{
    "info": {
        "author": "Ji Won Park",
        "author_email": "jiwon.christine.park@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python"
        ],
        "description": "# baobab\n\n[![Build Status](https://travis-ci.com/jiwoncpark/baobab.svg?branch=master)](https://travis-ci.org/jiwoncpark/baobab)\n[![Documentation Status](https://readthedocs.org/projects/pybaobab/badge/?version=latest)](https://pybaobab.readthedocs.io/en/latest/?badge=latest)\n\nTraining data generator for hierarchically modeling strong lenses with Bayesian neural networks\n\n## Installation\n\n0. You'll need a Fortran compiler, which you can get on a debian system by running\n```shell\nsudo apt-get install gfortran\n```\n\n1. Virtual environments are strongly recommended, to prevent dependencies with conflicting versions. Create a conda virtual environment and activate it.\n```shell\nconda create -n baobab python=3.6 -y\nconda activate baobab\n```\n\n2. Now do one of the following. \n\n### Option 2(a): clone the repo (please do this if you'd like to contribute to the development).\n```\ngit clone https://github.com/jiwoncpark/baobab.git\ncd baobab\npip install -e .\n```\n\n### Option 2(b): pip install the release version (only recommended if you're a user).\n```\npip install baobab\n```\n\n3. (Optional) To run the notebooks, add the Jupyter kernel.\n```shell\npython -m ipykernel install --user --name baobab --display-name \"Python (baobab)\"\n```\n\n## Usage\n\n1. Choose your favorite config file among the templates in the `configs` directory and *copy* it to a directory of your choice, e.g.\n```shell\nmkdir my_config_collection\ncp baobab/configs/tdlmc_diagonal_config.py my_config_collection/my_config.py\n```\n\n2. Customize it! You might want to change the `name` field first with something recognizable. Pay special attention to the `components` field, which determines which components of the lensed system (e.g. lens light, AGN light) become sampled from relevant priors and rendered in the image.\n\n2. Generate the training set, e.g. continuing with the example in #1,\n```shell\ngenerate my_config_collection/my_config.py\n```\n\nAlthough the `n_data` (size of training set) value is specified in the config file, you may choose to override it in the command line, as in\n```shell\ngenerate my_config_collection/my_config.py 100\n```\n\nPlease message @jiwoncpark with any questions.\n\nThere is an ongoing [document](https://www.overleaf.com/read/pswdqwttjbjr) that details our BNN prior choice, written and maintained by Ji Won.\n\n",
        "description_content_type": "text/markdown",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/jiwoncpark/baobab",
        "keywords": "physics",
        "license": "LICENSE.md",
        "maintainer": "",
        "maintainer_email": "",
        "name": "pybaobab",
        "package_url": "https://pypi.org/project/pybaobab/",
        "platform": "",
        "project_url": "https://pypi.org/project/pybaobab/",
        "project_urls": {
            "Homepage": "https://github.com/jiwoncpark/baobab"
        },
        "release_url": "https://pypi.org/project/pybaobab/0.1.0/",
        "requires_dist": [
            "lenstronomy",
            "astropy",
            "nose",
            "numpy",
            "matplotlib",
            "ipykernel",
            "pandas",
            "dynesty",
            "tqdm",
            "corner",
            "mpmath",
            "addict"
        ],
        "requires_python": "",
        "summary": "Data generator for hierarchically modeling strongly-lensed systems with Bayesian neural networks",
        "version": "0.1.0"
    },
    "last_serial": 5794565,
    "releases": {
        "0.1.0": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "61bdfb2843fedcdf6bacafbac0e3b551",
                    "sha256": "8348c32503193401e030fd3c5b4d0038c21a8913d2679f2d30d7aebdd62b98f3"
                },
                "downloads": -1,
                "filename": "pybaobab-0.1.0-py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "61bdfb2843fedcdf6bacafbac0e3b551",
                "packagetype": "bdist_wheel",
                "python_version": "py3",
                "requires_python": null,
                "size": 534811,
                "upload_time": "2019-09-07T00:32:47",
                "url": "https://files.pythonhosted.org/packages/a6/74/b794194273797e521484293ea6914ac987c1be4e441406c96394b3b397d9/pybaobab-0.1.0-py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "8f8753ddd0d36b5583152c10562d89ab",
                    "sha256": "a5c2c8516e819eb99c288236fbfecf2c6f47ae2ef6de92fa0d9f4e4dd0e03923"
                },
                "downloads": -1,
                "filename": "pybaobab-0.1.0.tar.gz",
                "has_sig": false,
                "md5_digest": "8f8753ddd0d36b5583152c10562d89ab",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 529463,
                "upload_time": "2019-09-07T00:32:50",
                "url": "https://files.pythonhosted.org/packages/cf/e0/4bf3b4e35fd50513abf69ff67953be7a642358af00d9f489129ab9b87b6e/pybaobab-0.1.0.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "61bdfb2843fedcdf6bacafbac0e3b551",
                "sha256": "8348c32503193401e030fd3c5b4d0038c21a8913d2679f2d30d7aebdd62b98f3"
            },
            "downloads": -1,
            "filename": "pybaobab-0.1.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "61bdfb2843fedcdf6bacafbac0e3b551",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": null,
            "size": 534811,
            "upload_time": "2019-09-07T00:32:47",
            "url": "https://files.pythonhosted.org/packages/a6/74/b794194273797e521484293ea6914ac987c1be4e441406c96394b3b397d9/pybaobab-0.1.0-py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "8f8753ddd0d36b5583152c10562d89ab",
                "sha256": "a5c2c8516e819eb99c288236fbfecf2c6f47ae2ef6de92fa0d9f4e4dd0e03923"
            },
            "downloads": -1,
            "filename": "pybaobab-0.1.0.tar.gz",
            "has_sig": false,
            "md5_digest": "8f8753ddd0d36b5583152c10562d89ab",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 529463,
            "upload_time": "2019-09-07T00:32:50",
            "url": "https://files.pythonhosted.org/packages/cf/e0/4bf3b4e35fd50513abf69ff67953be7a642358af00d9f489129ab9b87b6e/pybaobab-0.1.0.tar.gz"
        }
    ]
}