def __init__(self,Ti,ARCHIVE_DIR,search_type,postfix_dir=""):
'''
timelistcontainer is a reader class for files
produced by SatValidation, in operational chain or similar sequential runs
Arguments:
* Ti * a TimeInterval
* ARCHIVE_DIR * string indicating path of the chain archive directory e.g. /pico/home/usera07ogs/a07ogs00/OPA/V2C/archive/
* search_type * string, having one of these values: f0, f1, f2, a0, a1, a2
* postfix_dir * string to pass to TimeSeries objects
'''
self.timelist=[]
self.filelist=[]
self.bias = None
self.number=None
self.rmse =None
self.model =None
self.sat = None
self.rmselog = None
self.biaslog = None
self.search_type=search_type
TS=TimeSeries(Ti,archive_dir=ARCHIVE_DIR, postfix_dir=postfix_dir, glob_pattern="Validation_f0")
search_paths = TS.get_runs([2])
for _, directory in search_paths:
dirlist=glob.glob(directory + "Validation_" + search_type + "*")
if len(dirlist)> 0:
self.filelist.append(dirlist[0])
self.nFrames = len(self.filelist)
self.read_basic_info(self.filelist[0])
self.readfiles()
def __init__(self,Ti,ARCHIVE_DIR,postfix_dir=""):
self.timelist=[]
self.filelist=[]
self.bias = None
self.number=None
self.rmse =None
TS=TimeSeries(Ti,archive_dir=ARCHIVE_DIR, postfix_dir=postfix_dir, glob_pattern="BioFloat_Weekly_validation")
search_paths = TS.get_runs([2])
for _, directory in search_paths:
dirlist=glob.glob(directory + "BioFloat_Weekly_validation*")
if len(dirlist)> 0:
self.filelist.append(dirlist[0])
self.nFrames = len(self.filelist)
self.read_basic_info(self.filelist[0])
self.readfiles()
import numpy as np
import os
from commons import netcdf3
starttime='20160301'
end__time='20160308'
INPUTDIR='/gpfs/work/IscrC_MYMEDBIO/COPERNICUS/online_validation_data/TMP/' #args.inputdir
OUTDIR ='/gpfs/work/IscrC_MYMEDBIO/COPERNICUS/online_validation_data/TMP/' #args.outdir
maskfile='/pico/home/usera07ogs/a07ogs00/OPA/V2C/etc/static-data/MED1672_cut/MASK/meshmask.nc' # args.maskfile
TI=TimeInterval(starttime,end__time,'%Y%m%d')
archive_dir='/pico/home/usera07ogs/a07ogs00/OPA/V4/archive'
TheMask=Mask(maskfile)
TS = TimeSeries(TI, archive_dir,postfix_dir='POSTPROC/AVE_FREQ_1/',glob_pattern="ave*gz")
forecasts =TS.get_forecast_days(rundays=[2])
forecasts_sublist=TS.get_sublist(forecasts,[2,3,4]) #forecast tuesday and wed,thu
sat_archive="/gss/gss_work/DRES_OGS_BiGe/Observations/TIME_RAW_DATA/ONLINE/SAT/MODIS/DAILY/CHECKED/"
DAILY_SAT_LIST=TS.get_daily_sat(forecasts_sublist,sat_archive)
# float aggregator already done by others
day=0
surf_layer=Layer(0,10)
for time,archived_file,satfile in DAILY_SAT_LIST:
avefile=INPUTDIR + os.path.basename(archived_file)[:-3]
day=day+1
outfile=OUTDIR + "misfit+%dh.nc" % (day*24)
print avefile
continue
args = argument()
from commons.timeseries import TimeSeries
from commons.time_interval import TimeInterval
from commons.utils import addsep
starttime=args.starttime
end__time=args.endtime
LOC = addsep(args.outdir)
archive_dir= args.arcdir
TI=TimeInterval(starttime,end__time,'%Y%m%d')
if args.type=='analysis':
T_bio = TimeSeries(TI, archive_dir,postfix_dir='POSTPROC/AVE_FREQ_1/ARCHIVE/',glob_pattern="ave*gz")
T_phys= TimeSeries(TI, archive_dir,postfix_dir='OPAOPER_A/' ,glob_pattern="*gz" )
T_bio.extract_analysis( LOC + 'output_bio/')
T_phys.extract_analysis(LOC + 'output_phys/');
if args.type =='forecast':
T_bio = TimeSeries(TI, archive_dir,postfix_dir='POSTPROC/AVE_FREQ_1/ARCHIVE/',glob_pattern="ave*gz")
T_phys_s= TimeSeries(TI, archive_dir,postfix_dir='OPAOPER_A/' ,glob_pattern="*gz" )
T_phys_f= TimeSeries(TI, archive_dir,postfix_dir='OPAOPER_F/' ,glob_pattern="*gz" )
T_bio.extract_simulation(LOC + 'output_bio/')
T_phys_s.extract_simulation(LOC + 'output_phys/');
T_bio.extract_forecast( LOC + 'output_bio/')
