getting inf as lkl while using scf #400
Description
I'm running using the following param file
Experiment setup
data.experiments = ['flcdm_hz_lkl_new']
Cosmological parameters
data.parameters['omega_b'] = [0.022, 0.0, 1.0, 0.005, 1, 'cosmo']
data.parameters['omega_cdm'] = [0.12, 0.0, 1.0, 0.03, 1, 'cosmo']
data.parameters['H0'] = [70.0, None, None, 6.0, 1, 'cosmo']
data.parameters['scf_parameters__1'] = [1.0, 0.0, 2.1, 0.5, 1, 'cosmo']
data.parameters['scf_parameters__2'] = [2.0, 0.0, 10.0, 0.2, 1, 'cosmo']
data.parameters['scf_parameters__3'] = [0.0, None, None, 0.0, 1, 'cosmo']
data.parameters['scf_parameters__4'] = [0.0, None, None, 0.0, 1, 'cosmo']
data.parameters['scf_parameters__5'] = [5.0, None, None, 0.0, 1, 'cosmo']
data.parameters['scf_parameters__6'] = [1e-4, None, None, 0.0, 1, 'cosmo']
data.parameters['Omega_scf'] = [0.7, 0.6, 0.8, 0.01, 1, 'cosmo']
data.cosmo_arguments['attractor_ic_scf'] = 'no'
data.cosmo_arguments['scf_tuning_index'] = 0
data.cosmo_arguments['YHe'] = 0.245
Output folder
data.output = '/home/cosmo/outputfol/fpcdm_out'
The output I'm getting is
Running Monte Python v3.6.1
with CLASS v3.3.0
/!\ Detecting empty folder, logging the parameter file
Testing likelihoods for:
->flcdm_hz_lkl_new
Creating /home/amoghsriv/cosmo/outputfol/hz_analysis/fpcdm_out/2025-06-16_10__1.txt
Deduced starting covariance matrix:
['omega_b', 'omega_cdm', 'H0', 'scf_parameters__1', 'scf_parameters__2', 'Omega_scf']
[[2.5e-05 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00]
[0.0e+00 9.0e-04 0.0e+00 0.0e+00 0.0e+00 0.0e+00]
[0.0e+00 0.0e+00 3.6e+01 0.0e+00 0.0e+00 0.0e+00]
[0.0e+00 0.0e+00 0.0e+00 2.5e-01 0.0e+00 0.0e+00]
[0.0e+00 0.0e+00 0.0e+00 0.0e+00 4.0e-02 0.0e+00]
[0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 1.0e-04]]
Update routine is enabled with value 50 (recommended: 50)
This number is rescaled by cycle length 6 (N_slow + f_fast * N_fast) to 300
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
/!\ divide by zero encountered in double_scalars
-LogLkl omega_b omega_cdm H0 scf_parameters__1scf_parameters__2Omega_scf
/!\ PyMultiNest detected but MultiNest likely not installed correctly. You can
safely ignore this if not running with option -m NS
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 1.879624e-02 7.956697e-02 6.924076e+01 2.085838e+00 1.856458e+00 6.724413e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 2.410098e-02 6.271863e-02 6.658975e+01 1.837942e+00 1.843232e+00 6.783454e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
3 inf 2.814605e-02 5.643717e-02 5.980129e+01 1.395925e+00 1.633695e+00 6.930956e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 3.613725e-02 6.710061e-03 5.846484e+01 1.666665e+00 1.650395e+00 6.928909e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 4.398902e-02 2.995980e-02 5.445754e+01 1.511025e+00 1.412916e+00 7.141234e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 4.341038e-02 5.137635e-02 4.541653e+01 1.785469e+00 1.178728e+00 7.053400e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 5.460092e-02 6.007339e-02 4.475557e+01 1.820745e+00 1.407839e+00 6.943576e-01
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
Not using attractor initial conditions
1 inf 5.621073e-02 9.767171e-02 4.592003e+01 1.490823e+00 1.390748e+00 6.877165e-01
10 steps done, acceptance rate: 0.8
/!\ The acceptance rate is above 0.6, which means you might have difficulties
exploring the entire parameter space. Try analysing these chains, and use
the output covariance matrix to decrease the acceptance rate to a value
between 0.2 and 0.4 (roughly).
Can you help me figure out why lkl is giving inf (probably because it's failing but how is that happening)? Also CLASS independently is working so this must be a Montepython issue?