.. image:: _static/pymatgen.svg
   :width: 300 px
   :alt: pymatgen
   :align: center

============
Introduction
============


.. image:: https://github.com/materialsproject/pymatgen/actions/workflows/test.yml/badge.svg
      :alt: CI Status
      :target: https://github.com/materialsproject/pymatgen/actions/workflows/test.yml

.. image:: https://anaconda.org/conda-forge/pymatgen/badges/downloads.svg

.. image:: https://coveralls.io/repos/github/materialsproject/pymatgen/badge.svg?branch=master
      :alt: Coveralls Coverage Report
      :target: https://coveralls.io/github/materialsproject/pymatgen?branch=master

Pymatgen (Python Materials Genomics) is a robust, open-source Python library
for materials analysis. These are some of the main features:

1. Highly flexible classes for the representation of Element, Site, Molecule,
   Structure objects.
2. Extensive input/output support, including support for VASP
   (http://cms.mpi.univie.ac.at/vasp/), ABINIT (http://www.abinit.org/), CIF,
   Gaussian, XYZ, and many other file formats.
3. Powerful analysis tools, including generation of phase diagrams, Pourbaix
   diagrams, diffusion analyses, reactions, etc.
4. Electronic structure analyses, such as density of states and band structure.
5. Integration with the Materials Project REST API, Crystallography Open
   Database and other external data sources.

As of 2022, pymatgen only supports Python 3.8 and above. Our support schedule follows closely that of the Scientific
Python software stack, i.e., when packages such as numpy drops support for Python versions, we will drop support for
newer versions. Similarly, support for new Python versions will be adopted only when most of the core dependencies
support the new Python versions.

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 feature requests and bug
reports. The following are resources for pymatgen:

* `Official documentation <http://pymatgen.org>`_
* Offline docs: HTML are in the pymatgen Github repo's `docs` folder. `Dash <http://kapeli.com/dash>`_ or
  `Zeal <http://zealdocs.org/>`_ docs can be searched and downloaded from "User Contributed Docsets".
* Bug reports or feature requests: Please submit a `GitHub Issue <http://github.com/materialsproject/pymatgen/issues>`_.
* Code contributions via `pull requests <https://github.com/materialsproject/pymatgen/pulls>`_ welcome.
* For help with usage that are unrelated to bugs or feature requests, please use the pymatgen `Discourse page
  <https://discuss.matsci.org/c/pymatgen>`_.
* `matgenb <https://github.com/materialsvirtuallab/matgenb#introduction>`_ provides some Jupyter notebooks demonstrating functionality.
* Follow us on `Twitter <http://twitter.com/pymatgen>`_ to get news and tips.

    *The code is mightier than the pen.*

Major Announcement (v2022.0.*)
==============================

A **backwards incompatible** change has been introduced in v2022.0.*. Pymatgen root-level convenience imports have been
removed from in preparation for a change to a more modular, extensible namespace package architecture that will allow
more developers to contribute. If your existing code uses `from pymatgen import <something>`, you will need to make
modifications. MPRester should now be imported from `pymatgen.ext.matproj`. All other convenience objects such as
`Element`, `Species`, `Lattice`, `Structure`, etc. should be imported from `pymatgen.core`. There are a few simple ways
you can respond to this change:

* To migrate your code to be compatible with v2022.0.* (it will still be compatible with pymatgen<2022.0.0 since all
  the imports were already available in previous versions), you need to replace all instances of
  `from pymatgen import MPRester` with `from pymatgen.ext.matproj import MPRester`, followed by replacing all instances
  of `from pymatgen import` with `from pymatgen.core import`. These two steps have to be done in that sequence, since
  MPRester and the other core imports exist in different subpackages. The easiest way is to use an IDE such
  as Pycharm to run a `Replace in Files` on the root directory of your code.
* The pymatgen maintainers have also come up with the following terminal commands you can use to perform the migration.
  On a Mac::

    find . -name '*.py' | xargs sed -i "" 's/from pymatgen import MPRester/from pymatgen.ext.matproj import MPRester/g'
    find . -name '*.py' | xargs sed -i "" 's/from pymatgen import/from pymatgen.core import/g'

