schain Commit - r1279:c53fe2a4a291 · Jicamarca Repository

Modificación a kmamisr para ejecutarse en la versión 3, creación de scripts con terminación v3 para difereciarlos, se comentó la linea #720 de JroIO_param.py debido a que reiniciaba la lista de archivos, ocasionando la reescritura del archivo hdf5. Alguna otra modificación aparente es producto de algunas variaciones en espacios al usar la función print()

joabAM -

r1279:c53fe2a4a291

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r1279:c53fe2a4a291

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schainpy/controller.py +41 -36

                     nFiles = len(files)
                     if nFiles == 0:
                         continue
                     skip = int(math.ceil(nFiles / n))
                     while nFiles > cursor * skip:
                         rconf.update(startDate=dt_str, endDate=dt_str, cursor=cursor,
                                      skip=skip)
                     time.sleep(3)
             def wait(context):
                 time.sleep(1)
                 c = zmq.Context()
                 receiver = c.socket(zmq.SUB)
                 receiver.connect('ipc:///tmp/schain_{}_pub'.format(self.id))
                 receiver.setsockopt(zmq.SUBSCRIBE, self.id.encode())
                 msg = receiver.recv_multipart()[1]
                 context.terminate()
                         parmElement.set('name', self.name)
                         parmElement.set('value', self.value)
                         parmElement.set('format', self.format)
                 def readXml(self, parmElement):
                     self.id = parmElement.get('id')
                     self.name = opElement.get('name')
                     self.type = opElement.get('type')
                     self.priority = opElement.get('priority')
                     self.project_id = str(project_id)
                     # Compatible with old signal chain version
                     # Use of 'run' method instead 'init'
                     self.id = None
                     self.datatype = None
                     self.name = None
                     self.inputId = None
                     self.opConfObjList = []
                     self.procUnitObj = None
                     self.opObjDict = {}
                     return self.id
                 def updateId(self, new_id):
                     '''
                     new_id = int(parentId) * 10 + (int(self.id) % 10)
                     new_inputId = int(parentId) * 10 + (int(self.inputId) % 10)
                         id sera el topico a publicar
                         inputId sera el topico a subscribirse
                     '''
                     # Compatible with old signal chain version
                     if datatype == None and name == None:
                         raise ValueError('datatype or name should be defined')
                     self.lock = lock
                     self.opConfObjList = []
                     self.addOperation(name='run', optype='self')
                 def removeOperations(self):
                     '''
                     className = eval(self.name)
+                    #print(self.name)
                     kwargs = self.getKwargs()
+                    #print(kwargs)
+                    #print("mark_a")
                     procUnitObj = className(self.id, self.inputId, self.project_id, self.err_queue, self.lock, 'ProcUnit', **kwargs)
+                    #print("mark_b")
                     log.success('creating process...', self.name)
                     for opConfObj in self.opConfObjList:
                         if opConfObj.type == 'self' and opConfObj.name == 'run':
                             continue
                         elif opConfObj.type == 'self':
                             opObj = getattr(procUnitObj, opConfObj.name)
                         else:
                             opObj = opConfObj.createObject()
                         log.success('adding operation: {}, type:{}'.format(
                             opConfObj.name,
                             opConfObj.type), self.name)
                         procUnitObj.addOperation(opConfObj, opObj)
                     procUnitObj.start()
                     self.procUnitObj = procUnitObj
                 def close(self):
                     for opConfObj in self.opConfObjList:
                 def getElementName(self):
                     return self.ELEMENTNAME
                 def setup(self, project_id, id, name, datatype, err_queue, path='', startDate='', endDate='',
                           startTime='', endTime='', server=None, **kwargs):
                     kwargs deben ser trasmitidos en la instanciacion
                     '''
                     # Compatible with old signal chain version
                     if datatype == None and name == None:
                         raise ValueError('datatype or name should be defined')
                     self.datatype = datatype
                     if path != '':
                         self.path = os.path.abspath(path)
+                    print (self.path)
                     self.startDate = startDate
                     self.endDate = endDate
                     self.startTime = startTime
                     self.endTime = endTime
                     self.server = server
                     self.err_queue = err_queue
                     self.addRunOperation(**kwargs)
                 def update(self, **kwargs):
                 def addRunOperation(self, **kwargs):
                     opObj = self.addOperation(name='run', optype='self')
                     if self.server is None:
                         opObj.addParameter(
                     print('*' * 19)
                     print(' ')
                     self.id = str(id)
                     self.description = description
                     self.email = email
                     self.alarm = alarm
                     if name:
                     readUnitConfObj = ReadUnitConf()
                     readUnitConfObj.setup(self.id, idReadUnit, name, datatype, self.err_queue, **kwargs)
                     self.procUnitConfObjDict[readUnitConfObj.getId()] = readUnitConfObj
                     return readUnitConfObj
                 def addProcUnit(self, inputId='0', datatype=None, name=None):
                     idProcUnit = self.__getNewId()
                     procUnitConfObj = ProcUnitConf()
                     input_proc = self.procUnitConfObjDict[inputId]
                     procUnitConfObj.setup(self.id, idProcUnit, name, datatype, inputId, self.err_queue, input_proc.lock)
                     self.procUnitConfObjDict[procUnitConfObj.getId()] = procUnitConfObj
                     t = Thread(target=self.__monitor, args=(self.err_queue, self.ctx))
                     t.start()
                 def __monitor(self, queue, ctx):
                     import socket
                     procs = 0
                     err_msg = ''
                     while True:
                         msg = queue.get()
                         if '#_start_#' in msg:
                             procs -=1
                         else:
                             err_msg = msg
                         if procs == 0 or 'Traceback' in err_msg:
                             break
                         time.sleep(0.1)
                     if '|' in err_msg:
                         name, err = err_msg.split('|')
                         if 'SchainWarning' in err:
                             log.error(err.split('SchainError:')[-1].split('\n')[0].strip(), name)
                         else:
                             log.error(err, name)
                     else:
                         name, err = self.name, err_msg
                     time.sleep(2)
                     for conf in self.procUnitConfObjDict.values():
                             if confop.type == 'external':
                                 confop.opObj.terminate()
                         conf.procUnitObj.terminate()
                     ctx.term()
                     message = ''.join(err)
                             subtitle += '[End time = %s]\n' % readUnitConfObj.endTime
                         a = Alarm(
                             modes=self.alarm,
                             email=self.email,
                             message=message,
                             subject=subject,
                     if not os.path.exists('/tmp/schain'):
                         os.mkdir('/tmp/schain')
                     self.ctx = zmq.Context()
                     xpub = self.ctx.socket(zmq.XPUB)
                     xpub.bind('ipc:///tmp/schain/{}_pub'.format(self.id))
                 def run(self):
                     log.success('Starting {}: {}'.format(self.name, self.id), tag='')
                     self.start_time = time.time()
                     self.createObjects()
                     self.setProxy()
                     log.success('{} Done (Time: {}s)'.format(
                         self.name,
                         time.time()-self.start_time), '')

schainpy/model/data/jrodata.py +13 -13

                 flagNoData = True
                 def copy(self, inputObj=None):
                     if inputObj == None:
                         return copy.deepcopy(self)
                     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:
                 MAXNUMY = 100
                 def __init__(self, code, throttle_value, exp_code, buffering=True, snr=False):
                     self.key = code
                     self.throttle = throttle_value
                     self.exp_code = exp_code
                     return len(self.__times)
                 def __getitem__(self, key):
                     if key not in self.data:
                         raise KeyError(log.error('Missing key: {}'.format(key)))
                     if 'spc' in key or not self.buffering:
                         elif 'spc_moments' == plot:
                             plot = 'moments'
                         self.data[plot] = {}
                     if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data or 'moments' in self.data:
                         self.data['noise'] = {}
                         self.data['rti'] = {}
                             self.plottypes.append('noise')
                         if 'rti' not in self.plottypes:
                             self.plottypes.append('rti')
                 def shape(self, key):
                     '''
                     Get the shape of the one-element data for the given key
                     '''
                     Update data object with new dataOut
                     '''
                     if tm in self.__times:
                         return
                     self.profileIndex = dataOut.profileIndex
                     self.tm = tm
                     self.type = dataOut.type
                     self.parameters = getattr(dataOut, 'parameters', [])
                     if hasattr(dataOut, 'meta'):
                         self.meta.update(dataOut.meta)
                     self.pairs = dataOut.pairsList
                     self.interval = dataOut.getTimeInterval()
                     self.localtime = dataOut.useLocalTime
                     self.__heights.append(dataOut.heightList)
                     self.__all_heights.update(dataOut.heightList)
                     self.__times.append(tm)
                     for plot in self.plottypes:
                         if plot in ('spc', 'spc_moments'):
                             z = dataOut.data_spc/dataOut.normFactor
                         if plot == 'scope':
                             buffer = dataOut.data
                             self.flagDataAsBlock = dataOut.flagDataAsBlock
                             self.nProfiles = dataOut.nProfiles
                         if plot == 'spc':
                             self.data['spc'] = buffer
                         elif plot == 'cspc':
                     else:
                         meta['xrange'] = []
                     meta.update(self.meta)
                     ret['metadata'] = meta
                     return json.dumps(ret)

schainpy/model/data/jroheaderIO.py +2 -2

                 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
             def get_dtype_width(index):
-                return DTYPE_WIDTH[index]
  No newline at end of file
+                return DTYPE_WIDTH[index]

schainpy/model/graphics/jroplot_base.py +23 -25

                     self.__throttle_plot = apply_throttle(self.throttle)
                     self.data = PlotterData(
                         self.CODE, self.throttle, self.exp_code, self.buffering, snr=self.showSNR)
                     if self.plot_server:
                         if not self.plot_server.startswith('tcp://'):
                             self.plot_server = 'tcp://{}'.format(self.plot_server)
                     self.setup()
                     self.time_label = 'LT' if self.localtime else 'UTC'
                     if self.width is None:
                         self.width = 8
                             cmap = plt.get_cmap(self.colormap)
                         cmap.set_bad(self.bgcolor, 1.)
                         self.cmaps.append(cmap)
                     for fig in self.figures:
                         fig.canvas.mpl_connect('key_press_event', self.OnKeyPress)
                         fig.canvas.mpl_connect('scroll_event', self.OnBtnScroll)
                                 xmax += time.timezone
                         else:
                             xmax = self.xmax
                     ymin = self.ymin if self.ymin else numpy.nanmin(self.y)
                     ymax = self.ymax if self.ymax else numpy.nanmax(self.y)
                     #Y = numpy.array([1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000])
                     #i = 1 if numpy.where(
                     #    abs(ymax-ymin) <= Y)[0][0] < 0 else numpy.where(abs(ymax-ymin) <= Y)[0][0]
                     #ystep = Y[i] / 10.
                         ystep = ystep/5
                         ystep = ystep/(10**digD)
                     else:
                         ystep = ((ymax + (10**(dig)))//10**(dig))*(10**(dig))
                         ystep = ystep/5
                     if self.xaxis is not 'time':
                         dig = int(numpy.log10(xmax))
                         if dig <= 0:
                             digD = len(str(xmax)) - 2
                             xdec = xmax*(10**digD)
                             xstep = ((xdec + (10**(dig)))//10**(dig))*(10**(dig))
                             xstep = xstep*0.5
                             xstep = xstep/(10**digD)
                         else:
                             xstep = ((xmax + (10**(dig)))//10**(dig))*(10**(dig))
                             xstep = xstep/5
                     for n, ax in enumerate(self.axes):
                         if ax.firsttime:
                             ax.set_facecolor(self.bgcolor)
                         if self.save:
                             self.save_figure(n)
                     if self.plot_server:
                         self.send_to_server()
                         # t = Thread(target=self.send_to_server)
                         '{}{}_{}.png'.format(
                             self.CODE,
                             label,
-                            self.getDateTime(self.data.max_time).strftime(
+                            self.getDateTime(self.data.max_time).strftime('%Y%m%d_%H%M%S'),
-                                '%Y%m%d_%H%M%S'
-                                ),
                             )
                         )
                     log.log('Saving figure: {}'.format(figname), self.name)
                     if not os.path.isdir(os.path.dirname(figname)):
                         os.makedirs(os.path.dirname(figname))
                     self.ncols: number of cols
                     self.nplots: number of plots (channels or pairs)
                     self.ylabel: label for Y axes
                     self.titles: list of axes title
                     '''
                     raise NotImplementedError
                     Must be defined in the child class
                     '''
                     raise NotImplementedError
                 def run(self, dataOut, **kwargs):
                     '''
                     Main plotting routine
                     '''
                     if self.isConfig is False:
                         self.__setup(**kwargs)
                         if dataOut.type == 'Parameters':
                             t = dataOut.utctimeInit
                         else:
                             t = dataOut.utctime
                         if dataOut.useLocalTime:
                             self.getDateTime = datetime.datetime.fromtimestamp
                             self.getDateTime = datetime.datetime.utcfromtimestamp
                             if self.localtime:
                                 t -= time.timezone
                         if 'buffer' in self.plot_type:
                             if self.xmin is None:
                                 self.tmin = t
                             else:
                                 self.tmin = (
                                     self.getDateTime(t).replace(
                                         hour=self.xmin,
                                         minute=0,
                                         second=0) - self.getDateTime(0)).total_seconds()
                         self.data.setup()
                     if dataOut.useLocalTime and not self.localtime:
                         tm += time.timezone
                     if self.xaxis is 'time' and self.data and (tm - self.tmin) >= self.xrange*60*60:
                         self.save_counter = self.save_period
                         self.__plot()
                         self.xmin += self.xrange
                         self.__plot()
                     if self.data and self.pause:
                         figpause(10)

