`HFSRatioModel` Tutorial

Trey V. Wenger (c) June 2025

Here we demonstrate the HFSRatioModel, which predicts the hyperfine spectra for two species simultaneously. The fundamental assumption is that the two species share the overall excitation temperature. Neither, one, or both of the species may assume or not assume CTEX. In the case that both species do not assume CTEX, then we assume that both species share the CTEX_variance hyperparameter.

[1]:

# General imports
import time

import matplotlib.pyplot as plt
import arviz as az
import pandas as pd
import numpy as np
import pymc as pm

import pytensor
print("pytensor version:", pytensor.__version__)

print("pymc version:", pm.__version__)
print("arviz version:", az.__version__)

import bayes_spec
print("bayes_spec version:", bayes_spec.__version__)

import bayes_hfs
print("bayes_hfs version:", bayes_hfs.__version__)

# Notebook configuration
pd.options.display.max_rows = None

pytensor version: 2.30.3
pymc version: 5.22.0
arviz version: 0.22.0dev
bayes_spec version: 1.9.0
bayes_hfs version: 1+0.gc6f9e00.dirty

Preparing Molecule Data

Here we model the hyperfine structure of CN and \(^{13}\)CN transitions to the ground rotational state.

[2]:

from bayes_hfs import get_molecule_data, supplement_molecule_data
import pickle

try:
    all_mol_data_12CN, all_mol_metadata_12CN = get_molecule_data("CN, v=0,1", fmin=100.0, fmax=200.0)
    with open("mol_data_12CN.pkl", "wb") as f:
        pickle.dump(all_mol_data_12CN, f)
    with open("mol_metadata_12CN.pkl", "wb") as f:
        pickle.dump(all_mol_metadata_12CN, f)
except:
    with open("mol_data_12CN.pkl", "rb") as f:
        all_mol_data_12CN = pickle.load(f)
    with open("mol_metadata_12CN.pkl", "rb") as f:
        all_mol_metadata_12CN = pickle.load(f)

all_mol_data_12CN.pprint_all()

    FREQ     ERR    LGINT   DR    ELO    GUP MOLWT TAG  QNFMT  Ju  Ku  vu F1u F2u F3u  Jl  Kl  vl F1l F2l F3l    name    Lab
    MHz      MHz   nm2 MHz       1 / cm        u
----------- ------ ------- --- --------- --- ----- ---- ----- --- --- --- --- --- --- --- --- --- --- --- --- --------- -----
 112101.656   0.05 -8.0612   2 2042.4216   2    26 5041   234   1   1   1   1  --  --   0   1   1   2  --  -- CN, v=0,1  True
 112128.989   0.05  -8.069   2 2042.4222   4    26 5041   234   1   1   1   2  --  --   0   1   1   1  --  -- CN, v=0,1  True
 112148.503   0.05 -7.9593   2 2042.4216   4    26 5041   234   1   1   1   2  --  --   0   1   1   2  --  -- CN, v=0,1  True
 112442.806   0.05 -7.9569   2 2042.4222   4    26 5041   234   1   1   2   2  --  --   0   1   1   1  --  -- CN, v=0,1  True
 112445.015   0.05 -7.5311   2 2042.4216   6    26 5041   234   1   1   2   3  --  --   0   1   1   2  --  -- CN, v=0,1  True
 112453.876   0.05 -8.0586   2 2042.4222   2    26 5041   234   1   1   2   1  --  --   0   1   1   1  --  -- CN, v=0,1  True
 112462.292   0.05 -8.0664   2 2042.4216   4    26 5041   234   1   1   2   2  --  --   0   1   1   2  --  -- CN, v=0,1  True
113123.3701 0.0058 -4.7118   2    0.0007   2    26 5041   234   1   0   1   1  --  --   0   0   1   1  --  -- CN, v=0,1 False
113144.1573 0.0057 -3.7989   2      -0.0   2    26 5041   234   1   0   1   1  --  --   0   0   1   2  --  -- CN, v=0,1 False
113170.4915 0.0039  -3.809   2    0.0007   4    26 5041   234   1   0   1   2  --  --   0   0   1   1  --  -- CN, v=0,1 False
113191.2787 0.0034 -3.6955   2      -0.0   4    26 5041   234   1   0   1   2  --  --   0   0   1   2  --  -- CN, v=0,1 False
113488.1202 0.0033 -3.6932   2    0.0007   4    26 5041   234   1   0   2   2  --  --   0   0   1   1  --  -- CN, v=0,1 False
113490.9702 0.0024 -3.2691   2      -0.0   6    26 5041   234   1   0   2   3  --  --   0   0   1   2  --  -- CN, v=0,1 False
113499.6443 0.0028 -3.7962   2    0.0007   2    26 5041   234   1   0   2   1  --  --   0   0   1   1  --  -- CN, v=0,1 False
113508.9074 0.0028 -3.8065   2      -0.0   4    26 5041   234   1   0   2   2  --  --   0   0   1   2  --  -- CN, v=0,1 False
113520.4315 0.0044  -4.709   2      -0.0   2    26 5041   234   1   0   2   1  --  --   0   0   1   2  --  -- CN, v=0,1 False

[3]:

# Keep only Kl = 0 transitions
all_mol_data_12CN = all_mol_data_12CN[all_mol_data_12CN["Kl"] == 0]

# Add GLO
all_mol_data_12CN["GLO"] = 2 * all_mol_data_12CN["F1l"]

all_mol_data_12CN.pprint_all()

    FREQ     ERR    LGINT   DR  ELO   GUP MOLWT TAG  QNFMT  Ju  Ku  vu F1u F2u F3u  Jl  Kl  vl F1l F2l F3l    name    Lab  GLO
    MHz      MHz   nm2 MHz     1 / cm       u
----------- ------ ------- --- ------ --- ----- ---- ----- --- --- --- --- --- --- --- --- --- --- --- --- --------- ----- ---
113123.3701 0.0058 -4.7118   2 0.0007   2    26 5041   234   1   0   1   1  --  --   0   0   1   1  --  -- CN, v=0,1 False   2
113144.1573 0.0057 -3.7989   2   -0.0   2    26 5041   234   1   0   1   1  --  --   0   0   1   2  --  -- CN, v=0,1 False   4
113170.4915 0.0039  -3.809   2 0.0007   4    26 5041   234   1   0   1   2  --  --   0   0   1   1  --  -- CN, v=0,1 False   2
113191.2787 0.0034 -3.6955   2   -0.0   4    26 5041   234   1   0   1   2  --  --   0   0   1   2  --  -- CN, v=0,1 False   4
113488.1202 0.0033 -3.6932   2 0.0007   4    26 5041   234   1   0   2   2  --  --   0   0   1   1  --  -- CN, v=0,1 False   2
113490.9702 0.0024 -3.2691   2   -0.0   6    26 5041   234   1   0   2   3  --  --   0   0   1   2  --  -- CN, v=0,1 False   4
113499.6443 0.0028 -3.7962   2 0.0007   2    26 5041   234   1   0   2   1  --  --   0   0   1   1  --  -- CN, v=0,1 False   2
113508.9074 0.0028 -3.8065   2   -0.0   4    26 5041   234   1   0   2   2  --  --   0   0   1   2  --  -- CN, v=0,1 False   4
113520.4315 0.0044  -4.709   2   -0.0   2    26 5041   234   1   0   2   1  --  --   0   0   1   2  --  -- CN, v=0,1 False   4

[4]:

try:
    all_mol_data_13CN, all_mol_metadata_13CN = get_molecule_data("C-13-N", fmin=100.0, fmax=200.0)
    with open("mol_data_13CN.pkl", "wb") as f:
        pickle.dump(all_mol_data_13CN, f)
    with open("mol_metadata_13CN.pkl", "wb") as f:
        pickle.dump(all_mol_metadata_13CN, f)
except:
    with open("mol_data_13CN.pkl", "rb") as f:
        all_mol_data_13CN = pickle.load(f)
    with open("mol_metadata_13CN.pkl", "rb") as f:
        all_mol_metadata_13CN = pickle.load(f)

all_mol_data_13CN.pprint_all()

    FREQ     ERR    LGINT   DR  ELO   GUP MOLWT TAG QNFMT  Ju  Ku  vu F1u F2u F3u  Jl  Kl  vl F1l F2l F3l  name   Lab
    MHz      MHz   nm2 MHz     1 / cm       u
----------- ------ ------- --- ------ --- ----- --- ----- --- --- --- --- --- --- --- --- --- --- --- --- ------ -----
108056.1623 0.2014  -5.598   2 0.0194   3    27 505   144   1   1   1   1  --  --   0   1   1   0  --  -- C-13-N False
108057.1556 0.1995 -5.4828   2 0.0191   1    27 505   144   1   1   1   0  --  --   0   1   1   1  --  -- C-13-N False
108062.9306 0.2014 -5.8921   2 0.0191   3    27 505   144   1   1   1   1  --  --   0   1   1   1  --  -- C-13-N False
108076.9692 0.2014 -5.2448   2 0.0187   3    27 505   144   1   1   1   1  --  --   0   1   1   2  --  -- C-13-N False
108077.2965 0.2062 -5.4626   2 0.0191   5    27 505   144   1   1   1   2  --  --   0   1   1   1  --  -- C-13-N False
108091.3352 0.2062 -5.0344   2 0.0187   5    27 505   144   1   1   1   2  --  --   0   1   1   2  --  -- C-13-N False
108406.0905 0.0256 -4.9719   2 0.0194   3    27 505   144   1   1   0   1  --  --   0   1   1   0  --  -- C-13-N False
 108412.862   0.05 -4.4492   2 0.0191   3    27 505   144   1   1   0   1  --  --   0   1   1   1  --  -- C-13-N  True
 108426.889   0.05  -4.149   2 0.0187   3    27 505   144   1   1   0   1  --  --   0   1   1   2  --  -- C-13-N  True
 108631.121   0.05 -4.4427   2   -0.0   1    27 505   144   1   1   1   0  --  --   0   1   0   1  --  -- C-13-N  True
 108636.923   0.05  -3.964   2   -0.0   3    27 505   144   1   1   1   1  --  --   0   1   0   1  --  -- C-13-N  True
 108638.212   0.05 -4.3912   2 0.0194   3    27 505   144   1   2   1   1  --  --   0   1   1   0  --  -- C-13-N  True
  108643.59   0.05 -4.3176   2 0.0191   5    27 505   144   1   2   1   2  --  --   0   1   1   1  --  -- C-13-N  True
108644.3456 0.0157 -4.4427   2 0.0191   1    27 505   144   1   2   1   0  --  --   0   1   1   1  --  -- C-13-N False
 108645.064    0.1 -4.5088   2 0.0191   3    27 505   144   1   2   1   1  --  --   0   1   1   1  --  -- C-13-N  True
 108651.297   0.05 -3.7345   2   -0.0   5    27 505   144   1   1   1   2  --  --   0   1   0   1  --  -- C-13-N  True
 108657.646   0.05 -3.8661   2 0.0187   5    27 505   144   1   2   1   2  --  --   0   1   1   2  --  -- C-13-N  True
 108658.948   0.05 -4.4244   2 0.0187   3    27 505   144   1   2   1   1  --  --   0   1   1   2  --  -- C-13-N  True
 108780.201   0.05 -3.5582   2 0.0187   7    27 505   144   1   2   2   3  --  --   0   1   1   2  --  -- C-13-N  True
 108782.374   0.05 -3.8362   2 0.0191   5    27 505   144   1   2   2   2  --  --   0   1   1   1  --  -- C-13-N  True
 108786.982   0.05 -4.1904   2 0.0194   3    27 505   144   1   2   2   1  --  --   0   1   1   0  --  -- C-13-N  True
 108793.753   0.05 -4.2976   2 0.0191   3    27 505   144   1   2   2   1  --  --   0   1   1   1  --  -- C-13-N  True
   108796.4   0.05  -4.286   2 0.0187   5    27 505   144   1   2   2   2  --  --   0   1   1   2  --  -- C-13-N  True
108807.7879 0.0117 -5.4372   2 0.0187   3    27 505   144   1   2   2   1  --  --   0   1   1   2  --  -- C-13-N False
 108986.836 0.2119  -6.206   2   -0.0   3    27 505   144   1   1   0   1  --  --   0   1   0   1  --  -- C-13-N False
109217.5674 0.2165 -4.8875   2   -0.0   5    27 505   144   1   2   1   2  --  --   0   1   0   1  --  -- C-13-N False
109218.3227 0.2096 -5.4737   2   -0.0   1    27 505   144   1   2   1   0  --  --   0   1   0   1  --  -- C-13-N False
 109218.919 0.2123 -5.0423   2   -0.0   3    27 505   144   1   2   1   1  --  --   0   1   0   1  --  -- C-13-N False

[5]:

# Add GLO
all_mol_data_13CN["GLO"] = 2 * all_mol_data_13CN["F1l"] + 1

all_mol_data_13CN.pprint_all()

    FREQ     ERR    LGINT   DR  ELO   GUP MOLWT TAG QNFMT  Ju  Ku  vu F1u F2u F3u  Jl  Kl  vl F1l F2l F3l  name   Lab  GLO
    MHz      MHz   nm2 MHz     1 / cm       u
----------- ------ ------- --- ------ --- ----- --- ----- --- --- --- --- --- --- --- --- --- --- --- --- ------ ----- ---
108056.1623 0.2014  -5.598   2 0.0194   3    27 505   144   1   1   1   1  --  --   0   1   1   0  --  -- C-13-N False   1
108057.1556 0.1995 -5.4828   2 0.0191   1    27 505   144   1   1   1   0  --  --   0   1   1   1  --  -- C-13-N False   3
108062.9306 0.2014 -5.8921   2 0.0191   3    27 505   144   1   1   1   1  --  --   0   1   1   1  --  -- C-13-N False   3
108076.9692 0.2014 -5.2448   2 0.0187   3    27 505   144   1   1   1   1  --  --   0   1   1   2  --  -- C-13-N False   5
108077.2965 0.2062 -5.4626   2 0.0191   5    27 505   144   1   1   1   2  --  --   0   1   1   1  --  -- C-13-N False   3
108091.3352 0.2062 -5.0344   2 0.0187   5    27 505   144   1   1   1   2  --  --   0   1   1   2  --  -- C-13-N False   5
108406.0905 0.0256 -4.9719   2 0.0194   3    27 505   144   1   1   0   1  --  --   0   1   1   0  --  -- C-13-N False   1
 108412.862   0.05 -4.4492   2 0.0191   3    27 505   144   1   1   0   1  --  --   0   1   1   1  --  -- C-13-N  True   3
 108426.889   0.05  -4.149   2 0.0187   3    27 505   144   1   1   0   1  --  --   0   1   1   2  --  -- C-13-N  True   5
 108631.121   0.05 -4.4427   2   -0.0   1    27 505   144   1   1   1   0  --  --   0   1   0   1  --  -- C-13-N  True   3
 108636.923   0.05  -3.964   2   -0.0   3    27 505   144   1   1   1   1  --  --   0   1   0   1  --  -- C-13-N  True   3
 108638.212   0.05 -4.3912   2 0.0194   3    27 505   144   1   2   1   1  --  --   0   1   1   0  --  -- C-13-N  True   1
  108643.59   0.05 -4.3176   2 0.0191   5    27 505   144   1   2   1   2  --  --   0   1   1   1  --  -- C-13-N  True   3
108644.3456 0.0157 -4.4427   2 0.0191   1    27 505   144   1   2   1   0  --  --   0   1   1   1  --  -- C-13-N False   3
 108645.064    0.1 -4.5088   2 0.0191   3    27 505   144   1   2   1   1  --  --   0   1   1   1  --  -- C-13-N  True   3
 108651.297   0.05 -3.7345   2   -0.0   5    27 505   144   1   1   1   2  --  --   0   1   0   1  --  -- C-13-N  True   3
 108657.646   0.05 -3.8661   2 0.0187   5    27 505   144   1   2   1   2  --  --   0   1   1   2  --  -- C-13-N  True   5
 108658.948   0.05 -4.4244   2 0.0187   3    27 505   144   1   2   1   1  --  --   0   1   1   2  --  -- C-13-N  True   5
 108780.201   0.05 -3.5582   2 0.0187   7    27 505   144   1   2   2   3  --  --   0   1   1   2  --  -- C-13-N  True   5
 108782.374   0.05 -3.8362   2 0.0191   5    27 505   144   1   2   2   2  --  --   0   1   1   1  --  -- C-13-N  True   3
 108786.982   0.05 -4.1904   2 0.0194   3    27 505   144   1   2   2   1  --  --   0   1   1   0  --  -- C-13-N  True   1
 108793.753   0.05 -4.2976   2 0.0191   3    27 505   144   1   2   2   1  --  --   0   1   1   1  --  -- C-13-N  True   3
   108796.4   0.05  -4.286   2 0.0187   5    27 505   144   1   2   2   2  --  --   0   1   1   2  --  -- C-13-N  True   5
108807.7879 0.0117 -5.4372   2 0.0187   3    27 505   144   1   2   2   1  --  --   0   1   1   2  --  -- C-13-N False   5
 108986.836 0.2119  -6.206   2   -0.0   3    27 505   144   1   1   0   1  --  --   0   1   0   1  --  -- C-13-N False   3
109217.5674 0.2165 -4.8875   2   -0.0   5    27 505   144   1   2   1   2  --  --   0   1   0   1  --  -- C-13-N False   3
109218.3227 0.2096 -5.4737   2   -0.0   1    27 505   144   1   2   1   0  --  --   0   1   0   1  --  -- C-13-N False   3
 109218.919 0.2123 -5.0423   2   -0.0   3    27 505   144   1   2   1   1  --  --   0   1   0   1  --  -- C-13-N False   3

[6]:

mol_data_12CN = supplement_molecule_data(all_mol_data_12CN, all_mol_metadata_12CN)
print(mol_data_12CN.keys())
print("molecular weight (Daltons):", mol_data_12CN['mol_weight'])
print("transition frequency (MHz):", mol_data_12CN['freq'])
print("Einstein A coefficient (s-1):", mol_data_12CN['Aul'])
print("Relative intensities:", mol_data_12CN['relative_int'])
print("state info:", mol_data_12CN["states"])
print("upper state index:", mol_data_12CN["state_u_idx"])
print("lower state index:", mol_data_12CN["state_l_idx"])
print("upper state degeneracy:", mol_data_12CN["Gu"])
print("lower state degeneracy:", mol_data_12CN["Gl"])

dict_keys(['mol_weight', 'freq', 'Aul', 'relative_int', 'states', 'state_u_idx', 'state_l_idx', 'Gu', 'Gl'])
molecular weight (Daltons): 26
transition frequency (MHz): [113123.3701 113144.1573 113170.4915 113191.2787 113488.1202 113490.9702
 113499.6443 113508.9074 113520.4315]
Einstein A coefficient (s-1): [1.24997446e-06 1.02301076e-05 4.99866053e-06 6.49280964e-06
 6.54458098e-06 1.15851092e-05 1.03265758e-05 5.04267116e-06
 1.26251089e-06]
Relative intensities: [0.01204927 0.09859632 0.09632981 0.12510097 0.12576526 0.33393404
 0.0992112  0.09688593 0.0121272 ]
state info: {'state': [np.str_('0 0 1 1 -- --'), np.str_('0 0 1 2 -- --'), np.str_('1 0 1 1 -- --'), np.str_('1 0 1 2 -- --'), np.str_('1 0 2 1 -- --'), np.str_('1 0 2 2 -- --'), np.str_('1 0 2 3 -- --')], 'deg': array([2, 4, 2, 4, 2, 4, 6]), 'E': array([ 1.00714381e-03, -0.00000000e+00,  5.43007265e+00,  5.43233412e+00,
        5.44813096e+00,  5.44757789e+00,  5.44670753e+00])}
upper state index: [2, 2, 3, 3, 5, 6, 4, 5, 4]
lower state index: [0, 1, 0, 1, 0, 1, 0, 1, 1]
upper state degeneracy: [2 2 4 4 4 6 2 4 2]
lower state degeneracy: [2 4 2 4 2 4 2 4 4]

