Metadata-Version: 2.1
Name: ENPMDA
Version: 0.1.0
Summary: parallel analysis for ensemble simulations
Home-page: https://github.com/yuxuanzhuang/ENPMDA
Author: Yuxuan Zhuang
Author-email: yuxuan.zhuang@dbb.su.se
License: GNU General Public License v3
Keywords: ENPMDA
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
License-File: LICENSE
License-File: AUTHORS.rst

=================
Ensemble Analysis
=================

|mdanalysis|

.. |mdanalysis| image:: https://img.shields.io/badge/powered%20by-MDAnalysis-orange.svg?logoWidth=16&logo=data:image/x-icon;base64,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
    :alt: Powered by MDAnalysis
    :target: https://www.mdanalysis.org
    
.. image:: https://img.shields.io/pypi/v/ENPMDA.svg
        :target: https://pypi.python.org/pypi/ENPMDA

.. image:: https://img.shields.io/travis/yuxuanzhuang/ENPMDA.svg
        :target: https://travis-ci.com/yuxuanzhuang/ENPMDA

.. image:: https://readthedocs.org/projects/ENPMDA/badge/?version=latest
        :target: https://ENPMDA.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status

.. warning::
    This is still under constrution.

parallel analysis for ensemble simulations


* Free software: GNU General Public License v3
* Documentation: https://ENPMDA.readthedocs.io.


Features
--------

* Parallel analysis for ensemble simulations.
* Dataframe for storing and accessing results.
* dask-based task scheduler, suitable for both workstations and clusters.
* Expandable analysis library powered by MDAnalysis.
* ...


Workflow Illustration
---------------------
.. image:: ./example.png
  :width: 700
  :alt: Illustration of the ensemble analysis workflow.


TODO
----

* more analysis functions.
* unit testing
* benchmarking
* option to retrieve numerical results 
* switch between save to file and return values
* documentation
* add mechanims to cancel running tasks
* add mechanims to test and report errors when adding features
* ...


Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage



=======
History
=======

0.1.0 (2022-05-09)
------------------

* First release on PyPI.
