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'''
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Created on Aug 1, 2017
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@author: Juan C. Espinoza
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'''
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import os
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import sys
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import time
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import datetime
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import numpy
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from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation
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from schainpy.model.data.jrodata import Parameters
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from schainpy.model.data.jroheaderIO import RadarControllerHeader, SystemHeader
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from schainpy.model.graphics.jroplot_parameters import WindProfilerPlot
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from schainpy.model.io.jroIO_base import *
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try:
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import madrigal
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import madrigal.cedar
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from madrigal.cedar import MadrigalCatalogRecord
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except:
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print 'You should install "madrigal library" module if you want to read/write Madrigal data'
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class MADWriter(Operation):
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def __init__(self):
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Operation.__init__(self)
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self.dataOut = Parameters()
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self.path = None
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self.dataOut = None
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self.flagIsNewFile=1
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self.ext = ".hdf5"
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return
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def run(self, dataOut, path , modetowrite,**kwargs):
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if self.flagIsNewFile:
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flagdata = self.setup(dataOut, path, modetowrite)
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self.putData()
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return
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def setup(self, dataOut, path, modetowrite):
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'''
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Recovering data to write in new *.hdf5 file
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Inputs:
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modew -- mode to write (1 or 2)
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path -- destination path
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'''
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self.im = modetowrite-1
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if self.im!=0 and self.im!=1:
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raise ValueError, 'Check "modetowrite" value. Must be egual to 1 or 2, "{}" is not valid. '.format(modetowrite)
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self.dataOut = dataOut
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self.nmodes = self.dataOut.nmodes
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self.nchannels = self.dataOut.nchannels
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self.lat = self.dataOut.lat
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self.lon = self.dataOut.lon
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self.hcm = 3
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self.thisDate = self.dataOut.utctimeInit
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self.year = self.dataOut.year
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self.month = self.dataOut.month
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self.day = self.dataOut.day
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self.path = path
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self.flagIsNewFile = 0
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return 1
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def setFile(self):
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'''
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- Determining the file name for each mode of operation
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kinst - Kind of Instrument (mnemotic)
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kindat - Kind of Data (mnemotic)
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- Creating a cedarObject
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'''
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lat_piura = -5.17
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lat_huancayo = -12.04
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lat_porcuya = -5.8
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if '%2.2f' % self.lat == '%2.2f' % lat_piura:
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self.instMnemonic = 'pbr'
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elif '%2.2f' % self.lat == '%2.2f' % lat_huancayo:
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self.instMnemonic = 'hbr'
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elif '%2.2f' % self.lat == '%2.2f' % lat_porcuya:
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self.instMnemonic = 'obr'
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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 "
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mode = ['_mode1','_mode2']
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self.hdf5filename = '%s%4.4d%2.2d%2.2d%s%s' % (self.instMnemonic,
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self.year,
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self.month,
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self.day,
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mode[self.im],
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self.ext)
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self.fullname=os.path.join(self.path,self.hdf5filename)
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if os.path.isfile(self.fullname) :
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print "Destination path '%s' already exists. Previous file deleted. " %self.fullname
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os.remove(self.fullname)
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# Identify kinst and kindat
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InstName = self.hdf5filename[0:3]
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KinstList = [1000, 1001, 1002]
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KinstId = {'pbr':0, 'hbr':1, 'obr':2} # pbr:piura, hbr:huancayo, obr:porcuya
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KindatList = [1600, 1601] # mode 1, mode 2
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self.type = KinstId[InstName]
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self.kinst = KinstList[self.type]
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self.kindat = KindatList[self.im]
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try:
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self.cedarObj = madrigal.cedar.MadrigalCedarFile(self.fullname, True)
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except ValueError, message:
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print '[Error]: Impossible to create a cedar object with "madrigal.cedar.MadrigalCedarFile" '
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return
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return 1
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def writeBlock(self):
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'''
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- Selecting mode of operation:
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bltr high resolution mode 1 - Low Atmosphere (0 - 3km) // bltr high resolution mode 2 - High Atmosphere (0 - 10km)
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msnr - Average Signal Noise Ratio in dB
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hcm - 3 km
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- Filling the cedarObject by a block: each array data entry is assigned a code that defines the parameter to write to the file
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GDLATR - Reference geod latitude (deg)
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GDLONR - Reference geographic longitude (deg)
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GDLAT2 - Geodetic latitude of second inst (deg)
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GLON2 - Geographic longitude of second inst (deg)
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GDALT - Geodetic altitude (height) (km)
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SNL - Log10 (signal to noise ratio)
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VN1P2 - Neutral wind in direction 1 (eastward) (m/s), ie zonal wind
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VN2P2 - Neutral wind in direction 2 (northward) (m/s), ie meridional wind
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EL2 - Ending elevation angle (deg), ie vertical wind
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Other parameters: /madrigal3/metadata/parcodes.tab
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'''
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self.z_zon = self.dataOut.data_output[0,:,:]
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self.z_mer =self.dataOut.data_output[1,:,:]
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self.z_ver = self.dataOut.data_output[2,:,:]
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if self.im == 0:
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h_select = numpy.where(numpy.bitwise_and(self.dataOut.height[0, :] >= 0., self.dataOut.height[0, :] <= self.hcm, numpy.isfinite(self.dataOut.height[0, :])))
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else:
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h_select = numpy.where(numpy.bitwise_and(self.dataOut.height[0, :] >= 0., self.dataOut.height[0, :] < 20, numpy.isfinite(self.dataOut.height[0, :])))
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ht = h_select[0]
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self.o_height = self.dataOut.height[self.im, ht]
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self.o_zon = self.z_zon[ht, self.im]
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self.o_mer = self.z_mer[ht, self.im]
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self.o_ver = self.z_ver[ht, self.im]
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o_snr = self.dataOut.data_SNR[ :, :, self.im]
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o_snr = o_snr[ht, :]
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ndiv = numpy.nansum((numpy.isfinite(o_snr)), 1)
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ndiv = ndiv.astype(float)
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sel_div = numpy.where(ndiv == 0.)
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ndiv[sel_div] = numpy.nan
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if self.nchannels > 1:
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msnr = numpy.nansum(o_snr, axis=1)
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else:
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msnr = o_snr
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try:
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self.msnr = 10 * numpy.log10(msnr / ndiv)
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except ZeroDivisionError:
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self.msnr = 10 * numpy.log10(msnr /1)
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print 'Number of division (ndiv) egal to 1 by default. Check SNR'
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time_t = time.gmtime(self.dataOut.time1)
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year = time_t.tm_year
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month = time_t.tm_mon
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day = time_t.tm_mday
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hour = time_t.tm_hour
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minute = time_t.tm_min
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second = time_t.tm_sec
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timedate_0 = datetime.datetime(year, month, day, hour, minute, second)
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# 1d parameters
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GDLATR = self.lat
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GDLONR = self.lon
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GDLAT2 = self.lat
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GLON2 = self.lon
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# 2d parameters
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GDALT = self.o_height
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SNL = self.msnr
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VN1P2 = self.o_zon
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VN2P2 = self.o_mer
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EL2 = self.o_ver
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NROW = len(self.o_height)
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startTime = timedate_0
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endTime = startTime
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self.dataRec = madrigal.cedar.MadrigalDataRecord(self.kinst,
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self.kindat,
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startTime.year,
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startTime.month,
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startTime.day,
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startTime.hour,
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startTime.minute,
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startTime.second,
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0,
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endTime.year,
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endTime.month,
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endTime.day,
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endTime.hour,
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endTime.minute,
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endTime.second,
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0,
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('gdlatr', 'gdlonr', 'gdlat2', 'glon2'),
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('gdalt', 'snl', 'vn1p2', 'vn2p2', 'el2'),
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NROW, ind2DList=['gdalt'])
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# Setting 1d values
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self.dataRec.set1D('gdlatr', GDLATR)
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self.dataRec.set1D('gdlonr', GDLONR)
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self.dataRec.set1D('gdlat2', GDLAT2)
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self.dataRec.set1D('glon2', GLON2)
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# Setting 2d values
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for n in range(self.o_height.shape[0]):
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self.dataRec.set2D('gdalt', n, GDALT[n])
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self.dataRec.set2D('snl', n, SNL[n])
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self.dataRec.set2D('vn1p2', n, VN1P2[n])
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self.dataRec.set2D('vn2p2', n, VN2P2[n])
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self.dataRec.set2D('el2', n, EL2[n])
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# Appending new data record
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'''
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[MADRIGAL3]There are two ways to write to a MadrigalCedarFile. Either this method (write) is called after all the
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records have been appended to the MadrigalCedarFile, or dump is called after a certain number of records are appended,
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and then at the end dump is called a final time if there were any records not yet dumped, followed by addArray.
