schain Commit - r1481:f29ad632dda8 · Jicamarca Repository

Add h0 attribute to dataOut

Juan C. Espinoza -

r1481:f29ad632dda8

parent child

Context file:

r1481:f29ad632dda8

Collapse all files

schainpy/model/data/jrodata.py +1 0

             # Copyright (c) 2012-2020 Jicamarca Radio Observatory
             # All rights reserved.
             #
             # Distributed under the terms of the BSD 3-clause license.
             """Definition of diferent Data objects for different types of data
             Here you will find the diferent data objects for the different types
             of data, this data objects must be used as dataIn or dataOut objects in
             processing units and operations. Currently the supported data objects are:
             Voltage, Spectra, SpectraHeis, Fits, Correlation and Parameters
             """
             import copy
             import numpy
             import datetime
             import json
             import schainpy.admin
             from schainpy.utils import log
             from .jroheaderIO import SystemHeader, RadarControllerHeader
             from schainpy.model.data import _noise
             def getNumpyDtype(dataTypeCode):
                 if dataTypeCode == 0:
                     numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')])
                 elif dataTypeCode == 1:
                     numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')])
                 elif dataTypeCode == 2:
                     numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')])
                 elif dataTypeCode == 3:
                     numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')])
                 elif dataTypeCode == 4:
                     numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
                 elif dataTypeCode == 5:
                     numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')])
                 else:
                     raise ValueError('dataTypeCode was not defined')
                 return numpyDtype
             def getDataTypeCode(numpyDtype):
                 if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]):
                     datatype = 0
                 elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]):
                     datatype = 1
                 elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]):
                     datatype = 2
                 elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]):
                     datatype = 3
                 elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]):
                     datatype = 4
                 elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]):
                     datatype = 5
                 else:
                     datatype = None
                 return datatype
             def hildebrand_sekhon(data, navg):
                 """
                 This method is for the objective determination of the noise level in Doppler spectra. This
                 implementation technique is based on the fact that the standard deviation of the spectral
                 densities is equal to the mean spectral density for white Gaussian noise
                 Inputs:
                     Data    :    heights
                     navg    :    numbers of averages
                 Return:
                     mean    :    noise's level
                 """
                 sortdata = numpy.sort(data, axis=None)
                 '''
                 lenOfData = len(sortdata)
                 nums_min = lenOfData*0.2
                 if nums_min <= 5:
                     nums_min = 5
                 sump = 0.
                 sumq = 0.
                 j = 0
                 cont = 1
                 while((cont == 1)and(j < lenOfData)):
                     sump += sortdata[j]
                     sumq += sortdata[j]**2
                     if j > nums_min:
                         rtest = float(j)/(j-1) + 1.0/navg
                         if ((sumq*j) > (rtest*sump**2)):
                             j = j - 1
                             sump = sump - sortdata[j]
                             sumq = sumq - sortdata[j]**2
                             cont = 0
                     j += 1
                 lnoise = sump / j
                 '''
                 return _noise.hildebrand_sekhon(sortdata, navg)
             class Beam:
                 def __init__(self):
                     self.codeList = []
                     self.azimuthList = []
                     self.zenithList = []
             class GenericData(object):
                 flagNoData = True
                 def copy(self, inputObj=None):
                     if inputObj == None:
                         return copy.deepcopy(self)
                     for key in list(inputObj.__dict__.keys()):
                         attribute = inputObj.__dict__[key]
                         # If this attribute is a tuple or list
                         if type(inputObj.__dict__[key]) in (tuple, list):
                             self.__dict__[key] = attribute[:]
                             continue
                         # If this attribute is another object or instance
                         if hasattr(attribute, '__dict__'):
                             self.__dict__[key] = attribute.copy()
                             continue
                         self.__dict__[key] = inputObj.__dict__[key]
                 def deepcopy(self):
                     return copy.deepcopy(self)
                 def isEmpty(self):
                     return self.flagNoData
                 def isReady(self):
                     return not self.flagNoData
             class JROData(GenericData):
                 systemHeaderObj = SystemHeader()
                 radarControllerHeaderObj = RadarControllerHeader()
                 type = None
                 datatype = None  # dtype but in string
                 nProfiles = None
                 heightList = None
                 channelList = None
                 flagDiscontinuousBlock = False
                 useLocalTime = False
                 utctime = None
                 timeZone = None
                 dstFlag = None
                 errorCount = None
                 blocksize = None
                 flagDecodeData = False  # asumo q la data no esta decodificada
                 flagDeflipData = False  # asumo q la data no esta sin flip
                 flagShiftFFT = False
                 nCohInt = None
                 windowOfFilter = 1
                 C = 3e8
                 frequency = 49.92e6
                 realtime = False
                 beacon_heiIndexList = None
                 last_block = None
                 blocknow = None
                 azimuth = None
                 zenith = None
                 beam = Beam()
                 profileIndex = None
                 error = None
                 data = None
                 nmodes = None
+                h0 = 0
                 metadata_list = ['heightList', 'timeZone', 'type']
                 def __str__(self):
                     return '{} - {}'.format(self.type, self.datatime())
                 def getNoise(self):
                     raise NotImplementedError
                 @property
                 def nChannels(self):
                     return len(self.channelList)
                 @property
                 def channelIndexList(self):
                     return list(range(self.nChannels))
                 @property
                 def nHeights(self):
                     return len(self.heightList)
                 def getDeltaH(self):
                     return self.heightList[1] - self.heightList[0]
                 @property
                 def ltctime(self):
                     if self.useLocalTime:
                         return self.utctime - self.timeZone * 60
                     return self.utctime
                 @property
                 def datatime(self):
                     datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime)
                     return datatimeValue
                 def getTimeRange(self):
                     datatime = []
                     datatime.append(self.ltctime)
                     datatime.append(self.ltctime + self.timeInterval + 1)
                     datatime = numpy.array(datatime)
                     return datatime
                 def getFmaxTimeResponse(self):
                     period = (10**-6) * self.getDeltaH() / (0.15)
                     PRF = 1. / (period * self.nCohInt)
                     fmax = PRF
                     return fmax
                 def getFmax(self):
                     PRF = 1. / (self.ippSeconds * self.nCohInt)
                     fmax = PRF
                     return fmax
                 def getVmax(self):
                     _lambda = self.C / self.frequency
                     vmax = self.getFmax() * _lambda / 2
                     return vmax
                 @property
                 def ippSeconds(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.ippSeconds
                 @ippSeconds.setter
                 def ippSeconds(self, ippSeconds):
                     '''
                     '''
                     self.radarControllerHeaderObj.ippSeconds = ippSeconds
                 @property
                 def code(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.code
                 @code.setter
                 def code(self, code):
                     '''
                     '''
                     self.radarControllerHeaderObj.code = code
                 @property
                 def nCode(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.nCode
                 @nCode.setter
                 def nCode(self, ncode):
                     '''
                     '''
                     self.radarControllerHeaderObj.nCode = ncode
                 @property
                 def nBaud(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.nBaud
                 @nBaud.setter
                 def nBaud(self, nbaud):
                     '''
                     '''
                     self.radarControllerHeaderObj.nBaud = nbaud
                 @property
                 def ipp(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.ipp
                 @ipp.setter
                 def ipp(self, ipp):
                     '''
                     '''
                     self.radarControllerHeaderObj.ipp = ipp
                 @property
                 def metadata(self):
                     '''
                     '''
                     return {attr: getattr(self, attr) for attr in self.metadata_list}
             class Voltage(JROData):
                 dataPP_POW   = None
                 dataPP_DOP   = None
                 dataPP_WIDTH = None
                 dataPP_SNR   = None
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.useLocalTime = True
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Voltage"
                     self.data = None
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.timeZone = 0
                     self.dstFlag = None
                     self.errorCount = None
                     self.nCohInt = None
                     self.blocksize = None
                     self.flagCohInt = False
                     self.flagDecodeData = False  # asumo q la data no esta decodificada
                     self.flagDeflipData = False  # asumo q la data no esta sin flip
                     self.flagShiftFFT = False
                     self.flagDataAsBlock = False  # Asumo que la data es leida perfil a perfil
                     self.profileIndex = 0
                     self.metadata_list = ['type', 'heightList', 'timeZone', 'nProfiles', 'channelList', 'nCohInt',
