I keep getting the error message: "parameter ranges not found: param1", although I haven't defined param1 at all.
Does anyone have any ideas? See below for my params.ini file:
Code: Select allp>
#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/tut/ #Root name for files produced file_root = tut #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 = 6 param[omegabh2] = 0.0221 0.005 0.1 0.0001 0.0001 param[omegach2] = 0.12 0.001 0.99 0.001 0.0005 param[theta] = 1.0411 0.5 10 0.0004 0.0002 param[tau] = 0.09 0.01 0.8 0.01 0.005 neutrino_hierarchy = degenerate num_massive_neutrinos=1 param[mnu] = 0.06 param[meffsterile] = 0 #alternative using parameter names from your generic_example.paramnames file #Planck 2015 + BAO #—-highl temp + pol——-# DEFAULT(batch2/plik_dx11dr2_HM_v18_TTTEEE.ini) #—-lowl temp + pol——–# DEFAULT(batch2/lowTEB.ini) #———BAO—————# DEFAULT(batch2/BAO.ini) #General settings DEFAULT(batch2/common.ini) #Max samples to get samples = 100000 #Use vanilla MCMC here since no speed hierarchy defined sampling_method = 7 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 =