Metadata-Version: 1.1
Name: pymatgen
Version: 2.8.1
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 (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: 
        **Official docs:** http://www.pymatgen.org
        
        Pymatgen (Python Materials Genomics) is a robust, open-source Python library
        for materials analysis. It currently powers the public Materials Project
        (http://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, like all scientific research, will always be a work in progress.
        While the development team will always strive to avoid backward incompatible
        changes, they are sometimes unavoidable, and tough decisions have to be made
        for the long term health of the code.
        
        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/>`_.
        
        Why use pymatgen?
        =================
        
        There are many materials analysis codes out there, both commerical and free.
        So you might ask - why should I use pymatgen over others? Pymatgen offer
        several advantages over other codes out there:
        
        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 by vectorizing 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
        ================
        
        Stable version
        --------------
        
        The version at the Python Package Index (PyPI) is always the latest stable
        release that will be hopefully, be relatively bug-free. The easiest way to
        install pymatgen on any system is to use easy_install or pip, as follows::
        
            easy_install pymatgen
        
        or::
        
            pip install pymatgen
        
        Some extra functionality (e.g., generation of POTCARs) do require additional
        setup (please see the `official pymatgen page <http://pymatgen.org/>`_).
        
        **Note**: You may need to install numpy before installing pymatgen as numpy's
        distutils is needed to compile the spglib and pyhull dependencies.
        
        **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.
        
        Developmental version
        ---------------------
        
        The bleeding edge developmental version is at the pymatgen's `Github repo
        <https://github.com/materialsproject/pymatgen>`_. The developmental
        version is likely to be more buggy, but may contain new features. The
        Github version include test files as well for complete unit testing. After
        cloning the source, you can type::
        
            python setup.py install
        
        or to install the package in developmental mode::
        
            python setup.py develop
        
        Requirements
        ============
        
        All required dependencies should be automatically taken care of if you
        install pymatgen using easy_install or pip. Otherwise, these packages should
        be available on `PyPI <http://pypi.python.org>`_.
        
        1. Python 2.7+ required. New default modules such as json are used, as well as
           new unittest features in Python 2.7.
        2. numpy - For array, matrix and other numerical manipulations. Used extensively
           by all core modules.
        3. pyhull 1.3.6+: For generation of phase diagrams.
        4. PyCifRW 3.3+: For reading and writing Crystallographic Information Format
           (CIF) files.
        5. requests 1.0+: For the high-level interface to the Materials API.
        
        Optional dependencies
        ---------------------
        
        Optional libraries that are required if you need certain features:
        
        1. scipy 0.10+ (highly recommended): For use in Gaussian smearing.
        2. matplotlib 1.1+ (highly recommended): For plotting (e.g., Phase Diagrams).
        3. VTK with Python bindings 5.8+ (http://www.vtk.org/): For visualization of
           crystal structures using the pymatgen.vis package.
        4. Atomistic Simulation Environment or ASE 3.6+: Required for the usage of the
           adapters in pymatgen.io.aseio between pymatgen's core Structure object and
           the Atoms object used by ASE. Get it at https://wiki.fysik.dtu.dk/ase/.
        5. OpenBabel with Python bindings (http://openbabel.org): Required for the
           usage of the adapters in pymatgen.io.babelio between pymatgen's Molecule
           and OpenBabel's OBMol. Opens up input and output support for the very large
           number of input and output formats supported by OpenBabel.
        6. nose - For complete unittesting.
        
        Optional non-Python programs
        ----------------------------
        
        Optional non-python libraries (because no good python alternative exists at
        the moment) required only for certain features:
        
        1. ffmpeg: For generation of movies in structure_vtk.py. The executable ffmpeg
           must be in the path. Get it at http://www.ffmpeg.org.
        2. enum: For the use of EnumerateStructureTransformation and the
           pymatgen.command_line.enumlib_caller module. This library by Gus Hart
           provides a robust way to enumerate derivative structures. It can be used to
           completely enumerate all symmetrically distinct ordered structures of
           disordered structures via the EnumerateStructureTransformation. The
           multienum.x and makestr.x executables must be in the path. Get it at
           http://enum.sourceforge.org and follow the instructions to compile
           multienum.x and makestr.x.
        3. bader: For the use of the BaderAnalysis class in pymatgen.command_line.bader
           module. This library by Henkelmann et al. provides a robust way to
           calculate the Bader analysis from a CHGCAR. The bader executable must be
           in the path. Get it at http://theory.cm.utexas.edu/bader.
        
        Using pymatgen
        ==============
        
        .. figure:: http://pymatgen.org/images/overview.jpg
           :width: 70%
           :alt: pymatgen overview
           :align: center
        
        The figure above provides an overview of the functionality in pymatgen. A
        typical workflow would involve a user converting data (structure, calculations,
        etc.) from various sources (first principles calculations, crystallographic and
        molecule input files, Materials Project, etc.) into Python objects using
        pymatgen's io packages, which are then used to perform further structure
        manipulation or analyses.
        
