##Sample file of common parameters for baseline Planck set of runs
batch_name = batch3
local_dir = %LOCALDIR%
#directory, e.g. window functions in directory windows under data_dir
data_dir = %LOCALDIR%data/
INCLUDE(likelihood.ini)
INCLUDE(params_CMB_defaults.ini)
#whether to include prior on a parameter if it has non-varying value
include_fixed_parameter_priors = F
#Feedback level ( 2=lots,1=chatty,0=none)
feedback = 1
#Force computation of sigma_8 even if use_mpk = F
get_sigma8 = T
#This only has a small effect at very high L
use_nonlinear_lensing = T
#if using non-linear lensing, better turn of power spectrum fast/slow since is now non-linear
block_semi_fast = F
#Temperature at which to Monte-Carlo
temperature = 1
#Maximum number of chain steps
samples = 4000000
#Scale of proposal relative to covariance; 2.4 is recommended by astro-ph/0405462 for Gaussians
#If propose_matrix is much broader than the new distribution, make proportionately smaller
#Generally make smaller if your acceptance rate is too low
propose_scale = 1.9
#Increase to oversample fast parameters more, e.g. if space is odd shape
oversample_fast = 1
#if non-zero number of steps between sample info dumped to file file_root.data
#WANT THIS ON so we can do importance sampling runs quickly later for likelihood updates
#Off to save lots of disk space
indep_sample = 10
#number of samples to disgard at start; usually 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
#Do initial period of slice sampling; may be good idea if
#cov matrix or widths are likely to be very poor estimates
MPI_StartSliceSampling = F
#Can optionally also check for convergence of confidence limits (after MPI_Converge_Stop reached)
#Can be good idea as small value of MPI_Converge_Stop does not (necessarily) imply good exploration of tails
MPI_Check_Limit_Converge = T
#if MPI_Check_Limit_Converge = T, give tail fraction to check (checks both tails):
MPI_Limit_Converge = 0.025
#permitted quantile chain variance in units of the standard deviation (small values v slow):
MPI_Limit_Converge_Err = 0.2
#which parameters tails to check. If zero, check all parameters:
MPI_Limit_Param = 0
#if MPI_LearnPropose = T, the proposal density is continally updated from the covariance of samples so far (since burn in)
MPI_LearnPropose = T
#can set a value of converge at which to stop updating covariance (so that it becomes rigorously Markovian)
#e.g. MPI_R_StopProposeUpdate = 0.4 will stop updating when (variance of chain means)/(mean of variances) < 0.4
MPI_R_StopProposeUpdate = 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 = 3
#As above, but used if varying new parameters that were fixed in covmat
MPI_Max_R_ProposeUpdateNew = 50
#Initial power spectrum amplitude pivots (Mpc^{-1})
#if tensor_pivot_k/=pivot_k then r is defined so to that P_t(tensor_pivot_k)=r P_s(tensor_pivot_k)
pivot_k = 0.05
#tensor_pivot_k defaults to same as pivot_k
#tensor_pivot_k = 0.05
#Whether the CMB should be lensed (slows a lot unless also computing matter power)
CMB_lensing = T
accuracy_level = 1
high_accuracy_default = T
#1: Simple Metropolis, 2: slice sampling, 3: slice sampling fast parameters, 4: directional gridding
#7 is new dragging method
sampling_method = 7
dragging_steps = 3
use_fast_slow = T
##Rest are fairly irrelevant
#if sampling_method =4, iterations per gridded direction
directional_grid_steps = 20
#action = 0: MCMC, action=1: postprocess .data file, action=2: find best fit point only
action = 0
#If propose_matrix is blank (first run), can try to use numerical Hessian to
#estimate a good propose matrix. As a byproduct you also get an approx best fit point
estimate_propose_matrix = F
#when estimating best fit point (action=2 or estimate_propose_matrix),
#required relative accuracy of each parameter in units of the covariance width
max_like_radius = 0.05
max_like_iterations = 5000
minimization_points_factor = 2
minimize_loglike_tolerance = 0.05
minimize_separate_fast = T
#if non-zero do some low temperature MCMC steps to check minimum stable
minimize_mcmc_refine_num = 20
minimize_refine_temp = 0.01
minimize_temp_scale_factor = 5
minimize_random_start_pos = T
#max_like_radius = 0.002
#max_like_iterations = 40000
#minimization_points_factor = 6
#minimize_loglike_tolerance=0.05
#if blank this is set from system clock
rand_seed =
#If true, generate checkpoint files and terminated runs can be restarted using exactly the same command
#and chains continued from where they stopped
#With checkpoint=T note you must delete all chains/file_root.* files if you want new chains with an old file_root
checkpoint = T
#whether to stop on CAMB error, or continue ignoring point
stop_on_error= T
#If action = 1
redo_likelihoods = T
redo_theory = F
redo_cls = F
redo_pk = F
redo_skip = 0
redo_outroot =
redo_thin = 1
redo_add = F
redo_from_text = F
#If large difference in log likelihoods may need to offset to give sensible weights
#for exp(difference in likelihoods)
redo_likeoffset = 0