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A Python library that unifies Positron Emission Particle Tracking (PEPT) research, including tracking, simulation, data analysis and visualisation tools.

Project description

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PEPT

A Python library that integrates all the tools necessary to perform research using Positron Emission Particle Tracking (PEPT). The library includes algorithms for the location, identification and tracking of particles, in addition to tools for visualisation and analysis, and utilities allowing the realistic simulation of PEPT data.

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.

Getting Started

These instructions will help you get started PEPT data analysis.

Prerequisites

This package supports Python 3. You also need to have NumPy and Cython on your system in order to install it.

Installation

You can install pept from PyPI:

pip install pept

Or you can install the latest version from the GitHub repository:

pip install git+https://github.com/uob-positron-imaging-centre/pept

Example usage

You can download data samples from the UoB Positron Imaging Centre's Repository:

$> git clone https://github.com/uob-positron-imaging-centre/example_data

A minimal analysis script using the pept.tracking.peptml subpackage:

import pept
from pept.scanners import ParallelScreens
from pept.tracking import peptml
from pept.visualisation import PlotlyGrapher

lors = ParallelScreens('example_data/sample_2p_42rpm.csv', skiprows = 16)

max_distance = 0.1
cutpoints = peptml.Cutpoints(lors, max_distance)

clusterer = peptml.HDBSCANClusterer(min_sample_size = 30)
centres, clustered_cutpoints = clusterer.fit_cutpoints(cutpoints)

fig = PlotlyGrapher().create_figure()
fig.add_trace(centres.all_points_trace())
fig.show()

A more in-depth tutorial is available on Google Colab.

Performance

Significant effort has been put into making the algorithms in this package as fast as possible. The most compute-intensive parts have been implemented in C and parallelised, where possible, using joblib. For example, using the peptml subpackage, analysing 1,000,000 LoRs on the author's machine (mid 2012 MacBook Pro) takes ~26 s (with another 12 s to read in the data). This efficiency is largely due to the availabiliy of a great high-performance implementation of the HDBSCAN clustering algorithm.

Help and Support

We recommend you check out our tutorials. If your issue is not suitably resolved there, please check the issues page on our GitHub. Finally, if no solution is available there, feel free to open an issue; the authors will attempt to respond in a reasonably timely fashion.

Contributing

We welcome contributions in any form! Assistance with documentation, particularly expanding tutorials, is always welcome. To contribute please fork the project, make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.

Licensing

The pept package is GNU v3.0 licensed.

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