Metadata-Version: 2.1
Name: tidynamics
Version: 1.0.0
Summary: Tiny package to compute dynamics correlations
Home-page: https://pypi.org/project/tidynamics/
Author: Pierre de Buyl
Author-email: pdebuyl@pdebuyl.be
License: BSD
Description: tidynamics
        ==========
        
        .. image:: http://joss.theoj.org/papers/10.21105/joss.00877/status.svg
           :target: https://doi.org/10.21105/joss.00877
           :alt: DOI link to JOSS article
        
        .. image:: https://travis-ci.org/pdebuyl-lab/tidynamics.svg?branch=master
           :target: https://travis-ci.org/pdebuyl-lab/tidynamics
           :alt: Test status
        
        .. image:: https://anaconda.org/conda-forge/tidynamics/badges/version.svg
           :target: https://anaconda.org/conda-forge/tidynamics
           :alt: Link to conda-forge page
        
        .. image:: https://mybinder.org/badge.svg
           :target: https://mybinder.org/v2/gh/pdebuyl-lab/tidynamics/master?filepath=doc%2Findex.ipynb
           :alt: Link to binder example notebook
        
        A tiny package to compute the dynamics of stochastic and molecular simulations.
        
        :License: BSD 3-clause
        :Author: Pierre de Buyl
        :Website: http://lab.pdebuyl.be/tidynamics/
        
        tidynamics
        
        - performs the computation of mean-square displacements and correlation functions.
        - accepts as input NumPy arrays storing the positions and velocities of particles.
        - implements the so-called Fast Correlation Algorithm proposed by Kneller and others for the
          `nMOLDYN <http://dirac.cnrs-orleans.fr/plone/software/nmoldyn/>`_ analysis program.
        - depends only `Python <https://www.python.org/>`_ and `NumPy <http://www.numpy.org/>`_.
        
        For a quick jump into tidynamics, have a look at the examples.
        
        Goals and plans:
        
        - Minimal dependencies.
        - Serve as a reference implementation for common algorithms that are useful for molecular
          and stochastic simulations.
        - Provide later a bit more flexibility to handle cross correlations and many-body systems.
        
        
        Installation
        ------------
        
        It is necessary to have Python and NumPy to install and use tidynamics.
        
        tidynamics can be installed with pip::
        
            pip install --user tidynamics
        
        or with conda (via conda-forge)::
        
            conda install -c conda-forge tidynamics
        
        It is also possible to download the source code and execute the setup.py file.
        
        I ran the tests with Python 2.7, 3.5 and 3.6 and NumPy 1.11 and 1.13. If you encounter any
        issue, let me know (see Contact below).
        
        
        Citation
        --------
        
        When using tidynamics in a publication, please cite the following paper:
        
        Pierre de Buyl (2018), *tidynamics: A tiny package to compute the dynamics of
        stochastic and molecular simulations*, The Journal of Open Source
        Software https://doi.org/10.21105/joss.00877
        
        
        Testing
        -------
        
        We use `pytest <https://pypi.python.org/pypi/pytest/>`_ for testing::
        
            python -m pytest
        
        Installing tidynamics does not install the tests. It is necessary to download tidynamics'
        source and to install pytest to run the tests.
        
        Contact, support, and contribution information
        ----------------------------------------------
        
        To contact the author about tidynamics, you can either write an email to `Pierre de Buyl
        <https://www.kuleuven.be/wieiswie/nl/person/00092351>`_ or use the `issue tracker
        <https://github.com/pdebuyl-lab/tidynamics/issues>`_ of the GitHub project.
        Existing contributors are listed in the file CONTRIBUTORS.
        
        Bug reports are welcome. If you consider proposing a feature, please keep in mind the goals
        and plans exposed above.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/x-rst
