn_snCode:
289 289 print("EXISTE")
290 290 else:
291 291 print("No existe")
292 292
293 293 if Dyn_snCode: # if Bauds:
294 294 pulses = list(range(0,Num_Codes))
295 295 num_codes = Num_Codes
296 296 for i in range(num_codes):
297 297 pulse_size = Bauds*BaudWidth
298 298 pulses[i] = numpy.zeros(pulse_size)
299 299 for j in range(Bauds):
300 300 for k in range(BaudWidth):
301 301 pulses[i][j*BaudWidth+k] = int(Dyn_snCode[i][j]*600)
302 302 else:
303 303 print("sin code")
304 304 pulses = list(range(1))
305 305 if self.AcqDH_0>0.149:
306 306 pulse_size = int(self.FixRCP_TXB/0.15+0.5)
307 307 else:
308 308 pulse_size = int((self.FixRCP_TXB/self.AcqDH_0)+0.5) #0.0375
309 309 pulses[0] = numpy.ones(pulse_size)
310 310 pulses = 600*pulses[0]
311 311
312 312 return pulses,pulse_size
313 313
314 314 def jro_GenerateBlockOfData(self,Samples=Samples,DC_level= DC_level,stdev=stdev,
315 315 Reference= Reference,pulses= pulses,
316 316 Num_Codes= Num_Codes,pulse_size=pulse_size,
317 317 prof_gen= prof_gen,H0 = H0,DH0=DH0,
318 318 Adoppler=Adoppler,Fdoppler= Fdoppler,Hdoppler=Hdoppler):
319 319 Samples = Samples
320 320 DC_level = DC_level
321 321 stdev = stdev
322 322 m_nR = Reference
323 323 pulses = pulses
324 324 num_codes = Num_Codes
325 325 ps = pulse_size
326 326 prof_gen = prof_gen
327 327 channels = self.channels
328 328 H0 = H0
329 329 DH0 = DH0
330 330 ippSec = self.radarControllerHeaderObj.ippSeconds
331 331 Fdoppler = self.Fdoppler
332 332 Hdoppler = self.Hdoppler
333 333 Adoppler = self.Adoppler
334 334
335 335 self.datablock = numpy.zeros([channels,prof_gen,Samples],dtype= numpy.complex64)
336 336 for i in range(channels):
337 337 for k in range(prof_gen):
338 338 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·NOISEΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
339 339 Noise_r = numpy.random.normal(DC_level,stdev,Samples)
340 340 Noise_i = numpy.random.normal(DC_level,stdev,Samples)
341 341 Noise = numpy.zeros(Samples,dtype=complex)
342 342 Noise.real = Noise_r
343 343 Noise.imag = Noise_i
344 344 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·PULSOSΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
345 345 Pulso = numpy.zeros(pulse_size,dtype=complex)
346 346 Pulso.real = pulses[k%num_codes]
347 347 Pulso.imag = pulses[k%num_codes]
348 348 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· PULSES+NOISEΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·
349 349 InBuffer = numpy.zeros(Samples,dtype=complex)
350 350 InBuffer[m_nR:m_nR+ps] = Pulso
351 351 InBuffer = InBuffer+Noise
352 352 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· ANGLE Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
353 353 InBuffer.real[m_nR:m_nR+ps] = InBuffer.real[m_nR:m_nR+ps]*(math.cos( self.fAngle)*5)
354 354 InBuffer.imag[m_nR:m_nR+ps] = InBuffer.imag[m_nR:m_nR+ps]*(math.sin( self.fAngle)*5)
355 355 InBuffer=InBuffer
356 356 self.datablock[i][k]= InBuffer
357 357
358 358 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·DOPPLER SIGNAL...............................................
359 359 time_vec = numpy.linspace(0,(prof_gen-1)*ippSec,int(prof_gen))+self.nReadBlocks*ippSec*prof_gen+(self.nReadFiles-1)*ippSec*prof_gen
360 360 fd = Fdoppler #+(600.0/120)*self.nReadBlocks
361 361 d_signal = Adoppler*numpy.array(numpy.exp(1.0j*2.0*math.pi*fd*time_vec),dtype=numpy.complex64)
362 362 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·SeΓ±al con ancho espectralΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
363 #specw_sig = numpy.linspace(-149,150,300)
364 #w = 8
365 #A = 20
366 #specw_sig = specw_sig/w
367 #specw_sig = numpy.sinc(specw_sig)
368 #specw_sig = A*numpy.array(specw_sig,dtype=numpy.complex64)
363 if prof_gen%2==0:
364 min = int(prof_gen/2.0-1.0)
365 max = int(prof_gen/2.0)
366 else:
367 min = int(prof_gen/2.0)
368 max = int(prof_gen/2.0)
369 specw_sig = numpy.linspace(-min,max,prof_gen)
370 w = 4
371 A = 20
372 specw_sig = specw_sig/w
373 specw_sig = numpy.sinc(specw_sig)
374 specw_sig = A*numpy.array(specw_sig,dtype=numpy.complex64)
369 375 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· DATABLOCK + DOPPLERΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
370 376 HD=int(Hdoppler/self.AcqDH_0)
371 377 for i in range(12):
372 378 self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ d_signal# RESULT
373 379 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· DATABLOCK + DOPPLER*Sinc(x)Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
374 #HD=int(Hdoppler/self.AcqDH_0)
375 #HD=int(HD/2)
376 #for i in range(12):
377 # self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ specw_sig*d_signal# RESULT
380 HD=int(Hdoppler/self.AcqDH_0)
381 HD=int(HD/2)
382 for i in range(12):
383 self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ specw_sig*d_signal# RESULT
378 384
379 385 def readBlock(self):
380 386
381 387 self.jro_GenerateBlockOfData(Samples= self.samples,DC_level=self.DC_level,
382 388 stdev=self.stdev,Reference= self.Reference,
383 389 pulses = self.pulses,Num_Codes=self.Num_Codes,
384 390 pulse_size=self.pulse_size,prof_gen=self.profiles,
385 391 H0=self.H0,DH0=self.DH0)
386 392
387 393 self.profileIndex = 0
388 394 self.flagIsNewFile = 0
389 395 self.flagIsNewBlock = 1
390 396 self.nTotalBlocks += 1
391 397 self.nReadBlocks += 1
392 398
393 399 return 1
394 400
395 401
396 402 def getData(self):
397 403 if self.flagNoMoreFiles:
398 404 self.dataOut.flagNodata = True
399 405 return 0
400 406 self.flagDiscontinuousBlock = 0
401 407 self.flagIsNewBlock = 0
402 408 if self.__hasNotDataInBuffer(): # aqui es verdad
403 409 if not(self.readNextBlock()): # return 1 y por eso el if not salta a getBasic Header
404 410 return 0
405 411 self.getFirstHeader() # atributo
406 412
407 413 if not self.getByBlock:
408 414 self.dataOut.flagDataAsBlock = False
409 415 self.dataOut.data = self.datablock[:, self.profileIndex, :]
410 416 self.dataOut.profileIndex = self.profileIndex
411 417 self.profileIndex += 1
412 418 else:
413 419 pass
414 420 self.dataOut.flagNoData = False
415 421 self.getBasicHeader()
416 422 self.dataOut.realtime = self.online
417 423 return self.dataOut.data
418 424
419 425
420 426 def setup(self,frequency=49.92e6,incIntFactor= 1, nFFTPoints = 0, FixPP_IncInt=1,FixRCP_IPP=1000,
421 427 FixPP_CohInt= 1,Tau_0= 250,AcqH0_0 = 70 ,AcqDH_0=1.25, Bauds= 32,
422 428 FixRCP_TXA = 40, FixRCP_TXB = 50, fAngle = 2.0*math.pi*(1/16),DC_level= 50,
423 429 stdev= 8,Num_Codes = 1 , Dyn_snCode = None, samples=200,
424 channels=2,Fdoppler=20,Hdoppler=36,Adoppler=500,nTotalReadFiles=10000,
430 channels=2,Fdoppler=20,Hdoppler=36,Adoppler=500,
431 profilesPerBlock=300,dataBlocksPerFile=120,nTotalReadFiles=10000,
425 432 **kwargs):
426 433
427 434 self.set_kwargs(**kwargs)
428 435 self.nReadBlocks = 0
429 436 self.nReadFiles = 1
430 437 print('------------------- [Opening file: ] ------------------------------',self.nReadFiles)
431 438
432 439 tmp = time.time()
433 440 tmp_utc = int(tmp)
434 441 tmp_milisecond = int((tmp-tmp_utc)*1000)
435 442 print(" SETUP -basicHeaderObj.utc",datetime.datetime.utcfromtimestamp(tmp))
436 443 if Dyn_snCode is None:
437 444 Num_Codes=1
438 445 Bauds =1
439 446
440 447
441 448
442 449 self.set_BH(utc= tmp_utc,miliSecond= tmp_milisecond,timeZone=300 )
443 450 self.set_RCH( expType=0, nTx=150,ipp=FixRCP_IPP, txA=FixRCP_TXA, txB= FixRCP_TXB,
444 451 nWindows=1 , nHeights=samples, firstHeight=AcqH0_0, deltaHeight=AcqDH_0,
445 452 numTaus=1, line6Function=0, line5Function=0, fClock=None,
446 453 prePulseBefore=0, prePulseAfter=0,
447 454 codeType=0, nCode=Num_Codes, nBaud=32, code=Dyn_snCode,
448 455 flip1=0, flip2=0,Taus=Tau_0)
449 456
450 self.set_PH(dtype=0, blockSize=0, profilesPerBlock=300,
451 dataBlocksPerFile=120, nWindows=1, processFlags=numpy.array([1024]), nCohInt=1,
