Production Configuration

Modules provide functionality to configure a simulation production. This includes the derivation of energy, viewcone radius, and core scatter ranges and related production-configuration helpers used by the supported workflows.

derive_corsika_limits

Derive CORSIKA limits from trigger histograms.

production_configuration.derive_corsika_limits.compute_lower_energy_limit(histograms, threshold_fraction)

Compute the lower energy limit in TeV based on the threshold fraction.

Parameters:

histogramsEventDataHistograms

Histograms.

threshold_fractionfloat

Fraction of the stable peak used as threshold.

Returns:

astropy.units.Quantity

Lower energy limit.

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

Generate CORSIKA limits from precomputed trigger-histogram products.

Reads histograms, computes limits, optionally plots histograms, and writes results to ECSV files. A literal file or glob preserves the existing output behavior. A directory processes supported particle products independently and writes one result below a particle-specific subdirectory.

production_configuration.derive_corsika_limits.parse_allowed_losses(allowed_losses_args)

Parse repeatable –allowed_losses values for core/viewcone axes.

Parameters:

allowed_losses_argslist[str]

List of values in the form “axis,fraction,min_events”.

Returns:

dict

Mapping of axis name to dict with keys “loss_fraction” and “loss_min_events”.

production_configuration.derive_corsika_limits.validate_differential_loss_bins_per_decade(value)

Validate and return a non-negative differential-bin count.

Parameters:

valueint or str

Differential energy-bin count per decade.

Returns:

int

Validated bin count.

Raises:

ValueError

If value is not a non-negative integer. The raised exception is also an argparse.ArgumentTypeError for detailed CLI diagnostics.

production_configuration.derive_corsika_limits.write_results(results, args_dict, allowed_losses, energy_threshold_fraction, context=None)

Write the computed limits as astropy table to file.

Parameters:

resultslist[dict]

List of computed limits.

allowed_lossesdict

Per-axis loss settings for core_distance/angular_distance.

energy_threshold_fractionfloat

Fraction used for deriving the lower energy threshold.

corsika_limits_lookup

Lookup-table access and interpolation for CORSIKA production limits.

class production_configuration.corsika_limits_lookup.CorsikaLimitsLookup(lookup_table, array_layout_name=None)

Read and interpolate CORSIKA limits for production grids.

Parameters:

lookup_tablestr or Path

Path to the lookup-table ECSV file.

array_layout_namestr, optional

Array layout name used to select lookup-table rows.

interpolate_point(zenith, azimuth, nsb=1.0)

Interpolate lookup-table limits for a single point.

Parameters:

zenithfloat or Quantity

Zenith angle.

azimuthfloat or Quantity

Azimuth angle.

nsbfloat or Quantity, optional

Night-sky background rate stored in the lookup table.

Returns:

dict

Interpolated lower-energy threshold, scatter radius, and viewcone radius.

load_matching_lookup_arrays()

Load and filter lookup-table arrays for the selected array layout.

Returns:

dict

Lookup arrays for interpolation.

prepare_point_interpolators()

Prepare lookup arrays for per-point interpolation.

Returns:

dict

Interpolators keyed by lookup quantity.

production_configuration.corsika_limits_lookup.attach_lookup_limits_to_point(point, limits, lookup_field_units=None)

Attach interpolated CORSIKA limits to a grid point.

trigger_histograms

Build and load trigger-histogram products from reduced event data.

production_configuration.trigger_histograms.discover_event_data_groups(event_data_directory)

Discover reduced event-data files and group partitioned products by filename.

Parameters:

event_data_directorystr or pathlib.Path

Directory containing direct-child reduced event-data HDF5 files.

Returns:

list[tuple[str, list[pathlib.Path]]]

Sorted (group_name, files) pairs. A group name is the file stem with a trailing .part<digits> suffix removed.

Raises:

FileNotFoundError

If the input directory does not exist or is not a directory.

ValueError

If the directory does not contain matching reduced event-data files.

production_configuration.trigger_histograms.inspect_trigger_histogram_file(file_path, format_report=True)

Return trigger-histogram-specific consistency checks when applicable.

