def setUp ( self ):
"""Initialisation des tests."""
self.rfile = '../data/restart_10years.nc'
self.refile = 'test0.nc'
self.para = {'hphy','hdyn','uphy','udyn','uforc','uparam'}
self.ds = xr.open_dataset(self.refile)
SW = SWmodel(nx=80, ny=80)
SW.inistate_rst(self.rfile)
SW.set_time(0)
endtime = 12 * 30 * 12 * 1
SW.save(time=np.arange(1, endtime, 12 * 7), para=self.para, name='test.nc')
for i in range(endtime):
SW.next()
self.ds2 = xr.open_dataset('test.nc')
outfile_chg = '../../data/test-z0-change.nc'
outfile_nochg = '../../data/test-z0-nochange.nc'
#Run 2 versions of the model
# - one with the old z limit condition
# - one with the changed z limit condition
files2run = {outfile_chg, outfile_nochg}
#files2run = {} #comment to rerun
for outfile in files2run:
para = {'hphy', 'vphy', 'uphy'}
if 'nochange' in outfile:
SW = SWmodel(nx=80, ny=80)
else:
SW = SWz0(nx=80, ny=80)
SW.inistate_rst(rfile)
SW.set_time(0)
endtime = 12 * 30 * 12 * 10
SW.save(time=np.arange(0, endtime, 12 * 7), para=para, name=outfile)
for i in tqdm(range(endtime + 1)):
SW.next()
ds_chg = xr.open_dataset(outfile_chg)
ds_nochg = xr.open_dataset(outfile_nochg)
out = ds_nochg.assign(dh=ds_chg.hphy - ds_nochg.hphy)
out = out.assign(du=ds_chg.uphy - ds_nochg.uphy)
out = out.assign(dv=ds_chg.vphy - ds_nochg.vphy)
f2plot = {'dh', 'du', 'dv'}
for par in f2plot:
fig, ax = plt.subplots(nrows=2)
out[par].isel(time=1).plot(ax=ax[0])
except:
PLOT = False
PLOT = False
rfile = '../data/restart_10years.nc'
nnufile = '../data/nn-uparam-im/model_upar-im.pkl'
nnvfile = '../data/nn-vparam-im/model_vpar-im.pkl'
nhdynfile = '../data/nn-hdynparam-amsgrad-long2/model_hdyn-im.pkl'
SW0 = SWmodel(nx=80, ny=80)
SW = SWparnnim(nnupar=nnufile,nnvpar=nnvfile,nx=80,ny=80)
#SW = SWparnnhdyn(nnupar=nnufile,nnvpar=nnvfile,nnhdyn=nhdynfile, nx=80,ny=80)
SW.inistate_rst(rfile)
SW.set_time(0)
SW0.inistate_rst(rfile)
SW0.set_time(0)
# time of the spinup
# endtime = 12*30*12*10 #10 years
endtime = 48 * 30 * 12 * 15
#endtime = 48 * 30 * 12 *10
# Declare to save all phy parameters (default) every 12*30 time step(1 month)
# 10000 is approximatively 13 months
para = { 'hphy', 'hdyn', 'uphy', 'udyn', 'uparam','vparam','vphy' }
SW.save(time=np.arange(0, endtime,48*7 ), para=para, name='../data/egu/test-nn-new.nc')
SW0.save(time=np.arange(0, endtime,48*7 ), para=para, name='../data/egu/test-00-new.nc')
# Run the model
start = time.time()
for i in tqdm(range(endtime)):
SW.next()
t0 = 0
# Generation of the ensemble
epsb = dict()
for par in std_b:
epsb[par] = np.empty(shape=(m, ) + dr[par][0].shape)
for im in range(m):
eps = np.random.normal(0, std_b[par], dr[par][0].shape)
epsb[par][im] = ndimage.convolve(eps, k, mode='constant')
epsb[par][im] += dr[par][0]
#Model load:
SW = SWmodel()
SW.inistate_rst(rfile)
SW.set_time(t0)
#Reference model (to produce obs)
SW0 = SWmodel()
SW0.inistate_rst(rfile)
SW0.set_time(t0)
#parameters in control state variable (not all ?)
cparam = {'hphy', 'uphy', 'vphy', 'ufil', 'vfil', 'hfil'}
epsa = epsb
endtime = 3
for t in tqdm(ds.hphy_o.time[:endtime]):
#forecast
if t > t0:
epsb = forward(SW, epsa, int(t - t0))
for i in range(int(t0), int(t)):