457 self.set_PH(dtype=0, blockSize=0, profilesPerBlock=profilesPerBlock,
458 dataBlocksPerFile=dataBlocksPerFile, nWindows=1, processFlags=numpy.array([1024]), nCohInt=1,
452 459 nIncohInt=1, totalSpectra=0, nHeights=samples, firstHeight=AcqH0_0,
453 460 deltaHeight=AcqDH_0, samplesWin=samples, spectraComb=0, nCode=0,
454 461 code=0, nBaud=None, shif_fft=False, flag_dc=False,
455 462 flag_cspc=False, flag_decode=False, flag_deflip=False)
456 463
457 self.set_SH(nSamples=samples, nProfiles=300, nChannels=channels)
464 self.set_SH(nSamples=samples, nProfiles=profilesPerBlock, nChannels=channels)
458 465
459 466 self.readFirstHeader()
460 467
461 468 self.frequency = frequency
462 469 self.incIntFactor = incIntFactor
463 470 self.nFFTPoints = nFFTPoints
464 471 self.FixPP_IncInt = FixPP_IncInt
465 472 self.FixRCP_IPP = FixRCP_IPP
466 473 self.FixPP_CohInt = FixPP_CohInt
467 474 self.Tau_0 = Tau_0
468 475 self.AcqH0_0 = AcqH0_0
469 476 self.H0 = AcqH0_0
470 477 self.AcqDH_0 = AcqDH_0
471 478 self.DH0 = AcqDH_0
472 479 self.Bauds = Bauds
473 480 self.FixRCP_TXA = FixRCP_TXA
474 481 self.FixRCP_TXB = FixRCP_TXB
475 482 self.fAngle = fAngle
476 483 self.DC_level = DC_level
477 484 self.stdev = stdev
478 485 self.Num_Codes = Num_Codes
479 486 self.Dyn_snCode = Dyn_snCode
480 487 self.samples = samples
481 488 self.channels = channels
482 489 self.profiles = None
483 490 self.m_nReference = None
484 491 self.Baudwidth = None
485 492 self.Fdoppler = Fdoppler
486 493 self.Hdoppler = Hdoppler
487 494 self.Adoppler = Adoppler
488 495 self.nTotalReadFiles = int(nTotalReadFiles)
489 496
490 497 print("IPP ", self.FixRCP_IPP)
491 498 print("Tau_0 ",self.Tau_0)
492 499 print("AcqH0_0",self.AcqH0_0)
493 500 print("samples,window ",self.samples)
494 501 print("AcqDH_0",AcqDH_0)
495 502 print("FixRCP_TXA",self.FixRCP_TXA)
496 503 print("FixRCP_TXB",self.FixRCP_TXB)
497 504 print("Dyn_snCode",Dyn_snCode)
498 505 print("Fdoppler", Fdoppler)
499 506 print("Hdoppler",Hdoppler)
500 507 print("Vdopplermax",Fdoppler*(3.0e8/self.frequency)/2.0)
501 508 print("nTotalReadFiles", nTotalReadFiles)
502 509
503 510 self.init_acquisition()
504 511 self.pulses,self.pulse_size=self.init_pulse(Num_Codes=self.Num_Codes,Bauds=self.Bauds,BaudWidth=self.BaudWidth,Dyn_snCode=Dyn_snCode)
505 512 print(" [ END ] - SETUP metodo")
506 513 return
507 514
508 515 def run(self,**kwargs): # metodo propio
509 516 if not(self.isConfig):
510 517 self.setup(**kwargs)
511 518 self.isConfig = True
512 519 self.getData()
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1 1 import sys
2 2 import numpy,math
3 3 from scipy import interpolate
4 4 from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
5 from schainpy.model.data.jrodata import Voltage
5 from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon
6 from schainpy.model.data import _noise
6 7 from schainpy.utils import log
7 8 from time import time
8 9
9 10
10 11
11 12 class VoltageProc(ProcessingUnit):
12 13
13 14 def __init__(self):
14 15
15 16 ProcessingUnit.__init__(self)
16 17
17 18 self.dataOut = Voltage()
18 19 self.flip = 1
19 20 self.setupReq = False
20 21
21 22 def run(self):
22 23
23 24 if self.dataIn.type == 'AMISR':
24 25 self.__updateObjFromAmisrInput()
25 26
26 27 if self.dataIn.type == 'Voltage':
27 28 self.dataOut.copy(self.dataIn)
28 29
29 30 def __updateObjFromAmisrInput(self):
30 31
31 32 self.dataOut.timeZone = self.dataIn.timeZone
32 33 self.dataOut.dstFlag = self.dataIn.dstFlag
33 34 self.dataOut.errorCount = self.dataIn.errorCount
34 35 self.dataOut.useLocalTime = self.dataIn.useLocalTime
35 36
36 37 self.dataOut.flagNoData = self.dataIn.flagNoData
37 38 self.dataOut.data = self.dataIn.data
38 39 self.dataOut.utctime = self.dataIn.utctime
39 40 self.dataOut.channelList = self.dataIn.channelList
40 41 #self.dataOut.timeInterval = self.dataIn.timeInterval
41 42 self.dataOut.heightList = self.dataIn.heightList
42 43 self.dataOut.nProfiles = self.dataIn.nProfiles
43 44
44 45 self.dataOut.nCohInt = self.dataIn.nCohInt
45 46 self.dataOut.ippSeconds = self.dataIn.ippSeconds
46 47 self.dataOut.frequency = self.dataIn.frequency
47 48
48 49 self.dataOut.azimuth = self.dataIn.azimuth
49 50 self.dataOut.zenith = self.dataIn.zenith
50 51
51 52 self.dataOut.beam.codeList = self.dataIn.beam.codeList
52 53 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
53 54 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
54 55
55 56
56 57 class selectChannels(Operation):
57 58
58 59 def run(self, dataOut, channelList):
59 60
60 61 channelIndexList = []
61 62 self.dataOut = dataOut
62 63 for channel in channelList:
63 64 if channel not in self.dataOut.channelList:
64 65 raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList)))
65 66
66 67 index = self.dataOut.channelList.index(channel)
67 68 channelIndexList.append(index)
68 69 self.selectChannelsByIndex(channelIndexList)
69 70 return self.dataOut
70 71
71 72 def selectChannelsByIndex(self, channelIndexList):
72 73 """
73 74 Selecciona un bloque de datos en base a canales segun el channelIndexList
74 75
75 76 Input:
76 77 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
77 78
78 79 Affected:
79 80 self.dataOut.data
80 81 self.dataOut.channelIndexList
81 82 self.dataOut.nChannels
82 83 self.dataOut.m_ProcessingHeader.totalSpectra
83 84 self.dataOut.systemHeaderObj.numChannels
84 85 self.dataOut.m_ProcessingHeader.blockSize
85 86
86 87 Return:
87 88 None
88 89 """
89 90
90 91 for channelIndex in channelIndexList:
91 92 if channelIndex not in self.dataOut.channelIndexList:
92 93 raise ValueError("The value %d in channelIndexList is not valid" %channelIndex)
93 94
94 95 if self.dataOut.type == 'Voltage':
95 96 if self.dataOut.flagDataAsBlock:
96 97 """
97 98 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
98 99 """
99 100 data = self.dataOut.data[channelIndexList,:,:]
100 101 else:
101 102 data = self.dataOut.data[channelIndexList,:]
102 103
103 104 self.dataOut.data = data
104 105 # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
105 106 self.dataOut.channelList = range(len(channelIndexList))
106 107
107 108 elif self.dataOut.type == 'Spectra':
108 109 data_spc = self.dataOut.data_spc[channelIndexList, :]
109 110 data_dc = self.dataOut.data_dc[channelIndexList, :]
110 111
111 112 self.dataOut.data_spc = data_spc
112 113 self.dataOut.data_dc = data_dc
113 114
114 115 # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
115 116 self.dataOut.channelList = range(len(channelIndexList))
116 117 self.__selectPairsByChannel(channelIndexList)
117 118
118 119 return 1
119 120
120 121 def __selectPairsByChannel(self, channelList=None):
121 122
122 123 if channelList == None:
123 124 return
124 125
125 126 pairsIndexListSelected = []
126 127 for pairIndex in self.dataOut.pairsIndexList:
127 128 # First pair
128 129 if self.dataOut.pairsList[pairIndex][0] not in channelList:
129 130 continue
130 131 # Second pair
131 132 if self.dataOut.pairsList[pairIndex][1] not in channelList:
132 133 continue
133 134
134 135 pairsIndexListSelected.append(pairIndex)
135 136
136 137 if not pairsIndexListSelected:
137 138 self.dataOut.data_cspc = None
138 139 self.dataOut.pairsList = []
139 140 return
140 141
141 142 self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
142 143 self.dataOut.pairsList = [self.dataOut.pairsList[i]
143 144 for i in pairsIndexListSelected]
144 145
145 146 return
146 147
147 148 class selectHeights(Operation):
148 149
149 150 def run(self, dataOut, minHei=None, maxHei=None):
150 151 """
151 152 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
152 153 minHei <= height <= maxHei
153 154
154 155 Input:
155 156 minHei : valor minimo de altura a considerar
156 157 maxHei : valor maximo de altura a considerar
157 158
158 159 Affected:
159 160 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
160 161
