dapper.mods.Lorenz96.anderson2009
A land-ocean setup from bib.anderson2009spatially.
1"""A land-ocean setup from `bib.anderson2009spatially`.""" 2 3import numpy as np 4 5import dapper.mods as modelling 6from dapper.mods.Lorenz96.sakov2008 import X0, Dyn, LPs, Nx, Tplot 7from dapper.tools.localization import localization_setup, pairwise_distances 8from dapper.tools.viz import xtrema 9 10tseq = modelling.Chronology(0.05, dto=0.05, Ko=4000, Tplot=Tplot, BurnIn=2000 * 0.05) 11 12# Define obs sites 13obs_sites = 0.395 + 0.01 * np.arange(1, 21) 14obs_sites *= 40 15# Surrounding inds 16ii_below = obs_sites.astype(int) 17ii_above = ii_below + 1 18# Linear-interpolation weights 19w_above = obs_sites - ii_below 20w_below = 1 - w_above 21# Define obs matrix 22H = np.zeros((20, 40)) 23H[np.arange(20), ii_below] = w_below 24H[np.arange(20), ii_above] = w_above 25# Measure obs-state distances 26y2x_dists = pairwise_distances(obs_sites[:, None], np.arange(Nx)[:, None], domain=(Nx,)) 27batches = np.arange(40)[:, None] 28# Define operator 29Obs = { 30 "M": len(H), 31 "model": lambda E: E @ H.T, 32 "linear": lambda E: H, 33 "noise": 1, 34 "localizer": localization_setup(lambda: y2x_dists, batches), 35} 36 37HMM = modelling.HiddenMarkovModel( 38 Dyn, 39 Obs, 40 tseq, 41 X0, 42 LP=LPs(), 43 sectors={"land": np.arange(*xtrema(obs_sites)).astype(int)}, 44) 45 46#################### 47# Suggested tuning 48#################### 49 50# Reproduce Anderson Figure 2 51# ----------------------------------------------------------------------------------- 52# xp = SL_EAKF(N=6, infl=sqrt(1.1), loc_rad=0.2/1.82*40) 53# for lbl in ['err', 'spread']: 54# stat = getattr(xp.stats,lbl).f[HMM.tseq.masko] 55# plt.plot(sqrt(np.mean(stat**2, axis=0)),label=lbl) 56# 57# Note: for this xp, one must to be lucky with the random seed to avoid 58# blow up in the ocean sector (which is not constrained by obs) due to infl. 59# Instead, I recommend lowering dt (as in Miyoshi 2011) to stabilize integration.
tseq =
<Chronology>
- K: 4001
- Ko: 4000
- T: 200.05
- BurnIn: 100.0
- dto: 0.05
- dt: 0.05
obs_sites =
array([16.2, 16.6, 17. , 17.4, 17.8, 18.2, 18.6, 19. , 19.4, 19.8, 20.2, 20.6,
21. , 21.4, 21.8, 22.2, 22.6, 23. , 23.4, 23.8])
ii_below =
array([16, 16, 17, 17, 17, 18, 18, 19, 19, 19, 20, 20, 21, 21, 21, 22, 22, 23,
23, 23])
ii_above =
array([17, 17, 18, 18, 18, 19, 19, 20, 20, 20, 21, 21, 22, 22, 22, 23, 23, 24,
24, 24])
w_above =
array([0.2, 0.6, 0. , 0.4, 0.8, 0.2, 0.6, 0. , 0.4, 0.8, 0.2, 0.6, 0. , 0.4,
0.8, 0.2, 0.6, 0. , 0.4, 0.8])
w_below =
array([0.8, 0.4, 1. , 0.6, 0.2, 0.8, 0.4, 1. , 0.6, 0.2, 0.8, 0.4, 1. , 0.6,
0.2, 0.8, 0.4, 1. , 0.6, 0.2])
H =
array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.8, 0.2, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0.4, 0.6, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.6, 0.4, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0.2, 0.8, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.8, 0.2, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0.4, 0.6, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0.6, 0.4, 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0.2, 0.8, 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0.8, 0.2, 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0.4, 0.6, 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.6, 0.4, 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.2, 0.8, 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.8, 0.2, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.4, 0.6, 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.6, 0.4, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.2, 0.8, 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])
