def getROMSfilenames(self,startdatenum,operMode):
if operMode=='oper':
self.roms_parent_file = self.romsdir+'roms_BRIFS_parent_'+self.romsdatestring+'_his.nc'
romsfile = self.romsdir+'roms_BRIFS_parent_'+self.romsdatestring+'_his.nc'
romsfileop = self.romsdir+'roms_BRIFS_parent_'+self.romsdatestring+'_op_his.nc'
if os.path.isfile(romsfile):
self.roms_parent_file = romsfile
elif os.path.isfile(romsfileop):
self.roms_parent_file = romsfileop
else:
print "******modelData: getROMSfilenames ERROR: NEITHER FILE EXISTS:"
print romsfile
print romsfileop
print "1. CHECK FILENAME \nOR \n2. MODIFY self.roms_parent_file VARIABLE IN modelData.py!"
q()
else:
romsfile = self.romsdir+'roms_BRIFS_parent_'+self.romsdatestring+'_hind_his.nc'
if os.path.isfile(romsfile):
self.roms_parent_file = romsfile
else:
print "******modelData: getROMSfilenames ERROR: THE FILE DOES NOT EXIST:"
print romsfile
print "1. CHECK FILENAME \nOR \n2. MODIFY self.roms_parent_file VARIABLE IN modelData.py!"
q()
self.roms_child_file = self.roms_parent_file.replace('parent','child')
return self.roms_parent_file, self.roms_child_file
def getROMSfilenames(self, startdatenum, operMode):
if operMode == 'oper':
self.roms_parent_file = self.romsdir + 'roms_BRIFS_parent_' + self.romsdatestring + '_his.nc'
romsfile = self.romsdir + 'roms_BRIFS_parent_' + self.romsdatestring + '_his.nc'
romsfileop = self.romsdir + 'roms_BRIFS_parent_' + self.romsdatestring + '_op_his.nc'
if os.path.isfile(romsfile):
self.roms_parent_file = romsfile
elif os.path.isfile(romsfileop):
self.roms_parent_file = romsfileop
else:
print "******modelData: getROMSfilenames ERROR: NEITHER FILE EXISTS:"
print romsfile
print romsfileop
print "1. CHECK FILENAME \nOR \n2. MODIFY self.roms_parent_file VARIABLE IN modelData.py!"
q()
else:
romsfile = self.romsdir + 'roms_BRIFS_parent_' + self.romsdatestring + '_hind_his.nc'
if os.path.isfile(romsfile):
self.roms_parent_file = romsfile
else:
print "******modelData: getROMSfilenames ERROR: THE FILE DOES NOT EXIST:"
print romsfile
print "1. CHECK FILENAME \nOR \n2. MODIFY self.roms_parent_file VARIABLE IN modelData.py!"
q()
self.roms_child_file = self.roms_parent_file.replace('parent', 'child')
return self.roms_parent_file, self.roms_child_file
def getModelName(ptm, dateString):
if ptm().model == 'ADRIPOM' or ptm().model == 'NADRIPOM':
# here, dateString should be in YYYYMMDD format
return ptm().ncdir + ptm().model + '_' + dateString + '.nc'
elif ptm().model == 'sbADRIPOM' or ptm().model == 'sbNADRIPOM':
dtobjStart = datetime.strptime(dateString, '%Y%m%d')
dtobjEnd = dtobjStart + timedelta(hours=ptm().modelRuntimeInHours)
strStart = datetime.strftime(dtobjStart, "%Y%m%d")
return ptm().ncdir + 'zeta.' + ptm().model + '_' + strStart + '.nc'
else:
print("\nERROR in getModelName: unknown model name: " + ptm().model)
q()
def main():
# check for input date YYYYMMDD from console:
try:
strdate = sys.argv[1]
operMode = sys.argv[2]
except:
printHelp()
# set OPERATIONAL wrf and roms netCDF output directories:
if operMode == 'oper':
wrfdir = '/home/rissaga/new_setup/Archive/Outputs/WRF/' + strdate + '_op/'
# set HINDCAST wrf and roms netCDF output directories:
else:
wrfdir = '/home/rissaga/new_setup/Archive/Outputs/WRF/' + strdate + '_hind/'
# wrfdir = '/home/mlicer/'
romsdir = '/home/rissaga/new_setup/Archive/Outputs/ROMS/'
plotdir = '/home/mlicer/BRIFSverif/pyVerif/' + strdate + '_' + operMode + '/'
