Metadata-Version: 1.2
Name: pychemia
Version: 0.18.2.19
Summary: Python framework for Materials Discovery and Design
Home-page: https://github.com/MaterialsDiscovery/PyChemia
Author: Guillermo Avendano-Franco
Author-email: gufranco@mail.wvu.edu
License: LICENSE.txt
Description-Content-Type: UNKNOWN
Description: 
        PyChemia
        ========
        
        Python Materials Discovery Framework
        
        PyChemia is an open-source Python Library for materials structural
        search. The purpose of the initiative is to create a method agnostic
        framework for materials discovery and design using a variety of methods
        from Minima Hoping to Soft-computing based methods. PyChemia is also a
        library for data-mining, using several methods to discover interesting
        candidates among the materials already processed.
        
        The core of the library is the Structure python class, it is able to
        describe periodic and non-periodic structures. As the focus of this
        library is structural search the class defines extensive capabilities to
        modify atomic structures.
        
        The library includes capability to read and write in several ab-initio
        codes. At the level of DFT, PyChemia support VASP, ABINIT and Octopus.
        At Tight-binding level development is in process to support DFTB+ and
        Fireball. This allows the library to compute electronic-structure
        properties using state-of-the-art ab-initio software packages and
        extract properties from those calculations.
        
        
        PyChemia requirements
        =====================
        
        Before installing PyChemia, you may need to first install a few critical
        dependencies
        
        Mandatory
        ---------
        
        1.  Python >= 2.7 or Python >= 3.4 The library is tested on Travis for
            versions of Python 2.7, 3.4 and 3.5
        
            https://travis-ci.org/MaterialsDiscovery/PyChemia
        
        2.  Numpy >= 1.12 NumPy is a fundamental package for any Python
            scientific library. Numpy arrays are essential for efficient array
            manipulation. Many distributions come with relatively old version of
            numpy, so you should install a more recent version.
        
        3.  SciPy >= 0.18 SciPy is used for many linear algebra and FFT calls
            Most distros comes with scipy 0.13 or below. PyChemia uses
            scipy.spatial that have been actively developed since version 0.15,
            we have tested PyChemia on 0.17.
        
        4.  Spglib >= 1.9.9 Spglib is used to determine symmetry groups for
            structures
        
        5.  future This is a library that offers some easy hacks to support
            python 2.7 and python 3 on the same code source. The library is easy
            to install with pip
        
            pip install future
        
            or
        
            pip install future --user
        
        6.  PyMongo >= 3.4 At least for structural search PyChemia relies
            strongly in MongoDB and its python driver. For the MongoDB server,
            any version beyond 3.0 should be fine. We have tested pychemia on
            MongoDB 3.2
        
            pip install pymongo
        
            or
        
            pip install pymongo --user
        
        Optional Highly Recommended
        ---------------------------
        
        1.  nose (https://nose.readthedocs.io/en/latest/) >= 1.3.7 A python
            library for testing, simply go to the source directory and execute
        
            nosetests -v
        
        2.  Matplotlib >= 1.2 Used to plot band structures, densities of states
            and other 2D plots
        
        Optional
        --------
        
        1.  Pandas Library for Data Analysis used by the datamining modules
        
        2.  PyMC PyMC is a python module that implements Bayesian statistical
            models and fitting algorithms Important for the datamining
            capabilities of PyChemia
        
        3.  Mayavi >= 4.1 Some basic visualization tools are incorporated using
            this library
        
        4.  ScientificPython >2.6 This library is used for reading and writing
            NetCDF files
        
        5.  pymatgen >= 2.9 pymatgen is an excellent library for materials
            analysis
        
        6.  ASE Atomic Simulation Environment is another good library for
            ab-initio calculations. Quite impressive for the number of ab-initio
            packages supported
        
        7.  qmpy The Python library behind the Open Quantum Materials Database.
            The OQMD is a database of DFT calculated structures. For the time
            being the database contains more than 300000 structures, with more
            than 90% of them with the electronic ground-state computed.
        
        8.  [coverage] (https://bitbucket.org/ned/coveragepy) >= 4.0.1 Provides
            code coverage analysis
        
        9.  python-coveralls To submit coverage information to coveralls.io
        
            https://coveralls.io/github/MaterialsDiscovery/PyChemia
        
        
        Documentation
        =============
        
        Instructions for installation, using and programming scripts with
        PyChemia can be found on two repositories for documentation:
        
        -   Read The Docs:
        
        http://pychemia.readthedocs.io/en/latest
        
        -   Python Hosted:
        
        http://pythonhosted.org/pychemia
        
        
        Contributors
        ============
        
        1.  Prof. Aldo H. Romero [West Virginia University] (Project Director)
        
        2.  Guillermo Avendaño-Franco [West Virginia University]
            (Basic Infrastructure)
        
        3.  Adam Payne [West Virginia University] (Bug fixes (Populations,
            Relaxators, and KPoints) )
        
        4.  Irais Valencia Jaime [West Virginia University] (Simulation
            and testing)
        
        5.  Sobhit Singh [West Virginia University] (Data-mining)
        
        6.  Francisco Muñoz [Universidad de Chile] (PyPROCAR)
        
        7.  Wilfredo Ibarra Hernandez [West Virginia University] (Interface
            with MAISE)
        
Keywords: electronic,structure,analysis,materials,discovery,metaheuristics
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, <4
