From ae60ec260f8939c952167035df5b6957fdfa4e9a Mon Sep 17 00:00:00 2001 From: shivesh Date: Fri, 13 Apr 2018 22:00:22 -0500 Subject: Fri Apr 13 22:00:22 CDT 2018 --- submitter/make_dag.py | 231 ++++++++++++++++++++++++++------------------------ submitter/submit.sub | 4 +- 2 files changed, 124 insertions(+), 111 deletions(-) (limited to 'submitter') diff --git a/submitter/make_dag.py b/submitter/make_dag.py index 641e00e..be13ac8 100644 --- a/submitter/make_dag.py +++ b/submitter/make_dag.py @@ -12,7 +12,7 @@ f_fr = (2, 1, 0) g_fr = (1, 1, 0) full_scan_mfr = [ - (1, 1, 1), (1, 1, 0) + # (1, 1, 1), (1, 1, 0) ] fix_sfr_mfr = [ @@ -35,11 +35,11 @@ burnin = 500 nsteps = 2000 nwalkers = 60 seed = 24 -threads = 12 +threads = 4 mcmc_seed_type = 'uniform' # FR -dimension = [3, 6] +dimension = [6] energy = [1e6] likelihood = 'golemfit' no_bsm = 'False' @@ -56,30 +56,35 @@ fix_mixing_almost = 'False' likelihood = 'golemfit' # Nuisance -astroDeltaGamma = 2. -astroNorm = 1. convNorm = 1. -muonNorm = 1. promptNorm = 0. +muonNorm = 1. +astroNorm = 6.9 +astroDeltaGamma = 2.5 # GolemFit ast = 'p2_0' data = 'real' # Bayes Factor -run_bayes_factor = 'True' +run_bayes_factor = 'False' bayes_bins = 10 bayes_live_points = 200 +bayes_tolerance = 0.01 +bayes_eval_bin = True # set to 'all' to run normally # Plot plot_angles = 'False' plot_elements = 'False' -plot_bayes = 'True' +plot_bayes = 'False' outfile = 'dagman_FR.submit' golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit' condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/submit.sub' +if bayes_eval_bin != 'all': b_runs = bayes_bins +else: b_runs = 1 + with open(outfile, 'w') as f: job_number = 1 for dim in dimension: @@ -101,104 +106,112 @@ with open(outfile, 'w') as f: 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])) - f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) - f.write('VARS\tjob{0}\tsigma_ratio="{1}"\n'.format(job_number, sig)) - f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, 'True')) - f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, frs[3])) - f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, frs[4])) - f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, frs[5])) - f.write('VARS\tjob{0}\tfix_scale="{1}"\n'.format(job_number, 'False')) - f.write('VARS\tjob{0}\tscale="{1}"\n'.format(job_number, 0)) - f.write('VARS\tjob{0}\tscale_region="{1}"\n'.format(job_number, scale_region)) - f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - f.write('VARS\tjob{0}\tenergy="{1}"\n'.format(job_number, en)) - f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) - f.write('VARS\tjob{0}\tburnin="{1}"\n'.format(job_number, burnin)) - f.write('VARS\tjob{0}\tnwalkers="{1}"\n'.format(job_number, nwalkers)) - f.write('VARS\tjob{0}\tnsteps="{1}"\n'.format(job_number, nsteps)) - f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) - f.write('VARS\tjob{0}\tfix_mixing="{1}"\n'.format(job_number, fix_mixing)) - f.write('VARS\tjob{0}\tno_bsm="{1}"\n'.format(job_number, no_bsm)) - f.write('VARS\tjob{0}\trun_mcmc="{1}"\n'.format(job_number, run_mcmc)) - f.write('VARS\tjob{0}\tastroDeltaGamma="{1}"\n'.format(job_number, astroDeltaGamma)) - f.write('VARS\tjob{0}\tastroNorm="{1}"\n'.format(job_number, astroNorm)) - f.write('VARS\tjob{0}\tconvNorm="{1}"\n'.format(job_number, convNorm)) - f.write('VARS\tjob{0}\tmuonNorm="{1}"\n'.format(job_number, muonNorm)) - f.write('VARS\tjob{0}\tpromptNorm="{1}"\n'.format(job_number, promptNorm)) - f.write('VARS\tjob{0}\tdata="{1}"\n'.format(job_number, data)) - f.write('VARS\tjob{0}\tast="{1}"\n'.format(job_number, ast)) - f.write('VARS\tjob{0}\tplot_angles="{1}"\n'.format(job_number, plot_angles)) - f.write('VARS\tjob{0}\tplot_elements="{1}"\n'.format(job_number, plot_elements)) - f.write('VARS\tjob{0}\tseed="{1}"\n'.format(job_number, seed)) - f.write('VARS\tjob{0}\tthreads="{1}"\n'.format(job_number, threads)) - f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) - f.write('VARS\tjob{0}\tmcmc_seed_type="{1}"\n'.format(job_number, mcmc_seed_type)) - f.write('VARS\tjob{0}\tenergy_dependance="{1}"\n'.format(job_number, energy_dependance)) - f.write('VARS\tjob{0}\tspectral_index="{1}"\n'.format(job_number, spectral_index)) - f.write('VARS\tjob{0}\tbinning_0="{1}"\n'.format(job_number, binning[0])) - 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}'.