{ "info": { "author": "Espen Hagen", "author_email": "e.hagen@fz-juelich.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License (GPL)", "Operating System :: OS Independent", "Programming Language :: Cython", "Programming Language :: Python", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.3", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Physics" ], "description": "=====================\nModule **hybridLFPy**\n=====================\n\nPython module implementating a hybrid model scheme for predictions of\nextracellular potentials (local field potentials, LFPs) of spiking\nneuron network simulations. \n\n\nDevelopment\n-----------\n\nThe module hybridLFPy was mainly developed in the Computational Neuroscience\nGroup (http://compneuro.umb.no), Department of Mathemathical Sciences and\nTechnology (http://www.nmbu.no/imt), at the Norwegian University of Life\nSciences (http://www.nmbu.no), Aas, Norway, in collaboration with Institute of\nNeuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6),\nJuelich Research Centre and JARA, Juelich, Germany\n(http://www.fz-juelich.de/inm/inm-6/EN/).\n\n\nManuscript\n----------\n\nA preprint of our manuscript on the hybrid scheme implemented in ``hybridLFPy`` is available on arXiv.org at http://arxiv.org/abs/1511.01681\n\nCitation:\nEspen Hagen, David Dahmen, Maria L. Stavrinou, Henrik Linden, Tom Tetzlaff, Sacha Jennifer van Albada, Sonja Gruen, Markus Diesmann, Gaute T. Einevoll. Hybrid scheme for modeling local field potentials from point-neuron networks. arXiv:1511.01681 [q-bio.NC]\n\nBibtex source:\n::\n \n @ARTICLE{2015arXiv151101681H,\n author = {{Hagen}, E. and {Dahmen}, D. and {Stavrinou}, M.~L. and {Lind{\\'e}n}, H. and \n {Tetzlaff}, T. and {van Albada}, S.~J. and {Gr{\\\"u}n}, S. and \n {Diesmann}, M. and {Einevoll}, G.~T.},\n title = \"{Hybrid scheme for modeling local field potentials from point-neuron networks}\",\n journal = {ArXiv e-prints},\n archivePrefix = \"arXiv\",\n eprint = {1511.01681},\n primaryClass = \"q-bio.NC\",\n keywords = {Quantitative Biology - Neurons and Cognition},\n year = 2015,\n month = nov,\n adsurl = {http://adsabs.harvard.edu/abs/2015arXiv151101681H},\n adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n } \n\nTutorial slides\n---------------\n\nSlides from OCNS 2015 meeting tutorial `T2: Modeling and analysis of extracellular potentials `_ hosted in Prague, Czech Republic on LFPy and hybridLFPy: `CNS2015_LFPy_tutorial.pdf `_\n\n\n\nLicense\n-------\n\nThis software is released under the General Public License (see LICENSE file).\n\n\nWarranty\n--------\n\nThis software comes without any form of warranty. \n\n\n============\nInstallation\n============\n\nFirst download all the ``hybridLFPy`` source files using ``git``\n(http://git-scm.com). Open a terminal window and type:\n::\n \n cd $HOME/where/to/put/hybridLFPy\n git clone https://github.com/INM-6/hybridLFPy.git\n \n\nTo use ``hybridLFPy`` from any working folder without installing files, add this\npath to ``$PYTHONPATH``. Edit your ``.bash_profile`` or similar file, and add:\n:: \n \n export $PYTHONPATH=$PYTHONPATH:/PATH/TO/THIS/FOLDER:\n \nInstalling it is also possible, but not recommended as things might change with\nany pull request from the repository:\n:: \n \n (sudo) python setup.py install (--user)\n\n\n\nexamples folder\n---------------\n\nSome example script(s) on how to use this module\n\n\n\ndocs folder\n-----------\n\nSource files for autogenerated documentation using Sphinx.\n\nTo compile documentation source files in this directory using sphinx, use:\n::\n\n sphinx-build -b html docs documentation\n \n\nOnline documentation\n--------------------\n\nThe sphinx-generated html documentation can be accessed at\nhttp://INM-6.github.io/hybridLFPy", "description_content_type": null, "docs_url": null, "download_url": "https://github.com/INM-6/hybridLFPy/tarball/v0.1.2", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/INM-6/hybridLFPy", "keywords": null, "license": "LICENSE", "maintainer": null, "maintainer_email": null, "name": "hybridLFPy", "package_url": "https://pypi.org/project/hybridLFPy/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/hybridLFPy/", "project_urls": { "Download": "https://github.com/INM-6/hybridLFPy/tarball/v0.1.2", "Homepage": "https://github.com/INM-6/hybridLFPy" }, "release_url": "https://pypi.org/project/hybridLFPy/0.1.3/", "requires_dist": null, "requires_python": null, "summary": "methods to calculate LFPs with spike events from network sim", "version": "0.1.3" }, "last_serial": 2192682, "releases": { "0.1.1": [ { "comment_text": "", "digests": { "md5": "e30be5892d528abe98b1f95b2a2e66b4", "sha256": "4b352a50e0e68a5268e7edb001af5b9a2b8063ef9f7c87d4023210c336ac3b1e" }, "downloads": -1, "filename": "hybridLFPy-0.1.1.tar.gz", "has_sig": false, "md5_digest": "e30be5892d528abe98b1f95b2a2e66b4", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 143617, "upload_time": "2016-01-12T22:40:50", "url": "https://files.pythonhosted.org/packages/57/e0/9a4b546c1cc2c87b4f72b8d678ce4145ec53320cd07ad1849ad0cca7aaab/hybridLFPy-0.1.1.tar.gz" } ], "0.1.2": [ { "comment_text": "", "digests": { "md5": "ec34bd70a7cf2a93cca6dfdd94e63c49", "sha256": "74d20f804d94c984f9dd78f43300f656c5ca4217ca62ce69e5a689d14998eed3" }, "downloads": -1, "filename": "hybridLFPy-0.1.2.tar.gz", "has_sig": false, "md5_digest": "ec34bd70a7cf2a93cca6dfdd94e63c49", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 143609, "upload_time": "2016-01-13T20:47:15", "url": "https://files.pythonhosted.org/packages/7a/83/776ee07b0cdd7e0dc13b800c54d0afee95a93b517223092743b102c7745e/hybridLFPy-0.1.2.tar.gz" } ], "0.1.3": [ { "comment_text": "", "digests": { "md5": "207a276b3d8b5a0f65c571e84088327a", "sha256": "bfcdad4d3a13269c7569a7095535f63e8643fefc9fae03efa7de2e64473d8742" }, "downloads": -1, "filename": "hybridLFPy-0.1.3.tar.gz", "has_sig": false, "md5_digest": "207a276b3d8b5a0f65c571e84088327a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 143942, "upload_time": "2016-06-29T00:45:48", "url": "https://files.pythonhosted.org/packages/28/28/c625c82118b953c8b80018a16ada2cebcfd51665567130f51cb34a027fe5/hybridLFPy-0.1.3.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "207a276b3d8b5a0f65c571e84088327a", "sha256": "bfcdad4d3a13269c7569a7095535f63e8643fefc9fae03efa7de2e64473d8742" }, "downloads": -1, "filename": "hybridLFPy-0.1.3.tar.gz", "has_sig": false, "md5_digest": "207a276b3d8b5a0f65c571e84088327a", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 143942, "upload_time": "2016-06-29T00:45:48", "url": "https://files.pythonhosted.org/packages/28/28/c625c82118b953c8b80018a16ada2cebcfd51665567130f51cb34a027fe5/hybridLFPy-0.1.3.tar.gz" } ] }