update submitter script

1 files changed, 85 insertions, 36 deletions

diff --git a/submitter/make_dag.py b/submitter/make_dag.py
index 849020e..ccd1fee 100644
--- a/submitter/make_dag.py
+++ b/submitter/make_dag.py
@@ -29,15 +29,40 @@ fix_sfr_mfr = [
     # (1, 2, 0, 1, 2, 0)
 ]
 
-sigmas = ['0.01']
-dimensions = [3]
-energy = [1e6]
-flat = False
-burnin = 20
+# MCMC
+run_mcmc = 'True'
+burnin   = 20
+nsteps   = 100
 nwalkers = 200
-nsteps = 100
-scales = "1E-20 1E-30"
-no_bsm = False
+seed     = 24
+threads  = 1
+
+# FR
+dimension    = [3]
+energy       = [1e6]
+likelihood   = 'gaussian'
+no_bsm       = 'False'
+sigma_ratio  = ['0.01']
+scale        = "1E-20 1E-30"
+scale_region = "1E10"
+
+# Nuisance
+astroDeltaGamma = 2.
+astroNorm       = 1.
+convNorm        = 1.
+muonNorm        = 1.
+promptNorm      = 0.
+
+# GolemFit
+aft    = 'hesespl'
+ast    = 'baseline'
+axs    = 'nom'
+data   = 'real'
+priors = 'uniform'
+
+# Plot
+plot_angles   = 'True'
+plot_elements = 'False'
 
 outfile = 'dagman_FR.submit'
 golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
@@ -45,14 +70,14 @@ condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/submit.su
 
 with open(outfile, 'w') as f:
     job_number = 1
-    for dim in dimensions:
+    for dim in dimension:
         print 'dimension', dim
         for en in energy:
             print 'energy {0:.0E}'.format(en)
 
             outchain_head = '/data/user/smandalia/flavour_ratio/data/DIM{0}/{1:.0E}'.format(dim, en)
 
-            for sig in sigmas:
+            for sig in sigma_ratio:
                 print 'sigma', sig
                 for frs in fix_sfr_mfr:
                     print frs
@@ -61,45 +86,69 @@ with open(outfile, 'w') as f:
                     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="{1}"\n'.format(job_number, sig))
+                    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}\tflat_llh="{1}"\n'.format(job_number, flat))
+                    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}\taft="{1}"\n'.format(job_number, aft))
+                    f.write('VARS\tjob{0}\tast="{1}"\n'.format(job_number, ast))
+                    f.write('VARS\tjob{0}\taxs="{1}"\n'.format(job_number, axs))
+                    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))
                     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="{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}\tdimension="{1}"\n'.format(job_number, dim))
-                #     f.write('VARS\tjob{0}\tenergy="{1}"\n'.format(job_number, en))
-                #     f.write('VARS\tjob{0}\tflat_llh="{1}"\n'.format(job_number, flat))
-                #     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))
-                #     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}\taft="{1}"\n'.format(job_number, aft))
+                    f.write('VARS\tjob{0}\tast="{1}"\n'.format(job_number, ast))
+                    f.write('VARS\tjob{0}\taxs="{1}"\n'.format(job_number, axs))
+                    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))
+                    job_number += 1