def L96_table(): import dapper.mods.Lorenz96 as model from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM model.Force = 8.0 # undo pinheiro2019 HMM = _HMM.copy() HMM.t.BurnIn = 0 HMM.t.KObs = 10 dpr.set_seed(3000) # xps xps = dpr.xpList() xps += da.Climatology() xps += da.OptInterp() xps += da.Var3D(xB=0.02) xps += da.ExtKF(infl=6) xps += da.EnKF("PertObs", N=40, infl=1.06) xps += da.EnKF("Sqrt", N=28, infl=1.02, rot=True) xps += da.EnKF_N(N=24, rot=True) xps += da.EnKF_N(N=24, rot=True, xN=2) xps += da.iEnKS("Sqrt", N=40, infl=1.01, rot=True) xps += da.LETKF(N=7, rot=True, infl=1.04, loc_rad=4) xps += da.SL_EAKF(N=7, rot=True, infl=1.07, loc_rad=6) xps.launch(HMM, store_u=True) table = xps.tabulate_avrgs(statkeys, decimals=4) return table.splitlines(True)
def L63_gen(): from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM HMM = _HMM.copy() HMM.tseq.BurnIn = 0 HMM.tseq.Ko = 10 # xps xps = dpr.xpList() xps += da.Climatology() xps += da.OptInterp() xps += da.Persistence() xps += da.PreProg(lambda k, xx, yy: xx[k]) xps += da.Var3D(xB=0.1) xps += da.ExtKF(infl=90) xps += da.EnKF("Sqrt", N=3, infl=1.30) xps += da.EnKF("Sqrt", N=10, infl=1.02, rot=True) xps += da.EnKF("PertObs", N=500, infl=0.95, rot=False) xps += da.EnKF_N(N=10, rot=True) xps += da.iEnKS("Sqrt", N=10, infl=1.02, rot=True) xps += da.PartFilt(N=100, reg=2.4, NER=0.3) xps += da.PartFilt(N=800, reg=0.9, NER=0.2) xps += da.PartFilt(N=4000, reg=0.7, NER=0.05) xps += da.PFxN(xN=1000, N=30, Qs=2, NER=0.2) xps += da.OptPF(N=100, Qs=2, reg=0.7, NER=0.3) xps += da.EnKS("Serial", N=30, Lag=1) xps += da.EnRTS("Serial", N=30, DeCorr=0.99) for xp in xps: xp.seed = 3000 # Run xps.launch(HMM, False, store_u=True) return xps
def L63_gen(): from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM HMM = _HMM.copy() HMM.t.BurnIn = 0 HMM.t.KObs = 10 dpr.set_seed(3000) # xps xps = dpr.xpList() xps += da.Climatology() xps += da.OptInterp() xps += da.Var3D(xB=0.1) xps += da.ExtKF(infl=90) xps += da.EnKF("Sqrt", N=3, infl=1.30) xps += da.EnKF("Sqrt", N=10, infl=1.02, rot=True) xps += da.EnKF("PertObs", N=500, infl=0.95, rot=False) xps += da.EnKF_N(N=10, rot=True) xps += da.iEnKS("Sqrt", N=10, infl=1.02, rot=True) xps += da.PartFilt(N=100, reg=2.4, NER=0.3) xps += da.PartFilt(N=800, reg=0.9, NER=0.2) xps += da.PartFilt(N=4000, reg=0.7, NER=0.05) xps += da.PFxN(xN=1000, N=30, Qs=2, NER=0.2) # Run xps.launch(HMM, False, store_u=True) return xps
def test_L63(): from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM xps = dpr.xpList() xps += da.EnKF("Sqrt", N=10, infl=1.02, rot=True) xps += da.PartFilt(N=20, reg=2.4, NER=0.3) xps += da.OptInterp() # xps += da.iEnKS('Sqrt', N=10, infl=1.02,rot=True) HMM = _HMM.copy() HMM.tseq.BurnIn = HMM.tseq.dto HMM.tseq.Ko = 1 xps.launch( HMM, free=False, statkeys=True, liveplots=None, store_u=False, fail_gently=False, save_as=False, ) print(xps.tabulate_avrgs(["rmse.a"])) # Disabled coz magic doesn't work any more on python 3.7 # spell_out(HMM) # spell_out(xps[-1]) # spell_out(xps[-1].stats) # spell_out(xps[-1].avrgs) assert True # An assertion for pytest to count return HMM, xps # Return useful stuff
