Metadata-Version: 2.1
Name: hybridLFPy
Version: 0.2rc2
Summary: methods to calculate extracellular signals of neural activity from spike events from spiking neuron networks
Home-page: https://github.com/INM-6/hybridLFPy
Maintainer: Espen Hagen
Maintainer-email: espenhgn@users.noreply.github.com
License: LICENSE
Download-URL: https://github.com/INM-6/hybridLFPy/tarball/v0.2rc2
Description: # hybridLFPy
        
        Python module implementing a hybrid scheme for predictions of
        extracellular potentials (local field potentials, LFPs) of spiking
        neuron network simulations.
        
        
        ## Project Status
        
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.45185.svg)](https://doi.org/10.5281/zenodo.45185)
        [![Documentation Status](https://readthedocs.org/projects/hybridlfpy/badge/?version=latest)](https://hybridLFPy.readthedocs.io/en/latest/?badge=latest)
        [![Upload Python Package](https://github.com/INM-6/hybridLFPy/workflows/Upload%20Python%20Package/badge.svg)](https://pypi.org/project/hybridLFPy)
        [![Python pytest](https://github.com/INM-6/hybridLFPy/workflows/Python%20pytest/badge.svg)](https://github.com/INM-6/hybridLFPy/actions/workflows/python-pytest.yml)
        [![License](http://img.shields.io/:license-GPLv3+-green.svg)](http://www.gnu.org/licenses/gpl-3.0.html)
        
        
        ##  Development
        
        The module hybridLFPy was mainly developed in the Computational Neuroscience
        Group (http://compneuro.umb.no), Department of Mathemathical Sciences and
        Technology (http://www.nmbu.no/imt), at the Norwegian University of Life
        Sciences (http://www.nmbu.no), Aas, Norway, in collaboration with Institute of
        Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6),
        Juelich Research Centre and JARA, Juelich, Germany
        (http://www.fz-juelich.de/inm/inm-6/EN/).
        
        
        ## Citation
        
        Should you find `hybridLFPy` useful for your research, please cite the following paper:
        
            Espen Hagen, David Dahmen, Maria L. Stavrinou, Henrik Lindén, Tom Tetzlaff,
            Sacha J. van Albada, Sonja Grün, Markus Diesmann, Gaute T. Einevoll;
            Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks,
            Cerebral Cortex, Volume 26, Issue 12, 1 December 2016, Pages 4461–4496,
            https://doi.org/10.1093/cercor/bhw237
        
        
        Bibtex source:
        
            @article{doi:10.1093/cercor/bhw237,
            author = {Hagen, Espen and Dahmen, David and Stavrinou, Maria L. and Lindén, Henrik and Tetzlaff, Tom and van Albada, Sacha J. and Grün, Sonja and Diesmann, Markus and Einevoll, Gaute T.},
            title = {Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks},
            journal = {Cerebral Cortex},
            volume = {26},
            number = {12},
            pages = {4461-4496},
            year = {2016},
            doi = {10.1093/cercor/bhw237},
            URL = { + http://dx.doi.org/10.1093/cercor/bhw237},
            eprint = {/oup/backfile/content_public/journal/cercor/26/12/10.1093_cercor_bhw237/2/bhw237.pdf}
            }
        
        
        ## License
        
        This software is released under the General Public License (see the [LICENSE](https://github.com/INM-6/hybridLFPy/blob/master/LICENSE) file).
        
        
        ## Warranty
        
        This software comes without any form of warranty.
        
        
        ## Installation
        
        First download all the `hybridLFPy` source files using `git`
        (http://git-scm.com). Open a terminal window and type:
        
            $ cd $HOME/where/to/put/hybridLFPy
            $ git clone https://github.com/INM-6/hybridLFPy.git
        
        
        To use `hybridLFPy` from any working folder without copying files, run:
            $ (sudo) pip install -e . (--user)
        
        
        Installing it is also possible, but not recommended as things might change with
        future pulls from the repository:
        
            $ (sudo) pip install . (--user)
        
        
        ### examples folder
        
        Some example script(s) on how to use this module
        
        
        ### docs folder
        
        Source files for autogenerated documentation using `Sphinx` (https://www.sphinx-doc.org).
        
        To compile documentation source files in this directory using sphinx, use:
        
            $ sphinx-build -b html docs documentation
        
        
        ### Dockerfile
        
        The provided `Dockerfile` provides a Docker container recipe for `x86_64` hosts
        with all dependencies required to run simulation files provided in `examples`.
        To build and run the container locally, get Docker from https://www.docker.com
        and issue the following (replace `<image-name>` with a name of your choosing):
        
            $ docker build -t <image-name> -< Dockerfile
            $ docker run -it -p 5000:5000 <image-name>:latest
        
        
        The `--mount` option can be used to mount a folder on the host to a target folder as:
        
            $ docker run --mount type=bind,source="$(pwd)",target=/opt/hybridLFPy -it -p 5000:5000 <image-name>
        
        Then, code examples may be run as:
        
            $ cd /opt/hybridLFPy/examples
            $ nrnivmodl  # compile local .mod (NMODL) files
            $ mpirun --allow-run-as-root python3 example_brunel.py
        
        
        ## Online documentation
        
        The sphinx-generated html documentation can be accessed at
        https://hybridLFPy.readthedocs.io
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License (GPL)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Cython
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 4 - Beta
Provides: hybridLFPy
Provides-Extra: tests
Provides-Extra: docs
