nelpy 0.0.97

Neuroelectrophysiology object model and data analysis in Python.

Neuroelectrophysiology object model, data exploration, and analysis in Python.

Overview

Nelpy (Neuroelectrophysiology) is an open source package for analysis of neuroelectrophysiology data. Nelpy defines a number of data objects to make it easier to work with electrophysiology (ephys) data, and although it was originally designed for use with extracellular recorded data, it can be used much more broadly. Nelpy is intended to make interactive data analysis and exploration of these ephys data easy, by providing several convenience functions and common visualizations that operate directly on the nelpy objects.

More specifically, the functionality of this package includes:

• several container objects (SpikeTrain, BinnedSpikeTrain, AnalogSignal, EpochArray, …) with nice human-readable __repr__ methods

• powerful ways to interact with the data in the container objects

• hidden Markov model analysis of neural activity

• basic data exploration and visualization operating directly on the core nelpy objects

• and much more

Quick example

Let’s give it a try. Create a SpikeTrainArray:

import nelpy as nel  # main nelpy imports
import nelpy.plotting as npl  # optional plotting imports
spike_times = np.array([1, 2, 4, 5, 10])
st = nel.SpikeTrainArray(spike_times, fs=1)

Do something:

st.n_spikes
5

Related work and inspiration

Nelpy drew heavy inspiration from the python-vdmlab package from the van der Meer lab at Dartmouth College (https://github.com/vandermeerlab), which was created by Emily Irvine (https://github.com/emirvine). It is also inspired by the neuralensemble.org NEO project (http://neo.readthedocs.io).

Short history: Etienne A started the nelpy project for two main reasons, namely

1. he wanted / needed a BinnedSpikeTrain object for hidden Markov model analysis that wasn’t (at the time) avaialable in neo or python-vdmlab, and

2. he fundamentally wanted to add “support” attributes to all the container objects. Here “support” should be understood in the mathematical sense of “domain of definition”, whereas the mathematical support technically would not include some elements for which the function maps to zero. This is critical for spike trains, for example, where it is important to differentiate “no spike at time t” from “no record at time t”.

Scope of this work

The nelpy object model is expected to be quite similar to the python-vdmlab object model, which in turn has significant overlap with neuralensemble.org’s neo model. However, the nelpy object model extends the former by making binned data first class citizens, and by changing the API for indexing and extracting subsets of data, as well as making “functional support” an integral part of the model. It (nelpy) is currently simpler and less comprehensive than neo, and specifically lacks in terms of physical units and complex object hierarchies and nonlinear relationships. However, nelpy again makes binned data a core object, and nelpy further aims to add additional analysis code including filtering, smoothing, position analysis, subsampling, interpolation, spike rate estimation, spike generation / synthesis, ripple detection, Bayesian decoding, and so on. In short, nelpy is more than just an object model, but the nelpy core is designed to be a flexible, readable, yet powerful object model for neuroelectrophysiology.

Installation

The easiest way to install nelpy is to use pip. From the terminal, run:

pip install nelpy

Alternatively, you can install the latest version of nelpy by running the following commands:

git clone https://github.com/eackermann/nelpy.git
cd nelpy
python setup.py [install, develop]

where the develop argument should be used if you want to modify the code.

A weak prerequisite for installing nelpy is a modified version of hmmlearn. This requirement is weak, in the sense that installation will complete successfully without it, and most of nelpy can also be used without any problems. However, as soon as any of the hidden Markov model (HMM) functions are used, you will get an error if the correct version of hmmlearn is not installed. To make things easier, there is a handy 64-bit Windows wheel in the hmmlearn directory of this repository. Installation on Linux/Unix should be almost trivial.

Where

download	https://pypi.python.org/pypi/nelpy
docs	coming soon!
code	https://github.com/eackermann/nelpy

License

Nelpy is distributed under the MIT license. See the LICENSE file for details.