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MontePython with MultiNest - seg fault #122
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We will need more information, e.g. on which likelihoods are being used. The links to the new github page work fine for me: |
I'll reply through the new page. The problem is the link to 'forum'. It's fine if you go through 'issues' which clearly several people (like me) have not done. |
Oh, thanks a lot! I hadn't updated the link. I will do that for the next release. |
Hey! I'm also trying to run MontePython with MultiNest and I'm facing the following problem:Error in Class: background_init(L:634) :condition (pba->shooting_failed == TRUE) is true; Shooting failed, try optimising input_get_guess(). Error message: input_init(L:330) :error in input_find_root(&xzero, &fevals, &fzw, errmsg); The *.param file is written as follows: #------Experiments to test (separated with commas)----- Cosmological parameters listdata.parameters['omega_b'] = [2.249, 1.8, 3, 0.016, 0.01,'cosmo'] Nuisance parameter list, same call, except the name does not have to be a class namedata.parameters['A_cib_217'] = [ 61, 0, 200, 7, 1,'nuisance'] Derived parametersdata.parameters['z_reio'] = [1, None, None, 0, 1, 'derived'] Other cosmo parameters (fixed parameters, precision parameters, etc.)##data.cosmo_arguments['sBBN file'] = data.path['cosmo']+'/bbn/sBBN.dat' The base model features two masslessand one massive neutrino with m=0.06eV.The settings below ensures that Neff=3.046and m/omega = 93.14 eVdata.cosmo_arguments['N_ur'] = 2.0328 These two are required to get sigma8 as a derived parameter(class must compute the P(k) until sufficient k)data.cosmo_arguments['output'] = 'mPk' Thanks! |
Hi , I have been running MontePython to create the chains, but when I want to create new chains and I run a file.param where the parameters w0_fld and wa_fld are variying, I get the following error in the terminal : I don't know what is happening, because in my class version I have the file background.h where are defined wa_fld and w0_fld. Thanks! |
@Cristian1200 |
I have been running with Metropolis-Hastings with no problems. I just started using MultiNest instead and I'm getting segmentation faults - sometimes before any MultiNest output and sometimes after many lines of sample size/evidence values.
Also get CLASS error messages but I don't think these are the cause.
Error in Class: background_init(L:657) :error in background_solve(ppr,pba);
=>background_solve(L:1587) :condition ((tau_end-tau_start)/tau_start < ppr->smallest_allowed_variation) is true; integration step: relative change in time =1.164104e-16 < machine precision : leads either to numerical error or infinite loop
Any suggestions why the seg faults happen, what I can do about it, how I can track the problem down?
PS others have been advised to go to latest github to report issues, but the forum link there just comes back to here.
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