schain Commit - r1323:133a5771abd8 · Jicamarca Repository

Merge branch 'v3.0-devel' of http://jro-dev.igp.gob.pe/rhodecode/schain into v3.0-devel

Juan C. Espinoza -

r1323:133a5771abd8

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schainpy/scripts/pedestal_client.py created 644 +87 0

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	1	import numpy
	2	import sys
	3	import zmq
	4	import time
	5	import h5py
	6	import os
	7
	8	path="/home/alex/Downloads/pedestal"
	9	ext=".hdf5"
	10
	11	port ="5556"
	12	if len(sys.argv)>1:
	13	port = sys.argv[1]
	14	int(port)
	15
	16	if len(sys.argv)>2:
	17	port1 = sys.argv[2]
	18	int(port1)
	19
	20	#Socket to talk to server
	21	context = zmq.Context()
	22	socket = context.socket(zmq.SUB)
	23
	24	print("Collecting updates from weather server...")
	25	socket.connect("tcp://localhost:%s"%port)
	26
	27	if len(sys.argv)>2:
	28	socket.connect("tcp://localhost:%s"%port1)
	29
	30	#Subscribe to zipcode, default is NYC,10001
	31	topicfilter = "10001"
	32	socket.setsockopt_string(zmq.SUBSCRIBE,topicfilter)
	33	#Process 5 updates
	34	total_value=0
	35	count= -1
	36	azi= []
	37	elev=[]
	38	time0=[]
	39	#for update_nbr in range(250):
	40	while(True):
	41	string= socket.recv()
	42	topic,ang_elev,ang_elev_dec,ang_azi,ang_azi_dec,seconds,seconds_dec= string.split()
	43	ang_azi =float(ang_azi)+1e-3*float(ang_azi_dec)
	44	ang_elev =float(ang_elev)+1e-3*float(ang_elev_dec)
	45	seconds =float(seconds) +1e-6*float(seconds_dec)
	46	azi.append(ang_azi)
	47	elev.append(ang_elev)
	48	time0.append(seconds)
	49	count +=1
	50	if count == 100:
	51	timetuple=time.localtime()
	52	epoc = time.mktime(timetuple)
	53	#print(epoc)
	54	fullpath = path + ("/" if path[-1]!="/" else "")
	55
	56	if not os.path.exists(fullpath):
	57	os.mkdir(fullpath)
	58
	59	azi_array = numpy.array(azi)
	60	elev_array = numpy.array(elev)
	61	time0_array= numpy.array(time0)
	62	pedestal_array=numpy.array([azi,elev,time0])
	63	count=0
	64	azi= []
	65	elev=[]
	66	time0=[]
	67	#print(pedestal_array[0])
	68	#print(pedestal_array[1])
	69
	70	meta='PE'
	71	filex="%s%4.4d%3.3d%10.4d%s"%(meta,timetuple.tm_year,timetuple.tm_yday,epoc,ext)
	72	filename = os.path.join(fullpath,filex)
	73	fp = h5py.File(filename,'w')
	74	#print("Escribiendo HDF5...",epoc)
	75	#·················· Data·....······································
	76	grp = fp.create_group("Data")
	77	dset = grp.create_dataset("azimuth" , data=pedestal_array[0])
	78	dset = grp.create_dataset("elevacion", data=pedestal_array[1])
	79	dset = grp.create_dataset("utc" , data=pedestal_array[2])
	80	#·················· Metadata·······································
	81	grp = fp.create_group("Metadata")
	82	dset = grp.create_dataset("utctimeInit", data=pedestal_array[2][0])
	83	timeInterval = pedestal_array[2][1]-pedestal_array[2][0]
	84	dset = grp.create_dataset("timeInterval", data=timeInterval)
	85	fp.close()
	86
	87	#print ("Average messagedata value for topic '%s' was %dF" % ( topicfilter,total_value / update_nbr))

schainpy/scripts/pedestal_server.py created 644 +48 0

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	1	###########################################################################
	2	############################### SERVIDOR###################################
	3	######################### SIMULADOR DE PEDESTAL############################
	4	###########################################################################
	5	import time
	6	import math
	7	import numpy
	8	import struct
	9	from time import sleep
	10	import zmq
	11	import pickle
	12	port="5556"
	13	context = zmq.Context()
	14	socket = context.socket(zmq.PUB)
	15	socket.bind("tcp://*:%s"%port)
	16	###### PARAMETROS DE ENTRADA################################
	17	print("PEDESTAL RESOLUCION 0.01")
	18	print("MAXIMA VELOCIDAD DEL PEDESTAL")
	19	ang_elev = 4.12
	20	ang_azi = 30
	21	velocidad= input ("Ingresa velocidad:")
	22	velocidad= float(velocidad)
	23	print (velocidad)
	24	############################################################
	25	sleep(3)
	26	print("Start program")
	27	t1 = time.time()
	28	count=0
	29	while(True):
	30	tmp_vuelta = int(360/velocidad)
	31	t1=t1+tmp_vuelta*count
	32	count= count+1
	33	muestras_seg = 100
	34	t2 = time.time()
	35	for i in range(tmp_vuelta):
	36	for j in range(muestras_seg):
	37	tmp_variable = (i+j/100.0)
	38	ang_azi = (tmp_variable)*float(velocidad)
	39	seconds = t1+ tmp_variable
	40	topic=10001
	41	print ("Azim°: ","%.4f"%ang_azi,"Time:" ,"%.5f"%seconds)
	42	seconds_dec=(seconds-int(seconds))*1e6
	43	ang_azi_dec= (ang_azi-int(ang_azi))*1e3
	44	ang_elev_dec=(ang_elev-int(ang_elev))*1e3
	45	sleep(0.0088)
	46	socket.send_string("%d %d %d %d %d %d %d"%(topic,ang_elev,ang_elev_dec,ang_azi,ang_azi_dec,seconds,seconds_dec))
	47	t3 = time.time()
	48	print ("Total time for 1 vuelta in Seconds",t3-t2)

schainpy/scripts/test_sim00010.py created 644 +82 0

@@ -0,0 +1,82
	1	import os, sys
	2	import datetime
	3	import time
	4	from schainpy.controller import Project
	5
	6	desc = "USRP_test"
	7	filename = "USRP_processing.xml"
	8	controllerObj = Project()
	9	controllerObj.setup(id = '191', name='Test_USRP', description=desc)
	10
	11	############## USED TO PLOT IQ VOLTAGE, POWER AND SPECTRA #############
	12	######PATH DE LECTURA, ESCRITURA, GRAFICOS Y ENVIO WEB#################
	13	path = '/home/alex/Downloads/test_rawdata'
	14	figpath = '/home/alex/Downloads/hdf5_test'
	15	######################## UNIDAD DE LECTURA#############################
	16	'''
	17	readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader',
	18	path=path,
	19	startDate="2020/01/01", #"2020/01/01",#today,
	20	endDate= "2020/12/01", #"2020/12/30",#today,
	21	startTime='00:00:00',
	22	endTime='23:59:59',
	23	delay=0,
	24	#set=0,
	25	online=0,
	26	walk=1)
	27
	28	'''
	29	readUnitConfObj = controllerObj.addReadUnit(datatype='SimulatorReader',
	30	frequency=9.345e9,
	31	FixRCP_IPP= 60,
	32	Tau_0 = 30,
	33	AcqH0_0=0,
	34	samples=330,
	35	AcqDH_0=0.15,
	36	FixRCP_TXA=0.15,
	37	FixRCP_TXB=0.15,
	38	Fdoppler=600.0,
	39	Hdoppler=36,
	40	Adoppler=300,#300
	41	delay=0,
	42	online=0,
	43	walk=0,
	44	profilesPerBlock=625,
	45	dataBlocksPerFile=100)
	46	#nTotalReadFiles=2)
	47
	48
	49	#opObj11 = readUnitConfObj.addOperation(name='printInfo')
	50
	51	procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId())
	52
	53	procUnitConfObjB = controllerObj.addProcUnit(datatype='SpectraProc', inputId=procUnitConfObjA.getId())
	54	procUnitConfObjB.addParameter(name='nFFTPoints', value=625, format='int')
	55	procUnitConfObjB.addParameter(name='nProfiles', value=625, format='int')
	56
	57	opObj11 = procUnitConfObjB.addOperation(name='removeDC')
	58	opObj11.addParameter(name='mode', value=2)
	59	#opObj11 = procUnitConfObjB.addOperation(name='SpectraPlot')
	60	#opObj11 = procUnitConfObjB.addOperation(name='PowerProfilePlot')
	61
	62	procUnitConfObjC= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjB.getId())
	63	procUnitConfObjC.addOperation(name='SpectralMoments')
	64	#opObj11 = procUnitConfObjC.addOperation(name='PowerPlot')
	65
	66	'''
	67	opObj11 = procUnitConfObjC.addOperation(name='SpectralMomentsPlot')
	68	#opObj11.addParameter(name='xmin', value=14)
	69	#opObj11.addParameter(name='xmax', value=15)
	70	#opObj11.addParameter(name='save', value=figpath)
	71	opObj11.addParameter(name='showprofile', value=1)
	72	#opObj11.addParameter(name='save_period', value=10)
	73	'''
	74
	75	opObj10 = procUnitConfObjC.addOperation(name='ParameterWriter')
	76	opObj10.addParameter(name='path',value=figpath)
	77	#opObj10.addParameter(name='mode',value=0)
	78	opObj10.addParameter(name='blocksPerFile',value='100',format='int')
	79	opObj10.addParameter(name='metadataList',value='utctimeInit,timeInterval',format='list')
	80	opObj10.addParameter(name='dataList',value='data_POW,data_DOP,data_WIDTH,data_SNR')#,format='list'
	81
	82	controllerObj.start()

schainpy/scripts/wr_integrador.py created 644 +162 0

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	1	import os,numpy,h5py
	2	from shutil import copyfile
	3
	4	def isNumber(str):
	5	try:
	6	float(str)
	7	return True
	8	except:
	9	return False
	10
	11	def getfirstFilefromPath(path,meta,ext):
	12	validFilelist = []
	13	fileList = os.listdir(path)
	14	if len(fileList)<1:
	15	return None
	16	# meta 1234 567 8-18 BCDE
	17	# H,D,PE YYYY DDD EPOC .ext
	18
	19	for thisFile in fileList:
	20	if meta =="PE":
	21	try:
	22	number= int(thisFile[len(meta)+7:len(meta)+17])
	23	except:
	24	print("There is a file or folder with different format")
	25	if meta == "D":
	26	try:
	27	number= int(thisFile[8:11])
	28	except:
	29	print("There is a file or folder with different format")
	30
	31	if not isNumber(str=number):
	32	continue
	33	if (os.path.splitext(thisFile)[-1].lower() != ext.lower()):
	34	continue
	35	validFilelist.sort()
	36	validFilelist.append(thisFile)
	37	if len(validFilelist)>0:
	38	validFilelist = sorted(validFilelist,key=str.lower)
	39	return validFilelist
	40	return None
	41
	42	def gettimeutcfromDirFilename(path,file):
	43	dir_file= path+"/"+file
	44	fp = h5py.File(dir_file,'r')
	45	epoc = fp['Metadata'].get('utctimeInit')[()]
	46	fp.close()
	47	return epoc
	48
	49	def getDatavaluefromDirFilename(path,file,value):
	50	dir_file= path+"/"+file
	51	fp = h5py.File(dir_file,'r')
	52	array = fp['Data'].get(value)[()]
	53	fp.close()
	54	return array
	55
	56
	57	#·········· Velocidad de Pedestal·················
	58	w = input ("Ingresa velocidad de Pedestal: ")
	59	w = 4
	60	w = float(w)
	61	#·········· Resolucion minimo en grados···········
	62	alfa = input ("Ingresa resolucion minima en grados: ")
	63	alfa = 1
	64	alfa = float(alfa)
	65	#·········· IPP del Experimento ··················
	66	IPP = input ("Ingresa el IPP del experimento: ")
	67	IPP = 0.0004
	68	IPP = float(IPP)
	69	#·········· MODE ··················
	70	mode = input ("Ingresa el MODO del experimento T or F: ")
	71	mode = "T"
	72	mode = str(mode)
	73
	74	#·········· Tiempo en generar la resolucion min···
	75	#············ MCU ·· var_ang = w * (var_tiempo)···
	76	var_tiempo = alfa/w
	77	#·········· Tiempo Equivalente en perfiles········
	78	#·········· var_tiempo = IPP * ( num_perfiles )·
	79	num_perfiles = int(var_tiempo/IPP)
	80
	81	#··········DATA PEDESTAL··························
	82	dir_pedestal = "/home/alex/Downloads/pedestal"
	83	#·········· DATA ADQ······························
	84	if mode=="T":
	85	dir_adq = "/home/alex/Downloads/hdf5_testPP/d2020194" # Time domain
	86	else:
	87	dir_adq = "/home/alex/Downloads/hdf5_test/d2020194" # Frequency domain
	88
	89	print( "Velocidad angular :", w)
	90	print( "Resolucion minima en grados :", alfa)
	91	print( "Numero de perfiles equivalente:", num_perfiles)
	92	print( "Mode :", mode)
	93
	94	#············ First File·············
	95	list_pedestal = getfirstFilefromPath(path=dir_pedestal,meta="PE",ext=".hdf5")
	96	list_adq = getfirstFilefromPath(path=dir_adq ,meta="D",ext=".hdf5")
	97
	98	#············ utc time ··············
	99	utc_pedestal= gettimeutcfromDirFilename(path=dir_pedestal,file=list_pedestal[0])
	100	utc_adq = gettimeutcfromDirFilename(path=dir_adq ,file=list_adq[0])
	101
	102	print("utc_pedestal :",utc_pedestal)
	103	print("utc_adq :",utc_adq)
	104	#·············Relacion: utc_adq (+/-) var_tiempo*nro_file= utc_pedestal
	105	time_Interval_p = 0.01
	106	n_perfiles_p = 100
	107	if utc_adq>utc_pedestal:
	108	nro_file = int((int(utc_adq) - int(utc_pedestal))/(time_Interval_p*n_perfiles_p))
	109	ff_pedestal = list_pedestal[nro_file]
	110	utc_pedestal = gettimeutcfromDirFilename(path=dir_pedestal,file=ff_pedestal)
	111	nro_key_p = int((utc_adq-utc_pedestal)/time_Interval_p)
	112	if utc_adq >utc_pedestal:
	113	ff_pedestal = ff_pedestal
	114	else:
	115	nro_file = nro_file-1
	116	ff_pedestal = list_pedestal[nro_file]
	117	angulo = getDatavaluefromDirFilename(path=dir_pedestal,file=ff_pedestal,value="azimuth")
	118	nro_key_p = int((utc_adq-utc_pedestal)/time_Interval_p)
	119	print("nro_file :",nro_file)
	120	print("name_file :",ff_pedestal)
	121	print("utc_pedestal_file :",utc_pedestal)
	122	print("nro_key_p :",nro_key_p)
	123	print("utc_pedestal_init :",utc_pedestal+nro_key_p*time_Interval_p)
	124	print("angulo_array :",angulo[nro_key_p])
	125	#4+25+25+25+21
	126	#while True:
	127	list_pedestal = getfirstFilefromPath(path=dir_pedestal,meta="PE",ext=".hdf5")
	128	list_adq = getfirstFilefromPath(path=dir_adq ,meta="D",ext=".hdf5")
	129
	130	nro_file = nro_file #10
	131	nro_key_perfil = nro_key_p
	132	blocksPerFile = 100
	133	wr_path = "/home/alex/Downloads/hdf5_wr/"
	134	# Lectura de archivos de adquisicion para adicion de azimuth
	135	for thisFile in range(len(list_adq)):
	136	print("thisFileAdq",thisFile)
	137	angulo_adq = numpy.zeros(blocksPerFile)
	138	tmp = 0
	139	for j in range(blocksPerFile):
	140	iterador = nro_key_perfil + 25*(j-tmp)
	141	if iterador < n_perfiles_p:
	142	nro_file = nro_file
	143	else:
	144	nro_file = nro_file+1
	145	tmp = j
	146	iterador = nro_key_perfil
	147	ff_pedestal = list_pedestal[nro_file]
	148	angulo = getDatavaluefromDirFilename(path=dir_pedestal,file=ff_pedestal,value="azimuth")
	149	angulo_adq[j]= angulo[iterador]
	150	copyfile(dir_adq+"/"+list_adq[thisFile],wr_path+list_adq[thisFile])
	151	fp = h5py.File(wr_path+list_adq[thisFile],'a')
	152	grp = fp.create_group("Pedestal")
	153	dset = grp.create_dataset("azimuth" , data=angulo_adq)
	154	fp.close()
	155	print("Angulo",angulo_adq)
	156	print("Angulo",len(angulo_adq))
	157	nro_key_perfil=iterador + 25
	158	if nro_key_perfil< n_perfiles_p:
	159	nro_file = nro_file
	160	else:
	161	nro_file = nro_file+1
	162	nro_key_perfil= nro_key_p

