def extractTOWandCN0(SVprn, measData, TOWmeas, CN0meas, verbose=False):
"""
extractTOWandCN0 axtracts for a SV the TOW and CN0 values observed per signaltype
Parameters:
SVprn: the ID for this SV
measData: the measurement data from MEAS_EPOCH
TOWmeas: array of lists that contains the TOW for this PRN and per signalType
CN0meas: idem for CN0
Returns:
the signal types for this SVprn
"""
if verbose:
print(' Processing SVID = %d' % SVprn)
# find indices with data for this SVID
# print('SVprn = %s' % SVprn)
indexSVprn = sbf2stf.indicesSatellite(SVprn, measData['MEAS_SVID'],
verbose)
# print('indexSVprn = %s' % indexSVprn)
dataMeasSVprn = measData[indexSVprn]
# print('dataMeasSVprn = %s' % dataMeasSVprn)
# find indices that correspond to the signalTypes for this SVprn
signalTypesSVprn = sbf2stf.observedSignalTypes(
dataMeasSVprn['MEAS_SIGNALTYPE'], verbose)
indexSignalType = []
for index, signalType in enumerate(signalTypesSVprn):
if verbose:
print(' Treating signalType = %s (index=%d)' %
(signalType, index))
# get the observation time span and observed CN0 for this SVprn and SignalType
indexSignalType.extend(
np.array(
sbf2stf.indicesSignalType(signalType,
dataMeasSVprn['MEAS_SIGNALTYPE'],
verbose)))
TOWmeas.append(dataMeasSVprn[indexSignalType[index]]['MEAS_TOW'])
CN0meas.append(dataMeasSVprn[indexSignalType[index]]['MEAS_CN0'])
# # print last added values
if True:
print('TOWmeas[%d] = %d => %d (%d)' %
(len(TOWmeas), TOWmeas[-1][0], TOWmeas[-1][-1],
np.size(TOWmeas[-1])))
print('CN0meas[%d] = %f => %s (%d)' %
(len(CN0meas), CN0meas[-1][0], CN0meas[-1][-1],
np.size(CN0meas[-1])))
return signalTypesSVprn
def extractELEVATION(SVprn, dataVisibility, verbose=False):
"""
extractELEVATION Extracts for a SV the elevation values observed
Parameters:
SVprn: the ID for this SV
dataVisibility: the measurement data from SatVisibility_1
Returns:
the elevation for this SVprn
"""
indexSVprnVis = sbf2stf.indicesSatellite(SVprn, dataVisibility['VISIBILITY_SVID'], verbose)
dataVisibilitySVprn = dataVisibility[indexSVprnVis]['VISIBILITY_ELEVATION']
# print(SVprn)
# print(dataVisibilitySVprn)
# sys.exit(0)
return dataVisibilitySVprn
def extractTOWandCN0(SVprn, measData, TOWmeas, CN0meas, verbose=False):
"""
extractTOWandCN0 axtracts for a SV the TOW and CN0 values observed per signaltype
Parameters:
SVprn: the ID for this SV
measData: the measurement data from MEAS_EPOCH
TOWmeas: array of lists that contains the TOW for this PRN and per signalType
CN0meas: idem for CN0
Returns:
the signal types for this SVprn
"""
if verbose:
print(' Processing SVID = %d' % SVprn)
# find indices with data for this SVID
# print('SVprn = %s' % SVprn)
indexSVprn = sbf2stf.indicesSatellite(SVprn, measData['MEAS_SVID'], verbose)
# print('indexSVprn = %s' % indexSVprn)
dataMeasSVprn = measData[indexSVprn]
# print('dataMeasSVprn = %s' % dataMeasSVprn)
# find indices that correspond to the signalTypes for this SVprn
signalTypesSVprn = sbf2stf.observedSignalTypes(dataMeasSVprn['MEAS_SIGNALTYPE'], verbose)
indexSignalType = []
for index, signalType in enumerate(signalTypesSVprn):
if verbose:
print(' Treating signalType = %s (index=%d)' % (signalType, index))
# get the observation time span and observed CN0 for this SVprn and SignalType
indexSignalType.extend(np.array(sbf2stf.indicesSignalType(signalType, dataMeasSVprn['MEAS_SIGNALTYPE'], verbose)))
TOWmeas.append(dataMeasSVprn[indexSignalType[index]]['MEAS_TOW'])
CN0meas.append(dataMeasSVprn[indexSignalType[index]]['MEAS_CN0'])
# # print last added values
if True:
print('TOWmeas[%d] = %d => %d (%d)' % (len(TOWmeas), TOWmeas[-1][0], TOWmeas[-1][-1], np.size(TOWmeas[-1])))
print('CN0meas[%d] = %f => %s (%d)' % (len(CN0meas), CN0meas[-1][0], CN0meas[-1][-1], np.size(CN0meas[-1])))