                         'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp']
                 def getNoisebyHildebrand(self, channel=None):
                     """
                     Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
                     Return:
                         noiselevel
                     """
                     if channel != None:
                         data = self.data[channel]
                         nChannels = 1
                     else:
                         data = self.data
                         nChannels = self.nChannels
                     noise = numpy.zeros(nChannels)
                     power = data * numpy.conjugate(data)
                     for thisChannel in range(nChannels):
                         if nChannels == 1:
                             daux = power[:].real
                         else:
                             daux = power[thisChannel, :].real
                         noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt)
                     return noise
                 def getNoise(self, type=1, channel=None):
                     if type == 1:
                         noise = self.getNoisebyHildebrand(channel)
                     return noise
                 def getPower(self, channel=None):
                     if channel != None:
                         data = self.data[channel]
                     else:
                         data = self.data
                     power = data * numpy.conjugate(data)
                     powerdB = 10 * numpy.log10(power.real)
                     powerdB = numpy.squeeze(powerdB)
                     return powerdB
                 @property
                 def timeInterval(self):
                     return self.ippSeconds * self.nCohInt
                 noise = property(getNoise, "I'm the 'nHeights' property.")
             class Spectra(JROData):
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.data_dc = None
                     self.data_spc = None
                     self.data_cspc = None
                     self.useLocalTime = True
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Spectra"
                     self.timeZone = 0
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
                     self.pairsList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.nCohInt = None
                     self.nIncohInt = None
                     self.blocksize = None
                     self.nFFTPoints = None
                     self.wavelength = None
                     self.flagDecodeData = False  # asumo q la data no esta decodificada
                     self.flagDeflipData = False  # asumo q la data no esta sin flip
                     self.flagShiftFFT = False
                     self.ippFactor = 1
                     self.beacon_heiIndexList = []
                     self.noise_estimation = None
                     self.metadata_list = ['type', 'heightList', 'timeZone', 'pairsList', 'channelList', 'nCohInt',
                         'code', 'nCode', 'nBaud', 'ippSeconds', 'ipp','nIncohInt', 'nFFTPoints', 'nProfiles']
                 def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
                     """
                     Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
                     Return:
                         noiselevel
                     """
                     noise = numpy.zeros(self.nChannels)
                     for channel in range(self.nChannels):
                         daux = self.data_spc[channel,
                                              xmin_index:xmax_index, ymin_index:ymax_index]
                         noise[channel] = hildebrand_sekhon(daux, self.nIncohInt)
                     return noise
                 def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
                     if self.noise_estimation is not None:
                         # this was estimated by getNoise Operation defined in jroproc_spectra.py
                         return self.noise_estimation
                     else:
                         noise = self.getNoisebyHildebrand(
                             xmin_index, xmax_index, ymin_index, ymax_index)
                         return noise
                 def getFreqRangeTimeResponse(self, extrapoints=0):
                     deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor)
                     freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2
                     return freqrange
                 def getAcfRange(self, extrapoints=0):
                     deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor))
                     freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
                     return freqrange
                 def getFreqRange(self, extrapoints=0):
                     deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor)
                     freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
                     return freqrange
                 def getVelRange(self, extrapoints=0):
                     deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor)
                     velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.)
                     if self.nmodes:
                         return velrange/self.nmodes
                     else:
                         return velrange
                 @property
                 def nPairs(self):
                     return len(self.pairsList)
                 @property
                 def pairsIndexList(self):
                     return list(range(self.nPairs))
                 @property
                 def normFactor(self):
                     pwcode = 1
                     if self.flagDecodeData:
                         pwcode = numpy.sum(self.code[0]**2)
                     #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter
                     normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter
                     return normFactor
                 @property
                 def flag_cspc(self):
                     if self.data_cspc is None:
                         return True
                     return False
                 @property
                 def flag_dc(self):
                     if self.data_dc is None:
                         return True
                     return False
                 @property
                 def timeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor
                     if self.nmodes:
                         return self.nmodes*timeInterval
                     else:
                         return timeInterval
                 def getPower(self):
                     factor = self.normFactor
                     z = self.data_spc / factor
                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     avg = numpy.average(z, axis=1)
                     return 10 * numpy.log10(avg)
                 def getCoherence(self, pairsList=None, phase=False):
                     z = []
                     if pairsList is None:
                         pairsIndexList = self.pairsIndexList
                     else:
                         pairsIndexList = []
                         for pair in pairsList:
                             if pair not in self.pairsList:
                                 raise ValueError("Pair %s is not in dataOut.pairsList" % (
                                     pair))
                             pairsIndexList.append(self.pairsList.index(pair))
                     for i in range(len(pairsIndexList)):
                         pair = self.pairsList[pairsIndexList[i]]
                         ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0)
                         powa = numpy.average(self.data_spc[pair[0], :, :], axis=0)
                         powb = numpy.average(self.data_spc[pair[1], :, :], axis=0)
                         avgcoherenceComplex = ccf / numpy.sqrt(powa * powb)
                         if phase:
                             data = numpy.arctan2(avgcoherenceComplex.imag,
                                                  avgcoherenceComplex.real) * 180 / numpy.pi
                         else:
                             data = numpy.abs(avgcoherenceComplex)
                         z.append(data)
                     return numpy.array(z)
                 def setValue(self, value):
                     print("This property should not be initialized")
                     return
                 noise = property(getNoise, setValue, "I'm the 'nHeights' property.")
             class SpectraHeis(Spectra):
                 def __init__(self):
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "SpectraHeis"
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.blocksize = None
                     self.profileIndex = 0
                     self.nCohInt = 1
                     self.nIncohInt = 1
                 @property
                 def normFactor(self):
                     pwcode = 1
                     if self.flagDecodeData:
                         pwcode = numpy.sum(self.code[0]**2)
                     normFactor = self.nIncohInt * self.nCohInt * pwcode
                     return normFactor
                 @property
                 def timeInterval(self):
                     return self.ippSeconds * self.nCohInt * self.nIncohInt
             class Fits(JROData):
                 def __init__(self):
                     self.type = "Fits"
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
                     self.flagNoData = True
                     self.utctime = None
                     self.nCohInt = 1
                     self.nIncohInt = 1
                     self.useLocalTime = True
                     self.profileIndex = 0
                     self.timeZone = 0
                 def getTimeRange(self):
                     datatime = []
                     datatime.append(self.ltctime)
                     datatime.append(self.ltctime + self.timeInterval)
                     datatime = numpy.array(datatime)
                     return datatime
                 def getChannelIndexList(self):
                     return list(range(self.nChannels))
                 def getNoise(self, type=1):
                     if type == 1:
                         noise = self.getNoisebyHildebrand()
                     if type == 2:
                         noise = self.getNoisebySort()
                     if type == 3:
                         noise = self.getNoisebyWindow()
                     return noise
                 @property
                 def timeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt
                     return timeInterval
                 @property
                 def ippSeconds(self):
                     '''
                     '''
                     return self.ipp_sec
                 noise = property(getNoise, "I'm the 'nHeights' property.")
             class Correlation(JROData):
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Correlation"
                     self.data = None
                     self.dtype = None
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.timeZone = 0
                     self.dstFlag = None
                     self.errorCount = None
                     self.blocksize = None
                     self.flagDecodeData = False  # asumo q la data no esta decodificada
                     self.flagDeflipData = False  # asumo q la data no esta sin flip
                     self.pairsList = None
                     self.nPoints = None
                 def getPairsList(self):
                     return self.pairsList
                 def getNoise(self, mode=2):
                     indR = numpy.where(self.lagR == 0)[0][0]
                     indT = numpy.where(self.lagT == 0)[0][0]
                     jspectra0 = self.data_corr[:, :, indR, :]