161 162 Return:
162 163 1 si el metodo se ejecuto con exito caso contrario devuelve 0
163 164 """
164 165
165 166 self.dataOut = dataOut
166 167
167 168 if minHei == None:
168 169 minHei = self.dataOut.heightList[0]
169 170
170 171 if maxHei == None:
171 172 maxHei = self.dataOut.heightList[-1]
172 173
173 174 if (minHei < self.dataOut.heightList[0]):
174 175 minHei = self.dataOut.heightList[0]
175 176
176 177 if (maxHei > self.dataOut.heightList[-1]):
177 178 maxHei = self.dataOut.heightList[-1]
178 179
179 180 minIndex = 0
180 181 maxIndex = 0
181 182 heights = self.dataOut.heightList
182 183
183 184 inda = numpy.where(heights >= minHei)
184 185 indb = numpy.where(heights <= maxHei)
185 186
186 187 try:
187 188 minIndex = inda[0][0]
188 189 except:
189 190 minIndex = 0
190 191
191 192 try:
192 193 maxIndex = indb[0][-1]
193 194 except:
194 195 maxIndex = len(heights)
195 196
196 197 self.selectHeightsByIndex(minIndex, maxIndex)
197 198
198 199 return self.dataOut
199 200
200 201 def selectHeightsByIndex(self, minIndex, maxIndex):
201 202 """
202 203 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
203 204 minIndex <= index <= maxIndex
204 205
205 206 Input:
206 207 minIndex : valor de indice minimo de altura a considerar
207 208 maxIndex : valor de indice maximo de altura a considerar
208 209
209 210 Affected:
210 211 self.dataOut.data
211 212 self.dataOut.heightList
212 213
213 214 Return:
214 215 1 si el metodo se ejecuto con exito caso contrario devuelve 0
215 216 """
216 217
217 218 if self.dataOut.type == 'Voltage':
218 219 if (minIndex < 0) or (minIndex > maxIndex):
219 220 raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex))
220 221
221 222 if (maxIndex >= self.dataOut.nHeights):
222 223 maxIndex = self.dataOut.nHeights
223 224
224 225 #voltage
225 226 if self.dataOut.flagDataAsBlock:
226 227 """
227 228 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
228 229 """
229 230 data = self.dataOut.data[:,:, minIndex:maxIndex]
230 231 else:
231 232 data = self.dataOut.data[:, minIndex:maxIndex]
232 233
233 234 # firstHeight = self.dataOut.heightList[minIndex]
234 235
235 236 self.dataOut.data = data
236 237 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
237 238
238 239 if self.dataOut.nHeights <= 1:
239 240 raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights))
240 241 elif self.dataOut.type == 'Spectra':
241 242 if (minIndex < 0) or (minIndex > maxIndex):
242 243 raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (
243 244 minIndex, maxIndex))
244 245
245 246 if (maxIndex >= self.dataOut.nHeights):
246 247 maxIndex = self.dataOut.nHeights - 1
247 248
248 249 # Spectra
249 250 data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
250 251
251 252 data_cspc = None
252 253 if self.dataOut.data_cspc is not None:
253 254 data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
254 255
255 256 data_dc = None
256 257 if self.dataOut.data_dc is not None:
257 258 data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
258 259
259 260 self.dataOut.data_spc = data_spc
260 261 self.dataOut.data_cspc = data_cspc
261 262 self.dataOut.data_dc = data_dc
262 263
263 264 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
264 265
265 266 return 1
266 267
267 268
268 269 class filterByHeights(Operation):
269 270
270 271 def run(self, dataOut, window):
271 272
272 273 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
273 274
274 275 if window == None:
275 276 window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
276 277
277 278 newdelta = deltaHeight * window
278 279 r = dataOut.nHeights % window
279 280 newheights = (dataOut.nHeights-r)/window
280 281
281 282 if newheights <= 1:
282 283 raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window))
283 284
284 285 if dataOut.flagDataAsBlock:
285 286 """
286 287 Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
287 288 """
288 289 buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)]
289 290 buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window)
290 291 buffer = numpy.sum(buffer,3)
291 292
292 293 else:
293 294 buffer = dataOut.data[:,0:int(dataOut.nHeights-r)]
294 295 buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window))
295 296 buffer = numpy.sum(buffer,2)
296 297
297 298 dataOut.data = buffer
298 299 dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta
299 300 dataOut.windowOfFilter = window
300 301
301 302 return dataOut
302 303
303 304
304 305 class setH0(Operation):
305 306
306 307 def run(self, dataOut, h0, deltaHeight = None):
307 308
308 309 if not deltaHeight:
309 310 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
310 311
311 312 nHeights = dataOut.nHeights
312 313
313 314 newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
314 315
315 316 dataOut.heightList = newHeiRange
316 317
317 318 return dataOut
318 319
319 320
320 321 class deFlip(Operation):
321 322
322 323 def run(self, dataOut, channelList = []):
323 324
324 325 data = dataOut.data.copy()
325 326
326 327 if dataOut.flagDataAsBlock:
327 328 flip = self.flip
328 329 profileList = list(range(dataOut.nProfiles))
329 330
330 331 if not channelList:
331 332 for thisProfile in profileList:
332 333 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
333 334 flip *= -1.0
334 335 else:
335 336 for thisChannel in channelList:
336 337 if thisChannel not in dataOut.channelList:
337 338 continue
338 339
339 340 for thisProfile in profileList:
340 341 data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
341 342 flip *= -1.0
342 343
343 344 self.flip = flip
344 345
345 346 else:
346 347 if not channelList:
347 348 data[:,:] = data[:,:]*self.flip
348 349 else:
349 350 for thisChannel in channelList:
350 351 if thisChannel not in dataOut.channelList:
351 352 continue
352 353
353 354 data[thisChannel,:] = data[thisChannel,:]*self.flip
354 355
355 356 self.flip *= -1.
356 357
357 358 dataOut.data = data
358 359
359 360 return dataOut
360 361
361 362
362 363 class setAttribute(Operation):
363 364 '''
364 365 Set an arbitrary attribute(s) to dataOut
365 366 '''
366 367
367 368 def __init__(self):
368 369
369 370 Operation.__init__(self)
370 371 self._ready = False
371 372
372 373 def run(self, dataOut, **kwargs):
373 374
374 375 for key, value in kwargs.items():
375 376 setattr(dataOut, key, value)
376 377
377 378 return dataOut
378 379
379 380
380 381 class interpolateHeights(Operation):
381 382
382 383 def run(self, dataOut, topLim, botLim):
383 384 #69 al 72 para julia
384 385 #82-84 para meteoros
385 386 if len(numpy.shape(dataOut.data))==2:
386 387 sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2
387 388 sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
388 389 #dataOut.data[:,botLim:limSup+1] = sampInterp
389 390 dataOut.data[:,botLim:topLim+1] = sampInterp
390 391 else:
391 392 nHeights = dataOut.data.shape[2]
392 393 x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
393 394 y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))]
394 395 f = interpolate.interp1d(x, y, axis = 2)
395 396 xnew = numpy.arange(botLim,topLim+1)
396 397 ynew = f(xnew)
397 398 dataOut.data[:,:,botLim:topLim+1] = ynew
398 399
399 400 return dataOut
400 401
401 402
402 403 class CohInt(Operation):
403 404
404 405 isConfig = False
405 406 __profIndex = 0
406 407 __byTime = False
407 408 __initime = None
408 409 __lastdatatime = None
409 410 __integrationtime = None
410 411 __buffer = None
411 412 __bufferStride = []
412 413 __dataReady = False
413 414 __profIndexStride = 0
414 415 __dataToPutStride = False
415 416 n = None
416 417
417 418 def __init__(self, **kwargs):
418 419
419 420 Operation.__init__(self, **kwargs)
420 421
421 422 # self.isConfig = False
422 423
423 424 def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
424 425 """
425 426 Set the parameters of the integration class.
