From 01c77997f4212085a1cedc049e6c6bca98a5c1b6 Mon Sep 17 00:00:00 2001 From: shivesh Date: Tue, 10 Apr 2018 11:12:44 -0500 Subject: updates --- submitter/make_dag.py | 115 +++++++++++++++++++++++++------------------------- 1 file changed, 58 insertions(+), 57 deletions(-) (limited to 'submitter/make_dag.py') diff --git a/submitter/make_dag.py b/submitter/make_dag.py index 133819b..627e3ff 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, 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, 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) ] # MCMC run_mcmc = 'True' burnin = 1000 nsteps = 4000 -nwalkers = 70 +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' @@ -49,6 +49,7 @@ scale_region = "1E10" energy_dependance = 'spectral' spectral_index = -2 binning = [1e4, 1e7, 10] +fix_mixing = 'False' # Likelihood likelihood = 'golemfit' @@ -108,7 +109,7 @@ with open(outfile, 'w') as f: 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, 'False')) + 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)) @@ -131,47 +132,47 @@ with open(outfile, 'w') as f: f.write('VARS\tjob{0}\tbinning_2="{1}"\n'.format(job_number, binning[2])) job_number += 1 - for frs in full_scan_mfr: - print frs - outchains = outchain_head + '/full_scan/{0}/mcmc_chain'.format(str(sig).replace('.', '_')) - 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, 'False')) - 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])) - job_number += 1 + # for frs in full_scan_mfr: + # print frs + # outchains = outchain_head + '/full_scan/{0}/mcmc_chain'.format(str(sig).replace('.', '_')) + # 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])) + # job_number += 1 -- cgit v1.2.3