schain-jc Commit - r1181:eb60268405c6

Multiprocessing for BLTR (sswma) data

Juan C. Espinoza -

r1181:eb60268405c6

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r1181:eb60268405c6

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schainpy/model/io/bltrIO_param.py +4 -6

             '''
             Created on Nov 9, 2016
             @author: roj- LouVD
             '''
             import os
             import sys
             import time
             import glob
             import datetime
             import numpy
-            from schainpy.model.proc.jroproc_base import ProcessingUnit
+            from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator
             from schainpy.model.data.jrodata import Parameters
             from schainpy.model.io.jroIO_base import JRODataReader, isNumber
             from schainpy.utils import log
             FILE_HEADER_STRUCTURE = numpy.dtype([
                 ('FMN', '<u4'),
                 ('nrec', '<u4'),
                 ('fr_offset', '<u4'),
                 ('id', '<u4'),
                 ('site', 'u1', (32,))
             ])
             REC_HEADER_STRUCTURE = numpy.dtype([
                 ('rmn', '<u4'),
                 ('rcounter', '<u4'),
                 ('nr_offset', '<u4'),
                 ('tr_offset', '<u4'),
                 ('time', '<u4'),
                 ('time_msec', '<u4'),
                 ('tag', 'u1', (32,)),
                 ('comments', 'u1', (32,)),
                 ('lat', '<f4'),
                 ('lon', '<f4'),
                 ('gps_status', '<u4'),
                 ('freq', '<u4'),
                 ('freq0', '<u4'),
                 ('nchan', '<u4'),
                 ('delta_r', '<u4'),
                 ('nranges', '<u4'),
                 ('r0', '<u4'),
                 ('prf', '<u4'),
                 ('ncoh', '<u4'),
                 ('npoints', '<u4'),
                 ('polarization', '<i4'),
                 ('rx_filter', '<u4'),
                 ('nmodes', '<u4'),
                 ('dmode_index', '<u4'),
                 ('dmode_rngcorr', '<u4'),
                 ('nrxs', '<u4'),
                 ('acf_length', '<u4'),
                 ('acf_lags', '<u4'),
                 ('sea_to_atmos', '<f4'),
                 ('sea_notch', '<u4'),
                 ('lh_sea', '<u4'),
                 ('hh_sea', '<u4'),
                 ('nbins_sea', '<u4'),
                 ('min_snr', '<f4'),
                 ('min_cc', '<f4'),
                 ('max_time_diff', '<f4')
             ])
             DATA_STRUCTURE = numpy.dtype([
                 ('range', '<u4'),
                 ('status', '<u4'),
                 ('zonal', '<f4'),
                 ('meridional', '<f4'),
                 ('vertical', '<f4'),
                 ('zonal_a', '<f4'),
                 ('meridional_a', '<f4'),
                 ('corrected_fading', '<f4'),  # seconds
                 ('uncorrected_fading', '<f4'),  # seconds
                 ('time_diff', '<f4'),
                 ('major_axis', '<f4'),
                 ('axial_ratio', '<f4'),
                 ('orientation', '<f4'),
                 ('sea_power', '<u4'),
                 ('sea_algorithm', '<u4')
             ])
+            @MPDecorator
             class BLTRParamReader(JRODataReader, ProcessingUnit):
                 '''
                 Boundary Layer and Tropospheric Radar (BLTR) reader, Wind velocities and SNR from *.sswma files
                 '''
                 ext = '.sswma'
                 def __init__(self, **kwargs):
                     ProcessingUnit.__init__(self, **kwargs)
                     self.dataOut = Parameters()
                     self.counter_records = 0
                     self.flagNoMoreFiles = 0
                     self.isConfig = False
                     self.filename = None
                 def setup(self,
                           path=None,
                           startDate=None,
                           endDate=None,
                           ext=None,
                           startTime=datetime.time(0, 0, 0),
                           endTime=datetime.time(23, 59, 59),
                           timezone=0,
                           status_value=0,
                           **kwargs):
                     self.path = path
                     self.startDate = startDate
                     self.endDate = endDate
                     self.startTime = startTime
                     self.endTime = endTime
                     self.status_value = status_value
                     self.datatime = datetime.datetime(1900,1,1)
                     if self.path is None:
                         raise ValueError("The path is not valid")
                     if ext is None:
                         ext = self.ext
                     self.search_files(self.path, startDate, endDate, ext)
                     self.timezone = timezone
                     self.fileIndex = 0
                     if not self.fileList:
                         raise Warning("There is no files matching these date in the folder: %s. \n Check 'startDate' and 'endDate' " % (
                             path))
                     self.setNextFile()