os.system('mkdir -p ' + plotdir)
# determine timeWindow [hours] for comparisons:
timeWindow = 48
# parse start date:
startdatenum = datetime.strptime(strdate, '%Y%m%d')
enddatenum = startdatenum + timedelta(hours=timeWindow)
# set WRF files and dates to read:
today = startdatenum
yesterday = startdatenum - timedelta(days=1)
tomorrow = startdatenum + timedelta(days=1)
wrfdatestring = datetime.strftime(startdatenum, '%Y-%m-%d_12:00:00')
wrfdatestring_yesterday = datetime.strftime(
startdatenum - timedelta(days=1), '%Y-%m-%d_12:00:00')
wrfdatestring_tomorrow = datetime.strftime(
startdatenum + timedelta(days=1), '%Y-%m-%d_12:00:00')
wrf_file = wrfdir + 'wrfout_d02_' + wrfdatestring
wrf_file_yesterday = wrfdir + 'wrfout_d02_' + wrfdatestring_yesterday
wrf_file_tomorrow = wrfdir + 'wrfout_d02_' + wrfdatestring_tomorrow
# specify fields for comparisons:
observationFields=['time','LON','LAT','HEIGHT',\
'SLEV','QC_SLEV',\
'WTR_PRE','QC_WTR_PRE',\
'AIR_PRE','QC_AIR_PRE']
wrfFields = [
'location', 'Times', 'XLONG', 'XLAT', 'pointLon', 'pointLat',
'pointPSFC', 'pointMSLP'
]
romsFields = [
'location', 'ocean_time', 'lon_rho', 'lat_rho', 'h', 'pointLon',
'pointLat', 'pointSSH'
]
# get a list of all available observations:
fileList = getAllObservations(strdate, timeWindow)
# exit if empty:
if not fileList:
print('\n No observations found for this date: ' + strdate +
'! \n Exiting.')
q()
# keep working if not empty:
else:
print "\nReading observation files:"
sensorType = []
for k in range(len(fileList)):
print fileList[k], "..."
# determine the sensorType, if any, from the filename:
m = re.findall(r'.*_(\w+-\w+.?)_L1', fileList[k])
if m:
if 'station' in m[0]:
sensorType.append('')
else:
sensorType.append(m[0])
print ""
# read the files to 'stations' data object and merge files (sensor types) from different months(years) if neccessary:
stations, sensorType = obsData(fileList, sensorType,
observationFields).read()
# extract WRF for available grid points:
wrf_yesterday = modelData(stations, yesterday, wrfFields, romsFields,
wrfdir, romsdir, operMode).readWRF()
wrf_today = modelData(stations, today, wrfFields, romsFields, wrfdir,
romsdir, operMode).readWRF()
wrf_tomorrow = modelData(stations, tomorrow, wrfFields, romsFields, wrfdir,
romsdir, operMode).readWRF()
# merge WRF times and air pressures from all three days for all stations:
wrf_t_3days, wrf_p_3days = mergeWRF(stations, wrf_yesterday, wrf_today,
wrf_tomorrow, 'pointMSLP')
# wrf_t_3days,wrf_p_3days = mergeWRF(stations,wrf_yesterday,wrf_today,wrf_tomorrow,'pointPSFC')
# extract ROMS
roms = modelData(stations, startdatenum, wrfFields, romsFields, wrfdir,
romsdir, operMode).readROMS(startdatenum, operMode)
# compute basic statistics (BIAS, RMSE, CORR):
stats = basicStatistics(strdate, sensorType, stations, wrf_t_3days,
wrf_p_3days, roms)
# plot graphs:
plotBRIFS(plotdir, strdate, sensorType, stations, wrf_t_3days, wrf_p_3days,
roms, stats)
def __init__(self):
# model name:
self.model = 'sbNADRIPOM'
# set home directory:
self.homedir = '/home/momo/particleTracking/source_sb/'
# set out directory for namelist:
self.outdir = '/home/momo/particleTracking/rundir/'
# set netcdf directory (containing hydrodynamics netcdfs):
self.ncdir = '/home/momo/particleTracking/' + self.model + '_netcdf/'
# start date of particle tracking (YYYYMMDDHH):
startdate = str(2017032600)
