Metadata-Version: 1.2
Name: PyFraME
Version: 0.1.0
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
        
        [![build status](https://gitlab.com/FraME-projects/PyFraME/badges/master/build.svg)](https://gitlab.com/FraME-projects/PyFraME/commits/master)
        [![coverage report](https://gitlab.com/FraME-projects/PyFraME/badges/master/coverage.svg)](https://gitlab.com/FraME-projects/PyFraME/commits/master)
        [![Codacy Badge](https://api.codacy.com/project/badge/Grade/8cfac142c47040e0a9b2d318ee11becf)](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)
        [![Codacy Badge](https://api.codacy.com/project/badge/Coverage/8cfac142c47040e0a9b2d318ee11becf)](https://www.codacy.com/app/foeroyingur/PyFraME?utm_source=gitlab.com&utm_medium=referral&utm_content=FraME-projects/PyFraME&utm_campaign=Badge_Coverage)
        
        Archived copy of current release ([0.1.0](https://gitlab.com/FraME-projects/PyFraME/tags/v0.1.0)): [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.293765.svg)](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 here: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.293765.svg)](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 (note that currently there are very few tests):
         - [nose](http://nose.readthedocs.io/en/latest/)
        
        
        ## 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 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. Yu 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 (which can take a while).
        If python3 is not your default python version, change the last command to:
        ```
        python3 setup.py install
        ```
        
        ## Tests
        
        To run the test suite type
        ```
        nosetests
        ```
        from the PyFraME root directory. If python3 is not your default python version, type:
        ```
        nosetests3
        ```
        or
        ```
        nosetest-3
        ```
        depending on your specific setup.
        
        
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
