def process(config_file, binfile):
# Setup logging.
log = logging.getLogger(__name__)
start_time = time.time()
# Load config from file.
config = utils.load_config(config_file)
nominal_conf = {}
nominal_conf['alpha'] = [0.55, 1.0]
nominal_conf['missing_mass'] = [0.0, 5.0]
nominal_conf['p_mes'] = [0.35, 2.0]
# Load entire dataset, this
# should only be done once
# because it's 1.5 GB at load time.
data = utils.load_dataset(config)
if binfile is not None:
with open(binfile, 'rb') as bfile:
bins = pickle.load(bfile)
else:
bins = setup_binning(config, data)
kin_limits = find_kinematic_limits_in_bins(data, bins)
kin_limits.to_csv('kinematic_limits_mc.csv', index = False)
def process(config_file, samples):
# Setup logging.
log = logging.getLogger(__name__)
start_time = time.time()
# Load config from file.
config = utils.load_config(config_file)
# Load entire dataset, this
# should only be done once
# because it's 1.5 GB at load time.
data = utils.load_dataset(config)
# Applying nominal cuts to get the subset
# of events that I consider good when
# using the "best" cut values.
nominal_filter = utils.build_filter(data)
nominal_data = utils.build_dataframe(data, nominal_filter)
# Randomize the sectors to test
# if we can at least get the same
# answer.
utils.randomize_sector(data)
varfile = os.path.dirname(__file__) + '/../../variations.json'
variations = load_variations(varfile)
# Use quantile binning to get integrated bins
# for the axes listed in the configuration.
bins = setup_binning(config, nominal_data)
# Calculate the results for the nominal subset of data.
results = {}
results['nominal'] = utils.get_results(nominal_data, bins, config)
# Calculate the results for each sector.
for sector in range(1, 7):
sector_data = data[data['sector'] == sector]
for imc in range(samples):
var_time = time.time()
log.info('Doing sector {}'.format(sector))
random_filter = utils.get_random_config(sector_data, variations)
random_data = utils.build_dataframe(sector_data, random_filter)
sect_result = utils.get_results(random_data, bins, config)
elapsed_time = time.time() - var_time
log.info('Elapsed time %.3f' % elapsed_time)
output_filename = str(config['database_path'] +
'phi/random/sector_' + str(sector) +
'_{}.csv'.format(imc))
sect_result.to_csv(output_filename, index=False)
exe_time = time.time() - start_time
log.info('Finished execution in %.3f seconds.' % exe_time)
def process(config_file):
# Setup logging.
log = logging.getLogger(__name__)
start_time = time.time()
# Load config from file.
config = utils.load_config(config_file)
nominal_conf = {}
#nominal_conf['alpha'] = [0.55, 1.0]
#nominal_conf['missing_mass'] = [0.0, 5.0]
nominal_conf['p_mes'] = [0.35, 1.8]
# Load entire dataset, this
# should only be done once
# because it's 1.5 GB at load time.
data = utils.load_dataset(config)
#data = data.dropna(how='any')
print(data.info())
# Applying nominal cuts to get the subset
# of events that I consider good when
# using the "best" cut values.
#nominal_filter = utils.build_filter(data, nominal_conf)
#nominal_data = utils.build_dataframe(data, nominal_filter)
# Use quantile binning to get integrated bins
# for the axes listed in the configuration.
#bins = setup_binning(config, nominal_data)
bins = setup_binning(config, data)
with open('binning_mc.pkl', 'wb') as binf:
pickle.dump(bins, binf)
#kin_limits = find_kinematic_limits_in_bins(data, bins)
#kin_limits.to_csv('kinematic_limits_mc.csv', index = False)
# Calculate the results for the nominal subset of data.
results = utils.get_results(data, bins, config)
results.to_csv(config['output_filename'], index=False)
'dist_ecv_min': (-1.1, -0.9),
'dist_ecv_max': (0.9, 1.1),
'dist_ecw_min': (-1.1, -0.9),
'dist_ecw_max': (0.9, 1.1),
'dist_ec_edep_min': (-1.1, -0.9),
'dist_ec_edep_max': (0.9, 1.1),
'dist_vz_min': (-1.1, -0.9),
'dist_vz_max': (0.9, 1.1),
'missing_mass_min': (0.0, 1.75),
'p_mes_min': (0.3, 0.4),
'p_mes_max': (1.6, 1.8)