schainpy/model/data/jrodata.py +13 -7

             '''
             $Author: murco $
             $Id: JROData.py 173 2012-11-20 15:06:21Z murco $
             '''
             import copy
             import numpy
             import datetime
             import json
             import schainpy.admin
             from schainpy.utils import log
             from .jroheaderIO import SystemHeader, RadarControllerHeader
             from schainpy.model.data import _noise
             def getNumpyDtype(dataTypeCode):
                 if dataTypeCode == 0:
                     numpyDtype = numpy.dtype([('real', '<i1'), ('imag', '<i1')])
                 elif dataTypeCode == 1:
                     numpyDtype = numpy.dtype([('real', '<i2'), ('imag', '<i2')])
                 elif dataTypeCode == 2:
                     numpyDtype = numpy.dtype([('real', '<i4'), ('imag', '<i4')])
                 elif dataTypeCode == 3:
                     numpyDtype = numpy.dtype([('real', '<i8'), ('imag', '<i8')])
                 elif dataTypeCode == 4:
                     numpyDtype = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
                 elif dataTypeCode == 5:
                     numpyDtype = numpy.dtype([('real', '<f8'), ('imag', '<f8')])
                 else:
                     raise ValueError('dataTypeCode was not defined')
                 return numpyDtype
             def getDataTypeCode(numpyDtype):
                 if numpyDtype == numpy.dtype([('real', '<i1'), ('imag', '<i1')]):
                     datatype = 0
                 elif numpyDtype == numpy.dtype([('real', '<i2'), ('imag', '<i2')]):
                     datatype = 1
                 elif numpyDtype == numpy.dtype([('real', '<i4'), ('imag', '<i4')]):
                     datatype = 2
                 elif numpyDtype == numpy.dtype([('real', '<i8'), ('imag', '<i8')]):
                     datatype = 3
                 elif numpyDtype == numpy.dtype([('real', '<f4'), ('imag', '<f4')]):
                     datatype = 4
                 elif numpyDtype == numpy.dtype([('real', '<f8'), ('imag', '<f8')]):
                     datatype = 5
                 else:
                     datatype = None
                 return datatype
             def hildebrand_sekhon(data, navg):
                 """
                 This method is for the objective determination of the noise level in Doppler spectra. This
                 implementation technique is based on the fact that the standard deviation of the spectral
                 densities is equal to the mean spectral density for white Gaussian noise
                 Inputs:
                     Data    :    heights
                     navg    :    numbers of averages
                 Return:
                     mean    :    noise's level
                 """
                 sortdata = numpy.sort(data, axis=None)
                 '''
                 lenOfData = len(sortdata)
                 nums_min = lenOfData*0.2
                 if nums_min <= 5:
                     nums_min = 5
                 sump = 0.
                 sumq = 0.
                 j = 0
                 cont = 1
                 while((cont == 1)and(j < lenOfData)):
                     sump += sortdata[j]
                     sumq += sortdata[j]**2
                     if j > nums_min:
                         rtest = float(j)/(j-1) + 1.0/navg
                         if ((sumq*j) > (rtest*sump**2)):
                             j = j - 1
                             sump = sump - sortdata[j]
                             sumq = sumq - sortdata[j]**2
                             cont = 0
                     j += 1
                 lnoise = sump / j
                 '''
                 return _noise.hildebrand_sekhon(sortdata, navg)
             class Beam:
                 def __init__(self):
                     self.codeList = []
                     self.azimuthList = []
                     self.zenithList = []
             class GenericData(object):
                 flagNoData = True
                 def copy(self, inputObj=None):
                     if inputObj == None:
                         return copy.deepcopy(self)
                     for key in list(inputObj.__dict__.keys()):
                         attribute = inputObj.__dict__[key]
                         # If this attribute is a tuple or list
                         if type(inputObj.__dict__[key]) in (tuple, list):
                             self.__dict__[key] = attribute[:]
                             continue
                         # If this attribute is another object or instance
                         if hasattr(attribute, '__dict__'):
                             self.__dict__[key] = attribute.copy()
                             continue
                         self.__dict__[key] = inputObj.__dict__[key]
                 def deepcopy(self):
                     return copy.deepcopy(self)
                 def isEmpty(self):
                     return self.flagNoData
                 def isReady(self):
                     return not self.flagNoData
             class JROData(GenericData):
                 #    m_BasicHeader = BasicHeader()
                 #    m_ProcessingHeader = ProcessingHeader()
                 systemHeaderObj = SystemHeader()
                 radarControllerHeaderObj = RadarControllerHeader()
             #    data = None
                 type = None
                 datatype = None  # dtype but in string
             #     dtype = None
             #    nChannels = None
             #    nHeights = None
                 nProfiles = None
                 heightList = None
                 channelList = None
                 flagDiscontinuousBlock = False
                 useLocalTime = False
                 utctime = None
                 timeZone = None
                 dstFlag = None
                 errorCount = None
                 blocksize = None
             #     nCode = None
             #     nBaud = None
             #     code = None
                 flagDecodeData = False  # asumo q la data no esta decodificada
                 flagDeflipData = False  # asumo q la data no esta sin flip
                 flagShiftFFT = False
             #     ippSeconds = None
             #     timeInterval = None
                 nCohInt = None
             #     noise = None
                 windowOfFilter = 1
                 # Speed of ligth
                 C = 3e8
                 frequency = 49.92e6
                 realtime = False
                 beacon_heiIndexList = None
                 last_block = None
                 blocknow = None
                 azimuth = None
                 zenith = None
                 beam = Beam()
                 profileIndex = None
                 error = None
                 data = None
                 nmodes = None
                 def __str__(self):
                     return '{} - {}'.format(self.type, self.getDatatime())
                 def getNoise(self):
                     raise NotImplementedError
                 def getNChannels(self):
                     return len(self.channelList)
                 def getChannelIndexList(self):
                     return list(range(self.nChannels))
                 def getNHeights(self):
                     return len(self.heightList)
                 def getHeiRange(self, extrapoints=0):
                     heis = self.heightList
             #        deltah = self.heightList[1] - self.heightList[0]
             #
             #        heis.append(self.heightList[-1])
                     return heis
                 def getDeltaH(self):
                     delta = self.heightList[1] - self.heightList[0]
                     return delta
                 def getltctime(self):
                     if self.useLocalTime:
                         return self.utctime - self.timeZone * 60
                     return self.utctime
                 def getDatatime(self):
                     datatimeValue = datetime.datetime.utcfromtimestamp(self.ltctime)
                     return datatimeValue
                 def getTimeRange(self):
                     datatime = []
                     datatime.append(self.ltctime)
                     datatime.append(self.ltctime + self.timeInterval + 1)
                     datatime = numpy.array(datatime)
                     return datatime
                 def getFmaxTimeResponse(self):
                     period = (10**-6) * self.getDeltaH() / (0.15)
                     PRF = 1. / (period * self.nCohInt)
                     fmax = PRF
                     return fmax
                 def getFmax(self):
                     PRF = 1. / (self.ippSeconds * self.nCohInt)
                     fmax = PRF
                     return fmax
                 def getVmax(self):
                     _lambda = self.C / self.frequency
                     vmax = self.getFmax() * _lambda / 2
                     return vmax
                 def get_ippSeconds(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.ippSeconds
                 def set_ippSeconds(self, ippSeconds):
                     '''
                     '''
                     self.radarControllerHeaderObj.ippSeconds = ippSeconds
                     return
                 def get_dtype(self):
                     '''
                     '''
                     return getNumpyDtype(self.datatype)
                 def set_dtype(self, numpyDtype):
                     '''
                     '''
                     self.datatype = getDataTypeCode(numpyDtype)
                 def get_code(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.code
                 def set_code(self, code):
                     '''
                     '''
                     self.radarControllerHeaderObj.code = code
                     return
                 def get_ncode(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.nCode
                 def set_ncode(self, nCode):
                     '''
                     '''
                     self.radarControllerHeaderObj.nCode = nCode
                     return
                 def get_nbaud(self):
                     '''
                     '''
                     return self.radarControllerHeaderObj.nBaud
                 def set_nbaud(self, nBaud):
                     '''
                     '''
                     self.radarControllerHeaderObj.nBaud = nBaud
                     return
                 nChannels = property(getNChannels, "I'm the 'nChannel' property.")
                 channelIndexList = property(
                     getChannelIndexList, "I'm the 'channelIndexList' property.")
                 nHeights = property(getNHeights, "I'm the 'nHeights' property.")
                 #noise = property(getNoise, "I'm the 'nHeights' property.")
                 datatime = property(getDatatime, "I'm the 'datatime' property")
                 ltctime = property(getltctime, "I'm the 'ltctime' property")
                 ippSeconds = property(get_ippSeconds, set_ippSeconds)
                 dtype = property(get_dtype, set_dtype)
             #     timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
                 code = property(get_code, set_code)
                 nCode = property(get_ncode, set_ncode)
                 nBaud = property(get_nbaud, set_nbaud)
             class Voltage(JROData):
                 # data es un numpy array de 2 dmensiones (canales, alturas)
                 data = None
-                data_intensity = None
+                dataPP_POW   = None
-                data_velocity = None
+                dataPP_DOP   = None
-                data_specwidth = None
+                dataPP_WIDTH = None
+                dataPP_SNR   = None
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.useLocalTime = True
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Voltage"
                     self.data = None
             #         self.dtype = None
             #        self.nChannels = 0
             #        self.nHeights = 0
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
             #        self.channelIndexList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.timeZone = None
                     self.dstFlag = None
                     self.errorCount = None
                     self.nCohInt = None
                     self.blocksize = None
                     self.flagCohInt = False
                     self.flagDecodeData = False  # asumo q la data no esta decodificada
                     self.flagDeflipData = False  # asumo q la data no esta sin flip
                     self.flagShiftFFT = False
                     self.flagDataAsBlock = False  # Asumo que la data es leida perfil a perfil
                     self.profileIndex = 0
                 def getNoisebyHildebrand(self, channel=None):
                     """
                     Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
                     Return:
                         noiselevel
                     """
                     if channel != None:
                         data = self.data[channel]
                         nChannels = 1
                     else:
                         data = self.data
                         nChannels = self.nChannels
                     noise = numpy.zeros(nChannels)
                     power = data * numpy.conjugate(data)
                     for thisChannel in range(nChannels):
                         if nChannels == 1:
                             daux = power[:].real
                         else:
                             daux = power[thisChannel, :].real
                         noise[thisChannel] = hildebrand_sekhon(daux, self.nCohInt)
                     return noise
                 def getNoise(self, type=1, channel=None):
                     if type == 1:
                         noise = self.getNoisebyHildebrand(channel)
                     return noise
                 def getPower(self, channel=None):
                     if channel != None:
                         data = self.data[channel]
                     else:
                         data = self.data
                     power = data * numpy.conjugate(data)
                     powerdB = 10 * numpy.log10(power.real)
                     powerdB = numpy.squeeze(powerdB)
                     return powerdB
                 def getTimeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt
                     return timeInterval
                 noise = property(getNoise, "I'm the 'nHeights' property.")
                 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
             class Spectra(JROData):
                 # data spc es un numpy array de 2 dmensiones (canales, perfiles, alturas)
                 data_spc = None
                 # data cspc es un numpy array de 2 dmensiones (canales, pares, alturas)
                 data_cspc = None
                 # data dc es un numpy array de 2 dmensiones (canales, alturas)
                 data_dc = None
                 # data power
                 data_pwr = None
                 nFFTPoints = None
             #     nPairs = None
                 pairsList = None
                 nIncohInt = None
                 wavelength = None  # Necesario para cacular el rango de velocidad desde la frecuencia
                 nCohInt = None  # se requiere para determinar el valor de timeInterval
                 ippFactor = None
                 profileIndex = 0
                 plotting = "spectra"
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.useLocalTime = True
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Spectra"
             #        self.data = None
             #         self.dtype = None
             #        self.nChannels = 0
             #        self.nHeights = 0
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
             #        self.channelIndexList = None
                     self.pairsList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.nCohInt = None
                     self.nIncohInt = None
                     self.blocksize = None
                     self.nFFTPoints = None
                     self.wavelength = None
                     self.flagDecodeData = False  # asumo q la data no esta decodificada
                     self.flagDeflipData = False  # asumo q la data no esta sin flip
                     self.flagShiftFFT = False
                     self.ippFactor = 1
                     #self.noise = None
                     self.beacon_heiIndexList = []
                     self.noise_estimation = None
                 def getNoisebyHildebrand(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
                     """
                     Determino el nivel de ruido usando el metodo Hildebrand-Sekhon
                     Return:
                         noiselevel
                     """
                     noise = numpy.zeros(self.nChannels)
                     for channel in range(self.nChannels):
                         daux = self.data_spc[channel,
                                              xmin_index:xmax_index, ymin_index:ymax_index]
                         noise[channel] = hildebrand_sekhon(daux, self.nIncohInt)
                     return noise
                 def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
                     if self.noise_estimation is not None:
                         # this was estimated by getNoise Operation defined in jroproc_spectra.py
                         return self.noise_estimation
                     else:
                         noise = self.getNoisebyHildebrand(
                             xmin_index, xmax_index, ymin_index, ymax_index)
                         return noise
                 def getFreqRangeTimeResponse(self, extrapoints=0):
                     deltafreq = self.getFmaxTimeResponse() / (self.nFFTPoints * self.ippFactor)
                     freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.) - deltafreq / 2
                     return freqrange
                 def getAcfRange(self, extrapoints=0):
                     deltafreq = 10. / (self.getFmax() / (self.nFFTPoints * self.ippFactor))
                     freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
                     return freqrange
                 def getFreqRange(self, extrapoints=0):
                     deltafreq = self.getFmax() / (self.nFFTPoints * self.ippFactor)
                     freqrange = deltafreq * (numpy.arange(self.nFFTPoints + extrapoints) -self.nFFTPoints / 2.) - deltafreq / 2
                     return freqrange
                 def getVelRange(self, extrapoints=0):
                     deltav = self.getVmax() / (self.nFFTPoints * self.ippFactor)
                     velrange = deltav * (numpy.arange(self.nFFTPoints + extrapoints) - self.nFFTPoints / 2.)
                     if self.nmodes:
                         return velrange/self.nmodes
                     else:
                         return velrange
                 def getNPairs(self):
                     return len(self.pairsList)
                 def getPairsIndexList(self):
                     return list(range(self.nPairs))
                 def getNormFactor(self):
                     pwcode = 1
                     if self.flagDecodeData:
                         pwcode = numpy.sum(self.code[0]**2)
                     #normFactor = min(self.nFFTPoints,self.nProfiles)*self.nIncohInt*self.nCohInt*pwcode*self.windowOfFilter
                     normFactor = self.nProfiles * self.nIncohInt * self.nCohInt * pwcode * self.windowOfFilter
                     return normFactor
                 def getFlagCspc(self):
                     if self.data_cspc is None:
                         return True
                     return False
                 def getFlagDc(self):
                     if self.data_dc is None:
                         return True
                     return False
                 def getTimeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt * self.nProfiles * self.ippFactor
                     if self.nmodes:
                         return self.nmodes*timeInterval
                     else:
                         return timeInterval
                 def getPower(self):
                     factor = self.normFactor
                     z = self.data_spc / factor
                     z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     avg = numpy.average(z, axis=1)
                     return 10 * numpy.log10(avg)
                 def getCoherence(self, pairsList=None, phase=False):
                     z = []
                     if pairsList is None:
                         pairsIndexList = self.pairsIndexList
                     else:
                         pairsIndexList = []
                         for pair in pairsList:
                             if pair not in self.pairsList:
                                 raise ValueError("Pair %s is not in dataOut.pairsList" % (
                                     pair))
                             pairsIndexList.append(self.pairsList.index(pair))
                     for i in range(len(pairsIndexList)):
                         pair = self.pairsList[pairsIndexList[i]]
                         ccf = numpy.average(self.data_cspc[pairsIndexList[i], :, :], axis=0)
                         powa = numpy.average(self.data_spc[pair[0], :, :], axis=0)
                         powb = numpy.average(self.data_spc[pair[1], :, :], axis=0)
                         avgcoherenceComplex = ccf / numpy.sqrt(powa * powb)
                         if phase:
                             data = numpy.arctan2(avgcoherenceComplex.imag,
                                                  avgcoherenceComplex.real) * 180 / numpy.pi
                         else:
                             data = numpy.abs(avgcoherenceComplex)
                         z.append(data)
                     return numpy.array(z)
                 def setValue(self, value):
                     print("This property should not be initialized")
                     return
                 nPairs = property(getNPairs, setValue, "I'm the 'nPairs' property.")
                 pairsIndexList = property(
                     getPairsIndexList, setValue, "I'm the 'pairsIndexList' property.")
                 normFactor = property(getNormFactor, setValue,
                                       "I'm the 'getNormFactor' property.")
                 flag_cspc = property(getFlagCspc, setValue)
                 flag_dc = property(getFlagDc, setValue)
                 noise = property(getNoise, setValue, "I'm the 'nHeights' property.")
                 timeInterval = property(getTimeInterval, setValue,
                                         "I'm the 'timeInterval' property")
             class SpectraHeis(Spectra):
                 data_spc = None
                 data_cspc = None
                 data_dc = None
                 nFFTPoints = None
             #     nPairs = None
                 pairsList = None
                 nCohInt = None
                 nIncohInt = None
                 def __init__(self):
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "SpectraHeis"
             #         self.dtype = None
             #        self.nChannels = 0
             #        self.nHeights = 0
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
             #        self.channelIndexList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
             #         self.nPairs = 0
                     self.utctime = None
                     self.blocksize = None
                     self.profileIndex = 0
                     self.nCohInt = 1
                     self.nIncohInt = 1
                 def getNormFactor(self):
                     pwcode = 1
                     if self.flagDecodeData:
                         pwcode = numpy.sum(self.code[0]**2)
                     normFactor = self.nIncohInt * self.nCohInt * pwcode
                     return normFactor
                 def getTimeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt
                     return timeInterval
                 normFactor = property(getNormFactor, "I'm the 'getNormFactor' property.")
                 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
             class Fits(JROData):
                 heightList = None
                 channelList = None
                 flagNoData = True
                 flagDiscontinuousBlock = False
                 useLocalTime = False
                 utctime = None
                 timeZone = None
             #     ippSeconds = None
             #     timeInterval = None
                 nCohInt = None
                 nIncohInt = None
                 noise = None
                 windowOfFilter = 1
                 # Speed of ligth
                 C = 3e8
                 frequency = 49.92e6
                 realtime = False
                 def __init__(self):
                     self.type = "Fits"
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
             #         self.channelIndexList = None
                     self.flagNoData = True
                     self.utctime = None
                     self.nCohInt = 1
                     self.nIncohInt = 1
                     self.useLocalTime = True
                     self.profileIndex = 0
             #         self.utctime = None
             #         self.timeZone = None
             #         self.ltctime = None
             #         self.timeInterval = None
             #         self.header = None
             #         self.data_header = None
             #         self.data = None
             #         self.datatime = None
             #         self.flagNoData = False
             #         self.expName = ''
             #         self.nChannels = None
             #         self.nSamples = None
             #         self.dataBlocksPerFile = None
             #         self.comments = ''
             #
                 def getltctime(self):
                     if self.useLocalTime:
                         return self.utctime - self.timeZone * 60
                     return self.utctime
                 def getDatatime(self):
                     datatime = datetime.datetime.utcfromtimestamp(self.ltctime)
                     return datatime
                 def getTimeRange(self):
                     datatime = []
                     datatime.append(self.ltctime)
                     datatime.append(self.ltctime + self.timeInterval)
                     datatime = numpy.array(datatime)
                     return datatime
                 def getHeiRange(self):
                     heis = self.heightList
                     return heis
                 def getNHeights(self):
                     return len(self.heightList)
                 def getNChannels(self):
                     return len(self.channelList)
                 def getChannelIndexList(self):
                     return list(range(self.nChannels))
                 def getNoise(self, type=1):
                     #noise = numpy.zeros(self.nChannels)
                     if type == 1:
                         noise = self.getNoisebyHildebrand()
                     if type == 2:
                         noise = self.getNoisebySort()
                     if type == 3:
                         noise = self.getNoisebyWindow()
                     return noise
                 def getTimeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt * self.nIncohInt
                     return timeInterval
                 def get_ippSeconds(self):
                     '''
                     '''
                     return self.ipp_sec
                 datatime = property(getDatatime, "I'm the 'datatime' property")
                 nHeights = property(getNHeights, "I'm the 'nHeights' property.")
                 nChannels = property(getNChannels, "I'm the 'nChannel' property.")
                 channelIndexList = property(
                     getChannelIndexList, "I'm the 'channelIndexList' property.")
                 noise = property(getNoise, "I'm the 'nHeights' property.")
                 ltctime = property(getltctime, "I'm the 'ltctime' property")
                 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
                 ippSeconds = property(get_ippSeconds, '')
             class Correlation(JROData):
                 noise = None
                 SNR = None
                 #--------------------------------------------------
                 mode = None
                 split = False
                 data_cf = None
                 lags = None
                 lagRange = None
                 pairsList = None
                 normFactor = None
                 #--------------------------------------------------
             #     calculateVelocity = None
                 nLags = None
                 nPairs = None
                 nAvg = None
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Correlation"
                     self.data = None
                     self.dtype = None
                     self.nProfiles = None
                     self.heightList = None
                     self.channelList = None
                     self.flagNoData = True
                     self.flagDiscontinuousBlock = False
                     self.utctime = None
                     self.timeZone = None
                     self.dstFlag = None
                     self.errorCount = None
                     self.blocksize = None
                     self.flagDecodeData = False  # asumo q la data no esta decodificada
                     self.flagDeflipData = False  # asumo q la data no esta sin flip
                     self.pairsList = None
                     self.nPoints = None
                 def getPairsList(self):
                     return self.pairsList
                 def getNoise(self, mode=2):
                     indR = numpy.where(self.lagR == 0)[0][0]
                     indT = numpy.where(self.lagT == 0)[0][0]
                     jspectra0 = self.data_corr[:, :, indR, :]
                     jspectra = copy.copy(jspectra0)
                     num_chan = jspectra.shape[0]
                     num_hei = jspectra.shape[2]
                     freq_dc = jspectra.shape[1] / 2
                     ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
                     if ind_vel[0] < 0:
                         ind_vel[list(range(0, 1))] = ind_vel[list(
                             range(0, 1))] + self.num_prof
                     if mode == 1:
                         jspectra[:, freq_dc, :] = (
                             jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2  # CORRECCION
                     if mode == 2:
                         vel = numpy.array([-2, -1, 1, 2])
                         xx = numpy.zeros([4, 4])
                         for fil in range(4):
                             xx[fil, :] = vel[fil]**numpy.asarray(list(range(4)))
                         xx_inv = numpy.linalg.inv(xx)
                         xx_aux = xx_inv[0, :]
                         for ich in range(num_chan):
                             yy = jspectra[ich, ind_vel, :]
                             jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy)
                             junkid = jspectra[ich, freq_dc, :] <= 0
                             cjunkid = sum(junkid)
                             if cjunkid.any():
                                 jspectra[ich, freq_dc, junkid.nonzero()] = (
                                     jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2
                     noise = jspectra0[:, freq_dc, :] - jspectra[:, freq_dc, :]
                     return noise
                 def getTimeInterval(self):
                     timeInterval = self.ippSeconds * self.nCohInt * self.nProfiles
                     return timeInterval
                 def splitFunctions(self):
                     pairsList = self.pairsList
                     ccf_pairs = []
                     acf_pairs = []
                     ccf_ind = []
                     acf_ind = []
                     for l in range(len(pairsList)):
                         chan0 = pairsList[l][0]
                         chan1 = pairsList[l][1]
                         # Obteniendo pares de Autocorrelacion
                         if chan0 == chan1:
                             acf_pairs.append(chan0)
                             acf_ind.append(l)
                         else:
                             ccf_pairs.append(pairsList[l])
                             ccf_ind.append(l)
                     data_acf = self.data_cf[acf_ind]
                     data_ccf = self.data_cf[ccf_ind]
                     return acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf
                 def getNormFactor(self):
                     acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.splitFunctions()
                     acf_pairs = numpy.array(acf_pairs)
                     normFactor = numpy.zeros((self.nPairs, self.nHeights))
                     for p in range(self.nPairs):
                         pair = self.pairsList[p]
                         ch0 = pair[0]
                         ch1 = pair[1]
                         ch0_max = numpy.max(data_acf[acf_pairs == ch0, :, :], axis=1)
                         ch1_max = numpy.max(data_acf[acf_pairs == ch1, :, :], axis=1)
                         normFactor[p, :] = numpy.sqrt(ch0_max * ch1_max)
                     return normFactor
                 timeInterval = property(getTimeInterval, "I'm the 'timeInterval' property")
                 normFactor = property(getNormFactor, "I'm the 'normFactor property'")
             class Parameters(Spectra):
                 experimentInfo = None  # Information about the experiment
                 # Information from previous data
                 inputUnit = None  # Type of data to be processed
                 operation = None  # Type of operation to parametrize
                 # normFactor = None       #Normalization Factor
                 groupList = None  # List of Pairs, Groups, etc
                 # Parameters
                 data_param = None  # Parameters obtained
                 data_pre = None  # Data Pre Parametrization
                 data_SNR = None  # Signal to Noise Ratio
             #     heightRange = None      #Heights
                 abscissaList = None  # Abscissa, can be velocities, lags or time
             #    noise = None            #Noise Potency
                 utctimeInit = None  # Initial UTC time
                 paramInterval = None  # Time interval to calculate Parameters in seconds
                 useLocalTime = True
                 # Fitting
                 data_error = None  # Error of the estimation
                 constants = None
                 library = None
                 # Output signal
                 outputInterval = None  # Time interval to calculate output signal in seconds
                 data_output = None  # Out signal
                 nAvg = None
                 noise_estimation = None
                 GauSPC = None  # Fit gaussian SPC
                 def __init__(self):
                     '''
                     Constructor
                     '''
                     self.radarControllerHeaderObj = RadarControllerHeader()
                     self.systemHeaderObj = SystemHeader()
                     self.type = "Parameters"
                 def getTimeRange1(self, interval):
                     datatime = []
                     if self.useLocalTime:
                         time1 = self.utctimeInit - self.timeZone * 60
                     else:
                         time1 = self.utctimeInit
                     datatime.append(time1)
                     datatime.append(time1 + interval)
                     datatime = numpy.array(datatime)
                     return datatime
                 def getTimeInterval(self):
                     if hasattr(self, 'timeInterval1'):
                         return self.timeInterval1
                     else:
                         return self.paramInterval
                 def setValue(self, value):
                     print("This property should not be initialized")
                     return
                 def getNoise(self):
                     return self.spc_noise
                 timeInterval = property(getTimeInterval)
                 noise = property(getNoise, setValue, "I'm the 'Noise' property.")
             class PlotterData(object):
                 '''
                 Object to hold data to be plotted
                 '''
                 MAXNUMX = 200
                 MAXNUMY = 200
                 def __init__(self, code, throttle_value, exp_code, buffering=True, snr=False):
                     self.key = code
                     self.throttle = throttle_value
                     self.exp_code = exp_code
                     self.buffering = buffering
                     self.ready = False
                     self.flagNoData = False
                     self.localtime = False
                     self.data = {}
                     self.meta = {}
                     self.__heights = []
                     if 'snr' in code:
                         self.plottypes = ['snr']
                     elif code == 'spc':
                         self.plottypes = ['spc', 'noise', 'rti']
                     elif code == 'cspc':
                         self.plottypes = ['cspc', 'spc', 'noise', 'rti']
                     elif code == 'rti':
                         self.plottypes = ['noise', 'rti']
                     else:
                         self.plottypes = [code]
                     if 'snr' not in self.plottypes and snr:
                         self.plottypes.append('snr')
                     for plot in self.plottypes:
                         self.data[plot] = {}
                 def __str__(self):
                     dum = ['{}{}'.format(key, self.shape(key)) for key in self.data]
                     return 'Data[{}][{}]'.format(';'.join(dum), len(self.times))
                 def __len__(self):
                     return len(self.data[self.key])
                 def __getitem__(self, key):
                     if key not in self.data:
                         raise KeyError(log.error('Missing key: {}'.format(key)))
                     if 'spc' in key or not self.buffering:
                         ret = self.data[key][self.tm]
                     elif 'scope' in key:
                         ret = numpy.array(self.data[key][float(self.tm)])
                     else:
                         ret = numpy.array([self.data[key][x] for x in self.times])
                         if ret.ndim > 1:
                             ret = numpy.swapaxes(ret, 0, 1)
                     return ret
                 def __contains__(self, key):
                     return key in self.data
                 def setup(self):
                     '''
                     Configure object
                     '''
                     self.type = ''
                     self.ready = False
                     del self.data
                     self.data = {}
                     self.__heights = []
                     self.__all_heights = set()
                     for plot in self.plottypes:
                         if 'snr' in plot:
                             plot = 'snr'
                         elif 'spc_moments' == plot:
                             plot = 'moments'
                         self.data[plot] = {}
                     if 'spc' in self.data or 'rti' in self.data or 'cspc' in self.data or 'moments' in self.data:
                         self.data['noise'] = {}
                         self.data['rti'] = {}
                         if 'noise' not in self.plottypes:
                             self.plottypes.append('noise')
                         if 'rti' not in self.plottypes:
                             self.plottypes.append('rti')
                 def shape(self, key):
                     '''
                     Get the shape of the one-element data for the given key
                     '''
                     if len(self.data[key]):
                         if 'spc' in key or not self.buffering:
                             return self.data[key].shape
                         return self.data[key][self.times[0]].shape
                     return (0,)
                 def update(self, dataOut, tm):
                     '''
                     Update data object with new dataOut
                     '''
                     self.profileIndex = dataOut.profileIndex
                     self.tm = tm
                     self.type = dataOut.type
                     self.parameters = getattr(dataOut, 'parameters', [])
                     if hasattr(dataOut, 'meta'):
                         self.meta.update(dataOut.meta)
                     if hasattr(dataOut, 'pairsList'):
                         self.pairs = dataOut.pairsList
                     self.interval = dataOut.getTimeInterval()
                     self.localtime = dataOut.useLocalTime
                     if True in ['spc' in ptype for ptype in self.plottypes]:
                         self.xrange = (dataOut.getFreqRange(1)/1000.,
                                        dataOut.getAcfRange(1), dataOut.getVelRange(1))
                     self.__heights.append(dataOut.heightList)
                     self.__all_heights.update(dataOut.heightList)
                     for plot in self.plottypes:
                         if plot in ('spc', 'spc_moments', 'spc_cut'):
                             z = dataOut.data_spc/dataOut.normFactor
                             buffer = 10*numpy.log10(z)
                         if plot == 'cspc':
                             buffer = (dataOut.data_spc, dataOut.data_cspc)
                         if plot == 'noise':
                             buffer = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
                         if plot in ('rti', 'spcprofile'):
                             buffer = dataOut.getPower()
                         if plot == 'snr_db':
                             buffer = dataOut.data_SNR
                         if plot == 'snr':
                             buffer = 10*numpy.log10(dataOut.data_SNR)
                         if plot == 'dop':
                             buffer = dataOut.data_DOP
                         if plot == 'pow':
                             buffer = 10*numpy.log10(dataOut.data_POW)
                         if plot == 'width':
                             buffer = dataOut.data_WIDTH
                         if plot == 'coh':
                             buffer = dataOut.getCoherence()
                         if plot == 'phase':
                             buffer = dataOut.getCoherence(phase=True)
                         if plot == 'output':
                             buffer = dataOut.data_output
                         if plot == 'param':
                             buffer = dataOut.data_param
                         if plot == 'scope':
                             buffer = dataOut.data
                             self.flagDataAsBlock = dataOut.flagDataAsBlock
                             self.nProfiles = dataOut.nProfiles
                         if plot == 'pp_power':
-                            buffer = dataOut.data_intensity
+                            buffer = dataOut.dataPP_POWER
+                            self.flagDataAsBlock = dataOut.flagDataAsBlock
+                            self.nProfiles = dataOut.nProfiles
+                        if plot == 'pp_signal':
+                            buffer = dataOut.dataPP_POW
                             self.flagDataAsBlock = dataOut.flagDataAsBlock
                             self.nProfiles = dataOut.nProfiles
                         if plot == 'pp_velocity':
-                            buffer = dataOut.data_velocity
+                            buffer = dataOut.dataPP_DOP
                             self.flagDataAsBlock = dataOut.flagDataAsBlock
                             self.nProfiles = dataOut.nProfiles
                         if plot == 'pp_specwidth':
-                            buffer = dataOut.data_specwidth
+                            buffer = dataOut.dataPP_WIDTH
                             self.flagDataAsBlock = dataOut.flagDataAsBlock
                             self.nProfiles = dataOut.nProfiles
                         if plot == 'spc':
                             self.data['spc'][tm] = buffer
                         elif plot == 'cspc':
                             self.data['cspc'][tm] = buffer
                         elif plot == 'spc_moments':
                             self.data['spc'][tm] = buffer
                             self.data['moments'][tm] = dataOut.moments
                         else:
                             if self.buffering:
                                 self.data[plot][tm] = buffer
                             else:
                                 self.data[plot][tm] = buffer
                     if dataOut.channelList is None:
                         self.channels = range(buffer.shape[0])
                     else:
                         self.channels = dataOut.channelList
                     if buffer is None:
                         self.flagNoData = True
                         raise schainpy.admin.SchainWarning('Attribute data_{} is empty'.format(self.key))
                 def normalize_heights(self):
                     '''
                     Ensure same-dimension of the data for different heighList
                     '''
                     H = numpy.array(list(self.__all_heights))
                     H.sort()
                     for key in self.data:
                         shape = self.shape(key)[:-1] + H.shape
                         for tm, obj in list(self.data[key].items()):
                             h = self.__heights[self.times.tolist().index(tm)]
                             if H.size == h.size:
                                 continue
                             index = numpy.where(numpy.in1d(H, h))[0]
                             dummy = numpy.zeros(shape) + numpy.nan
                             if len(shape) == 2:
                                 dummy[:, index] = obj
                             else:
                                 dummy[index] = obj
                             self.data[key][tm] = dummy
                     self.__heights = [H for tm in self.times]
                 def jsonify(self, tm, plot_name, plot_type, decimate=False):
                     '''
                     Convert data to json
                     '''
                     dy = int(self.heights.size/self.MAXNUMY) + 1
                     if self.key in ('spc', 'cspc'):
                         dx = int(self.data[self.key][tm].shape[1]/self.MAXNUMX) + 1
                         data = self.roundFloats(
                             self.data[self.key][tm][::, ::dx, ::dy].tolist())
                     else:
                         if self.key is 'noise':
                             data = [[x] for x in self.roundFloats(self.data[self.key][tm].tolist())]
                         else:
                             data = self.roundFloats(self.data[self.key][tm][::, ::dy].tolist())
                     meta = {}
                     ret = {
                         'plot': plot_name,
                         'code': self.exp_code,
                         'time': float(tm),
                         'data': data,
                         }
                     meta['type'] = plot_type
                     meta['interval'] = float(self.interval)
                     meta['localtime'] = self.localtime
                     meta['yrange'] = self.roundFloats(self.heights[::dy].tolist())
                     if 'spc' in self.data or 'cspc' in self.data:
                         meta['xrange'] = self.roundFloats(self.xrange[2][::dx].tolist())
                     else:
                         meta['xrange'] = []
                     meta.update(self.meta)
                     ret['metadata'] = meta
                     return json.dumps(ret)
                 @property
                 def times(self):
                     '''
                     Return the list of times of the current data
                     '''
                     ret = numpy.array([*self.data[self.key]])
                     if self:
                         ret.sort()
                     return ret
                 @property
                 def min_time(self):
                     '''
                     Return the minimun time value
                     '''
                     return self.times[0]
                 @property
                 def max_time(self):
                     '''
                     Return the maximun time value
                     '''
                     return self.times[-1]
                 @property
                 def heights(self):
                     '''
                     Return the list of heights of the current data
                     '''
                     return numpy.array(self.__heights[-1])
                 @staticmethod
                 def roundFloats(obj):
                     if isinstance(obj, list):
                         return list(map(PlotterData.roundFloats, obj))
                     elif isinstance(obj, float):
                         return round(obj, 2)

schainpy/model/graphics/jroplot_voltage.py +29 -3

             '''
             Created on Jul 9, 2014
             @author: roj-idl71
             '''
             import os
             import datetime
             import numpy
             from schainpy.model.graphics.jroplot_base import Plot, plt
             class ScopePlot(Plot):
                 '''
                    Plot for Scope
                 '''
                 CODE = 'scope'
                 plot_name = 'Scope'
                 plot_type = 'scatter'
                 def setup(self):
                     self.xaxis = 'Range (Km)'
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Intensity [dB]'
                     self.titles = ['Scope']
                     self.colorbar = False
                     self.width = 6
                     self.height = 4
                 def plot_iq(self, x, y, channelIndexList, thisDatetime, wintitle):
                     yreal = y[channelIndexList,:].real
                     yimag = y[channelIndexList,:].imag
                     title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y"))
                     self.xlabel = "Range (Km)"
                     self.ylabel = "Intensity - IQ"
                     self.y = yreal
                     self.x = x
                     self.xmin = min(x)
                     self.xmax = max(x)
                     self.titles[0] = title
                     for i,ax in enumerate(self.axes):
                         title = "Channel %d" %(i)
                         if ax.firsttime:
                             ax.plt_r = ax.plot(x, yreal[i,:], color='b')[0]
                             ax.plt_i = ax.plot(x, yimag[i,:], color='r')[0]
                         else:
                             ax.plt_r.set_data(x, yreal[i,:])
                             ax.plt_i.set_data(x, yimag[i,:])
                 def plot_power(self, x, y, channelIndexList, thisDatetime, wintitle):
                     y = y[channelIndexList,:] * numpy.conjugate(y[channelIndexList,:])
                     yreal = y.real
                     yreal = 10*numpy.log10(yreal)
                     self.y = yreal
                     title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y"))
                     self.xlabel = "Range (Km)"
                     self.ylabel = "Intensity"
                     self.xmin = min(x)
                     self.xmax = max(x)
                     self.titles[0] = title
                     for i,ax in enumerate(self.axes):
                         title = "Channel %d" %(i)
                         ychannel = yreal[i,:]
                         if ax.firsttime:
                             ax.plt_r = ax.plot(x, ychannel)[0]
                         else:
                             #pass
                             ax.plt_r.set_data(x, ychannel)
                 def plot_weatherpower(self, x, y, channelIndexList, thisDatetime, wintitle):
                     y      = y[channelIndexList,:]
                     yreal  = y.real
                     yreal  = 10*numpy.log10(yreal)
                     self.y = yreal
                     title  = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
                     self.xlabel = "Range (Km)"
                     self.ylabel = "Intensity"
                     self.xmin   = min(x)
                     self.xmax   = max(x)
                     self.titles[0] =title
                     for i,ax in enumerate(self.axes):
                         title    = "Channel %d" %(i)
                         ychannel = yreal[i,:]
                         if ax.firsttime:
                             ax.plt_r = ax.plot(x, ychannel)[0]
                         else:
                             #pass
                             ax.plt_r.set_data(x, ychannel)
                 def plot_weathervelocity(self, x, y, channelIndexList, thisDatetime, wintitle):
                     x = x[channelIndexList,:]
                     yreal  = y
                     self.y = yreal
                     title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
                     self.xlabel = "Velocity (m/s)"
                     self.ylabel = "Range (Km)"
                     self.xmin   = numpy.min(x)
                     self.xmax   = numpy.max(x)
                     self.titles[0] =title
                     for i,ax in enumerate(self.axes):
                         title    = "Channel %d" %(i)
                         xchannel    = x[i,:]
                         if ax.firsttime:
                             ax.plt_r = ax.plot(xchannel, yreal)[0]
                         else:
                             #pass
                             ax.plt_r.set_data(xchannel, yreal)
                 def plot_weatherspecwidth(self, x, y, channelIndexList, thisDatetime, wintitle):
                     x = x[channelIndexList,:]
                     yreal  = y
                     self.y = yreal
                     title = wintitle + " Scope: %s" %(thisDatetime.strftime("%d-%b-%Y %H:%M:%S"))
                     self.xlabel = "width "
                     self.ylabel = "Range (Km)"
                     self.xmin   = numpy.min(x)
                     self.xmax   = numpy.max(x)
                     self.titles[0] =title
                     for i,ax in enumerate(self.axes):
                         title    = "Channel %d" %(i)
                         xchannel    = x[i,:]
                         if ax.firsttime:
                             ax.plt_r = ax.plot(xchannel, yreal)[0]
                         else:
                             #pass
                             ax.plt_r.set_data(xchannel, yreal)
                 def plot(self):
                     if self.channels:
                         channels = self.channels
                     else:
                         channels = self.data.channels
                     thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1])
                     if self.CODE == "pp_power":
                         scope = self.data['pp_power']
+                    elif self.CODE == "pp_signal":
+                        scope = self.data["pp_signal"]
                     elif self.CODE == "pp_velocity":
                         scope = self.data["pp_velocity"]
                     elif self.CODE == "pp_specwidth":
                         scope = self.data["pp_specwidth"]
                     else:
                         scope =self.data["scope"]
                     if self.data.flagDataAsBlock:
                         for i in range(self.data.nProfiles):
                             wintitle1 = " [Profile = %d] " %i
                             if self.CODE =="scope":
                                 if self.type == "power":
                                     self.plot_power(self.data.heights,
                                                     scope[:,i,:],
                                                     channels,
                                                     thisDatetime,
                                                     wintitle1
                                                     )
                                 if self.type == "iq":
                                     self.plot_iq(self.data.heights,
                                                  scope[:,i,:],
                                                  channels,
                                                  thisDatetime,
                                                  wintitle1
                                                 )
                             if self.CODE=="pp_power":
                                 self.plot_weatherpower(self.data.heights,
                                            scope[:,i,:],
                                            channels,
                                            thisDatetime,
                                            wintitle
                                            )
+                            if self.CODE=="pp_signal":
+                                self.plot_weatherpower(self.data.heights,
+                                           scope[:,i,:],
+                                           channels,
+                                           thisDatetime,
+                                           wintitle
+                                           )
                             if self.CODE=="pp_velocity":
                                 self.plot_weathervelocity(scope[:,i,:],
                                            self.data.heights,
                                            channels,
                                            thisDatetime,
                                            wintitle
                                            )
                             if self.CODE=="pp_spcwidth":
                                 self.plot_weatherspecwidth(scope[:,i,:],
                                            self.data.heights,
                                            channels,
                                            thisDatetime,
                                            wintitle
                                            )
                     else:
                         wintitle = " [Profile = %d] " %self.data.profileIndex
                         if self.CODE== "scope":
                             if self.type == "power":
                                  self.plot_power(self.data.heights,
                                             scope,
                                             channels,
                                             thisDatetime,
                                             wintitle
                                             )
                             if self.type == "iq":
                                 self.plot_iq(self.data.heights,
                                             scope,
                                             channels,
                                             thisDatetime,
                                             wintitle
                                             )
                         if self.CODE=="pp_power":
                             self.plot_weatherpower(self.data.heights,
                                                 scope,
                                                 channels,
                                                 thisDatetime,
                                                 wintitle
                                                    )
+                        if self.CODE=="pp_signal":
+                            self.plot_weatherpower(self.data.heights,
+                                                scope,
+                                                channels,
+                                                thisDatetime,
+                                                wintitle
+                                                   )
                         if self.CODE=="pp_velocity":
                             self.plot_weathervelocity(scope,
                                                    self.data.heights,
                                                    channels,
                                                    thisDatetime,
                                                    wintitle
                                                    )
                         if self.CODE=="pp_specwidth":
                             self.plot_weatherspecwidth(scope,
                                                    self.data.heights,
                                                    channels,
                                                    thisDatetime,
                                                    wintitle
                                                    )
             class PulsepairPowerPlot(ScopePlot):
                 '''
-                Plot for
+                Plot for  P= S+N
                 '''
                 CODE = 'pp_power'
                 plot_name = 'PulsepairPower'
                 plot_type = 'scatter'
                 buffering = False
             class PulsepairVelocityPlot(ScopePlot):
                 '''
-                Plot for
+                Plot for VELOCITY
                 '''
                 CODE = 'pp_velocity'
                 plot_name = 'PulsepairVelocity'
                 plot_type = 'scatter'
                 buffering = False
             class PulsepairSpecwidthPlot(ScopePlot):
                 '''
-                Plot for
+                Plot for WIDTH
                 '''
                 CODE = 'pp_specwidth'
                 plot_name = 'PulsepairSpecwidth'
                 plot_type = 'scatter'
                 buffering = False
+            class PulsepairSignalPlot(ScopePlot):
+                '''
+                Plot for S
+                '''
+                CODE = 'pp_signal'
+                plot_name = 'PulsepairSignal'
+                plot_type = 'scatter'
+                buffering = False