                 def search_files(self, path, startDate, endDate, ext):
                     '''
                      Searching for BLTR rawdata file in path
                      Creating a list of file to proces included in [startDate,endDate]
                      Input:
                          path - Path to find BLTR rawdata files
                          startDate - Select file from this date
                          enDate - Select file until this date
                          ext - Extension of the file to read
                     '''
                     log.success('Searching files in {} '.format(path), 'BLTRParamReader')
                     foldercounter = 0
                     fileList0 = glob.glob1(path, "*%s" % ext)
                     fileList0.sort()
                     self.fileList = []
                     self.dateFileList = []
                     for thisFile in fileList0:
                         year = thisFile[-14:-10]
                         if not isNumber(year):
                             continue
                         month = thisFile[-10:-8]
                         if not isNumber(month):
                             continue
                         day = thisFile[-8:-6]
                         if not isNumber(day):
                             continue
                         year, month, day = int(year), int(month), int(day)
                         dateFile = datetime.date(year, month, day)
                         if (startDate > dateFile) or (endDate < dateFile):
                             continue
                         self.fileList.append(thisFile)
                         self.dateFileList.append(dateFile)
                     return
                 def setNextFile(self):
                     file_id = self.fileIndex
                     if file_id == len(self.fileList):
-                        log.success('No more files in the folder', 'BLTRParamReader')
                         self.flagNoMoreFiles = 1
                         return 0
                     log.success('Opening {}'.format(self.fileList[file_id]), 'BLTRParamReader')
                     filename = os.path.join(self.path, self.fileList[file_id])
                     dirname, name = os.path.split(filename)
                     # 'peru2' ---> Piura  -   'peru1' ---> Huancayo or Porcuya
                     self.siteFile = name.split('.')[0]
                     if self.filename is not None:
                         self.fp.close()
                     self.filename = filename
                     self.fp = open(self.filename, 'rb')
                     self.header_file = numpy.fromfile(self.fp, FILE_HEADER_STRUCTURE, 1)
                     self.nrecords = self.header_file['nrec'][0]
                     self.sizeOfFile = os.path.getsize(self.filename)
                     self.counter_records = 0
                     self.flagIsNewFile = 0
                     self.fileIndex += 1
                     return 1
                 def readNextBlock(self):
                     while True:
                         if self.counter_records == self.nrecords:
                             self.flagIsNewFile = 1
                             if not self.setNextFile():
                                 return 0
                         self.readBlock()
                         if (self.datatime < datetime.datetime.combine(self.startDate, self.startTime)) or \
                            (self.datatime > datetime.datetime.combine(self.endDate, self.endTime)):
                             log.warning(
                                 'Reading Record No. {}/{} -> {} [Skipping]'.format(
                                     self.counter_records,
                                     self.nrecords,
                                     self.datatime.ctime()),
                                 'BLTRParamReader')
                             continue
                         break
                     log.log('Reading Record No. {}/{} -> {}'.format(
                         self.counter_records,
                         self.nrecords,
                         self.datatime.ctime()), 'BLTRParamReader')
                     return 1
                 def readBlock(self):
                     pointer = self.fp.tell()
                     header_rec = numpy.fromfile(self.fp, REC_HEADER_STRUCTURE, 1)
-                    self.nchannels = header_rec['nchan'][0] / 2
+                    self.nchannels = int(header_rec['nchan'][0] / 2)
                     self.kchan = header_rec['nrxs'][0]
                     self.nmodes = header_rec['nmodes'][0]
                     self.nranges = header_rec['nranges'][0]
                     self.fp.seek(pointer)
                     self.height = numpy.empty((self.nmodes, self.nranges))
                     self.snr = numpy.empty((self.nmodes, self.nchannels, self.nranges))
                     self.buffer = numpy.empty((self.nmodes, 3, self.nranges))
                     self.flagDiscontinuousBlock = 0
                     for mode in range(self.nmodes):
                         self.readHeader()
                         data = self.readData()
                         self.height[mode] = (data[0] - self.correction) / 1000.
