else:
print 'Did not find pre-saved data, compute!'
radarmean = []
detmean = []
detrmse = []
fss01 = []
fss10 = []
ensrmse = []
ensrmv = []
ensbs = []
ensmean = []
ensmean_std = []
# Loop over time
for t in timelist:
# Determine 3hrly storage time
date_ana = yyyymmddhhmmss(t)
print 'date_ana = ', date_ana
date_fg = yyyymmddhhmmss(t - timedelta(hours=1))
date_store = yyyymmddhhmmss(t - timedelta(hours=t.hour % 3))
# Directory where lff*_prec files are stored
DATA_DIR = (datadir_da + expid + '/' + date_store + '/')
# Load relevant files
try:
radardata = load_radar(date_ana, return_array=True)
detdata = load_det_da(DATA_DIR, date_fg, return_array=True)
ensdatalist = load_ens_da(DATA_DIR, date_fg, return_array=True)
except:
print 'Some file was not found, write nans.'
radardata = np.zeros((461, 421)) * np.nan
print 'Try to load pre-saved data:', savefn
if os.path.exists(savefn) and not args.recompute:
print 'Found pre-saved data.'
mean_bias, rmse, count = np.load(savefn)
else:
obs_time = [] # in mins relative to obs_date
obs = []
verif = []
bias = []
obs_date = [] # datetime object
obs_lev = [] # in Pa
for t in timelist:
print t
# Load fof file
foffn = (DATA_DIR + '/' + yyyymmddhhmmss(t) + '/det/fof_' +
yyyymmddhhmmss(t) + '.nc')
print foffn
fof = fdbkfile(foffn)
var_tab = fof.table_varno
obs_tab = fof.table_obstype
# print var_tab, obs_tab
varno = var_tab[args.var]
obsno = obs_tab[args.obs]
if fof.fb_times()[-1] / 60. > 24.:
hint = 48
# Get temp data, T
try:
ov = fof.obs_veri(varno=varno, obstype=obsno)
except:
fss01_list = []
fss10_list = []
radar_hist_list = []
hist_list = []
else:
radar_list = []
ensrmse_list = []
ensrmv_list = []
ensbs_list = []
ensmean_list = []
ensmean_std_list = []
crps_list = []
for t in timelist:
print t
date = yyyymmddhhmmss(t)
savestr = (args.ana + '_' + expid + '_' + date + '_n_' +
str(args.n_kernel) + '_norm_' + str(args.ens_norm_type))
savefn = savedir + savestr + '.npy'
print 'Try to load pre-saved data:', savefn
if os.path.exists(savefn) and not args.recompute:
print 'Found pre-saved data.'
if args.ana == 'det':
radar, detmean, detrmse, fss01, fss10, radar_hist, hist = \
np.load(savefn)
if args.ana == 'ens':
radar, ensrmse, ensrmv, ensbs, ensmean, ensmean_std, crps = \
np.load(savefn)
else:
print 'Did not find pre-saved data, compute!'
# Loop over time
tstart = yyyymmddhhmmss_strtotime(args.date_start)
tend = yyyymmddhhmmss_strtotime(args.date_stop)
tint = timedelta(hours=args.date_inc)
if tstart == tend:
datelist = [tstart]
else:
datelist = make_timelist(tstart, tend, tint)
hourlist = []
for h in range(1, args.hint + 1):
hourlist
for idate, date in enumerate(datelist):
print date
datestr = yyyymmddhhmmss(date)
for h in range(1, args.hint + 1):
hstr = ddhhmmss(timedelta(hours=h))
detfobjlist = []
expid_str = ''
for exp in args.expid:
expid_str += exp + '_'
DATA_DIR = datadir + exp
detfobjlist.append(load_det(DATA_DIR, datestr, hstr))
radarfobj = load_radar(datestr, hstr)
plotfobjlist = [radarfobj] + detfobjlist
titlelist = ['Radar']
for e in args.expid:
titlelist.append(e)
print 'Try to load pre-saved data:', savefn
if os.path.exists(savefn):
print 'Found pre-saved data.'
mean_spread, mean_bias, rmse = np.load(savefn)
else:
print 'Did not find saved data'
mean_spread = []
mean_bias = []
rmse = []
spread = []
bias = []
lev = []
hourlist = []
for t in timelist:
# Determine 3hrly storage time
date_ana = yyyymmddhhmmss(t)
print 'date_ana = ', date_ana
date_fg = yyyymmddhhmmss(t - timedelta(hours=1))
date_store = yyyymmddhhmmss(t - timedelta(hours=t.hour % 3))
# Directory where ekf's and fof's are stored
FEED_DIR = (feeddir + expid + '/' + date_store + '/')
# Load files
try:
ekf = fdbkfile(FEED_DIR + 'ekf' + args.obs + '_' + date_ana +
'.nc')
fof = fdbkfile(FEED_DIR + 'fof_' + date_fg + '.nc')
ov_ekf = ekf.obs_veri(
varno=args.var,
savedir = '/e/uwork/extsrasp/save/' + exptag + '/prec_time/'
plotdir = '/e/uwork/extsrasp/plots/' + exptag + '/prec_time/'
if not os.path.exists(plotdir): os.makedirs(plotdir)
# Loop over time
tstart = yyyymmddhhmmss_strtotime(args.date_ini)
tend = yyyymmddhhmmss_strtotime(args.date_end)
tint = timedelta(days=1)
if tstart == tend:
timelist = [tstart]
else:
timelist = make_timelist(tstart, tend, tint)
matlist = []
for t in timelist:
savefn = savedir + yyyymmddhhmmss(t) + '_' + str(args.time[0]) + '_' + str(
args.time[1])
tplot, savemat, labelslist = np.load(savefn + '.npy')
matlist.append(savemat)
cdict = {
'radar': 'k',
'REF': 'b',
'REF_TL500': 'green',
'PSP_TL500': 'r',
#'sig1':'green',
#'time20':'orange',
#'time10':'orange',
#'const3':'purple',
#'const1':'purple',
#'nolowest':'gray',