Metadata-Version: 2.1
Name: trackstar
Version: 1.0.0a0.dev0
Summary: Bayesian likelihood estimates for curves in data space
Author-email: "James W. Johnson" <giganano9@gmail.com>
Maintainer-email: "James W. Johnson" <giganano9@gmail.com>
License: MIT License
        
        Copyright (c) 2023 James W. Johnson
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Project-URL: Repository, https://github.com/giganano/TrackStar
Project-URL: Issues, https://github.com/giganano/TrackStar/issues
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: numpy>=1.21.0; extra == "dev"
Provides-Extra: docs
Requires-Dist: sphinx>=6.0; extra == "docs"
Requires-Dist: sphinx-rtd-theme>=2.0; extra == "docs"
Requires-Dist: nbsphinx>=0.8; extra == "docs"
Requires-Dist: matplotlib>=3.0; extra == "docs"


# TrackStar

<div align="center">
	<a href="https://github.com/giganano/TrackStar/actions/workflows/ci.yml"
		target="_blank">
		<img src="https://github.com/giganano/TrackStar/workflows/GitHub%20CI/badge.svg">
	</a><a
		href="https://github.com/giganano/TrackStar/issues" target="_blank">
		<img src="https://img.shields.io/github/issues/giganano/TrackStar.svg">
	</a>
</div>

<div align="center">
	<a href="https://github.com/giganano/TrackStar/blob/main/LICENSE"
		target="_blank">
		<img src="https://img.shields.io/badge/License-MIT-blue.svg">
	</a>
</div>

TrackStar is a highly optimized, user-friendly library designed to compute
Bayesian likelihood estimates for arbitrary lines and curves when the data have
errors in every direction.
When combined with your favorite optimization routine (e.g. Markov chain Monte
Carlo, maximum a posteriori esimation), TrackStar provides blazing fast
best-fit parameters!

TrackStar users enjoy the following features:

<ul>
	<li>
		Support for samples in which some quantities are not available for
		every data vector.
		For example, if &theta;<sub>1</sub> and &theta;<sub>2</sub> are
		available for the whole sample, but only half of the data have
		measurements of &theta;<sub>3</sub>, TrackStar will automatically use
		&theta;<sub>3</sub> where it is available.
	</li>
	<li>
		Multi-threading with the
		<a href="https://openmp.org/" target="_blank">OpenMP</a> library.
		If these features are enabled when TrackStar is installed, simply
		tell it how many threads you'd like it to use, and hit run!
	</li>
	<li>
		Full control over the expected N-dimensional distribution of the data,
		both due to selection effects and arising from model predictions.
	</li>
</ul>

### Developers: To-Do Items

<ul>
	<li>
		Finish implementing unit tests
	</li>
	<li>
		Ensure the likelihood estimates is properly normalized when the data
		vectors do not have the same dimensionality (i.e. when not all
		quantities are measured for each datum).
	</li>
	<li>
		Finish writing documentation: API reference, science documentation,
		installation instructions, developers documentation, Changelog
	</li>
	<li>
		Write example codes
	</li>
</ul>

