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        "author": "Daniele Coslovich",
        "author_email": "daniele.coslovich@umontpellier.fr",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering :: Physics"
        ],
        "description": "Post processing\n===============\n\nPython post processing tools to compute static and dynamic correlation\nfunctions from particle simulations\n\n-  Real space: radial distribution function, mean square displacement,\n   time-dependent overlap functions, non-Gaussian parameter\n-  Fourier space: structure factor, intermediate scattering functions,\n   four-point dynamic susceptibility\n\nThis package relies on\n`atooms <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git>`__\nto read trajectory files.\n\nQuick start\n-----------\n\nInstallation is easy (see `Installation <#installation>`__ for more\ndetails)\n\n::\n\n    pip install atooms-pp\n\nWe can now compute correlation functions from trajectories produced by\nparticle simulation codes. Any trajectory format recognized by atooms\ncan be processed, for instance most \"xyz\" files should work fine.\n\nAs an example, we compute the structure factor S(k) for the trajectory\nfile ``trajectory.xyz`` contained in the ``data/`` directory.\n\n.. figure:: https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing/raw/develop/docs/anim.gif\n   :alt: https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/anim.gif\n\n   https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/anim.gif\n\nIn the example above, we used 20% of the available time frames to\ncompute the averages using the ``--norigins`` flag. Without it,\natooms-pp applies an heuristics to determine the number of time frames\nrequired to achieve a reasonable data quality.\n\nThe results of the calculation are stored in\n``data/trajectory.xyz.pp.sk``. If the system is a mixture of different\ntypes of particles, say A and B, the program will create additional\nfiles for partial correlations, named ``trajectory.xyz.pp.sk.A-A``,\n``trajectory.xyz.pp.sk.B-B`` and ``trajectory.xyz.pp.sk.A-B``.\n\nThe same calculation can be done from python:\n\n.. code:: python\n\n    from atooms.trajectory import Trajectory\n    import atooms.postprocessing as pp\n\n    with Trajectory('data/trajectory.xyz') as t:\n         p = pp.StructureFactor(t)\n         p.do()\n\nCheckout the\n`tutorial <https://www.coulomb.univ-montp2.fr/perso/daniele.coslovich/pp_notebook/>`__\nand\n`notebook <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing/raw/develop/docs/tutorial.ipynb>`__\nfor more details.\n\nRequirements\n------------\n\n-  `numpy <https://pypi.org/project/numpy/>`__\n-  `atooms <https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git>`__\n-  [optional] [argh](https://pypi.org/project/argh/) (only needed when\n   using ``pp.py``)\n-  [optional] [tqdm](https://pypi.org/project/tqdm/) (enable progress\n   bars)\n-  [optional] [argcomplete](https://pypi.org/project/argcomplete/)\n   (enable tab-completion for ``pp.py``)\n\nInstallation\n------------\n\nIf you cannot install the package system-wide, you can still install it\nin the user space. Either from pypi\n\n::\n\n    pip install --user atooms-pp\n\nor cloning the project repo\n\n::\n\n    git clone https://gitlab.info-ufr.univ-montp2.fr/atooms/postprocessing.git\n    cd postprocessing\n    make user\n\nThe commands above will install ``pp.py`` under ``~/.local/bin``. Make\nsure this folder is in your ``$PATH``. To install system-wide,\n``sudo make install``.",
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        "license": "GPLv3",
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