# initial location of spill:
#self.lat0=44.806286
#self.lon0=13.143596
# release type:
# 'singlePoint' = point release for releaseDuration from the first releasePoints point
# 'multiplePoints'= point release for releaseDuration from all releasePoints point
# 'polygon' = instantaneous (releaseDuration will be set to 1!!) linear
# release along the SIDES of releaseShape polygon points.
# Choosing multiple (N) points will increase total number of particles N-times!
self.releaseType = 'singlePoint'
#self.releaseType='multiplePoints'
# self.releaseType='polygon'
# releasePoints: [ [lat1,lon1] , [lat2,lon2] , ... , [latN,lonN] ]
#self.releasePoints=[ [45.7239,13.603] , [45.5042,12.742], [44.9711, 12.609] ]
self.releasePoints = [[45.5904065, 13.6993]]
# self.releasePoints=[ [45.1793,12.7857], [44.673,13.0229],[45.1793,13.26],[44.882,12.601],[44.882,13.4447],[45.1793,12.7857] ]
# depth level of the spill (1=surface)
self.level = 1
# perform 3D tracking: (True=3D, False=single depth level)
self.tracking3D = False
if self.tracking3D:
print "WARNING: 3D tracking IS NOT YET SUPPORTED in sbADRIPOM!"
print "Setting: self.tracking3D=False!"
self.tracking3D = False
# perform back-tracking (True) or regular particle tracking (False):
self.performBacktracking = False
# number of hours of tracking simulation ('polygon' release type will set this to 1):
self.numHoursOfTracking = 24
# perform daily hotstart (initialize particles from previous step) - to be continued:
# self.performHotstart=False
# duration of particle release in hours:
self.releaseDuration = 2
# number of particles PER RELEASE POINT:
numberOfParticles = 120
# total mass of polutant [kg]:
self.totalMass = 20000.
# oil type index:
# 1 = arabian light
# 2 = arabian medium
# 3 = arabian heavy
self.oilIndex = 2
# what percentage of particles evaporates completely:
self.completeEvapPercentage = 0.2
# evaporation threshold for particles that do not evaporate completely:
# i.e., self.evapThreshold=0.77 implies that 77 percent of each particle
# which does not evaporate completely, will evaporate.
self.evapThreshold = 0.77
# do we read turbulent diffusivity from the ocean model or not:
self.useOceanModelDiffusivity = False
if self.useOceanModelDiffusivity:
print "WARNING: useOceanModelDiffusivity IS NOT YET SUPPORTED in sbADRIPOM!!!"
print "Setting: self.useOceanModelDiffusivity=False!!!"
