diff options
Diffstat (limited to 'submitter/sens_dag_source.py')
| -rw-r--r-- | submitter/sens_dag_source.py | 183 |
1 files changed, 0 insertions, 183 deletions
diff --git a/submitter/sens_dag_source.py b/submitter/sens_dag_source.py deleted file mode 100644 index bdb5924..0000000 --- a/submitter/sens_dag_source.py +++ /dev/null @@ -1,183 +0,0 @@ -#! /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) |