.. File : index.rst License: GNU v3.0 Author : Andrei Leonard Nicusan Date : 28.06.2020 ================================ The PEPT Library's Documentation ================================ A Python library that unifies Positron Emission Particle Tracking (PEPT) research, including tracking, simulation, data analysis and visualisation tools. Positron Emission Particle Tracking =================================== PEPT is a technique developed at the University of Birmingham which allows the non-invasive, three-dimensional tracking of one or more 'tracer' particles through particulate, fluid or multiphase systems. The technique allows particle or fluid motion to be tracked with sub-millimetre accuracy and sub-millisecond temporal resolution and, due to its use of highly-penetrating 511keV gamma rays, can be used to probe the internal dynamics of even large, dense, optically opaque systems - making it ideal for industrial as well as scientific applications. PEPT is performed by radioactively labelling a particle with a positron- emitting radioisotope such as fluorine-18 (18F) or gallium-68 (68Ga), and using the back-to-back gamma rays produced by electron-positron annihilation events in and around the tracer to triangulate its spatial position. Each detected gamma ray represents a line of response (LoR). .. image:: imgs/pept_transformation.png :alt: Transforming LoRs into trajectories using `pept` Transforming gamma rays, or lines of response (left) into individual tracer trajectories (right) using the `pept` library. Depicted is experimental data of two tracers rotating at 42 RPM, imaged using the University of Birmingham Positron Imaging Centre's parallel screens PEPT camera. Tutorials and Documentation =========================== A very fast-paced introduction to Python is available `here (Google Colab tutorial link) `_; it is aimed at engineers whose background might be a few lines written MATLAB, as well as moderate C/C++ programmers. A beginner-friendly tutorial for using the `pept` package is available `here (Google Colab link) `_. The links above point to Google Colaboratory, a Jupyter notebook-hosting website that lets you combine text with Python code, executing it on Google servers. Pretty neat, isn't it? Performance =========== Significant effort has been put into making the algorithms in this package as fast as possible. Most computationally intensive code has been implemented in `Cython`, `C` or `C++` and allows policy-based parallel execution, either on shared-memory machines using `joblib` / `ThreadPoolExecutor`, or on distributed computing clusters using `mpi4py.futures.MPIPoolExecutor`. Copyright ========= Copyright (C) 2021 the `pept` developers. Until now, this library was built directly or indirectly through the brain-time of: - Andrei Leonard Nicusan (University of Birmingham) - Dr. Kit Windows-Yule (University of Birmingham) - Dr. Sam Manger (University of Birmingham) - Matthew Herald (University of Birmingham) - Chris Jones (University of Birmingham) - Mark Al-Shemmeri (University of Birmingham) - Prof. David Parker (University of Birmingham) - Dr. Antoine Renaud (University of Edinburgh) - Dr. Cody Wiggins (Virginia Commonwealth University) - Dawid Michał Hampel - Dr. Tom Leadbeater Thank you. Indices and tables ================== .. toctree:: :caption: Documentation :maxdepth: 2 getting_started tutorials/index manual/index contributing citing Pages * :ref:`genindex` * :ref:`modindex` * :ref:`search`