Commit 54ec9f84 authored by brunner's avatar brunner
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parent 0ba7dfc7
......@@ -154,15 +154,14 @@ class ObservationOperator(object):
os.chdir(dacycle['dir.da_run'])
args = [
(dacycle, hstart, hstop, self.forecast_nmembers)
for dacycle, (hstart, hstop), self.forecast_nmembers
in zip(repeat(dacycle),
[(starth+168*lag,endh+168*lag-1)],
repeat(self.forecast_nmembers))
(dacycle, starth+168*lag, endh+168*lag-1, n)
for n in range(0,self.forecast_nmembers)
]
with Pool(3) as pool:
with Pool(self.forecast_nmembers) as pool:
pool.starmap(self.extract_model_data, args)
pool.close()
pool.join()
for i in range(0,self.forecast_nmembers):
idx = str(i).zfill(3)
......@@ -187,7 +186,6 @@ class ObservationOperator(object):
def extract_model_data(self,dacycle,hstart,hstop,ensnum):
self.dacycle = dacycle
# time_stamp = str((dacycle['time.start']+timedelta(hours=hstart)).strftime('%Y%m%d%H'))+'_'+str((dacycle['time.start']+timedelta(hours=hstop)).strftime('%Y%m%d%H'))
time_stamp = dacycle['time.sample.stamp']
abs_start_time = str((to_datetime(dacycle['abs.time.start'])).strftime('%Y%m%d%H'))
......@@ -195,43 +193,54 @@ class ObservationOperator(object):
hhl_cosmo_out = "/scratch/snx3000/parsenov/ctdas/"+abs_start_time+"_0_168/cosmo/output/"
cosmo_save = "/store/empa/em05/parsenov/cosmo_data/"
hhl_fn = hhl_cosmo_out+'lffd'+abs_start_time+'c.nc'
cdo.selname("HHL", input = hhl_fn, output = cosmo_out+"hhl.nc")
for ens in range(0,ensnum):
ens = str(ens).zfill(3)
files2cat=[]
for dt in rrule.rrule(rrule.HOURLY, dtstart=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstart), until=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstop)):
dt=dt.strftime('%Y%m%d%H')
logging.info('Extracting output for ens %s, time %s' % (str(ens),str(dt)))
co2_in_fn = cosmo_out+'lffd'+dt+'.nc'
co2_out_fn = cosmo_out+'CO2_'+ens+'_'+dt+'.nc'
cdo.expr("'CO2=(BG_"+ens+"-GPP_"+ens+"+RESP_"+ens+"+CO2_A_CH+CO2_A)/(1.-QV)'", input = "-selname,QV,BG_"+ens+",GPP_"+ens+",RESP_"+ens+",CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_fn)
files2cat.append(co2_out_fn)
cdo.cat(input = files2cat, output = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc")
cdo.remapnn("lon=7.99_lat=46.54,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_jfj_"+ens+"_"+time_stamp+".nc")
cdo.remapnn("lon=7.99_lat=46.54,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_jfj.nc")
cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_lhw_"+ens+"_"+time_stamp+".nc")
cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_lhw.nc")
ens = str(ensnum).zfill(3)
files2cat_jfj=[]
files2cat_lhw=[]
files2cat_brm=[]
files2cat_ssl=[]
cdo.remapnn("lon=8.18_lat=47.19,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_brm_"+ens+"_"+time_stamp+".nc")
if ens == "000":
cdo.selname("HHL", input = hhl_fn, output = cosmo_out+"hhl.nc")
cdo.remapnn("lon=7.99_lat=46.54,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_jfj.nc")
cdo.remapnn("lon=8.40_lat=47.48,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_lhw.nc")
cdo.remapnn("lon=8.18_lat=47.19,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_brm.nc")
cdo.remapnn("lon=7.92_lat=47.92,", input = cosmo_out+"CO2_"+ens+"_"+time_stamp+".nc", output = cosmo_out+"CO2_ssl_"+ens+"_"+time_stamp+".nc")
cdo.remapnn("lon=7.92_lat=47.92,", input = cosmo_out+"hhl.nc", output = cosmo_out+"hhl_ssl.nc")
sites = ("lhw","brm","jfj","ssl")
for s,ss in enumerate(sites):
site_height.main(cosmo_out, str(ens), ss, time_stamp)
cdo.intlevel("860", input = cosmo_out+"CO2_60lev_"+ens+"_lhw_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_lhw_"+time_stamp+".nc")
