schain Commit - r1224:092ea8e2ae46 · Jicamarca Repository

Update from master

Juan C. Espinoza -

r1224:092ea8e2ae46

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r1224:092ea8e2ae46

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schainpy/model/io/jroIO_param.py +3 -1

             import numpy
             import time
             import os
             import h5py
             import re
             import datetime
             from schainpy.model.data.jrodata import *
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
             # from .jroIO_base import *
             from schainpy.model.io.jroIO_base import *
             import schainpy
             from schainpy.utils import log
             @MPDecorator
             class ParamReader(JRODataReader,ProcessingUnit):
                 '''
                 Reads HDF5 format files
                 path
                 startDate
                 endDate
                 startTime
                 endTime
                 '''
                 ext = ".hdf5"
                 optchar = "D"
                 timezone = None
                 startTime = None
                 endTime = None
                 fileIndex = None
                 utcList = None      #To select data in the utctime list
                 blockList = None    #List to blocks to be read from the file
                 blocksPerFile = None    #Number of blocks to be read
                 blockIndex = None
                 path = None
                 #List of Files
                 filenameList = None
                 datetimeList = None
                 #Hdf5 File
                 listMetaname = None
                 listMeta = None
                 listDataname = None
                 listData = None
                 listShapes = None
                 fp = None
                 #dataOut reconstruction
                 dataOut = None
                 def __init__(self):#, **kwargs):
                     ProcessingUnit.__init__(self) #, **kwargs)
                     self.dataOut = Parameters()
                     return
                 def setup(self, **kwargs):
                     path = kwargs['path']
                     startDate = kwargs['startDate']
                     endDate = kwargs['endDate']
                     startTime = kwargs['startTime']
                     endTime = kwargs['endTime']
                     walk = kwargs['walk']
                     if 'ext' in kwargs:
                         ext = kwargs['ext']
                     else:
                         ext = '.hdf5'
                     if 'timezone' in kwargs:
                         self.timezone = kwargs['timezone']
                     else:
                         self.timezone = 'lt'
                     print("[Reading] Searching files in offline mode ...")
                     pathList, filenameList = self.searchFilesOffLine(path, startDate=startDate, endDate=endDate,
                                                                            startTime=startTime, endTime=endTime,
                                                                            ext=ext, walk=walk)
                     if not(filenameList):
                         print("There is no files into the folder: %s"%(path))
                         sys.exit(-1)
                     self.fileIndex = -1
                     self.startTime = startTime
                     self.endTime = endTime
                     self.__readMetadata()
                     self.__setNextFileOffline()
                     return
                 def searchFilesOffLine(self,
                                         path,
                                         startDate=None,
                                         endDate=None,
                                         startTime=datetime.time(0,0,0),
                                         endTime=datetime.time(23,59,59),
                                         ext='.hdf5',
                                         walk=True):
                     expLabel = ''
                     self.filenameList = []
                     self.datetimeList = []
                     pathList = []
                     JRODataObj = JRODataReader()
                     dateList, pathList = JRODataObj.findDatafiles(path, startDate, endDate, expLabel, ext, walk, include_path=True)
                     if dateList == []:
                         print("[Reading] No *%s files in %s from %s to %s)"%(ext, path,
                                                                     datetime.datetime.combine(startDate,startTime).ctime(),
                                                                     datetime.datetime.combine(endDate,endTime).ctime()))
                         return None, None
                     if len(dateList) > 1:
                         print("[Reading] %d days were found in date range: %s - %s" %(len(dateList), startDate, endDate))
                     else:
                         print("[Reading] data was found for the date %s" %(dateList[0]))
                     filenameList = []
                     datetimeList = []
                     #----------------------------------------------------------------------------------
                     for thisPath in pathList:
              #             thisPath = pathList[pathDict[file]]
                         fileList = glob.glob1(thisPath, "*%s" %ext)
                         fileList.sort()
                         for file in fileList:
                             filename = os.path.join(thisPath,file)
                             if not isFileInDateRange(filename, startDate, endDate):
                                 continue
                             thisDatetime = self.__isFileInTimeRange(filename, startDate, endDate, startTime, endTime)
                             if not(thisDatetime):
                                 continue
                             filenameList.append(filename)
                             datetimeList.append(thisDatetime)
                     if not(filenameList):
                         print("[Reading] Any file was found int time range %s - %s" %(datetime.datetime.combine(startDate,startTime).ctime(), datetime.datetime.combine(endDate,endTime).ctime()))
                         return None, None
                     print("[Reading] %d file(s) was(were) found in time range: %s - %s" %(len(filenameList), startTime, endTime))
                     print()
                     self.filenameList = filenameList
                     self.datetimeList = datetimeList
                     return pathList, filenameList
                 def __isFileInTimeRange(self,filename, startDate, endDate, startTime, endTime):
                     """
                     Retorna 1 si el archivo de datos se encuentra dentro del rango de horas especificado.
                     Inputs:
                         filename            :    nombre completo del archivo de datos en formato Jicamarca (.r)
                         startDate          :    fecha inicial del rango seleccionado en formato datetime.date
                         endDate            :    fecha final del rango seleccionado en formato datetime.date
                         startTime          :    tiempo inicial del rango seleccionado en formato datetime.time
                         endTime            :    tiempo final del rango seleccionado en formato datetime.time
                     Return:
                         Boolean    :    Retorna True si el archivo de datos contiene datos en el rango de
                                         fecha especificado, de lo contrario retorna False.
                     Excepciones:
                         Si el archivo no existe o no puede ser abierto
                         Si la cabecera no puede ser leida.
                     """
                     try:
                         fp = h5py.File(filename,'r')
                         grp1 = fp['Data']
                     except IOError:
                         traceback.print_exc()
                         raise IOError("The file %s can't be opened" %(filename))
                     #chino rata
                     #In case has utctime attribute
                     grp2 = grp1['utctime']
              #         thisUtcTime = grp2.value[0] - 5*3600 #To convert to local time
                     thisUtcTime = grp2.value[0]
                     fp.close()
                     if self.timezone == 'lt':
                         thisUtcTime -= 5*3600
                     thisDatetime = datetime.datetime.fromtimestamp(thisUtcTime[0] + 5*3600)
              #         thisDatetime = datetime.datetime.fromtimestamp(thisUtcTime[0])
                     thisDate = thisDatetime.date()
                     thisTime = thisDatetime.time()
                     startUtcTime = (datetime.datetime.combine(thisDate,startTime)- datetime.datetime(1970, 1, 1)).total_seconds()
                     endUtcTime = (datetime.datetime.combine(thisDate,endTime)- datetime.datetime(1970, 1, 1)).total_seconds()
                     #General case
                     #           o>>>>>>>>>>>>>><<<<<<<<<<<<<<o
                     #-----------o----------------------------o-----------
                     #       startTime                     endTime
                     if endTime >= startTime:
                         thisUtcLog = numpy.logical_and(thisUtcTime > startUtcTime, thisUtcTime < endUtcTime)
                         if numpy.any(thisUtcLog):   #If there is one block between the hours mentioned
                             return thisDatetime
                         return None
                     #If endTime < startTime then endTime belongs to the next day
                     #<<<<<<<<<<<o                            o>>>>>>>>>>>
                     #-----------o----------------------------o-----------
                     #        endTime                    startTime
                     if (thisDate == startDate) and numpy.all(thisUtcTime < startUtcTime):
                         return None
                     if (thisDate == endDate) and numpy.all(thisUtcTime > endUtcTime):
                         return None
                     if numpy.all(thisUtcTime < startUtcTime) and numpy.all(thisUtcTime > endUtcTime):
                         return None
                     return thisDatetime
                 def __setNextFileOffline(self):
                     self.fileIndex += 1
                     idFile = self.fileIndex
                     if not(idFile < len(self.filenameList)):
                         print("No more Files")
                         return 0
                     filename = self.filenameList[idFile]
                     filePointer = h5py.File(filename,'r')
                     self.filename = filename
                     self.fp = filePointer
                     print("Setting the file: %s"%self.filename)
              #         self.__readMetadata()
                     self.__setBlockList()
                     self.__readData()
              #         self.nRecords = self.fp['Data'].attrs['blocksPerFile']
              #         self.nRecords = self.fp['Data'].attrs['nRecords']
                     self.blockIndex = 0
                     return 1
                 def __setBlockList(self):
                     '''
                     Selects the data within the times defined
                     self.fp
                     self.startTime
                     self.endTime
                     self.blockList
                     self.blocksPerFile
                     '''
                     fp = self.fp
                     startTime = self.startTime
                     endTime = self.endTime
                     grp = fp['Data']
                     thisUtcTime = grp['utctime'].value.astype(numpy.float)[0]
                     #ERROOOOR
                     if self.timezone == 'lt':
                         thisUtcTime -= 5*3600
                     thisDatetime = datetime.datetime.fromtimestamp(thisUtcTime[0] + 5*3600)
                     thisDate = thisDatetime.date()
                     thisTime = thisDatetime.time()
                     startUtcTime = (datetime.datetime.combine(thisDate,startTime) - datetime.datetime(1970, 1, 1)).total_seconds()
                     endUtcTime = (datetime.datetime.combine(thisDate,endTime) - datetime.datetime(1970, 1, 1)).total_seconds()
                     ind = numpy.where(numpy.logical_and(thisUtcTime >= startUtcTime, thisUtcTime < endUtcTime))[0]
                     self.blockList = ind
                     self.blocksPerFile = len(ind)
                     return
                 def __readMetadata(self):
                     '''
                     Reads Metadata
                     self.pathMeta
                     self.listShapes
                     self.listMetaname
                     self.listMeta
                     '''
              #         grp = self.fp['Data']
              #         pathMeta = os.path.join(self.path, grp.attrs['metadata'])
              #
              #         if pathMeta == self.pathMeta:
              #             return
              #         else:
              #             self.pathMeta = pathMeta
              #
              #         filePointer = h5py.File(self.pathMeta,'r')
