schain-jc Commit - r1010:0630a39b8282

BLTRParamreader ready

Juan C. Espinoza -

r1010:0630a39b8282

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r1010:0630a39b8282

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schainpy/model/io/bltrIO_param.py created 644 +364 0

@@ -0,0 +1,364
	1	'''
	2	Created on Nov 9, 2016
	3
	4	@author: roj- LouVD
	5	'''
	6
	7
	8	import os
	9	import sys
	10	import time
	11	import glob
	12	import datetime
	13	import numpy
	14
	15	from schainpy.model.proc.jroproc_base import ProcessingUnit
	16	from schainpy.model.data.jrodata import Parameters
	17	from schainpy.model.io.jroIO_base import JRODataReader, isNumber
	18
	19	FILE_HEADER_STRUCTURE = numpy.dtype([
	20	('FMN', '<u4'),
	21	('nrec', '<u4'),
	22	('fr_offset', '<u4'),
	23	('id', '<u4'),
	24	('site', 'u1', (32,))
	25	])
	26
	27	REC_HEADER_STRUCTURE = numpy.dtype([
	28	('rmn', '<u4'),
	29	('rcounter', '<u4'),
	30	('nr_offset', '<u4'),
	31	('tr_offset', '<u4'),
	32	('time', '<u4'),
	33	('time_msec', '<u4'),
	34	('tag', 'u1', (32,)),
	35	('comments', 'u1', (32,)),
	36	('lat', '<f4'),
	37	('lon', '<f4'),
	38	('gps_status', '<u4'),
	39	('freq', '<u4'),
	40	('freq0', '<u4'),
	41	('nchan', '<u4'),
	42	('delta_r', '<u4'),
	43	('nranges', '<u4'),
	44	('r0', '<u4'),
	45	('prf', '<u4'),
	46	('ncoh', '<u4'),
	47	('npoints', '<u4'),
	48	('polarization', '<i4'),
	49	('rx_filter', '<u4'),
	50	('nmodes', '<u4'),
	51	('dmode_index', '<u4'),
	52	('dmode_rngcorr', '<u4'),
	53	('nrxs', '<u4'),
	54	('acf_length', '<u4'),
	55	('acf_lags', '<u4'),
	56	('sea_to_atmos', '<f4'),
	57	('sea_notch', '<u4'),
	58	('lh_sea', '<u4'),
	59	('hh_sea', '<u4'),
	60	('nbins_sea', '<u4'),
	61	('min_snr', '<f4'),
	62	('min_cc', '<f4'),
	63	('max_time_diff', '<f4')
	64	])
	65
	66	DATA_STRUCTURE = numpy.dtype([
	67	('range', '<u4'),
	68	('status', '<u4'),
	69	('zonal', '<f4'),
	70	('meridional', '<f4'),
	71	('vertical', '<f4'),
	72	('zonal_a', '<f4'),
	73	('meridional_a', '<f4'),
	74	('corrected_fading', '<f4'), # seconds
	75	('uncorrected_fading', '<f4'), # seconds
	76	('time_diff', '<f4'),
	77	('major_axis', '<f4'),
	78	('axial_ratio', '<f4'),
	79	('orientation', '<f4'),
	80	('sea_power', '<u4'),
	81	('sea_algorithm', '<u4')
	82	])
	83
	84	class BLTRParamReader(JRODataReader, ProcessingUnit):
	85	'''
	86	Boundary Layer and Tropospheric Radar (BLTR) reader, Wind velocities and SNR from *.sswma files
	87	'''
	88
	89	ext = '.sswma'
	90
	91	def __init__(self, **kwargs):
	92
	93	ProcessingUnit.__init__(self , **kwargs)
	94
	95	self.dataOut = Parameters()
	96	self.counter_records = 0
	97	self.flagNoMoreFiles = 0
	98	self.isConfig = False
	99	self.filename = None
	100
	101	def setup(self,
	102	path=None,
	103	startDate=None,
	104	endDate=None,
	105	ext=None,
	106	startTime=datetime.time(0, 0, 0),
	107	endTime=datetime.time(23, 59, 59),
	108	timezone=0,
	109	status_value=0,
	110	**kwargs):
	111
	112	self.path = path
	113	self.startTime = startTime
	114	self.endTime = endTime
	115	self.status_value = status_value
	116
	117	if self.path is None:
	118	raise ValueError, "The path is not valid"
	119
	120	if ext is None:
	121	ext = self.ext
	122
	123	self.search_files(self.path, startDate, endDate, ext)
	124	self.timezone = timezone
	125	self.fileIndex = 0
	126
	127	if not self.fileList:
	128	raise Warning, "There is no files matching these date in the folder: %s. \n Check 'startDate' and 'endDate' "%(path)
	129
