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jroproc_spectra_lags added to schainpy
Miguel Valdez -
r768:b6e838a8cfb8
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1 import numpy
2
3 from jroproc_base import ProcessingUnit, Operation
4 from schainpy.model.data.jrodata import Spectra
5 from schainpy.model.data.jrodata import hildebrand_sekhon
6
7 class SpectraLagsProc(ProcessingUnit):
8
9 def __init__(self):
10
11 ProcessingUnit.__init__(self)
12
13 self.buffer = None
14 self.firstdatatime = None
15 self.profIndex = 0
16 self.dataOut = Spectra()
17 self.id_min = None
18 self.id_max = None
19
20 def __updateSpecFromVoltage(self):
21
22 self.dataOut.timeZone = self.dataIn.timeZone
23 self.dataOut.dstFlag = self.dataIn.dstFlag
24 self.dataOut.errorCount = self.dataIn.errorCount
25 self.dataOut.useLocalTime = self.dataIn.useLocalTime
26
27 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
28 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
29 self.dataOut.ippSeconds = self.dataIn.getDeltaH()*(10**-6)/0.15
30
31 self.dataOut.channelList = self.dataIn.channelList
32 self.dataOut.heightList = self.dataIn.heightList
33 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
34
35 self.dataOut.nBaud = self.dataIn.nBaud
36 self.dataOut.nCode = self.dataIn.nCode
37 self.dataOut.code = self.dataIn.code
38 self.dataOut.nProfiles = self.dataOut.nFFTPoints
39
40 self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
41 self.dataOut.utctime = self.firstdatatime
42 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
43 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
44 self.dataOut.flagShiftFFT = False
45
46 self.dataOut.nCohInt = self.dataIn.nCohInt
47 self.dataOut.nIncohInt = 1
48
49 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
50
51 self.dataOut.frequency = self.dataIn.frequency
52 self.dataOut.realtime = self.dataIn.realtime
53
54 self.dataOut.azimuth = self.dataIn.azimuth
55 self.dataOut.zenith = self.dataIn.zenith
56
57 self.dataOut.beam.codeList = self.dataIn.beam.codeList
58 self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
59 self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
60
61 def __decodeData(self, nProfiles, code):
62
63 if code is None:
64 return
65
66 for i in range(nProfiles):
67 self.buffer[:,i,:] = self.buffer[:,i,:]*code[0][i]
68
69 def __getFft(self):
70 """
71 Convierte valores de Voltaje a Spectra
72
73 Affected:
74 self.dataOut.data_spc
75 self.dataOut.data_cspc
76 self.dataOut.data_dc
77 self.dataOut.heightList
78 self.profIndex
79 self.buffer
80 self.dataOut.flagNoData
81 """
82 nsegments = self.dataOut.nHeights
83
84 _fft_buffer = numpy.zeros((self.dataOut.nChannels, self.dataOut.nProfiles, nsegments), dtype='complex')
85
86 for i in range(nsegments):
87 try:
88 _fft_buffer[:,:,i] = self.buffer[:,i:i+self.dataOut.nProfiles]
89
90 if self.code is not None:
91 _fft_buffer[:,:,i] = _fft_buffer[:,:,i]*self.code[0]
92 except:
93 pass
94
95 fft_volt = numpy.fft.fft(_fft_buffer, n=self.dataOut.nFFTPoints, axis=1)
96 fft_volt = fft_volt.astype(numpy.dtype('complex'))
97 dc = fft_volt[:,0,:]
98
99 #calculo de self-spectra
100 fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
101 spc = fft_volt * numpy.conjugate(fft_volt)
102 spc = spc.real
103
104 blocksize = 0
105 blocksize += dc.size
106 blocksize += spc.size
107
108 cspc = None
109 pairIndex = 0
110
111 if self.dataOut.pairsList != None:
112 #calculo de cross-spectra
113 cspc = numpy.zeros((self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
114 for pair in self.dataOut.pairsList:
115 if pair[0] not in self.dataOut.channelList:
116 raise ValueError, "Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList))
117 if pair[1] not in self.dataOut.channelList:
118 raise ValueError, "Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" %(str(pair), str(self.dataOut.channelList))