Parameters:

file_pathstr or Path

Path to the file to inspect.

format_reportbool, optional

Whether to format the report as human-readable text. Default is True.

Returns:

str or dict or None

Formatted report string, structured report dictionary, or None if the file does not contain trigger-histogram products.

production_configuration.trigger_histograms.load_dense_histogram_payloads(reference_file)

Load dense histograms grouped by reference id.

production_configuration.trigger_histograms.load_event_data_histograms(reference_file, array_names=None, production_indices=None)

Load finalized EventDataHistograms objects from a trigger-histogram HDF5 file.

Parameters:

reference_filestr or pathlib.Path

Path to the trigger-histogram HDF5 file.

array_nameslist[str], optional

Restrict loaded histograms to these array names.

production_indiceslist[int], optional

Restrict loaded histograms to these production indices.

Returns:

list[tuple[astropy.table.Row, EventDataHistograms]]

Metadata row and reconstructed histogram object for each reference.

production_configuration.trigger_histograms.load_trigger_histograms(reference_file)

Load trigger-histogram metadata and bin tables from HDF5.

Parameters:

reference_filestr or pathlib.Path

Path to the trigger-histogram HDF5 file.

Returns:

tuple[astropy.table.Table, astropy.table.Table]

Metadata table and per-bin histogram table read from the input file.

production_configuration.trigger_histograms.validate_derived_products(configuration)

Check histogram metadata, run coverage, and source freshness without publishing it.

production_configuration.trigger_histograms.validate_derived_workflow(applications)

Check trigger products owned by histogram-writer workflow steps.

production_configuration.trigger_histograms.write_trigger_histograms(args_dict, context=None)

Build trigger histograms and write them to file.

Parameters:

args_dictdict

Application arguments.

Returns:

tuple[astropy.table.Table, astropy.table.Table] or simtools.job_execution.job.SubmissionHandle

Metadata and per-bin histogram tables for explicit input patterns, or the submission handle for directory-mode jobs.

Raises:

ValueError

If the output file does not use an HDF5 suffix or no supported telescope selection is provided.

monte_carlo_statistics_estimator

Estimate required Monte Carlo statistics from trigger histogram products.

production_configuration.monte_carlo_statistics_estimator.estimate_monte_carlo_statistics(args_dict=None, metadata=None, context=None)

Estimate required total thrown events for one or more trigger histograms.

Parameters:

args_dictdict, optional

Estimator configuration. Uses the active application configuration when omitted.

metadatadict, optional

Top-level product metadata to include in the ECSV header.

Returns:

astropy.table.Table

Results table containing required thrown-event estimates and limiting-bin diagnostics for each selected trigger histogram.

histogram_output_metadata

Shared helpers for production outputs derived from trigger-histogram metadata.

production_configuration.histogram_output_metadata.extract_histogram_output_metadata(source, file_info_columns, include_array_name=False)

Return production-identifying metadata for one histogram-derived output row.

production_event_data_helpers

Shared helpers for production workflows based on reduced event-data files.

production_configuration.production_event_data_helpers.accumulate_histograms_by_telescope_config(file_path, telescope_configs, *, energy_bins_per_decade, angular_distance_bin_count=100, angular_distance_bin_width=None, core_distance_bin_width=None, skip_invalid_event_data_files=False, fill_efficiency_histogram=False, collect_trigger_topology=False, minimum_triggered_telescopes=2)

Read one production once and accumulate histograms for all telescope configurations.