426 427
427 428 Inputs:
428 429
429 430 n : Number of coherent integrations
430 431 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
431 432 overlapping :
432 433 """
433 434
434 435 self.__initime = None
435 436 self.__lastdatatime = 0
436 437 self.__buffer = None
437 438 self.__dataReady = False
438 439 self.byblock = byblock
439 440 self.stride = stride
440 441
441 442 if n == None and timeInterval == None:
442 443 raise ValueError("n or timeInterval should be specified ...")
443 444
444 445 if n != None:
445 446 self.n = n
446 447 self.__byTime = False
447 448 else:
448 449 self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
449 450 self.n = 9999
450 451 self.__byTime = True
451 452
452 453 if overlapping:
453 454 self.__withOverlapping = True
454 455 self.__buffer = None
455 456 else:
456 457 self.__withOverlapping = False
457 458 self.__buffer = 0
458 459
459 460 self.__profIndex = 0
460 461
461 462 def putData(self, data):
462 463
463 464 """
464 465 Add a profile to the __buffer and increase in one the __profileIndex
465 466
466 467 """
467 468
468 469 if not self.__withOverlapping:
469 470 self.__buffer += data.copy()
470 471 self.__profIndex += 1
471 472 return
472 473
473 474 #Overlapping data
474 475 nChannels, nHeis = data.shape
475 476 data = numpy.reshape(data, (1, nChannels, nHeis))
476 477
477 478 #If the buffer is empty then it takes the data value
478 479 if self.__buffer is None:
479 480 self.__buffer = data
480 481 self.__profIndex += 1
481 482 return
482 483
483 484 #If the buffer length is lower than n then stakcing the data value
484 485 if self.__profIndex < self.n:
485 486 self.__buffer = numpy.vstack((self.__buffer, data))
486 487 self.__profIndex += 1
487 488 return
488 489
489 490 #If the buffer length is equal to n then replacing the last buffer value with the data value
490 491 self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
491 492 self.__buffer[self.n-1] = data
492 493 self.__profIndex = self.n
493 494 return
494 495
495 496
496 497 def pushData(self):
497 498 """
498 499 Return the sum of the last profiles and the profiles used in the sum.
499 500
500 501 Affected:
501 502
502 503 self.__profileIndex
503 504
504 505 """
505 506
506 507 if not self.__withOverlapping:
507 508 data = self.__buffer
508 509 n = self.__profIndex
509 510
510 511 self.__buffer = 0
511 512 self.__profIndex = 0
512 513
513 514 return data, n
514 515
515 516 #Integration with Overlapping
516 517 data = numpy.sum(self.__buffer, axis=0)
517 518 # print data
518 519 # raise
519 520 n = self.__profIndex
520 521
521 522 return data, n
522 523
523 524 def byProfiles(self, data):
524 525
525 526 self.__dataReady = False
526 527 avgdata = None
527 528 # n = None
528 529 # print data
529 530 # raise
530 531 self.putData(data)
531 532
532 533 if self.__profIndex == self.n:
533 534 avgdata, n = self.pushData()
534 535 self.__dataReady = True
535 536
536 537 return avgdata
537 538
538 539 def byTime(self, data, datatime):
539 540
540 541 self.__dataReady = False
541 542 avgdata = None
542 543 n = None
543 544
544 545 self.putData(data)
545 546
546 547 if (datatime - self.__initime) >= self.__integrationtime:
547 548 avgdata, n = self.pushData()
548 549 self.n = n
549 550 self.__dataReady = True
550 551
551 552 return avgdata
552 553
553 554 def integrateByStride(self, data, datatime):
554 555 # print data
555 556 if self.__profIndex == 0:
556 557 self.__buffer = [[data.copy(), datatime]]
557 558 else:
558 559 self.__buffer.append([data.copy(),datatime])
559 560 self.__profIndex += 1
560 561 self.__dataReady = False
561 562
562 563 if self.__profIndex == self.n * self.stride :
563 564 self.__dataToPutStride = True
564 565 self.__profIndexStride = 0
565 566 self.__profIndex = 0
566 567 self.__bufferStride = []
567 568 for i in range(self.stride):
568 569 current = self.__buffer[i::self.stride]
569 570 data = numpy.sum([t[0] for t in current], axis=0)
570 571 avgdatatime = numpy.average([t[1] for t in current])
571 572 # print data
572 573 self.__bufferStride.append((data, avgdatatime))
573 574
574 575 if self.__dataToPutStride:
575 576 self.__dataReady = True
576 577 self.__profIndexStride += 1
577 578 if self.__profIndexStride == self.stride:
578 579 self.__dataToPutStride = False
579 580 # print self.__bufferStride[self.__profIndexStride - 1]
580 581 # raise
581 582 return self.__bufferStride[self.__profIndexStride - 1]
582 583
583 584
584 585 return None, None
585 586
586 587 def integrate(self, data, datatime=None):
587 588
588 589 if self.__initime == None:
589 590 self.__initime = datatime
590 591
591 592 if self.__byTime:
592 593 avgdata = self.byTime(data, datatime)
593 594 else:
594 595 avgdata = self.byProfiles(data)
595 596
596 597
597 598 self.__lastdatatime = datatime
598 599
599 600 if avgdata is None:
600 601 return None, None
601 602
602 603 avgdatatime = self.__initime
603 604
604 605 deltatime = datatime - self.__lastdatatime
605 606
606 607 if not self.__withOverlapping:
607 608 self.__initime = datatime
608 609 else:
609 610 self.__initime += deltatime
610 611
611 612 return avgdata, avgdatatime
612 613
613 614 def integrateByBlock(self, dataOut):
614 615
615 616 times = int(dataOut.data.shape[1]/self.n)
616 617 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
617 618
618 619 id_min = 0
619 620 id_max = self.n
620 621
621 622 for i in range(times):
622 623 junk = dataOut.data[:,id_min:id_max,:]
623 624 avgdata[:,i,:] = junk.sum(axis=1)
624 625 id_min += self.n
625 626 id_max += self.n
626 627
627 628 timeInterval = dataOut.ippSeconds*self.n
628 629 avgdatatime = (times - 1) * timeInterval + dataOut.utctime
629 630 self.__dataReady = True
630 631 return avgdata, avgdatatime
631 632
632 633 def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
633 634
634 635 if not self.isConfig:
635 636 self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
636 637 self.isConfig = True
637 638
638 639 if dataOut.flagDataAsBlock:
639 640 """
640 641 Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
641 642 """
642 643 avgdata, avgdatatime = self.integrateByBlock(dataOut)
643 644 dataOut.nProfiles /= self.n
644 645 else:
645 646 if stride is None:
646 647 avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
647 648 else:
648 649 avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
649 650
650 651
651 652 # dataOut.timeInterval *= n
652 653 dataOut.flagNoData = True
653 654
654 655 if self.__dataReady:
655 656 dataOut.data = avgdata
656 657 dataOut.nCohInt *= self.n
657 658 dataOut.utctime = avgdatatime
658 659 # print avgdata, avgdatatime
659 660 # raise
660 661 # dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
661 662 dataOut.flagNoData = False
662 663 return dataOut
663 664
664 665 class Decoder(Operation):
665 666
666 667 isConfig = False
667 668 __profIndex = 0
668 669
669 670 code = None
670 671
671 672 nCode = None
672 673 nBaud = None
673 674
674 675 def __init__(self, **kwargs):
675 676
676 677 Operation.__init__(self, **kwargs)
677 678
678 679 self.times = None
679 680 self.osamp = None
680 681 # self.__setValues = False
681 682 self.isConfig = False
682 683 self.setupReq = False
683 684 def setup(self, code, osamp, dataOut):
684 685
685 686 self.__profIndex = 0
686 687
687 688 self.code = code
688 689
689 690 self.nCode = len(code)
690 691 self.nBaud = len(code[0])
691 692
692 693 if (osamp != None) and (osamp >1):
693 694 self.osamp = osamp
694 695 self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
695 696 self.nBaud = self.nBaud*self.osamp
696 697
697 698 self.__nChannels = dataOut.nChannels
698 699 self.__nProfiles = dataOut.nProfiles
699 700 self.__nHeis = dataOut.nHeights
700 701
701 702 if self.__nHeis < self.nBaud:
702 703 raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
703 704
704 705 #Frequency
705 706 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
706 707
707 708 __codeBuffer[:,0:self.nBaud] = self.code
708 709
709 710 self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
710 711
711 712 if dataOut.flagDataAsBlock:
712 713
713 714 self.ndatadec = self.__nHeis #- self.nBaud + 1
714 715
715 716 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
716 717
717 718 else:
718 719
719 720 #Time
720 721 self.ndatadec = self.__nHeis #- self.nBaud + 1
721 722
722 723 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
723 724
724 725 def __convolutionInFreq(self, data):
725 726
726 727 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
727 728
728 729 fft_data = numpy.fft.fft(data, axis=1)
729 730
730 731 conv = fft_data*fft_code
731 732
732 733 data = numpy.fft.ifft(conv,axis=1)
733 734
734 735 return data
735 736
736 737 def __convolutionInFreqOpt(self, data):
737 738
738 739 raise NotImplementedError
739 740
740 741 def __convolutionInTime(self, data):
741 742
742 743 code = self.code[self.__profIndex]
743 744 for i in range(self.__nChannels):
744 745 self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
745 746
746 747 return self.datadecTime
747 748
748 749 def __convolutionByBlockInTime(self, data):
749 750
750 751 repetitions = int(self.__nProfiles / self.nCode)
751 752 junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
752 753 junk = junk.flatten()
753 754 code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
754 755 profilesList = range(self.__nProfiles)
755 756
756 757 for i in range(self.__nChannels):
757 758 for j in profilesList:
758 759 self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
759 760 return self.datadecTime
760 761
761 762 def __convolutionByBlockInFreq(self, data):
762 763
763 764 raise NotImplementedError("Decoder by frequency fro Blocks not implemented")
764 765
765 766
766 767 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
767 768
768 769 fft_data = numpy.fft.fft(data, axis=2)
769 770
770 771 conv = fft_data*fft_code
771 772
772 773 data = numpy.fft.ifft(conv,axis=2)
773 774
774 775 return data
775 776
776 777
777 778 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
778 779
779 780 if dataOut.flagDecodeData:
780 781 print("This data is already decoded, recoding again ...")
