diff options
Diffstat (limited to 'submitter/make_dag.py')
| -rw-r--r-- | submitter/make_dag.py | 55 |
1 files changed, 39 insertions, 16 deletions
diff --git a/submitter/make_dag.py b/submitter/make_dag.py index 735d213..641e00e 100644 --- a/submitter/make_dag.py +++ b/submitter/make_dag.py @@ -16,30 +16,30 @@ full_scan_mfr = [ ] fix_sfr_mfr = [ - (1, 1, 1, 1, 0, 0), - (1, 1, 1, 0, 1, 0), - (1, 1, 1, 0, 0, 1), (1, 1, 1, 1, 2, 0), - (1, 1, 0, 0, 1, 0), - (1, 1, 0, 1, 2, 0), - (1, 1, 0, 1, 0, 0), - (1, 0, 0, 1, 0, 0), - (0, 1, 0, 0, 1, 0), - (1, 2, 0, 0, 1, 0), - (1, 2, 0, 1, 2, 0) + # (1, 1, 0, 1, 2, 0), + # (1, 2, 0, 1, 2, 0), + # (1, 1, 1, 1, 0, 0), + # (1, 1, 0, 1, 0, 0), + # (1, 0, 0, 1, 0, 0), + # (1, 1, 1, 0, 1, 0), + # (1, 1, 0, 0, 1, 0), + # (0, 1, 0, 0, 1, 0), + # (1, 2, 0, 0, 1, 0), + # (1, 1, 1, 0, 0, 1), ] # MCMC -run_mcmc = 'True' +run_mcmc = 'False' burnin = 500 nsteps = 2000 nwalkers = 60 seed = 24 -threads = 4 +threads = 12 mcmc_seed_type = 'uniform' # FR -dimension = [4, 5, 7, 8] +dimension = [3, 6] energy = [1e6] likelihood = 'golemfit' no_bsm = 'False' @@ -66,9 +66,15 @@ promptNorm = 0. ast = 'p2_0' data = 'real' +# Bayes Factor +run_bayes_factor = 'True' +bayes_bins = 10 +bayes_live_points = 200 + # Plot -plot_angles = 'True' +plot_angles = 'False' plot_elements = 'False' +plot_bayes = 'True' outfile = 'dagman_FR.submit' golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' @@ -86,11 +92,15 @@ with open(outfile, 'w') as f: elif energy_dependance == 'spectral': outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}/SI_{2}'.format(likelihood, dim, spectral_index) + bayes_output = 'None' for sig in sigma_ratio: print 'sigma', sig for frs in fix_sfr_mfr: print frs - outchains = outchain_head + '/fix_ifr/{0}/mcmc_chain'.format(str(sig).replace('.', '_')) + outchains = outchain_head + '/fix_ifr/{0}/'.format(str(sig).replace('.', '_')) + if run_bayes_factor == 'True': + bayes_output = outchains + '/bayes_factor/' + outchains += 'mcmc_chain' f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0])) f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1])) @@ -132,11 +142,19 @@ with open(outfile, 'w') as f: f.write('VARS\tjob{0}\tbinning_1="{1}"\n'.format(job_number, binning[1])) f.write('VARS\tjob{0}\tbinning_2="{1}"\n'.format(job_number, binning[2])) f.write('VARS\tjob{0}\tfix_mixing_almost="{1}"\n'.format(job_number, fix_mixing_almost)) + f.write('VARS\tjob{0}\trun_bayes_factor="{1}"\n'.format(job_number, run_bayes_factor)) + f.write('VARS\tjob{0}\tbayes_bins="{1}"\n'.format(job_number, bayes_bins)) + f.write('VARS\tjob{0}\tbayes_output="{1}"\n'.format(job_number, bayes_output)) + f.write('VARS\tjob{0}\tbayes_live_points="{1}"\n'.format(job_number, bayes_live_points)) + f.write('VARS\tjob{0}\tplot_bayes="{1}"\n'.format(job_number, plot_bayes)) job_number += 1 # for frs in full_scan_mfr: # print frs - # outchains = outchain_head + '/full_scan/{0}/mcmc_chain'.format(str(sig).replace('.', '_')) + # outchains = outchain_head + '/full_scan/{0}'.format(str(sig).replace('.', '_')) + # if run_bayes_factor == 'True': + # bayes_output = outchains + '/bayes_factor/' + # outchains += 'mcmc_chain' # f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) # f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, frs[0])) # f.write('VARS\tjob{0}\tmr1="{1}"\n'.format(job_number, frs[1])) @@ -178,4 +196,9 @@ with open(outfile, 'w') as f: # f.write('VARS\tjob{0}\tbinning_1="{1}"\n'.format(job_number, binning[1])) # f.write('VARS\tjob{0}\tbinning_2="{1}"\n'.format(job_number, binning[2])) # f.write('VARS\tjob{0}\tfix_mixing_almost="{1}"\n'.format(job_number, fix_mixing_almost)) + # f.write('VARS\tjob{0}\trun_bayes_factor="{1}"\n'.format(job_number, run_bayes_factor)) + # f.write('VARS\tjob{0}\tbayes_bins="{1}"\n'.format(job_number, bayes_bins)) + # f.write('VARS\tjob{0}\tbayes_output="{1}"\n'.format(job_number, bayes_output)) + # f.write('VARS\tjob{0}\tbayes_live_points="{1}"\n'.format(job_number, bayes_live_points)) + # f.write('VARS\tjob{0}\tplot_bayes="{1}"\n'.format(job_number, plot_bayes)) # job_number += 1 |