                         self.buffer[mode] = data[1]
                         self.snr[mode] = data[2]
                     self.counter_records = self.counter_records + self.nmodes
                     return
                 def readHeader(self):
                     '''
                     RecordHeader of BLTR rawdata file
                     '''
                     header_structure = numpy.dtype(
                         REC_HEADER_STRUCTURE.descr + [
                             ('antenna_coord', 'f4', (2, self.nchannels)),
                             ('rx_gains', 'u4', (self.nchannels,)),
                             ('rx_analysis', 'u4', (self.nchannels,))
                         ]
                     )
                     self.header_rec = numpy.fromfile(self.fp, header_structure, 1)
                     self.lat = self.header_rec['lat'][0]
                     self.lon = self.header_rec['lon'][0]
                     self.delta = self.header_rec['delta_r'][0]
                     self.correction = self.header_rec['dmode_rngcorr'][0]
                     self.imode = self.header_rec['dmode_index'][0]
                     self.antenna = self.header_rec['antenna_coord']
                     self.rx_gains = self.header_rec['rx_gains']
                     self.time = self.header_rec['time'][0]
                     dt = datetime.datetime.utcfromtimestamp(self.time)
                     if dt.date()>self.datatime.date():
                         self.flagDiscontinuousBlock = 1
                     self.datatime = dt
                 def readData(self):
                     '''
                     Reading and filtering data block record of BLTR rawdata file, filtering is according to status_value.
                     Input:
                         status_value - Array data is set to NAN for values that are not equal to status_value
                     '''
                     data_structure = numpy.dtype(
                         DATA_STRUCTURE.descr + [
                             ('rx_saturation', 'u4', (self.nchannels,)),
                             ('chan_offset', 'u4', (2 * self.nchannels,)),
                             ('rx_amp', 'u4', (self.nchannels,)),
                             ('rx_snr', 'f4', (self.nchannels,)),
                             ('cross_snr', 'f4', (self.kchan,)),
                             ('sea_power_relative', 'f4', (self.kchan,))]
                     )
                     data = numpy.fromfile(self.fp, data_structure, self.nranges)
                     height = data['range']
                     winds = numpy.array(
                         (data['zonal'], data['meridional'], data['vertical']))
                     snr = data['rx_snr'].T
                     winds[numpy.where(winds == -9999.)] = numpy.nan
                     winds[:, numpy.where(data['status'] != self.status_value)] = numpy.nan
                     snr[numpy.where(snr == -9999.)] = numpy.nan
                     snr[:, numpy.where(data['status'] != self.status_value)] = numpy.nan
                     snr = numpy.power(10, snr / 10)
                     return height, winds, snr
                 def set_output(self):
                     '''
                     Storing data from databuffer to dataOut object
                     '''
                     self.dataOut.data_SNR = self.snr
                     self.dataOut.height = self.height
                     self.dataOut.data = self.buffer
                     self.dataOut.utctimeInit = self.time
                     self.dataOut.utctime = self.dataOut.utctimeInit
                     self.dataOut.useLocalTime = False
                     self.dataOut.paramInterval = 157
                     self.dataOut.timezone = self.timezone
                     self.dataOut.site = self.siteFile
                     self.dataOut.nrecords = self.nrecords / self.nmodes
                     self.dataOut.sizeOfFile = self.sizeOfFile
                     self.dataOut.lat = self.lat
                     self.dataOut.lon = self.lon
                     self.dataOut.channelList = list(range(self.nchannels))
                     self.dataOut.kchan = self.kchan
                     self.dataOut.delta = self.delta
                     self.dataOut.correction = self.correction
                     self.dataOut.nmodes = self.nmodes
                     self.dataOut.imode = self.imode
                     self.dataOut.antenna = self.antenna
                     self.dataOut.rx_gains = self.rx_gains
                     self.dataOut.flagNoData = False
