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Python module that offers functions for measuring the similarity between two segmented multi-neuronal spiking activities.

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Copyright (c) 2019 Keita Watanabe

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Description: # <img src=”docs/spykesim_logo/wtext/spykesim_wtext.svg” width=”320px”>

[![PyPI](https://img.shields.io/pypi/v/spykesim.svg)](https://pypi.org/project/spykesim/) [![MIT License](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](LICENSE) [![Build Status](https://travis-ci.org/KeitaW/spykesim.svg?branch=master)](https://travis-ci.org/KeitaW/spykesim)

spykesim is a Python module that offers functions for measuring the similarity between two segmented multi-neuronal spiking activities.

Extended edit similarity measurement is implemented. You can find details in the following paper.

https://www.frontiersin.org/articles/10.3389/fninf.2019.00039

This library is re-implementation of the algorithm. The original implementation can be found in [this repo](https://github.com/KeitaW/Chaldea).

# Supported Operating Systems This library tested on Ubuntu and MacOS.

For Windows users: Please consider to use Ubuntu via [Windows Subsystem for Linux](https://docs.microsoft.com/en-us/windows/wsl/install-win10).

# Installation If you do not have Python3.7 on your environment, you may use [Anaconda](https://www.anaconda.com/distribution/).

[Cython](https://github.com/cython/cython) and [Numpy](https://github.com/numpy/numpy) needs to be preinstalled as these will be used in the installation process.

If you have not installed these packages, run the following: `bash pip install numpy cython ` You can install this library via pip as well: `bash pip install spykesim ` or you may clone and build by yourself: `bash git clone https://github.com/KeitaW/spykesim.git cd spykesim python setup.py build_ext --inplace install `

## Dependencies

  • Python (>= 3.7)
  • Numpy(Needs to be preinstalled)
  • Cython(Needs to be preinstalled)
  • scipy
  • tqdm
  • h5py

# Tutorial You can find a tutorial in [doc](https://github.com/KeitaW/spykesim/blob/master/docs/tutorial.ipynb).

# Citation You can use the following bib entry to cite this work: ` @article{Watanabe:2019eq, author = {Watanabe, Keita and Haga, Tatsuya and Tatsuno, Masami and Euston, David R and Fukai, Tomoki}, title = {{Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering}}, journal = {Frontiers in Neuroinformatics}, year = {2019}, volume = {13}, month = may } ` #

This project uses the following repository as a template.

https://github.com/kennethreitz/samplemod The original LICENSE file can be found in [here](misc/original_license.md).

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