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#! /usr/bin/env python
import os
import numpy as np
full_scan_mfr = [
# (1, 1, 1), (1, 2, 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),
]
GLOBAL_PARAMS = {}
# Bayes Factor
sens_eval_bin = 'true' # set to 'all' to run normally
GLOBAL_PARAMS.update(dict(
sens_run = 'True',
run_method = 'fixed_angle', # full, fixed_angle, corr_angle
stat_method = 'bayesian',
sens_bins = 10,
seed = None
))
# MultiNest
GLOBAL_PARAMS.update(dict(
mn_live_points = 1000,
# mn_live_points = 500,
# mn_live_points = 300,
# mn_tolerance = 0.1,
mn_tolerance = 0.3,
mn_output = './mnrun'
))
# FR
# dimension = [6]
# dimension = [3, 6]
dimension = [3, 4, 5, 6, 7, 8]
GLOBAL_PARAMS.update(dict(
threads = 1,
binning = '6e4 1e7 20',
no_bsm = 'False',
scale_region = "1E10",
energy_dependance = 'spectral',
spectral_index = -2,
fix_mixing = 'None',
fix_mixing_almost = 'False',
fold_index = 'True',
save_measured_fr = 'False',
output_measured_fr = './frs/'
))
# Likelihood
GLOBAL_PARAMS.update(dict(
likelihood = 'golemfit',
sigma_ratio = '0.01'
))
# GolemFit
GLOBAL_PARAMS.update(dict(
ast = 'p2_0',
data = 'real'
))
# Plot
GLOBAL_PARAMS.update(dict(
plot_statistic = 'True'
))
outfile = 'dagman_FR_SENS_{0}_{1}_{2}_{3}'.format(
GLOBAL_PARAMS['stat_method'], GLOBAL_PARAMS['run_method'],
GLOBAL_PARAMS['likelihood'], GLOBAL_PARAMS['data']
)
# outfile += '_seed2'
# outfile += '_tol03'
# outfile += '_NULL'
# outfile += '_prior'
# outfile += '_strictprior'
# outfile += '_noprior'
outfile += '.submit'
golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/sens_submit.sub'
if sens_eval_bin.lower() != 'all':
if GLOBAL_PARAMS['run_method'].lower() == 'corr_angle':
raise NotImplementedError
sens_runs = GLOBAL_PARAMS['sens_bins']**2
else:
sens_runs = GLOBAL_PARAMS['sens_bins'] + 1
else: sens_runs = 1
with open(outfile, 'w') as f:
job_number = 1
for dim in dimension:
print 'dimension', dim
outchain_head = '/data/user/smandalia/flavour_ratio/data/{0}/DIM{1}'.format(
GLOBAL_PARAMS['likelihood'], dim
)
for frs in fix_sfr_mfr:
print 'frs', frs
output = outchain_head + '/fix_ifr/'
if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian':
output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_'))
# output += 'seed2/'
# output += 'mn_noverlap/'
# output += 'tol_03/'
# output += 'prior/'
# output += 'strictprior/'
# output += 'noprior/'
for r in xrange(sens_runs):
print 'run', r
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, 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]))
if sens_eval_bin.lower() != 'all':
f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r))
else:
f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, 'all'))
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, output))
job_number += 1
# break
# for frs in full_scan_mfr:
# print 'frs', frs
# output = outchain_head + '/full/'
# if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian':
# output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_'))
# for r in xrange(sens_runs):
# print 'run', r
# 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))
# if sens_eval_bin.lower() != 'all':
# f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r))
# else:
# f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, 'all'))
# 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, output))
# job_number += 1
print 'dag file = {0}'.format(outfile)
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