                     jspectra = copy.copy(jspectra0)
                     num_chan = jspectra.shape[0]
                     num_hei = jspectra.shape[2]
                     freq_dc = jspectra.shape[1] / 2
                     ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
                     if ind_vel[0] < 0:
                         ind_vel[list(range(0, 1))] = ind_vel[list(
                             range(0, 1))] + self.num_prof
                     if mode == 1:
                         jspectra[:, freq_dc, :] = (
                             jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2  # CORRECCION
                     if mode == 2:
                         vel = numpy.array([-2, -1, 1, 2])
                         xx = numpy.zeros([4, 4])
                         for fil in range(4):
                             xx[fil, :] = vel[fil]**numpy.asarray(list(range(4)))
                         xx_inv = numpy.linalg.inv(xx)
                         xx_aux = xx_inv[0, :]
                         for ich in range(num_chan):
                             yy = jspectra[ich, ind_vel, :]
                             jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy)
                             junkid = jspectra[ich, freq_dc, :] <= 0
                             cjunkid = sum(junkid)
                             if cjunkid.any():
                                 jspectra[ich, freq_dc, junkid.nonzero()] = (
                                     jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2
                     noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :]
                     return noise
                 @property
                 def timeInterval(self):
                     return self.ippSeconds * self.nCohInt * self.nProfiles
                 def splitFunctions(self):
                     pairsList = self.pairsList
                     ccf_pairs = []
                     acf_pairs = []
                     ccf_ind = []
                     acf_ind = []
                     for l in range(len(pairsList)):
                         chan0 = pairsList[l][0]
                         chan1 = pairsList[l][1]
                         # Obteniendo pares de Autocorrelacion
                         if chan0 == chan1:
                             acf_pairs.append(chan0)
                             acf_ind.append(l)
                         else:
                             ccf_pairs.append(pairsList[l])
                             ccf_ind.append(l)
                     data_acf = self.data_cf[acf_ind]
                     data_ccf = self.data_cf[ccf_ind]
                     return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf
                 @property
                 def normFactor(self):
                     acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions()
                     acf_pairs = numpy.array(acf_pairs)
                     normFactor = numpy.zeros((self.nPairs, self.nHeights))
                     for p in range(self.nPairs):
                         pair = self.pairsList[p]
                         ch0 = pair[0]
                         ch1 = pair[1]
                         ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1)
                         ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1)
                         normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max)
                     return normFactor
             class Parameters(Spectra):
                 groupList = None  # List of Pairs, Groups, etc
                 data_param = None  # Parameters obtained
                 data_pre = None  # Data Pre Parametrization
                 data_SNR = None  # Signal to Noise Ratio
                 abscissaList = None  # Abscissa, can be velocities, lags or time
                 utctimeInit = None  # Initial UTC time
                 paramInterval = None  # Time interval to calculate Parameters in seconds
                 useLocalTime = True
                 # Fitting
                 data_error = None  # Error of the estimation
                 constants = None
                 library = None
                 # Output signal
                 outputInterval = None  # Time interval to calculate output signal in seconds
                 data_output = None  # Out signal
                 nAvg = None
                 noise_estimation = None
                 GauSPC = None  # Fit gaussian SPC
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Parameters"
                     self.timeZone = 0
                 def getTimeRange1(self, interval):
                     datatime = []
                     if self.useLocalTime:
                         time1 = self.utctimeInit - self.timeZone * 60
                     else:
                         time1 = self.utctimeInit
                     datatime.append(time1)
                     datatime.append(time1 + interval)
                     datatime = numpy.array(datatime)
                     return datatime
                 @property
                 def timeInterval(self):
                     if hasattr(self, 'timeInterval1'):
                         return self.timeInterval1
                     else:
                         return self.paramInterval
                 def setValue(self, value):
                     print("This property should not be initialized")
                     return
                 def getNoise(self):
                     return self.spc_noise
                 noise = property(getNoise, setValue, "I'm the 'Noise' property.")
             class PlotterData(object):
                 '''
                 Object to hold data to be plotted
                 '''
                 MAXNUMX = 200
                 MAXNUMY = 200
                 def __init__(self, code, exp_code, localtime=True):
                     self.key = code
                     self.exp_code = exp_code
                     self.ready = False
                     self.flagNoData = False
                     self.localtime = localtime
                     self.data = {}
                     self.meta = {}
                     self.__heights = []
                 def __str__(self):
                     dum = ['{}{}'.format(key, self.shape(key)) for key in self.data]
                     return 'Data[{}][{}]'.format(';'.join(dum), len(self.times))
                 def __len__(self):
                     return len(self.data)
                 def __getitem__(self, key):
                     if isinstance(key, int):
                         return self.data[self.times[key]]
                     elif isinstance(key, str):
                         ret = numpy.array([self.data[x][key] for x in self.times])
                         if ret.ndim > 1:
                             ret = numpy.swapaxes(ret, 0, 1)
                         return ret
                 def __contains__(self, key):
                     return key in self.data[self.min_time]
                 def setup(self):
                     '''
                     Configure object
                     '''
                     self.type = ''
                     self.ready = False
                     del self.data
                     self.data = {}
                     self.__heights = []
                     self.__all_heights = set()
                 def shape(self, key):
                     '''
                     Get the shape of the one-element data for the given key
                     '''
                     if len(self.data[self.min_time][key]):
                         return self.data[self.min_time][key].shape
                     return (0,)
                 def update(self, data, tm, meta={}):
                     '''
                     Update data object with new dataOut
                     '''
                     self.data[tm] = data
                     for key, value in meta.items():
                         setattr(self, key, value)
                 def normalize_heights(self):
                     '''
                     Ensure same-dimension of the data for different heighList
                     '''
                     H = numpy.array(list(self.__all_heights))
                     H.sort()
                     for key in self.data:
                         shape = self.shape(key)[:-1] + H.shape
                         for tm, obj in list(self.data[key].items()):
                             h = self.__heights[self.times.tolist().index(tm)]
                             if H.size == h.size:
                                 continue
                             index = numpy.where(numpy.in1d(H, h))[0]
                             dummy = numpy.zeros(shape) + numpy.nan
                             if len(shape) == 2:
                                 dummy[:, index] = obj
                             else:
                                 dummy[index] = obj
                             self.data[key][tm] = dummy
                     self.__heights = [H for tm in self.times]
                 def jsonify(self, tm, plot_name, plot_type, decimate=False):
                     '''
                     Convert data to json
                     '''
                     meta = {}
                     meta['xrange'] = []
                     dy = int(len(self.yrange)/self.MAXNUMY) + 1
                     tmp = self.data[tm][self.key]
                     shape = tmp.shape
                     if len(shape) == 2:
                         data = self.roundFloats(self.data[tm][self.key][::, ::dy].tolist())
                     elif len(shape) == 3:
                         dx = int(self.data[tm][self.key].shape[1]/self.MAXNUMX) + 1
                         data = self.roundFloats(
                             self.data[tm][self.key][::, ::dx, ::dy].tolist())
                         meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist())
                     else:
                         data = self.roundFloats(self.data[tm][self.key].tolist())
                     ret = {
                         'plot': plot_name,
                         'code': self.exp_code,
                         'time': float(tm),
                         'data': data,
                         }
                     meta['type'] = plot_type
                     meta['interval'] = float(self.interval)
                     meta['localtime'] = self.localtime
                     meta['yrange'] = self.roundFloats(self.yrange[::dy].tolist())
                     meta.update(self.meta)
                     ret['metadata'] = meta
                     return json.dumps(ret)
                 @property
                 def times(self):
                     '''
                     Return the list of times of the current data
                     '''
                     ret = [t for t in self.data]
                     ret.sort()
                     return numpy.array(ret)
                 @property
                 def min_time(self):
                     '''
                     Return the minimun time value
                     '''
                     return self.times[0]
                 @property
                 def max_time(self):
                     '''
                     Return the maximun time value
                     '''
                     return self.times[-1]
                 # @property
                 # def heights(self):
                 #     '''
                 #     Return the list of heights of the current data
                 #     '''
                 #     return numpy.array(self.__heights[-1])
                 @staticmethod
                 def roundFloats(obj):
                     if isinstance(obj, list):
                         return list(map(PlotterData.roundFloats, obj))
                     elif isinstance(obj, float):
                         return round(obj, 2)

