{
    "info": {
        "author": "Andrei Leonard Nicusan",
        "author_email": "a.l.nicusan@bham.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
            "Natural Language :: English",
            "Programming Language :: C",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Scientific/Engineering :: Visualization",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1G8XHP9zWMMDVu23PXzANLCOKNP_RjBEO)\n\n# PEPT\n\n\nA Python library that integrates all the tools necessary to\nperform research using Positron Emission Particle Tracking (PEPT). The library\nincludes algorithms for the location, identification and tracking of particles,\nin addition to tools for visualisation and analysis, and utilities allowing the\nrealistic simulation of PEPT data.\n\n\n## Positron Emission Particle Tracking\nPEPT is a technique developed at the University of Birmingham which allows the\nnon-invasive, three-dimensional tracking of one or more 'tracer' particles through\nparticulate, fluid or multiphase systems. The technique allows particle or fluid\nmotion to be tracked with sub-millimetre accuracy and sub-millisecond temporal\nresolution and, due to its use of highly-penetrating 511keV gamma rays, can be\nused to probe the internal dynamics of even large, dense, optically opaque\nsystems - making it ideal for industrial as well as scientific applications.\n\n\n## Getting Started\n\nThese instructions will help you get started PEPT data analysis.\n\n### Prerequisites\n\nThis package supports Python 3. You also need to have `NumPy` and `Cython`\non your system in order to install it.\n\n### Installation\n\nYou can install `pept` from PyPI:\n\n```\npip install pept\n```\n\nOr you can install the latest version from the GitHub repository:\n\n```\npip install git+https://github.com/uob-positron-imaging-centre/pept\n```\n\n### Example usage\n\nYou can download data samples from the [UoB Positron Imaging Centre's\nRepository](https://github.com/uob-positron-imaging-centre/example_data):\n\n```\n$> git clone https://github.com/uob-positron-imaging-centre/example_data\n```\n\nA minimal analysis script using the `pept.tracking.peptml` subpackage:\n\n```\nimport pept\nfrom pept.scanners import ParallelScreens\nfrom pept.tracking import peptml\nfrom pept.visualisation import PlotlyGrapher\n\nlors = ParallelScreens('example_data/sample_2p_42rpm.csv', skiprows = 16)\n\nmax_distance = 0.1\ncutpoints = peptml.Cutpoints(lors, max_distance)\n\nclusterer = peptml.HDBSCANClusterer(min_sample_size = 30)\ncentres, clustered_cutpoints = clusterer.fit_cutpoints(cutpoints)\n\nfig = PlotlyGrapher().create_figure()\nfig.add_trace(centres.all_points_trace())\nfig.show()\n```\n\nA more in-depth tutorial is available on [Google\nColab](https://colab.research.google.com/drive/1G8XHP9zWMMDVu23PXzANLCOKNP_RjBEO).\n\n\n## Performance\n\nSignificant effort has been put into making the algorithms in this package as\nfast as possible. The most compute-intensive parts have been implemented in\n`C` and parallelised, where possible, using `joblib`. For example, using the `peptml`\nsubpackage, analysing 1,000,000 LoRs on the author's machine (mid 2012 MacBook Pro)\ntakes ~26 s (with another 12 s to read in the data). This efficiency is largely\ndue to the availabiliy of a great high-performance [implementation of the\nHDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) clustering algorithm.\n\n\n## Help and Support\n\nWe recommend you check out [our tutorials](https://colab.research.google.com/drive/1G8XHP9zWMMDVu23PXzANLCOKNP_RjBEO). If your issue is not suitably resolved there, please\ncheck the [issues](https://github.com/uob-positron-imaging-centre/pept/issues)\npage on our GitHub. Finally, if no solution is available there, feel free to\n[open an\nissue](https://github.com/uob-positron-imaging-centre/pept/issues/new); the\nauthors will attempt to respond in a reasonably timely fashion.\n\n## Contributing\n\nWe welcome contributions in any form! Assistance with documentation, particularly\nexpanding tutorials, is always welcome. To contribute please fork the project, make\nyour changes and submit a pull request. We will do our best to work through any\nissues with you and get your code merged into the main branch.\n\n## Licensing\n\nThe `pept` package is GNU v3.0 licensed.\n\n\n\n\n\n\n",
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        "name": "pept",
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