schainpy/model/io/jroIO_simulator.py +21 -14

             import numpy,math,random,time
             #---------------1  Heredamos JRODatareader
             from schainpy.model.io.jroIO_base import *
             #---------------2 Heredamos las propiedades de ProcessingUnit
             from schainpy.model.proc.jroproc_base import ProcessingUnit,Operation,MPDecorator
             #---------------3 Importaremos las clases BascicHeader, SystemHeader, RadarControlHeader, ProcessingHeader
             from schainpy.model.data.jroheaderIO import PROCFLAG, BasicHeader,SystemHeader,RadarControllerHeader, ProcessingHeader
             #---------------4 Importaremos el objeto Voltge
             from schainpy.model.data.jrodata import Voltage
             class SimulatorReader(JRODataReader, ProcessingUnit):
                 incIntFactor                   = 1
                 nFFTPoints                     = 0
                 FixPP_IncInt                   = 1
                 FixRCP_IPP                     = 1000
                 FixPP_CohInt                   = 1
                 Tau_0                          = 250
                 AcqH0_0                        = 70
                 H0                             = AcqH0_0
                 AcqDH_0                        = 1.25
                 DH0                            = AcqDH_0
                 Bauds                          = 32
                 BaudWidth                      = None
                 FixRCP_TXA                     = 40
                 FixRCP_TXB                     = 70
                 fAngle                         = 2.0*math.pi*(1/16)
                 DC_level                       = 500
                 stdev                          = 8
                 Num_Codes                      = 2
                 #code0                          = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1])
                 #code1                          = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0])
                 #Dyn_snCode                     = numpy.array([Num_Codes,Bauds])
                 Dyn_snCode                     = None
                 Samples                        = 200
                 channels                       = 2
                 pulses                         = None
                 Reference                      = None
                 pulse_size                     = None
                 prof_gen                       = None
                 Fdoppler                       = 100
                 Hdoppler                       = 36
                 Adoppler                       = 300
                 frequency                      = 9345
                 nTotalReadFiles                = 1000
                 def __init__(self):
                     """
                     Inicializador de la clases SimulatorReader para
                     generar datos de voltage simulados.
                     Input:
                         dataOut: Objeto de la clase Voltage.
                         Este Objeto sera utilizado apra almacenar
                         un perfil de datos  cada vez qe se haga
                         un requerimiento (getData)
                     """
                     ProcessingUnit.__init__(self)
                     print(" [ START ] init - Metodo Simulator Reader")
                     self.isConfig                  = False
                     self.basicHeaderObj            = BasicHeader(LOCALTIME)
                     self.systemHeaderObj           = SystemHeader()
                     self.radarControllerHeaderObj  = RadarControllerHeader()
                     self.processingHeaderObj       = ProcessingHeader()
                     self.profileIndex              = 2**32-1
                     self.dataOut                   = Voltage()
                     #code0                          = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,1,1,1,0,1,1,0,1,0,0,0,1,1,1,0,1])
                     code0                          = numpy.array([1,1,1,-1,1,1,-1,1,1,1,1,-1,-1,-1,1,-1,1,1,1,-1,1,1,-1,1,-1,-1,-1,1,1,1,-1,1])
                     #code1                          = numpy.array([1,1,1,0,1,1,0,1,1,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,1,0])
                     code1                          = numpy.array([1,1,1,-1,1,1,-1,1,1,1,1,-1,-1,-1,1,-1,-1,-1,-1,1,-1,-1,1,-1,1,1,1,-1,-1,-1,1,-1])
                     #self.Dyn_snCode                = numpy.array([code0,code1])
                     self.Dyn_snCode                = None
                 def set_kwargs(self, **kwargs):
                     for key, value in kwargs.items():
                         setattr(self, key, value)
                 def __hasNotDataInBuffer(self):
                     if self.profileIndex >= self.processingHeaderObj.profilesPerBlock* self.nTxs:
                         if self.nReadBlocks>0:
                             tmp                           = self.dataOut.utctime
                             tmp_utc                       = int(self.dataOut.utctime)
                             tmp_milisecond                = int((tmp-tmp_utc)*1000)
                             self.basicHeaderObj.utc       = tmp_utc
                             self.basicHeaderObj.miliSecond= tmp_milisecond
                         return 1
                     return 0
                 def setNextFile(self):
                     """Set the next file to be readed open it and parse de file header"""
                     if (self.nReadBlocks >= self.processingHeaderObj.dataBlocksPerFile):
                         self.nReadFiles=self.nReadFiles+1
                         if self.nReadFiles > self.nTotalReadFiles:
                             self.flagNoMoreFiles=1
                             raise schainpy.admin.SchainWarning('No more files to read')
                         print('------------------- [Opening file] ------------------------------',self.nReadFiles)
                         self.nReadBlocks  = 0
                     #if self.nReadBlocks==0:
                     #    self.readFirstHeader()
                 def __setNewBlock(self):
                     self.setNextFile()
                     if self.flagIsNewFile:
                         return 1
                 def readNextBlock(self):
                     while True:
                         self.__setNewBlock()
                         if not(self.readBlock()):
                             return 0
                         self.getBasicHeader()
                         break
                     if self.verbose:
                         print("[Reading] Block No. %d/%d -> %s" %(self.nReadBlocks,
                                                                   self.processingHeaderObj.dataBlocksPerFile,
                                                                   self.dataOut.datatime.ctime()) )
                     return 1
                 def getFirstHeader(self):
                     self.getBasicHeader()
                     self.dataOut.processingHeaderObj      = self.processingHeaderObj.copy()
                     self.dataOut.systemHeaderObj          = self.systemHeaderObj.copy()
                     self.dataOut.radarControllerHeaderObj = self.radarControllerHeaderObj.copy()
                     self.dataOut.dtype       = self.dtype
                     self.dataOut.nProfiles   = self.processingHeaderObj.profilesPerBlock
                     self.dataOut.heightList  = numpy.arange(self.processingHeaderObj.nHeights) * self.processingHeaderObj.deltaHeight + self.processingHeaderObj.firstHeight
                     self.dataOut.channelList = list(range(self.systemHeaderObj.nChannels))
                     self.dataOut.nCohInt     = self.processingHeaderObj.nCohInt
                     # asumo q la data no esta decodificada
                     self.dataOut.flagDecodeData  = self.processingHeaderObj.flag_decode
                     # asumo q la data no esta sin flip
                     self.dataOut.flagDeflipData  = self.processingHeaderObj.flag_deflip
                     self.dataOut.flagShiftFFT    = self.processingHeaderObj.shif_fft
                     self.dataOut.frequency       = self.frequency
                 def getBasicHeader(self):
                     self.dataOut.utctime = self.basicHeaderObj.utc + self.basicHeaderObj.miliSecond / \
 . + self.profileIndex * self.radarControllerHeaderObj.ippSeconds
                     self.dataOut.flagDiscontinuousBlock = self.flagDiscontinuousBlock
                     self.dataOut.timeZone               = self.basicHeaderObj.timeZone
                     self.dataOut.dstFlag                = self.basicHeaderObj.dstFlag
                     self.dataOut.errorCount             = self.basicHeaderObj.errorCount
                     self.dataOut.useLocalTime           = self.basicHeaderObj.useLocalTime
                     self.dataOut.ippSeconds             = self.radarControllerHeaderObj.ippSeconds / self.nTxs
                 def readFirstHeader(self):
                     datatype = int(numpy.log2((self.processingHeaderObj.processFlags &
                                                PROCFLAG.DATATYPE_MASK)) - numpy.log2(PROCFLAG.DATATYPE_CHAR))
                     if datatype == 0:
                         datatype_str = numpy.dtype([('real', '<i1'), ('imag', '<i1')])
                     elif datatype == 1:
                         datatype_str = numpy.dtype([('real', '<i2'), ('imag', '<i2')])
                     elif datatype == 2:
                         datatype_str = numpy.dtype([('real', '<i4'), ('imag', '<i4')])
                     elif datatype == 3:
                         datatype_str = numpy.dtype([('real', '<i8'), ('imag', '<i8')])
                     elif datatype == 4:
                         datatype_str = numpy.dtype([('real', '<f4'), ('imag', '<f4')])
                     elif datatype == 5:
                         datatype_str = numpy.dtype([('real', '<f8'), ('imag', '<f8')])
                     else:
                         raise ValueError('Data type was not defined')
                     self.dtype = datatype_str
                 def set_RCH(self, expType=2, nTx=1,ipp=None, txA=0, txB=0,
                              nWindows=None, nHeights=None, firstHeight=None, deltaHeight=None,
                              numTaus=0, line6Function=0, line5Function=0, fClock=None,
                              prePulseBefore=0, prePulseAfter=0,
                              codeType=0, nCode=0, nBaud=0, code=None,
                              flip1=0, flip2=0,Taus=0):
                     self.radarControllerHeaderObj.expType       = expType
                     self.radarControllerHeaderObj.nTx           = nTx
                     self.radarControllerHeaderObj.ipp           = float(ipp)
                     self.radarControllerHeaderObj.txA           = float(txA)
                     self.radarControllerHeaderObj.txB           = float(txB)
                     self.radarControllerHeaderObj.rangeIpp      = b'A\n'#ipp
                     self.radarControllerHeaderObj.rangeTxA      = b''
                     self.radarControllerHeaderObj.rangeTxB      = b''
                     self.radarControllerHeaderObj.nHeights      = int(nHeights)
                     self.radarControllerHeaderObj.firstHeight   = numpy.array([firstHeight])
                     self.radarControllerHeaderObj.deltaHeight   = numpy.array([deltaHeight])
                     self.radarControllerHeaderObj.samplesWin    = numpy.array([nHeights])
                     self.radarControllerHeaderObj.nWindows      = nWindows
                     self.radarControllerHeaderObj.numTaus       = numTaus
                     self.radarControllerHeaderObj.codeType      = codeType
                     self.radarControllerHeaderObj.line6Function = line6Function
                     self.radarControllerHeaderObj.line5Function = line5Function
                     #self.radarControllerHeaderObj.fClock        = fClock
                     self.radarControllerHeaderObj.prePulseBefore= prePulseBefore
                     self.radarControllerHeaderObj.prePulseAfter = prePulseAfter
                     self.radarControllerHeaderObj.flip1         = flip1
                     self.radarControllerHeaderObj.flip2         = flip2
                     self.radarControllerHeaderObj.code_size     = 0
                     if self.radarControllerHeaderObj.codeType  != 0:
                         self.radarControllerHeaderObj.nCode         = nCode
                         self.radarControllerHeaderObj.nBaud         = nBaud
                         self.radarControllerHeaderObj.code          = code
                         self.radarControllerHeaderObj.code_size     = int(numpy.ceil(nBaud / 32.)) * nCode * 4
                     if fClock is None and deltaHeight is not None:
                         self.fClock = 0.15 / (deltaHeight * 1e-6)
                         self.radarControllerHeaderObj.fClock     = self.fClock
                     if numTaus==0:
                         self.radarControllerHeaderObj.Taus       =  numpy.array(0,'<f4')
                     else:
                         self.radarControllerHeaderObj.Taus       = numpy.array(Taus,'<f4')
                 def set_PH(self, dtype=0, blockSize=0, profilesPerBlock=0,
                               dataBlocksPerFile=0, nWindows=0, processFlags=0, nCohInt=0,
                               nIncohInt=0, totalSpectra=0, nHeights=0, firstHeight=0,
                               deltaHeight=0, samplesWin=0, spectraComb=0, nCode=0,
                               code=0, nBaud=None, shif_fft=False, flag_dc=False,
                               flag_cspc=False, flag_decode=False, flag_deflip=False):
                     self.processingHeaderObj.dtype             = dtype
                     self.processingHeaderObj.profilesPerBlock  = profilesPerBlock
                     self.processingHeaderObj.dataBlocksPerFile = dataBlocksPerFile
                     self.processingHeaderObj.nWindows          = nWindows
                     self.processingHeaderObj.processFlags      = processFlags
                     self.processingHeaderObj.nCohInt           = nCohInt
                     self.processingHeaderObj.nIncohInt         = nIncohInt
                     self.processingHeaderObj.totalSpectra      = totalSpectra
                     self.processingHeaderObj.nHeights          = int(nHeights)
                     self.processingHeaderObj.firstHeight       = firstHeight#numpy.array([firstHeight])#firstHeight
                     self.processingHeaderObj.deltaHeight       = deltaHeight#numpy.array([deltaHeight])#deltaHeight
                     self.processingHeaderObj.samplesWin        = nHeights#numpy.array([nHeights])#nHeights
                 def set_BH(self, utc = 0, miliSecond = 0, timeZone = 0):
                     self.basicHeaderObj.utc                    = utc
                     self.basicHeaderObj.miliSecond             = miliSecond
                     self.basicHeaderObj.timeZone               = timeZone
                 def set_SH(self, nSamples=0, nProfiles=0, nChannels=0, adcResolution=14, pciDioBusWidth=32):
                     #self.systemHeaderObj.size           = size
                     self.systemHeaderObj.nSamples       = nSamples
                     self.systemHeaderObj.nProfiles      = nProfiles
                     self.systemHeaderObj.nChannels      = nChannels
                     self.systemHeaderObj.adcResolution  = adcResolution
                     self.systemHeaderObj.pciDioBusWidth = pciDioBusWidth
                 def init_acquisition(self):
                     if self.nFFTPoints != 0:
                         self.incIntFactor = m_nProfilesperBlock/self.nFFTPoints
                         if (self.FixPP_IncInt > self.incIntFactor):
                             self.incIntFactor = self.FixPP_IncInt/ self.incIntFactor
                         elif(self.FixPP_IncInt< self.incIntFactor):
                             print("False alert...")
                     ProfilesperBlock  = self.processingHeaderObj.profilesPerBlock
                     self.timeperblock =int(((self.FixRCP_IPP
                                     *ProfilesperBlock
                                     *self.FixPP_CohInt
                                     *self.incIntFactor)
                                    /150.0)
                                   *0.9
                                   +0.5)
                     # para cada canal
                     self.profiles     =  ProfilesperBlock*self.FixPP_CohInt
                     self.profiles     =  ProfilesperBlock
                     self.Reference    =  int((self.Tau_0-self.AcqH0_0)/(self.AcqDH_0)+0.5)
                     self.BaudWidth    =  int((self.FixRCP_TXA/self.AcqDH_0)/self.Bauds + 0.5 )
                     if (self.BaudWidth==0):
                         self.BaudWidth=1
                 def init_pulse(self,Num_Codes=Num_Codes,Bauds=Bauds,BaudWidth=BaudWidth,Dyn_snCode=Dyn_snCode):
                     Num_Codes   = Num_Codes
                     Bauds       = Bauds
                     BaudWidth   = BaudWidth
                     Dyn_snCode  = Dyn_snCode
                     if Dyn_snCode:
                         print("EXISTE")
                     else:
                         print("No existe")
                     if Dyn_snCode: # if Bauds:
                         pulses         = list(range(0,Num_Codes))
                         num_codes      = Num_Codes
                         for i in range(num_codes):
                             pulse_size = Bauds*BaudWidth
                             pulses[i]  = numpy.zeros(pulse_size)
                             for j in range(Bauds):
                                 for k in range(BaudWidth):
                                     pulses[i][j*BaudWidth+k] = int(Dyn_snCode[i][j]*600)
                     else:
                         print("sin code")
                         pulses     = list(range(1))
                         if self.AcqDH_0>0.149:
                             pulse_size = int(self.FixRCP_TXB/0.15+0.5)
                         else:
                             pulse_size = int((self.FixRCP_TXB/self.AcqDH_0)+0.5) #0.0375
                         pulses[0]  = numpy.ones(pulse_size)
                         pulses     = 600*pulses[0]
                     return pulses,pulse_size
                 def jro_GenerateBlockOfData(self,Samples=Samples,DC_level= DC_level,stdev=stdev,
                                             Reference= Reference,pulses= pulses,
                                             Num_Codes= Num_Codes,pulse_size=pulse_size,
                                             prof_gen= prof_gen,H0 = H0,DH0=DH0,
                                             Adoppler=Adoppler,Fdoppler= Fdoppler,Hdoppler=Hdoppler):
                     Samples    = Samples
                     DC_level   = DC_level
                     stdev      = stdev
                     m_nR       = Reference
                     pulses     = pulses
                     num_codes  = Num_Codes
                     ps         = pulse_size
                     prof_gen   = prof_gen
                     channels   = self.channels
                     H0         = H0
                     DH0        = DH0
                     ippSec     = self.radarControllerHeaderObj.ippSeconds
                     Fdoppler   = self.Fdoppler
                     Hdoppler   = self.Hdoppler
                     Adoppler   = self.Adoppler
                     self.datablock = numpy.zeros([channels,prof_gen,Samples],dtype= numpy.complex64)
                     for i in range(channels):
                         for k in range(prof_gen):
                             #·······················NOISE···············
                             Noise_r    = numpy.random.normal(DC_level,stdev,Samples)
                             Noise_i    = numpy.random.normal(DC_level,stdev,Samples)
                             Noise      = numpy.zeros(Samples,dtype=complex)
                             Noise.real = Noise_r
                             Noise.imag = Noise_i
                             #·······················PULSOS··············
                             Pulso      = numpy.zeros(pulse_size,dtype=complex)
                             Pulso.real =  pulses[k%num_codes]
                             Pulso.imag =  pulses[k%num_codes]
                             #····················· PULSES+NOISE··········
                             InBuffer                    = numpy.zeros(Samples,dtype=complex)
                             InBuffer[m_nR:m_nR+ps]      = Pulso
                             InBuffer                    =  InBuffer+Noise
                             #····················· ANGLE ·······························
                             InBuffer.real[m_nR:m_nR+ps] = InBuffer.real[m_nR:m_nR+ps]*(math.cos( self.fAngle)*5)
                             InBuffer.imag[m_nR:m_nR+ps] = InBuffer.imag[m_nR:m_nR+ps]*(math.sin( self.fAngle)*5)
                             InBuffer=InBuffer
                             self.datablock[i][k]= InBuffer
                     #················DOPPLER SIGNAL...............................................
                     time_vec   = numpy.linspace(0,(prof_gen-1)*ippSec,int(prof_gen))+self.nReadBlocks*ippSec*prof_gen+(self.nReadFiles-1)*ippSec*prof_gen
                     fd         = Fdoppler #+(600.0/120)*self.nReadBlocks
                     d_signal   = Adoppler*numpy.array(numpy.exp(1.0j*2.0*math.pi*fd*time_vec),dtype=numpy.complex64)
                     #·············Señal con ancho espectral····················
-                    #specw_sig  = numpy.linspace(-149,150,300)
+                    if prof_gen%2==0:
-                    #w          = 8
+                        min = int(prof_gen/2.0-1.0)
-                    #A          = 20
+                        max = int(prof_gen/2.0)
-                    #specw_sig   = specw_sig/w
+                    else:
-                    #specw_sig   = numpy.sinc(specw_sig)
+                        min = int(prof_gen/2.0)
-                    #specw_sig   =  A*numpy.array(specw_sig,dtype=numpy.complex64)
+                        max = int(prof_gen/2.0)
+                    specw_sig  = numpy.linspace(-min,max,prof_gen)
+                    w          = 4
+                    A          = 20
+                    specw_sig   = specw_sig/w
+                    specw_sig   = numpy.sinc(specw_sig)
+                    specw_sig   =  A*numpy.array(specw_sig,dtype=numpy.complex64)
                     #·················· DATABLOCK + DOPPLER····················
                     HD=int(Hdoppler/self.AcqDH_0)
                     for  i in range(12):
                         self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ d_signal# RESULT
                     #·················· DATABLOCK + DOPPLER*Sinc(x)····················
-                    #HD=int(Hdoppler/self.AcqDH_0)
+                    HD=int(Hdoppler/self.AcqDH_0)
-                    #HD=int(HD/2)
+                    HD=int(HD/2)
-                    #for  i in range(12):
+                    for  i in range(12):
-                    #    self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ specw_sig*d_signal# RESULT
+                        self.datablock[0,:,HD+i]=self.datablock[0,:,HD+i]+ specw_sig*d_signal# RESULT
                 def readBlock(self):
                     self.jro_GenerateBlockOfData(Samples= self.samples,DC_level=self.DC_level,
                                                  stdev=self.stdev,Reference= self.Reference,
                                                  pulses = self.pulses,Num_Codes=self.Num_Codes,
                                                  pulse_size=self.pulse_size,prof_gen=self.profiles,
                                                  H0=self.H0,DH0=self.DH0)
                     self.profileIndex   = 0
                     self.flagIsNewFile  = 0
                     self.flagIsNewBlock = 1
                     self.nTotalBlocks  += 1
                     self.nReadBlocks   += 1
                     return 1
                 def getData(self):
                     if self.flagNoMoreFiles:
                         self.dataOut.flagNodata = True
                         return 0
                     self.flagDiscontinuousBlock = 0
                     self.flagIsNewBlock         = 0
                     if self.__hasNotDataInBuffer():   # aqui es verdad
                         if not(self.readNextBlock()): # return 1 y por eso el if not salta  a getBasic Header
                             return 0
                         self.getFirstHeader()         # atributo
                     if not self.getByBlock:
                         self.dataOut.flagDataAsBlock = False
                         self.dataOut.data = self.datablock[:, self.profileIndex, :]
                         self.dataOut.profileIndex = self.profileIndex
                         self.profileIndex += 1
                     else:
                         pass
                     self.dataOut.flagNoData = False
                     self.getBasicHeader()
                     self.dataOut.realtime = self.online
                     return self.dataOut.data
                 def setup(self,frequency=49.92e6,incIntFactor= 1, nFFTPoints = 0, FixPP_IncInt=1,FixRCP_IPP=1000,
                                FixPP_CohInt= 1,Tau_0= 250,AcqH0_0 = 70 ,AcqDH_0=1.25, Bauds= 32,
                                FixRCP_TXA = 40, FixRCP_TXB = 50, fAngle = 2.0*math.pi*(1/16),DC_level= 50,
                                stdev= 8,Num_Codes = 1 , Dyn_snCode = None, samples=200,
-                               channels=2,Fdoppler=20,Hdoppler=36,Adoppler=500,nTotalReadFiles=10000,
+                               channels=2,Fdoppler=20,Hdoppler=36,Adoppler=500,
+                               profilesPerBlock=300,dataBlocksPerFile=120,nTotalReadFiles=10000,
                                **kwargs):
                     self.set_kwargs(**kwargs)
                     self.nReadBlocks = 0
                     self.nReadFiles  = 1
                     print('------------------- [Opening file: ] ------------------------------',self.nReadFiles)
                     tmp              = time.time()
                     tmp_utc          = int(tmp)
                     tmp_milisecond   = int((tmp-tmp_utc)*1000)
                     print(" SETUP -basicHeaderObj.utc",datetime.datetime.utcfromtimestamp(tmp))
                     if Dyn_snCode is None:
                         Num_Codes=1
                         Bauds    =1
                     self.set_BH(utc= tmp_utc,miliSecond= tmp_milisecond,timeZone=300 )
                     self.set_RCH( expType=0, nTx=150,ipp=FixRCP_IPP, txA=FixRCP_TXA, txB= FixRCP_TXB,
                              nWindows=1 , nHeights=samples, firstHeight=AcqH0_0, deltaHeight=AcqDH_0,
                              numTaus=1, line6Function=0, line5Function=0, fClock=None,
                              prePulseBefore=0, prePulseAfter=0,
                              codeType=0, nCode=Num_Codes, nBaud=32, code=Dyn_snCode,
                              flip1=0, flip2=0,Taus=Tau_0)
-                    self.set_PH(dtype=0, blockSize=0, profilesPerBlock=300,
+                    self.set_PH(dtype=0, blockSize=0, profilesPerBlock=profilesPerBlock,
-                                  dataBlocksPerFile=120, nWindows=1, processFlags=numpy.array([1024]), nCohInt=1,
+                                  dataBlocksPerFile=dataBlocksPerFile, nWindows=1, processFlags=numpy.array([1024]), nCohInt=1,
                                   nIncohInt=1, totalSpectra=0, nHeights=samples, firstHeight=AcqH0_0,
                                   deltaHeight=AcqDH_0, samplesWin=samples, spectraComb=0, nCode=0,
                                   code=0, nBaud=None, shif_fft=False, flag_dc=False,
                                   flag_cspc=False, flag_decode=False, flag_deflip=False)
-                    self.set_SH(nSamples=samples, nProfiles=300, nChannels=channels)
+                    self.set_SH(nSamples=samples, nProfiles=profilesPerBlock, nChannels=channels)
                     self.readFirstHeader()
                     self.frequency                      = frequency
                     self.incIntFactor                   = incIntFactor
                     self.nFFTPoints                     = nFFTPoints
                     self.FixPP_IncInt                   = FixPP_IncInt
                     self.FixRCP_IPP                     = FixRCP_IPP
                     self.FixPP_CohInt                   = FixPP_CohInt
                     self.Tau_0                          = Tau_0
                     self.AcqH0_0                        = AcqH0_0
                     self.H0                             = AcqH0_0
                     self.AcqDH_0                        = AcqDH_0
                     self.DH0                            = AcqDH_0
                     self.Bauds                          = Bauds
                     self.FixRCP_TXA                     = FixRCP_TXA
                     self.FixRCP_TXB                     = FixRCP_TXB
                     self.fAngle                         = fAngle
                     self.DC_level                       = DC_level
                     self.stdev                          = stdev
                     self.Num_Codes                      = Num_Codes
                     self.Dyn_snCode                     = Dyn_snCode
                     self.samples                        = samples
                     self.channels                       = channels
                     self.profiles                       = None
                     self.m_nReference                   = None
                     self.Baudwidth                      = None
                     self.Fdoppler                       = Fdoppler
                     self.Hdoppler                       = Hdoppler
                     self.Adoppler                       = Adoppler
                     self.nTotalReadFiles                = int(nTotalReadFiles)
                     print("IPP    ", self.FixRCP_IPP)
                     print("Tau_0  ",self.Tau_0)
                     print("AcqH0_0",self.AcqH0_0)
                     print("samples,window ",self.samples)
                     print("AcqDH_0",AcqDH_0)
                     print("FixRCP_TXA",self.FixRCP_TXA)
                     print("FixRCP_TXB",self.FixRCP_TXB)
                     print("Dyn_snCode",Dyn_snCode)
                     print("Fdoppler", Fdoppler)
                     print("Hdoppler",Hdoppler)
                     print("Vdopplermax",Fdoppler*(3.0e8/self.frequency)/2.0)
                     print("nTotalReadFiles", nTotalReadFiles)
                     self.init_acquisition()
                     self.pulses,self.pulse_size=self.init_pulse(Num_Codes=self.Num_Codes,Bauds=self.Bauds,BaudWidth=self.BaudWidth,Dyn_snCode=Dyn_snCode)
                     print(" [ END ] - SETUP metodo")
                     return
                 def run(self,**kwargs): # metodo propio
                     if not(self.isConfig):
                         self.setup(**kwargs)
                         self.isConfig = True
                     self.getData()