781 782
782 783 if not self.isConfig:
783 784
784 785 if code is None:
785 786 if dataOut.code is None:
786 787 raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type)
787 788
788 789 code = dataOut.code
789 790 else:
790 791 code = numpy.array(code).reshape(nCode,nBaud)
791 792 self.setup(code, osamp, dataOut)
792 793
793 794 self.isConfig = True
794 795
795 796 if mode == 3:
796 797 sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
797 798
798 799 if times != None:
799 800 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
800 801
801 802 if self.code is None:
802 803 print("Fail decoding: Code is not defined.")
803 804 return
804 805
805 806 self.__nProfiles = dataOut.nProfiles
806 807 datadec = None
807 808
808 809 if mode == 3:
809 810 mode = 0
810 811
811 812 if dataOut.flagDataAsBlock:
812 813 """
813 814 Decoding when data have been read as block,
814 815 """
815 816
816 817 if mode == 0:
817 818 datadec = self.__convolutionByBlockInTime(dataOut.data)
818 819 if mode == 1:
819 820 datadec = self.__convolutionByBlockInFreq(dataOut.data)
820 821 else:
821 822 """
822 823 Decoding when data have been read profile by profile
823 824 """
824 825 if mode == 0:
825 826 datadec = self.__convolutionInTime(dataOut.data)
826 827
827 828 if mode == 1:
828 829 datadec = self.__convolutionInFreq(dataOut.data)
829 830
830 831 if mode == 2:
831 832 datadec = self.__convolutionInFreqOpt(dataOut.data)
832 833
833 834 if datadec is None:
834 835 raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode)
835 836
836 837 dataOut.code = self.code
837 838 dataOut.nCode = self.nCode
838 839 dataOut.nBaud = self.nBaud
839 840
840 841 dataOut.data = datadec
841 842
842 843 dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
843 844
844 845 dataOut.flagDecodeData = True #asumo q la data esta decodificada
845 846
846 847 if self.__profIndex == self.nCode-1:
847 848 self.__profIndex = 0
848 849 return dataOut
849 850
850 851 self.__profIndex += 1
851 852
852 853 return dataOut
853 854 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
854 855
855 856
856 857 class ProfileConcat(Operation):
857 858
858 859 isConfig = False
859 860 buffer = None
860 861
861 862 def __init__(self, **kwargs):
862 863
863 864 Operation.__init__(self, **kwargs)
864 865 self.profileIndex = 0
865 866
866 867 def reset(self):
867 868 self.buffer = numpy.zeros_like(self.buffer)
868 869 self.start_index = 0
869 870 self.times = 1
870 871
871 872 def setup(self, data, m, n=1):
872 873 self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
873 874 self.nHeights = data.shape[1]#.nHeights
874 875 self.start_index = 0
875 876 self.times = 1
876 877
877 878 def concat(self, data):
878 879
879 880 self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
880 881 self.start_index = self.start_index + self.nHeights
881 882
882 883 def run(self, dataOut, m):
883 884 dataOut.flagNoData = True
884 885
885 886 if not self.isConfig:
886 887 self.setup(dataOut.data, m, 1)
887 888 self.isConfig = True
888 889
889 890 if dataOut.flagDataAsBlock:
890 891 raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False")
891 892
892 893 else:
893 894 self.concat(dataOut.data)
894 895 self.times += 1
895 896 if self.times > m:
896 897 dataOut.data = self.buffer
897 898 self.reset()
898 899 dataOut.flagNoData = False
899 900 # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
900 901 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
901 902 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
902 903 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
903 904 dataOut.ippSeconds *= m
904 905 return dataOut
905 906
906 907 class ProfileSelector(Operation):
907 908
908 909 profileIndex = None
909 910 # Tamanho total de los perfiles
910 911 nProfiles = None
911 912
912 913 def __init__(self, **kwargs):
913 914
914 915 Operation.__init__(self, **kwargs)
915 916 self.profileIndex = 0
916 917
917 918 def incProfileIndex(self):
918 919
919 920 self.profileIndex += 1
920 921
921 922 if self.profileIndex >= self.nProfiles:
922 923 self.profileIndex = 0
923 924
924 925 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
925 926
926 927 if profileIndex < minIndex:
927 928 return False
928 929
929 930 if profileIndex > maxIndex:
930 931 return False
931 932
932 933 return True
933 934
934 935 def isThisProfileInList(self, profileIndex, profileList):
935 936
936 937 if profileIndex not in profileList:
937 938 return False
938 939
939 940 return True
940 941
941 942 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
942 943
943 944 """
944 945 ProfileSelector:
945 946
946 947 Inputs:
947 948 profileList : Index of profiles selected. Example: profileList = (0,1,2,7,8)
948 949
949 950 profileRangeList : Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
950 951
951 952 rangeList : List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
952 953
953 954 """
954 955
955 956 if rangeList is not None:
956 957 if type(rangeList[0]) not in (tuple, list):
957 958 rangeList = [rangeList]
958 959
959 960 dataOut.flagNoData = True
960 961
961 962 if dataOut.flagDataAsBlock:
962 963 """
963 964 data dimension = [nChannels, nProfiles, nHeis]
964 965 """
965 966 if profileList != None:
966 967 dataOut.data = dataOut.data[:,profileList,:]
967 968
968 969 if profileRangeList != None:
969 970 minIndex = profileRangeList[0]
970 971 maxIndex = profileRangeList[1]
971 972 profileList = list(range(minIndex, maxIndex+1))
972 973
973 974 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
974 975
975 976 if rangeList != None:
976 977
977 978 profileList = []
978 979
979 980 for thisRange in rangeList:
980 981 minIndex = thisRange[0]
981 982 maxIndex = thisRange[1]
982 983
983 984 profileList.extend(list(range(minIndex, maxIndex+1)))
984 985
985 986 dataOut.data = dataOut.data[:,profileList,:]
986 987
987 988 dataOut.nProfiles = len(profileList)
988 989 dataOut.profileIndex = dataOut.nProfiles - 1
989 990 dataOut.flagNoData = False
990 991
991 992 return dataOut
992 993
993 994 """
994 995 data dimension = [nChannels, nHeis]
995 996 """
996 997
997 998 if profileList != None:
998 999
999 1000 if self.isThisProfileInList(dataOut.profileIndex, profileList):
1000 1001
1001 1002 self.nProfiles = len(profileList)
1002 1003 dataOut.nProfiles = self.nProfiles
1003 1004 dataOut.profileIndex = self.profileIndex
1004 1005 dataOut.flagNoData = False
1005 1006
1006 1007 self.incProfileIndex()
1007 1008 return dataOut
1008 1009
1009 1010 if profileRangeList != None:
1010 1011
1011 1012 minIndex = profileRangeList[0]
1012 1013 maxIndex = profileRangeList[1]
1013 1014
1014 1015 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
1015 1016
1016 1017 self.nProfiles = maxIndex - minIndex + 1
1017 1018 dataOut.nProfiles = self.nProfiles
1018 1019 dataOut.profileIndex = self.profileIndex
1019 1020 dataOut.flagNoData = False
1020 1021
1021 1022 self.incProfileIndex()
1022 1023 return dataOut
1023 1024
1024 1025 if rangeList != None:
1025 1026
1026 1027 nProfiles = 0
1027 1028
1028 1029 for thisRange in rangeList:
1029 1030 minIndex = thisRange[0]
1030 1031 maxIndex = thisRange[1]
1031 1032
1032 1033 nProfiles += maxIndex - minIndex + 1
1033 1034
1034 1035 for thisRange in rangeList:
1035 1036
1036 1037 minIndex = thisRange[0]
1037 1038 maxIndex = thisRange[1]
1038 1039
1039 1040 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
1040 1041
1041 1042 self.nProfiles = nProfiles
1042 1043 dataOut.nProfiles = self.nProfiles
1043 1044 dataOut.profileIndex = self.profileIndex
1044 1045 dataOut.flagNoData = False
1045 1046
1046 1047 self.incProfileIndex()
1047 1048
1048 1049 break
1049 1050
1050 1051 return dataOut
1051 1052
1052 1053
1053 1054 if beam != None: #beam is only for AMISR data
1054 1055 if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
1055 1056 dataOut.flagNoData = False
1056 1057 dataOut.profileIndex = self.profileIndex
1057 1058
1058 1059 self.incProfileIndex()
1059 1060
1060 1061 return dataOut
