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#! /usr/bin/env python
import os
import numpy as np
full_scan_mfr = [
# (1, 1, 1), (1, 0, 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 = {}
# MCMC
GLOBAL_PARAMS.update(dict(
run_mcmc = 'True',
burnin = 2500,
nsteps = 10000,
nwalkers = 60,
seed = 'None',
mcmc_seed_type = 'uniform'
))
# FR
dimension = [3, 6]
GLOBAL_PARAMS.update(dict(
threads = 1,
binning = '1e4 1e7 5',
no_bsm = 'False',
scale_region = "1E10",
energy_dependance = 'spectral',
spectral_index = -2,
fix_mixing = 'False',
fix_mixing_almost = 'False'
))
# Likelihood
GLOBAL_PARAMS.update(dict(
likelihood = 'gaussian',
sigma_ratio = '0.01'
))
# GolemFit
GLOBAL_PARAMS.update(dict(
ast = 'p2_0',
data = 'real'
))
# Plot
GLOBAL_PARAMS.update(dict(
plot_angles = 'True',
plot_elements = 'False',
))
outfile = 'dagman_FR_MCMC.submit'
golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/mcmc_submit.sub'
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}/SI_{2}'.format(
GLOBAL_PARAMS['likelihood'], dim, GLOBAL_PARAMS['spectral_index']
)
for frs in fix_sfr_mfr:
print 'frs', frs
outchains = outchain_head + '/fix_ifr/'
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, 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]))
for key in GLOBAL_PARAMS.keys():
f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, GLOBAL_PARAMS[key]))
f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outfile))
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.keys():
f.write('VARS\tjob{0}\tmr0="{1}"\n'.format(job_number, GLOBAL_PARAMS[key]))
f.write('VARS\tjob{0}\toutfile="{1}"\n'.format(job_number, outfile))
job_number += 1
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