schainpy/model/proc/jroproc_voltage.py +1 0

             import sys
             import numpy,math
             from scipy import interpolate
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
             from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon
             from schainpy.utils import log
             from time import time
             class VoltageProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.dataOut = Voltage()
                     self.flip = 1
                     self.setupReq = False
                 def run(self):
                     if self.dataIn.type == 'AMISR':
                         self.__updateObjFromAmisrInput()
                     if self.dataIn.type == 'Voltage':
                         self.dataOut.copy(self.dataIn)
                 def __updateObjFromAmisrInput(self):
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     self.dataOut.flagNoData = self.dataIn.flagNoData
                     self.dataOut.data = self.dataIn.data
                     self.dataOut.utctime = self.dataIn.utctime
                     self.dataOut.channelList = self.dataIn.channelList
                     #self.dataOut.timeInterval = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.nProfiles = self.dataIn.nProfiles
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.ippSeconds = self.dataIn.ippSeconds
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.azimuth = self.dataIn.azimuth
                     self.dataOut.zenith = self.dataIn.zenith
                     self.dataOut.beam.codeList = self.dataIn.beam.codeList
                     self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
                     self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
             class selectChannels(Operation):
                 def run(self, dataOut, channelList):
                     channelIndexList = []
                     self.dataOut = dataOut
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList)))
                         index = self.dataOut.channelList.index(channel)
                         channelIndexList.append(index)
                     self.selectChannelsByIndex(channelIndexList)
                     return self.dataOut
                 def selectChannelsByIndex(self, channelIndexList):
                     """
                     Selecciona un bloque de datos en base a canales segun el channelIndexList
                     Input:
                         channelIndexList    :    lista sencilla de canales a seleccionar por ej. [2,3,7]
                     Affected:
                         self.dataOut.data
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                         self.dataOut.m_ProcessingHeader.totalSpectra
                         self.dataOut.systemHeaderObj.numChannels
                         self.dataOut.m_ProcessingHeader.blockSize
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             raise ValueError("The value %d in channelIndexList is not valid" %channelIndex)
                     if self.dataOut.type == 'Voltage':
                         if self.dataOut.flagDataAsBlock:
                             """
                             Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                             """
                             data = self.dataOut.data[channelIndexList,:,:]
                         else:
                             data = self.dataOut.data[channelIndexList,:]
                         self.dataOut.data = data
                         # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
                         self.dataOut.channelList = range(len(channelIndexList))
                     elif self.dataOut.type == 'Spectra':
                         data_spc = self.dataOut.data_spc[channelIndexList, :]
                         data_dc = self.dataOut.data_dc[channelIndexList, :]
                         self.dataOut.data_spc = data_spc
                         self.dataOut.data_dc = data_dc
                         # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
                         self.dataOut.channelList = range(len(channelIndexList))
                         self.__selectPairsByChannel(channelIndexList)
                     return 1
                 def __selectPairsByChannel(self, channelList=None):
                     if channelList == None:
                         return
                     pairsIndexListSelected = []
                     for pairIndex in self.dataOut.pairsIndexList:
                         # First pair
                         if self.dataOut.pairsList[pairIndex][0] not in channelList:
                             continue
                         # Second pair
                         if self.dataOut.pairsList[pairIndex][1] not in channelList:
                             continue
                         pairsIndexListSelected.append(pairIndex)
                     if not pairsIndexListSelected:
                         self.dataOut.data_cspc = None
                         self.dataOut.pairsList = []
                         return
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
                     self.dataOut.pairsList = [self.dataOut.pairsList[i]
                                               for i in pairsIndexListSelected]
                     return
             class selectHeights(Operation):
                 def run(self, dataOut, minHei=None, maxHei=None, minIndex=None, maxIndex=None):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
                     minHei <= height <= maxHei
                     Input:
                         minHei    :    valor minimo de altura a considerar
                         maxHei    :    valor maximo de altura a considerar
                     Affected:
                         Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     self.dataOut = dataOut
                     if minHei and maxHei:
                         if (minHei < self.dataOut.heightList[0]):
                             minHei = self.dataOut.heightList[0]
                         if (maxHei > self.dataOut.heightList[-1]):
                             maxHei = self.dataOut.heightList[-1]
                         minIndex = 0
                         maxIndex = 0
                         heights = self.dataOut.heightList
                         inda = numpy.where(heights >= minHei)
                         indb = numpy.where(heights <= maxHei)
                         try:
                             minIndex = inda[0][0]
                         except:
                             minIndex = 0
                         try:
                             maxIndex = indb[0][-1]
                         except:
                             maxIndex = len(heights)
                     self.selectHeightsByIndex(minIndex, maxIndex)
                     return self.dataOut
                 def selectHeightsByIndex(self, minIndex, maxIndex):
                     """
                     Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
                     minIndex <= index <= maxIndex
                     Input:
                         minIndex    :    valor de indice minimo de altura a considerar
                         maxIndex    :    valor de indice maximo de altura a considerar
                     Affected:
                         self.dataOut.data
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if self.dataOut.type == 'Voltage':
                         if (minIndex < 0) or (minIndex > maxIndex):
                             raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex))
                         if (maxIndex >= self.dataOut.nHeights):
                             maxIndex = self.dataOut.nHeights
                         #print("shapeeee",self.dataOut.data.shape)
                         #voltage
                         if self.dataOut.flagDataAsBlock:
                             """
                             Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                             """
                             data = self.dataOut.data[:,:, minIndex:maxIndex]
                         else:
                             data = self.dataOut.data[:, minIndex:maxIndex]
                         #         firstHeight = self.dataOut.heightList[minIndex]
                         self.dataOut.data = data
                         self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
                         if self.dataOut.nHeights <= 1:
                             raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights))
                     elif self.dataOut.type == 'Spectra':
                         if (minIndex < 0) or (minIndex > maxIndex):
                             raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (
                                 minIndex, maxIndex))
                         if (maxIndex >= self.dataOut.nHeights):
                             maxIndex = self.dataOut.nHeights - 1
                         # Spectra
                         data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                         data_cspc = None
                         if self.dataOut.data_cspc is not None:
                             data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                         data_dc = None
                         if self.dataOut.data_dc is not None:
                             data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                         self.dataOut.data_spc = data_spc
                         self.dataOut.data_cspc = data_cspc
                         self.dataOut.data_dc = data_dc
                         self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     return 1
             class filterByHeights(Operation):
                 def run(self, dataOut, window):
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     if window == None:
                         window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
                     newdelta = deltaHeight * window
                     r = dataOut.nHeights % window
                     newheights = (dataOut.nHeights-r)/window
                     if newheights <= 1:
                         raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window))
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)]
                         buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window)
                         buffer = numpy.sum(buffer,3)
                     else:
                         buffer = dataOut.data[:,0:int(dataOut.nHeights-r)]
                         buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window))
                         buffer = numpy.sum(buffer,2)
                     dataOut.data = buffer
                     dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta
                     dataOut.windowOfFilter = window
                     return dataOut
             class setH0(Operation):
                 def run(self, dataOut, h0, deltaHeight = None):
                     if not deltaHeight:
                         deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     nHeights = dataOut.nHeights
                     newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
                     dataOut.heightList = newHeiRange
+                    dataOut.h0 = h0
                     return dataOut
             class deFlip(Operation):
                 def run(self, dataOut, channelList = []):
                     data = dataOut.data.copy()
                     if dataOut.flagDataAsBlock:
                         flip = self.flip
                         profileList = list(range(dataOut.nProfiles))
                         if not channelList:
                             for thisProfile in profileList:
                                 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
                                 flip *= -1.0
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in dataOut.channelList:
                                     continue
                                 for thisProfile in profileList:
                                     data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
                                     flip *= -1.0
                         self.flip = flip
                     else:
                         if not channelList:
                             data[:,:] = data[:,:]*self.flip
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in dataOut.channelList:
                                     continue
                                 data[thisChannel,:] = data[thisChannel,:]*self.flip
                         self.flip *= -1.
                     dataOut.data = data
                     return dataOut
             class setAttribute(Operation):
                 '''
                 Set an arbitrary attribute(s) to dataOut
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self._ready = False
                 def run(self, dataOut, **kwargs):
                     for key, value in kwargs.items():
                         setattr(dataOut, key, value)
                     return dataOut
             @MPDecorator
             class printAttribute(Operation):
                 '''
                 Print an arbitrary attribute of dataOut
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                 def run(self, dataOut, attributes):
                     if isinstance(attributes, str):
                         attributes = [attributes]
                     for attr in attributes:
                         if hasattr(dataOut, attr):
                             log.log(getattr(dataOut, attr), attr)
             class interpolateHeights(Operation):
                 def run(self, dataOut, topLim, botLim):
                     #69 al 72 para julia
                     #82-84 para meteoros
                     if len(numpy.shape(dataOut.data))==2:
                         sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2
                         sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
                         #dataOut.data[:,botLim:limSup+1] = sampInterp
