Metadata-Version: 1.2
Name: PyFraME
Version: 0.1.1
Summary: PyFraME: Python tools for Fragment-based Multiscale Embedding
Home-page: https://gitlab.com/FraME-projects/PyFraME
Author: Jógvan Magnus Haugaard Olsen
Author-email: foeroyingur@gmail.com
License: GPLv3+
Description-Content-Type: UNKNOWN
Description: PyFraME: Python tools for Fragment-based Multiscale Embedding calculations
        ==========================================================================
        
        .. image:: https://gitlab.com/FraME-projects/PyFraME/badges/master/build.svg
           :target: https://gitlab.com/FraME-projects/PyFraME/commits/master
        .. image:: https://gitlab.com/FraME-projects/PyFraME/badges/master/coverage.svg
           :target: https://gitlab.com/FraME-projects/PyFraME/commits/master
        .. image:: https://api.codacy.com/project/badge/Grade/8cfac142c47040e0a9b2d318ee11becf
           :target: https://www.codacy.com/app/foeroyingur/PyFraME?utm_source=gitlab.com&amp;utm_medium=referral&amp;utm_content=FraME-projects/PyFraME&amp;utm_campaign=Badge_Grade
        .. image:: https://api.codacy.com/project/badge/Coverage/8cfac142c47040e0a9b2d318ee11becf
           :target: https://www.codacy.com/app/foeroyingur/PyFraME?utm_source=gitlab.com&amp;utm_medium=referral&amp;utm_content=FraME-projects/PyFraME&amp;utm_campaign=Badge_Coverage
        
        Archived copies: |DOI|
        
        .. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.293765.svg
           :target: https://doi.org/10.5281/zenodo.293765
        
        
        Description
        -----------
        
        PyFraME is a Python package that provides tools for setting up and
        running fragment-based multiscale embedding calculations. The aim is to
        provide tools that can automatize the workflow of such calculations in a
        flexible manner.
        
        The typical workflow is as follows:
        
        1. a part of the total molecular system is chosen as the core region
           which is typically treated a high level of theory
        2. the remainder is split into a number of regions each of which can be
           treated at different levels of theory
        3. each region (except the core) is divided into fragments that consist
           of either small molecules or parts of larger molecules that have been
           fragmented into smaller computationally manageable fragments
        4. a calculation is run on each fragment to obtain fragment parameters
           (if necessary)
        5. all fragment parameters of all regions are assembled and constitute
           the embedding potential
        6. a final calculation is run on the core region using the embedding
           potential to model the effect from the remainder of the molecular
           system
        
        How to cite
        -----------
        
        To cite PyFraME please use a format similar to the following
        
        "J. M. H. Olsen, *PyFraME: Python tools for Fragment-based Multiscale
        Embedding (version 0.1.0)*, **2017**,
        https://doi.org/10.5281/zenodo.293765"
        
        where the version and DOI should of course correspond to the actual
        version that was used. A possible BibTeX entry could be::
        
            @misc{pyframe,
                  author = {Olsen, J. M. H.},
                  title = {{PyFraME}: {P}ython tools for {F}ragment-based {M}ultiscale {E}mbedding (version 0.1.0)},
                  year = {2017},
                  note = {https://doi.org/10.5281/zenodo.293765}}
        
        Alternatively, BibTeX and other formats can be generated by `Zenodo <https://doi.org/10.5281/zenodo.293765>`_.
        
        Requirements
        ------------
        
        To use PyFraME you need:
        
        * `Python 3 <http://www.python.org/>`_
        * `NumPy <http://www.numpy.org/>`_
        * `Numba <https://numba.pydata.org/>`_
        
        For certain functionality you will need one or more of the following:
        
        * `Dalton <http://www.daltonprogram.org/>`_
        * `LoProp for Dalton <https://github.com/vahtras/loprop>`_
        * `Molcas 8 <http://www.molcas.org/>`_
        
        To run the test suite you need:
        
        * `nose <http://nose.readthedocs.io>`_
        
        or
        
        * `pytest <http://pytest.org>`_
        
        Installation
        ------------
        
        The source can be downloaded from
        `GitLab <https://gitlab.com/FraME-projects/PyFraME>`_ or
        `Zenodo <https://doi.org/10.5281/zenodo.293765>`_. Alternatively, it can be
        installed from `PyPI <https://pypi.org/>`_::
        
            pip install pyframe
        
        Alternatively, it can be cloned from the repository::
        
            git clone https://gitlab.com/FraME-projects/PyFraME.git
        
        The package is installed by running::
        
            python setup.py install
        
        from the PyFraME root directory. You may wish to add ``--user`` in the
        last line if you do not have root access / sudo rights. Note that this
        will install NumPy and Numba if they are not installed already.
        
        
        Tests
        -----
        
        To run the test suite type::
        
            nosetests
        
        or::
        
            pytest
        
        from the PyFraME root directory.
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3