schainpy/model/proc/jroproc_parameters.py +1077 -1064

             import numpy
             import math
             from scipy import optimize, interpolate, signal, stats, ndimage
             import scipy
             import re
             import datetime
             import copy
             import sys
             import importlib
             import itertools
             from multiprocessing import Pool, TimeoutError
             from multiprocessing.pool import ThreadPool
             import time
             from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
             from .jroproc_base import ProcessingUnit, Operation, MPDecorator
             from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
             from scipy import asarray as ar,exp
             from scipy.optimize import curve_fit
             from schainpy.utils import log
             import warnings
             from numpy import NaN
             from scipy.optimize.optimize import OptimizeWarning
             warnings.filterwarnings('ignore')
             import matplotlib.pyplot as plt
             SPEED_OF_LIGHT = 299792458
             '''solving pickling issue'''
             def _pickle_method(method):
                 func_name = method.__func__.__name__
                 obj = method.__self__
                 cls = method.__self__.__class__
                 return _unpickle_method, (func_name, obj, cls)
             def _unpickle_method(func_name, obj, cls):
                 for cls in cls.mro():
                     try:
                         func = cls.__dict__[func_name]
                     except KeyError:
                         pass
                     else:
                         break
                 return func.__get__(obj, cls)
             class ParametersProc(ProcessingUnit):
                 METHODS = {}
                 nSeconds = None
                 def __init__(self):
                     ProcessingUnit.__init__(self)
             #         self.objectDict = {}
                     self.buffer = None
                     self.firstdatatime = None
                     self.profIndex = 0
                     self.dataOut = Parameters()
                     self.setupReq = False #Agregar a todas las unidades de proc
                 def __updateObjFromInput(self):
                     self.dataOut.inputUnit = self.dataIn.type
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
             #         self.dataOut.nHeights = self.dataIn.nHeights
             #         self.dataOut.nChannels = self.dataIn.nChannels
                     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
                     self.dataOut.utctime = self.dataIn.utctime
                     self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
                     self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
                     self.dataOut.nCohInt = self.dataIn.nCohInt
             #        self.dataOut.nIncohInt = 1
                     self.dataOut.ippSeconds = self.dataIn.ippSeconds
             #        self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.timeInterval1 = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.getHeiRange()
                     self.dataOut.frequency = self.dataIn.frequency
                     # self.dataOut.noise = self.dataIn.noise
                 def run(self):
                     #----------------------    Voltage Data    ---------------------------
                     if self.dataIn.type == "Voltage":
                         self.__updateObjFromInput()
                         self.dataOut.data_pre = self.dataIn.data.copy()
                         self.dataOut.flagNoData = False
                         self.dataOut.utctimeInit = self.dataIn.utctime
                         self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds
+                        if hasattr(self.dataIn, 'dataPP_POW'):
+                            self.dataOut.dataPP_POW = self.dataIn.dataPP_POW
+                        if hasattr(self.dataIn, 'dataPP_POWER'):
+                            self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER
+                        if hasattr(self.dataIn, 'dataPP_DOP'):
+                            self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP
+                        if hasattr(self.dataIn, 'dataPP_SNR'):
+                            self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR
+                        if hasattr(self.dataIn, 'dataPP_WIDTH'):
+                            self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH
                         return
                     #----------------------    Spectra Data    ---------------------------
                     if self.dataIn.type == "Spectra":
                         self.dataOut.data_pre = (self.dataIn.data_spc, self.dataIn.data_cspc)
                         self.dataOut.data_spc = self.dataIn.data_spc
                         self.dataOut.data_cspc = self.dataIn.data_cspc
                         self.dataOut.nProfiles = self.dataIn.nProfiles
                         self.dataOut.nIncohInt = self.dataIn.nIncohInt
                         self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
                         self.dataOut.ippFactor = self.dataIn.ippFactor
                         self.dataOut.abscissaList = self.dataIn.getVelRange(1)
                         self.dataOut.spc_noise = self.dataIn.getNoise()
                         self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
                         # self.dataOut.normFactor = self.dataIn.normFactor
                         self.dataOut.pairsList = self.dataIn.pairsList
                         self.dataOut.groupList = self.dataIn.pairsList
                         self.dataOut.flagNoData = False
                         if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
                             self.dataOut.ChanDist = self.dataIn.ChanDist
                         else: self.dataOut.ChanDist = None
                         #if hasattr(self.dataIn, 'VelRange'): #Velocities range
                         #    self.dataOut.VelRange = self.dataIn.VelRange
                         #else: self.dataOut.VelRange = None
                         if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
                             self.dataOut.RadarConst = self.dataIn.RadarConst
                         if hasattr(self.dataIn, 'NPW'): #NPW
                             self.dataOut.NPW = self.dataIn.NPW
                         if hasattr(self.dataIn, 'COFA'): #COFA
                             self.dataOut.COFA = self.dataIn.COFA
                     #----------------------    Correlation Data    ---------------------------
                     if self.dataIn.type == "Correlation":
                         acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
                         self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
                         self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
                         self.dataOut.groupList = (acf_pairs, ccf_pairs)
                         self.dataOut.abscissaList = self.dataIn.lagRange
                         self.dataOut.noise = self.dataIn.noise
                         self.dataOut.data_SNR = self.dataIn.SNR
                         self.dataOut.flagNoData = False
                         self.dataOut.nAvg = self.dataIn.nAvg
                     #----------------------    Parameters Data    ---------------------------
                     if self.dataIn.type == "Parameters":
                         self.dataOut.copy(self.dataIn)
                         self.dataOut.flagNoData = False
                         return True
                     self.__updateObjFromInput()
                     self.dataOut.utctimeInit = self.dataIn.utctime
                     self.dataOut.paramInterval = self.dataIn.timeInterval
                     return
             def target(tups):
                 obj, args = tups
                 return obj.FitGau(args)
             class SpectralFilters(Operation):
                 '''This class allows the Rainfall / Wind Selection for CLAIRE RADAR
                     LimitR :    It is the limit in m/s of Rainfall
                     LimitW :    It is the limit in m/s for Winds
                     Input:
                     self.dataOut.data_pre :        SPC and CSPC
                     self.dataOut.spc_range :       To select wind and rainfall velocities
                     Affected:
                     self.dataOut.data_pre :        It is used for the new SPC and CSPC ranges of wind
                     self.dataOut.spcparam_range :  Used in SpcParamPlot
                     self.dataOut.SPCparam :        Used in PrecipitationProc
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def run(self, dataOut, PositiveLimit=1.5, NegativeLimit=2.5):
                     #Limite de vientos
                     LimitR = PositiveLimit
                     LimitN = NegativeLimit
                     self.spc = dataOut.data_pre[0].copy()
                     self.cspc = dataOut.data_pre[1].copy()
                     self.Num_Hei = self.spc.shape[2]
                     self.Num_Bin = self.spc.shape[1]
                     self.Num_Chn = self.spc.shape[0]
                     VelRange = dataOut.spc_range[2]
                     TimeRange = dataOut.spc_range[1]
                     FrecRange = dataOut.spc_range[0]
                     Vmax= 2*numpy.max(dataOut.spc_range[2])
                     Tmax= 2*numpy.max(dataOut.spc_range[1])
                     Fmax= 2*numpy.max(dataOut.spc_range[0])
                     Breaker1R=VelRange[numpy.abs(VelRange-(-LimitN)).argmin()]
                     Breaker1R=numpy.where(VelRange == Breaker1R)
                     Delta = self.Num_Bin/2 - Breaker1R[0]
                     '''Reacomodando SPCrange'''
                     VelRange=numpy.roll(VelRange,-(int(self.Num_Bin/2)) ,axis=0)
                     VelRange[-(int(self.Num_Bin/2)):]+= Vmax
                     FrecRange=numpy.roll(FrecRange,-(int(self.Num_Bin/2)),axis=0)
                     FrecRange[-(int(self.Num_Bin/2)):]+= Fmax
                     TimeRange=numpy.roll(TimeRange,-(int(self.Num_Bin/2)),axis=0)
                     TimeRange[-(int(self.Num_Bin/2)):]+= Tmax
                     ''' ------------------ '''
                     Breaker2R=VelRange[numpy.abs(VelRange-(LimitR)).argmin()]
                     Breaker2R=numpy.where(VelRange == Breaker2R)
                     SPCroll = numpy.roll(self.spc,-(int(self.Num_Bin/2)) ,axis=1)
                     SPCcut = SPCroll.copy()
                     for i in range(self.Num_Chn):
                         SPCcut[i,0:int(Breaker2R[0]),:] = dataOut.noise[i]
                         SPCcut[i,-int(Delta):,:] = dataOut.noise[i]
                         SPCcut[i]=SPCcut[i]- dataOut.noise[i]
                         SPCcut[ numpy.where( SPCcut<0 ) ] = 1e-20
                         SPCroll[i]=SPCroll[i]-dataOut.noise[i]
                         SPCroll[ numpy.where( SPCroll<0 ) ] = 1e-20
                     SPC_ch1 = SPCroll
                     SPC_ch2 = SPCcut
                     SPCparam = (SPC_ch1, SPC_ch2, self.spc)
                     dataOut.SPCparam = numpy.asarray(SPCparam)
                     dataOut.spcparam_range=numpy.zeros([self.Num_Chn,self.Num_Bin+1])
                     dataOut.spcparam_range[2]=VelRange
                     dataOut.spcparam_range[1]=TimeRange
                     dataOut.spcparam_range[0]=FrecRange
                     return dataOut
             class GaussianFit(Operation):
                 '''
                     Function that fit of one and two generalized gaussians (gg) based
                     on the PSD shape across an "power band" identified from a cumsum of
                     the measured spectrum - noise.
                     Input:
                         self.dataOut.data_pre    :    SelfSpectra
                     Output:
                         self.dataOut.SPCparam :    SPC_ch1, SPC_ch2
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
                     """This routine will find a couple of generalized Gaussians to a power spectrum
                     input: spc
                     output:
                         Amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1,noise
                     """
                     self.spc = dataOut.data_pre[0].copy()
                     self.Num_Hei = self.spc.shape[2]
                     self.Num_Bin = self.spc.shape[1]
                     self.Num_Chn = self.spc.shape[0]
                     Vrange = dataOut.abscissaList
                     GauSPC = numpy.empty([self.Num_Chn,self.Num_Bin,self.Num_Hei])
                     SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch1[:] = numpy.NaN
                     SPC_ch2[:] = numpy.NaN
                     start_time = time.time()
                     noise_ = dataOut.spc_noise[0].copy()
                     pool = Pool(processes=self.Num_Chn)
                     args = [(Vrange, Ch, pnoise, noise_, num_intg, SNRlimit) for Ch in range(self.Num_Chn)]
                     objs = [self for __ in range(self.Num_Chn)]
                     attrs = list(zip(objs, args))
                     gauSPC = pool.map(target, attrs)
                     dataOut.SPCparam = numpy.asarray(SPCparam)
                     ''' Parameters:
 . Amplitude
 . Shift
 . Width
 . Power
                            '''
                 def FitGau(self, X):
                     Vrange, ch, pnoise, noise_, num_intg, SNRlimit = X
                     SPCparam = []
                     SPC_ch1 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch2 = numpy.empty([self.Num_Bin,self.Num_Hei])
                     SPC_ch1[:] = 0#numpy.NaN
                     SPC_ch2[:] = 0#numpy.NaN
                     for ht in range(self.Num_Hei):
                         spc =  numpy.asarray(self.spc)[ch,:,ht]
                         #############################################
                         # normalizing spc and noise
                         # This part differs from gg1
                         spc_norm_max = max(spc)
                         #spc = spc / spc_norm_max
                         pnoise = pnoise #/ spc_norm_max
                         #############################################
                         fatspectra=1.0
                         wnoise = noise_ #/ spc_norm_max
                             #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
                             #if wnoise>1.1*pnoise: # to be tested later
                             #    wnoise=pnoise
                         noisebl=wnoise*0.9;
                         noisebh=wnoise*1.1
                         spc=spc-wnoise
                         minx=numpy.argmin(spc)
                         #spcs=spc.copy()
                         spcs=numpy.roll(spc,-minx)
                         cum=numpy.cumsum(spcs)
                         tot_noise=wnoise * self.Num_Bin  #64;
                         snr = sum(spcs)/tot_noise
                         snrdB=10.*numpy.log10(snr)
                         if snrdB < SNRlimit :
                             snr = numpy.NaN
                             SPC_ch1[:,ht] = 0#numpy.NaN
                             SPC_ch1[:,ht] = 0#numpy.NaN
                             SPCparam = (SPC_ch1,SPC_ch2)
                             continue
                         #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
                         #    return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
                         cummax=max(cum);
                         epsi=0.08*fatspectra # cumsum to narrow down the energy region
                         cumlo=cummax*epsi;
                         cumhi=cummax*(1-epsi)
                         powerindex=numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
                         if len(powerindex) < 1:# case for powerindex 0
                             continue
                         powerlo=powerindex[0]
                         powerhi=powerindex[-1]
                         powerwidth=powerhi-powerlo
                         firstpeak=powerlo+powerwidth/10.# first gaussian energy location
                         secondpeak=powerhi-powerwidth/10.#second gaussian energy location
                         midpeak=(firstpeak+secondpeak)/2.
                         firstamp=spcs[int(firstpeak)]
                         secondamp=spcs[int(secondpeak)]
                         midamp=spcs[int(midpeak)]
                         x=numpy.arange( self.Num_Bin )
                         y_data=spc+wnoise
                         '''    single Gaussian    '''
                         shift0=numpy.mod(midpeak+minx, self.Num_Bin )
                         width0=powerwidth/4.#Initialization entire power of spectrum divided by 4
                         power0=2.
                         amplitude0=midamp
                         state0=[shift0,width0,amplitude0,power0,wnoise]
                         bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
                         lsq1=fmin_l_bfgs_b(self.misfit1,state0,args=(y_data,x,num_intg),bounds=bnds,approx_grad=True)
-                        chiSq1=lsq1[1];
+                        chiSq1=lsq1[1];
                         if fatspectra<1.0 and powerwidth<4:
                                 choice=0
                                 Amplitude0=lsq1[0][2]
                                 shift0=lsq1[0][0]
                                 width0=lsq1[0][1]
                                 p0=lsq1[0][3]
                                 Amplitude1=0.
                                 shift1=0.
                                 width1=0.
                                 p1=0.
                                 noise=lsq1[0][4]
                                 #return (numpy.array([shift0,width0,Amplitude0,p0]),
                                 #        numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
                         '''    two gaussians    '''
                         #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
                         shift0=numpy.mod(firstpeak+minx, self.Num_Bin );
                         shift1=numpy.mod(secondpeak+minx, self.Num_Bin )
                         width0=powerwidth/6.;
                         width1=width0
                         power0=2.;
                         power1=power0
                         amplitude0=firstamp;
                         amplitude1=secondamp
                         state0=[shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
                         #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
                         bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
                         #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
                         lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True )
                         chiSq2=lsq2[1];
                         oneG=(chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
                         if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error
                             if oneG:
                                 choice=0
                             else:
                                 w1=lsq2[0][1]; w2=lsq2[0][5]
                                 a1=lsq2[0][2]; a2=lsq2[0][6]
                                 p1=lsq2[0][3]; p2=lsq2[0][7]
                                 s1=(2**(1+1./p1))*scipy.special.gamma(1./p1)/p1;
                                 s2=(2**(1+1./p2))*scipy.special.gamma(1./p2)/p2;
                                 gp1=a1*w1*s1; gp2=a2*w2*s2 # power content of each ggaussian with proper p scaling
                                 if gp1>gp2:
                                     if a1>0.7*a2:
                                         choice=1
                                     else:
                                         choice=2
                                 elif gp2>gp1:
                                     if a2>0.7*a1:
                                         choice=2
                                     else:
                                         choice=1
                                 else:
                                     choice=numpy.argmax([a1,a2])+1
                                     #else:
                                     #choice=argmin([std2a,std2b])+1
                         else: # with low SNR go to the most energetic peak
                             choice=numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
                         shift0=lsq2[0][0];
                         vel0=Vrange[0] + shift0*(Vrange[1]-Vrange[0])
                         shift1=lsq2[0][4];
                         vel1=Vrange[0] + shift1*(Vrange[1]-Vrange[0])
                         max_vel = 1.0
                         #first peak will be 0, second peak will be 1
                         if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range
                             shift0=lsq2[0][0]
                             width0=lsq2[0][1]
                             Amplitude0=lsq2[0][2]
                             p0=lsq2[0][3]
                             shift1=lsq2[0][4]
                             width1=lsq2[0][5]
                             Amplitude1=lsq2[0][6]
                             p1=lsq2[0][7]
                             noise=lsq2[0][8]
                         else:
                             shift1=lsq2[0][0]
                             width1=lsq2[0][1]
                             Amplitude1=lsq2[0][2]
                             p1=lsq2[0][3]
                             shift0=lsq2[0][4]
                             width0=lsq2[0][5]
                             Amplitude0=lsq2[0][6]
                             p0=lsq2[0][7]
                             noise=lsq2[0][8]
                         if Amplitude0<0.05: # in case the peak is noise
                             shift0,width0,Amplitude0,p0 = [0,0,0,0]#4*[numpy.NaN]
                         if Amplitude1<0.05:
                             shift1,width1,Amplitude1,p1 = [0,0,0,0]#4*[numpy.NaN]
                         SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0))/width0)**p0
                         SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1))/width1)**p1
                         SPCparam = (SPC_ch1,SPC_ch2)
                     return GauSPC
                 def y_model1(self,x,state):
                     shift0,width0,amplitude0,power0,noise=state
                     model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
                     model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
                     model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
                     return model0+model0u+model0d+noise
                 def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
                     shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,noise=state
                     model0=amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
                     model0u=amplitude0*numpy.exp(-0.5*abs((x-shift0- self.Num_Bin )/width0)**power0)
                     model0d=amplitude0*numpy.exp(-0.5*abs((x-shift0+ self.Num_Bin )/width0)**power0)
                     model1=amplitude1*numpy.exp(-0.5*abs((x-shift1)/width1)**power1)
                     model1u=amplitude1*numpy.exp(-0.5*abs((x-shift1- self.Num_Bin )/width1)**power1)
                     model1d=amplitude1*numpy.exp(-0.5*abs((x-shift1+ self.Num_Bin )/width1)**power1)
                     return model0+model0u+model0d+model1+model1u+model1d+noise
                 def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is.
                     return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
                 def misfit2(self,state,y_data,x,num_intg):
                     return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
             class PrecipitationProc(Operation):
                 '''
                      Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
                      Input:
                         self.dataOut.data_pre    :    SelfSpectra
                      Output:
                         self.dataOut.data_output :    Reflectivity factor, rainfall Rate
                      Parameters affected:
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def gaus(self,xSamples,Amp,Mu,Sigma):
                     return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) ))
                 def Moments(self, ySamples, xSamples):
                     Pot = numpy.nansum( ySamples )                              # Potencia, momento 0
                     yNorm = ySamples / Pot
                     Vr = numpy.nansum( yNorm * xSamples )                       # Velocidad radial, mu, corrimiento doppler, primer momento
                     Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 ))  # Segundo Momento
                     Desv = Sigma2**0.5                                          # Desv. Estandar, Ancho espectral
                     return numpy.array([Pot, Vr, Desv])
                 def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118,
                         tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km = 0.93, Altitude=3350):
                     Velrange = dataOut.spcparam_range[2]
                     FrecRange = dataOut.spcparam_range[0]
                     dV= Velrange[1]-Velrange[0]
                     dF= FrecRange[1]-FrecRange[0]
                     if radar == "MIRA35C" :
                         self.spc = dataOut.data_pre[0].copy()
                         self.Num_Hei = self.spc.shape[2]
                         self.Num_Bin = self.spc.shape[1]
                         self.Num_Chn = self.spc.shape[0]
                         Ze = self.dBZeMODE2(dataOut)
                     else:
                         self.spc = dataOut.SPCparam[1].copy() #dataOut.data_pre[0].copy() #
                         """NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX"""
                         self.spc[:,:,0:7]= numpy.NaN
                         """##########################################"""
                         self.Num_Hei = self.spc.shape[2]
                         self.Num_Bin = self.spc.shape[1]
                         self.Num_Chn = self.spc.shape[0]
                         ''' Se obtiene la constante del RADAR '''
                         self.Pt = Pt
                         self.Gt = Gt
                         self.Gr = Gr
                         self.Lambda = Lambda
                         self.aL = aL
                         self.tauW = tauW
                         self.ThetaT = ThetaT
                         self.ThetaR = ThetaR
                         Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
                         Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR)
                         RadarConstant = 10e-26 * Numerator / Denominator #
                         ''' =============================  '''
                         self.spc[0] = (self.spc[0]-dataOut.noise[0])
                         self.spc[1] = (self.spc[1]-dataOut.noise[1])
                         self.spc[2] = (self.spc[2]-dataOut.noise[2])
                         self.spc[ numpy.where(self.spc < 0)] = 0
                         SPCmean = (numpy.mean(self.spc,0) - numpy.mean(dataOut.noise))
                         SPCmean[ numpy.where(SPCmean < 0)] = 0
                         ETAn = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         ETAv = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         ETAd = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         Pr = SPCmean[:,:]
                         VelMeteoro = numpy.mean(SPCmean,axis=0)
                         D_range = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         SIGMA = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         N_dist = numpy.zeros([self.Num_Bin,self.Num_Hei])
                         V_mean = numpy.zeros(self.Num_Hei)
                         del_V = numpy.zeros(self.Num_Hei)
                         Z = numpy.zeros(self.Num_Hei)
                         Ze = numpy.zeros(self.Num_Hei)
                         RR = numpy.zeros(self.Num_Hei)
                         Range = dataOut.heightList*1000.
                         for R in range(self.Num_Hei):
                             h = Range[R] + Altitude #Range from ground to radar pulse altitude
                             del_V[R] = 1 + 3.68 * 10**-5 * h + 1.71 * 10**-9 * h**2    #Density change correction for velocity
                             D_range[:,R] = numpy.log( (9.65 - (Velrange[0:self.Num_Bin] / del_V[R])) / 10.3 ) / -0.6 #Diameter range [m]x10**-3
                             '''NOTA: ETA(n) dn = ETA(f) df
                                 dn = 1     Diferencial de muestreo
                                 df = ETA(n) / ETA(f)
                             '''
                             ETAn[:,R] = RadarConstant * Pr[:,R] * (Range[R] )**2 #Reflectivity (ETA)
                             ETAv[:,R]=ETAn[:,R]/dV
                             ETAd[:,R]=ETAv[:,R]*6.18*exp(-0.6*D_range[:,R])
                             SIGMA[:,R] = Km * (D_range[:,R] * 1e-3 )**6 * numpy.pi**5 / Lambda**4      #Equivalent Section of drops (sigma)
                             N_dist[:,R] = ETAn[:,R] / SIGMA[:,R]
                             DMoments = self.Moments(Pr[:,R], Velrange[0:self.Num_Bin])
                             try:
                                 popt01,pcov = curve_fit(self.gaus, Velrange[0:self.Num_Bin] , Pr[:,R] , p0=DMoments)
                             except:
                                 popt01=numpy.zeros(3)
                                 popt01[1]= DMoments[1]
                             if popt01[1]<0 or popt01[1]>20:
                                 popt01[1]=numpy.NaN
                             V_mean[R]=popt01[1]
                             Z[R] = numpy.nansum( N_dist[:,R] * (D_range[:,R])**6 )#*10**-18
                             RR[R] = 0.0006*numpy.pi * numpy.nansum( D_range[:,R]**3 * N_dist[:,R] * Velrange[0:self.Num_Bin] ) #Rainfall rate
                             Ze[R] = (numpy.nansum( ETAn[:,R]) * Lambda**4) / ( 10**-18*numpy.pi**5 * Km)
                     RR2 = (Z/200)**(1/1.6)
                     dBRR = 10*numpy.log10(RR)
                     dBRR2 = 10*numpy.log10(RR2)
                     dBZe = 10*numpy.log10(Ze)
                     dBZ = 10*numpy.log10(Z)
                     dataOut.data_output = RR[8]
                     dataOut.data_param = numpy.ones([3,self.Num_Hei])
                     dataOut.channelList = [0,1,2]
                     dataOut.data_param[0]=dBZ
                     dataOut.data_param[1]=V_mean
                     dataOut.data_param[2]=RR
                     return dataOut
                 def dBZeMODE2(self, dataOut): #    Processing for MIRA35C
                     NPW = dataOut.NPW
                     COFA = dataOut.COFA
                     SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
                     RadarConst = dataOut.RadarConst
                     #frequency = 34.85*10**9
                     ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
                     data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
                     ETA = numpy.sum(SNR,1)
                     ETA = numpy.where(ETA is not 0. , ETA, numpy.NaN)
                     Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
                     for r in range(self.Num_Hei):
                         Ze[0,r] =  ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
                         #Ze[1,r] =  ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
                     return Ze
             #     def GetRadarConstant(self):
+            #
             #         """
             #         Constants:
+            #
             #         Pt:     Transmission Power               dB        5kW                5000
             #         Gt:     Transmission Gain                dB        24.7 dB            295.1209
             #         Gr:     Reception Gain                   dB        18.5 dB            70.7945
             #         Lambda: Wavelenght                       m         0.6741 m           0.6741
             #         aL:     Attenuation loses                dB        4dB                2.5118
             #         tauW:   Width of transmission pulse      s         4us                4e-6
             #         ThetaT: Transmission antenna bean angle  rad       0.1656317 rad      0.1656317
             #         ThetaR: Reception antenna beam angle     rad       0.36774087 rad     0.36774087
+            #
             #         """
+            #
             #         Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
             #         Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
             #         RadarConstant =  Numerator / Denominator
+            #
             #         return RadarConstant
             class FullSpectralAnalysis(Operation):
                 """
                     Function that implements Full Spectral Analysis technique.
                     Input:
                         self.dataOut.data_pre    :    SelfSpectra and CrossSpectra data
                         self.dataOut.groupList   :    Pairlist of channels
                         self.dataOut.ChanDist    :    Physical distance between receivers
                     Output:
                         self.dataOut.data_output :    Zonal wind, Meridional wind and Vertical wind
                     Parameters affected:    Winds, height range, SNR
                 """
                 def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRlimit=7, minheight=None, maxheight=None):
                     self.indice=int(numpy.random.rand()*1000)
                     spc = dataOut.data_pre[0].copy()
                     cspc = dataOut.data_pre[1]
                     """Erick: NOTE THE RANGE OF THE PULSE TX MUST BE REMOVED"""
                     SNRspc = spc.copy()
                     SNRspc[:,:,0:7]= numpy.NaN
                     """##########################################"""
                     nChannel = spc.shape[0]
                     nProfiles = spc.shape[1]
                     nHeights = spc.shape[2]
                     # first_height = 0.75 #km (ref: data header 20170822)
                     # resolution_height = 0.075 #km
                     '''
                         finding height range. check this when radar parameters are changed!
                     '''
                     if maxheight is not None:
                         # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical
                         range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better
                     else:
                         range_max = nHeights
                     if minheight is not None:
                         # range_min = int((minheight - first_height) / resolution_height) # theoretical
                         range_min = int(13.26 * minheight - 5) # empirical, works better
                         if range_min < 0:
                             range_min = 0
                     else:
                         range_min = 0
                     pairsList = dataOut.groupList
                     if dataOut.ChanDist is not None :
                         ChanDist = dataOut.ChanDist
                     else:
                         ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]])
                     FrecRange = dataOut.spc_range[0]
                     data_SNR=numpy.zeros([nProfiles])
                     noise = dataOut.noise
                     dataOut.data_SNR = (numpy.mean(SNRspc,axis=1)- noise[0]) / noise[0]
                     dataOut.data_SNR[numpy.where( dataOut.data_SNR <0 )] = 1e-20
                     data_output=numpy.ones([spc.shape[0],spc.shape[2]])*numpy.NaN
                     velocityX=[]
                     velocityY=[]
                     velocityV=[]
                     dbSNR = 10*numpy.log10(dataOut.data_SNR)
                     dbSNR = numpy.average(dbSNR,0)
                     '''***********************************************WIND ESTIMATION**************************************'''
                     for Height in range(nHeights):
                         if Height >= range_min and Height < range_max:
                             # error_code unused, yet maybe useful for future analysis.
                             [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList, ChanDist, Height, noise, dataOut.spc_range, dbSNR[Height], SNRlimit)
                         else:
                             Vzon,Vmer,Vver = 0., 0., numpy.NaN
                         if abs(Vzon) < 100. and abs(Vzon) > 0. and abs(Vmer) < 100. and abs(Vmer) > 0.:
                             velocityX=numpy.append(velocityX, Vzon)
                             velocityY=numpy.append(velocityY, -Vmer)
                         else:
                             velocityX=numpy.append(velocityX, numpy.NaN)
                             velocityY=numpy.append(velocityY, numpy.NaN)
                         if dbSNR[Height] > SNRlimit:
                             velocityV=numpy.append(velocityV, -Vver) # reason for this minus sign -> convention? (taken from Ericks version)
                         else:
                             velocityV=numpy.append(velocityV, numpy.NaN)
                     '''Change the numpy.array (velocityX) sign when trying to process BLTR data (Erick)'''
                     data_output[0] = numpy.array(velocityX)
                     data_output[1] = numpy.array(velocityY)
                     data_output[2] = velocityV
                     dataOut.data_output = data_output
                     return dataOut
                 def moving_average(self,x, N=2):
                     """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """
                     return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
                 def gaus(self,xSamples,Amp,Mu,Sigma):
                     return ( Amp / ((2*numpy.pi)**0.5 * Sigma) ) * numpy.exp( -( xSamples - Mu )**2 / ( 2 * (Sigma**2) ))
                 def Moments(self, ySamples, xSamples):
                     '''***
                     Variables corresponding to moments of distribution.
                     Also used as initial coefficients for curve_fit.
                     Vr was corrected. Only a velocity when x is velocity, of course.
                     ***'''
                     Pot = numpy.nansum( ySamples )                                  # Potencia, momento 0
                     yNorm = ySamples / Pot
                     x_range = (numpy.max(xSamples)-numpy.min(xSamples))
                     Vr = numpy.nansum( yNorm * xSamples )*x_range/len(xSamples)     # Velocidad radial, mu, corrimiento doppler, primer momento
                     Sigma2 = abs(numpy.nansum( yNorm * ( xSamples - Vr )**2 ))      # Segundo Momento
                     Desv = Sigma2**0.5                                              # Desv. Estandar, Ancho espectral
                     return numpy.array([Pot, Vr, Desv])
                 def StopWindEstimation(self, error_code):
                     '''
                         the wind calculation and returns zeros
                     '''
                     Vzon = 0
                     Vmer = 0
                     Vver = numpy.nan
                     return Vzon, Vmer, Vver, error_code
                 def AntiAliasing(self, interval, maxstep):
                     """
                         function to prevent errors from aliased values when computing phaseslope
                     """
                     antialiased = numpy.zeros(len(interval))*0.0
                     copyinterval = interval.copy()
                     antialiased[0] = copyinterval[0]
                     for i in range(1,len(antialiased)):
                         step = interval[i] - interval[i-1]
                         if step > maxstep:
                             copyinterval -= 2*numpy.pi
                             antialiased[i] = copyinterval[i]
                         elif step < maxstep*(-1):
                             copyinterval += 2*numpy.pi
                             antialiased[i] = copyinterval[i]
                         else:
                             antialiased[i] = copyinterval[i].copy()
                     return antialiased
                 def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit):
                     """
                         Function that Calculates Zonal, Meridional and Vertical wind velocities.
                         Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019.
                         Input:
                             spc, cspc       : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively.
                             pairsList       : Pairlist of channels
                             ChanDist        : array of xi_ij and eta_ij
                             Height          : height at which data is processed
                             noise           : noise in [channels] format for specific height
                             Abbsisarange    : range of the frequencies or velocities
                             dbSNR, SNRlimit : signal to noise ratio in db, lower limit
                         Output:
                             Vzon, Vmer, Vver         : wind velocities
                             error_code               : int that states where code is terminated
 : no error detected
 : Gaussian of mean spc exceeds widthlimit
 : no Gaussian of mean spc found
 : SNR to low or velocity to high -> prec. e.g.
 : at least one Gaussian of cspc exceeds widthlimit
 : zero out of three cspc Gaussian fits converged
 : phase slope fit could not be found
 : arrays used to fit phase have different length
 : frequency range is either too short (len <= 5) or very long (> 30% of cspc)
                     """
                     error_code = 0
                     SPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]])           # for normalized spc values for one height
                     phase = numpy.ones([spc.shape[0],spc.shape[1]])                 # phase between channels
                     CSPC_Samples = numpy.ones([spc.shape[0],spc.shape[1]],dtype=numpy.complex_)      # for normalized cspc values
                     PhaseSlope = numpy.zeros(spc.shape[0])                          # slope of the phases, channelwise
                     PhaseInter = numpy.ones(spc.shape[0])                           # intercept to the slope of the phases, channelwise
                     xFrec = AbbsisaRange[0][0:spc.shape[1]]                         # frequency range
                     xVel = AbbsisaRange[2][0:spc.shape[1]]                          # velocity range
                     SPCav = numpy.average(spc, axis=0)-numpy.average(noise)         # spc[0]-noise[0]
                     SPCmoments_vel = self.Moments(SPCav, xVel )  # SPCmoments_vel[1] corresponds to vertical velocity and is used to determine if signal corresponds to wind (if .. <3)
                     CSPCmoments = []
                     '''Getting Eij and Nij'''
                     Xi01, Xi02, Xi12 = ChanDist[:,0]
                     Eta01, Eta02, Eta12 = ChanDist[:,1]
                     # update nov 19
                     widthlimit = 7 # maximum width in Hz of the gaussian, empirically determined. Anything above 10 is unrealistic, often values between 1 and 5 correspond to proper fits.
                     '''************************* SPC is normalized ********************************'''
                     spc_norm = spc.copy() # need copy() because untouched spc is needed for normalization of cspc below
                     spc_norm = numpy.where(numpy.isfinite(spc_norm), spc_norm, numpy.NAN)
                     for i in range(spc.shape[0]):
                         spc_sub = spc_norm[i,:] - noise[i] # spc not smoothed here or in previous version.
                         Factor_Norm = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc_sub)) # usually = Freq range / nfft
                         normalized_spc = spc_sub / (numpy.nansum(numpy.abs(spc_sub)) * Factor_Norm)
                         xSamples = xFrec                   # the frequency range is taken
                         SPC_Samples[i] = normalized_spc             # Normalized SPC values are taken
                     '''********************** FITTING MEAN SPC GAUSSIAN **********************'''
                     """ the gaussian of the mean: first subtract noise, then normalize. this is legal because
