Benchmarks (protopipe.benchmarks)

The protopipe.benchmarks module is composed of 3 parts:

• notebooks, a folder containing benchmarking Jupyter notebooks divided by analysis stage,

• operations, a sub-module containing functions to perform many different operations on data related to benchmarking

• plot, a sub-module containing plotting functions

• utils, a sub-module containing utility functions for the notebooks

• book_template, a folder containing the Jupyter Book template for a CTA analysis

Note

Much of what is contained in the sub-modules is the product of a long refactoring process of old material from the notebooks. Many things can be improved or imported by ctaplot/cta-benchmarks and ctapipe as the refactoring of the pipeline takes progress. Also, not all notebooks are exatcly the same in terms of global options, a notebook template will be added.

All benchmarks can be launched by means of the protopipe-BENCHMARK script (Benchmarking). This is the recommended method, as it integrates with the rest of the analysis interface.

API reference

protopipe.benchmarks Package

Functions

add_BTEL1010_weigths_to_data(data[, subarray])	Update data table with weights from requirement B-TEL-1010-Intensity-Resolution.
add_stats(data, ax[, x, y, fontsize])	Add a textbox containing statistical information.
average_bias_of_charge_resolution(...[, ...])	Calculate the average bias of charge resolution.
calculate_RMS_around_1(values, weights)	Root Mean Square around 1 as proposed from comparison with CTA-MARS.
compute_bias(x_bin_edges, reco, true[, ...])	Compute bias as a binned statistic.
compute_psf(data, ebins, radius)
compute_resolution(x_bin_edges, reco, true)	Compute a resolution as a binned statistic.
compute_weight_BTEL1010(true_energy[, ...])	Compute the weight from requirement B-TEL-1010-Intensity-Resolution.
create_lookup_function(binned_stat)	Returns a function f(x,y) that evaluates the lookup at a point.
get_evt_subarray_model_output(data[, ...])	Returns DataStore with keepcols + score/target columns of model at the level-subarray-event.
get_fig_size([ratio, scale])	Get size of figure given a ratio and a scale.
get_single_pixels_spectrum(x, bins, ...)	Log-Log data for single pixels spectrum.
load_tel_id([file_name, tel_id])	Load R0 and R1 waveforms for 1 telescope.
plot_DL1a_reco_true_correlation(ax, reco, true)	Plot the correlation between reconstructed and true number of photoelectrons.
plot_background_rate(input_file, ax, label, ...)
plot_bias(ax, x, bias, **opt)	Plot a bias.
plot_binned_mean(x, y[, bins, yerr, ax])	Plot mean of y in each bin in x with standard deviation as errorbars.
plot_binned_median(x, y[, bins, yerr, ax])	Plot median of y in each bin in x with the central 1-sigma interval as errorbars.
plot_distributions(suptitle, feature_list, ...)	Plot feature distributions for several data set.
plot_evt_roc_curve_variation(ax, data_test, ...)	Parameters
plot_hist(ax, data, nbin, limit[, norm, ...])	Utility function to plot histogram
plot_profile(ax, data, xcol, ycol, n_xbin, ...)	Plot a profiled histogram.
plot_psf(ax, x, y, err, **kwargs)
plot_resolution(ax, x, resolution[, label, fmt])	Plot a resolution.
plot_roc_curve(ax, model_output, y, **kwargs)	Plot ROC curve for a given set of model outputs and labels
plot_sensitivity_from_pyirf(ax, data[, ...])	Produce a sensitivity plot.
prepare_requirements(input_directory, site, ...)	Prepare requirements data as a dictionary.
raise_(ex)	Raise an exception as a statement.
string_to_boolean(variables)	Convert True/False strings to booleans.
sum_of_squares(x)

Classes

BoostedDecisionTreeDiagnostic()	Class producing diagnostic plot for the BDT method
ClassifierDiagnostic(model, ...[, ...])	Class to plot several diagnostic plot for classification.
ModelDiagnostic(model, feature_name_list, ...)	Base class for model diagnostics.
OnlineBinnedStats(bin_edges)	Class to dynamically compute one-dimensional binned statistics.
RegressorDiagnostic(model, ...)	Class to plot several diagnostic plots for regression.

Class Inheritance Diagram

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