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 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 data(): from dapper.mods.LA.small import HMM as _HMM HMM = _HMM.copy() HMM.t.BurnIn = 0 HMM.t.KObs = 10 xps = dpr.xpList(unique=True) # yapf: disable xps += da.EnKF('Sqrt' , N=20) xps += da.EnKF('PertObs', N=20) xps += da.EnKF('DEnKF' , N=20) for Lag in [0, 1, 3]: xps += da.EnKS('Sqrt' , N=20, Lag=Lag) xps += da.EnKS('PertObs', N=20, Lag=Lag) xps += da.EnKS('DEnKF' , N=20, Lag=Lag) for nIter in [1, 4]: for MDA in [False, True]: xps += da.iEnKS('Sqrt' , N=20, Lag=Lag, nIter=nIter, MDA=MDA) xps += da.iEnKS('PertObs', N=20, Lag=Lag, nIter=nIter, MDA=MDA) xps += da.iEnKS('Order1' , N=20, Lag=Lag, nIter=nIter, MDA=MDA) # yapf: enable for xp in xps: xp.seed = 3000 xps.launch(HMM, store_u=True, save_as=False) print(xps.tabulate_avrgs(["rmse.a", "rmse.f", "rmse.u", "rmse.s"])) 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 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
# #### 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() # #### Print some averages print(xp.avrgs.tabulate(['rmse.a', 'rmv.a']))
core.diffusion = xp.Diffus1 xx, yy = HMMs("Tay2", xp.resoltn, R=xp.ObsErr2).simulate() if xp.resoltn == "Low": xx = xx[::2] hmm = HMMs(xp.stepper, xp.resoltn, R=xp.ObsErr2) return hmm, xx, yy # #### DA method and experiment listing xps = dpr.xpList() for resolution in ["Low", "High"]: for step_kind in ["RK4", "EM"]: for diffusion_stddev in [0.1, 0.25, 0.5, 0.75, 1.0]: for obs_noise_variance in [0.1, 0.25, 0.5, 0.75, 1.0]: xp = da.EnKF('PertObs', N=100) xp.resoltn = resolution xp.stepper = step_kind xp.Diffus1 = diffusion_stddev xp.ObsErr2 = obs_noise_variance xps.append(xp) # #### Run experiments save_as = xps.launch(HMMs(), __file__, setup=setup) # #### Load data # This block is redundant if running in the same script # as generates the data and not using multiprocessing. # +
import dapper as dpr import dapper.da_methods as da seed = dpr.set_seed(3000) # #### DA method configurations xps = dpr.xpList() from dapper.mods.Lorenz63.sakov2012 import HMM # Expected rmse.a: xps += da.Climatology() # 7.6 xps += da.OptInterp() # 1.25 xps += da.Var3D(xB=0.1) # 1.03 xps += da.ExtKF(infl=90) # 0.87 xps += da.EnKF('Sqrt', N=3, infl=1.30) # 0.82 xps += da.EnKF('Sqrt', N=10, infl=1.02, rot=True) # 0.63 xps += da.EnKF('PertObs', N=500, infl=0.95, rot=False) # 0.56 xps += da.EnKF_N(N=10, rot=True) # 0.54 xps += da.iEnKS('Sqrt', N=10, infl=1.02, rot=True) # 0.31 xps += da.PartFilt(N=100, reg=2.4, NER=0.3) # 0.38 xps += da.PartFilt(N=800, reg=0.9, NER=0.2) # 0.28 # xps += da.PartFilt( N=4000, reg=0.7 , NER=0.05) # 0.27 # xps += da.PFxN(xN=1000, N=30 , Qs=2 , NER=0.2) # 0.56 # #### With Lorenz-96 instead # + # from dapper.mods.Lorenz96.sakov2008 import HMM # Expected rmse.a: # xps += da.Climatology() # 3.6 # xps += da.OptInterp() # 0.95