	130	self.setNextFile()
	131
	132	def search_files(self, path, startDate, endDate, ext):
	133	'''
	134	Searching for BLTR rawdata file in path
	135	Creating a list of file to proces included in [startDate,endDate]
	136
	137	Input:
	138	path - Path to find BLTR rawdata files
	139	startDate - Select file from this date
	140	enDate - Select file until this date
	141	ext - Extension of the file to read
	142
	143	'''
	144
	145	print 'Searching file in %s ' % (path)
	146	foldercounter = 0
	147	fileList0 = glob.glob1(path, "*%s" % ext)
	148	fileList0.sort()
	149
	150	self.fileList = []
	151	self.dateFileList = []
	152
	153	for thisFile in fileList0:
	154	year = thisFile[-14:-10]
	155	if not isNumber(year):
	156	continue
	157
	158	month = thisFile[-10:-8]
	159	if not isNumber(month):
	160	continue
	161
	162	day = thisFile[-8:-6]
	163	if not isNumber(day):
	164	continue
	165
	166	year, month, day = int(year), int(month), int(day)
	167	dateFile = datetime.date(year, month, day)
	168
	169	if (startDate > dateFile) or (endDate < dateFile):
	170	continue
	171
	172	self.fileList.append(thisFile)
	173	self.dateFileList.append(dateFile)
	174
	175	return
	176
	177	def setNextFile(self):
	178
	179	file_id = self.fileIndex
	180
	181	if file_id == len(self.fileList):
	182	print '\nNo more files in the folder'
	183	print 'Total number of file(s) read : {}'.format(self.fileIndex + 1)
	184	self.flagNoMoreFiles = 1
	185	return 0
	186
	187	print '\n[Setting file] (%s) ...' % self.fileList[file_id]
	188	filename = os.path.join(self.path, self.fileList[file_id])
	189
	190	dirname, name = os.path.split(filename)
	191	self.siteFile = name.split('.')[0] # 'peru2' ---> Piura - 'peru1' ---> Huancayo or Porcuya
	192	if self.filename is not None:
	193	self.fp.close()
	194	self.filename = filename
	195	self.fp = open(self.filename, 'rb')
	196	self.header_file = numpy.fromfile(self.fp, FILE_HEADER_STRUCTURE, 1)
	197	self.nrecords = self.header_file['nrec'][0]
	198	self.sizeOfFile = os.path.getsize(self.filename)
	199	self.counter_records = 0
	200	self.flagIsNewFile = 0
	201	self.fileIndex += 1
	202
	203	return 1
	204
	205	def readNextBlock(self):
	206
	207	while True:
	208	if self.counter_records == self.nrecords:
	209	self.flagIsNewFile = 1
	210	if not self.setNextFile():
	211	return 0
	212
	213	self.readBlock()
	214
	215	if (self.datatime.time() < self.startTime) or (self.datatime.time() > self.endTime):
	216	print "[Reading] Record No. %d/%d -> %s [Skipping]" %(
	217	self.counter_records,
	218	self.nrecords,
	219	self.datatime.ctime())
	220	continue
	221	break
	222
	223	print "[Reading] Record No. %d/%d -> %s" %(
	224	self.counter_records,
	225	self.nrecords,
	226	self.datatime.ctime())
	227
	228	return 1
	229
	230	def readBlock(self):
	231
	232	pointer = self.fp.tell()
	233	header_rec = numpy.fromfile(self.fp, REC_HEADER_STRUCTURE, 1)
	234	self.nchannels = header_rec['nchan'][0]/2
	235	self.kchan = header_rec['nrxs'][0]
	236	self.nmodes = header_rec['nmodes'][0]
	237	self.nranges = header_rec['nranges'][0]
	238	self.fp.seek(pointer)
	239	self.height = numpy.empty((self.nmodes, self.nranges))
	240	self.snr = numpy.empty((self.nmodes, self.nchannels, self.nranges))
	241	self.buffer = numpy.empty((self.nmodes, 3, self.nranges))
	242
	243	for mode in range(self.nmodes):
	244	self.readHeader()
	245	data = self.readData()
	246	self.height[mode] = (data[0] - self.correction) / 1000.