schainpy/model/io/jroIO_kamisr.py +205 -185

             from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader
             from schainpy.model.data.jrodata import Voltage
-            from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
+            from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
             from numpy import imag
+            @MPDecorator
             class AMISRReader(ProcessingUnit):
                 '''
                 classdocs
                     '''
                     Constructor
                     '''
                     ProcessingUnit.__init__(self)
                     self.set = None
                     self.subset = None
                     self.extension_file = '.h5'
                     self.flagIsNewFile = 0
                     self.filename = ''
                     self.amisrFilePointer = None
-                    self.dataset = None
+                    #self.dataset = None
                     self.profileIndex = 0
                     self.beamCodeByFrame = None
                     self.radacTimeByFrame = None
                     self.dataset = None
                     self.__firstFile = True
                     self.buffer = None
                     self.timezone = 'ut'
                     self.__waitForNewFile = 20
                     self.__filename_online = None
                     #Is really necessary create the output object in the initializer
                     self.dataOut = Voltage()
+                    self.dataOut.error=False
                 def setup(self,path=None,
                                 startDate=None,
                                 endDate=None,
                                 startTime=None,
                                 endTime=None,
                                 walk=True,
                                 timezone='ut',
                                 nCode = 0,
                                 nBaud = 0,
                                 online=False):
+                    #print ("T",path)
                     self.timezone = timezone
                     self.all = all
                     self.online = online
                     self.code = code
                     self.nCode = int(nCode)
                     self.nBaud = int(nBaud)
                     #self.findFiles()
                     if not(online):
                         #Busqueda de archivos offline
                         self.searchFilesOffLine(path, startDate, endDate, startTime, endTime, walk)
                     else:
                         self.searchFilesOnLine(path, startDate, endDate, startTime,endTime,walk)
                     if not(self.filenameList):
                         print("There is no files into the folder: %s"%(path))
                         sys.exit(-1)
                     self.fileIndex = -1
                     self.readNextFile(online)
                     '''
                     Add code
                     '''
                     self.isConfig = True
                     pass
                 def readAMISRHeader(self,fp):
                     header = 'Raw11/Data/RadacHeader'
                     self.beamCodeByPulse = fp.get(header+'/BeamCode') # LIST OF BEAMS PER PROFILE, TO BE USED ON REARRANGE
                     self.rangeFromFile = fp.get('Raw11/Data/Samples/Range')
                     self.frequency =  fp.get('Rx/Frequency')
                     txAus = fp.get('Raw11/Data/Pulsewidth')
                     self.nblocks = self.pulseCount.shape[0] #nblocks
                     self.nprofiles = self.pulseCount.shape[1] #nprofile
                     self.nsa = self.nsamplesPulse[0,0] #ngates
                     self.nchannels = self.beamCode.shape[1]
                     self.ippSeconds = (self.radacTime[0][1] -self.radacTime[0][0]) #Ipp in seconds
                     #self.__waitForNewFile = self.nblocks  # wait depending on the number of blocks since each block is 1 sec
                     self.__waitForNewFile = self.nblocks * self.nprofiles * self.ippSeconds # wait until new file is created
                     #filling radar controller header parameters
                     self.__ippKm = self.ippSeconds *.15*1e6 # in km
                     self.__txA = (txAus.value)*.15 #(ipp[us]*.15km/1us) in km
                     self.__txB = 0
                     nWindows=1
                     self.__nSamples = self.nsa
                     self.__firstHeight = self.rangeFromFile[0][0]/1000 #in km
                     self.__deltaHeight = (self.rangeFromFile[0][1] - self.rangeFromFile[0][0])/1000
                     #for now until understand why the code saved is different (code included even though code not in tuf file)
                     #self.__codeType = 0
                    # self.__nCode = None
                     self.__nCode = self.nCode
                     self.__nBaud = self.nBaud
                     #self.__code = 0
                     #filling system header parameters
                     self.__nSamples = self.nsa
                     self.newProfiles = self.nprofiles/self.nchannels
                     self.__channelList = list(range(self.nchannels))
                     self.__frequency = self.frequency[0][0]
                 def createBuffers(self):
                     pass
                 def __setParameters(self,path='', startDate='',endDate='',startTime='', endTime='', walk=''):
                     self.path = path
                     self.startDate = startDate
                     self.startTime = startTime
                     self.endTime = endTime
                     self.walk = walk
                 def __checkPath(self):
                     if os.path.exists(self.path):
                         self.status = 1
                     else:
                         self.status = 0
                         print('Path:%s does not exists'%self.path)
                     return
                 def __selDates(self, amisr_dirname_format):
                     try:
                         year = int(amisr_dirname_format[0:4])
                         month = int(amisr_dirname_format[4:6])
                         dom = int(amisr_dirname_format[6:8])
                         thisDate = datetime.date(year,month,dom)
                         if (thisDate>=self.startDate and thisDate <= self.endDate):
                             return amisr_dirname_format
                     except:
                         return None
                 def __findDataForDates(self,online=False):
                     if not(self.status):
                         return None
                     pat = '\d+.\d+'
                     dirnameList = [re.search(pat,x) for x in os.listdir(self.path)]
                     dirnameList = [x for x in dirnameList if x!=None]
                     else:
                         self.status = 0
                         return None
                 def __getTimeFromData(self):
                     startDateTime_Reader = datetime.datetime.combine(self.startDate,self.startTime)
                     endDateTime_Reader = datetime.datetime.combine(self.endDate,self.endTime)
                         filename = self.filenameList[i]
                         fp = h5py.File(filename,'r')
                         time_str = fp.get('Time/RadacTimeString')
-                        startDateTimeStr_File = time_str[0][0].split('.')[0]
+                        startDateTimeStr_File = time_str[0][0].decode('UTF-8').split('.')[0]
+                        #startDateTimeStr_File = "2019-12-16 09:21:11"
                         junk = time.strptime(startDateTimeStr_File, '%Y-%m-%d %H:%M:%S')
                         startDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec)
-                        endDateTimeStr_File = time_str[-1][-1].split('.')[0]
+                        #endDateTimeStr_File = "2019-12-16 11:10:11"
+                        endDateTimeStr_File = time_str[-1][-1].decode('UTF-8').split('.')[0]
                         junk = time.strptime(endDateTimeStr_File, '%Y-%m-%d %H:%M:%S')
                         endDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec)
                         fp.close()
+                        #print("check time", startDateTime_File)
                         if self.timezone == 'lt':
                             startDateTime_File = startDateTime_File - datetime.timedelta(minutes = 300)
                             endDateTime_File = endDateTime_File - datetime.timedelta(minutes = 300)
                         if (endDateTime_File>=startDateTime_Reader and endDateTime_File<endDateTime_Reader):
                             #self.filenameList.remove(filename)
                             filter_filenameList.append(filename)
                         if (endDateTime_File>=endDateTime_Reader):
                             break
                     filter_filenameList.sort()
                     self.filenameList = filter_filenameList
                     return 1
                 def __filterByGlob1(self, dirName):
                     filter_files = glob.glob1(dirName, '*.*%s'%self.extension_file)
                     filter_files.sort()
                     filterDict.setdefault(dirName)
                     filterDict[dirName] = filter_files
                     return filterDict
                 def __getFilenameList(self, fileListInKeys, dirList):
                     for value in fileListInKeys:
                         dirName = list(value.keys())[0]
                         for file in value[dirName]:
                             filename = os.path.join(dirName, file)
                             self.filenameList.append(filename)
                 def __selectDataForTimes(self, online=False):
                     #aun no esta implementado el filtro for tiempo
                     if not(self.status):
                         return None
                     dirList = [os.path.join(self.path,x) for x in self.dirnameList]
                     fileListInKeys = [self.__filterByGlob1(x) for x in dirList]
                     self.__getFilenameList(fileListInKeys, dirList)
                     if not(online):
                         #filtro por tiempo
                         else:
                             self.status = 0
                             return None
                     else:
                         #get the last file - 1
                         self.filenameList = [self.filenameList[-2]]
                     new_dirnameList = []
                     for dirname in self.dirnameList:
                         junk = numpy.array([dirname in x for x in self.filenameList])
                             new_dirnameList.append(dirname)
                     self.dirnameList = new_dirnameList
                     return 1
                 def searchFilesOnLine(self, path, startDate, endDate, startTime=datetime.time(0,0,0),
                                         endTime=datetime.time(23,59,59),walk=True):
                     if endDate ==None:
                      startDate = datetime.datetime.utcnow().date()
                      endDate = datetime.datetime.utcnow().date()
                     self.__setParameters(path=path, startDate=startDate, endDate=endDate,startTime = startTime,endTime=endTime, walk=walk)
                     self.__checkPath()
                     self.__findDataForDates(online=True)
                     self.dirnameList = [self.dirnameList[-1]]
                     self.__selectDataForTimes(online=True)
                     return
                 def searchFilesOffLine(self,
                                         path,
                                         startDate,
                                         startTime=datetime.time(0,0,0),
                                         endTime=datetime.time(23,59,59),
                                         walk=True):
                     self.__setParameters(path, startDate, endDate, startTime, endTime, walk)
                     self.__checkPath()
                     self.__findDataForDates()
                     self.__selectDataForTimes()
                     for i in range(len(self.filenameList)):
                         print("%s" %(self.filenameList[i]))
                     return
                 def __setNextFileOffline(self):
                     idFile = self.fileIndex
                         if not(idFile < len(self.filenameList)):
                             self.flagNoMoreFiles = 1
                             print("No more Files")
+                            self.dataOut.error = True
                             return 0
                         filename = self.filenameList[idFile]
                         amisrFilePointer = h5py.File(filename,'r')
                         break
                     self.flagIsNewFile = 1
                     print("Setting the file: %s"%self.filename)
                     return 1
                 def __setNextFileOnline(self):
                     filename = self.filenameList[0]
                     if self.__filename_online != None:
                             self.__selectDataForTimes(online=True)
                             filename = self.filenameList[0]
                             wait += 1
                     self.__filename_online = filename
                     self.amisrFilePointer = h5py.File(filename,'r')
                     self.flagIsNewFile = 1
                     self.filename = filename
                     print("Setting the file: %s"%self.filename)
                     return 1
                 def readData(self):
                     buffer = self.amisrFilePointer.get('Raw11/Data/Samples/Data')
                     re = buffer[:,:,:,0]
                     im = buffer[:,:,:,1]
                     dataset = re + im*1j
                     self.radacTime = self.amisrFilePointer.get('Raw11/Data/RadacHeader/RadacTime')
                     timeset = self.radacTime[:,0]
                     return dataset,timeset
                 def reshapeData(self):
                 #self.beamCodeByPulse, self.beamCode, self.nblocks, self.nprofiles, self.nsa,
                     channels = self.beamCodeByPulse[0,:]
                     nchan = self.nchannels
                     #self.newProfiles = self.nprofiles/nchan #must be defined on filljroheader
                     nblocks = self.nblocks
                     nsamples = self.nsa
                     #Dimensions : nChannels, nProfiles, nSamples
-                    new_block = numpy.empty((nblocks, nchan, self.newProfiles, nsamples), dtype="complex64")
+                    new_block = numpy.empty((nblocks, nchan, numpy.int_(self.newProfiles), nsamples), dtype="complex64")
                     ############################################
                     for thisChannel in range(nchan):
                         new_block[:,thisChannel,:,:] = self.dataset[:,numpy.where(channels==self.beamCode[0][thisChannel])[0],:]
                     new_block = numpy.transpose(new_block, (1,0,2,3))
                     new_block = numpy.reshape(new_block, (nchan,-1, nsamples))
                     return new_block
                 def updateIndexes(self):
                     pass
                 def fillJROHeader(self):
                     #fill radar controller header
-                    self.dataOut.radarControllerHeaderObj = RadarControllerHeader(ippKm=self.__ippKm,
+                    self.dataOut.radarControllerHeaderObj = RadarControllerHeader(ipp=self.__ippKm,
                                                                                   txA=self.__txA,
                                                                                   txB=0,
                                                                                   nWindows=1,
                                                                                   nCode=self.__nCode, nBaud=self.__nBaud,
                                                                                   code = self.__code,
                                                                                   fClock=1)
                     #fill system header
                     self.dataOut.systemHeaderObj = SystemHeader(nSamples=self.__nSamples,
                                                                 nProfiles=self.newProfiles,
                                                                 nChannels=len(self.__channelList),
                                                                 adcResolution=14,
-                                                                pciDioBusWith=32)
+                                                                pciDioBusWidth=32)
                     self.dataOut.type = "Voltage"
                     self.dataOut.data = None
                     self.dataOut.dtype = numpy.dtype([('real','<i8'),('imag','<i8')])
             #        self.dataOut.nChannels = 0
             #        self.dataOut.nHeights = 0
                     self.dataOut.nProfiles = self.newProfiles*self.nblocks
                     #self.dataOut.heightList = self.__firstHeigth + numpy.arange(self.__nSamples, dtype = numpy.float)*self.__deltaHeigth
                     ranges = numpy.reshape(self.rangeFromFile.value,(-1))
                     self.dataOut.heightList =  ranges/1000.0 #km
                     self.dataOut.channelList = self.__channelList
                     self.dataOut.blocksize = self.dataOut.getNChannels() * self.dataOut.getNHeights()
             #        self.dataOut.channelIndexList = None
                     self.dataOut.flagNoData = True
                     #Set to TRUE if the data is discontinuous
                     self.dataOut.flagDiscontinuousBlock = False
                     self.dataOut.utctime = None
                     #self.dataOut.timeZone = -5 #self.__timezone/60  #timezone like jroheader, difference in minutes between UTC and localtime
                     if self.timezone == 'lt':
                         self.dataOut.timeZone = time.timezone / 60. #get the timezone in minutes
                     else:
                         self.dataOut.timeZone = 0 #by default time is UTC
                     self.dataOut.dstFlag = 0
                     self.dataOut.errorCount = 0
                     self.dataOut.nCohInt = 1
                     self.dataOut.flagDecodeData = False #asumo que la data esta decodificada
                     self.dataOut.flagDeflipData = False #asumo que la data esta sin flip
                     self.dataOut.flagShiftFFT = False
                     self.dataOut.ippSeconds = self.ippSeconds
                     #Time interval between profiles
                     #self.dataOut.timeInterval = self.dataOut.ippSeconds * self.dataOut.nCohInt
                     self.dataOut.frequency = self.__frequency
                     self.dataOut.realtime = self.online
                     pass
                 def readNextFile(self,online=False):
                     if not(online):
                         newFile = self.__setNextFileOffline()
                     else:
                         newFile = self.__setNextFileOnline()
                     if not(newFile):
                         return 0
                     #if self.__firstFile:
                     self.readAMISRHeader(self.amisrFilePointer)
                     self.createBuffers()
                     self.fillJROHeader()
                     #self.__firstFile = False
                     self.dataset,self.timeset = self.readData()
                     if self.endDate!=None:
                      endDateTime_Reader = datetime.datetime.combine(self.endDate,self.endTime)
                      time_str = self.amisrFilePointer.get('Time/RadacTimeString')
-                     startDateTimeStr_File = time_str[0][0].split('.')[0]
+                     startDateTimeStr_File = time_str[0][0].decode('UTF-8').split('.')[0]
                      junk = time.strptime(startDateTimeStr_File, '%Y-%m-%d %H:%M:%S')
                      startDateTime_File = datetime.datetime(junk.tm_year,junk.tm_mon,junk.tm_mday,junk.tm_hour, junk.tm_min, junk.tm_sec)
                      if self.timezone == 'lt':
                       startDateTime_File = startDateTime_File - datetime.timedelta(minutes = 300)
                      if (startDateTime_File>endDateTime_Reader):
                          return 0
                     self.jrodataset = self.reshapeData()
                     #----self.updateIndexes()
                     self.profileIndex = 0
                     return 1
                 def __hasNotDataInBuffer(self):
                     if self.profileIndex >= (self.newProfiles*self.nblocks):
                         return 1
                     return 0
                 def getData(self):
                     if self.flagNoMoreFiles:
                         self.dataOut.flagNoData = True
                         return 0
                     if self.__hasNotDataInBuffer():
                         if not (self.readNextFile(self.online)):
                             return 0
-                    if self.dataset is None: # setear esta condicion cuando no hayan datos por leers
+                    if self.dataset is None: # setear esta condicion cuando no hayan datos por leer
                         self.dataOut.flagNoData = True
                         return 0
                     #self.dataOut.data = numpy.reshape(self.jrodataset[self.profileIndex,:],(1,-1))
                     self.dataOut.data = self.jrodataset[:,self.profileIndex,:]
+                    #print("R_t",self.timeset)
                     #self.dataOut.utctime = self.jrotimeset[self.profileIndex]
                     #verificar basic header de jro data y ver si es compatible con este valor
                     #self.dataOut.utctime = self.timeset + (self.profileIndex * self.ippSeconds * self.nchannels)
                     indexprof = numpy.mod(self.profileIndex, self.newProfiles)
                     indexblock = self.profileIndex/self.newProfiles
-                    #print indexblock, indexprof
+                    #print (indexblock, indexprof)
-                    self.dataOut.utctime = self.timeset[indexblock] + (indexprof * self.ippSeconds * self.nchannels)
+                    diffUTC = 1.8e4 #UTC diference from peru in seconds --Joab
+                    diffUTC = 0
+                    t_comp = (indexprof * self.ippSeconds * self.nchannels) + diffUTC #
+                    #cambio posible 18/02/2020
+                    #print("utc :",indexblock," __ ",t_comp)
+                    #print(numpy.shape(self.timeset))
+                    self.dataOut.utctime = self.timeset[numpy.int_(indexblock)] + t_comp
+                    #self.dataOut.utctime = self.timeset[self.profileIndex] + t_comp
+                    #print(self.dataOut.utctime)
                     self.dataOut.profileIndex = self.profileIndex
                     self.dataOut.flagNoData = False
             #         if indexprof == 0:
             #             print self.dataOut.utctime
                     self.profileIndex += 1
                     return self.dataOut.data
                 def run(self, **kwargs):
                     '''
                     This method will be called many times so here you should put all your code
                     '''
                     if not self.isConfig:
                         self.setup(**kwargs)
                         self.isConfig = True
                     self.getData()

schainpy/model/io/jroIO_param.py +36 -36

                     except IOError:
                         traceback.print_exc()
                         raise IOError("The file %s can't be opened" %(filename))
                     #In case has utctime attribute
                     grp2 = grp1['utctime']
              #         thisUtcTime = grp2.value[0] - 5*3600 #To convert to local time
                 setType = None
                 def __init__(self):
                     Operation.__init__(self)
                     return
                         dsDict['variable'] = self.dataList[i]
                         #---------------------    Conditionals    ------------------------
                         #There is no data
                         if dataAux is None:
                             return 0
                         if isinstance(dataAux, (int, float, numpy.integer, numpy.float)):
                         return False
                 def setNextFile(self):
                     ext = self.ext
                     path = self.path
                     setFile = self.setFile
                     if os.path.exists(fullpath):
                         filesList = os.listdir( fullpath )
-                        filesList = [k for k in filesList if 'M' in k]
+                        ##filesList = [k for k in filesList if 'M' in k]
                         if len( filesList ) > 0:
                             filesList = sorted( filesList, key=str.lower )
                             filen = filesList[-1]
                             for j in range(dsInfo['dsNumber']):
                                 dsInfo = dsList[i]
                                 tableName = dsInfo['dsName']
                                 if dsInfo['nDim'] == 3:
                                     shape = dsInfo['shape'].astype(int)
                     self.dataOut = dataOut
                     if not(self.isConfig):
                         self.setup(dataOut, path=path, blocksPerFile=blocksPerFile,
                                    metadataList=metadataList, dataList=dataList, mode=mode,
                                    setType=setType)
                     self.putData()
                     return
             @MPDecorator
             class ParameterReader(Reader, ProcessingUnit):
                     self.set_kwargs(**kwargs)
                     if not self.ext.startswith('.'):
                         self.ext = '.{}'.format(self.ext)
                     if self.online:
                         log.log("Searching files in online mode...", self.name)
                         for nTries in range(self.nTries):
                             fullpath = self.searchFilesOnLine(self.path, self.startDate,
                                 self.endDate, self.expLabel, self.ext, self.walk,
                                 self.filefmt, self.folderfmt)
                             try:
                                 fullpath = next(fullpath)
                             except:
                                 fullpath = None
                             if fullpath:
                                 break
                             log.warning(
                                 'Waiting {} sec for a valid file in {}: try {} ...'.format(
                                     self.delay, self.path, nTries + 1),
                                 self.name)
                             time.sleep(self.delay)
                         if not(fullpath):
                             raise schainpy.admin.SchainError(
                                 'There isn\'t any valid file in {}'.format(self.path))
                         pathname, filename = os.path.split(fullpath)
                         self.year = int(filename[1:5])
                         self.doy = int(filename[5:8])
                         self.set = int(filename[8:11]) - 1
                     else:
                         log.log("Searching files in {}".format(self.path), self.name)
                         self.filenameList = self.searchFilesOffLine(self.path, self.startDate,
                             self.endDate, self.expLabel, self.ext, self.walk, self.filefmt, self.folderfmt)
                     self.setNextFile()
                     return
                 def readFirstHeader(self):
                     '''Read metadata and data'''
                     self.__readMetadata()
                     self.__readData()
                     self.__setBlockList()
                     self.blockIndex = 0
                     return
                 def __setBlockList(self):
                             else:
                                 data = gp[name].value
                                 listMetaname.append(name)
                                 listMetadata.append(data)
                     elif self.metadata:
                         metadata = json.loads(self.metadata)
                         listShapes = {}
                     self.listShapes = listShapes
                     self.listMetaname = listMetaname
                     self.listMeta = listMetadata
                     return
                     listdataname = []
                     listdata = []
                     if 'Data' in self.fp:
                         grp = self.fp['Data']
                         for item in list(grp.items()):
                                 for i in range(dim):
                                     array.append(grp[name]['table{:02d}'.format(i)].value)
                                 array = numpy.array(array)
                             listdata.append(array)
                     elif self.metadata:
                         metadata = json.loads(self.metadata)
                     self.listDataname = listdataname
                     self.listData = listdata
                     return
                 def getData(self):
                     for i in range(len(self.listMeta)):
                 lastTime = None
                 def __init__(self):
                     Operation.__init__(self)
                     return
                             dsDict['nDim'] = len(dataAux.shape)
                             dsDict['shape'] = dataAux.shape
                             dsDict['dsNumber'] = dataAux.shape[0]
                         dsList.append(dsDict)
                         tableList.append((self.dataList[i], dsDict['nDim']))
                         self.lastTime = currentTime
                         self.currentDay = dataDay
                         return False
                     timeDiff = currentTime - self.lastTime
                     #Si el dia es diferente o si la diferencia entre un dato y otro supera la hora
                     self.dataOut = dataOut
                     if not(self.isConfig):
                         self.setup(path=path, blocksPerFile=blocksPerFile,
                                    metadataList=metadataList, dataList=dataList,
                                    setType=setType)
                     self.putData()
                     return
                 def setNextFile(self):
                     ext = self.ext
                     path = self.path
                     setFile = self.setFile
                     return
                 def writeData(self, fp):
                     grp = fp.create_group("Data")
                     dtsets = []
                     data = []
                     for dsInfo in self.dsList:
                         if dsInfo['nDim'] == 0:
                             ds = grp.create_dataset(
                                 dsInfo['variable'],
                                 (self.blocksPerFile, ),
                                 chunks=True,
                                 dtype=numpy.float64)
                             dtsets.append(ds)
                             data.append((dsInfo['variable'], -1))
                             sgrp = grp.create_group(dsInfo['variable'])
                             for i in range(dsInfo['dsNumber']):
                                 ds = sgrp.create_dataset(
                                     'table{:02d}'.format(i),
                                     (self.blocksPerFile, ) + dsInfo['shape'][1:],
                                     chunks=True)
                                 dtsets.append(ds)
                     fp.flush()
                     log.log('Creating file: {}'.format(fp.filename), self.name)
                     self.ds = dtsets
                     self.data = data
                     self.firsttime = True

