{
    "info": {
        "author": "Shyue Ping Ong, Dan Gunter",
        "author_email": "shyuep@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Database",
            "Topic :: Scientific/Engineering :: Chemistry",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Scientific/Engineering :: Physics",
            "Topic :: Software Development :: Libraries :: Python Modules"
        ],
        "description": "Pymatgen-db is a database add-on for the Python Materials Genomics (pymatgen)\nmaterials analysis library. It enables the creation of Materials\nProject-style `MongoDB`_ databases for management of materials data. A query\nengine is also provided to enable the easy translation of MongoDB docs to\nuseful pymatgen objects for analysis purposes.\n\nGetting pymatgen-db\n===================\n\nStable version\n--------------\n\nThe version at the Python Package Index (PyPI) is always the latest stable\nrelease that will be hopefully, be relatively bug-free. The easiest way to\ninstall pymatgen-db on any system is to use easy_install or pip, as follows::\n\n    easy_install pymatgen-db\n\nor::\n\n    pip install pymatgen-db\n\nDevelopmental version\n---------------------\n\nThe bleeding edge developmental version is at the pymatgen-db's `Github repo\n<https://github.com/materialsproject/pymatgen-db>`_. The developmental\nversion is likely to be more buggy, but may contain new features. The\nGithub version include test files as well for complete unit testing. After\ncloning the source, you can type::\n\n    python setup.py install\n\nor to install the package in developmental mode::\n\n    python setup.py develop\n\nThe version number is in the file `VERSION` in the same directory as this\nREADME.\n\nRequirements\n============\n\nAll required python dependencies should be automatically taken care of if you\ninstall pymatgen-db using easy_install or pip. Otherwise, these packages should\nbe available on `PyPI <http://pypi.python.org>`_.\n\n1. Python 2.7+ or 3.5+ required.\n2. Pymatgen 4+, including all dependencies associated with it. Please refer\n   to the `pymatgen docs <http://pythonhosted.org//pymatgen>`_ for detailed\n   installation instructions.\n3. Pymongo 3.3+: For interfacing with MongoDb.\n4. MongoDB 2.2+: Get it at the `MongoDB`_ website.\n\nUsage\n=====\n\nA powerful command-line script (mgdb) provides most of the access to many of\nthe features in pymatgen-db, including db initialization, insertion of data,\nrunning the materials genomics ui, etc. To see all options available, type::\n\n    mgdb --help\n\nInitial setup\n-------------\n\nThe first step is to install and setup MongoDB on a server of your choice.\nThe `MongoDB manual`_ is an excellent place to start. For the purposes of\ntesting out the tools here, you may simply download the binary distributions\ncorresponding to your OS from the `MongoDB`_ website, and then running the\nfollowing commands::\n\n    # For Mac and Linux OS.\n    mkdir test_db && mongod --dbpath test_db\n\nThis will create a test database and start the Mongo daemon. Once you are\ndone with testing, you can simply press Ctrl-C to stop the server and delete\nthe \"test_db\" folder. Running a Mongo server this way is insecure as Mongo\ndoes not enable authentication by default. Please refer to the `MongoDB\nmanual`_ when setting up your production database.\n\nAfter your server is up, you should create a database config file by running\nthe following command::\n\n    mgdb init -c db.json\n\nThis will prompt you for a few parameters to create a database config file,\nwhich will make it much easier to use mgdb in future. Note that the config file\nname can be anything of your choice, but using \"db.json\" will allow you to use\nmgdb without explicitly specifying the filename in future. If you are just\ntesting using the test database, simply hit Enter to accept the defaults for\nall settings.\n\nFor more advanced use of the \"db.json\" config file (e.g., specifying aliases,\ndefaults, etc., please refer to the following `sample\n<http://pythonhosted.org/pymatgen-db/_static/db.json>`_.