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Update from schain_tmp Joab Version - AVP
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r1753:0a6e6a51ad73
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@@ -26,6 +26,10 from numpy import NaN
26 from scipy.optimize.optimize import OptimizeWarning
26 from scipy.optimize.optimize import OptimizeWarning
27 warnings.filterwarnings('ignore')
27 warnings.filterwarnings('ignore')
28
28
29 import os
30 import csv
31 from scipy import signal
32 import matplotlib.pyplot as plt
29
33
30 SPEED_OF_LIGHT = 299792458
34 SPEED_OF_LIGHT = 299792458
31
35
@@ -72,27 +76,47 class ParametersProc(ProcessingUnit):
72 self.dataOut.useLocalTime = self.dataIn.useLocalTime
76 self.dataOut.useLocalTime = self.dataIn.useLocalTime
73
77
74 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
78 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
79 self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
75 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
80 self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
81
76 self.dataOut.channelList = self.dataIn.channelList
82 self.dataOut.channelList = self.dataIn.channelList
77 self.dataOut.heightList = self.dataIn.heightList
83 self.dataOut.heightList = self.dataIn.heightList
84 self.dataOut.ipp = self.dataIn.ipp
85 self.dataOut.ippSeconds = self.dataIn.ippSeconds
86 self.dataOut.deltaHeight = self.dataIn.deltaHeight
78 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
87 self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
79 # self.dataOut.nBaud = self.dataIn.nBaud
88
80 # self.dataOut.nCode = self.dataIn.nCode
89 self.dataOut.nBaud = self.dataIn.nBaud
81 # self.dataOut.code = self.dataIn.code
90 self.dataOut.nCode = self.dataIn.nCode
91 self.dataOut.code = self.dataIn.code
92 self.dataOut.nProfiles = self.dataIn.nProfiles
93
82 self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
94 self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
83 self.dataOut.utctime = self.dataIn.utctime
95 self.dataOut.utctime = self.dataIn.utctime
84 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
96 self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
85 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
97 self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
86 self.dataOut.nCohInt = self.dataIn.nCohInt
98 self.dataOut.nCohInt = self.dataIn.nCohInt
99 self.dataOut.nIncohInt = self.dataIn.nIncohInt
100 self.dataOut.ippSeconds = self.dataIn.ippSeconds
101 self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
102
87 self.dataOut.timeInterval1 = self.dataIn.timeInterval
103 self.dataOut.timeInterval1 = self.dataIn.timeInterval
88 self.dataOut.heightList = self.dataIn.heightList
104 self.dataOut.heightList = self.dataIn.heightList
89 self.dataOut.frequency = self.dataIn.frequency
105 self.dataOut.frequency = self.dataIn.frequency
106 self.dataOut.codeList = self.dataIn.codeList
107 self.dataOut.azimuthList = self.dataIn.azimuthList
108 self.dataOut.elevationList = self.dataIn.elevationList
90 self.dataOut.runNextUnit = self.dataIn.runNextUnit
109 self.dataOut.runNextUnit = self.dataIn.runNextUnit
91
110
92 def run(self, runNextUnit=0):
111 def run(self, runNextUnit=0):
93
112
94 self.dataIn.runNextUnit = runNextUnit
113 self.dataIn.runNextUnit = runNextUnit
95 #---------------------- Voltage Data ---------------------------
114 #---------------------- Voltage Data ---------------------------
115 try:
116 intype = self.dataIn.type.decode("utf-8")