Parameters:

file_pathstr

Reduced event-data input path or glob pattern for one production.

telescope_configslist[dict]

Telescope configurations with array_name and normalized telescope_ids.

energy_bins_per_decadeint

Number of logarithmic energy bins per decade.

angular_distance_bin_countint, optional

Number of angular-distance bins used by the accumulators.

angular_distance_bin_widthastropy.units.Quantity, optional

Angular-distance bin width used to derive bins from broad-range viewcone limits.

core_distance_bin_widthastropy.units.Quantity, optional

Core-distance bin width used to derive a common physical grid across ranges.

skip_invalid_event_data_filesbool, optional

Skip malformed event-data files instead of aborting the run.

fill_efficiency_histogrambool, optional

Whether to finalize with efficiency histograms enabled.

collect_trigger_topologybool, optional

Whether to also collect trigger topology summaries.

minimum_triggered_telescopesint, optional

Minimum number of triggered telescopes within each telescope configuration.

Returns:

list[tuple]

One finalized (config, histograms) pair per telescope configuration, with a trigger-topology accumulator appended when collect_trigger_topology is set.

production_configuration.production_event_data_helpers.normalize_telescope_configs(telescope_configs)

Normalize telescope configurations to a list of array-name descriptors.

Parameters:

telescope_configsdict or list[dict]

Raw telescope configuration payload.

Returns:

list[dict]

List of dictionaries with array_name and normalized telescope_ids.

production_configuration.production_event_data_helpers.resolve_telescope_configs(args_dict, context=None)

Resolve telescope configurations from supported input options.

Parameters:

args_dictdict

Application arguments containing layout or array-element selection.

Returns:

dict or list[dict]

Telescope configuration payload returned by the chosen selector.

Raises:

ValueError

If no supported telescope selector is provided.

production_file_selection

Discover, filter, group, and validate production metadata manifests.

class production_configuration.production_file_selection.ProductionFileGroup(configuration, run_numbers=<factory>, file_paths=<factory>, missing_run_numbers=<factory>, duplicate_run_numbers=<factory>, files_by_run_number=<factory>)

Selected production files sharing one simulation configuration.

class production_configuration.production_file_selection.ProductionManifest(path, data)

A loaded production metadata manifest and its file path.

property directory

Return the manifest parent directory.

property run_number

Return the run number recorded in this manifest.

production_configuration.production_file_selection.check_manifest(manifest)

Validate a manifest against its output directory and listed files.

production_configuration.production_file_selection.discover_manifests(production_path, manifest_name='simulate_prod_job_metadata.yml')

Load all production metadata manifests below a directory.

production_configuration.production_file_selection.discover_product_manifests(production_path, product_type, manifest_pattern='*.yml')

Load all manifests of one product type below a directory.

production_configuration.production_file_selection.filter_manifests(manifests, selections)

Return manifests matching all selection expressions.

production_configuration.production_file_selection.find_manifests(production_path, manifest_name='simulate_prod_job_metadata.yml')

Return sorted production metadata manifests below a directory.

production_configuration.production_file_selection.get_manifest_schema_metadata(product_type)

Return the current schema name and version for a manifest product type.

production_configuration.production_file_selection.group_selected_files(manifests, file_type='reduced_event_data')

Group selected files by all configuration fields except the run number.

production_configuration.production_file_selection.inventory_production_files(job_directory)

Return nested production outputs grouped by manifest file type.

Paths in the returned inventory are relative to job_directory so that manifests can describe the standard sim_telarray/runNNNNNN and corsika/runNNNNNN output layout.

production_configuration.production_file_selection.load_manifest(manifest_path)

Load and validate one production manifest YAML file.

production_configuration.production_file_selection.normalize_for_comparison(value)

Normalize quantities and containers for exact matching and grouping.

production_configuration.production_file_selection.resolve_relative_manifest_path(directory, relative_path)

Resolve one manifest-relative path and reject paths escaping the job directory.

production_configuration.production_file_selection.select_file_groups(production_path, selections=None, file_type='reduced_event_data', require_complete_runs=False, manifest_name='simulate_prod_job_metadata.yml')

Discover manifests, filter jobs, and group selected files by configuration.

production_configuration.production_file_selection.selection_summary(selection_result)

Return a compact summary string for logging or console output.

production_configuration.production_file_selection.stable_configuration_hash(value, length=8)

Return a short stable hash for normalized configuration data.

production_configuration.production_file_selection.validate_required_production_outputs(file_inventory, simulation_software, job_directory)