              #         groupPointer = filePointer['Metadata']
                     filename = self.filenameList[0]
                     fp = h5py.File(filename,'r')
                     gp = fp['Metadata']
                     listMetaname = []
                     listMetadata = []
                     for item in list(gp.items()):
                         name = item[0]
                         if name=='array dimensions':
                             table = gp[name][:]
                             listShapes = {}
                             for shapes in table:
                                 listShapes[shapes[0]] = numpy.array([shapes[1],shapes[2],shapes[3],shapes[4],shapes[5]])
                         else:
                             data = gp[name].value
                             listMetaname.append(name)
                             listMetadata.append(data)
              #                 if name=='type':
              #                     self.__initDataOut(data)
                     self.listShapes = listShapes
                     self.listMetaname = listMetaname
                     self.listMeta = listMetadata
                     fp.close()
                     return
                 def __readData(self):
                     grp = self.fp['Data']
                     listdataname = []
                     listdata = []
                     for item in list(grp.items()):
                         name = item[0]
                         listdataname.append(name)
                         array = self.__setDataArray(grp[name],self.listShapes[name])
                         listdata.append(array)
                     self.listDataname = listdataname
                     self.listData = listdata
                     return
                 def __setDataArray(self, dataset, shapes):
                     nDims = shapes[0]
                     nDim2 = shapes[1]      #Dimension 0
                     nDim1 = shapes[2]      #Dimension 1, number of Points or Parameters
                     nDim0 = shapes[3]      #Dimension 2, number of samples or ranges
                     mode = shapes[4]        #Mode of storing
                     blockList = self.blockList
                     blocksPerFile = self.blocksPerFile
                     #Depending on what mode the data was stored
                     if mode == 0:       #Divided in channels
                         arrayData = dataset.value.astype(numpy.float)[0][blockList]
                     if mode == 1:     #Divided in parameter
                         strds = 'table'
                         nDatas = nDim1
                         newShapes = (blocksPerFile,nDim2,nDim0)
                     elif mode==2:       #Concatenated in a table
                         strds = 'table0'
                         arrayData = dataset[strds].value
                         #Selecting part of the dataset
                         utctime = arrayData[:,0]
                         u, indices = numpy.unique(utctime, return_index=True)
                         if blockList.size != indices.size:
                             indMin = indices[blockList[0]]
                             if blockList[1] + 1 >= indices.size:
                                 arrayData = arrayData[indMin:,:]
                             else:
                                 indMax = indices[blockList[1] + 1]
                                 arrayData = arrayData[indMin:indMax,:]
                         return arrayData
                     #    One dimension
                     if nDims == 0:
                         arrayData = dataset.value.astype(numpy.float)[0][blockList]
                     #    Two dimensions
                     elif nDims == 2:
                         arrayData = numpy.zeros((blocksPerFile,nDim1,nDim0))
                         newShapes = (blocksPerFile,nDim0)
                         nDatas = nDim1
                         for i in range(nDatas):
                             data = dataset[strds + str(i)].value
                             arrayData[:,i,:] = data[blockList,:]
                     #    Three dimensions
                     else:
                         arrayData = numpy.zeros((blocksPerFile,nDim2,nDim1,nDim0))
                         for i in range(nDatas):
                             data = dataset[strds + str(i)].value
                             for b in range(blockList.size):
                                 arrayData[b,:,i,:] = data[:,:,blockList[b]]
                     return arrayData
                 def __setDataOut(self):
                     listMeta = self.listMeta
                     listMetaname = self.listMetaname
                     listDataname = self.listDataname
                     listData = self.listData
                     listShapes = self.listShapes
                     blockIndex = self.blockIndex
              #         blockList = self.blockList
                     for i in range(len(listMeta)):
                         setattr(self.dataOut,listMetaname[i],listMeta[i])
                     for j in range(len(listData)):
                         nShapes = listShapes[listDataname[j]][0]
                         mode = listShapes[listDataname[j]][4]
                         if nShapes == 1:
                             setattr(self.dataOut,listDataname[j],listData[j][blockIndex])
                         elif nShapes > 1:
                             setattr(self.dataOut,listDataname[j],listData[j][blockIndex,:])
                         elif mode==0:
                             setattr(self.dataOut,listDataname[j],listData[j][blockIndex])
                         #Mode Meteors
                         elif mode ==2:
                             selectedData = self.__selectDataMode2(listData[j], blockIndex)
                             setattr(self.dataOut, listDataname[j], selectedData)
                     return
                 def __selectDataMode2(self, data, blockIndex):
                     utctime = data[:,0]
                     aux, indices = numpy.unique(utctime, return_inverse=True)
                     selInd = numpy.where(indices == blockIndex)[0]
                     selData = data[selInd,:]
                     return selData
                 def getData(self):
                     if self.blockIndex==self.blocksPerFile:
                          if not( self.__setNextFileOffline() ):
                             self.dataOut.flagNoData = True
                             return 0
                     self.__setDataOut()
                     self.dataOut.flagNoData = False
                     self.blockIndex += 1
                     return
                 def run(self, **kwargs):
                     if not(self.isConfig):
                         self.setup(**kwargs)
              #             self.setObjProperties()
                         self.isConfig = True
                     self.getData()
                     return
             @MPDecorator
             class ParamWriter(Operation):
                 '''
                 HDF5 Writer, stores parameters data in HDF5 format files
                 path:             path where the files will be stored
                 blocksPerFile:    number of blocks that will be saved in per HDF5 format file
                 mode:             selects the data stacking mode: '0' channels, '1' parameters, '3' table (for meteors)
                 metadataList:     list of attributes that will be stored as metadata
                 dataList:         list of attributes that will be stores as data
                 '''
                 ext = ".hdf5"
                 optchar = "D"
                 metaoptchar = "M"
                 metaFile = None
                 filename = None
                 path = None
                 setFile = None
                 fp = None
                 grp = None
                 ds = None
                 firsttime = True
                 #Configurations
                 blocksPerFile = None
                 blockIndex = None
                 dataOut = None
                 #Data Arrays
                 dataList = None
                 metadataList = None
                 dsList = None   #List of dictionaries with dataset properties
                 tableDim = None
                 dtype = [('arrayName', 'S20'),('nDimensions', 'i'), ('dim2', 'i'), ('dim1', 'i'),('dim0', 'i'),('mode', 'b')]
                 currentDay = None
                 lastTime = None
+                setType = None
                 def __init__(self):
                     Operation.__init__(self)
                     return
                 def setup(self, dataOut, path=None, blocksPerFile=10, metadataList=None, dataList=None, mode=None, setType=None):
                     self.path = path
                     self.blocksPerFile = blocksPerFile
                     self.metadataList = metadataList
                     self.dataList = dataList
                     self.dataOut = dataOut
                     self.mode = mode
                     if self.mode is not None:
                         self.mode = numpy.zeros(len(self.dataList)) + mode
                     else:
                         self.mode = numpy.ones(len(self.dataList))
                     self.setType = setType
                     arrayDim = numpy.zeros((len(self.dataList),5))
                     #Table dimensions
                     dtype0 = self.dtype
                     tableList = []
                     #Dictionary and list of tables
                     dsList = []
                     for i in range(len(self.dataList)):
                         dsDict = {}
                         dataAux = getattr(self.dataOut, self.dataList[i])
                         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)):
                             dsDict['mode'] = 0
                             dsDict['nDim'] = 0
                             arrayDim[i,0] = 0
                             dsList.append(dsDict)
                         #Mode 2: meteors
                         elif self.mode[i] == 2:
                             dsDict['dsName'] = 'table0'
                             dsDict['mode'] = 2      # Mode meteors
                             dsDict['shape'] = dataAux.shape[-1]
                             dsDict['nDim'] = 0
                             dsDict['dsNumber'] = 1
                             arrayDim[i,3] = dataAux.shape[-1]
                             arrayDim[i,4] = self.mode[i]         #Mode the data was stored
                             dsList.append(dsDict)
                         #Mode 1
                         else:
                             arrayDim0 = dataAux.shape       #Data dimensions
                             arrayDim[i,0] = len(arrayDim0)  #Number of array dimensions
                             arrayDim[i,4] = self.mode[i]         #Mode the data was stored
                             strtable = 'table'
                             dsDict['mode'] = 1      # Mode parameters
                             # Three-dimension arrays
                             if len(arrayDim0) == 3:
                                 arrayDim[i,1:-1] = numpy.array(arrayDim0)
                                 nTables = int(arrayDim[i,2])
                                 dsDict['dsNumber'] = nTables
                                 dsDict['shape'] = arrayDim[i,2:4]
                                 dsDict['nDim'] = 3
                                 for j in range(nTables):
                                     dsDict = dsDict.copy()
                                     dsDict['dsName'] = strtable + str(j)
                                     dsList.append(dsDict)
                             # Two-dimension arrays
                             elif len(arrayDim0) == 2:
                                 arrayDim[i,2:-1] = numpy.array(arrayDim0)
                                 nTables = int(arrayDim[i,2])
                                 dsDict['dsNumber'] = nTables
                                 dsDict['shape'] = arrayDim[i,3]
                                 dsDict['nDim'] = 2
                                 for j in range(nTables):
                                     dsDict = dsDict.copy()
                                     dsDict['dsName'] = strtable + str(j)
                                     dsList.append(dsDict)
                             # One-dimension arrays
                             elif len(arrayDim0) == 1:
                                 arrayDim[i,3] = arrayDim0[0]
                                 dsDict['shape'] = arrayDim0[0]
                                 dsDict['dsNumber'] = 1
                                 dsDict['dsName'] = strtable + str(0)
                                 dsDict['nDim'] = 1
                                 dsList.append(dsDict)
                         table = numpy.array((self.dataList[i],) + tuple(arrayDim[i,:]),dtype = dtype0)
                         tableList.append(table)
                     self.dsList = dsList
                     self.tableDim = numpy.array(tableList, dtype = dtype0)
                     self.blockIndex = 0
                     timeTuple = time.localtime(dataOut.utctime)
                     self.currentDay = timeTuple.tm_yday
                 def putMetadata(self):
                     fp = self.createMetadataFile()