[7]:

mol_data_13CN = supplement_molecule_data(all_mol_data_13CN, all_mol_metadata_13CN)
print(mol_data_13CN.keys())
print("molecular weight (Daltons):", mol_data_13CN['mol_weight'])
print("transition frequency (MHz):", mol_data_13CN['freq'])
print("Einstein A coefficient (s-1):", mol_data_13CN['Aul'])
print("Relative intensities:", mol_data_13CN['relative_int'])
print("state info:", mol_data_13CN["states"])
print("upper state index:", mol_data_13CN["state_u_idx"])
print("lower state index:", mol_data_13CN["state_l_idx"])
print("upper state degeneracy:", mol_data_13CN["Gu"])
print("lower state degeneracy:", mol_data_13CN["Gl"])

dict_keys(['mol_weight', 'freq', 'Aul', 'relative_int', 'states', 'state_u_idx', 'state_l_idx', 'Gu', 'Gl'])
molecular weight (Daltons): 27
transition frequency (MHz): [108056.1623 108057.1556 108062.9306 108076.9692 108077.2965 108091.3352
 108406.0905 108412.862  108426.889  108631.121  108636.923  108638.212
 108643.59   108644.3456 108645.064  108651.297  108657.646  108658.948
 108780.201  108782.374  108786.982  108793.753  108796.4    108807.7879
 108986.836  109217.5674 109218.3227 109218.919 ]
Einstein A coefficient (s-1): [2.16171194e-07 8.45517260e-07 1.09830923e-07 4.87619092e-07
 1.77187921e-07 4.74996242e-07 9.16883349e-07 3.05520828e-06
 6.09953409e-06 9.32187564e-06 9.35632817e-06 3.49905729e-06
 2.48727247e-06 9.32387439e-06 2.66918119e-06 9.52389619e-06
 7.03524460e-06 3.24215072e-06 1.02222142e-05 7.54540569e-06
 5.56353117e-06 4.34687514e-06 2.67878736e-06 3.15233846e-07
 5.37667957e-08 6.73119004e-07 8.72699820e-07 7.85499700e-07]
Relative intensities: [0.00177717 0.00231702 0.00090288 0.00400801 0.00242733 0.00650623
 0.00751327 0.02503394 0.04997229 0.02541144 0.07651194 0.02861075
 0.0338945  0.02541144 0.02182374 0.12978641 0.09585819 0.02650509
 0.19477322 0.10269026 0.04542859 0.0354919  0.03645266 0.00257353
 0.00043826 0.00912492 0.00236608 0.00638895]
state info: {'state': [np.str_('0 1 0 1 -- --'), np.str_('0 1 1 0 -- --'), np.str_('0 1 1 1 -- --'), np.str_('0 1 1 2 -- --'), np.str_('1 1 0 1 -- --'), np.str_('1 1 1 0 -- --'), np.str_('1 1 1 1 -- --'), np.str_('1 1 1 2 -- --'), np.str_('1 2 1 0 -- --'), np.str_('1 2 1 1 -- --'), np.str_('1 2 1 2 -- --'), np.str_('1 2 2 1 -- --'), np.str_('1 2 2 2 -- --'), np.str_('1 2 2 3 -- --')], 'deg': array([3, 1, 3, 5, 3, 1, 3, 5, 1, 3, 5, 3, 5, 7]), 'E': array([-0.        ,  0.02791227,  0.02748064,  0.02690513,  5.23058406,
        5.21340619,  5.21379016,  5.2143728 ,  5.24158687,  5.24172414,
        5.24155061,  5.24886397,  5.24821119,  5.24753139])}
upper state index: [6, 5, 6, 6, 7, 7, 4, 4, 4, 5, 6, 9, 10, 8, 9, 7, 10, 9, 13, 12, 11, 11, 12, 11, 4, 10, 8, 9]
lower state index: [1, 2, 2, 3, 2, 3, 1, 2, 3, 0, 0, 1, 2, 2, 2, 0, 3, 3, 3, 2, 1, 2, 3, 3, 0, 0, 0, 0]
upper state degeneracy: [3 1 3 3 5 5 3 3 3 1 3 3 5 1 3 5 5 3 7 5 3 3 5 3 3 5 1 3]
lower state degeneracy: [1 3 3 5 3 5 1 3 5 3 3 1 3 3 3 3 5 5 5 3 1 3 5 5 3 3 3 3]

Simulate Data

[8]:

from bayes_spec import SpecData
from bayes_hfs import HFSRatioModel

# spectral axis definition
freq_axis_12CN_1 = np.arange(113110.0, 113200.0, 0.2) # MHz
freq_axis_12CN_2 = np.arange(113470.0, 113530.0, 0.2) # MHz
freq_axis_13CN_1 = np.arange(108620.0, 108670.0, 0.2) # MHz
freq_axis_13CN_2 = np.arange(108770.0, 108810.0, 0.2) # MHz

# data noise can either be a scalar (assumed constant noise across the spectrum)
# or an array of the same length as the data
noise = 0.001 # K

# brightness data. In this case, we just throw in some random data for now
# since we are only doing this in order to simulate some actual data.
brightness_data_12CN_1 = noise * np.random.randn(len(freq_axis_12CN_1)) # K
brightness_data_12CN_2 = noise * np.random.randn(len(freq_axis_12CN_2)) # K
brightness_data_13CN_1 = noise * np.random.randn(len(freq_axis_13CN_1)) # K
brightness_data_13CN_2 = noise * np.random.randn(len(freq_axis_13CN_2)) # K

# CNRatioModel expects observation names to contain either "12CN" or "13CN"
observation_12CN_1 = SpecData(
    freq_axis_12CN_1,
    brightness_data_12CN_1,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"CN $T_B$ (K)",
)
observation_12CN_2 = SpecData(
    freq_axis_12CN_2,
    brightness_data_12CN_2,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"CN $T_B$ (K)",
)
observation_13CN_1 = SpecData(
    freq_axis_13CN_1,
    brightness_data_13CN_1,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"$^{13}$CN $T_B$ (K)",
)
observation_13CN_2 = SpecData(
    freq_axis_13CN_2,
    brightness_data_13CN_2,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"$^{13}$CN $T_B$ (K)",
)
dummy_data = {
    "12CN-1": observation_12CN_1,
    "12CN-2": observation_12CN_2,
    "13CN-1": observation_13CN_1,
    "13CN-2": observation_13CN_2,
}

The order of the molecular species passed to HFSRatioModel is important. The order must be the same in the following mol_keys variable as the mol_data dictionaries passed during initialization. That is, if 12CN is the first key in mol_keys, then mol_data_12CN must also be the first dictionary passed to HFSModel. The inferred column density ratio is the ratio of the second species to the first species (i.e., 13CN to 12CN in this example).

[9]:

# association each dataset with the related species
mol_keys = {
    "12CN": ["12CN-1", "12CN-2"],
    "13CN": ["13CN-1", "13CN-2"],
}

[10]:

from bayes_hfs import HFSRatioModel
from bayes_hfs import physics

# Initialize and define the model
n_clouds = 3 # number of cloud components
baseline_degree = 0 # polynomial baseline degree
model = HFSRatioModel(
    mol_data_12CN, # molecular data for species 1
    mol_data_13CN, # molecular data for species 2
    mol_keys, # dataset association
    dummy_data,
    bg_temp = 2.7, # assumed background temperature (K)
    Beff = 1.0, # beam efficiency
    Feff = 1.0, # forward efficiency
    n_clouds=n_clouds,
    baseline_degree=baseline_degree,
    seed=1234,
    verbose=True
)
model.add_priors(
    prior_log10_Ntot1 = [13.5, 0.5], # mean and width of log10 total column density prior of first species (cm-2)
    prior_ratio = 0.1, # width of the column density ratio between the second and first species
    prior_fwhm2 = 1.0, # width of FWHM^2 prior (km2 s-2)
    prior_velocity = [-3.0, 3.0], # upper and lower limit of velocity prior (km/s)
    prior_log10_Tex_CTEX = [0.75, 0.25], # mean and width of log10 CTEX excitation temperature prior (K)
    assume_CTEX1 = False, # do not assume CTEX for the first species
    assume_CTEX2 = True, # assume CTEX for the second species for speed
    prior_log10_CTEX_variance = [-4.0, 1.0], # offset and width of log10 CTEX variance prior
    clip_weights = 1.0e-9, # clip statistical weights between [clip_weights, 1-clip_weights]
    clip_tau = -10.0, # clip optical depths below to prevent masers
    prior_fwhm_L = None, # assume Gaussian line profile
    prior_baseline_coeffs = None, # use default baseline priors
)
model.add_likelihood()

sim_params = {
    "log10_Ntot_12CN": np.array([13.8, 13.9, 14.0]),
    "ratio": np.array([1.0/65.0, 1.0/60.0, 1.0/55.0]),
    "fwhm2": np.array([1.0, 1.25, 1.5])**2.0,
    "velocity": [-2.0, 0.0, 2.5],
    "log10_Tex_CTEX": np.log10([4.46, 3.98, 3.16]),
    "log10_CTEX_variance": np.array([-1.5, -2.0, -3.0]),
    "baseline_12CN-1_norm": [0.0],
    "baseline_12CN-2_norm": [0.0],
    "baseline_13CN-1_norm": [0.0],
    "baseline_13CN-2_norm": [0.0],
}

CTEX_weights_12CN = physics.calc_stat_weight(
    model.mol1_data["states"]["deg"][None, :],
    model.mol1_data["states"]["E"][None, :],
    10.0 ** sim_params["log10_Tex_CTEX"][:, None],
).eval()

CTEX_concentration_12CN = (
    len(model.mol1_data["states"]["state"])
    * CTEX_weights_12CN
    / 10.0**sim_params["log10_CTEX_variance"][:, None]
)

from scipy.stats import dirichlet

sim_params["weights_12CN"] = np.stack([
    dirichlet(CTEX_concentration_12CN[i]).rvs()[0] for i in range(n_clouds)
])

# add derived quantities to sim_params
for key in model.cloud_deterministics:
    if key not in sim_params.keys():
        sim_params[key] = model.model[key].eval(sim_params, on_unused_input="ignore")

# Evaluate and save simulated observations
sim_12CN_1 = model.model["12CN-1"].eval(sim_params, on_unused_input="ignore")
sim_12CN_2 = model.model["12CN-2"].eval(sim_params, on_unused_input="ignore")
sim_13CN_1 = model.model["13CN-1"].eval(sim_params, on_unused_input="ignore")
sim_13CN_2 = model.model["13CN-2"].eval(sim_params, on_unused_input="ignore")

# pack simulated data
observation_12CN_1 = SpecData(
    freq_axis_12CN_1,
    sim_12CN_1,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"CN $T_B$ (K)",
)
observation_12CN_2 = SpecData(
    freq_axis_12CN_2,
    sim_12CN_2,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"CN $T_B$ (K)",
)
observation_13CN_1 = SpecData(
    freq_axis_13CN_1,
    sim_13CN_1,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"$^{13}$CN $T_B$ (K)",
)
observation_13CN_2 = SpecData(
    freq_axis_13CN_2,
    sim_13CN_2,
    noise,
    xlabel=r"LSRK Frequency (MHz)",
    ylabel=r"$^{13}$CN $T_B$ (K)",
)
data = {
    "12CN-1": observation_12CN_1,
    "12CN-2": observation_12CN_2,
    "13CN-1": observation_13CN_1,
    "13CN-2": observation_13CN_2,
}

[11]:

# Plot the simulated data
fig, axes = plt.subplots(4, layout="constrained", figsize=(8, 12))
for i, dataset in enumerate(data.values()):
    axes[i].plot(dataset.spectral, dataset.brightness, 'k-')
    axes[i].set_ylabel(dataset.ylabel)
    _ = axes[i].set_xlabel(dataset.xlabel)

../_images/notebooks_hfs_ratio_model_14_0.png

[12]:

sim_params

[12]:

{'log10_Ntot_12CN': array([13.8, 13.9, 14. ]),
 'ratio': array([0.01538462, 0.01666667, 0.01818182]),
 'fwhm2': array([1.    , 1.5625, 2.25  ]),
 'velocity': [-2.0, 0.0, 2.5],
 'log10_Tex_CTEX': array([0.64933486, 0.59988307, 0.49968708]),
 'log10_CTEX_variance': array([-1.5, -2. , -3. ]),
 'baseline_12CN-1_norm': [0.0],
 'baseline_12CN-2_norm': [0.0],
 'baseline_13CN-1_norm': [0.0],
 'baseline_13CN-2_norm': [0.0],
 'weights_12CN': array([[0.18217207, 0.3420491 , 0.0549568 , 0.1090344 , 0.05406404,
         0.10595788, 0.15176571],
        [0.18558518, 0.37959808, 0.04676937, 0.09318156, 0.05182396,
         0.09195015, 0.1510917 ],
        [0.21533183, 0.43479939, 0.03894158, 0.07836171, 0.03816636,
         0.07787778, 0.11652136]]),
 'log10_Ntot_13CN': array([11.98708664, 12.12184875, 12.25963731]),
 'CTEX_weights_12CN': array([[1.99954842, 4.        , 0.59193533, 1.18327051, 0.58954346,
         1.17923313, 1.76919492],
        [1.99949396, 4.        , 0.51109864, 1.02161662, 0.5087849 ,
         1.01771121, 1.52690069],
        [1.99936267, 4.        , 0.35871387, 0.71691448, 0.35666979,
         0.71346443, 1.07049146]]),
 'Tex_12CN': array([[5.45609577, 4.16767468, 3.10369069],
        [6.39367861, 3.71675158, 3.06058314],
        [4.73552571, 5.10483093, 3.45336134],
        [5.42596228, 4.44446339, 3.40002935],
        [4.13353693, 4.49236295, 3.28049712],
        [4.59834107, 4.12620471, 3.27293171],
        [3.29446805, 4.85806014, 3.29146908],
        [4.64851558, 3.97424955, 3.23249552],
        [3.61363781, 4.25779355, 3.24315109]]),
 'tau_12CN': array([[0.02644032, 0.03277413, 0.06020208],
        [0.16976539, 0.33129313, 0.50759888],
        [0.22875863, 0.23555336, 0.46155043],
        [0.23798933, 0.38595574, 0.61840658],
        [0.3195878 , 0.32897129, 0.61409773],
        [0.6952564 , 1.06750181, 1.67325136],
        [0.27837553, 0.2489999 , 0.48377674],
        [0.20055953, 0.31524977, 0.487758  ],
        [0.02831459, 0.03817425, 0.06097173]]),
 'tau_total_12CN': array([2.18504752, 2.98447339, 4.96761353]),
 'TR_12CN': array([[3.18448583, 2.02647233, 1.14292095],
        [4.05841154, 1.64043357, 1.10919082],
        [2.52793983, 2.86187888, 1.42182878],
        [3.15562932, 2.26830954, 1.37813454],
        [1.99168919, 2.30633949, 1.2782896 ],
        [2.40045388, 1.98529335, 1.27221585],
        [1.28932308, 2.63311092, 1.28692365],
        [2.44493729, 1.85406827, 1.23982848],
        [1.54946488, 2.09940913, 1.24815497]]),
 'CTEX_weights_13CN': array([[3.        , 0.99376119, 2.9815721 , 4.9699281 , 0.92851903,
         0.31070072, 0.93202191, 1.55316693, 0.30874373, 0.92620269,
         1.54373121, 0.92472116, 1.54142752, 2.15832747],
        [3.        , 0.9930114 , 2.9793573 , 4.96631358, 0.80605378,
         0.26984676, 0.80946217, 1.34890613, 0.26794283, 0.80380078,
         1.33972638, 0.80236011, 1.3374862 , 1.87280054],
        [3.        , 0.9912059 , 2.9740239 , 4.95760931, 0.57313394,
         0.192086  , 0.57618799, 0.96013626, 0.19038061, 0.57111701,
         0.95191395, 0.56982806, 0.94990964, 1.33015962]]),
 'Tex_13CN': array([4.46, 3.98, 3.16]),
 'tau_13CN': array([[5.70799516e-05, 8.80497885e-05, 1.53606660e-04],
        [7.44257389e-05, 1.14808165e-04, 2.00293271e-04],
        [2.90012788e-05, 4.47368494e-05, 7.80469670e-05],
        [1.28748420e-04, 1.98607139e-04, 3.46495315e-04],
        [7.79624920e-05, 1.20262957e-04, 2.09806064e-04],
        [2.08982767e-04, 3.22374989e-04, 5.62418213e-04],
        [2.40953532e-04, 3.71636220e-04, 6.48143115e-04],
        [8.02909913e-04, 1.23838330e-03, 2.15981662e-03],
        [1.60284552e-03, 2.47220591e-03, 4.31179712e-03],
        [8.19200201e-04, 1.26434124e-03, 2.20864205e-03],
        [2.46651583e-03, 3.80677135e-03, 6.64989652e-03],
        [9.16647148e-04, 1.41366727e-03, 2.46498781e-03],
        [1.08600947e-03, 1.67487643e-03, 2.92052274e-03],
        [8.14202082e-04, 1.25568654e-03, 2.18956974e-03],
        [6.99254685e-04, 1.07841005e-03, 1.88044406e-03],
        [4.18362274e-03, 6.45688903e-03, 1.12791653e-02],
        [3.07155555e-03, 4.73709661e-03, 8.26041668e-03],
        [8.49299422e-04, 1.30982742e-03, 2.28403347e-03],
        [6.23785394e-03, 9.61983811e-03, 1.67730383e-02],
        [3.28833428e-03, 5.07109086e-03, 8.84154731e-03],
        [1.45454111e-03, 2.24308420e-03, 3.91073643e-03],
        [1.13647401e-03, 1.75259923e-03, 3.05565634e-03],
        [1.16734938e-03, 1.80024246e-03, 3.13884435e-03],
        [8.24108460e-05, 1.27090237e-04, 2.21587868e-04],
        [1.41069931e-05, 2.17694543e-05, 3.80169519e-05],
        [2.93428170e-04, 4.52768189e-04, 7.90536651e-04],
        [7.60852382e-05, 1.17401698e-04, 2.04984114e-04],
        [2.05448996e-04, 3.17013319e-04, 5.53505945e-04]]),
 'tau_total_13CN': array([0.03208525, 0.04949153, 0.08633656]),
 'TR_13CN': array([[2.35858208, 1.93483872, 1.24635168],
        [2.35856709, 1.93482468, 1.24633981],
        [2.35847991, 1.93474309, 1.24627084],
        [2.35826801, 1.93454474, 1.24610318],
        [2.35826307, 1.93454011, 1.24609927],
        [2.35805118, 1.93434178, 1.24593163],
        [2.35330437, 1.92989924, 1.24217787],
        [2.35320233, 1.92980376, 1.24209722],
        [2.35299097, 1.92960597, 1.24193015],
        [2.34991527, 1.92672806, 1.23949983],
        [2.34982794, 1.92664635, 1.23943084],
        [2.34980854, 1.9266282 , 1.23941552],
        [2.34972759, 1.92655246, 1.23935158],
        [2.34971622, 1.92654182, 1.23934259],
        [2.34970541, 1.92653171, 1.23933405],
        [2.34961159, 1.92644393, 1.23925995],
        [2.34951604, 1.92635453, 1.23918447],
        [2.34949644, 1.9263362 , 1.23916899],
        [2.34767213, 1.92462946, 1.23772826],
        [2.34763945, 1.92459888, 1.23770245],
        [2.34757014, 1.92453404, 1.23764773],
        [2.3474683 , 1.92443878, 1.23756732],
        [2.34742849, 1.92440153, 1.23753588],
        [2.34725723, 1.92424131, 1.23740066],
        [2.34456575, 1.92172365, 1.23527616],
        [2.34110091, 1.91848309, 1.23254281],
        [2.34108957, 1.91847249, 1.23253387],
        [2.34108062, 1.91846412, 1.23252681]])}

Model Definition

[13]:

# Initialize and define the model
mol_keys_rev = {"13CN": mol_keys["13CN"], "12CN": mol_keys["12CN"]}
model = HFSRatioModel(
    mol_data_12CN, # molecular data for species 1
    mol_data_13CN, # molecular data for species 2
    mol_keys, # dataset association
    data,
    bg_temp = 2.7, # assumed background temperature (K)
    Beff = 1.0, # beam efficiency
    Feff = 1.0, # forward efficiency
    n_clouds=n_clouds,
    baseline_degree=baseline_degree,
    seed=1234,
    verbose=True
)
model.add_priors(
    prior_log10_Ntot1 = [13.5, 0.5], # mean and width of log10 total column density prior of first species (cm-2)
    prior_ratio = 0.1, # width of the column density ratio between the second and first species
    prior_fwhm2 = 1.0, # width of FWHM^2 prior (km2 s-2)
    prior_velocity = [-3.0, 3.0], # upper and lower limit of velocity prior (km/s)
    prior_log10_Tex_CTEX = [0.75, 0.25], # mean and width of log10 CTEX excitation temperature prior (K)
    assume_CTEX1 = False, # do not assume CTEX for the first species
    assume_CTEX2 = True, # assume CTEX for the second species for efficiency
    prior_log10_CTEX_variance = [-4.0, 1.0], # offset and width of log10 CTEX variance prior
    clip_weights = 1.0e-9, # clip statistical weights between [clip_weights, 1-clip_weights]
    clip_tau = -10.0, # clip optical depths below to prevent masers
    prior_fwhm_L = None, # assume Gaussian line profile
    prior_baseline_coeffs = None, # use default baseline priors
)
model.add_likelihood()