schainpy/model/proc/jroproc_base.py +37 -40

             Jan 2018
             Abstract:
                 Base class for processing units and operations. A decorator provides multiprocessing features and interconnect the processes created.
                 The argument (kwargs) sent from the controller is parsed and filtered via the decorator for each processing unit or operation instantiated.
                 The decorator handle also the methods inside the processing unit to be called from the main script (not as operations) (OPERATION -> type ='self').
             Based on:
                 $Author: murco $
                 """
                 Update - Jan 2018 - MULTIPROCESSING
                 All the "call" methods present in the previous base were removed.
                 The majority of operations are independant processes, thus
                 the decorator is in charge of communicate the operation processes
                 with the proccessing unit via IPC.
                 The constructor does not receive any argument. The remaining methods
                 are related with the operations to execute.
                 """
                 proc_type = 'processing'
                 def addOperation(self, conf, operation):
                     """
                     This method is used in the controller, and update the dictionary containing the operations to execute. The dict
                     posses the id of the operation process (IPC purposes)
                         Agrega un objeto del tipo "Operation" (opObj) a la lista de objetos "self.objectList" y retorna el
                     self.operations.append(
                         (operation, conf.type, conf.id, conf.getKwargs()))
                     if 'plot' in self.name.lower():
                         self.plots.append(operation.CODE)
                     return
             class InputQueue(Thread):
                 '''
                 Class to hold input data for Proccessing Units and external Operations,
                 '''
                 def get(self):
                     if not self.islocked and self.size/1000000 > 512:
                         self.lock.n.value += 1
                         self.islocked = True
                         self.lock.clear()
                     elif self.islocked and self.size/1000000 <= 512:
                         self.islocked = False
                         self.lock.n.value -= 1
                         if self.lock.n.value == 0:
                             self.lock.set()
                     obj = self.queue.get()
                     self.size -= sys.getsizeof(obj)
                     return pickle.loads(obj)
             def MPDecorator(BaseClass):
                 """
                 Multiprocessing class decorator
                 This function add multiprocessing features to a BaseClass. Also, it handle
                 the communication beetween processes (readers, procUnits and operations).
                 """
                 class MPClass(BaseClass, Process):
                         self.t = time.time()
                         self.name = BaseClass.__name__
                         self.__doc__ = BaseClass.__doc__
                         if 'plot' in self.name.lower() and not self.name.endswith('_'):
                             self.name = '{}{}'.format(self.CODE.upper(), 'Plot')
                         self.start_time = time.time()
                         self.id = args[0]
                         self.inputId = args[1]
                         self.project_id = args[2]
                         '''
                         self.queue.start()
                     def listen(self):
                         '''
                         This function waits for objects
                         '''
                         return self.queue.get()
                     def set_publisher(self):
                         '''
                         This function create a zmq socket for publishing objects.
                         '''
                         time.sleep(0.5)
                         c = zmq.Context()
                         self.sender = c.socket(zmq.PUB)
                         self.sender.connect(
                         '''
                         This function publish an object, to an specific topic.
                         It blocks publishing when receiver queue is full to avoid data loss
                         '''
                         if self.inputId is None:
                             self.lock.wait()
                         self.sender.send_multipart([str(id).encode(), pickle.dumps(data)])
                     def runReader(self):
                         '''
                         Run fuction for read units
                             try:
                                 BaseClass.run(self, **self.kwargs)
                             except:
                                 err = traceback.format_exc()
                                 if 'No more files' in err:
                                     log.warning('No more files to read', self.name)
                                 else:
                                     self.err_queue.put('{}|{}'.format(self.name, err))
                                 self.dataOut.error = True
                             for op, optype, opId, kwargs in self.operations:
                                 if optype == 'self' and not self.dataOut.flagNoData:
                                     op(**kwargs)
                                 continue
                             self.publish(self.dataOut, self.id)
+                            if self.dataOut.error:
-                            if self.dataOut.error:
                                 break
                         time.sleep(0.5)
                         '''
                         while True:
                             self.dataIn = self.listen()
                             if self.dataIn.flagNoData and self.dataIn.error is None:
                                 continue
                             elif self.dataIn.error:
                                 self.dataOut.error = self.dataIn.error
                                 self.dataOut.flagNoData = True
                             for op, optype, opId, kwargs in self.operations:
                                 if optype == 'self' and not self.dataOut.flagNoData:
                                     op(**kwargs)
                                 elif optype == 'other' and not self.dataOut.flagNoData:
                                     self.dataOut = op.run(self.dataOut, **kwargs)
                                 elif optype == 'external' and not self.dataOut.flagNoData:
                                     self.publish(self.dataOut, opId)
                             self.publish(self.dataOut, self.id)
                             for op, optype, opId, kwargs in self.operations:
                                 if optype == 'external' and self.dataOut.error:
                                     self.publish(self.dataOut, opId)
                             if self.dataOut.error:
                                 break
                         time.sleep(0.5)
                     def runOp(self):
                         Run function for external operations (this operations just receive data
                         ex: plots, writers, publishers)
                         '''
                         while True:
                             dataOut = self.listen()
                                     self.err_queue.put('{}|{}'.format(self.name, traceback.format_exc()))
                                     dataOut.error = True
                             else:
                                 break
                     def run(self):
                         if self.typeProc is "ProcUnit":
                             if self.inputId is not None:
                                 self.subscribe()
                             self.set_publisher()
                             if 'Reader' not in BaseClass.__name__:
                                 self.runProc()
                             else:
                                 self.runReader()
                         elif self.typeProc is "Operation":
                             self.subscribe()

