text: # Small-ish
  
  # NB: Since there is no noise, and the system is stable,
  #     the benchmarks obtained from this configuration
  #     - go to zero as T-->infty
  #     - are highly dependent on the initial error.
  
  from common import *
  
  from mods.LA.core import Fmat, homogeneous_1D_cov
  
  tseq = Chronology(dt=1,dkObs=5,T=300,BurnIn=-1)
  
  m = 100
  
  #def step(x,t,dt):
    #return np.roll(x,1,axis=x.ndim-1)
  Fm = Fmat(m,-1,1,tseq.dt)
  def step(x,t,dt):
    assert dt == tseq.dt
    return x @ Fm.T
  
  f = {
      'm': m,
      'model': step,
      'noise': 0
      }
  
  X0 = GaussRV(C=homogeneous_1D_cov(m,m/8,kind='Gauss'))
  
  p  = 4
  jj = equi_spaced_integers(m,p)
  h  = partial_direct_obs_setup(m,jj)
  h['noise'] = 0.01
  
   
  other = {'name': os.path.relpath(__file__,'mods/')}
  setup = TwinSetup(f,h,tseq,X0,**other)
  
  
  
  ####################
  # Suggested tuning
  ####################
  
  #config.N = 100
  