	247	self.buffer[mode] = data[1]
	248	self.snr[mode] = data[2]
	249
	250	self.counter_records = self.counter_records + self.nmodes
	251
	252	return
	253
	254	def readHeader(self):
	255	'''
	256	RecordHeader of BLTR rawdata file
	257	'''
	258
	259	header_structure = numpy.dtype(
	260	REC_HEADER_STRUCTURE.descr + [
	261	('antenna_coord', 'f4', (2, self.nchannels)),
	262	('rx_gains', 'u4', (self.nchannels,)),
	263	('rx_analysis', 'u4', (self.nchannels,))
	264	]
	265	)
	266
	267	self.header_rec = numpy.fromfile(self.fp, header_structure, 1)
	268	self.lat = self.header_rec['lat'][0]
	269	self.lon = self.header_rec['lon'][0]
	270	self.delta = self.header_rec['delta_r'][0]
	271	self.correction = self.header_rec['dmode_rngcorr'][0]
	272	self.imode = self.header_rec['dmode_index'][0]
	273	self.antenna = self.header_rec['antenna_coord']
	274	self.rx_gains = self.header_rec['rx_gains']
	275	self.time1 = self.header_rec['time'][0]
	276	tseconds = self.header_rec['time'][0]
	277	local_t1 = time.localtime(tseconds)
	278	self.year = local_t1.tm_year
	279	self.month = local_t1.tm_mon
	280	self.day = local_t1.tm_mday
	281	self.t = datetime.datetime(self.year, self.month, self.day)
	282	self.datatime = datetime.datetime.utcfromtimestamp(self.time1)
	283
	284	def readData(self):
	285	'''
	286	Reading and filtering data block record of BLTR rawdata file, filtering is according to status_value.
	287
	288	Input:
	289	status_value - Array data is set to NAN for values that are not equal to status_value
	290
	291	'''
	292
	293	data_structure = numpy.dtype(
	294	DATA_STRUCTURE.descr + [
	295	('rx_saturation', 'u4', (self.nchannels,)),
	296	('chan_offset', 'u4', (2 * self.nchannels,)),
	297	('rx_amp', 'u4', (self.nchannels,)),
	298	('rx_snr', 'f4', (self.nchannels,)),
	299	('cross_snr', 'f4', (self.kchan,)),
	300	('sea_power_relative', 'f4', (self.kchan,))]
	301	)
	302
	303	data = numpy.fromfile(self.fp, data_structure, self.nranges)
	304
	305	height = data['range']
	306	winds = numpy.array((data['zonal'], data['meridional'], data['vertical']))
	307	snr = data['rx_snr'].T
	308
	309	winds[numpy.where(winds == -9999.)] = numpy.nan
	310	winds[:, numpy.where(data['status'] != self.status_value)] = numpy.nan
	311	snr[numpy.where(snr == -9999.)] = numpy.nan
	312	snr[:, numpy.where(data['status'] != self.status_value)] = numpy.nan
	313	snr = numpy.power(10, snr / 10)
	314
	315	return height, winds, snr
	316
	317	def set_output(self):
	318	'''
	319	Storing data from databuffer to dataOut object
	320	'''
	321
	322	self.dataOut.time1 = self.time1
	323	self.dataOut.data_SNR = self.snr
	324	self.dataOut.height= self.height
	325	self.dataOut.data_output = self.buffer
	326	self.dataOut.utctimeInit = self.time1
	327	self.dataOut.utctime = self.dataOut.utctimeInit
	328	self.dataOut.counter_records = self.counter_records
	329	self.dataOut.nrecords = self.nrecords
	330	self.dataOut.useLocalTime = False
	331	self.dataOut.paramInterval = 157
	332	self.dataOut.timezone = self.timezone
	333	self.dataOut.site = self.siteFile
	334	self.dataOut.nrecords = self.nrecords
	335	self.dataOut.sizeOfFile = self.sizeOfFile
	336	self.dataOut.lat = self.lat
	337	self.dataOut.lon = self.lon