self.useOceanModelDiffusivity = False
# default turbulent diffusivity
# (this is overridden by the Dh and Dv from the model, if
# self.useOceanModelDiffusivity=True
self.Dh = 0.001
# create startdate datetime object:
self.startdate = datetime.strptime(startdate, "%Y%m%d%H")
# specify number of hours in each model netcdf:
if self.model == 'sbADRIPOM':
self.modelRuntimeInHours = 74 #54
elif self.model == 'sbNADRIPOM':
self.modelRuntimeInHours = 72
else:
print "INVALID MODEL: " + self.model
q()
# specify time increment in hours in each model netcdf:
self.dt = 1
#=======END OF USER INPUT===============================================
#=======================================================================
#=======DO NOT MODIFY THE CODE BELOW!===================================
#=======================================================================
#determine temporal sign and limit duration of spill:
if self.performBacktracking:
self.sign_t = int(-1)
self.releaseDuration = 1
else:
self.sign_t = int(+1)
#determine enddate:
self.enddate = self.startdate + self.sign_t * timedelta(
hours=self.numHoursOfTracking)
self.startingHour = int(datetime.strftime(self.startdate, "%H"))
# how many dates does the simulation span (we need to include starting Hour because this may
# brind an extra day, depending on the hours of tracking):
self.numDaysOfTracking = int(
math.ceil((self.startingHour + self.numHoursOfTracking) / 24) + 1)
self.modelRuntimeInDays = int(self.modelRuntimeInHours / 24)
# determine release coordinates according to releaseType:
if self.releaseType == 'singlePoint':
self.releasePoints = [[
self.releasePoints[0][0], self.releasePoints[0][1]
]]
# elif self.releaseType=='multiplePoints':
# pass
elif self.releaseType == 'polygon':
self.releaseDuration = 1
else:
print("SETUP ERROR: unknown releaseType: " + self.releaseType)
print("Should be 'singlePoint' or 'polygon'!")
q()
self.numberOfReleasePoints = len(self.releasePoints)
self.numberOfParticles = max(
numberOfParticles,
self.releaseDuration * self.numberOfReleasePoints)
# if numberOfParticles<release duration, create at least one particle per step:
self.releaseDuration = min(self.releaseDuration,
self.numberOfParticles,
self.numHoursOfTracking)
self.numCreatedParticlesPerStep = max(
1, int(self.numberOfParticles / self.releaseDuration))
def checkForNeccessaryFiles(ptm):
print("-------------------------------------------")
print("Starting particle tracking preprocessing...")
print("Checking for neccessary files...")
# create integers of start date and end date:
startDate = int(datetime.strftime(ptm().startdate, "%Y%m%d%H"))
endDate = int(datetime.strftime(ptm().enddate, "%Y%m%d%H"))
# MAIN LOOP over days of tracking:
numOfHoursToBeReadFromFiles = []
neccessaryFiles = []
days = []
days_dt = []
files = []
hourReadStart = []
hourReadEnd = []
daysToRead = []
for k in range(ptm().numDaysOfTracking):
# current date datetime object:
currentDatetime = ptm().startdate + ptm().sign_t * timedelta(days=k)
# current day string:
day_str = datetime.strftime(currentDatetime, "%Y%m%d")
# netcdf file of current day:
filename = getModelName(ptm, day_str)
# add day to days if not already there:
if day_str not in days:
days = np.append(days, day_str)
days_dt = np.append(days_dt, currentDatetime)
# add current file to files if not already there:
if filename not in files:
files = np.append(files, filename)
# filter out only dates for actually existing files:
existingDates = [
d for i, d in enumerate(days_dt) if os.path.isfile(files[i])
]
existingFiles = [
f for i, f in enumerate(files) if os.path.isfile(files[i])
]
# check for first day:
firstday = datetime.strftime(ptm().startdate, "%Y%m%d")
# first file name - it's not yet known if it exists:
firstfile = getModelName(ptm, firstday)
# this will later be added to how many days we neet to read:
increaseDaysToRead0 = 0
if not os.path.isfile(firstfile):
possibleFirstFileExists = False
# loop over model runtime days at current date:
for k in range(ptm().modelRuntimeInDays):
# go back in time as many days as neccessary and possible to locate a
# possible first file:
currentDatetime = ptm().startdate - timedelta(days=k)
daynowstr = datetime.strftime(currentDatetime, "%Y%m%d")
filenow = getModelName(ptm, daynowstr)
# if current file exists:
if os.path.isfile(filenow) and not possibleFirstFileExists:
# set flag to true and increase days to read by 1:
possibleFirstFileExists = True
increaseDaysToRead0 = increaseDaysToRead0 + 1
# update existing dates and files:
if ptm().performBacktracking:
#change nothing
existingDates = existingDates
existingFiles = existingFiles
else:
# prepend current date and filename:
existingDates = np.insert(existingDates, 0,
currentDatetime)
existingFiles = np.insert(existingFiles, 0, filenow)
break
else:
possibleFirstFileExists = True
# overall check if we have the file containing the first day data:
if not possibleFirstFileExists:
print("\nERROR: first date " + firstday +
" data could not be retrieved from existing files:\n")
print(existingFiles)
q()
# check for gaps in the data:
for i in range(len(existingDates) - 1):
# difference in days between consecutive dates in existing dates array:
deltaDays = abs(int((existingDates[i + 1] - existingDates[i]).days))
daysToRead = np.append(daysToRead, deltaDays)
# if difference in insurmountable by all the hours contained in the
# last existing file, then abort:
if deltaDays > ptm().modelRuntimeInDays:
daynowstr = datetime.strftime(existingDates[i], "%Y%m%d")
print("ERROR: data gap on date " + daynowstr +
" exceeds modelRuntimeInDays!")