119
120 chan_index0 = self.dataOut.channelList.index(pair[0])
121 chan_index1 = self.dataOut.channelList.index(pair[1])
122
123 cspc[pairIndex,:,:] = fft_volt[chan_index0,:,:] * numpy.conjugate(fft_volt[chan_index1,:,:])
124 pairIndex += 1
125 blocksize += cspc.size
126
127 self.dataOut.data_spc = spc
128 self.dataOut.data_cspc = cspc
129 self.dataOut.data_dc = dc
130 self.dataOut.blockSize = blocksize
131 self.dataOut.flagShiftFFT = True
132
133 def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], code=None, nCode=1, nBaud=1):
134
135 self.dataOut.flagNoData = True
136
137 if code is not None:
138 self.code = numpy.array(code).reshape(nCode,nBaud)
139 else:
140 self.code = None
141
142 if self.dataIn.type == "Voltage":
143
144 if nFFTPoints == None:
145 raise ValueError, "This SpectraProc.run() need nFFTPoints input variable"
146
147 if nProfiles == None:
148 nProfiles = nFFTPoints
149
150 self.dataOut.ippFactor = 1
151
152 self.dataOut.nFFTPoints = nFFTPoints
153 self.dataOut.pairsList = pairsList
154
155 # if self.buffer is None:
156 # self.buffer = numpy.zeros( (self.dataIn.nChannels, nProfiles, self.dataIn.nHeights),
157 # dtype='complex')
158
159 if not self.dataIn.flagDataAsBlock:
160 self.buffer = self.dataIn.data.copy()
161
162 # for i in range(self.dataIn.nHeights):
163 # self.buffer[:, self.profIndex, self.profIndex:] = voltage_data[:,:self.dataIn.nHeights - self.profIndex]
164 #
165 # self.profIndex += 1
166
167 else:
168 raise ValueError, ""
169
170 self.firstdatatime = self.dataIn.utctime
171
172 self.profIndex == nProfiles
173
174 self.__updateSpecFromVoltage()
175
176 self.__getFft()
177
178 self.dataOut.flagNoData = False
179
180 return True
181
182 raise ValueError, "The type of input object '%s' is not valid"%(self.dataIn.type)
183
184 def __selectPairs(self, pairsList):
185
186 if channelList == None:
187 return
188
189 pairsIndexListSelected = []
190
191 for thisPair in pairsList:
192
193 if thisPair not in self.dataOut.pairsList:
194 continue
195
196 pairIndex = self.dataOut.pairsList.index(thisPair)
197
198 pairsIndexListSelected.append(pairIndex)
199
200 if not pairsIndexListSelected:
201 self.dataOut.data_cspc = None
202 self.dataOut.pairsList = []
203 return
204
205 self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
206 self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]
207
208 return
209
210 def __selectPairsByChannel(self, channelList=None):
211
212 if channelList == None:
213 return
214
215 pairsIndexListSelected = []
216 for pairIndex in self.dataOut.pairsIndexList:
217 #First pair
218 if self.dataOut.pairsList[pairIndex][0] not in channelList:
219 continue
220 #Second pair
221 if self.dataOut.pairsList[pairIndex][1] not in channelList:
222 continue
223
224 pairsIndexListSelected.append(pairIndex)
225
226 if not pairsIndexListSelected:
227 self.dataOut.data_cspc = None
228 self.dataOut.pairsList = []
229 return
230
231 self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
232 self.dataOut.pairsList = [self.dataOut.pairsList[i] for i in pairsIndexListSelected]
233
234 return
235
236 def selectChannels(self, channelList):
237
238 channelIndexList = []
239
240 for channel in channelList:
241 if channel not in self.dataOut.channelList:
242 raise ValueError, "Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" %(channel, str(self.dataOut.channelList))
243
244 index = self.dataOut.channelList.index(channel)
245 channelIndexList.append(index)
246
247 self.selectChannelsByIndex(channelIndexList)
248
249 def selectChannelsByIndex(self, channelIndexList):
250 """
251 Selecciona un bloque de datos en base a canales segun el channelIndexList
252
253 Input:
254 channelIndexList : lista sencilla de canales a seleccionar por ej. [2,3,7]
255
256 Affected:
257 self.dataOut.data_spc
258 self.dataOut.channelIndexList
259 self.dataOut.nChannels
260
261 Return:
262 None
263 """
264
265 for channelIndex in channelIndexList:
266 if channelIndex not in self.dataOut.channelIndexList:
267 raise ValueError, "Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " %(channelIndex, self.dataOut.channelIndexList)
268
269 # nChannels = len(channelIndexList)
270
271 data_spc = self.dataOut.data_spc[channelIndexList,:]
272 data_dc = self.dataOut.data_dc[channelIndexList,:]
273
274 self.dataOut.data_spc = data_spc
275 self.dataOut.data_dc = data_dc
276
277 self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
278 # self.dataOut.nChannels = nChannels
279
280 self.__selectPairsByChannel(self.dataOut.channelList)
281
282 return 1
283
284 def selectHeights(self, minHei, maxHei):
285 """
286 Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
287 minHei <= height <= maxHei
288
289 Input:
290 minHei : valor minimo de altura a considerar
291 maxHei : valor maximo de altura a considerar
292
293 Affected:
294 Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
295
296 Return:
297 1 si el metodo se ejecuto con exito caso contrario devuelve 0
298 """
299
300 if (minHei > maxHei):
301 raise ValueError, "Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei)
302
303 if (minHei < self.dataOut.heightList[0]):
304 minHei = self.dataOut.heightList[0]
305
306 if (maxHei > self.dataOut.heightList[-1]):
307 maxHei = self.dataOut.heightList[-1]
308
309 minIndex = 0
310 maxIndex = 0
311 heights = self.dataOut.heightList
312
313 inda = numpy.where(heights >= minHei)
314 indb = numpy.where(heights <= maxHei)
315
316 try:
317 minIndex = inda[0][0]
318 except:
319 minIndex = 0
320
321 try:
322 maxIndex = indb[0][-1]
323 except:
324 maxIndex = len(heights)
325
326 self.selectHeightsByIndex(minIndex, maxIndex)
327
328 return 1
329
330 def getBeaconSignal(self, tauindex = 0, channelindex = 0, hei_ref=None):
331 newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
332
333 if hei_ref != None:
334 newheis = numpy.where(self.dataOut.heightList>hei_ref)
335
336 minIndex = min(newheis[0])
337 maxIndex = max(newheis[0])
338 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
339 heightList = self.dataOut.heightList[minIndex:maxIndex+1]
340
341 # determina indices
342 nheis = int(self.dataOut.radarControllerHeaderObj.txB/(self.dataOut.heightList[1]-self.dataOut.heightList[0]))
343 avg_dB = 10*numpy.log10(numpy.sum(data_spc[channelindex,:,:],axis=0))
344 beacon_dB = numpy.sort(avg_dB)[-nheis:]
345 beacon_heiIndexList = []
346 for val in avg_dB.tolist():
347 if val >= beacon_dB[0]:
348 beacon_heiIndexList.append(avg_dB.tolist().index(val))
349
350 #data_spc = data_spc[:,:,beacon_heiIndexList]
351 data_cspc = None
352 if self.dataOut.data_cspc is not None:
353 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
354 #data_cspc = data_cspc[:,:,beacon_heiIndexList]
355
356 data_dc = None
357 if self.dataOut.data_dc is not None:
358 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
359 #data_dc = data_dc[:,beacon_heiIndexList]
360
361 self.dataOut.data_spc = data_spc
362 self.dataOut.data_cspc = data_cspc
363 self.dataOut.data_dc = data_dc
364 self.dataOut.heightList = heightList
365 self.dataOut.beacon_heiIndexList = beacon_heiIndexList
366
367 return 1
368
369
370 def selectHeightsByIndex(self, minIndex, maxIndex):
371 """
372 Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
373 minIndex <= index <= maxIndex
374
375 Input:
376 minIndex : valor de indice minimo de altura a considerar
377 maxIndex : valor de indice maximo de altura a considerar
378
379 Affected:
380 self.dataOut.data_spc
381 self.dataOut.data_cspc
382 self.dataOut.data_dc
383 self.dataOut.heightList
384
385 Return:
386 1 si el metodo se ejecuto con exito caso contrario devuelve 0
387 """
388
389 if (minIndex < 0) or (minIndex > maxIndex):
390 raise ValueError, "Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex)
391
392 if (maxIndex >= self.dataOut.nHeights):
393 maxIndex = self.dataOut.nHeights-1
394
395 #Spectra
396 data_spc = self.dataOut.data_spc[:,:,minIndex:maxIndex+1]
397
398 data_cspc = None
399 if self.dataOut.data_cspc is not None:
400 data_cspc = self.dataOut.data_cspc[:,:,minIndex:maxIndex+1]
401
402 data_dc = None
403 if self.dataOut.data_dc is not None:
404 data_dc = self.dataOut.data_dc[:,minIndex:maxIndex+1]
405
406 self.dataOut.data_spc = data_spc
407 self.dataOut.data_cspc = data_cspc
408 self.dataOut.data_dc = data_dc
409
410 self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex+1]
411
412 return 1
413
414 def removeDC(self, mode = 2):
415 jspectra = self.dataOut.data_spc
416 jcspectra = self.dataOut.data_cspc
417
418
419 num_chan = jspectra.shape[0]
420 num_hei = jspectra.shape[2]
421
422 if jcspectra is not None:
423 jcspectraExist = True
424 num_pairs = jcspectra.shape[0]
425 else: jcspectraExist = False
426
427 freq_dc = jspectra.shape[1]/2
428 ind_vel = numpy.array([-2,-1,1,2]) + freq_dc
429
430 if ind_vel[0]<0:
431 ind_vel[range(0,1)] = ind_vel[range(0,1)] + self.num_prof
432
433 if mode == 1:
434 jspectra[:,freq_dc,:] = (jspectra[:,ind_vel[1],:] + jspectra[:,ind_vel[2],:])/2 #CORRECCION
435
436 if jcspectraExist:
437 jcspectra[:,freq_dc,:] = (jcspectra[:,ind_vel[1],:] + jcspectra[:,ind_vel[2],:])/2
438
439 if mode == 2:
440
441 vel = numpy.array([-2,-1,1,2])
442 xx = numpy.zeros([4,4])
443
444 for fil in range(4):
445 xx[fil,:] = vel[fil]**numpy.asarray(range(4))
446
447 xx_inv = numpy.linalg.inv(xx)
448 xx_aux = xx_inv[0,:]
449
450 for ich in range(num_chan):
451 yy = jspectra[ich,ind_vel,:]
452 jspectra[ich,freq_dc,:] = numpy.dot(xx_aux,yy)
453
454 junkid = jspectra[ich,freq_dc,:]<=0
455 cjunkid = sum(junkid)
456
457 if cjunkid.any():
458 jspectra[ich,freq_dc,junkid.nonzero()] = (jspectra[ich,ind_vel[1],junkid] + jspectra[ich,ind_vel[2],junkid])/2
459
460 if jcspectraExist:
461 for ip in range(num_pairs):
462 yy = jcspectra[ip,ind_vel,:]
463 jcspectra[ip,freq_dc,:] = numpy.dot(xx_aux,yy)
464
465
466 self.dataOut.data_spc = jspectra
467 self.dataOut.data_cspc = jcspectra
468
469 return 1
470
471 def removeInterference(self, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):
472
473 jspectra = self.dataOut.data_spc
474 jcspectra = self.dataOut.data_cspc
475 jnoise = self.dataOut.getNoise()
476 num_incoh = self.dataOut.nIncohInt
477
478 num_channel = jspectra.shape[0]
479 num_prof = jspectra.shape[1]
480 num_hei = jspectra.shape[2]
481
482 #hei_interf
483 if hei_interf is None:
484 count_hei = num_hei/2 #Como es entero no importa
485 hei_interf = numpy.asmatrix(range(count_hei)) + num_hei - count_hei
486 hei_interf = numpy.asarray(hei_interf)[0]
487 #nhei_interf
488 if (nhei_interf == None):
489 nhei_interf = 5
490 if (nhei_interf < 1):
491 nhei_interf = 1
492 if (nhei_interf > count_hei):
493 nhei_interf = count_hei
494 if (offhei_interf == None):
495 offhei_interf = 0
496
497 ind_hei = range(num_hei)
498 # mask_prof = numpy.asarray(range(num_prof - 2)) + 1
499 # mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
500 mask_prof = numpy.asarray(range(num_prof))
501 num_mask_prof = mask_prof.size
502 comp_mask_prof = [0, num_prof/2]
503
504
505 #noise_exist: Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
506 if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
507 jnoise = numpy.nan
508 noise_exist = jnoise[0] < numpy.Inf
509
510 #Subrutina de Remocion de la Interferencia
511 for ich in range(num_channel):
512 #Se ordena los espectros segun su potencia (menor a mayor)
513 power = jspectra[ich,mask_prof,:]
514 power = power[:,hei_interf]
515 power = power.sum(axis = 0)