                     self.writeMetadata(fp)
                     fp.close()
                     return
                 def createMetadataFile(self):
                     ext = self.ext
                     path = self.path
                     setFile = self.setFile
                     timeTuple = time.localtime(self.dataOut.utctime)
                     subfolder = ''
                     fullpath = os.path.join( path, subfolder )
                     if not( os.path.exists(fullpath) ):
                         os.mkdir(fullpath)
                         setFile = -1 #inicializo mi contador de seteo
                     subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday)
                     fullpath = os.path.join( path, subfolder )
                     if not( os.path.exists(fullpath) ):
                         os.mkdir(fullpath)
                         setFile = -1 #inicializo mi contador de seteo
                     else:
                         filesList = os.listdir( fullpath )
                         filesList = sorted( filesList, key=str.lower )
                         if len( filesList ) > 0:
                             filesList = [k for k in filesList if k.startswith(self.metaoptchar)]
                             filen = filesList[-1]
                             # el filename debera tener el siguiente formato
                             # 0 1234 567 89A BCDE (hex)
                             # x YYYY DDD SSS .ext
                             if isNumber( filen[8:11] ):
                                 setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file
                             else:
                                 setFile = -1
                         else:
                             setFile = -1 #inicializo mi contador de seteo
                     if self.setType is None:
                         setFile += 1
                         file = '%s%4.4d%3.3d%03d%s' % (self.metaoptchar,
                                                        timeTuple.tm_year,
                                                        timeTuple.tm_yday,
                                                        setFile,
                                                        ext )
                     else:
                         setFile = timeTuple.tm_hour*60+timeTuple.tm_min
                         file = '%s%4.4d%3.3d%04d%s' % (self.metaoptchar,
                                                        timeTuple.tm_year,
                                                        timeTuple.tm_yday,
                                                        setFile,
                                                        ext )
                     filename = os.path.join( path, subfolder, file )
                     self.metaFile = file
                     #Setting HDF5 File
                     fp = h5py.File(filename,'w')
                     return fp
                 def writeMetadata(self, fp):
                     grp = fp.create_group("Metadata")
                     grp.create_dataset('array dimensions', data = self.tableDim, dtype = self.dtype)
                     for i in range(len(self.metadataList)):
                         grp.create_dataset(self.metadataList[i], data=getattr(self.dataOut, self.metadataList[i]))
                     return
                 def timeFlag(self):
                     currentTime = self.dataOut.utctime
                     if self.lastTime is None:
                         self.lastTime = currentTime
                     #Day
                     timeTuple = time.localtime(currentTime)
                     dataDay = timeTuple.tm_yday
                     #Time
                     timeDiff = currentTime - self.lastTime
                     #Si el dia es diferente o si la diferencia entre un dato y otro supera la hora
                     if dataDay != self.currentDay:
                         self.currentDay = dataDay
                         return True
                     elif timeDiff > 3*60*60:
                         self.lastTime = currentTime
                         return True
                     else:
                         self.lastTime = currentTime
                         return False
                 def setNextFile(self):
                     ext = self.ext
                     path = self.path
                     setFile = self.setFile
                     mode = self.mode
                     timeTuple = time.localtime(self.dataOut.utctime)
                     subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday)
                     fullpath = os.path.join( path, subfolder )
                     if os.path.exists(fullpath):
                         filesList = os.listdir( fullpath )
-                        filesList = [k for k in filesList if k.startswith(self.optchar)]
+                        filesList = [k for k in filesList if 'M' in k]
                         if len( filesList ) > 0:
                             filesList = sorted( filesList, key=str.lower )
                             filen = filesList[-1]
                             # el filename debera tener el siguiente formato
                             # 0 1234 567 89A BCDE (hex)
                             # x YYYY DDD SSS .ext
                             if isNumber( filen[8:11] ):
                                 setFile = int( filen[8:11] ) #inicializo mi contador de seteo al seteo del ultimo file
                             else:
                                 setFile = -1
                         else:
                             setFile = -1 #inicializo mi contador de seteo
                     else:
                         os.makedirs(fullpath)
                         setFile = -1 #inicializo mi contador de seteo
                     if self.setType is None:
                         setFile += 1
                         file = '%s%4.4d%3.3d%03d%s' % (self.optchar,
                                                        timeTuple.tm_year,
                                                        timeTuple.tm_yday,
                                                        setFile,
                                                        ext )
                     else:
                         setFile = timeTuple.tm_hour*60+timeTuple.tm_min
                         file = '%s%4.4d%3.3d%04d%s' % (self.optchar,
                                                        timeTuple.tm_year,
                                                        timeTuple.tm_yday,
                                                        setFile,
                                                        ext )
                     filename = os.path.join( path, subfolder, file )
                     #Setting HDF5 File
                     fp = h5py.File(filename,'w')
                     #write metadata
                     self.writeMetadata(fp)
                     #Write data
                     grp = fp.create_group("Data")
                     ds = []
                     data = []
                     dsList = self.dsList
                     i = 0
                     while i < len(dsList):
                         dsInfo = dsList[i]
                         #One-dimension data
                         if dsInfo['mode'] == 0:
                             ds0 = grp.create_dataset(dsInfo['variable'], (1,1), maxshape=(1,self.blocksPerFile) , chunks = True, dtype=numpy.float64)
                             ds.append(ds0)
                             data.append([])
                             i += 1
                             continue
                         elif dsInfo['mode'] == 2:
                             grp0 = grp.create_group(dsInfo['variable'])
                             ds0 = grp0.create_dataset(dsInfo['dsName'], (1,dsInfo['shape']), data = numpy.zeros((1,dsInfo['shape'])) , maxshape=(None,dsInfo['shape']), chunks=True)
                             ds.append(ds0)
                             data.append([])
                             i += 1
                             continue
                         elif dsInfo['mode'] == 1:
                             grp0 = grp.create_group(dsInfo['variable'])
                             for j in range(dsInfo['dsNumber']):
                                 dsInfo = dsList[i]
                                 tableName = dsInfo['dsName']
                                 if dsInfo['nDim'] == 3:
                                     shape = dsInfo['shape'].astype(int)
                                     ds0 = grp0.create_dataset(tableName, (shape[0],shape[1],1) , data = numpy.zeros((shape[0],shape[1],1)), maxshape = (None,shape[1],None), chunks=True)
                                 else:
                                     shape = int(dsInfo['shape'])
                                     ds0 = grp0.create_dataset(tableName, (1,shape), data = numpy.zeros((1,shape)) , maxshape=(None,shape), chunks=True)
                                 ds.append(ds0)
                                 data.append([])
                                 i += 1
                     fp.flush()
                     fp.close()
                     log.log('creating file: {}'.format(filename), 'Writing')
                     self.filename = filename
                     self.ds = ds
                     self.data = data
                     self.firsttime = True
                     self.blockIndex = 0
                     return
                 def putData(self):
                     if self.blockIndex == self.blocksPerFile or self.timeFlag():
                         self.setNextFile()
                     self.readBlock()
                     self.setBlock()     #Prepare data to be written
                     self.writeBlock()   #Write data
                     return
                 def readBlock(self):
                     '''
                     data Array configured
                     self.data
                     '''
                     dsList = self.dsList
                     ds = self.ds
                             #Setting HDF5 File
                     fp = h5py.File(self.filename,'r+')
                     grp = fp["Data"]
                     ind = 0
                     while ind < len(dsList):
                         dsInfo = dsList[ind]
                         if dsInfo['mode'] == 0:
                             ds0 = grp[dsInfo['variable']]
                             ds[ind] = ds0
                             ind += 1
                         else:
                             grp0 = grp[dsInfo['variable']]
                             for j in range(dsInfo['dsNumber']):
                                 dsInfo = dsList[ind]
                                 ds0 = grp0[dsInfo['dsName']]
                                 ds[ind] = ds0
                                 ind += 1
                     self.fp = fp
                     self.grp = grp
                     self.ds = ds
                     return
                 def setBlock(self):
                     '''
                     data Array configured
                     self.data
                     '''
                     #Creating Arrays
                     dsList = self.dsList
                     data = self.data
                     ind = 0
                     while ind < len(dsList):
                         dsInfo = dsList[ind]
                         dataAux = getattr(self.dataOut, dsInfo['variable'])
                         mode = dsInfo['mode']
                         nDim = dsInfo['nDim']
                         if mode == 0 or mode == 2 or nDim == 1:
                             data[ind] = dataAux
                             ind += 1
              #             elif nDim == 1:
              #                 data[ind] = numpy.reshape(dataAux,(numpy.size(dataAux),1))
              #                 ind += 1
                         elif nDim == 2:
                             for j in range(dsInfo['dsNumber']):
                                 data[ind] = dataAux[j,:]
                                 ind += 1
                         elif nDim == 3:
                             for j in range(dsInfo['dsNumber']):
                                 data[ind] = dataAux[:,j,:]
                                 ind += 1
                     self.data = data
                     return
                 def writeBlock(self):
                     '''
                     Saves the block in the HDF5 file
                     '''
                     dsList = self.dsList
                     for i in range(len(self.ds)):
                         dsInfo = dsList[i]
                         nDim = dsInfo['nDim']
                         mode = dsInfo['mode']
                         #    First time
                         if self.firsttime:
                             if type(self.data[i]) == numpy.ndarray:
                                 if nDim == 3:
                                     self.data[i] = self.data[i].reshape((self.data[i].shape[0],self.data[i].shape[1],1))
                                     self.ds[i].resize(self.data[i].shape)
                                 if mode == 2:
                                     self.ds[i].resize(self.data[i].shape)
                             self.ds[i][:] = self.data[i]
                         else:
                         #    From second time
                             #    Meteors!
                             if mode == 2:
                                 dataShape = self.data[i].shape
                                 dsShape = self.ds[i].shape
                                 self.ds[i].resize((self.ds[i].shape[0] + dataShape[0],self.ds[i].shape[1]))
                                 self.ds[i][dsShape[0]:,:] = self.data[i]
                             #    No dimension
                             elif mode == 0:
                                 self.ds[i].resize((self.ds[i].shape[0], self.ds[i].shape[1] + 1))
                                 self.ds[i][0,-1] = self.data[i]
                             #    One dimension
                             elif nDim == 1:
                                 self.ds[i].resize((self.ds[i].shape[0] + 1, self.ds[i].shape[1]))
                                 self.ds[i][-1,:] = self.data[i]
                             #    Two dimension
                             elif nDim == 2:
                                 self.ds[i].resize((self.ds[i].shape[0] + 1,self.ds[i].shape[1]))
                                 self.ds[i][self.blockIndex,:] = self.data[i]
                             #    Three dimensions
                             elif nDim == 3:
                                 self.ds[i].resize((self.ds[i].shape[0],self.ds[i].shape[1],self.ds[i].shape[2]+1))
                                 self.ds[i][:,:,-1] = self.data[i]
                     self.firsttime = False
                     self.blockIndex += 1
                     #Close to save changes
                     self.fp.flush()
                     self.fp.close()
                     return
                 def run(self, dataOut, path, blocksPerFile=10, metadataList=None, dataList=None, mode=None, setType=None):
+                    self.dataOut = dataOut
                     if not(self.isConfig):
                         self.setup(dataOut, path=path, blocksPerFile=blocksPerFile,
                                    metadataList=metadataList, dataList=dataList, mode=mode,
                                    setType=setType)
                         self.isConfig = True
                         self.setNextFile()
                     self.putData()
                     return
   No newline at end of file

schainpy/model/proc/jroproc_spectra.py +4 -8

             import itertools
             import numpy
             from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation
             from schainpy.model.data.jrodata import Spectra
             from schainpy.model.data.jrodata import hildebrand_sekhon
             from schainpy.utils import log
             @MPDecorator
             class SpectraProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.buffer = None
                     self.firstdatatime = None
                     self.profIndex = 0
                     self.dataOut = Spectra()
                     self.id_min = None
                     self.id_max = None
                     self.setupReq = False #Agregar a todas las unidades de proc
                 def __updateSpecFromVoltage(self):
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     try:
                         self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
                     except:
                         pass
                     self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.dtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
                     self.dataOut.nBaud = self.dataIn.nBaud
                     self.dataOut.nCode = self.dataIn.nCode
                     self.dataOut.code = self.dataIn.code
                     self.dataOut.nProfiles = self.dataOut.nFFTPoints
                     self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
                     self.dataOut.utctime = self.firstdatatime
                     # asumo q la data esta decodificada
                     self.dataOut.flagDecodeData = self.dataIn.flagDecodeData
                     # asumo q la data esta sin flip
                     self.dataOut.flagDeflipData = self.dataIn.flagDeflipData
                     self.dataOut.flagShiftFFT = False
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.nIncohInt = 1
                     self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.realtime = self.dataIn.realtime
                     self.dataOut.azimuth = self.dataIn.azimuth
                     self.dataOut.zenith = self.dataIn.zenith
                     self.dataOut.beam.codeList = self.dataIn.beam.codeList
                     self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
                     self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
                 def __getFft(self):
                     """
                     Convierte valores de Voltaje a Spectra
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                         self.profIndex
                         self.buffer
                         self.dataOut.flagNoData
                     """
                     fft_volt = numpy.fft.fft(
                         self.buffer, n=self.dataOut.nFFTPoints, axis=1)
                     fft_volt = fft_volt.astype(numpy.dtype('complex'))
                     dc = fft_volt[:, 0, :]
                     # calculo de self-spectra
                     fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
                     spc = fft_volt * numpy.conjugate(fft_volt)
                     spc = spc.real
                     blocksize = 0
                     blocksize += dc.size
                     blocksize += spc.size
                     cspc = None
                     pairIndex = 0
                     if self.dataOut.pairsList != None:
                         # calculo de cross-spectra
                         cspc = numpy.zeros(
                             (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
                         for pair in self.dataOut.pairsList:
                             if pair[0] not in self.dataOut.channelList:
                                 raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % (
                                     str(pair), str(self.dataOut.channelList)))
                             if pair[1] not in self.dataOut.channelList:
                                 raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % (
                                     str(pair), str(self.dataOut.channelList)))
                             cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \
                                 numpy.conjugate(fft_volt[pair[1], :, :])
                             pairIndex += 1
                         blocksize += cspc.size
                     self.dataOut.data_spc = spc
                     self.dataOut.data_cspc = cspc
                     self.dataOut.data_dc = dc
                     self.dataOut.blockSize = blocksize
                     self.dataOut.flagShiftFFT = True
                 def run(self, nProfiles=None, nFFTPoints=None, pairsList=[], ippFactor=None, shift_fft=False):
                     if self.dataIn.type == "Spectra":
                         self.dataOut.copy(self.dataIn)
                         if shift_fft:
                             #desplaza a la derecha en el eje 2 determinadas posiciones
                             shift = int(self.dataOut.nFFTPoints/2)
                             self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1)
                             if self.dataOut.data_cspc is not None:
                                 #desplaza a la derecha en el eje 2 determinadas posiciones
                                 self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1)
                         return True
                     if self.dataIn.type == "Voltage":
                         self.dataOut.flagNoData = True
                         if nFFTPoints == None:
                             raise ValueError("This SpectraProc.run() need nFFTPoints input variable")
                         if nProfiles == None:
                             nProfiles = nFFTPoints
                         if ippFactor == None:
                             ippFactor = 1
                         self.dataOut.ippFactor = ippFactor
                         self.dataOut.nFFTPoints = nFFTPoints
                         self.dataOut.pairsList = pairsList
                         if self.buffer is None:
                             self.buffer = numpy.zeros((self.dataIn.nChannels,
                                                        nProfiles,
                                                        self.dataIn.nHeights),
                                                       dtype='complex')
                         if self.dataIn.flagDataAsBlock:
                             nVoltProfiles = self.dataIn.data.shape[1]
                             if nVoltProfiles == nProfiles:
                                 self.buffer = self.dataIn.data.copy()
                                 self.profIndex = nVoltProfiles
                             elif nVoltProfiles < nProfiles:
                                 if self.profIndex == 0:
                                     self.id_min = 0
                                     self.id_max = nVoltProfiles
                                 self.buffer[:, self.id_min:self.id_max,
                                             :] = self.dataIn.data
                                 self.profIndex += nVoltProfiles
                                 self.id_min += nVoltProfiles
                                 self.id_max += nVoltProfiles
                             else:
                                 raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % (
                                     self.dataIn.type, self.dataIn.data.shape[1], nProfiles))
                                 self.dataOut.flagNoData = True
                                 return 0
                         else:
                             self.buffer[:, self.profIndex, :] = self.dataIn.data.copy()
                             self.profIndex += 1
                         if self.firstdatatime == None:
                             self.firstdatatime = self.dataIn.utctime
                         if self.profIndex == nProfiles:
                             self.__updateSpecFromVoltage()
                             self.__getFft()
                             self.dataOut.flagNoData = False
                             self.firstdatatime = None
                             self.profIndex = 0
                         return True
                     raise ValueError("The type of input object '%s' is not valid" % (
                         self.dataIn.type))
                 def __selectPairs(self, pairsList):
                     if not pairsList:
                         return
                     pairs = []
                     pairsIndex = []
                     for pair in pairsList:
                         if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList:
                             continue
                         pairs.append(pair)
                         pairsIndex.append(pairs.index(pair))
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex]
                     self.dataOut.pairsList = pairs
                     return
                 def __selectPairsByChannel(self, channelList=None):
                     if channelList == None:
                         return
                     pairsIndexListSelected = []
                     for pairIndex in self.dataOut.pairsIndexList:
                         # First pair
                         if self.dataOut.pairsList[pairIndex][0] not in channelList:
                             continue
                         # Second pair
                         if self.dataOut.pairsList[pairIndex][1] not in channelList:
                             continue
                         pairsIndexListSelected.append(pairIndex)
                     if not pairsIndexListSelected:
                         self.dataOut.data_cspc = None
                         self.dataOut.pairsList = []
                         return
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
                     self.dataOut.pairsList = [self.dataOut.pairsList[i]
                                               for i in pairsIndexListSelected]
                     return
                 def selectChannels(self, channelList):
                     channelIndexList = []
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError("Error selecting channels, Channel %d is not valid.\nAvailable channels = %s" % (
                                 channel, str(self.dataOut.channelList)))
                         index = self.dataOut.channelList.index(channel)
                         channelIndexList.append(index)
                     self.selectChannelsByIndex(channelIndexList)
                 def selectChannelsByIndex(self, channelIndexList):
                     """
                     Selecciona un bloque de datos en base a canales segun el channelIndexList
                     Input:
                         channelIndexList    :    lista sencilla de canales a seleccionar por ej. [2,3,7]
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             raise ValueError("Error selecting channels: The value %d in channelIndexList is not valid.\nAvailable channel indexes = " % (
                                 channelIndex, self.dataOut.channelIndexList))
-            #         nChannels = len(channelIndexList)
                     data_spc = self.dataOut.data_spc[channelIndexList, :]
                     data_dc = self.dataOut.data_dc[channelIndexList, :]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_dc = data_dc
-                    self.dataOut.channelList = [
+                    # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
-                        self.dataOut.channelList[i] for i in channelIndexList]
+                    self.dataOut.channelList = range(len(channelIndexList))
-            #        self.dataOut.nChannels = nChannels
+                    self.__selectPairsByChannel(channelIndexList)
-                    self.__selectPairsByChannel(self.dataOut.channelList)
                     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))
                     if (minFFT < self.dataOut.getFreqRange()[0]):
                         minFFT = self.dataOut.getFreqRange()[0]
                     if (maxFFT > self.dataOut.getFreqRange()[-1]):
                         maxFFT = self.dataOut.getFreqRange()[-1]
                     minIndex = 0
                     maxIndex = 0
                     FFTs = self.dataOut.getFreqRange()
                     inda = numpy.where(FFTs >= minFFT)
                     indb = numpy.where(FFTs <= maxFFT)
                     try:
                         minIndex = inda[0][0]
                     except:
                         minIndex = 0
                     try:
                         maxIndex = indb[0][-1]
                     except:
                         maxIndex = len(FFTs)
                     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
                     minHei <= height <= maxHei
                     Input:
                         minHei    :    valor minimo de altura a considerar
                         maxHei    :    valor maximo de altura a considerar
                     Affected:
                         Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minHei > maxHei):
                         raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minHei, maxHei))
                     if (minHei < self.dataOut.heightList[0]):
                         minHei = self.dataOut.heightList[0]
                     if (maxHei > self.dataOut.heightList[-1]):
                         maxHei = self.dataOut.heightList[-1]
                     minIndex = 0
                     maxIndex = 0
                     heights = self.dataOut.heightList
                     inda = numpy.where(heights >= minHei)
                     indb = numpy.where(heights <= maxHei)
                     try:
                         minIndex = inda[0][0]
                     except:
                         minIndex = 0
                     try:
                         maxIndex = indb[0][-1]
                     except:
                         maxIndex = len(heights)
                     self.selectHeightsByIndex(minIndex, maxIndex)
                     return 1
                 def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None):
                     newheis = numpy.where(
                         self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex])
                     if hei_ref != None:
                         newheis = numpy.where(self.dataOut.heightList > hei_ref)
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                     heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     # determina indices
                     nheis = int(self.dataOut.radarControllerHeaderObj.txB /
                                 (self.dataOut.heightList[1] - self.dataOut.heightList[0]))
                     avg_dB = 10 * \
                         numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0))
                     beacon_dB = numpy.sort(avg_dB)[-nheis:]
                     beacon_heiIndexList = []
                     for val in avg_dB.tolist():
                         if val >= beacon_dB[0]:
                             beacon_heiIndexList.append(avg_dB.tolist().index(val))