Require the principal simulation output before marking a job complete.

production_configuration.production_file_selection.values_match(value, expected)

Return whether a manifest value matches a command-line selection value.

production_configuration.production_file_selection.write_selection_file(selection_result, output_file)

Write selected file groups to a YAML file.

comparison

Compare trigger-histogram products from simulation productions.

class production_configuration.comparison.ProductionDescriptor(label, input_files)

Descriptor for one production input provided.

class production_configuration.comparison.ProductionEventMetrics(label, trigger_combinations, telescope_participation, simulated_event_count, triggered_event_count, simulated_energies=<factory>, triggered_energies=<factory>, simulated_core_distances=<factory>, triggered_core_distances=<factory>, simulated_angular_distances=<factory>, triggered_angular_distances=<factory>, trigger_multiplicity=<factory>, per_type=<factory>, quantity_histograms=<factory>, trigger_multiplicity_histogram=<factory>)

Aggregated event-level metrics for one production.

class production_configuration.comparison.ProductionSignalMetrics(label, pedestals, signals, peak_timing, triggered_pixels)

Aggregated signal metrics for one production and telescope.

production_configuration.comparison.collect_production_metrics(production_descriptors, array_names=None)

Collect comparison metrics from trigger histogram files for each production.

Parameters:

production_descriptorslist[ProductionDescriptor]

Input descriptor for each production.

array_nameslist[str] or str, optional

Restrict loaded histogram references to these array layout names.

Returns:

list[ProductionEventMetrics]

Aggregated metrics per production.

production_configuration.comparison.collect_signal_metrics(production_descriptors, telescope_names=None)

Collect telescope-level signal metrics for each production.

Parameters:

production_descriptorslist[ProductionDescriptor]

Production descriptors containing sim_telarray input files.

telescope_namesstr or list[str], optional

Restrict the metrics to one or more telescope names. If omitted, all telescopes shared by the input files are processed.

Returns:

dict[str, list[ProductionSignalMetrics]]

Metrics grouped by telescope name, with one entry per production.

Raises:

ValueError

If a required telescope is absent or no event data is available for a telescope.

production_configuration.comparison.parse_production_arguments(production_arguments)

Parse repeated production arguments into validated descriptors.

Parameters:

production_argumentslist[list[str]]

Repeated --production arguments in the shape [label, patterns].

Returns:

list[ProductionDescriptor]

Validated and normalized production descriptors.

Raises:

ValueError

If configuration is malformed or does not contain at least two productions.

production_configuration.comparison.validate_comparison_inputs(applications, production_paths)

Check source coverage and freshness before trigger-product comparisons.

production_configuration.comparison.write_production_comparison(args_dict, output_directory)

Compare selected trigger-histogram productions and write their statistics.

Parameters:

args_dictdict

Application arguments, including either explicit production descriptors or metadata paths.

output_directorypathlib.Path

Directory in which comparison products are written.

resource_requirements

Collect and summarize CORSIKA and sim_telarray resource records.

production_configuration.resource_requirements.collect_resource_requirements(production_path, selections=None, label='baseline', comparison_role=None)

Collect one resource row per recorded simulation process.

Parameters:

production_pathstr or pathlib.Path

Production directory containing job-* directories.

selectionslist[str], optional

Manifest selection expressions accepted by filter_manifests.

labelstr, optional

Label identifying this production in combined results.

comparison_rolestr, optional

Stable comparison role, either "baseline" or "candidate".

Returns:

tuple[list[dict], list[dict]]

Valid resource rows and diagnostics for resource records that cannot be used. Missing CORSIKA output files are represented by None.

production_configuration.resource_requirements.resource_table(rows)

Return resource rows as an ECSV-compatible Astropy table.

production_configuration.resource_requirements.summarize_resource_requirements(rows)

Return grouped summary statistics for resource rows.

