3DLearn.py - This file takes absolute file paths to data generated from a GEANT4 simulation for muon tomography, as well as the relevant parameters, at the command line and creates 3D images of the objects in the field of view of the detector. There are a number of free parameters that may need to be tuned (listed at the beginning of the file) for clustering and visualising etc. Skeleton functions have been written to train a deep learning algorithm for regression (of object density etc.) and for classification (whether an object is in the field of view).
Angular\ Distributions.py - Takes empircal distribuition of incident CR Muons at sea level and compares it to Energy and Angular Distribution of simulated data.
Landau\ Fit\ Cases.py - Takes test data for muons through top layer of scintillators and fits various curves to the distribution.