                     #data_spc = data_spc[:,:,beacon_heiIndexList]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                         #data_cspc = data_cspc[:,:,beacon_heiIndexList]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                         #data_dc = data_dc[:,beacon_heiIndexList]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = heightList
                     self.dataOut.beacon_heiIndexList = beacon_heiIndexList
                     return 1
                 def selectFFTsByIndex(self, minIndex, maxIndex):
                     """
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nProfiles):
                         maxIndex = self.dataOut.nProfiles-1
                     #Spectra
                     data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.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]
                     return 1
                 def selectHeightsByIndex(self, minIndex, maxIndex):
                     """
                     Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
                     minIndex <= index <= maxIndex
                     Input:
                         minIndex    :    valor de indice minimo de altura a considerar
                         maxIndex    :    valor de indice maximo de altura a considerar
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (
                             minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights - 1
                     # Spectra
                     data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     return 1
                 def removeDC(self, mode=2):
                     jspectra = self.dataOut.data_spc
                     jcspectra = self.dataOut.data_cspc
                     num_chan = jspectra.shape[0]
                     num_hei = jspectra.shape[2]
                     if jcspectra is not None:
                         jcspectraExist = True
                         num_pairs = jcspectra.shape[0]
                     else:
                         jcspectraExist = False
                     freq_dc = int(jspectra.shape[1] / 2)
                     ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
                     ind_vel = ind_vel.astype(int)
                     if ind_vel[0] < 0:
                         ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof
                     if mode == 1:
                         jspectra[:, freq_dc, :] = (
                             jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2  # CORRECCION
                         if jcspectraExist:
                             jcspectra[:, freq_dc, :] = (
                                 jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2
                     if mode == 2:
                         vel = numpy.array([-2, -1, 1, 2])
                         xx = numpy.zeros([4, 4])
                         for fil in range(4):
                             xx[fil, :] = vel[fil]**numpy.asarray(list(range(4)))
                         xx_inv = numpy.linalg.inv(xx)
                         xx_aux = xx_inv[0, :]
                         for ich in range(num_chan):
                             yy = jspectra[ich, ind_vel, :]
                             jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy)
                             junkid = jspectra[ich, freq_dc, :] <= 0
                             cjunkid = sum(junkid)
                             if cjunkid.any():
                                 jspectra[ich, freq_dc, junkid.nonzero()] = (
                                     jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2
                         if jcspectraExist:
                             for ip in range(num_pairs):
                                 yy = jcspectra[ip, ind_vel, :]
                                 jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy)
                     self.dataOut.data_spc = jspectra
                     self.dataOut.data_cspc = jcspectra
                     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)]
                         SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ]
                         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
                     jcspectra = self.dataOut.data_cspc
                     jnoise = self.dataOut.getNoise()
                     num_incoh = self.dataOut.nIncohInt
                     num_channel = jspectra.shape[0]
                     num_prof = jspectra.shape[1]
                     num_hei = jspectra.shape[2]
                     # hei_interf
                     if hei_interf is None:
                         count_hei = int(num_hei / 2)
                         hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei
                         hei_interf = numpy.asarray(hei_interf)[0]
                     # nhei_interf
                     if (nhei_interf == None):
                         nhei_interf = 5
                     if (nhei_interf < 1):
                         nhei_interf = 1
                     if (nhei_interf > count_hei):
                         nhei_interf = count_hei
                     if (offhei_interf == None):
                         offhei_interf = 0
                     ind_hei = list(range(num_hei))
             #         mask_prof = numpy.asarray(range(num_prof - 2)) + 1
             #         mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
                     mask_prof = numpy.asarray(list(range(num_prof)))
                     num_mask_prof = mask_prof.size
                     comp_mask_prof = [0, num_prof / 2]
                     # noise_exist:    Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
                     if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
                         jnoise = numpy.nan
                     noise_exist = jnoise[0] < numpy.Inf
                     # Subrutina de Remocion de la Interferencia
                     for ich in range(num_channel):
                         # Se ordena los espectros segun su potencia (menor a mayor)
                         power = jspectra[ich, mask_prof, :]
                         power = power[:, hei_interf]
                         power = power.sum(axis=0)
                         psort = power.ravel().argsort()
                         # Se estima la interferencia promedio en los Espectros de Potencia empleando
                         junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range(
                             offhei_interf, nhei_interf + offhei_interf))]]]
                         if noise_exist:
                             #    tmp_noise = jnoise[ich] / num_prof
                             tmp_noise = jnoise[ich]
                         junkspc_interf = junkspc_interf - tmp_noise
                         #junkspc_interf[:,comp_mask_prof] = 0
                         jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf
                         jspc_interf = jspc_interf.transpose()
                         # Calculando el espectro de interferencia promedio
                         noiseid = numpy.where(
                             jspc_interf <= tmp_noise / numpy.sqrt(num_incoh))
                         noiseid = noiseid[0]
                         cnoiseid = noiseid.size
                         interfid = numpy.where(
                             jspc_interf > tmp_noise / numpy.sqrt(num_incoh))
                         interfid = interfid[0]
                         cinterfid = interfid.size
                         if (cnoiseid > 0):
                             jspc_interf[noiseid] = 0
                         # Expandiendo los perfiles a limpiar
                         if (cinterfid > 0):
                             new_interfid = (
                                 numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof
                             new_interfid = numpy.asarray(new_interfid)
                             new_interfid = {x for x in new_interfid}
                             new_interfid = numpy.array(list(new_interfid))
                             new_cinterfid = new_interfid.size
                         else:
                             new_cinterfid = 0
                         for ip in range(new_cinterfid):
                             ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort()
                             jspc_interf[new_interfid[ip]
                                         ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]]
                         jspectra[ich, :, ind_hei] = jspectra[ich, :,
                                                              ind_hei] - jspc_interf  # Corregir indices
                         # Removiendo la interferencia del punto de mayor interferencia
                         ListAux = jspc_interf[mask_prof].tolist()
                         maxid = ListAux.index(max(ListAux))
                         if cinterfid > 0:
                             for ip in range(cinterfid * (interf == 2) - 1):
                                 ind = (jspectra[ich, interfid[ip], :] < tmp_noise *
                                        (1 + 1 / numpy.sqrt(num_incoh))).nonzero()
                                 cind = len(ind)
                                 if (cind > 0):
                                     jspectra[ich, interfid[ip], ind] = tmp_noise * \
                                         (1 + (numpy.random.uniform(cind) - 0.5) /
                                          numpy.sqrt(num_incoh))
                             ind = numpy.array([-2, -1, 1, 2])
                             xx = numpy.zeros([4, 4])
                             for id1 in range(4):
                                 xx[:, id1] = ind[id1]**numpy.asarray(list(range(4)))
                             xx_inv = numpy.linalg.inv(xx)
                             xx = xx_inv[:, 0]
                             ind = (ind + maxid + num_mask_prof) % num_mask_prof
                             yy = jspectra[ich, mask_prof[ind], :]
                             jspectra[ich, mask_prof[maxid], :] = numpy.dot(
                                 yy.transpose(), xx)
                         indAux = (jspectra[ich, :, :] < tmp_noise *
                                   (1 - 1 / numpy.sqrt(num_incoh))).nonzero()
                         jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \
                             (1 - 1 / numpy.sqrt(num_incoh))
                     # Remocion de Interferencia en el Cross Spectra
                     if jcspectra is None:
                         return jspectra, jcspectra
                     num_pairs = int(jcspectra.size / (num_prof * num_hei))
                     jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
                     for ip in range(num_pairs):
                         #-------------------------------------------
                         cspower = numpy.abs(jcspectra[ip, mask_prof, :])
                         cspower = cspower[:, hei_interf]
                         cspower = cspower.sum(axis=0)
                         cspsort = cspower.ravel().argsort()
                         junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range(
                             offhei_interf, nhei_interf + offhei_interf))]]]
                         junkcspc_interf = junkcspc_interf.transpose()
                         jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf
                         ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
                         median_real = int(numpy.median(numpy.real(
                             junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
                         median_imag = int(numpy.median(numpy.imag(
                             junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
                         comp_mask_prof = [int(e) for e in comp_mask_prof]
                         junkcspc_interf[comp_mask_prof, :] = numpy.complex(
                             median_real, median_imag)
                         for iprof in range(num_prof):
                             ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()
                             jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]]
                         # Removiendo la Interferencia
                         jcspectra[ip, :, ind_hei] = jcspectra[ip,
                                                               :, ind_hei] - jcspc_interf
                         ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
                         maxid = ListAux.index(max(ListAux))
                         ind = numpy.array([-2, -1, 1, 2])
                         xx = numpy.zeros([4, 4])
                         for id1 in range(4):
                             xx[:, id1] = ind[id1]**numpy.asarray(list(range(4)))
                         xx_inv = numpy.linalg.inv(xx)
                         xx = xx_inv[:, 0]
                         ind = (ind + maxid + num_mask_prof) % num_mask_prof
                         yy = jcspectra[ip, mask_prof[ind], :]
                         jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx)
                     # Guardar Resultados
                     self.dataOut.data_spc = jspectra
                     self.dataOut.data_cspc = jcspectra
                     return 1
                 def setRadarFrequency(self, frequency=None):
                     if frequency != None:
                         self.dataOut.frequency = frequency
                     return 1
                 def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
                     # validacion de rango
                     if minHei == None:
                         minHei = self.dataOut.heightList[0]
                     if maxHei == None:
                         maxHei = self.dataOut.heightList[-1]
                     if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
                         print('minHei: %.2f is out of the heights range' % (minHei))
                         print('minHei is setting to %.2f' % (self.dataOut.heightList[0]))
                         minHei = self.dataOut.heightList[0]
                     if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
                         print('maxHei: %.2f is out of the heights range' % (maxHei))
                         print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1]))
                         maxHei = self.dataOut.heightList[-1]
                     # validacion de velocidades
                     velrange = self.dataOut.getVelRange(1)
                     if minVel == None:
                         minVel = velrange[0]
                     if maxVel == None:
                         maxVel = velrange[-1]
                     if (minVel < velrange[0]) or (minVel > maxVel):
                         print('minVel: %.2f is out of the velocity range' % (minVel))
                         print('minVel is setting to %.2f' % (velrange[0]))
                         minVel = velrange[0]
                     if (maxVel > velrange[-1]) or (maxVel < minVel):
                         print('maxVel: %.2f is out of the velocity range' % (maxVel))
                         print('maxVel is setting to %.2f' % (velrange[-1]))
                         maxVel = velrange[-1]
                     # seleccion de indices para rango
                     minIndex = 0
                     maxIndex = 0
                     heights = self.dataOut.heightList
                     inda = numpy.where(heights >= minHei)
                     indb = numpy.where(heights <= maxHei)
                     try:
                         minIndex = inda[0][0]
                     except:
                         minIndex = 0
                     try:
                         maxIndex = indb[0][-1]
                     except:
                         maxIndex = len(heights)
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("some value in (%d,%d) is not valid" % (
                             minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights - 1
                     # seleccion de indices para velocidades
                     indminvel = numpy.where(velrange >= minVel)
                     indmaxvel = numpy.where(velrange <= maxVel)
                     try:
                         minIndexVel = indminvel[0][0]
                     except:
                         minIndexVel = 0
                     try:
                         maxIndexVel = indmaxvel[0][-1]
                     except:
                         maxIndexVel = len(velrange)
                     # seleccion del espectro
                     data_spc = self.dataOut.data_spc[:,
                                                      minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1]
                     # estimacion de ruido
                     noise = numpy.zeros(self.dataOut.nChannels)
                     for channel in range(self.dataOut.nChannels):
                         daux = data_spc[channel, :, :]
                         noise[channel] = hildebrand_sekhon(daux, self.dataOut.nIncohInt)
                     self.dataOut.noise_estimation = noise.copy()
                     return 1
             class IncohInt(Operation):
                 __profIndex = 0
                 __withOverapping = False
                 __byTime = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer_spc = None
                 __buffer_cspc = None
                 __buffer_dc = None
                 __dataReady = False
                 __timeInterval = None
                 n = None
                 def __init__(self):
                     Operation.__init__(self)
                 def setup(self, n=None, timeInterval=None, overlapping=False):
                     """
                     Set the parameters of the integration class.
