Repository for machine learning-assisted analysis of Polarized Neutron Reflectometry (PNR) data.

Setup

• Clone the repository with git clone https://github.com/ninarina12/ML_PNR.git.
• Create a conda environment with necessary dependencies using conda create --name myenv --file env_file.txt.

Workflow

• Generate synthetic data by updating pnr_generate.py with the appropriate sample parameters and executing mpiexec -n num_process python pnr_generate.py, with num_process being the number of processes on which to run parallel simulations. Example parameters are based on nominal values of measurements in the experiments directory.
• Visualize properties of the synthetic data directly within the pnr_properties.ipynb notebook.
• Train and evaluate a machine learning model within the pnr_vae.ipynb notebook.

Helper scripts

• pnr_models.py contains all network components and architectures.
• pnr_utils.py contains various utilities to assist with data import, processing, and plotting.
• plot_imports.py contains some typical imports for plotting.

Other

• plot_exp.py can be used to fit and plot experimental data only.