cdo.intlevel("797", input = cosmo_out+"CO2_60lev_"+ens+"_brm_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_brm_"+time_stamp+".nc")
cdo.intlevel("3580", input = cosmo_out+"CO2_60lev_"+ens+"_jfj_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_jfj_"+time_stamp+".nc")
cdo.intlevel("1205", input = cosmo_out+"CO2_60lev_"+ens+"_ssl_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_ssl_"+time_stamp+".nc")
for dt in rrule.rrule(rrule.HOURLY, dtstart=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstart), until=to_datetime(dacycle['abs.time.start'])+timedelta(hours=hstop)):
dt=dt.strftime('%Y%m%d%H')
logging.info('Extracting output for ens %s, time %s' % (str(ens),str(dt)))
co2_in_fn = cosmo_out+'lffd'+dt+'.nc'
co2_out_jfj = cosmo_out+'CO2_jfj_'+ens+'_'+dt+'.nc'
co2_out_lhw = cosmo_out+'CO2_lhw_'+ens+'_'+dt+'.nc'
co2_out_brm = cosmo_out+'CO2_brm_'+ens+'_'+dt+'.nc'
co2_out_ssl = cosmo_out+'CO2_ssl_'+ens+'_'+dt+'.nc'
cdo.expr("'CO2=(BG_"+ens+"-GPP_"+ens+"+RESP_"+ens+"+CO2_A_CH+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.99_lat=46.54 -selname,QV,BG_"+ens+",GPP_"+ens+",RESP_"+ens+",CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_jfj)
cdo.expr("'CO2=(BG_"+ens+"-GPP_"+ens+"+RESP_"+ens+"+CO2_A_CH+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.40_lat=47.48 -selname,QV,BG_"+ens+",GPP_"+ens+",RESP_"+ens+",CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_lhw)
cdo.expr("'CO2=(BG_"+ens+"-GPP_"+ens+"+RESP_"+ens+"+CO2_A_CH+CO2_A)/(1.-QV)'", input = "-remapnn,lon=8.18_lat=47.19 -selname,QV,BG_"+ens+",GPP_"+ens+",RESP_"+ens+",CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_brm)
cdo.expr("'CO2=(BG_"+ens+"-GPP_"+ens+"+RESP_"+ens+"+CO2_A_CH+CO2_A)/(1.-QV)'", input = "-remapnn,lon=7.92_lat=47.92 -selname,QV,BG_"+ens+",GPP_"+ens+",RESP_"+ens+",CO2_A_CH,CO2_A "+co2_in_fn, output = co2_out_ssl)
files2cat_jfj.append(co2_out_jfj)
files2cat_lhw.append(co2_out_lhw)
files2cat_brm.append(co2_out_brm)
files2cat_ssl.append(co2_out_ssl)
cdo.cat(input = files2cat_jfj, output = cosmo_out+"CO2_jfj_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_lhw, output = cosmo_out+"CO2_lhw_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_brm, output = cosmo_out+"CO2_brm_"+ens+"_"+time_stamp+".nc")
cdo.cat(input = files2cat_ssl, output = cosmo_out+"CO2_ssl_"+ens+"_"+time_stamp+".nc")
sites = ("lhw","brm","jfj","ssl")
for s,ss in enumerate(sites):
site_height.main(cosmo_out, str(ens), ss, time_stamp)
cdo.intlevel("860", input = cosmo_out+"CO2_60lev_"+ens+"_lhw_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_lhw_"+time_stamp+".nc")
cdo.intlevel("797", input = cosmo_out+"CO2_60lev_"+ens+"_brm_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_brm_"+time_stamp+".nc") # this needs changing to 1009 (797 + 212)
cdo.intlevel("3580", input = cosmo_out+"CO2_60lev_"+ens+"_jfj_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_jfj_"+time_stamp+".nc")
cdo.intlevel("1205", input = cosmo_out+"CO2_60lev_"+ens+"_ssl_"+time_stamp+".nc", output = cosmo_out+"modelled_"+ens+"_ssl_"+time_stamp+".nc")
cdo.cat(input = cosmo_out+"modelled_"+ens+"_brm_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_jfj_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_lhw_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_ssl_"+time_stamp+".nc ", output = cosmo_save+"model_"+ens+"_"+time_stamp+".nc")
logging.info('Extracting done for ens %s' % (ens))
cdo.cat(input = cosmo_out+"modelled_"+ens+"_brm_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_jfj_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_lhw_"+time_stamp+".nc "+cosmo_out+"modelled_"+ens+"_ssl_"+time_stamp+".nc ", output = cosmo_save+"model_"+ens+"_"+time_stamp+".nc")
################### End Class ObservationOperator ###################
......