                     Inputs:
                         n        :    Number of coherent integrations
                         timeInterval   :    Time of integration. If the parameter "n" is selected this one does not work
                         overlapping    :
                     """
                     self.__initime = None
                     self.__lastdatatime = 0
                     self.__buffer_spc = 0
                     self.__buffer_cspc = 0
                     self.__buffer_dc = 0
                     self.__profIndex = 0
                     self.__dataReady = False
                     self.__byTime = False
                     if n is None and timeInterval is None:
                         raise ValueError("n or timeInterval should be specified ...")
                     if n is not None:
                         self.n = int(n)
                     else:
                         self.__integrationtime = int(timeInterval)
                         self.n = None
                         self.__byTime = True
                 def putData(self, data_spc, data_cspc, data_dc):
                     """
                     Add a profile to the __buffer_spc and increase in one the __profileIndex
                     """
                     self.__buffer_spc += data_spc
                     if data_cspc is None:
                         self.__buffer_cspc = None
                     else:
                         self.__buffer_cspc += data_cspc
                     if data_dc is None:
                         self.__buffer_dc = None
                     else:
                         self.__buffer_dc += data_dc
                     self.__profIndex += 1
                     return
                 def pushData(self):
                     """
                     Return the sum of the last profiles and the profiles used in the sum.
                     Affected:
                     self.__profileIndex
                     """
                     data_spc = self.__buffer_spc
                     data_cspc = self.__buffer_cspc
                     data_dc = self.__buffer_dc
                     n = self.__profIndex
                     self.__buffer_spc = 0
                     self.__buffer_cspc = 0
                     self.__buffer_dc = 0
                     self.__profIndex = 0
                     return data_spc, data_cspc, data_dc, n
                 def byProfiles(self, *args):
                     self.__dataReady = False
                     avgdata_spc = None
                     avgdata_cspc = None
                     avgdata_dc = None
                     self.putData(*args)
                     if self.__profIndex == self.n:
                         avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata_spc, avgdata_cspc, avgdata_dc
                 def byTime(self, datatime, *args):
                     self.__dataReady = False
                     avgdata_spc = None
                     avgdata_cspc = None
                     avgdata_dc = None
                     self.putData(*args)
                     if (datatime - self.__initime) >= self.__integrationtime:
                         avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata_spc, avgdata_cspc, avgdata_dc
                 def integrate(self, datatime, *args):
                     if self.__profIndex == 0:
                         self.__initime = datatime
                     if self.__byTime:
                         avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(
                             datatime, *args)
                     else:
                         avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
                     if not self.__dataReady:
                         return None, None, None, None
                     return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc
                 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
                     if n == 1:
                         return
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         self.setup(n, timeInterval, overlapping)
                         self.isConfig = True
                     avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
                                                                                         dataOut.data_spc,
                                                                                         dataOut.data_cspc,
                                                                                         dataOut.data_dc)
                     if self.__dataReady:
                         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

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

             import sys
             import numpy
             from scipy import interpolate
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
             from schainpy.model.data.jrodata import Voltage
             from schainpy.utils import log
             from time import time
             @MPDecorator
             class VoltageProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.dataOut = Voltage()
                     self.flip = 1
                     self.setupReq = False
                 def run(self):
                     if self.dataIn.type == 'AMISR':
                         self.__updateObjFromAmisrInput()
                     if self.dataIn.type == 'Voltage':
                         self.dataOut.copy(self.dataIn)
                 #         self.dataOut.copy(self.dataIn)
                 def __updateObjFromAmisrInput(self):
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     self.dataOut.flagNoData = self.dataIn.flagNoData
                     self.dataOut.data = self.dataIn.data
                     self.dataOut.utctime = self.dataIn.utctime
                     self.dataOut.channelList = self.dataIn.channelList
                     #self.dataOut.timeInterval = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.nProfiles = self.dataIn.nProfiles
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.ippSeconds = self.dataIn.ippSeconds
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.azimuth = self.dataIn.azimuth
                     self.dataOut.zenith = self.dataIn.zenith
                     self.dataOut.beam.codeList = self.dataIn.beam.codeList
                     self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
                     self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
                 #
                 #        pass#
                 #
                 #    def init(self):
                 #
                 #
                 #        if self.dataIn.type == 'AMISR':
                 #            self.__updateObjFromAmisrInput()
                 #
                 #        if self.dataIn.type == 'Voltage':
                 #            self.dataOut.copy(self.dataIn)
                 #        # No necesita copiar en cada init() los atributos de dataIn
                 #        # la copia deberia hacerse por cada nuevo bloque de datos
                 def selectChannels(self, channelList):
                     channelIndexList = []
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList)))
                         index = self.dataOut.channelList.index(channel)
                         channelIndexList.append(index)
                     self.selectChannelsByIndex(channelIndexList)
                 def selectChannelsByIndex(self, channelIndexList):
                     """
                     Selecciona un bloque de datos en base a canales segun el channelIndexList
                     Input:
                         channelIndexList    :    lista sencilla de canales a seleccionar por ej. [2,3,7]
                     Affected:
                         self.dataOut.data
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                         self.dataOut.m_ProcessingHeader.totalSpectra
                         self.dataOut.systemHeaderObj.numChannels
                         self.dataOut.m_ProcessingHeader.blockSize
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             print(channelIndexList)
                             raise ValueError("The value %d in channelIndexList is not valid" %channelIndex)
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         data = self.dataOut.data[channelIndexList,:,:]
                     else:
                         data = self.dataOut.data[channelIndexList,:]
                     self.dataOut.data = data
-                    self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
+                    # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
-                    #        self.dataOut.nChannels = nChannels
+                    self.dataOut.channelList = range(len(channelIndexList))
                     return 1
                 def selectHeights(self, minHei=None, maxHei=None):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
                     minHei <= height <= maxHei
                     Input:
                         minHei    :    valor minimo de altura a considerar
                         maxHei    :    valor maximo de altura a considerar
                     Affected:
                         Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if minHei == None:
                         minHei = self.dataOut.heightList[0]
                     if maxHei == None:
                         maxHei = self.dataOut.heightList[-1]
                     if (minHei < self.dataOut.heightList[0]):
                         minHei = self.dataOut.heightList[0]
                     if (maxHei > self.dataOut.heightList[-1]):
                         maxHei = self.dataOut.heightList[-1]
                     minIndex = 0
                     maxIndex = 0
                     heights = self.dataOut.heightList
                     inda = numpy.where(heights >= minHei)
                     indb = numpy.where(heights <= maxHei)
                     try:
                         minIndex = inda[0][0]
                     except:
                         minIndex = 0
                     try:
                         maxIndex = indb[0][-1]
                     except:
                         maxIndex = len(heights)
                     self.selectHeightsByIndex(minIndex, maxIndex)
                     return 1
                 def selectHeightsByIndex(self, minIndex, maxIndex):
                     """
                     Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
                     minIndex <= index <= maxIndex
                     Input:
                         minIndex    :    valor de indice minimo de altura a considerar
                         maxIndex    :    valor de indice maximo de altura a considerar
                     Affected:
                         self.dataOut.data
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights
                     #voltage
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         data = self.dataOut.data[:,:, minIndex:maxIndex]
                     else:
                         data = self.dataOut.data[:, minIndex:maxIndex]
                     #         firstHeight = self.dataOut.heightList[minIndex]
                     self.dataOut.data = data
                     self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
                     if self.dataOut.nHeights <= 1:
                         raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights))
                     return 1
                 def filterByHeights(self, window):
                     deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                     if window == None:
                         window = (self.dataOut.radarControllerHeaderObj.txA/self.dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
                     newdelta = deltaHeight * window
                     r = self.dataOut.nHeights % window
                     newheights = (self.dataOut.nHeights-r)/window
                     if newheights <= 1:
                         raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(self.dataOut.nHeights, window))
                     if self.dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         buffer = self.dataOut.data[:, :, 0:int(self.dataOut.nHeights-r)]
                         buffer = buffer.reshape(self.dataOut.nChannels,self.dataOut.nProfiles,self.dataOut.nHeights/window,window)
                         buffer = numpy.sum(buffer,3)
                     else:
                         buffer = self.dataOut.data[:,0:int(self.dataOut.nHeights-r)]
                         buffer = buffer.reshape(self.dataOut.nChannels,int(self.dataOut.nHeights/window),int(window))
                         buffer = numpy.sum(buffer,2)
                     self.dataOut.data = buffer
                     self.dataOut.heightList = self.dataOut.heightList[0] + numpy.arange( newheights )*newdelta
                     self.dataOut.windowOfFilter = window
                 def setH0(self, h0, deltaHeight = None):
                     if not deltaHeight:
                         deltaHeight = self.dataOut.heightList[1] - self.dataOut.heightList[0]
                     nHeights = self.dataOut.nHeights
                     newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
                     self.dataOut.heightList = newHeiRange
                 def deFlip(self, channelList = []):
                     data = self.dataOut.data.copy()
                     if self.dataOut.flagDataAsBlock:
                         flip = self.flip
                         profileList = list(range(self.dataOut.nProfiles))
                         if not channelList:
                             for thisProfile in profileList:
                                 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
                                 flip *= -1.0
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in self.dataOut.channelList:
                                     continue
                                 for thisProfile in profileList:
                                     data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
                                     flip *= -1.0
                         self.flip = flip
                     else:
                         if not channelList:
                             data[:,:] = data[:,:]*self.flip
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in self.dataOut.channelList:
                                     continue
                                 data[thisChannel,:] = data[thisChannel,:]*self.flip
                         self.flip *= -1.