516 psort = power.ravel().argsort()
517
518 #Se estima la interferencia promedio en los Espectros de Potencia empleando
519 junkspc_interf = jspectra[ich,:,hei_interf[psort[range(offhei_interf, nhei_interf + offhei_interf)]]]
520
521 if noise_exist:
522 # tmp_noise = jnoise[ich] / num_prof
523 tmp_noise = jnoise[ich]
524 junkspc_interf = junkspc_interf - tmp_noise
525 #junkspc_interf[:,comp_mask_prof] = 0
526
527 jspc_interf = junkspc_interf.sum(axis = 0) / nhei_interf
528 jspc_interf = jspc_interf.transpose()
529 #Calculando el espectro de interferencia promedio
530 noiseid = numpy.where(jspc_interf <= tmp_noise/ numpy.sqrt(num_incoh))
531 noiseid = noiseid[0]
532 cnoiseid = noiseid.size
533 interfid = numpy.where(jspc_interf > tmp_noise/ numpy.sqrt(num_incoh))
534 interfid = interfid[0]
535 cinterfid = interfid.size
536
537 if (cnoiseid > 0): jspc_interf[noiseid] = 0
538
539 #Expandiendo los perfiles a limpiar
540 if (cinterfid > 0):
541 new_interfid = (numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof)%num_prof
542 new_interfid = numpy.asarray(new_interfid)
543 new_interfid = {x for x in new_interfid}
544 new_interfid = numpy.array(list(new_interfid))
545 new_cinterfid = new_interfid.size
546 else: new_cinterfid = 0
547
548 for ip in range(new_cinterfid):
549 ind = junkspc_interf[:,new_interfid[ip]].ravel().argsort()
550 jspc_interf[new_interfid[ip]] = junkspc_interf[ind[nhei_interf/2],new_interfid[ip]]
551
552
553 jspectra[ich,:,ind_hei] = jspectra[ich,:,ind_hei] - jspc_interf #Corregir indices
554
555 #Removiendo la interferencia del punto de mayor interferencia
556 ListAux = jspc_interf[mask_prof].tolist()
557 maxid = ListAux.index(max(ListAux))
558
559
560 if cinterfid > 0:
561 for ip in range(cinterfid*(interf == 2) - 1):
562 ind = (jspectra[ich,interfid[ip],:] < tmp_noise*(1 + 1/numpy.sqrt(num_incoh))).nonzero()
563 cind = len(ind)
564
565 if (cind > 0):
566 jspectra[ich,interfid[ip],ind] = tmp_noise*(1 + (numpy.random.uniform(cind) - 0.5)/numpy.sqrt(num_incoh))
567
568 ind = numpy.array([-2,-1,1,2])
569 xx = numpy.zeros([4,4])
570
571 for id1 in range(4):
572 xx[:,id1] = ind[id1]**numpy.asarray(range(4))
573
574 xx_inv = numpy.linalg.inv(xx)
575 xx = xx_inv[:,0]
576 ind = (ind + maxid + num_mask_prof)%num_mask_prof
577 yy = jspectra[ich,mask_prof[ind],:]
578 jspectra[ich,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
579
580
581 indAux = (jspectra[ich,:,:] < tmp_noise*(1-1/numpy.sqrt(num_incoh))).nonzero()
582 jspectra[ich,indAux[0],indAux[1]] = tmp_noise * (1 - 1/numpy.sqrt(num_incoh))
583
584 #Remocion de Interferencia en el Cross Spectra
585 if jcspectra is None: return jspectra, jcspectra
586 num_pairs = jcspectra.size/(num_prof*num_hei)
587 jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
588
589 for ip in range(num_pairs):
590
591 #-------------------------------------------
592
593 cspower = numpy.abs(jcspectra[ip,mask_prof,:])
594 cspower = cspower[:,hei_interf]
595 cspower = cspower.sum(axis = 0)
596
597 cspsort = cspower.ravel().argsort()
598 junkcspc_interf = jcspectra[ip,:,hei_interf[cspsort[range(offhei_interf, nhei_interf + offhei_interf)]]]
599 junkcspc_interf = junkcspc_interf.transpose()
600 jcspc_interf = junkcspc_interf.sum(axis = 1)/nhei_interf
601
602 ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
603
604 median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
605 median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[range(3*num_prof/4)]],:]))
606 junkcspc_interf[comp_mask_prof,:] = numpy.complex(median_real, median_imag)
607
608 for iprof in range(num_prof):
609 ind = numpy.abs(junkcspc_interf[iprof,:]).ravel().argsort()
610 jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf/2]]
611
612 #Removiendo la Interferencia
613 jcspectra[ip,:,ind_hei] = jcspectra[ip,:,ind_hei] - jcspc_interf