                         you only fit the curve and don't need the absolute value of height for calculation,
                         only for estimation of width. for normalization of cross spectra, you need initial,
                         unnormalized self-spectra With noise.
                         Technically, you don't even need to normalize the self-spectra, as you only need the
                         width of the peak. However, it was left this way. Note that the normalization has a flaw:
                         due to subtraction of the noise, some values are below zero. Raw "spc" values should be
                         >= 0, as it is the modulus squared of the signals (complex * it's conjugate)
                     """
                     SPCMean = numpy.average(SPC_Samples, axis=0)
                     popt = [1e-10,0,1e-10]
                     SPCMoments = self.Moments(SPCMean, xSamples)
                     if dbSNR > SNRlimit and numpy.abs(SPCmoments_vel[1]) < 3:
                         try:
                             popt,pcov = curve_fit(self.gaus,xSamples,SPCMean,p0=SPCMoments)#, bounds=(-numpy.inf, [numpy.inf, numpy.inf, 10])). Setting bounds does not make the code faster but only keeps the fit from finding the minimum.
                             if popt[2] > widthlimit: # CONDITION
                                 return self.StopWindEstimation(error_code = 1)
                             FitGauss = self.gaus(xSamples,*popt)
                         except :#RuntimeError:
                             return self.StopWindEstimation(error_code = 2)
                     else:
                         return self.StopWindEstimation(error_code = 3)
                     '''***************************** CSPC Normalization *************************
                         new  section:
                             The Spc spectra are used to normalize the crossspectra. Peaks from precipitation
                             influence the norm which is not desired. First, a range is identified where the
                             wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area
                             around it gets cut off and values replaced by mean determined by the boundary
                             data -> sum_noise (spc is not normalized here, thats why the noise is important)
                             The sums are then added and multiplied by range/datapoints, because you need
                             an integral and not a sum for normalization.
                             A norm is found according to Briggs 92.
                     '''
                     radarWavelength = 0.6741 # meters
                     count_limit_freq = numpy.abs(popt[1]) + widthlimit # Hz, m/s can be also used if velocity is desired abscissa.
                     # count_limit_freq = numpy.max(xFrec)
                     channel_integrals = numpy.zeros(3)
                     for i in range(spc.shape[0]):
                         '''
                             find the point in array corresponding to count_limit frequency.
                             sum over all frequencies in the range around zero Hz @ math.ceil(N_freq/2)
                         '''
                         N_freq = numpy.count_nonzero(~numpy.isnan(spc[i,:]))
                         count_limit_int = int(math.ceil( count_limit_freq / numpy.max(xFrec) * (N_freq / 2)  )) # gives integer point
                         sum_wind = numpy.nansum( spc[i, (math.ceil(N_freq/2) - count_limit_int) : (math.ceil(N_freq / 2) + count_limit_int)] ) #N_freq/2 is where frequency (velocity) is zero, i.e. middle of spectrum.
                         sum_noise = (numpy.mean(spc[i, :4]) + numpy.mean(spc[i, -6:-2]))/2.0 * (N_freq - 2*count_limit_int)
                         channel_integrals[i] = (sum_noise + sum_wind) * (2*numpy.max(xFrec) / N_freq)
                     cross_integrals_peak = numpy.zeros(3)
                     # cross_integrals_totalrange = numpy.zeros(3)
                     for i in range(spc.shape[0]):
                         cspc_norm = cspc[i,:].copy()    # cspc not smoothed here or in previous version
                         chan_index0 = pairsList[i][0]
                         chan_index1 = pairsList[i][1]
                         cross_integrals_peak[i] = channel_integrals[chan_index0]*channel_integrals[chan_index1]
                         normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_peak[i])
                         CSPC_Samples[i] = normalized_cspc
                         ''' Finding cross integrals without subtracting any peaks:'''
                         # FactorNorm0 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index0,:]))
                         # FactorNorm1 = 2*numpy.max(xFrec) / numpy.count_nonzero(~numpy.isnan(spc[chan_index1,:]))
                         # cross_integrals_totalrange[i] = (numpy.nansum(spc[chan_index0,:])) * FactorNorm0  *  (numpy.nansum(spc[chan_index1,:])) * FactorNorm1
                         # normalized_cspc = cspc_norm / numpy.sqrt(cross_integrals_totalrange[i])
                         # CSPC_Samples[i] = normalized_cspc
                         phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real)
                     CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0]), xSamples),
                                                 self.Moments(numpy.abs(CSPC_Samples[1]), xSamples),
                                                 self.Moments(numpy.abs(CSPC_Samples[2]), xSamples)])
                     '''***Sorting out NaN entries***'''
                     CSPCMask01 = numpy.abs(CSPC_Samples[0])
                     CSPCMask02 = numpy.abs(CSPC_Samples[1])
                     CSPCMask12 = numpy.abs(CSPC_Samples[2])
                     mask01 = ~numpy.isnan(CSPCMask01)
                     mask02 = ~numpy.isnan(CSPCMask02)
                     mask12 = ~numpy.isnan(CSPCMask12)
                     CSPCMask01 = CSPCMask01[mask01]
                     CSPCMask02 = CSPCMask02[mask02]
                     CSPCMask12 = CSPCMask12[mask12]
                     popt01, popt02, popt12 = [1e-10,1e-10,1e-10], [1e-10,1e-10,1e-10] ,[1e-10,1e-10,1e-10]
                     FitGauss01, FitGauss02, FitGauss12 = numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0, numpy.empty(len(xSamples))*0
                     '''*******************************FIT GAUSS CSPC************************************'''
                     try:
                         popt01,pcov = curve_fit(self.gaus,xSamples[mask01],numpy.abs(CSPCMask01),p0=CSPCmoments[0])
                         if popt01[2] > widthlimit: # CONDITION
                             return self.StopWindEstimation(error_code = 4)
                         popt02,pcov = curve_fit(self.gaus,xSamples[mask02],numpy.abs(CSPCMask02),p0=CSPCmoments[1])
                         if popt02[2] > widthlimit: # CONDITION
                             return self.StopWindEstimation(error_code = 4)
                         popt12,pcov = curve_fit(self.gaus,xSamples[mask12],numpy.abs(CSPCMask12),p0=CSPCmoments[2])
                         if popt12[2] > widthlimit: # CONDITION
                             return self.StopWindEstimation(error_code = 4)
                         FitGauss01 = self.gaus(xSamples, *popt01)
                         FitGauss02 = self.gaus(xSamples, *popt02)
                         FitGauss12 = self.gaus(xSamples, *popt12)
                     except:
                         return self.StopWindEstimation(error_code = 5)
                     '''************* Getting Fij ***************'''
                     #Punto en Eje X de la Gaussiana donde se encuentra el centro -- x-axis point of the gaussian where the center is located
                     # -> PointGauCenter
                     GaussCenter = popt[1]
                     ClosestCenter = xSamples[numpy.abs(xSamples-GaussCenter).argmin()]
                     PointGauCenter = numpy.where(xSamples==ClosestCenter)[0][0]
                     #Punto e^-1 hubicado en la Gaussiana  -- point where e^-1 is located in the gaussian
                     PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1)
                     FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # El punto mas cercano a "Peminus1" dentro de "FitGauss"
                     PointFij = numpy.where(FitGauss==FijClosest)[0][0]
                     Fij = numpy.abs(xSamples[PointFij] - xSamples[PointGauCenter])
                     '''********** Taking frequency ranges from mean SPCs **********'''
                     #GaussCenter = popt[1]     #Primer momento 01
                     GauWidth = popt[2] * 3/2        #Ancho de banda de Gau01 -- Bandwidth of Gau01 TODO why *3/2?
                     Range = numpy.empty(2)
                     Range[0] = GaussCenter - GauWidth
                     Range[1] = GaussCenter + GauWidth
                     #Punto en Eje X de la Gaussiana donde se encuentra ancho de banda (min:max) -- Point in x-axis where the bandwidth is located (min:max)
                     ClosRangeMin = xSamples[numpy.abs(xSamples-Range[0]).argmin()]
                     ClosRangeMax = xSamples[numpy.abs(xSamples-Range[1]).argmin()]
                     PointRangeMin = numpy.where(xSamples==ClosRangeMin)[0][0]
                     PointRangeMax = numpy.where(xSamples==ClosRangeMax)[0][0]
                     Range = numpy.array([ PointRangeMin, PointRangeMax ])
                     FrecRange = xFrec[ Range[0] : Range[1] ]
                     '''************************** Getting Phase Slope ***************************'''
                     for i in range(1,3): # Changed to only compute two
                         if len(FrecRange) > 5 and len(FrecRange) < spc.shape[1] * 0.3:
                             # PhaseRange=self.moving_average(phase[i,Range[0]:Range[1]],N=1)  #used before to smooth phase with N=3
                             PhaseRange = phase[i,Range[0]:Range[1]].copy()
                             mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange)
                             if len(FrecRange) == len(PhaseRange):
                                 try:
                                     slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5))
                                     PhaseSlope[i] = slope
                                     PhaseInter[i] = intercept
                                 except:
                                     return self.StopWindEstimation(error_code = 6)
                             else:
                                 return self.StopWindEstimation(error_code = 7)
                         else:
                             return self.StopWindEstimation(error_code = 8)
                     '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***'''
                     '''Getting constant C'''
                     cC=(Fij*numpy.pi)**2
                     '''****** Getting constants F and G ******'''
                     MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]])
                     MijResult0 = (-PhaseSlope[1] * cC) / (2*numpy.pi)
                     MijResult1 = (-PhaseSlope[2] * cC) / (2*numpy.pi)
                     MijResults = numpy.array([MijResult0,MijResult1])
                     (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
                     '''****** Getting constants A, B and H ******'''
                     W01 = numpy.nanmax( FitGauss01 )
                     W02 = numpy.nanmax( FitGauss02 )
                     W12 = numpy.nanmax( FitGauss12 )
                     WijResult0 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC))
                     WijResult1 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC))
                     WijResult2 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC))
                     WijResults = numpy.array([WijResult0, WijResult1, WijResult2])
                     WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ])
                     (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
                     VxVy = numpy.array([[cA,cH],[cH,cB]])
                     VxVyResults = numpy.array([-cF,-cG])
                     (Vx,Vy) = numpy.linalg.solve(VxVy, VxVyResults)
                     Vzon = Vy
                     Vmer = Vx
                     # Vmag=numpy.sqrt(Vzon**2+Vmer**2) # unused
                     # Vang=numpy.arctan2(Vmer,Vzon)   # unused
                     ''' using frequency as abscissa. Due to three channels, the offzenith angle is zero
                         and Vrad equal to Vver. formula taken from Briggs 92, figure 4.
                     '''
                     if numpy.abs( popt[1] ) < 3.5 and len(FrecRange) > 4:
                         Vver = 0.5 * radarWavelength * popt[1] * 100 # *100 to get cm (/s)
                     else:
                         Vver = numpy.NaN
                     error_code = 0
                     return Vzon, Vmer, Vver, error_code
             class SpectralMoments(Operation):
                 '''
                     Function SpectralMoments()
                     Calculates moments (power, mean, standard deviation) and SNR of the signal
                     Type of dataIn:    Spectra
                     Configuration Parameters:
                         dirCosx    :     Cosine director in X axis
                         dirCosy    :     Cosine director in Y axis
                         elevation  :
                         azimuth    :
                     Input:
                         channelList    :    simple channel list to select e.g. [2,3,7]
                         self.dataOut.data_pre        :    Spectral data
                         self.dataOut.abscissaList    :    List of frequencies
                         self.dataOut.noise           :    Noise level per channel
                     Affected:
                         self.dataOut.moments        :    Parameters per channel
                         self.dataOut.data_SNR       :    SNR per channel
                 '''
                 def run(self, dataOut):
                     data = dataOut.data_pre[0]
                     absc = dataOut.abscissaList[:-1]
                     noise = dataOut.noise
                     nChannel = data.shape[0]
                     data_param = numpy.zeros((nChannel, 4, data.shape[2]))
                     for ind in range(nChannel):
                         data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
                     dataOut.moments = data_param[:,1:,:]
                     dataOut.data_SNR = data_param[:,0]
                     dataOut.data_POW = data_param[:,1]
                     dataOut.data_DOP = data_param[:,2]
                     dataOut.data_WIDTH = data_param[:,3]
                     return dataOut
                 def __calculateMoments(self, oldspec, oldfreq, n0,
                                        nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
                     if (nicoh is None): nicoh = 1
                     if (graph is None): graph = 0
                     if (smooth is None): smooth = 0
                     elif (self.smooth < 3): smooth = 0
                     if (type1 is None): type1 = 0
                     if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1
                     if (snrth is None): snrth = -3
                     if (dc is None): dc = 0
                     if (aliasing is None): aliasing = 0
                     if (oldfd is None): oldfd = 0
                     if (wwauto is None): wwauto = 0
                     if (n0 < 1.e-20):   n0 = 1.e-20
                     freq = oldfreq
                     vec_power = numpy.zeros(oldspec.shape[1])
                     vec_fd = numpy.zeros(oldspec.shape[1])
                     vec_w = numpy.zeros(oldspec.shape[1])
                     vec_snr = numpy.zeros(oldspec.shape[1])
                     # oldspec = numpy.ma.masked_invalid(oldspec)
                     for ind in range(oldspec.shape[1]):
                         spec = oldspec[:,ind]
                         aux = spec*fwindow
                         max_spec = aux.max()
                         m = aux.tolist().index(max_spec)
                         #Smooth
                         if (smooth == 0):
                             spec2 = spec
                         else:
                             spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
                         #    Calculo de Momentos
                         bb = spec2[numpy.arange(m,spec2.size)]
                         bb = (bb<n0).nonzero()
                         bb = bb[0]
                         ss = spec2[numpy.arange(0,m + 1)]
                         ss = (ss<n0).nonzero()
                         ss = ss[0]
                         if (bb.size == 0):
                             bb0 = spec.size - 1 - m
                         else:
                             bb0 = bb[0] - 1
                             if (bb0 < 0):
                                 bb0 = 0
                         if (ss.size == 0):
                             ss1 = 1
                         else:
                             ss1 = max(ss) + 1
                         if (ss1 > m):
                             ss1 = m
                         valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1
                         power = ((spec2[valid] - n0) * fwindow[valid]).sum()
                         fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power
                         w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power)
                         snr = (spec2.mean()-n0)/n0
                         if (snr < 1.e-20) :
                             snr = 1.e-20
                         vec_power[ind] = power
                         vec_fd[ind] = fd
                         vec_w[ind] = w
                         vec_snr[ind] = snr
                     return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
                 #------------------    Get SA Parameters    --------------------------
                 def GetSAParameters(self):
                     #SA en frecuencia
                     pairslist = self.dataOut.groupList
                     num_pairs = len(pairslist)
                     vel = self.dataOut.abscissaList
                     spectra = self.dataOut.data_pre
                     cspectra = self.dataIn.data_cspc
                     delta_v = vel[1] - vel[0]
                     #Calculating the power spectrum
                     spc_pow = numpy.sum(spectra, 3)*delta_v
                     #Normalizing Spectra
                     norm_spectra = spectra/spc_pow
                     #Calculating the norm_spectra at peak
                     max_spectra = numpy.max(norm_spectra, 3)
                     #Normalizing Cross Spectra
                     norm_cspectra = numpy.zeros(cspectra.shape)
                     for i in range(num_chan):
                         norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
                     max_cspectra = numpy.max(norm_cspectra,2)
                     max_cspectra_index = numpy.argmax(norm_cspectra, 2)
                     for i in range(num_pairs):
                         cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
                 #-------------------    Get Lags    ----------------------------------
             class SALags(Operation):
                 '''
                 Function GetMoments()
                 Input:
                     self.dataOut.data_pre
                     self.dataOut.abscissaList
                     self.dataOut.noise
                     self.dataOut.normFactor
                     self.dataOut.data_SNR
                     self.dataOut.groupList
                     self.dataOut.nChannels
                 Affected:
                     self.dataOut.data_param
                 '''
                 def run(self, dataOut):
                     data_acf = dataOut.data_pre[0]
                     data_ccf = dataOut.data_pre[1]
                     normFactor_acf = dataOut.normFactor[0]
                     normFactor_ccf = dataOut.normFactor[1]
                     pairs_acf = dataOut.groupList[0]
                     pairs_ccf = dataOut.groupList[1]
                     nHeights = dataOut.nHeights
                     absc = dataOut.abscissaList
                     noise = dataOut.noise
                     SNR = dataOut.data_SNR
                     nChannels = dataOut.nChannels
             #         pairsList = dataOut.groupList
             #         pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels)
                     for l in range(len(pairs_acf)):
                         data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
                     for l in range(len(pairs_ccf)):
                         data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
                     dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
                     dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
                     dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
                     return
             #     def __getPairsAutoCorr(self, pairsList, nChannels):
+            #
             #         pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
+            #
             #         for l in range(len(pairsList)):
             #             firstChannel = pairsList[l][0]
             #             secondChannel = pairsList[l][1]
+            #
             #             #Obteniendo pares de Autocorrelacion
             #             if firstChannel == secondChannel:
             #                 pairsAutoCorr[firstChannel] = int(l)
+            #
             #         pairsAutoCorr = pairsAutoCorr.astype(int)
+            #
             #         pairsCrossCorr = range(len(pairsList))
             #         pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
+            #
             #         return pairsAutoCorr, pairsCrossCorr
                 def __calculateTaus(self, data_acf, data_ccf, lagRange):
                     lag0 = data_acf.shape[1]/2
                     #Funcion de Autocorrelacion
                     mean_acf = stats.nanmean(data_acf, axis = 0)
                     #Obtencion Indice de TauCross
                     ind_ccf = data_ccf.argmax(axis = 1)
                     #Obtencion Indice de TauAuto
                     ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
                     ccf_lag0 = data_ccf[:,lag0,:]
                     for i in range(ccf_lag0.shape[0]):
                         ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
                     #Obtencion de TauCross y TauAuto
                     tau_ccf = lagRange[ind_ccf]
                     tau_acf  = lagRange[ind_acf]
                     Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
                     tau_ccf[Nan1,Nan2] = numpy.nan
                     tau_acf[Nan1,Nan2] = numpy.nan
                     tau = numpy.vstack((tau_ccf,tau_acf))
                     return tau
                 def __calculateLag1Phase(self, data, lagTRange):
                     data1 = stats.nanmean(data, axis = 0)
                     lag1 = numpy.where(lagTRange == 0)[0][0] + 1
                     phase = numpy.angle(data1[lag1,:])
                     return phase
             class SpectralFitting(Operation):
                 '''
                     Function GetMoments()
                     Input:
                     Output:
                     Variables modified:
                 '''
                 def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
                     if path != None:
                         sys.path.append(path)
                     self.dataOut.library = importlib.import_module(file)
                     #To be inserted as a parameter
                     groupArray = numpy.array(groupList)
             #         groupArray = numpy.array([[0,1],[2,3]])
                     self.dataOut.groupList = groupArray
                     nGroups = groupArray.shape[0]
                     nChannels = self.dataIn.nChannels
                     nHeights=self.dataIn.heightList.size
                     #Parameters Array
                     self.dataOut.data_param = None
                     #Set constants
                     constants = self.dataOut.library.setConstants(self.dataIn)
                     self.dataOut.constants = constants
                     M = self.dataIn.normFactor
                     N = self.dataIn.nFFTPoints
                     ippSeconds = self.dataIn.ippSeconds
                     K = self.dataIn.nIncohInt
                     pairsArray = numpy.array(self.dataIn.pairsList)
                     #List of possible combinations
                     listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
                     indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
                     if getSNR:
                         listChannels = groupArray.reshape((groupArray.size))
                         listChannels.sort()
                         noise = self.dataIn.getNoise()
                         self.dataOut.data_SNR = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
                     for i in range(nGroups):
                         coord = groupArray[i,:]
                         #Input data array
                         data = self.dataIn.data_spc[coord,:,:]/(M*N)
                         data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
                         #Cross Spectra data array for Covariance Matrixes
                         ind = 0
                         for pairs in listComb:
                             pairsSel = numpy.array([coord[x],coord[y]])
                             indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0])
                             ind += 1
                         dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
                         dataCross = dataCross**2/K
                         for h in range(nHeights):
                             #Input
                             d = data[:,h]
                             #Covariance Matrix
                             D = numpy.diag(d**2/K)
                             ind = 0
                             for pairs in listComb:
                                 #Coordinates in Covariance Matrix
                                 x = pairs[0]
                                 y = pairs[1]
                                 #Channel Index
                                 S12 = dataCross[ind,:,h]
                                 D12 = numpy.diag(S12)
                                 #Completing Covariance Matrix with Cross Spectras
                                 D[x*N:(x+1)*N,y*N:(y+1)*N] = D12
                                 D[y*N:(y+1)*N,x*N:(x+1)*N] = D12
                                 ind += 1
                             Dinv=numpy.linalg.inv(D)
                             L=numpy.linalg.cholesky(Dinv)
                             LT=L.T
                             dp = numpy.dot(LT,d)
                             #Initial values
                             data_spc = self.dataIn.data_spc[coord,:,h]
                             if (h>0)and(error1[3]<5):
                                 p0 = self.dataOut.data_param[i,:,h-1]
                             else:
                                 p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
                             try:
                                 #Least Squares
                                 minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
             #                   minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants))
                                 #Chi square error
                                 error0 = numpy.sum(infodict['fvec']**2)/(2*N)
                                 #Error with Jacobian
                                 error1 = self.dataOut.library.errorFunction(minp,constants,LT)
                             except:
                                 minp = p0*numpy.nan
                                 error0 = numpy.nan
                                 error1 = p0*numpy.nan
                             #Save
                             if self.dataOut.data_param is None:
                                 self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
                                 self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
                             self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
                             self.dataOut.data_param[i,:,h] = minp
                     return
                 def __residFunction(self, p, dp, LT, constants):
                     fm = self.dataOut.library.modelFunction(p, constants)
                     fmp=numpy.dot(LT,fm)
                     return  dp-fmp
                 def __getSNR(self, z, noise):
                     avg = numpy.average(z, axis=1)
                     SNR = (avg.T-noise)/noise
                     SNR = SNR.T
                     return SNR
                 def __chisq(p,chindex,hindex):
                     #similar to Resid but calculates CHI**2
                     [LT,d,fm]=setupLTdfm(p,chindex,hindex)
                     dp=numpy.dot(LT,d)
                     fmp=numpy.dot(LT,fm)
                     chisq=numpy.dot((dp-fmp).T,(dp-fmp))
                     return chisq
             class WindProfiler(Operation):
                 __isConfig = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __dataReady = False
                 __firstdata = None
                 n = None
                 def __init__(self):
                     Operation.__init__(self)
                 def __calculateCosDir(self, elev, azim):
                     zen = (90 - elev)*numpy.pi/180
                     azim = azim*numpy.pi/180
                     cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
                     cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
                     signX = numpy.sign(numpy.cos(azim))
                     signY = numpy.sign(numpy.sin(azim))
                     cosDirX = numpy.copysign(cosDirX, signX)
                     cosDirY = numpy.copysign(cosDirY, signY)
                     return cosDirX, cosDirY
                 def __calculateAngles(self, theta_x, theta_y, azimuth):
                     dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
                     zenith_arr = numpy.arccos(dir_cosw)
                     azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
                     dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
                     dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
                     return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
                 def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
+            #
                     if horOnly:
                         A = numpy.c_[dir_cosu,dir_cosv]
                     else:
                         A = numpy.c_[dir_cosu,dir_cosv,dir_cosw]
                     A = numpy.asmatrix(A)
                     A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose()
                     return A1
                 def __correctValues(self, heiRang, phi, velRadial, SNR):
                     listPhi = phi.tolist()
                     maxid = listPhi.index(max(listPhi))
                     minid = listPhi.index(min(listPhi))
                     rango = list(range(len(phi)))
                #     rango = numpy.delete(rango,maxid)
                     heiRang1 = heiRang*math.cos(phi[maxid])
                     heiRangAux = heiRang*math.cos(phi[minid])
                     indOut = (heiRang1 < heiRangAux[0]).nonzero()
                     heiRang1 = numpy.delete(heiRang1,indOut)
                     velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
                     SNR1 = numpy.zeros([len(phi),len(heiRang1)])
                     for i in rango:
                         x = heiRang*math.cos(phi[i])
                         y1 = velRadial[i,:]
                         f1 = interpolate.interp1d(x,y1,kind = 'cubic')
                         x1 = heiRang1
                         y11 = f1(x1)
                         y2 = SNR[i,:]
                         f2 = interpolate.interp1d(x,y2,kind = 'cubic')
                         y21 = f2(x1)
                         velRadial1[i,:] = y11
                         SNR1[i,:] = y21
                     return heiRang1, velRadial1, SNR1
                 def __calculateVelUVW(self, A, velRadial):
                     #Operacion Matricial
             #         velUVW = numpy.zeros((velRadial.shape[1],3))
             #         for ind in range(velRadial.shape[1]):
             #             velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
             #         velUVW = velUVW.transpose()
                     velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
                     velUVW[:,:] = numpy.dot(A,velRadial)
                     return velUVW
             #     def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
                 def techniqueDBS(self, kwargs):
                     """
                     Function that implements Doppler Beam Swinging (DBS) technique.
                     Input:    Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
                                 Direction correction (if necessary), Ranges and SNR
                     Output:    Winds estimation (Zonal, Meridional and Vertical)
                     Parameters affected:    Winds, height range, SNR
                     """
                     velRadial0 = kwargs['velRadial']
                     heiRang = kwargs['heightList']
                     SNR0 = kwargs['SNR']
                     if 'dirCosx' in kwargs and 'dirCosy' in kwargs:
                         theta_x = numpy.array(kwargs['dirCosx'])
                         theta_y = numpy.array(kwargs['dirCosy'])
                     else:
                         elev = numpy.array(kwargs['elevation'])
                         azim = numpy.array(kwargs['azimuth'])
                         theta_x, theta_y = self.__calculateCosDir(elev, azim)
                     azimuth = kwargs['correctAzimuth']
                     if 'horizontalOnly' in kwargs:
                         horizontalOnly = kwargs['horizontalOnly']
                     else:   horizontalOnly = False
                     if 'correctFactor' in kwargs:
                         correctFactor = kwargs['correctFactor']
                     else:   correctFactor = 1
                     if 'channelList' in kwargs:
                         channelList = kwargs['channelList']
                         if len(channelList) == 2:
                             horizontalOnly = True
                         arrayChannel = numpy.array(channelList)
                         param = param[arrayChannel,:,:]
                         theta_x = theta_x[arrayChannel]
                         theta_y = theta_y[arrayChannel]
                     azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
                     heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
                     A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
                     #Calculo de Componentes de la velocidad con DBS
                     winds = self.__calculateVelUVW(A,velRadial1)
                     return winds, heiRang1, SNR1
                 def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
                     nPairs = len(pairs_ccf)
                     posx = numpy.asarray(posx)
                     posy = numpy.asarray(posy)
                     #Rotacion Inversa para alinear con el azimuth
                     if azimuth!= None:
                         azimuth = azimuth*math.pi/180
                         posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth)
                         posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth)
                     else:
                         posx1 = posx
                         posy1 = posy
                     #Calculo de Distancias
                     distx = numpy.zeros(nPairs)
                     disty = numpy.zeros(nPairs)
                     dist = numpy.zeros(nPairs)
                     ang = numpy.zeros(nPairs)
                     for i in range(nPairs):
                         distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
                         disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
                         dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
                         ang[i] = numpy.arctan2(disty[i],distx[i])
                     return distx, disty, dist, ang
                     #Calculo de Matrices
             #         nPairs = len(pairs)
             #         ang1 = numpy.zeros((nPairs, 2, 1))
             #         dist1 = numpy.zeros((nPairs, 2, 1))
+            #
             #         for j in range(nPairs):
             #             dist1[j,0,0] = dist[pairs[j][0]]
             #             dist1[j,1,0] = dist[pairs[j][1]]
             #             ang1[j,0,0] = ang[pairs[j][0]]
             #             ang1[j,1,0] = ang[pairs[j][1]]
+            #
             #         return distx,disty, dist1,ang1
                 def __calculateVelVer(self, phase, lagTRange, _lambda):
                     Ts = lagTRange[1] - lagTRange[0]
                     velW = -_lambda*phase/(4*math.pi*Ts)
                     return velW
                 def __calculateVelHorDir(self, dist, tau1, tau2, ang):
                     nPairs = tau1.shape[0]
                     nHeights = tau1.shape[1]
                     vel = numpy.zeros((nPairs,3,nHeights))
                     dist1 = numpy.reshape(dist, (dist.size,1))
                     angCos = numpy.cos(ang)
                     angSin = numpy.sin(ang)
                     vel0 = dist1*tau1/(2*tau2**2)
                     vel[:,0,:] = (vel0*angCos).sum(axis = 1)
                     vel[:,1,:] = (vel0*angSin).sum(axis = 1)
                     ind = numpy.where(numpy.isinf(vel))
                     vel[ind] = numpy.nan
                     return vel
             #     def __getPairsAutoCorr(self, pairsList, nChannels):
+            #
             #         pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
+            #
             #         for l in range(len(pairsList)):
             #             firstChannel = pairsList[l][0]
             #             secondChannel = pairsList[l][1]
+            #
             #             #Obteniendo pares de Autocorrelacion
             #             if firstChannel == secondChannel:
             #                 pairsAutoCorr[firstChannel] = int(l)
+            #
             #         pairsAutoCorr = pairsAutoCorr.astype(int)
+            #
             #         pairsCrossCorr = range(len(pairsList))
             #         pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
+            #
             #         return pairsAutoCorr, pairsCrossCorr
             #     def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
                 def techniqueSA(self, kwargs):
                     """
                     Function that implements Spaced Antenna (SA) technique.
                     Input:    Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
                                 Direction correction (if necessary), Ranges and SNR
                     Output:    Winds estimation (Zonal, Meridional and Vertical)
                     Parameters affected:    Winds
                     """
                     position_x = kwargs['positionX']
                     position_y = kwargs['positionY']
                     azimuth = kwargs['azimuth']
                     if 'correctFactor' in kwargs:
                         correctFactor = kwargs['correctFactor']
                     else:
                         correctFactor = 1
                     groupList = kwargs['groupList']
                     pairs_ccf = groupList[1]
                     tau = kwargs['tau']
                     _lambda = kwargs['_lambda']
                     #Cross Correlation pairs obtained
             #         pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
             #         pairsArray = numpy.array(pairsList)[pairsCrossCorr]
             #         pairsSelArray = numpy.array(pairsSelected)
             #         pairs = []
+            #
             #         #Wind estimation pairs obtained
             #         for i in range(pairsSelArray.shape[0]/2):
             #             ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
             #             ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
             #             pairs.append((ind1,ind2))
                     indtau = tau.shape[0]/2
                     tau1 = tau[:indtau,:]
                     tau2 = tau[indtau:-1,:]
             #         tau1 = tau1[pairs,:]
             #         tau2 = tau2[pairs,:]
                     phase1 = tau[-1,:]
                     #---------------------------------------------------------------------
                     #Metodo Directo
                     distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
                     winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
                     winds = stats.nanmean(winds, axis=0)
                     #---------------------------------------------------------------------
                     #Metodo General
             #         distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
             #         #Calculo Coeficientes de Funcion de Correlacion
             #         F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
             #         #Calculo de Velocidades
             #         winds = self.calculateVelUV(F,G,A,B,H)
                     #---------------------------------------------------------------------
                     winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
                     winds = correctFactor*winds
                     return winds
                 def __checkTime(self, currentTime, paramInterval, outputInterval):
                     dataTime = currentTime + paramInterval
                     deltaTime = dataTime - self.__initime
                     if deltaTime >= outputInterval or deltaTime < 0:
                         self.__dataReady = True
                     return
                 def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
                     '''
                     Function that implements winds estimation technique with detected meteors.
                     Input:    Detected meteors, Minimum meteor quantity to wind estimation
                     Output:    Winds estimation (Zonal and Meridional)
                     Parameters affected:    Winds
                     '''
                     #Settings
                     nInt = (heightMax - heightMin)/2
                     nInt = int(nInt)
                     winds = numpy.zeros((2,nInt))*numpy.nan
                     #Filter errors
                     error = numpy.where(arrayMeteor[:,-1] == 0)[0]
                     finalMeteor = arrayMeteor[error,:]
                     #Meteor Histogram
                     finalHeights = finalMeteor[:,2]
                     hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
                     nMeteorsPerI = hist[0]
                     heightPerI = hist[1]
                     #Sort of meteors
                     indSort = finalHeights.argsort()
                     finalMeteor2 = finalMeteor[indSort,:]
                     #    Calculating winds
                     ind1 = 0
                     ind2 = 0
                     for i in range(nInt):
                         nMet = nMeteorsPerI[i]
                         ind1 = ind2
                         ind2 = ind1 + nMet
                         meteorAux = finalMeteor2[ind1:ind2,:]
                         if meteorAux.shape[0] >= meteorThresh:
                             vel = meteorAux[:, 6]
                             zen = meteorAux[:, 4]*numpy.pi/180
                             azim = meteorAux[:, 3]*numpy.pi/180
                             n = numpy.cos(zen)
                     #         m = (1 - n**2)/(1 - numpy.tan(azim)**2)
                     #         l = m*numpy.tan(azim)
                             l = numpy.sin(zen)*numpy.sin(azim)
                             m = numpy.sin(zen)*numpy.cos(azim)
                             A = numpy.vstack((l, m)).transpose()
                             A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
                             windsAux = numpy.dot(A1, vel)
                             winds[0,i] = windsAux[0]
                             winds[1,i] = windsAux[1]
                     return winds, heightPerI[:-1]