......@@ -295,6 +295,87 @@ class StateVector(object):
rands = np.random.uniform(low=-1., high=1., size=self.nparams-1)
rands_bg = np.random.uniform(low=-0.05, high=0.05, size=1)
# if member == 1 and lag == 0:
# rands=np.array([-0.2236048,0.1492591,-0.4134172,0.2030501,-0.3056341,0.6603037,0.3247675,0.6550926,0.8024981,0.1593491,-1,0.08171216,-0.4006799,-0.3123662, 0.1022556, 0.1977177, -0.4678199, 0.8751256, 0.9531838,0.9820679,0.3594339,-1,0.03129737])
# if member == 2 and lag == 0:
# rands=np.array([-0.1477133,-0.6238207,-0.8987545,-1,-1,0.1647741,0.6627979,0.4307449,-0.4548403,-0.1154654,-1,-0.05427124,-0.9482519,0.7116647,0.03384373,-0.09207293,0.4631868,-0.5646629,0.7750968,-0.8704156,0.1906742,-1,0.004112379])
# if member == 3 and lag == 0:
# rands=np.array([-0.5120361,-0.6635633,0.4805151,0.3135487,0.02116868,0.0115789,-0.2708648, 0.6396611,-0.4579386,-0.8980371,-1,0.4571423,0.2513106,0.3329439,0.5895487,-0.2265133,-0.3762723,0.9784772,0.6362864,-0.0876907,-0.1079749,-1,0.00497188])
# if member == 4 and lag == 0:
# rands=np.array([0.5773118,0.8732759,1.126802,-0.3782958,0.662484,1.199195,-0.5346411,-0.0550764,-0.6245533,0.6759356,-1,-0.8961901,0.3352093,-0.5532659,-0.7597447,0.5648068,-0.3713143,0.124998,-0.6321908,0.2655768,-0.02420997,-1,-0.01510416])
# if member == 5 and lag == 0:
# rands=np.array([0.1289302,-0.08508042,-0.3619103,-0.5414423,-0.07692706,-0.9641049,-0.268249,-0.7405945,-0.2146955,-0.1976647,-1,-0.1085423,-0.946457,-0.008138934,-0.530806,0.315332,-0.2215554,0.7034806,0.527541,-0.4504287,-0.3679401,-1,0.02846291])
# if member == 6 and lag == 0:
# rands=np.array([-0.2574656,-0.3259587,0.8530074,0.04455876,0.5508205,0.553733,0.5282572,-0.7782328,0.7970823,-0.4580753,-1,-0.1998393,0.04793931,0.01853116,0.6679623,-0.4434216,-0.1505937,-0.1531717,-0.1313033,0.3774659,-0.8304669,-1,0.0197916,])
#if member == 7 and lag == 0:
# rands=np.array([0.6881989,0.3826058,-0.5495766,0.1132997,-0.57166,0.3214818,-0.9898017,0.6837509,-0.3501504,0.7928728,-1,-0.2640067,0.5886974,0.262185,0.2781147,0.7389106,0.6068769,0.1230468,-0.4471386,0.09954312,-0.4604041,-1,-0.04947798])
# if member == 8 and lag == 0:
# rands=np.array([-0.8718347,-0.03896444,-0.384823,-0.5786896,0.2542168,-0.5035606,0.7728764,-0.6899376,-0.5994669,0.1059537,-1,-0.859535,0.2382655,-0.3679568,0.450976,0.6301627,-0.6471004,-0.8724778,0.7950776,-0.1000319,-0.1237619,-1,-0.04825296])
# if member == 9 and lag == 0:
# rands=np.array([0.06249616,0.78708,-0.5142601,-0.1251998,-0.5136412,-0.9106983,0.7841995,-0.8327041,-0.03150041,-0.7065354,-1,-0.1541739,0.7130719,0.6997306,0.5323737,-0.6959949,0.1497954,0.2615451,-0.451424,-0.8742322,-0.4250237,-1,0.03196495])
# if member == 10 and lag == 0:
# rands=np.array([-0.1827562,0.3512708,0.3367097,-0.6161442,0.2948794,0.3662989,0.7885535,-0.6752307,0.4505339,0.9947404,-1,-0.1427709,-0.4374289,-0.4191162,-0.9822996,-0.2934023,-0.2808521,-0.8965437, 0.7602426,-0.1778743,-0.000989437,-1,-0.01274748])
# if member == 11 and lag == 0:
# rands=np.array([0.3784615,-0.7083653,-0.821948,0.06502473,-0.6035533,-0.7092476,0.9460906,0.2481164,0.772471,0.6445355,-1,-0.1532128,-0.5447619,-0.5863768,0.2508805,-0.2562239,-0.9050175,-0.6196863,-1,-0.3799991,0.5817245,-1,-0.02425112])
# if member == 12 and lag == 0:
# rands=np.array([-0.3772987,-0.09633347,-0.9725795,-0.9913203,-0.1097328,0.3683293,-0.3654553,0.3282615,0.5734389,-0.03295924,-1,0.6370476,-0.4982804,0.174771,-0.740661,-0.02542321,-0.7887223,0.7312356,-0.3222232,-0.1027924,0.02633788,-1,-0.040399])
# if member == 13 and lag == 0:
# rands=np.array([0.7854867,0.3317518,0.6469278,0.5409229,-0.7125678,-0.5082978,0.4885957,-0.490857,-0.1638525,-0.3768145,-1,-0.8093954,0.5902002,0.03597473,-0.7890476,0.5711794,-0.3381689,-0.67668,-1,0.7070413,-0.1368402,-1,0.01318651])
#if member == 14 and lag == 0:
# rands=np.array([0.7588015,0.4496917,-0.1509738,0.5066012,0.7388234,0.310389,-0.2899449,-0.6862295,-0.5775338,0.02781044,-1,-0.3429568,-0.7947903,-0.6043629,-0.8869596,-0.5914347,-0.2657907,-0.6107077,-0.2889067,-0.283401,0.8258069,-1,-0.03961588])
# if member == 15 and lag == 0:
# rands=np.array([-0.1068678,-0.6150778,0.2749358,-0.006487241,0.3801498,-0.2595585,-0.1739224,0.4673905,0.2893905,-0.7730854,-1,-0.6906102,0.3519762,0.6020219,-0.1512664,0.4392967,0.4572283,-0.109156,0.1201844,-0.8120148,-1,-1,0.008621466])
# if member == 16 and lag == 0:
# rands=np.array([-0.4203033,0.6120592,-0.07144696,0.07225665,-0.7220178,-0.5390107,-0.3260586,0.7489389,0.4817865,0.04893194,-1,0.3428221,0.3335567,1.011872,0.3968997,1.008683,0.6700837,0.886711,0.5548246,0.4422186,-0.3110386,-1,0.04391268])
# if member == 17 and lag == 0:
# rands=np.array([0.6939667,-0.02326318,-0.6011316,0.5709327,-0.6587268,-0.2600601,0.09742739,-0.6605019,0.8611858,-0.3578327,-1,0.8069043,1.003004,0.5246789,-0.2310518,-0.3621024,-0.1325169,-0.2894324,0.5083101,-0.04920054,0.2199543,-1,0.009945367])
# if member == 18 and lag == 0:
# rands=np.array([0.3914793,-0.06666035,0.7248741,-0.4003048,-0.6606267,-0.807146,-0.5127164,-0.3519507,0.3180182,0.1996123,-1,-0.9029648,0.1214889,0.02123361,0.6533288,-0.3655816,0.7919229,-0.02215478,0.246939,0.4821936,-0.5654917,-1,-0.003499507])
# if member == 19 and lag == 0:
# rands=np.array([-0.8513823,-1,-0.9796664,0.3713398,-0.4331474,0.3044456,-0.761591,-0.5368128,0.2107361,0.3060099,-1,-0.2859453,-0.5175856,-0.4946607,0.3281591,-0.2430111,0.05990436,0.01103085,-0.6508834,-0.002251296,0.3083046,-1,0.005067795])
# if member == 20 and lag == 0:
# rands=np.array([0.1491016,-0.5305333,-0.4203315,-0.08089796,0.4151005,-0.7401157,0.3687969,-0.5454835,0.714487,1.023472,-1,0.3852504,0.2557997,0.1142526,-0.3280363,0.6893721,-0.4994218,0.7723718,1.014927,0.1936809,0.4526471,-1,0.01181981])
#if member == 1 and lag == 1:
# rands=np.array([-0.8534008,-1,-0.01288013,-0.9141065,0.4783214,-0.1723187,-0.8843083,-0.1162029,-0.009133766,-0.6961404,-1,-0.992416,-0.2270698,-0.795909,-0.9905862,-1,-1,0.5997077,-0.08789548,0.9465016,-0.3178105,-1,0.02080303])
# if member == 2 and lag == 1:
# rands=np.array([0.2119585,-0.06496529,-0.113645,-0.3219482,0.3354161,-0.2561546,-0.8578026,0.488537,0.4687303,0.9537283,-1,0.2903501,-0.2037528,0.2844993,-0.2640329,0.6626308,1.016451,-0.2284834,-0.9198995,-0.772432,-0.9456592,-1,-0.00193539])
# if member == 3 and lag == 1:
# rands=np.array([-0.8951237,-0.03746896,0.6338864,-0.7385425,-0.5215907,-0.2300481,-1,-0.5272307,-0.3447395,-0.161121,-1,-0.3376194,-0.5966941,0.5816291,0.4801027,0.8620167,0.02183707,-0.5763896,0.3380404,-0.4263453,0.5804913,-1,0.04326432])
# if member == 4 and lag == 1:
# rands=np.array([-0.3111334,0.1525611,-0.5140871,-0.9283414,0.08702877,-0.8027726,0.7558329,-0.3327861,-0.3074336,0.4950007,-1,-0.3270844,0.6640525,-0.2391291,-0.08126307,-0.01146206,0.01725924,0.7988746,1.104426,0.1875366,0.5716885,-1,-0.01126271])
# if member == 5 and lag == 1:
# rands=np.array([0.7825249,-0.4134479,-0.4143417,-0.9416251,0.497882,-0.7672758,0.4174937,0.8023338,0.9017133,0.4341536,-1,0.1975201,0.7627142,0.2342599,-0.5487284,-0.8285513,-0.1792318,0.7551883,0.7257428,0.6304003,0.4330811,-1,-0.03357022])
# if member == 6 and lag == 1:
# rands=np.array([-0.3209096,-0.5156574,0.4877877,-0.235484,-0.4256767,0.6646661,0.2886034,0.9879024,-0.06839389,0.6481704,-1,-0.5804082,-0.6514803,0.7308455,0.4101343,-0.8554199,-1,-0.254794,-0.1424363,-0.1296123,0.1170656,-1,0.007876628])
# if member == 7 and lag == 1:
# rands=np.array([-0.4625655,0.6606569,-0.1525902,-0.627054,-0.5481565,-0.7688485,-0.6856943,-0.3053931,-0.03701457,-0.3725091,-1,0.155129,0.136141,-0.7843549,-0.0782493,-0.5983688,0.4498052,0.7143801,-0.1611456,1.007675,0.9826942,-1,0.04915765])
#if member == 8 and lag == 1:
# rands=np.array([-0.3430965,-0.4227155,-0.07735894,0.1337408,0.3767924,-0.3956357,-0.2084762,0.2861364,0.9596422,0.718588,-1,0.7803586,-0.2091809,0.02144417,-0.269459,0.5019733,0.4792759,0.4641314,1.016747,1.19465,0.3354242,-1,0.02544297])
# if member == 9 and lag == 1:
# rands=np.array([-0.7412553,-0.5466392,0.6302599,0.6477938,0.3541165,-0.3394596,0.346593,0.7322681,-0.7179851,-0.731463,-1,0.5184832,-0.1218895,0.7145109,1.139154,0.1859014,-0.6142078,-0.6477512,0.799903,-0.2647903,0.264973,-1,0.01599488])
# if member == 10 and lag == 1:
# rands=np.array([-0.03720611,-0.4162885,0.3607477,0.223355,0.1092008,-0.3264467,-0.721734,-0.6520889,-0.6977112,-0.9723021,-1,-0.693766,-0.4895594,-0.6218721,-0.9569306,-0.009422544,-0.09222607,0.6750132,-0.7697481,-0.6925188,0.6922073,-1,0.04024281])
# if member == 11 and lag == 1:
# rands=np.array([-0.5116026,0.343556,-0.9518977,0.4320086,0.7536819,-0.6906458,-0.8697743,-0.5209014,-0.0006806303,-0.7983823,-1,-0.6035937,-0.6045027,0.2035089,-0.01446889,-0.4622703,0.04925987,-0.1290628,-0.8433856,-0.5047902,-0.4363325,-1,0.04609365])
# if member == 12 and lag == 1:
# rands=np.array([-0.03203867,0.1992727,0.01177486,-0.8682783,-0.6371214,-0.8578926,-0.5281639,0.4448915,0.05798108,0.4604711,-1,-0.6847421,-1,-0.2765961,-0.4123928,-0.1756036,0.459426,0.303864,-0.6993556,0.1463735,0.06817089,-1,-0.02779672])
# if member == 