y2x_dists =
array([[16.2, 15.2, 14.2, 13.2, 12.2, 11.2, 10.2, 9.2, 8.2, 7.2, 6.2,
5.2, 4.2, 3.2, 2.2, 1.2, 0.2, 0.8, 1.8, 2.8, 3.8, 4.8,
5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8,
16.8, 17.8, 18.8, 19.8, 19.2, 18.2, 17.2],
[16.6, 15.6, 14.6, 13.6, 12.6, 11.6, 10.6, 9.6, 8.6, 7.6, 6.6,
5.6, 4.6, 3.6, 2.6, 1.6, 0.6, 0.4, 1.4, 2.4, 3.4, 4.4,
5.4, 6.4, 7.4, 8.4, 9.4, 10.4, 11.4, 12.4, 13.4, 14.4, 15.4,
16.4, 17.4, 18.4, 19.4, 19.6, 18.6, 17.6],
[17. , 16. , 15. , 14. , 13. , 12. , 11. , 10. , 9. , 8. , 7. ,
6. , 5. , 4. , 3. , 2. , 1. , 0. , 1. , 2. , 3. , 4. ,
5. , 6. , 7. , 8. , 9. , 10. , 11. , 12. , 13. , 14. , 15. ,
16. , 17. , 18. , 19. , 20. , 19. , 18. ],
[17.4, 16.4, 15.4, 14.4, 13.4, 12.4, 11.4, 10.4, 9.4, 8.4, 7.4,
6.4, 5.4, 4.4, 3.4, 2.4, 1.4, 0.4, 0.6, 1.6, 2.6, 3.6,
4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 14.6,
15.6, 16.6, 17.6, 18.6, 19.6, 19.4, 18.4],
[17.8, 16.8, 15.8, 14.8, 13.8, 12.8, 11.8, 10.8, 9.8, 8.8, 7.8,
6.8, 5.8, 4.8, 3.8, 2.8, 1.8, 0.8, 0.2, 1.2, 2.2, 3.2,
4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2,
15.2, 16.2, 17.2, 18.2, 19.2, 19.8, 18.8],
[18.2, 17.2, 16.2, 15.2, 14.2, 13.2, 12.2, 11.2, 10.2, 9.2, 8.2,
7.2, 6.2, 5.2, 4.2, 3.2, 2.2, 1.2, 0.2, 0.8, 1.8, 2.8,
3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8, 12.8, 13.8,
14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 19.2],
[18.6, 17.6, 16.6, 15.6, 14.6, 13.6, 12.6, 11.6, 10.6, 9.6, 8.6,
7.6, 6.6, 5.6, 4.6, 3.6, 2.6, 1.6, 0.6, 0.4, 1.4, 2.4,
3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 9.4, 10.4, 11.4, 12.4, 13.4,
14.4, 15.4, 16.4, 17.4, 18.4, 19.4, 19.6],
[19. , 18. , 17. , 16. , 15. , 14. , 13. , 12. , 11. , 10. , 9. ,
8. , 7. , 6. , 5. , 4. , 3. , 2. , 1. , 0. , 1. , 2. ,
3. , 4. , 5. , 6. , 7. , 8. , 9. , 10. , 11. , 12. , 13. ,
14. , 15. , 16. , 17. , 18. , 19. , 20. ],
[19.4, 18.4, 17.4, 16.4, 15.4, 14.4, 13.4, 12.4, 11.4, 10.4, 9.4,
8.4, 7.4, 6.4, 5.4, 4.4, 3.4, 2.4, 1.4, 0.4, 0.6, 1.6,
2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6,
13.6, 14.6, 15.6, 16.6, 17.6, 18.6, 19.6],
[19.8, 18.8, 17.8, 16.8, 15.8, 14.8, 13.8, 12.8, 11.8, 10.8, 9.8,
8.8, 7.8, 6.8, 5.8, 4.8, 3.8, 2.8, 1.8, 0.8, 0.2, 1.2,
2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2,
13.2, 14.2, 15.2, 16.2, 17.2, 18.2, 19.2],
[19.8, 19.2, 18.2, 17.2, 16.2, 15.2, 14.2, 13.2, 12.2, 11.2, 10.2,
9.2, 8.2, 7.2, 6.2, 5.2, 4.2, 3.2, 2.2, 1.2, 0.2, 0.8,
1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8,
12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8],
[19.4, 19.6, 18.6, 17.6, 16.6, 15.6, 14.6, 13.6, 12.6, 11.6, 10.6,
9.6, 8.6, 7.6, 6.6, 5.6, 4.6, 3.6, 2.6, 1.6, 0.6, 0.4,
1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 9.4, 10.4, 11.4,
12.4, 13.4, 14.4, 15.4, 16.4, 17.4, 18.4],
[19. , 20. , 19. , 18. , 17. , 16. , 15. , 14. , 13. , 12. , 11. ,
10. , 9. , 8. , 7. , 6. , 5. , 4. , 3. , 2. , 1. , 0. ,
1. , 2. , 3. , 4. , 5. , 6. , 7. , 8. , 9. , 10. , 11. ,
12. , 13. , 14. , 15. , 16. , 17. , 18. ],
[18.6, 19.6, 19.4, 18.4, 17.4, 16.4, 15.4, 14.4, 13.4, 12.4, 11.4,
10.4, 9.4, 8.4, 7.4, 6.4, 5.4, 4.4, 3.4, 2.4, 1.4, 0.4,
0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6,
11.6, 12.6, 13.6, 14.6, 15.6, 16.6, 17.6],
[18.2, 19.2, 19.8, 18.8, 17.8, 16.8, 15.8, 14.8, 13.8, 12.8, 11.8,
10.8, 9.8, 8.8, 7.8, 6.8, 5.8, 4.8, 3.8, 2.8, 1.8, 0.8,
0.2, 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2,
11.2, 12.2, 13.2, 14.2, 15.2, 16.2, 17.2],
