Metadata-Version: 1.1
Name: atooms-pp
Version: 1.0.0
Summary: Post-processing tools for particle simulations
Home-page: http://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/
Author: Daniele Coslovich
Author-email: daniele.coslovich@umontpellier.fr
License: GPLv3
Description: Post processing
        ===============
        
        Python post processing tools to compute static and dynamic correlation
        functions from particle simulations: - Real space: radial distribution
        function, mean square displacement, time-dependent overlap, ... -
        Fourier space: structure factor, intermediate scattering function,
        dynamic susceptibility, ...
        
        This package relies on
        `atooms <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git>`__
        to read trajectory files.
        
        Quick start
        -----------
        
        Installation is easy (see `Installation <#installation>`__ for more
        details)
        
        ::
        
            pip install atooms-pp
        
        We can now compute correlation functions from trajectories produced by
        particle simulation codes. Any trajectory format recognized by atooms
        can be processed, for instance most "xyz" files should work fine.
        
        As an example, we compute the structure factor S(k) for the trajectory
        file ``trajectory.xyz`` contained in the ``data/`` directory.
        
        .. figure:: https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing/raw/develop/docs/anim.gif
           :alt: terminal
        
           terminal
        
        In the example above, we used 20% of the available time frames via the
        flag ``--norigins``. Without it, atooms-pp applies an heuristics to
        determine the number of time frames required to achieve a reasonable
        data quality.
        
        The results of the calculation are stored in
        ``data/trajectory.xyz.pp.sk``. If the system is a mixture of different
        types of particles, say A and B, the program will create additional
        files for partial correlations, named ``trajectory.xyz.pp.sk.A-A``,
        ``trajectory.xyz.pp.sk.B-B`` and ``trajectory.xyz.pp.sk.A-B``.
        
        The same calculation can be done from python:
        
        .. code:: python
        
            from atooms.trajectory import Trajectory
            import atooms.postprocessing as pp
        
            with Trajectory('data/trajectory.xyz') as t:
                 p = pp.StructureFactor(t)
                 p.do()
        
        Checkout the
        `tutorial <https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/pp_notebook/>`__
        and
        `notebook <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing/raw/develop/docs/tutorial.ipynb>`__
        for more details.
        
        Requirements
        ------------
        
        -  numpy
        -  `atooms <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git>`__
        -  [optional] argh (only needed when using ``pp.py``)
        -  [optional] tqdm (enable progress bars)
        -  [optional] argcomplete (enable tab-completion for ``pp.py``)
        
        Installation
        ------------
        
        If you cannot install the package system-wide, you can still install it
        in the user space. Either from pypi
        
        ::
        
            pip install --user atooms-pp
        
        or cloning the project repo
        
        ::
        
            git clone https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git
            cd postprocessing
            make user
        
        The commands above will install ``pp.py`` under ``~/.local/bin``. Make
        sure this folder is in your ``$PATH``. To install system-wide,
        ``sudo make install``.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Scientific/Engineering :: Physics
