Production Configuration

Modules provide functionality to configure a simulation production. This includes the derivation of energy, viewcone radius, and core scatter ranges and the calculation of the number of events to be simulated given a pre-determined metric.

calculate_statistical_uncertainties_grid_point

Evaluate statistical uncertainties from DL2 MC event files.

class production_configuration.calculate_statistical_uncertainties_grid_point.StatisticalUncertaintyEvaluator(file_path: str, metrics: dict[str, float], grid_point: tuple[float, float, float, float, float] | None = None)

Evaluate statistical uncertainties for a metric at a point in the observational parameter grid.

Parameters:

file_pathstr

Path to the DL2 MC event file.

metricsdict

Dictionary of metrics to evaluate.

grid_pointtuple, optional

Grid point (energy, azimuth, zenith, NSB, offset).

calculate_energy_estimate()

Calculate the uncertainties in energy estimation.

Returns:

float

The calculated uncertainty for energy estimation.

calculate_energy_threshold(requested_eff_area_fraction=0.1)

Calculate the energy threshold where the effective area exceeds 10% of its maximum value.

Returns:

float

Energy threshold value.

calculate_max_error_for_effective_area()

Calculate the maximum relative uncertainty for effective area within the validity range.

Returns:

max_errorfloat

Maximum relative error.

calculate_metrics()

Calculate all defined metrics as specified in self.metrics and store results.

calculate_overall_metric(metric='average')

Calculate an overall metric for the statistical uncertainties.

Parameters:

metricstr

The metric to calculate (‘average’, ‘maximum’).

Returns:

float

The overall metric value.

calculate_uncertainty_effective_area()

Calculate the uncertainties on the effective collection area.

Returns:

dict

Dictionary with uncertainties for the file.

compute_efficiency_and_uncertainties(reconstructed_event_counts, simulated_event_counts)

Compute reconstructed event efficiency and its uncertainty assuming binomial distribution.

Parameters:

reconstructed_event_countsarray with units

Histogram counts of reconstructed events.

simulated_event_countsarray with units

Histogram counts of simulated events.

Returns:

efficienciesarray

Array of calculated efficiencies.

relative_uncertaintiesarray

Array of relative uncertainties.

compute_reconstructed_event_histogram(event_energies_reco, bin_edges)

Compute histogram of events as function of reconstructed energy.

Parameters:

event_energies_recoarray

Array of reconstructed energy per event.

bin_edgesarray

Array of energy bin edges.

Returns:

reconstructed_event_histogramarray

Histogram of reconstructed events.

create_energy_bin_edges()

Create unique energy bin edges.

Returns:

bin_edgesarray

Array of unique energy bin edges.

load_data_from_file(file_path)

Load data from the DL2 MC event file and return dictionaries with units.

Returns:

dict

Dictionary containing data from the DL2 MC event file with units.

derive_corsika_limits

Derive CORSIKA limits from a reduced event data file.

production_configuration.derive_corsika_limits.compute_lower_energy_limit(histograms, loss_fraction)

Compute the lower energy limit in TeV based on the event loss fraction.

Parameters:

histogramsSimtelIOEventHistograms

Histograms.

loss_fractionfloat

Fraction of events to be lost.

Returns:

astropy.units.Quantity

Lower energy limit.

production_configuration.derive_corsika_limits.compute_upper_radius_limit(histograms, loss_fraction)

Compute the upper radial distance based on the event loss fraction.

Parameters:

histogramsSimtelIOEventHistograms

Histograms.

loss_fractionfloat

Fraction of events to be lost.

Returns:

astropy.units.Quantity

Upper radial distance in m.

production_configuration.derive_corsika_limits.compute_viewcone(histograms, loss_fraction)

Compute the viewcone based on the event loss fraction.

The shower IDs of triggered events are used to create a mask for the azimuth and altitude of the triggered events. A mapping is created between the triggered events and the simulated events using the shower IDs.

Parameters:

histogramsSimtelIOEventHistograms

Histograms.

loss_fractionfloat

Fraction of events to be lost.

Returns:

astropy.units.Quantity

Viewcone radius in degrees.

production_configuration.derive_corsika_limits.generate_corsika_limits_grid(args_dict, db_config=None)

Generate CORSIKA limits.

Parameters:

args_dictdict

Dictionary containing command line arguments.

db_configdict, optional

Database configuration dictionary.

production_configuration.derive_corsika_limits.write_results(results, args_dict)

Write the computed limits as astropy table to file.

Parameters:

resultslist[dict]

List of computed limits.

args_dictdict

Dictionary containing command line arguments.

derive_production_statistics

Calculate the event production statistics based on metrics.

Module for calculating the production event statistics based on statistical error metrics. Contains the ProductionStatisticsDerivator class, which derives the number of events for both the entire dataset and specific grid points. Event statistic is calculated using error metrics and the evaluator’s results.

class production_configuration.derive_production_statistics.ProductionStatisticsDerivator(evaluator, metrics: dict)

Derives the production statistics based on statistical error metrics.

Supports deriving statistics for both the entire dataset and specific grid points like energy values.

calculate_production_statistics_at_grid_point(grid_point: tuple) → Quantity

Derive the production statistics for a specific energy grid point.

Parameters:

grid_pointtuple

The grid point specifying energy, azimuth, zenith, NSB, and offset.

Returns:

float

The derived production statistics at the specified grid point (energy).

derive_statistics(return_sum: bool = True) → Quantity

Derive the production statistics based on statistical error metrics.

Parameters:

return_sumbool, optional

If True, returns the sum of production statistics for the entire set of MC events. If False, returns the production statistics for each grid point along the energy axis. Default is True.

Returns:

u.Quantity

If ‘return_sum’ is True, returns the total derived production statistics as a u.Quantity. If ‘return_sum’ is False, returns an array of production statistics along the energy axis as a u.Quantity.

derive_production_statistics_handler

Derives the required statistics for a requested set of production parameters through interpolation.

This module provides the ProductionStatisticsHandler class, which manages the workflow for derivation of required number of events for a simulation production using pre-defined metrics.

The module includes functionality to: - Initialize evaluators for statistical uncertainty calculations based on input parameters. - Perform interpolation using the initialized evaluators to estimate production statistics at a query point. - Write the results of the interpolation to an output file.

class production_configuration.derive_production_statistics_handler.ProductionStatisticsHandler(args_dict, output_path)

Handles the workflow for deriving production statistics.

This class manages the evaluation of statistical uncertainties from DL2 MC event files and performs interpolation to estimate the required number of events for a simulation production at a specified query point.

initialize_evaluators()

Initialize StatisticalUncertaintyEvaluator instances for the given grid point.

perform_interpolation()

Perform interpolation for the query point.

plot_production_statistics_comparison()

Plot the derived event statistics.

run()

Run the scaling and interpolation workflow.

write_output(production_statistics)

Write the derived event statistics to a file.

generate_production_grid

Module defines the GridGeneration class.

Used to generate a grid of simulation points based on flexible axes definitions such azimuth, zenith angle, night-sky background, and camera offset. The module handles axis binning, scaling and interpolation of energy thresholds, viewcone, and radius limits from a lookup table. Additionally, it allows for converting between Altitude/Azimuth and Right Ascension Declination coordinates. The resulting grid points are saved to a file.

class production_configuration.generate_production_grid.GridGeneration(axes: dict, coordinate_system: str = 'zenith_azimuth', observing_location=None, observing_time=None, lookup_table: str | None = None, telescope_ids: list | None = None)

Defines and generates a grid of simulation points based on flexible axes definitions.

This class generates a grid of points for a simulation based on parameters such as azimuth, zenith angle, night-sky background, and camera offset, taking into account axis definitions, scaling, and units and interpolating values for simulations from a lookup table.

convert_altaz_to_radec(alt, az)

Convert Altitude/Azimuth (AltAz) coordinates to Right Ascension/Declination (RA/Dec).

Parameters:

altfloat

Altitude angle in degrees.

azfloat

Azimuth angle in degrees.

Returns:

SkyCoord

SkyCoord object containing the RA/Dec coordinates.

Raises:

ValueError

If observing_time is not set.

convert_coordinates(grid_points: list[dict]) → list[dict]

Convert the grid points to RA/Dec coordinates if necessary.

Parameters:

grid_pointslist of dict

List of grid points, each represented as a dictionary with axis

names as keys and values.

Returns:

list of dict

The grid points with converted RA/Dec coordinates.

create_circular_binning(azimuth_range, num_bins)

Create bin centers for azimuth angles, handling circular wraparound (0 deg to 360 deg).

Parameters:

azimuth_rangetuple

(min_azimuth, max_azimuth), can wrap around 0 deg.

num_binsint

Number of bins.

Returns:

np.ndarray

Array of bin centers.

generate_grid() → list[dict]

Generate the grid based on the required axes and include interpolated limits.

Takes energy threshold, viewcone, and radius from the interpolated lookup table.

Returns:

list of dict

A list of grid points, each represented as a dictionary with axis names as keys and axis values as values. Axis values may include units where defined.

serialize_grid_points(grid_points, output_file)

Serialize the grid output and save to a file or print to the console.

serialize_quantity(value)

Serialize Quantity.

interpolation_handler

Handle interpolation between multiple StatisticalUncertaintyEvaluator instances.

class production_configuration.interpolation_handler.InterpolationHandler(evaluators, metrics: dict, grid_points_production: list)

Calculate the required events for production via interpolation from a grid.