117 self.dataIn.type = intype
118 except:
119 pass
96
120
97 if self.dataIn.type == "Voltage":
121 if self.dataIn.type == "Voltage":
98
122
@@ -124,6 +148,21 class ParametersProc(ProcessingUnit):
124 self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc]
148 self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc]
125 self.dataOut.data_spc = self.dataIn.data_spc
149 self.dataOut.data_spc = self.dataIn.data_spc
126 self.dataOut.data_cspc = self.dataIn.data_cspc
150 self.dataOut.data_cspc = self.dataIn.data_cspc
151 if hasattr(self.dataIn, 'data_outlier'):
152 self.dataOut.data_outlier = self.dataIn.data_outlier
153 if hasattr(self.dataIn,'flagPRofilesByRange'):
154 self.dataOut.flagProfilesByRange = self.dataIn.flagProfilesByRange
155 if hasattr(self.dataIn,'nProfilesByRange'):
156 self.dataOut.nProfilesByRange = self.dataIn.nProfilesByRange
157 if hasattr(self.dataIn,'deltaHeight'):
158 self.dataOut.deltaHeight = self.dataIn.deltaHeight
159 if hasattr(self.dataIn,'noise_estimation'):
160 self.dataOut.noise_estimation = self.dataIn.noise_estimation
161 if hasattr(self.dataIn, 'channelList'):
162 self.dataOut.channelList = self.dataIn.channelList
163 if hasattr(self.dataIn, 'pairsList'):
164 self.dataOut.pairsList = self.dataIn.pairsList
165 self.dataOut.groupList = self.dataIn.pairsList
127 self.dataOut.nProfiles = self.dataIn.nProfiles
166 self.dataOut.nProfiles = self.dataIn.nProfiles
128 self.dataOut.nIncohInt = self.dataIn.nIncohInt
167 self.dataOut.nIncohInt = self.dataIn.nIncohInt
129 self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
168 self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
@@ -132,8 +171,6 class ParametersProc(ProcessingUnit):
132 self.dataOut.spc_noise = self.dataIn.getNoise()
171 self.dataOut.spc_noise = self.dataIn.getNoise()
133 self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
172 self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
134 # self.dataOut.normFactor = self.dataIn.normFactor
173 # self.dataOut.normFactor = self.dataIn.normFactor
135 self.dataOut.pairsList = self.dataIn.pairsList
136 self.dataOut.groupList = self.dataIn.pairsList
137 self.dataOut.flagNoData = False
174 self.dataOut.flagNoData = False
138
175
139 if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
176 if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
@@ -174,8 +211,14 class ParametersProc(ProcessingUnit):
174
211
175 if self.dataIn.type == "Parameters":
212 if self.dataIn.type == "Parameters":
176 self.dataOut.copy(self.dataIn)
213 self.dataOut.copy(self.dataIn)
214 self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
215 self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
177 self.dataOut.flagNoData = False
216 self.dataOut.flagNoData = False
178
217 if isinstance(self.dataIn.nIncohInt,numpy.ndarray):
218 nch, nheis = self.dataIn.nIncohInt.shape
219 if nch != self.dataIn.nChannels:
220 aux = numpy.repeat(self.dataIn.nIncohInt, self.dataIn.nChannels, axis=0)
221 self.dataOut.nIncohInt = aux
179 return True
222 return True
180
223
181 self.__updateObjFromInput()
224 self.__updateObjFromInput()
@@ -2454,8 +2497,7 class SpectralMoments(Operation):
2454
2497
2455 return dataOut
2498 return dataOut
2456
2499
2457 def __calculateMoments(self, oldspec, oldfreq, n0,
2500 def __calculateMoments(self, oldspec, oldfreq, n0, normFactor = 1,nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,id_ch=0):
2458 nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None,id_ch=0):
2459
2501
2460 def __GAUSSWINFIT1(A, flagPDER=0):