def L96_gen(): import dapper.mods.Lorenz96 as model from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM model.Force = 8.0 # undo pinheiro2019 HMM = _HMM.copy() HMM.tseq.BurnIn = 0 HMM.tseq.Ko = 10 # xps xps = dpr.xpList() xps += da.Climatology() xps += da.OptInterp() xps += da.Persistence() xps += da.PreProg(lambda k, xx, yy: xx[k]) xps += da.Var3D(xB=0.02) xps += da.ExtKF(infl=6) xps += da.EnKF("PertObs", N=40, infl=1.06) xps += da.EnKF("Sqrt", N=28, infl=1.02, rot=True) xps += da.EnKF_N(N=24, rot=True) xps += da.EnKF_N(N=24, rot=True, xN=2) xps += da.iEnKS("Sqrt", N=40, infl=1.01, rot=True) xps += da.LETKF(N=7, rot=True, infl=1.04, loc_rad=4) xps += da.SL_EAKF(N=7, rot=True, infl=1.07, loc_rad=6) for xp in xps: xp.seed = 3000 xps.launch(HMM, store_u=True) return xps
def test_L63(): from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM xps = dpr.xpList() xps += da.EnKF('Sqrt', N=10, infl=1.02, rot=True) xps += da.PartFilt(N=20, reg=2.4, NER=0.3) xps += da.OptInterp() # xps += da.iEnKS('Sqrt', N=10, infl=1.02,rot=True) HMM = _HMM.copy() HMM.t.BurnIn = HMM.t.dtObs HMM.t.KObs = 1 xps.launch(HMM, free=False, liveplots="all", store_u=False, fail_gently=False) for xp in xps: xp.stats.replay("all") xp.stats.replay(t2=1) xp.stats.replay(t2=0.0) xp.stats.replay(t2=0.3) xp.stats.replay(t2=0.8) xp.stats.replay(t2=0.8, t1=0.2) xp.stats.replay(t2=np.inf) xp.stats.replay(t2=np.inf, speed=1) xp.stats.replay(t2=np.inf, pause_a=0, pause_f=0) assert True # An assertion for pytest to count return HMM, xps # Return useful stuff
def test_L96(): xps = dpr.xpList() from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM xps += da.EnKF('PertObs', N=40, infl=1.06) xps += da.EnKF('Serial', N=28, infl=1.02, rot=True) xps += da.OptInterp() xps += da.Var3D(xB=0.02) xps += da.ExtKF(infl=10) xps += da.LETKF(N=6, rot=True, infl=1.05, loc_rad=4, taper='Step') # from dapper.mods.Lorenz96.bocquet2015loc import HMM # xps += da.EnKF_N( N=24, rot=True ,infl=1.01) # xps += da.PartFilt(N=3000,NER=0.20,reg=1.2) # xps += da.PFxN( N=1000,xN=100, NER=0.9,Qs=0.6) HMM = _HMM.copy() # HMM.t.BurnIn = 10*HMM.t.dtObs # HMM.t.KObs = 30 HMM.t.BurnIn = HMM.t.dtObs HMM.t.KObs = 2 xps.launch(HMM, free=False, liveplots="all", store_u=False, fail_gently=False, save_as=False) for xp in xps: xp.stats.replay("all") xp.stats.replay(t2=1) xp.stats.replay(t2=0.0) xp.stats.replay(t2=0.3) xp.stats.replay(t2=0.8) xp.stats.replay(t2=0.8, t1=0.2) xp.stats.replay(t2=np.inf) xp.stats.replay(t2=np.inf, speed=1) xp.stats.replay(t2=np.inf, pause_a=0, pause_f=0) assert True # An assertion for pytest to count return HMM, xps # Return useful stuff