                         dataOut.data[:,botLim:topLim+1] = sampInterp
                     else:
                         nHeights = dataOut.data.shape[2]
                         x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
                         y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))]
                         f = interpolate.interp1d(x, y, axis = 2)
                         xnew = numpy.arange(botLim,topLim+1)
                         ynew = f(xnew)
                         dataOut.data[:,:,botLim:topLim+1]  = ynew
                     return dataOut
             class CohInt(Operation):
                 isConfig = False
                 __profIndex = 0
                 __byTime = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __bufferStride = []
                 __dataReady = False
                 __profIndexStride = 0
                 __dataToPutStride = False
                 n = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
                     """
                     Set the parameters of the integration class.
                     Inputs:
                         n               :    Number of coherent integrations
                         timeInterval    :    Time of integration. If the parameter "n" is selected this one does not work
                         overlapping     :
                     """
                     self.__initime = None
                     self.__lastdatatime = 0
                     self.__buffer = None
                     self.__dataReady = False
                     self.byblock = byblock
                     self.stride = stride
                     if n == None and timeInterval == None:
                         raise ValueError("n or timeInterval should be specified ...")
                     if n != None:
                         self.n = n
                         self.__byTime = False
                     else:
                         self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
                         self.n = 9999
                         self.__byTime = True
                     if overlapping:
                         self.__withOverlapping = True
                         self.__buffer = None
                     else:
                         self.__withOverlapping = False
                         self.__buffer = 0
                     self.__profIndex = 0
                 def putData(self, data):
                     """
                     Add a profile to the __buffer and increase in one the __profileIndex
                     """
                     if not self.__withOverlapping:
                         self.__buffer += data.copy()
                         self.__profIndex += 1
                         return
                     #Overlapping data
                     nChannels, nHeis = data.shape
                     data = numpy.reshape(data, (1, nChannels, nHeis))
                     #If the buffer is empty then it takes the data value
                     if self.__buffer is None:
                         self.__buffer = data
                         self.__profIndex += 1
                         return
                     #If the buffer length is lower than n then stakcing the data value
                     if self.__profIndex < self.n:
                         self.__buffer = numpy.vstack((self.__buffer, data))
                         self.__profIndex += 1
                         return
                     #If the buffer length is equal to n then replacing the last buffer value with the data value
                     self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
                     self.__buffer[self.n-1] = data
                     self.__profIndex = self.n
                     return
                 def pushData(self):
                     """
                     Return the sum of the last profiles and the profiles used in the sum.
                     Affected:
                     self.__profileIndex
                     """
                     if not self.__withOverlapping:
                         data = self.__buffer
                         n = self.__profIndex
                         self.__buffer = 0
                         self.__profIndex = 0
                         return data, n
                     #Integration with Overlapping
                     data = numpy.sum(self.__buffer, axis=0)
                     # print data
                     # raise
                     n = self.__profIndex
                     return data, n
                 def byProfiles(self, data):
                     self.__dataReady = False
                     avgdata = None
                     #         n = None
                     # print data
                     # raise
                     self.putData(data)
                     if self.__profIndex == self.n:
                         avgdata, n = self.pushData()
                         self.__dataReady = True
                     return avgdata
                 def byTime(self, data, datatime):
                     self.__dataReady = False
                     avgdata = None
                     n = None
                     self.putData(data)
                     if (datatime - self.__initime) >= self.__integrationtime:
                         avgdata, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata
                 def integrateByStride(self, data, datatime):
                     # print data
                     if self.__profIndex == 0:
                         self.__buffer = [[data.copy(), datatime]]
                     else:
                         self.__buffer.append([data.copy(),datatime])
                     self.__profIndex += 1
                     self.__dataReady = False
                     if self.__profIndex == self.n * self.stride :
                         self.__dataToPutStride = True
                         self.__profIndexStride = 0
                         self.__profIndex = 0
                         self.__bufferStride = []
                         for i in range(self.stride):
                             current = self.__buffer[i::self.stride]
                             data = numpy.sum([t[0] for t in current], axis=0)
                             avgdatatime = numpy.average([t[1] for t in current])
                             # print data
                             self.__bufferStride.append((data, avgdatatime))
                     if self.__dataToPutStride:
                         self.__dataReady = True
                         self.__profIndexStride += 1
                         if self.__profIndexStride == self.stride:
                             self.__dataToPutStride = False
                         # print self.__bufferStride[self.__profIndexStride - 1]
                         # raise
                         return self.__bufferStride[self.__profIndexStride - 1]
                     return None, None
                 def integrate(self, data, datatime=None):
                     if self.__initime == None:
                         self.__initime = datatime
                     if self.__byTime:
                         avgdata = self.byTime(data, datatime)
                     else:
                         avgdata = self.byProfiles(data)
                     self.__lastdatatime = datatime
                     if avgdata is None:
                         return None, None
                     avgdatatime = self.__initime
                     deltatime = datatime - self.__lastdatatime
                     if not self.__withOverlapping:
                         self.__initime = datatime
                     else:
                         self.__initime += deltatime
                     return avgdata, avgdatatime
                 def integrateByBlock(self, dataOut):
                     times = int(dataOut.data.shape[1]/self.n)
                     avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
                     id_min = 0
                     id_max = self.n
                     for i in range(times):
                         junk = dataOut.data[:,id_min:id_max,:]
                         avgdata[:,i,:] = junk.sum(axis=1)
                         id_min += self.n
                         id_max += self.n
                     timeInterval = dataOut.ippSeconds*self.n
                     avgdatatime = (times - 1) * timeInterval + dataOut.utctime
                     self.__dataReady = True
                     return avgdata, avgdatatime
                 def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
                     if not self.isConfig:
                         self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         avgdata, avgdatatime = self.integrateByBlock(dataOut)
                         dataOut.nProfiles /= self.n
                     else:
                         if stride is None:
                             avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
                         else:
                             avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
                     #   dataOut.timeInterval *= n
                     dataOut.flagNoData = True
                     if self.__dataReady:
                         dataOut.data = avgdata
                         if not dataOut.flagCohInt:
                             dataOut.nCohInt *= self.n
                             dataOut.flagCohInt = True
                         ####################################dataOut.utctime = avgdatatime
                         # print avgdata, avgdatatime
                         # raise
                         #   dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
                         dataOut.flagNoData = False
                     return dataOut
             class Decoder(Operation):
                 isConfig = False
                 __profIndex = 0
                 code = None
                 nCode = None
                 nBaud = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.times = None
                     self.osamp = None
                 #         self.__setValues = False
                     self.isConfig = False
                     self.setupReq = False
                 def setup(self, code, osamp, dataOut):
                     self.__profIndex = 0
                     self.code = code
                     self.nCode = len(code)
                     self.nBaud = len(code[0])
                     if (osamp != None) and (osamp >1):
                         self.osamp = osamp
                         self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
                         self.nBaud = self.nBaud*self.osamp
                     self.__nChannels = dataOut.nChannels
                     self.__nProfiles = dataOut.nProfiles
                     self.__nHeis = dataOut.nHeights
                     if self.__nHeis < self.nBaud:
                         raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
                     #Frequency
                     __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
                     __codeBuffer[:,0:self.nBaud] = self.code
                     self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
                     if dataOut.flagDataAsBlock:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
                     else:
                         #Time
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
                 def __convolutionInFreq(self, data):
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=1)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=1)
                     return data
                 def __convolutionInFreqOpt(self, data):
                     raise NotImplementedError
                 def __convolutionInTime(self, data):
                     code = self.code[self.__profIndex]
                     for i in range(self.__nChannels):
                         self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInTime(self, data):
                     repetitions = int(self.__nProfiles / self.nCode)
                     junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
                     junk = junk.flatten()
                     code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
                     profilesList = range(self.__nProfiles)
                     for i in range(self.__nChannels):
                         for j in profilesList:
                             self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInFreq(self, data):
                     raise NotImplementedError("Decoder by frequency fro Blocks not implemented")
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=2)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=2)
                     return data
                 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
                     if dataOut.flagDecodeData:
                         print("This data is already decoded, recoding again ...")
                     if not self.isConfig:
                         if code is None:
                             if dataOut.code is None:
                                 raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type)
                             code = dataOut.code
                         else:
                             code = numpy.array(code).reshape(nCode,nBaud)
                         self.setup(code, osamp, dataOut)
                         self.isConfig = True
                         if mode == 3:
                             sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
                         if times != None:
                             sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
                     if self.code is None:
                         print("Fail decoding: Code is not defined.")
                         return
                     self.__nProfiles = dataOut.nProfiles
                     datadec = None
                     if mode == 3:
                         mode = 0
                     if dataOut.flagDataAsBlock:
                         """