\n\nInserting calculations\n----------------------\n\nTo insert an entire directory of runs (where the topmost directory is\n\"dir_name\") into the database, use the following command::\n\n    # Note that \"-c db.json\" may be omitted if the config filename is the\n    # current directory under the default filename of db.json.\n\n    mgdb insert -c db.json dir_name\n\nA sample run has been provided for `download\n<http://pythonhosted.org/pymatgen-db/_static/Li2O.zip>`_ for testing\npurposes. Unzip the file and run the above command in the directory.\n\nQuerying a database\n-------------------\n\nSometimes, more fine-grained querying is needed (e.g., for subsequent\npostprocessing and analysis).\n\nThe mgdb script allows you to make simple queries from the command line::\n\n    # Query for the task id and energy per atom of all calculations with\n    # formula Li2O. Note that the criteria has to be specified in the form of\n    # a json string. Note that \"-c db.json\" may be omitted if the config\n    # filename is the current directory under the default filename of db.json.\n\n    mgdb query -c db.json --crit '{\"pretty_formula\": \"Li2O\"}' --props task_id energy_per_atom\n\nFor more advanced queries, you can use the QueryEngine class for which an\nalias is provided at the root package. Some examples are as follows::\n\n    >>> from matgendb import QueryEngine\n    # Depending on your db.json, you may need to supply keyword args below\n    # for `port`, `database`, `collection`, etc.\n    >>> qe = QueryEngine()\n\n    #Print the task id and formula of all entries in the database.\n    >>> for r in qe.query(properties=[\"pretty_formula\", \"task_id\"]):\n    ...     print \"{task_id} - {pretty_formula}\".format(**r)\n    ...\n    12 - Li2O\n\n    # Get a pymatgen Structure from the task_id.\n    >>> structure = qe.get_structure_from_id(12)\n\n    # Get pymatgen ComputedEntries using a criteria.\n    >>> entries = qe.get_entries({})\n\nThe language follows very closely to pymongo/MongoDB syntax, except that\nQueryEngine provides useful aliases for commonly used fields as well as\ntranslation to commonly used pymatgen objects like Structure and\nComputedEntries.\n\nExtending pymatgen-db\n---------------------\n\nCurrently, pymatgen-db is written with standard VASP runs in mind. However,\nit is perfectly extensible to any kind of data, e.g., other kinds of VASP runs\n(bandstructure, NEB, etc.) or just any form of data in general. Developers\nlooking to adapt pymatgen-db for other purposes should look at the\nVaspToDbTaskDrone class as an example and write similar drones for their\nneeds. The QueryEngine can generally be applied to any Mongo collection,\nwith suitable specification of aliases if desired.\n\nHow to cite pymatgen-db\n=======================\n\nIf you use pymatgen and pymatgen-db in your research, please consider citing\nthe following work:\n\n    Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier,\n    Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent Chevrier, Kristin A.\n    Persson, Gerbrand Ceder. *Python Materials Genomics (pymatgen) : A Robust,\n    Open-Source Python Library for Materials Analysis.* Computational\n    Materials Science, 2013, 68, 314-319. `doi:10.1016/j.commatsci.2012.10.028\n    <http://dx.doi.org/10.1016/j.commatsci.2012.10.028>`_\n\n.. _`MongoDB` : http://www.mongodb.org/\n.. _`Github repo` : https://github.com/materialsproject/pymatgen-db\n.. _`MongoDB manual` : http://docs.mongodb.org/manual/\n\n\n",
        "description_content_type": "",
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        "home_page": "https://github.com/materialsproject/pymatgen-db",
        "keywords": "vasp,gaussian,materials,project,electronic,structure,mongo",
        "license": "MIT",
        "maintainer": "Dan Gunter",
        "maintainer_email": "dkgunter@lbl.gov",
        "name": "pymatgen-db",
        "package_url": "https://pypi.org/project/pymatgen-db/",
        "platform": "",
        "project_url": "https://pypi.org/project/pymatgen-db/",
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        "summary": "Pymatgen-db is a database add-on for the Python Materials Genomics (pymatgen) materials analysis library.",
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