schain-jc Commit - r1168:889c94859eae

Python 2to3, Voltage (all operations) working

George Yong -

r1168:889c94859eae

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r1168:889c94859eae

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schainpy/model/data/jroheaderIO.py 0 -1

             '''
             $Author: murco $
             $Id: JROHeaderIO.py 151 2012-10-31 19:00:51Z murco $
             '''
             import sys
             import numpy
             import copy
             import datetime
             import inspect
             from schainpy.utils import log
             SPEED_OF_LIGHT = 299792458
             SPEED_OF_LIGHT = 3e8
             BASIC_STRUCTURE = numpy.dtype([
                 ('nSize', '<u4'),
                 ('nVersion', '<u2'),
                 ('nDataBlockId', '<u4'),
                 ('nUtime', '<u4'),
                 ('nMilsec', '<u2'),
                 ('nTimezone', '<i2'),
                 ('nDstflag', '<i2'),
                 ('nErrorCount', '<u4')
             ])
             SYSTEM_STRUCTURE = numpy.dtype([
                 ('nSize', '<u4'),
                 ('nNumSamples', '<u4'),
                 ('nNumProfiles', '<u4'),
                 ('nNumChannels', '<u4'),
                 ('nADCResolution', '<u4'),
                 ('nPCDIOBusWidth', '<u4'),
             ])
             RADAR_STRUCTURE = numpy.dtype([
                                          ('nSize', '<u4'),
                                          ('nExpType', '<u4'),
                                          ('nNTx', '<u4'),
                                          ('fIpp', '<f4'),
                                          ('fTxA', '<f4'),
                                          ('fTxB', '<f4'),
                                          ('nNumWindows', '<u4'),
                                          ('nNumTaus', '<u4'),
                                          ('nCodeType', '<u4'),
                                          ('nLine6Function', '<u4'),
                                          ('nLine5Function', '<u4'),
                                          ('fClock', '<f4'),
                                          ('nPrePulseBefore', '<u4'),
                                          ('nPrePulseAfter', '<u4'),
                                          ('sRangeIPP', '<a20'),
                                          ('sRangeTxA', '<a20'),
                                          ('sRangeTxB', '<a20'),
             ])
             SAMPLING_STRUCTURE = numpy.dtype(
                 [('h0', '<f4'), ('dh', '<f4'), ('nsa', '<u4')])
             PROCESSING_STRUCTURE = numpy.dtype([
                 ('nSize', '<u4'),
                 ('nDataType', '<u4'),
                 ('nSizeOfDataBlock', '<u4'),
                 ('nProfilesperBlock', '<u4'),
                 ('nDataBlocksperFile', '<u4'),
                 ('nNumWindows', '<u4'),
                 ('nProcessFlags', '<u4'),
                 ('nCoherentIntegrations', '<u4'),
                 ('nIncoherentIntegrations', '<u4'),
                 ('nTotalSpectra', '<u4')
             ])
             class Header(object):
                 def __init__(self):
                     raise NotImplementedError
                 def copy(self):
                     return copy.deepcopy(self)
                 def read(self):
                     raise NotImplementedError
                 def write(self):
                     raise NotImplementedError
                 def getAllowedArgs(self):
                     args = inspect.getargspec(self.__init__).args
                     try:
                         args.remove('self')
                     except:
                         pass
                     return args
                 def getAsDict(self):
                     args = self.getAllowedArgs()
                     asDict = {}
                     for x in args:
                         asDict[x] = self[x]
                     return asDict
                 def __getitem__(self, name):
                     return getattr(self, name)
                 def printInfo(self):
                     message = "#" * 50 + "\n"
                     message += self.__class__.__name__.upper() + "\n"
                     message += "#" * 50 + "\n"
                     keyList = list(self.__dict__.keys())
                     keyList.sort()
                     for key in keyList:
                         message += "%s = %s" % (key, self.__dict__[key]) + "\n"
                     if "size" not in keyList:
                         attr = getattr(self, "size")
                         if attr:
                             message += "%s = %s" % ("size", attr) + "\n"
                     print(message)
             class BasicHeader(Header):
                 size = None
                 version = None
                 dataBlock = None
                 utc = None
                 ltc = None
                 miliSecond = None
                 timeZone = None
                 dstFlag = None
                 errorCount = None
                 datatime = None
                 structure = BASIC_STRUCTURE
                 __LOCALTIME = None
                 def __init__(self, useLocalTime=True):
                     self.size = 24
                     self.version = 0
                     self.dataBlock = 0
                     self.utc = 0
                     self.miliSecond = 0
                     self.timeZone = 0
                     self.dstFlag = 0
                     self.errorCount = 0
                     self.useLocalTime = useLocalTime
                 def read(self, fp):
                     self.length = 0
                     try:
                         if hasattr(fp, 'read'):
                             header = numpy.fromfile(fp, BASIC_STRUCTURE, 1)
                         else:
                             header = numpy.fromstring(fp, BASIC_STRUCTURE, 1)
                     except Exception as e:
                         print("BasicHeader: ")
                         print(e)
                         return 0
                     self.size = int(header['nSize'][0])
                     self.version = int(header['nVersion'][0])
                     self.dataBlock = int(header['nDataBlockId'][0])
                     self.utc = int(header['nUtime'][0])
                     self.miliSecond = int(header['nMilsec'][0])
                     self.timeZone = int(header['nTimezone'][0])
                     self.dstFlag = int(header['nDstflag'][0])
                     self.errorCount = int(header['nErrorCount'][0])
                     if self.size < 24:
                         return 0
                     self.length = header.nbytes
                     return 1
                 def write(self, fp):
                     headerTuple = (self.size, self.version, self.dataBlock, self.utc,
                                    self.miliSecond, self.timeZone, self.dstFlag, self.errorCount)
                     header = numpy.array(headerTuple, BASIC_STRUCTURE)
                     header.tofile(fp)
                     return 1
                 def get_ltc(self):
                     return self.utc - self.timeZone * 60
                 def set_ltc(self, value):
                     self.utc = value + self.timeZone * 60
                 def get_datatime(self):
                     return datetime.datetime.utcfromtimestamp(self.ltc)
                 ltc = property(get_ltc, set_ltc)
                 datatime = property(get_datatime)
             class SystemHeader(Header):
                 size = None
                 nSamples = None
                 nProfiles = None
                 nChannels = None
                 adcResolution = None
                 pciDioBusWidth = None
                 structure = SYSTEM_STRUCTURE
                 def __init__(self, nSamples=0, nProfiles=0, nChannels=0, adcResolution=14, pciDioBusWidth=0):
                     self.size = 24
                     self.nSamples = nSamples
                     self.nProfiles = nProfiles
                     self.nChannels = nChannels
                     self.adcResolution = adcResolution
                     self.pciDioBusWidth = pciDioBusWidth
                 def read(self, fp):
                     self.length = 0
                     try:
                         startFp = fp.tell()
                     except Exception as e:
                         startFp = None
                         pass
                     try:
                         if hasattr(fp, 'read'):
                             header = numpy.fromfile(fp, SYSTEM_STRUCTURE, 1)
                         else:
                             header = numpy.fromstring(fp, SYSTEM_STRUCTURE, 1)
                     except Exception as e:
                         print("System Header: " + str(e))
                         return 0
                     self.size = header['nSize'][0]
                     self.nSamples = header['nNumSamples'][0]
                     self.nProfiles = header['nNumProfiles'][0]
                     self.nChannels = header['nNumChannels'][0]
                     self.adcResolution = header['nADCResolution'][0]
                     self.pciDioBusWidth = header['nPCDIOBusWidth'][0]
                     if startFp is not None:
                         endFp = self.size + startFp
                         if fp.tell() > endFp:
                             sys.stderr.write(
                                 "Warning %s: Size value read from System Header is lower than it has to be\n" % fp.name)
                             return 0
                         if fp.tell() < endFp:
                             sys.stderr.write(
                                 "Warning %s: Size value read from System Header size is greater than it has to be\n" % fp.name)
                             return 0
                     self.length = header.nbytes
                     return 1
                 def write(self, fp):
                     headerTuple = (self.size, self.nSamples, self.nProfiles,
                                    self.nChannels, self.adcResolution, self.pciDioBusWidth)
                     header = numpy.array(headerTuple, SYSTEM_STRUCTURE)
                     header.tofile(fp)
                     return 1
             class RadarControllerHeader(Header):
                 expType = None
                 nTx = None
                 ipp = None
                 txA = None
                 txB = None
                 nWindows = None
                 numTaus = None
                 codeType = None
                 line6Function = None
                 line5Function = None
                 fClock = None
                 prePulseBefore = None
                 prePulseAfter = None
                 rangeIpp = None
                 rangeTxA = None
                 rangeTxB = None
                 structure = RADAR_STRUCTURE
                 __size = None
                 def __init__(self, expType=2, nTx=1,
                              ipp=None, txA=0, txB=0,
                              nWindows=None, nHeights=None, firstHeight=None, deltaHeight=None,
                              numTaus=0, line6Function=0, line5Function=0, fClock=None,
                              prePulseBefore=0, prePulseAfter=0,
                              codeType=0, nCode=0, nBaud=0, code=None,
                              flip1=0, flip2=0):
                     #         self.size = 116
                     self.expType = expType
                     self.nTx = nTx
                     self.ipp = ipp
                     self.txA = txA
                     self.txB = txB