2502 def __GAUSSWINFIT1(A, flagPDER=0):
2461 nonlocal truex, xvalid
2503 nonlocal truex, xvalid
@@ -2631,6 +2673,7 class SpectralMoments(Operation):
2631 vec_n1 = numpy.empty(oldspec.shape[1])
2673 vec_n1 = numpy.empty(oldspec.shape[1])
2632 vec_fp = numpy.empty(oldspec.shape[1])
2674 vec_fp = numpy.empty(oldspec.shape[1])
2633 vec_sigma_fd = numpy.empty(oldspec.shape[1])
2675 vec_sigma_fd = numpy.empty(oldspec.shape[1])
2676 norm = 1
2634
2677
2635 for ind in range(oldspec.shape[1]):
2678 for ind in range(oldspec.shape[1]):
2636
2679
@@ -2644,6 +2687,10 class SpectralMoments(Operation):
2644 max_spec = aux.max()
2687 max_spec = aux.max()
2645 m = aux.tolist().index(max_spec)
2688 m = aux.tolist().index(max_spec)
2646
2689
2690 if hasattr(normFactor, "ndim"):
2691 if normFactor.ndim >= 1:
2692 norm = normFactor[ind]
2693
2647 if m > 2 and m < oldfreq.size - 3:
2694 if m > 2 and m < oldfreq.size - 3:
2648 newindex = m + numpy.array([-2,-1,0,1,2])
2695 newindex = m + numpy.array([-2,-1,0,1,2])
2649 newfreq = numpy.arange(20)/20.0*(numpy.max(freq[newindex])-numpy.min(freq[newindex]))+numpy.min(freq[newindex])
2696 newfreq = numpy.arange(20)/20.0*(numpy.max(freq[newindex])-numpy.min(freq[newindex]))+numpy.min(freq[newindex])
@@ -2691,6 +2738,7 class SpectralMoments(Operation):
2691 power = ((spec2[valid] - n0) * fwindow[valid]).sum()
2738 power = ((spec2[valid] - n0) * fwindow[valid]).sum()
2692 fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power
2739 fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power
2693 w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power)
2740 w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power)
2741 spec2 /=(norm) #compensation for sats remove
2694 snr = (spec2.mean()-n0)/n0
2742 snr = (spec2.mean()-n0)/n0
2695 if (snr < 1.e-20): snr = 1.e-20
2743 if (snr < 1.e-20): snr = 1.e-20
2696
2744
@@ -3149,6 +3197,7 class SpectralFitting(Operation):
3149 self.bloque0 = numpy.zeros([nChan, nProf, nHei, nBlocks])
3197 self.bloque0 = numpy.zeros([nChan, nProf, nHei, nBlocks])
3150
3198
3151 def __calculateMoments(self,oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
3199 def __calculateMoments(self,oldspec, oldfreq, n0, nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
3200
3152 if (nicoh is None): nicoh = 1
3201 if (nicoh is None): nicoh = 1
3153 if (graph is None): graph = 0
3202 if (graph is None): graph = 0
3154 if (smooth is None): smooth = 0
3203 if (smooth is None): smooth = 0
@@ -3396,7 +3445,7 class SpectralFitting(Operation):
3396 nPairs = len(crosspairs)
3445 nPairs = len(crosspairs)
3397 hval=(heights >= min_hei).nonzero()
3446 hval=(heights >= min_hei).nonzero()
3398 ih=hval[0]
3447 ih=hval[0]
3399
3448
3400 for ih in range(hval[0][0],nHei):
3449 for ih in range(hval[0][0],nHei):
3401 for ifreq in range(nProf):
3450 for ifreq in range(nProf):
3402 for ii in range(n_funct):
3451 for ii in range(n_funct):
@@ -3868,7 +3917,7 class SpectralFitting(Operation):
3868 buffer3 = 0
3917 buffer3 = 0
3869
3918
3870 def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None, filec=None,coh_th=None, hei_th=None,taver=None,proc=None,nhei=None,nprofs=None,ipp=None,channelList=None,Gaussian_Windowed=0):
3919 def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None, filec=None,coh_th=None, hei_th=None,taver=None,proc=None,nhei=None,nprofs=None,ipp=None,channelList=None,Gaussian_Windowed=0):
3871
3920
3872 if not numpy.any(proc):
3921 if not numpy.any(proc):
3873 nChannels = dataOut.nChannels
3922 nChannels = dataOut.nChannels