[14]:

# Plot model graph
model.graph().render('hfs_ratio_model', format='png')
model.graph()

[14]:

../_images/notebooks_hfs_ratio_model_18_0.svg

[15]:

# model string representation
print(model.model.str_repr())

    baseline_12CN-1_norm ~ Normal(0, 1)
    baseline_12CN-2_norm ~ Normal(0, 1)
    baseline_13CN-1_norm ~ Normal(0, 1)
    baseline_13CN-2_norm ~ Normal(0, 1)
    log10_Ntot_12CN_norm ~ Normal(0, 1)
              ratio_norm ~ HalfNormal(0, 1)
              fwhm2_norm ~ Gamma(0.5, f())
           velocity_norm ~ Beta(2, 2)
     log10_Tex_CTEX_norm ~ Normal(0, 1)
log10_CTEX_variance_norm ~ HalfNormal(0, 1)
       weights_12CN_norm ~ Dirichlet(f(log10_CTEX_variance_norm, log10_Tex_CTEX_norm))
         log10_Ntot_12CN ~ Deterministic(f(log10_Ntot_12CN_norm))
                   ratio ~ Deterministic(f(ratio_norm))
                   fwhm2 ~ Deterministic(f(fwhm2_norm))
                velocity ~ Deterministic(f(velocity_norm))
          log10_Tex_CTEX ~ Deterministic(f(log10_Tex_CTEX_norm))
         log10_Ntot_13CN ~ Deterministic(f(ratio_norm, log10_Ntot_12CN_norm))
     log10_CTEX_variance ~ Deterministic(f(log10_CTEX_variance_norm))
       CTEX_weights_12CN ~ Deterministic(f(log10_Tex_CTEX_norm))
                Tex_12CN ~ Deterministic(f(weights_12CN_norm, log10_Ntot_12CN_norm))
                tau_12CN ~ Deterministic(f(weights_12CN_norm, log10_Ntot_12CN_norm))
          tau_total_12CN ~ Deterministic(f(weights_12CN_norm, log10_Ntot_12CN_norm))
                 TR_12CN ~ Deterministic(f(weights_12CN_norm, log10_Ntot_12CN_norm))
       CTEX_weights_13CN ~ Deterministic(f(log10_Tex_CTEX_norm))
                Tex_13CN ~ Deterministic(f(log10_Tex_CTEX_norm))
                tau_13CN ~ Deterministic(f(ratio_norm, log10_Ntot_12CN_norm, log10_Tex_CTEX_norm))
          tau_total_13CN ~ Deterministic(f(ratio_norm, log10_Ntot_12CN_norm, log10_Tex_CTEX_norm))
                 TR_13CN ~ Deterministic(f(log10_Tex_CTEX_norm))
                  12CN-1 ~ Normal(f(baseline_12CN-1_norm, weights_12CN_norm, log10_Ntot_12CN_norm, velocity_norm, fwhm2_norm), <constant>)
                  12CN-2 ~ Normal(f(baseline_12CN-2_norm, weights_12CN_norm, log10_Ntot_12CN_norm, velocity_norm, fwhm2_norm), <constant>)
                  13CN-1 ~ Normal(f(baseline_13CN-1_norm, log10_Tex_CTEX_norm, velocity_norm, fwhm2_norm, ratio_norm, log10_Ntot_12CN_norm), <constant>)
                  13CN-2 ~ Normal(f(baseline_13CN-2_norm, log10_Tex_CTEX_norm, velocity_norm, fwhm2_norm, ratio_norm, log10_Ntot_12CN_norm), <constant>)

[16]:

from bayes_spec.plots import plot_predictive

# prior predictive check
prior = model.sample_prior_predictive(
    samples=1000,  # prior predictive samples
)
axes = plot_predictive(model.data, prior.prior_predictive.sel(draw=slice(None, None, 20)))
axes.ravel()[0].figure.set_size_inches(8, 12)

Sampling: [12CN-1, 12CN-2, 13CN-1, 13CN-2, baseline_12CN-1_norm, baseline_12CN-2_norm, baseline_13CN-1_norm, baseline_13CN-2_norm, fwhm2_norm, log10_CTEX_variance_norm, log10_Ntot_12CN_norm, log10_Tex_CTEX_norm, ratio_norm, velocity_norm, weights_12CN_norm]

../_images/notebooks_hfs_ratio_model_20_1.png

[17]:

print(model.cloud_freeRVs)
print(model.cloud_deterministics)

['log10_Ntot_12CN_norm', 'ratio_norm', 'fwhm2_norm', 'velocity_norm', 'log10_Tex_CTEX_norm', 'log10_CTEX_variance_norm', 'weights_12CN_norm']
['log10_Ntot_12CN', 'ratio', 'fwhm2', 'velocity', 'log10_Tex_CTEX', 'log10_Ntot_13CN', 'log10_CTEX_variance', 'CTEX_weights_12CN', 'Tex_12CN', 'tau_12CN', 'tau_total_12CN', 'TR_12CN', 'CTEX_weights_13CN', 'Tex_13CN', 'tau_13CN', 'tau_total_13CN', 'TR_13CN']

[18]:

from bayes_spec.plots import plot_pair

var_names = [
    param for param in model.cloud_deterministics + [p for p in model.cloud_freeRVs if "_norm" not in p]
    if not set(model.model.named_vars_to_dims[param]).intersection(set(["transition_12CN", "state_12CN", "transition_13CN", "state_13CN"]))
]
print(var_names)
_ = plot_pair(
    prior.prior, # samples
    var_names, # var_names to plot
    combine_dims=["cloud"], # concatenate clouds
    labeller=model.labeller, # label manager
    kind="scatter", # plot type
    reference_values=sim_params, # truths
)

['log10_Ntot_12CN', 'ratio', 'fwhm2', 'velocity', 'log10_Tex_CTEX', 'log10_Ntot_13CN', 'log10_CTEX_variance', 'tau_total_12CN', 'Tex_13CN', 'tau_total_13CN']

../_images/notebooks_hfs_ratio_model_22_1.png

Variational Inference

[19]:

start = time.time()
model.fit(
    n = 1_000_000, # maximum number of VI iterations
    draws = 1_000, # number of posterior samples
    rel_tolerance = 0.01, # VI relative convergence threshold
    abs_tolerance = 0.01, # VI absolute convergence threshold
    learning_rate = 0.001, # VI learning rate
    start = {"velocity_norm": np.linspace(0.1, 0.9, n_clouds)},
)
end = time.time()
print(f"Runtime: {(end-start)/60.0:.2f} minutes")

Convergence achieved at 52400
Interrupted at 52,399 [5%]: Average Loss = 5.5843e+28

Adding log-likelihood to trace

Runtime: 19.86 minutes

[20]:

pm.summary(model.trace.posterior)

arviz - WARNING - Shape validation failed: input_shape: (1, 1000), minimum_shape: (chains=2, draws=4)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4

[20]:

	mean	sd	hdi_3%	hdi_97%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
baseline_12CN-1_norm[0]	0.036	0.050	-0.048	0.139	0.002	0.001	878.0	834.0	NaN
baseline_12CN-2_norm[0]	0.024	0.060	-0.082	0.145	0.002	0.001	991.0	1071.0	NaN
baseline_13CN-1_norm[0]	-0.021	0.064	-0.147	0.098	0.002	0.002	993.0	1022.0	NaN
baseline_13CN-2_norm[0]	0.055	0.073	-0.090	0.181	0.002	0.002	936.0	982.0	NaN
log10_Ntot_12CN_norm[0]	0.597	0.000	0.596	0.597	0.000	0.000	976.0	908.0	NaN
log10_Ntot_12CN_norm[1]	0.797	0.000	0.796	0.798	0.000	0.000	1006.0	942.0	NaN
log10_Ntot_12CN_norm[2]	0.986	0.001	0.985	0.989	0.000	0.000	902.0	767.0	NaN
log10_Tex_CTEX_norm[0]	-0.316	0.030	-0.375	-0.262	0.001	0.001	839.0	982.0	NaN
log10_Tex_CTEX_norm[1]	-0.461	0.024	-0.510	-0.419	0.001	0.001	933.0	937.0	NaN
log10_Tex_CTEX_norm[2]	-0.934	0.017	-0.963	-0.901	0.001	0.000	1030.0	812.0	NaN
ratio_norm[0]	0.141	0.004	0.135	0.149	0.000	0.000	981.0	942.0	NaN
ratio_norm[1]	0.140	0.004	0.134	0.147	0.000	0.000	913.0	982.0	NaN
ratio_norm[2]	0.138	0.007	0.124	0.151	0.000	0.000	1019.0	952.0	NaN
fwhm2_norm[0]	1.003	0.001	1.001	1.005	0.000	0.000	886.0	1011.0	NaN
fwhm2_norm[1]	1.563	0.002	1.559	1.567	0.000	0.000	931.0	872.0	NaN
fwhm2_norm[2]	2.245	0.006	2.233	2.256	0.000	0.000	1025.0	1117.0	NaN
velocity_norm[0]	0.167	0.000	0.167	0.167	0.000	0.000	858.0	948.0	NaN
velocity_norm[1]	0.500	0.000	0.500	0.500	0.000	0.000	967.0	1000.0	NaN
velocity_norm[2]	0.917	0.000	0.916	0.917	0.000	0.000	1116.0	984.0	NaN
log10_CTEX_variance_norm[0]	2.434	0.227	2.046	2.880	0.008	0.005	916.0	912.0	NaN
log10_CTEX_variance_norm[1]	1.931	0.219	1.568	2.375	0.007	0.005	1089.0	877.0	NaN
log10_CTEX_variance_norm[2]	1.206	0.242	0.796	1.669	0.008	0.006	1005.0	978.0	NaN
weights_12CN_norm[0, 0 0 1 1 -- --]	0.190	0.000	0.190	0.190	0.000	0.000	1090.0	782.0	NaN
weights_12CN_norm[0, 0 0 1 2 -- --]	0.329	0.000	0.329	0.329	0.000	0.000	1005.0	843.0	NaN
weights_12CN_norm[0, 1 0 1 1 -- --]	0.070	0.000	0.070	0.070	0.000	0.000	989.0	983.0	NaN
weights_12CN_norm[0, 1 0 1 2 -- --]	0.121	0.000	0.121	0.121	0.000	0.000	905.0	785.0	NaN
weights_12CN_norm[0, 1 0 2 1 -- --]	0.036	0.000	0.036	0.036	0.000	0.000	960.0	992.0	NaN
weights_12CN_norm[0, 1 0 2 2 -- --]	0.102	0.000	0.102	0.102	0.000	0.000	1033.0	910.0	NaN
weights_12CN_norm[0, 1 0 2 3 -- --]	0.151	0.000	0.151	0.151	0.000	0.000	1085.0	848.0	NaN
weights_12CN_norm[1, 0 0 1 1 -- --]	0.162	0.000	0.162	0.162	0.000	0.000	939.0	942.0	NaN
weights_12CN_norm[1, 0 0 1 2 -- --]	0.380	0.000	0.380	0.380	0.000	0.000	901.0	983.0	NaN
weights_12CN_norm[1, 1 0 1 1 -- --]	0.044	0.000	0.044	0.044	0.000	0.000	878.0	915.0	NaN
weights_12CN_norm[1, 1 0 1 2 -- --]	0.112	0.000	0.112	0.112	0.000	0.000	967.0	969.0	NaN
weights_12CN_norm[1, 1 0 2 1 -- --]	0.053	0.000	0.053	0.053	0.000	0.000	968.0	1024.0	NaN
weights_12CN_norm[1, 1 0 2 2 -- --]	0.097	0.000	0.097	0.097	0.000	0.000	1092.0	857.0	NaN
weights_12CN_norm[1, 1 0 2 3 -- --]	0.152	0.000	0.152	0.152	0.000	0.000	951.0	661.0	NaN
weights_12CN_norm[2, 0 0 1 1 -- --]	0.208	0.000	0.208	0.209	0.000	0.000	899.0	874.0	NaN
weights_12CN_norm[2, 0 0 1 2 -- --]	0.427	0.000	0.427	0.428	0.000	0.000	880.0	837.0	NaN
weights_12CN_norm[2, 1 0 1 1 -- --]	0.036	0.000	0.036	0.036	0.000	0.000	1023.0	1026.0	NaN
weights_12CN_norm[2, 1 0 1 2 -- --]	0.087	0.000	0.087	0.087	0.000	0.000	784.0	953.0	NaN
weights_12CN_norm[2, 1 0 2 1 -- --]	0.040	0.000	0.040	0.040	0.000	0.000	1045.0	975.0	NaN
weights_12CN_norm[2, 1 0 2 2 -- --]	0.079	0.000	0.079	0.080	0.000	0.000	814.0	908.0	NaN
weights_12CN_norm[2, 1 0 2 3 -- --]	0.122	0.000	0.121	0.122	0.000	0.000	968.0	739.0	NaN
log10_Ntot_12CN[0]	13.798	0.000	13.798	13.799	0.000	0.000	976.0	908.0	NaN
log10_Ntot_12CN[1]	13.898	0.000	13.898	13.899	0.000	0.000	1006.0	942.0	NaN
log10_Ntot_12CN[2]	13.993	0.001	13.992	13.994	0.000	0.000	902.0	767.0	NaN
ratio[0]	0.014	0.000	0.013	0.015	0.000	0.000	981.0	942.0	NaN
ratio[1]	0.014	0.000	0.013	0.015	0.000	0.000	913.0	982.0	NaN
ratio[2]	0.014	0.001	0.012	0.015	0.000	0.000	1019.0	952.0	NaN
fwhm2[0]	1.003	0.001	1.001	1.005	0.000	0.000	886.0	1011.0	NaN
fwhm2[1]	1.563	0.002	1.559	1.567	0.000	0.000	931.0	872.0	NaN
fwhm2[2]	2.245	0.006	2.233	2.256	0.000	0.000	1025.0	1117.0	NaN
velocity[0]	-2.000	0.000	-2.001	-2.000	0.000	0.000	858.0	948.0	NaN
velocity[1]	0.000	0.000	-0.000	0.001	0.000	0.000	967.0	1000.0	NaN
velocity[2]	2.500	0.001	2.499	2.502	0.000	0.000	1116.0	984.0	NaN
log10_Tex_CTEX[0]	0.671	0.008	0.656	0.685	0.000	0.000	839.0	982.0	NaN
log10_Tex_CTEX[1]	0.635	0.006	0.622	0.645	0.000	0.000	933.0	937.0	NaN
log10_Tex_CTEX[2]	0.517	0.004	0.509	0.525	0.000	0.000	1030.0	812.0	NaN
log10_Ntot_13CN[0]	11.949	0.011	11.928	11.971	0.000	0.000	984.0	942.0	NaN
log10_Ntot_13CN[1]	12.044	0.011	12.024	12.065	0.000	0.000	916.0	982.0	NaN
log10_Ntot_13CN[2]	12.132	0.022	12.088	12.171	0.001	0.000	1020.0	944.0	NaN
log10_CTEX_variance[0]	-1.566	0.227	-1.954	-1.120	0.008	0.005	916.0	912.0	NaN
log10_CTEX_variance[1]	-2.069	0.219	-2.432	-1.625	0.007	0.005	1089.0	877.0	NaN
log10_CTEX_variance[2]	-2.794	0.242	-3.204	-2.331	0.008	0.006	1005.0	978.0	NaN
CTEX_weights_12CN[0, 0 0 1 1 -- --]	2.000	0.000	2.000	2.000	0.000	0.000	839.0	982.0	NaN
CTEX_weights_12CN[0, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000	0.000	NaN	1000.0	1000.0	NaN
CTEX_weights_12CN[0, 1 0 1 1 -- --]	0.628	0.013	0.603	0.651	0.000	0.000	839.0	982.0	NaN
CTEX_weights_12CN[0, 1 0 1 2 -- --]	1.256	0.025	1.206	1.301	0.001	0.001	839.0	982.0	NaN
CTEX_weights_12CN[0, 1 0 2 1 -- --]	0.626	0.013	0.601	0.648	0.000	0.000	839.0	982.0	NaN
CTEX_weights_12CN[0, 1 0 2 2 -- --]	1.252	0.025	1.202	1.297	0.001	0.001	839.0	982.0	NaN
CTEX_weights_12CN[0, 1 0 2 3 -- --]	1.878	0.038	1.803	1.946	0.001	0.001	839.0	982.0	NaN
CTEX_weights_12CN[1, 0 0 1 1 -- --]	2.000	0.000	2.000	2.000	0.000	0.000	933.0	937.0	NaN
CTEX_weights_12CN[1, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000	0.000	NaN	1000.0	1000.0	NaN
CTEX_weights_12CN[1, 1 0 1 1 -- --]	0.568	0.010	0.548	0.585	0.000	0.000	933.0	937.0	NaN
CTEX_weights_12CN[1, 1 0 1 2 -- --]	1.135	0.020	1.095	1.170	0.001	0.000	933.0	937.0	NaN
CTEX_weights_12CN[1, 1 0 2 1 -- --]	0.566	0.010	0.545	0.583	0.000	0.000	933.0	937.0	NaN
CTEX_weights_12CN[1, 1 0 2 2 -- --]	1.131	0.020	1.091	1.166	0.001	0.000	933.0	937.0	NaN
CTEX_weights_12CN[1, 1 0 2 3 -- --]	1.697	0.030	1.636	1.749	0.001	0.001	933.0	937.0	NaN
CTEX_weights_12CN[2, 0 0 1 1 -- --]	1.999	0.000	1.999	1.999	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_12CN[2, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000	0.000	NaN	1000.0	1000.0	NaN
CTEX_weights_12CN[2, 1 0 1 1 -- --]	0.383	0.006	0.373	0.395	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_12CN[2, 1 0 1 2 -- --]	0.766	0.012	0.744	0.790	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_12CN[2, 1 0 2 1 -- --]	0.381	0.006	0.370	0.393	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_12CN[2, 1 0 2 2 -- --]	0.762	0.012	0.741	0.786	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_12CN[2, 1 0 2 3 -- --]	1.143	0.018	1.112	1.179	0.001	0.000	1030.0	812.0	NaN
Tex_12CN[113123.3701, 0]	5.465	0.002	5.461	5.469	0.000	0.000	1057.0	906.0	NaN
Tex_12CN[113123.3701, 1]	4.171	0.001	4.169	4.174	0.000	0.000	925.0	881.0	NaN
Tex_12CN[113123.3701, 2]	3.108	0.001	3.106	3.110	0.000	0.000	943.0	983.0	NaN
Tex_12CN[113144.1573, 0]	6.406	0.002	6.402	6.410	0.000	0.000	1013.0	979.0	NaN
Tex_12CN[113144.1573, 1]	3.721	0.001	3.719	3.722	0.000	0.000	879.0	948.0	NaN
Tex_12CN[113144.1573, 2]	3.064	0.001	3.063	3.065	0.000	0.000	1026.0	1026.0	NaN
Tex_12CN[113170.4915, 0]	4.744	0.001	4.742	4.747	0.000	0.000	872.0	773.0	NaN
Tex_12CN[113170.4915, 1]	5.111	0.002	5.108	5.114	0.000	0.000	944.0	981.0	NaN
Tex_12CN[113170.4915, 2]	3.462	0.001	3.460	3.464	0.000	0.000	836.0	893.0	NaN
Tex_12CN[113191.2787, 0]	5.437	0.002	5.435	5.441	0.000	0.000	897.0	791.0	NaN
Tex_12CN[113191.2787, 1]	4.451	0.001	4.449	4.453	0.000	0.000	898.0	981.0	NaN
Tex_12CN[113191.2787, 2]	3.408	0.001	3.407	3.410	0.000	0.000	825.0	928.0	NaN
Tex_12CN[113488.1202, 0]	4.139	0.001	4.137	4.141	0.000	0.000	932.0	1023.0	NaN
Tex_12CN[113488.1202, 1]	4.497	0.001	4.494	4.499	0.000	0.000	888.0	983.0	NaN
Tex_12CN[113488.1202, 2]	3.287	0.001	3.285	3.289	0.000	0.000	852.0	908.0	NaN
Tex_12CN[113490.9702, 0]	4.599	0.003	4.595	4.604	0.000	0.000	1038.0	914.0	NaN
Tex_12CN[113490.9702, 1]	4.129	0.002	4.125	4.133	0.000	0.000	936.0	669.0	NaN
Tex_12CN[113490.9702, 2]	3.275	0.002	3.272	3.279	0.000	0.000	989.0	875.0	NaN
Tex_12CN[113499.6443, 0]	3.297	0.001	3.295	3.298	0.000	0.000	912.0	868.0	NaN
Tex_12CN[113499.6443, 1]	4.865	0.002	4.862	4.868	0.000	0.000	927.0	849.0	NaN
Tex_12CN[113499.6443, 2]	3.298	0.001	3.296	3.300	0.000	0.000	844.0	743.0	NaN
Tex_12CN[113508.9074, 0]	4.655	0.001	4.653	4.657	0.000	0.000	1004.0	979.0	NaN
Tex_12CN[113508.9074, 1]	3.979	0.001	3.977	3.981	0.000	0.000	1057.0	945.0	NaN
Tex_12CN[113508.9074, 2]	3.238	0.001	3.237	3.240	0.000	0.000	807.0	908.0	NaN
Tex_12CN[113520.4315, 0]	3.616	0.001	3.615	3.618	0.000	0.000	941.0	1026.0	NaN
Tex_12CN[113520.4315, 1]	4.265	0.001	4.263	4.267	0.000	0.000	986.0	983.0	NaN
Tex_12CN[113520.4315, 2]	3.248	0.001	3.247	3.250	0.000	0.000	1049.0	1021.0	NaN
tau_12CN[113123.3701, 0]	0.026	0.000	0.026	0.026	0.000	0.000	984.0	1005.0	NaN
tau_12CN[113123.3701, 1]	0.033	0.000	0.033	0.033	0.000	0.000	997.0	845.0	NaN
tau_12CN[113123.3701, 2]	0.059	0.000	0.059	0.059	0.000	0.000	913.0	786.0	NaN
tau_12CN[113144.1573, 0]	0.169	0.000	0.169	0.169	0.000	0.000	963.0	729.0	NaN
tau_12CN[113144.1573, 1]	0.330	0.000	0.329	0.330	0.000	0.000	1022.0	874.0	NaN
tau_12CN[113144.1573, 2]	0.499	0.001	0.498	0.500	0.000	0.000	912.0	713.0	NaN
tau_12CN[113170.4915, 0]	0.228	0.000	0.227	0.228	0.000	0.000	917.0	901.0	NaN
tau_12CN[113170.4915, 1]	0.234	0.000	0.234	0.235	0.000	0.000	996.0	947.0	NaN
tau_12CN[113170.4915, 2]	0.453	0.001	0.452	0.454	0.000	0.000	874.0	788.0	NaN
tau_12CN[113191.2787, 0]	0.237	0.000	0.236	0.237	0.000	0.000	862.0	983.0	NaN
tau_12CN[113191.2787, 1]	0.384	0.000	0.384	0.384	0.000	0.000	996.0	781.0	NaN
tau_12CN[113191.2787, 2]	0.608	0.001	0.606	0.609	0.000	0.000	874.0	788.0	NaN
tau_12CN[113488.1202, 0]	0.318	0.000	0.318	0.318	0.000	0.000	954.0	985.0	NaN
tau_12CN[113488.1202, 1]	0.328	0.000	0.327	0.328	0.000	0.000	953.0	983.0	NaN
tau_12CN[113488.1202, 2]	0.603	0.001	0.602	0.605	0.000	0.000	895.0	809.0	NaN
tau_12CN[113490.9702, 0]	0.692	0.000	0.691	0.693	0.000	0.000	946.0	896.0	NaN
tau_12CN[113490.9702, 1]	1.062	0.001	1.061	1.064	0.000	0.000	1056.0	912.0	NaN
tau_12CN[113490.9702, 2]	1.646	0.002	1.642	1.650	0.000	0.000	903.0	663.0	NaN
tau_12CN[113499.6443, 0]	0.277	0.000	0.277	0.277	0.000	0.000	960.0	1022.0	NaN
tau_12CN[113499.6443, 1]	0.248	0.000	0.247	0.248	0.000	0.000	1017.0	983.0	NaN
tau_12CN[113499.6443, 2]	0.475	0.001	0.474	0.476	0.000	0.000	901.0	788.0	NaN
tau_12CN[113508.9074, 0]	0.200	0.000	0.199	0.200	0.000	0.000	979.0	983.0	NaN
tau_12CN[113508.9074, 1]	0.314	0.000	0.313	0.314	0.000	0.000	1023.0	912.0	NaN
tau_12CN[113508.9074, 2]	0.480	0.001	0.478	0.481	0.000	0.000	884.0	740.0	NaN
tau_12CN[113520.4315, 0]	0.028	0.000	0.028	0.028	0.000	0.000	961.0	942.0	NaN
tau_12CN[113520.4315, 1]	0.038	0.000	0.038	0.038	0.000	0.000	1040.0	773.0	NaN
tau_12CN[113520.4315, 2]	0.060	0.000	0.060	0.060	0.000	0.000	901.0	724.0	NaN
tau_total_12CN[0]	2.174	0.001	2.172	2.176	0.000	0.000	905.0	904.0	NaN
tau_total_12CN[1]	2.970	0.002	2.967	2.973	0.000	0.000	1040.0	943.0	NaN
tau_total_12CN[2]	4.883	0.006	4.872	4.895	0.000	0.000	904.0	685.0	NaN
TR_12CN[113123.3701, 0]	3.192	0.002	3.189	3.196	0.000	0.000	1057.0	906.0	NaN
TR_12CN[113123.3701, 1]	2.029	0.001	2.027	2.032	0.000	0.000	925.0	881.0	NaN
TR_12CN[113123.3701, 2]	1.146	0.001	1.145	1.148	0.000	0.000	943.0	983.0	NaN
TR_12CN[113144.1573, 0]	4.070	0.002	4.066	4.074	0.000	0.000	1013.0	979.0	NaN
TR_12CN[113144.1573, 1]	1.644	0.001	1.643	1.645	0.000	0.000	879.0	948.0	NaN
TR_12CN[113144.1573, 2]	1.112	0.001	1.111	1.113	0.000	0.000	1026.0	1026.0	NaN
TR_12CN[113170.4915, 0]	2.536	0.001	2.533	2.538	0.000	0.000	872.0	773.0	NaN
TR_12CN[113170.4915, 1]	2.868	0.002	2.865	2.871	0.000	0.000	944.0	981.0	NaN
TR_12CN[113170.4915, 2]	1.429	0.001	1.428	1.431	0.000	0.000	836.0	893.0	NaN
TR_12CN[113191.2787, 0]	3.166	0.001	3.164	3.169	0.000	0.000	897.0	791.0	NaN
TR_12CN[113191.2787, 1]	2.274	0.001	2.272	2.276	0.000	0.000	898.0	981.0	NaN
TR_12CN[113191.2787, 2]	1.385	0.001	1.383	1.386	0.000	0.000	825.0	928.0	NaN
TR_12CN[113488.1202, 0]	1.996	0.001	1.994	1.998	0.000	0.000	932.0	1023.0	NaN
TR_12CN[113488.1202, 1]	2.310	0.001	2.308	2.312	0.000	0.000	888.0	983.0	NaN
TR_12CN[113488.1202, 2]	1.284	0.001	1.282	1.285	0.000	0.000	852.0	908.0	NaN
TR_12CN[113490.9702, 0]	2.401	0.002	2.397	2.406	0.000	0.000	1038.0	914.0	NaN
TR_12CN[113490.9702, 1]	1.988	0.002	1.984	1.991	0.000	0.000	936.0	669.0	NaN
TR_12CN[113490.9702, 2]	1.274	0.001	1.271	1.277	0.000	0.000	989.0	875.0	NaN
TR_12CN[113499.6443, 0]	1.291	0.001	1.290	1.292	0.000	0.000	912.0	868.0	NaN
TR_12CN[113499.6443, 1]	2.640	0.002	2.637	2.642	0.000	0.000	927.0	849.0	NaN
TR_12CN[113499.6443, 2]	1.292	0.001	1.290	1.293	0.000	0.000	844.0	743.0	NaN
TR_12CN[113508.9074, 0]	2.451	0.001	2.449	2.453	0.000	0.000	1004.0	979.0	NaN
TR_12CN[113508.9074, 1]	1.858	0.001	1.857	1.860	0.000	0.000	1057.0	945.0	NaN
TR_12CN[113508.9074, 2]	1.244	0.001	1.243	1.246	0.000	0.000	807.0	908.0	NaN
TR_12CN[113520.4315, 0]	1.552	0.001	1.551	1.553	0.000	0.000	941.0	1026.0	NaN
TR_12CN[113520.4315, 1]	2.106	0.001	2.104	2.108	0.000	0.000	986.0	983.0	NaN
TR_12CN[113520.4315, 2]	1.252	0.001	1.251	1.254	0.000	0.000	1049.0	1021.0	NaN
CTEX_weights_13CN[0, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000	0.000	NaN	1000.0	1000.0	NaN
CTEX_weights_13CN[0, 0 1 1 0 -- --]	0.994	0.000	0.994	0.994	0.000	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 0 1 1 1 -- --]	2.982	0.000	2.982	2.983	0.000	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 0 1 1 2 -- --]	4.971	0.000	4.970	4.972	0.000	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 1 0 1 -- --]	0.983	0.019	0.946	1.017	0.001	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 1 1 0 -- --]	0.329	0.006	0.316	0.340	0.000	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 1 1 1 -- --]	0.987	0.019	0.949	1.021	0.001	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 1 1 2 -- --]	1.644	0.032	1.582	1.701	0.001	0.001	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 2 1 0 -- --]	0.327	0.006	0.314	0.338	0.000	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 2 1 1 -- --]	0.981	0.019	0.943	1.015	0.001	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 2 1 2 -- --]	1.635	0.032	1.573	1.692	0.001	0.001	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 2 2 1 -- --]	0.979	0.019	0.942	1.014	0.001	0.000	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 2 2 2 -- --]	1.632	0.032	1.570	1.689	0.001	0.001	839.0	982.0	NaN
CTEX_weights_13CN[0, 1 2 2 3 -- --]	2.286	0.044	2.199	2.366	0.002	0.001	839.0	982.0	NaN
CTEX_weights_13CN[1, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000	0.000	NaN	1000.0	1000.0	NaN
CTEX_weights_13CN[1, 0 1 1 0 -- --]	0.994	0.000	0.993	0.994	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 0 1 1 1 -- --]	2.981	0.000	2.980	2.981	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 0 1 1 2 -- --]	4.969	0.000	4.968	4.970	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 1 0 1 -- --]	0.892	0.015	0.861	0.918	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 1 1 0 -- --]	0.299	0.005	0.288	0.307	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 1 1 1 -- --]	0.896	0.015	0.865	0.922	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 1 1 2 -- --]	1.493	0.025	1.441	1.536	0.001	0.001	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 2 1 0 -- --]	0.297	0.005	0.286	0.305	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 2 1 1 -- --]	0.890	0.015	0.859	0.916	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 2 1 2 -- --]	1.483	0.025	1.432	1.527	0.001	0.001	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 2 2 1 -- --]	0.888	0.015	0.858	0.914	0.000	0.000	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 2 2 2 -- --]	1.481	0.025	1.430	1.524	0.001	0.001	933.0	937.0	NaN
CTEX_weights_13CN[1, 1 2 2 3 -- --]	2.074	0.035	2.002	2.134	0.001	0.001	933.0	937.0	NaN
CTEX_weights_13CN[2, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000	0.000	NaN	1000.0	1000.0	NaN
CTEX_weights_13CN[2, 0 1 1 0 -- --]	0.992	0.000	0.991	0.992	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 0 1 1 1 -- --]	2.975	0.000	2.975	2.975	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 0 1 1 2 -- --]	4.959	0.000	4.959	4.960	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 1 0 1 -- --]	0.611	0.009	0.594	0.629	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 1 1 0 -- --]	0.205	0.003	0.199	0.211	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 1 1 1 -- --]	0.614	0.009	0.597	0.632	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 1 1 2 -- --]	1.023	0.016	0.996	1.053	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 2 1 0 -- --]	0.203	0.003	0.197	0.209	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 2 1 1 -- --]	0.608	0.009	0.592	0.627	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 2 1 2 -- --]	1.014	0.016	0.987	1.045	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 2 2 1 -- --]	0.607	0.009	0.591	0.626	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 2 2 2 -- --]	1.012	0.016	0.985	1.043	0.000	0.000	1030.0	812.0	NaN
CTEX_weights_13CN[2, 1 2 2 3 -- --]	1.417	0.022	1.380	1.460	0.001	0.001	1030.0	812.0	NaN
Tex_13CN[0]	4.689	0.081	4.531	4.837	0.003	0.002	839.0	982.0	NaN
Tex_13CN[1]	4.313	0.060	4.192	4.418	0.002	0.002	933.0	937.0	NaN
Tex_13CN[2]	3.286	0.032	3.231	3.348	0.001	0.001	1030.0	812.0	NaN
tau_13CN[108056.1623, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108056.1623, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108056.1623, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108057.1556, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108057.1556, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108057.1556, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108062.9306, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108062.9306, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108062.9306, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108076.9692, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108076.9692, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108076.9692, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108077.2965, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108077.2965, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108077.2965, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108091.3352, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108091.3352, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108091.3352, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108406.0905, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108406.0905, 1]	0.000	0.000	0.000	0.000	0.000	0.000	874.0	1015.0	NaN
tau_13CN[108406.0905, 2]	0.000	0.000	0.000	0.001	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108412.862, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108412.862, 1]	0.001	0.000	0.001	0.001	0.000	0.000	874.0	1015.0	NaN
tau_13CN[108412.862, 2]	0.002	0.000	0.001	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108426.889, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108426.889, 1]	0.002	0.000	0.002	0.002	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108426.889, 2]	0.003	0.000	0.003	0.003	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108631.121, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108631.121, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108631.121, 2]	0.002	0.000	0.001	0.002	0.000	0.000	1006.0	1019.0	NaN
tau_13CN[108636.923, 0]	0.002	0.000	0.002	0.002	0.000	0.000	845.0	951.0	NaN
tau_13CN[108636.923, 1]	0.003	0.000	0.003	0.003	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108636.923, 2]	0.005	0.000	0.004	0.005	0.000	0.000	1006.0	1019.0	NaN
tau_13CN[108638.212, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108638.212, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108638.212, 2]	0.002	0.000	0.002	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108643.59, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108643.59, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108643.59, 2]	0.002	0.000	0.002	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108644.3456, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108644.3456, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108644.3456, 2]	0.002	0.000	0.001	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108645.064, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108645.064, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108645.064, 2]	0.001	0.000	0.001	0.001	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108651.297, 0]	0.004	0.000	0.003	0.004	0.000	0.000	845.0	951.0	NaN
tau_13CN[108651.297, 1]	0.005	0.000	0.005	0.005	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108651.297, 2]	0.008	0.000	0.007	0.009	0.000	0.000	1006.0	1019.0	NaN
tau_13CN[108657.646, 0]	0.003	0.000	0.002	0.003	0.000	0.000	845.0	951.0	NaN
tau_13CN[108657.646, 1]	0.004	0.000	0.003	0.004	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108657.646, 2]	0.006	0.000	0.005	0.007	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108658.948, 0]	0.001	0.000	0.001	0.001	0.000	0.000	845.0	951.0	NaN
tau_13CN[108658.948, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108658.948, 2]	0.002	0.000	0.001	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108780.201, 0]	0.005	0.000	0.005	0.006	0.000	0.000	845.0	951.0	NaN
tau_13CN[108780.201, 1]	0.007	0.000	0.007	0.008	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108780.201, 2]	0.012	0.001	0.011	0.013	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108782.374, 0]	0.003	0.000	0.003	0.003	0.000	0.000	846.0	951.0	NaN
tau_13CN[108782.374, 1]	0.004	0.000	0.004	0.004	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108782.374, 2]	0.006	0.000	0.006	0.007	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108786.982, 0]	0.001	0.000	0.001	0.001	0.000	0.000	846.0	951.0	NaN
tau_13CN[108786.982, 1]	0.002	0.000	0.002	0.002	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108786.982, 2]	0.003	0.000	0.003	0.003	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108793.753, 0]	0.001	0.000	0.001	0.001	0.000	0.000	846.0	951.0	NaN
tau_13CN[108793.753, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108793.753, 2]	0.002	0.000	0.002	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108796.4, 0]	0.001	0.000	0.001	0.001	0.000	0.000	846.0	951.0	NaN
tau_13CN[108796.4, 1]	0.001	0.000	0.001	0.001	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108796.4, 2]	0.002	0.000	0.002	0.002	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108807.7879, 0]	0.000	0.000	0.000	0.000	0.000	0.000	846.0	951.0	NaN
tau_13CN[108807.7879, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108807.7879, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	992.0	NaN
tau_13CN[108986.836, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[108986.836, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[108986.836, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	1019.0	NaN
tau_13CN[109217.5674, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[109217.5674, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[109217.5674, 2]	0.001	0.000	0.001	0.001	0.000	0.000	1006.0	1019.0	NaN
tau_13CN[109218.3227, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[109218.3227, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[109218.3227, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	1019.0	NaN
tau_13CN[109218.919, 0]	0.000	0.000	0.000	0.000	0.000	0.000	845.0	951.0	NaN
tau_13CN[109218.919, 1]	0.000	0.000	0.000	0.000	0.000	0.000	873.0	1015.0	NaN
tau_13CN[109218.919, 2]	0.000	0.000	0.000	0.000	0.000	0.000	1006.0	1019.0	NaN
tau_total_13CN[0]	0.028	0.001	0.026	0.030	0.000	0.000	845.0	951.0	NaN
tau_total_13CN[1]	0.038	0.001	0.036	0.040	0.000	0.000	873.0	1015.0	NaN
tau_total_13CN[2]	0.062	0.003	0.056	0.068	0.000	0.000	1006.0	992.0	NaN
TR_13CN[108056.1623, 0]	2.565	0.073	2.422	2.699	0.003	0.002	839.0	982.0	NaN
TR_13CN[108056.1623, 1]	2.228	0.053	2.121	2.321	0.002	0.001	933.0	937.0	NaN
TR_13CN[108056.1623, 2]	1.348	0.026	1.304	1.399	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108057.1556, 0]	2.565	0.073	2.422	2.699	0.003	0.002	839.0	982.0	NaN
TR_13CN[108057.1556, 1]	2.228	0.053	2.121	2.321	0.002	0.001	933.0	937.0	NaN
TR_13CN[108057.1556, 2]	1.348	0.026	1.304	1.399	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108062.9306, 0]	2.565	0.073	2.422	2.699	0.003	0.002	839.0	982.0	NaN
TR_13CN[108062.9306, 1]	2.228	0.053	2.120	2.321	0.002	0.001	933.0	937.0	NaN
TR_13CN[108062.9306, 2]	1.348	0.026	1.304	1.399	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108076.9692, 0]	2.564	0.073	2.422	2.698	0.003	0.002	839.0	982.0	NaN
TR_13CN[108076.9692, 1]	2.228	0.053	2.120	2.321	0.002	0.001	933.0	937.0	NaN
TR_13CN[108076.9692, 2]	1.348	0.026	1.303	1.399	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108077.2965, 0]	2.564	0.073	2.422	2.698	0.003	0.002	839.0	982.0	NaN
TR_13CN[108077.2965, 1]	2.228	0.053	2.120	2.321	0.002	0.001	933.0	937.0	NaN
TR_13CN[108077.2965, 2]	1.348	0.026	1.303	1.399	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108091.3352, 0]	2.564	0.073	2.422	2.698	0.003	0.002	839.0	982.0	NaN
TR_13CN[108091.3352, 1]	2.227	0.053	2.120	2.320	0.002	0.001	933.0	937.0	NaN
TR_13CN[108091.3352, 2]	1.348	0.026	1.303	1.399	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108406.0905, 0]	2.559	0.073	2.417	2.693	0.003	0.002	839.0	982.0	NaN
TR_13CN[108406.0905, 1]	2.223	0.053	2.115	2.316	0.002	0.001	933.0	937.0	NaN
TR_13CN[108406.0905, 2]	1.344	0.026	1.299	1.395	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108412.862, 0]	2.559	0.073	2.417	2.693	0.003	0.002	839.0	982.0	NaN
TR_13CN[108412.862, 1]	2.223	0.053	2.115	2.316	0.002	0.001	933.0	937.0	NaN
TR_13CN[108412.862, 2]	1.344	0.026	1.299	1.395	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108426.889, 0]	2.559	0.073	2.417	2.693	0.003	0.002	839.0	982.0	NaN
TR_13CN[108426.889, 1]	2.223	0.053	2.115	2.315	0.002	0.001	933.0	937.0	NaN
TR_13CN[108426.889, 2]	1.344	0.026	1.299	1.395	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108631.121, 0]	2.556	0.073	2.414	2.690	0.003	0.002	839.0	982.0	NaN
TR_13CN[108631.121, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108631.121, 2]	1.341	0.026	1.297	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108636.923, 0]	2.556	0.073	2.414	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108636.923, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108636.923, 2]	1.341	0.026	1.297	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108638.212, 0]	2.556	0.073	2.414	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108638.212, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108638.212, 2]	1.341	0.026	1.297	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108643.59, 0]	2.556	0.073	2.413	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108643.59, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108643.59, 2]	1.341	0.026	1.296	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108644.3456, 0]	2.556	0.073	2.413	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108644.3456, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108644.3456, 2]	1.341	0.026	1.296	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108645.064, 0]	2.556	0.073	2.413	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108645.064, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108645.064, 2]	1.341	0.026	1.296	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108651.297, 0]	2.556	0.073	2.413	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108651.297, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108651.297, 2]	1.341	0.026	1.296	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108657.646, 0]	2.555	0.073	2.413	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108657.646, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108657.646, 2]	1.341	0.026	1.296	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108658.948, 0]	2.555	0.073	2.413	2.689	0.003	0.002	839.0	982.0	NaN
TR_13CN[108658.948, 1]	2.219	0.053	2.112	2.312	0.002	0.001	933.0	937.0	NaN
TR_13CN[108658.948, 2]	1.341	0.026	1.296	1.392	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108780.201, 0]	2.554	0.073	2.411	2.687	0.003	0.002	839.0	982.0	NaN
TR_13CN[108780.201, 1]	2.217	0.053	2.110	2.310	0.002	0.001	933.0	937.0	NaN
TR_13CN[108780.201, 2]	1.339	0.026	1.295	1.390	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108782.374, 0]	2.553	0.073	2.411	2.687	0.003	0.002	839.0	982.0	NaN
TR_13CN[108782.374, 1]	2.217	0.053	2.110	2.310	0.002	0.001	933.0	937.0	NaN
TR_13CN[108782.374, 2]	1.339	0.026	1.295	1.390	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108786.982, 0]	2.553	0.073	2.411	2.687	0.003	0.002	839.0	982.0	NaN
TR_13CN[108786.982, 1]	2.217	0.053	2.110	2.310	0.002	0.001	933.0	937.0	NaN
TR_13CN[108786.982, 2]	1.339	0.026	1.295	1.390	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108793.753, 0]	2.553	0.073	2.411	2.687	0.003	0.002	839.0	982.0	NaN
TR_13CN[108793.753, 1]	2.217	0.053	2.110	2.310	0.002	0.001	933.0	937.0	NaN
TR_13CN[108793.753, 2]	1.339	0.026	1.295	1.390	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108796.4, 0]	2.553	0.073	2.411	2.687	0.003	0.002	839.0	982.0	NaN
TR_13CN[108796.4, 1]	2.217	0.053	2.110	2.310	0.002	0.001	933.0	937.0	NaN
TR_13CN[108796.4, 2]	1.339	0.026	1.295	1.390	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108807.7879, 0]	2.553	0.073	2.411	2.687	0.003	0.002	839.0	982.0	NaN
TR_13CN[108807.7879, 1]	2.217	0.053	2.110	2.310	0.002	0.001	933.0	937.0	NaN
TR_13CN[108807.7879, 2]	1.339	0.026	1.294	1.390	0.001	0.001	1030.0	812.0	NaN
TR_13CN[108986.836, 0]	2.550	0.073	2.408	2.684	0.003	0.002	839.0	982.0	NaN
TR_13CN[108986.836, 1]	2.214	0.053	2.107	2.307	0.002	0.001	933.0	937.0	NaN
TR_13CN[108986.836, 2]	1.337	0.026	1.292	1.388	0.001	0.001	1030.0	812.0	NaN
TR_13CN[109217.5674, 0]	2.547	0.073	2.405	2.680	0.003	0.002	839.0	982.0	NaN
TR_13CN[109217.5674, 1]	2.211	0.053	2.104	2.304	0.002	0.001	933.0	937.0	NaN
TR_13CN[109217.5674, 2]	1.334	0.026	1.290	1.385	0.001	0.001	1030.0	812.0	NaN
TR_13CN[109218.3227, 0]	2.547	0.073	2.405	2.680	0.003	0.002	839.0	982.0	NaN
TR_13CN[109218.3227, 1]	2.211	0.053	2.104	2.304	0.002	0.001	933.0	937.0	NaN
TR_13CN[109218.3227, 2]	1.334	0.026	1.290	1.385	0.001	0.001	1030.0	812.0	NaN
TR_13CN[109218.919, 0]	2.547	0.073	2.405	2.680	0.003	0.002	839.0	982.0	NaN
TR_13CN[109218.919, 1]	2.211	0.053	2.104	2.303	0.002	0.001	933.0	937.0	NaN
TR_13CN[109218.919, 2]	1.334	0.026	1.290	1.385	0.001	0.001	1030.0	812.0	NaN

