"""Uses `nU`, `J`, `F` as in `dapper.mods.LorenzUV` ie. from `bib.wilks2005effects`.
Obs settings taken from different places (=> quasi-linear regime).
"""
import numpy as np
import dapper.mods as modelling
from dapper.mods.LorenzUV import model_instance
from ..utils import rel2mods
LUV = model_instance()
nU = LUV.nU
# Wilks2005 uses dt=1e-4 with RK4 for the full model,
# and dt=5e-3 with RK2 for the forecast/truncated model.
# As berry2014linear notes, this is possible coz
# "numerical stiffness disappears when fast processes are removed".
################
# Full
################
# tseq = modelling.Chronology(dt=0.001,dto=0.05,T=4**3,BurnIn=6) # allows using rk2
tseq = modelling.Chronology(dt=0.005, dto=0.05, T=4**3,
BurnIn=6) # requires rk4
Dyn = {
'M': LUV.M,
'model': modelling.with_rk4(LUV.dxdt, autonom=True),
def test_LUV():
from dapper.mods.LorenzUV import model_instance
LUV = model_instance(nU=10, J=4, F=10)
assert _allclose(LUV.dxdt, LUV.d2x_dtdx, LUV.x0)
"""As in `bib.lorenz1996predictability`."""
import numpy as np
import dapper.mods as modelling
from dapper.mods.LorenzUV import model_instance
from ..utils import rel2mods
LUV = model_instance(nU=36, J=10, F=10)
nU = LUV.nU
################
# Full
################
t = modelling.Chronology(dt=0.005, dtObs=0.05, T=4**3, BurnIn=6)
Dyn = {
'M': LUV.M,
'model': modelling.with_rk4(LUV.dxdt, autonom=True),
'noise': 0,
'linear': LUV.dstep_dx,
}
X0 = modelling.GaussRV(mu=LUV.x0, C=0.01)
R = 1.0
jj = np.arange(nU)
Obs = modelling.partial_Id_Obs(LUV.M, jj)
Obs['noise'] = R
def test_VL20():
from dapper.mods.VL20 import model_instance
VL20 = model_instance(nX=18, F=10, G=10)
assert _allclose(VL20.dxdt, VL20.d2x_dtdx, VL20.x0)