                     self.rangeIpp = ipp
                     self.rangeTxA = txA
                     self.rangeTxB = txB
                     self.nWindows = nWindows
                     self.numTaus = numTaus
                     self.codeType = codeType
                     self.line6Function = line6Function
                     self.line5Function = line5Function
                     self.fClock = fClock
                     self.prePulseBefore = prePulseBefore
                     self.prePulseAfter = prePulseAfter
                     self.nHeights = nHeights
                     self.firstHeight = firstHeight
                     self.deltaHeight = deltaHeight
                     self.samplesWin = nHeights
                     self.nCode = nCode
                     self.nBaud = nBaud
                     self.code = code
                     self.flip1 = flip1
                     self.flip2 = flip2
                     self.code_size = int(numpy.ceil(self.nBaud / 32.)) * self.nCode * 4
             #         self.dynamic = numpy.array([],numpy.dtype('byte'))
                     if self.fClock is None and self.deltaHeight is not None:
                         self.fClock = 0.15 / (deltaHeight * 1e-6)  # 0.15Km / (height * 1u)
                 def read(self, fp):
                     self.length = 0
                     try:
                         startFp = fp.tell()
                     except Exception as e:
                         startFp = None
                         pass
                     try:
                         if hasattr(fp, 'read'):
                             header = numpy.fromfile(fp, RADAR_STRUCTURE, 1)
                         else:
                             header = numpy.fromstring(fp, RADAR_STRUCTURE, 1)
                         self.length += header.nbytes
                     except Exception as e:
                         print("RadarControllerHeader: " + str(e))
                         return 0
                     size = int(header['nSize'][0])
                     self.expType = int(header['nExpType'][0])
                     self.nTx = int(header['nNTx'][0])
                     self.ipp = float(header['fIpp'][0])
                     self.txA = float(header['fTxA'][0])
                     self.txB = float(header['fTxB'][0])
                     self.nWindows = int(header['nNumWindows'][0])
                     self.numTaus = int(header['nNumTaus'][0])
                     self.codeType = int(header['nCodeType'][0])
                     self.line6Function = int(header['nLine6Function'][0])
                     self.line5Function = int(header['nLine5Function'][0])
                     self.fClock = float(header['fClock'][0])
                     self.prePulseBefore = int(header['nPrePulseBefore'][0])
                     self.prePulseAfter = int(header['nPrePulseAfter'][0])
                     self.rangeIpp = header['sRangeIPP'][0]
                     self.rangeTxA = header['sRangeTxA'][0]
                     self.rangeTxB = header['sRangeTxB'][0]
                     try:
                         if hasattr(fp, 'read'):
                             samplingWindow = numpy.fromfile(
                                 fp, SAMPLING_STRUCTURE, self.nWindows)
                         else:
                             samplingWindow = numpy.fromstring(
                                 fp[self.length:], SAMPLING_STRUCTURE, self.nWindows)
                         self.length += samplingWindow.nbytes
                     except Exception as e:
                         print("RadarControllerHeader: " + str(e))
                         return 0
                     self.nHeights = int(numpy.sum(samplingWindow['nsa']))
                     self.firstHeight = samplingWindow['h0']
                     self.deltaHeight = samplingWindow['dh']
                     self.samplesWin = samplingWindow['nsa']
                     try:
                         if hasattr(fp, 'read'):
                             self.Taus = numpy.fromfile(fp, '<f4', self.numTaus)
                         else:
                             self.Taus = numpy.fromstring(
                                 fp[self.length:], '<f4', self.numTaus)
                         self.length += self.Taus.nbytes
                     except Exception as e:
                         print("RadarControllerHeader: " + str(e))
                         return 0
                     self.code_size = 0
                     if self.codeType != 0:
                         try:
                             if hasattr(fp, 'read'):
                                 self.nCode = numpy.fromfile(fp, '<u4', 1)[0]
                                 self.length += self.nCode.nbytes
                                 self.nBaud = numpy.fromfile(fp, '<u4', 1)[0]
                                 self.length += self.nBaud.nbytes
                             else:
                                 self.nCode = numpy.fromstring(
                                     fp[self.length:], '<u4', 1)[0]
                                 self.length += self.nCode.nbytes
                                 self.nBaud = numpy.fromstring(
                                     fp[self.length:], '<u4', 1)[0]
                                 self.length += self.nBaud.nbytes
                         except Exception as e:
                             print("RadarControllerHeader: " + str(e))
                             return 0
                         code = numpy.empty([self.nCode, self.nBaud], dtype='i1')
                         for ic in range(self.nCode):
                             try:
                                 if hasattr(fp, 'read'):
                                     temp = numpy.fromfile(fp, 'u4', int(
                                         numpy.ceil(self.nBaud / 32.)))
                                 else:
                                     temp = numpy.fromstring(
                                         fp, 'u4', int(numpy.ceil(self.nBaud / 32.)))
                                 self.length += temp.nbytes
                             except Exception as e:
                                 print("RadarControllerHeader: " + str(e))
                                 return 0
                             for ib in range(self.nBaud - 1, -1, -1):
-                                log.error(ib / 32)
                                 code[ic, ib] = temp[int(ib / 32)] % 2
                                 temp[int(ib / 32)] = temp[int(ib / 32)] / 2
                         self.code = 2.0 * code - 1.0
                         self.code_size = int(numpy.ceil(self.nBaud / 32.)) * self.nCode * 4
             #             if self.line5Function == RCfunction.FLIP:
             #                 self.flip1 = numpy.fromfile(fp,'<u4',1)
             #
             #             if self.line6Function == RCfunction.FLIP:
             #                 self.flip2 = numpy.fromfile(fp,'<u4',1)
                     if startFp is not None:
                         endFp = size + startFp
                         if fp.tell() != endFp:
                             #             fp.seek(endFp)
                             print("%s: Radar Controller Header size is not consistent: from data [%d] != from header field [%d]" % (fp.name, fp.tell() - startFp, size))
                 #             return 0
                         if fp.tell() > endFp:
                             sys.stderr.write(
                                 "Warning %s: Size value read from Radar Controller header is lower than it has to be\n" % fp.name)
                 #             return 0
                         if fp.tell() < endFp:
                             sys.stderr.write(
                                 "Warning %s: Size value read from Radar Controller header is greater than it has to be\n" % fp.name)
                     return 1
                 def write(self, fp):
                     headerTuple = (self.size,
                                    self.expType,
                                    self.nTx,
                                    self.ipp,
                                    self.txA,
                                    self.txB,
                                    self.nWindows,
                                    self.numTaus,
                                    self.codeType,
                                    self.line6Function,
                                    self.line5Function,
                                    self.fClock,
                                    self.prePulseBefore,
                                    self.prePulseAfter,
                                    self.rangeIpp,
                                    self.rangeTxA,
                                    self.rangeTxB)
                     header = numpy.array(headerTuple, RADAR_STRUCTURE)
                     header.tofile(fp)
                     sampleWindowTuple = (
                         self.firstHeight, self.deltaHeight, self.samplesWin)
                     samplingWindow = numpy.array(sampleWindowTuple, SAMPLING_STRUCTURE)
                     samplingWindow.tofile(fp)
                     if self.numTaus > 0:
                         self.Taus.tofile(fp)
                     if self.codeType != 0:
                         nCode = numpy.array(self.nCode, '<u4')
                         nCode.tofile(fp)
                         nBaud = numpy.array(self.nBaud, '<u4')
                         nBaud.tofile(fp)
                         code1 = (self.code + 1.0) / 2.
                         for ic in range(self.nCode):
                             tempx = numpy.zeros(int(numpy.ceil(self.nBaud / 32.)))
                             start = 0
                             end = 32
                             for i in range(len(tempx)):
                                 code_selected = code1[ic, start:end]
                                 for j in range(len(code_selected) - 1, -1, -1):
                                     if code_selected[j] == 1:
                                         tempx[i] = tempx[i] + \
 **(len(code_selected) - 1 - j)
                                 start = start + 32
                                 end = end + 32
                             tempx = tempx.astype('u4')
                             tempx.tofile(fp)
             #         if self.line5Function == RCfunction.FLIP:
             #             self.flip1.tofile(fp)
             #
             #         if self.line6Function == RCfunction.FLIP:
             #             self.flip2.tofile(fp)
                     return 1
                 def get_ippSeconds(self):
                     '''
                     '''
                     ippSeconds = 2.0 * 1000 * self.ipp / SPEED_OF_LIGHT
                     return ippSeconds
                 def set_ippSeconds(self, ippSeconds):
                     '''
                     '''
                     self.ipp = ippSeconds * SPEED_OF_LIGHT / (2.0 * 1000)
                     return
                 def get_size(self):
                     self.__size = 116 + 12 * self.nWindows + 4 * self.numTaus
                     if self.codeType != 0:
                         self.__size += 4 + 4 + 4 * self.nCode * \
                             numpy.ceil(self.nBaud / 32.)