13 and lag == 1:
# rands=np.array([-0.1434858,0.6530933,0.4572614,1.004458,-0.1688031,0.2899839,-0.4890676,-0.08017457,0.2399375,0.8467381,-1,-0.9818414,-0.7904143,0.6110988,-0.2375934,0.008658743,-0.6596891,0.804915,0.8108714,1.026871,-0.632525,-1,0.008359899])
# if member == 14 and lag == 1:
# rands=np.array([0.1951734,0.1356803,-0.7984297,0.6222939,-0.3783839,-0.2109271,-0.5047454,0.7663568,-0.2467663,0.7969002,-1,-0.1039866,-0.0799861,-0.9506351,-0.2622426,-0.5555511,-0.6443623,-0.3867293,0.8174118,-0.2623511,-0.6504129,-1,0.02077365])
#if member == 15 and lag == 1:
# rands=np.array([0.5399131,0.9709167,0.1353998,0.9513429,0.1061087,-0.1906423,0.2546594,0.6244627,0.4253108,-0.5599692,-1,-0.6188933,-0.009267714,0.5136899,0.7686136,-0.7976044,0.6507949,0.2095091,-0.8000021,0.7896537,-0.6757631,-1,-0.04675981])
# if member == 16 and lag == 1:
# rands=np.array([0.2039032,0.4437117,-0.8730499,-0.8436514,-0.3197937,0.1300565,0.2079234,0.07507716,-0.1368318,-0.8760222,-1,0.7775679,0.1503317,-0.6096121,-0.4738224,-0.4769458,0.01603237,-0.5856133,0.1603515,0.639724,0.8044817,-1,-0.03076992])
# if member == 17 and lag == 1:
# rands=np.array([0.6912341,0.03829452,0.0645803,-0.1688701,-0.06769498,0.5935946,0.794823,-0.1764786,-0.5644639,0.1006729,-1,0.3818233,0.8611205,0.8617951,1.227797,0.06026031,-0.3915816,-0.2597351,0.03994489,0.04319654,0.4627483,-1,0.02341671])
# if member == 18 and lag == 1:
# rands=np.array([0.1631074,-0.7144772,0.5909197,0.8623705,-0.157497,0.5982791,-0.2239446,0.5731422,0.6889938,-0.04528522,-1,0.07149924,-0.6744667,0.4025916,0.9270787,0.1585069,0.8808218,0.5500813,1.050181,0.6608209,-0.4115532,-1,0.0001819044])
# if member == 19 and lag == 1:
# rands=np.array([-0.4331279,-0.2030248,-0.2923387,-0.1422065,0.04285587,-0.6101277,-0.5285338,0.820045,0.4597438,-0.7786282,-1,0.3376869,0.1703605,-0.5970429,-0.2520662,-0.6760406,0.3587383,0.1665516,-0.351487,-0.3638605,0.4790523,-1,-0.01560173])
# if member == 20 and lag == 1:
# rands=np.array([0.4845566,0.6727741,0.9107583,-0.2242058,0.8334284,-0.0900023,-0.1698219,-0.1709382,-0.1608661,-0.9621966,-1,0.9589482,-0.5061898,0.02963674,-0.5164318,-0.05424574,-0.8521295,0.7594227,-0.5421007,0.8706058,0.7356571,-1,-0.03166761])
#newmember.param_values = rands + newmean
newmember = EnsembleMember(member)
newmember.param_values = (np.hstack((np.dot(C, rands[0:11]),np.dot(C, rands[11:22]), rands_bg)) + newmean).ravel()
newmember.param_values[10] = 0.
......
......@@ -75,7 +75,7 @@ da.resources.ntasks : 1
! any form appropriate for your system. Typically, HPC queueing systems allow you a certain number of hours of usage before
! your job is killed, and you are expected to finalize and submit a next job before that time. Valid entries are strings.
da.resources.ntime : 04:00:00
da.resources.ntime : 44:00:00
! The resource settings above will cause the creation of a job file in which 2 cycles will be run, and 30 threads
! are asked for a duration of 4 hours
......
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