3874 nHeights= dataOut.heightList.size
3923 nHeights= dataOut.heightList.size
@@ -4058,6 +4107,8 class SpectralFitting(Operation):
4058 if getSNR:
4107 if getSNR:
4059 listChannels = groupArray.reshape((groupArray.size))
4108 listChannels = groupArray.reshape((groupArray.size))
4060 listChannels.sort()
4109 listChannels.sort()
4110 # norm Este factor debe estar implementado para ploteo o grabado como metadata
4111 # norm = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter
4061 dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise[listChannels])
4112 dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], noise[listChannels])
4062 else:
4113 else:
4063 if numpy.any(taver): taver=int(taver)
4114 if numpy.any(taver): taver=int(taver)
@@ -4307,6 +4358,8 class SpectralFitting(Operation):
4307 if 0:
4358 if 0:
4308 listChannels = groupArray.reshape((groupArray.size))
4359 listChannels = groupArray.reshape((groupArray.size))
4309 listChannels.sort()
4360 listChannels.sort()
4361 # norm Este factor debe estar implementado para ploteo o grabado como metadata
4362 # norm = dataOut.nProfiles * dataOut.nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter
4310 dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], my_noises[listChannels])
4363 dataOut.data_snr = self.__getSNR(dataOut.data_spc[listChannels,:,:], my_noises[listChannels])
4311 #print(dataOut.data_snr1_i)
4364 #print(dataOut.data_snr1_i)
4312 # Adding coherent echoes from possible satellites.
4365 # Adding coherent echoes from possible satellites.
@@ -4348,7 +4401,7 class SpectralFitting(Operation):
4348 fmp=numpy.dot(LT,fm)
4401 fmp=numpy.dot(LT,fm)
4349 return dp-fmp
4402 return dp-fmp
4350
4403
4351 def __getSNR(self, z, noise):
4404 def __getSNR(self, z, noise, norm=1):
4352
4405
4353 avg = numpy.average(z, axis=1)
4406 avg = numpy.average(z, axis=1)
4354 SNR = (avg.T-noise)/noise
4407 SNR = (avg.T-noise)/noise
@@ -4363,7 +4416,7 class SpectralFitting(Operation):
4363 chisq=numpy.dot((dp-fmp).T,(dp-fmp))
4416 chisq=numpy.dot((dp-fmp).T,(dp-fmp))
4364 return chisq
4417 return chisq
4365
4418
4366 class WindProfiler_V0(Operation):
4419 class WindProfiler(Operation):
4367
4420
4368 __isConfig = False
4421 __isConfig = False
4369
4422
@@ -4423,7 +4476,6 class WindProfiler_V0(Operation):
4423 minid = listPhi.index(min(listPhi))
4476 minid = listPhi.index(min(listPhi))
4424
4477
4425 rango = list(range(len(phi)))
4478 rango = list(range(len(phi)))
4426 # rango = numpy.delete(rango,maxid)
4427
4479
4428 heiRang1 = heiRang*math.cos(phi[maxid])
4480 heiRang1 = heiRang*math.cos(phi[maxid])
4429 heiRangAux = heiRang*math.cos(phi[minid])
4481 heiRangAux = heiRang*math.cos(phi[minid])
@@ -4453,18 +4505,12 class WindProfiler_V0(Operation):
4453 def __calculateVelUVW(self, A, velRadial):
4505 def __calculateVelUVW(self, A, velRadial):
4454
4506
4455 #Operacion Matricial
4507 #Operacion Matricial
4456 # velUVW = numpy.zeros((velRadial.shape[1],3))
4457 # for ind in range(velRadial.shape[1]):
4458 # velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
4459 # velUVW = velUVW.transpose()
4460 velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
4508 velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
4461 velUVW[:,:] = numpy.dot(A,velRadial)
4509 velUVW[:,:] = numpy.dot(A,velRadial)
4462
4510
4463
4511
4464 return velUVW
4512 return velUVW
4465
4513
4466 # def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
4467
4468 def techniqueDBS(self, kwargs):
4514 def techniqueDBS(self, kwargs):
4469 """
4515 """
