1 files changed, 134 insertions, 0 deletions
diff --git a/submitter/sens_dag.py b/submitter/sens_dag.py
new file mode 100644
index 0000000..2cecfe9
--- /dev/null
+++ b/submitter/sens_dag.py
@@ -0,0 +1,134 @@
+#! /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
+GLOBAL_PARAMS.update(dict(
+    sens_run      = 'True',
+    run_method    = 'full',
+    stat_method   = 'bayesian',
+    sens_bins     = 10,
+    sens_eval_bin = 'all' # set to 'all' to run normally
+))
+
+# MultiNest
+GLOBAL_PARAMS.update(dict(
+    mn_live_points = 400,
+    mn_tolerance   = 0.01,
+    mn_output      = './mnrun'
+))
+
+# 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_statistic = 'True'
+))
+
+outfile = 'dagman_FR_SENS.submit'
+golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
+condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/sens_submit.sub'
+
+if GLOBAL_PARAMS['sens_eval_bin'].lower() != 'all':
+    if GLOBAL_PARAMS['run_method'].lower() == 'corr_angle':
+        sens_runs = GLOBAL_PARAMS['sens_bins']**2
+    else:
+        sens_runs = GLOBAL_PARAMS['sens_bins']
+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}/SI_{2}'.format(
+            GLOBAL_PARAMS['likelihood'], dim, GLOBAL_PARAMS['spectral_index']
+        )
+        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 += 'fr_stat'
+            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]))
+                f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r))
+                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
+            output = outchain_head + '/full/'
+            if GLOBAL_PARAMS['likelihood'].lower() == 'gaussian':
+                output += '{0}/'.format(str(GLOBAL_PARAMS['sigma_ratio']).replace('.', '_'))
+            output += 'fr_stat'
+            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))
+                f.write('VARS\tjob{0}\tsens_eval_bin="{1}"\n'.format(job_number, r))
+                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