param_names:
    SIM_c
    SIM_beta
    SIM_x1
    SIM_EBV
    SIM_RV
    SCATTER


parameters_to_condition_on:
    ['c', 'mB', 'x1', 'zHD', 'cERR', 'mBERR', 'x1ERR', 'MU', 'HOST_LOGMASS', 'MURES']

sim_parameters:
    n_sim:   60_000
    n_batch: 500
    simname: 'simulations.v5.LOG.h5'
    posteriorname: 'posterior.v5.LOG.pt'

Data_File:
    "../INVERSE_H0/D5YR_DATA/FITOPT000_MUOPT000.FITRES.gz"
Simbank_File:
    "SIM_BANK_SB.FITRES.gz"

#########################
# Dictionaries 
#########################

Functions:
    SIM_c:    DistGaussian  #The shape of this parameter.
    SIM_RV:   DistLogistic
    SIM_EBV:  DistExponential
    SIM_beta: DistGaussian
    SIM_x1:   DistGaussian

Splits:
    SIM_EBV: ["Stepwise","HOST_LOGMASS", 10]


#If using higher-order functions such as DistLogistic, which require two parameters
#this dictionary tells the correlation parameter.
#ONLY works when/if calling 2-parameter functions. 
Correlations:
    SIM_c:    None
    SIM_RV:   HOST_LOGMASS
    SIM_EBV:  None
    SIM_beta: None
    SIM_x1:   None

# We assume BoxUniform priors for our parameters; 
# so each tuple needs to include the lower and upper bound you expect for that parameter. 
# Gaussians are formatted as mu then sigma

Priors:
    SIM_c:    ["(-.1, 0)", "(0.02, 0.1)"]
    SIM_EBV:  ["(0.05, 0.5)"]
    SIM_beta: ["(1,2)", "(0.05, 0.3)"]
    SIM_x1:   ["(-2,2)", "(0.2, 1)"]
    SIM_x1_EVOL: ["(-1,1)"]
    STEP:     ["(0,0.2)"]
    SCATTER:  ["(0,0.1)"]
    SIM_RV:   ["(1,3)","(-1.5, 2.1)", "(0,.5)"] #L, k, sigma


# Fancy latex labels for plotting. Add as you see fit. 
Latex_Names:
    DistExponential:  [r'$\tau$']
    DistGaussian:     [r'$\mu$', r'$\sigma$']
    SIM_c:            r"$c_{\rm int}$"
    SIM_beta:         r"$\beta_{\rm int}$"
    SIM_RV:           r"$R_V$"
    SIM_EBV:          r"$EBV$"
    SIM_x1:           r"$x_1$"
    STEP:             r"$\gamma$"
    SCATTER:          r"$\sigma_{\rm int}$"
    DistLogistic:     [r"$L$", r"$k$", r"$\sigma$"]
