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-rw-r--r--submitter/make_dag.py241
1 files changed, 0 insertions, 241 deletions
diff --git a/submitter/make_dag.py b/submitter/make_dag.py
deleted file mode 100644
index 0e41c9a..0000000
--- a/submitter/make_dag.py
+++ /dev/null
@@ -1,241 +0,0 @@
-#! /usr/bin/env python
-
-import os
-import numpy as np
-
-a_fr = (1, 2, 0)
-b_fr = (1, 0, 0)
-c_fr = (0, 1, 0)
-d_fr = (0, 0, 1)
-e_fr = (1, 1, 1)
-f_fr = (2, 1, 0)
-g_fr = (1, 1, 0)
-
-full_scan_mfr = [
- # (1, 1, 1), (1, 1, 0)
-]
-
-fix_sfr_mfr = [
- (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),
- # (1, 1, 0, 1, 0, 0),
- # (1, 1, 0, 0, 1, 0),
- # (1, 0, 0, 1, 0, 0),
- # (0, 1, 0, 0, 1, 0),
- # (1, 2, 0, 1, 2, 0),
- # (1, 2, 0, 0, 1, 0),
-]
-
-# MCMC
-run_mcmc = 'True'
-burnin = 2500
-nsteps = 10000
-nwalkers = 60
-seed = 'None'
-threads = 1
-mcmc_seed_type = 'uniform'
-
-# FR
-dimension = [3, 6]
-energy = [1e6]
-no_bsm = 'False'
-sigma_ratio = ['0.01']
-scale = "1E-20 1E-30"
-scale_region = "1E10"
-energy_dependance = 'spectral'
-spectral_index = -2
-binning = [1e4, 1e7, 5]
-fix_mixing = 'False'
-fix_mixing_almost = 'False'
-
-# Likelihood
-likelihood = 'gaussian'
-
-# Nuisance
-convNorm = 1.
-promptNorm = 0.
-muonNorm = 1.
-astroNorm = 6.9
-astroDeltaGamma = 2.5
-
-# GolemFit
-ast = 'p2_0'
-data = 'real'
-
-# Bayes Factor
-run_bayes_factor = 'False'
-run_angles_limit = 'False'
-run_angles_correlation = 'False'
-bayes_bins = 100
-bayes_live_points = 3000
-bayes_tolerance = 0.01
-bayes_eval_bin = 'all' # set to 'all' to run normally
-
-# Plot
-plot_angles = 'True'
-plot_elements = 'False'
-plot_bayes = 'False'
-plot_angles_limit = 'False'
-
-outfile = 'dagman_FR.submit'
-golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
-condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/submit.sub'
-
-if bayes_eval_bin != 'all':
- if run_angles_correlation == 'True':
- b_runs = bayes_bins**2
- else:
- b_runs = bayes_bins
-else: b_runs = 1
-
-with open(outfile, 'w') as f:
- job_number = 1
- for dim in dimension:
- print 'dimension', dim
- for en in energy:
- print 'energy {0:.0E}'.format(en)
-
- if energy_dependance == 'mono':
- outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}/{2:.0E}'.format(likelihood, dim, en)
- elif energy_dependance == 'spectral':
- outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}/SI_{2}'.format(likelihood, dim, spectral_index)
-
- bayes_output = 'None'
- angles_lim_output = 'None'
- angles_corr_output = 'None'
- for sig in sigma_ratio:
- print 'sigma', sig
- for frs in fix_sfr_mfr:
- print frs
- outchains = outchain_head + '/fix_ifr/{0}/'.format(str(sig).replace('.', '_'))
- if run_bayes_factor == 'True':
- bayes_output = outchains + '/bayes_factor/'
- if run_angles_limit == 'True':
- angles_lim_output = outchains + '/angles_limit/'
- if run_angles_correlation == 'True':
- angles_corr_output = outchains + '/angles_corr/'
- 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, '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}\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))
- # f.write('VARS\tjob{0}\trun_angles_limit="{1}"\n'.format(job_number, run_angles_limit))
- # f.write('VARS\tjob{0}\tangles_lim_output="{1}"\n'.format(job_number, angles_lim_output))
- # f.write('VARS\tjob{0}\tplot_angles_limit="{1}"\n'.format(job_number, plot_angles_limit))
- # f.write('VARS\tjob{0}\trun_angles_correlation="{1}"\n'.format(job_number, run_angles_correlation))
- # f.write('VARS\tjob{0}\tangles_corr_output="{1}"\n'.format(job_number, angles_corr_output))
- 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/'
- if run_angles_limit == 'True':
- angles_lim_output = outchains + '/angles_limit/'
- if run_angles_correlation == 'True':
- angles_corr_output = outchains + '/angles_corr/'
- 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}\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))
- f.write('VARS\tjob{0}\trun_angles_limit="{1}"\n'.format(job_number, run_angles_limit))
- f.write('VARS\tjob{0}\tangles_lim_output="{1}"\n'.format(job_number, angles_lim_output))
- f.write('VARS\tjob{0}\tplot_angles_limit="{1}"\n'.format(job_number, plot_angles_limit))
- f.write('VARS\tjob{0}\trun_angles_correlation="{1}"\n'.format(job_number, run_angles_correlation))
- f.write('VARS\tjob{0}\tangles_corr_output="{1}"\n'.format(job_number, angles_corr_output))
- job_number += 1
generated by cgit v1.2.3 (git 2.25.1) at 2025-12-17 21:07:03 +0000