schainpy/model/proc/jroproc_parameters.py +1076 -1076

             import sys
             import importlib
             import itertools
             from multiprocessing import Pool, TimeoutError
             from multiprocessing.pool import ThreadPool
             import time
             from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
             from .jroproc_base import ProcessingUnit, Operation, MPDecorator
             from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
             from scipy import asarray as ar,exp
             from scipy.optimize import curve_fit
             @MPDecorator
             class ParametersProc(ProcessingUnit):
                 METHODS = {}
                 nSeconds = None
                 def __init__(self):
                     ProcessingUnit.__init__(self)
             #         self.objectDict = {}
                     self.buffer = None
                     self.firstdatatime = None
                     self.setupReq = False #Agregar a todas las unidades de proc
                 def __updateObjFromInput(self):
                     self.dataOut.inputUnit = self.dataIn.type
                     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.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.ippSeconds = self.dataIn.ippSeconds
             #        self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.timeInterval1 = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.getHeiRange()
                     self.dataOut.frequency = self.dataIn.frequency
                     # self.dataOut.noise = self.dataIn.noise
                 def run(self):
                     #----------------------    Voltage Data    ---------------------------
                     if self.dataIn.type == "Voltage":
                         self.__updateObjFromInput()
                         self.dataOut.data_pre = self.dataIn.data.copy()
                         self.dataOut.flagNoData = False
                         self.dataOut.utctimeInit = self.dataIn.utctime
                         self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds
                         return
                     #----------------------    Spectra Data    ---------------------------
                     if self.dataIn.type == "Spectra":
                         self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc)
                         self.dataOut.spc_noise = self.dataIn.getNoise()
                         self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
                         # self.dataOut.normFactor = self.dataIn.normFactor
                         self.dataOut.pairsList = self.dataIn.pairsList
                         self.dataOut.groupList = self.dataIn.pairsList
                         self.dataOut.flagNoData = False
                         if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
                             self.dataOut.ChanDist = self.dataIn.ChanDist
                         else: self.dataOut.ChanDist = None
                         #if hasattr(self.dataIn, 'VelRange'): #Velocities range
                         #    self.dataOut.VelRange = self.dataIn.VelRange
                         #else: self.dataOut.VelRange = None
                         if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
                             self.dataOut.RadarConst = self.dataIn.RadarConst
                         if hasattr(self.dataIn, 'NPW'): #NPW
                             self.dataOut.NPW = self.dataIn.NPW
                         if hasattr(self.dataIn, 'COFA'): #COFA
                             self.dataOut.COFA = self.dataIn.COFA
                     #----------------------    Correlation Data    ---------------------------
                     if self.dataIn.type == "Correlation":
                         acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
                         self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
                         self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
                         self.dataOut.groupList = (acf_pairs, ccf_pairs)
                         self.dataOut.abscissaList = self.dataIn.lagRange
                         self.dataOut.noise = self.dataIn.noise
                         self.dataOut.data_SNR = self.dataIn.SNR
                         self.dataOut.flagNoData = False
                         self.dataOut.nAvg = self.dataIn.nAvg
                     #----------------------    Parameters Data    ---------------------------
                     if self.dataIn.type == "Parameters":
                         self.dataOut.copy(self.dataIn)
                         self.dataOut.flagNoData = False
                         return True
                     self.__updateObjFromInput()
                     self.dataOut.utctimeInit = self.dataIn.utctime
                     self.dataOut.paramInterval = self.dataIn.timeInterval
                     return
             def target(tups):
                 obj, args = tups
                 return obj.FitGau(args)
             class SpectralFilters(Operation):
                 '''This class allows the Rainfall / Wind Selection for CLAIRE RADAR
                     LimitR :    It is the limit in m/s of Rainfall
                     LimitW :    It is the limit in m/s for Winds
                     Input:
                     self.dataOut.data_pre :        SPC and CSPC
                     self.dataOut.spc_range :       To select wind and rainfall velocities
                     Affected:
                     self.dataOut.data_pre :        It is used for the new SPC and CSPC ranges of wind
                     self.dataOut.spcparam_range :  Used in SpcParamPlot
                     self.dataOut.SPCparam :        Used in PrecipitationProc
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5):
                     #Limite de vientos
                     LimitR = PositiveLimit
                     LimitN = NegativeLimit
                     self.spc = dataOut.data_pre[0].copy()
                     self.cspc = dataOut.data_pre[1].copy()
                     self.Num_Hei = self.spc.shape[2]
                     self.Num_Bin = self.spc.shape[1]
                     self.Num_Chn = self.spc.shape[0]
                     VelRange = dataOut.spc_range[2]
                     TimeRange = dataOut.spc_range[1]
                     FrecRange = dataOut.spc_range[0]
                     Vmax= 2*numpy.max(dataOut.spc_range[2])
                     Tmax= 2*numpy.max(dataOut.spc_range[1])
                     Fmax= 2*numpy.max(dataOut.spc_range[0])
                     Breaker1R=VelRange[numpy.abs(VelRange-(-LimitN)).argmin()]
                     Breaker1R=numpy.where(VelRange == Breaker1R)
                     Delta = self.Num_Bin/2 - Breaker1R[0]
                     '''Reacomodando SPCrange'''
                     VelRange=numpy.roll(VelRange,-(int(self.Num_Bin/2)) ,axis=0)
                     VelRange[-(int(self.Num_Bin/2)):]+= Vmax
                     FrecRange=numpy.roll(FrecRange,-(int(self.Num_Bin/2)),axis=0)
                     FrecRange[-(int(self.Num_Bin/2)):]+= Fmax
                     TimeRange=numpy.roll(TimeRange,-(int(self.Num_Bin/2)),axis=0)
                     TimeRange[-(int(self.Num_Bin/2)):]+= Tmax
                     ''' ------------------ '''
                     Breaker2R=VelRange[numpy.abs(VelRange-(LimitR)).argmin()]
                     Breaker2R=numpy.where(VelRange == Breaker2R)
                     SPCroll = numpy.roll(self.spc,-(int(self.Num_Bin/2)) ,axis=1)
                     SPCcut = SPCroll.copy()
                     for i in range(self.Num_Chn):
                         SPCcut[i,0:int(Breaker2R[0]),:] = dataOut.noise[i]
                         SPCcut[i,-int(Delta):,:] = dataOut.noise[i]
                         SPCcut[i]=SPCcut[i]- dataOut.noise[i]
                         SPCcut[ numpy.where( SPCcut<0 ) ] = 1e-20
                         SPCroll[i]=SPCroll[i]-dataOut.noise[i]
                         SPCroll[ numpy.where( SPCroll<0 ) ] = 1e-20
                     SPC_ch1 = SPCroll
                     SPC_ch2 = SPCcut
                     SPCparam = (SPC_ch1, SPC_ch2, self.spc)
                     dataOut.SPCparam = numpy.asarray(SPCparam)
                     dataOut.spcparam_range=numpy.zeros([self.Num_Chn,self.Num_Bin+1])
                     dataOut.spcparam_range[2]=VelRange
                     dataOut.spcparam_range[1]=TimeRange
                     dataOut.spcparam_range[0]=FrecRange
                     return dataOut
             class GaussianFit(Operation):
                 '''
                     Function that fit of one and two generalized gaussians (gg) based
                     on the PSD shape across an "power band" identified from a cumsum of
                     the measured spectrum - noise.
                     Input:
                         self.dataOut.data_pre    :    SelfSpectra
                     Output:
                         self.dataOut.SPCparam :    SPC_ch1, SPC_ch2
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
                     """This routine will find a couple of generalized Gaussians to a power spectrum
                     input: spc
                     output:
                         Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise
                     """
                     self.spc = dataOut.data_pre[0].copy()
                     self.Num_Hei = self.spc.shape[2]
                     self.Num_Bin = self.spc.shape[1]
                     self.Num_Chn = self.spc.shape[0]
                     Vrange = dataOut.abscissaList
                     GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei])
                     SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch1[:] = numpy.NaN
                     SPC_ch2[:] = numpy.NaN
                     start_time = time.time()
                     noise_ = dataOut.spc_noise[0].copy()
                     pool = Pool(processes=self.Num_Chn)
                     args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)]
                     objs = [self for __ in range(self.Num_Chn)]
                     attrs = list(zip(objs, args))
                     gauSPC = pool.map(target, attrs)
                     dataOut.SPCparam = numpy.asarray(SPCparam)
                     ''' Parameters:
 . Amplitude
 . Shift
 . Width
 . Power
                            '''
                 def FitGau(self, X):
                     Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X
                     SPCparam = []
                     SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch1[:] = 0#numpy.NaN
                     SPC_ch2[:] = 0#numpy.NaN
                     for ht in range(self.Num_Hei):
                         spc =  numpy.asarray(self.spc)[ch,:,ht]
                         #############################################
                         # normalizing spc and noise
                         # This part differs from gg1
                         #spc = spc / spc_norm_max
                         pnoise = pnoise #/ spc_norm_max
                         #############################################
                         fatspectra=1.0
                         wnoise = noise_ #/ spc_norm_max
                             #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
                             #if wnoise>1.1*pnoise: # to be tested later
                             #    wnoise=pnoise
                         noisebl=wnoise*0.9;
                         noisebh=wnoise*1.1
                         spc=spc-wnoise
                         minx=numpy.argmin(spc)
                         #spcs=spc.copy()
                         spcs=numpy.roll(spc,-minx)
                         cum=numpy.cumsum(spcs)
                         tot_noise=wnoise * self.Num_Bin  #64;
                         snr = sum(spcs)/tot_noise
                         snrdB=10.*numpy.log10(snr)
                         if snrdB < SNRlimit :
                             snr = numpy.NaN
                             SPC_ch1[:,ht] = 0#numpy.NaN
                             SPC_ch1[:,ht] = 0#numpy.NaN
                             SPCparam = (SPC_ch1,SPC_ch2)
                             continue
                         #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
                         #    return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
                         cummax=max(cum);
                         epsi=0.08*fatspectra # cumsum to narrow down the energy region
                         cumlo=cummax*epsi;
                         cumhi=cummax*(1-epsi)
                         powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
                         if len(powerindex) < 1:# case for powerindex 0
                             continue
                         powerlo=powerindex[0]
                         powerhi=powerindex[-1]
                         powerwidth=powerhi-powerlo
                         firstpeak=powerlo+powerwidth/10.# first gaussian energy location
                         secondpeak=powerhi-powerwidth/10.#second gaussian energy location
                         midpeak=(firstpeak+secondpeak)/2.
                         firstamp=spcs[int(firstpeak)]
                         secondamp=spcs[int(secondpeak)]
                         midamp=spcs[int(midpeak)]
                         x=numpy.arange( self.Num_Bin )
                         y_data=spc+wnoise
                         '''    single Gaussian    '''
                         shift0=numpy.mod(midpeak+minx, self.Num_Bin )
                         width0=powerwidth/4.#Initialization entire power of spectrum divided by 4
                         state0=[shift0,width0,amplitude0,power0,wnoise]
                         bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
                         lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
-                        chiSq1=lsq1[1];
+                        chiSq1=lsq1[1];
                         if fatspectra<1.0 and powerwidth<4:
                                 choice=0
                                 Amplitude0=lsq1[0][2]
                                 noise=lsq1[0][4]
                                 #return (numpy.array([shift0,width0,Amplitude0,p0]),
                                 #        numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
                         '''    two gaussians    '''
                         #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
                         shift0=numpy.mod(firstpeak+minx, self.Num_Bin );
                         shift1=numpy.mod(secondpeak+minx, self.Num_Bin )
                         width0=powerwidth/6.;
                         width1=width0
                         power0=2.;
                         power1=power0
                         amplitude0=firstamp;
                         amplitude1=secondamp
                         state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
                         #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
                         bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
                         #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
                         lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True )
                         chiSq2=lsq2[1];
                         oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
                         if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error
                             if oneG:
                                 choice=0
                                 w1=lsq2[0][1]; w2=lsq2[0][5]
                                 a1=lsq2[0][2]; a2=lsq2[0][6]
                                 p1=lsq2[0][3]; p2=lsq2[0][7]
                                 s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1;
                                 s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2;
                                 gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling
                                 if gp1>gp2:
                                     if a1>0.7*a2:
                                         choice=1
                                     choice=numpy.argmax([a1,a2])+1
                                     #else:
                                     #choice=argmin([std2a,std2b])+1
                         else: # with low SNR go to the most energetic peak
                             choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
                         shift0=lsq2[0][0];
                         vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0])
                         shift1=lsq2[0][4];
                         vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0])
                         max_vel = 1.0
                         #first peak will be 0, second peak will be 1
                         if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range
                             shift0=lsq2[0][0]
                             width0=lsq2[0][1]
                             Amplitude0=lsq2[0][2]
                             p0=lsq2[0][3]
                             shift1=lsq2[0][4]
                             width1=lsq2[0][5]
                             Amplitude1=lsq2[0][6]
                             p1=lsq2[0][7]
                             noise=lsq2[0][8]
                         else:
                             shift1=lsq2[0][0]
                             width1=lsq2[0][1]
                             Amplitude1=lsq2[0][2]
                             p1=lsq2[0][3]
                             shift0=lsq2[0][4]
                             width0=lsq2[0][5]
                             Amplitude0=lsq2[0][6]
                             p0=lsq2[0][7]
                             noise=lsq2[0][8]
                         if Amplitude0<0.05: # in case the peak is noise
                             shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN]
                         if Amplitude1<0.05:
                             shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN]
                         SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0
                         SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1
                         SPCparam = (SPC_ch1,SPC_ch2)
                     return GauSPC
                 def y_model1(self,x,state):
                     shift0,width0,amplitude0,power0,noise=state
                     model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
                     model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
                     model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
                     return model0+model0u+model0d+noise
                 def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
                     shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state
                     model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
                     model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
                     model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
                     model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1)
                     model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1)
                     model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1)
                     return model0+model0u+model0d+model1+model1u+model1d+noise
                 def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is.
                     return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
                 def misfit2(self,state,y_data,x,num_intg):
                     return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
             class PrecipitationProc(Operation):
                 '''
                      Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
                      Input:
                         self.dataOut.data_pre    :    SelfSpectra
                      Output:
                         self.dataOut.data_output :    Reflectivity factor, rainfall Rate
                      Parameters affected:
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def gaus(self,xSamples,Amp,Mu,Sigma):
                     return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) ))
                 def Moments(self, ySamples, xSamples):
                     Pot = numpy.nansum( ySamples )                              # Potencia, momento 0
                     yNorm = ySamples / Pot
                     Vr = numpy.nansum( yNorm * xSamples )                       # Velocidad radial, mu, corrimiento doppler, primer momento
                     Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 ))  # Segundo Momento
                     Desv = Sigma2**0.5                                          # Desv. Estandar, Ancho espectral
                     return numpy.array([Pot, Vr, Desv])
                 def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118,
                         tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km = 0.93, Altitude=3350):
                     Velrange = dataOut.spcparam_range[2]
                     FrecRange = dataOut.spcparam_range[0]
                     dV= Velrange[1]-Velrange[0]
                     dF= FrecRange[1]-FrecRange[0]
                     if radar == "MIRA35C" :
                         self.spc = dataOut.data_pre[0].copy()
                         self.Num_Hei = self.spc.shape[2]
                         self.Num_Bin = self.spc.shape[1]
                         self.Num_Chn = self.spc.shape[0]
                         Ze = self.dBZeMODE2(dataOut)
                     else:
                         self.spc = dataOut.SPCparam[1].copy() #dataOut.data_pre[0].copy() #
                         """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX"""
                         self.spc[:,:,0:7]= numpy.NaN
                         """##########################################"""
                         self.Num_Hei = self.spc.shape[2]
                         self.Num_Bin = self.spc.shape[1]
                         self.Num_Chn = self.spc.shape[0]
                         ''' Se obtiene la constante del RADAR '''
                         self.Pt = Pt
                         self.Gt = Gt
                         self.Gr = Gr
                         self.tauW = tauW
                         self.ThetaT = ThetaT
                         self.ThetaR = ThetaR
                         Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
                         Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR)
                         RadarConstant = 10e-26 * Numerator / Denominator #
                         ''' =============================  '''
                         self.spc[0] = (self.spc[0]-dataOut.noise[0])
                         self.spc[1] = (self.spc[1]-dataOut.noise[1])
                         self.spc[2] = (self.spc[2]-dataOut.noise[2])
                         self.spc[ numpy.where(self.spc < 0)] = 0
                         SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise))
                         SPCmean[ numpy.where(SPCmean < 0)] = 0
                         ETAn = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         ETAv = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         ETAd = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         Pr = SPCmean[:,:]
                         VelMeteoro = numpy.mean(SPCmean,axis=0)
                         D_range = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         SIGMA = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         N_dist = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         Z = numpy.zeros(self.Num_Hei)
                         Ze = numpy.zeros(self.Num_Hei)
                         RR = numpy.zeros(self.Num_Hei)
                         Range = dataOut.heightList*1000.
                         for R in range(self.Num_Hei):
                             h = Range[R] + Altitude #Range from ground to radar pulse altitude
                             del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2    #Density change correction for velocity
                             D_range[:,R] = numpy.log( (9.65 - (Velrange[0:self.Num_Bin] / del_V[R])) / 10.3 ) / -0.6 #Diameter range [m]x10**-3
                             '''NOTA: ETA(n) dn = ETA(f) df
                                 dn = 1     Diferencial de muestreo
                                 df = ETA(n) / ETA(f)
                             '''
                             ETAn[:,R] = RadarConstant * Pr[:,R] * (Range[R] )**2 #Reflectivity (ETA)
                             ETAv[:,R]=ETAn[:,R]/dV
                             ETAd[:,R]=ETAv[:,R]*6.18*exp(-0.6*D_range[:,R])
                             SIGMA[:,R] = Km * (D_range[:,R] * 1e-3 )**6 * numpy.pi**5 / Lambda**4      #Equivalent Section of drops (sigma)
                             N_dist[:,R] = ETAn[:,R] / SIGMA[:,R]
                             DMoments = self.Moments(Pr[:,R], Velrange[0:self.Num_Bin])
                             try:
                                 popt01,pcov = curve_fit(self.gaus, Velrange[0:self.Num_Bin] , Pr[:,R] , p0=DMoments)
                             except:
                                 popt01=numpy.zeros(3)
                                 popt01[1]= DMoments[1]
                             if popt01[1]<0 or popt01[1]>20:
                                 popt01[1]=numpy.NaN
                             V_mean[R]=popt01[1]
                             Z[R] = numpy.nansum( N_dist[:,R] * (D_range[:,R])**6 )#*10**-18
                             RR[R] = 0.0006*numpy.pi * numpy.nansum( D_range[:,R]**3 * N_dist[:,R] * Velrange[0:self.Num_Bin] ) #Rainfall rate
                             Ze[R] = (numpy.nansum( ETAn[:,R]) * Lambda**4) / ( 10**-18*numpy.pi**5 * Km)
                     RR2 = (Z/200)**(1/1.6)
                     dBRR = 10*numpy.log10(RR)
                     dBRR2 = 10*numpy.log10(RR2)
                     dBZe = 10*numpy.log10(Ze)
                     dBZ = 10*numpy.log10(Z)
                     dataOut.data_output = RR[8]
                     dataOut.data_param = numpy.ones([3,self.Num_Hei])
                     dataOut.channelList = [0,1,2]
                     dataOut.data_param[0]=dBZ
                     dataOut.data_param[1]=V_mean
                     dataOut.data_param[2]=RR
                     return dataOut
                 def dBZeMODE2(self, dataOut): #    Processing for MIRA35C
                     NPW = dataOut.NPW
                     COFA = dataOut.COFA
                     SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
                     RadarConst = dataOut.RadarConst
                     #frequency = 34.85*10**9
                     ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
                     data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
                     ETA = numpy.sum(SNR,1)
                     ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN)
                     Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
                     for r in range(self.Num_Hei):
                         Ze[0,r] =  ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
                         #Ze[1,r] =  ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
                     return Ze
             #     def GetRadarConstant(self):
+            #
             #         """
             #         Constants:
+            #
             #         Pt:     Transmission Power               dB        5kW                5000
             #         Gt:     Transmission Gain                dB        24.7 dB            295.1209
             #         Gr:     Reception Gain                   dB        18.5 dB            70.7945
             #         tauW:   Width of transmission pulse      s         4us                4e-6
             #         ThetaT: Transmission antenna bean angle  rad       0.1656317 rad      0.1656317
             #         ThetaR: Reception antenna beam angle     rad       0.36774087 rad     0.36774087
+            #
             #         """
+            #
             #         Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
             #         Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
             #         RadarConstant =  Numerator / Denominator
+            #
             #         return RadarConstant
             class FullSpectralAnalysis(Operation):
                 """
                     Function that implements Full Spectral Analisys technique.
                     Input:
                         self.dataOut.data_pre    :    SelfSpectra and CrossSPectra data
                         self.dataOut.groupList   :    Pairlist of channels
                         self.dataOut.ChanDist    :    Physical distance between receivers
                     Output:
                         self.dataOut.data_output :    Zonal wind, Meridional wind and Vertical wind
                     Parameters affected:    Winds, height range, SNR
                 """
                 def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7):
                     self.indice=int(numpy.random.rand()*1000)
                     spc = dataOut.data_pre[0].copy()
                     cspc = dataOut.data_pre[1]
                     """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX"""
                     SNRspc = spc.copy()
                     SNRspc[:,:,0:7]= numpy.NaN
                     """##########################################"""
                     nChannel = spc.shape[0]
                     nProfiles = spc.shape[1]
                     nHeights = spc.shape[2]
                     pairsList = dataOut.groupList
                     if dataOut.ChanDist is not None :
                         ChanDist = dataOut.ChanDist
                     else:
                         ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]])
                     FrecRange = dataOut.spc_range[0]
                     ySamples=numpy.ones([nChannel,nProfiles])
                     phase=numpy.ones([nChannel,nProfiles])
                     CSPCSamples=numpy.ones([nChannel,nProfiles],dtype=numpy.complex_)
                     PhaseSlope=numpy.ones(nChannel)
                     PhaseInter=numpy.ones(nChannel)
                     data_SNR=numpy.zeros([nProfiles])
                     data = dataOut.data_pre
                     noise = dataOut.noise
                     dataOut.data_SNR = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0]
                     dataOut.data_SNR[numpy.where( dataOut.data_SNR <0 )] = 1e-20
                     data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN
                     velocityX=[]
                     velocityY=[]
                     velocityV=[]
                     PhaseLine=[]
                     dbSNR = 10*numpy.log10(dataOut.data_SNR)
                     dbSNR = numpy.average(dbSNR,0)
                     for Height in range(nHeights):
                         [Vzon,Vmer,Vver, GaussCenter, PhaseSlope, FitGaussCSPC]= self.WindEstimation(spc, cspc, pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit)
                         PhaseLine = numpy.append(PhaseLine, PhaseSlope)
                         if abs(Vzon)<100. and abs(Vzon)> 0.:
                             velocityX=numpy.append(velocityX, Vzon)#Vmag
                         else:
                             velocityX=numpy.append(velocityX, numpy.NaN)
                         if abs(Vmer)<100. and abs(Vmer) > 0.:
                             velocityY=numpy.append(velocityY, -Vmer)#Vang
                         else:
                             velocityY=numpy.append(velocityY, numpy.NaN)
                         if dbSNR[Height] > SNRlimit:
                             velocityV=numpy.append(velocityV, -Vver)#FirstMoment[Height])
                         else:
                             velocityV=numpy.append(velocityV, numpy.NaN)
                     '''Nota: Cambiar el signo de numpy.array(velocityX) cuando se intente procesar datos de BLTR'''
                     data_output[0] = numpy.array(velocityX)  #self.moving_average(numpy.array(velocityX) , N=1)
                     data_output[1] = numpy.array(velocityY)  #self.moving_average(numpy.array(velocityY) , N=1)
                     data_output[2] = velocityV#FirstMoment
                     xFrec=FrecRange[0:spc.shape[1]]
                     dataOut.data_output=data_output
                     return dataOut
                 def moving_average(self,x, N=2):
                     return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
                 def gaus(self,xSamples,Amp,Mu,Sigma):
                     return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) ))
                 def Moments(self, ySamples, xSamples):
                     Pot = numpy.nansum( ySamples )                              # Potencia, momento 0
                     yNorm = ySamples / Pot
                     Vr = numpy.nansum( yNorm * xSamples )                       # Velocidad radial, mu, corrimiento doppler, primer momento
                     Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 ))  # Segundo Momento
                     Desv = Sigma2**0.5                                          # Desv. Estandar, Ancho espectral
                     return numpy.array([Pot, Vr, Desv])
                 def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit):
                     ySamples=numpy.ones([spc.shape[0],spc.shape[1]])
                     phase=numpy.ones([spc.shape[0],spc.shape[1]])
                     CSPCSamples=numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_)
                     xVel =AbbsisaRange[2][0:spc.shape[1]]
                     Vv=numpy.empty(spc.shape[2])*0
                     SPCav = numpy.average(spc, axis=0)-numpy.average(noise) #spc[0]-noise[0]#
                     SPCmoments = self.Moments(SPCav[:,Height], xVel )
                     CSPCmoments = []
                     cspcNoise = numpy.empty(3)
                     '''Getting Eij and Nij'''
                     Xi01=ChanDist[0][0]
                     Eta01=ChanDist[0][1]
                     Xi02=ChanDist[1][0]
                     Eta02=ChanDist[1][1]
                     Xi12=ChanDist[2][0]
                     Eta12=ChanDist[2][1]
                     z = spc.copy()
                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     for i in range(spc.shape[0]):
                         '''****** Line of Data SPC ******'''
                         zline=z[i,:,Height].copy() - noise[i]   # Se resta ruido
                         '''****** SPC is normalized ******'''
                         SmoothSPC =self.moving_average(zline.copy(),N=1)     # Se suaviza el ruido
                         FactNorm = SmoothSPC/numpy.nansum(SmoothSPC)         # SPC Normalizado y suavizado
                         xSamples = xFrec                   # Se toma el rango de frecuncias
                         ySamples[i] = FactNorm             # Se toman los valores de SPC normalizado
                     for i in range(spc.shape[0]):
                         '''****** Line of Data CSPC ******'''
                         cspcLine = ( cspc[i,:,Height].copy())# - noise[i] )     # no! Se resta el ruido
                         SmoothCSPC =self.moving_average(cspcLine,N=1)         # Se suaviza el ruido
                         cspcNorm = SmoothCSPC/numpy.nansum(SmoothCSPC)        # CSPC normalizado y suavizado
                         '''****** CSPC is normalized with respect to Briggs and Vincent ******'''
                         chan_index0 = pairsList[i][0]
                         chan_index1 = pairsList[i][1]
                         CSPCFactor= numpy.abs(numpy.nansum(ySamples[chan_index0]))**2  *  numpy.abs(numpy.nansum(ySamples[chan_index1]))**2
                         CSPCNorm = cspcNorm / numpy.sqrt(CSPCFactor)
                         CSPCSamples[i] = CSPCNorm
                         coherence[i] = numpy.abs(CSPCSamples[i]) / numpy.sqrt(CSPCFactor)
                         #coherence[i]= self.moving_average(coherence[i],N=1)
                         phase[i] = self.moving_average( numpy.arctan2(CSPCSamples[i].imag, CSPCSamples[i].real),N=1)#*180/numpy.pi
                     CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPCSamples[0]), xSamples),
                                                 self.Moments(numpy.abs(CSPCSamples[1]), xSamples),
                                                 self.Moments(numpy.abs(CSPCSamples[2]), xSamples)])
                     popt=[1e-10,0,1e-10]
                     popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10]
                     FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0
                     CSPCMask01 = numpy.abs(CSPCSamples[0])
                     CSPCMask02 = numpy.abs(CSPCSamples[1])
                     CSPCMask12 = numpy.abs(CSPCSamples[2])
                     mask01 = ~numpy.isnan(CSPCMask01)
                     mask02 = ~numpy.isnan(CSPCMask02)
                     mask12 = ~numpy.isnan(CSPCMask12)
                     #mask = ~numpy.isnan(CSPCMask01)
                     CSPCMask01 = CSPCMask01[mask01]
                     CSPCMask02 = CSPCMask02[mask02]
