smevans19 / nept

Neuroelectrophysiology tools used for the analysis of neural recording data and related behaviors

nept: Neuroelectrophysiology tools

Formerly vdmlab, renamed to emphasize general abilities of this library.

Getting started

If you don't already have python 3, we recommend you download it using Miniconda from Continuum Analytics.

We recommend using a separate python environment.

Open a new terminal, create and activate a new conda environment:

conda create -n yourenv python=3.5
activate yourenv [Windows] or source activate yourenv [Linux]

Install package dependencies:

conda install matplotlib jupyter scipy numpy pandas seaborn pytest coverage

For Shapely, try:

pip install shapely

If that fails, in Windows, download the most recent wheel file here. Once downloaded, install with wheel.

pip install yourshapelyinstall.whl

Installation

Clone nept from Github and use a developer installation:

git clone https://github.com/vandermeerlab/nept.git
cd nept
python setup.py develop

Documentation

Users

Check GitHub Pages for the latest version of the nept documentation.

Developers

Ensure you have sphinx, numpydic, and mock:

conda install ghp-import sphinx numpydoc sphinx_rtd_theme

Install nbsphinx so notebooks in the documentations can be executed:

pip install nbsphinx --user

Build the latest version of the documentation using in the nept directory prior to pushing it to Github:

sphinx-build docs docs/_build

And push it to Github:

docs/update.sh

Testing

Run tests with pytest.

Check coverage with codecov.

License

The nept codebase is made available under made available under the MIT license that allows using, copying and sharing.

The file nept/neuralynx_loaders.py contains code from nlxio by Bernard Willers, used with permission.

Projects using nept

emi_shortcut

emi_biconditional