                         Decoding when data have been read as block,
                         """
                         if mode == 0:
                             datadec = self.__convolutionByBlockInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionByBlockInFreq(dataOut.data)
                     else:
                         """
                         Decoding when data have been read profile by profile
                         """
                         if mode == 0:
                             datadec = self.__convolutionInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionInFreq(dataOut.data)
                         if mode == 2:
                             datadec = self.__convolutionInFreqOpt(dataOut.data)
                     if datadec is None:
                         raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode)
                     dataOut.code = self.code
                     dataOut.nCode = self.nCode
                     dataOut.nBaud = self.nBaud
                     dataOut.data = datadec
                     dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
                     dataOut.flagDecodeData = True #asumo q la data esta decodificada
                     if self.__profIndex == self.nCode-1:
                         self.__profIndex = 0
                         return dataOut
                     self.__profIndex += 1
                     return dataOut
                 #        dataOut.flagDeflipData = True #asumo q la data no esta sin flip
             class ProfileConcat(Operation):
                 isConfig = False
                 buffer = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def reset(self):
                     self.buffer = numpy.zeros_like(self.buffer)
                     self.start_index = 0
                     self.times = 1
                 def setup(self, data, m, n=1):
                     self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
                     self.nHeights = data.shape[1]#.nHeights
                     self.start_index = 0
                     self.times = 1
                 def concat(self, data):
                     self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
                     self.start_index = self.start_index + self.nHeights
                 def run(self, dataOut, m):
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         self.setup(dataOut.data, m, 1)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False")
                     else:
                         self.concat(dataOut.data)
                         self.times += 1
                         if self.times > m:
                             dataOut.data = self.buffer
                             self.reset()
                             dataOut.flagNoData = False
                             # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
                             deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                             xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
                             dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
                             dataOut.ippSeconds *= m
                     return dataOut
             class ProfileSelector(Operation):
                 profileIndex = None
                 # Tamanho total de los perfiles
                 nProfiles = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def incProfileIndex(self):
                     self.profileIndex += 1
                     if self.profileIndex >= self.nProfiles:
                         self.profileIndex = 0
                 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
                     if profileIndex < minIndex:
                         return False
                     if profileIndex > maxIndex:
                         return False
                     return True
                 def isThisProfileInList(self, profileIndex, profileList):
                     if profileIndex not in profileList:
                         return False
                     return True
                 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
                     #print("before",dataOut.data.shape)
                     """
                     ProfileSelector:
                     Inputs:
                         profileList        :    Index of profiles selected. Example: profileList = (0,1,2,7,8)
                         profileRangeList    :    Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
                         rangeList            :    List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
                     """
                     if rangeList is not None:
                         if type(rangeList[0]) not in (tuple, list):
                             rangeList = [rangeList]
                     dataOut.flagNoData = True
                     if dataOut.flagDataAsBlock:
                         """
                         data dimension  = [nChannels, nProfiles, nHeis]
                         """
                         if profileList != None:
                             dataOut.data = dataOut.data[:,profileList,:]
                         if profileRangeList != None:
                             minIndex = profileRangeList[0]
                             maxIndex = profileRangeList[1]
                             profileList = list(range(minIndex, maxIndex+1))
                             dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
                         if rangeList != None:
                             profileList = []
                             for thisRange in rangeList:
                                 minIndex = thisRange[0]
                                 maxIndex = thisRange[1]
                                 profileList.extend(list(range(minIndex, maxIndex+1)))
                             dataOut.data = dataOut.data[:,profileList,:]
                         dataOut.nProfiles = len(profileList)
                         dataOut.profileIndex = dataOut.nProfiles - 1
                         dataOut.flagNoData = False
                         #print(dataOut.data.shape)
                         return dataOut
                     """
                     data dimension  = [nChannels, nHeis]
                     """
                     if profileList != None:
                         if self.isThisProfileInList(dataOut.profileIndex, profileList):
                             self.nProfiles = len(profileList)
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return dataOut
                     if profileRangeList != None:
                         minIndex = profileRangeList[0]
                         maxIndex = profileRangeList[1]
                         if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                             self.nProfiles = maxIndex - minIndex + 1
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return dataOut
                     if rangeList != None:
                         nProfiles = 0
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             nProfiles += maxIndex - minIndex + 1
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                                 self.nProfiles = nProfiles
                                 dataOut.nProfiles = self.nProfiles
                                 dataOut.profileIndex = self.profileIndex
                                 dataOut.flagNoData = False
                                 self.incProfileIndex()
                                 break
                         return dataOut
                     if beam != None: #beam is only for AMISR data
                         if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
                             dataOut.flagNoData = False
                             dataOut.profileIndex = self.profileIndex
                             self.incProfileIndex()
                         return dataOut
                     raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter")
             class Reshaper(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__buffer = None
                     self.__nitems = 0
                 def __appendProfile(self, dataOut, nTxs):
                     if self.__buffer is None:
                         shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
                         self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
                     ini = dataOut.nHeights * self.__nitems
                     end = ini + dataOut.nHeights
                     self.__buffer[:, ini:end] = dataOut.data
                     self.__nitems += 1
                     return int(self.__nitems*nTxs)
                 def __getBuffer(self):
                     if self.__nitems == int(1./self.__nTxs):
                         self.__nitems = 0
                         return self.__buffer.copy()
                     return None
                 def __checkInputs(self, dataOut, shape, nTxs):
                     if shape is None and nTxs is None:
                         raise ValueError("Reshaper: shape of factor should be defined")
                     if nTxs:
                         if nTxs < 0:
                             raise ValueError("nTxs should be greater than 0")
                         if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
                             raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)))
                         shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
                         return shape, nTxs
                     if len(shape) != 2 and len(shape) !=  3:
                         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))
                     if len(shape) == 2:
                         shape_tuple = [dataOut.nChannels]
                         shape_tuple.extend(shape)
                     else:
                         shape_tuple = list(shape)
                     nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
                     return shape_tuple, nTxs
                 def run(self, dataOut, shape=None, nTxs=None):
                     shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
                     else:
                         if self.__nTxs < 1:
                             self.__appendProfile(dataOut, self.__nTxs)
                             new_data = self.__getBuffer()
                             if new_data is not None:
                                 dataOut.data = new_data
                                 dataOut.flagNoData = False
                                 profileIndex = dataOut.profileIndex*nTxs
                         else:
                             raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)")
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= self.__nTxs
                     return dataOut
             class SplitProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         if shape[2] % n != 0:
                             raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]))
                         new_shape = shape[0], shape[1]*n, int(shape[2]/n)
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles/n) - 1
                     else:
                         raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)")
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= n
                     return dataOut
             class CombineProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__remData = None
                     self.__profileIndex = 0
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         new_shape = shape[0], shape[1]/n, shape[2]*n
                         if shape[1] % n != 0:
                             raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]))
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*n) - 1
                     else:
                         #nchannels, nsamples
                         if self.__remData is None:
                             newData = dataOut.data
                         else:
                             newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
                         self.__profileIndex += 1
                         if self.__profileIndex < n:
                             self.__remData = newData
                             #continue
                             return
                         self.__profileIndex = 0
                         self.__remData = None
                         dataOut.data = newData
                         dataOut.flagNoData = False
                         profileIndex = dataOut.profileIndex/n
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles/n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds *= n
                     return dataOut
             class PulsePair(Operation):
                 '''
                 Function PulsePair(Signal Power, Velocity)
                 The real component of Lag[0] provides Intensity Information
                 The imag component of Lag[1] Phase provides Velocity Information
                 Configuration Parameters:
                 nPRF = Number of Several PRF
                 theta = Degree Azimuth angel Boundaries
                 Input:
                       self.dataOut
                       lag[N]
                 Affected:
                       self.dataOut.spc
                 '''
                 isConfig       = False
                 __profIndex    = 0
                 __initime      = None
                 __lastdatatime = None
                 __buffer       = None
                 noise          = None
                 __dataReady    = False
                 n              = None
                 __nch          = 0
                 __nHeis        = 0
                 removeDC       = False
                 ipp            = None
                 lambda_        = 0
                 def __init__(self,**kwargs):
                     Operation.__init__(self,**kwargs)
                 def setup(self, dataOut, n = None, removeDC=False):
                     '''
                     n= Numero de PRF's de entrada
                     '''
                     self.__initime        = None
