# ////////////////////////////////////////////////////////////////////// # For each trial, generate two pseudo-data experiemnts (one for each # hierarchy), and for each find the best matching template in each of the # hierarchy hypothesis. # ////////////////////////////////////////////////////////////////////// results = {} for data_tag, data_normal in [('data_NMH',True),('data_IMH',False)]: results[data_tag] = {} # 0) get a random seed and store with the data results[data_tag]['seed'] = get_seed() # 1) get a pseudo data fmap from fiducial model (best fit vals of params). fmap = get_pseudo_data_fmap(template_maker, get_values(select_hierarchy(params, normal_hierarchy=data_normal)), seed=results[data_tag]['seed']) # 2) find max llh (and best fit free params) from matching pseudo data # to templates. for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]: physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag)) profile.info("start optimizer") llh_data = find_max_llh_bfgs(fmap,template_maker,params, minimizer_settings,args.save_steps,normal_hierarchy=hypo_normal) profile.info("stop optimizer") #Store the LLH data results[data_tag][hypo_tag] = llh_data
for itrial in xrange(1, args.ntrials + 1): profile.info("start trial %d" % itrial) logging.info(">" * 10 + "Running trial: %05d" % itrial + "<" * 10) # ////////////////////////////////////////////////////////////////////// # For each trial, generate two pseudo-data experiemnts (one for each # hierarchy), and for each find the best matching template in each of the # hierarchy hypothesis. # ////////////////////////////////////////////////////////////////////// results = {} for data_tag, data_normal in [("data_NMH", True), ("data_IMH", False)]: results[data_tag] = {} # 1) get a pseudo data fmap from fiducial model (best fit vals of params). fmap = get_pseudo_data_fmap(template_maker, get_values(select_hierarchy(params, normal_hierarchy=data_normal))) # 2) find max llh (and best fit free params) from matching pseudo data # to templates. for hypo_tag, hypo_normal in [("hypo_NMH", True), ("hypo_IMH", False)]: physics.info("Finding best fit for %s under %s assumption" % (data_tag, hypo_tag)) profile.info("start scan") llh_data = find_max_grid( fmap, template_maker, params, grid_settings, args.save_steps, normal_hierarchy=hypo_normal ) profile.info("stop scan") # Store the LLH data results[data_tag][hypo_tag] = llh_data
# For each trial, generate two pseudo-data experiemnts (one for each # hierarchy), and for each find the best matching template in each of the # hierarchy hypotheses. # ////////////////////////////////////////////////////////////////////// results = {} for data_tag, data_normal in [('data_NMH', True), ('data_IMH', False)]: results[data_tag] = {} # 0) get a random seed and store with the data results[data_tag]['seed'] = get_seed() logging.info(" RNG seed: %ld" % results[data_tag]['seed']) # 1) get a pseudo data fmap from fiducial model (best fit vals of params). fmap = get_pseudo_data_fmap(pseudo_data_template_maker, get_values( select_hierarchy( pseudo_data_settings['params'], normal_hierarchy=data_normal)), seed=results[data_tag]['seed'], chan=channel) # 2) find max llh (and best fit free params) from matching pseudo data # to templates. for hypo_tag, hypo_normal in [('hypo_NMH', True), ('hypo_IMH', False)]: physics.info("Finding best fit for %s under %s assumption" % (data_tag, hypo_tag)) with Timer() as t: llh_data = find_max_llh_bfgs(fmap, template_maker, template_settings['params'],
# ////////////////////////////////////////////////////////////////////// # For each trial, generate two pseudo-data experiemnts (one for each # hierarchy), and for each find the best matching template in each of the # hierarchy hypotheses. # ////////////////////////////////////////////////////////////////////// results = {} for data_tag, data_normal in [('data_NMH',True),('data_IMH',False)]: results[data_tag] = {} # 0) get a random seed and store with the data results[data_tag]['seed'] = get_seed() logging.info(" RNG seed: %ld"%results[data_tag]['seed']) # 1) get a pseudo data fmap from fiducial model (best fit vals of params). fmap = get_pseudo_data_fmap( pseudo_data_template_maker, get_values(select_hierarchy(pseudo_data_settings['params'], normal_hierarchy=data_normal)), seed=results[data_tag]['seed'],chan=channel) # 2) find max llh (and best fit free params) from matching pseudo data # to templates. for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]: physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag)) with Timer() as t: llh_data = find_max_llh_bfgs(fmap,template_maker,template_settings['params'], minimizer_settings,args.save_steps, normal_hierarchy=hypo_normal) profile.info("==> elapsed time for optimizer: %s sec"%t.secs) # Store the LLH data
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10) # ////////////////////////////////////////////////////////////////////// # For each trial, generate two pseudo-data experiemnts (one for each # hierarchy), and for each find the best matching template in each of the # hierarchy hypothesis. # ////////////////////////////////////////////////////////////////////// results = {} for data_tag, data_normal in [('data_NMH',True),('data_IMH',False)]: results[data_tag] = {} # 1) get a pseudo data fmap from fiducial model (best fit vals of params). fiducial_param_values = get_values(select_hierarchy(params, normal_hierarchy=data_normal)) fmap = get_pseudo_data_fmap(template_maker=template_maker, fiducial_params=fiducial_param_values, channel=fiducial_param_values['channel']) # 2) find max llh (and best fit free params) from matching pseudo data # to templates. for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]: physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag)) profile.info("start scan") llh_data = find_max_grid(fmap=fmap, template_maker=template_maker, params=params, grid_settings=grid_settings, save_steps=args.save_steps, normal_hierarchy=hypo_normal) profile.info("stop scan")
for itrial in xrange(1, args.ntrials + 1): profile.info("start trial %d" % itrial) logging.info(">" * 10 + "Running trial: %05d" % itrial + "<" * 10) # ////////////////////////////////////////////////////////////////////// # For each trial, generate two pseudo-data experiemnts (one for each # hierarchy), and for each find the best matching template in each of the # hierarchy hypothesis. # ////////////////////////////////////////////////////////////////////// results = {} for data_tag, data_normal in [('data_NMH', True), ('data_IMH', False)]: results[data_tag] = {} # 1) get a pseudo data fmap from fiducial model (best fit vals of params). fmap = get_pseudo_data_fmap( template_maker, get_values(select_hierarchy(params, normal_hierarchy=data_normal))) # 2) find max llh (and best fit free params) from matching pseudo data # to templates. for hypo_tag, hypo_normal in [('hypo_NMH', True), ('hypo_IMH', False)]: physics.info("Finding best fit for %s under %s assumption" % (data_tag, hypo_tag)) profile.info("start scan") llh_data = find_max_grid(fmap, template_maker, params, grid_settings, args.save_steps, normal_hierarchy=hypo_normal)