  On Linux::

    find . -name '*.py' | xargs sed -i 's/from pymatgen import MPRester/from pymatgen.ext.matproj import MPRester/g'
    find . -name '*.py' | xargs sed -i 's/from pymatgen import/from pymatgen.core import/g'

  This should resolve most import errors, though you may have to fix a few issues manually, e.g., if your code contains
  something like `from pymatgen import Element, MPRester`, which will now need to be split into two lines.

Last but not least, one option is to pin to pymatgen==2021.*.*, which is the last version to contain the root-level
convenience imports, if you are not planning to use future new pymatgen functionality. The new breaking change will
become default from year 2022. Backports to 2021.*.* will still occur for critical bug fixes.

Matgenie & Examples
===================

The `Materials Virtual Lab`_ has developed a `matgenie web app <http://matgenie.materialsvirtuallab.org>`_ which
demonstrates some of the basic functionality of pymatgen, as well as a
`matgenb repository <http://matgenb.materialsvirtuallab.org>`_ of Jupyter notebooks for common and advanced use cases.
One of the ways you can contribute is to fork the matgenb repo and add your own examples.

Below are a quick look at some of the graphical output possible.

.. figure:: _static/phase_diagram.png
   :width: 100%
   :alt: Examples
   :align: center

   Top: (left) Phase and (right) Pourbaix diagram from the Materials API.
   Bottom left: Calculated bandstructure plot using pymatgen's parsing and
   plotting utilities. Bottom right: Arrhenius plot using pymatgen's
   DiffusionAnalyzer.

Why use pymatgen?
=================

There are many materials analysis codes out there, both commercial 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 by thousands of researchers, and is the analysis code powering the
   `Materials Project`_. The analysis it produces survives rigorous scrutiny every single day. Bugs tend to be
   found and corrected quickly. Pymatgen also uses Github Actions for continuous integration, which ensures that every
   new code passes a comprehensive suite of unittests.
2. **It is well documented.** A fairly comprehensive documentation has been written to help you get to grips with it
   quickly.
3. **It is open.** You are free to use and contribute to pymatgen. It also means that pymatgen is continuously being
   improved. We will attribute any code you contribute to any publication you specify. 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/scipy.
   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.
5. **It will be around.** Pymatgen is not a pet research project. It is used in the well-established Materials Project.
   It is also actively being developed and maintained by the `Materials Virtual Lab`_, the ABINIT group and many
   other research groups.
6. **A growing ecosystem of developers and add-ons**. Pymatgen has contributions from materials scientists all over the
   world. We also now have an architecture to support add-ons that expand pymatgen's functionality even further. Check
   out the `contributing page <http://pymatgen.org/contributing>`_ and `add-ons page <http://pymatgen.org/addons>`_ for
   details and examples.

Please review the `coding guidelines </contributing>`_.

.. include:: latest_changes.rst

:doc:`Older versions </change_log>`

Getting pymatgen
================

Via conda (recommended)
~~~~~~~~~~~~~~~~~~~~~~~

If you are absolutely new to Python and/or are using Windows, the easiest
installation process is using `conda <http://conda.pydata.org>`_. If you
already have conda installed, pymatgen can be installed from the `conda-forge
channel <https://anaconda.org/conda-forge>`_ using the following command::

    conda install --channel conda-forge pymatgen

Note that you might need to ensure a relatively recent version of gcc is
available to compile pymatgen. You can use conda to get that::

    conda install gcc

Pymatgen is under active development, and new features are added regularly. To
upgrade pymatgen to the latest version, use the following command::

    conda upgrade pymatgen

Step-by-step instructions for all platforms are available at the
:doc:`installation page </installation>`.

Via pip
~~~~~~~

The version at the Python Package Index (PyPI) is always the latest stable
release that is relatively bug-free. The easiest way to install pymatgen on
any system is to use pip::

    pip install pymatgen

Wheels for Mac and Windows have been built for convenience. Similarly, you might
need to ensure you have a relatively recent version of gcc.

