ephysiopy 2.0.57

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:

├── 2020-03-20_12-40-15
├── Record Node 101
|    └── settings.xml
         experiment1
|        └── recording1
|            ├── structure.oebin
|            ├── sync_messages.txt
|            ├── continuous
|            |   └── Neuropix-PXI-107.0
|            |       └── continuous.dat
|            └── events
├── Record Node 102

Then the “/path/to/top_level” is the folder “2020-03-20_12-40-15”

On insantiation of an OpenEphysBase object the directory structure containing the recording is traversed and various file locations are noted for later processing of the data in them.

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

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

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 “plot” e.g “_rate_map” and return an instance of a matplotlib axis. The plotting functions in turn call a corresponding “get” function e.g. “get_rate_map” that will return an instance of the BinnedData class containing the binned data, the histogram edges, the variable being binned (XY, SPEED etc) and the map type (RATE, SPK, POS).