1061 1062
1062 1063 raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter")
1063 1064
1064 1065
1065 1066 class Reshaper(Operation):
1066 1067
1067 1068 def __init__(self, **kwargs):
1068 1069
1069 1070 Operation.__init__(self, **kwargs)
1070 1071
1071 1072 self.__buffer = None
1072 1073 self.__nitems = 0
1073 1074
1074 1075 def __appendProfile(self, dataOut, nTxs):
1075 1076
1076 1077 if self.__buffer is None:
1077 1078 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
1078 1079 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
1079 1080
1080 1081 ini = dataOut.nHeights * self.__nitems
1081 1082 end = ini + dataOut.nHeights
1082 1083
1083 1084 self.__buffer[:, ini:end] = dataOut.data
1084 1085
1085 1086 self.__nitems += 1
1086 1087
1087 1088 return int(self.__nitems*nTxs)
1088 1089
1089 1090 def __getBuffer(self):
1090 1091
1091 1092 if self.__nitems == int(1./self.__nTxs):
1092 1093
1093 1094 self.__nitems = 0
1094 1095
1095 1096 return self.__buffer.copy()
1096 1097
1097 1098 return None
1098 1099
1099 1100 def __checkInputs(self, dataOut, shape, nTxs):
1100 1101
1101 1102 if shape is None and nTxs is None:
1102 1103 raise ValueError("Reshaper: shape of factor should be defined")
1103 1104
1104 1105 if nTxs:
1105 1106 if nTxs < 0:
1106 1107 raise ValueError("nTxs should be greater than 0")
1107 1108
1108 1109 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
1109 1110 raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)))
1110 1111
1111 1112 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
1112 1113
1113 1114 return shape, nTxs
1114 1115
1115 1116 if len(shape) != 2 and len(shape) != 3:
1116 1117 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))
1117 1118
1118 1119 if len(shape) == 2:
1119 1120 shape_tuple = [dataOut.nChannels]
1120 1121 shape_tuple.extend(shape)
1121 1122 else:
1122 1123 shape_tuple = list(shape)
1123 1124
1124 1125 nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
1125 1126
1126 1127 return shape_tuple, nTxs
1127 1128
1128 1129 def run(self, dataOut, shape=None, nTxs=None):
1129 1130
1130 1131 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
1131 1132
1132 1133 dataOut.flagNoData = True
1133 1134 profileIndex = None
1134 1135
1135 1136 if dataOut.flagDataAsBlock:
1136 1137
1137 1138 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
1138 1139 dataOut.flagNoData = False
1139 1140
1140 1141 profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
1141 1142
1142 1143 else:
1143 1144
1144 1145 if self.__nTxs < 1:
1145 1146
1146 1147 self.__appendProfile(dataOut, self.__nTxs)
1147 1148 new_data = self.__getBuffer()
1148 1149
1149 1150 if new_data is not None:
1150 1151 dataOut.data = new_data
1151 1152 dataOut.flagNoData = False
1152 1153
1153 1154 profileIndex = dataOut.profileIndex*nTxs
1154 1155
1155 1156 else:
1156 1157 raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)")
1157 1158
1158 1159 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1159 1160
1160 1161 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
1161 1162
1162 1163 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
1163 1164
1164 1165 dataOut.profileIndex = profileIndex
1165 1166
1166 1167 dataOut.ippSeconds /= self.__nTxs
1167 1168
1168 1169 return dataOut
1169 1170
1170 1171 class SplitProfiles(Operation):
1171 1172
1172 1173 def __init__(self, **kwargs):
1173 1174
1174 1175 Operation.__init__(self, **kwargs)
1175 1176
1176 1177 def run(self, dataOut, n):
1177 1178
1178 1179 dataOut.flagNoData = True
1179 1180 profileIndex = None
1180 1181
1181 1182 if dataOut.flagDataAsBlock:
1182 1183
1183 1184 #nchannels, nprofiles, nsamples
1184 1185 shape = dataOut.data.shape
1185 1186
1186 1187 if shape[2] % n != 0:
1187 1188 raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]))
1188 1189
1189 1190 new_shape = shape[0], shape[1]*n, int(shape[2]/n)
1190 1191
1191 1192 dataOut.data = numpy.reshape(dataOut.data, new_shape)
1192 1193 dataOut.flagNoData = False
1193 1194
1194 1195 profileIndex = int(dataOut.nProfiles/n) - 1
1195 1196
1196 1197 else:
1197 1198
1198 1199 raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)")
1199 1200
1200 1201 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1201 1202
1202 1203 dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
1203 1204
1204 1205 dataOut.nProfiles = int(dataOut.nProfiles*n)
1205 1206
1206 1207 dataOut.profileIndex = profileIndex
1207 1208
1208 1209 dataOut.ippSeconds /= n
1209 1210
1210 1211 return dataOut
1211 1212
1212 1213 class CombineProfiles(Operation):
1213 1214 def __init__(self, **kwargs):
1214 1215
1215 1216 Operation.__init__(self, **kwargs)
1216 1217
1217 1218 self.__remData = None
1218 1219 self.__profileIndex = 0
1219 1220
1220 1221 def run(self, dataOut, n):
1221 1222
1222 1223 dataOut.flagNoData = True
1223 1224 profileIndex = None
1224 1225
1225 1226 if dataOut.flagDataAsBlock:
1226 1227
1227 1228 #nchannels, nprofiles, nsamples
1228 1229 shape = dataOut.data.shape
1229 1230 new_shape = shape[0], shape[1]/n, shape[2]*n
1230 1231
1231 1232 if shape[1] % n != 0:
1232 1233 raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]))
1233 1234
1234 1235 dataOut.data = numpy.reshape(dataOut.data, new_shape)
1235 1236 dataOut.flagNoData = False
1236 1237
1237 1238 profileIndex = int(dataOut.nProfiles*n) - 1
1238 1239
1239 1240 else:
1240 1241
1241 1242 #nchannels, nsamples
1242 1243 if self.__remData is None:
1243 1244 newData = dataOut.data
1244 1245 else:
1245 1246 newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
1246 1247
1247 1248 self.__profileIndex += 1
1248 1249
1249 1250 if self.__profileIndex < n:
1250 1251 self.__remData = newData
1251 1252 #continue
1252 1253 return
1253 1254
1254 1255 self.__profileIndex = 0
1255 1256 self.__remData = None
1256 1257
1257 1258 dataOut.data = newData
1258 1259 dataOut.flagNoData = False
1259 1260
1260 1261 profileIndex = dataOut.profileIndex/n
1261 1262
1262 1263
1263 1264 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1264 1265
1265 1266 dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
1266 1267
1267 1268 dataOut.nProfiles = int(dataOut.nProfiles/n)
1268 1269
1269 1270 dataOut.profileIndex = profileIndex
1270 1271
1271 1272 dataOut.ippSeconds *= n
1272 1273
1273 1274 return dataOut
1274 1275
1275 1276 class PulsePairVoltage(Operation):
1276 1277 '''
1277 1278 Function PulsePair(Signal Power, Velocity)
1278 1279 The real component of Lag[0] provides Intensity Information
1279 1280 The imag component of Lag[1] Phase provides Velocity Information
1280 1281
1281 1282 Configuration Parameters:
1282 1283 nPRF = Number of Several PRF
1283 1284 theta = Degree Azimuth angel Boundaries
1284 1285
1285 1286 Input:
1286 1287 self.dataOut
1287 1288 lag[N]
1288 1289 Affected:
1289 1290 self.dataOut.spc
1290 1291 '''
1291 1292 isConfig = False
1292 1293 __profIndex = 0
1293 1294 __initime = None
1294 1295 __lastdatatime = None
1295 1296 __buffer = None
1296 1297 noise = None
1297 1298 __dataReady = False
1298 1299 n = None
1299 1300 __nch = 0
1300 1301 __nHeis = 0
1301 1302 removeDC = False
1302 1303 ipp = None
1303 1304 lambda_ = 0
1304 1305
1305 1306 def __init__(self,**kwargs):
1306 1307 Operation.__init__(self,**kwargs)
1307 1308
1308 1309 def setup(self, dataOut, n = None, removeDC=False):
1309 1310 '''
1310 1311 n= Numero de PRF's de entrada
1311 1312 '''
1312 1313 self.__initime = None
1313 1314 self.__lastdatatime = 0
1314 1315 self.__dataReady = False
1315 1316 self.__buffer = 0
1316 1317 self.__profIndex = 0
1317 1318 self.noise = None
1318 1319 self.__nch = dataOut.nChannels
1319 1320 self.__nHeis = dataOut.nHeights
1320 1321 self.removeDC = removeDC
1321 1322 self.lambda_ = 3.0e8/(9345.0e6)
1322 1323 self.ippSec = dataOut.ippSeconds
1323 1324 self.nCohInt = dataOut.nCohInt
1324 1325 print("IPPseconds",dataOut.ippSeconds)
1325 1326
1326 1327 print("ELVALOR DE n es:", n)
1327 1328 if n == None:
1328 1329 raise ValueError("n should be specified.")