To upgrade pymatgen via pip::

    pip install --upgrade pymatgen

The bleeding edge developmental version is at the pymatgen `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 complete test files. After
cloning the source, you can type in the root of the repo::

    pip install .

or to install the package in developmental mode::

    pip install -e .

Detailed installation instructions, including installation of
option dependencies, set up for POTCAR generation, Materials Project REST
interface usage, setup for developers, etc.are given on this
:doc:`page </installation>`.

For some extras, you can also install the optional dependencies using::

   pip install pymatgen[extra]

For an always up-to-date list of extras, consult the `setup.py's extras_require
<https://github.com/materialsproject/pymatgen/blob/master/setup.py>`_.

Shared cluster installation
~~~~~~~~~~~~~~~~~~~~~~~~~~~

If you are installing pymatgen on shared computing clusters, e.g., the XSEDE
or NERSC resources in the US, the best way is to use conda to perform a local
install. This guarantees the right version of python and all dependencies::

    wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
    bash Miniconda3-latest-Linux-x86_64.sh -b

    # Reload bash profile.
    source $HOME/.bashrc
    source $HOME/.bash_profile

    # Install numpy and other pydata stack packages via conda.
    conda install --yes numpy scipy pandas
    conda install --yes --channel conda-forge pymatgen

Usage
=====

.. figure:: _static/overview.jpg
   :width: 100%
   :alt: pymatgen overview
   :align: center

   Overview of a typical workflow for pymatgen.

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, crystal 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.

.. _quick_start:

Quick start
~~~~~~~~~~~

Here are some quick examples of the core capabilities and objects:

.. code-block:: pycon

    >>> import pymatgen.core as mg
    >>>
    >>> si = mg.Element("Si")
    >>> si.atomic_mass
    28.0855
    >>> print(si.melting_point)
    1687.0 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]
    >>>
    >>> # You can create a Structure using spacegroup symmetry as well.
    >>> li2o = mg.Structure.from_spacegroup("Fm-3m", mg.Lattice.cubic(3),
                                            ["Li", "O"],
                                            [[0.25, 0.25, 0.25], [0, 0, 0]])
    >>>
    >>> # Integrated symmetry analysis tools from spglib.
    >>> from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
    >>> finder = SpacegroupAnalyzer(structure)
    >>> finder.get_space_group_symbol()
    'Pm-3m'
    >>>
    >>> # Convenient IO to various formats. You can specify various formats.
    >>> # Without a filename, a string is returned. Otherwise,
    >>> # the output is written to the file. If only the filenmae is provided,
    >>> # the format is intelligently determined from a file.
    >>> structure.to(fmt="poscar")
    >>> structure.to(filename="POSCAR")
    >>> structure.to(filename="CsCl.cif")
    >>>
    >>> # Reading a structure is similarly easy.
    >>> structure = mg.Structure.from_str(open("CsCl.cif").read(), fmt="cif")
    >>> structure = mg.Structure.from_file("CsCl.cif")
    >>>
    >>> # Reading and writing a molecule from a file. Supports XYZ and
    >>> # Gaussian input and output by default. Support for many other
    >>> # formats via the optional openbabel dependency (if installed).
    >>> methane = mg.Molecule.from_file("methane.xyz")
    >>> mol.to("methane.gjf")
    >>>
    >>> # Pythonic API for editing Structures and Molecules (v2.9.1 onwards)
    >>> # Changing the specie of a site.
    >>> structure[1] = "F"
    >>> print(structure)
    Structure Summary (Cs1 F1)
    Reduced Formula: CsF
    abc   :   4.200000   4.200000   4.200000
    angles:  90.000000  90.000000  90.000000
    Sites (2)
    1 Cs     0.000000     0.000000     0.000000
    2 F     0.500000     0.500000     0.500000
    >>>
    >>> # Changes species and coordinates (fractional assumed for structures)
    >>> structure[1] = "Cl", [0.51, 0.51, 0.51]
    >>> print(structure)
    Structure Summary (Cs1 Cl1)
    Reduced Formula: CsCl
    abc   :   4.200000   4.200000   4.200000
    angles:  90.000000  90.000000  90.000000
    Sites (2)
    1 Cs     0.000000     0.000000     0.000000
    2 Cl     0.510000     0.510000     0.510000
    >>>
    >>> # Replaces all Cs in the structure with K
    >>> structure["Cs"] = "K"
    >>> print(structure)
    Structure Summary (K1 Cl1)
    Reduced Formula: KCl
    abc   :   4.200000   4.200000   4.200000
    angles:  90.000000  90.000000  90.000000
    Sites (2)
    1 K     0.000000     0.000000     0.000000
    2 Cl     0.510000     0.510000     0.510000
    >>>
    >>> # Replaces all K in the structure with K: 0.5, Na: 0.5, i.e.,
    >>> # a disordered structure is created.
    >>> structure["K"] = "K0.5Na0.5"
    >>> print(structure)
    Full Formula (K0.5 Na0.5 Cl1)
    Reduced Formula: K0.5Na0.5Cl1
    abc   :   4.209000   4.209000   4.209000
    angles:  90.000000  90.000000  90.000000
    Sites (2)
      #  SP                   a    b    c
    ---  -----------------  ---  ---  ---
      0  K:0.500, Na:0.500  0    0    0
      1  Cl                 0.5  0.5  0.5
    >>>
    >>> # Because structure is like a list, it supports most list-like methods
    >>> # such as sort, reverse, etc.
    >>> structure.reverse()
    >>> print(structure)
    Structure Summary (Cs1 Cl1)
    Reduced Formula: CsCl
    abc   :   4.200000   4.200000   4.200000
    angles:  90.000000  90.000000  90.000000
    Sites (2)
    1 Cl     0.510000     0.510000     0.510000
    2 Cs     0.000000     0.000000     0.000000
    >>>
    >>> # Molecules function similarly, but with Site and Cartesian coords.
    >>> # The following changes the C in CH4 to an N and displaces it by 0.01A
    >>> # in the x-direction.
    >>> methane[0] = "N", [0.01, 0, 0]
    >>>

The above illustrates only the most basic capabilities of pymatgen. Users are
strongly encouraged to explore the :doc:`usage pages </usage>` (toc given below).

API documentation
~~~~~~~~~~~~~~~~~

For detailed documentation of all modules and classes, please refer to the
:doc:`API docs </modules>`.

More resources
~~~~~~~~~~~~~~

The founder and maintainer of pymatgen, Shyue Ping Ong, has conducted several
workshops (together with Anubhav Jain) on how to effectively use pymatgen (as
well as the extremely useful `custodian`_ error management and `FireWorks`_
workflow software. The slides for these workshops are available on the
`Materials Virtual Lab`_.

pmg - 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. These are
installed to your path by default when you install pymatgen through the
typical installation routes.

Here, we will discuss the most versatile of these scripts, known as
pmg. The typical usage of pmg is::

    pmg {setup, config, analyze, plotdos, plotchgint, convert, symm, view, compare} additional_arguments

At any time, you can use ``"pmg --help"`` or ``"pmg subcommand
--help"`` to bring up a useful help message on how to use these subcommands.
With effect from v4.6.0, ``pmg`` also supports bash completion using
argcomplete, which is useful given the many options available in the cli tool.
To enable argcomplete, ``pip install argcomplete`` and either follow
argcomplete's instructions for enabling global completion, or add the following
line to your ``.bash_profile`` (this method usually works more reliably)::

   eval "$(register-python-argcomplete pmg)"

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.

    pmg analyze .