The grouping dimensions deliberately exclude azimuth and run number, but preserve separate CORSIKA and sim_telarray values.

production_configuration.resource_requirements.write_markdown_report(summary, diagnostics, output_file)

Write a compact human-readable resource requirements report.

production_configuration.resource_requirements.write_resource_requirements(args_dict, output_directory, plotter)

Collect, write, and plot resource requirements for one or two productions.

Parameters:

args_dictdict

Compute-comparison application arguments.

output_directorypathlib.Path

Destination directory for result products.

plottercallable

Plot function accepting rows, output path, and figure formats.

Returns:

dict

Paths of the ECSV, JSON, Markdown, and plot outputs plus diagnostics.

production_definition

Build and validate production definitions from resolved application options.

production_configuration.production_definition.production_definition_from_arguments(args_dict)

Create validated production data from resolved grid application arguments.

production_metadata

Write and validate metadata manifests for completed simulation production jobs.

production_configuration.production_metadata.build_production_job_manifest(args_dict, simulator, output_directory, file_inventory=None, catalog_metadata=None, atmosphere_configuration=None)

Build a versioned production-job manifest from resolved simulation output.

Parameters:

args_dictdict

Resolved simulate_prod application arguments.

simulatorsimtools.simulator.Simulator

Simulator for the completed and validated run.

output_directorystr or pathlib.Path

Directory containing the packaged output files.

file_inventorydict, optional

Precomputed manifest file inventory. Used when backfilling existing jobs.

catalog_metadatadict, optional

Catalog metadata to store instead of deriving it from the simulator.

atmosphere_configurationdict, optional

Resolved atmosphere configuration to store instead of deriving it from the simulator.

Returns:

dict

Versioned production-job manifest used for downstream file selection.

production_configuration.production_metadata.build_simulation_job_metadata(args_dict, simulator, include_sct=True)

Build DIRAC catalog metadata from resolved simulation configuration.

Parameters:

args_dictdict

Resolved simulate_prod application arguments.

simulatorsimtools.simulator.Simulator

Simulator for the completed run.

include_sctbool, optional

Include the SCT-presence catalog field when the resolved array elements are available.

Returns:

dict

Job-level metadata using DIRAC file-catalog field names.

production_configuration.production_metadata.get_sim_telarray_event_counts(output_directory, file_inventory)

Return sim_telarray event counts without reading simtel output files.

Parameters:

output_directorystr or pathlib.Path

Directory containing the production outputs.

file_inventorydict

Manifest file inventory with relative paths grouped by file type.

Returns:

dict or None

Mapping with simulated_events and triggered_events when counts can be recovered from reduced-event HDF5 files or sim_telarray logs.

production_configuration.production_metadata.write_production_metadata(args_dict, context=None)

Write or validate production job metadata manifests.

Parameters:

args_dictdict

Application arguments containing production_path and, in write mode, job_grid_file.

contextRunContext, optional

Run context for model access.

grid

Expand observation grids into full simulation job matrices.

Combines grids (from ProductionGridEngine or explicit axes) with primaries, interactions, model versions, energy ranges, and run counts into a complete job parameter set.

class production_configuration.grid.ProductionGridEngine(axes, coordinate_system='horizontal', observing_location=None, lookup_table=None, array_layout_name=None, lookup_nsb_rate=None)

Generate observation grids with direction-dependent CORSIKA physics limits.

convert_altaz_to_hadec(alt, az)

Convert Altitude/Azimuth (AltAz) coordinates to HA/Dec.

Parameters:

altfloat

Altitude angle in degrees.

azfloat

Azimuth angle in degrees.

Returns:

SimpleNamespace

Object exposing ha and dec angle attributes.

convert_coordinates(grid_points, keep_horizontal_coordinates=False)

Convert the grid points HA/Dec coordinates if necessary.

Parameters:

grid_pointslist of dict

List of grid points.

keep_horizontal_coordinatesbool

Keep zenith_angle and azimuth in the converted points.

Returns:

list of dict

The grid points with converted HA/Dec coordinates.

create_circular_binning(azimuth_range, num_bins)

Create bin centers for azimuth angles, handling circular wraparound.

