#Sample parameters for cosmomc used as a generic sampler
#Write your likelihood function in calclike.f90
generic_mcmc=T
parameterization=generic
#Folder where files (chains, checkpoints, etc.) are stored
root_dir = chains/
#Root name for files produced
file_root = test
#action = 0, to MCMC, action=1, postprocess .data file
action = 0
#You can either use numbered parameters, or, usually better, define a parameter name file as below
num_theory_params = 2
#e.g. for 2D Gaussian
param[param1] = 0 -20 20 1 1
param[param2] = 0 -20 20 1 1
#alternative using parameter names from your generic_example.paramnames file
#ParamNamesFile = paramnames/params_generic.paramnames
#param[mypar1] = 0 -20 20 1 1
#param[mypar2] = 0 -20 20 1 1
#Max samples to get
samples = 100000
#Use vanilla MCMC here since no speed hierarchy defined
sampling_method = 1
use_fast_slow = F
estimate_propose_matrix = F
delta_loglike = 2
propose_scale = 2.4
#Temperature at which to Monte-Carlo
temperature = 1
#Feedback level ( 2=lots,1=chatty,0=less,-1=minimal)
feedback = 1
#Can re-start from the last line of previous run (.txt file)
continue_from =
#Increase to oversample fast parameters,e.g. if space is odd shape
oversample_fast = 1
#Can use covariance matrix for proposal density, otherwise use settings below
#Covariance matrix can be produced using "getdist" prorgram.
propose_matrix =
#If action = 1
redo_likelihoods = T
redo_theory = F
redo_cls = F
redo_pk = F
redo_skip = 0
redo_outroot =
redo_thin = 1
#If large difference in log likelihoods may need to offset to give sensible weights
#for exp(difference in likelihoods)
redo_likeoffset = 0
#Number of distinct points to sample
#Every accepted point is included
#number of steps between independent samples
#if non-zero all info is dumped to file file_root.data
#if you change this probably have to change output routines in code too
indep_sample = 0
#number of samples to disgard at start
#May prefer to set to zero and remove later
burn_in = 0
#If zero set automatically
num_threads = 0
#MPI mode multi-chain options (recommended)
#MPI_Converge_Stop is a (variance of chain means)/(mean of variances) parameter that can be used to stop the chains
#Set to a negative number not to use this feature. Does not guarantee good accuracy of confidence limits.
MPI_Converge_Stop = 0.01
#if MPI_LearnPropose = T, the proposal density is continally updated from the covariance of samples so far (since burn in)
MPI_LearnPropose = T
#Can optionally also check for convergence of confidence limits (after MPI_Converge_Stop reached)
MPI_Check_Limit_Converge = T
#if MPI_Check_Limit_Converge = T, give tail fraction to check (checks both tails):
MPI_Limit_Converge = 0.025
#And the permitted percentil chain variance in units of the standard deviation (small values v slow):
MPI_Limit_Converge_Err = 0.1
#which parameter's tails to check. If zero, check all parameters:
MPI_Limit_Param = 0
#If have covmat, R to reach before updating proposal density (increase if covmat likely to be poor)
#Only used if not varying new parameters that are fixed in covmat
MPI_Max_R_ProposeUpdate = 2
#As above, but used if varying new parameters that were fixed in covmat
MPI_Max_R_ProposeUpdateNew = 30
#if blank this is set from system clock
rand_seed =