	338	self.dataOut.channelList = range(self.nchannels)
	339	self.dataOut.kchan = self.kchan
	340	# self.dataOut.nHeights = self.nranges
	341	self.dataOut.delta = self.delta
	342	self.dataOut.correction = self.correction
	343	self.dataOut.nmodes = self.nmodes
	344	self.dataOut.imode = self.imode
	345	self.dataOut.antenna = self.antenna
	346	self.dataOut.rx_gains = self.rx_gains
	347	self.dataOut.flagNoData = False
	348
	349	def getData(self):
	350	'''
	351	Storing data from databuffer to dataOut object
	352	'''
	353	if self.flagNoMoreFiles:
	354	self.dataOut.flagNoData = True
	355	print 'No file left to process'
	356	return 0
	357
	358	if not(self.readNextBlock()):
	359	self.dataOut.flagNoData = True
	360	return 0
	361
	362	self.set_output()
	363
	364	return 1

schainpy/model/io/jroIO_madrigal.py created 644 +375 0

@@ -0,0 +1,375
	1	'''
	2	Created on Aug 1, 2017
	3
	4	@author: Juan C. Espinoza
	5	'''
	6
	7	import os
	8	import sys
	9	import time
	10	import datetime
	11
	12	import numpy
	13
	14	from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
	15	from schainpy.model.data.jrodata import Parameters
	16	from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader
	17	from schainpy.model.graphics.jroplot_parameters import WindProfilerPlot
	18	from schainpy.model.io.jroIO_base import *
	19
	20	try:
	21	import madrigal
	22	import madrigal.cedar
	23	from madrigal.cedar import MadrigalCatalogRecord
	24	except:
	25	print 'You should install "madrigal library" module if you want to read/write Madrigal data'
	26
	27
	28	class MADWriter(Operation):
	29
	30	def __init__(self):
	31
	32	Operation.__init__(self)
	33	self.dataOut = Parameters()
	34	self.path = None
	35	self.dataOut = None
	36	self.flagIsNewFile=1
	37	self.ext = ".hdf5"
	38
	39	return
	40
	41	def run(self, dataOut, path , modetowrite,**kwargs):
	42
	43	if self.flagIsNewFile:
	44	flagdata = self.setup(dataOut, path, modetowrite)
	45
	46	self.putData()
	47	return
	48
	49	def setup(self, dataOut, path, modetowrite):
	50	'''
	51	Recovering data to write in new *.hdf5 file
	52	Inputs:
	53	modew -- mode to write (1 or 2)
	54	path -- destination path
	55
	56	'''
	57
	58	self.im = modetowrite-1
	59	if self.im!=0 and self.im!=1:
	60	raise ValueError, 'Check "modetowrite" value. Must be egual to 1 or 2, "{}" is not valid. '.format(modetowrite)
	61
	62	self.dataOut = dataOut
	63	self.nmodes = self.dataOut.nmodes
	64	self.nchannels = self.dataOut.nchannels
	65	self.lat = self.dataOut.lat
	66	self.lon = self.dataOut.lon
	67	self.hcm = 3
	68	self.thisDate = self.dataOut.utctimeInit
	69	self.year = self.dataOut.year
	70	self.month = self.dataOut.month
	71	self.day = self.dataOut.day
	72	self.path = path
	73
	74	self.flagIsNewFile = 0
	75
	76	return 1
	77
	78	def setFile(self):
	79	'''
	80	- Determining the file name for each mode of operation
	81	kinst - Kind of Instrument (mnemotic)
	82	kindat - Kind of Data (mnemotic)
	83
	84	- Creating a cedarObject
	85
	86	'''
	87	lat_piura = -5.17
	88	lat_huancayo = -12.04
	89	lat_porcuya = -5.8
	90
	91	if '%2.2f' % self.lat == '%2.2f' % lat_piura:
	92	self.instMnemonic = 'pbr'
	93
	94	elif '%2.2f' % self.lat == '%2.2f' % lat_huancayo:
	95	self.instMnemonic = 'hbr'
	96