q()
# check for last day (note that the loop above leaves the last day out):
deltaEndHours = int(abs((existingDates[-1] - days_dt[-1]).seconds) / 3600.)
lastday = datetime.strftime(ptm().enddate, "%Y%m%d")
allowed_days = [
ptm().enddate - timedelta(days=k)
for k in range(ptm().modelRuntimeInDays)
]
allowed_lastfiles = [
getModelName(ptm, datetime.strftime(dy, "%Y%m%d"))
for dy in allowed_days
]
# check if last file exists:
existLastFile = False
for lastfile in allowed_lastfiles:
if os.path.isfile(lastfile):
existLastFile = True
break
if not existLastFile:
print("ERROR: last date could not be extracted from existing files:")
print(lastday)
print(allowed_lastfiles)
q()
# similar condition for the last date:
if deltaEndHours > ptm().modelRuntimeInHours and not ptm(
).performBacktracking:
print(
"ERROR: end date file missing and could not be recovered from previous dates!"
)
q()
# or in case of back-tracking, the initial date:
elif ptm().performBacktracking and not os.path.isfile(lastfile):
print("ERROR: neccessary file " + lastfile +
" (with earliest dates) missing for backtracking!")
q()
# if there where no aborts until now, the final day exists,
# therefore add final day to dayToRead:
if not ptm().performBacktracking:
daysToRead = np.append(daysToRead, 1)
else:
daysToRead = np.insert(daysToRead, 0, 1)
# correct neccessary reading intervals for missing first day, if neccessary:
if ptm().performBacktracking:
daysToRead[0] = daysToRead[0] + increaseDaysToRead0
# set temporal hourly indices for start and end of netcdf array
# reading IN FORTRAN 1-BASED NOTATION:
hourReadStart = []
hourReadEnd = []
lastHourLastDay = abs(int(datetime.strftime(ptm().enddate, "%H")))
for i in range(len(existingDates)):
print("I,LEN:", i, len(existingDates), range(len(existingDates)))
# if we read only first file, up until runtimeInHours:
# w/o backtracking:
if i == 0 and not ptm().performBacktracking and len(
existingDates) == 1:
hourReadStart=np.append(hourReadStart,\
int(datetime.strftime(ptm().startdate,"%H"))+1)
if ptm().modelRuntimeInHours < ptm().numHoursOfTracking:
print("ERROR: numHoursOfTracking exceeds modelRuntimeInHours.")
print("More NetCDF files are needed!")
