README
This folder contains files used for unit testing and files that serve
as example scripts for the CTA interface of GammaLib.
benchmark_irf_computing.py
This script performs a benchmark of the response computation for all
different analysis methods (unbinned, binned, stacked). Specifically it
compares the total number of modelled counts to the expected number from
Monte-Carlo simulations. This script is used to validate the absolute
normalization of the CTA response computations.
benchmark_ml_fitting.py
This script performs a benchmark for maximum likelihood fitting of CTA
data.
example_binned_ml_fit.py
Illustrates a binned maximum likelihood fit of CTA data.
This script implements a full analysis chain, and computes
also the TS statistics value that estimates the significance
of the source detection.
example_make_model.py
Illustrates how to make a model in counts space for a counts
map given a specific model in XML format.
example_sim_photons.py
Illustrates how photons are simulated from a source model. If
matplotlib is installed, the simulated photons are shown as
a spectrum.
test_CTA.cpp
Unit test C++ file for CTA specific GammaLib components.
test_gauss.py
Test Gaussian.
test_irf_offset.py
Creates images of source models for various offset angles. This
tests the IRF variation with offset angle.
test_irf_trafo.py
Test coordinate transformations for IRF computation.
test_model.py
Creates images of sources models convolved with the CTA instrument
response function. Also allows the creation of gradient images.
test_npred_computation.py
Test the Npred computation of the CTA response.
test_npred_integration.py
Compare numerical to analytical Npred computation for CTA response.
test_radial_acceptance.py
Displays the various radial acceptance models.
test_response_table.py
Display CTA response table.
test_sim_edisp.py
Compares energy dispersion simulation to energy dispersion
model.
test_sim_psf.py
Compares Point Spread function simulation to Point Spread function
model.