                     CSPCMask12 = CSPCMask12[mask12]
                     #CSPCMask01 = numpy.ma.masked_invalid(CSPCMask01)
                     '''***Fit Gauss CSPC01***'''
                     if dbSNR > SNRlimit and numpy.abs(SPCmoments[1])<3 :
                         try:
                             FitGauss01=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[0]))
                             FitGauss02=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[1]))
                             FitGauss12=numpy.ones(len(xSamples))*numpy.mean(numpy.abs(CSPCSamples[2]))
                     CSPCopt = numpy.vstack([popt01,popt02,popt12])
                     '''****** Getting fij width ******'''
                     yMean = numpy.average(ySamples, axis=0)  # ySamples[0]
                     '''******* Getting fitting Gaussian *******'''
                     meanGauss = sum(xSamples*yMean) / len(xSamples)              # Mu, velocidad radial (frecuencia)
                     sigma2 = sum(yMean*(xSamples-meanGauss)**2) / len(xSamples)  # Varianza, Ancho espectral (frecuencia)
                     yMoments = self.Moments(yMean, xSamples)
                     if dbSNR > SNRlimit and numpy.abs(SPCmoments[1])<3: # and abs(meanGauss/sigma2) > 0.00001:
                         try:
                             popt,pcov = curve_fit(self.gaus,xSamples,yMean,p0=yMoments)
                             FitGauss=self.gaus(xSamples,*popt)
                         except :#RuntimeError:
                             FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
                     else:
                         FitGauss=numpy.ones(len(xSamples))*numpy.mean(yMean)
                     '''****** Getting Fij ******'''
                     Fijcspc = CSPCopt[:,2]/2*3
                     GaussCenter = popt[1] #xFrec[GCpos]
                     #Punto en Eje X de la Gaussiana donde se encuentra el centro
                     ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()]
                     PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0]
                     #Punto e^-1 hubicado en la Gaussiana
                     PeMinus1 = numpy.max(FitGauss)* numpy.exp(-1)
                     FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss"
                     PointFij = numpy.where(FitGauss==FijClosest)[0][0]
                     if xSamples[PointFij] > xSamples[PointGauCenter]:
                         Fij = xSamples[PointFij] - xSamples[PointGauCenter]
                     else:
                         Fij = xSamples[PointGauCenter] - xSamples[PointFij]
                     '''****** Taking frequency ranges from SPCs ******'''
                     #GaussCenter = popt[1]     #Primer momento 01
                     GauWidth = popt[2] *3/2        #Ancho de banda de Gau01
                     Range = numpy.empty(2)
                     Range[0] = GaussCenter - GauWidth
                     Range[1] = GaussCenter + GauWidth
                     #Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max)
                     ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()]
                     ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()]
                     PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0]
                     PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0]
                     Range=numpy.array([ PointRangeMin, PointRangeMax ])
                     FrecRange = xFrec[ Range[0] : Range[1] ]
                     VelRange  = xVel[ Range[0] : Range[1] ]
                     '''****** Getting SCPC Slope ******'''
                     for i in range(spc.shape[0]):
                         if len(FrecRange)>5 and len(FrecRange)<spc.shape[1]*0.3:
                             PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=3)
                             '''***********************VelRange******************'''
                             mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange)
                             if len(FrecRange) == len(PhaseRange):
                                 try:
                                     slope, intercept, r_value, p_value, std_err = stats.linregress(FrecRange[mask], PhaseRange[mask])
                         else:
                             PhaseSlope[i]=0
                             PhaseInter[i]=0
                         '''Getting constant C'''
                         cC=(Fij*numpy.pi)**2
                         '''****** Getting constants F and G ******'''
                         MijEijNij=numpy.array([[Xi02,Eta02], [Xi12,Eta12]])
                         MijResult0=(-PhaseSlope[1]*cC) / (2*numpy.pi)
                         MijResult1=(-PhaseSlope[2]*cC) / (2*numpy.pi)
                         MijResults=numpy.array([MijResult0,MijResult1])
                         (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
                         '''****** Getting constants A, B and H ******'''
                         W01=numpy.nanmax( FitGauss01 ) #numpy.abs(CSPCSamples[0]))
                         W02=numpy.nanmax( FitGauss02 ) #numpy.abs(CSPCSamples[1]))
                         W12=numpy.nanmax( FitGauss12 ) #numpy.abs(CSPCSamples[2]))
                         WijResult0=((cF*Xi01+cG*Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi/cC))
                         WijResult1=((cF*Xi02+cG*Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi/cC))
                         WijResult2=((cF*Xi12+cG*Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi/cC))
                         WijResults=numpy.array([WijResult0, WijResult1, WijResult2])
                         WijEijNij=numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ])
                         (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
                         VxVy=numpy.array([[cA,cH],[cH,cB]])
                         VxVyResults=numpy.array([-cF,-cG])
                         (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults)
                         Vzon = Vy
                         Vmer = Vx
                         Vmag=numpy.sqrt(Vzon**2+Vmer**2)
                         else:
                             Vver=numpy.NaN
                         FitGaussCSPC = numpy.array([FitGauss01,FitGauss02,FitGauss12])
                     return Vzon, Vmer, Vver, GaussCenter, PhaseSlope, FitGaussCSPC
             class SpectralMoments(Operation):
                 '''
                     Function SpectralMoments()
                     Calculates moments (power, mean, standard deviation) and SNR of the signal
                     Type of dataIn:    Spectra
                     Configuration Parameters:
                         dirCosx    :     Cosine director in X axis
                         dirCosy    :     Cosine director in Y axis
                         elevation  :
                         azimuth    :
                     Input:
                         channelList    :    simple channel list to select e.g. [2,3,7]
                         self.dataOut.data_pre        :    Spectral data
                         self.dataOut.abscissaList    :    List of frequencies
                         self.dataOut.noise           :    Noise level per channel
                     Affected:
                         self.dataOut.moments        :    Parameters per channel
                         self.dataOut.data_SNR       :    SNR per channel
                 '''
                 def run(self, dataOut):
                     #dataOut.data_pre = dataOut.data_pre[0]
                     data = dataOut.data_pre[0]
                     absc = dataOut.abscissaList[:-1]
                     noise = dataOut.noise
                     nChannel = data.shape[0]
                     data_param = numpy.zeros((nChannel, 4, data.shape[2]))
                     for ind in range(nChannel):
                         data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
                     dataOut.moments = data_param[:,1:,:]
                     dataOut.data_SNR = data_param[:,0]
                     dataOut.data_POW = data_param[:,1]
                     dataOut.data_DOP = data_param[:,2]
                     dataOut.data_WIDTH = data_param[:,3]
                     return dataOut
                 def __calculateMoments(self, oldspec, oldfreq, n0,
                                        nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
                     if (nicoh is None): nicoh = 1
                     if (graph is None): graph = 0
                     if (smooth is None): smooth = 0
                     elif (self.smooth < 3): smooth = 0
                     if (aliasing is None): aliasing = 0
                     if (oldfd is None): oldfd = 0
                     if (wwauto is None): wwauto = 0
                     if (n0 < 1.e-20):   n0 = 1.e-20
                     freq = oldfreq
                     vec_power = numpy.zeros(oldspec.shape[1])
                     vec_fd = numpy.zeros(oldspec.shape[1])
                     vec_w = numpy.zeros(oldspec.shape[1])
                     vec_snr = numpy.zeros(oldspec.shape[1])
                     oldspec = numpy.ma.masked_invalid(oldspec)
                     for ind in range(oldspec.shape[1]):
                         spec = oldspec[:,ind]
                         aux = spec*fwindow
                         max_spec = aux.max()
                         m = list(aux).index(max_spec)
                         #Smooth
                         if (smooth == 0):   spec2 = spec
                         else:   spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
                         #    Calculo de Momentos
                         bb = spec2[list(range(m,spec2.size))]
                         bb = (bb<n0).nonzero()
                         bb = bb[0]
                         ss = spec2[list(range(0,m + 1))]
                         ss = (ss<n0).nonzero()
                         ss = ss[0]
                         if (bb.size == 0):
                             bb0 = spec.size - 1 - m
                         else:
                             bb0 = bb[0] - 1
                             if (bb0 < 0):
                                 bb0 = 0
                         if (ss.size == 0):   ss1 = 1
                         else: ss1 = max(ss) + 1
                         if (ss1 > m):   ss1 = m
                         valid = numpy.asarray(list(range(int(m + bb0 - ss1 + 1)))) + ss1
                         power = ((spec2[valid] - n0)*fwindow[valid]).sum()
                         fd = ((spec2[valid]- n0)*freq[valid]*fwindow[valid]).sum()/power
                         w = math.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum()/power)
                         snr = (spec2.mean()-n0)/n0
                         if (snr < 1.e-20) :
                             snr = 1.e-20
                         vec_power[ind] = power
                         vec_fd[ind] = fd
                         vec_w[ind] = w
                         vec_snr[ind] = snr
                     moments = numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
                     return moments
                 #------------------    Get SA Parameters    --------------------------
                 def GetSAParameters(self):
                     #SA en frecuencia
                     pairslist = self.dataOut.groupList
                     num_pairs = len(pairslist)
                     vel = self.dataOut.abscissaList
                     spectra = self.dataOut.data_pre
                     cspectra = self.dataIn.data_cspc
                     delta_v = vel[1] - vel[0]
                     #Calculating the power spectrum
                     spc_pow = numpy.sum(spectra, 3)*delta_v
                     #Normalizing Spectra
                     norm_spectra = spectra/spc_pow
                     #Calculating the norm_spectra at peak
                     max_spectra = numpy.max(norm_spectra, 3)
                     #Normalizing Cross Spectra
                     norm_cspectra = numpy.zeros(cspectra.shape)
                     for i in range(num_chan):
                         norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
                     max_cspectra = numpy.max(norm_cspectra,2)
                     max_cspectra_index = numpy.argmax(norm_cspectra, 2)
                     for i in range(num_pairs):
                         cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
                 #-------------------    Get Lags    ----------------------------------
             class SALags(Operation):
                 '''
                 Function GetMoments()
                     self.dataOut.data_SNR
                     self.dataOut.groupList
                     self.dataOut.nChannels
                 Affected:
                     self.dataOut.data_param
                 '''
                 def run(self, dataOut):
                     data_acf = dataOut.data_pre[0]
                     data_ccf = dataOut.data_pre[1]
                     normFactor_acf = dataOut.normFactor[0]
                     normFactor_ccf = dataOut.normFactor[1]
                     pairs_acf = dataOut.groupList[0]
                     pairs_ccf = dataOut.groupList[1]
                     nHeights = dataOut.nHeights
                     absc = dataOut.abscissaList
                     noise = dataOut.noise
                     for l in range(len(pairs_acf)):
                         data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
                     for l in range(len(pairs_ccf)):
                         data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
                     dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
                     dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
                     dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
                     return
             #     def __getPairsAutoCorr(self, pairsList, nChannels):
+            #
             #         pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
+            #
             #         for l in range(len(pairsList)):
             #             firstChannel = pairsList[l][0]
             #             secondChannel = pairsList[l][1]
+            #
             #             #Obteniendo pares de Autocorrelacion
             #             if firstChannel == secondChannel:
             #                 pairsAutoCorr[firstChannel] = int(l)
+            #
             #         pairsAutoCorr = pairsAutoCorr.astype(int)
+            #
             #         pairsCrossCorr = range(len(pairsList))
             #         pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
+            #
             #         return pairsAutoCorr, pairsCrossCorr
                 def __calculateTaus(self, data_acf, data_ccf, lagRange):
                     lag0 = data_acf.shape[1]/2
                     #Funcion de Autocorrelacion
                     mean_acf = stats.nanmean(data_acf, axis = 0)
                     #Obtencion Indice de TauCross
                     ind_ccf = data_ccf.argmax(axis = 1)
                     #Obtencion Indice de TauAuto
                     ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
                     ccf_lag0 = data_ccf[:,lag0,:]
                     for i in range(ccf_lag0.shape[0]):
                         ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
                     #Obtencion de TauCross y TauAuto
                     tau_ccf = lagRange[ind_ccf]
                     tau_acf  = lagRange[ind_acf]
                     Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
                     tau_ccf[Nan1,Nan2] = numpy.nan
                     tau_acf[Nan1,Nan2] = numpy.nan
                     tau = numpy.vstack((tau_ccf,tau_acf))
                     return tau
                 def __calculateLag1Phase(self, data, lagTRange):
                     data1 = stats.nanmean(data, axis = 0)
                     lag1 = numpy.where(lagTRange == 0)[0][0] + 1
                     phase = numpy.angle(data1[lag1,:])
                     return phase
             class SpectralFitting(Operation):
                 '''
                     Function GetMoments()
                     Input:
                     Output:
                     Variables modified:
                 '''
                 def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
                     if path != None:
                         sys.path.append(path)
                     self.dataOut.library = importlib.import_module(file)
                     #To be inserted as a parameter
                     groupArray = numpy.array(groupList)
             #         groupArray = numpy.array([[0,1],[2,3]])
                     self.dataOut.groupList = groupArray
                     nGroups = groupArray.shape[0]
                     nChannels = self.dataIn.nChannels
                     nHeights=self.dataIn.heightList.size
                     #Parameters Array
                     self.dataOut.data_param = None
                     #Set constants
                     constants = self.dataOut.library.setConstants(self.dataIn)
                     self.dataOut.constants = constants
                     ippSeconds = self.dataIn.ippSeconds
                     K = self.dataIn.nIncohInt
                     pairsArray = numpy.array(self.dataIn.pairsList)
                     #List of possible combinations
                     listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
                     indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
                     if getSNR:
                         listChannels = groupArray.reshape((groupArray.size))
                         listChannels.sort()
                         noise = self.dataIn.getNoise()
                         self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
                     for i in range(nGroups):
                         coord = groupArray[i,:]
                         #Input data array
                         data = self.dataIn.data_spc[coord,:,:]/(M*N)
                         data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
                         #Cross Spectra data array for Covariance Matrixes
                         ind = 0
                         for pairs in listComb:
                             ind += 1
                         dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
                         dataCross = dataCross**2/K
                         for h in range(nHeights):
                             #Input
                             d = data[:,h]
                             ind = 0
                             for pairs in listComb:
                                 #Coordinates in Covariance Matrix
                                 x = pairs[0]
                                 y = pairs[1]
                                 #Channel Index
                                 S12 = dataCross[ind,:,h]
                             LT=L.T
                             dp = numpy.dot(LT,d)
                             #Initial values
                             data_spc = self.dataIn.data_spc[coord,:,h]
                             if (h>0)and(error1[3]<5):
                                 p0 = self.dataOut.data_param[i,:,h-1]
                             else:
                                 p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
                             try:
                                 #Least Squares
                                 minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
                                 minp = p0*numpy.nan
                                 error0 = numpy.nan
                                 error1 = p0*numpy.nan
                             #Save
                             if self.dataOut.data_param is None:
                                 self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
                                 self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
                             self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
                             self.dataOut.data_param[i,:,h] = minp
                     return
                 def __residFunction(self, p, dp, LT, constants):
                     fm = self.dataOut.library.modelFunction(p, constants)
                     fmp=numpy.dot(LT,fm)
                     return  dp-fmp
                 def __getSNR(self, z, noise):
                     avg = numpy.average(z, axis=1)
                     SNR = (avg.T-noise)/noise
                     SNR = SNR.T
                     return SNR
                 def __chisq(p,chindex,hindex):
                     #similar to Resid but calculates CHI**2
                     [LT,d,fm]=setupLTdfm(p,chindex,hindex)
                     fmp=numpy.dot(LT,fm)
                     chisq=numpy.dot((dp-fmp).T,(dp-fmp))
                     return chisq
             class WindProfiler(Operation):
                 __isConfig = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __dataReady = False
                 __firstdata = None
                 n = None
                 def __init__(self):
                     Operation.__init__(self)
                 def __calculateCosDir(self, elev, azim):
                     zen = (90 - elev)*numpy.pi/180
                     azim = azim*numpy.pi/180
                     cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
                     cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
                     signX = numpy.sign(numpy.cos(azim))
                     signY = numpy.sign(numpy.sin(azim))
                     cosDirX = numpy.copysign(cosDirX, signX)
                     cosDirY = numpy.copysign(cosDirY, signY)
                     return cosDirX, cosDirY
                 def __calculateAngles(self, theta_x, theta_y, azimuth):
                     dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
                     zenith_arr = numpy.arccos(dir_cosw)
                     azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
                     dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
                     dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
                     return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
                 def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
+            #
                     if horOnly:
                         A = numpy.c_[dir_cosu,dir_cosv]
                     else:
                     listPhi = phi.tolist()
                     maxid = listPhi.index(max(listPhi))
                     minid = listPhi.index(min(listPhi))
                     rango = list(range(len(phi)))
                #     rango = numpy.delete(rango,maxid)
                     heiRang1 = heiRang*math.cos(phi[maxid])
                     heiRangAux = heiRang*math.cos(phi[minid])
                     indOut = (heiRang1 < heiRangAux[0]).nonzero()
                     heiRang1 = numpy.delete(heiRang1,indOut)
                     velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
                     SNR1 = numpy.zeros([len(phi),len(heiRang1)])
                     for i in rango:
                         x = heiRang*math.cos(phi[i])
                         y1 = velRadial[i,:]
                         f1 = interpolate.interp1d(x,y1,kind = 'cubic')
                         x1 = heiRang1
                         y11 = f1(x1)
                         y2 = SNR[i,:]
                         f2 = interpolate.interp1d(x,y2,kind = 'cubic')
                         y21 = f2(x1)
                         velRadial1[i,:] = y11
                         SNR1[i,:] = y21
                     return heiRang1, velRadial1, SNR1
                 def __calculateVelUVW(self, A, velRadial):
                     #Operacion Matricial
             #         velUVW = numpy.zeros((velRadial.shape[1],3))
             #         for ind in range(velRadial.shape[1]):
             #         velUVW = velUVW.transpose()
                     velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
                     velUVW[:,:] = numpy.dot(A,velRadial)
                     return velUVW
             #     def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
                 def techniqueDBS(self, kwargs):
                     """
                     Function that implements Doppler Beam Swinging (DBS) technique.
                     Input:    Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
                                 Direction correction (if necessary), Ranges and SNR
                     Output:    Winds estimation (Zonal, Meridional and Vertical)
                     Parameters affected:    Winds, height range, SNR
                     """
                     velRadial0 = kwargs['velRadial']
                     heiRang = kwargs['heightList']
                     SNR0 = kwargs['SNR']
                     if 'dirCosx' in kwargs and 'dirCosy' in kwargs:
                         theta_x = numpy.array(kwargs['dirCosx'])
                         theta_y = numpy.array(kwargs['dirCosy'])
                         elev = numpy.array(kwargs['elevation'])
                         azim = numpy.array(kwargs['azimuth'])
                         theta_x, theta_y = self.__calculateCosDir(elev, azim)
                     azimuth = kwargs['correctAzimuth']
                     if 'horizontalOnly' in kwargs:
                         horizontalOnly = kwargs['horizontalOnly']
                     else:   horizontalOnly = False
                         param = param[arrayChannel,:,:]
                         theta_x = theta_x[arrayChannel]
                         theta_y = theta_y[arrayChannel]
                     azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
                     heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
                     A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
                     #Calculo de Componentes de la velocidad con DBS
                     winds = self.__calculateVelUVW(A,velRadial1)
                     return winds, heiRang1, SNR1
                 def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
                     nPairs = len(pairs_ccf)
                     posx = numpy.asarray(posx)
                     posy = numpy.asarray(posy)
                     #Rotacion Inversa para alinear con el azimuth
                     if azimuth!= None:
                         azimuth = azimuth*math.pi/180
                     else:
                         posx1 = posx
                         posy1 = posy
                     #Calculo de Distancias
                     distx = numpy.zeros(nPairs)
                     disty = numpy.zeros(nPairs)
                     dist = numpy.zeros(nPairs)
                     ang = numpy.zeros(nPairs)
                     for i in range(nPairs):
                         distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
                         disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
                         dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
                         ang[i] = numpy.arctan2(disty[i],distx[i])
                     return distx, disty, dist, ang
                     #Calculo de Matrices
             #         nPairs = len(pairs)
             #         ang1 = numpy.zeros((nPairs, 2, 1))
             #         dist1 = numpy.zeros((nPairs, 2, 1))
+            #
             #         for j in range(nPairs):
             #             dist1[j,0,0] = dist[pairs[j][0]]
             #             dist1[j,1,0] = dist[pairs[j][1]]
             #             ang1[j,0,0] = ang[pairs[j][0]]
             #             ang1[j,1,0] = ang[pairs[j][1]]
+            #
             #         return distx,disty, dist1,ang1
                 def __calculateVelVer(self, phase, lagTRange, _lambda):
                     Ts = lagTRange[1] - lagTRange[0]
                     velW = -_lambda*phase/(4*math.pi*Ts)
                     return velW
                 def __calculateVelHorDir(self, dist, tau1, tau2, ang):
                     nPairs = tau1.shape[0]
                     nHeights = tau1.shape[1]
                     vel = numpy.zeros((nPairs,3,nHeights))
                     dist1 = numpy.reshape(dist, (dist.size,1))
                     angCos = numpy.cos(ang)
                     angSin = numpy.sin(ang)
                     vel0 = dist1*tau1/(2*tau2**2)
                     vel[:,0,:] = (vel0*angCos).sum(axis = 1)
                     vel[:,1,:] = (vel0*angSin).sum(axis = 1)
                     ind = numpy.where(numpy.isinf(vel))
                     vel[ind] = numpy.nan
                     return vel
             #     def __getPairsAutoCorr(self, pairsList, nChannels):
+            #
             #         pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
+            #
             #         for l in range(len(pairsList)):
             #             firstChannel = pairsList[l][0]
             #             secondChannel = pairsList[l][1]
+            #
             #             #Obteniendo pares de Autocorrelacion
             #             if firstChannel == secondChannel:
             #                 pairsAutoCorr[firstChannel] = int(l)
+            #
             #         pairsAutoCorr = pairsAutoCorr.astype(int)
+            #
             #         pairsCrossCorr = range(len(pairsList))
             #         pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
+            #
             #         return pairsAutoCorr, pairsCrossCorr
             #     def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
                 def techniqueSA(self, kwargs):
                     """
                     Function that implements Spaced Antenna (SA) technique.
                     Input:    Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
                                 Direction correction (if necessary), Ranges and SNR
                     Output:    Winds estimation (Zonal, Meridional and Vertical)
                     Parameters affected:    Winds
                     """
                     position_x = kwargs['positionX']
                     position_y = kwargs['positionY']
                     azimuth = kwargs['azimuth']
                     if 'correctFactor' in kwargs:
                         correctFactor = kwargs['correctFactor']
                     else:
                         correctFactor = 1
                     groupList = kwargs['groupList']
                     pairs_ccf = groupList[1]
                     tau = kwargs['tau']
                     _lambda = kwargs['_lambda']
                     #Cross Correlation pairs obtained
             #         pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
             #         pairsArray = numpy.array(pairsList)[pairsCrossCorr]
             #         pairsSelArray = numpy.array(pairsSelected)
             #         pairs = []
+            #
             #         #Wind estimation pairs obtained
             #         for i in range(pairsSelArray.shape[0]/2):
             #             ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
             #             ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
             #             pairs.append((ind1,ind2))
                     indtau = tau.shape[0]/2
                     tau1 = tau[:indtau,:]
                     tau2 = tau[indtau:-1,:]
             #         tau1 = tau1[pairs,:]
             #         tau2 = tau2[pairs,:]
                     phase1 = tau[-1,:]
                     #---------------------------------------------------------------------
                     #Metodo Directo
                     distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
                     winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
                     winds = stats.nanmean(winds, axis=0)
                     winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
                     winds = correctFactor*winds
                     return winds
                 def __checkTime(self, currentTime, paramInterval, outputInterval):
                     dataTime = currentTime + paramInterval
                     deltaTime = dataTime - self.__initime
                     if deltaTime >= outputInterval or deltaTime < 0:
                         self.__dataReady = True
                     return
                 def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
                     '''
                     Function that implements winds estimation technique with detected meteors.
                     Input:    Detected meteors, Minimum meteor quantity to wind estimation
                     Output:    Winds estimation (Zonal and Meridional)
                     Parameters affected:    Winds
                     '''
                     #Settings
                     nInt = (heightMax - heightMin)/2
                     nInt = int(nInt)
                     winds = numpy.zeros((2,nInt))*numpy.nan
                     #Filter errors
                     error = numpy.where(arrayMeteor[:,-1] == 0)[0]
                     finalMeteor = arrayMeteor[error,:]
                     #Meteor Histogram
                     finalHeights = finalMeteor[:,2]
                     hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
                     nMeteorsPerI = hist[0]
                     heightPerI = hist[1]
                     #Sort of meteors
                     indSort = finalHeights.argsort()
                     finalMeteor2 = finalMeteor[indSort,:]
                     #    Calculating winds
                     ind1 = 0
                     ind2 = 0
                     for i in range(nInt):
                         nMet = nMeteorsPerI[i]
                         ind1 = ind2
                         ind2 = ind1 + nMet
                         meteorAux = finalMeteor2[ind1:ind2,:]
                         if meteorAux.shape[0] >= meteorThresh:
                             vel = meteorAux[:, 6]
                             zen = meteorAux[:, 4]*numpy.pi/180
                             azim = meteorAux[:, 3]*numpy.pi/180
                             n = numpy.cos(zen)
                     #         m = (1 - n**2)/(1 - numpy.tan(azim)**2)
                     #         l = m*numpy.tan(azim)
                             l = numpy.sin(zen)*numpy.sin(azim)
                             m = numpy.sin(zen)*numpy.cos(azim)
                             A = numpy.vstack((l, m)).transpose()
                             A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
                             windsAux = numpy.dot(A1, vel)
                             winds[0,i] = windsAux[0]
                             winds[1,i] = windsAux[1]
                     return winds, heightPerI[:-1]
                 def techniqueNSM_SA(self, **kwargs):
                     metArray = kwargs['metArray']
                     heightList = kwargs['heightList']
                     timeList = kwargs['timeList']
                     rx_location = kwargs['rx_location']
                     groupList = kwargs['groupList']
                     azimuth = kwargs['azimuth']
                     dfactor = kwargs['dfactor']
                     k = kwargs['k']
                     azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
                     d = dist*dfactor
                     #Phase calculation
                     metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
                     metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
                     velEst = numpy.zeros((heightList.size,2))*numpy.nan
                     azimuth1 = azimuth1*numpy.pi/180
                     for i in range(heightList.size):
                         h = heightList[i]
                         indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
                             A = numpy.asmatrix(A)
                             A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
                             velHor = numpy.dot(A1,velAux)
                             velEst[i,:] = numpy.squeeze(velHor)
                     return velEst
                 def __getPhaseSlope(self, metArray, heightList, timeList):
                     meteorList = []
                     #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
                     #Putting back together the meteor matrix
                     utctime = metArray[:,0]
                     uniqueTime = numpy.unique(utctime)
                     phaseDerThresh = 0.5
                     ippSeconds = timeList[1] - timeList[0]
                     sec = numpy.where(timeList>1)[0][0]
                     nPairs = metArray.shape[1] - 6
                     nHeights = len(heightList)
                     for t in uniqueTime:
                         metArray1 = metArray[utctime==t,:]
             #         phaseDerThresh = numpy.pi/4 #reducir Phase thresh
                         tmet = metArray1[:,1].astype(int)
                         hmet = metArray1[:,2].astype(int)
                         metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
                         metPhase[:,:] = numpy.nan
                         metPhase[:,hmet,tmet] = metArray1[:,6:].T
                         #Delete short trails
                         metBool = ~numpy.isnan(metPhase[0,:,:])
                         heightVect = numpy.sum(metBool, axis = 1)
