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update remHeightsIppInterf
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r1583:96ac032e04a6
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@@ -599,7 +599,7 class Spectra(JROData):
599 599 normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter
600 600 if self.flagProfilesByRange:
601 601 normFactor *= (self.nProfilesByRange/self.nProfilesByRange.max())
602
602 #print("normFactor: ", normFactor)
603 603 return normFactor
604 604
605 605 @property
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@@ -1062,7 +1062,7 class NoiselessRTIPlot(RTIPlot):
1062 1062 data = {}
1063 1063 meta = {}
1064 1064 #print(dataOut.max_nIncohInt, dataOut.nIncohInt)
1065 #print(dataOut.windowOfFilter,dataOut.nCohInt,dataOut.nProfiles,dataOut.max_nIncohInt,dataOut.nIncohInt)
1065 #print(dataOut.windowOfFilter,dataOut.nCohInt,dataOut.nProfiles,dataOut.max_nIncohInt,dataOut.nIncohInt
1066 1066 norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter
1067 1067 n0 = 10*numpy.log10(dataOut.getNoise()/norm)
1068 1068 data['noise'] = n0
@@ -1342,7 +1342,6 class NoiselessRTILinePlot(Plot):
1342 1342 #print(dataOut.windowOfFilter,dataOut.nCohInt,dataOut.nProfiles,dataOut.max_nIncohInt,dataOut.nIncohInt)
1343 1343 norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter
1344 1344
1345
1346 1345 n0 = 10*numpy.log10(dataOut.getNoise()/norm)
1347 1346 data['noise'] = n0
1348 1347
@@ -1355,7 +1354,7 class NoiselessRTILinePlot(Plot):
1355 1354
1356 1355 data['noiseless_rtiLine'] = noiseless_data
1357 1356
1358 print(noiseless_data.shape, self.name)
1357 #print(noiseless_data.shape, self.name)
1359 1358 data['time'] = dataOut.utctime
1360 1359
1361 1360 return data, meta
@@ -1364,7 +1363,7 class NoiselessRTILinePlot(Plot):
1364 1363
1365 1364 self.x = self.data.times
1366 1365 self.y = self.data.yrange
1367 print(self.data['noiseless_rtiLine'].shape, self.y.shape, self.name)
1366 #print(self.data['noiseless_rtiLine'].shape, self.y.shape, self.name)
1368 1367 #ts = self.data['time'][0].squeeze()
1369 1368 if len(self.data['noiseless_rtiLine'])>2 :
1370 1369 self.z = self.data['noiseless_rtiLine'][:, -1,:]
@@ -1435,7 +1434,7 class GeneralProfilePlot(Plot):
1435 1434
1436 1435 data['noiseless_rtiLine'] = noiseless_data
1437 1436
1438 print(noiseless_data.shape, self.name)
1437 #print(noiseless_data.shape, self.name)
1439 1438 data['time'] = dataOut.utctime
1440 1439
1441 1440 return data, meta
@@ -1444,7 +1443,7 class GeneralProfilePlot(Plot):
1444 1443
1445 1444 self.x = self.data.times
1446 1445 self.y = self.data.yrange
1447 print(self.data['noiseless_rtiLine'].shape, self.y.shape, self.name)
1446 #print(self.data['noiseless_rtiLine'].shape, self.y.shape, self.name)
1448 1447 #ts = self.data['time'][0].squeeze()
1449 1448 if len(self.data['noiseless_rtiLine'])>2 :
1450 1449 self.z = self.data['noiseless_rtiLine'][:, -1,:]
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@@ -727,6 +727,7 class getNoiseB(Operation):
727 727 norm = 1
728 728 else:
729 729 norm = self.dataOut.max_nIncohInt[channel]/self.dataOut.nIncohInt[channel, self.minIndex:self.maxIndex]
730
730 731 #print("norm nIncoh: ", norm ,self.dataOut.data_spc.shape, self.dataOut.max_nIncohInt)
731 732 daux = self.dataOut.data_spc[channel,self.minIndexFFT:self.maxIndexFFT, self.minIndex:self.maxIndex]
732 733 daux = numpy.multiply(daux, norm)
@@ -742,11 +743,12 class getNoiseB(Operation):
742 743 #noise = self.dataOut.getNoise(xmin_index=self.minIndexFFT, xmax_index=self.maxIndexFFT, ymin_index=self.minIndex, ymax_index=self.maxIndex)