1329 1330
1330 1331 if n != None:
1331 1332 if n<2:
1332 1333 raise ValueError("n should be greater than 2")
1333 1334
1334 1335 self.n = n
1335 1336 self.__nProf = n
1336 1337
1337 1338 self.__buffer = numpy.zeros((dataOut.nChannels,
1338 1339 n,
1339 1340 dataOut.nHeights),
1340 1341 dtype='complex')
1341 #self.noise = numpy.zeros([self.__nch,self.__nHeis])
1342 #for i in range(self.__nch):
1343 # self.noise[i]=dataOut.getNoise(channel=i)
1344 1342
1345 1343 def putData(self,data):
1346 1344 '''
1347 1345 Add a profile to he __buffer and increase in one the __profiel Index
1348 1346 '''
1349 1347 self.__buffer[:,self.__profIndex,:]= data
1350 1348 self.__profIndex += 1
1351 1349 return
1352 1350
1353 1351 def pushData(self,dataOut):
1354 1352 '''
1355 1353 Return the PULSEPAIR and the profiles used in the operation
1356 1354 Affected : self.__profileIndex
1357 1355 '''
1356 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Remove DCΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1358 1357 if self.removeDC==True:
1359 1358 mean = numpy.mean(self.__buffer,1)
1360 1359 tmp = mean.reshape(self.__nch,1,self.__nHeis)
1361 1360 dc= numpy.tile(tmp,[1,self.__nProf,1])
1362 1361 self.__buffer = self.__buffer - dc
1362 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Calculo de Potencia Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1363 pair0 = self.__buffer*numpy.conj(self.__buffer)
1364 pair0 = pair0.real
1365 lag_0 = numpy.sum(pair0,1)
1366 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Calculo de Ruido x canalΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1367 self.noise = numpy.zeros(self.__nch)
1368 for i in range(self.__nch):
1369 daux = numpy.sort(pair0[i,:,:],axis= None)
1370 self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt)
1371
1372 self.noise = self.noise.reshape(self.__nch,1)
1373 self.noise = numpy.tile(self.noise,[1,self.__nHeis])
1374 noise_buffer = self.noise.reshape(self.__nch,1,self.__nHeis)
1375 noise_buffer = numpy.tile(noise_buffer,[1,self.__nProf,1])
1376 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Potencia recibida= P , Potencia senal = S , Ruido= NΒ·Β·
1377 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· P= S+N ,P=lag_0/N Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1378 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Power Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1379 data_power = lag_0/(self.n*self.nCohInt)
1380 #------------------ Senal Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1381 data_intensity = pair0 - noise_buffer
1382 data_intensity = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt)
1383 #data_intensity = (lag_0-self.noise*self.n)*(self.n*self.nCohInt)
1384 for i in range(self.__nch):
1385 for j in range(self.__nHeis):
1386 if data_intensity[i][j] < 0:
1387 data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j]))
1363 1388
1364 lag_0 = numpy.sum(self.__buffer*numpy.conj(self.__buffer),1)
1365 data_intensity = lag_0/(self.n*self.nCohInt)#*self.nCohInt)
1366
1389 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Calculo de Frecuencia y Velocidad dopplerΒ·Β·Β·Β·Β·Β·Β·Β·
1367 1390 pair1 = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:])
1368 1391 lag_1 = numpy.sum(pair1,1)
1369 #angle = numpy.angle(numpy.sum(pair1,1))*180/(math.pi)
1370 data_velocity = (-1.0*self.lambda_/(4*math.pi*self.ippSec))*numpy.angle(lag_1)#self.ippSec*self.nCohInt
1392 data_freq = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1)
1393 data_velocity = (self.lambda_/2.0)*data_freq
1371 1394
1372 self.noise = numpy.zeros([self.__nch,self.__nHeis])
1373 for i in range(self.__nch):
1374 self.noise[i]=dataOut.getNoise(channel=i)
1395 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Potencia promedio estimada de la SenalΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1396 lag_0 = lag_0/self.n
1397 S = lag_0-self.noise
1375 1398
1376 lag_0 = lag_0.real/(self.n)
1399 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Frecuencia Doppler promedio Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1377 1400 lag_1 = lag_1/(self.n-1)
1378 1401 R1 = numpy.abs(lag_1)
1379 S = (lag_0-self.noise)
1380 1402
1403 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Calculo del SNRΒ·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1381 1404 data_snrPP = S/self.noise
1382 data_snrPP = numpy.where(data_snrPP<0,1,data_snrPP)
1405 for i in range(self.__nch):
1406 for j in range(self.__nHeis):
1407 if data_snrPP[i][j] < 1.e-20:
1408 data_snrPP[i][j] = 1.e-20
1383 1409
1410 #Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β· Calculo del ancho espectral Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·
1384 1411 L = S/R1
1385 1412 L = numpy.where(L<0,1,L)
1386 1413 L = numpy.log(L)
1387
1388 1414 tmp = numpy.sqrt(numpy.absolute(L))
1389
1390 data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec))*tmp*numpy.sign(L)
1391 #data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec))*k
1415 data_specwidth = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L)
1392 1416 n = self.__profIndex
1393 1417
1394 1418 self.__buffer = numpy.zeros((self.__nch, self.__nProf,self.__nHeis), dtype='complex')
1395 1419 self.__profIndex = 0
1396 return data_intensity,data_velocity,data_snrPP,data_specwidth,n
1420 return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n
1397 1421
1398 1422 def pulsePairbyProfiles(self,dataOut):
1399 1423
1400 1424 self.__dataReady = False
1425 data_power = None
1401 1426 data_intensity = None
1402 1427 data_velocity = None
1403 1428 data_specwidth = None
1404 1429 data_snrPP = None
1405 1430 self.putData(data=dataOut.data)
1406 1431 if self.__profIndex == self.n:
1407 #self.noise = numpy.zeros([self.__nch,self.__nHeis])
1408 #for i in range(self.__nch):
1409 # self.noise[i]=data.getNoise(channel=i)
1410 #print(self.noise.shape)
1411 data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut)
1432 data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n = self.pushData(dataOut=dataOut)
1412 1433 self.__dataReady = True
1413 1434
1414 return data_intensity, data_velocity,data_snrPP,data_specwidth
1435 return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth
1415 1436
1416 1437 def pulsePairOp(self, dataOut, datatime= None):
1417 1438
1418 1439 if self.__initime == None:
1419 1440 self.__initime = datatime
1420 #print("hola")
1421 data_intensity, data_velocity,data_snrPP,data_specwidth = self.pulsePairbyProfiles(dataOut)
1441 data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut)
1422 1442 self.__lastdatatime = datatime
1423 1443
1424 if data_intensity is None:
1425 return None, None,None,None,None
1444 if data_power is None:
1445 return None, None, None,None,None,None
1426 1446
1427 1447 avgdatatime = self.__initime
1428 1448 deltatime = datatime - self.__lastdatatime
1429 1449 self.__initime = datatime
1430 1450
1431 return data_intensity, data_velocity,data_snrPP,data_specwidth,avgdatatime
1451 return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime
1432 1452
1433 1453 def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs):
1434 1454
1435 1455 if not self.isConfig:
1436 1456 self.setup(dataOut = dataOut, n = n , removeDC=removeDC , **kwargs)
1437 1457 self.isConfig = True
1438 data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime)
1458 data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime)
1439 1459 dataOut.flagNoData = True
1440 1460
1441 1461 if self.__dataReady:
1442 1462 dataOut.nCohInt *= self.n
1443 dataOut.data_intensity = data_intensity #valor para intensidad
1444 dataOut.data_velocity = data_velocity #valor para velocidad
1445 dataOut.data_snrPP = data_snrPP # valor para snr
1446 dataOut.data_specwidth = data_specwidth
1463 dataOut.dataPP_POW = data_intensity # S
1464 dataOut.dataPP_POWER = data_power # P
1465 dataOut.dataPP_DOP = data_velocity
1466 dataOut.dataPP_SNR = data_snrPP
1467 dataOut.dataPP_WIDTH = data_specwidth
1447 1468 dataOut.PRFbyAngle = self.n #numero de PRF*cada angulo rotado que equivale a un tiempo.