4470 Function that implements Doppler Beam Swinging (DBS) technique.
4516 Function that implements Doppler Beam Swinging (DBS) technique.
@@ -4541,17 +4587,6 class WindProfiler_V0(Operation):
4541
4587
4542 return distx, disty, dist, ang
4588 return distx, disty, dist, ang
4543 #Calculo de Matrices
4589 #Calculo de Matrices
4544 # nPairs = len(pairs)
4545 # ang1 = numpy.zeros((nPairs, 2, 1))
4546 # dist1 = numpy.zeros((nPairs, 2, 1))
4547 #
4548 # for j in range(nPairs):
4549 # dist1[j,0,0] = dist[pairs[j][0]]
4550 # dist1[j,1,0] = dist[pairs[j][1]]
4551 # ang1[j,0,0] = ang[pairs[j][0]]
4552 # ang1[j,1,0] = ang[pairs[j][1]]
4553 #
4554 # return distx,disty, dist1,ang1
4555
4590
4556
4591
4557 def __calculateVelVer(self, phase, lagTRange, _lambda):
4592 def __calculateVelVer(self, phase, lagTRange, _lambda):
@@ -4579,26 +4614,6 class WindProfiler_V0(Operation):
4579
4614
4580 return vel
4615 return vel
4581
4616
4582 # def __getPairsAutoCorr(self, pairsList, nChannels):
4583 #
4584 # pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
4585 #
4586 # for l in range(len(pairsList)):
4587 # firstChannel = pairsList[l][0]
4588 # secondChannel = pairsList[l][1]
4589 #
4590 # #Obteniendo pares de Autocorrelacion
4591 # if firstChannel == secondChannel:
4592 # pairsAutoCorr[firstChannel] = int(l)
4593 #
4594 # pairsAutoCorr = pairsAutoCorr.astype(int)
4595 #
4596 # pairsCrossCorr = range(len(pairsList))
4597 # pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
4598 #
4599 # return pairsAutoCorr, pairsCrossCorr
4600
4601 # def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
4602 def techniqueSA(self, kwargs):
4617 def techniqueSA(self, kwargs):
4603
4618
4604 """
4619 """
@@ -4626,22 +4641,10 class WindProfiler_V0(Operation):
4626 _lambda = kwargs['_lambda']
4641 _lambda = kwargs['_lambda']
4627
4642
4628 #Cross Correlation pairs obtained
4643 #Cross Correlation pairs obtained
4629 # pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
4630 # pairsArray = numpy.array(pairsList)[pairsCrossCorr]
4631 # pairsSelArray = numpy.array(pairsSelected)
4632 # pairs = []
4633 #
4634 # #Wind estimation pairs obtained
4635 # for i in range(pairsSelArray.shape[0]/2):
4636 # ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
4637 # ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
4638 # pairs.append((ind1,ind2))
4639
4644
4640 indtau = tau.shape[0]/2
4645 indtau = tau.shape[0]/2
4641 tau1 = tau[:indtau,:]
4646 tau1 = tau[:indtau,:]
4642 tau2 = tau[indtau:-1,:]
4647 tau2 = tau[indtau:-1,:]
4643 # tau1 = tau1[pairs,:]
4644 # tau2 = tau2[pairs,:]
4645 phase1 = tau[-1,:]
4648 phase1 = tau[-1,:]
4646
4649
4647 #---------------------------------------------------------------------
4650 #---------------------------------------------------------------------
@@ -4651,11 +4654,6 class WindProfiler_V0(Operation):
4651 winds = stats.nanmean(winds, axis=0)
4654 winds = stats.nanmean(winds, axis=0)
4652 #---------------------------------------------------------------------
4655 #---------------------------------------------------------------------
4653 #Metodo General
4656 #Metodo General
4654 # distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
4655 # #Calculo Coeficientes de Funcion de Correlacion
4656 # F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
4657 # #Calculo de Velocidades
4658 # winds = self.calculateVelUV(F,G,A,B,H)
4659
4657