                         metBool[heightVect<sec,:] = False
                         metPhase[:,heightVect<sec,:] = numpy.nan
                         #Derivative
                         metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
                         phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
                         metPhase[phDerAux] = numpy.nan
                         #--------------------------METEOR DETECTION    -----------------------------------------
                         indMet = numpy.where(numpy.any(metBool,axis=1))[0]
                         for p in numpy.arange(nPairs):
                             phase = metPhase[p,:,:]
                             phDer = metDer[p,:,:]
                             for h in indMet:
                                 height = heightList[h]
                                 phase1 = phase[h,:] #82
                                 phDer1 = phDer[h,:]
                                 phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)])   #Unwrap
                                 indValid = numpy.where(~numpy.isnan(phase1))[0]
                                 initMet = indValid[0]
                                 endMet = 0
                                 for i in range(len(indValid)-1):
                                     #Time difference
                                     inow = indValid[i]
                                     inext = indValid[i+1]
                                     idiff = inext - inow
                                     #Phase difference
                                     phDiff = numpy.abs(phase1[inext] - phase1[inow])
                                     if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]:   #End of Meteor
                                         sizeTrail = inow - initMet + 1
                                         if sizeTrail>3*sec:  #Too short meteors
                                                 vel = slope#*height*1000/(k*d)
                                                 estAux = numpy.array([utctime,p,height, vel, rsq])
                                                 meteorList.append(estAux)
                                         initMet = inext
                     metArray2 = numpy.array(meteorList)
                     return metArray2
                 def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
                     azimuth1 = numpy.zeros(len(pairslist))
                     dist = numpy.zeros(len(pairslist))
                     for i in range(len(rx_location)):
                         ch0 = pairslist[i][0]
                         ch1 = pairslist[i][1]
                         diffX = rx_location[ch0][0] - rx_location[ch1][0]
                         diffY = rx_location[ch0][1] - rx_location[ch1][1]
                         azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
                         dist[i] = numpy.sqrt(diffX**2 + diffY**2)
                     azimuth1 -= azimuth0
                     return azimuth1, dist
                 def techniqueNSM_DBS(self, **kwargs):
                     metArray = kwargs['metArray']
                     heightList = kwargs['heightList']
                     timeList = kwargs['timeList']
                     azimuth = kwargs['azimuth']
                     theta_x = numpy.array(kwargs['theta_x'])
                     theta_y = numpy.array(kwargs['theta_y'])
                     utctime = metArray[:,0]
                     cmet = metArray[:,1].astype(int)
                     hmet = metArray[:,3].astype(int)
                     SNRmet = metArray[:,4]
                     vmet = metArray[:,5]
                     spcmet = metArray[:,6]
                     nChan = numpy.max(cmet) + 1
                     nHeights = len(heightList)
                         thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
                         indthisH = numpy.where(thisH)
                         if numpy.size(indthisH) > 3:
                             vel_aux = vmet[thisH]
                             chan_aux = cmet[thisH]
                             cosu_aux = dir_cosu[chan_aux]
                             cosv_aux = dir_cosv[chan_aux]
                             cosw_aux = dir_cosw[chan_aux]
                             nch = numpy.size(numpy.unique(chan_aux))
                             if  nch > 1:
                                 A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
                                 velEst[i,:] = numpy.dot(A,vel_aux)
                     return velEst
                 def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
                     # noise = dataOut.noise
                     heightList = dataOut.heightList
                     SNR = dataOut.data_SNR
                     if technique == 'DBS':
                         kwargs['velRadial'] = param[:,1,:] #Radial velocity
                         kwargs['heightList'] = heightList
                         kwargs['SNR'] = SNR
                         dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function
                         dataOut.utctimeInit = dataOut.utctime
                         dataOut.outputInterval = dataOut.paramInterval
                     elif technique == 'SA':
                         #Parameters
             #             position_x = kwargs['positionX']
             #             position_y = kwargs['positionY']
             #             azimuth = kwargs['azimuth']
+            #
             #             if kwargs.has_key('crosspairsList'):
             #                 pairs = kwargs['crosspairsList']
             #             else:
             #                 pairs = None
+            #
             #             if kwargs.has_key('correctFactor'):
             #                 correctFactor = kwargs['correctFactor']
             #             else:
             #                 correctFactor = 1
             #             tau = dataOut.data_param
             #             _lambda = dataOut.C/dataOut.frequency
             #             pairsList = dataOut.groupList
             #             nChannels = dataOut.nChannels
                         kwargs['groupList'] = dataOut.groupList
                         kwargs['tau'] = dataOut.data_param
                         kwargs['_lambda'] = dataOut.C/dataOut.frequency
                         dataOut.data_output = self.techniqueSA(kwargs)
                         dataOut.utctimeInit = dataOut.utctime
                         dataOut.outputInterval = dataOut.timeInterval
                     elif technique == 'Meteors':
                         dataOut.flagNoData = True
                         self.__dataReady = False
                         if 'nHours' in kwargs:
                             nHours = kwargs['nHours']
                         else:
                             nHours = 1
                         if 'meteorsPerBin' in kwargs:
                             meteorThresh = kwargs['meteorsPerBin']
                         else:
                             meteorThresh = 6
                         if 'hmin' in kwargs:
                             hmin = kwargs['hmin']
                         else:   hmin = 70
                         if 'hmax' in kwargs:
                             hmax = kwargs['hmax']
                         else:   hmax = 110
                         dataOut.outputInterval = nHours*3600
                         if self.__isConfig == False:
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                             #Get Initial LTC time
                             self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                             self.__isConfig = True
                         if self.__buffer is None:
                             self.__buffer = dataOut.data_param
                             self.__firstdata = copy.copy(dataOut)
                         else:
                             self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                         self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                         if self.__dataReady:
                             dataOut.utctimeInit = self.__initime
                             self.__initime += dataOut.outputInterval #to erase time offset
                             dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
                             dataOut.flagNoData = False
                             self.__buffer = None
                     elif technique == 'Meteors1':
                         dataOut.flagNoData = True
                         self.__dataReady = False
                         if 'nMins' in kwargs:
                             nMins = kwargs['nMins']
                         else: nMins = 20
                         if 'mode' in kwargs:
                             mode = kwargs['mode']
                         if 'theta_x' in kwargs:
                             theta_x = kwargs['theta_x']
                         if 'theta_y' in kwargs:
                             theta_y = kwargs['theta_y']
                         else: mode = 'SA'
                         freq = 50e6
                         lamb = C/freq
                         k = 2*numpy.pi/lamb
                         timeList = dataOut.abscissaList
                         heightList = dataOut.heightList
                         if self.__isConfig == False:
                             dataOut.outputInterval = nMins*60
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                             self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                             self.__isConfig = True
                         if self.__buffer is None:
                             self.__buffer = dataOut.data_param
                             self.__firstdata = copy.copy(dataOut)
                         else:
                             self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                         self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                         if self.__dataReady:
                             dataOut.utctimeInit = self.__initime
                             self.__initime += dataOut.outputInterval #to erase time offset
                             metArray = self.__buffer
                             if mode == 'SA':
                                 dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
                             self.__buffer = None
                     return
             class EWDriftsEstimation(Operation):
                 def __init__(self):
                     Operation.__init__(self)
                 def __correctValues(self, heiRang, phi, velRadial, SNR):
                     listPhi = phi.tolist()
                     maxid = listPhi.index(max(listPhi))
                     minid = listPhi.index(min(listPhi))
                     rango = list(range(len(phi)))
                #     rango = numpy.delete(rango,maxid)
                     heiRang1 = heiRang*math.cos(phi[maxid])
                     heiRangAux = heiRang*math.cos(phi[minid])
                     indOut = (heiRang1 < heiRangAux[0]).nonzero()
                     heiRang1 = numpy.delete(heiRang1,indOut)
                     velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
                     SNR1 = numpy.zeros([len(phi),len(heiRang1)])
                     for i in rango:
                         x = heiRang*math.cos(phi[i])
                         y1 = velRadial[i,:]
                         f1 = interpolate.interp1d(x,y1,kind = 'cubic')
                         x1 = heiRang1
                         y11 = f1(x1)
                         y2 = SNR[i,:]
                         f2 = interpolate.interp1d(x,y2,kind = 'cubic')
                         y21 = f2(x1)
                         velRadial1[i,:] = y11
                         SNR1[i,:] = y21
                     return heiRang1, velRadial1, SNR1
                 def run(self, dataOut, zenith, zenithCorrection):
                     heiRang = dataOut.heightList
                     velRadial = dataOut.data_param[:,3,:]
                     SNR = dataOut.data_SNR
                     zenith = numpy.array(zenith)
                     zenith -= zenithCorrection
                     zenith *= numpy.pi/180
                     heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
                     alp = zenith[0]
                     bet = zenith[1]
                     w_w = velRadial1[0,:]
                     w_e = velRadial1[1,:]
                     w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
                     u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
                     winds = numpy.vstack((u,w))
                     dataOut.heightList = heiRang1
                     dataOut.data_output = winds
                     dataOut.data_SNR = SNR1
                     dataOut.utctimeInit = dataOut.utctime
                     dataOut.outputInterval = dataOut.timeInterval
                     return
                     data_acf = dataOut.data_pre[0]
                     data_ccf = dataOut.data_pre[1]
                     pairsList = dataOut.groupList[1]
                     lamb = dataOut.C/dataOut.frequency
                     tSamp = dataOut.ippSeconds*dataOut.nCohInt
                     paramInterval = dataOut.paramInterval
                     nChannels = data_acf.shape[0]
                     nLags = data_acf.shape[1]
                     nProfiles = data_acf.shape[2]
                     heightList = dataOut.heightList
                     ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
                     utctime = dataOut.utctime
                     dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
                     #------------------------    SNR    --------------------------------------
                         SNR[i] = (power[i]-noise[i])/noise[i]
                     SNRm = numpy.nanmean(SNR, axis = 0)
                     SNRdB = 10*numpy.log10(SNR)
                     if mode == 'SA':
                         dataOut.groupList = dataOut.groupList[1]
                         nPairs = data_ccf.shape[0]
                         phase = numpy.zeros(data_ccf[:,0,:,:].shape)
             #             phase1 = numpy.copy(phase)
                         coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
                         for p in range(nPairs):
                             ch0 = pairsList[p][0]
                             ch1 = pairsList[p][1]
                             ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
                             phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
             #                 phase1[p,:,:] = numpy.angle(ccf) #median filter
                             coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
             #                 coh1[p,:,:] = numpy.abs(ccf) #median filter
                         coh = numpy.nanmax(coh1, axis = 0)
             #             struc = numpy.ones((5,1))
             #             coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
                         #----------------------    Radial Velocity    ----------------------------
                         phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
                         velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
                         if allData:
                             boolMetFin = ~numpy.isnan(SNRm)
             #                 coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
                             #------------------------    Meteor mask    ---------------------------------
             #                 #SNR mask
             #                 boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
+            #
             #                 #Erase small objects
             #                 boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
+            #
             #                 auxEEJ = numpy.sum(boolMet1,axis=0)
             #                 indOver = auxEEJ>nProfiles*0.8  #Use this later
             #                 indEEJ = numpy.where(indOver)[0]
             #                 indNEEJ = numpy.where(~indOver)[0]
+            #
             #                 boolMetFin = boolMet1
+            #
             #                 if indEEJ.size > 0:
             #                     boolMet1[:,indEEJ] = False  #Erase heights with EEJ
+            #
             #                     boolMet2 = coh > cohThresh
             #                     boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
+            #
             #                     #Final Meteor mask
             #                     boolMetFin = boolMet1|boolMet2
                             #Coherence mask
                             boolMet1 = coh > 0.75
                             struc = numpy.ones((30,1))
                             boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
                             #Derivative mask
                             derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
                             boolMet2 = derPhase < 0.2
                         tmet = coordMet[0]
                         hmet = coordMet[1]
                         data_param = numpy.zeros((tmet.size, 6 + nPairs))
                         data_param[:,0] = utctime
                         data_param[:,1] = tmet
                         data_param[:,4] = velRad[tmet,hmet]
                         data_param[:,5] = coh[tmet,hmet]
                         data_param[:,6:] = phase[:,tmet,hmet].T
                     elif mode == 'DBS':
                         dataOut.groupList = numpy.arange(nChannels)
                         phase = numpy.angle(data_acf[:,1,:,:])
             #             phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
                         velRad = phase*lamb/(4*numpy.pi*tSamp)
                         #Spectral width
             #             acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
             #             acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
                             #SNR
                             boolMet1 = (SNRdB>SNRthresh) #SNR mask
                             boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
                             #Radial velocity
                             boolMet2 = numpy.abs(velRad) < 20
                             boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
                             #Spectral Width
                             boolMet3 = spcWidth < 30
                             boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
             #                 boolMetFin = self.__erase_small(boolMet1, 10,5)
                             boolMetFin = boolMet1&boolMet2&boolMet3
                         #Creating data_param
                         coordMet = numpy.where(boolMetFin)
                         cmet = coordMet[0]
                         tmet = coordMet[1]
                         hmet = coordMet[2]
                         data_param = numpy.zeros((tmet.size, 7))
                         data_param[:,0] = utctime
                         data_param[:,1] = cmet
                         data_param[:,4] = SNR[cmet,tmet,hmet].T
                         data_param[:,5] = velRad[cmet,tmet,hmet].T
                         data_param[:,6] = spcWidth[cmet,tmet,hmet].T
             #         self.dataOut.data_param = data_int
                     if len(data_param) == 0:
                         dataOut.flagNoData = True
                 def __erase_small(self, binArray, threshX, threshY):
                     labarray, numfeat = ndimage.measurements.label(binArray)
                     binArray1 = numpy.copy(binArray)
                     for i in range(1,numfeat + 1):
                         auxBin = (labarray==i)
                         auxSize = auxBin.sum()
                         x,y = numpy.where(auxBin)
                         widthX = x.max() - x.min()
                         widthY = y.max() - y.min()
                         #width X: 3 seg -> 12.5*3
                         #width Y:
                         if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
                             binArray1[auxBin] = False
                     return binArray1
             #---------------    Specular Meteor    ----------------
                     Function DetectMeteors()
                         Project developed with paper:
                         HOLDSWORTH ET AL. 2004
                     Input:
                         self.dataOut.data_pre
                         centerReceiverIndex:      From the channels, which is the center receiver
                         hei_ref:                  Height reference for the Beacon signal extraction
                         tauindex:
                         predefinedPhaseShifts:    Predefined phase offset for the voltge signals
                         cohDetection:             Whether to user Coherent detection or not
                         cohDet_timeStep:          Coherent Detection calculation time step
                         cohDet_thresh:            Coherent Detection phase threshold to correct phases
                         noise_timeStep:           Noise calculation time step
                         noise_multiple:           Noise multiple to define signal threshold
                         multDet_timeLimit:        Multiple Detection Removal time limit in seconds
                         multDet_rangeLimit:       Multiple Detection Removal range limit in km
                         phaseThresh:              Maximum phase difference between receiver to be consider a meteor
                         SNRThresh:                Minimum SNR threshold of the meteor signal to be consider a meteor
                         hmin:                     Minimum Height of the meteor to use it in the further wind estimations
                         hmax:                     Maximum Height of the meteor to use it in the further wind estimations
                         azimuth:                  Azimuth angle correction
                     Affected:
                         self.dataOut.data_param
                     Rejection Criteria (Errors):
 : No error; analysis OK
 : SNR < SNR threshold
 : height ambiguous echo: more then one possible height within 70 to 110 km
 : radial drift velocity or projected horizontal velocity exceeds 200 m/s
 : oscilatory echo, indicating event most likely not an underdense echo
 : phase difference in meteor Reestimation
                     Data Storage:
                         Meteors for Wind Estimation   (8):
                         Utc Time   |    Range    Height
                         VelRad    errorVelRad
                         Phase0 Phase1 Phase2 Phase3
                         TypeError
                      '''
                 def run(self, dataOut, hei_ref = None, tauindex = 0,
                                   phaseOffsets = None,
                                   cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
                                   noise_timeStep = 4, noise_multiple = 4,
                                   multDet_timeLimit = 1, multDet_rangeLimit = 3,
                                   phaseThresh = 20, SNRThresh = 5,
                                   hmin = 50, hmax=150, azimuth = 0,
                                   channelPositions = None) :
                     #Getting Pairslist
                     if channelPositions is None:
             #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                     heiRang = dataOut.getHeiRange()
                     #Get Beacon signal - No Beacon signal anymore
             #         newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
+            #
             #         if hei_ref != None:
             #             newheis = numpy.where(self.dataOut.heightList>hei_ref)
+            #
                     #****************REMOVING HARDWARE PHASE DIFFERENCES***************
                     # see if the user put in pre defined phase shifts
                     voltsPShift = dataOut.data_pre.copy()
             #         if predefinedPhaseShifts != None:
             #             hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
+            #
             # #         elif beaconPhaseShifts:
             # #             #get hardware phase shifts using beacon signal
             # #             hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
             # #             hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
+            #
             #         else:
             #             hardwarePhaseShifts = numpy.zeros(5)
+            #
             #         voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
             #         for i in range(self.dataOut.data_pre.shape[0]):
             #             voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
                     #******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
                     #Remove DC
                     voltsDC = numpy.mean(voltsPShift,1)
                     voltsDC = numpy.mean(voltsDC,1)
                     for i in range(voltsDC.shape[0]):
                         voltsPShift[i] = voltsPShift[i] - voltsDC[i]
                     #Don't considerate last heights, theyre used to calculate Hardware Phase Shift
             #         voltsPShift = voltsPShift[:,:,:newheis[0][0]]
                     #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
                     #Coherent Detection
                     if cohDetection:
                         #use coherent detection to get the net power
                         cohDet_thresh = cohDet_thresh*numpy.pi/180
                         voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
                     #Non-coherent detection!
                     powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
                     #********** END OF COH/NON-COH POWER CALCULATION**********************
                     #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
                     #Get noise
                     noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
                     #Meteor echoes detection
                     listMeteors = self.__findMeteors(powerNet, signalThresh)
                     #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
                     #************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
                     #Parameters
                     heiRange = dataOut.getHeiRange()
                     #Multiple detection removals
                     listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
                     #************ END OF REMOVE MULTIPLE DETECTIONS **********************
                     #*********************     METEOR REESTIMATION  (3.7, 3.8, 3.9, 3.10)   ********************
                     #Parameters
                     phaseThresh = phaseThresh*numpy.pi/180
                     #Estimation of decay times (Errors N 7, 8, 11)
                     listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
                     #*******************     END OF METEOR REESTIMATION    *******************
                     #*********************    METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13)    **************************
                     #Calculating Radial Velocity (Error N 15)
                     radialStdThresh = 10
                     listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
                     if len(listMeteors4) > 0:
                         #Setting New Array
                         date = dataOut.utctime
                         arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
                         #Correcting phase offset
                         if phaseOffsets != None:
                             phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
                             arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
                         #Second Pairslist
                         pairsList = []
                         pairx = (0,1)
                         pairy = (2,3)
                         pairsList.append(pairx)
                         pairsList.append(pairy)
                         jph = numpy.array([0,0,0,0])
                         h = (hmin,hmax)
                         arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
             #             #Calculate AOA (Error N 3, 4)
             #             #JONES ET AL. 1998
             #             error = arrayParameters[:,-1]
             #             AOAthresh = numpy.pi/8
             #             phases = -arrayParameters[:,9:13]
             #             arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
+            #
             #             #Calculate Heights (Error N 13 and 14)
             #             error = arrayParameters[:,-1]
             #             Ranges = arrayParameters[:,2]
             #             arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
             #             error = arrayParameters[:,-1]
                     #*********************    END OF PARAMETERS CALCULATION    **************************
                     #***************************+     PASS DATA TO NEXT STEP    **********************
             #             arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
                         dataOut.data_param = arrayParameters
                         if arrayParameters is None:
                             dataOut.flagNoData = True
                     else:
                         dataOut.flagNoData = True
                     return
                 def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     voltage = voltage0[:,:,minIndex:maxIndex+1]
                     nLength = voltage.shape[1]/n
                     nMin = 0
                     nMax = 0
                     phaseOffset = numpy.zeros((len(pairslist),n))
                     for i in range(n):
                         nMax += nLength
                         phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
                         phaseCCF = numpy.mean(phaseCCF, axis = 2)
                         phaseOffset[:,i] = phaseCCF.transpose()
                         nMin = nMax
             #         phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
                     #Remove Outliers
                     factor = 2
                     wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
                     dw = numpy.std(wt,axis = 1)
                     dw = dw.reshape((dw.size,1))
                     ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
                     phaseOffset[ind] = numpy.nan
                     phaseOffset = stats.nanmean(phaseOffset, axis=1)
                     return phaseOffset
                 def __shiftPhase(self, data, phaseShift):
                     #this will shift the phase of a complex number
                     dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
                     return dataShifted
                 def __estimatePhaseDifference(self, array, pairslist):
                     nChannel = array.shape[0]
                     nHeights = array.shape[2]
                     numPairs = len(pairslist)
             #         phaseCCF = numpy.zeros((nChannel, 5, nHeights))
                     phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
                     #Correct phases
                     derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
                     indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
                     if indDer[0].shape[0] > 0:
                         for i in range(indDer[0].shape[0]):
                             signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
                             phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
             #         for j in range(numSides):
             #             phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
             #             phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
+            #
                     #Linear
                     phaseInt = numpy.zeros((numPairs,1))
                     angAllCCF = phaseCCF[:,[0,1,3,4],0]
                     #Phase Differences
                     phaseDiff = phaseInt - phaseCCF[:,2,:]
                     phaseArrival = phaseInt.reshape(phaseInt.size)
                     #Dealias
                     phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
             #         indAlias = numpy.where(phaseArrival > numpy.pi)
             #         phaseArrival[indAlias] -= 2*numpy.pi
             #         indAlias = numpy.where(phaseArrival < -numpy.pi)
             #         phaseArrival[indAlias] += 2*numpy.pi
                     return phaseDiff, phaseArrival
                 def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
                     #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
                     #find the phase shifts of each channel over 1 second intervals
                     numHeights = volts.shape[2]
                     nChannel = volts.shape[0]
                     voltsCohDet = volts.copy()
                     pairsarray = numpy.array(pairslist)
                     indSides = pairsarray[:,1]
             #         indSides = numpy.array(range(nChannel))
             #         indSides = numpy.delete(indSides, indCenter)
+            #
             #         listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
                     listBlocks = numpy.array_split(volts, numBlocks, 1)
                     startInd = 0
                     endInd = 0
                     for i in range(numBlocks):
                         startInd = endInd
                         endInd = endInd + listBlocks[i].shape[1]
                         arrayBlock = listBlocks[i]
             #             arrayBlockCenter = listCenter[i]
                         #Estimate the Phase Difference
                         phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
                         #Phase Difference RMS
                             for j in range(indSides.size):
                                 arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
                             voltsCohDet[:,startInd:endInd,:] = arrayBlock
                     return voltsCohDet
                 def __calculateCCF(self, volts, pairslist ,laglist):
                     nHeights = volts.shape[2]
                     nPoints = volts.shape[1]
                     voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
                     for i in range(len(pairslist)):
                         volts1 = volts[pairslist[i][0]]
                         volts2 = volts[pairslist[i][1]]
                         for t in range(len(laglist)):
                             idxT = laglist[t]
                             if idxT >= 0:
                                 vStacked = numpy.vstack((volts2[idxT:,:],
                                                        numpy.zeros((idxT, nHeights),dtype='complex')))
                                 vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
                                                        volts2[:(nPoints + idxT),:]))
                             voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
                             vStacked = None
                     return voltsCCF
                 def __getNoise(self, power, timeSegment, timeInterval):
                     numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
                     numBlocks = int(power.shape[0]/numProfPerBlock)
                     listPower = numpy.array_split(power, numBlocks, 0)
                     noise = numpy.zeros((power.shape[0], power.shape[1]))
                     noise1 = numpy.zeros((power.shape[0], power.shape[1]))
                     startInd = 0
                     endInd = 0
                     for i in range(numBlocks):             #split por canal
                         startInd = endInd
                         endInd = endInd + listPower[i].shape[0]
                         arrayBlock = listPower[i]
                         noiseAux = numpy.mean(arrayBlock, 0)
             #             noiseAux = numpy.median(noiseAux)
             #             noiseAux = numpy.mean(arrayBlock)
                         noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
                         noiseAux1 = numpy.mean(arrayBlock)
                         noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
                     return noise, noise1
                 def __findMeteors(self, power, thresh):
                     nProf = power.shape[0]
                     nHeights = power.shape[1]
                     listMeteors = []
                     for i in range(nHeights):
                         powerAux = power[:,i]
                         threshAux = thresh[:,i]
                         indUPthresh = numpy.where(powerAux > threshAux)[0]
                         indDNthresh = numpy.where(powerAux <= threshAux)[0]
                         j = 0
                         while (j < indUPthresh.size - 2):
                             if (indUPthresh[j + 2] == indUPthresh[j] + 2):