                     self.dataOut.data = data
                 def setRadarFrequency(self, frequency=None):
                     if frequency != None:
                         self.dataOut.frequency = frequency
                     return 1
                 def interpolateHeights(self, topLim, botLim):
                     #69 al 72 para julia
                     #82-84 para meteoros
                     if len(numpy.shape(self.dataOut.data))==2:
                         sampInterp = (self.dataOut.data[:,botLim-1] + self.dataOut.data[:,topLim+1])/2
                         sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
                         #self.dataOut.data[:,botLim:limSup+1] = sampInterp
                         self.dataOut.data[:,botLim:topLim+1] = sampInterp
                     else:
                         nHeights = self.dataOut.data.shape[2]
                         x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
                         y = self.dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))]
                         f = interpolate.interp1d(x, y, axis = 2)
                         xnew = numpy.arange(botLim,topLim+1)
                         ynew = f(xnew)
                         self.dataOut.data[:,:,botLim:topLim+1]  = ynew
                 # import collections
             class CohInt(Operation):
                 isConfig = False
                 __profIndex = 0
                 __byTime = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __bufferStride = []
                 __dataReady = False
                 __profIndexStride = 0
                 __dataToPutStride = False
                 n = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     #   self.isConfig = False
                 def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
                     """
                     Set the parameters of the integration class.
                     Inputs:
                         n               :    Number of coherent integrations
                         timeInterval    :    Time of integration. If the parameter "n" is selected this one does not work
                         overlapping     :
                     """
                     self.__initime = None
                     self.__lastdatatime = 0
                     self.__buffer = None
                     self.__dataReady = False
                     self.byblock = byblock
                     self.stride = stride
                     if n == None and timeInterval == None:
                         raise ValueError("n or timeInterval should be specified ...")
                     if n != None:
                         self.n = n
                         self.__byTime = False
                     else:
                         self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
                         self.n = 9999
                         self.__byTime = True
                     if overlapping:
                         self.__withOverlapping = True
                         self.__buffer = None
                     else:
                         self.__withOverlapping = False
                         self.__buffer = 0
                     self.__profIndex = 0
                 def putData(self, data):
                     """
                     Add a profile to the __buffer and increase in one the __profileIndex
                     """
                     if not self.__withOverlapping:
                         self.__buffer += data.copy()
                         self.__profIndex += 1
                         return
                     #Overlapping data
                     nChannels, nHeis = data.shape
                     data = numpy.reshape(data, (1, nChannels, nHeis))
                     #If the buffer is empty then it takes the data value
                     if self.__buffer is None:
                         self.__buffer = data
                         self.__profIndex += 1
                         return
                     #If the buffer length is lower than n then stakcing the data value
                     if self.__profIndex < self.n:
                         self.__buffer = numpy.vstack((self.__buffer, data))
                         self.__profIndex += 1
                         return
                     #If the buffer length is equal to n then replacing the last buffer value with the data value
                     self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
                     self.__buffer[self.n-1] = data
                     self.__profIndex = self.n
                     return
                 def pushData(self):
                     """
                     Return the sum of the last profiles and the profiles used in the sum.
                     Affected:
                     self.__profileIndex
                     """
                     if not self.__withOverlapping:
                         data = self.__buffer
                         n = self.__profIndex
                         self.__buffer = 0
                         self.__profIndex = 0
                         return data, n
                     #Integration with Overlapping
                     data = numpy.sum(self.__buffer, axis=0)
                     # print data
                     # raise
                     n = self.__profIndex
                     return data, n
                 def byProfiles(self, data):
                     self.__dataReady = False
                     avgdata = None
                     #         n = None
                     # print data
                     # raise
                     self.putData(data)
                     if self.__profIndex == self.n:
                         avgdata, n = self.pushData()
                         self.__dataReady = True
                     return avgdata
                 def byTime(self, data, datatime):
                     self.__dataReady = False
                     avgdata = None
                     n = None
                     self.putData(data)
                     if (datatime - self.__initime) >= self.__integrationtime:
                         avgdata, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata
                 def integrateByStride(self, data, datatime):
                     # print data
                     if self.__profIndex == 0:
                         self.__buffer = [[data.copy(), datatime]]
                     else:
                         self.__buffer.append([data.copy(),datatime])
                     self.__profIndex += 1
                     self.__dataReady = False
                     if self.__profIndex == self.n * self.stride :
                         self.__dataToPutStride = True
                         self.__profIndexStride = 0
                         self.__profIndex = 0
                         self.__bufferStride = []
                         for i in range(self.stride):
                             current = self.__buffer[i::self.stride]
                             data = numpy.sum([t[0] for t in current], axis=0)
                             avgdatatime = numpy.average([t[1] for t in current])
                             # print data
                             self.__bufferStride.append((data, avgdatatime))
                     if self.__dataToPutStride:
                         self.__dataReady = True
                         self.__profIndexStride += 1
                         if self.__profIndexStride == self.stride:
                             self.__dataToPutStride = False
                         # print self.__bufferStride[self.__profIndexStride - 1]
                         # raise
                         return self.__bufferStride[self.__profIndexStride - 1]
                     return None, None
                 def integrate(self, data, datatime=None):
                     if self.__initime == None:
                         self.__initime = datatime
                     if self.__byTime:
                         avgdata = self.byTime(data, datatime)
                     else:
                         avgdata = self.byProfiles(data)
                     self.__lastdatatime = datatime
                     if avgdata is None:
                         return None, None
                     avgdatatime = self.__initime
                     deltatime = datatime - self.__lastdatatime
                     if not self.__withOverlapping:
                         self.__initime = datatime
                     else:
                         self.__initime += deltatime
                     return avgdata, avgdatatime
                 def integrateByBlock(self, dataOut):
                     times = int(dataOut.data.shape[1]/self.n)
                     avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
                     id_min = 0
                     id_max = self.n
                     for i in range(times):
                         junk = dataOut.data[:,id_min:id_max,:]
                         avgdata[:,i,:] = junk.sum(axis=1)
                         id_min += self.n
                         id_max += self.n
                     timeInterval = dataOut.ippSeconds*self.n
                     avgdatatime = (times - 1) * timeInterval + dataOut.utctime
                     self.__dataReady = True
                     return avgdata, avgdatatime
                 def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
                     if not self.isConfig:
                         self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         avgdata, avgdatatime = self.integrateByBlock(dataOut)
                         dataOut.nProfiles /= self.n
                     else:
                         if stride is None:
                             avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
                         else:
                             avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
                     #   dataOut.timeInterval *= n
                     dataOut.flagNoData = True
                     if self.__dataReady:
                         dataOut.data = avgdata
                         dataOut.nCohInt *= self.n
                         dataOut.utctime = avgdatatime
                         # print avgdata, avgdatatime
                         # raise
                         #   dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
                         dataOut.flagNoData = False
                     return dataOut
             class Decoder(Operation):
                 isConfig = False
                 __profIndex = 0
                 code = None
                 nCode = None
                 nBaud = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.times = None
                     self.osamp = None
                 #         self.__setValues = False
                     self.isConfig = False
                     self.setupReq = False
                 def setup(self, code, osamp, dataOut):
                     self.__profIndex = 0
                     self.code = code
                     self.nCode = len(code)
                     self.nBaud = len(code[0])
                     if (osamp != None) and (osamp >1):
                         self.osamp = osamp
                         self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
                         self.nBaud = self.nBaud*self.osamp
                     self.__nChannels = dataOut.nChannels
                     self.__nProfiles = dataOut.nProfiles
                     self.__nHeis = dataOut.nHeights
                     if self.__nHeis < self.nBaud:
                         raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
                     #Frequency
                     __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
                     __codeBuffer[:,0:self.nBaud] = self.code
                     self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
                     if dataOut.flagDataAsBlock:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
                     else:
                         #Time
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
                 def __convolutionInFreq(self, data):
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=1)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=1)
                     return data
                 def __convolutionInFreqOpt(self, data):
                     raise NotImplementedError
                 def __convolutionInTime(self, data):
                     code = self.code[self.__profIndex]
                     for i in range(self.__nChannels):
                         self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInTime(self, data):
                     repetitions = self.__nProfiles / self.nCode
                     junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
                     junk = junk.flatten()
                     code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
                     profilesList = range(self.__nProfiles)
                     for i in range(self.__nChannels):
                         for j in profilesList:
                             self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInFreq(self, data):
                     raise NotImplementedError("Decoder by frequency fro Blocks not implemented")
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=2)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=2)
                     return data
                 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
                     if dataOut.flagDecodeData:
                         print("This data is already decoded, recoding again ...")
                     if not self.isConfig:
                         if code is None:
                             if dataOut.code is None:
                                 raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type)
                             code = dataOut.code
                         else:
                             code = numpy.array(code).reshape(nCode,nBaud)
                         self.setup(code, osamp, dataOut)
                         self.isConfig = True
                         if mode == 3:
                             sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
                         if times != None:
                             sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
                     if self.code is None:
                         print("Fail decoding: Code is not defined.")