743 744 else:
744 745 noise = self.dataOut.getNoise(xmin_index=self.minIndexFFT, xmax_index=self.maxIndexFFT, ymin_index=self.minIndex, ymax_index=self.maxIndex)
746
745 747 self.dataOut.noise_estimation = noise.copy() # dataOut.noise
746 748 #print("2: ",10*numpy.log10(self.dataOut.noise_estimation/64))
747 749 #print("2: ",self.dataOut.noise_estimation)
748 750 #print(self.dataOut.flagNoData)
749 #print("getNoise Done", noise, self.dataOut.nProfiles ,self.dataOut.ippFactor)
751 #print("getNoise Done", 10*numpy.log10(noise))
750 752 return self.dataOut
751 753
752 754 def getNoiseByMean(self,data):
@@ -1381,7 +1383,6 class IntegrationFaradaySpectra(Operation):
1381 1383 self.minHei_ind = ind_list1[0][0]
1382 1384 self.maxHei_ind = ind_list2[0][-1]
1383 1385
1384
1385 1386 def putData(self, data_spc, data_cspc, data_dc):
1386 1387 """
1387 1388 Add a profile to the __buffer_spc and increase in one the __profileIndex
@@ -1637,7 +1638,7 class IntegrationFaradaySpectra(Operation):
1637 1638 return self.dataOut
1638 1639 self.dataOut.processingHeaderObj.timeIncohInt = timeInterval
1639 1640
1640 if dataOut.flagProfilesByRange == True:
1641 if dataOut.flagProfilesByRange:
1641 1642 self._flagProfilesByRange = True
1642 1643
1643 1644 if self.dataOut.nChannels == 1:
@@ -1700,7 +1701,7 class IntegrationFaradaySpectra(Operation):
1700 1701 self.dataOut.flagNoData = False
1701 1702
1702 1703 dataOut.nProfilesByRange = self._nProfilesByRange
1703 self._nProfilesByRange = 0
1704 self._nProfilesByRange = numpy.zeros(len(dataOut.heightList))
1704 1705 self._flagProfilesByRange = False
1705 1706
1706 1707 # #update Processing Header:
@@ -1849,7 +1850,7 class removeInterference(Operation):
1849 1850 power = power[:, hei_interf]
1850 1851 power = power.sum(axis=0)
1851 1852 psort = power.ravel().argsort()
1852 print(hei_interf[psort[list(range(offhei_interf, nhei_interf + offhei_interf))]])
1853 #print(hei_interf[psort[list(range(offhei_interf, nhei_interf + offhei_interf))]])
1853 1854 # Se estima la interferencia promedio en los Espectros de Potencia empleando
1854 1855 junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range(
1855 1856 offhei_interf, nhei_interf + offhei_interf))]]]
@@ -1859,7 +1860,7 class removeInterference(Operation):
1859 1860 tmp_noise = jnoise[ich]
1860 1861 junkspc_interf = junkspc_interf - tmp_noise
1861 1862 #junkspc_interf[:,comp_mask_prof] = 0
1862 print(junkspc_interf.shape)
1863 #print(junkspc_interf.shape)
1863 1864 jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf
1864 1865 jspc_interf = jspc_interf.transpose()
1865 1866 # Calculando el espectro de interferencia promedio
@@ -1892,7 +1893,7 class removeInterference(Operation):
1892 1893 # Removiendo la interferencia del punto de mayor interferencia
1893 1894 ListAux = jspc_interf[mask_prof].tolist()
1894 1895 maxid = ListAux.index(max(ListAux))
1895 print(cinterfid)
1896 #print(cinterfid)
1896 1897 if cinterfid > 0:
1897 1898 for ip in range(cinterfid * (interf == 2) - 1):
1898 1899 ind = (jspectra[ich, interfid[ip], :] < tmp_noise *
@@ -1917,7 +1918,7 class removeInterference(Operation):
1917 1918
1918 1919 indAux = (jspectra[ich, :, :] < tmp_noise *
1919 1920 (1 - 1 / numpy.sqrt(num_incoh))).nonzero()
1920 print(indAux)
1921 #print(indAux)
1921 1922 jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \
1922 1923 (1 - 1 / numpy.sqrt(num_incoh))
1923 1924
@@ -2057,6 +2058,7 class IncohInt(Operation):
2057 2058 self.__byTime = True
2058 2059
2059 2060
2061
