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| author | shivesh <s.p.mandalia@qmul.ac.uk> | 2018-04-09 23:14:56 +0100 |
|---|---|---|
| committer | shivesh <s.p.mandalia@qmul.ac.uk> | 2018-04-09 23:14:56 +0100 |
| commit | 970277d845ff165c4a81ba7772c5bba001eb0e21 (patch) | |
| tree | 1176e78a05107512dd6f33b2417fb5a24516a229 /submitter/make_dag.py | |
| parent | bc07035994fa8630ea6ef14fea87287330851244 (diff) | |
| download | GolemFlavor-970277d845ff165c4a81ba7772c5bba001eb0e21.tar.gz GolemFlavor-970277d845ff165c4a81ba7772c5bba001eb0e21.zip | |
add spectral index implementation
Diffstat (limited to 'submitter/make_dag.py')
| -rw-r--r-- | submitter/make_dag.py | 27 |
1 files changed, 20 insertions, 7 deletions
diff --git a/submitter/make_dag.py b/submitter/make_dag.py index 63a94d7..f5a06dd 100644 --- a/submitter/make_dag.py +++ b/submitter/make_dag.py @@ -39,13 +39,16 @@ threads = 12 mcmc_seed_type = 'uniform' # FR -dimension = [3] -energy = [1e6] -likelihood = 'gaussian' -no_bsm = 'False' -sigma_ratio = ['0.01'] -scale = "1E-20 1E-30" -scale_region = "1E10" +dimension = [3] +energy = [1e6] +likelihood = 'gaussian' +no_bsm = 'False' +sigma_ratio = ['0.01'] +scale = "1E-20 1E-30" +scale_region = "1E10" +energy_dependance = 'spectral' +spectral_index = -2 +binning = [4, 7, 51] # Likelihood likelihood = 'golemfit' @@ -118,6 +121,11 @@ with open(outfile, 'w') as f: 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: @@ -158,4 +166,9 @@ with open(outfile, 'w') as f: 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 |