Fri Apr 13 22:00:22 CDT 2018

1 files changed, 122 insertions, 109 deletions

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