4660 #---------------------------------------------------------------------
4658 #---------------------------------------------------------------------
4661 winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
4659 winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
@@ -5781,7 +5779,6 class EWDriftsEstimation(Operation):
5781
5779
5782 rbufc=dataOut.data_paramC[:,:,0]
5780 rbufc=dataOut.data_paramC[:,:,0]
5783 ebufc=dataOut.data_paramC[:,:,1]
5781 ebufc=dataOut.data_paramC[:,:,1]
5784 #SNR = dataOut.data_snr
5785 SNR = dataOut.data_snr1_i
5782 SNR = dataOut.data_snr1_i
5786 rbufi = dataOut.data_snr1_i
5783 rbufi = dataOut.data_snr1_i
5787 velRerr = dataOut.data_error[:,4,:]
5784 velRerr = dataOut.data_error[:,4,:]
@@ -7189,7 +7186,6 class SMOperations():
7189
7186
7190 ph0_aux = ph0 + ph1
7187 ph0_aux = ph0 + ph1
7191 ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
7188 ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
7192
7193 #First Estimation
7189 #First Estimation
7194 cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
7190 cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
7195
7191
@@ -7466,7 +7462,6 class MergeProc(ProcessingUnit):
7466 #exit(1)
7462 #exit(1)
7467 self.dataOut.NLAG = 16
7463 self.dataOut.NLAG = 16
7468 self.dataOut.NLAG = self.dataOut.data_acf.shape[1]
7464 self.dataOut.NLAG = self.dataOut.data_acf.shape[1]
7469
7470 self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1]
7465 self.dataOut.NRANGE = self.dataOut.data_acf.shape[-1]
7471
7466
7472 #print(numpy.shape(self.dataOut.data_spc))
7467 #print(numpy.shape(self.dataOut.data_spc))
@@ -7571,3 +7566,48 class MST_Den_Conv(Operation):
7571 print("pow den shape", numpy.shape(dataOut.PowDen))
7566 print("pow den shape", numpy.shape(dataOut.PowDen))
7572 print("far den shape", numpy.shape(dataOut.FarDen))
7567 print("far den shape", numpy.shape(dataOut.FarDen))
7573 return dataOut
7568 return dataOut
7569
7570 class addTxPower(Operation):
7571 '''
7572 Transmited power level integrated in the dataOut ->AMISR
7573 resolution 1 min
7574 The power files have the pattern power_YYYYMMDD.csv
7575 '''
7576 __slots__ =('isConfig','dataDatetimes','txPowers')
7577 def __init__(self):
7578
7579 Operation.__init__(self)
7580 self.isConfig = False
7581 self.dataDatetimes = []
7582 self.txPowers = []
7583
7584 def setup(self, powerFile, dutyCycle):
7585 if not os.path.isfile(powerFile):
7586 raise schainpy.admin.SchainError('There is no file named :{}'.format(powerFile))
7587 return
7588
7589 with open(powerFile, newline='') as pfile:
7590 reader = csv.reader(pfile, delimiter=',', quotechar='|')
7591 next(reader)
7592 for row in reader:
7593 #'2022-10-25 00:00:00'
7594 self.dataDatetimes.append(datetime.datetime.strptime(row[0], "%Y-%m-%d %H:%M:%S"))
7595 self.txPowers.append(float(row[1])/dutyCycle)
7596 self.isConfig = True
7597
7598 def run(self, dataOut, path, DS=0.05):
7599
7600 #dataOut.flagNoData = True
7601
7602 if not(self.isConfig):
7603 self.setup(path, DS)
7604
7605 dataDate = datetime.datetime.utcfromtimestamp(dataOut.utctime).replace(second=0, microsecond=0)#no seconds
7606 try:
7607 indx = self.dataDatetimes.index(dataDate)
7608 dataOut.txPower = self.txPowers[indx]
7609 except:
7610 log.warning("No power available for the datetime {}, setting power to 0 w", self.name)
7611 dataOut.txPower = 0
7612
7613 return dataOut No newline at end of file
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