                     self.dataOut.flagDiscontinuousBlock = self.flagDiscontinuousBlock
                 def getData(self):
                     '''
                     Storing data from databuffer to dataOut object
                     '''
                     if self.flagNoMoreFiles:
                         self.dataOut.flagNoData = True
-                        log.success('No file left to process', 'BLTRParamReader')
+                        self.dataOut.error = (1, 'No More files to read')
-                        return 0
                     if not self.readNextBlock():
                         self.dataOut.flagNoData = True
                         return 0
                     self.set_output()
                     return 1

schainpy/model/proc/bltrproc_parameters.py +4 -5

             '''
             Created on Oct 24, 2016
             @author: roj- LouVD
             '''
             import numpy
             import copy
             import datetime
             import time
             from time import gmtime
             from numpy import transpose
-            from .jroproc_base import ProcessingUnit, Operation
+            from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
             from schainpy.model.data.jrodata import Parameters
+            @MPDecorator
             class BLTRParametersProc(ProcessingUnit):
                 '''
                 Processing unit for BLTR parameters data (winds)
                 Inputs:
                     self.dataOut.nmodes - Number of operation modes
                     self.dataOut.nchannels - Number of channels
                     self.dataOut.nranges - Number of ranges
                     self.dataOut.data_SNR - SNR array
                     self.dataOut.data_output - Zonal, Vertical and Meridional velocity array
                     self.dataOut.height - Height array (km)
                     self.dataOut.time - Time array (seconds)
                     self.dataOut.fileIndex -Index of the file currently read
                     self.dataOut.lat - Latitude coordinate of BLTR location
                     self.dataOut.doy - Experiment doy (number of the day in the current year)
                     self.dataOut.month - Experiment month
                     self.dataOut.day - Experiment day
                     self.dataOut.year - Experiment year
                 '''
-                def __init__(self, **kwargs):
+                def __init__(self):
                     '''
                     Inputs: None
                     '''
-                    ProcessingUnit.__init__(self, **kwargs)
+                    ProcessingUnit.__init__(self)
                     self.dataOut = Parameters()
-                    self.isConfig = False
                 def setup(self, mode):
                     '''
                     '''
                     self.dataOut.mode = mode
                 def run(self, mode, snr_threshold=None):
                     '''
                     Inputs:
                         mode = High resolution (0) or Low resolution (1) data
                         snr_threshold = snr filter value
                     '''
                     if not self.isConfig:
                         self.setup(mode)
                         self.isConfig = True
                     if self.dataIn.type == 'Parameters':
                         self.dataOut.copy(self.dataIn)
                     self.dataOut.data_param = self.dataOut.data[mode]
                     self.dataOut.heightList = self.dataOut.height[0]
                     self.dataOut.data_SNR = self.dataOut.data_SNR[mode]
                     if snr_threshold is not None:
                         SNRavg = numpy.average(self.dataOut.data_SNR, axis=0)
                         SNRavgdB = 10*numpy.log10(SNRavg)
                         for i in range(3):
                             self.dataOut.data_param[i][SNRavgdB <= snr_threshold] = numpy.nan
             # TODO
             class OutliersFilter(Operation):
                 def __init__(self, **kwargs):
                     '''
                     '''
                     Operation.__init__(self, **kwargs)
                 def run(self, svalue2, method, factor, filter, npoints=9):
                     '''
                     Inputs:
                         svalue    -  string to select array velocity
                         svalue2    -  string to choose axis filtering
                         method    - 0 for SMOOTH or 1 for MEDIAN