[21]:

posterior = model.sample_posterior_predictive(
    thin=10, # keep one in {thin} posterior samples
)
axes = plot_predictive(model.data, posterior.posterior_predictive)
axes.ravel()[0].figure.set_size_inches(8, 12)

Sampling: [12CN-1, 12CN-2, 13CN-1, 13CN-2]

../_images/notebooks_hfs_ratio_model_26_3.png

Posterior Sampling: MCMC

[22]:

start = time.time()
model.sample(
    init="advi+adapt_diag",  # initialization strategy
    tune=1000,  # tuning samples
    draws=1000,  # posterior samples
    chains=8,  # number of independent chains
    cores=8,  # number of parallel chains
    init_kwargs={
        "rel_tolerance": 0.01,
        "abs_tolerance": 0.01,
        "learning_rate": 0.001,
        "start": {"velocity_norm": np.linspace(0.1, 0.9, n_clouds)},
    },  # VI initialization arguments
    nuts_kwargs={"target_accept": 0.9},  # NUTS arguments
)
end = time.time()
print(f"Runtime: {(end-start)/60.0:.2f} minutes")

Initializing NUTS using custom advi+adapt_diag strategy

Convergence achieved at 52400
Interrupted at 52,399 [5%]: Average Loss = 5.5843e+28
Multiprocess sampling (8 chains in 8 jobs)
NUTS: [baseline_12CN-1_norm, baseline_12CN-2_norm, baseline_13CN-1_norm, baseline_13CN-2_norm, log10_Ntot_12CN_norm, ratio_norm, fwhm2_norm, velocity_norm, log10_Tex_CTEX_norm, log10_CTEX_variance_norm, weights_12CN_norm]

Sampling 8 chains for 1_000 tune and 1_000 draw iterations (8_000 + 8_000 draws total) took 944 seconds.

Adding log-likelihood to trace

Runtime: 36.27 minutes

[23]:

model.solve(
    init_params="random_from_data", # GMM initialization strategy
    n_init=10, # number of GMM initilizations
    max_iter=1_000, # maximum number of GMM iterations
    kl_div_threshold=0.1, # covergence threshold
)

GMM converged to unique solution

[24]:

print("solutions:", model.solutions)

pm.summary(model.trace.solution_0)

solutions: [0]

/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
  (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)
/home/twenger/miniforge3/envs/bayes_spec-dev/lib/python3.13/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
  varsd = varvar / evar / 4

[24]:

	mean	sd	hdi_3%	hdi_97%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
baseline_12CN-1_norm[0]	0.036	0.050	-0.058	0.130	0.000	0.001	10282.0	6277.0	1.0
baseline_12CN-2_norm[0]	0.024	0.065	-0.098	0.145	0.001	0.001	10368.0	6214.0	1.0
baseline_13CN-1_norm[0]	-0.028	0.067	-0.156	0.096	0.001	0.001	10280.0	6420.0	1.0
baseline_13CN-2_norm[0]	0.050	0.074	-0.089	0.186	0.001	0.001	9580.0	6058.0	1.0
log10_Ntot_12CN_norm[0]	0.597	0.005	0.588	0.606	0.000	0.000	2615.0	3721.0	1.0
log10_Ntot_12CN_norm[1]	0.797	0.005	0.788	0.805	0.000	0.000	2265.0	3342.0	1.0
log10_Ntot_12CN_norm[2]	0.987	0.009	0.971	1.003	0.000	0.000	2561.0	3317.0	1.0
log10_Tex_CTEX_norm[0]	-0.342	0.163	-0.644	-0.030	0.003	0.003	4393.0	3492.0	1.0
log10_Tex_CTEX_norm[1]	-0.463	0.095	-0.633	-0.286	0.002	0.003	4586.0	3620.0	1.0
log10_Tex_CTEX_norm[2]	-0.934	0.034	-0.999	-0.872	0.001	0.001	4457.0	4072.0	1.0
ratio_norm[0]	0.148	0.024	0.112	0.192	0.000	0.001	4387.0	3510.0	1.0
ratio_norm[1]	0.142	0.015	0.115	0.168	0.000	0.000	4673.0	4026.0	1.0
ratio_norm[2]	0.140	0.016	0.111	0.168	0.000	0.001	4795.0	4186.0	1.0
fwhm2_norm[0]	1.002	0.002	0.998	1.007	0.000	0.000	3292.0	5074.0	1.0
fwhm2_norm[1]	1.564	0.004	1.555	1.572	0.000	0.000	2745.0	4453.0	1.0
fwhm2_norm[2]	2.246	0.011	2.226	2.267	0.000	0.000	3166.0	4842.0	1.0
velocity_norm[0]	0.167	0.000	0.167	0.167	0.000	0.000	7686.0	5948.0	1.0
velocity_norm[1]	0.500	0.000	0.500	0.500	0.000	0.000	8595.0	6090.0	1.0
velocity_norm[2]	0.917	0.000	0.916	0.917	0.000	0.000	8465.0	6448.0	1.0
log10_CTEX_variance_norm[0]	2.472	0.237	2.033	2.914	0.003	0.003	7353.0	5668.0	1.0
log10_CTEX_variance_norm[1]	1.999	0.247	1.558	2.466	0.003	0.003	7720.0	5523.0	1.0
log10_CTEX_variance_norm[2]	1.255	0.266	0.782	1.751	0.003	0.004	7597.0	4874.0	1.0
weights_12CN_norm[0, 0 0 1 1 -- --]	0.190	0.000	0.190	0.191	0.000	0.000	3618.0	5091.0	1.0
weights_12CN_norm[0, 0 0 1 2 -- --]	0.329	0.001	0.328	0.330	0.000	0.000	2748.0	4096.0	1.0
weights_12CN_norm[0, 1 0 1 1 -- --]	0.070	0.000	0.070	0.071	0.000	0.000	2641.0	3765.0	1.0
weights_12CN_norm[0, 1 0 1 2 -- --]	0.121	0.000	0.121	0.121	0.000	0.000	2640.0	3888.0	1.0
weights_12CN_norm[0, 1 0 2 1 -- --]	0.036	0.000	0.036	0.037	0.000	0.000	7180.0	6037.0	1.0
weights_12CN_norm[0, 1 0 2 2 -- --]	0.102	0.000	0.102	0.102	0.000	0.000	2715.0	3989.0	1.0
weights_12CN_norm[0, 1 0 2 3 -- --]	0.151	0.000	0.151	0.151	0.000	0.000	4213.0	5529.0	1.0
weights_12CN_norm[1, 0 0 1 1 -- --]	0.162	0.000	0.162	0.163	0.000	0.000	2613.0	3818.0	1.0
weights_12CN_norm[1, 0 0 1 2 -- --]	0.380	0.000	0.379	0.380	0.000	0.000	2400.0	3690.0	1.0
weights_12CN_norm[1, 1 0 1 1 -- --]	0.044	0.000	0.044	0.044	0.000	0.000	2695.0	4284.0	1.0
weights_12CN_norm[1, 1 0 1 2 -- --]	0.112	0.000	0.112	0.112	0.000	0.000	2269.0	3281.0	1.0
weights_12CN_norm[1, 1 0 2 1 -- --]	0.053	0.000	0.053	0.053	0.000	0.000	2385.0	3534.0	1.0
weights_12CN_norm[1, 1 0 2 2 -- --]	0.097	0.000	0.096	0.097	0.000	0.000	2301.0	3767.0	1.0
weights_12CN_norm[1, 1 0 2 3 -- --]	0.152	0.000	0.152	0.153	0.000	0.000	3254.0	4718.0	1.0
weights_12CN_norm[2, 0 0 1 1 -- --]	0.208	0.000	0.208	0.209	0.000	0.000	3279.0	4184.0	1.0
weights_12CN_norm[2, 0 0 1 2 -- --]	0.427	0.000	0.427	0.428	0.000	0.000	2869.0	3758.0	1.0
weights_12CN_norm[2, 1 0 1 1 -- --]	0.036	0.000	0.036	0.036	0.000	0.000	3285.0	5170.0	1.0
weights_12CN_norm[2, 1 0 1 2 -- --]	0.087	0.000	0.087	0.087	0.000	0.000	2598.0	3417.0	1.0
weights_12CN_norm[2, 1 0 2 1 -- --]	0.040	0.000	0.040	0.040	0.000	0.000	2893.0	3812.0	1.0
weights_12CN_norm[2, 1 0 2 2 -- --]	0.079	0.000	0.079	0.080	0.000	0.000	2695.0	3439.0	1.0
weights_12CN_norm[2, 1 0 2 3 -- --]	0.122	0.000	0.121	0.122	0.000	0.000	4188.0	5349.0	1.0
log10_Ntot_12CN[0]	13.799	0.002	13.794	13.803	0.000	0.000	2615.0	3721.0	1.0
log10_Ntot_12CN[1]	13.898	0.002	13.894	13.903	0.000	0.000	2265.0	3342.0	1.0
log10_Ntot_12CN[2]	13.993	0.004	13.985	14.002	0.000	0.000	2561.0	3317.0	1.0
ratio[0]	0.015	0.002	0.011	0.019	0.000	0.000	4387.0	3510.0	1.0
ratio[1]	0.014	0.001	0.012	0.017	0.000	0.000	4673.0	4026.0	1.0
ratio[2]	0.014	0.002	0.011	0.017	0.000	0.000	4795.0	4186.0	1.0
fwhm2[0]	1.002	0.002	0.998	1.007	0.000	0.000	3292.0	5074.0	1.0
fwhm2[1]	1.564	0.004	1.555	1.572	0.000	0.000	2745.0	4453.0	1.0
fwhm2[2]	2.246	0.011	2.226	2.267	0.000	0.000	3166.0	4842.0	1.0
velocity[0]	-2.000	0.000	-2.001	-2.000	0.000	0.000	7686.0	5948.0	1.0
velocity[1]	0.000	0.000	-0.001	0.001	0.000	0.000	8595.0	6090.0	1.0
velocity[2]	2.501	0.001	2.499	2.502	0.000	0.000	8465.0	6448.0	1.0
log10_Tex_CTEX[0]	0.664	0.041	0.589	0.743	0.001	0.001	4393.0	3492.0	1.0
log10_Tex_CTEX[1]	0.634	0.024	0.592	0.678	0.000	0.001	4586.0	3620.0	1.0
log10_Tex_CTEX[2]	0.516	0.009	0.500	0.532	0.000	0.000	4457.0	4072.0	1.0
log10_Ntot_13CN[0]	11.965	0.065	11.847	12.081	0.001	0.002	4378.0	3519.0	1.0
log10_Ntot_13CN[1]	12.047	0.044	11.967	12.131	0.001	0.001	4707.0	4091.0	1.0
log10_Ntot_13CN[2]	12.137	0.048	12.044	12.221	0.001	0.001	4769.0	4335.0	1.0
log10_CTEX_variance[0]	-1.528	0.237	-1.967	-1.086	0.003	0.003	7353.0	5668.0	1.0
log10_CTEX_variance[1]	-2.001	0.247	-2.442	-1.534	0.003	0.003	7720.0	5523.0	1.0
log10_CTEX_variance[2]	-2.745	0.266	-3.218	-2.249	0.003	0.004	7597.0	4874.0	1.0
CTEX_weights_12CN[0, 0 0 1 1 -- --]	2.000	0.000	1.999	2.000	0.000	0.000	4393.0	3492.0	1.0
CTEX_weights_12CN[0, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000	0.000	NaN	8000.0	8000.0	NaN
CTEX_weights_12CN[0, 1 0 1 1 -- --]	0.618	0.068	0.492	0.747	0.001	0.001	4393.0	3492.0	1.0
CTEX_weights_12CN[0, 1 0 1 2 -- --]	1.234	0.135	0.983	1.494	0.002	0.002	4393.0	3492.0	1.0
CTEX_weights_12CN[0, 1 0 2 1 -- --]	0.615	0.068	0.490	0.745	0.001	0.001	4393.0	3492.0	1.0
CTEX_weights_12CN[0, 1 0 2 2 -- --]	1.230	0.135	0.979	1.490	0.002	0.002	4393.0	3492.0	1.0
CTEX_weights_12CN[0, 1 0 2 3 -- --]	1.846	0.203	1.470	2.235	0.003	0.003	4393.0	3492.0	1.0
CTEX_weights_12CN[1, 0 0 1 1 -- --]	2.000	0.000	1.999	2.000	0.000	0.000	4586.0	3620.0	1.0
CTEX_weights_12CN[1, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000	0.000	NaN	8000.0	8000.0	NaN
CTEX_weights_12CN[1, 1 0 1 1 -- --]	0.567	0.039	0.494	0.636	0.001	0.001	4586.0	3620.0	1.0
CTEX_weights_12CN[1, 1 0 1 2 -- --]	1.134	0.078	0.987	1.272	0.001	0.003	4586.0	3620.0	1.0
CTEX_weights_12CN[1, 1 0 2 1 -- --]	0.565	0.039	0.492	0.634	0.001	0.001	4586.0	3620.0	1.0
CTEX_weights_12CN[1, 1 0 2 2 -- --]	1.130	0.078	0.983	1.268	0.001	0.003	4586.0	3620.0	1.0
CTEX_weights_12CN[1, 1 0 2 3 -- --]	1.696	0.117	1.475	1.902	0.002	0.004	4586.0	3620.0	1.0
CTEX_weights_12CN[2, 0 0 1 1 -- --]	1.999	0.000	1.999	1.999	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_12CN[2, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000	0.000	NaN	8000.0	8000.0	NaN
CTEX_weights_12CN[2, 1 0 1 1 -- --]	0.383	0.013	0.358	0.405	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_12CN[2, 1 0 1 2 -- --]	0.765	0.025	0.716	0.809	0.000	0.001	4457.0	4072.0	1.0
CTEX_weights_12CN[2, 1 0 2 1 -- --]	0.381	0.012	0.356	0.403	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_12CN[2, 1 0 2 2 -- --]	0.762	0.025	0.713	0.805	0.000	0.001	4457.0	4072.0	1.0
CTEX_weights_12CN[2, 1 0 2 3 -- --]	1.143	0.037	1.069	1.209	0.001	0.001	4457.0	4072.0	1.0
Tex_12CN[113123.3701, 0]	5.463	0.019	5.428	5.498	0.000	0.000	2707.0	3940.0	1.0
Tex_12CN[113123.3701, 1]	4.171	0.011	4.150	4.191	0.000	0.000	2517.0	3633.0	1.0
Tex_12CN[113123.3701, 2]	3.108	0.004	3.100	3.115	0.000	0.000	2988.0	4218.0	1.0
Tex_12CN[113144.1573, 0]	6.402	0.030	6.348	6.459	0.001	0.000	2623.0	3838.0	1.0
Tex_12CN[113144.1573, 1]	3.721	0.006	3.710	3.731	0.000	0.000	2362.0	3730.0	1.0
Tex_12CN[113144.1573, 2]	3.064	0.003	3.057	3.070	0.000	0.000	2821.0	3629.0	1.0
Tex_12CN[113170.4915, 0]	4.742	0.012	4.720	4.765	0.000	0.000	2687.0	3904.0	1.0
Tex_12CN[113170.4915, 1]	5.111	0.018	5.078	5.145	0.000	0.000	2346.0	3458.0	1.0
Tex_12CN[113170.4915, 2]	3.463	0.007	3.450	3.475	0.000	0.000	2620.0	3367.0	1.0
Tex_12CN[113191.2787, 0]	5.435	0.019	5.400	5.471	0.000	0.000	2647.0	3902.0	1.0
Tex_12CN[113191.2787, 1]	4.451	0.010	4.433	4.469	0.000	0.000	2253.0	3459.0	1.0
Tex_12CN[113191.2787, 2]	3.408	0.006	3.398	3.419	0.000	0.000	2600.0	3257.0	1.0
Tex_12CN[113488.1202, 0]	4.138	0.008	4.123	4.152	0.000	0.000	2649.0	4068.0	1.0
Tex_12CN[113488.1202, 1]	4.497	0.012	4.474	4.520	0.000	0.000	2307.0	3556.0	1.0
Tex_12CN[113488.1202, 2]	3.287	0.005	3.278	3.297	0.000	0.000	2621.0	3389.0	1.0
Tex_12CN[113490.9702, 0]	4.602	0.007	4.589	4.616	0.000	0.000	2622.0	3894.0	1.0
Tex_12CN[113490.9702, 1]	4.129	0.004	4.122	4.138	0.000	0.000	2309.0	3483.0	1.0
Tex_12CN[113490.9702, 2]	3.277	0.002	3.273	3.281	0.000	0.000	2848.0	3258.0	1.0
Tex_12CN[113499.6443, 0]	3.296	0.004	3.289	3.303	0.000	0.000	3782.0	5101.0	1.0
Tex_12CN[113499.6443, 1]	4.865	0.016	4.837	4.897	0.000	0.000	2290.0	3506.0	1.0
Tex_12CN[113499.6443, 2]	3.298	0.005	3.288	3.308	0.000	0.000	2600.0	3579.0	1.0
Tex_12CN[113508.9074, 0]	4.654	0.013	4.629	4.676	0.000	0.000	2695.0	4143.0	1.0
Tex_12CN[113508.9074, 1]	3.979	0.007	3.966	3.991	0.000	0.000	2281.0	3631.0	1.0
Tex_12CN[113508.9074, 2]	3.238	0.004	3.230	3.246	0.000	0.000	2685.0	3450.0	1.0
Tex_12CN[113520.4315, 0]	3.616	0.006	3.604	3.627	0.000	0.000	3652.0	5240.0	1.0
Tex_12CN[113520.4315, 1]	4.265	0.009	4.249	4.283	0.000	0.000	2343.0	3497.0	1.0
Tex_12CN[113520.4315, 2]	3.249	0.005	3.240	3.258	0.000	0.000	2820.0	3525.0	1.0
tau_12CN[113123.3701, 0]	0.026	0.000	0.026	0.027	0.000	0.000	2619.0	3644.0	1.0
tau_12CN[113123.3701, 1]	0.033	0.000	0.032	0.033	0.000	0.000	2307.0	3378.0	1.0
tau_12CN[113123.3701, 2]	0.059	0.001	0.058	0.060	0.000	0.000	2564.0	3338.0	1.0
tau_12CN[113144.1573, 0]	0.169	0.002	0.166	0.172	0.000	0.000	2614.0	3895.0	1.0
tau_12CN[113144.1573, 1]	0.330	0.002	0.325	0.334	0.000	0.000	2235.0	3353.0	1.0
tau_12CN[113144.1573, 2]	0.499	0.005	0.489	0.510	0.000	0.000	2553.0	3313.0	1.0
tau_12CN[113170.4915, 0]	0.228	0.002	0.225	0.231	0.000	0.000	2615.0	3790.0	1.0
tau_12CN[113170.4915, 1]	0.234	0.002	0.230	0.238	0.000	0.000	2301.0	3350.0	1.0
tau_12CN[113170.4915, 2]	0.454	0.005	0.444	0.464	0.000	0.000	2565.0	3331.0	1.0
tau_12CN[113191.2787, 0]	0.237	0.002	0.233	0.241	0.000	0.000	2622.0	3765.0	1.0
tau_12CN[113191.2787, 1]	0.384	0.003	0.379	0.389	0.000	0.000	2239.0	3279.0	1.0
tau_12CN[113191.2787, 2]	0.608	0.007	0.595	0.621	0.000	0.000	2555.0	3292.0	1.0
tau_12CN[113488.1202, 0]	0.318	0.002	0.314	0.322	0.000	0.000	2607.0	3802.0	1.0
tau_12CN[113488.1202, 1]	0.328	0.003	0.322	0.333	0.000	0.000	2293.0	3381.0	1.0
tau_12CN[113488.1202, 2]	0.604	0.007	0.591	0.617	0.000	0.000	2564.0	3338.0	1.0
tau_12CN[113490.9702, 0]	0.692	0.005	0.683	0.702	0.000	0.000	2617.0	3788.0	1.0
tau_12CN[113490.9702, 1]	1.062	0.007	1.049	1.075	0.000	0.000	2245.0	3336.0	1.0
tau_12CN[113490.9702, 2]	1.646	0.018	1.612	1.680	0.000	0.000	2560.0	3282.0	1.0
tau_12CN[113499.6443, 0]	0.277	0.002	0.274	0.281	0.000	0.000	2622.0	3804.0	1.0
tau_12CN[113499.6443, 1]	0.248	0.002	0.244	0.252	0.000	0.000	2286.0	3377.0	1.0
tau_12CN[113499.6443, 2]	0.476	0.005	0.465	0.486	0.000	0.000	2563.0	3309.0	1.0
tau_12CN[113508.9074, 0]	0.200	0.002	0.197	0.203	0.000	0.000	2621.0	3816.0	1.0
tau_12CN[113508.9074, 1]	0.314	0.002	0.309	0.318	0.000	0.000	2237.0	3301.0	1.0
tau_12CN[113508.9074, 2]	0.480	0.005	0.470	0.490	0.000	0.000	2555.0	3277.0	1.0
tau_12CN[113520.4315, 0]	0.028	0.000	0.028	0.029	0.000	0.000	2642.0	4095.0	1.0
tau_12CN[113520.4315, 1]	0.038	0.000	0.037	0.038	0.000	0.000	2236.0	3279.0	1.0
tau_12CN[113520.4315, 2]	0.060	0.001	0.059	0.061	0.000	0.000	2552.0	3208.0	1.0
tau_total_12CN[0]	2.176	0.017	2.146	2.209	0.000	0.000	2601.0	3773.0	1.0
tau_total_12CN[1]	2.970	0.022	2.927	3.010	0.000	0.000	2245.0	3276.0	1.0
tau_total_12CN[2]	4.885	0.055	4.784	4.989	0.001	0.001	2556.0	3219.0	1.0
TR_12CN[113123.3701, 0]	3.190	0.017	3.159	3.223	0.000	0.000	2707.0	3940.0	1.0
TR_12CN[113123.3701, 1]	2.029	0.009	2.012	2.047	0.000	0.000	2517.0	3633.0	1.0
TR_12CN[113123.3701, 2]	1.146	0.003	1.140	1.152	0.000	0.000	2988.0	4218.0	1.0
TR_12CN[113144.1573, 0]	4.067	0.028	4.016	4.120	0.001	0.000	2623.0	3838.0	1.0
TR_12CN[113144.1573, 1]	1.644	0.005	1.635	1.653	0.000	0.000	2362.0	3730.0	1.0
TR_12CN[113144.1573, 2]	1.112	0.003	1.107	1.116	0.000	0.000	2821.0	3629.0	1.0
TR_12CN[113170.4915, 0]	2.534	0.011	2.514	2.554	0.000	0.000	2687.0	3904.0	1.0
TR_12CN[113170.4915, 1]	2.867	0.016	2.838	2.899	0.000	0.000	2346.0	3458.0	1.0
TR_12CN[113170.4915, 2]	1.429	0.005	1.419	1.439	0.000	0.000	2620.0	3367.0	1.0
TR_12CN[113191.2787, 0]	3.164	0.018	3.132	3.197	0.000	0.000	2647.0	3902.0	1.0
TR_12CN[113191.2787, 1]	2.274	0.009	2.258	2.290	0.000	0.000	2253.0	3459.0	1.0
TR_12CN[113191.2787, 2]	1.385	0.005	1.376	1.394	0.000	0.000	2600.0	3257.0	1.0
TR_12CN[113488.1202, 0]	1.995	0.007	1.982	2.008	0.000	0.000	2649.0	4068.0	1.0
TR_12CN[113488.1202, 1]	2.310	0.011	2.290	2.331	0.000	0.000	2307.0	3556.0	1.0
TR_12CN[113488.1202, 2]	1.284	0.004	1.276	1.291	0.000	0.000	2621.0	3389.0	1.0
TR_12CN[113490.9702, 0]	2.404	0.006	2.392	2.416	0.000	0.000	2622.0	3894.0	1.0
TR_12CN[113490.9702, 1]	1.988	0.004	1.982	1.995	0.000	0.000	2309.0	3483.0	1.0
TR_12CN[113490.9702, 2]	1.275	0.002	1.272	1.279	0.000	0.000	2848.0	3258.0	1.0
TR_12CN[113499.6443, 0]	1.291	0.003	1.285	1.296	0.000	0.000	3782.0	5101.0	1.0
TR_12CN[113499.6443, 1]	2.640	0.014	2.614	2.668	0.000	0.000	2290.0	3506.0	1.0
TR_12CN[113499.6443, 2]	1.292	0.004	1.284	1.300	0.000	0.000	2600.0	3579.0	1.0
TR_12CN[113508.9074, 0]	2.449	0.011	2.428	2.470	0.000	0.000	2695.0	4143.0	1.0
TR_12CN[113508.9074, 1]	1.858	0.006	1.847	1.869	0.000	0.000	2281.0	3631.0	1.0
TR_12CN[113508.9074, 2]	1.244	0.003	1.238	1.251	0.000	0.000	2685.0	3450.0	1.0
TR_12CN[113520.4315, 0]	1.551	0.005	1.541	1.561	0.000	0.000	3652.0	5240.0	1.0
TR_12CN[113520.4315, 1]	2.106	0.008	2.091	2.121	0.000	0.000	2343.0	3497.0	1.0
TR_12CN[113520.4315, 2]	1.253	0.004	1.245	1.260	0.000	0.000	2820.0	3525.0	1.0
CTEX_weights_13CN[0, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000	0.000	NaN	8000.0	8000.0	NaN
CTEX_weights_13CN[0, 0 1 1 0 -- --]	0.994	0.001	0.993	0.995	0.000	0.000	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 0 1 1 1 -- --]	2.982	0.002	2.979	2.985	0.000	0.000	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 0 1 1 2 -- --]	4.971	0.003	4.966	4.976	0.000	0.000	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 1 0 1 -- --]	0.967	0.102	0.780	1.165	0.002	0.002	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 1 1 0 -- --]	0.324	0.034	0.261	0.389	0.001	0.001	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 1 1 1 -- --]	0.970	0.102	0.783	1.168	0.002	0.002	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 1 1 2 -- --]	1.617	0.170	1.305	1.947	0.003	0.003	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 2 1 0 -- --]	0.322	0.034	0.259	0.387	0.001	0.001	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 2 1 1 -- --]	0.965	0.102	0.777	1.162	0.002	0.002	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 2 1 2 -- --]	1.608	0.170	1.296	1.937	0.003	0.003	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 2 2 1 -- --]	0.963	0.102	0.776	1.161	0.002	0.002	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 2 2 2 -- --]	1.605	0.170	1.293	1.935	0.003	0.003	4393.0	3492.0	1.0
CTEX_weights_13CN[0, 1 2 2 3 -- --]	2.248	0.238	1.811	2.709	0.004	0.004	4393.0	3492.0	1.0
CTEX_weights_13CN[1, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000	0.000	NaN	8000.0	8000.0	NaN
CTEX_weights_13CN[1, 0 1 1 0 -- --]	0.994	0.000	0.993	0.994	0.000	0.000	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 0 1 1 1 -- --]	2.981	0.001	2.979	2.983	0.000	0.000	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 0 1 1 2 -- --]	4.969	0.002	4.966	4.972	0.000	0.000	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 1 0 1 -- --]	0.891	0.059	0.780	0.995	0.001	0.002	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 1 1 0 -- --]	0.298	0.020	0.261	0.333	0.000	0.001	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 1 1 1 -- --]	0.895	0.059	0.783	0.999	0.001	0.002	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 1 1 2 -- --]	1.491	0.099	1.305	1.665	0.002	0.003	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 2 1 0 -- --]	0.296	0.020	0.259	0.331	0.000	0.001	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 2 1 1 -- --]	0.889	0.059	0.778	0.993	0.001	0.002	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 2 1 2 -- --]	1.482	0.099	1.296	1.655	0.002	0.003	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 2 2 1 -- --]	0.888	0.059	0.776	0.992	0.001	0.002	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 2 2 2 -- --]	1.479	0.099	1.294	1.653	0.002	0.003	4586.0	3620.0	1.0
CTEX_weights_13CN[1, 1 2 2 3 -- --]	2.072	0.138	1.812	2.314	0.002	0.004	4586.0	3620.0	1.0
CTEX_weights_13CN[2, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000	0.000	NaN	8000.0	8000.0	NaN
CTEX_weights_13CN[2, 0 1 1 0 -- --]	0.992	0.000	0.991	0.992	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 0 1 1 1 -- --]	2.975	0.000	2.974	2.976	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 0 1 1 2 -- --]	4.959	0.001	4.958	4.961	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 1 0 1 -- --]	0.610	0.019	0.573	0.644	0.000	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 1 1 0 -- --]	0.204	0.006	0.192	0.216	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 1 1 1 -- --]	0.613	0.019	0.576	0.647	0.000	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 1 1 2 -- --]	1.022	0.032	0.959	1.078	0.001	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 2 1 0 -- --]	0.203	0.006	0.190	0.214	0.000	0.000	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 2 1 1 -- --]	0.608	0.019	0.571	0.642	0.000	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 2 1 2 -- --]	1.014	0.032	0.951	1.070	0.001	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 2 2 1 -- --]	0.607	0.019	0.569	0.640	0.000	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 2 2 2 -- --]	1.012	0.032	0.949	1.068	0.001	0.001	4457.0	4072.0	1.0
CTEX_weights_13CN[2, 1 2 2 3 -- --]	1.417	0.045	1.329	1.495	0.001	0.001	4457.0	4072.0	1.0
Tex_13CN[0]	4.638	0.443	3.872	5.515	0.007	0.008	4393.0	3492.0	1.0
Tex_13CN[1]	4.315	0.243	3.878	4.737	0.004	0.011	4586.0	3620.0	1.0
Tex_13CN[2]	3.285	0.065	3.158	3.399	0.001	0.002	4457.0	4072.0	1.0
tau_13CN[108056.1623, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108056.1623, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108056.1623, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108057.1556, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108057.1556, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108057.1556, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108062.9306, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108062.9306, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108062.9306, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108076.9692, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108076.9692, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108076.9692, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108077.2965, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108077.2965, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108077.2965, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108091.3352, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108091.3352, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108091.3352, 2]	0.000	0.000	0.000	0.001	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108406.0905, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108406.0905, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108406.0905, 2]	0.000	0.000	0.000	0.001	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108412.862, 0]	0.001	0.000	0.000	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108412.862, 1]	0.001	0.000	0.001	0.001	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108412.862, 2]	0.002	0.000	0.001	0.002	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108426.889, 0]	0.002	0.000	0.001	0.002	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108426.889, 1]	0.002	0.000	0.001	0.003	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108426.889, 2]	0.003	0.000	0.002	0.004	0.000	0.000	4660.0	3974.0	1.0
tau_13CN[108631.121, 0]	0.001	0.000	0.000	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108631.121, 1]	0.001	0.000	0.001	0.001	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108631.121, 2]	0.002	0.000	0.001	0.002	0.000	0.000	4660.0	4016.0	1.0
tau_13CN[108636.923, 0]	0.002	0.001	0.001	0.003	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108636.923, 1]	0.003	0.000	0.002	0.004	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108636.923, 2]	0.005	0.001	0.004	0.006	0.000	0.000	4660.0	4016.0	1.0
tau_13CN[108638.212, 0]	0.001	0.000	0.000	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108638.212, 1]	0.001	0.000	0.001	0.001	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108638.212, 2]	0.002	0.000	0.001	0.002	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108643.59, 0]	0.001	0.000	0.001	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108643.59, 1]	0.001	0.000	0.001	0.002	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108643.59, 2]	0.002	0.000	0.002	0.003	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108644.3456, 0]	0.001	0.000	0.000	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108644.3456, 1]	0.001	0.000	0.001	0.001	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108644.3456, 2]	0.002	0.000	0.001	0.002	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108645.064, 0]	0.001	0.000	0.000	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108645.064, 1]	0.001	0.000	0.001	0.001	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108645.064, 2]	0.001	0.000	0.001	0.002	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108651.297, 0]	0.004	0.001	0.002	0.006	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108651.297, 1]	0.005	0.001	0.004	0.007	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108651.297, 2]	0.008	0.001	0.006	0.010	0.000	0.000	4660.0	4016.0	1.0
tau_13CN[108657.646, 0]	0.003	0.001	0.002	0.004	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108657.646, 1]	0.004	0.001	0.003	0.005	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108657.646, 2]	0.006	0.001	0.005	0.007	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108658.948, 0]	0.001	0.000	0.000	0.001	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108658.948, 1]	0.001	0.000	0.001	0.001	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108658.948, 2]	0.002	0.000	0.001	0.002	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108780.201, 0]	0.006	0.002	0.003	0.009	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108780.201, 1]	0.008	0.001	0.005	0.010	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108780.201, 2]	0.012	0.002	0.009	0.015	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108782.374, 0]	0.003	0.001	0.002	0.005	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108782.374, 1]	0.004	0.001	0.003	0.005	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108782.374, 2]	0.006	0.001	0.005	0.008	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108786.982, 0]	0.001	0.000	0.001	0.002	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108786.982, 1]	0.002	0.000	0.001	0.002	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108786.982, 2]	0.003	0.000	0.002	0.004	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108793.753, 0]	0.001	0.000	0.001	0.002	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108793.753, 1]	0.001	0.000	0.001	0.002	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108793.753, 2]	0.002	0.000	0.002	0.003	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108796.4, 0]	0.001	0.000	0.001	0.002	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108796.4, 1]	0.001	0.000	0.001	0.002	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108796.4, 2]	0.002	0.000	0.002	0.003	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108807.7879, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108807.7879, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108807.7879, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4661.0	3974.0	1.0
tau_13CN[108986.836, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[108986.836, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[108986.836, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	4016.0	1.0
tau_13CN[109217.5674, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[109217.5674, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[109217.5674, 2]	0.001	0.000	0.000	0.001	0.000	0.000	4660.0	4016.0	1.0
tau_13CN[109218.3227, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[109218.3227, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[109218.3227, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	4016.0	1.0
tau_13CN[109218.919, 0]	0.000	0.000	0.000	0.000	0.000	0.000	4366.0	3498.0	1.0
tau_13CN[109218.919, 1]	0.000	0.000	0.000	0.000	0.000	0.000	4606.0	3783.0	1.0
tau_13CN[109218.919, 2]	0.000	0.000	0.000	0.000	0.000	0.000	4660.0	4016.0	1.0
tau_total_13CN[0]	0.030	0.008	0.017	0.044	0.000	0.000	4366.0	3498.0	1.0
tau_total_13CN[1]	0.039	0.006	0.028	0.050	0.000	0.000	4606.0	3783.0	1.0
tau_total_13CN[2]	0.063	0.009	0.048	0.078	0.000	0.000	4660.0	3974.0	1.0
TR_13CN[108056.1623, 0]	2.522	0.401	1.798	3.278	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108056.1623, 1]	2.231	0.217	1.836	2.597	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108056.1623, 2]	1.348	0.053	1.245	1.441	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108057.1556, 0]	2.522	0.401	1.798	3.278	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108057.1556, 1]	2.231	0.217	1.836	2.597	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108057.1556, 2]	1.348	0.053	1.245	1.441	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108062.9306, 0]	2.522	0.401	1.798	3.278	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108062.9306, 1]	2.231	0.217	1.836	2.597	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108062.9306, 2]	1.348	0.053	1.245	1.441	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108076.9692, 0]	2.522	0.401	1.798	3.277	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108076.9692, 1]	2.230	0.217	1.836	2.597	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108076.9692, 2]	1.347	0.053	1.245	1.441	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108077.2965, 0]	2.522	0.401	1.798	3.277	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108077.2965, 1]	2.230	0.217	1.836	2.597	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108077.2965, 2]	1.347	0.053	1.245	1.441	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108091.3352, 0]	2.522	0.401	1.798	3.277	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108091.3352, 1]	2.230	0.217	1.836	2.597	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108091.3352, 2]	1.347	0.053	1.245	1.441	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108406.0905, 0]	2.517	0.401	1.793	3.272	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108406.0905, 1]	2.226	0.217	1.831	2.592	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108406.0905, 2]	1.343	0.053	1.241	1.437	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108412.862, 0]	2.517	0.401	1.793	3.272	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108412.862, 1]	2.225	0.217	1.831	2.592	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108412.862, 2]	1.343	0.053	1.241	1.437	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108426.889, 0]	2.516	0.401	1.793	3.272	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108426.889, 1]	2.225	0.217	1.831	2.591	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108426.889, 2]	1.343	0.053	1.241	1.437	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108631.121, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108631.121, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108631.121, 2]	1.341	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108636.923, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108636.923, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108636.923, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108638.212, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108638.212, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108638.212, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108643.59, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108643.59, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108643.59, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108644.3456, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108644.3456, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108644.3456, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108645.064, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108645.064, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108645.064, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108651.297, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108651.297, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108651.297, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108657.646, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108657.646, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108657.646, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108658.948, 0]	2.513	0.401	1.790	3.268	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108658.948, 1]	2.222	0.217	1.828	2.588	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108658.948, 2]	1.340	0.053	1.238	1.434	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108780.201, 0]	2.511	0.401	1.788	3.266	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108780.201, 1]	2.220	0.217	1.826	2.586	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108780.201, 2]	1.339	0.053	1.236	1.432	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108782.374, 0]	2.511	0.401	1.788	3.266	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108782.374, 1]	2.220	0.217	1.826	2.586	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108782.374, 2]	1.339	0.053	1.236	1.432	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108786.982, 0]	2.511	0.401	1.788	3.266	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108786.982, 1]	2.220	0.217	1.826	2.586	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108786.982, 2]	1.339	0.053	1.236	1.432	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108793.753, 0]	2.511	0.401	1.788	3.265	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108793.753, 1]	2.220	0.217	1.826	2.586	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108793.753, 2]	1.339	0.053	1.236	1.432	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108796.4, 0]	2.511	0.401	1.788	3.265	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108796.4, 1]	2.220	0.217	1.826	2.586	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108796.4, 2]	1.339	0.053	1.236	1.432	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108807.7879, 0]	2.511	0.401	1.788	3.265	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108807.7879, 1]	2.220	0.217	1.826	2.585	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108807.7879, 2]	1.338	0.053	1.236	1.432	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[108986.836, 0]	2.508	0.400	1.785	3.262	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[108986.836, 1]	2.217	0.217	1.823	2.583	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[108986.836, 2]	1.336	0.053	1.234	1.430	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[109217.5674, 0]	2.504	0.400	1.782	3.258	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[109217.5674, 1]	2.214	0.217	1.820	2.579	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[109217.5674, 2]	1.333	0.053	1.231	1.427	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[109218.3227, 0]	2.504	0.400	1.782	3.258	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[109218.3227, 1]	2.214	0.217	1.820	2.579	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[109218.3227, 2]	1.333	0.053	1.231	1.427	0.001	0.002	4457.0	4072.0	1.0
TR_13CN[109218.919, 0]	2.504	0.400	1.782	3.258	0.006	0.008	4393.0	3492.0	1.0
TR_13CN[109218.919, 1]	2.214	0.217	1.820	2.579	0.004	0.010	4586.0	3620.0	1.0
TR_13CN[109218.919, 2]	1.333	0.053	1.231	1.427	0.001	0.002	4457.0	4072.0	1.0

