{
    "info": {
        "author": "Henry Leung",
        "author_email": "henrysky.leung@mail.utoronto.ca",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Astronomy"
        ],
        "description": ".. image:: http://astronn.readthedocs.io/en/latest/_static/astroNN_icon_withname.png\n\n|\n\n.. image:: https://readthedocs.org/projects/astronn/badge/?version=latest\n   :target: http://astronn.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation Status\n\n.. image:: https://img.shields.io/github/license/henrysky/astroNN.svg\n   :target: https://github.com/henrysky/astroNN/blob/master/LICENSE\n   :alt: GitHub license\n\n.. image:: https://travis-ci.org/henrysky/astroNN.svg?branch=master\n   :target: https://travis-ci.org/henrysky/astroNN\n   :alt: Build Status\n\n.. image:: https://img.shields.io/coveralls/henrysky/astroNN.svg\n   :target: https://coveralls.io/github/henrysky/astroNN?branch=master\n   :alt: Coverage Status\n\n.. image:: https://badge.fury.io/py/astroNN.svg\n    :target: https://badge.fury.io/py/astroNN\n\n.. image:: http://img.shields.io/badge/DOI-10.1093/mnras/sty3217-blue.svg\n   :target: http://dx.doi.org/10.1093/mnras/sty3217\n\nGetting Started\n=================\n\nastroNN is a python package to do various kinds of neural networks with targeted application in astronomy by using Keras\nas model and training prototyping, but at the same time take advantage of Tensorflow's flexibility.\n\nFor non-astronomy applications, astroNN contains custom loss functions and layers which are compatible with Tensorflow\nor Keras with Tensorflow backend. The custom loss functions mostly designed to deal with incomplete labels.\nastroNN contains demo for implementing Bayesian Neural Net with Dropout Variational Inference in which you can get\nreasonable uncertainty estimation and other neural nets.\n\nFor astronomy applications, astroNN contains some tools to deal with APOGEE, Gaia and LAMOST data. astroNN is mainly designed\nto apply neural nets on APOGEE spectra analysis and predicting luminosity from spectra using data from Gaia\nparallax with reasonable uncertainty from Bayesian Neural Net. Generally, astroNN can handle 2D and 2D colored images too.\nCurrently astroNN is a python package being developed by the main author to facilitate his research\nproject on deep learning application in stellar and galactic astronomy using SDSS APOGEE, Gaia and LAMOST data.\n\nFor learning purpose, astroNN includes a deep learning toy dataset for astronomer - `Galaxy10 Dataset`_.\n\n\n`astroNN Documentation`_\n\n`Quick Start guide`_\n\n`Uncertainty Analysis of Neural Nets with Variational Methods`_\n\n\nAcknowledging astroNN\n-----------------------\n\n| Please cite the following paper that describes astroNN if astroNN used in your research as well as consider linking it to https://github.com/henrysky/astroNN\n| **Deep learning of multi-element abundances from high-resolution spectroscopic data** [`arXiv:1804.08622`_][`ADS`_]\n\n.. _arXiv:1804.08622: https://arxiv.org/abs/1808.04428\n.. _ADS: https://ui.adsabs.harvard.edu/#abs/2019MNRAS.483.3255L/\n\nAuthors\n-------------\n-  | **Henry Leung** - *Initial work and developer* - henrysky_\n   | Astronomy Student, University of Toronto\n   | Contact Henry: henrysky.leung [at] mail.utoronto.ca\n\n-  | **Jo Bovy** - *Project Supervisor* - jobovy_\n   | Astronomy Professor, University of Toronto\n\nLicense\n-------------\nThis project is licensed under the MIT License - see the `LICENSE`_ file for details\n\n.. _LICENSE: LICENSE\n.. _henrysky: https://github.com/henrysky\n.. _jobovy: https://github.com/jobovy\n\n.. _astroNN Documentation: http://astronn.readthedocs.io/\n.. _Quick Start guide: http://astronn.readthedocs.io/en/latest/quick_start.html\n.. _Galaxy10 Dataset: http://astronn.readthedocs.io/en/latest/galaxy10.html\n.. _Galaxy10 Tutorial Notebook: https://github.com/henrysky/astroNN/blob/master/demo_tutorial/galaxy10/Galaxy10_Tutorial.ipynb\n.. _Uncertainty Analysis of Neural Nets with Variational Methods: https://github.com/henrysky/astroNN/tree/master/demo_tutorial/NN_uncertainty_analysis",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/henrysky/astroNN",
        "keywords": "",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "astroNN",
        "package_url": "https://pypi.org/project/astroNN/",
        "platform": "",
        "project_url": "https://pypi.org/project/astroNN/",
        "project_urls": {
            "Bug Tracker": "https://github.com/henrysky/astroNN/issues",
            "Documentation": "http://astronn.readthedocs.io/",
            "Homepage": "https://github.com/henrysky/astroNN",
            "Source Code": "https://github.com/henrysky/astroNN"
        },
        "release_url": "https://pypi.org/project/astroNN/1.0.1/",
        "requires_dist": null,
        "requires_python": ">=3.6",
        "summary": "Deep Learning for Astronomers with Tensorflow",
        "version": "1.0.1"
    },
    "last_serial": 4902719,
    "releases": {
        "1.0.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "fb204579fae74c00adb6130dddf7ca50",
                    "sha256": "e374bf557e5cb17e72198d125499006fc40cfb0d36afb54befed6828349d07ac"
                },
                "downloads": -1,
                "filename": "astroNN-1.0.0.tar.gz",
                "has_sig": false,
                "md5_digest": "fb204579fae74c00adb6130dddf7ca50",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 9213560,
                "upload_time": "2018-08-01T23:26:30",
                "url": "https://files.pythonhosted.org/packages/ed/d2/f33e61b8a193054d100a3247108fcf8db2d8aee9f75f159e2884d9606698/astroNN-1.0.0.tar.gz"
            }
        ],
        "1.0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "9a777240879b307640720135a3c364c6",
                    "sha256": "47e7180ab93515480bec7a1f8c540ff67850f6a688d8a5ca9be9457f12c44316"
                },
                "downloads": -1,
                "filename": "astroNN-1.0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "9a777240879b307640720135a3c364c6",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": ">=3.6",
                "size": 9271021,
                "upload_time": "2019-03-06T00:25:22",
                "url": "https://files.pythonhosted.org/packages/0b/a6/5a0dfea08413801774dd599a08298ed80cae66d106d1831b9791d61dcb36/astroNN-1.0.1.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "9a777240879b307640720135a3c364c6",
                "sha256": "47e7180ab93515480bec7a1f8c540ff67850f6a688d8a5ca9be9457f12c44316"
            },
            "downloads": -1,
            "filename": "astroNN-1.0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "9a777240879b307640720135a3c364c6",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 9271021,
            "upload_time": "2019-03-06T00:25:22",
            "url": "https://files.pythonhosted.org/packages/0b/a6/5a0dfea08413801774dd599a08298ed80cae66d106d1831b9791d61dcb36/astroNN-1.0.1.tar.gz"
        }
    ]
}