                     return self.__size
                 def set_size(self, value):
                     raise IOError("size is a property and it cannot be set, just read")
                     return
                 ippSeconds = property(get_ippSeconds, set_ippSeconds)
                 size = property(get_size, set_size)
             class ProcessingHeader(Header):
                 #     size = None
                 dtype = None
                 blockSize = None
                 profilesPerBlock = None
                 dataBlocksPerFile = None
                 nWindows = None
                 processFlags = None
                 nCohInt = None
                 nIncohInt = None
                 totalSpectra = None
                 structure = PROCESSING_STRUCTURE
                 flag_dc = None
                 flag_cspc = None
                 def __init__(self, dtype=0, blockSize=0, profilesPerBlock=0, dataBlocksPerFile=0, nWindows=0, processFlags=0, nCohInt=0,
                              nIncohInt=0, totalSpectra=0, nHeights=0, firstHeight=0, deltaHeight=0, samplesWin=0, spectraComb=0, nCode=0,
                              code=0, nBaud=None, shif_fft=False, flag_dc=False, flag_cspc=False, flag_decode=False, flag_deflip=False
                              ):
                     #         self.size = 0
                     self.dtype = dtype
                     self.blockSize = blockSize
                     self.profilesPerBlock = 0
                     self.dataBlocksPerFile = 0
                     self.nWindows = 0
                     self.processFlags = 0
                     self.nCohInt = 0
                     self.nIncohInt = 0
                     self.totalSpectra = 0
                     self.nHeights = 0
                     self.firstHeight = 0
                     self.deltaHeight = 0
                     self.samplesWin = 0
                     self.spectraComb = 0
                     self.nCode = None
                     self.code = None
                     self.nBaud = None
                     self.shif_fft = False
                     self.flag_dc = False
                     self.flag_cspc = False
                     self.flag_decode = False
                     self.flag_deflip = False
                     self.length = 0
                 def read(self, fp):
                     self.length = 0
                     try:
                         startFp = fp.tell()
                     except Exception as e:
                         startFp = None
                         pass
                     try:
                         if hasattr(fp, 'read'):
                             header = numpy.fromfile(fp, PROCESSING_STRUCTURE, 1)
                         else:
                             header = numpy.fromstring(fp, PROCESSING_STRUCTURE, 1)
                         self.length += header.nbytes
                     except Exception as e:
                         print("ProcessingHeader: " + str(e))
                         return 0
                     size = int(header['nSize'][0])
                     self.dtype = int(header['nDataType'][0])
                     self.blockSize = int(header['nSizeOfDataBlock'][0])
                     self.profilesPerBlock = int(header['nProfilesperBlock'][0])
                     self.dataBlocksPerFile = int(header['nDataBlocksperFile'][0])
                     self.nWindows = int(header['nNumWindows'][0])
                     self.processFlags = header['nProcessFlags']
                     self.nCohInt = int(header['nCoherentIntegrations'][0])
                     self.nIncohInt = int(header['nIncoherentIntegrations'][0])
                     self.totalSpectra = int(header['nTotalSpectra'][0])
                     try:
                         if hasattr(fp, 'read'):
                             samplingWindow = numpy.fromfile(
                                 fp, SAMPLING_STRUCTURE, self.nWindows)
                         else:
                             samplingWindow = numpy.fromstring(
                                 fp[self.length:], SAMPLING_STRUCTURE, self.nWindows)
                         self.length += samplingWindow.nbytes
                     except Exception as e:
                         print("ProcessingHeader: " + str(e))
                         return 0
                     self.nHeights = int(numpy.sum(samplingWindow['nsa']))
                     self.firstHeight = float(samplingWindow['h0'][0])
                     self.deltaHeight = float(samplingWindow['dh'][0])
                     self.samplesWin = samplingWindow['nsa'][0]
                     try:
                         if hasattr(fp, 'read'):
                             self.spectraComb = numpy.fromfile(
                                 fp, 'u1', 2 * self.totalSpectra)
                         else:
                             self.spectraComb = numpy.fromstring(
                                 fp[self.length:], 'u1', 2 * self.totalSpectra)
