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trouble with standard run: "paramranges not found"
 
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Albin Nilsson



Joined: 05 Oct 2016
Posts: 13
Affiliation: NCBJ, Warsaw

PostPosted: January 26 2017  Reply with quote

I'm trying to do a generic run to reproduce some standard plots and results. I have not defined any new parameters or changed the code anywhere.
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:

#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 =

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Jason Dossett



Joined: 19 Mar 2010
Posts: 97
Affiliation: The University of Texas at Dallas

PostPosted: January 26 2017  Reply with quote

You should not be doing a generic run if you want to reproduce the standard plots.

A generic run will not invoke camb or any of the standard likelihoods at all.
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Albin Nilsson



Joined: 05 Oct 2016
Posts: 13
Affiliation: NCBJ, Warsaw

PostPosted: January 27 2017  Reply with quote

Hi, and thank for replying. By generic run, I mean a standard one. I haven't changed anything, just trying to reproduce some standard results.
Any ideas as to what the problem might be?
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Antony Lewis



Joined: 23 Sep 2004
Posts: 1293
Affiliation: University of Sussex

PostPosted: January 27 2017  Reply with quote

Albin Nilsson wrote:
Hi, and thank for replying. By generic run, I mean a standard one. I haven't changed anything, just trying to reproduce some standard results.
Any ideas as to what the problem might be?


You have

generic_mcmc=T
parameterization=generic

that are not in the supplied test_planck.ini (which is probably the best starting point for non-grid single Planck-like runs).
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