ctlearn_manager.utils.DL2_processing

---

ctlearn_manager.utils.DL2_processing.extract_cuts(args)

ctlearn_manager.utils.DL2_processing.get_energy_dependent_mask_data(DL2_file, pointing_table, pointing_alt_key, pointing_az_key, CTLearn, data, source_position, energy_key, gammaness_key, tri_model, reco_coord, theta_cut=True, cuts=<ctlearn_manager.utils.utils.Cuts object>)

Parameters:

• tri_model (CTLearnTriModelManager)

• cuts (Cuts)

ctlearn_manager.utils.DL2_processing.compute_eff_time(events, CTLearn, time_key)

ctlearn_manager.utils.DL2_processing.load_one_worker(args)

ctlearn_manager.utils.DL2_processing.process_gt_tuple(args)

class ctlearn_manager.utils.DL2_processing.DL2DataProcessor(DL2_files, CTLearn_TriModel_Manager, cuts=[<ctlearn_manager.utils.utils.Cuts object>], source_position=<SkyCoord (ICRS): (ra, dec) in deg     (83.6324, 22.0174)>, source_name='Crab Nebula', pointing_table='dl1/monitoring/telescope/pointing/tel_001', default_E_bins=<Quantity [ 0.02      ,  0.03169786,  0.05023773,  0.07962143,  0.12619147,             0.2       ,  0.31697864,  0.50237729,  0.79621434,  1.26191469,             2.        ,  3.16978638,  5.02377286,  7.96214341, 12.61914689,            20.        ] TeV>, e_edges=(-inf, inf), intensity_cut=80, global_gammaness_cut=0.0, workers=None, reco_field_suffix=None, reconstruction_method='CTLearn')

Bases: object

A class to process DL2 data and perform various analyses such as plotting theta^2 distributions and computing on-off counts.

Parameters:

• DL2_files (list[str])

• CTLearn_TriModel_Manager (CTLearnTriModelManager)

• cuts (list[Cuts])

• source_name (str)

• intensity_cut (int)

• global_gammaness_cut (float)

DL2_files

List of DL2 file paths to be processed.

Type:

list

CTLearnTriModelManager

An instance of CTLearnTriModelManager containing telescope information.

Type:

CTLearnTriModelManager

source_position

The sky coordinates of the source position. Default is the Crab Nebula.

Type:

SkyCoord

telescope_ids

List of telescope IDs from CTLearnTriModelManager.

Type:

list

telescope_names

List of telescope names from CTLearnTriModelManager.

Type:

list

stereo

Indicates if stereo mode is used.

Type:

bool

gammaness_cut

The gammaness cut value for event selection. Default is 0.9.

Type:

float

reconstruction_method

The method used for reconstruction. Default is “CTLearn”.

Type:

str

reco_field_suffix

Suffix for the reconstruction field, based on stereo mode.

Type:

str

telescope_location

The location of the telescope, if LST1 is in the telescope names.

Type:

EarthLocation

reco_directions

List of reconstructed sky directions.

Type:

list

pointings

List of pointing directions.

Type:

list

dl2s

List of loaded DL2 data.

Type:

list

dl2s_cuts

List of DL2 data after applying cuts.

Type:

list

__init__(self, DL2_files, CTLearnTriModelManager, gammaness_cut=0.9, source_position=SkyCoord.from_name("Crab")):

Initializes the DL2DataProcessor with the given parameters and processes the DL2 data.

process_DL2_data(self):

Processes the DL2 data files, applying cuts and computing sky positions.

plot_theta2_distribution(self, bins, n_off=5):

Plots the theta^2 distribution for the processed DL2 data.

compute_off_regions(self, pointing, n_off):

Computes the off-source regions for background estimation.

compute_eff_time(self, events):

Computes the effective observation time and elapsed time from the event data.

compute_on_off_counts(self, events, reco_coord, pointing_coord, n_off, theta2_cut=0.04*u.deg**2, gcut=0.5, E_min=0, E_max=100, I_min=None, I_max=None):

Computes the on-source and off-source counts, as well as the Li & Ma significance.

set_keys()

process_DL2_data()

load_processed_data()

plot_theta2_distribution(bins=25, n_off=5, output_file=None, cuts_index=0, t2_max=0.4)

compute_off_regions(pointing, n_off)

compute_on_off_counts_array_nul(events, on_sep, off_sep, theta2_cut, gcut, E_min=0, E_max=100)

compute_on_off_counts_array(events, on_sep, off_sep, theta2_cut, gcut, E_min=0, E_max=100)

compute_on_off_counts(events, reco_coord, pointing_coord, n_off, theta2_cut=<Quantity 0.04 deg2>, gcut=0.5, E_min=0, E_max=100, I_min=None, I_max=None)

plot_skymap(output_file=None, cuts_index=0, n_off=5)

optimize_cuts_on_crab(n_off=5, output_suffix='', max_gammaness_cut=1.0, max_theta2_cut=0.2, gcut_step=0.01, theta2_cut_step=0.001, E_bins=None)

Compute and store optimal gammaness/theta2 cuts for even and odd events for each energy bin.

static read_cuts_optimized_on_crab_from_csv(csv_filename)

Read optimal gammaness/theta2 cuts for even and odd events from a CSV file. Returns a dict with keys: ‘even’ and ‘odd’, each containing a dict with keys ‘gammaness’, ‘theta2’, ‘E_bins’. The bins are sorted by bin_index.

plot_cuts_optimized_on_crab(cuts_h5_file)

plot_sensitivity(n_off=5, ax=None, label='CTLearn', output_file=None, export_to_h5=None, import_from_h5=None, import_label=None, optimized_on_crab=False, output_suffix='')

Parameters:

• export_to_h5 (str)

• import_from_h5 (str)

• import_label (str)

• optimized_on_crab (bool)

• output_suffix (str)

plot_PSF(n_off=5, ax=None, label='CTLearn', output_file=None, plot_MC=[], export_to_h5=None, import_from_h5=None, import_label=None, ylim=(0, 0.6))

Parameters:

• plot_MC (list[str])

• export_to_h5 (str)

• import_from_h5 (str)

• import_label (str)

get_gammaness_cuts_for_efficiencies(MC_dl2, efficiencies, E_min=None, E_max=None, I_min=None, I_max=None)

get_efficiency_for_gamaness_cuts(MC_dl2, gammaness_cuts, E_min=None, E_max=None, I_min=None, I_max=None)

plot_bkg_discrimination_capability(n_off=5, axs=None, label='CTLearn', output_file=None)

plot_excess_vs_background_rates(n_off=5, output_file=None)

plot_excess_and_background_rates_vs_energy(n_off=5, output_file=None, cuts_index=0)

plot_gammaness_distribution(output_file=None)

plot_energy_distribution(output_file=None, bins=None, gammaness_cut=0.9)

plot_everything(output_directory, suffix='')

Parameters:

• output_directory (str)

• suffix (str)

produce_dl3(output_dl3_directory, source_name='Crab', source_ra=<Quantity 83.633 deg>, source_dec=<Quantity 22.01 deg>, cuts_index=0, overwrite=False, dl3_file_pattern='LST-1.Run*.dl3.fits')

Parameters:

• output_dl3_directory (str)

• source_name (str)

• source_ra (float)

• source_dec (float)

• cuts_index (int)

• overwrite (bool)

• dl3_file_pattern (str)