2060 2062 def putData(self, data_spc, data_cspc, data_dc):
2061 2063 """
2062 2064 Add a profile to the __buffer_spc and increase in one the __profileIndex
@@ -2165,6 +2167,7 class IncohInt(Operation):
2165 2167 dataOut.flagNoData = True
2166 2168 dataOut.processingHeaderObj.timeIncohInt = timeInterval
2167 2169 if not self.isConfig:
2170 self._nProfilesByRange = numpy.zeros(len(dataOut.heightList))
2168 2171 self.setup(n, timeInterval, overlapping)
2169 2172 self.isConfig = True
2170 2173
@@ -2197,7 +2200,7 class IncohInt(Operation):
2197 2200 self.nOutliers = 0
2198 2201 self.__profIndex = 0
2199 2202 dataOut.nProfilesByRange = self._nProfilesByRange
2200 self._nProfilesByRange = 0
2203 self._nProfilesByRange = numpy.zeros(len(dataOut.heightList))
2201 2204 self._flagProfilesByRange = False
2202 2205 #print("IncohInt Done")
2203 2206 return dataOut
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@@ -2024,10 +2024,8 class SSheightProfiles2(Operation):
2024 2024 # #assuming the same remotion for all channels
2025 2025 aux = [ self.nsamples - numpy.count_nonzero(dataOut.data[0, :, h]==0) for h in range(len(dataOut.heightList))]
2026 2026 dataOut.nProfilesByRange = numpy.asarray(aux)
2027 #print(dataOut.nProfilesByRange)
2028 2027 else:
2029 2028 dataOut.nProfilesByRange = numpy.ones(len(dataOut.heightList))*dataOut.nProfiles
2030 print(dataOut.nProfilesByRange)
2031 2029 return dataOut
2032 2030
2033 2031
@@ -3581,9 +3579,12 class RemoveProfileSats2(Operation):
3581 3579
3582 3580 class remHeightsIppInterf(Operation):
3583 3581
3584 def __init__(self):
3582 def __init__(self, **kwargs):
3585 3583
3586 self.config = False
3584
3585 Operation.__init__(self, **kwargs)
3586
3587 self.isConfig = False
3587 3588
3588 3589 self.heights_indx = None
3589 3590 self.heightsList = []
@@ -3618,12 +3619,15 class remHeightsIppInterf(Operation):
3618 3619 self.heights_indx = [ numpy.asarray([k for k in range(_n_hIntf+2)])+(getHei_index(h,h,heiList)[0] -1) for h in self.heightsList]
3619 3620
3620 3621 self.heights_indx = numpy.asarray(self.heights_indx )
3621 self.config = True
3622 self.isConfig = True
3622 3623 self.startTime = datetime.datetime.combine(idate,startH)
3623 3624 self.endTime = datetime.datetime.combine(idate,endH)
3624 3625 #print(self.startTime, self.endTime)
3625 3626 #print("nrepeats: ", _n_repeats, " _nH: ",_n_hIntf )
3626 #print("H interf:",self.heightsList, dataOut.heightList[self.heights_indx] )
3627
3628 log.warning("Heights set to zero (km): ", self.name)
3629 log.warning(str((dataOut.heightList[self.heights_indx].flatten())), self.name)
3630 log.warning("Be careful with the selection of heights for noise calculation!")
3627 3631
3628 3632 def run(self, dataOut, ipp1=None, ipp2=None, tx1=None, tx2=None, dh1=None, idate=None,
3629 3633 startH=None, endH=None):
@@ -3633,17 +3637,16 class remHeightsIppInterf(Operation):
3633 3637 return dataOut
3634 3638
3635 3639
3636 if not self.config:
3640 if not self.isConfig:
3637 3641 self.setup(dataOut, ipp1=ipp1, ipp2=ipp2, tx1=tx1, tx2=tx2, dh1=dh1,
3638 3642 idate=idate, startH=startH, endH=endH)
3639 3643
3640 flagProfilesByRange = False
3644 dataOut.flagProfilesByRange = False
3641 3645 currentTime = datetime.datetime.fromtimestamp(dataOut.utctime)
3642 3646
3643 3647 if currentTime < self.startTime or currentTime > self.endTime:
3644 3648 return dataOut
3645 3649
3646
3647 3650 for ch in range(dataOut.data.shape[0]):
3648 3651
3649 3652 for hk in self.heights_indx.flatten():
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