return signalTypesSVprn
if not sbf2stf.verifySignalTypeOrder(dataMeas['MEAS_SIGNALTYPE'], dataExtra['EXTRA_SIGNALTYPE'], dataMeas['MEAS_TOW'], verbose):
sys.exit(E_SIGNALTYPE_MISMATCH)
# correct the smoothed PR Code and work with the raw PR
dataMeas['MEAS_CODE'] = sbf2stf.removeSmoothing(dataMeas['MEAS_CODE'], dataExtra['EXTRA_SMOOTHINGCORR'], dataExtra['EXTRA_MPCORR'])
# print('dataMeas['MEAS_CODE'] = %s\n' % dataMeas['MEAS_CODE'])
# find list of SVIDs observed
SVIDs = sbf2stf.observedSatellites(dataMeas['MEAS_SVID'], verbose)
for SVID in SVIDs:
print('=' * 50)
gnssSyst, gnssSystShort, gnssPRN = mSSN.svPRN(SVID)
print('SVID = %d - %s - %s%d' % (SVID, gnssSyst, gnssSystShort, gnssPRN))
indexSVID = sbf2stf.indicesSatellite(SVID, dataMeas['MEAS_SVID'], verbose)
dataMeasSVID = dataMeas[indexSVID]
print("indexSVID = %s" % indexSVID)
# store temporaray results ONLY for inspection
nameDataMeasSVID = str(SVID) + '.csv'
print('nameDataMeasSVID = %s' % nameDataMeasSVID)
print('dataMeasSVID = %s' % dataMeasSVID)
np.savetxt(nameDataMeasSVID, dataMeasSVID, fmt='%i,%.1f,%i,%i,%i,%i,%i,%.2f,%.2f,%.2f,%.2f,%i,%i,%i')
signalTypesSVID = sbf2stf.observedSignalTypes(dataMeasSVID['MEAS_SIGNALTYPE'], verbose)
print('signalTypesSVID = %s' % signalTypesSVID)
# print("len dataMeas['MEAS_CODE'] %d" % len(dataMeas['MEAS_CODE']))
# print("len dataMeasSVID['MEAS_CODE'] %d" % len(dataMeasSVID['MEAS_CODE']))
# print dataMeasSVID['MEAS_SVID']
# correct the smoothed PR Code and work with the raw PR
dataMeas['MEAS_CODE'] = sbf2stf.removeSmoothing(
dataMeas['MEAS_CODE'], dataExtra['EXTRA_SMOOTHINGCORR'],
dataExtra['EXTRA_MPCORR'])
# print 'dataMeas['MEAS_CODE'] = %s\n' % dataMeas['MEAS_CODE']
# find list of SVIDs observed
SVIDs = sbf2stf.observedSatellites(dataMeas['MEAS_SVID'], verbose)
for SVID in SVIDs:
print('=' * 50)
gnssSyst, gnssSystShort, gnssPRN = mSSN.svPRN(SVID)
print('SVID = %d - %s - %s%d' %
(SVID, gnssSyst, gnssSystShort, gnssPRN))
indexSVID = sbf2stf.indicesSatellite(SVID, dataMeas['MEAS_SVID'],
verbose)
dataMeasSVID = dataMeas[indexSVID]
# print "indexSVID = %s" % indexSVID
# store temporaray results ONLY for inspection
# nameDataMeasSVID = str(SVID) + '.csv'
# np.savetxt(nameDataMeasSVID, dataMeasSVID, fmt=colFmtMeasEpoch)
signalTypesSVID = sbf2stf.observedSignalTypes(
dataMeasSVID['MEAS_SIGNALTYPE'], verbose)
# print 'signalTypesSVID = %s' % signalTypesSVID
# print "len dataMeas['MEAS_CODE'] %d" % len(dataMeas['MEAS_CODE'])
# print "len dataMeasSVID['MEAS_CODE'] %d" % len(dataMeasSVID['MEAS_CODE'])
# print dataMeasSVID['MEAS_SVID']
dateString = gpstime.UTCFromWT(WkNr, float(dataMeas['MEAS_TOW'][0])).strftime("%d/%m/%Y")
if verbose:
print('WkNr = %d - dateString = %s' % (WkNr, dateString))
# correct the smoothed PR Code and work with the raw PR
dataMeas['MEAS_CODE'] = sbf2stf.removeSmoothing(dataMeas['MEAS_CODE'], dataExtra['EXTRA_SMOOTHINGCORR'], dataExtra['EXTRA_MPCORR'])
# print('rawPR = %s\n' % dataMeas['MEAS_CODE'])
# find list of SVIDs from MeasEpoch and SatVisibility blocks and SignalTypes observed
SVIDs = sbf2stf.observedSatellites(dataMeas['MEAS_SVID'], verbose)
SVIDsVis = sbf2stf.observedSatellites(dataVisibility['VISIBILITY_SVID'], verbose)
# print(SVIDsVis)
# print(SVIDs)
# sys.exit(0)
signalTypes = sbf2stf.observedSignalTypes(dataMeas['MEAS_SIGNALTYPE'], verbose)
indexSVprnVis = sbf2stf.indicesSatellite(dataVisibility['VISIBILITY_SVID'], verbose)
# print(SVIDsVis)
# create the CN0 plots for all SVs and SignalTypes
indexSignalType = []
dataMeasSignalType = []
# storing data in arrays per SV and per signalType
measTOW = [] # TOWs with measurements
visibilityTOW = []
measCN0 = [] # CNO values @ measTOW