	97	elif '%2.2f' % self.lat == '%2.2f' % lat_porcuya:
	98	self.instMnemonic = 'obr'
	99	else: raise Warning, "The site of file read doesn't match any site known. Only file from Huancayo, Piura and Porcuya can be processed.\n Check the file "
	100
	101	mode = ['_mode1','_mode2']
	102
	103	self.hdf5filename = '%s%4.4d%2.2d%2.2d%s%s' % (self.instMnemonic,
	104	self.year,
	105	self.month,
	106	self.day,
	107	mode[self.im],
	108	self.ext)
	109
	110	self.fullname=os.path.join(self.path,self.hdf5filename)
	111
	112	if os.path.isfile(self.fullname) :
	113	print "Destination path '%s' already exists. Previous file deleted. " %self.fullname
	114	os.remove(self.fullname)
	115
	116	# Identify kinst and kindat
	117	InstName = self.hdf5filename[0:3]
	118	KinstList = [1000, 1001, 1002]
	119	KinstId = {'pbr':0, 'hbr':1, 'obr':2} # pbr:piura, hbr:huancayo, obr:porcuya
	120	KindatList = [1600, 1601] # mode 1, mode 2
	121	self.type = KinstId[InstName]
	122	self.kinst = KinstList[self.type]
	123	self.kindat = KindatList[self.im]
	124
	125	try:
	126	self.cedarObj = madrigal.cedar.MadrigalCedarFile(self.fullname, True)
	127	except ValueError, message:
	128	print '[Error]: Impossible to create a cedar object with "madrigal.cedar.MadrigalCedarFile" '
	129	return
	130
	131	return 1
	132
	133	def writeBlock(self):
	134	'''
	135	- Selecting mode of operation:
	136
	137	bltr high resolution mode 1 - Low Atmosphere (0 - 3km) // bltr high resolution mode 2 - High Atmosphere (0 - 10km)
	138	msnr - Average Signal Noise Ratio in dB
	139	hcm - 3 km
	140
	141	- Filling the cedarObject by a block: each array data entry is assigned a code that defines the parameter to write to the file
	142
	143	GDLATR - Reference geod latitude (deg)
	144	GDLONR - Reference geographic longitude (deg)
	145	GDLAT2 - Geodetic latitude of second inst (deg)
	146	GLON2 - Geographic longitude of second inst (deg)
	147
	148	GDALT - Geodetic altitude (height) (km)
	149	SNL - Log10 (signal to noise ratio)
	150	VN1P2 - Neutral wind in direction 1 (eastward) (m/s), ie zonal wind
	151	VN2P2 - Neutral wind in direction 2 (northward) (m/s), ie meridional wind
	152	EL2 - Ending elevation angle (deg), ie vertical wind
	153
	154	Other parameters: /madrigal3/metadata/parcodes.tab
	155
	156	'''
	157
	158	self.z_zon = self.dataOut.data_output[0,:,:]
	159	self.z_mer =self.dataOut.data_output[1,:,:]
	160	self.z_ver = self.dataOut.data_output[2,:,:]
	161
	162	if self.im == 0:
	163	h_select = numpy.where(numpy.bitwise_and(self.dataOut.height[0, :] >= 0., self.dataOut.height[0, :] <= self.hcm, numpy.isfinite(self.dataOut.height[0, :])))
	164	else:
	165	h_select = numpy.where(numpy.bitwise_and(self.dataOut.height[0, :] >= 0., self.dataOut.height[0, :] < 20, numpy.isfinite(self.dataOut.height[0, :])))
	166
	167	ht = h_select[0]
	168
	169	self.o_height = self.dataOut.height[self.im, ht]
	170	self.o_zon = self.z_zon[ht, self.im]
	171	self.o_mer = self.z_mer[ht, self.im]
	172	self.o_ver = self.z_ver[ht, self.im]
	173	o_snr = self.dataOut.data_SNR[ :, :, self.im]
	174
	175	o_snr = o_snr[ht, :]
	176
	177	ndiv = numpy.nansum((numpy.isfinite(o_snr)), 1)
	178	ndiv = ndiv.astype(float)
	179
	180	sel_div = numpy.where(ndiv == 0.)