                         self.length += self.spectraComb.nbytes
                     except Exception as e:
                         print("ProcessingHeader: " + str(e))
                         return 0
                     if ((self.processFlags & PROCFLAG.DEFINE_PROCESS_CODE) == PROCFLAG.DEFINE_PROCESS_CODE):
                         self.nCode = int(numpy.fromfile(fp, '<u4', 1))
                         self.nBaud = int(numpy.fromfile(fp, '<u4', 1))
                         self.code = numpy.fromfile(
                             fp, '<f4', self.nCode * self.nBaud).reshape(self.nCode, self.nBaud)
                     if ((self.processFlags & PROCFLAG.EXP_NAME_ESP) == PROCFLAG.EXP_NAME_ESP):
                         exp_name_len = int(numpy.fromfile(fp, '<u4', 1))
                         exp_name = numpy.fromfile(fp, 'u1', exp_name_len + 1)
                     if ((self.processFlags & PROCFLAG.SHIFT_FFT_DATA) == PROCFLAG.SHIFT_FFT_DATA):
                         self.shif_fft = True
                     else:
                         self.shif_fft = False
                     if ((self.processFlags & PROCFLAG.SAVE_CHANNELS_DC) == PROCFLAG.SAVE_CHANNELS_DC):
                         self.flag_dc = True
                     else:
                         self.flag_dc = False
                     if ((self.processFlags & PROCFLAG.DECODE_DATA) == PROCFLAG.DECODE_DATA):
                         self.flag_decode = True
                     else:
                         self.flag_decode = False
                     if ((self.processFlags & PROCFLAG.DEFLIP_DATA) == PROCFLAG.DEFLIP_DATA):
                         self.flag_deflip = True
                     else:
                         self.flag_deflip = False
                     nChannels = 0
                     nPairs = 0
                     pairList = []
                     for i in range(0, self.totalSpectra * 2, 2):
                         if self.spectraComb[i] == self.spectraComb[i + 1]:
                             nChannels = nChannels + 1  # par de canales iguales
                         else:
                             nPairs = nPairs + 1  # par de canales diferentes
                             pairList.append((self.spectraComb[i], self.spectraComb[i + 1]))
                     self.flag_cspc = False
                     if nPairs > 0:
                         self.flag_cspc = True
                     if startFp is not None:
                         endFp = size + startFp
                         if fp.tell() > endFp:
                             sys.stderr.write(
                                 "Warning: Processing header size is lower than it has to be")
                             return 0
                         if fp.tell() < endFp:
                             sys.stderr.write(
                                 "Warning: Processing header size is greater than it is considered")
                     return 1
                 def write(self, fp):
                     # Clear DEFINE_PROCESS_CODE
                     self.processFlags = self.processFlags & (~PROCFLAG.DEFINE_PROCESS_CODE)
                     headerTuple = (self.size,
                                    self.dtype,
                                    self.blockSize,
                                    self.profilesPerBlock,
                                    self.dataBlocksPerFile,
                                    self.nWindows,
                                    self.processFlags,
                                    self.nCohInt,
                                    self.nIncohInt,
                                    self.totalSpectra)
                     header = numpy.array(headerTuple, PROCESSING_STRUCTURE)
                     header.tofile(fp)
                     if self.nWindows != 0:
                         sampleWindowTuple = (
                             self.firstHeight, self.deltaHeight, self.samplesWin)
                         samplingWindow = numpy.array(sampleWindowTuple, SAMPLING_STRUCTURE)
                         samplingWindow.tofile(fp)
                     if self.totalSpectra != 0:
                         #             spectraComb = numpy.array([],numpy.dtype('u1'))
                         spectraComb = self.spectraComb
                         spectraComb.tofile(fp)
             #         if self.processFlags & PROCFLAG.DEFINE_PROCESS_CODE == PROCFLAG.DEFINE_PROCESS_CODE:
             #             nCode = numpy.array([self.nCode], numpy.dtype('u4')) #Probar con un dato que almacene codigo, hasta el momento no se hizo la prueba
             #             nCode.tofile(fp)
             #
             #             nBaud = numpy.array([self.nBaud], numpy.dtype('u4'))
             #             nBaud.tofile(fp)
             #
             #             code = self.code.reshape(self.nCode*self.nBaud)
             #             code = code.astype(numpy.dtype('<f4'))
             #             code.tofile(fp)
                     return 1
                 def get_size(self):