                     ####print("[INICIO]-setup  del METODO PULSE PAIR")
                     self.__lastdatatime   = 0
                     self.__dataReady      = False
                     self.__buffer         = 0
                     self.__profIndex      = 0
                     self.noise            = None
                     self.__nch            = dataOut.nChannels
                     self.__nHeis          = dataOut.nHeights
                     self.removeDC         = removeDC
                     self.lambda_          = 3.0e8/(9345.0e6)
                     self.ippSec           = dataOut.ippSeconds
                     self.nCohInt          = dataOut.nCohInt
                     ####print("IPPseconds",dataOut.ippSeconds)
                     ####print("ELVALOR DE n es:", n)
                     if n == None:
                         raise ValueError("n should be specified.")
                     if n != None:
                         if n<2:
                             raise ValueError("n should be greater than 2")
                     self.n       = n
                     self.__nProf = n
                     self.__buffer = numpy.zeros((dataOut.nChannels,
                                                        n,
                                                        dataOut.nHeights),
                                                       dtype='complex')
                 def putData(self,data):
                     '''
                     Add a profile to he __buffer and increase in one the __profiel Index
                     '''
                     self.__buffer[:,self.__profIndex,:]= data
                     self.__profIndex      += 1
                     return
                 def pushData(self,dataOut):
                     '''
                     Return the PULSEPAIR and the profiles used in the operation
                     Affected :  self.__profileIndex
                     '''
                     #----------------- Remove DC-----------------------------------
                     if self.removeDC==True:
                         mean    = numpy.mean(self.__buffer,1)
                         tmp     = mean.reshape(self.__nch,1,self.__nHeis)
                         dc= numpy.tile(tmp,[1,self.__nProf,1])
                         self.__buffer = self.__buffer -  dc
                     #------------------Calculo de Potencia ------------------------
                     pair0       = self.__buffer*numpy.conj(self.__buffer)
                     pair0       = pair0.real
                     lag_0       = numpy.sum(pair0,1)
                     #-----------------Calculo de Cscp------------------------------ New
                     cspc_pair01 = self.__buffer[0]*self.__buffer[1]
                     #------------------Calculo de Ruido x canal--------------------
                     self.noise  = numpy.zeros(self.__nch)
                     for i in range(self.__nch):
                         daux         = numpy.sort(pair0[i,:,:],axis= None)
                         self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt)
                     self.noise       = self.noise.reshape(self.__nch,1)
                     self.noise       = numpy.tile(self.noise,[1,self.__nHeis])
                     noise_buffer     = self.noise.reshape(self.__nch,1,self.__nHeis)
                     noise_buffer     = numpy.tile(noise_buffer,[1,self.__nProf,1])
                     #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N--
                     #------------------   P= S+N  ,P=lag_0/N ---------------------------------
                     #-------------------- Power --------------------------------------------------
                     data_power       = lag_0/(self.n*self.nCohInt)
                     #--------------------CCF------------------------------------------------------
                     data_ccf         =numpy.sum(cspc_pair01,axis=0)/(self.n*self.nCohInt)
                     #------------------  Senal  --------------------------------------------------
                     data_intensity   = pair0 - noise_buffer
                     data_intensity   = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt)
                     #data_intensity   = (lag_0-self.noise*self.n)*(self.n*self.nCohInt)
                     for i in range(self.__nch):
                         for j in range(self.__nHeis):
                             if data_intensity[i][j]  < 0:
                                 data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j]))
                     #----------------- Calculo de Frecuencia y Velocidad doppler--------
                     pair1            = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:])
                     lag_1            = numpy.sum(pair1,1)
                     data_freq        = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1)
                     data_velocity    = (self.lambda_/2.0)*data_freq
                     #---------------- Potencia promedio estimada de la Senal-----------
                     lag_0            = lag_0/self.n
                     S                = lag_0-self.noise
                     #---------------- Frecuencia Doppler promedio ---------------------
                     lag_1            = lag_1/(self.n-1)
                     R1               = numpy.abs(lag_1)
                     #---------------- Calculo del SNR----------------------------------
                     data_snrPP       = S/self.noise
                     for i in range(self.__nch):
                         for j in range(self.__nHeis):
                             if data_snrPP[i][j]  < 1.e-20:
                                 data_snrPP[i][j] = 1.e-20
                     #----------------- Calculo del ancho espectral ----------------------
                     L                = S/R1
                     L                = numpy.where(L<0,1,L)
                     L                = numpy.log(L)
                     tmp              = numpy.sqrt(numpy.absolute(L))
                     data_specwidth   = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L)
                     n                = self.__profIndex
                     self.__buffer    = numpy.zeros((self.__nch, self.__nProf,self.__nHeis),  dtype='complex')
                     self.__profIndex = 0
                     return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,data_ccf,n
                 def pulsePairbyProfiles(self,dataOut):
                     self.__dataReady     =  False
                     data_power           =  None
                     data_intensity       =  None
                     data_velocity        =  None
                     data_specwidth       =  None
                     data_snrPP           =  None
                     data_ccf             =  None
                     self.putData(data=dataOut.data)
                     if self.__profIndex  == self.n:
                         data_power,data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, n   = self.pushData(dataOut=dataOut)
                         self.__dataReady                   = True
                     return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf
                 def pulsePairOp(self, dataOut, datatime= None):
                     if self.__initime == None:
                         self.__initime = datatime
                     data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf = self.pulsePairbyProfiles(dataOut)
                     self.__lastdatatime           = datatime
                     if data_power is None:
                         return None, None, None,None,None,None,None
                     avgdatatime    = self.__initime
                     deltatime      = datatime - self.__lastdatatime
                     self.__initime = datatime
                     return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf, avgdatatime
                 def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs):
                     #print("hey")
                     #print(dataOut.data.shape)
                     #exit(1)
                     #print(self.__profIndex)
                     if not self.isConfig:
                         self.setup(dataOut = dataOut, n    = n , removeDC=removeDC , **kwargs)
                         self.isConfig   = True
                     data_power, data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime)
                     dataOut.flagNoData                         = True
                     if self.__dataReady:
                         ###print("READY ----------------------------------")
                         dataOut.nCohInt        *= self.n
                         dataOut.dataPP_POW      = data_intensity # S
                         dataOut.dataPP_POWER    = data_power     # P valor que corresponde a POTENCIA MOMENTO
                         dataOut.dataPP_DOP      = data_velocity
                         dataOut.dataPP_SNR      = data_snrPP
                         dataOut.dataPP_WIDTH    = data_specwidth
                         dataOut.dataPP_CCF      = data_ccf
                         dataOut.PRFbyAngle      = self.n         #numero de PRF*cada angulo rotado que equivale a un tiempo.
                         dataOut.nProfiles       = int(dataOut.nProfiles/n)
                         dataOut.utctime         = avgdatatime
                         dataOut.flagNoData      = False
                     return dataOut
             class PulsePair_vRF(Operation):
                 '''
                 Function PulsePair(Signal Power, Velocity)
                 The real component of Lag[0] provides Intensity Information
                 The imag component of Lag[1] Phase provides Velocity Information
                 Configuration Parameters:
                 nPRF = Number of Several PRF
                 theta = Degree Azimuth angel Boundaries
                 Input:
                       self.dataOut
                       lag[N]
                 Affected:
                       self.dataOut.spc
                 '''
                 isConfig       = False
                 __profIndex    = 0
                 __initime      = None
                 __lastdatatime = None
                 __buffer       = None
                 noise          = None
                 __dataReady    = False
                 n              = None
                 __nch          = 0
                 __nHeis        = 0
                 removeDC       = False
                 ipp            = None
                 lambda_        = 0
                 def __init__(self,**kwargs):
                     Operation.__init__(self,**kwargs)
                 def setup(self, dataOut, n = None, removeDC=False):
                     '''
                     n= Numero de PRF's de entrada
                     '''
                     self.__initime        = None
                     ####print("[INICIO]-setup  del METODO PULSE PAIR")
                     self.__lastdatatime   = 0
                     self.__dataReady      = False
                     self.__buffer         = 0
                     self.__profIndex      = 0
                     self.noise            = None
                     self.__nch            = dataOut.nChannels
                     self.__nHeis          = dataOut.nHeights
                     self.removeDC         = removeDC
                     self.lambda_          = 3.0e8/(9345.0e6)
                     self.ippSec           = dataOut.ippSeconds
                     self.nCohInt          = dataOut.nCohInt
                     ####print("IPPseconds",dataOut.ippSeconds)
                     ####print("ELVALOR DE n es:", n)
                     if n == None:
                         raise ValueError("n should be specified.")
                     if n != None:
                         if n<2:
                             raise ValueError("n should be greater than 2")
                     self.n       = n
                     self.__nProf = n
                     self.__buffer = numpy.zeros((dataOut.nChannels,
                                                        n,
                                                        dataOut.nHeights),
                                                       dtype='complex')
                 def putData(self,data):
                     '''
                     Add a profile to he __buffer and increase in one the __profiel Index
                     '''
                     self.__buffer[:,self.__profIndex,:]= data
                     self.__profIndex      += 1
                     return
                 def putDataByBlock(self,data,n):
                     '''
                     Add a profile to he __buffer and increase in one the __profiel Index
                     '''
                     self.__buffer[:]= data
                     self.__profIndex      = n
                     return
                 def pushData(self,dataOut):
                     '''
                     Return the PULSEPAIR and the profiles used in the operation
                     Affected :  self.__profileIndex
                     NOTA:
 .)  Calculo de Potencia
                     PdBm = 10 *log10(10*(I**2  + Q**2)) Unidades dBm
                     self.__buffer = I + Qj
 .) Data decodificada
                     Se toma como referencia el factor estimado en jrodata.py y se adiciona
                     en PulsePair solo pwcode.
                     if self.flagDecodeData:
                         pwcode = numpy.sum(self.code[0]**2)
                     normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter
 .) hildebrand_sekhon
                     Se pasa el arreglo de Potencia pair0 que contiene canales perfiles y altura dividiendole entre el
                     factor pwcode.
 .) data_power
                     Este parametro esta dividido por los factores: nro. perfiles, nro intCoh y pwcode
 .) lag_0
                     Este parametro esta dividido por los factores: nro. perfiles, nro intCoh y pwcode
                     Igual a data_power
                     '''