                                 indDNAux = numpy.where(indDNthresh > indUPthresh[j])
                                 indDNthresh = indDNthresh[indDNAux]
                                 if (indDNthresh.size > 0):
                                     indEnd = indDNthresh[0] - 1
                                     indInit = indUPthresh[j]
                                     meteor = powerAux[indInit:indEnd + 1]
                                     indPeak = meteor.argmax() + indInit
                                     FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
                                     listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
                                     j = numpy.where(indUPthresh == indEnd)[0] + 1
                                 else: j+=1
                             else: j+=1
                     return listMeteors
                 def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
                     arrayMeteors = numpy.asarray(listMeteors)
                     listMeteors1 = []
                     while arrayMeteors.shape[0] > 0:
                         FLAs = arrayMeteors[:,4]
                         maxFLA = FLAs.argmax()
                         listMeteors1.append(arrayMeteors[maxFLA,:])
                         MeteorInitTime = arrayMeteors[maxFLA,1]
                         MeteorEndTime = arrayMeteors[maxFLA,3]
                         MeteorHeight = arrayMeteors[maxFLA,0]
                         #Check neighborhood
                         maxHeightIndex = MeteorHeight + rangeLimit
                         minHeightIndex = MeteorHeight - rangeLimit
                         minTimeIndex = MeteorInitTime - timeLimit
                         maxTimeIndex = MeteorEndTime + timeLimit
                         #Check Heights
                         indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
                         indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
                         indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
                         arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
                     return listMeteors1
                 def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
                     numHeights = volts.shape[2]
                     nChannel = volts.shape[0]
                     thresholdPhase = thresh[0]
                     thresholdNoise = thresh[1]
                     thresholdDB = float(thresh[2])
                     thresholdDB1 = 10**(thresholdDB/10)
                     pairsarray = numpy.array(pairslist)
                     indSides = pairsarray[:,1]
                     pairslist1 = list(pairslist)
                     pairslist1.append((0,1))
                     pairslist1.append((3,4))
                     listPowerSeries = []
                     listVoltageSeries = []
                     #volts has the war data
                     if frequency == 30e6:
                         timeLag = 45*10**-3
                     else:
                         timeLag = 15*10**-3
                     lag = numpy.ceil(timeLag/timeInterval)
                     for i in range(len(listMeteors)):
                         ######################   3.6 - 3.7 PARAMETERS REESTIMATION    #########################
                         meteorAux = numpy.zeros(16)
                         #Loading meteor Data (mHeight, mStart, mPeak, mEnd)
                         mHeight = listMeteors[i][0]
                         mStart = listMeteors[i][1]
                         mPeak = listMeteors[i][2]
                         mEnd = listMeteors[i][3]
                         #get the volt data between the start and end times of the meteor
                         meteorVolts = volts[:,mStart:mEnd+1,mHeight]
                         meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
                         #3.6. Phase Difference estimation
                         phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
                         #3.7. Phase difference removal & meteor start, peak and end times reestimated
                         #meteorVolts0.- all Channels, all Profiles
                         meteorVolts0 = volts[:,:,mHeight]
                         meteorNoise = noise[:,mHeight]
                         meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
                         powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0)  #Power
                         #Times reestimation
                         mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
                         if mStart1.size > 0:
                             mStart1 = mStart1[-1] + 1
                         else:
                             mStart1 = mPeak
                         mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
                         mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
                         if mEndDecayTime1.size == 0:
                         else:
                             mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
             #             mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
                         #meteorVolts1.- all Channels, from start to end
                         meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
                         meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
                         meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
                         meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
                         #####################    END PARAMETERS REESTIMATION    #########################
                         #####################   3.8 PHASE DIFFERENCE REESTIMATION  ########################
             #             if mEnd1 - mStart1 > 4:       #Error Number 6: echo less than 5 samples long; too short for analysis
                         if meteorVolts2.shape[1] > 0:
                             #Phase Difference re-estimation
                             phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1)   #Phase Difference Estimation
             #                 phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
                             meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
                             phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
                             meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4])     #Phase Shifting
                             #Phase Difference RMS
                             phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
                             powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
                             #Vectorize
                             meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
                             meteorAux[7:11] = phaseDiffint[0:4]
                             #Rejection Criterions
                             if phaseRMS1 > thresholdPhase:  #Error Number 17: Phase variation
                                 meteorAux[-1] = 17
                             elif mSNR1 < thresholdDB1:      #Error Number 1: SNR < threshold dB
                                 meteorAux[-1] = 1
                         else:
                             meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
                             meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
                             PowerSeries = 0
                         listMeteors1.append(meteorAux)
                         listPowerSeries.append(PowerSeries)
                         listVoltageSeries.append(meteorVolts1)
                     return listMeteors1, listPowerSeries, listVoltageSeries
                 def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
                     threshError = 10
                     #Depending if it is 30 or 50 MHz
                     if frequency == 30e6:
                     else:
                         timeLag = 15*10**-3
                     lag = numpy.ceil(timeLag/timeInterval)
                     listMeteors1 = []
                     for i in range(len(listMeteors)):
                         meteorPower = listPower[i]
                         meteorAux = listMeteors[i]
                         if meteorAux[-1] == 0:
                             try:
                                 indmax = meteorPower.argmax()
                                 indlag = indmax + lag
                                 y = meteorPower[indlag:]
                                 x = numpy.arange(0, y.size)*timeLag
                                 #first guess
                                 a = y[0]
                                 tau = timeLag
                                 y1 = self.__exponential_function(x, *popt)
                                 #error estimation
                                 error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
                                 decayTime = popt[1]
                                 riseTime = indmax*timeInterval
                                 meteorAux[11:13] = [decayTime, error]
                                 #Table items 7, 8 and 11
                                 if (riseTime > 0.3):            #Number 7: Echo rise exceeds 0.3s
                                     meteorAux[-1] = 7
                                 elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
                                     meteorAux[-1] = 8
                                 if (error > threshError):       #Number 11: Poor fit to amplitude for estimation of decay time
                                     meteorAux[-1] = 11
                             except:
                                 meteorAux[-1] = 11
                         listMeteors1.append(meteorAux)
                     return listMeteors1
                 #Exponential Function
                 def __exponential_function(self, x, a, tau):
                     y = a*numpy.exp(-x/tau)
                     return y
                 def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist,  timeInterval):
                     pairslist1 = list(pairslist)
                     pairslist1.append((0,1))
                     pairslist1.append((3,4))
                     c = 3e8
                     lag = numpy.ceil(timeLag/timeInterval)
                     freq = 30e6
                     listMeteors1 = []
                     for i in range(len(listMeteors)):
                         meteorAux = listMeteors[i]
                         if meteorAux[-1] == 0:
                             mStart = listMeteors[i][1]
                             mPeak = listMeteors[i][2]
                             mLag = mPeak - mStart + lag
                             #get the volt data between the start and end times of the meteor
                             meteorVolts = listVolts[i]
                             meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
                             #Get CCF
                             allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
                             #Method 2
                             slopes = numpy.zeros(numPairs)
                             time = numpy.array([-2,-1,1,2])*timeInterval
                             angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
                             #Correct phases
                             derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
                             indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
                             if indDer[0].shape[0] > 0:
                                 for i in range(indDer[0].shape[0]):
                                     signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
                                     angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
                             for j in range(numPairs):
                                 fit = stats.linregress(time, angAllCCF[j,:])
                                 slopes[j] = fit[0]
                             #Remove Outlier
             #                 indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
             #                 slopes = numpy.delete(slopes,indOut)
             #                 indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
             #                 slopes = numpy.delete(slopes,indOut)
                             radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
                             radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
                             meteorAux[-2] = radialError
                             meteorAux[-3] = radialVelocity
                             #Setting Error
                             #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
                             if numpy.abs(radialVelocity) > 200:
                                 meteorAux[-1] = 15
                             #Number 12: Poor fit to CCF variation for estimation of radial drift velocity
                             elif radialError > radialStdThresh:
                                 meteorAux[-1] = 12
                         listMeteors1.append(meteorAux)
                     return listMeteors1
                 def __setNewArrays(self, listMeteors, date, heiRang):
                     #New arrays
                     arrayMeteors = numpy.array(listMeteors)
                     arrayParameters = numpy.zeros((len(listMeteors), 13))
                     #Date inclusion
             #         date = re.findall(r'\((.*?)\)', date)
             #         date = date[0].split(',')
             #         date = map(int, date)
+            #
             #         if len(date)<6:
             #             date.append(0)
+            #
             #         date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
             #         arrayDate = numpy.tile(date, (len(listMeteors), 1))
                     arrayDate = numpy.tile(date, (len(listMeteors)))
                     #Meteor array
             #         arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
             #         arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
                     #Parameters Array
                     arrayParameters[:,0] = arrayDate #Date
                     arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
                     arrayParameters[:,8:12] = arrayMeteors[:,7:11]  #Phases
                     arrayParameters[:,-1] = arrayMeteors[:,-1]  #Error
                     return arrayParameters
             class CorrectSMPhases(Operation):
                 def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
                     arrayParameters = dataOut.data_param
                     pairsList = []
                     pairx = (0,1)
                     pairsList.append(pairx)
                     pairsList.append(pairy)
                     jph = numpy.zeros(4)
                     phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
                 #         arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
                     arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
                     meteorOps = SMOperations()
                     if channelPositions is None:
                 #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                         channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                     pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                     h = (hmin,hmax)
                     arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
                     dataOut.data_param = arrayParameters
                     return
             class SMPhaseCalibration(Operation):
                 __buffer = None
                 __initime = None
                 __dataReady = False
                 __isConfig = False
                 def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
                     dataTime = currentTime + paramInterval
                     deltaTime = dataTime - initTime
                     if deltaTime >= outputInterval or deltaTime < 0:
                         return True
                     return False
                 def __getGammas(self, pairs, d, phases):
                     gammas = numpy.zeros(2)
                     for i in range(len(pairs)):
                         pairi = pairs[i]
                         phip3 = phases[:,pairi[0]]
                         jgamma = numpy.angle(numpy.exp(1j*jgamma))
             #             jgamma[jgamma>numpy.pi] -= 2*numpy.pi
             #             jgamma[jgamma<-numpy.pi] += 2*numpy.pi
                         #Revised distribution
                         jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
                         rmin = -0.5*numpy.pi
                         rmax = 0.5*numpy.pi
                         phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
                         meteorsY = phaseHisto[0]
                         phasesX = phaseHisto[1][:-1]
                         width = phasesX[1] - phasesX[0]
                         phasesX += width/2
                         #Gaussian aproximation
                         bpeak = meteorsY.argmax()
                         peak = meteorsY.max()
                         jmin = bpeak - 5
                         jmax = bpeak + 5 + 1
                         if jmin<0:
                             jmin = 0
                             jmax = 6
                         elif jmax > meteorsY.size:
                             jmin = meteorsY.size - 6
                             jmax = meteorsY.size
                         x0 = numpy.array([peak,bpeak,50])
                         coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
                         #Gammas
                         gammas[i] = coeff[0][1]
                     return gammas
                 def __residualFunction(self, coeffs, y, t):
                     return y - self.__gauss_function(t, coeffs)
                 def __gauss_function(self, t, coeffs):
                     return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
                 def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
                         max_xangle = range_angle[iz]/2 + center_xangle
                         min_yangle = -range_angle[iz]/2 + center_yangle
                         max_yangle = range_angle[iz]/2 + center_yangle
                         inc_x = (max_xangle-min_xangle)/nstepsx
                         inc_y = (max_yangle-min_yangle)/nstepsy
                         alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
                         alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
                         penalty = numpy.zeros((nstepsx,nstepsy))
                         jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
                         jph = numpy.zeros(nchan)
                         # Iterations looking for the offset
                         for iy in range(int(nstepsy)):
                             for ix in range(int(nstepsx)):
                                 d2 = d[pairsList[1][1]]
                                 d5 = d[pairsList[0][0]]
                                 d4 = d[pairsList[0][1]]
                                 alp2 = alpha_y[iy]  #gamma 1
                                 alp4 = alpha_x[ix]  #gamma 0
                                 alp3 = -alp2*d3/d2 - gammas[1]
                                 alp5 = -alp4*d5/d4 - gammas[0]
             #                     jph[pairy[1]] = alpha_y[iy]
             #                     jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
             #                     jph[pairx[1]] = alpha_x[ix]
             #                     jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
                                 jph[pairsList[0][1]] = alp4
                                 jph[pairsList[0][0]] = alp5
                                 jph[pairsList[1][0]] = alp3
                                 jph[pairsList[1][1]] = alp2
                                 jph_array[:,ix,iy] = jph
             #                     d = [2.0,2.5,2.5,2.0]
                                 #falta chequear si va a leer bien  los meteoros
                                 meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
                                 error = meteorsArray1[:,-1]
                                 ind1 = numpy.where(error==0)[0]
                                 penalty[ix,iy] = ind1.size
                         i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
                         phOffset = jph_array[:,i,j]
                         center_xangle = phOffset[pairx[1]]
                         center_yangle = phOffset[pairy[1]]
                     phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
                     phOffset = phOffset*180/numpy.pi
                     return phOffset
                 def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
                     dataOut.flagNoData = True
                     self.__dataReady = False
                     dataOut.outputInterval = nHours*3600
                     if self.__isConfig == False:
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                         #Get Initial LTC time
                         self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                         self.__isConfig = True
                     if self.__buffer is None:
                         self.__buffer = dataOut.data_param.copy()
                     else:
                         self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                     self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                     if self.__dataReady:
                         dataOut.utctimeInit = self.__initime
                         self.__initime += dataOut.outputInterval #to erase time offset
                         freq = dataOut.frequency
                         c = dataOut.C #m/s
                         lamb = c/freq
                             pairs.append((1,0))
                         else:
                             pairs.append((0,1))
                         if distances[3] > distances[2]:
                             pairs.append((3,2))
                         else:
                             pairs.append((2,3))
             #             distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
                         meteorsArray = self.__buffer
                         error = meteorsArray[:,-1]
                         boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
                         meteorsArray = meteorsArray[ind1,:]
                         meteorsArray[:,-1] = 0
                         phases = meteorsArray[:,8:12]
                         #Calculate Gammas
                         gammas = self.__getGammas(pairs, distances, phases)
             #             gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
                         dataOut.data_output = -phasesOff
                         dataOut.flagNoData = False
                         self.__buffer = None
                     return
             class SMOperations():
                 def __init__(self):
                     return
                 def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
                     arrayParameters = arrayParameters0.copy()
                     hmin = h[0]
                     hmax = h[1]
                     #Calculate AOA (Error N 3, 4)
                     #JONES ET AL. 1998
                     AOAthresh = numpy.pi/8
                     phases = -arrayParameters[:,8:12] + jph
             #         phases = numpy.unwrap(phases)
                     arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
                     #Calculate Heights (Error N 13 and 14)
                     error = arrayParameters[:,-1]
                     Ranges = arrayParameters[:,1]
                     zenith = arrayParameters[:,4]
                     arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
                     #----------------------- Get Final data    ------------------------------------
             #         error = arrayParameters[:,-1]
             #         ind1 = numpy.where(error==0)[0]
             #         arrayParameters = arrayParameters[ind1,:]
                     return arrayParameters
                 def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
                     arrayAOA = numpy.zeros((phases.shape[0],3))
                     cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
                     arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
                     cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
                     arrayAOA[:,2] = cosDirError
                     azimuthAngle = arrayAOA[:,0]
                     zenithAngle = arrayAOA[:,1]
                     #Setting Error
                     indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
                     error[indError] = 0
                     #Number 3: AOA not fesible
                     indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
                     error[indInvalid] = 3
                     #Number 4: Large difference in AOAs obtained from different antenna baselines
                     indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
                     error[indInvalid] = 4
                     return arrayAOA, error
                 def __getDirectionCosines(self, arrayPhase, pairsList, distances):
                     #Initializing some variables
                     ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
                     ang_aux = ang_aux.reshape(1,ang_aux.size)
                     cosdir = numpy.zeros((arrayPhase.shape[0],2))
                     cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
                     for i in range(2):
                         ph0 = arrayPhase[:,pairsList[i][0]]
                         ph1 = arrayPhase[:,pairsList[i][1]]
                         d0 = distances[pairsList[i][0]]
                         d1 = distances[pairsList[i][1]]
                         ph0_aux = ph0 + ph1
                         ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
             #             ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
             #             ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
                         #First Estimation
                         cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
                         #Most-Accurate Second Estimation
                         phi1_aux =  ph0 - ph1
                         phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
                         #Direction Cosine 1
                         cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
                         #Searching the correct Direction Cosine
                         cosdir0_aux = cosdir0[:,i]
                         cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
                         indcos = cosDiff.argmin(axis = 1)
                         #Saving Value obtained
                         cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
                     return cosdir0, cosdir
                 def __calculateAOA(self, cosdir, azimuth):
                     cosdirX = cosdir[:,0]
                     cosdirY = cosdir[:,1]
                     zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
                     azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
                     angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
                     return angles
                 def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
                     Ramb = 375  #Ramb = c/(2*PRF)
                     Re = 6371   #Earth Radius
                     heights = numpy.zeros(Ranges.shape)
                     R_aux = numpy.array([0,1,2])*Ramb
                     R_aux = R_aux.reshape(1,R_aux.size)
                     Ranges = Ranges.reshape(Ranges.size,1)
                     Ri = Ranges + R_aux
                     hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
                     #Check if there is a height between 70 and 110 km
                     h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
                     ind_h = numpy.where(h_bool == 1)[0]
                     hCorr = hi[ind_h, :]
                     ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
                     hCorr = hi[ind_hCorr][:len(ind_h)]
                     heights[ind_h] = hCorr
                     #Setting Error
                     #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
                     #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
                     indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
                     error[indError] = 0
                     indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
                     error[indInvalid2] = 14
                     indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
                     error[indInvalid1] = 13
                     return heights, error
                 def getPhasePairs(self, channelPositions):
                     chanPos = numpy.array(channelPositions)
                     listOper = list(itertools.combinations(list(range(5)),2))
                     distances = numpy.zeros(4)
                     axisX = []
                     axisY = []
                     distY = numpy.zeros(3)
                     ix = 0
                     iy = 0
                     pairX = numpy.zeros((2,2))
                     pairY = numpy.zeros((2,2))
                     for i in range(len(listOper)):
                         pairi = listOper[i]
                         posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
                         if posDif[0] == 0:
                             axisY.append(pairi)
                             distY[iy] = posDif[1]
                             axisX.append(pairi)
                             distX[ix] = posDif[0]
                             ix += 1
                     for i in range(2):
                         if i==0:
                             dist0 = distX
                         else:
                             dist0 = distY
                             axis0 = axisY
                         side = numpy.argsort(dist0)[:-1]
                         axis0 = numpy.array(axis0)[side,:]
                         chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
                         side = axis1[axis1 != chanC]
                         diff1 = chanPos[chanC,i] - chanPos[side[0],i]
                         diff2 = chanPos[chanC,i] - chanPos[side[1],i]
                         if diff1<0:
                             chan2 = side[0]
                             d2 = numpy.abs(diff1)
                             chan1 = side[1]
                             d2 = numpy.abs(diff2)
                             chan1 = side[0]
                             d1 = numpy.abs(diff1)
                         if i==0:
                             chanCX = chanC
                             chan1X = chan1
                             chan2Y = chan2
                             distances[2:4] = numpy.array([d1,d2])
             #         axisXsides = numpy.reshape(axisX[ix,:],4)
+            #
             #         channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
             #         channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
+            #
             #         ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
             #         ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
             #         channel25X = int(pairX[0,ind25X])
             #         ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
             #         channel25Y = int(pairY[0,ind25Y])
             #         channel20Y = int(pairY[1,ind20Y])
             #         pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
                     pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
                     return pairslist, distances
             #     def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
+            #
             #         arrayAOA = numpy.zeros((phases.shape[0],3))
             #         cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
+            #
             #         arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
             #         cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
             #         arrayAOA[:,2] = cosDirError
+            #
             #         azimuthAngle = arrayAOA[:,0]
             #         zenithAngle = arrayAOA[:,1]
+            #
             #         #Setting Error
             #         #Number 3: AOA not fesible
             #         indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
             #         error[indInvalid] = 3
             #         #Number 4: Large difference in AOAs obtained from different antenna baselines
             #         indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
             #         error[indInvalid] = 4
             #         return arrayAOA, error
+            #
             #     def __getDirectionCosines(self, arrayPhase, pairsList):
+            #
             #         #Initializing some variables
             #         ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
             #         ang_aux = ang_aux.reshape(1,ang_aux.size)
+            #
             #         cosdir = numpy.zeros((arrayPhase.shape[0],2))
             #         cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
+            #
+            #
             #         for i in range(2):
             #             #First Estimation
             #             phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
             #             #Dealias
             #             indcsi = numpy.where(phi0_aux > numpy.pi)
             #             phi0_aux[indcsi] -= 2*numpy.pi
             #             indcsi = numpy.where(phi0_aux < -numpy.pi)
             #             phi0_aux[indcsi] += 2*numpy.pi
             #             #Direction Cosine 0
             #             cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
+            #
             #             #Most-Accurate Second Estimation
             #             phi1_aux =  arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
             #             phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
             #             #Direction Cosine 1
             #             cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
+            #
             #             #Searching the correct Direction Cosine
             #             cosdir0_aux = cosdir0[:,i]
             #             cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
             #             indcos = cosDiff.argmin(axis = 1)
             #             #Saving Value obtained
             #             cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
+            #
             #         return cosdir0, cosdir
+            #
             #     def __calculateAOA(self, cosdir, azimuth):
             #         cosdirX = cosdir[:,0]
             #         cosdirY = cosdir[:,1]
+            #
             #         zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
             #         azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
             #         angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
+            #
             #         return angles
+            #
             #     def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
+            #
             #         Ramb = 375  #Ramb = c/(2*PRF)
             #         Re = 6371   #Earth Radius
             #         heights = numpy.zeros(Ranges.shape)
+            #
             #         R_aux = numpy.array([0,1,2])*Ramb
             #         R_aux = R_aux.reshape(1,R_aux.size)
+            #
             #         Ranges = Ranges.reshape(Ranges.size,1)
+            #
             #         Ri = Ranges + R_aux
             #         hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
+            #
             #         #Check if there is a height between 70 and 110 km
             #         h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
             #         ind_h = numpy.where(h_bool == 1)[0]
+            #
             #         hCorr = hi[ind_h, :]
             #         ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
+            #
             #         hCorr = hi[ind_hCorr]
             #         heights[ind_h] = hCorr
+            #
             #         #Setting Error
             #         #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
             #         #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
+            #
             #         indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
             #         error[indInvalid2] = 14
             #         indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
             #         error[indInvalid1] = 13
+            #
             #         return heights, error
-  No newline at end of file