This class provides methods to interpolate production statistics across a grid of parameter values (azimuth, zenith, NSB, offset) and energy.

build_grid_points_no_energy()

Build grid points without energy dimension.

Returns:

tuple

(production_statistics, grid_points_no_energy)

interpolate() → ndarray

Interpolate production statistics at the grid points specified in grid_points_production.

This method performs two types of interpolation: 1. Energy-independent interpolation using the sum of production statistics 2. Energy-dependent interpolation for each energy bin

Returns:

np.ndarray

Interpolated values at the query points.

plot_comparison(grid_point_index=0)

Plot a comparison between interpolated production statistics and reconstructed events.

Parameters:

grid_point_indexint, optional

Index of the grid point to plot, by default 0

Returns:

matplotlib.axes.Axes

The Axes object containing the plot.

merge_corsika_limits

Class for merging CORSIKA limit tables and checking grid completeness.

class production_configuration.merge_corsika_limits.CorsikaMergeLimits(output_dir=None)

Class for merging CORSIKA limit tables and checking grid completeness.

check_grid_completeness(merged_table, grid_definition)

Check if the grid is complete by verifying all expected combinations exist.

This function checks whether all combinations of zenith, azimuth, nsb_level, and array_name specified in the grid_definition are present in the merged_table.

Parameters:

merged_tableastropy.table.Table

The merged table containing CORSIKA limit data.

grid_definitiondict

Dictionary defining the grid dimensions with keys: ‘zenith’: list of zenith angles, ‘azimuth’: list of azimuth angles, ‘nsb_level’: list of NSB levels, ‘array_name’: list of array name

Returns:

tuple

A tuple containing: is_complete (bool) that is True if all expected combinations are found in the table, and info_dict (dict) with detailed information about the completeness check including expected points, found points, and missing combinations.

merge_tables(input_files)

Merge multiple CORSIKA limit tables into a single table.

This function reads and merges CORSIKA limit tables from multiple files, handling duplicate grid points by checking for consistency and raising an error if inconsistent duplicates are found. It also converts the loss_fraction value from metadata to a table column and logs a message if multiple loss_fraction values are found.

Parameters:

input_fileslist of Path or str

List of paths to CORSIKA limit table files to merge.

Returns:

astropy.table.Table

The merged table with duplicates removed, containing all rows from input files. The table will be sorted by array_name, zenith, azimuth, and nsb_level.

Raises:

ValueError

If inconsistent duplicate grid points are found.

plot_grid_coverage(merged_table, grid_definition)

Generate plots showing grid coverage for each combination of NSB level and array name.

Creates a series of heatmap plots showing which grid points (combinations of zenith and azimuth angles) are present or missing in the merged table, for each combination of NSB level and array name.

Parameters:

merged_tableastropy.table.Table

The merged table containing CORSIKA limit data.

grid_definitiondict

Dictionary defining the grid dimensions with keys: ‘zenith’: list of zenith angles, ‘azimuth’: list of azimuth angles, ‘nsb_level’: list of NSB levels, ‘array_name’: list of array names

Returns:

list

List of Path objects pointing to the saved plot files.

plot_limits(merged_table)

Create plots showing the derived limits for each combination of array_name and azimuth.

Creates plots showing the lower energy limit, upper radius limit, and viewcone radius versus zenith angle for each combination of array_name and azimuth angle. Each plot has lines for different NSB levels.

Parameters:

merged_tableastropy.table.Table

The merged table containing CORSIKA limit data.

Returns:

list

List of Path objects pointing to the saved plot files.

read_file_list(file_list_path)

Read a list of input files from a text file.

The text file should contain one file path per line. Lines starting with ‘#’ are treated as comments and ignored. Empty lines are also ignored.

Parameters:

file_list_pathPath or str

Path to the text file containing the list of input files.

Returns:

list

List of Path objects for the input files.

Raises:

FileNotFoundError

If the file list does not exist.

write_merged_table(merged_table, output_file, input_files, grid_completeness)

Write the merged table to file and save metadata.

Writes the merged table to the specified output file in ECSV format and saves relevant metadata about the merge process, including input files, grid completeness statistics, and row count.

Parameters:

merged_tableastropy.table.Table

The merged table to write to file.

output_filePath or str

Path where the merged table will be written.

input_fileslist of Path or str

List of input files used to create the merged table.

grid_completenessdict

Dictionary with grid completeness information from check_grid_completeness.

Returns:

Path or str

The path to the written file (same as output_file).