I'm running the following python file with version 3.4.1 of COBAYA, and version 3.2.1 of CLASS:
Code: Select all
info={
"params": {
"clamp": {
"latex": "10^9 A_\\mathrm{s} e^{-2\\tau}",
"derived": "lambda A_s, tau_reio: 1e9*A_s*np.exp(-2*tau_reio)"
},
"A": {
"latex": "10^9 A_\\mathrm{s}",
"derived": "lambda A_s: 1e9*A_s"
},
"Omega_m": {
"latex": "\\Omega_\\mathrm{m}"
},
"Omega_Lambda": {
"latex": "\\Omega_\\Lambda"
},
"tau_reio": {
"prior": {"min": 0.01, "max": 0.8},
"proposal": 0.003,
"ref": {
"loc": 0.055,
"scale": 0.006,
"dist": "norm"
},
"latex": "\\tau_\\mathrm{reio}"
},
"age": {
"latex": "{\\rm{Age}}/\\mathrm{Gyr}"
},
"n_s": {
"prior": {
"max": 1.2,
"min": 0.8
},
"proposal": 0.002,
"ref": {
"loc": 0.965,
"scale": 0.004,
"dist": "norm"
},
"latex": "n_\\mathrm{s}"
},
"s8h5": {
"latex": "\\sigma_8/h^{0.5}",
"derived": "lambda sigma8, H0: sigma8*(H0*1e-2)**(-0.5)"
},
############### IDM PARAMS BEGIN ###############################
"m_idm": {
"latex": "m_\\mathrm{idm}",
"value": 2e9
},
"xi_idr":{
"prior": {
"max": 0.5,
"min": 0
},
"proposal": 0.05,
"ref": {
"loc": 0.1,
"scale": 0.05,
"dist": "norm"
},
"latex": r"\xi"
},
"log10_a_idm_dr_p1":{
"prior":{
"min": 0,
"max": 8
},
"ref":{"dist": "norm","loc": 4, "scale": 3},
"proposal": 1.5,
"drop": True,
"latex": r"\log_{10}(\mathrm{a}_\mathrm{dark}\mathrm{Mpc})"
},
"a_idm_dr": {
"value": "lambda log10_a_idm_dr_p1: 10**(log10_a_idm_dr_p1)-1.0",
"latex": "\mathrm{a}_\mathrm{dark}"
},
################ IDM PARAMS FINISH ########################
"sigma8": {
"latex": "\\sigma_8"
},
"z_reio": {
"latex": "z_\\mathrm{re}"
},
"omega_b": {
"prior": {
"max": 0.1,
"min": 0.005
},
"proposal": 0.0001,
"ref": {
"loc": 0.0224,
"scale": 0.0001,
"dist": "norm"
},
"latex": "\\Omega_\\mathrm{b} h^2"
},
"rs_drag": {
"latex": "r_\\mathrm{drag}"
},
"omega_cdm": {
"prior": {
"max": 0.99,
"min": 0.001
},
"proposal": 0.0005,
"ref": {
"loc": 0.12,
"scale": 0.001,
"dist": "norm"
},
"latex": "\\Omega_\\mathrm{c} h^2"
},
"s8omegamp25": {
"latex": "\\sigma_8 \\Omega_\\mathrm{m}^{0.25}",
"derived": "lambda sigma8, Omega_m: sigma8*Omega_m**0.25"
},
"theta_s_1e2": {
"prior": {
"max": 10,
"min": 0.5
},
"proposal": 0.0002,
"drop": True,
"ref": {
"loc": 1.0416,
"scale": 0.0004,
"dist": "norm"
},
"latex": "100\\theta_\\mathrm{s}"
},
"H0": {
"latex": "H_0"
},
"m_ncdm": {
"value": 0.02,
"renames": "mnu"
},
"m_ncdm_tot":{
"latex": r'\Sigma\mathrm{m}_{\nu}\mathrm(eV)'
},
"YHe": {
"latex": "Y_\\mathrm{P}"
},
"s8omegamp5": {
"latex": "\\sigma_8 \\Omega_\\mathrm{m}^{0.5}",
"derived": "lambda sigma8, Omega_m: sigma8*Omega_m**0.5"
},
"omegamh2": {
"latex": "\\Omega_\\mathrm{m} h^2",
"derived": "lambda Omega_m, H0: Omega_m*(H0/100)**2"
},
"100*theta_s": {
"derived": False,
"value": "lambda theta_s_1e2: theta_s_1e2"
},
"logA": {
"prior": {
"max": 3.91,
"min": 1.61
},
"proposal": 0.001,
"drop": True,
"ref": {
"loc": 3.05,
"scale": 0.001,
"dist": "norm"
},
"latex": "\\log(10^{10} A_\\mathrm{s})"
},
"A_s": {
"latex": "A_\\mathrm{s}",
"value": "lambda logA: 1e-10*np.exp(logA)"
}
},
"theory": {
"classy": {
"extra_args": {
"nindex_idm_dr": 4.0,
"stat_f_idr": 0.875, #1
"idr_nature": "free_streaming", # "fluid"
######## PRECISION PARAMS ##########
#"idm_dr_tight_coupling_trigger_tau_c_over_tau_k":, # when to switch off the dark-tight-coupling approximation, first condition
#"idm_dr_tight_coupling_trigger_tau_c_over_tau_h":, # when to switch off the dark-tight-coupling approximation, second condition
"output": "tCl,pCl,lCl",
"f_idm": 1,
"N_ncdm": 1,
"N_ur": 0.00441,
"nonlinear_min_k_max": 20,
"non linear": "halofit",
"deg_ncdm": 3,
},
"path": "home/Codes/code/classy/",
"ignore_obsolete": True
}
},
"sampler": {
"mcmc": {
"Rminus1_cl_stop": 0.2,
"drag": True,
"Rminus1_stop": 0.02,
"covmat": 'home/IDM/DM_DR/ALL_005/CLASS.covmat',
"measure_speeds": True,
"oversample_power": 0.4,
"output_every": 1,
"proposal_scale": 1,
"max_tries": 1.e4
}
},
"resume": True,
"debug": True,
"timing": True,
"output": "home/IDM/DM_DR/ALL_1/CLASS",
"likelihood": {
"SPT3G_Y1.TTTEEE": {
"python_path": "home/spt3g"
},
"planck_2018_lowl.TT": {},
"planck_2018_lowl.EE": {},
"planck_2018_lensing.clik": {},
"planck_2018_highl_plik.TTTEEE": {},
"sn.pantheon": {},
"bao.sixdf_2011_bao": {},
"bao.sdss_dr7_mgs": {},
"bao.sdss_dr16_baoplus_lrg": {},
"bao.sdss_dr16_lrg_bao_dmdh": {},
#"des_y1.joint": {}
}
}
import warnings
from sklearn.exceptions import DataConversionWarning
warnings.filterwarnings(action='ignore', category=DataConversionWarning)
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
from cobaya.run import run
import cobaya
updated_info, sampler = run(info)
Thanks in advance for the replies!