schainpy/model/proc/jroproc_spectra.py +31 -31

                     # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
                     self.dataOut.channelList = range(len(channelIndexList))
                     self.__selectPairsByChannel(channelIndexList)
                     return 1
                 def selectFFTs(self, minFFT, maxFFT ):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango
                     minFFT<= FFT <= maxFFT
                     """
                     if (minFFT > maxFFT):
                         raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT))
                     self.selectFFTsByIndex(minIndex, maxIndex)
                     return 1
                 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 selectHeights(self, minHei, maxHei):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minHei > maxHei):
                         raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei))
                         maxIndex = len(heights)
                     self.selectHeightsByIndex(minIndex, maxIndex)
                     return 1
                 def selectFFTsByIndex(self, minIndex, maxIndex):
                     """
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1])
                     self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1]
                     self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1]
                         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)
                     return 1
                 def removeInterference2(self):
                     cspc = self.dataOut.data_cspc
                     spc = self.dataOut.data_spc
                     Heights = numpy.arange(cspc.shape[2])
                     realCspc = numpy.abs(cspc)
                     for i in range(cspc.shape[0]):
                         LinePower= numpy.sum(realCspc[i], axis=0)
                         Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)]
                         InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 )
                         InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)]
                         InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)]
                         InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) )
                         #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax]))
                         if len(InterferenceRange)<int(cspc.shape[1]*0.3):
                             cspc[i,InterferenceRange,:] = numpy.NaN
                     self.dataOut.data_cspc = cspc
                 def removeInterference(self,  interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None):
                     jspectra = self.dataOut.data_spc
                     if n is not None:
                         self.n = int(n)
                     else:
                         self.__integrationtime = int(timeInterval)
                         self.n = None
                         self.__byTime = True
                 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
                     if n == 1:
                         return
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         dataOut.data_spc = avgdata_spc
                         dataOut.data_cspc = avgdata_cspc
                         dataOut.data_dc = avgdata_dc
                         dataOut.nIncohInt *= self.n
                         dataOut.utctime = avgdatatime
                         dataOut.flagNoData = False
-                    return dataOut
  No newline at end of file
+                    return dataOut

schainpy/model/proc/jroproc_voltage.py +20 -21

             @MPDecorator
             class VoltageProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.dataOut.data = data
                     # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
                     self.dataOut.channelList = range(len(channelIndexList))
                     return 1
                 def selectHeights(self, minHei=None, maxHei=None):
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         buffer = self.dataOut.data[:, :, 0:int(self.dataOut.nHeights-r)]
                         buffer = buffer.reshape(self.dataOut.nChannels, self.dataOut.nProfiles, int(self.dataOut.nHeights/window), window)
                         buffer = numpy.sum(buffer,3)
                         # print self.__bufferStride[self.__profIndexStride - 1]
                         # raise
                         return self.__bufferStride[self.__profIndexStride - 1]
                     return None, None
                 def integrate(self, data, datatime=None):
                     avgdatatime = self.__initime
                     deltatime = datatime - self.__lastdatatime
                     if not self.__withOverlapping:
                         self.__initime = datatime
                     else:
                     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:
                         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
                     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)
                     #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))
                     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):
                     return data
                 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
                     if dataOut.flagDecodeData:
                     self.__nProfiles = dataOut.nProfiles
                     datadec = None
                     if mode == 3:
                         mode = 0
                         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

schainpy/model/utils/jroutils_ftp.py +27 -27

                     self.mutex.acquire()
                     #         init = time.time()
+                    #
                     #         while(self.bussy):
                     #             sleep(0.1)
                     #             if time.time() - init > 2*self.period:
                     #                 return 0
                     self.fileList = fileList
                     self.mutex.release()
                     return 1
                         if self.stopFlag:
                             break
                         #   self.bussy = True
                         self.mutex.acquire()
                     """
                         This method is used to set SSH parameters and establish a connection to a remote server
                         Inputs:
                             server    - remote server IP Address
                             username    - remote server Username
                             password    - remote server password
                             remotefolder    - remote server current working directory
                         Return: void
                         Affects:
                             self.status        - in case of error or fail connection this parameter is set to 0 else 1
                     """
                 def __execute(self, command):
                     """
                         __execute a command on remote server
                         Input:
                             command    - Exmaple 'ls -l'
                         Return:
 in error case else 1
                     """
                 def mkdir(self, remotefolder):
                     """
                         mkdir is used to make a new directory in remote server
                         Input:
                             remotefolder    - directory name
                         Return:
 in error case else 1
                     """
                 def cd(self, remotefolder):
                     """
                         cd is used to change remote working directory on server
                         Input:
                             remotefolder    - current working directory
                         Affects:
                             self.remotefolder
                         Return:
 in case of error else 1
                     """
                     if not self.status:
                     ProcessingUnit.__init__(self, **kwargs)
                     self.isConfig = False
                     self.clientObj = None
                 def setup(self, server, username, password, remotefolder, localfolder, ext='.png', period=60, protocol='ftp', **kwargs):
                     self.clientObj = None
                         self.init = time.time()
                         self.setup(**kwargs)
                         self.isConfig = True
                     if not self.clientObj.is_alive():
                         print("[Remote Server]: Restarting connection ")
                         self.setup(**kwargs)
                     if time.time() - self.init >= self.period:
                         fullfilenameList = self.findFiles()
                     try:
                         self.ftp = ftplib.FTP(self.server)
                         self.ftp.login(self.username,self.password)
                         self.ftp.cwd(self.remotefolder)
                         #   print 'Connect to FTP Server: Successfully'
                     except ftplib.all_errors:
                         print('Error FTP Service')
                         self.status = 1
                         self.counter = 0
-                    self.status = 1
  No newline at end of file
+                    self.status = 1

schainpy/model/utils/jroutils_publish.py +20 -20

             def get_plot_code(s):
                 label = s.split('_')[0]
                 codes = [key for key in PLOT_CODES if key in label]
                 if codes:
                     return PLOT_CODES[codes[0]]
                 else:
                     return 24
                     self.counter = 0
                     self.delay = kwargs.get('delay', 0)
                     self.cnt = 0
                     self.verbose = verbose
                     context = zmq.Context()
                     self.zmq_socket = context.socket(zmq.PUSH)
                     server = kwargs.get('server', 'zmq.pipe')
                 def publish_data(self):
                     self.dataOut.finished = False
                     if self.verbose:
                         log.log(
                             'Sending {} - {}'.format(self.dataOut.type, self.dataOut.datatime),
                     time.sleep(self.delay)
                 def close(self):
                     self.dataOut.finished = True
                     self.zmq_socket.send_pyobj(self.dataOut)
                     time.sleep(0.1)
                     self.zmq_socket.close()
             class ReceiverData(ProcessingUnit):
                             self.ftp.close()
                         self.ftp = None
                         self.ready = False
                         return
                     try:
                         self.ftp.login(self.username, self.password)
                 def upload(self, src, dst):
                     log.log('Uploading {} -> {} '.format(
                         src.split('/')[-1], dst.split('/')[-1]),
                         self.name,
                         nl=False
                         )
                     fp.close()
                     log.success('OK', tag='')
                     return 1
                 def send_files(self):
                     for x, pattern in enumerate(self.patterns):
                             srcname = self.find_files(local, ext)
                             src = os.path.join(local, srcname)
                             if os.path.getmtime(src) < time.time() - 30*60:
                                 log.warning('Skipping old file {}'.format(srcname))
                                 continue
                             if srcname is None or srcname == self.latest[x]:
                                 log.warning('File alreday uploaded {}'.format(srcname))
                                 continue
                             if 'png' in ext:
                                 dstname = self.getftpname(srcname, int(exp_code), int(sub_exp_code))
                             else:
                                 dstname = srcname
                             dst = os.path.join(remote, dstname)
                             if self.upload(src, dst):
                                 self.times[x] = time.time()
                                 self.latest[x] = srcname
                             else:
                                 self.ready = False
                                 break
                 def run(self, dataOut, server, username, password, timeout=10, **kwargs):
                     if not self.isConfig:
                         self.setup(
                             server=server,
                             username=username,
                             password=password,
                             timeout=timeout,
                             **kwargs
                             )
                         self.isConfig = True

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