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        "author": "Blake A. Wilson",
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            "License :: OSI Approved :: MIT License",
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        "description": "# GAlibrate\n\n![Python version badge](https://img.shields.io/badge/python-3.6,3.7-blue.svg)\n[![license](https://img.shields.io/github/license/blakeaw/GAlibrate.svg)](LICENSE)\n![version](https://img.shields.io/badge/version-0.4.0-orange.svg)\n[![release](https://img.shields.io/github/release-pre/blakeaw/GAlibrate.svg)](https://github.com/blakeaw/GAlibrate/releases/tag/v0.4.0)\n[![anaconda cloud](https://anaconda.org/blakeaw/galibrate/badges/version.svg)](https://anaconda.org/blakeaw/galibrate)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/6cdd91c06b11458384becb85db9adb15)](https://www.codacy.com/app/blakeaw1102/GAlibrate?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=blakeaw/GAlibrate&amp;utm_campaign=Badge_Grade)\n[![DOI](https://zenodo.org/badge/197295657.svg)](https://zenodo.org/badge/latestdoi/197295657)\n\n<p align=\"center\">\n  <img width=\"200\" height=\"200\" src=\"./images/GAlibrate_logo.png\">\n</p>\n\n**GAlibrate** is a python toolkit that provides an easy to use interface for model calibration/parameter estimation using an implementation of continuous genetic algorithm-based optimization. Its functionality and API were designed to be familiar to users of the [PyDREAM](https://github.com/LoLab-VU/PyDREAM), [simplePSO](https://github.com/LoLab-VU/ParticleSwarmOptimization), and [Gleipnir](https://github.com/LoLab-VU/Gleipnir) packages.\n\nAlthough **GAlibrate** provides a general framework for running continuous\ngenetic algorithm-based optimizations, it was created with systems biology models in mind. It therefore supplies additional tools for working with biological models in the [PySB](http://pysb.org/) format.\n\n------\n\n# Install\n\n| **! Warning** |\n| :--- |\n|  GAlibrate is still under heavy development and may rapidly change. |\n\n**GAlibrate** installs as the `galibrate` package. It is compatible (i.e., tested) with Python 3.6 and 3.7.\n\nNote that `galibrate` has the following core dependencies:\n   * [NumPy](http://www.numpy.org/)\n   * [SciPy](https://www.scipy.org/)\n\n#### pip install\nYou can install the latest release of the `galibrate` package using `pip` sourced from the GitHub repo:\n```\npip install -e git+https://github.com/blakeaw/GAlibrate@v0.4.0#egg=galibrate\n```\nHowever, this will not automatically install the core dependencies. You will have to do that separately:\n```\npip install numpy scipy\n```\n#### conda install\nYou can install the `galibrate` package from the `blakeaw` channel:\n```\nconda install -c blakeaw galibrate\n```\nNumPy and SciPy dependencies will be automatically installed with this version.\n\n### Recommended additional software\n\nThe following software is not required for the basic operation of **GAlibrate**, but provides extra capabilities and features when installed.\n\n#### Cython\n**GAlibrate** includes an implementation of the core genetic algorithm that is written in   [Cython](https://cython.org/), which takes advantage of Cython-based optimizations and compilation to accelerate the algorithm. This version of genetic algorithm is used if Cython is installed.\n\n#### Numba\n**GAlibrate** also includes an implementation of the core genetic algorithm that takes advantage of [Numba](https://numba.pydata.org/)-based JIT compilation and optimization to accelerate the algorithm. This version of genetic algorithm is used if Numba is installed.\n\n#### PySB\n[PySB](http://pysb.org/) is needed to run PySB models, and it is therfore needed if you want to use tools from the galibrate.pysb_utils package.\n\n------\n\n# License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details\n\n------\n\n# Documentation and Usage\n\n### Quick Overview\nPrincipally, **GAlibrate** defines the **GAO** (continuous **G**enetic **A**lgorithm-based **O**ptimizer ) class,\n```python\nfrom galibrate import GAO\n```\nwhich defines an object that can be used setup and run a continuous genetic algorithm-based optimization (i.e., a maximization) of a user-defined fitness function over the search space of a given set of (model) parameters.\n\nAdditionally, **GAlibrate** has a `pysb_utils` sub-package that provides the\n`galibrate_it` module, which defines the GaoIt and GAlibrateIt classes (importable from the pysb_utils package level),\n```python\nfrom galibrate.pysb_utils import GaoIt, GAlibrateIt\n```  \nwhich create objects that abstract away some of the effort to setup and generate GAO instances for PySB models; [examples/pysb_dimerization_model](./examples/pysb_dimerization_model) provides some\nexamples for using GaoIt and GAlibrateIt objects. The `galibrate_it` module can also be called from the command line to generate a template run script for a PySB model,\n```python\npython -m galibrate.pysb_utils.galibrate_it pysb_model.py output_path\n```\nwhich users can then modify to fit their needs.\n\n### Examples\nAdditional example scripts that show how to setup and launch Genetic Algorithm runs using **GAlibrate** can be found under [examples](./examples).\n\n------\n\n# Contact\n\nTo report problems or bugs please open a\n[GitHub Issue](https://github.com/blakeaw/GAlibrate/issues). Additionally, any\ncomments, suggestions, or feature requests for **GAlibrate** can also be submitted as a\n[GitHub Issue](https://github.com/blakeaw/GAlibrate/issues).\n\n------\n\n# Citing\n\nIf you use the **GAlibrate** software in your research, please cite it. You can export the  **GAlibrate** citation in your preferred format from its [Zenodo DOI](https://doi.org/10.5281/zenodo.3345232) entry.\n\nAlso, please cite the following references as appropriate for software used with/via **GAlibrate**:\n\n#### Packages from the SciPy ecosystem\n\nThese include NumPy and SciPy for which references can be obtained from:\nhttps://www.scipy.org/citing.html\n\n#### PySB\n  1. Lopez, C. F., Muhlich, J. L., Bachman, J. A. & Sorger, P. K. Programming biological models in Python using PySB. Mol Syst Biol 9, (2013). doi:[10.1038/msb.2013.1](dx.doi.org/10.1038/msb.2013.1)\n\n\n",
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