q()
else:
hourReadEnd = np.append(hourReadEnd, ptm().numHoursOfTracking)
#if we read more files than just first file:
# first day w/o backtracking:
if i == 0 and not ptm().performBacktracking and len(existingDates) > 1:
hourReadStart=np.append(hourReadStart,\
int(datetime.strftime(ptm().startdate,"%H"))+1)
hourReadEnd=np.append(hourReadEnd,\
daysToRead[i]*24)
print('d1 ', hourReadEnd)
#first day w backtracking:
elif i == 0 and ptm().performBacktracking:
hourReadStart = np.append(hourReadStart, 1)
hourReadEnd=np.append(hourReadEnd,\
abs(int(datetime.strftime(ptm().startdate,"%H"))+1+(daysToRead[i]-1)*24))
print('d2 ', hourReadEnd)
elif i > 0 and i < len(
existingDates) - 1: # other days, but not last day:
hourReadStart = np.append(hourReadStart, 1)
hourReadEnd=np.append(hourReadEnd,\
abs(daysToRead[i]*24))
print('d3 ', hourReadEnd)
# last day w/o backtracking if last file exists:
elif i > 0 and i == len(existingDates) - 1 and not ptm(
).performBacktracking and os.path.isfile(lastfile):
hourReadStart = np.append(hourReadStart, 1)
hourReadEnd=np.append(hourReadEnd,\
lastHourLastDay+24)
print('d4', hourReadEnd)
# last day w/o backtracking if last file does NOT exist:
elif i > 0 and i == len(existingDates) - 1 and not ptm(
).performBacktracking and not os.path.isfile(lastfile):
hourReadStart = np.append(hourReadStart, 1)
hourReadEnd=np.append(hourReadEnd,\
lastHourLastDay+24)
print('d5', hourReadEnd)
elif i > 0 and i == len(existingDates) - 1 and ptm(
).performBacktracking: # last day w backtracking:
hourReadStart = np.append(hourReadStart, 1)
hourReadEnd=np.append(hourReadEnd,\
abs(daysToRead[i]*24))
print('d6', hourReadEnd)
else:
continue
# return to main:
vsota = 0
print('ure:', hourReadStart, hourReadEnd)
for l, ura in enumerate(hourReadEnd):
vsota = vsota + abs(hourReadEnd[l] - hourReadStart[l] + 1)
print('vsota: ', vsota)
print('led: ', existingDates)
print('ef:', existingFiles)
if len(existingDates) > 1:
sumHours = int(vsota)
else:
sumHours = int(vsota) - 24
existingAtmFiles = [f.replace('zeta', 'atm') for f in existingFiles]
existingFiles = [
f.replace('zeta.sbNADRIPOM', 'sbnadripom_uvtide')
for f in existingFiles
]
return existingFiles, existingAtmFiles, hourReadStart, hourReadEnd, sumHours
def main():
# check for input date YYYYMMDD from console:
try:
strdate=sys.argv[1]
operMode = sys.argv[2]
except:
printHelp()
# set OPERATIONAL wrf and roms netCDF output directories:
if operMode=='oper':
wrfdir = '/home/rissaga/new_setup/Archive/Outputs/WRF/'+strdate+'_op/'
# set HINDCAST wrf and roms netCDF output directories:
else:
wrfdir = '/home/rissaga/new_setup/Archive/Outputs/WRF/'+strdate+'_hind/'
# wrfdir = '/home/mlicer/'
romsdir = '/home/rissaga/new_setup/Archive/Outputs/ROMS/'
plotdir = '/home/mlicer/BRIFSverif/pyVerif/'+strdate+'_'+operMode+'/'
os.system('mkdir -p '+plotdir)
# determine timeWindow [hours] for comparisons:
timeWindow=48
# parse start date:
startdatenum = datetime.strptime(strdate,'%Y%m%d')
enddatenum = startdatenum+timedelta(hours=timeWindow)
# set WRF files and dates to read:
today = startdatenum
yesterday = startdatenum-timedelta(days=1)
tomorrow = startdatenum+timedelta(days=1)
wrfdatestring = datetime.strftime(startdatenum,'%Y-%m-%d_12:00:00')
wrfdatestring_yesterday = datetime.strftime(startdatenum-timedelta(days=1),'%Y-%m-%d_12:00:00')
wrfdatestring_tomorrow = datetime.strftime(startdatenum+timedelta(days=1),'%Y-%m-%d_12:00:00')
wrf_file = wrfdir+'wrfout_d02_'+wrfdatestring
wrf_file_yesterday = wrfdir+'wrfout_d02_'+wrfdatestring_yesterday
wrf_file_tomorrow = wrfdir+'wrfout_d02_'+wrfdatestring_tomorrow
# specify fields for comparisons:
observationFields=['time','LON','LAT','HEIGHT',\
'SLEV','QC_SLEV',\
'WTR_PRE','QC_WTR_PRE',\
'AIR_PRE','QC_AIR_PRE']
wrfFields=['location','Times','XLONG','XLAT', 'pointLon','pointLat', 'pointPSFC','pointMSLP']
romsFields=['location','ocean_time','lon_rho','lat_rho','h','pointLon','pointLat', 'pointSSH']
# get a list of all available observations:
fileList = getAllObservations(strdate,timeWindow)
# exit if empty:
if not fileList:
print('\n No observations found for this date: '+strdate+'! \n Exiting.')