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])) - # f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) - # f.write('VARS\tjob{0}\tsigma_ratio="{1}"\n'.format(job_number, sig)) - # f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, 'False')) - # f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tfix_scale="{1}"\n'.format(job_number, 'False')) - # f.write('VARS\tjob{0}\tscale="{1}"\n'.format(job_number, 0)) - # f.write('VARS\tjob{0}\tscale_region="{1}"\n'.format(job_number, scale_region)) - # f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - # f.write('VARS\tjob{0}\tenergy="{1}"\n'.format(job_number, en)) - # f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) - # f.write('VARS\tjob{0}\tburnin="{1}"\n'.format(job_number, burnin)) - # f.write('VARS\tjob{0}\tnwalkers="{1}"\n'.format(job_number, nwalkers)) - # f.write('VARS\tjob{0}\tnsteps="{1}"\n'.format(job_number, nsteps)) - # f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) - # f.write('VARS\tjob{0}\tfix_mixing="{1}"\n'.format(job_number, fix_mixing)) - # f.write('VARS\tjob{0}\tno_bsm="{1}"\n'.format(job_number, no_bsm)) - # f.write('VARS\tjob{0}\trun_mcmc="{1}"\n'.format(job_number, run_mcmc)) - # f.write('VARS\tjob{0}\tastroDeltaGamma="{1}"\n'.format(job_number, astroDeltaGamma)) - # f.write('VARS\tjob{0}\tastroNorm="{1}"\n'.format(job_number, astroNorm)) - # f.write('VARS\tjob{0}\tconvNorm="{1}"\n'.format(job_number, convNorm)) - # f.write('VARS\tjob{0}\tmuonNorm="{1}"\n'.format(job_number, muonNorm)) - # f.write('VARS\tjob{0}\tpromptNorm="{1}"\n'.format(job_number, promptNorm)) - # f.write('VARS\tjob{0}\tdata="{1}"\n'.format(job_number, data)) - # f.write('VARS\tjob{0}\tast="{1}"\n'.format(job_number, ast)) - # f.write('VARS\tjob{0}\tplot_angles="{1}"\n'.format(job_number, plot_angles)) - # f.write('VARS\tjob{0}\tplot_elements="{1}"\n'.format(job_number, plot_elements)) - # f.write('VARS\tjob{0}\tseed="{1}"\n'.format(job_number, seed)) - # f.write('VARS\tjob{0}\tthreads="{1}"\n'.format(job_number, threads)) - # f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) - # f.write('VARS\tjob{0}\tmcmc_seed_type="{1}"\n'.format(job_number, mcmc_seed_type)) - # f.write('VARS\tjob{0}\tenergy_dependance="{1}"\n'.format(job_number, energy_dependance)) - # f.write('VARS\tjob{0}\tspectral_index="{1}"\n'.format(job_number, spectral_index)) - # f.write('VARS\tjob{0}\tbinning_0="{1}"\n'.format(job_number, binning[0])) - # 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 r in range(b_runs): + print 'run', r + 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])) + f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) + f.write('VARS\tjob{0}\tsigma_ratio="{1}"\n'.format(job_number, sig)) + f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, 'True')) + f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, frs[3])) + f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, frs[4])) + f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, frs[5])) + f.write('VARS\tjob{0}\tfix_scale="{1}"\n'.format(job_number, 'False')) + f.write('VARS\tjob{0}\tscale="{1}"\n'.format(job_number, 0)) + f.write('VARS\tjob{0}\tscale_region="{1}"\n'.format(job_number, scale_region)) + f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) + f.write('VARS\tjob{0}\tenergy="{1}"\n'.format(job_number, en)) + f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) + f.write('VARS\tjob{0}\tburnin="{1}"\n'.format(job_number, burnin)) + f.write('VARS\tjob{0}\tnwalkers="{1}"\n'.format(job_number, nwalkers)) + f.write('VARS\tjob{0}\tnsteps="{1}"\n'.format(job_number, nsteps)) + f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) + f.write('VARS\tjob{0}\tfix_mixing="{1}"\n'.format(job_number, fix_mixing)) + f.write('VARS\tjob{0}\tno_bsm="{1}"\n'.format(job_number, no_bsm)) + f.write('VARS\tjob{0}\trun_mcmc="{1}"\n'.format(job_number, run_mcmc)) + f.write('VARS\tjob{0}\tastroDeltaGamma="{1}"\n'.format(job_number, astroDeltaGamma)) + f.write('VARS\tjob{0}\tastroNorm="{1}"\n'.format(job_number, astroNorm)) + f.write('VARS\tjob{0}\tconvNorm="{1}"\n'.format(job_number, convNorm)) + f.write('VARS\tjob{0}\tmuonNorm="{1}"\n'.format(job_number, muonNorm)) + f.write('VARS\tjob{0}\tpromptNorm="{1}"\n'.format(job_number, promptNorm)) + f.write('VARS\tjob{0}\tdata="{1}"\n'.format(job_number, data)) + f.write('VARS\tjob{0}\tast="{1}"\n'.format(job_number, ast)) + f.write('VARS\tjob{0}\tplot_angles="{1}"\n'.format(job_number, plot_angles)) + f.write('VARS\tjob{0}\tplot_elements="{1}"\n'.format(job_number, plot_elements)) + f.write('VARS\tjob{0}\tseed="{1}"\n'.format(job_number, seed)) + f.write('VARS\tjob{0}\tthreads="{1}"\n'.format(job_number, threads)) + f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) + f.write('VARS\tjob{0}\tmcmc_seed_type="{1}"\n'.format(job_number, mcmc_seed_type)) + f.write('VARS\tjob{0}\tenergy_dependance="{1}"\n'.format(job_number, energy_dependance)) + f.write('VARS\tjob{0}\tspectral_index="{1}"\n'.format(job_number, spectral_index)) + f.write('VARS\tjob{0}\tbinning_0="{1}"\n'.format(job_number, binning[0])) + 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}\tbayes_tolerance="{1}"\n'.format(job_number, bayes_tolerance)) + f.write('VARS\tjob{0}\tplot_bayes="{1}"\n'.format(job_number, plot_bayes)) + f.write('VARS\tjob{0}\tbayes_eval_bin="{1}"\n'.format(job_number, r)) + job_number += 1 + + for frs in full_scan_mfr: + print frs + outchains = outchain_head + '/full_scan/{0}'.format(str(sig).replace('.', '_')) + if run_bayes_factor == 'True': + bayes_output = outchains + '/bayes_factor/' + outchains += 'mcmc_chain' + for r in range(b_runs): + print 'run', r + 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])) + f.write('VARS\tjob{0}\tmr2="{1}"\n'.format(job_number, frs[2])) + f.write('VARS\tjob{0}\tsigma_ratio="{1}"\n'.format(job_number, sig)) + f.write('VARS\tjob{0}\tfix_source_ratio="{1}"\n'.format(job_number, 'False')) + f.write('VARS\tjob{0}\tsr0="{1}"\n'.format(job_number, 0)) + f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, 0)) + f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, 0)) + f.write('VARS\tjob{0}\tfix_scale="{1}"\n'.format(job_number, 'False')) + f.write('VARS\tjob{0}\tscale="{1}"\n'.format(job_number, 0)) + f.write('VARS\tjob{0}\tscale_region="{1}"\n'.format(job_number, scale_region)) + f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) + f.write('VARS\tjob{0}\tenergy="{1}"\n'.format(job_number, en)) + f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) + f.write('VARS\tjob{0}\tburnin="{1}"\n'.format(job_number, burnin)) + f.write('VARS\tjob{0}\tnwalkers="{1}"\n'.format(job_number, nwalkers)) + f.write('VARS\tjob{0}\tnsteps="{1}"\n'.format(job_number, nsteps)) + f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) + f.write('VARS\tjob{0}\tfix_mixing="{1}"\n'.format(job_number, fix_mixing)) + f.write('VARS\tjob{0}\tno_bsm="{1}"\n'.format(job_number, no_bsm)) + f.write('VARS\tjob{0}\trun_mcmc="{1}"\n'.format(job_number, run_mcmc)) + f.write('VARS\tjob{0}\tastroDeltaGamma="{1}"\n'.format(job_number, astroDeltaGamma)) + f.write('VARS\tjob{0}\tastroNorm="{1}"\n'.format(job_number, astroNorm)) + f.write('VARS\tjob{0}\tconvNorm="{1}"\n'.format(job_number, convNorm)) + f.write('VARS\tjob{0}\tmuonNorm="{1}"\n'.format(job_number, muonNorm)) + f.write('VARS\tjob{0}\tpromptNorm="{1}"\n'.format(job_number, promptNorm)) + f.write('VARS\tjob{0}\tdata="{1}"\n'.format(job_number, data)) + f.write('VARS\tjob{0}\tast="{1}"\n'.format(job_number, ast)) + f.write('VARS\tjob{0}\tplot_angles="{1}"\n'.format(job_number, plot_angles)) + f.write('VARS\tjob{0}\tplot_elements="{1}"\n'.format(job_number, plot_elements)) + f.write('VARS\tjob{0}\tseed="{1}"\n'.format(job_number, seed)) + f.write('VARS\tjob{0}\tthreads="{1}"\n'.format(job_number, threads)) + f.write('VARS\tjob{0}\tlikelihood="{1}"\n'.format(job_number, likelihood)) + f.write('VARS\tjob{0}\tmcmc_seed_type="{1}"\n'.format(job_number, mcmc_seed_type)) + f.write('VARS\tjob{0}\tenergy_dependance="{1}"\n'.format(job_number, energy_dependance)) + f.write('VARS\tjob{0}\tspectral_index="{1}"\n'.format(job_number, spectral_index)) + f.write('VARS\tjob{0}\tbinning_0="{1}"\n'.format(job_number, binning[0])) + 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}\tbayes_tolerance="{1}"\n'.format(job_number, bayes_tolerance)) + f.write('VARS\tjob{0}\tplot_bayes="{1}"\n'.format(job_number, plot_bayes)) + f.write('VARS\tjob{0}\tbayes_eval_bin="{1}"\n'.format(job_number, r)) + job_number += 1 diff --git a/submitter/submit.sub b/submitter/submit.sub index e563924..e9e66bd 100644 --- a/submitter/submit.sub +++ b/submitter/submit.sub @@ -1,5 +1,5 @@ Executable = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/fr.py -Arguments = "--ast $(ast) --astroDeltaGamma $(astroDeltaGamma) --astroNorm $(astroNorm) --burnin $(burnin) --convNorm $(convNorm) --data $(data) --dimension $(dimension) --energy $(energy) --fix-mixing $(fix_mixing) --fix-scale $(fix_scale) --fix-source-ratio $(fix_source_ratio) --likelihood $(likelihood) --measured-ratio $(mr0) $(mr1) $(mr2) --muonNorm $(muonNorm) --no-bsm $(no_bsm) --nsteps $(nsteps) --nwalkers $(nwalkers) --outfile $(outfile) --plot-angles $(plot_angles) --plot-elements $(plot_elements) --promptNorm $(promptNorm) --run-mcmc $(run_mcmc) --scale $(scale) --scale-region $(scale_region) --seed $(seed) --sigma-ratio $(sigma_ratio) --source-ratio $(sr0) $(sr1) $(sr2) --threads $(threads) --likelihood $(likelihood) --mcmc-seed-type $(mcmc_seed_type) --energy-dependance $(energy_dependance) --spectral-index $(spectral_index) --binning $(binning_0) $(binning_1) $(binning_2) --fix-mixing-almost $(fix_mixing_almost) --run-bayes-factor $(run_bayes_factor) --bayes-bins $(bayes_bins) --bayes-output $(bayes_output) --bayes-live-points $(bayes_live_points) --plot-bayes $(plot_bayes)" +Arguments = "--ast $(ast) --astroDeltaGamma $(astroDeltaGamma) --astroNorm $(astroNorm) --burnin $(burnin) --convNorm $(convNorm) --data $(data) --dimension $(dimension) --energy $(energy) --fix-mixing $(fix_mixing) --fix-scale $(fix_scale) --fix-source-ratio $(fix_source_ratio) --likelihood $(likelihood) --measured-ratio $(mr0) $(mr1) $(mr2) --muonNorm $(muonNorm) --no-bsm $(no_bsm) --nsteps $(nsteps) --nwalkers $(nwalkers) --outfile $(outfile) --plot-angles $(plot_angles) --plot-elements $(plot_elements) --promptNorm $(promptNorm) --run-mcmc $(run_mcmc) --scale $(scale) --scale-region $(scale_region) --seed $(seed) --sigma-ratio $(sigma_ratio) --source-ratio $(sr0) $(sr1) $(sr2) --threads $(threads) --likelihood $(likelihood) --mcmc-seed-type $(mcmc_seed_type) --energy-dependance $(energy_dependance) --spectral-index $(spectral_index) --binning $(binning_0) $(binning_1) $(binning_2) --fix-mixing-almost $(fix_mixing_almost) --run-bayes-factor $(run_bayes_factor) --bayes-bins $(bayes_bins) --bayes-output $(bayes_output) --bayes-live-points $(bayes_live_points) --plot-bayes $(plot_bayes) --bayes-tolerance $(bayes_tolerance) --bayes-eval-bin $(bayes_eval_bin)" # All logs will go to a single file log = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/logs/job_$(Cluster).log @@ -17,7 +17,7 @@ getenv = True transfer_output_files = /data/user/smandalia/GolemTools/sources/GolemFit/scripts/flavour_ratio/submitter/metaouts/ request_memory = 30GB -request_cpus = 12 +request_cpus = 4 Universe = vanilla Notification = never -- cgit v1.2.3