                 def techniqueNSM_SA(self, **kwargs):
                     metArray = kwargs['metArray']
                     heightList = kwargs['heightList']
                     timeList = kwargs['timeList']
                     rx_location = kwargs['rx_location']
                     groupList = kwargs['groupList']
                     azimuth = kwargs['azimuth']
                     dfactor = kwargs['dfactor']
                     k = kwargs['k']
                     azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
                     d = dist*dfactor
                     #Phase calculation
                     metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
                     metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
                     velEst = numpy.zeros((heightList.size,2))*numpy.nan
                     azimuth1 = azimuth1*numpy.pi/180
                     for i in range(heightList.size):
                         h = heightList[i]
                         indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
                         metHeight = metArray1[indH,:]
                         if metHeight.shape[0] >= 2:
                             velAux = numpy.asmatrix(metHeight[:,-2]).T    #Radial Velocities
                             iazim = metHeight[:,1].astype(int)
                             azimAux = numpy.asmatrix(azimuth1[iazim]).T    #Azimuths
                             A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux)))
                             A = numpy.asmatrix(A)
                             A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
                             velHor = numpy.dot(A1,velAux)
                             velEst[i,:] = numpy.squeeze(velHor)
                     return velEst
                 def __getPhaseSlope(self, metArray, heightList, timeList):
                     meteorList = []
                     #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
                     #Putting back together the meteor matrix
                     utctime = metArray[:,0]
                     uniqueTime = numpy.unique(utctime)
                     phaseDerThresh = 0.5
                     ippSeconds = timeList[1] - timeList[0]
                     sec = numpy.where(timeList>1)[0][0]
                     nPairs = metArray.shape[1] - 6
                     nHeights = len(heightList)
                     for t in uniqueTime:
                         metArray1 = metArray[utctime==t,:]
             #         phaseDerThresh = numpy.pi/4 #reducir Phase thresh
                         tmet = metArray1[:,1].astype(int)
                         hmet = metArray1[:,2].astype(int)
                         metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
                         metPhase[:,:] = numpy.nan
                         metPhase[:,hmet,tmet] = metArray1[:,6:].T
                         #Delete short trails
                         metBool = ~numpy.isnan(metPhase[0,:,:])
                         heightVect = numpy.sum(metBool, axis = 1)
                         metBool[heightVect<sec,:] = False
                         metPhase[:,heightVect<sec,:] = numpy.nan
                         #Derivative
                         metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
                         phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
                         metPhase[phDerAux] = numpy.nan
                         #--------------------------METEOR DETECTION    -----------------------------------------
                         indMet = numpy.where(numpy.any(metBool,axis=1))[0]
                         for p in numpy.arange(nPairs):
                             phase = metPhase[p,:,:]
                             phDer = metDer[p,:,:]
                             for h in indMet:
                                 height = heightList[h]
                                 phase1 = phase[h,:] #82
                                 phDer1 = phDer[h,:]
                                 phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)])   #Unwrap
                                 indValid = numpy.where(~numpy.isnan(phase1))[0]
                                 initMet = indValid[0]
                                 endMet = 0
                                 for i in range(len(indValid)-1):
                                     #Time difference
                                     inow = indValid[i]
                                     inext = indValid[i+1]
                                     idiff = inext - inow
                                     #Phase difference
                                     phDiff = numpy.abs(phase1[inext] - phase1[inow])
                                     if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]:   #End of Meteor
                                         sizeTrail = inow - initMet + 1
                                         if sizeTrail>3*sec:  #Too short meteors
                                             x = numpy.arange(initMet,inow+1)*ippSeconds
                                             y = phase1[initMet:inow+1]
                                             ynnan = ~numpy.isnan(y)
                                             x = x[ynnan]
                                             y = y[ynnan]
                                             slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
                                             ylin = x*slope + intercept
                                             rsq = r_value**2
                                             if rsq > 0.5:
                                                 vel = slope#*height*1000/(k*d)
                                                 estAux = numpy.array([utctime,p,height, vel, rsq])
                                                 meteorList.append(estAux)
                                         initMet = inext
                     metArray2 = numpy.array(meteorList)
                     return metArray2
                 def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
                     azimuth1 = numpy.zeros(len(pairslist))
                     dist = numpy.zeros(len(pairslist))
                     for i in range(len(rx_location)):
                         ch0 = pairslist[i][0]
                         ch1 = pairslist[i][1]
                         diffX = rx_location[ch0][0] - rx_location[ch1][0]
                         diffY = rx_location[ch0][1] - rx_location[ch1][1]
                         azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
                         dist[i] = numpy.sqrt(diffX**2 + diffY**2)
                     azimuth1 -= azimuth0
                     return azimuth1, dist
                 def techniqueNSM_DBS(self, **kwargs):
                     metArray = kwargs['metArray']
                     heightList = kwargs['heightList']
                     timeList = kwargs['timeList']
                     azimuth = kwargs['azimuth']
                     theta_x = numpy.array(kwargs['theta_x'])
                     theta_y = numpy.array(kwargs['theta_y'])
                     utctime = metArray[:,0]
                     cmet = metArray[:,1].astype(int)
                     hmet = metArray[:,3].astype(int)
                     SNRmet = metArray[:,4]
                     vmet = metArray[:,5]
                     spcmet = metArray[:,6]
                     nChan = numpy.max(cmet) + 1
                     nHeights = len(heightList)
                     azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
                     hmet = heightList[hmet]
                     h1met = hmet*numpy.cos(zenith_arr[cmet])      #Corrected heights
                     velEst = numpy.zeros((heightList.size,2))*numpy.nan
                     for i in range(nHeights - 1):
                         hmin = heightList[i]
                         hmax = heightList[i + 1]
                         thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
                         indthisH = numpy.where(thisH)
                         if numpy.size(indthisH) > 3:
                             vel_aux = vmet[thisH]
                             chan_aux = cmet[thisH]
                             cosu_aux = dir_cosu[chan_aux]
                             cosv_aux = dir_cosv[chan_aux]
                             cosw_aux = dir_cosw[chan_aux]
                             nch = numpy.size(numpy.unique(chan_aux))
                             if  nch > 1:
                                 A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
                                 velEst[i,:] = numpy.dot(A,vel_aux)
                     return velEst
                 def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
                     param = dataOut.data_param
                     if dataOut.abscissaList != None:
                         absc = dataOut.abscissaList[:-1]
                     # noise = dataOut.noise
                     heightList = dataOut.heightList
                     SNR = dataOut.data_SNR
                     if technique == 'DBS':
                         kwargs['velRadial'] = param[:,1,:] #Radial velocity
                         kwargs['heightList'] = heightList
                         kwargs['SNR'] = SNR
                         dataOut.data_output, dataOut.heightList, dataOut.data_SNR = self.techniqueDBS(kwargs) #DBS Function
                         dataOut.utctimeInit = dataOut.utctime
                         dataOut.outputInterval = dataOut.paramInterval
                     elif technique == 'SA':
                         #Parameters
             #             position_x = kwargs['positionX']
             #             position_y = kwargs['positionY']
             #             azimuth = kwargs['azimuth']
+            #
             #             if kwargs.has_key('crosspairsList'):
             #                 pairs = kwargs['crosspairsList']
             #             else:
             #                 pairs = None
+            #
             #             if kwargs.has_key('correctFactor'):
             #                 correctFactor = kwargs['correctFactor']
             #             else:
             #                 correctFactor = 1
             #             tau = dataOut.data_param
             #             _lambda = dataOut.C/dataOut.frequency
             #             pairsList = dataOut.groupList
             #             nChannels = dataOut.nChannels
                         kwargs['groupList'] = dataOut.groupList
                         kwargs['tau'] = dataOut.data_param
                         kwargs['_lambda'] = dataOut.C/dataOut.frequency
             #             dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor)
                         dataOut.data_output = self.techniqueSA(kwargs)
                         dataOut.utctimeInit = dataOut.utctime
                         dataOut.outputInterval = dataOut.timeInterval
                     elif technique == 'Meteors':
                         dataOut.flagNoData = True
                         self.__dataReady = False
                         if 'nHours' in kwargs:
                             nHours = kwargs['nHours']
                         else:
                             nHours = 1
                         if 'meteorsPerBin' in kwargs:
                             meteorThresh = kwargs['meteorsPerBin']
                         else:
                             meteorThresh = 6
                         if 'hmin' in kwargs:
                             hmin = kwargs['hmin']
                         else:   hmin = 70
                         if 'hmax' in kwargs:
                             hmax = kwargs['hmax']
                         else:   hmax = 110
                         dataOut.outputInterval = nHours*3600
                         if self.__isConfig == False:
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                             #Get Initial LTC time
                             self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
                             self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                             self.__isConfig = True
                         if self.__buffer is None:
                             self.__buffer = dataOut.data_param
                             self.__firstdata = copy.copy(dataOut)
                         else:
                             self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                         self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                         if self.__dataReady:
                             dataOut.utctimeInit = self.__initime
                             self.__initime += dataOut.outputInterval #to erase time offset
                             dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
                             dataOut.flagNoData = False
                             self.__buffer = None
                     elif technique == 'Meteors1':
                         dataOut.flagNoData = True
                         self.__dataReady = False
                         if 'nMins' in kwargs:
                             nMins = kwargs['nMins']
                         else: nMins = 20
                         if 'rx_location' in kwargs:
                             rx_location = kwargs['rx_location']
                         else: rx_location = [(0,1),(1,1),(1,0)]
                         if 'azimuth' in kwargs:
                             azimuth = kwargs['azimuth']
                         else: azimuth = 51.06
                         if 'dfactor' in kwargs:
                             dfactor = kwargs['dfactor']
                         if 'mode' in kwargs:
                             mode = kwargs['mode']
                         if 'theta_x' in kwargs:
                             theta_x = kwargs['theta_x']
                         if 'theta_y' in kwargs:
                             theta_y = kwargs['theta_y']
                         else: mode = 'SA'
                         #Borrar luego esto
                         if dataOut.groupList is None:
                             dataOut.groupList = [(0,1),(0,2),(1,2)]
                         groupList = dataOut.groupList
                         C = 3e8
                         freq = 50e6
                         lamb = C/freq
                         k = 2*numpy.pi/lamb
                         timeList = dataOut.abscissaList
                         heightList = dataOut.heightList
                         if self.__isConfig == False:
                             dataOut.outputInterval = nMins*60
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                             #Get Initial LTC time
                             initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
                             minuteAux = initime.minute
                             minuteNew = int(numpy.floor(minuteAux/nMins)*nMins)
                             self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                             self.__isConfig = True
                         if self.__buffer is None:
                             self.__buffer = dataOut.data_param
                             self.__firstdata = copy.copy(dataOut)
                         else:
                             self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                         self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                         if self.__dataReady:
                             dataOut.utctimeInit = self.__initime
                             self.__initime += dataOut.outputInterval #to erase time offset
                             metArray = self.__buffer
                             if mode == 'SA':
                                 dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
                             elif mode == 'DBS':
                                 dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y)
                             dataOut.data_output = dataOut.data_output.T
                             dataOut.flagNoData = False
                             self.__buffer = None
                     return
             class EWDriftsEstimation(Operation):
                 def __init__(self):
                     Operation.__init__(self)
                 def __correctValues(self, heiRang, phi, velRadial, SNR):
                     listPhi = phi.tolist()
                     maxid = listPhi.index(max(listPhi))
                     minid = listPhi.index(min(listPhi))
                     rango = list(range(len(phi)))
                #     rango = numpy.delete(rango,maxid)
                     heiRang1 = heiRang*math.cos(phi[maxid])
                     heiRangAux = heiRang*math.cos(phi[minid])
                     indOut = (heiRang1 < heiRangAux[0]).nonzero()
                     heiRang1 = numpy.delete(heiRang1,indOut)
                     velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
                     SNR1 = numpy.zeros([len(phi),len(heiRang1)])
                     for i in rango:
                         x = heiRang*math.cos(phi[i])
                         y1 = velRadial[i,:]
                         f1 = interpolate.interp1d(x,y1,kind = 'cubic')
                         x1 = heiRang1
                         y11 = f1(x1)
                         y2 = SNR[i,:]
                         f2 = interpolate.interp1d(x,y2,kind = 'cubic')
                         y21 = f2(x1)
                         velRadial1[i,:] = y11
                         SNR1[i,:] = y21
                     return heiRang1, velRadial1, SNR1
                 def run(self, dataOut, zenith, zenithCorrection):
                     heiRang = dataOut.heightList
                     velRadial = dataOut.data_param[:,3,:]
                     SNR = dataOut.data_SNR
                     zenith = numpy.array(zenith)
                     zenith -= zenithCorrection
                     zenith *= numpy.pi/180
                     heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
                     alp = zenith[0]
                     bet = zenith[1]
                     w_w = velRadial1[0,:]
                     w_e = velRadial1[1,:]
                     w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
                     u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
                     winds = numpy.vstack((u,w))
                     dataOut.heightList = heiRang1
                     dataOut.data_output = winds
                     dataOut.data_SNR = SNR1
                     dataOut.utctimeInit = dataOut.utctime
                     dataOut.outputInterval = dataOut.timeInterval
                     return
             #---------------    Non Specular Meteor    ----------------
             class NonSpecularMeteorDetection(Operation):
                 def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False):
                     data_acf = dataOut.data_pre[0]
                     data_ccf = dataOut.data_pre[1]
                     pairsList = dataOut.groupList[1]
                     lamb = dataOut.C/dataOut.frequency
                     tSamp = dataOut.ippSeconds*dataOut.nCohInt
                     paramInterval = dataOut.paramInterval
                     nChannels = data_acf.shape[0]
                     nLags = data_acf.shape[1]
                     nProfiles = data_acf.shape[2]
                     nHeights = dataOut.nHeights
                     nCohInt = dataOut.nCohInt
                     sec = numpy.round(nProfiles/dataOut.paramInterval)
                     heightList = dataOut.heightList
                     ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
                     utctime = dataOut.utctime
                     dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
                     #------------------------    SNR    --------------------------------------
                     power = data_acf[:,0,:,:].real
                     noise = numpy.zeros(nChannels)
                     SNR = numpy.zeros(power.shape)
                     for i in range(nChannels):
                         noise[i] = hildebrand_sekhon(power[i,:], nCohInt)
                         SNR[i] = (power[i]-noise[i])/noise[i]
                     SNRm = numpy.nanmean(SNR, axis = 0)
                     SNRdB = 10*numpy.log10(SNR)
                     if mode == 'SA':
                         dataOut.groupList = dataOut.groupList[1]
                         nPairs = data_ccf.shape[0]
                         #----------------------    Coherence and Phase   --------------------------
                         phase = numpy.zeros(data_ccf[:,0,:,:].shape)
             #             phase1 = numpy.copy(phase)
                         coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
                         for p in range(nPairs):
                             ch0 = pairsList[p][0]
                             ch1 = pairsList[p][1]
                             ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
                             phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
             #                 phase1[p,:,:] = numpy.angle(ccf) #median filter
                             coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
             #                 coh1[p,:,:] = numpy.abs(ccf) #median filter
                         coh = numpy.nanmax(coh1, axis = 0)
             #             struc = numpy.ones((5,1))
             #             coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
                         #----------------------    Radial Velocity    ----------------------------
                         phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
                         velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
                         if allData:
                             boolMetFin = ~numpy.isnan(SNRm)
             #                 coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
                         else:
                             #------------------------    Meteor mask    ---------------------------------
             #                 #SNR mask
             #                 boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
+            #
             #                 #Erase small objects
             #                 boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
+            #
             #                 auxEEJ = numpy.sum(boolMet1,axis=0)
             #                 indOver = auxEEJ>nProfiles*0.8  #Use this later
             #                 indEEJ = numpy.where(indOver)[0]
             #                 indNEEJ = numpy.where(~indOver)[0]
+            #
             #                 boolMetFin = boolMet1
+            #
             #                 if indEEJ.size > 0:
             #                     boolMet1[:,indEEJ] = False  #Erase heights with EEJ
+            #
             #                     boolMet2 = coh > cohThresh
             #                     boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
+            #
             #                     #Final Meteor mask
             #                     boolMetFin = boolMet1|boolMet2
                             #Coherence mask
                             boolMet1 = coh > 0.75
                             struc = numpy.ones((30,1))
                             boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
                             #Derivative mask
                             derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
                             boolMet2 = derPhase < 0.2
             #                 boolMet2 = ndimage.morphology.binary_opening(boolMet2)
             #                 boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1)))
                             boolMet2 = ndimage.median_filter(boolMet2,size=5)
                             boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool)))
             #                 #Final mask
             #                 boolMetFin = boolMet2
                             boolMetFin = boolMet1&boolMet2
             #                 boolMetFin = ndimage.morphology.binary_dilation(boolMetFin)
                         #Creating data_param
                         coordMet = numpy.where(boolMetFin)
                         tmet = coordMet[0]
                         hmet = coordMet[1]
                         data_param = numpy.zeros((tmet.size, 6 + nPairs))
                         data_param[:,0] = utctime
                         data_param[:,1] = tmet
                         data_param[:,2] = hmet
                         data_param[:,3] = SNRm[tmet,hmet]
                         data_param[:,4] = velRad[tmet,hmet]
                         data_param[:,5] = coh[tmet,hmet]
                         data_param[:,6:] = phase[:,tmet,hmet].T
                     elif mode == 'DBS':
                         dataOut.groupList = numpy.arange(nChannels)
                         #Radial Velocities
                         phase = numpy.angle(data_acf[:,1,:,:])
             #             phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
                         velRad = phase*lamb/(4*numpy.pi*tSamp)
                         #Spectral width
             #             acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
             #             acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
                         acf1 = data_acf[:,1,:,:]
                         acf2 = data_acf[:,2,:,:]
                         spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2))
             #             velRad = ndimage.median_filter(velRad, size = (1,5,1))
                         if allData:
                             boolMetFin = ~numpy.isnan(SNRdB)
                         else:
                             #SNR
                             boolMet1 = (SNRdB>SNRthresh) #SNR mask
                             boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
                             #Radial velocity
                             boolMet2 = numpy.abs(velRad) < 20
                             boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
                             #Spectral Width
                             boolMet3 = spcWidth < 30
                             boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
             #                 boolMetFin = self.__erase_small(boolMet1, 10,5)
                             boolMetFin = boolMet1&boolMet2&boolMet3
                         #Creating data_param
                         coordMet = numpy.where(boolMetFin)
                         cmet = coordMet[0]
                         tmet = coordMet[1]
                         hmet = coordMet[2]
                         data_param = numpy.zeros((tmet.size, 7))
                         data_param[:,0] = utctime
                         data_param[:,1] = cmet
                         data_param[:,2] = tmet
                         data_param[:,3] = hmet
                         data_param[:,4] = SNR[cmet,tmet,hmet].T
                         data_param[:,5] = velRad[cmet,tmet,hmet].T
                         data_param[:,6] = spcWidth[cmet,tmet,hmet].T
             #         self.dataOut.data_param = data_int
                     if len(data_param) == 0:
                         dataOut.flagNoData = True
                     else:
                         dataOut.data_param = data_param
                 def __erase_small(self, binArray, threshX, threshY):
                     labarray, numfeat = ndimage.measurements.label(binArray)
                     binArray1 = numpy.copy(binArray)
                     for i in range(1,numfeat + 1):
                         auxBin = (labarray==i)
                         auxSize = auxBin.sum()
                         x,y = numpy.where(auxBin)
                         widthX = x.max() - x.min()
                         widthY = y.max() - y.min()
                         #width X: 3 seg -> 12.5*3
                         #width Y:
                         if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
                             binArray1[auxBin] = False
                     return binArray1
             #---------------    Specular Meteor    ----------------
             class SMDetection(Operation):
                 '''
                     Function DetectMeteors()
                         Project developed with paper:
                         HOLDSWORTH ET AL. 2004
                     Input:
                         self.dataOut.data_pre
                         centerReceiverIndex:      From the channels, which is the center receiver
                         hei_ref:                  Height reference for the Beacon signal extraction
                         tauindex:
                         predefinedPhaseShifts:    Predefined phase offset for the voltge signals
                         cohDetection:             Whether to user Coherent detection or not
                         cohDet_timeStep:          Coherent Detection calculation time step
                         cohDet_thresh:            Coherent Detection phase threshold to correct phases
                         noise_timeStep:           Noise calculation time step
                         noise_multiple:           Noise multiple to define signal threshold
                         multDet_timeLimit:        Multiple Detection Removal time limit in seconds
                         multDet_rangeLimit:       Multiple Detection Removal range limit in km
                         phaseThresh:              Maximum phase difference between receiver to be consider a meteor
                         SNRThresh:                Minimum SNR threshold of the meteor signal to be consider a meteor
                         hmin:                     Minimum Height of the meteor to use it in the further wind estimations
                         hmax:                     Maximum Height of the meteor to use it in the further wind estimations
                         azimuth:                  Azimuth angle correction
                     Affected:
                         self.dataOut.data_param
                     Rejection Criteria (Errors):
 : No error; analysis OK
 : SNR < SNR threshold
 : angle of arrival (AOA) ambiguously determined
 : AOA estimate not feasible
 : Large difference in AOAs obtained from different antenna baselines
 : echo at start or end of time series
 : echo less than 5 examples long; too short for analysis
 : echo rise exceeds 0.3s
 : echo decay time less than twice rise time
 : large power level before echo
 : large power level after echo
 : poor fit to amplitude for estimation of decay time
 : poor fit to CCF phase variation for estimation of radial drift velocity
 : height unresolvable echo: not valid height within 70 to 110 km
 : height ambiguous echo: more then one possible height within 70 to 110 km
 : radial drift velocity or projected horizontal velocity exceeds 200 m/s
 : oscilatory echo, indicating event most likely not an underdense echo
 : phase difference in meteor Reestimation
                     Data Storage:
                         Meteors for Wind Estimation   (8):
                         Utc Time   |    Range    Height
                         Azimuth    Zenith    errorCosDir
                         VelRad    errorVelRad
                         Phase0 Phase1 Phase2 Phase3
                         TypeError
                      '''
                 def run(self, dataOut, hei_ref = None, tauindex = 0,
                                   phaseOffsets = None,
                                   cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
                                   noise_timeStep = 4, noise_multiple = 4,
                                   multDet_timeLimit = 1, multDet_rangeLimit = 3,
                                   phaseThresh = 20, SNRThresh = 5,
                                   hmin = 50, hmax=150, azimuth = 0,
                                   channelPositions = None) :
                     #Getting Pairslist
                     if channelPositions is None:
             #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                         channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                     meteorOps = SMOperations()
                     pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                     heiRang = dataOut.getHeiRange()
                     #Get Beacon signal - No Beacon signal anymore
             #         newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
+            #
             #         if hei_ref != None:
             #             newheis = numpy.where(self.dataOut.heightList>hei_ref)
+            #
                     #****************REMOVING HARDWARE PHASE DIFFERENCES***************
                     # see if the user put in pre defined phase shifts
                     voltsPShift = dataOut.data_pre.copy()
             #         if predefinedPhaseShifts != None:
             #             hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
+            #
             # #         elif beaconPhaseShifts:
             # #             #get hardware phase shifts using beacon signal
             # #             hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
             # #             hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
+            #
             #         else:
             #             hardwarePhaseShifts = numpy.zeros(5)
+            #
             #         voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
             #         for i in range(self.dataOut.data_pre.shape[0]):
             #             voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
                     #******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
                     #Remove DC
                     voltsDC = numpy.mean(voltsPShift,1)
                     voltsDC = numpy.mean(voltsDC,1)
                     for i in range(voltsDC.shape[0]):
                         voltsPShift[i] = voltsPShift[i] - voltsDC[i]
                     #Don't considerate last heights, theyre used to calculate Hardware Phase Shift
             #         voltsPShift = voltsPShift[:,:,:newheis[0][0]]
                     #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
                     #Coherent Detection
                     if cohDetection:
                         #use coherent detection to get the net power
                         cohDet_thresh = cohDet_thresh*numpy.pi/180
                         voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
                     #Non-coherent detection!
                     powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
                     #********** END OF COH/NON-COH POWER CALCULATION**********************
                     #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
                     #Get noise
                     noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
             #         noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval)
                     #Get signal threshold
                     signalThresh = noise_multiple*noise
                     #Meteor echoes detection
                     listMeteors = self.__findMeteors(powerNet, signalThresh)
                     #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
                     #************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
                     #Parameters
                     heiRange = dataOut.getHeiRange()
                     rangeInterval = heiRange[1] - heiRange[0]
                     rangeLimit = multDet_rangeLimit/rangeInterval
                     timeLimit = multDet_timeLimit/dataOut.timeInterval
                     #Multiple detection removals
                     listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
                     #************ END OF REMOVE MULTIPLE DETECTIONS **********************
                     #*********************     METEOR REESTIMATION  (3.7, 3.8, 3.9, 3.10)   ********************
                     #Parameters
                     phaseThresh = phaseThresh*numpy.pi/180
                     thresh = [phaseThresh, noise_multiple, SNRThresh]
                     #Meteor reestimation  (Errors N 1, 6, 12, 17)
                     listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency)
             #         listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise)
                     #Estimation of decay times (Errors N 7, 8, 11)
                     listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
                     #*******************     END OF METEOR REESTIMATION    *******************
                     #*********************    METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13)    **************************
                     #Calculating Radial Velocity (Error N 15)
                     radialStdThresh = 10
                     listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
                     if len(listMeteors4) > 0:
                         #Setting New Array
                         date = dataOut.utctime
                         arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
                         #Correcting phase offset
                         if phaseOffsets != None:
                             phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
                             arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
                         #Second Pairslist
                         pairsList = []
                         pairx = (0,1)
                         pairy = (2,3)
                         pairsList.append(pairx)
                         pairsList.append(pairy)
                         jph = numpy.array([0,0,0,0])
                         h = (hmin,hmax)
                         arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
             #             #Calculate AOA (Error N 3, 4)
             #             #JONES ET AL. 1998
             #             error = arrayParameters[:,-1]
             #             AOAthresh = numpy.pi/8
             #             phases = -arrayParameters[:,9:13]
             #             arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
+            #
             #             #Calculate Heights (Error N 13 and 14)
             #             error = arrayParameters[:,-1]
             #             Ranges = arrayParameters[:,2]
             #             zenith = arrayParameters[:,5]
             #             arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
             #             error = arrayParameters[:,-1]
                     #*********************    END OF PARAMETERS CALCULATION    **************************
                     #***************************+     PASS DATA TO NEXT STEP    **********************
             #             arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
                         dataOut.data_param = arrayParameters
                         if arrayParameters is None:
                             dataOut.flagNoData = True
                     else:
                         dataOut.flagNoData = True
                     return
                 def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     voltage = voltage0[:,:,minIndex:maxIndex+1]
                     nLength = voltage.shape[1]/n
                     nMin = 0
                     nMax = 0
                     phaseOffset = numpy.zeros((len(pairslist),n))
                     for i in range(n):
                         nMax += nLength
                         phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
                         phaseCCF = numpy.mean(phaseCCF, axis = 2)
                         phaseOffset[:,i] = phaseCCF.transpose()
                         nMin = nMax
             #         phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
                     #Remove Outliers
                     factor = 2
                     wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
                     dw = numpy.std(wt,axis = 1)
                     dw = dw.reshape((dw.size,1))
                     ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
                     phaseOffset[ind] = numpy.nan
                     phaseOffset = stats.nanmean(phaseOffset, axis=1)
                     return phaseOffset
                 def __shiftPhase(self, data, phaseShift):
                     #this will shift the phase of a complex number
                     dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
                     return dataShifted
                 def __estimatePhaseDifference(self, array, pairslist):
                     nChannel = array.shape[0]
                     nHeights = array.shape[2]
                     numPairs = len(pairslist)
             #         phaseCCF = numpy.zeros((nChannel, 5, nHeights))
                     phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
                     #Correct phases
                     derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
                     indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
                     if indDer[0].shape[0] > 0:
                         for i in range(indDer[0].shape[0]):
                             signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
                             phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
             #         for j in range(numSides):
             #             phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
             #             phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
+            #
                     #Linear
                     phaseInt = numpy.zeros((numPairs,1))
                     angAllCCF = phaseCCF[:,[0,1,3,4],0]
                     for j in range(numPairs):
                         fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:])
                         phaseInt[j] = fit[1]
                     #Phase Differences
                     phaseDiff = phaseInt - phaseCCF[:,2,:]
                     phaseArrival = phaseInt.reshape(phaseInt.size)
                     #Dealias
                     phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
             #         indAlias = numpy.where(phaseArrival > numpy.pi)
             #         phaseArrival[indAlias] -= 2*numpy.pi
             #         indAlias = numpy.where(phaseArrival < -numpy.pi)
             #         phaseArrival[indAlias] += 2*numpy.pi
                     return phaseDiff, phaseArrival
                 def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
                     #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
                     #find the phase shifts of each channel over 1 second intervals
                     #only look at ranges below the beacon signal
                     numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
                     numBlocks = int(volts.shape[1]/numProfPerBlock)
                     numHeights = volts.shape[2]
                     nChannel = volts.shape[0]
                     voltsCohDet = volts.copy()
                     pairsarray = numpy.array(pairslist)
                     indSides = pairsarray[:,1]
             #         indSides = numpy.array(range(nChannel))
             #         indSides = numpy.delete(indSides, indCenter)
+            #
             #         listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
                     listBlocks = numpy.array_split(volts, numBlocks, 1)
                     startInd = 0
                     endInd = 0
                     for i in range(numBlocks):
                         startInd = endInd
                         endInd = endInd + listBlocks[i].shape[1]
                         arrayBlock = listBlocks[i]
             #             arrayBlockCenter = listCenter[i]
                         #Estimate the Phase Difference
                         phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
                         #Phase Difference RMS
                         arrayPhaseRMS = numpy.abs(phaseDiff)
                         phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0)
                         indPhase = numpy.where(phaseRMSaux==4)
                         #Shifting
                         if indPhase[0].shape[0] > 0:
                             for j in range(indSides.size):
                                 arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
                             voltsCohDet[:,startInd:endInd,:] = arrayBlock
                     return voltsCohDet
                 def __calculateCCF(self, volts, pairslist ,laglist):