	181	ndiv[sel_div] = numpy.nan
	182
	183	if self.nchannels > 1:
	184	msnr = numpy.nansum(o_snr, axis=1)
	185	else:
	186	msnr = o_snr
	187
	188	try:
	189	self.msnr = 10 * numpy.log10(msnr / ndiv)
	190	except ZeroDivisionError:
	191	self.msnr = 10 * numpy.log10(msnr /1)
	192	print 'Number of division (ndiv) egal to 1 by default. Check SNR'
	193
	194	time_t = time.gmtime(self.dataOut.time1)
	195	year = time_t.tm_year
	196	month = time_t.tm_mon
	197	day = time_t.tm_mday
	198	hour = time_t.tm_hour
	199	minute = time_t.tm_min
	200	second = time_t.tm_sec
	201	timedate_0 = datetime.datetime(year, month, day, hour, minute, second)
	202
	203	# 1d parameters
	204	GDLATR = self.lat
	205	GDLONR = self.lon
	206	GDLAT2 = self.lat
	207	GLON2 = self.lon
	208
	209	# 2d parameters
	210	GDALT = self.o_height
	211
	212	SNL = self.msnr
	213	VN1P2 = self.o_zon
	214	VN2P2 = self.o_mer
	215	EL2 = self.o_ver
	216	NROW = len(self.o_height)
	217
	218	startTime = timedate_0
	219	endTime = startTime
	220	self.dataRec = madrigal.cedar.MadrigalDataRecord(self.kinst,
	221	self.kindat,
	222	startTime.year,
	223	startTime.month,
	224	startTime.day,
	225	startTime.hour,
	226	startTime.minute,
	227	startTime.second,
	228	0,
	229	endTime.year,
	230	endTime.month,
	231	endTime.day,
	232	endTime.hour,
	233	endTime.minute,
	234	endTime.second,
	235	0,
	236	('gdlatr', 'gdlonr', 'gdlat2', 'glon2'),
	237	('gdalt', 'snl', 'vn1p2', 'vn2p2', 'el2'),
	238	NROW, ind2DList=['gdalt'])
	239
	240	# Setting 1d values
	241	self.dataRec.set1D('gdlatr', GDLATR)
	242	self.dataRec.set1D('gdlonr', GDLONR)
	243	self.dataRec.set1D('gdlat2', GDLAT2)
	244	self.dataRec.set1D('glon2', GLON2)
	245
	246	# Setting 2d values
	247	for n in range(self.o_height.shape[0]):
	248	self.dataRec.set2D('gdalt', n, GDALT[n])
	249	self.dataRec.set2D('snl', n, SNL[n])
	250	self.dataRec.set2D('vn1p2', n, VN1P2[n])
	251	self.dataRec.set2D('vn2p2', n, VN2P2[n])
	252	self.dataRec.set2D('el2', n, EL2[n])
	253
	254	# Appending new data record
	255	'''
	256	[MADRIGAL3]There are two ways to write to a MadrigalCedarFile. Either this method (write) is called after all the
	257	records have been appended to the MadrigalCedarFile, or dump is called after a certain number of records are appended,
	258	and then at the end dump is called a final time if there were any records not yet dumped, followed by addArray.
	259	'''
	260
	261	self.cedarObj.append(self.dataRec)
	262	print ' [Writing] records {} (mode {}).'.format(self.dataOut.counter_records,self.im+1)
	263	self.cedarObj.dump()
	264
	265
	266
	267
	268	def setHeader(self):
	269	'''
	270	- Creating self.catHeadObj
	271	- Adding information catalog
	272	- Writing file header
	273
	274	'''
	275	self.catHeadObj = madrigal.cedar.CatalogHeaderCreator(self.fullname)
	276	kindatDesc, comments, analyst, history, principleInvestigator = self._info_BLTR()
	277
	278	self.catHeadObj.createCatalog(principleInvestigator="Jarjar",
	279	expPurpose='characterize the atmospheric dynamics in this region where frequently it happens the El Nino',
	280	sciRemarks="http://madrigal3.haystack.mit.edu/static/CEDARMadrigalHdf5Format.pdf")
	281
	282	self.catHeadObj.createHeader(kindatDesc, analyst, comments, history)
	283
	284	self.catHeadObj.write()
	285
	286	print '[File created] path: %s' % (self.fullname)
	287
	288	def putData(self):
	289
	290	if self.dataOut.flagNoData:
	291	return 0
	292
	293	if self.dataOut.counter_records == 1:
	294	self.setFile()
	295	print '[Writing] Setting new hdf5 file for the mode {}'.format(self.im+1)
	296
	297	if self.dataOut.counter_records <= self.dataOut.nrecords:
	298	self.writeBlock()
	299
	300
	301	if self.dataOut.counter_records == self.dataOut.nrecords:
	302	self.cedarObj.addArray()
	303
	304	self.setHeader()
	305	self.flagIsNewFile = 1
	306
	307	def _info_BLTR(self):
	308
	309	kindatDesc = '''--This header is for KINDAT = %d''' % self.kindat
	310	history = None
	311	analyst = '''Jarjar'''
	312	principleInvestigator = '''
	313	Jarjar
	314	Radio Observatorio de Jicamarca
	315	Instituto Geofisico del Peru
	316
	317	'''
	318	if self.type == 1:
	319	comments = '''
	320
	321	--These data are provided by two Boundary Layer and Tropospheric Radar (BLTR) deployed at two different locations at Peru(GMT-5), one of them at Piura(5.17 S, 80.64W) and another located at Huancayo (12.04 S, 75.32 W).
