diff options
| -rwxr-xr-x | plot_sens.py | 2 | ||||
| -rw-r--r-- | submitter/mcmc_dag.py | 44 | ||||
| -rw-r--r-- | submitter/mcmc_submit.sub | 2 | ||||
| -rw-r--r-- | utils/multinest.py | 4 |
4 files changed, 26 insertions, 26 deletions
diff --git a/plot_sens.py b/plot_sens.py index 2ecde12..a957fe5 100755 --- a/plot_sens.py +++ b/plot_sens.py @@ -353,7 +353,7 @@ def main(): elif args.run_method in fixed_angle_categ: plot_utils.plot_sens_fixed_angle_pretty( data = data, - outfile = baseoutfile + '/fixed_angle_pretty', + outfile = baseoutfile + '/fixed_angle_pretty_1108', outformat = ['png', 'pdf'], args = args, ) diff --git a/submitter/mcmc_dag.py b/submitter/mcmc_dag.py index 5a44411..1356965 100644 --- a/submitter/mcmc_dag.py +++ b/submitter/mcmc_dag.py @@ -8,8 +8,8 @@ full_scan_mfr = [ ] fix_sfr_mfr = [ - (1, 1, 1, 1, 2, 0), - (1, 1, 1, 1, 0, 0), + # (1, 1, 1, 1, 2, 0), + # (1, 1, 1, 1, 0, 0), (1, 1, 1, 0, 1, 0), # (1, 1, 1, 0, 0, 1), # (1, 1, 0, 1, 2, 0), @@ -44,7 +44,7 @@ GLOBAL_PARAMS.update(dict( scale_region = "1E10", energy_dependance = 'spectral', spectral_index = -2, - fix_mixing = 'T23', + fix_mixing = 'T13', fix_mixing_almost = 'False', fold_index = 'True' )) @@ -100,24 +100,24 @@ with open(outfile, 'w') as f: f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) job_number += 1 - for frs in full_scan_mfr: - print 'frs', frs - outchains = outchain_head + '/full/' - if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian': - outchains += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_')) - outchains += 'mcmc_chain' - f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) - f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) - 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}\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)) - for key in GLOBAL_PARAMS.iterkeys(): - f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) - f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) - job_number += 1 + # for frs in full_scan_mfr: + # print 'frs', frs + # outchains = outchain_head + '/full/' + # if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian': + # outchains += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_')) + # outchains += 'mcmc_chain' + # f.write('JOB\tjob{0}\t{1}\n'.format(job_number, condor_script)) + # f.write('VARS\tjob{0}\tdimension="{1}"\n'.format(job_number, dim)) + # 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}\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)) + # for key in GLOBAL_PARAMS.iterkeys(): + # f.write('VARS\tjob{0}\t{1}="{2}"\n'.format(job_number, key, GLOBAL_PARAMS[key])) + # f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outchains)) + # job_number += 1 print 'dag file = {0}'.format(outfile) diff --git a/submitter/mcmc_submit.sub b/submitter/mcmc_submit.sub index fa36250..f98049c 100644 --- a/submitter/mcmc_submit.sub +++ b/submitter/mcmc_submit.sub @@ -22,7 +22,7 @@ request_cpus = 1 Universe = vanilla Notification = never -+AccountingGroup="sanctioned.$ENV(USER)" +# +AccountingGroup="sanctioned.$ENV(USER)" # run on both SL5 and 6 # +WantRHEL6 = True # +WantSLC6 = False diff --git a/utils/multinest.py b/utils/multinest.py index 63437bc..e9eece9 100644 --- a/utils/multinest.py +++ b/utils/multinest.py @@ -79,8 +79,8 @@ def mn_evidence(mn_paramset, llh_paramset, asimov_paramset, args, fitter): fitter = fitter ) - prefix = './mnrun/DIM{0}/{1}_{2:>010}_'.format( - args.dimension, gen_identifier(args), np.random.randint(0, 2**30) + prefix = './mnrun/DIM{0}/{1}_{2}_{3:>010}_'.format( + args.dimension, args.likelihood, gen_identifier(args), np.random.randint(0, 2**30) ) make_dir(prefix) print 'Running evidence calculation for {0}'.format(prefix) |