                     self.__size = 40 + 12 * self.nWindows + 2 * self.totalSpectra
             #         if self.processFlags & PROCFLAG.DEFINE_PROCESS_CODE == PROCFLAG.DEFINE_PROCESS_CODE:
             #             self.__size += 4 + 4 + 4*self.nCode*numpy.ceil(self.nBaud/32.)
             #             self.__size += 4 + 4 + 4 * self.nCode * self.nBaud
                     return self.__size
                 def set_size(self, value):
                     raise IOError("size is a property and it cannot be set, just read")
                     return
                 size = property(get_size, set_size)
             class RCfunction:
                 NONE = 0
                 FLIP = 1
                 CODE = 2
                 SAMPLING = 3
                 LIN6DIV256 = 4
                 SYNCHRO = 5
             class nCodeType:
                 NONE = 0
                 USERDEFINE = 1
                 BARKER2 = 2
                 BARKER3 = 3
                 BARKER4 = 4
                 BARKER5 = 5
                 BARKER7 = 6
                 BARKER11 = 7
                 BARKER13 = 8
                 AC128 = 9
                 COMPLEMENTARYCODE2 = 10
                 COMPLEMENTARYCODE4 = 11
                 COMPLEMENTARYCODE8 = 12
                 COMPLEMENTARYCODE16 = 13
                 COMPLEMENTARYCODE32 = 14
                 COMPLEMENTARYCODE64 = 15
                 COMPLEMENTARYCODE128 = 16
                 CODE_BINARY28 = 17
             class PROCFLAG:
                 COHERENT_INTEGRATION = numpy.uint32(0x00000001)
                 DECODE_DATA = numpy.uint32(0x00000002)
                 SPECTRA_CALC = numpy.uint32(0x00000004)
                 INCOHERENT_INTEGRATION = numpy.uint32(0x00000008)
                 POST_COHERENT_INTEGRATION = numpy.uint32(0x00000010)
                 SHIFT_FFT_DATA = numpy.uint32(0x00000020)
                 DATATYPE_CHAR = numpy.uint32(0x00000040)
                 DATATYPE_SHORT = numpy.uint32(0x00000080)
                 DATATYPE_LONG = numpy.uint32(0x00000100)
                 DATATYPE_INT64 = numpy.uint32(0x00000200)
                 DATATYPE_FLOAT = numpy.uint32(0x00000400)
                 DATATYPE_DOUBLE = numpy.uint32(0x00000800)
                 DATAARRANGE_CONTIGUOUS_CH = numpy.uint32(0x00001000)
                 DATAARRANGE_CONTIGUOUS_H = numpy.uint32(0x00002000)
                 DATAARRANGE_CONTIGUOUS_P = numpy.uint32(0x00004000)
                 SAVE_CHANNELS_DC = numpy.uint32(0x00008000)
                 DEFLIP_DATA = numpy.uint32(0x00010000)
                 DEFINE_PROCESS_CODE = numpy.uint32(0x00020000)
                 ACQ_SYS_NATALIA = numpy.uint32(0x00040000)
                 ACQ_SYS_ECHOTEK = numpy.uint32(0x00080000)
                 ACQ_SYS_ADRXD = numpy.uint32(0x000C0000)
                 ACQ_SYS_JULIA = numpy.uint32(0x00100000)
                 ACQ_SYS_XXXXXX = numpy.uint32(0x00140000)
                 EXP_NAME_ESP = numpy.uint32(0x00200000)
                 CHANNEL_NAMES_ESP = numpy.uint32(0x00400000)
                 OPERATION_MASK = numpy.uint32(0x0000003F)
                 DATATYPE_MASK = numpy.uint32(0x00000FC0)
                 DATAARRANGE_MASK = numpy.uint32(0x00007000)
                 ACQ_SYS_MASK = numpy.uint32(0x001C0000)
             dtype0 = numpy.dtype([('real', '<i1'), ('imag', '<i1')])
             dtype1 = numpy.dtype([('real', '<i2'), ('imag', '<i2')])
             dtype2 = numpy.dtype([('real', '<i4'), ('imag', '<i4')])
             dtype3 = numpy.dtype([('real', '<i8'), ('imag', '<i8')])
             dtype4 = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
             dtype5 = numpy.dtype([('real', '<f8'), ('imag', '<f8')])
             NUMPY_DTYPE_LIST = [dtype0, dtype1, dtype2, dtype3, dtype4, dtype5]
             PROCFLAG_DTYPE_LIST = [PROCFLAG.DATATYPE_CHAR,
                                    PROCFLAG.DATATYPE_SHORT,
                                    PROCFLAG.DATATYPE_LONG,
                                    PROCFLAG.DATATYPE_INT64,
                                    PROCFLAG.DATATYPE_FLOAT,
                                    PROCFLAG.DATATYPE_DOUBLE]
             DTYPE_WIDTH = [1, 2, 4, 8, 4, 8]
             def get_dtype_index(numpy_dtype):
                 index = None
                 for i in range(len(NUMPY_DTYPE_LIST)):
                     if numpy_dtype == NUMPY_DTYPE_LIST[i]:
                         index = i
                         break
                 return index
             def get_numpy_dtype(index):
                 return NUMPY_DTYPE_LIST[index]
             def get_procflag_dtype(index):
                 return PROCFLAG_DTYPE_LIST[index]
             def get_dtype_width(index):
                 return DTYPE_WIDTH[index]

schainpy/model/proc/jroproc_voltage.py +3 -2

             import sys
             import numpy
             from scipy import interpolate
             #TODO
             #from schainpy import cSchain
             from .jroproc_base import ProcessingUnit, Operation
             from schainpy.model.data.jrodata import Voltage
             from time import time
+            from schainpy.utils import log
             class VoltageProc(ProcessingUnit):
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
                     #         self.objectDict = {}
                     self.dataOut = Voltage()
                     self.flip = 1
                 def run(self):
                     if self.dataIn.type == 'AMISR':
                         self.__updateObjFromAmisrInput()
                     if self.dataIn.type == 'Voltage':
                         self.dataOut.copy(self.dataIn)
                 #         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
                 #
                 #        pass#
                 #
                 #    def init(self):
                 #
                 #
                 #        if self.dataIn.type == 'AMISR':
                 #            self.__updateObjFromAmisrInput()
                 #
                 #        if self.dataIn.type == 'Voltage':
                 #            self.dataOut.copy(self.dataIn)
                 #        # No necesita copiar en cada init() los atributos de dataIn
                 #        # la copia deberia hacerse por cada nuevo bloque de datos
                 def selectChannels(self, channelList):
                     channelIndexList = []