[17.8, 18.8, 19.8, 19.2, 18.2, 17.2, 16.2, 15.2, 14.2, 13.2, 12.2,
11.2, 10.2, 9.2, 8.2, 7.2, 6.2, 5.2, 4.2, 3.2, 2.2, 1.2,
0.2, 0.8, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8,
10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8],
[17.4, 18.4, 19.4, 19.6, 18.6, 17.6, 16.6, 15.6, 14.6, 13.6, 12.6,
11.6, 10.6, 9.6, 8.6, 7.6, 6.6, 5.6, 4.6, 3.6, 2.6, 1.6,
0.6, 0.4, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 9.4,
10.4, 11.4, 12.4, 13.4, 14.4, 15.4, 16.4],
[17. , 18. , 19. , 20. , 19. , 18. , 17. , 16. , 15. , 14. , 13. ,
12. , 11. , 10. , 9. , 8. , 7. , 6. , 5. , 4. , 3. , 2. ,
1. , 0. , 1. , 2. , 3. , 4. , 5. , 6. , 7. , 8. , 9. ,
10. , 11. , 12. , 13. , 14. , 15. , 16. ],
[16.6, 17.6, 18.6, 19.6, 19.4, 18.4, 17.4, 16.4, 15.4, 14.4, 13.4,
12.4, 11.4, 10.4, 9.4, 8.4, 7.4, 6.4, 5.4, 4.4, 3.4, 2.4,
1.4, 0.4, 0.6, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6,
9.6, 10.6, 11.6, 12.6, 13.6, 14.6, 15.6],
[16.2, 17.2, 18.2, 19.2, 19.8, 18.8, 17.8, 16.8, 15.8, 14.8, 13.8,
12.8, 11.8, 10.8, 9.8, 8.8, 7.8, 6.8, 5.8, 4.8, 3.8, 2.8,
1.8, 0.8, 0.2, 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2,
9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 15.2]])
batches =
array([[ 0],
[ 1],
[ 2],
[ 3],
[ 4],
[ 5],
[ 6],
[ 7],
[ 8],
[ 9],
[10],
[11],
[12],
[13],
[14],
[15],
[16],
[17],
[18],
[19],
[20],
[21],
[22],
[23],
[24],
[25],
[26],
[27],
[28],
[29],
[30],
[31],
[32],
[33],
[34],
[35],
[36],
[37],
[38],
[39]])
Obs =
{'M': 20, 'model': <function <lambda>>, 'linear': <function <lambda>>, 'noise': 1, 'localizer': <function localization_setup.<locals>.localization_now>}
HMM =
HiddenMarkovModel({
'Dyn':
Operator({
'M': 40,
'model': <function step>,
'noise':
GaussRV({
'mu': array([0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0.]),
'M': 40,
'C': 0
}),
'linear': <function dstep_dx>
}),
'Obs':
<TimeDependentOperator> CONSTANT operator sepcified by .Op1:
Operator({
'M': 20,
'model': <function <lambda>>,
'noise':
GaussRV({
'C': <CovMat>
M: 20
kind: 'diag'
trunc: 1.0
rk: 20
full:
[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.
0.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
1.
0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
1. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 1. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 1. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 1. 0.]
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.
0. 0. 0. 0. 0. 0. 1.]]
diag:
[1. 1. 1. ... 1. 1. 1.],
'mu': array([0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0.]),
'M': 20
}),
'linear': <function <lambda>>,
'localizer':
<function
localization_setup.<locals>.localization_now at
0x7fa23ff62830>
}),
'tseq':
<Chronology>
- K: 4001
- Ko: 4000
- T: 200.05
- BurnIn: 100.0
- dto: 0.05
- dt: 0.05,
'X0':
GaussRV({
'C': <CovMat>
M: 40
kind: 'diag'
trunc: 1.0
rk: 40
full: (only computing/printing corners)
[[0.001 0. 0. ... 0. 0. 0. ]
[0. 0.001 0. ... 0. 0. 0. ]
[0. 0. 0.001 ... 0. 0. 0. ]
...
[0. 0. 0. ... 0.001 0. 0. ]
[0. 0. 0. ... 0. 0.001 0. ]
[0. 0. 0. ... 0. 0. 0.001]]
diag:
[0.001 0.001 0.001 ... 0.001 0.001 0.001],
'mu': array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0.]),
'M': 40
}),
'liveplotters':
[(1, <function spatial1d.<locals>.init at
0x7fa23ff628c0>), (1, <class
'dapper.tools.liveplotting.correlations'>), (0, <class
'dapper.tools.liveplotting.spectral_errors'>), (0,
<function phase_particles.<locals>.init at
0x7fa23ff62950>), (0, <function
sliding_marginals.<locals>.init>)],
'sectors': {'land': array([16, 17, 18, 19, 20, 21, 22, 23])},
'name': 'Lorenz96/anderson2009.py'
})