ephysiopy 1.8.91

Analysis of electrophysiology data

[](https://github.com/rhayman/ephysiopy/actions/workflows/python-package.yml)

Synopsis

Tools for the analysis of electrophysiological data collected with the Axona or openephys recording systems.

Installation

ephysiopy requires python3.7 or greater. The easiest way to install is using pip:

python3 -m pip install ephysiopy

or,

pip3 install ephysiopy

Or similar.

Code Example

Neuropixels / openephys tetrode recordings

For openephys-type analysis there are two main entry classes depending on whether you are doing OpenEphys- or Axona-based analysis. Both classes inherit from the same abstract base class (TrialInterface) and so share a high degree of overlap in what they can do. Because of the inheritance structure, the methods you call on each concrete class are the same

`python from ephysiopy.io.recording import OpenEphysBase trial = OpenEphysBase("/path/to/top_level") `

The “/path/to/top_level” bit here means that if your directory hierarchy looks like this:

├── settings.xml
├── 2020-03-20_12-40-15
|    └── experiment1
|        └── recording1
|            ├── structure.oebin
|            ├── sync_messages.txt
|            ├── continuous
|            |   └── Neuropix-PXI-107.0
|            |       └── continuous.dat
|            └── events

Walk through the folders/ files to see where the data is:

`python trial.find_files("/path/to/top_level", "experiment1", "recording1") `

The pos data is loaded by calling the load_pos_data() method:

`python npx.load_pos_data(ppm=300, jumpmax=100) `

Note ppm = pixels per metre, used to convert pixel coords to cms. jumpmax = maximum “jump” in cms for point to be considered “bad” and smoothed over

The same principles apply to the other classes that inherit from TrialInterface (AxonaTrial and OpenEphysNWB)

Plotting data

A mixin class called FigureMaker allows consistent plots, regardless of recording technique. All plotting functions there begin with “make” e.g “makeRateMap” and return an instance of a matplotlib axis

Motivation

Analysis using Axona’s Tint cluster cutting program or phy/ phy2 (openephys) is great but limited. This extends that functionality.

Optional packages include:

• klustakwik

Download the files and extract to a folder and make sure it’s on your Python path NB this is limited to data recorded using Axona as it has now been superceded by tools such as KiloSort/ KiloSort2 etc.

Contributors

Robin Hayman.