{
    "info": {
        "author": "Matthias Hennig Lab, University of Edinburgh",
        "author_email": "m.hennig@ed.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# Herding Spikes 2\n\n[![Build Status](https://travis-ci.org/mhhennig/HS2.svg?branch=master)](https://travis-ci.org/mhhennig/HS2)\n[![Gitter chat](https://badges.gitter.im/HerdingSpikes.png)](https://gitter.im/HerdingSpikes)\n\n![Spikes](documentation/pictures/spikes.png)\n\n## Software for high density electrophysiology\n\nThis software provides functionality for the detection, localisation and clustering of spike data from dense multielectrode arrays based on the methods described in the following papers:\n\nJ.-O. Muthmann, H. Amin, E. Sernagor, A. Maccione, D. Panas, L. Berdondini, U.S. Bhalla, M.H. Hennig MH (2015). [Spike detection for large neural populations using high density multielectrode arrays](http://journal.frontiersin.org/article/10.3389/fninf.2015.00028/abstract). Front. Neuroinform. 9:28. doi: 10.3389/fninf.2015.00028.\n\nG. Hilgen, M. Sorbaro, S. Pirmoradian, J.-O. Muthmann, I. Kepiro, S. Ullo, C. Juarez Ramirez, A. Puente Encinas, A. Maccione, L. Berdondini, V. Murino, D. Sona, F. Cella Zanacchi, E. Sernagor, M.H. Hennig (2016). [Unsupervised spike sorting for large scale, high density multielectrode arrays.](http://www.cell.com/cell-reports/fulltext/S2211-1247(17)30236-X) Cell Reports 18, 2521\u20132532. bioRxiv: <http://dx.doi.org/10.1101/048645>.\n\nThis implementation is highly efficient, spike detection and localisation runs in real time on recordings from 4,096 channels at 7kHz on a desktop PC. Large recordings with millions of events can be sorted in minutes.\n\nSince we believe publicly funded research code should be free and open, all code is released under GPL-3.0.\n\n### Supported systems  <a name=\"systems\"></a>\n\n- [3Brain](http://3brain.com/) BIOCAM and BIOCAM X\n- [Neuropixel array](https://www.ucl.ac.uk/neuropixels)\n- [ETH MEA1K](https://www.bsse.ethz.ch/bel/research/cmos-microsystems/microelectrode-systems.html)\n- [128 channel Neuroseeker array](http://neuroseeker.eu/)\n\n## Contributors, alphabetical <a name=\"people\"></a>\n\n- [Matthias Hennig](http://homepages.inf.ed.ac.uk/mhennig/index.html): Spike sorting\n- [Jano Horvath](https://github.com/JanoHorvath): Parameter optimisation\n- [Cole Hurwitz](https://github.com/colehurwitz31): Spike detection, localisation and sorting, C++ code\n- [Oliver Muthmann](mailto:ollimuh@googlemail.com): Spike detection and localisation\n- [Albert Puente Encinas](https://github.com/albertpuente): C++ implementation, optimisation and parallelisation\n- [Martino Sorbaro](http://martinosorb.github.io): Spike sorting, class structure and much of the python code\n- [Cesar Juarez Ramirez](mailto:cesaripn2@gmail.com): Visualisation\n- [Raimon Wintzer](https://github.com/lsIand): GUI and visualisation\n\n## Quick start <a name=\"quickstart\"></a>\n\nThe code has been tested with Python version 3.6. It is essential `numpy` is available before installing.\nThe other dependencies will be installed by the installer.\n\nIf your system does not have Python pre-installed, the [Anaconda distribution](https://www.anaconda.com/download/) may be used.\n\n### All operative systems - Installation via pip\n\nWe suggest you use Anaconda if you don't have a favourite Python installed yet. We also recommend installing the code in a virtual environment (see below in the \"from source\" sections).\n\nA pip distribution is available and can be installed as follows:\n\n    pip install numpy scipy\n    pip install herdingspikes\n\n**Note:** HerdingSpikes is tested on Python 3.5 and above. It *may* work on Python 3.0-3.4.\n\nTo install from source, clone this repository and follow the instructions below.\n\n### Linux/Mac - from source\n\nWe suggest you install the code in a virtual environment. You can create one by running\n\n    python3 -m venv --system-site-packages desired/location/HS2venv\n    source desired/location/HS2venv/bin/activate\n\nYou can omit `--system-site-packages` if you don't want to use the local versions of common Python libraries. You will need to `activate` whenever you're using the module.\n\nThe module can automatically be installed, including all dependencies, by running\n\n    pip install numpy scipy\n    python setup.py install\n\n\n### Windows - from source\n\n#### 1. Creating a Python virtual environment\n\n##### Option 1: GUI\n\nOnce [Anaconda](https://www.anaconda.com/download/#windows) is installed, create and activate a virtual environment called ``HS2env``. This can be done with the ``Anaconda Navigator`` per mouse click. To make sure ``numpy`` and ``Cython`` are available, type\n\n    conda install -n C:\\Users\\HS2env numpy\n\n\n##### Option 2: Command line (from source)\n\nAlternatively, open the ``Anaconda Prompt`` and type:\n\n    conda search \"^python$\"\n\nThis will display a list of available python versions. Here we choose 3.6.5:\n\n    conda create -n C:\\Users\\HS2env python=3.6.5 anaconda\n\nThis environment should be activated every time HS2 is used with the command\n\n    conda activate C:\\Users\\HS2env\n\nTo make sure ``numpy`` is available, type\n\n    conda install -n C:\\Users\\HS2env numpy\n\n#### 2. Installing a C++ Compiler\n\nHS2 contains fast C++ code, which requires a compiler. If you don't have a C++ compiler installed, the easiest solution is to download and install the Microsoft Visual Studio Build Tools: [https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017](https://www.visualstudio.com/downloads/#build-tools-for-visual-studio-2017).\n\n\n#### 3. Obtaining and installing HS2\n\n##### Getting the code\n\n**Either** download and uncompress: [https://github.com/mhhennig/HS2/archive/master.zip](https://github.com/mhhennig/HS2/archive/master.zip)\n\n**Or** install ``git`` from [https://git-scm.com/download/win](https://git-scm.com/download/win). Then open a command prompt and type\n\n    git clone https://github.com/mhhennig/HS2.git\n\nThis will create a folder ``HS2`` in the current directory. Note that updates can now be simply retrieved by typing ``git pull``.\n\n##### Install\n\nTo install, go to the HS2 directory, e.g.\n\n    cd HS2\n\nand type\n\n    python setup.py install\n\nNow HS2 will be available in the current virtual environment.\n\n\n\n## Example code\n\nExample code for the different supported systems is in the folder [notebooks](notebooks). These can be run without installing HS2 system-wide, but requires to run ``python setup.py build_ext --inplace`` in the ``HS2`` directory. Next, run ``jupyter notebook`` and navigate to the directory to try the code. Each notebook will download a short segment of raw data.\n\nGo [here](documentation) for documentation. A worked example for Biocam data is [here](documentation/biocam/BioCam-demo.md).\n\n## Contact\n\nThe herders are based at the School of Informatics, University of Edinburgh. Contact us [here](http://homepages.inf.ed.ac.uk/mhennig/contact/), we are happy to help.",
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