[25]:

posterior = model.sample_posterior_predictive(
    thin=10, # keep one in {thin} posterior samples
)
axes = plot_predictive(model.data, posterior.posterior_predictive)
axes.ravel()[0].figure.set_size_inches(8, 12)

Sampling: [12CN-1, 12CN-2, 13CN-1, 13CN-2]

../_images/notebooks_hfs_ratio_model_31_3.png

[26]:

from bayes_spec.plots import plot_traces

axes = plot_traces(model.trace.posterior, model.cloud_freeRVs + model.baseline_freeRVs + model.hyper_freeRVs)
fig = axes.ravel()[0].figure
fig.tight_layout()

../_images/notebooks_hfs_ratio_model_32_0.png

[27]:

var_names = [
    param for param in model.cloud_freeRVs
    if not set(model.model.named_vars_to_dims[param]).intersection(set(["transition_12CN", "state_12CN", "transition_13CN", "state_13CN"]))
]
print(var_names)
_ = plot_pair(
    model.trace.solution_0.sel(draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    combine_dims=["cloud"], # concatenate clouds
    kind="scatter", # plot type
)

['log10_Ntot_12CN_norm', 'ratio_norm', 'fwhm2_norm', 'velocity_norm', 'log10_Tex_CTEX_norm', 'log10_CTEX_variance_norm']

../_images/notebooks_hfs_ratio_model_33_1.png

[28]:

_ = plot_pair(
    model.trace.solution_0.sel(draw=slice(None, None, 10)), # samples
    ["velocity", "fwhm2"], # var_names to plot
    combine_dims=None, # do not concatenate clouds
    kind="scatter", # plot type
)

../_images/notebooks_hfs_ratio_model_34_0.png

[29]:

_ = plot_pair(
    model.trace.solution_0.sel(cloud=0, draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    kind="scatter", # plot type
)

../_images/notebooks_hfs_ratio_model_35_0.png

[30]:

_ = plot_pair(
    model.trace.solution_0.sel(cloud=1, draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    kind="scatter", # plot type
)

../_images/notebooks_hfs_ratio_model_36_0.png

[31]:

var_names = [
    param for param in model.cloud_deterministics + [p for p in model.cloud_freeRVs if "_norm" not in p]
    if not set(model.model.named_vars_to_dims[param]).intersection(set(["transition_12CN", "state_12CN", "transition_13CN", "state_13CN"]))
]
print(var_names)
_ = plot_pair(
    model.trace.solution_0.sel(draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    combine_dims=["cloud"], # concatenate clouds
    labeller=model.labeller, # label manager
    kind="scatter", # plot type
    reference_values=sim_params, # truths
)

['log10_Ntot_12CN', 'ratio', 'fwhm2', 'velocity', 'log10_Tex_CTEX', 'log10_Ntot_13CN', 'log10_CTEX_variance', 'tau_total_12CN', 'Tex_13CN', 'tau_total_13CN']

../_images/notebooks_hfs_ratio_model_37_1.png

[32]:

# identify simulation cloud corresponding to each posterior cloud
sim_cloud_map = {}
for i in range(n_clouds):
    posterior_velocity = model.trace.solution_0['velocity'].sel(cloud=i).data.mean()
    match = np.argmin(np.abs(sim_params["velocity"] - posterior_velocity))
    sim_cloud_map[i] = match
sim_cloud_map

[32]:

{0: np.int64(0), 1: np.int64(1), 2: np.int64(2)}

[33]:

cloud = 0

# subset of sim_params
my_sim_params = {}
for var_name in var_names:
    my_sim_params[var_name] = sim_params[var_name][sim_cloud_map[cloud]]

_ = plot_pair(
    model.trace.solution_0.sel(cloud=cloud, draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    labeller=model.labeller, # label manager
    kind="scatter", # plot type
    reference_values=my_sim_params, # truths
)

../_images/notebooks_hfs_ratio_model_39_0.png

[34]:

cloud = 1

# subset of sim_params
my_sim_params = {}
for var_name in var_names:
    my_sim_params[var_name] = sim_params[var_name][sim_cloud_map[cloud]]

_ = plot_pair(
    model.trace.solution_0.sel(cloud=cloud, draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    labeller=model.labeller, # label manager
    kind="scatter", # plot type
    reference_values=my_sim_params, # truths
)

../_images/notebooks_hfs_ratio_model_40_0.png

[35]:

cloud = 2

# subset of sim_params
my_sim_params = {}
for var_name in var_names:
    my_sim_params[var_name] = sim_params[var_name][sim_cloud_map[cloud]]

_ = plot_pair(
    model.trace.solution_0.sel(cloud=cloud, draw=slice(None, None, 10)), # samples
    var_names, # var_names to plot
    labeller=model.labeller, # label manager
    kind="scatter", # plot type
    reference_values=my_sim_params, # truths
)

../_images/notebooks_hfs_ratio_model_41_0.png

[36]:

point_stats = az.summary(model.trace.solution_0, kind='stats')
print("BIC:", model.bic())
display(point_stats)