614
615 ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
616 maxid = ListAux.index(max(ListAux))
617
618 ind = numpy.array([-2,-1,1,2])
619 xx = numpy.zeros([4,4])
620
621 for id1 in range(4):
622 xx[:,id1] = ind[id1]**numpy.asarray(range(4))
623
624 xx_inv = numpy.linalg.inv(xx)
625 xx = xx_inv[:,0]
626
627 ind = (ind + maxid + num_mask_prof)%num_mask_prof
628 yy = jcspectra[ip,mask_prof[ind],:]
629 jcspectra[ip,mask_prof[maxid],:] = numpy.dot(yy.transpose(),xx)
630
631 #Guardar Resultados
632 self.dataOut.data_spc = jspectra
633 self.dataOut.data_cspc = jcspectra
634
635 return 1
636
637 def setRadarFrequency(self, frequency=None):
638
639 if frequency != None:
640 self.dataOut.frequency = frequency
641
642 return 1
643
644 def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
645 #validacion de rango
646 if minHei == None:
647 minHei = self.dataOut.heightList[0]
648
649 if maxHei == None:
650 maxHei = self.dataOut.heightList[-1]
651
652 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
653 print 'minHei: %.2f is out of the heights range'%(minHei)
654 print 'minHei is setting to %.2f'%(self.dataOut.heightList[0])
655 minHei = self.dataOut.heightList[0]
656
657 if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
658 print 'maxHei: %.2f is out of the heights range'%(maxHei)
659 print 'maxHei is setting to %.2f'%(self.dataOut.heightList[-1])
660 maxHei = self.dataOut.heightList[-1]
661
662 # validacion de velocidades
663 velrange = self.dataOut.getVelRange(1)
664
665 if minVel == None:
666 minVel = velrange[0]
667
668 if maxVel == None:
669 maxVel = velrange[-1]
670
671 if (minVel < velrange[0]) or (minVel > maxVel):
672 print 'minVel: %.2f is out of the velocity range'%(minVel)
673 print 'minVel is setting to %.2f'%(velrange[0])
674 minVel = velrange[0]
675
676 if (maxVel > velrange[-1]) or (maxVel < minVel):
677 print 'maxVel: %.2f is out of the velocity range'%(maxVel)
678 print 'maxVel is setting to %.2f'%(velrange[-1])
679 maxVel = velrange[-1]
680
681 # seleccion de indices para rango
682 minIndex = 0
683 maxIndex = 0
684 heights = self.dataOut.heightList
685
686 inda = numpy.where(heights >= minHei)
687 indb = numpy.where(heights <= maxHei)
688
689 try:
690 minIndex = inda[0][0]
691 except:
692 minIndex = 0
693
694 try:
695 maxIndex = indb[0][-1]
696 except:
697 maxIndex = len(heights)
698
699 if (minIndex < 0) or (minIndex > maxIndex):
700 raise ValueError, "some value in (%d,%d) is not valid" % (minIndex, maxIndex)
701
702 if (maxIndex >= self.dataOut.nHeights):
703 maxIndex = self.dataOut.nHeights-1
704
705 # seleccion de indices para velocidades
706 indminvel = numpy.where(velrange >= minVel)
707 indmaxvel = numpy.where(velrange <= maxVel)
708 try:
709 minIndexVel = indminvel[0][0]
710 except:
711 minIndexVel = 0
712
713 try:
714 maxIndexVel = indmaxvel[0][-1]
715 except:
716 maxIndexVel = len(velrange)
717
718 #seleccion del espectro
719 data_spc = self.dataOut.data_spc[:,minIndexVel:maxIndexVel+1,minIndex:maxIndex+1]
720 #estimacion de ruido
721 noise = numpy.zeros(self.dataOut.nChannels)
722
723 for channel in range(self.dataOut.nChannels):
724 daux = data_spc[channel,:,:]
725 noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
726
727 self.dataOut.noise_estimation = noise.copy()
728
729 return 1
730
731 class IncohIntLags(Operation):
732
733
734 __profIndex = 0
735 __withOverapping = False
736
737 __byTime = False
738 __initime = None
739 __lastdatatime = None
740 __integrationtime = None
741
742 __buffer_spc = None
743 __buffer_cspc = None
744 __buffer_dc = None
745
746 __dataReady = False
747
748 __timeInterval = None
749
750 n = None
751
752
753
754 def __init__(self):
755
756 Operation.__init__(self)
757 # self.isConfig = False
758
759 def setup(self, n=None, timeInterval=None, overlapping=False):
760 """