                     nHeights = volts.shape[2]
                     nPoints = volts.shape[1]
                     voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
                     for i in range(len(pairslist)):
                         volts1 = volts[pairslist[i][0]]
                         volts2 = volts[pairslist[i][1]]
                         for t in range(len(laglist)):
                             idxT = laglist[t]
                             if idxT >= 0:
                                 vStacked = numpy.vstack((volts2[idxT:,:],
                                                        numpy.zeros((idxT, nHeights),dtype='complex')))
                             else:
                                 vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
                                                        volts2[:(nPoints + idxT),:]))
                             voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
                             vStacked = None
                     return voltsCCF
                 def __getNoise(self, power, timeSegment, timeInterval):
                     numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
                     numBlocks = int(power.shape[0]/numProfPerBlock)
                     numHeights = power.shape[1]
                     listPower = numpy.array_split(power, numBlocks, 0)
                     noise = numpy.zeros((power.shape[0], power.shape[1]))
                     noise1 = numpy.zeros((power.shape[0], power.shape[1]))
                     startInd = 0
                     endInd = 0
                     for i in range(numBlocks):             #split por canal
                         startInd = endInd
                         endInd = endInd + listPower[i].shape[0]
                         arrayBlock = listPower[i]
                         noiseAux = numpy.mean(arrayBlock, 0)
             #             noiseAux = numpy.median(noiseAux)
             #             noiseAux = numpy.mean(arrayBlock)
                         noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
                         noiseAux1 = numpy.mean(arrayBlock)
                         noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
                     return noise, noise1
                 def __findMeteors(self, power, thresh):
                     nProf = power.shape[0]
                     nHeights = power.shape[1]
                     listMeteors = []
                     for i in range(nHeights):
                         powerAux = power[:,i]
                         threshAux = thresh[:,i]
                         indUPthresh = numpy.where(powerAux > threshAux)[0]
                         indDNthresh = numpy.where(powerAux <= threshAux)[0]
                         j = 0
                         while (j < indUPthresh.size - 2):
                             if (indUPthresh[j + 2] == indUPthresh[j] + 2):
                                 indDNAux = numpy.where(indDNthresh > indUPthresh[j])
                                 indDNthresh = indDNthresh[indDNAux]
                                 if (indDNthresh.size > 0):
                                     indEnd = indDNthresh[0] - 1
                                     indInit = indUPthresh[j]
                                     meteor = powerAux[indInit:indEnd + 1]
                                     indPeak = meteor.argmax() + indInit
                                     FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
                                     listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
                                     j = numpy.where(indUPthresh == indEnd)[0] + 1
                                 else: j+=1
                             else: j+=1
                     return listMeteors
                 def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
                     arrayMeteors = numpy.asarray(listMeteors)
                     listMeteors1 = []
                     while arrayMeteors.shape[0] > 0:
                         FLAs = arrayMeteors[:,4]
                         maxFLA = FLAs.argmax()
                         listMeteors1.append(arrayMeteors[maxFLA,:])
                         MeteorInitTime = arrayMeteors[maxFLA,1]
                         MeteorEndTime = arrayMeteors[maxFLA,3]
                         MeteorHeight = arrayMeteors[maxFLA,0]
                         #Check neighborhood
                         maxHeightIndex = MeteorHeight + rangeLimit
                         minHeightIndex = MeteorHeight - rangeLimit
                         minTimeIndex = MeteorInitTime - timeLimit
                         maxTimeIndex = MeteorEndTime + timeLimit
                         #Check Heights
                         indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
                         indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
                         indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
                         arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
                     return listMeteors1
                 def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
                     numHeights = volts.shape[2]
                     nChannel = volts.shape[0]
                     thresholdPhase = thresh[0]
                     thresholdNoise = thresh[1]
                     thresholdDB = float(thresh[2])
                     thresholdDB1 = 10**(thresholdDB/10)
                     pairsarray = numpy.array(pairslist)
                     indSides = pairsarray[:,1]
                     pairslist1 = list(pairslist)
                     pairslist1.append((0,1))
                     pairslist1.append((3,4))
                     listMeteors1 = []
                     listPowerSeries = []
                     listVoltageSeries = []
                     #volts has the war data
                     if frequency == 30e6:
                         timeLag = 45*10**-3
                     else:
                         timeLag = 15*10**-3
                     lag = numpy.ceil(timeLag/timeInterval)
                     for i in range(len(listMeteors)):
                         ######################   3.6 - 3.7 PARAMETERS REESTIMATION    #########################
                         meteorAux = numpy.zeros(16)
                         #Loading meteor Data (mHeight, mStart, mPeak, mEnd)
                         mHeight = listMeteors[i][0]
                         mStart = listMeteors[i][1]
                         mPeak = listMeteors[i][2]
                         mEnd = listMeteors[i][3]
                         #get the volt data between the start and end times of the meteor
                         meteorVolts = volts[:,mStart:mEnd+1,mHeight]
                         meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
                         #3.6. Phase Difference estimation
                         phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
                         #3.7. Phase difference removal & meteor start, peak and end times reestimated
                         #meteorVolts0.- all Channels, all Profiles
                         meteorVolts0 = volts[:,:,mHeight]
                         meteorThresh = noise[:,mHeight]*thresholdNoise
                         meteorNoise = noise[:,mHeight]
                         meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
                         powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0)  #Power
                         #Times reestimation
                         mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
                         if mStart1.size > 0:
                             mStart1 = mStart1[-1] + 1
                         else:
                             mStart1 = mPeak
                         mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
                         mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
                         if mEndDecayTime1.size == 0:
                             mEndDecayTime1 = powerNet0.size
                         else:
                             mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
             #             mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
                         #meteorVolts1.- all Channels, from start to end
                         meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
                         meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
                         if meteorVolts2.shape[1] == 0:
                             meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1]
                         meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
                         meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
                         #####################    END PARAMETERS REESTIMATION    #########################
                         #####################   3.8 PHASE DIFFERENCE REESTIMATION  ########################
             #             if mEnd1 - mStart1 > 4:       #Error Number 6: echo less than 5 samples long; too short for analysis
                         if meteorVolts2.shape[1] > 0:
                             #Phase Difference re-estimation
                             phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1)   #Phase Difference Estimation
             #                 phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
                             meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
                             phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
                             meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4])     #Phase Shifting
                             #Phase Difference RMS
                             phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
                             powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
                             #Data from Meteor
                             mPeak1 = powerNet1.argmax() + mStart1
                             mPeakPower1 = powerNet1.max()
                             noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight])
                             mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux
                             Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1])
                             Meteor1 = numpy.hstack((Meteor1,phaseDiffint))
                             PowerSeries  = powerNet0[mStart1:mEndDecayTime1 + 1]
                             #Vectorize
                             meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
                             meteorAux[7:11] = phaseDiffint[0:4]
                             #Rejection Criterions
                             if phaseRMS1 > thresholdPhase:  #Error Number 17: Phase variation
                                 meteorAux[-1] = 17
                             elif mSNR1 < thresholdDB1:      #Error Number 1: SNR < threshold dB
                                 meteorAux[-1] = 1
                         else:
                             meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
                             meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
                             PowerSeries = 0
                         listMeteors1.append(meteorAux)
                         listPowerSeries.append(PowerSeries)
                         listVoltageSeries.append(meteorVolts1)
                     return listMeteors1, listPowerSeries, listVoltageSeries
                 def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
                     threshError = 10
                     #Depending if it is 30 or 50 MHz
                     if frequency == 30e6:
                         timeLag = 45*10**-3
                     else:
                         timeLag = 15*10**-3
                     lag = numpy.ceil(timeLag/timeInterval)
                     listMeteors1 = []
                     for i in range(len(listMeteors)):
                         meteorPower = listPower[i]
                         meteorAux = listMeteors[i]
                         if meteorAux[-1] == 0:
                             try:
                                 indmax = meteorPower.argmax()
                                 indlag = indmax + lag
                                 y = meteorPower[indlag:]
                                 x = numpy.arange(0, y.size)*timeLag
                                 #first guess
                                 a = y[0]
                                 tau = timeLag
                                 #exponential fit
                                 popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau])
                                 y1 = self.__exponential_function(x, *popt)
                                 #error estimation
                                 error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
                                 decayTime = popt[1]
                                 riseTime = indmax*timeInterval
                                 meteorAux[11:13] = [decayTime, error]
                                 #Table items 7, 8 and 11
                                 if (riseTime > 0.3):            #Number 7: Echo rise exceeds 0.3s
                                     meteorAux[-1] = 7
                                 elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
                                     meteorAux[-1] = 8
                                 if (error > threshError):       #Number 11: Poor fit to amplitude for estimation of decay time
                                     meteorAux[-1] = 11
                             except:
                                 meteorAux[-1] = 11
                         listMeteors1.append(meteorAux)
                     return listMeteors1
                 #Exponential Function
                 def __exponential_function(self, x, a, tau):
                     y = a*numpy.exp(-x/tau)
                     return y
                 def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist,  timeInterval):
                     pairslist1 = list(pairslist)
                     pairslist1.append((0,1))
                     pairslist1.append((3,4))
                     numPairs = len(pairslist1)
                     #Time Lag
                     timeLag = 45*10**-3
                     c = 3e8
                     lag = numpy.ceil(timeLag/timeInterval)
                     freq = 30e6
                     listMeteors1 = []
                     for i in range(len(listMeteors)):
                         meteorAux = listMeteors[i]
                         if meteorAux[-1] == 0:
                             mStart = listMeteors[i][1]
                             mPeak = listMeteors[i][2]
                             mLag = mPeak - mStart + lag
                             #get the volt data between the start and end times of the meteor
                             meteorVolts = listVolts[i]
                             meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
                             #Get CCF
                             allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
                             #Method 2
                             slopes = numpy.zeros(numPairs)
                             time = numpy.array([-2,-1,1,2])*timeInterval
                             angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
                             #Correct phases
                             derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
                             indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
                             if indDer[0].shape[0] > 0:
                                 for i in range(indDer[0].shape[0]):
                                     signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
                                     angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
             #                     fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]]))
                             for j in range(numPairs):
                                 fit = stats.linregress(time, angAllCCF[j,:])
                                 slopes[j] = fit[0]
                             #Remove Outlier
             #                 indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
             #                 slopes = numpy.delete(slopes,indOut)
             #                 indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
             #                 slopes = numpy.delete(slopes,indOut)
                             radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
                             radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
                             meteorAux[-2] = radialError
                             meteorAux[-3] = radialVelocity
                             #Setting Error
                             #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
                             if numpy.abs(radialVelocity) > 200:
                                 meteorAux[-1] = 15
                             #Number 12: Poor fit to CCF variation for estimation of radial drift velocity
                             elif radialError > radialStdThresh:
                                 meteorAux[-1] = 12
                         listMeteors1.append(meteorAux)
                     return listMeteors1
                 def __setNewArrays(self, listMeteors, date, heiRang):
                     #New arrays
                     arrayMeteors = numpy.array(listMeteors)
                     arrayParameters = numpy.zeros((len(listMeteors), 13))
                     #Date inclusion
             #         date = re.findall(r'\((.*?)\)', date)
             #         date = date[0].split(',')
             #         date = map(int, date)
+            #
             #         if len(date)<6:
             #             date.append(0)
+            #
             #         date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
             #         arrayDate = numpy.tile(date, (len(listMeteors), 1))
                     arrayDate = numpy.tile(date, (len(listMeteors)))
                     #Meteor array
             #         arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
             #         arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
                     #Parameters Array
                     arrayParameters[:,0] = arrayDate #Date
                     arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
                     arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error
                     arrayParameters[:,8:12] = arrayMeteors[:,7:11]  #Phases
                     arrayParameters[:,-1] = arrayMeteors[:,-1]  #Error
                     return arrayParameters
             class CorrectSMPhases(Operation):
                 def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
                     arrayParameters = dataOut.data_param
                     pairsList = []
                     pairx = (0,1)
                     pairy = (2,3)
                     pairsList.append(pairx)
                     pairsList.append(pairy)
                     jph = numpy.zeros(4)
                     phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
                 #         arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
                     arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
                     meteorOps = SMOperations()
                     if channelPositions is None:
                 #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                         channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                     pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                     h = (hmin,hmax)
                     arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
                     dataOut.data_param = arrayParameters
                     return
             class SMPhaseCalibration(Operation):
                 __buffer = None
                 __initime = None
                 __dataReady = False
                 __isConfig = False
                 def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
                     dataTime = currentTime + paramInterval
                     deltaTime = dataTime - initTime
                     if deltaTime >= outputInterval or deltaTime < 0:
                         return True
                     return False
                 def __getGammas(self, pairs, d, phases):
                     gammas = numpy.zeros(2)
                     for i in range(len(pairs)):
                         pairi = pairs[i]
                         phip3 = phases[:,pairi[0]]
                         d3 = d[pairi[0]]
                         phip2 = phases[:,pairi[1]]
                         d2 = d[pairi[1]]
                         #Calculating gamma
             #             jdcos = alp1/(k*d1)
             #             jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0)))
                         jgamma = -phip2*d3/d2 - phip3
                         jgamma = numpy.angle(numpy.exp(1j*jgamma))
             #             jgamma[jgamma>numpy.pi] -= 2*numpy.pi
             #             jgamma[jgamma<-numpy.pi] += 2*numpy.pi
                         #Revised distribution
                         jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
                         #Histogram
                         nBins = 64
                         rmin = -0.5*numpy.pi
                         rmax = 0.5*numpy.pi
                         phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
                         meteorsY = phaseHisto[0]
                         phasesX = phaseHisto[1][:-1]
                         width = phasesX[1] - phasesX[0]
                         phasesX += width/2
                         #Gaussian aproximation
                         bpeak = meteorsY.argmax()
                         peak = meteorsY.max()
                         jmin = bpeak - 5
                         jmax = bpeak + 5 + 1
                         if jmin<0:
                             jmin = 0
                             jmax = 6
                         elif jmax > meteorsY.size:
                             jmin = meteorsY.size - 6
                             jmax = meteorsY.size
                         x0 = numpy.array([peak,bpeak,50])
                         coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
                         #Gammas
                         gammas[i] = coeff[0][1]
                     return gammas
                 def __residualFunction(self, coeffs, y, t):
                     return y - self.__gauss_function(t, coeffs)
                 def __gauss_function(self, t, coeffs):
                     return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
                 def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
                     meteorOps = SMOperations()
                     nchan = 4
                     pairx = pairsList[0] #x es 0
                     pairy = pairsList[1] #y es 1
                     center_xangle = 0
                     center_yangle = 0
                     range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4])
                     ntimes = len(range_angle)
                     nstepsx = 20
                     nstepsy = 20
                     for iz in range(ntimes):
                         min_xangle = -range_angle[iz]/2 + center_xangle
                         max_xangle = range_angle[iz]/2 + center_xangle
                         min_yangle = -range_angle[iz]/2 + center_yangle
                         max_yangle = range_angle[iz]/2 + center_yangle
                         inc_x = (max_xangle-min_xangle)/nstepsx
                         inc_y = (max_yangle-min_yangle)/nstepsy
                         alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
                         alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
                         penalty = numpy.zeros((nstepsx,nstepsy))
                         jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
                         jph = numpy.zeros(nchan)
                         # Iterations looking for the offset
                         for iy in range(int(nstepsy)):
                             for ix in range(int(nstepsx)):
                                 d3 = d[pairsList[1][0]]
                                 d2 = d[pairsList[1][1]]
                                 d5 = d[pairsList[0][0]]
                                 d4 = d[pairsList[0][1]]
                                 alp2 = alpha_y[iy]  #gamma 1
                                 alp4 = alpha_x[ix]  #gamma 0
                                 alp3 = -alp2*d3/d2 - gammas[1]
                                 alp5 = -alp4*d5/d4 - gammas[0]
             #                     jph[pairy[1]] = alpha_y[iy]
             #                     jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
             #                     jph[pairx[1]] = alpha_x[ix]
             #                     jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
                                 jph[pairsList[0][1]] = alp4
                                 jph[pairsList[0][0]] = alp5
                                 jph[pairsList[1][0]] = alp3
                                 jph[pairsList[1][1]] = alp2
                                 jph_array[:,ix,iy] = jph
             #                     d = [2.0,2.5,2.5,2.0]
                                 #falta chequear si va a leer bien  los meteoros
                                 meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
                                 error = meteorsArray1[:,-1]
                                 ind1 = numpy.where(error==0)[0]
                                 penalty[ix,iy] = ind1.size
                         i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
                         phOffset = jph_array[:,i,j]
                         center_xangle = phOffset[pairx[1]]
                         center_yangle = phOffset[pairy[1]]
                     phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
                     phOffset = phOffset*180/numpy.pi
                     return phOffset
                 def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
                     dataOut.flagNoData = True
                     self.__dataReady = False
                     dataOut.outputInterval = nHours*3600
                     if self.__isConfig == False:
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                         #Get Initial LTC time
                         self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
                         self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                         self.__isConfig = True
                     if self.__buffer is None:
                         self.__buffer = dataOut.data_param.copy()
                     else:
                         self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                     self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                     if self.__dataReady:
                         dataOut.utctimeInit = self.__initime
                         self.__initime += dataOut.outputInterval #to erase time offset
                         freq = dataOut.frequency
                         c = dataOut.C #m/s
                         lamb = c/freq
                         k = 2*numpy.pi/lamb
                         azimuth = 0
                         h = (hmin, hmax)
             #             pairs = ((0,1),(2,3)) #Estrella
             #             pairs = ((1,0),(2,3)) #T
                         if channelPositions is None:
             #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                             channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                         meteorOps = SMOperations()
                         pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                         #Checking correct order of pairs
                         pairs = []
                         if distances[1] > distances[0]:
                             pairs.append((1,0))
                         else:
                             pairs.append((0,1))
                         if distances[3] > distances[2]:
                             pairs.append((3,2))
                         else:
                             pairs.append((2,3))
             #             distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
                         meteorsArray = self.__buffer
                         error = meteorsArray[:,-1]
                         boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
                         ind1 = numpy.where(boolError)[0]
                         meteorsArray = meteorsArray[ind1,:]
                         meteorsArray[:,-1] = 0
                         phases = meteorsArray[:,8:12]
                         #Calculate Gammas
                         gammas = self.__getGammas(pairs, distances, phases)
             #             gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
                         #Calculate Phases
                         phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray)
                         phasesOff = phasesOff.reshape((1,phasesOff.size))
                         dataOut.data_output = -phasesOff
                         dataOut.flagNoData = False
                         self.__buffer = None
                     return
             class SMOperations():
                 def __init__(self):
                     return
                 def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
                     arrayParameters = arrayParameters0.copy()
                     hmin = h[0]
                     hmax = h[1]
                     #Calculate AOA (Error N 3, 4)
                     #JONES ET AL. 1998
                     AOAthresh = numpy.pi/8
                     error = arrayParameters[:,-1]
                     phases = -arrayParameters[:,8:12] + jph
             #         phases = numpy.unwrap(phases)
                     arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
                     #Calculate Heights (Error N 13 and 14)
                     error = arrayParameters[:,-1]
                     Ranges = arrayParameters[:,1]
                     zenith = arrayParameters[:,4]
                     arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
                     #----------------------- Get Final data    ------------------------------------
             #         error = arrayParameters[:,-1]
             #         ind1 = numpy.where(error==0)[0]
             #         arrayParameters = arrayParameters[ind1,:]
                     return arrayParameters
                 def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
                     arrayAOA = numpy.zeros((phases.shape[0],3))
                     cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
                     arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
                     cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
                     arrayAOA[:,2] = cosDirError
                     azimuthAngle = arrayAOA[:,0]
                     zenithAngle = arrayAOA[:,1]
                     #Setting Error
                     indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
                     error[indError] = 0
                     #Number 3: AOA not fesible
                     indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
                     error[indInvalid] = 3
                     #Number 4: Large difference in AOAs obtained from different antenna baselines
                     indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
                     error[indInvalid] = 4
                     return arrayAOA, error
                 def __getDirectionCosines(self, arrayPhase, pairsList, distances):
                     #Initializing some variables
                     ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
                     ang_aux = ang_aux.reshape(1,ang_aux.size)
                     cosdir = numpy.zeros((arrayPhase.shape[0],2))
                     cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
                     for i in range(2):
                         ph0 = arrayPhase[:,pairsList[i][0]]
                         ph1 = arrayPhase[:,pairsList[i][1]]
                         d0 = distances[pairsList[i][0]]
                         d1 = distances[pairsList[i][1]]
                         ph0_aux = ph0 + ph1
                         ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
             #             ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
             #             ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
                         #First Estimation
                         cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
                         #Most-Accurate Second Estimation
                         phi1_aux =  ph0 - ph1
                         phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
                         #Direction Cosine 1
                         cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
                         #Searching the correct Direction Cosine
                         cosdir0_aux = cosdir0[:,i]
                         cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
                         #Minimum Distance
                         cosDiff = (cosdir1 - cosdir0_aux)**2
                         indcos = cosDiff.argmin(axis = 1)
                         #Saving Value obtained
                         cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
                     return cosdir0, cosdir
                 def __calculateAOA(self, cosdir, azimuth):
                     cosdirX = cosdir[:,0]
                     cosdirY = cosdir[:,1]
                     zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
                     azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
                     angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
                     return angles
                 def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
                     Ramb = 375  #Ramb = c/(2*PRF)
                     Re = 6371   #Earth Radius
                     heights = numpy.zeros(Ranges.shape)
                     R_aux = numpy.array([0,1,2])*Ramb
                     R_aux = R_aux.reshape(1,R_aux.size)
                     Ranges = Ranges.reshape(Ranges.size,1)
                     Ri = Ranges + R_aux
                     hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
                     #Check if there is a height between 70 and 110 km
                     h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
                     ind_h = numpy.where(h_bool == 1)[0]
                     hCorr = hi[ind_h, :]
                     ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
                     hCorr = hi[ind_hCorr][:len(ind_h)]
                     heights[ind_h] = hCorr
                     #Setting Error
                     #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
                     #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
                     indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
                     error[indError] = 0
                     indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
                     error[indInvalid2] = 14
                     indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
                     error[indInvalid1] = 13
                     return heights, error
                 def getPhasePairs(self, channelPositions):
                     chanPos = numpy.array(channelPositions)
                     listOper = list(itertools.combinations(list(range(5)),2))
                     distances = numpy.zeros(4)
                     axisX = []
                     axisY = []
                     distX = numpy.zeros(3)
                     distY = numpy.zeros(3)
                     ix = 0
                     iy = 0
                     pairX = numpy.zeros((2,2))
                     pairY = numpy.zeros((2,2))
                     for i in range(len(listOper)):
                         pairi = listOper[i]
                         posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
                         if posDif[0] == 0:
                             axisY.append(pairi)
                             distY[iy] = posDif[1]
                             iy += 1
                         elif posDif[1] == 0:
                             axisX.append(pairi)
                             distX[ix] = posDif[0]
                             ix += 1
                     for i in range(2):
                         if i==0:
                             dist0 = distX
                             axis0 = axisX
                         else:
                             dist0 = distY
                             axis0 = axisY
                         side = numpy.argsort(dist0)[:-1]
                         axis0 = numpy.array(axis0)[side,:]
                         chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
                         axis1 = numpy.unique(numpy.reshape(axis0,4))
                         side = axis1[axis1 != chanC]
                         diff1 = chanPos[chanC,i] - chanPos[side[0],i]
                         diff2 = chanPos[chanC,i] - chanPos[side[1],i]
                         if diff1<0:
                             chan2 = side[0]
                             d2 = numpy.abs(diff1)
                             chan1 = side[1]
                             d1 = numpy.abs(diff2)
                         else:
                             chan2 = side[1]
                             d2 = numpy.abs(diff2)
                             chan1 = side[0]
                             d1 = numpy.abs(diff1)
                         if i==0:
                             chanCX = chanC
                             chan1X = chan1
                             chan2X = chan2
                             distances[0:2] = numpy.array([d1,d2])
                         else:
                             chanCY = chanC
                             chan1Y = chan1
                             chan2Y = chan2
                             distances[2:4] = numpy.array([d1,d2])
             #         axisXsides = numpy.reshape(axisX[ix,:],4)
+            #
             #         channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
             #         channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
+            #
             #         ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
             #         ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
             #         channel25X = int(pairX[0,ind25X])
             #         channel20X = int(pairX[1,ind20X])
             #         ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0]
             #         ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
             #         channel25Y = int(pairY[0,ind25Y])
             #         channel20Y = int(pairY[1,ind20Y])
             #         pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
                     pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
                     return pairslist, distances
             #     def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
+            #
             #         arrayAOA = numpy.zeros((phases.shape[0],3))
             #         cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
+            #
             #         arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
             #         cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
             #         arrayAOA[:,2] = cosDirError
+            #
             #         azimuthAngle = arrayAOA[:,0]
             #         zenithAngle = arrayAOA[:,1]
+            #
             #         #Setting Error
             #         #Number 3: AOA not fesible
             #         indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
             #         error[indInvalid] = 3
             #         #Number 4: Large difference in AOAs obtained from different antenna baselines
             #         indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
             #         error[indInvalid] = 4
             #         return arrayAOA, error
+            #
             #     def __getDirectionCosines(self, arrayPhase, pairsList):
+            #
             #         #Initializing some variables
             #         ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
             #         ang_aux = ang_aux.reshape(1,ang_aux.size)
+            #
             #         cosdir = numpy.zeros((arrayPhase.shape[0],2))
             #         cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
+            #
+            #
             #         for i in range(2):
             #             #First Estimation
             #             phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
             #             #Dealias
             #             indcsi = numpy.where(phi0_aux > numpy.pi)
             #             phi0_aux[indcsi] -= 2*numpy.pi
             #             indcsi = numpy.where(phi0_aux < -numpy.pi)
             #             phi0_aux[indcsi] += 2*numpy.pi
             #             #Direction Cosine 0
             #             cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
+            #
             #             #Most-Accurate Second Estimation
             #             phi1_aux =  arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
             #             phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
             #             #Direction Cosine 1
             #             cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
+            #
             #             #Searching the correct Direction Cosine
             #             cosdir0_aux = cosdir0[:,i]
             #             cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
             #             #Minimum Distance
             #             cosDiff = (cosdir1 - cosdir0_aux)**2
             #             indcos = cosDiff.argmin(axis = 1)
             #             #Saving Value obtained
             #             cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
+            #
             #         return cosdir0, cosdir
+            #
             #     def __calculateAOA(self, cosdir, azimuth):
             #         cosdirX = cosdir[:,0]
             #         cosdirY = cosdir[:,1]
+            #
             #         zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
             #         azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
             #         angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
+            #
             #         return angles
+            #
             #     def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
+            #
             #         Ramb = 375  #Ramb = c/(2*PRF)
             #         Re = 6371   #Earth Radius
             #         heights = numpy.zeros(Ranges.shape)
+            #
             #         R_aux = numpy.array([0,1,2])*Ramb
             #         R_aux = R_aux.reshape(1,R_aux.size)
+            #
             #         Ranges = Ranges.reshape(Ranges.size,1)
+            #
             #         Ri = Ranges + R_aux
             #         hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
+            #
             #         #Check if there is a height between 70 and 110 km
             #         h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
             #         ind_h = numpy.where(h_bool == 1)[0]
+            #
             #         hCorr = hi[ind_h, :]
             #         ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
+            #
             #         hCorr = hi[ind_hCorr]
             #         heights[ind_h] = hCorr
+            #
             #         #Setting Error
             #         #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
             #         #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
+            #
             #         indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
             #         error[indInvalid2] = 14
             #         indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
             #         error[indInvalid1] = 13
+            #
             #         return heights, error
-  No newline at end of file