                         factor    - number used to set threshold
                         filter    - 1 for data filtering using the standard deviation criteria else 0
                         npoints    - number of points for mask filter
                     '''
                     print('    Outliers Filter {} {} / threshold = {}'.format(svalue, svalue, factor))
                     yaxis = self.dataOut.heightList
                     xaxis = numpy.array([[self.dataOut.utctime]])
                     # Zonal
                     value_temp = self.dataOut.data_output[0]
                     # Zonal
                     value_temp = self.dataOut.data_output[1]
                     # Vertical
                     value_temp = numpy.transpose(self.dataOut.data_output[2])
                     htemp = yaxis
                     std = value_temp
                     for h in range(len(htemp)):
                         nvalues_valid = len(numpy.where(numpy.isfinite(value_temp[h]))[0])
                         minvalid = npoints
                         #only if valid values greater than the minimum required (10%)
                         if nvalues_valid > minvalid:
                             if method == 0:
                                 #SMOOTH
                                 w = value_temp[h] - self.Smooth(input=value_temp[h], width=npoints, edge_truncate=1)
                             if method == 1:
                                 #MEDIAN
                                 w = value_temp[h] - self.Median(input=value_temp[h], width = npoints)
                             dw = numpy.std(w[numpy.where(numpy.isfinite(w))],ddof = 1)
                             threshold = dw*factor
                             value_temp[numpy.where(w > threshold),h] = numpy.nan
                             value_temp[numpy.where(w < -1*threshold),h] = numpy.nan
                     #At the end
                     if svalue2 == 'inHeight':
                         value_temp = numpy.transpose(value_temp)
                     output_array[:,m] = value_temp
                     if svalue == 'zonal':
                         self.dataOut.data_output[0] = output_array
                     elif svalue == 'meridional':
                         self.dataOut.data_output[1] = output_array
                     elif svalue == 'vertical':
                         self.dataOut.data_output[2] = output_array
                     return self.dataOut.data_output
                 def Median(self,input,width):
                     '''
                     Inputs:
                         input - Velocity array
                         width - Number of points for mask filter
                     '''
                     if numpy.mod(width,2) == 1:
                         pc = int((width - 1) / 2)
                     cont = 0
                     output = []
                     for i in range(len(input)):
                         if i >= pc and i < len(input) - pc:
                             new2 = input[i-pc:i+pc+1]
                             temp = numpy.where(numpy.isfinite(new2))
                             new = new2[temp]
                             value = numpy.median(new)
                             output.append(value)
                     output = numpy.array(output)
                     output = numpy.hstack((input[0:pc],output))
                     output = numpy.hstack((output,input[-pc:len(input)]))
                     return output
                 def Smooth(self,input,width,edge_truncate = None):
                     '''
                     Inputs:
                         input - Velocity array
                         width - Number of points for mask filter
                         edge_truncate - 1 for truncate the convolution product else
                     '''
                     if numpy.mod(width,2) == 0:
                         real_width = width + 1
                         nzeros = width / 2
                     else:
                         real_width = width
                         nzeros = (width - 1) / 2
                     half_width = int(real_width)/2
                     length = len(input)
                     gate = numpy.ones(real_width,dtype='float')
                     norm_of_gate = numpy.sum(gate)
                     nan_process = 0
                     nan_id = numpy.where(numpy.isnan(input))
                     if len(nan_id[0]) > 0:
                         nan_process = 1
                         pb = numpy.zeros(len(input))
                         pb[nan_id] = 1.