        Basic usage
        -----------
        
        Useful aliases for commonly used objects are now provided, similar in style to
        numpy. Supported objects include Element, Composition, Structure, Molecule,
        Spin and Orbital. Here are some quick examples of the core capabilities and
        objects:
        
        .. code-block:: pycon
        
            >>> import pymatgen as mg
            >>>
            >>> si = mg.Element("Si")
            >>> si.atomic_mass
            28.0855
            >>> si.melting_point
            u'1687 K'
            >>>
            >>> comp = mg.Composition("Fe2O3")
            >>> comp.weight
            159.6882
            >>> #Note that Composition conveniently allows strings to be treated just
            >>> #like an Element object.
            >>> comp["Fe"]
            2.0
            >>> comp.get_atomic_fraction("Fe")
            0.4
            >>> lattice = mg.Lattice.cubic(4.2)
            >>> structure = mg.Structure(lattice, ["Cs", "Cl"],
            ...                          [[0, 0, 0], [0.5, 0.5, 0.5]])
            >>> structure.volume
            74.088000000000008
            >>> structure[0]
            PeriodicSite: Cs (0.0000, 0.0000, 0.0000) [0.0000, 0.0000, 0.0000]
            >>>
            >>> #Integrated symmetry tools from spglib.
            >>> from pymatgen.symmetry.finder import SymmetryFinder
            >>> finder = SymmetryFinder(structure)
            >>> finder.get_spacegroup_symbol()
            'Pm-3m'
            >>>
            >>> # Convenient IO to various formats. Format is intelligently determined
            >>> # from file name and extension.
            >>> mg.write_structure(structure, "POSCAR")
            >>> mg.write_structure(structure, "CsCl.cif")
            >>>
            >>> #Reading a structure from a file.
            >>> structure = mg.read_structure("POSCAR")
        
        The above illustrates only the most basic capabilities of pymatgen.
        
        .. note:: Examples
        
            A good way to explore the functionality of pymatgen is to look at examples.
            We have created a `Github wiki page
            <https://github.com/materialsproject/pymatgen/wiki>`_ to allow users to
            share their Github gists (essentially mini git repos of scripts)
            performing various kinds of functions with pymatgen. Please feel free to
            check them out and we welcome your contributions as well!
        
        matgenie.py - Command line tool
        -------------------------------
        
        To demonstrate the capabilities of pymatgen and to make it easy for users to
        quickly use the functionality, pymatgen comes with a set of useful scripts
        that utilize the library to perform all kinds of analyses. You can find these
        scripts in `scripts directory of pymatgen's github repo
        <https://github.com/materialsproject/pymatgen/tree/master/scripts>`_.
        
        Here, we will discuss the most versatile of these scripts,
        known as matgenie.py. The typical usage of matgenie.py is::
        
            matgenie.py {analyze, plotdos, plotchgint, convert, symm, view, compare} additional_arguments
        
        At any time, you can use "matgenie.py --help" or "matgenie.py subcommand
        --help" to bring up a useful help message on how to use these subcommands.
        Here are a few examples of typical usages::
        
            #Parses all vasp runs in a directory and display the basic energy
            #information. Saves the data in a file called vasp_data.gz for subsequent
            #reuse.
        
            matgenie.py analyze .
        
            #Plot the dos from the vasprun.xml file.
        
            matgenie.py plotdos vasprun.xml
        
            #Convert between file formats. The script attempts to intelligently
            #determine the file type. Input file types supported include CIF,
            #vasprun.xml, POSCAR, CSSR. You can force the script to assume certain file
            #types by specifying additional arguments. See matgenie.py convert -h.
        
            matgenie.py convert input_filename output_filename.
        
            #Obtain spacegroup information.
        
            matgenie.py symm -s filename1 filename2
        
            #Visualize a structure. Requires VTK to be installed.
        
            matgenie.py view filename
        
            #Compare two structures for similarity
        
            matgenie.py compare filename1 filename2
        
            #Generate a POTCAR with symbols Li_sv O and the PBE functional
        
            matgenie.py generate --potcar Li_sv O --functional PBE
        
        ipmg - A Custom ipython shell
        -----------------------------
        
        From version 2.5.2, A custom ipython shell for pymatgen has been implemented.
        Upon installing pymatgen in the usual manner, the "ipmg" script will be
        installed. Running ipmg will bring users into a custom ipython environment
        where the most commonly used pymatgen objects (see Aliases below) are
        automatically loaded into the environment.
        
        Advanced Usage
        --------------
        
        Users are strongly encouraged to explore the detailed `usage pages
        <http://pymatgen.org/usage.html>`_ and `api docs
        <http://pymatgen.org/modules.html>`_.
        
        Add-ons
        -------
        
        Some add-ons are available for pymatgen today:
        
        1. The `pymatgen-db <https://pypi.python.org/pypi/pymatgen-db>`_ add-on
           provides tools to create databases of calculated run data using pymatgen.
        2. The `custodian <https://pypi.python.org/pypi/custodian>`_ pacakge provides
           a JIT job management and error correction for calculations, particularly
           VASP calculations.
        
        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.
Keywords: VASP,gaussian,ABINIT,nwchem,materials,project,electronic,structure,analysis,phase,diagrams
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 2.7
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