}
# Load the configuration file and entire
# dataset (once).
config = utils.load_config(args.config)
data = utils.load_dataset(config)
# Nominal data to get binning
nominal_filter = utils.build_filter(data)
nominal_data = utils.build_dataframe(data, nominal_filter)
bins = setup_binning(config, nominal_data)
objective_fn = partial(process_par_set, data=data, config=config)
opt = BayesianOptimization(f=objective_fn,
pbounds=parameter_bounds,
random_state=1)
opt.maximize(init_points=args.init_points, n_iter=args.n_iter)
def process(config_file):
# Setup logging.
log = logging.getLogger(__name__)
start_time = time.time()
# Load config from file.
config = utils.load_config(config_file)
# Load entire dataset, this
# should only be done once
# because it's 1.5 GB at load time.
data = utils.load_dataset(config)
utils.randomize_sector(data)
# Applying nominal cuts to get the subset
# of events that I consider good when
# using the "best" cut values.
if args.bayes_opt_pars is not None:
log.info("Using Bayesian Optimized parameters for nominal.")
with open(args.bayes_opt_pars, 'rb') as f:
bayes_pars = pickle.load(f)
params = {str(k): float(v) for k, v in bayes_pars['params'].items()}
bayes_conf = build_bayesian_optimized_config(**params)
nominal_filter = utils.build_filter(data, bayes_conf)
else:
nominal_filter = utils.build_filter(data)
nominal_data = utils.build_dataframe(data, nominal_filter)
# Use quantile binning to get integrated bins
# for the axes listed in the configuration.
bins = setup_binning(config, nominal_data)
# Calculate the results for the nominal subset of data.
results = {}
results['nominal'] = utils.get_results(nominal_data, bins, config)
# Calculate the results for each sector.
for sector in range(1, 7):
var_time = time.time()
log.info('Doing sector {}'.format(sector))
sector_data = nominal_data[nominal_data['sector'] == sector]
sect_result = utils.get_results(sector_data, bins, config)
elapsed_time = time.time() - var_time
log.info('Elapsed time %.3f' % elapsed_time)
output_filename = str(config['database_path'] + 'phi/sector_' +
str(sector) + '.csv')
sect_result.to_csv(output_filename, index=False)
del nominal_data
# Define variations to consider. These
# are the systematics that are applied.
variations = load_variations(config['variation_file'])
for par in variations.keys():
results[par] = {}
for index in variations[par].keys():
var_time = time.time()
log.info(
'Doing %.3f < %s < %.3f' %
(variations[par][index][0], par, variations[par][index][1]))
# get these cut values
temp_dict = {}
temp_dict[par] = variations[par][index]
# get data
temp_filter = utils.build_filter(data, temp_dict)
temp_data = utils.build_dataframe(data, temp_filter)
results[par][index] = utils.get_results(temp_data, bins, config)
del temp_data
end_var_time = time.time() - var_time
log.info('Elapsed time %.3f' % end_var_time)
# Using all variations, systematic
# uncertainties are added to the dataframe.
systematic_sources = assign_systematics(results)
with open(config['systematics_file'], 'wb') as outputfile:
pickle.dump(systematic_sources, outputfile)
#pickle.dump(systematic_sources, config['systematics_file'])
# Write results to file.
results['nominal'].to_csv(config['output_filename'], index=False)
# Write other results too.
dont_write = ['sector'.format(s) for s in range(1, 7)]
dont_write.append('nominal')
for key in results.keys():
if key not in dont_write:
for conf in results[key]:
output_filename = str(config['database_path'] +
'phi/variation_' + key + '_' +
str(conf) + '.csv')
results[key][conf].to_csv(output_filename, index=False)
exe_time = time.time() - start_time
log.info('Finished execution in %.3f seconds.' % exe_time)