q()
# keep working if not empty:
else:
print "\nReading observation files:"
sensorType=[]
for k in range(len(fileList)):
print fileList[k],"..."
# determine the sensorType, if any, from the filename:
m = re.findall(r'.*_(\w+-\w+.?)_L1',fileList[k])
if m:
if 'station' in m[0]:
sensorType.append('')
else:
sensorType.append(m[0])
print ""
# read the files to 'stations' data object and merge files (sensor types) from different months(years) if neccessary:
stations,sensorType = obsData(fileList,sensorType,observationFields).read()
# extract WRF for available grid points:
wrf_yesterday = modelData(stations,yesterday,wrfFields,romsFields,wrfdir,romsdir, operMode).readWRF()
wrf_today = modelData(stations,today,wrfFields,romsFields,wrfdir,romsdir, operMode).readWRF()
wrf_tomorrow = modelData(stations,tomorrow,wrfFields,romsFields,wrfdir,romsdir, operMode).readWRF()
# merge WRF times and air pressures from all three days for all stations:
wrf_t_3days,wrf_p_3days = mergeWRF(stations,wrf_yesterday,wrf_today,wrf_tomorrow,'pointMSLP')
# wrf_t_3days,wrf_p_3days = mergeWRF(stations,wrf_yesterday,wrf_today,wrf_tomorrow,'pointPSFC')
# extract ROMS
roms = modelData(stations,startdatenum,wrfFields,romsFields,wrfdir,romsdir, operMode).readROMS(startdatenum,operMode)
# compute basic statistics (BIAS, RMSE, CORR):
stats = basicStatistics(strdate,sensorType,stations,wrf_t_3days,wrf_p_3days,roms)
# plot graphs:
plotBRIFS(plotdir,strdate,sensorType,stations,wrf_t_3days,wrf_p_3days,roms,stats)
'buffer_size': 1,
'train_steps': None,
'eval_steps': 100,
'verbose_shapes': True,
'pool_stride': 2,
'n_layers': 4,
'rnn_layer': True,
'dense_layer': True,
'one_output_per_epoch': True,
'training_hook_n_iter': 50,
'throttle_secs': 120,
}
if params['n_epoch_samples'] % params['time_steps'] != 0:
print('Warning: time steps has to divide epoch in equal parts. Quitting.')
q()
# Setup partitions and batches in the DataHandler-class
DataHandler.setup_partitions(data_folder, params['train_pct'],
params['val_pct'], params['batch_size'])
if model_memory:
with open('partitions.pkl', 'rb') as f:
DataHandler.set_partitions(pickle.load(f))
else:
with open('partitions.pkl', 'wb') as f:
pickle.dump(DataHandler.get_partitions(), f)
config = tf.estimator.RunConfig(
save_checkpoints_steps=params['save_checkpoint_steps'],
save_summary_steps=params['save_summary_steps'])
model = Model('CRNN', params)