                     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)
                 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:
                             print(channelIndexList)
                             raise ValueError("The value %d in channelIndexList is not valid" %channelIndex)
                     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.nChannels = nChannels
                     return 1
                 def selectHeights(self, minHei=None, maxHei=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
                     """
                     if minHei == None:
                         minHei = self.dataOut.heightList[0]
                     if maxHei == None:
                         maxHei = self.dataOut.heightList[-1]
                     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 1
                 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 (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
                     #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))
                     return 1
                 def filterByHeights(self, window):
                     deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                     if window == None:
                         window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
                     newdelta = deltaHeight * window
                     r = self.dataOut.nHeights % window
                     newheights = (self.dataOut.nHeights-r)/window
                     if newheights <= 1:
                         raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window))
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         buffer = self.dataOut.data[:, :, 0:self.dataOut.nHeights-r]
                         buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
                         buffer = numpy.sum(buffer,3)
                     else:
                         buffer = self.dataOut.data[:,0:self.dataOut.nHeights-r]
                         buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nHeights/window,window)
                         buffer = numpy.sum(buffer,2)
                     self.dataOut.data = buffer
                     self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
                     self.dataOut.windowOfFilter = window
                 def setH0(self, h0, deltaHeight = None):
                     if not deltaHeight:
                         deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                     nHeights = self.dataOut.nHeights
                     newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
                     self.dataOut.heightList = newHeiRange
                 def deFlip(self, channelList = []):
                     data = self.dataOut.data.copy()
                     if self.dataOut.flagDataAsBlock:
                         flip = self.flip
                         profileList = list(range(self.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 self.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 self.dataOut.channelList:
                                     continue
                                 data[thisChannel,:] = data[thisChannel,:]*self.flip
                         self.flip *= -1.
                     self.dataOut.data = data
                 def setRadarFrequency(self, frequency=None):
                     if frequency != None:
                         self.dataOut.frequency = frequency
                     return 1
                 def interpolateHeights(self, topLim, botLim):
                     #69 al 72 para julia
                     #82-84 para meteoros
                     if len(numpy.shape(self.dataOut.data))==2:
                         sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
                         sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
                         #self.dataOut.data[:,botLim:limSup+1] = sampInterp
                         self.dataOut.data[:,botLim:topLim+1] = sampInterp
                     else:
                         nHeights = self.dataOut.data.shape[2]
                         x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
                         y = self.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)
                         self.dataOut.data[:,:,botLim:topLim+1]  = ynew
                 # import collections
             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)
                     #   self.isConfig = False
                 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
                         dataOut.nCohInt *= self.n
                         dataOut.utctime = avgdatatime
                         # print avgdata, avgdatatime
                         # raise
                         #   dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
                         dataOut.flagNoData = False
             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
                 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 = 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 1
                     self.__profIndex += 1
                     return 1
                 #        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
             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):
                     """
                     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
                         return True
                     """
                     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 True
                     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 True
                     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 True
                     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 True
                     raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter")
                     return False
             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
             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, shape[2]/n
+                        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
             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
             # 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

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