Metadata-Version: 1.1
Name: pymatgen
Version: 3.0.9
Summary: Python Materials Genomics is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project (https://www.materialsproject.org).
Home-page: https://github.com/materialsproject/pymatgen/
Author: Shyue Ping Ong
Author-email: ongsp@ucsd.edu, anubhavj@mit.edu, mpkocher@lbnl.gov, geoffroy.hautier@uclouvain.be, wrichard@mit.edu, sdacek@mit.edu, dkgunter@lbl.gov, scholia@lbl.gov, gmatteo@gmail.com, vincentchevrier@gmail.com, armiento@mit.edu
License: MIT
Description: 
        Pymatgen (Python Materials Genomics) is a robust, open-source Python library
        for materials analysis. It currently powers the public Materials Project
        (https://www.materialsproject.org), an initiative to make calculated
        properties of all known inorganic materials available to materials
        researchers. These are some of the main features:
        
        1. Highly flexible classes for the representation of Element, Site, Molecule,
           Structure objects.
        2. Extensive io capabilities to manipulate many VASP
           (http://cms.mpi.univie.ac.at/vasp/) and ABINIT (http://www.abinit.org/)
           input and output files and the crystallographic information file format.
           This includes generating Structure objects from vasp input and output.
           There is also support for Gaussian input files and XYZ file for molecules.
        3. Comprehensive tool to generate and view compositional and grand canonical
           phase diagrams.
        4. Electronic structure analyses (DOS and Bandstructure).
        5. Integration with the Materials Project REST API.
        
        Pymatgen is free to use. However, we also welcome your help to improve this
        library by making your own contributions.  These contributions can be in the
        form of additional tools or modules you develop, or even simple things such
        as bug reports. Please report any bugs and issues at pymatgen's `Github page
        <https://github.com/materialsproject/pymatgen>`_. If you wish to be notified
        of pymatgen releases, you may become a member of `pymatgen's Google Groups page
        <https://groups.google.com/forum/?fromgroups#!forum/pymatgen/>`_.
        
        Python 3.x support
        ==================
        
        With effect from version 3.0, pymatgen now supports both Python 2.7 as well
        as Python 3.x. All underlying core dependencies (numpy,
        pyhull and the spglib library) have been made Python 3 compatible,
        and a completely rewritten CIF parser module (courtesy of William Davidson
        Richards) has removed the dependency on PyCIFRW. We will support Python >= 3.3
        (ignoring v3.1 and v3.2). With the release of a new major version,
        we also took the opportunity to streamline and cleanup some of the code,
        which introduces a few backward incompatibilities.
        
        Why use pymatgen?
        =================
        
        There are many materials analysis codes out there, both commerical and free,
        but pymatgen offer several advantages:
        
        1. **It is (fairly) robust.** Pymatgen is used in the Materials Project. As
           such, the analysis it produces survives rigorous scrutiny every single
           day. Bugs tend to be found and corrected quickly. Furthermore,
           pymatgen uses `CircleCI <https://circleci.com>`_ for continuous
           integration, which ensures that all unittests pass with every commit.
        2. **It is well documented.** A fairly comprehensive documentation has been
           written to help you get to grips with it quickly. That means more
           efficient research.
        3. **It is open.** That means you are free to use it, and you can also
           contribute to it. It also means that pymatgen is continuously being
           improved. We have a policy of attributing any code you contribute to any
           publication you choose. Contributing to pymatgen means your research
           becomes more visible, which translates to greater impact.
        4. **It is fast.** Many of the core numerical methods in pymatgen have been
           optimized in numpy. This means that coordinate manipulations are extremely
           fast and are in fact comparable to codes written in other languages.
           Pymatgen also comes with a complete system for handling periodic boundary
           conditions.
        
        Getting pymatgen
        ================
        
        Before installing pymatgen, you may need to first install a few critical
        dependencies manually. Please refer to the official `pymatgen page`_ for
        installation details and requirements, including instructions for the
        bleeding edge developmental version.
        
        The version at the Python Package Index (PyPI) is always the latest stable
        release that is relatively bug-free. The recommended way to install pymatgen
        on any system is to use pip (or easy_install), as follows::
        
            pip install pymatgen
        
        or::
        
            easy_install pymatgen
        
        Some extra functionality (e.g., generation of POTCARs) do require additional
        setup (please see the `pymatgen page`_).
        
        **Note for Windows users**: Given that pymatgen requires several Python C
        extensions, it is generally recommended that you install it in a cygwin or
        equivalent environment with the necessary compilers.
        
        Change Log
        ==========
        The latest change log is available `here <http://pymatgen.org/change_log>`_.
        
        Using pymatgen
        ==============
        
        Please refer to the official `pymatgen page`_ for tutorials and examples.
        
        How to cite pymatgen
        ====================
        
        If you use pymatgen in your research, please consider citing the following
        work:
        
            Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier,
            Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent Chevrier, Kristin A.
            Persson, Gerbrand Ceder. *Python Materials Genomics (pymatgen) : A Robust,
            Open-Source Python Library for Materials Analysis.* Computational
            Materials Science, 2013, 68, 314-319. `doi:10.1016/j.commatsci.2012.10.028
            <http://dx.doi.org/10.1016/j.commatsci.2012.10.028>`_
        
        In addition, some of pymatgen's functionality is based on scientific advances
        / principles developed by the computational materials scientists in our team.
        Please refer to `pymatgen's documentation <http://pymatgen.org/>`_ on how to
        cite them.
        
        License
        =======
        
        Pymatgen is released under the MIT License. The terms of the license are as
        follows::
        
            The MIT License (MIT)
            Copyright (c) 2011-2012 MIT & LBNL
        
            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
            copies or substantial portions of the Software.
        
            THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
            IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
            FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
            COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
            IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
            CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
        
        .. _`pymatgen page` : http://www.pymatgen.org
        
Keywords: VASP,gaussian,ABINIT,nwchem,materials,project,electronic,structure,analysis,phase,diagrams
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Software Development :: Libraries :: Python Modules