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_simulation_grid()

Generate a grid for the configured coordinate system.

production_configuration.grid.build_axes_dict_from_cli_args(args_dict)

Build ProductionGridEngine-compatible axes configuration from CLI arguments.

production_configuration.grid.build_job_grid_metadata(args_dict)

Build metadata stored alongside serialized executable job grids.

production_configuration.grid.build_observing_location(site, model_version, context=None)

Build observing location from the site model.

Parameters:

sitestr

Site name.

model_versionstr or list[str]

Model version used to read site coordinates.

contextRunContext, optional

Run configuration and model reader.

production_configuration.grid.build_production_grid_engine(args_dict, array_layout_name=None, model_version=None, context=None)

Build a production-grid engine from application arguments.

Parameters:

args_dictdict

Application arguments (axis definitions, site, model version, optional CORSIKA lookup table, array layout name).

array_layout_namestr, optional

Resolved array layout name; resolved from args_dict if omitted.

model_versionstr, optional

Model version for observing location, array layout, and NSB rate. Defaults to the first model version in args_dict.

contextRunContext, optional

Run configuration and model reader.

Returns:

ProductionGridEngine

Configured production-grid engine.

production_configuration.grid.build_simulation_jobs(args_dict, context=None)

Expand production config into full simulation job matrix.

Cartesian product of primaries, model versions, interactions, observation directions, energy ranges, and run counts. Energy ranges are clipped by direction-dependent CORSIKA limits.

Returns:

list[dict]

Each job: primary, model_version, interactions, directions (Alt/Az), energy_min/max (clipped), showers_per_run, run_number, scatter/viewcone values (clipped by physics limits).

production_configuration.grid.calculate_log_energy_midpoint(energy_range_pair)

Return the geometric-mean energy for an energy range pair.

production_configuration.grid.calculate_scaled_showers_per_run(energy_range_pair, baseline_showers_per_run, showers_per_run_power_law=None)

Return an energy-dependent showers per run value.

production_configuration.grid.calculate_zenith_scaled_showers_per_run(zenith_angle, baseline_showers_per_run, showers_per_run_scaling='fixed')

Return a zenith-angle-dependent showers per run value.

Parameters:

zenith_angleastropy.units.Quantity

Zenith angle for one simulation point.

baseline_showers_per_runint

Showers-per-run value before zenith-angle scaling.

showers_per_run_scalingstr

Scaling mode (‘fixed’ or ‘cosine_zenith’).

Returns:

int

Zenith-scaled showers-per-run value.

production_configuration.grid.ceil_with_tolerance(value)

Ceil a float while avoiding near-integer floating-point artifacts.

production_configuration.grid.directed_circular_span_degrees(axis_range)

Return the directed circular span in degrees; full-circle ranges give 360.0.

production_configuration.grid.generate_job_grid(args_dict, output_file, metadata=None, context=None)

Generate and serialize a production job grid.

Parameters:

args_dictdict

Production job-grid configuration arguments.

output_filestr or Path

Path to the ECSV file to write.

metadatadict, optional

Default metadata to include in the ECSV header.

contextRunContext, optional

Run configuration and model reader.

production_configuration.grid.normalize_energy_ranges(energy_range)

Normalize energy range argument to a list of (e_min, e_max) pairs.

production_configuration.grid.normalize_grid_axes(args_dict)

Return normalized grid axes for cartesian product expansion.

production_configuration.grid.renumber_job_rows(job_rows, run_number_offset)

Set output run numbers continuously.

Parameters:

job_rowslist[dict]

List of job rows to renumber.

run_number_offsetint

Offset to apply to the run numbers (first run number will be offset + 1).

Returns:

list[dict]

List of job rows with updated run numbers.

production_configuration.grid.resolve_single_model_version(model_version)

Resolve one model version for helpers that require a scalar version.

production_configuration.grid.scale_energy_max_for_zenith_angle(zenith_angle, energy_range_pair, energy_max_scaling=None)

Scale energy_max with zenith angle and return an updated energy range pair.

Parameters:

zenith_angleastropy.units.Quantity

Zenith angle for one simulation point.

energy_range_pairtuple

Tuple containing the minimum and maximum energy values.

energy_max_scalingtuple or None

Tuple containing the scaling index and reference energy, or None.

Returns:

tuple or None

Updated energy range pair, or None if the scaling results in an invalid range.

jobs_io

Read and write executable job grids for production preparation.

production_configuration.jobs_io.build_job_grid_summary(rows)

Build a user-facing summary of generated job-grid values.

production_configuration.jobs_io.build_simulate_prod_job_specs(args_dict, rows, parser, metadata=None)

Build backend-neutral command jobs for production-grid rows.

Parameters:

args_dictdict

Controller application arguments.

rowslist[dict]

Deserialized production-grid rows.

parserargparse.ArgumentParser

Parser defining the nested simulate_prod command.

metadatadict, optional

Production-grid metadata.

Returns:

list[JobSpec]

Ordered command jobs that force local execution inside each worker.

production_configuration.jobs_io.format_quantity_field(rows, key, summary_unit=None, *, log_wording=False)

Format one quantity field across rows as a single value or a range.

log_wording selects the sentence style used in log messages instead of the summary style used in the job-grid metadata.

production_configuration.jobs_io.job_grid_row_to_simulate_prod_args(job_row, metadata=None)

Convert an in-memory job grid row to simulate_prod argument format.

Callers are responsible for not combining these production-defining values ambiguously with independently supplied production arguments.

Parameters:

job_rowdict

A single deserialized job row as returned by read_job_grid().

metadatadict, optional

Job grid metadata as returned alongside the rows. When provided, site and simulation_software are included in the result.

Returns:

dict

Argument dictionary compatible with simulate_prod args_dict keys.

production_configuration.jobs_io.read_job_grid(input_file)

Read executable job rows from ECSV input.

Parameters:

input_filestr or Path

Input file path.

Returns:

tuple[list[dict], dict]

Deserialized job rows and metadata.

production_configuration.jobs_io.resolve_job_grid_arguments(args_dict, config_sources, parser)

Merge selected job-grid row values into args after rejecting ambiguous input.

production_configuration.jobs_io.serialize_job_grid(job_rows, output_file, metadata=None)

Serialize executable job rows to ECSV output.

Parameters:

job_rowslist[dict]

Job rows in the in-memory schema.

output_filestr or Path

Output file path. Must use the .ecsv suffix.

metadatadict, optional

Metadata to store alongside the rows.

Dependency compatibility

Validate models against the actual production runtime before submission.

production_configuration.compatibility.prepare_jobs(jobs, args, options)

Reject incompatible models before submitting a production grid.

production_configuration.compatibility.preview(args, image=None)

Check selected models using the target installation’s bundled catalog.

Parameter scans

Parameter scan grids and NSB/proton bias-curve scan submissions.

A parameter scan expands a production job grid with the cartesian product of scan values. Each scan combination is stored once as a named parameter set in the ECSV metadata (model_parameter_sets); every expanded row references its set via model_parameter_set and carries the combination name in scan_label.

Bias curves use two scans (NSB and proton), each with a production grid, a scan configuration, and the expanded scan grid. The NSB overwrite sets min_photons and min_photoelectrons to zero; both curves set nsb_scaling_factor.

production_configuration.parameter_scans.expand_job_grid_with_scan(base_grid_file, scan_config_path, output_file)

Expand a production job grid with parameter scan combinations.

Parameters:

base_grid_filestr or Path

Base job grid file (ECSV format).

scan_config_pathstr or Path

Parameter scan configuration file (YAML format).

output_filestr or Path

Output file for the expanded grid.

production_configuration.parameter_scans.generate_scan_grids(args, io_handler, context=None)

Generate the NSB and proton bias-curve scan grids.

Parameters:

argsdict

Application arguments for the production grid and scan configuration.

io_handlerOutputPaths

Output paths providing the output directories.

contextRunContext, optional

Run context for model access.