1 files changed, 0 insertions, 123 deletions
diff --git a/submitter/mcmc_dag.py b/submitter/mcmc_dag.py
deleted file mode 100644
index 1356965..0000000
--- a/submitter/mcmc_dag.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#! /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   = 250,
-    nsteps   = 1000,
-    nwalkers = 60,
-    seed     = 25,
-    mcmc_seed_type = 'uniform'
-))
-
-# FR
-dimension         = [6]
-# dimension         = [4, 5, 7, 8]
-# 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        = 'T13',
-    fix_mixing_almost = 'False',
-    fold_index        = 'True'
-))
-
-# 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_angles       = 'True',
-    plot_elements     = 'False',
-))
-
-outfile = 'dagman_FR_MCMC_{0}_{1}.submit'.format(GLOBAL_PARAMS['likelihood'],
-                                                 GLOBAL_PARAMS['fix_mixing'])
-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}/'.format(
-            GLOBAL_PARAMS['likelihood'], dim
-        )
-        for frs in fix_sfr_mfr:
-            print 'frs', frs
-            outchains = outchain_head + '/fix_ifr/' + '{0}/'.format(GLOBAL_PARAMS['fix_mixing'])
-            if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian':
-                outchains += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_'))
-            outchains += '{0}/'.format(GLOBAL_PARAMS['data'].lower())
-            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.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, outchains))
-            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.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, outchains))
-        #     job_number += 1
-
-    print 'dag file = {0}'.format(outfile)