                         input[nan_id] = 0.
                     if edge_truncate == True:
                         output = numpy.convolve(input/norm_of_gate,gate,mode='same')
                     elif edge_truncate == False or edge_truncate == None:
                         output = numpy.convolve(input/norm_of_gate,gate,mode='valid')
                         output = numpy.hstack((input[0:half_width],output))
                         output = numpy.hstack((output,input[len(input)-half_width:len(input)]))
                     if nan_process:
                         pb = numpy.convolve(pb/norm_of_gate,gate,mode='valid')
                         pb = numpy.hstack((numpy.zeros(half_width),pb))
                         pb = numpy.hstack((pb,numpy.zeros(half_width)))
                         output[numpy.where(pb > 0.9999)] = numpy.nan
                         input[nan_id] = numpy.nan
                     return output
                 def Average(self,aver=0,nhaver=1):
                     '''
                     Inputs:
                         aver - Indicates the time period over which is averaged or consensus data
                         nhaver - Indicates the decimation factor in heights
                     '''
                     nhpoints = 48
                     lat_piura = -5.17
                     lat_huancayo = -12.04
                     lat_porcuya = -5.8
                     if '%2.2f'%self.dataOut.lat == '%2.2f'%lat_piura:
                         hcm = 3.
                         if self.dataOut.year == 2003 :
                             if self.dataOut.doy >= 25 and self.dataOut.doy < 64:
                                 nhpoints = 12
                     elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_huancayo:
                         hcm = 3.
                         if self.dataOut.year == 2003 :
                             if self.dataOut.doy >= 25 and self.dataOut.doy < 64:
                                 nhpoints = 12
                     elif '%2.2f'%self.dataOut.lat == '%2.2f'%lat_porcuya:
                         hcm = 5.#2
                     pdata = 0.2
                     taver = [1,2,3,4,6,8,12,24]
                     t0 = 0
                     tf = 24
                     ntime =(tf-t0)/taver[aver]
                     ti = numpy.arange(ntime)
                     tf = numpy.arange(ntime) + taver[aver]
                     old_height = self.dataOut.heightList
                     if nhaver > 1:
                         num_hei = len(self.dataOut.heightList)/nhaver/self.dataOut.nmodes
                         deltha = 0.05*nhaver
                         minhvalid = pdata*nhaver
                         for im in range(self.dataOut.nmodes):
                             new_height = numpy.arange(num_hei)*deltha + self.dataOut.height[im,0] + deltha/2.
                     data_fHeigths_List = []
                     data_fZonal_List = []
                     data_fMeridional_List = []
                     data_fVertical_List = []
                     startDTList = []
                     for i in range(ntime):
                         height = old_height
                         start = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(ti[i])) - datetime.timedelta(hours = 5)
                         stop = datetime.datetime(self.dataOut.year,self.dataOut.month,self.dataOut.day) + datetime.timedelta(hours = int(tf[i])) - datetime.timedelta(hours = 5)
                         limit_sec1 = time.mktime(start.timetuple())
                         limit_sec2 = time.mktime(stop.timetuple())
                         t1 = numpy.where(self.f_timesec >= limit_sec1)
                         t2 = numpy.where(self.f_timesec < limit_sec2)
                         time_select = []
                         for val_sec in t1[0]:
                             if val_sec in t2[0]:
                                 time_select.append(val_sec)
                         time_select = numpy.array(time_select,dtype = 'int')
                         minvalid = numpy.ceil(pdata*nhpoints)
                         zon_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
                         mer_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
                         ver_aver = numpy.zeros([self.dataOut.nranges,self.dataOut.nmodes],dtype='f4') + numpy.nan
                         if nhaver > 1:
                             new_zon_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
                             new_mer_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
                             new_ver_aver = numpy.zeros([num_hei,self.dataOut.nmodes],dtype='f4') + numpy.nan
                         if len(time_select) > minvalid:
                             time_average = self.f_timesec[time_select]
                             for im in range(self.dataOut.nmodes):
                                 for ih in range(self.dataOut.nranges):
                                     if numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im])) >= minvalid:
                                         zon_aver[ih,im] = numpy.nansum(self.f_zon[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_zon[time_select,ih,im]))
                                     if numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im])) >= minvalid:
                                         mer_aver[ih,im] = numpy.nansum(self.f_mer[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_mer[time_select,ih,im]))
                                     if numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im])) >= minvalid:
                                         ver_aver[ih,im] = numpy.nansum(self.f_ver[time_select,ih,im]) / numpy.sum(numpy.isfinite(self.f_ver[time_select,ih,im]))
                                 if nhaver > 1:
                                     for ih in range(num_hei):
                                         hvalid = numpy.arange(nhaver) + nhaver*ih
                                         if numpy.sum(numpy.isfinite(zon_aver[hvalid,im])) >= minvalid:
                                             new_zon_aver[ih,im] = numpy.nansum(zon_aver[hvalid,im]) /  numpy.sum(numpy.isfinite(zon_aver[hvalid,im]))
                                         if numpy.sum(numpy.isfinite(mer_aver[hvalid,im])) >= minvalid:
                                             new_mer_aver[ih,im] = numpy.nansum(mer_aver[hvalid,im]) /  numpy.sum(numpy.isfinite(mer_aver[hvalid,im]))
                                         if numpy.sum(numpy.isfinite(ver_aver[hvalid,im])) >= minvalid:
                                             new_ver_aver[ih,im] = numpy.nansum(ver_aver[hvalid,im]) /  numpy.sum(numpy.isfinite(ver_aver[hvalid,im]))
                             if nhaver > 1:
                                 zon_aver = new_zon_aver
                                 mer_aver = new_mer_aver
                                 ver_aver = new_ver_aver
                                 height = new_height
                             tstart = time_average[0]
                             tend = time_average[-1]
                             startTime = time.gmtime(tstart)
                             year = startTime.tm_year
                             month = startTime.tm_mon
                             day = startTime.tm_mday
                             hour = startTime.tm_hour
                             minute = startTime.tm_min
                             second = startTime.tm_sec
                             startDTList.append(datetime.datetime(year,month,day,hour,minute,second))
                             o_height = numpy.array([])
                             o_zon_aver = numpy.array([])
                             o_mer_aver = numpy.array([])
                             o_ver_aver = numpy.array([])
                             if self.dataOut.nmodes > 1:
                                 for im in range(self.dataOut.nmodes):
                                     if im == 0:
                                         h_select = numpy.where(numpy.bitwise_and(height[0,:] >=0,height[0,:] <= hcm,numpy.isfinite(height[0,:])))
                                     else:
                                         h_select = numpy.where(numpy.bitwise_and(height[1,:] > hcm,height[1,:] < 20,numpy.isfinite(height[1,:])))
                                     ht = h_select[0]
                                     o_height = numpy.hstack((o_height,height[im,ht]))
                                     o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im]))
                                     o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im]))
                                     o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im]))
                                 data_fHeigths_List.append(o_height)
                                 data_fZonal_List.append(o_zon_aver)
                                 data_fMeridional_List.append(o_mer_aver)
                                 data_fVertical_List.append(o_ver_aver)
                             else:
                                 h_select = numpy.where(numpy.bitwise_and(height[0,:] <= hcm,numpy.isfinite(height[0,:])))
                                 ht = h_select[0]
                                 o_height = numpy.hstack((o_height,height[im,ht]))
                                 o_zon_aver = numpy.hstack((o_zon_aver,zon_aver[ht,im]))
                                 o_mer_aver = numpy.hstack((o_mer_aver,mer_aver[ht,im]))
                                 o_ver_aver = numpy.hstack((o_ver_aver,ver_aver[ht,im]))
                                 data_fHeigths_List.append(o_height)
                                 data_fZonal_List.append(o_zon_aver)
                                 data_fMeridional_List.append(o_mer_aver)
                                 data_fVertical_List.append(o_ver_aver)
                     return startDTList, data_fHeigths_List, data_fZonal_List, data_fMeridional_List, data_fVertical_List

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