	322
	323	--The purpose of conducting these observations is to measure wind in the differents levels of height, this radar makes measurements the Zonal(U), Meridional(V) and Vertical(W) wind velocities component in northcoast from Peru. And the main purpose of these mensurations is to characterize the atmospheric dynamics in this region where frequently it happens the 'El Nino Phenomenon'
	324
	325	--In Kindat = 1600, contains information of wind velocities component since 0 Km to 3 Km.
	326
	327	--In Kindat = 1601, contains information of wind velocities component since 0 Km to 10 Km.
	328
	329	--The Huancayo-BLTR is a VHF Profiler Radar System is a 3 channel coherent receiver pulsed radar utilising state-of-the-art software and computing techniques to acquire, decode, and translate signals obtained from partial reflection echoes in the troposphere, lower stratosphere and mesosphere. It uses an array of three horizontal spaced and vertically directed receiving antennas. The data is recorded thirty seconds, averaged to one minute mean values of Height, Zonal, Meridional and Vertical wind.
	330
	331	--The Huancayo-BLTR was installed in January 2010. This instrument was designed and constructed by Genesis Soft Pty. Ltd. Is constituted by three groups of spaced antennas (distributed) forming an isosceles triangle.
	332
	333
	334	Station _______ Geographic Coord ______ Geomagnetic Coord
	335
	336	_______________ Latitude _ Longitude __ Latitude _ Longitude
	337
	338	Huancayo (HUA) __12.04 S ___ 75.32 W _____ -12.05 ____ 352.85
	339	Piura (PIU) _____ 5.17 S ___ 80.64 W ______ 5.18 ____ 350.93
	340
	341	WIND OBSERVATIONS
	342
	343	--To obtain wind the BLTR uses Spaced Antenna technique (e.g., Briggs 1984). The scatter and reflection it still provided by variations in the refractive index as in the Doppler method(Gage and Basley,1978; Balsley and Gage 1982; Larsen and Rottger 1982), but instead of using the Doppler shift to derive the velocity components, the cross-correlation between signals in an array of three horizontally spaced and vertically directed receiving antennas is used.
	344
	345	......................................................................
	346	For more information, consult the following references:
	347	- Balsley, B. B., and K. S. Gage., On the use of radars for operational wind profiling, Bull. Amer. Meteor.Soc.,63, 1009-1018, 1982.
	348
	349	- Briggs, B. H., The analysis of spaced sensor data by correations techniques, Handbook for MAP, Vol. 13, SCOTEP Secretariat, University of Illinois, Urbana, 166-186, 1984.
	350
	351	- Gage, K. S., and B.B. Balsley., Doppler radar probing of the clear atmosphere, Bull. Amer. Meteor.Soc., 59, 1074-1093, 1978.
	352
	353	- Larsen, M. F., The Spaced Antenna Technique for Radar Wind Profiling, Journal of Atm. and Ocean. Technology. , Vol.6, 920-937, 1989.
	354
	355	- Larsen, M. F., A method for single radar voracity measurements?, Handbook for MAP,SCOSTEP Secretariat, University of the Illinois, Urban, in press, 1989.
	356	......................................................................
	357
	358	ACKNOWLEDGEMENTS:
	359
	360	--The Piura and Huancayo BLTR are part of the network of instruments operated by the Jicamarca Radio Observatory.
	361
	362	--The Jicamarca Radio Observatory is a facility of the Instituto Geofisico del Peru operated with support from the NSF Cooperative Agreement ATM-0432565 through Cornell University
	363
	364	......................................................................
	365
	366	Further questions and comments should be addressed to:
	367	Radio Observatorio de Jicamarca
	368	Instituto Geofisico del Peru
	369	Lima, Peru
	370	Web URL: http://jro.igp.gob.pe
	371	......................................................................
	372	'''
	373
	374	return kindatDesc, comments, analyst, history, principleInvestigator
	375

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