text: # Settings from
  # Pajonk, Oliver, et al. 
  #   "A deterministic filter for non-Gaussian Bayesian estimation—applications
  #    to dynamical system estimation with noisy measurements."
  #   Physica D: Nonlinear Phenomena 241.7 (2012): 775-788.
  #
  # There is nothing to reproduce from the paper as there are no
  # statistically converged numbers.
  
  # More interesting settings: mods.Lorenz84.harder
  
  from common import *
  
  from mods.Lorenz84.core import step, dfdx, Nx
  from mods.Lorenz63.core import LPs
  
  Ny = Nx
  
  day = 0.05/6 * 24 # coz dt=0.05 <--> 6h in "model time scale"
  t = Chronology(0.05,dkObs=1,T=200*day,BurnIn=10*day)
  
  Dyn = {
      'M'    : Nx,
      'model': step,
      'jacob': dfdx,
      'noise': 0
      }
  
  X0 = GaussRV(C=0.01,M=Nx) # Decreased from Pajonk's C=1.
  
  jj = arange(Nx)
  Obs = partial_direct_Obs(Nx, jj)
  Obs['noise'] = 0.1
  
  HMM = HiddenMarkovModel(Dyn,Obs,t,X0,LP=LPs(jj))
  
  ####################
  # Suggested tuning
  ####################
  # cfgs += ExtKF(infl=2)
  # cfgs += EnKF('Sqrt',N=3,infl=1.01)
  # cfgs += PartFilt(reg=1.0, N=100, NER=0.4) # add reg!
  # cfgs += PartFilt(reg=1.0, N=1000, NER=0.1) # add reg!
  