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#! /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, 0, 1, 2, 0),
# (1, 2, 0, 1, 2, 0),
# (1, 1, 1, 1, 0, 0),
# (1, 1, 0, 1, 0, 0),
# (1, 0, 0, 1, 0, 0),
# (1, 1, 1, 0, 1, 0),
# (1, 1, 0, 0, 1, 0),
# (0, 1, 0, 0, 1, 0),
# (1, 2, 0, 0, 1, 0),
# (1, 1, 1, 0, 0, 1),
]
# MCMC
run_mcmc = 'False'
burnin = 500
nsteps = 2000
nwalkers = 60
seed = 24
threads = 1
mcmc_seed_type = 'uniform'
# FR
dimension = [6]
energy = [1e6]
likelihood = 'golemfit'
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 = 'golemfit'
# 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 = 'True'
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 = 'False'
outfile = 'dagman_FR_angles_limit.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:
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'
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/'
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}\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))
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))
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/'
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))
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))
job_number += 1
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