Skip to main content

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

Project 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).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

spykesim-1.2.4.tar.gz (16.5 kB view details)

Uploaded Source

File details

Details for the file spykesim-1.2.4.tar.gz.

File metadata

  • Download URL: spykesim-1.2.4.tar.gz
  • Upload date:
  • Size: 16.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: Python-urllib/3.7

File hashes

Hashes for spykesim-1.2.4.tar.gz
Algorithm Hash digest
SHA256 5a95848aae036108a46677ebbc3e5a8d9be7377453bc8c856ff9b1c69164ee33
MD5 a7be5c779983291d35b9b88092d6e350
BLAKE2b-256 e778b2a6a7f368e8cdb19dd7492a60d54fdef082105964c34980f7889f3d1445

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page