BIC: -12924.17555539098

	mean	sd	hdi_3%	hdi_97%
baseline_12CN-1_norm[0]	0.036	0.050	-0.058	0.130
baseline_12CN-2_norm[0]	0.024	0.065	-0.098	0.145
baseline_13CN-1_norm[0]	-0.028	0.067	-0.156	0.096
baseline_13CN-2_norm[0]	0.050	0.074	-0.089	0.186
log10_Ntot_12CN_norm[0]	0.597	0.005	0.588	0.606
log10_Ntot_12CN_norm[1]	0.797	0.005	0.788	0.805
log10_Ntot_12CN_norm[2]	0.987	0.009	0.971	1.003
log10_Tex_CTEX_norm[0]	-0.342	0.163	-0.644	-0.030
log10_Tex_CTEX_norm[1]	-0.463	0.095	-0.633	-0.286
log10_Tex_CTEX_norm[2]	-0.934	0.034	-0.999	-0.872
ratio_norm[0]	0.148	0.024	0.112	0.192
ratio_norm[1]	0.142	0.015	0.115	0.168
ratio_norm[2]	0.140	0.016	0.111	0.168
fwhm2_norm[0]	1.002	0.002	0.998	1.007
fwhm2_norm[1]	1.564	0.004	1.555	1.572
fwhm2_norm[2]	2.246	0.011	2.226	2.267
velocity_norm[0]	0.167	0.000	0.167	0.167
velocity_norm[1]	0.500	0.000	0.500	0.500
velocity_norm[2]	0.917	0.000	0.916	0.917
log10_CTEX_variance_norm[0]	2.472	0.237	2.033	2.914
log10_CTEX_variance_norm[1]	1.999	0.247	1.558	2.466
log10_CTEX_variance_norm[2]	1.255	0.266	0.782	1.751
weights_12CN_norm[0, 0 0 1 1 -- --]	0.190	0.000	0.190	0.191
weights_12CN_norm[0, 0 0 1 2 -- --]	0.329	0.001	0.328	0.330
weights_12CN_norm[0, 1 0 1 1 -- --]	0.070	0.000	0.070	0.071
weights_12CN_norm[0, 1 0 1 2 -- --]	0.121	0.000	0.121	0.121
weights_12CN_norm[0, 1 0 2 1 -- --]	0.036	0.000	0.036	0.037
weights_12CN_norm[0, 1 0 2 2 -- --]	0.102	0.000	0.102	0.102
weights_12CN_norm[0, 1 0 2 3 -- --]	0.151	0.000	0.151	0.151
weights_12CN_norm[1, 0 0 1 1 -- --]	0.162	0.000	0.162	0.163
weights_12CN_norm[1, 0 0 1 2 -- --]	0.380	0.000	0.379	0.380
weights_12CN_norm[1, 1 0 1 1 -- --]	0.044	0.000	0.044	0.044
weights_12CN_norm[1, 1 0 1 2 -- --]	0.112	0.000	0.112	0.112
weights_12CN_norm[1, 1 0 2 1 -- --]	0.053	0.000	0.053	0.053
weights_12CN_norm[1, 1 0 2 2 -- --]	0.097	0.000	0.096	0.097
weights_12CN_norm[1, 1 0 2 3 -- --]	0.152	0.000	0.152	0.153
weights_12CN_norm[2, 0 0 1 1 -- --]	0.208	0.000	0.208	0.209
weights_12CN_norm[2, 0 0 1 2 -- --]	0.427	0.000	0.427	0.428
weights_12CN_norm[2, 1 0 1 1 -- --]	0.036	0.000	0.036	0.036
weights_12CN_norm[2, 1 0 1 2 -- --]	0.087	0.000	0.087	0.087
weights_12CN_norm[2, 1 0 2 1 -- --]	0.040	0.000	0.040	0.040
weights_12CN_norm[2, 1 0 2 2 -- --]	0.079	0.000	0.079	0.080
weights_12CN_norm[2, 1 0 2 3 -- --]	0.122	0.000	0.121	0.122
log10_Ntot_12CN[0]	13.799	0.002	13.794	13.803
log10_Ntot_12CN[1]	13.898	0.002	13.894	13.903
log10_Ntot_12CN[2]	13.993	0.004	13.985	14.002
ratio[0]	0.015	0.002	0.011	0.019
ratio[1]	0.014	0.001	0.012	0.017
ratio[2]	0.014	0.002	0.011	0.017
fwhm2[0]	1.002	0.002	0.998	1.007
fwhm2[1]	1.564	0.004	1.555	1.572
fwhm2[2]	2.246	0.011	2.226	2.267
velocity[0]	-2.000	0.000	-2.001	-2.000
velocity[1]	0.000	0.000	-0.001	0.001
velocity[2]	2.501	0.001	2.499	2.502
log10_Tex_CTEX[0]	0.664	0.041	0.589	0.743
log10_Tex_CTEX[1]	0.634	0.024	0.592	0.678
log10_Tex_CTEX[2]	0.516	0.009	0.500	0.532
log10_Ntot_13CN[0]	11.965	0.065	11.847	12.081
log10_Ntot_13CN[1]	12.047	0.044	11.967	12.131
log10_Ntot_13CN[2]	12.137	0.048	12.044	12.221
log10_CTEX_variance[0]	-1.528	0.237	-1.967	-1.086
log10_CTEX_variance[1]	-2.001	0.247	-2.442	-1.534
log10_CTEX_variance[2]	-2.745	0.266	-3.218	-2.249
CTEX_weights_12CN[0, 0 0 1 1 -- --]	2.000	0.000	1.999	2.000
CTEX_weights_12CN[0, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000
CTEX_weights_12CN[0, 1 0 1 1 -- --]	0.618	0.068	0.492	0.747
CTEX_weights_12CN[0, 1 0 1 2 -- --]	1.234	0.135	0.983	1.494
CTEX_weights_12CN[0, 1 0 2 1 -- --]	0.615	0.068	0.490	0.745
CTEX_weights_12CN[0, 1 0 2 2 -- --]	1.230	0.135	0.979	1.490
CTEX_weights_12CN[0, 1 0 2 3 -- --]	1.846	0.203	1.470	2.235
CTEX_weights_12CN[1, 0 0 1 1 -- --]	2.000	0.000	1.999	2.000
CTEX_weights_12CN[1, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000
CTEX_weights_12CN[1, 1 0 1 1 -- --]	0.567	0.039	0.494	0.636
CTEX_weights_12CN[1, 1 0 1 2 -- --]	1.134	0.078	0.987	1.272
CTEX_weights_12CN[1, 1 0 2 1 -- --]	0.565	0.039	0.492	0.634
CTEX_weights_12CN[1, 1 0 2 2 -- --]	1.130	0.078	0.983	1.268
CTEX_weights_12CN[1, 1 0 2 3 -- --]	1.696	0.117	1.475	1.902
CTEX_weights_12CN[2, 0 0 1 1 -- --]	1.999	0.000	1.999	1.999
CTEX_weights_12CN[2, 0 0 1 2 -- --]	4.000	0.000	4.000	4.000
CTEX_weights_12CN[2, 1 0 1 1 -- --]	0.383	0.013	0.358	0.405
CTEX_weights_12CN[2, 1 0 1 2 -- --]	0.765	0.025	0.716	0.809
CTEX_weights_12CN[2, 1 0 2 1 -- --]	0.381	0.012	0.356	0.403
CTEX_weights_12CN[2, 1 0 2 2 -- --]	0.762	0.025	0.713	0.805
CTEX_weights_12CN[2, 1 0 2 3 -- --]	1.143	0.037	1.069	1.209
Tex_12CN[113123.3701, 0]	5.463	0.019	5.428	5.498
Tex_12CN[113123.3701, 1]	4.171	0.011	4.150	4.191
Tex_12CN[113123.3701, 2]	3.108	0.004	3.100	3.115
Tex_12CN[113144.1573, 0]	6.402	0.030	6.348	6.459
Tex_12CN[113144.1573, 1]	3.721	0.006	3.710	3.731
Tex_12CN[113144.1573, 2]	3.064	0.003	3.057	3.070
Tex_12CN[113170.4915, 0]	4.742	0.012	4.720	4.765
Tex_12CN[113170.4915, 1]	5.111	0.018	5.078	5.145
Tex_12CN[113170.4915, 2]	3.463	0.007	3.450	3.475
Tex_12CN[113191.2787, 0]	5.435	0.019	5.400	5.471
Tex_12CN[113191.2787, 1]	4.451	0.010	4.433	4.469
Tex_12CN[113191.2787, 2]	3.408	0.006	3.398	3.419
Tex_12CN[113488.1202, 0]	4.138	0.008	4.123	4.152
Tex_12CN[113488.1202, 1]	4.497	0.012	4.474	4.520
Tex_12CN[113488.1202, 2]	3.287	0.005	3.278	3.297
Tex_12CN[113490.9702, 0]	4.602	0.007	4.589	4.616
Tex_12CN[113490.9702, 1]	4.129	0.004	4.122	4.138
Tex_12CN[113490.9702, 2]	3.277	0.002	3.273	3.281
Tex_12CN[113499.6443, 0]	3.296	0.004	3.289	3.303
Tex_12CN[113499.6443, 1]	4.865	0.016	4.837	4.897
Tex_12CN[113499.6443, 2]	3.298	0.005	3.288	3.308
Tex_12CN[113508.9074, 0]	4.654	0.013	4.629	4.676
Tex_12CN[113508.9074, 1]	3.979	0.007	3.966	3.991
Tex_12CN[113508.9074, 2]	3.238	0.004	3.230	3.246
Tex_12CN[113520.4315, 0]	3.616	0.006	3.604	3.627
Tex_12CN[113520.4315, 1]	4.265	0.009	4.249	4.283
Tex_12CN[113520.4315, 2]	3.249	0.005	3.240	3.258
tau_12CN[113123.3701, 0]	0.026	0.000	0.026	0.027
tau_12CN[113123.3701, 1]	0.033	0.000	0.032	0.033
tau_12CN[113123.3701, 2]	0.059	0.001	0.058	0.060
tau_12CN[113144.1573, 0]	0.169	0.002	0.166	0.172
tau_12CN[113144.1573, 1]	0.330	0.002	0.325	0.334
tau_12CN[113144.1573, 2]	0.499	0.005	0.489	0.510
tau_12CN[113170.4915, 0]	0.228	0.002	0.225	0.231
tau_12CN[113170.4915, 1]	0.234	0.002	0.230	0.238
tau_12CN[113170.4915, 2]	0.454	0.005	0.444	0.464
tau_12CN[113191.2787, 0]	0.237	0.002	0.233	0.241
tau_12CN[113191.2787, 1]	0.384	0.003	0.379	0.389
tau_12CN[113191.2787, 2]	0.608	0.007	0.595	0.621
tau_12CN[113488.1202, 0]	0.318	0.002	0.314	0.322
tau_12CN[113488.1202, 1]	0.328	0.003	0.322	0.333
tau_12CN[113488.1202, 2]	0.604	0.007	0.591	0.617
tau_12CN[113490.9702, 0]	0.692	0.005	0.683	0.702
tau_12CN[113490.9702, 1]	1.062	0.007	1.049	1.075
tau_12CN[113490.9702, 2]	1.646	0.018	1.612	1.680
tau_12CN[113499.6443, 0]	0.277	0.002	0.274	0.281
tau_12CN[113499.6443, 1]	0.248	0.002	0.244	0.252
tau_12CN[113499.6443, 2]	0.476	0.005	0.465	0.486
tau_12CN[113508.9074, 0]	0.200	0.002	0.197	0.203
tau_12CN[113508.9074, 1]	0.314	0.002	0.309	0.318
tau_12CN[113508.9074, 2]	0.480	0.005	0.470	0.490
tau_12CN[113520.4315, 0]	0.028	0.000	0.028	0.029
tau_12CN[113520.4315, 1]	0.038	0.000	0.037	0.038
tau_12CN[113520.4315, 2]	0.060	0.001	0.059	0.061
tau_total_12CN[0]	2.176	0.017	2.146	2.209
tau_total_12CN[1]	2.970	0.022	2.927	3.010
tau_total_12CN[2]	4.885	0.055	4.784	4.989
TR_12CN[113123.3701, 0]	3.190	0.017	3.159	3.223
TR_12CN[113123.3701, 1]	2.029	0.009	2.012	2.047
TR_12CN[113123.3701, 2]	1.146	0.003	1.140	1.152
TR_12CN[113144.1573, 0]	4.067	0.028	4.016	4.120
TR_12CN[113144.1573, 1]	1.644	0.005	1.635	1.653
TR_12CN[113144.1573, 2]	1.112	0.003	1.107	1.116
TR_12CN[113170.4915, 0]	2.534	0.011	2.514	2.554
TR_12CN[113170.4915, 1]	2.867	0.016	2.838	2.899
TR_12CN[113170.4915, 2]	1.429	0.005	1.419	1.439
TR_12CN[113191.2787, 0]	3.164	0.018	3.132	3.197
TR_12CN[113191.2787, 1]	2.274	0.009	2.258	2.290
TR_12CN[113191.2787, 2]	1.385	0.005	1.376	1.394
TR_12CN[113488.1202, 0]	1.995	0.007	1.982	2.008
TR_12CN[113488.1202, 1]	2.310	0.011	2.290	2.331
TR_12CN[113488.1202, 2]	1.284	0.004	1.276	1.291
TR_12CN[113490.9702, 0]	2.404	0.006	2.392	2.416
TR_12CN[113490.9702, 1]	1.988	0.004	1.982	1.995
TR_12CN[113490.9702, 2]	1.275	0.002	1.272	1.279
TR_12CN[113499.6443, 0]	1.291	0.003	1.285	1.296
TR_12CN[113499.6443, 1]	2.640	0.014	2.614	2.668
TR_12CN[113499.6443, 2]	1.292	0.004	1.284	1.300
TR_12CN[113508.9074, 0]	2.449	0.011	2.428	2.470
TR_12CN[113508.9074, 1]	1.858	0.006	1.847	1.869
TR_12CN[113508.9074, 2]	1.244	0.003	1.238	1.251
TR_12CN[113520.4315, 0]	1.551	0.005	1.541	1.561
TR_12CN[113520.4315, 1]	2.106	0.008	2.091	2.121
TR_12CN[113520.4315, 2]	1.253	0.004	1.245	1.260
CTEX_weights_13CN[0, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000
CTEX_weights_13CN[0, 0 1 1 0 -- --]	0.994	0.001	0.993	0.995
CTEX_weights_13CN[0, 0 1 1 1 -- --]	2.982	0.002	2.979	2.985
CTEX_weights_13CN[0, 0 1 1 2 -- --]	4.971	0.003	4.966	4.976
CTEX_weights_13CN[0, 1 1 0 1 -- --]	0.967	0.102	0.780	1.165
CTEX_weights_13CN[0, 1 1 1 0 -- --]	0.324	0.034	0.261	0.389
CTEX_weights_13CN[0, 1 1 1 1 -- --]	0.970	0.102	0.783	1.168
CTEX_weights_13CN[0, 1 1 1 2 -- --]	1.617	0.170	1.305	1.947
CTEX_weights_13CN[0, 1 2 1 0 -- --]	0.322	0.034	0.259	0.387
CTEX_weights_13CN[0, 1 2 1 1 -- --]	0.965	0.102	0.777	1.162
CTEX_weights_13CN[0, 1 2 1 2 -- --]	1.608	0.170	1.296	1.937
CTEX_weights_13CN[0, 1 2 2 1 -- --]	0.963	0.102	0.776	1.161
CTEX_weights_13CN[0, 1 2 2 2 -- --]	1.605	0.170	1.293	1.935
CTEX_weights_13CN[0, 1 2 2 3 -- --]	2.248	0.238	1.811	2.709
CTEX_weights_13CN[1, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000
CTEX_weights_13CN[1, 0 1 1 0 -- --]	0.994	0.000	0.993	0.994
CTEX_weights_13CN[1, 0 1 1 1 -- --]	2.981	0.001	2.979	2.983
CTEX_weights_13CN[1, 0 1 1 2 -- --]	4.969	0.002	4.966	4.972
CTEX_weights_13CN[1, 1 1 0 1 -- --]	0.891	0.059	0.780	0.995
CTEX_weights_13CN[1, 1 1 1 0 -- --]	0.298	0.020	0.261	0.333
CTEX_weights_13CN[1, 1 1 1 1 -- --]	0.895	0.059	0.783	0.999
CTEX_weights_13CN[1, 1 1 1 2 -- --]	1.491	0.099	1.305	1.665
CTEX_weights_13CN[1, 1 2 1 0 -- --]	0.296	0.020	0.259	0.331
CTEX_weights_13CN[1, 1 2 1 1 -- --]	0.889	0.059	0.778	0.993
CTEX_weights_13CN[1, 1 2 1 2 -- --]	1.482	0.099	1.296	1.655
CTEX_weights_13CN[1, 1 2 2 1 -- --]	0.888	0.059	0.776	0.992
CTEX_weights_13CN[1, 1 2 2 2 -- --]	1.479	0.099	1.294	1.653
CTEX_weights_13CN[1, 1 2 2 3 -- --]	2.072	0.138	1.812	2.314
CTEX_weights_13CN[2, 0 1 0 1 -- --]	3.000	0.000	3.000	3.000
CTEX_weights_13CN[2, 0 1 1 0 -- --]	0.992	0.000	0.991	0.992
CTEX_weights_13CN[2, 0 1 1 1 -- --]	2.975	0.000	2.974	2.976
CTEX_weights_13CN[2, 0 1 1 2 -- --]	4.959	0.001	4.958	4.961
CTEX_weights_13CN[2, 1 1 0 1 -- --]	0.610	0.019	0.573	0.644
CTEX_weights_13CN[2, 1 1 1 0 -- --]	0.204	0.006	0.192	0.216
CTEX_weights_13CN[2, 1 1 1 1 -- --]	0.613	0.019	0.576	0.647
CTEX_weights_13CN[2, 1 1 1 2 -- --]	1.022	0.032	0.959	1.078
CTEX_weights_13CN[2, 1 2 1 0 -- --]	0.203	0.006	0.190	0.214
CTEX_weights_13CN[2, 1 2 1 1 -- --]	0.608	0.019	0.571	0.642
CTEX_weights_13CN[2, 1 2 1 2 -- --]	1.014	0.032	0.951	1.070
CTEX_weights_13CN[2, 1 2 2 1 -- --]	0.607	0.019	0.569	0.640
CTEX_weights_13CN[2, 1 2 2 2 -- --]	1.012	0.032	0.949	1.068
CTEX_weights_13CN[2, 1 2 2 3 -- --]	1.417	0.045	1.329	1.495
Tex_13CN[0]	4.638	0.443	3.872	5.515
Tex_13CN[1]	4.315	0.243	3.878	4.737
Tex_13CN[2]	3.285	0.065	3.158	3.399
tau_13CN[108056.1623, 0]	0.000	0.000	0.000	0.000
tau_13CN[108056.1623, 1]	0.000	0.000	0.000	0.000
tau_13CN[108056.1623, 2]	0.000	0.000	0.000	0.000
tau_13CN[108057.1556, 0]	0.000	0.000	0.000	0.000
tau_13CN[108057.1556, 1]	0.000	0.000	0.000	0.000
tau_13CN[108057.1556, 2]	0.000	0.000	0.000	0.000
tau_13CN[108062.9306, 0]	0.000	0.000	0.000	0.000
tau_13CN[108062.9306, 1]	0.000	0.000	0.000	0.000
tau_13CN[108062.9306, 2]	0.000	0.000	0.000	0.000
tau_13CN[108076.9692, 0]	0.000	0.000	0.000	0.000
tau_13CN[108076.9692, 1]	0.000	0.000	0.000	0.000
tau_13CN[108076.9692, 2]	0.000	0.000	0.000	0.000
tau_13CN[108077.2965, 0]	0.000	0.000	0.000	0.000
tau_13CN[108077.2965, 1]	0.000	0.000	0.000	0.000
tau_13CN[108077.2965, 2]	0.000	0.000	0.000	0.000
tau_13CN[108091.3352, 0]	0.000	0.000	0.000	0.000
tau_13CN[108091.3352, 1]	0.000	0.000	0.000	0.000
tau_13CN[108091.3352, 2]	0.000	0.000	0.000	0.001
tau_13CN[108406.0905, 0]	0.000	0.000	0.000	0.000
tau_13CN[108406.0905, 1]	0.000	0.000	0.000	0.000
tau_13CN[108406.0905, 2]	0.000	0.000	0.000	0.001
tau_13CN[108412.862, 0]	0.001	0.000	0.000	0.001
tau_13CN[108412.862, 1]	0.001	0.000	0.001	0.001
tau_13CN[108412.862, 2]	0.002	0.000	0.001	0.002
tau_13CN[108426.889, 0]	0.002	0.000	0.001	0.002
tau_13CN[108426.889, 1]	0.002	0.000	0.001	0.003
tau_13CN[108426.889, 2]	0.003	0.000	0.002	0.004
tau_13CN[108631.121, 0]	0.001	0.000	0.000	0.001
tau_13CN[108631.121, 1]	0.001	0.000	0.001	0.001
tau_13CN[108631.121, 2]	0.002	0.000	0.001	0.002
tau_13CN[108636.923, 0]	0.002	0.001	0.001	0.003
tau_13CN[108636.923, 1]	0.003	0.000	0.002	0.004
tau_13CN[108636.923, 2]	0.005	0.001	0.004	0.006
tau_13CN[108638.212, 0]	0.001	0.000	0.000	0.001
tau_13CN[108638.212, 1]	0.001	0.000	0.001	0.001
tau_13CN[108638.212, 2]	0.002	0.000	0.001	0.002
tau_13CN[108643.59, 0]	0.001	0.000	0.001	0.001
tau_13CN[108643.59, 1]	0.001	0.000	0.001	0.002
tau_13CN[108643.59, 2]	0.002	0.000	0.002	0.003
tau_13CN[108644.3456, 0]	0.001	0.000	0.000	0.001
tau_13CN[108644.3456, 1]	0.001	0.000	0.001	0.001
tau_13CN[108644.3456, 2]	0.002	0.000	0.001	0.002
tau_13CN[108645.064, 0]	0.001	0.000	0.000	0.001
tau_13CN[108645.064, 1]	0.001	0.000	0.001	0.001
tau_13CN[108645.064, 2]	0.001	0.000	0.001	0.002
tau_13CN[108651.297, 0]	0.004	0.001	0.002	0.006
tau_13CN[108651.297, 1]	0.005	0.001	0.004	0.007
tau_13CN[108651.297, 2]	0.008	0.001	0.006	0.010
tau_13CN[108657.646, 0]	0.003	0.001	0.002	0.004
tau_13CN[108657.646, 1]	0.004	0.001	0.003	0.005
tau_13CN[108657.646, 2]	0.006	0.001	0.005	0.007
tau_13CN[108658.948, 0]	0.001	0.000	0.000	0.001
tau_13CN[108658.948, 1]	0.001	0.000	0.001	0.001
tau_13CN[108658.948, 2]	0.002	0.000	0.001	0.002
tau_13CN[108780.201, 0]	0.006	0.002	0.003	0.009
tau_13CN[108780.201, 1]	0.008	0.001	0.005	0.010
tau_13CN[108780.201, 2]	0.012	0.002	0.009	0.015
tau_13CN[108782.374, 0]	0.003	0.001	0.002	0.005
tau_13CN[108782.374, 1]	0.004	0.001	0.003	0.005
tau_13CN[108782.374, 2]	0.006	0.001	0.005	0.008
tau_13CN[108786.982, 0]	0.001	0.000	0.001	0.002
tau_13CN[108786.982, 1]	0.002	0.000	0.001	0.002
tau_13CN[108786.982, 2]	0.003	0.000	0.002	0.004
tau_13CN[108793.753, 0]	0.001	0.000	0.001	0.002
tau_13CN[108793.753, 1]	0.001	0.000	0.001	0.002
tau_13CN[108793.753, 2]	0.002	0.000	0.002	0.003
tau_13CN[108796.4, 0]	0.001	0.000	0.001	0.002
tau_13CN[108796.4, 1]	0.001	0.000	0.001	0.002
tau_13CN[108796.4, 2]	0.002	0.000	0.002	0.003
tau_13CN[108807.7879, 0]	0.000	0.000	0.000	0.000
tau_13CN[108807.7879, 1]	0.000	0.000	0.000	0.000
tau_13CN[108807.7879, 2]	0.000	0.000	0.000	0.000
tau_13CN[108986.836, 0]	0.000	0.000	0.000	0.000
tau_13CN[108986.836, 1]	0.000	0.000	0.000	0.000
tau_13CN[108986.836, 2]	0.000	0.000	0.000	0.000
tau_13CN[109217.5674, 0]	0.000	0.000	0.000	0.000
tau_13CN[109217.5674, 1]	0.000	0.000	0.000	0.000
tau_13CN[109217.5674, 2]	0.001	0.000	0.000	0.001
tau_13CN[109218.3227, 0]	0.000	0.000	0.000	0.000
tau_13CN[109218.3227, 1]	0.000	0.000	0.000	0.000
tau_13CN[109218.3227, 2]	0.000	0.000	0.000	0.000
tau_13CN[109218.919, 0]	0.000	0.000	0.000	0.000
tau_13CN[109218.919, 1]	0.000	0.000	0.000	0.000
tau_13CN[109218.919, 2]	0.000	0.000	0.000	0.000
tau_total_13CN[0]	0.030	0.008	0.017	0.044
tau_total_13CN[1]	0.039	0.006	0.028	0.050
tau_total_13CN[2]	0.063	0.009	0.048	0.078
TR_13CN[108056.1623, 0]	2.522	0.401	1.798	3.278
TR_13CN[108056.1623, 1]	2.231	0.217	1.836	2.597
TR_13CN[108056.1623, 2]	1.348	0.053	1.245	1.441
TR_13CN[108057.1556, 0]	2.522	0.401	1.798	3.278
TR_13CN[108057.1556, 1]	2.231	0.217	1.836	2.597
TR_13CN[108057.1556, 2]	1.348	0.053	1.245	1.441
TR_13CN[108062.9306, 0]	2.522	0.401	1.798	3.278
TR_13CN[108062.9306, 1]	2.231	0.217	1.836	2.597
TR_13CN[108062.9306, 2]	1.348	0.053	1.245	1.441
TR_13CN[108076.9692, 0]	2.522	0.401	1.798	3.277
TR_13CN[108076.9692, 1]	2.230	0.217	1.836	2.597
TR_13CN[108076.9692, 2]	1.347	0.053	1.245	1.441
TR_13CN[108077.2965, 0]	2.522	0.401	1.798	3.277
TR_13CN[108077.2965, 1]	2.230	0.217	1.836	2.597
TR_13CN[108077.2965, 2]	1.347	0.053	1.245	1.441
TR_13CN[108091.3352, 0]	2.522	0.401	1.798	3.277
TR_13CN[108091.3352, 1]	2.230	0.217	1.836	2.597
TR_13CN[108091.3352, 2]	1.347	0.053	1.245	1.441
TR_13CN[108406.0905, 0]	2.517	0.401	1.793	3.272
TR_13CN[108406.0905, 1]	2.226	0.217	1.831	2.592
TR_13CN[108406.0905, 2]	1.343	0.053	1.241	1.437
TR_13CN[108412.862, 0]	2.517	0.401	1.793	3.272
TR_13CN[108412.862, 1]	2.225	0.217	1.831	2.592
TR_13CN[108412.862, 2]	1.343	0.053	1.241	1.437
TR_13CN[108426.889, 0]	2.516	0.401	1.793	3.272
TR_13CN[108426.889, 1]	2.225	0.217	1.831	2.591
TR_13CN[108426.889, 2]	1.343	0.053	1.241	1.437
TR_13CN[108631.121, 0]	2.513	0.401	1.790	3.268
TR_13CN[108631.121, 1]	2.222	0.217	1.828	2.588
TR_13CN[108631.121, 2]	1.341	0.053	1.238	1.434
TR_13CN[108636.923, 0]	2.513	0.401	1.790	3.268
TR_13CN[108636.923, 1]	2.222	0.217	1.828	2.588
TR_13CN[108636.923, 2]	1.340	0.053	1.238	1.434
TR_13CN[108638.212, 0]	2.513	0.401	1.790	3.268
TR_13CN[108638.212, 1]	2.222	0.217	1.828	2.588
TR_13CN[108638.212, 2]	1.340	0.053	1.238	1.434
TR_13CN[108643.59, 0]	2.513	0.401	1.790	3.268
TR_13CN[108643.59, 1]	2.222	0.217	1.828	2.588
TR_13CN[108643.59, 2]	1.340	0.053	1.238	1.434
TR_13CN[108644.3456, 0]	2.513	0.401	1.790	3.268
TR_13CN[108644.3456, 1]	2.222	0.217	1.828	2.588
TR_13CN[108644.3456, 2]	1.340	0.053	1.238	1.434
TR_13CN[108645.064, 0]	2.513	0.401	1.790	3.268
TR_13CN[108645.064, 1]	2.222	0.217	1.828	2.588
TR_13CN[108645.064, 2]	1.340	0.053	1.238	1.434
TR_13CN[108651.297, 0]	2.513	0.401	1.790	3.268
TR_13CN[108651.297, 1]	2.222	0.217	1.828	2.588
TR_13CN[108651.297, 2]	1.340	0.053	1.238	1.434
TR_13CN[108657.646, 0]	2.513	0.401	1.790	3.268
TR_13CN[108657.646, 1]	2.222	0.217	1.828	2.588
TR_13CN[108657.646, 2]	1.340	0.053	1.238	1.434
TR_13CN[108658.948, 0]	2.513	0.401	1.790	3.268
TR_13CN[108658.948, 1]	2.222	0.217	1.828	2.588
TR_13CN[108658.948, 2]	1.340	0.053	1.238	1.434
TR_13CN[108780.201, 0]	2.511	0.401	1.788	3.266
TR_13CN[108780.201, 1]	2.220	0.217	1.826	2.586
TR_13CN[108780.201, 2]	1.339	0.053	1.236	1.432
TR_13CN[108782.374, 0]	2.511	0.401	1.788	3.266
TR_13CN[108782.374, 1]	2.220	0.217	1.826	2.586
TR_13CN[108782.374, 2]	1.339	0.053	1.236	1.432
TR_13CN[108786.982, 0]	2.511	0.401	1.788	3.266
TR_13CN[108786.982, 1]	2.220	0.217	1.826	2.586
TR_13CN[108786.982, 2]	1.339	0.053	1.236	1.432
TR_13CN[108793.753, 0]	2.511	0.401	1.788	3.265
TR_13CN[108793.753, 1]	2.220	0.217	1.826	2.586
TR_13CN[108793.753, 2]	1.339	0.053	1.236	1.432
TR_13CN[108796.4, 0]	2.511	0.401	1.788	3.265
TR_13CN[108796.4, 1]	2.220	0.217	1.826	2.586
TR_13CN[108796.4, 2]	1.339	0.053	1.236	1.432
TR_13CN[108807.7879, 0]	2.511	0.401	1.788	3.265
TR_13CN[108807.7879, 1]	2.220	0.217	1.826	2.585
TR_13CN[108807.7879, 2]	1.338	0.053	1.236	1.432
TR_13CN[108986.836, 0]	2.508	0.400	1.785	3.262
TR_13CN[108986.836, 1]	2.217	0.217	1.823	2.583
TR_13CN[108986.836, 2]	1.336	0.053	1.234	1.430
TR_13CN[109217.5674, 0]	2.504	0.400	1.782	3.258
TR_13CN[109217.5674, 1]	2.214	0.217	1.820	2.579
TR_13CN[109217.5674, 2]	1.333	0.053	1.231	1.427
TR_13CN[109218.3227, 0]	2.504	0.400	1.782	3.258
TR_13CN[109218.3227, 1]	2.214	0.217	1.820	2.579
TR_13CN[109218.3227, 2]	1.333	0.053	1.231	1.427
TR_13CN[109218.919, 0]	2.504	0.400	1.782	3.258
TR_13CN[109218.919, 1]	2.214	0.217	1.820	2.579
TR_13CN[109218.919, 2]	1.333	0.053	1.231	1.427

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