761 Set the parameters of the integration class.
762
763 Inputs:
764
765 n : Number of coherent integrations
766 timeInterval : Time of integration. If the parameter "n" is selected this one does not work
767 overlapping :
768
769 """
770
771 self.__initime = None
772 self.__lastdatatime = 0
773
774 self.__buffer_spc = 0
775 self.__buffer_cspc = 0
776 self.__buffer_dc = 0
777
778 self.__profIndex = 0
779 self.__dataReady = False
780 self.__byTime = False
781
782 if n is None and timeInterval is None:
783 raise ValueError, "n or timeInterval should be specified ..."
784
785 if n is not None:
786 self.n = int(n)
787 else:
788 self.__integrationtime = int(timeInterval) #if (type(timeInterval)!=integer) -> change this line
789 self.n = None
790 self.__byTime = True
791
792 def putData(self, data_spc, data_cspc, data_dc):
793
794 """
795 Add a profile to the __buffer_spc and increase in one the __profileIndex
796
797 """
798
799 self.__buffer_spc += data_spc
800
801 if data_cspc is None:
802 self.__buffer_cspc = None
803 else:
804 self.__buffer_cspc += data_cspc
805
806 if data_dc is None:
807 self.__buffer_dc = None
808 else:
809 self.__buffer_dc += data_dc
810
811 self.__profIndex += 1
812
813 return
814
815 def pushData(self):
816 """
817 Return the sum of the last profiles and the profiles used in the sum.
818
819 Affected:
820
821 self.__profileIndex
822
823 """
824
825 data_spc = self.__buffer_spc
826 data_cspc = self.__buffer_cspc
827 data_dc = self.__buffer_dc
828 n = self.__profIndex
829
830 self.__buffer_spc = 0
831 self.__buffer_cspc = 0
832 self.__buffer_dc = 0
833 self.__profIndex = 0
834
835 return data_spc, data_cspc, data_dc, n
836
837 def byProfiles(self, *args):
838
839 self.__dataReady = False
840 avgdata_spc = None
841 avgdata_cspc = None
842 avgdata_dc = None
843
844 self.putData(*args)
845
846 if self.__profIndex == self.n:
847
848 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
849 self.n = n
850 self.__dataReady = True
851
852 return avgdata_spc, avgdata_cspc, avgdata_dc
853
854 def byTime(self, datatime, *args):
855
856 self.__dataReady = False
857 avgdata_spc = None
858 avgdata_cspc = None
859 avgdata_dc = None
860
861 self.putData(*args)
862
863 if (datatime - self.__initime) >= self.__integrationtime:
864 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
865 self.n = n
866 self.__dataReady = True
867
868 return avgdata_spc, avgdata_cspc, avgdata_dc
869
870 def integrate(self, datatime, *args):
871
872 if self.__profIndex == 0:
873 self.__initime = datatime
874
875 if self.__byTime:
876 avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(datatime, *args)
877 else:
878 avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
879
880 if not self.__dataReady:
881 return None, None, None, None
882
883 return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc
884
885 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
886
887 if n==1:
888 return
889
890 dataOut.flagNoData = True
891
892 if not self.isConfig:
893 self.setup(n, timeInterval, overlapping)
894 self.isConfig = True
895
896 avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
897 dataOut.data_spc,
898 dataOut.data_cspc,
899 dataOut.data_dc)
900
901 if self.__dataReady:
902
903 dataOut.data_spc = avgdata_spc
904 dataOut.data_cspc = avgdata_cspc
905 dataOut.data_dc = avgdata_dc
906
907 dataOut.nIncohInt *= self.n
908 dataOut.utctime = avgdatatime
909 dataOut.flagNoData = False
Show file before | Show file after | Hide comments
@@ -9,4 +9,5 from jroproc_spectra import *
9 9 from jroproc_heispectra import *
10 10 from jroproc_amisr import *
11 11 from jroproc_correlation import *
12 from jroproc_parameters import * No newline at end of file
12 from jroproc_parameters import *
13 from jroproc_spectra_lags import * No newline at end of file
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