                         return
                     self.__nProfiles = dataOut.nProfiles
                     datadec = None
                     if mode == 3:
                         mode = 0
                     if dataOut.flagDataAsBlock:
                         """
                         Decoding when data have been read as block,
                         """
                         if mode == 0:
                             datadec = self.__convolutionByBlockInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionByBlockInFreq(dataOut.data)
                     else:
                         """
                         Decoding when data have been read profile by profile
                         """
                         if mode == 0:
                             datadec = self.__convolutionInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionInFreq(dataOut.data)
                         if mode == 2:
                             datadec = self.__convolutionInFreqOpt(dataOut.data)
                     if datadec is None:
                         raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode)
                     dataOut.code = self.code
                     dataOut.nCode = self.nCode
                     dataOut.nBaud = self.nBaud
                     dataOut.data = datadec
                     dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
                     dataOut.flagDecodeData = True #asumo q la data esta decodificada
                     if self.__profIndex == self.nCode-1:
                         self.__profIndex = 0
                         return dataOut
                     self.__profIndex += 1
                     return dataOut
                 #        dataOut.flagDeflipData = True #asumo q la data no esta sin flip
             class ProfileConcat(Operation):
                 isConfig = False
                 buffer = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def reset(self):
                     self.buffer = numpy.zeros_like(self.buffer)
                     self.start_index = 0
                     self.times = 1
                 def setup(self, data, m, n=1):
                     self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
                     self.nHeights = data.shape[1]#.nHeights
                     self.start_index = 0
                     self.times = 1
                 def concat(self, data):
                     self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
                     self.start_index = self.start_index + self.nHeights
                 def run(self, dataOut, m):
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         self.setup(dataOut.data, m, 1)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False")
                     else:
                         self.concat(dataOut.data)
                         self.times += 1
                         if self.times > m:
                             dataOut.data = self.buffer
                             self.reset()
                             dataOut.flagNoData = False
                             # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
                             deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                             xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
                             dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
                             dataOut.ippSeconds *= m
                     return dataOut
             class ProfileSelector(Operation):
                 profileIndex = None
                 # Tamanho total de los perfiles
                 nProfiles = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def incProfileIndex(self):
                     self.profileIndex += 1
                     if self.profileIndex >= self.nProfiles:
                         self.profileIndex = 0
                 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
                     if profileIndex < minIndex:
                         return False
                     if profileIndex > maxIndex:
                         return False
                     return True
                 def isThisProfileInList(self, profileIndex, profileList):
                     if profileIndex not in profileList:
                         return False
                     return True
                 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
                     """
                     ProfileSelector:
                     Inputs:
                         profileList        :    Index of profiles selected. Example: profileList = (0,1,2,7,8)
                         profileRangeList    :    Minimum and maximum profile indexes. Example: profileRangeList = (4, 30)
                         rangeList            :    List of profile ranges. Example: rangeList = ((4, 30), (32, 64), (128, 256))
                     """
                     if rangeList is not None:
                         if type(rangeList[0]) not in (tuple, list):
                             rangeList = [rangeList]
                     dataOut.flagNoData = True
                     if dataOut.flagDataAsBlock:
                         """
                         data dimension  = [nChannels, nProfiles, nHeis]
                         """
                         if profileList != None:
                             dataOut.data = dataOut.data[:,profileList,:]
                         if profileRangeList != None:
                             minIndex = profileRangeList[0]
                             maxIndex = profileRangeList[1]
                             profileList = list(range(minIndex, maxIndex+1))
                             dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
                         if rangeList != None:
                             profileList = []
                             for thisRange in rangeList:
                                 minIndex = thisRange[0]
                                 maxIndex = thisRange[1]
                                 profileList.extend(list(range(minIndex, maxIndex+1)))
                             dataOut.data = dataOut.data[:,profileList,:]
                         dataOut.nProfiles = len(profileList)
                         dataOut.profileIndex = dataOut.nProfiles - 1
                         dataOut.flagNoData = False
                         return dataOut
                     """
                     data dimension  = [nChannels, nHeis]
                     """
                     if profileList != None:
                         if self.isThisProfileInList(dataOut.profileIndex, profileList):
                             self.nProfiles = len(profileList)
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return dataOut
                     if profileRangeList != None:
                         minIndex = profileRangeList[0]
                         maxIndex = profileRangeList[1]
                         if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                             self.nProfiles = maxIndex - minIndex + 1
                             dataOut.nProfiles = self.nProfiles
                             dataOut.profileIndex = self.profileIndex
                             dataOut.flagNoData = False
                             self.incProfileIndex()
                         return dataOut
                     if rangeList != None:
                         nProfiles = 0
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             nProfiles += maxIndex - minIndex + 1
                         for thisRange in rangeList:
                             minIndex = thisRange[0]
                             maxIndex = thisRange[1]
                             if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
                                 self.nProfiles = nProfiles
                                 dataOut.nProfiles = self.nProfiles
                                 dataOut.profileIndex = self.profileIndex
                                 dataOut.flagNoData = False
                                 self.incProfileIndex()
                                 break
                         return dataOut
                     if beam != None: #beam is only for AMISR data
                         if self.isThisProfileInList(dataOut.profileIndex, dataOut.beamRangeDict[beam]):
                             dataOut.flagNoData = False
                             dataOut.profileIndex = self.profileIndex
                             self.incProfileIndex()
                         return dataOut
                     raise ValueError("ProfileSelector needs profileList, profileRangeList or rangeList parameter")
                     #return False
                     return dataOut
             class Reshaper(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__buffer = None
                     self.__nitems = 0
                 def __appendProfile(self, dataOut, nTxs):
                     if self.__buffer is None:
                         shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
                         self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
                     ini = dataOut.nHeights * self.__nitems
                     end = ini + dataOut.nHeights
                     self.__buffer[:, ini:end] = dataOut.data
                     self.__nitems += 1
                     return int(self.__nitems*nTxs)
                 def __getBuffer(self):
                     if self.__nitems == int(1./self.__nTxs):
                         self.__nitems = 0
                         return self.__buffer.copy()
                     return None
                 def __checkInputs(self, dataOut, shape, nTxs):
                     if shape is None and nTxs is None:
                         raise ValueError("Reshaper: shape of factor should be defined")
                     if nTxs:
                         if nTxs < 0:
                             raise ValueError("nTxs should be greater than 0")
                         if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
                             raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)))
                         shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
                         return shape, nTxs
                     if len(shape) != 2 and len(shape) !=  3:
                         raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights))
                     if len(shape) == 2:
                         shape_tuple = [dataOut.nChannels]
                         shape_tuple.extend(shape)
                     else:
                         shape_tuple = list(shape)
                     nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
                     return shape_tuple, nTxs
                 def run(self, dataOut, shape=None, nTxs=None):
                     shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
                     else:
                         if self.__nTxs < 1:
                             self.__appendProfile(dataOut, self.__nTxs)
                             new_data = self.__getBuffer()
                             if new_data is not None:
                                 dataOut.data = new_data
                                 dataOut.flagNoData = False
                                 profileIndex = dataOut.profileIndex*nTxs
                         else:
                             raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)")
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= self.__nTxs
                     return dataOut
             class SplitProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         if shape[2] % n != 0:
                             raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]))
                         new_shape = shape[0], shape[1]*n, int(shape[2]/n)
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles/n) - 1
                     else:
                         raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)")
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= n
                     return dataOut
             class CombineProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__remData = None
                     self.__profileIndex = 0
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         new_shape = shape[0], shape[1]/n, shape[2]*n
                         if shape[1] % n != 0:
                             raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]))
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*n) - 1
                     else:
                         #nchannels, nsamples
                         if self.__remData is None:
                             newData = dataOut.data
                         else:
                             newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
                         self.__profileIndex += 1
                         if self.__profileIndex < n:
                             self.__remData = newData
                             #continue
                             return
                         self.__profileIndex = 0
                         self.__remData = None
                         dataOut.data = newData
                         dataOut.flagNoData = False
                         profileIndex = dataOut.profileIndex/n
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles/n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds *= n
                     return dataOut
             # import collections
             # from scipy.stats import mode
             #
             # class Synchronize(Operation):
             #
             #     isConfig = False
             #     __profIndex = 0
             #
             #     def __init__(self, **kwargs):
             #
             #         Operation.__init__(self, **kwargs)
             # #         self.isConfig = False
             #         self.__powBuffer = None
             #         self.__startIndex = 0
             #         self.__pulseFound = False
             #
             #     def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
             #
             #         #Read data
             #
             #         powerdB = dataOut.getPower(channel = channel)
             #         noisedB = dataOut.getNoise(channel = channel)[0]
             #
             #         self.__powBuffer.extend(powerdB.flatten())
             #
             #         dataArray = numpy.array(self.__powBuffer)
             #
             #         filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
             #
             #         maxValue = numpy.nanmax(filteredPower)
             #
             #         if maxValue < noisedB + 10:
             #             #No se encuentra ningun pulso de transmision
             #             return None
             #
             #         maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
             #
             #         if len(maxValuesIndex) < 2:
             #             #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
             #             return None
             #
             #         phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
             #
             #         #Seleccionar solo valores con un espaciamiento de nSamples
             #         pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
             #
             #         if len(pulseIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         spacing = pulseIndex[1:] - pulseIndex[:-1]
             #
             #         #remover senales que se distancien menos de 10 unidades o muestras
             #         #(No deberian existir IPP menor a 10 unidades)
             #
             #         realIndex = numpy.where(spacing > 10 )[0]
             #
             #         if len(realIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
             #         realPulseIndex = pulseIndex[realIndex]
             #
             #         period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
             #
             #         print "IPP = %d samples" %period
             #
             #         self.__newNSamples = dataOut.nHeights #int(period)
             #         self.__startIndex = int(realPulseIndex[0])
             #
             #         return 1
             #
             #
             #     def setup(self, nSamples, nChannels, buffer_size = 4):
             #
             #         self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
             #                                           maxlen = buffer_size*nSamples)
             #
             #         bufferList = []
             #
             #         for i in range(nChannels):
             #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) +  numpy.NAN,
             #                                           maxlen = buffer_size*nSamples)
             #
             #             bufferList.append(bufferByChannel)
             #
             #         self.__nSamples = nSamples
             #         self.__nChannels = nChannels
             #         self.__bufferList = bufferList
             #
             #     def run(self, dataOut, channel = 0):
             #
             #         if not self.isConfig:
             #             nSamples = dataOut.nHeights
             #             nChannels = dataOut.nChannels
             #             self.setup(nSamples, nChannels)
             #             self.isConfig = True
             #
             #         #Append new data to internal buffer
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             bufferByChannel.extend(dataOut.data[thisChannel])
             #
             #         if self.__pulseFound:
             #             self.__startIndex -= self.__nSamples
             #
             #         #Finding Tx Pulse
             #         if not self.__pulseFound:
             #             indexFound = self.__findTxPulse(dataOut, channel)
             #
             #             if indexFound == None:
             #                 dataOut.flagNoData = True
             #                 return
             #
             #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
             #             self.__pulseFound = True
             #             self.__startIndex = indexFound
             #
             #         #If pulse was found ...
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             #print self.__startIndex
             #             x = numpy.array(bufferByChannel)
             #             self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
             #
             #         deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
             #         dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
             # #             dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
             #
             #         dataOut.data = self.__arrayBuffer
             #
             #         self.__startIndex += self.__newNSamples
             #
             #         return

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