1448 1469 dataOut.utctime = avgdatatime
1449 1470 dataOut.flagNoData = False
1450 1471 return dataOut
1451 1472
1452 1473
1453 1474 # import collections
1454 1475 # from scipy.stats import mode
1455 1476 #
1456 1477 # class Synchronize(Operation):
1457 1478 #
1458 1479 # isConfig = False
1459 1480 # __profIndex = 0
1460 1481 #
1461 1482 # def __init__(self, **kwargs):
1462 1483 #
1463 1484 # Operation.__init__(self, **kwargs)
1464 1485 # # self.isConfig = False
1465 1486 # self.__powBuffer = None
1466 1487 # self.__startIndex = 0
1467 1488 # self.__pulseFound = False
1468 1489 #
1469 1490 # def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
1470 1491 #
1471 1492 # #Read data
1472 1493 #
1473 1494 # powerdB = dataOut.getPower(channel = channel)
1474 1495 # noisedB = dataOut.getNoise(channel = channel)[0]
1475 1496 #
1476 1497 # self.__powBuffer.extend(powerdB.flatten())
1477 1498 #
1478 1499 # dataArray = numpy.array(self.__powBuffer)
1479 1500 #
1480 1501 # filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
1481 1502 #
1482 1503 # maxValue = numpy.nanmax(filteredPower)
1483 1504 #
1484 1505 # if maxValue < noisedB + 10:
1485 1506 # #No se encuentra ningun pulso de transmision
1486 1507 # return None
1487 1508 #
1488 1509 # maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
1489 1510 #
1490 1511 # if len(maxValuesIndex) < 2:
1491 1512 # #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
1492 1513 # return None
1493 1514 #
1494 1515 # phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
1495 1516 #
1496 1517 # #Seleccionar solo valores con un espaciamiento de nSamples
1497 1518 # pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
1498 1519 #
1499 1520 # if len(pulseIndex) < 2:
1500 1521 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1501 1522 # return None
1502 1523 #
1503 1524 # spacing = pulseIndex[1:] - pulseIndex[:-1]
1504 1525 #
1505 1526 # #remover senales que se distancien menos de 10 unidades o muestras
1506 1527 # #(No deberian existir IPP menor a 10 unidades)
1507 1528 #
1508 1529 # realIndex = numpy.where(spacing > 10 )[0]
1509 1530 #
1510 1531 # if len(realIndex) < 2:
1511 1532 # #Solo se encontro un pulso de transmision con ancho mayor a 1
1512 1533 # return None
1513 1534 #
1514 1535 # #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
1515 1536 # realPulseIndex = pulseIndex[realIndex]
1516 1537 #
1517 1538 # period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
1518 1539 #
1519 1540 # print "IPP = %d samples" %period
1520 1541 #
1521 1542 # self.__newNSamples = dataOut.nHeights #int(period)
1522 1543 # self.__startIndex = int(realPulseIndex[0])
1523 1544 #
1524 1545 # return 1
1525 1546 #
1526 1547 #
1527 1548 # def setup(self, nSamples, nChannels, buffer_size = 4):
1528 1549 #
1529 1550 # self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
1530 1551 # maxlen = buffer_size*nSamples)
1531 1552 #
1532 1553 # bufferList = []
1533 1554 #
1534 1555 # for i in range(nChannels):
1535 1556 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
1536 1557 # maxlen = buffer_size*nSamples)
1537 1558 #
1538 1559 # bufferList.append(bufferByChannel)
1539 1560 #
1540 1561 # self.__nSamples = nSamples
1541 1562 # self.__nChannels = nChannels
1542 1563 # self.__bufferList = bufferList
1543 1564 #
1544 1565 # def run(self, dataOut, channel = 0):
1545 1566 #
1546 1567 # if not self.isConfig:
1547 1568 # nSamples = dataOut.nHeights
1548 1569 # nChannels = dataOut.nChannels
1549 1570 # self.setup(nSamples, nChannels)
1550 1571 # self.isConfig = True
1551 1572 #
1552 1573 # #Append new data to internal buffer
1553 1574 # for thisChannel in range(self.__nChannels):
1554 1575 # bufferByChannel = self.__bufferList[thisChannel]
1555 1576 # bufferByChannel.extend(dataOut.data[thisChannel])
1556 1577 #
1557 1578 # if self.__pulseFound:
1558 1579 # self.__startIndex -= self.__nSamples
1559 1580 #
1560 1581 # #Finding Tx Pulse
1561 1582 # if not self.__pulseFound:
1562 1583 # indexFound = self.__findTxPulse(dataOut, channel)
1563 1584 #
1564 1585 # if indexFound == None:
1565 1586 # dataOut.flagNoData = True
1566 1587 # return
1567 1588 #
1568 1589 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
1569 1590 # self.__pulseFound = True
1570 1591 # self.__startIndex = indexFound
1571 1592 #
1572 1593 # #If pulse was found ...
1573 1594 # for thisChannel in range(self.__nChannels):
1574 1595 # bufferByChannel = self.__bufferList[thisChannel]
1575 1596 # #print self.__startIndex
1576 1597 # x = numpy.array(bufferByChannel)
1577 1598 # self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
1578 1599 #
1579 1600 # deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
1580 1601 # dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
1581 1602 # # dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
1582 1603 #
1583 1604 # dataOut.data = self.__arrayBuffer
1584 1605 #
1585 1606 # self.__startIndex += self.__newNSamples
1586 1607 #
1587 1608 # return
Show file before | Show file after | Hide comments
@@ -1,49 +1,76
1 1 import os,sys
2 2 import datetime
3 3 import time
4 4 from schainpy.controller import Project
5 path = '/home/alex/Downloads/NEW_WR2/spc16removeDC'
6 figpath = path
5 #path = '/home/alex/Downloads/NEW_WR2/spc16removeDC'
6 #figpath = path
7
8 path = '/home/alex/Downloads/test_rawdata'
9 figpath = '/home/alex/Downloads/hdf5_testPP'
7 10 desc = "Simulator Test"
8 11
9 12 controllerObj = Project()
10 13
11 14 controllerObj.setup(id='10',name='Test Simulator',description=desc)
12 15
13 16 readUnitConfObj = controllerObj.addReadUnit(datatype='SimulatorReader',
14 17 frequency=9.345e9,
15 18 FixRCP_IPP= 60,
16 19 Tau_0 = 30,
17 20 AcqH0_0=0,
18 21 samples=330,
19 22 AcqDH_0=0.15,
20 23 FixRCP_TXA=0.15,
21 24 FixRCP_TXB=0.15,
22 25 Fdoppler=600.0,
23 26 Hdoppler=36,
24 27 Adoppler=300,#300
25 28 delay=0,
26 29 online=0,
27 30 walk=0,
28 nTotalReadFiles=3)
29
30 opObj11 = readUnitConfObj.addOperation(name='printInfo')
31 profilesPerBlock=625,
32 dataBlocksPerFile=100)#,#nTotalReadFiles=2)
31 33
34 '''
35 readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader',
36 path=path,
37 startDate="2020/01/01", #"2020/01/01",#today,
38 endDate= "2020/12/01", #"2020/12/30",#today,
39 startTime='00:00:00',
40 endTime='23:59:59',
41 delay=0,
42 #set=0,
43 online=0,
44 walk=1)
45 '''
46 #opObj11 = readUnitConfObj.addOperation(name='printInfo')
32 47 procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId())
33 48 #opObj11 = procUnitConfObjA.addOperation(name='CohInt', optype='other')
34 #opObj11.addParameter(name='n', value='10', format='int')
49 #opObj11.addParameter(name='n', value='4', format='int')
35 50
36 51 #opObj10 = procUnitConfObjA.addOperation(name='selectChannels')
37 52 #opObj10.addParameter(name='channelList', value=[0])
38 53 opObj11 = procUnitConfObjA.addOperation(name='PulsePairVoltage', optype='other')
39 opObj11.addParameter(name='n', value='300', format='int')#10
54 opObj11.addParameter(name='n', value='625', format='int')#10
40 55 opObj11.addParameter(name='removeDC', value=1, format='int')
41 56
42 57 #opObj11 = procUnitConfObjA.addOperation(name='PulsepairPowerPlot', optype='other')
58 #opObj11 = procUnitConfObjA.addOperation(name='PulsepairSignalPlot', optype='other')
43 59
44 opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other')
60
61 #opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other')
45 62 #opObj11.addParameter(name='xmax', value=8)
46 63
47 opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other')
64 #opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other')
65
66 procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId())
67
68
69 opObj10 = procUnitConfObjB.addOperation(name='ParameterWriter')
70 opObj10.addParameter(name='path',value=figpath)
71 #opObj10.addParameter(name='mode',value=0)
72 opObj10.addParameter(name='blocksPerFile',value='100',format='int')
73 opObj10.addParameter(name='metadataList',value='utctimeInit,timeInterval',format='list')
74 opObj10.addParameter(name='dataList',value='dataPP_POW,dataPP_DOP,dataPP_SNR,dataPP_WIDTH')#,format='list'
48 75
49 76 controllerObj.start()
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