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'''
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self.cedarObj.append(self.dataRec)
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print ' [Writing] records {} (mode {}).'.format(self.dataOut.counter_records,self.im+1)
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self.cedarObj.dump()
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def setHeader(self):
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'''
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- Creating self.catHeadObj
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- Adding information catalog
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- Writing file header
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'''
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self.catHeadObj = madrigal.cedar.CatalogHeaderCreator(self.fullname)
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kindatDesc, comments, analyst, history, principleInvestigator = self._info_BLTR()
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self.catHeadObj.createCatalog(principleInvestigator="Jarjar",
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expPurpose='characterize the atmospheric dynamics in this region where frequently it happens the El Nino',
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sciRemarks="http://madrigal3.haystack.mit.edu/static/CEDARMadrigalHdf5Format.pdf")
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self.catHeadObj.createHeader(kindatDesc, analyst, comments, history)
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self.catHeadObj.write()
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print '[File created] path: %s' % (self.fullname)
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def putData(self):
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if self.dataOut.flagNoData:
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return 0
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if self.dataOut.counter_records == 1:
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self.setFile()
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print '[Writing] Setting new hdf5 file for the mode {}'.format(self.im+1)
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if self.dataOut.counter_records <= self.dataOut.nrecords:
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self.writeBlock()
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if self.dataOut.counter_records == self.dataOut.nrecords:
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self.cedarObj.addArray()
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self.setHeader()
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self.flagIsNewFile = 1
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def _info_BLTR(self):
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kindatDesc = '''--This header is for KINDAT = %d''' % self.kindat
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history = None
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analyst = '''Jarjar'''
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principleInvestigator = '''
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Jarjar
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Radio Observatorio de Jicamarca
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Instituto Geofisico del Peru
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'''
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if self.type == 1:
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comments = '''
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--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).
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--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'
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--In Kindat = 1600, contains information of wind velocities component since 0 Km to 3 Km.
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--In Kindat = 1601, contains information of wind velocities component since 0 Km to 10 Km.
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--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.
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--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.
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Station _______ Geographic Coord ______ Geomagnetic Coord
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_______________ Latitude _ Longitude __ Latitude _ Longitude
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Huancayo (HUA) __12.04 S ___ 75.32 W _____ -12.05 ____ 352.85
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Piura (PIU) _____ 5.17 S ___ 80.64 W ______ 5.18 ____ 350.93
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WIND OBSERVATIONS
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--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.
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......................................................................
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For more information, consult the following references:
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- Balsley, B. B., and K. S. Gage., On the use of radars for operational wind profiling, Bull. Amer. Meteor.Soc.,63, 1009-1018, 1982.
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- 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.
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- Gage, K. S., and B.B. Balsley., Doppler radar probing of the clear atmosphere, Bull. Amer. Meteor.Soc., 59, 1074-1093, 1978.
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- Larsen, M. F., The Spaced Antenna Technique for Radar Wind Profiling, Journal of Atm. and Ocean. Technology. , Vol.6, 920-937, 1989.
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- Larsen, M. F., A method for single radar voracity measurements?, Handbook for MAP,SCOSTEP Secretariat, University of the Illinois, Urban, in press, 1989.
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......................................................................
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ACKNOWLEDGEMENTS:
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--The Piura and Huancayo BLTR are part of the network of instruments operated by the Jicamarca Radio Observatory.
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--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
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......................................................................
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Further questions and comments should be addressed to:
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Radio Observatorio de Jicamarca
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Instituto Geofisico del Peru
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Lima, Peru
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Web URL: http://jro.igp.gob.pe
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......................................................................
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'''
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return kindatDesc, comments, analyst, history, principleInvestigator
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