=====
VASPy
=====

.. image:: https://travis-ci.org/PytLab/VASPy.svg?branch=master
    :target: https://travis-ci.org/PytLab/VASPy
    :alt: Build Status

.. image:: https://landscape.io/github/PytLab/VASPy/master/landscape.svg?style=flat
   :target: https://landscape.io/github/PytLab/VASPy/master
   :alt: Code Health

.. image:: https://codecov.io/gh/PytLab/VASPy/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/PytLab/VASPy

.. image:: https://img.shields.io/badge/python-3.5, 2.7-green.svg
    :target: https://www.python.org/downloads/release/python-351/
    :alt: platform

.. image:: https://img.shields.io/badge/pypi-v0.8.2-blue.svg
    :target: https://pypi.python.org/pypi/vaspy/
    :alt: versions

Introduction
------------

VASPy is a pure Python library designed to make it easy and quick to manipulate VASP files.

You can use VASPy to manipulate VASP files in command lins or write your own python scripts to process VASP files and visualize VASP data.

In `/scripts <https://github.com/PytLab/VASPy/tree/master/scripts>`_ , there are some scripts written by me for daily use.

Installation
------------
1. Via pip(recommend)::

    pip install vaspy

2. Via easy_install::

    easy_install vaspy

3. From source::

    python setup.py install

If you want to use **mayavi** to visualize VASP data, it is recommened to install `Canopy environment <https://store.enthought.com/downloads/#default>`_ on your device instead of installing it manually.

After installing canopy, you can set corresponding aliases, for example:

.. code-block:: shell

    alias canopy='/Users/<yourname>/Library/Enthought/Canopy/edm/envs/User/bin/python'
    alias canopy-pip='/Users/zjshao/Library/Enthought/Canopy/edm/envs/User/bin/pip'
    alias canopy-ipython='/Users/<yourname>/Library/Enthought/Canopy/edm/envs/User/bin/ipython'
    alias canopy-jupyter='/Users/<yourname>/Library/Enthought/Canopy/edm/envs/User/bin/jupyter'

Then you can install VASPy to canopy::

    canopy-pip install vaspy

Examples
--------

manipulate splited DOS file in command-line
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    # Manipulate splited DOS files.
    >>> from vaspy.electro import DosX
    >>> a = DosX('DOS1')
    >>> b = DosX('DOS8')
    
    # Merge DOS data.
    >>> c = a
    >>> c.reset_data()              # Initialize DOS data
    >>> for i in range(1, 10):
    >>>    c += DosX('DOS'+str(i))  # Merge DOS data.
    >>> ...
    >>> c.data                      # Get data(numpy array/matrix)
    >>> c.tofile()                  # Get new DOS file with merged data
    
    # Plot.
    >>> c.plotsum(0, (5, 10))

**Output**

.. image:: https://github.com/PytLab/VASPy/blob/dev/pic/pDOS.png

Visualize ELFCAR
~~~~~~~~~~~~~~~~

.. code-block:: python

    >>> from vaspy.electro import ElfCar
    >>> a = ElfCar() 
    >>> a.plot_contour()   # Plot coutour
    >>> a.plot_mcontour()  # Plot coutour using mlab(with Mayavi installed)
    >>> a.plot_contour3d() # Plot 3D coutour
    >>> a.plot_field()     # Plot scalar field

**Output**

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/contour2d.png

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/contours.png

**3D contour**

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/contour3d.png

**Scalar field**

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/field.png

Charge difference (Use ChgCar class)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/contourf.png

Manipulate XDATCAR:
~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    >>> from vaspy.atomco import XdatCar
    >>> xdatcar = XdatCar()
    >>> # Get Cartisan coordinates and step number in XDATCAR.
    >>> for item in xdatcar:
    >>>     print(item.step)
    >>>     print(xdatcar.dir2cart(xdatcar.bases, item.coordinates))

    >>> python xdatcar_to_arc.py

**animation**

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/sn2_my.gif


Process animation file:
~~~~~~~~~~~~~~~~~~~~~~~

Now VASPy can manipulate animation files to get more realistice results like atom trajectories or 2D/3D probability distribution.

.. image:: https://github.com/PytLab/VASPy/blob/master/pic/pd.png


**You can write your OWN script to process VASP files**

From the author
---------------
Welcome to use **VASPy**  (●'◡'●)ﾉ♥

- If you find any bug, please report it to me by opening a issue.
- **VASPy** needs to be improved, your contribution will be welcomed.

Important update log
--------------------

.. csv-table::
    :header: "Date", "Version", "Description"

    "2016-08-08", "0.7.0", "Enhance universality"
    "2016-07-15", "0.6.0", "Compatible with python 3"
    "2015-08-04", "0.1.0", "Initial version"

