Mon 8 Apr 15:15:12 CDT 2019

2 files changed, 183 insertions, 0 deletions

diff --git a/submitter/out b/submitter/out
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/submitter/out
diff --git a/submitter/sens_dag_source.py b/submitter/sens_dag_source.py
new file mode 100644
index 0000000..bdb5924
--- /dev/null
+++ b/submitter/sens_dag_source.py
@@ -0,0 +1,183 @@
+#! /usr/bin/env python
+
+import os
+import numpy as np
+
+full_scan_mfr = [
+    # (1, 1, 1), (1, 2, 0)
+]
+
+bins = 5
+binning = np.linspace(0, 1, bins)
+grid = np.dstack(np.meshgrid(binning, binning)).reshape(bins*bins, 2)
+sources = []
+for x in grid:
+    if x[0]+x[1] > 1:
+        continue
+    sources.append([x[0], x[1], 1-x[0]-x[1]])
+
+# 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),
+# ]
+fix_sfr_mfr = []
+for s in sources:
+    fix_sfr_mfr.append((1, 1, 1, s[0], s[1], s[2]))
+print 'fix_sfr_mfr', fix_sfr_mfr
+print 'len(fix_sfr_mfr)', len(fix_sfr_mfr)
+
+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 = 600,
+    mn_live_points = 100,
+    # 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 += '_sourcescan'
+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/'
+            output += 'sourcescan/'
+            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)