refactor sensitivity/limit scripts

1 files changed, 51 insertions, 129 deletions

diff --git a/submitter/sens_dag.py b/submitter/sens_dag.py
index 2046efb..e60477c 100644
--- a/submitter/sens_dag.py
+++ b/submitter/sens_dag.py
@@ -3,34 +3,36 @@
 import os
 import numpy as np
 
-full_scan_mfr = [
-    # (1, 1, 1), (1, 2, 0)
+sources = [
+    (1, 2, 0),
+    (1, 0, 0),
+    (0, 1, 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),
+dims = [
+    3, 4, 5, 6, 7, 8
 ]
 
+textures = [
+    'OEU', 'OET', 'OUT'
+]
+
+datadir = '/data/user/smandalia/flavour_ratio/data/sensitivity'
+scratchdir = '/scratch/smandalia/flavour_ratio'
+
+prefix = ''
+# prefix = '_noprior'
+
+golemfitsourcepath = os.environ['GOLEMSOURCEPATH'] + '/GolemFit'
+condor_script = golemfitsourcepath + '/scripts/flavour_ratio/submitter/sens_submit.sub'
+
 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
+    stat_method = 'bayesian',
+    segments    = 10,
+    seed        = 26
 ))
 
 # MultiNest
@@ -40,31 +42,14 @@ GLOBAL_PARAMS.update(dict(
     # mn_live_points = 300,
     # mn_tolerance   = 0.1,
     mn_tolerance   = 0.3,
-    mn_output      = './mnrun'
+    mn_output      = scratchdir + '/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'
+    threads = 4,
+    binning = '6e4 1e7 20',
+    no_bsm  = 'False'
 ))
 
 # GolemFit
@@ -73,95 +58,32 @@ GLOBAL_PARAMS.update(dict(
     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']
+dagfile = 'dagman_SENS_{0}_{1}'.format(
+    GLOBAL_PARAMS['stat_method'], 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'
+dagfile += prefix + '.submit'
 
-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:
+with open(dagfile, '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
+    for dim in dims:
+        print 'dims', dim
+        of_d = datadir + '/DIM{0}/{1}'.format(dim, prefix)
+        for src in sources:
+            print 'source flavour', src
+            for tex in textures:
+                print 'texture', tex
+                for r in xrange(GLOBAL_PARAMS['segments']):
+                    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}\tsr0="{1}"\n'.format(job_number, src[0]))
+                    f.write('VARS\tjob{0}\tsr1="{1}"\n'.format(job_number, src[1]))
+                    f.write('VARS\tjob{0}\tsr2="{1}"\n'.format(job_number, src[2]))
+                    f.write('VARS\tjob{0}\ttexture="{1}"\n'.format(job_number, tex))
+                    f.write('VARS\tjob{0}\teval_segment="{1}"\n'.format(job_number, r))
+                    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}\tdatadir="{1}"\n'.format(job_number, of_d))
+                    job_number += 1
 
-    print 'dag file = {0}'.format(outfile)
+    print 'dag file = {0}'.format(dagfile)