    # Plot the dos from the vasprun.xml file.

    pmg plot --dos 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 pmg convert -h.

    pmg structure --convert --filenames input_filename output_filename.

    # Obtain spacegroup information using a tolerance of 0.1 angstroms.

    pmg structure --symmetry 0.1 --filenames filename1 filename2

    # Visualize a structure. Requires VTK to be installed.

    pmg view filename

    # Compare two structures for similarity

    pmg structure --group element --filenames filename1 filename2

    # Generate a POTCAR with symbols Li_sv O and the PBE functional

    pmg potcar --symbols Li_sv O --functional PBE

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`_ package provides a JIT job management and error correction for calculations and is used by the
   Materials Project for high-throughput calculations.
3. `pymatgen-analysis-diffusion <http://pypi.org/project/pymatgen-analysis-diffusion/>`_ by the `Materials Virtual Lab`_
   provides modules for diffusion analysis, including path determination for NEB calculations, analysis of MD
   trajectories (RDF, van Hove, Arrhenius plots, etc.)

A comprehensive listing is provided at the :doc:`addons page </addons>`.

Contributing
============

Pymatgen is developed by a team of volunteers. It is started by a team comprising of MIT and Lawrence Berkeley National
Laboratory staff to be a robust toolkit for materials researchers to perform advanced manipulations of structures and
analyses.

For pymatgen to continue to grow in functionality and robustness, we rely on other volunteers to develop new analyses
and report and fix bugs. We welcome anyone to use our code as-is, but if you could take a few moment to give back
to pymatgen in some small way, it would be greatly appreciated. A benefit of contributing is that your code will now be
used by other researchers who use pymatgen, and we will include an acknowledgement to you (and any related
publications) in pymatgen.

Reporting bugs
--------------

A simple way that anyone can contribute is simply to report bugs and issues
to the developing team. Please report any bugs and issues at pymatgen's
`Github Issues page <https://github.com/materialsproject/pymatgen/issues>`_.
For help with any pymatgen issue, consult `Stack Overflow
<https://stackoverflow.com/questions/tagged/pymatgen>`_ and if
you cannot find an answer, please post a question with the tag `pymatgen`.

Developing new functionality
----------------------------

Another way to contribute is to submit new code/bugfixes to pymatgen. The
best way for anyone to develop pymatgen is by adopting the collaborative
Github workflow (see `contributing page </contributing>`_).

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
    <https://doi.org/10.1016/j.commatsci.2012.10.028>`_

In addition, some of pymatgen's functionality is based on scientific advances / principles developed by various
scientists. Please refer to the :doc:`references page </references>` for citation info.

License
=======

Pymatgen is released under the MIT License.

.. literalinclude:: ../LICENSE.rst

About the Team
==============

Shyue Ping Ong of the `Materials Virtual Lab`_ started Pymatgen in 2011, and is still the project lead.

The Pymatgen Development Team is the set of all contributors to the pymatgen project, including all subprojects.

The full list of contributors are listed in the :doc:`team page </team>`.

Copyright Policy
================

Pymatgen uses a shared copyright model. Each contributor maintains copyright
over their contributions to pymatgen. But, it is important to note that these
contributions are typically only changes to the repositories. Thus, the
pymatgen source code, in its entirety is not the copyright of any
single person or institution. Instead, it is the collective copyright of the
entire pymatgen Development Team. If individual contributors want to maintain a
record of what changes/contributions they have specific copyright on, they
should indicate their copyright in the commit message of the change, when
they commit the change to one of the pymatgen repositories.

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

.. _`PyPI`: http://pypi.python.org/pypi/pymatgen
.. _`Github page`: https://github.com/materialsproject/pymatgen/issues
.. _`custodian`: https://pypi.python.org/pypi/custodian
.. _`FireWorks`: https://materialsproject.github.io/fireworks
.. _`Materials Project`: https://www.materialsproject.org
.. _`Materials Virtual Lab`: http://www.materialsvirtuallab.org