def test_L96(): xps = dpr.xpList() from dapper.mods.Lorenz96.sakov2008 import HMM as _HMM xps += da.EnKF("PertObs", N=40, infl=1.06) xps += da.EnKF("Serial", N=28, infl=1.02, rot=True) xps += da.OptInterp() xps += da.Var3D(xB=0.02) xps += da.ExtKF(infl=10) xps += da.LETKF(N=6, rot=True, infl=1.05, loc_rad=4, taper="Step") # from dapper.mods.Lorenz96.bocquet2015loc import HMM # xps += da.EnKF_N( N=24, rot=True ,infl=1.01) # xps += da.PartFilt(N=3000,NER=0.20,reg=1.2) # xps += da.PFxN( N=1000,xN=100, NER=0.9,Qs=0.6) HMM = _HMM.copy() # HMM.tseq.BurnIn = 10*HMM.tseq.dto # HMM.tseq.Ko = 30 HMM.tseq.BurnIn = HMM.tseq.dto HMM.tseq.Ko = 2 xps.launch( HMM, free=False, statkeys=True, liveplots=None, store_u=False, fail_gently=False, save_as=False, ) print(xps.tabulate_avrgs(["rmse.a"])) # spell_out(HMM) # spell_out(xps[-1]) # spell_out(xps[-1].stats) # spell_out(xps[-1].avrgs) assert True # An assertion for pytest to count return HMM, xps # Return useful stuff
"""Settings from `bib.wiljes2016second`.""" import numpy as np import dapper.mods as modelling from dapper.mods.Lorenz63.sakov2012 import HMM as _HMM from dapper.mods.Lorenz63.sakov2012 import Nx HMM = _HMM.copy() HMM.tseq = modelling.Chronology(0.01, dko=12, T=4**5, BurnIn=4) jj = np.array([0]) Obs = modelling.partial_Id_Obs(Nx, jj) Obs['noise'] = 8 HMM.Obs = modelling.Operator(**Obs) #################### # Suggested tuning #################### # Reproduce benchmarks for NETF and ESRF (here EnKF-N) from left pane of Fig 1. # from dapper.mods.Lorenz63.wiljes2017 import HMM # rmse.a reported by DAPPER / PAPER: # ------------------------------------------------------------------------------ # HMM.tseq.Ko = 10**2 # xps += OptInterp() # 5.4 / N/A # xps += Var3D(xB=0.3) # 3.0 / N/A # xps += EnKF_N(N=5) # 2.68 / N/A # xps += EnKF_N(N=30,rot=True) # 2.52 / 2.5 # xps += LNETF(N=40,rot=True,infl=1.02,Rs=1.0,loc_rad='NA') # 2.61 / ~2.2 # xps += PartFilt(N=35 ,reg=1.4,NER=0.3) # 2.05 / 1.4 * # *: tuning settings not given #
import dapper as dpr import dapper.da_methods as da # #### Load experiment setup: the hidden Markov model (HMM) from dapper.mods.Lorenz63.sakov2012 import HMM # isort:skip # #### Generate the same random numbers each time this script is run seed = dpr.set_seed(3000) # #### Simulate synthetic truth (xx) and noisy obs (yy) HMM.t.T = 30 # shorten experiment xx, yy = HMM.simulate() # #### Specify a DA method configuration ("xp" for "experiment") xp = da.EnKF('Sqrt', N=10, infl=1.02, rot=True) # xp = da.Var3D() # xp = da.PartFilt(N=100, reg=2.4, NER=0.3) # #### Assimilate yy, knowing the HMM; xx is used to assess the performance xp.assimilate(HMM, xx, yy, liveplots=not nb) # #### Average the time series of various statistics xp.stats.average_in_time()