def parse_sinex(self):
for sinex in os.listdir(self.pwd_glbf):
if sinex.endswith('.snx'):
snx = snxParse.snxFileParser(os.path.join(self.pwd_glbf, sinex))
snx.parse()
self.polyhedron = snx.stationDict
self.VarianceFactor = snx.varianceFactor
if self.polyhedron:
# remame any aliases and change keys to net.stn
for stn in self.StationInstances:
# replace the key
try:
self.polyhedron[stationID(stn)] = self.polyhedron.pop(stn.StationAlias.upper())
except KeyError:
# maybe the station didn't have a solution
pass
return self.polyhedron, self.VarianceFactor
def __init__(self, expt, org, dataType, dataSrcLable, filePath):
# call super class constructor for data object
DataObj.__init__(self, expt, org, dataType, dataSrcLable, filePath)
# init sinex parser for current sinex file
snxParser = snxParse.snxFileParser(filePath).parse()
# organize the coordinates for the data object
for stn in snxParser:
# initialize the station if we haven't seen it before
if not self.data.has_key(stn):
self.data[stn] = dict()
# get the snx data from the parser
stnData = snxParser.get(stn)
# pack the data away
self.data[stn]['xyz'] = [stnData.X, stnData.Y, stnData.Z]
def main():
(file, outdir) = get_input_args()
# get the date from the sinex file
gpsWeek = int(os.path.basename(file)[3:7])
gpsWeekDay = int(os.path.basename(file)[7])
# compute a data from the information
date = pyDate.Date(gpsweek=gpsWeek, gpsweekday=gpsWeekDay)
# check outdir
# if out dir is none then put soln file
# in same directory as snx files
if outdir == None: outdir = '.'
# make full path for solution file
solnFilePath = os.path.join(outdir, getOutFileName(file))
# init sinex parser for current sinex file
snxParser = snxParse.snxFileParser(file).parse()
# construct npvs and npvs sigma from the sinex data
npvs, npvs_sigma = npv(snxParser.stationDict)
# create station list from dictionary keys
stn_list = snxParser.stationDict.keys()
# compute epoch in fractional year
epochs = date.fyear
#extract the variance factor
var_factor = snxParser.varianceFactor
# save as a mat file
scipy.io.savemat(solnFilePath, mdict={'stnm' :stn_list , \
'epochs' :epochs , \
'npvs' :npvs , \
'npv_sigma' :npvs_sigma , \
'var_factor':var_factor}, \
oned_as = 'column')
def process_sinex(cnn, project, dates, sinex):
# parse the SINEX to get the station list
snx = snxParse.snxFileParser(sinex)
snx.parse()
stnlist = ('\'' + '\',\''.join(snx.stationDict.keys()) + '\'').lower()
# insert the statistical data
zg = cnn.query_float(
'SELECT count("Year")*2 as ss FROM gamit_soln '
'WHERE "Project" = \'%s\' AND "FYear" BETWEEN %.4f AND %.4f AND "StationCode" IN (%s) '
'GROUP BY "Year", "DOY"' % (project, dates[0].first_epoch('fyear'),
dates[1].last_epoch('fyear'), stnlist))
zg = sum([s[0] for s in zg])
zd = cnn.query_float('SELECT count("ZTD") + %i as implicit FROM gamit_ztd '
'WHERE "Date" BETWEEN \'%s\' AND \'%s\' ' %
(zg, dates[0].first_epoch(), dates[1].last_epoch()))
zd = zd[0][0]
print ' >> Adding NUMBER OF UNKNOWNS: %i (previous value: %i)' % (
zd, snx.unknowns)
replace_in_sinex(sinex, snx.observations, snx.unknowns,
snx.unknowns + zg + zd)
rs = cnn.query('SELECT "NetworkCode", "StationCode", dome FROM stations '
'WHERE "StationCode" IN (%s) '
'ORDER BY "NetworkCode", "StationCode"' % stnlist)
stations = rs.dictresult()
print ' >> Adding DOMES'
# add domes
add_domes(sinex, stations)
def parse_sinex(self):
for sinex in os.listdir(self.pwd_comb):
if sinex.endswith('.snx'):
snx = snxParse.snxFileParser(os.path.join(
self.pwd_comb, sinex))
snx.parse()
self.polyhedron = snx.stationDict
self.VarianceFactor = snx.varianceFactor
if self.polyhedron:
# rename the dict keys to net.stn format (and replace any aliases)
for GamitSession in self.Sessions:
for StationInstance in GamitSession.StationInstances:
# replace the key
try:
self.polyhedron[StationInstance.NetworkCode + '.' +
StationInstance.StationCode] = \
self.polyhedron.pop(StationInstance.StationAlias.upper())
except KeyError:
# maybe the station didn't have a solution
pass
return self.polyhedron, self.VarianceFactor
def mk_mat(self, snxfile):
# minimize depencencies if not interested in mat files
import pyDate
import snxParse
import snx2mat
import scipy.io
import gzip
# get the date from the sinex file
gpsWeek = int(os.path.basename(snxfile)[3:7])
gpsWeekDay = int(os.path.basename(snxfile)[7])
# compute a data from the information
date = pyDate.Date(gpsweek=gpsWeek, gpsweekday=gpsWeekDay)
# parse the file path in to dir and name
file_path, file_name = os.path.split(snxfile)
# create the output file name
file_name = file_name.split('.')[0]
# make full path for solution file
solnFilePath = os.path.join(file_path, file_name)
# init sinex parser for current sinex file
snxParser = snxParse.snxFileParser(snxfile).parse()
# construct npvs and npvs sigma from the sinex data
npvs, npvs_sigma = snx2mat.npv(snxParser.stationDict)
# create station list from dictionary keys
stn_list = snxParser.stationDict.keys()
# compute epoch in fractional year
epochs = date.fyear
#extract the variance factor
var_factor = snxParser.varianceFactor
# save as a mat file
scipy.io.savemat(solnFilePath,
mdict={
'stnm': stn_list,
'epochs': epochs,
'npvs': npvs,
'npv_sigma': npvs_sigma,
'var_factor': var_factor
},
oned_as='column')
# if we have a mat file then gzip it
if os.path.isfile(solnFilePath + '.mat'):
# gzip the file up with open file handles
with open(solnFilePath + '.mat', 'rb') as orig_file:
with gzip.open(solnFilePath + '.mat.gz', 'wb') as zipped_file:
zipped_file.writelines(orig_file)
# clean up
os.remove(solnFilePath + '.mat')