schainpy/model/proc/jroproc_voltage.py +59 -36

             import sys
             import numpy,math
             from scipy import interpolate
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
-            from schainpy.model.data.jrodata import Voltage
+            from schainpy.model.data.jrodata import Voltage,hildebrand_sekhon
             from schainpy.utils import log
             from time import time
             class VoltageProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.dataOut = Voltage()
                     self.flip = 1
                     self.setupReq = False
                 def run(self):
                     if self.dataIn.type == 'AMISR':
                         self.__updateObjFromAmisrInput()
                     if self.dataIn.type == 'Voltage':
                         self.dataOut.copy(self.dataIn)
                 def __updateObjFromAmisrInput(self):
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     self.dataOut.flagNoData = self.dataIn.flagNoData
                     self.dataOut.data = self.dataIn.data
                     self.dataOut.utctime = self.dataIn.utctime
                     self.dataOut.channelList = self.dataIn.channelList
                     #self.dataOut.timeInterval = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.nProfiles = self.dataIn.nProfiles
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.ippSeconds = self.dataIn.ippSeconds
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.azimuth = self.dataIn.azimuth
                     self.dataOut.zenith = self.dataIn.zenith
                     self.dataOut.beam.codeList = self.dataIn.beam.codeList
                     self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
                     self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
             class selectChannels(Operation):
                 def run(self, dataOut, channelList):
                     channelIndexList = []
                     self.dataOut = dataOut
                     for channel in channelList:
                         if channel not in self.dataOut.channelList:
                             raise ValueError("Channel %d is not in %s" %(channel, str(self.dataOut.channelList)))
                         index = self.dataOut.channelList.index(channel)
                         channelIndexList.append(index)
                     self.selectChannelsByIndex(channelIndexList)
                     return self.dataOut
                 def selectChannelsByIndex(self, channelIndexList):
                     """
                     Selecciona un bloque de datos en base a canales segun el channelIndexList
                     Input:
                         channelIndexList    :    lista sencilla de canales a seleccionar por ej. [2,3,7]
                     Affected:
                         self.dataOut.data
                         self.dataOut.channelIndexList
                         self.dataOut.nChannels
                         self.dataOut.m_ProcessingHeader.totalSpectra
                         self.dataOut.systemHeaderObj.numChannels
                         self.dataOut.m_ProcessingHeader.blockSize
                     Return:
                         None
                     """
                     for channelIndex in channelIndexList:
                         if channelIndex not in self.dataOut.channelIndexList:
                             raise ValueError("The value %d in channelIndexList is not valid" %channelIndex)
                     if self.dataOut.type == 'Voltage':
                         if self.dataOut.flagDataAsBlock:
                             """
                             Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                             """
                             data = self.dataOut.data[channelIndexList,:,:]
                         else:
                             data = self.dataOut.data[channelIndexList,:]
                         self.dataOut.data = data
                         # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
                         self.dataOut.channelList = range(len(channelIndexList))
                     elif self.dataOut.type == 'Spectra':
                         data_spc = self.dataOut.data_spc[channelIndexList, :]
                         data_dc = self.dataOut.data_dc[channelIndexList, :]
                         self.dataOut.data_spc = data_spc
                         self.dataOut.data_dc = data_dc
                         # self.dataOut.channelList = [self.dataOut.channelList[i] for i in channelIndexList]
                         self.dataOut.channelList = range(len(channelIndexList))
                         self.__selectPairsByChannel(channelIndexList)
                     return 1
                 def __selectPairsByChannel(self, channelList=None):
                     if channelList == None:
                         return
                     pairsIndexListSelected = []
                     for pairIndex in self.dataOut.pairsIndexList:
                         # First pair
                         if self.dataOut.pairsList[pairIndex][0] not in channelList:
                             continue
                         # Second pair
                         if self.dataOut.pairsList[pairIndex][1] not in channelList:
                             continue
                         pairsIndexListSelected.append(pairIndex)
                     if not pairsIndexListSelected:
                         self.dataOut.data_cspc = None
                         self.dataOut.pairsList = []
                         return
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndexListSelected]
                     self.dataOut.pairsList = [self.dataOut.pairsList[i]
                                               for i in pairsIndexListSelected]
                     return
             class selectHeights(Operation):
                 def run(self, dataOut, minHei=None, maxHei=None):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de alturas segun el rango
                     minHei <= height <= maxHei
                     Input:
                         minHei    :    valor minimo de altura a considerar
                         maxHei    :    valor maximo de altura a considerar
                     Affected:
                         Indirectamente son cambiados varios valores a travez del metodo selectHeightsByIndex
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     self.dataOut = dataOut
                     if minHei == 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 self.dataOut
                 def selectHeightsByIndex(self, minIndex, maxIndex):
                     """
                     Selecciona un bloque de datos en base a un grupo indices de alturas segun el rango
                     minIndex <= index <= maxIndex
                     Input:
                         minIndex    :    valor de indice minimo de altura a considerar
                         maxIndex    :    valor de indice maximo de altura a considerar
                     Affected:
                         self.dataOut.data
                         self.dataOut.heightList
                     Return:
 si el metodo se ejecuto con exito caso contrario devuelve 0
                     """
                     if self.dataOut.type == 'Voltage':
                         if (minIndex < 0) or (minIndex > maxIndex):
                             raise ValueError("Height index range (%d,%d) is not valid" % (minIndex, maxIndex))
                         if (maxIndex >= self.dataOut.nHeights):
                             maxIndex = self.dataOut.nHeights
                         #voltage
                         if self.dataOut.flagDataAsBlock:
                             """
                             Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                             """
                             data = self.dataOut.data[:,:, minIndex:maxIndex]
                         else:
                             data = self.dataOut.data[:, minIndex:maxIndex]
                         #         firstHeight = self.dataOut.heightList[minIndex]
                         self.dataOut.data = data
                         self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex]
                         if self.dataOut.nHeights <= 1:
                             raise ValueError("selectHeights: Too few heights. Current number of heights is %d" %(self.dataOut.nHeights))
                     elif self.dataOut.type == 'Spectra':
                         if (minIndex < 0) or (minIndex > maxIndex):
                             raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (
                                 minIndex, maxIndex))
                         if (maxIndex >= self.dataOut.nHeights):
                             maxIndex = self.dataOut.nHeights - 1
                         # Spectra
                         data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                         data_cspc = None
                         if self.dataOut.data_cspc is not None:
                             data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                         data_dc = None
                         if self.dataOut.data_dc is not None:
                             data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                         self.dataOut.data_spc = data_spc
                         self.dataOut.data_cspc = data_cspc
                         self.dataOut.data_dc = data_dc
                         self.dataOut.heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     return 1
             class filterByHeights(Operation):
                 def run(self, dataOut, window):
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     if window == None:
                         window = (dataOut.radarControllerHeaderObj.txA/dataOut.radarControllerHeaderObj.nBaud) / deltaHeight
                     newdelta = deltaHeight * window
                     r = dataOut.nHeights % window
                     newheights = (dataOut.nHeights-r)/window
                     if newheights <= 1:
                         raise ValueError("filterByHeights: Too few heights. Current number of heights is %d and window is %d" %(dataOut.nHeights, window))
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es obtenida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         buffer = dataOut.data[:, :, 0:int(dataOut.nHeights-r)]
                         buffer = buffer.reshape(dataOut.nChannels, dataOut.nProfiles, int(dataOut.nHeights/window), window)
                         buffer = numpy.sum(buffer,3)
                     else:
                         buffer = dataOut.data[:,0:int(dataOut.nHeights-r)]
                         buffer = buffer.reshape(dataOut.nChannels,int(dataOut.nHeights/window),int(window))
                         buffer = numpy.sum(buffer,2)
                     dataOut.data = buffer
                     dataOut.heightList = dataOut.heightList[0] + numpy.arange( newheights )*newdelta
                     dataOut.windowOfFilter = window
                     return dataOut
             class setH0(Operation):
                 def run(self, dataOut, h0, deltaHeight = None):
                     if not deltaHeight:
                         deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     nHeights = dataOut.nHeights
                     newHeiRange = h0 + numpy.arange(nHeights)*deltaHeight
                     dataOut.heightList = newHeiRange
                     return dataOut
             class deFlip(Operation):
                 def run(self, dataOut, channelList = []):
                     data = dataOut.data.copy()
                     if dataOut.flagDataAsBlock:
                         flip = self.flip
                         profileList = list(range(dataOut.nProfiles))
                         if not channelList:
                             for thisProfile in profileList:
                                 data[:,thisProfile,:] = data[:,thisProfile,:]*flip
                                 flip *= -1.0
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in dataOut.channelList:
                                     continue
                                 for thisProfile in profileList:
                                     data[thisChannel,thisProfile,:] = data[thisChannel,thisProfile,:]*flip
                                     flip *= -1.0
                         self.flip = flip
                     else:
                         if not channelList:
                             data[:,:] = data[:,:]*self.flip
                         else:
                             for thisChannel in channelList:
                                 if thisChannel not in dataOut.channelList:
                                     continue
                                 data[thisChannel,:] = data[thisChannel,:]*self.flip
                         self.flip *= -1.
                     dataOut.data = data
                     return dataOut
             class setAttribute(Operation):
                 '''
                 Set an arbitrary attribute(s) to dataOut
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self._ready = False
                 def run(self, dataOut, **kwargs):
                     for key, value in kwargs.items():
                         setattr(dataOut, key, value)
                     return dataOut
             @MPDecorator
             class printAttribute(Operation):
                 '''
                 Print an arbitrary attribute of dataOut
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                 def run(self, dataOut, attributes):
                     for attr in attributes:
                         if hasattr(dataOut, attr):
                             log.log(getattr(dataOut, attr), attr)
             class interpolateHeights(Operation):
                 def run(self, dataOut, topLim, botLim):
                     #69 al 72 para julia
                     #82-84 para meteoros
                     if len(numpy.shape(dataOut.data))==2:
                         sampInterp = (dataOut.data[:,botLim-1] + dataOut.data[:,topLim+1])/2
                         sampInterp = numpy.transpose(numpy.tile(sampInterp,(topLim-botLim + 1,1)))
                         #dataOut.data[:,botLim:limSup+1] = sampInterp
                         dataOut.data[:,botLim:topLim+1] = sampInterp
                     else:
                         nHeights = dataOut.data.shape[2]
                         x = numpy.hstack((numpy.arange(botLim),numpy.arange(topLim+1,nHeights)))
                         y = dataOut.data[:,:,list(range(botLim))+list(range(topLim+1,nHeights))]
                         f = interpolate.interp1d(x, y, axis = 2)
                         xnew = numpy.arange(botLim,topLim+1)
                         ynew = f(xnew)
                         dataOut.data[:,:,botLim:topLim+1]  = ynew
                     return dataOut
             class CohInt(Operation):
                 isConfig = False
                 __profIndex = 0
                 __byTime = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __bufferStride = []
                 __dataReady = False
                 __profIndexStride = 0
                 __dataToPutStride = False
                 n = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def setup(self, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False):
                     """
                     Set the parameters of the integration class.
                     Inputs:
                         n               :    Number of coherent integrations
                         timeInterval    :    Time of integration. If the parameter "n" is selected this one does not work
                         overlapping     :
                     """
                     self.__initime = None
                     self.__lastdatatime = 0
                     self.__buffer = None
                     self.__dataReady = False
                     self.byblock = byblock
                     self.stride = stride
                     if n == None and timeInterval == None:
                         raise ValueError("n or timeInterval should be specified ...")
                     if n != None:
                         self.n = n
                         self.__byTime = False
                     else:
                         self.__integrationtime = timeInterval #* 60. #if (type(timeInterval)!=integer) -> change this line
                         self.n = 9999
                         self.__byTime = True
                     if overlapping:
                         self.__withOverlapping = True
                         self.__buffer = None
                     else:
                         self.__withOverlapping = False
                         self.__buffer = 0
                     self.__profIndex = 0
                 def putData(self, data):
                     """
                     Add a profile to the __buffer and increase in one the __profileIndex
                     """
                     if not self.__withOverlapping:
                         self.__buffer += data.copy()
                         self.__profIndex += 1
                         return
                     #Overlapping data
                     nChannels, nHeis = data.shape
                     data = numpy.reshape(data, (1, nChannels, nHeis))
                     #If the buffer is empty then it takes the data value
                     if self.__buffer is None:
                         self.__buffer = data
                         self.__profIndex += 1
                         return
                     #If the buffer length is lower than n then stakcing the data value
                     if self.__profIndex < self.n:
                         self.__buffer = numpy.vstack((self.__buffer, data))
                         self.__profIndex += 1
                         return
                     #If the buffer length is equal to n then replacing the last buffer value with the data value
                     self.__buffer = numpy.roll(self.__buffer, -1, axis=0)
                     self.__buffer[self.n-1] = data
                     self.__profIndex = self.n
                     return
                 def pushData(self):
                     """
                     Return the sum of the last profiles and the profiles used in the sum.
                     Affected:
                     self.__profileIndex
                     """
                     if not self.__withOverlapping:
                         data = self.__buffer
                         n = self.__profIndex
                         self.__buffer = 0
                         self.__profIndex = 0
                         return data, n
                     #Integration with Overlapping
                     data = numpy.sum(self.__buffer, axis=0)
                     # print data
                     # raise
                     n = self.__profIndex
                     return data, n
                 def byProfiles(self, data):
                     self.__dataReady = False
                     avgdata = None
                     #         n = None
                     # print data
                     # raise
                     self.putData(data)
                     if self.__profIndex == self.n:
                         avgdata, n = self.pushData()
                         self.__dataReady = True
                     return avgdata
                 def byTime(self, data, datatime):
                     self.__dataReady = False
                     avgdata = None
                     n = None
                     self.putData(data)
                     if (datatime - self.__initime) >= self.__integrationtime:
                         avgdata, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata
                 def integrateByStride(self, data, datatime):
                     # print data
                     if self.__profIndex == 0:
                         self.__buffer = [[data.copy(), datatime]]
                     else:
                         self.__buffer.append([data.copy(),datatime])
                     self.__profIndex += 1
                     self.__dataReady = False
                     if self.__profIndex == self.n * self.stride :
                         self.__dataToPutStride = True
                         self.__profIndexStride = 0
                         self.__profIndex = 0
                         self.__bufferStride = []
                         for i in range(self.stride):
                             current = self.__buffer[i::self.stride]
                             data = numpy.sum([t[0] for t in current], axis=0)
                             avgdatatime = numpy.average([t[1] for t in current])
                             # print data
                             self.__bufferStride.append((data, avgdatatime))
                     if self.__dataToPutStride:
                         self.__dataReady = True
                         self.__profIndexStride += 1
                         if self.__profIndexStride == self.stride:
                             self.__dataToPutStride = False
                         # print self.__bufferStride[self.__profIndexStride - 1]
                         # raise
                         return self.__bufferStride[self.__profIndexStride - 1]
                     return None, None
                 def integrate(self, data, datatime=None):
                     if self.__initime == None:
                         self.__initime = datatime
                     if self.__byTime:
                         avgdata = self.byTime(data, datatime)
                     else:
                         avgdata = self.byProfiles(data)
                     self.__lastdatatime = datatime
                     if avgdata is None:
                         return None, None
                     avgdatatime = self.__initime
                     deltatime = datatime - self.__lastdatatime
                     if not self.__withOverlapping:
                         self.__initime = datatime
                     else:
                         self.__initime += deltatime
                     return avgdata, avgdatatime
                 def integrateByBlock(self, dataOut):
                     times = int(dataOut.data.shape[1]/self.n)
                     avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
                     id_min = 0
                     id_max = self.n
                     for i in range(times):
                         junk = dataOut.data[:,id_min:id_max,:]
                         avgdata[:,i,:] = junk.sum(axis=1)
                         id_min += self.n
                         id_max += self.n
                     timeInterval = dataOut.ippSeconds*self.n
                     avgdatatime = (times - 1) * timeInterval + dataOut.utctime
                     self.__dataReady = True
                     return avgdata, avgdatatime
                 def run(self, dataOut, n=None, timeInterval=None, stride=None, overlapping=False, byblock=False, **kwargs):
                     if not self.isConfig:
                         self.setup(n=n, stride=stride, timeInterval=timeInterval, overlapping=overlapping, byblock=byblock, **kwargs)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         """
                         Si la data es leida por bloques, dimension = [nChannels, nProfiles, nHeis]
                         """
                         avgdata, avgdatatime = self.integrateByBlock(dataOut)
                         dataOut.nProfiles /= self.n
                     else:
                         if stride is None:
                             avgdata, avgdatatime = self.integrate(dataOut.data, dataOut.utctime)
                         else:
                             avgdata, avgdatatime = self.integrateByStride(dataOut.data, dataOut.utctime)
                     #   dataOut.timeInterval *= n
                     dataOut.flagNoData = True
                     if self.__dataReady:
                         dataOut.data = avgdata
                         if not dataOut.flagCohInt:
                             dataOut.nCohInt *= self.n
                             dataOut.flagCohInt = True
                         dataOut.utctime = avgdatatime
                         # print avgdata, avgdatatime
                         # raise
                         #   dataOut.timeInterval = dataOut.ippSeconds * dataOut.nCohInt
                         dataOut.flagNoData = False
                     return dataOut
             class Decoder(Operation):
                 isConfig = False
                 __profIndex = 0
                 code = None
                 nCode = None
                 nBaud = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.times = None
                     self.osamp = None
                 #         self.__setValues = False
                     self.isConfig = False
                     self.setupReq = False
                 def setup(self, code, osamp, dataOut):
                     self.__profIndex = 0
                     self.code = code
                     self.nCode = len(code)
                     self.nBaud = len(code[0])
                     if (osamp != None) and (osamp >1):
                         self.osamp = osamp
                         self.code = numpy.repeat(code, repeats=self.osamp, axis=1)
                         self.nBaud = self.nBaud*self.osamp
                     self.__nChannels = dataOut.nChannels
                     self.__nProfiles = dataOut.nProfiles
                     self.__nHeis = dataOut.nHeights
                     if self.__nHeis < self.nBaud:
                         raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
                     #Frequency
                     __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
                     __codeBuffer[:,0:self.nBaud] = self.code
                     self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
                     if dataOut.flagDataAsBlock:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
                     else:
                         #Time
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
                         self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
                 def __convolutionInFreq(self, data):
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=1)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=1)
                     return data
                 def __convolutionInFreqOpt(self, data):
                     raise NotImplementedError
                 def __convolutionInTime(self, data):
                     code = self.code[self.__profIndex]
                     for i in range(self.__nChannels):
                         self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInTime(self, data):
                     repetitions = int(self.__nProfiles / self.nCode)
                     junk = numpy.lib.stride_tricks.as_strided(self.code, (repetitions, self.code.size), (0, self.code.itemsize))
                     junk = junk.flatten()
                     code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud))
                     profilesList = range(self.__nProfiles)
                     for i in range(self.__nChannels):
                         for j in profilesList:
                             self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:]
                     return self.datadecTime
                 def __convolutionByBlockInFreq(self, data):
                     raise NotImplementedError("Decoder by frequency fro Blocks not implemented")
                     fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
                     fft_data = numpy.fft.fft(data, axis=2)
                     conv = fft_data*fft_code
                     data = numpy.fft.ifft(conv,axis=2)
                     return data
                 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0, osamp=None, times=None):
                     if dataOut.flagDecodeData:
                         print("This data is already decoded, recoding again ...")
                     if not self.isConfig:
                         if code is None:
                             if dataOut.code is None:
                                 raise ValueError("Code could not be read from %s instance. Enter a value in Code parameter" %dataOut.type)
                             code = dataOut.code
                         else:
                             code = numpy.array(code).reshape(nCode,nBaud)
                         self.setup(code, osamp, dataOut)
                         self.isConfig = True
                         if mode == 3:
                             sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
                         if times != None:
                             sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
                     if self.code is None:
                         print("Fail decoding: Code is not defined.")
                         return
                     self.__nProfiles = dataOut.nProfiles
                     datadec = None
                     if mode == 3:
                         mode = 0
                     if dataOut.flagDataAsBlock:
                         """
                         Decoding when data have been read as block,
                         """
                         if mode == 0:
                             datadec = self.__convolutionByBlockInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionByBlockInFreq(dataOut.data)
                     else:
                         """
                         Decoding when data have been read profile by profile
                         """
                         if mode == 0:
                             datadec = self.__convolutionInTime(dataOut.data)
                         if mode == 1:
                             datadec = self.__convolutionInFreq(dataOut.data)
                         if mode == 2:
                             datadec = self.__convolutionInFreqOpt(dataOut.data)
                     if datadec is None:
                         raise ValueError("Codification mode selected is not valid: mode=%d. Try selecting 0 or 1" %mode)
                     dataOut.code = self.code
                     dataOut.nCode = self.nCode
                     dataOut.nBaud = self.nBaud
                     dataOut.data = datadec
                     dataOut.heightList = dataOut.heightList[0:datadec.shape[-1]]
                     dataOut.flagDecodeData = True #asumo q la data esta decodificada
                     if self.__profIndex == self.nCode-1:
                         self.__profIndex = 0
                         return dataOut
                     self.__profIndex += 1
                     return dataOut
                 #        dataOut.flagDeflipData = True #asumo q la data no esta sin flip
             class ProfileConcat(Operation):
                 isConfig = False
                 buffer = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def reset(self):
                     self.buffer = numpy.zeros_like(self.buffer)
                     self.start_index = 0
                     self.times = 1
                 def setup(self, data, m, n=1):
                     self.buffer = numpy.zeros((data.shape[0],data.shape[1]*m),dtype=type(data[0,0]))
                     self.nHeights = data.shape[1]#.nHeights
                     self.start_index = 0
                     self.times = 1
                 def concat(self, data):
                     self.buffer[:,self.start_index:self.nHeights*self.times] = data.copy()
                     self.start_index = self.start_index + self.nHeights
                 def run(self, dataOut, m):
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         self.setup(dataOut.data, m, 1)
                         self.isConfig = True
                     if dataOut.flagDataAsBlock:
                         raise ValueError("ProfileConcat can only be used when voltage have been read profile by profile, getBlock = False")
                     else:
                         self.concat(dataOut.data)
                         self.times += 1
                         if self.times > m:
                             dataOut.data = self.buffer
                             self.reset()
                             dataOut.flagNoData = False
                             # se deben actualizar mas propiedades del header y del objeto dataOut, por ejemplo, las alturas
                             deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                             xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * m
                             dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
                             dataOut.ippSeconds *= m
                     return dataOut
             class ProfileSelector(Operation):
                 profileIndex = None
                 # Tamanho total de los perfiles
                 nProfiles = None
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.profileIndex = 0
                 def incProfileIndex(self):
                     self.profileIndex += 1
                     if self.profileIndex >= self.nProfiles:
                         self.profileIndex = 0
                 def isThisProfileInRange(self, profileIndex, minIndex, maxIndex):
                     if profileIndex < minIndex:
                         return False
                     if profileIndex > maxIndex:
                         return False
                     return True
                 def isThisProfileInList(self, profileIndex, profileList):
                     if profileIndex not in profileList:
                         return False
                     return True
                 def run(self, dataOut, profileList=None, profileRangeList=None, beam=None, byblock=False, rangeList = None, nProfiles=None):
                     """
                     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")
             class Reshaper(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__buffer = None
                     self.__nitems = 0
                 def __appendProfile(self, dataOut, nTxs):
                     if self.__buffer is None:
                         shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
                         self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
                     ini = dataOut.nHeights * self.__nitems
                     end = ini + dataOut.nHeights
                     self.__buffer[:, ini:end] = dataOut.data
                     self.__nitems += 1
                     return int(self.__nitems*nTxs)
                 def __getBuffer(self):
                     if self.__nitems == int(1./self.__nTxs):
                         self.__nitems = 0
                         return self.__buffer.copy()
                     return None
                 def __checkInputs(self, dataOut, shape, nTxs):
                     if shape is None and nTxs is None:
                         raise ValueError("Reshaper: shape of factor should be defined")
                     if nTxs:
                         if nTxs < 0:
                             raise ValueError("nTxs should be greater than 0")
                         if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
                             raise ValueError("nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs)))
                         shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
                         return shape, nTxs
                     if len(shape) != 2 and len(shape) !=  3:
                         raise ValueError("shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights))
                     if len(shape) == 2:
                         shape_tuple = [dataOut.nChannels]
                         shape_tuple.extend(shape)
                     else:
                         shape_tuple = list(shape)
                     nTxs = 1.0*shape_tuple[1]/dataOut.nProfiles
                     return shape_tuple, nTxs
                 def run(self, dataOut, shape=None, nTxs=None):
                     shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*self.__nTxs) - 1
                     else:
                         if self.__nTxs < 1:
                             self.__appendProfile(dataOut, self.__nTxs)
                             new_data = self.__getBuffer()
                             if new_data is not None:
                                 dataOut.data = new_data
                                 dataOut.flagNoData = False
                                 profileIndex = dataOut.profileIndex*nTxs
                         else:
                             raise ValueError("nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)")
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= self.__nTxs
                     return dataOut
             class SplitProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         if shape[2] % n != 0:
                             raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[2]))
                         new_shape = shape[0], shape[1]*n, int(shape[2]/n)
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles/n) - 1
                     else:
                         raise ValueError("Could not split the data when is read Profile by Profile. Use VoltageReader(..., getblock=True)")
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights/n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles*n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds /= n
                     return dataOut
             class CombineProfiles(Operation):
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                     self.__remData = None
                     self.__profileIndex = 0
                 def run(self, dataOut, n):
                     dataOut.flagNoData = True
                     profileIndex = None
                     if dataOut.flagDataAsBlock:
                         #nchannels, nprofiles, nsamples
                         shape = dataOut.data.shape
                         new_shape = shape[0], shape[1]/n, shape[2]*n
                         if shape[1] % n != 0:
                             raise ValueError("Could not split the data, n=%d has to be multiple of %d" %(n, shape[1]))
                         dataOut.data = numpy.reshape(dataOut.data, new_shape)
                         dataOut.flagNoData = False
                         profileIndex = int(dataOut.nProfiles*n) - 1
                     else:
                         #nchannels, nsamples
                         if self.__remData is None:
                             newData = dataOut.data
                         else:
                             newData = numpy.concatenate((self.__remData, dataOut.data), axis=1)
                         self.__profileIndex += 1
                         if self.__profileIndex < n:
                             self.__remData = newData
                             #continue
                             return
                         self.__profileIndex = 0
                         self.__remData = None
                         dataOut.data = newData
                         dataOut.flagNoData = False
                         profileIndex = dataOut.profileIndex/n
                     deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
                     dataOut.heightList = numpy.arange(dataOut.nHeights*n) * deltaHeight + dataOut.heightList[0]
                     dataOut.nProfiles = int(dataOut.nProfiles/n)
                     dataOut.profileIndex = profileIndex
                     dataOut.ippSeconds *= n
                     return dataOut
             class PulsePairVoltage(Operation):
                 '''
                 Function PulsePair(Signal Power, Velocity)
                 The real component of Lag[0] provides Intensity Information
                 The imag component of Lag[1] Phase provides Velocity Information
                 Configuration Parameters:
                 nPRF = Number of Several PRF
                 theta = Degree Azimuth angel Boundaries
                 Input:
                       self.dataOut
                       lag[N]
                 Affected:
                       self.dataOut.spc
                 '''
                 isConfig       = False
                 __profIndex    = 0
                 __initime      = None
                 __lastdatatime = None
                 __buffer       = None
                 noise          = None
                 __dataReady    = False
                 n              = None
                 __nch          = 0
                 __nHeis        = 0
                 removeDC       = False
                 ipp            = None
                 lambda_        = 0
                 def __init__(self,**kwargs):
                     Operation.__init__(self,**kwargs)
                 def setup(self, dataOut, n = None, removeDC=False):
                     '''
                     n= Numero de PRF's de entrada
                     '''
                     self.__initime        = None
                     self.__lastdatatime   = 0
                     self.__dataReady      = False
                     self.__buffer         = 0
                     self.__profIndex      = 0
                     self.noise            = None
                     self.__nch            = dataOut.nChannels
                     self.__nHeis          = dataOut.nHeights
                     self.removeDC         = removeDC
                     self.lambda_          = 3.0e8/(9345.0e6)
                     self.ippSec           = dataOut.ippSeconds
                     self.nCohInt          = dataOut.nCohInt
                     print("IPPseconds",dataOut.ippSeconds)
                     print("ELVALOR DE n es:", n)
                     if n == None:
                         raise ValueError("n should be specified.")
                     if n != None:
                         if n<2:
                             raise ValueError("n should be greater than 2")
                     self.n       = n
                     self.__nProf = n
                     self.__buffer = numpy.zeros((dataOut.nChannels,
                                                        n,
                                                        dataOut.nHeights),
                                                       dtype='complex')
-                    #self.noise  = numpy.zeros([self.__nch,self.__nHeis])
-                    #for i in range(self.__nch):
-                    #    self.noise[i]=dataOut.getNoise(channel=i)
                 def putData(self,data):
                     '''
                     Add a profile to he __buffer and increase in one the __profiel Index
                     '''
                     self.__buffer[:,self.__profIndex,:]= data
                     self.__profIndex      += 1
                     return
                 def pushData(self,dataOut):
                     '''
                     Return the PULSEPAIR and the profiles used in the operation
                     Affected :  self.__profileIndex
                     '''
+                    #················· Remove DC···································
                     if self.removeDC==True:
                         mean    = numpy.mean(self.__buffer,1)
                         tmp     = mean.reshape(self.__nch,1,self.__nHeis)
                         dc= numpy.tile(tmp,[1,self.__nProf,1])
                         self.__buffer = self.__buffer -  dc
+                    #··················Calculo de Potencia ························
+                    pair0       = self.__buffer*numpy.conj(self.__buffer)
+                    pair0       = pair0.real
+                    lag_0       = numpy.sum(pair0,1)
+                    #··················Calculo de Ruido x canal····················
+                    self.noise  = numpy.zeros(self.__nch)
+                    for i in range(self.__nch):
+                        daux         = numpy.sort(pair0[i,:,:],axis= None)
+                        self.noise[i]=hildebrand_sekhon( daux ,self.nCohInt)
+                    self.noise       = self.noise.reshape(self.__nch,1)
+                    self.noise       = numpy.tile(self.noise,[1,self.__nHeis])
+                    noise_buffer     = self.noise.reshape(self.__nch,1,self.__nHeis)
+                    noise_buffer     = numpy.tile(noise_buffer,[1,self.__nProf,1])
+                    #·················· Potencia recibida= P , Potencia senal = S , Ruido= N··
+                    #··················   P= S+N  ,P=lag_0/N ·································
+                    #···················· Power ··················································
+                    data_power       = lag_0/(self.n*self.nCohInt)
+                    #------------------  Senal  ···················································
+                    data_intensity   = pair0 - noise_buffer
+                    data_intensity   = numpy.sum(data_intensity,axis=1)*(self.n*self.nCohInt)#*self.nCohInt)
+                    #data_intensity   = (lag_0-self.noise*self.n)*(self.n*self.nCohInt)
+                    for i in range(self.__nch):
+                        for j in range(self.__nHeis):
+                            if data_intensity[i][j]  < 0:
+                                data_intensity[i][j] = numpy.min(numpy.absolute(data_intensity[i][j]))
-                    lag_0            = numpy.sum(self.__buffer*numpy.conj(self.__buffer),1)
+                    #················· Calculo de Frecuencia y Velocidad doppler········
-                    data_intensity   = lag_0/(self.n*self.nCohInt)#*self.nCohInt)
                     pair1            = self.__buffer[:,:-1,:]*numpy.conjugate(self.__buffer[:,1:,:])
                     lag_1            = numpy.sum(pair1,1)
-                    #angle            = numpy.angle(numpy.sum(pair1,1))*180/(math.pi)
+                    data_freq        = (-1/(2.0*math.pi*self.ippSec*self.nCohInt))*numpy.angle(lag_1)
-                    data_velocity    = (-1.0*self.lambda_/(4*math.pi*self.ippSec))*numpy.angle(lag_1)#self.ippSec*self.nCohInt
+                    data_velocity    = (self.lambda_/2.0)*data_freq
-                    self.noise  = numpy.zeros([self.__nch,self.__nHeis])
+                    #················ Potencia promedio estimada de la Senal···········
-                    for i in range(self.__nch):
+                    lag_0            = lag_0/self.n
-                        self.noise[i]=dataOut.getNoise(channel=i)
+                    S                = lag_0-self.noise
-                    lag_0            = lag_0.real/(self.n)
+                    #················ Frecuencia Doppler promedio ·····················
                     lag_1            = lag_1/(self.n-1)
                     R1               = numpy.abs(lag_1)
-                    S                = (lag_0-self.noise)
+                    #················ Calculo del SNR··································
                     data_snrPP       = S/self.noise
-                    data_snrPP       = numpy.where(data_snrPP<0,1,data_snrPP)
+                    for i in range(self.__nch):
+                        for j in range(self.__nHeis):
+                            if data_snrPP[i][j]  < 1.e-20:
+                                data_snrPP[i][j] = 1.e-20
+                    #················· Calculo del ancho espectral ······················
                     L                = S/R1
                     L                = numpy.where(L<0,1,L)
                     L                = numpy.log(L)
                     tmp              = numpy.sqrt(numpy.absolute(L))
+                    data_specwidth   = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec*self.nCohInt))*tmp*numpy.sign(L)
-                    data_specwidth   = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec))*tmp*numpy.sign(L)
-                    #data_specwidth   = (self.lambda_/(2*math.sqrt(2)*math.pi*self.ippSec))*k
                     n                = self.__profIndex
                     self.__buffer    = numpy.zeros((self.__nch, self.__nProf,self.__nHeis),  dtype='complex')
                     self.__profIndex = 0
-                    return data_intensity,data_velocity,data_snrPP,data_specwidth,n
+                    return data_power,data_intensity,data_velocity,data_snrPP,data_specwidth,n
                 def pulsePairbyProfiles(self,dataOut):
                     self.__dataReady     =  False
+                    data_power           =  None
                     data_intensity       =  None
                     data_velocity        =  None
                     data_specwidth       =  None
                     data_snrPP           =  None
                     self.putData(data=dataOut.data)
                     if self.__profIndex  == self.n:
-                        #self.noise  = numpy.zeros([self.__nch,self.__nHeis])
+                        data_power,data_intensity, data_velocity,data_snrPP,data_specwidth, n   = self.pushData(dataOut=dataOut)
-                        #for i in range(self.__nch):
-                        #    self.noise[i]=data.getNoise(channel=i)
-                        #print(self.noise.shape)
-                        data_intensity, data_velocity,data_snrPP,data_specwidth, n   = self.pushData(dataOut=dataOut)
                         self.__dataReady                   = True
-                    return data_intensity, data_velocity,data_snrPP,data_specwidth
+                    return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth
                 def pulsePairOp(self, dataOut, datatime= None):
                     if self.__initime == None:
                         self.__initime = datatime
-                    #print("hola")
+                    data_power, data_intensity, data_velocity, data_snrPP, data_specwidth = self.pulsePairbyProfiles(dataOut)
-                    data_intensity, data_velocity,data_snrPP,data_specwidth = self.pulsePairbyProfiles(dataOut)
                     self.__lastdatatime           = datatime
-                    if data_intensity is None:
+                    if data_power is None:
-                        return None, None,None,None,None
+                        return None, None, None,None,None,None
                     avgdatatime    = self.__initime
                     deltatime      = datatime - self.__lastdatatime
                     self.__initime = datatime
-                    return data_intensity, data_velocity,data_snrPP,data_specwidth,avgdatatime
+                    return data_power, data_intensity, data_velocity, data_snrPP, data_specwidth, avgdatatime
                 def run(self, dataOut,n = None,removeDC= False, overlapping= False,**kwargs):
                     if not self.isConfig:
                         self.setup(dataOut = dataOut, n    = n , removeDC=removeDC , **kwargs)
                         self.isConfig   = True
-                    data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime)
+                    data_power, data_intensity, data_velocity,data_snrPP,data_specwidth, avgdatatime = self.pulsePairOp(dataOut, dataOut.utctime)
                     dataOut.flagNoData                         = True
                     if self.__dataReady:
                         dataOut.nCohInt        *= self.n
-                        dataOut.data_intensity  = data_intensity #valor para intensidad
+                        dataOut.dataPP_POW      = data_intensity # S
-                        dataOut.data_velocity   = data_velocity  #valor para velocidad
+                        dataOut.dataPP_POWER    = data_power     # P
-                        dataOut.data_snrPP      = data_snrPP     # valor para snr
+                        dataOut.dataPP_DOP      = data_velocity
-                        dataOut.data_specwidth  = data_specwidth
+                        dataOut.dataPP_SNR      = data_snrPP
+                        dataOut.dataPP_WIDTH    = data_specwidth
                         dataOut.PRFbyAngle      = self.n         #numero de PRF*cada angulo rotado que equivale a un tiempo.
                         dataOut.utctime         = avgdatatime
                         dataOut.flagNoData      = False
                     return dataOut
             # import collections
             # from scipy.stats import mode
             #
             # class Synchronize(Operation):
             #
             #     isConfig = False
             #     __profIndex = 0
             #
             #     def __init__(self, **kwargs):
             #
             #         Operation.__init__(self, **kwargs)
             # #         self.isConfig = False
             #         self.__powBuffer = None
             #         self.__startIndex = 0
             #         self.__pulseFound = False
             #
             #     def __findTxPulse(self, dataOut, channel=0, pulse_with = None):
             #
             #         #Read data
             #
             #         powerdB = dataOut.getPower(channel = channel)
             #         noisedB = dataOut.getNoise(channel = channel)[0]
             #
             #         self.__powBuffer.extend(powerdB.flatten())
             #
             #         dataArray = numpy.array(self.__powBuffer)
             #
             #         filteredPower = numpy.correlate(dataArray, dataArray[0:self.__nSamples], "same")
             #
             #         maxValue = numpy.nanmax(filteredPower)
             #
             #         if maxValue < noisedB + 10:
             #             #No se encuentra ningun pulso de transmision
             #             return None
             #
             #         maxValuesIndex = numpy.where(filteredPower > maxValue - 0.1*abs(maxValue))[0]
             #
             #         if len(maxValuesIndex) < 2:
             #             #Solo se encontro un solo pulso de transmision de un baudio, esperando por el siguiente TX
             #             return None
             #
             #         phasedMaxValuesIndex = maxValuesIndex - self.__nSamples
             #
             #         #Seleccionar solo valores con un espaciamiento de nSamples
             #         pulseIndex = numpy.intersect1d(maxValuesIndex, phasedMaxValuesIndex)
             #
             #         if len(pulseIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         spacing = pulseIndex[1:] - pulseIndex[:-1]
             #
             #         #remover senales que se distancien menos de 10 unidades o muestras
             #         #(No deberian existir IPP menor a 10 unidades)
             #
             #         realIndex = numpy.where(spacing > 10 )[0]
             #
             #         if len(realIndex) < 2:
             #             #Solo se encontro un pulso de transmision con ancho mayor a 1
             #             return None
             #
             #         #Eliminar pulsos anchos (deja solo la diferencia entre IPPs)
             #         realPulseIndex = pulseIndex[realIndex]
             #
             #         period = mode(realPulseIndex[1:] - realPulseIndex[:-1])[0][0]
             #
             #         print "IPP = %d samples" %period
             #
             #         self.__newNSamples = dataOut.nHeights #int(period)
             #         self.__startIndex = int(realPulseIndex[0])
             #
             #         return 1
             #
             #
             #     def setup(self, nSamples, nChannels, buffer_size = 4):
             #
             #         self.__powBuffer = collections.deque(numpy.zeros( buffer_size*nSamples,dtype=numpy.float),
             #                                           maxlen = buffer_size*nSamples)
             #
             #         bufferList = []
             #
             #         for i in range(nChannels):
             #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) +  numpy.NAN,
             #                                           maxlen = buffer_size*nSamples)
             #
             #             bufferList.append(bufferByChannel)
             #
             #         self.__nSamples = nSamples
             #         self.__nChannels = nChannels
             #         self.__bufferList = bufferList
             #
             #     def run(self, dataOut, channel = 0):
             #
             #         if not self.isConfig:
             #             nSamples = dataOut.nHeights
             #             nChannels = dataOut.nChannels
             #             self.setup(nSamples, nChannels)
             #             self.isConfig = True
             #
             #         #Append new data to internal buffer
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             bufferByChannel.extend(dataOut.data[thisChannel])
             #
             #         if self.__pulseFound:
             #             self.__startIndex -= self.__nSamples
             #
             #         #Finding Tx Pulse
             #         if not self.__pulseFound:
             #             indexFound = self.__findTxPulse(dataOut, channel)
             #
             #             if indexFound == None:
             #                 dataOut.flagNoData = True
             #                 return
             #
             #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
             #             self.__pulseFound = True
             #             self.__startIndex = indexFound
             #
             #         #If pulse was found ...
             #         for thisChannel in range(self.__nChannels):
             #             bufferByChannel = self.__bufferList[thisChannel]
             #             #print self.__startIndex
             #             x = numpy.array(bufferByChannel)
             #             self.__arrayBuffer[thisChannel] = x[self.__startIndex:self.__startIndex+self.__newNSamples]
             #
             #         deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
             #         dataOut.heightList = numpy.arange(self.__newNSamples)*deltaHeight
             # #             dataOut.ippSeconds = (self.__newNSamples / deltaHeight)/1e6
             #
             #         dataOut.data = self.__arrayBuffer
             #
             #         self.__startIndex += self.__newNSamples
             #
             #         return

schainpy/scripts/test_sim0009.py +29 -5

             import os,sys
             import datetime
             import time
             from schainpy.controller import Project
             path    = '/home/alex/Downloads/NEW_WR2/spc16removeDC'
             figpath = path
             desc            = "Simulator Test"
             controllerObj   = Project()
             controllerObj.setup(id='10',name='Test Simulator',description=desc)
             readUnitConfObj = controllerObj.addReadUnit(datatype='SimulatorReader',
                                                         frequency=9.345e9,
                                                         FixRCP_IPP= 60,
                                                         Tau_0 = 30,
                                                         AcqH0_0=0,
                                                         samples=330,
                                                         AcqDH_0=0.15,
                                                         FixRCP_TXA=0.15,
                                                         FixRCP_TXB=0.15,
                                                         Fdoppler=600.0,
                                                         Hdoppler=36,
                                                         Adoppler=300,#300
                                                         delay=0,
                                                         online=0,
                                                         walk=0,
-                                                        nTotalReadFiles=3)
+                                                        profilesPerBlock=625,
+                                                        dataBlocksPerFile=100)#,#nTotalReadFiles=2)
+            '''
+            readUnitConfObj = controllerObj.addReadUnit(datatype='VoltageReader',
+                                                        path=path,
+                                                        startDate="2020/01/01",   #"2020/01/01",#today,
+                                                        endDate= "2020/12/01",  #"2020/12/30",#today,
+                                                        startTime='00:00:00',
+                                                        endTime='23:59:59',
+                                                        delay=0,
+                                                        #set=0,
+                                                        online=0,
+                                                        walk=1)
+            '''
             opObj11         = readUnitConfObj.addOperation(name='printInfo')
             procUnitConfObjA = controllerObj.addProcUnit(datatype='VoltageProc', inputId=readUnitConfObj.getId())
             #opObj11 = procUnitConfObjA.addOperation(name='CohInt', optype='other')
             #opObj11.addParameter(name='n', value='10', format='int')
             #opObj10 = procUnitConfObjA.addOperation(name='selectChannels')
             #opObj10.addParameter(name='channelList', value=[0])
             opObj11 = procUnitConfObjA.addOperation(name='PulsePairVoltage', optype='other')
-            opObj11.addParameter(name='n', value='300', format='int')#10
+            opObj11.addParameter(name='n', value='625', format='int')#10
             opObj11.addParameter(name='removeDC', value=1, format='int')
             #opObj11 = procUnitConfObjA.addOperation(name='PulsepairPowerPlot', optype='other')
+            #opObj11 = procUnitConfObjA.addOperation(name='PulsepairSignalPlot', optype='other')
-            opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other')
+            #opObj11 = procUnitConfObjA.addOperation(name='PulsepairVelocityPlot', optype='other')
             #opObj11.addParameter(name='xmax', value=8)
-            opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other')
+            #opObj11 = procUnitConfObjA.addOperation(name='PulsepairSpecwidthPlot', optype='other')
+            procUnitConfObjB= controllerObj.addProcUnit(datatype='ParametersProc',inputId=procUnitConfObjA.getId())
+            opObj10 = procUnitConfObjB.addOperation(name='ParameterWriter')
+            opObj10.addParameter(name='path',value=figpath)
+            #opObj10.addParameter(name='mode',value=0)
+            opObj10.addParameter(name='blocksPerFile',value='100',format='int')
+            opObj10.addParameter(name='metadataList',value='utctimeInit,timeInterval',format='list')
+            opObj10.addParameter(name='dataList',value='dataPP_POW,dataPP_DOP,dataPP_SNR,dataPP_WIDTH')#,format='list'
             controllerObj.start()

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