                     #----------------- Remove DC-----------------------------------
                     if self.removeDC==True:
                         mean    = numpy.mean(self.__buffer,1)
                         tmp     = mean.reshape(self.__nch,1,self.__nHeis)
                         dc= numpy.tile(tmp,[1,self.__nProf,1])
                         self.__buffer = self.__buffer -  dc
                     #------------------Calculo de Potencia ------------------------
                     pair0       = self.__buffer*numpy.conj(self.__buffer) * 10.0
                     pair0       = pair0.real
                     lag_0       = numpy.sum(pair0,1)
                     #-----------------Calculo de Cscp------------------------------ New
                     cspc_pair01 = self.__buffer[0]*self.__buffer[1]
                     #------------------  Data Decodificada------------------------
                     pwcode =  1
                     if dataOut.flagDecodeData == True:
                         pwcode = numpy.sum(dataOut.code[0]**2)
                     #------------------Calculo de Ruido x canal--------------------
                     self.noise  = numpy.zeros(self.__nch)
                     for i in range(self.__nch):
                         daux         = numpy.sort(pair0[i,:,:],axis= None)
                         self.noise[i]=hildebrand_sekhon( daux/pwcode ,self.nCohInt)
                     self.noise       = self.noise.reshape(self.__nch,1)
                     self.noise       = numpy.tile(self.noise,[1,self.__nHeis])
                     noise_buffer     = self.noise.reshape(self.__nch,1,self.__nHeis)
                     noise_buffer     = numpy.tile(noise_buffer,[1,self.__nProf,1])
                     #------------------ Potencia recibida= P , Potencia senal = S , Ruido= N--
                     #------------------   P= S+N  ,P=lag_0/N ---------------------------------
                     #-------------------- Power --------------------------------------------------
                     data_power       = lag_0/(self.n*self.nCohInt*pwcode)
                     #--------------------CCF------------------------------------------------------
                     data_ccf         =numpy.sum(cspc_pair01,axis=0)/(self.n*self.nCohInt)
                     #------------------  Senal  --------------------------------------------------
                     data_intensity   = pair0 - noise_buffer
                     data_intensity   = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt)
                     #data_intensity   = (lag_0-self.noise*self.n)*(self.n*self.nCohInt)
                     for i in range(self.__nch):
                         for j in range(self.__nHeis):
                             if data_intensity[i][j]  < 0:
                                 data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j]))
                     #----------------- Calculo de Frecuencia y Velocidad doppler--------
                     pair1            = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:])
                     lag_1            = numpy.sum(pair1,1)
                     data_freq        = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1)
                     data_velocity    = (self.lambda_/2.0)*data_freq
                     #---------------- Potencia promedio estimada de la Senal-----------
                     lag_0            = data_power
                     S                = lag_0-self.noise
                     #---------------- Frecuencia Doppler promedio ---------------------
                     lag_1            = lag_1/(self.n-1)
                     R1               = numpy.abs(lag_1)
                     #---------------- Calculo del SNR----------------------------------
                     data_snrPP       = S/self.noise
                     for i in range(self.__nch):
                         for j in range(self.__nHeis):
                             if data_snrPP[i][j]  < 1.e-20:
                                 data_snrPP[i][j] = 1.e-20
                     #----------------- Calculo del ancho espectral ----------------------
                     L                = S/R1
                     L                = numpy.where(L<0,1,L)
                     L                = numpy.log(L)
                     tmp              = numpy.sqrt(numpy.absolute(L))
                     data_specwidth   = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L)
                     n                = self.__profIndex
                     self.__buffer    = numpy.zeros((self.__nch, self.__nProf,self.__nHeis),  dtype='complex')
                     self.__profIndex = 0
                     return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,data_ccf,n
                 def pulsePairbyProfiles(self,dataOut,n):
                     self.__dataReady     =  False
                     data_power           =  None
                     data_intensity       =  None
                     data_velocity        =  None
                     data_specwidth       =  None
                     data_snrPP           =  None
                     data_ccf             =  None
                     if dataOut.flagDataAsBlock:
                         self.putDataByBlock(data=dataOut.data,n=n)
                     else:
                         self.putData(data=dataOut.data)
                     if self.__profIndex  == self.n:
                         data_power,data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, n   = self.pushData(dataOut=dataOut)
                         self.__dataReady                   = True
                     return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf
                 def pulsePairOp(self, dataOut, n, datatime= None):
                     if self.__initime == None:
                         self.__initime = datatime
                     data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf = self.pulsePairbyProfiles(dataOut,n)
                     self.__lastdatatime           = datatime
                     if data_power is None:
                         return None, None, None,None,None,None,None
                     avgdatatime    = self.__initime
                     deltatime      = datatime - self.__lastdatatime
                     self.__initime = datatime
                     return data_power, data_intensity, data_velocity, data_snrPP,data_specwidth,data_ccf, avgdatatime
                 def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs):
                     if dataOut.flagDataAsBlock:
                         n = int(dataOut.nProfiles)
                         #print("n",n)
                     if not self.isConfig:
                         self.setup(dataOut = dataOut, n    = n , removeDC=removeDC , **kwargs)
                         self.isConfig   = True
                     data_power, data_intensity, data_velocity,data_snrPP,data_specwidth,data_ccf, avgdatatime = self.pulsePairOp(dataOut, n, dataOut.utctime)
                     dataOut.flagNoData                         = True
                     if self.__dataReady:
                         ###print("READY ----------------------------------")
                         dataOut.nCohInt        *= self.n
                         dataOut.dataPP_POW      = data_intensity # S
                         dataOut.dataPP_POWER    = data_power     # P valor que corresponde a POTENCIA MOMENTO
                         dataOut.dataPP_DOP      = data_velocity
                         dataOut.dataPP_SNR      = data_snrPP
                         dataOut.dataPP_WIDTH    = data_specwidth
                         dataOut.dataPP_CCF      = data_ccf
                         dataOut.PRFbyAngle      = self.n         #numero de PRF*cada angulo rotado que equivale a un tiempo.
                         dataOut.nProfiles       = int(dataOut.nProfiles/n)
                         dataOut.utctime         = avgdatatime
                         dataOut.flagNoData      = False
                     return dataOut
             # import collections
             # from scipy.stats import mode
             #
             # class Synchronize(Operation):
             #
             #     isConfig = False
             #     __profIndex = 0
             #
             #     def __init__(self, **kwargs):
             #
             #         Operation.__init__(self, **kwargs)
             # #         self.isConfig = False
             #         self.__powBuffer = None
             #         self.__startIndex = 0
             #         self.__pulseFound = False
             #
             #     def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
             #
             #         #Read data
             #
             #         powerdB = dataOut.getPower(channel = channel)
             #         noisedB = dataOut.getNoise(channel = channel)[0]
             #
             #         self.__powBuffer.extend(powerdB.flatten())
             #
             #         dataArray = numpy.array(self.__powBuffer)
             #
             #         filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
             #
             #         maxValue = numpy.nanmax(filteredPower)
             #
             #         if maxValue < noisedB + 10:
             #             #No se encuentra ningun pulso de transmision
             #             return None
             #
             #         maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
             #
             #         if len(maxValuesIndex) < 2:
             #             #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
             #             return None
             #
             #         phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
             #
             #         #Seleccionar solo valores con un espaciamiento de nSamples
             #         pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
             #
             #         if len(pulseIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         spacing = pulseIndex[1:] - pulseIndex[:-1]
             #
             #         #remover senales que se distancien menos de 10 unidades o muestras
             #         #(No deberian existir IPP menor a 10 unidades)
             #
             #         realIndex = numpy.where(spacing > 10 )[0]
             #
             #         if len(realIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
             #         realPulseIndex = pulseIndex[realIndex]
             #
             #         period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
             #
             #         print "IPP = %d samples" %period
             #
             #         self.__newNSamples = dataOut.nHeights #int(period)
             #         self.__startIndex = int(realPulseIndex[0])
             #
             #         return 1
             #
             #
             #     def setup(self, nSamples, nChannels, buffer_size = 4):
             #
             #         self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
             #                                           maxlen = buffer_size*nSamples)
             #
             #         bufferList = []
             #
             #         for i in range(nChannels):
             #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) +  numpy.NAN,
             #                                           maxlen = buffer_size*nSamples)
             #
             #             bufferList.append(bufferByChannel)
             #
             #         self.__nSamples = nSamples
             #         self.__nChannels = nChannels
             #         self.__bufferList = bufferList
             #
             #     def run(self, dataOut, channel = 0):
             #
             #         if not self.isConfig:
             #             nSamples = dataOut.nHeights
             #             nChannels = dataOut.nChannels
             #             self.setup(nSamples, nChannels)
             #             self.isConfig = True
             #
             #         #Append new data to internal buffer
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             bufferByChannel.extend(dataOut.data[thisChannel])
             #
             #         if self.__pulseFound:
             #             self.__startIndex -= self.__nSamples
             #
             #         #Finding Tx Pulse
             #         if not self.__pulseFound:
             #             indexFound = self.__findTxPulse(dataOut, channel)
             #
             #             if indexFound == None:
             #                 dataOut.flagNoData = True
             #                 return
             #
             #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
             #             self.__pulseFound = True
             #             self.__startIndex = indexFound
             #
             #         #If pulse was found ...
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             #print self.__startIndex
             #             x = numpy.array(bufferByChannel)
             #             self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
             #
             #         deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
             #         dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
             # #             dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
             #
             #         dataOut.data = self.__arrayBuffer
             #
             #         self.__startIndex += self.__newNSamples
             #
             #         return

General Comments 0

Write
Preview

You need to be logged in to leave comments. Login now

No TODOs yet

	Site-wide shortcuts
/	Use quick search box
g h	Goto home page
g g	Goto my private gists page
g G	Goto my public gists page
g 0-9	Goto bookmarked items from 0-9
n r	New repository page
n g	New gist page

	Repositories
g s	Goto summary page
g c	Goto changelog page
g f	Goto files page
g F	Goto files page with file search activated
g p	Goto pull requests page
g o	Goto repository settings
g O	Goto repository access permissions settings
t s	Toggle sidebar on some pages