STlist = [] # list of signaltypes traversed
SVIDlist = [] # list of SVIDs traversed
ELEVATIONVisibility = [] # list of Elevation traversed
# extract first TOW and CN0 arrays for all SVs and signaltypes
# create subset with only valid elevation angles
indexValid = sbf2stf.findValidElevation(dataChanSt['CHST_Elevation'], verbose)
dataChanStValid = dataChanSt[indexValid]
# find the list of SVIDs with valid elev/azim data
SVIDs = sbf2stf.observedSatellites(dataChanStValid['CHST_SVID'], verbose)
# extract for each satellite the elevation and azimuth angles
prnElev = []
prnAzim = []
prnHour = []
prnHourElev = []
prnHourAzim = []
for i, PRN in enumerate(SVIDs):
print('PRN = %d' % PRN)
indexPRN = sbf2stf.indicesSatellite(PRN, dataChanStValid['CHST_SVID'], verbose)
prnElev.append(dataChanStValid[indexPRN]['CHST_Elevation'])
prnAzim.append(dataChanStValid[indexPRN]['CHST_Azimuth'])
# retain only multiples of hours to display
prnTime = dataChanStValid[indexPRN]['CHST_TOW']
indexHour = np.where(np.fmod(prnTime, 3600.) == 0)
# print('indexHour = %s' % indexHour)
prnHour.append(prnTime[indexHour])
prnHourElev.append(dataChanStValid[indexPRN]['CHST_Elevation'][indexHour])
prnHourAzim.append(dataChanStValid[indexPRN]['CHST_Azimuth'][indexHour])
# print('PRN = %d - prnElev = %s' % (PRN, prnElev[-1]))
# print('PRN = %d - prnAzim = %s' % (PRN, prnAzim[-1]))
# print('PRN = %d - prnHour = %s' % (PRN, prnHour[-1]))
# print('PRN = %d - prnHourElev = %s' % (PRN, prnHourElev[-1]))
# correct the smoothed PR Code and work with the raw PR
dataMeas['MEAS_CODE'] = sbf2stf.removeSmoothing(
dataMeas['MEAS_CODE'], dataExtra['EXTRA_SMOOTHINGCORR'],
dataExtra['EXTRA_MPCORR'])
# print('rawPR = %s\n' % dataMeas['MEAS_CODE'])
# find list of SVIDs from MeasEpoch and SatVisibility blocks and SignalTypes observed
SVIDs = sbf2stf.observedSatellites(dataMeas['MEAS_SVID'], verbose)
SVIDsVis = sbf2stf.observedSatellites(dataVisibility['VISIBILITY_SVID'],
verbose)
# print(SVIDsVis)
# print(SVIDs)
# sys.exit(0)
signalTypes = sbf2stf.observedSignalTypes(dataMeas['MEAS_SIGNALTYPE'],
verbose)
indexSVprnVis = sbf2stf.indicesSatellite(dataVisibility['VISIBILITY_SVID'],
verbose)
# print(SVIDsVis)
# create the CN0 plots for all SVs and SignalTypes
indexSignalType = []
dataMeasSignalType = []
# storing data in arrays per SV and per signalType
measTOW = [] # TOWs with measurements
visibilityTOW = []
measCN0 = [] # CNO values @ measTOW
STlist = [] # list of signaltypes traversed
SVIDlist = [] # list of SVIDs traversed
ELEVATIONVisibility = [] # list of Elevation traversed
# extract first TOW and CN0 arrays for all SVs and signaltypes
indexValid = sbf2stf.findValidElevation(dataChanSt['CHST_Elevation'],
verbose)
dataChanStValid = dataChanSt[indexValid]
# find the list of SVIDs with valid elev/azim data
SVIDs = sbf2stf.observedSatellites(dataChanStValid['CHST_SVID'], verbose)
# extract for each satellite the elevation and azimuth angles
prnElev = []
prnAzim = []
prnHour = []
prnHourElev = []
prnHourAzim = []
for i, PRN in enumerate(SVIDs):
print('PRN = %d' % PRN)
indexPRN = sbf2stf.indicesSatellite(PRN, dataChanStValid['CHST_SVID'],
verbose)
prnElev.append(dataChanStValid[indexPRN]['CHST_Elevation'])
prnAzim.append(dataChanStValid[indexPRN]['CHST_Azimuth'])
# retain only multiples of hours to display
prnTime = dataChanStValid[indexPRN]['CHST_TOW']
indexHour = np.where(np.fmod(prnTime, 3600.) == 0)
# print('indexHour = %s' % indexHour)
prnHour.append(prnTime[indexHour])
prnHourElev.append(
dataChanStValid[indexPRN]['CHST_Elevation'][indexHour])
prnHourAzim.append(
dataChanStValid[indexPRN]['CHST_Azimuth'][indexHour])
# print('PRN = %d - prnElev = %s' % (PRN, prnElev[-1]))
# print('PRN = %d - prnAzim = %s' % (PRN, prnAzim[-1]))
