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')
def predictfield(self, data, outfield, infield, forcfield, SW=None):
if SW is None:
SW = SWmodel()
nout = (1, 1)
delta = (1, 1)
field = mydata(data,
outfield=outfield,
infield=infield,
forcfield=forcfield,
nout=nout,
delta=delta,
dt=1,
SW=SW)
field.make_base()
y_predict = self.predict(field._X)
outfield = MakeBigImage(y_predict,
ind=(field._indouty, field._indoutx))
return outfield
def __init__( self, fname, outfield,infield, forcfield, nout=(1,1),delta=(1,1),dt=1, SW=None): self._outfield = outfield self._infield = infield self._forcfield = forcfield self._nout = nout self._delta = delta self._dt = dt if SW is None: self._SW = SWmodel() else: self._SW = SW if isinstance(fname,str): self._fname = fname self._data = xr.open_dataset(self._fname) else: self._fname = '' self._data = fname #define times self._t = [] if self.dt == 0: tslice = slice(0,None) else: tslice = slice(0,-self.dt) for t in self.data.time[tslice]: if int(t+dt) in self.data.time.values: self._t.append((t,t+dt)) self._X = None self._y = None self._indinx = None self._indiny = None self._indoutx = None self._indouty = None self._indint = None self._indoutt = None
from shalw import SWmodel
import numpy as np
import xarray as xr
try:
import matplotlib.pyplot as plt
PLOT = True
except:
PLOT = False
endtime = 400
midtime = 200
#Run : one step
SW = SWmodel(nx=80,ny=80)
SW.initstate_cst(0,0,0)
SW.save(time=np.arange(0,endtime,1),name='longrun.nc')
for i in range(endtime):
SW.next()
SW.remove_save('longrun.nc')
#Run : two steps
SW.initstate_cst(0,0,0)
SW.save(time=np.arange(0,midtime,1),name='firstrun.nc')
for i in range(0,midtime):
SW.next()
SW.save_rst('restart_'+str(midtime)+'.nc')
SW.remove_save('firstrun.nc')
SW.initstate_cst(0,0,0)
SW.save(time=np.arange(midtime,endtime,1),name='secondrun.nc')
SW.inistate_rst('restart_'+str(midtime)+'.nc')
for i in range(midtime,endtime):
SW.next()
import xarray as xr
from tqdm import tqdm
try:
import matplotlib.pyplot as plt
PLOT = True
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')
if not os.path.isfile(rfile) or rewrite:
if '_hr' in rfile:
fact = 8
outname = '../../data/restartrun_20years_hr.nc'
#SW = SWmodel(nx=320, ny=320, dx=5e3, dy=5e3,nu=0.18,dt=450)
elif '_lr' in rfile:
fact = 1
outname = '../../data/restartrun_20years_lr.nc'
#SW = SWmodel(nx=40,ny=40,dx = 40e3, dy=40e3, nu=1.44, dt=3600//fact)
else:
fact = 2
outname = '../../data/restartrun_20years_mr.nc'
SW = SWmodel(nx=40 * fact,
ny=40 * fact,
dx=40e3 // fact,
dy=40e3 // fact,
nu=1.44 / fact,
dt=3600 // fact,
alpha=0.025)
SW.initstate_cst(0, 0, 0)
endtime = (fact * 24) * 30 * 12 * 20 #10 years of spinup
SW.save(time=np.arange(0, endtime, (fact * 24) * 30),
name=outname) #monthly
#run the model
for i in tqdm(range(endtime)):
SW.next()
#Save the restart
SW.save_rst(rfile)
# starting time
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: