ctlearn_manager.tri_model_collection

Plotting and predicting with a collection of CTLearnTriModelManager models.

class ctlearn_manager.tri_model_collection.TriModelCollection(tri_models, cluster_configuration=<ctlearn_manager.utils.utils.ClusterConfiguration object>, model_labels=None, allow_muliple_projects=False)[source]

Bases: object

TriModelCollection is a class that manages a collection of tri-models.

Use for predicting and analyzing data from telescopes. It provides methods for predicting data, finding the closest model to a given pointing, and visualizing various performance metrics.

tri_models

A list of tri-models to be managed by the collection.

Type:

list

cluster_configuration

Configuration for the cluster where the models will run.

Type:

ClusterConfiguration

model_labels

Labels for the tri-models in the collection.

Type:

list[str]

__init__(tri_models, cluster_configuration, model_labels)[source]

Initialize the TriModelCollection with a list of tri-models, cluster configuration, and optional model labels.

predict_lstchain_run(run, output_dir, DL1_data_dir=None, overwrite=False, plot=False, batch_size=64)[source]

Predict data for a specific LST chain run and save the results.

Parameters:

  • run (int)

  • output_dir (str)

predict_lstchain_data(input_file, output_file, pointing_table, config_dir=None, overwrite=False, run=None, subrun=None, plot=False, batch_size=64)[source]

Predict data for a specific input file using the closest tri-model.

predict_data(input_file, output_file, pointing_table, config_dir=None, overwrite=False, plot=False)[source]

Predict data for a given input file and save the results.

find_closest_model_to(input_file, pointing_table, plot=False, alt_key='alt_tel', az_key='az_tel', verbose=True)[source]

Find the closest tri-model to a given pointing based on angular distance.

plot_zenith_azimuth_ranges(plot_testing_nodes=True)[source]

Plot the zenith and azimuth ranges for all tri-models in the collection.

plot_energy_resolution_DL2(cuts, zenith=None, azimuth=None, ylim=None, particle_type, figsize=None, plot_RF=False, compare_with=None)[source]

Plot the energy resolution for DL2 data with optional comparison to a reference model.

Parameters:

  • cuts (Cuts)

  • zenith (float)

  • azimuth (float)

  • particle_type (ParticleType)

  • compare_with (str)

plot_angular_resolution_DL2(cuts, zenith=None, azimuth=None, ylim=None, particle_type, figsize=None, plot_RF=False, compare_with=None)[source]

Plot the angular resolution for DL2 data with optional comparison to a reference model.

Parameters:

  • cuts (Cuts)

  • zenith (float)

  • azimuth (float)

  • particle_type (ParticleType)

  • compare_with (str)

plot_cuts(cuts)[source]

Plot the cuts applied to the data for all tri-models in the collection.

Parameters:

cuts (Cuts)

plot_everything_dl2(output_directory, dl2_files, gammaness_cut=0.9, edep_cuts=False, pointing_table='/dl1/monitoring/telescope/pointing/tel_001')[source]

Generate and save plots for angular resolution, energy resolution, and gammaness for DL2 data.

Parameters:

  • dl2_files (list[str])

  • gammaness_cut (float)

  • edep_cuts (bool)

  • pointing_table (str)

Initialize the TriModelCollection object.

Parameters:

  • tri_models (list) – A list of tri-model objects to be included in the collection.

  • cluster_configuration (ClusterConfiguration, optional) – The cluster configuration to be applied to all tri-models in the collection. Defaults to a new instance of ClusterConfiguration.

  • model_labels (list of str, optional) – A list of labels for the tri-models. If not provided, default labels in the format “Model_{j}” will be generated.

tri_models

The list of tri-models in the collection.

Type:

list

cluster_configuration

The cluster configuration applied to all tri-models.

Type:

ClusterConfiguration

model_labels

The labels for the tri-models in the collection.

Type:

list of str

Raises:

AssertionError – If model_labels is provided and its length does not match the number of tri-models. If the stereos of all tri-models are not the same.

Parameters:
predict_lstchain_run(run, output_dir, DL1_data_dir=None, overwrite=False, plot=False, batch_size=64)[source]

Predict DL2 data for a given LST run using the specified cluster configuration.

Parameters:

  • run (int) – The run number to process.

  • output_dir (str) – Directory where the output DL2 files will be saved.

  • DL1_data_dir (str, optional) – Directory containing the DL1 input data. If None, a default path is used based on the cluster configuration.

  • overwrite (bool, default=False) – Whether to overwrite existing DL2 files.

  • plot (bool, default=False) – Whether to generate and save plots during the prediction process.

  • batch_size (int, default=64) – Batch size to use during prediction.

Raises:

ValueError – If the cluster configuration is not recognized (neither ‘cscs’ nor ‘lst-cluster’).

Notes

  • For the ‘cscs’ cluster, DL1 files are fetched from dCache and copied to a scratch directory before prediction.

  • For the ‘lst-cluster’, DL1 files are directly accessed from the specified directory.

  • The method generates DL2 files for each subrun of the specified run.

predict_lstchain_data(input_file, output_file, pointing_table='/dl1/event/telescope/parameters/LST_LSTCam', config_dir=None, overwrite=False, run=None, subrun=None, plot=False, batch_size=64)[source]

Predict data using the closest trained model for lstchain data.

This method identifies the closest trained model to the input data and uses it to make predictions. The results are saved to the specified output file.

Parameters:

  • input_file (str) – Path to the input file containing lstchain data.

  • output_file (str) – Path to the output file where predictions will be saved.

  • pointing_table (str, optional) – Path to the pointing table in the input file. Default is “/dl1/event/telescope/parameters/LST_LSTCam”.

  • config_dir (str, optional) – Directory containing configuration files for the model. Default is None.

  • overwrite (bool, optional) – Whether to overwrite the output file if it already exists. Default is False.

  • run (int, optional) – Run number to filter the data. Default is None.

  • subrun (int, optional) – Subrun number to filter the data. Default is None.

  • plot (bool, optional) – Whether to generate and display plots for model selection. Default is False.

  • batch_size (int, optional) – Batch size to use during prediction. Default is 64.

Returns:

This method does not return any value. The predictions are saved to the specified output file.

Return type:

None

Notes

If no suitable trained model is found, the method will return without performing any predictions. If the output file already exists and overwrite is set to False, the method will also return without performing any predictions.

predict_data(input_file, output_file, pointing_table='dl0/monitoring/subarray/pointing', config_dir=None, overwrite=False, plot=False)[source]

Predict data using the closest trained model.

Parameters:

  • input_file (str) – Path to the input file containing the data to be predicted.

  • output_file (str) – Path to the output file where the predictions will be saved.

  • pointing_table (str, optional) – Path to the pointing table within the input file, by default “dl0/monitoring/subarray/pointing”.

  • config_dir (str, optional) – Directory containing configuration files for the prediction, by default None.

  • overwrite (bool, optional) – Whether to overwrite the output file if it already exists, by default False.

  • plot (bool, optional) – Whether to generate and display plots during the process, by default False.

Returns:

This method does not return anything. If no suitable model is found, the function exits early.

Return type:

None

find_closest_model_to(input_file, pointing_table, plot=False, alt_key='alt_tel', az_key='az_tel', verbose=True)[source]

Find the closest model to the average pointing of a given input file.

Parameters:

  • input_file (str) – Path to the input file containing pointing data.

  • pointing_table (str) – Path to the pointing table file.

  • plot (bool, optional) – If True, plot the pointing and model ranges on a polar plot. Default is False.

  • alt_key (str, optional) – Key for altitude in the pointing table. Default is “alt_tel”.

  • az_key (str, optional) – Key for azimuth in the pointing table. Default is “az_tel”.

  • verbose (bool, optional) – If True, print detailed information about the closest model. Default is True.

Returns:

closest_model – The model from self.tri_models that is closest to the average pointing.

Return type:

object

Notes

This method calculates the average pointing (zenith and azimuth) of the input file and compares it to the average pointing ranges of the models in self.tri_models. The closest model is determined based on the angular distance.

Raises:

Exception – If the pointing data cannot be found in the provided pointing table.

plot_zenith_azimuth_ranges(plot_testing_nodes=True)[source]

Plot the zenith and azimuth ranges for all tri-models in a polar plot.

Parameters:

plot_testing_nodes (bool, optional) – If True, include testing nodes in the plot. Default is True.

Notes

This method creates a polar plot using Matplotlib and iterates over all tri-models to plot their respective zenith and azimuth ranges. The plot is displayed using plt.show().

plot_energy_resolution_DL2(cuts=<ctlearn_manager.utils.utils.Cuts object>, zenith=None, azimuth=None, ylim=None, particle_type=ParticleType.GAMMA_POINT, figsize=None, plot_RF=False, compare_with=None, output_file=None)[source]

Plot the energy resolution for DL2 data.

Parameters:

  • cuts (Cuts, optional) – The cuts to apply for the analysis. Defaults to DefaultCuts.GH_0_9.value.

  • zenith (float, optional) – The zenith angle in degrees. Required if plot_RF is True or compare_with is provided.

  • azimuth (float, optional) – The azimuth angle in degrees. Required if compare_with is provided.

  • ylim (tuple, optional) – The y-axis limits for the plot.

  • particle_type (ParticleType, optional) – The type of particle to analyze. Defaults to ParticleType.GAMMA_POINT.

  • figsize (tuple, optional) – The size of the figure.

  • plot_RF (bool, optional) – Whether to include the Random Forest (RF) benchmark in the plot. Defaults to False.

  • compare_with (str, optional) – The label of the model to compare with. If provided, zenith and azimuth must also be specified.

Raises:

ValueError – If compare_with is provided but zenith or azimuth is not specified.

Notes

  • If plot_RF is True, the function will load the corresponding IRF file based on the zenith angle.

  • When compare_with is specified, the function will plot the relative improvement of the energy resolution compared to the reference model.

plot_angular_resolution_DL2(cuts=<ctlearn_manager.utils.utils.Cuts object>, zenith=None, azimuth=None, ylim=None, particle_type=ParticleType.GAMMA_POINT, figsize=None, plot_RF=False, compare_with=None, output_file=None)[source]
Parameters:

  • cuts (Cuts)

  • zenith (float)

  • azimuth (float)

  • particle_type (ParticleType)

  • compare_with (str)

plot_cuts(cuts=<ctlearn_manager.utils.utils.Cuts object>)[source]
Parameters:

cuts (Cuts)

plot_everything_dl2(dl2_files, gammaness_cut=0.9, edep_cuts=False, pointing_table='/dl1/monitoring/telescope/pointing/tel_001')[source]

Plot the angular resolution, energy resolution, and gammaness for DL2 data. This function generates plots for the angular resolution, energy resolution, and gammaness for the given DL2 files. It uses ctaplot to create the plots and saves them in the specified output directory.

Parameters:

  • output_directory (str) – The directory where the plots will be saved.

  • dl2_files (list[str]) – List of DL2 files to be processed.

  • dl2_processed_dir (str) – The directory where the processed DL2 files are stored.

  • gammaness_cut (float, optional) – The gammaness cut value to be applied. Default is 0.9.

  • edep_cuts (bool)

  • pointing_table (str)

Return type:

None

get_tri_model_by_nickname(tri_model_nickname)[source]

Get a tri-model by its nickname.

Parameters:

nickname (str) – The nickname of the tri-model to retrieve.

Returns:

The tri-model with the specified nickname, or None if not found.

Return type:

CTLearnTriModel