pyxdf-tools

Install

Install into an existing environment

pip install -e git+https://github.com/jamieforth/pyxdf-tools.git#egg=pyxdftools

Create a new environment from this repo using PDM

PDM is a package and dependency manager, this is simplest way to set up a self-contained virtual environment with everything installed.

First install PDM: https://pdm-project.org/.

git clone https://github.com/jamieforth/pyxdf-tools.git
cd pyxdf-tools
pdm install

Updating

git pull
pdm update

Using

The virtual environment can be activated/deactivated in the usual way.

source .venv/bin/activate
deactivate

Inspecting streams

from pyxdftools import Xdf

xdf_data_path = '<filename>.xdf'

# Inspect all available streams.
Xdf(xdf_data_path).resolve_streams()

# Inspect streams by ID.
Xdf(xdf_data_path).resolve_streams(stream_id=[1,2])

# Inspect streams by properties (accepts multiple keyword args).
Xdf(xdf_data_path).resolve_streams(type='eeg')

Returns a pandas DataFrame.

stream_id	name	type	source_id	created_at	uid	session_id	hostname	channel_count	channel_format	nominal_srate
1	Test stream 0	eeg	simulate.py:78559	80860.5	209ecbcf-08f6-414b-b4ab-6eaa9484174e	default	kassia	2	float32	1
2	Test stream 1	eeg	simulate.py:78559	80860.5	79a34624-1171-4988-96fd-cb43b79d7fa4	default	kassia	2	float32	1

Loading data

# Load all streams.
xdf = Xdf(xdf_data_path).load()

# Load subset of streams by ID.
xdf = Xdf(xdf_data_path).load(1, 2)

# Load streams matching properties.
xdf = Xdf(xdf_data_path).load(type='EEG')

Inspecting metadata for loaded streams

Stream metadata

xdf.metadata()     # Accepts optional stream IDs

Returns a pandas DataFrame including all stream header and footer metadata.

stream_id	name	type	channel_count	channel_format	source_id	nominal_srate	version	created_at	uid	session_id	hostname	v4address	v4data_port	v4service_port	v6address	v6data_port	v6service_port	stream_id	effective_srate	manufacturer	first_timestamp	last_timestamp	sample_count
1	Test stream 0	eeg	2	float32	simulate.py:78559	1	1.1	80860.5	209ecbcf-08f6-414b-b4ab-6eaa9484174e	default	kassia		16573	16596		16575	16598	1	0.999821	Neurolive	80863.5	80871.5	8
2	Test stream 1	eeg	2	float32	simulate.py:78559	1	1.1	80860.5	79a34624-1171-4988-96fd-cb43b79d7fa4	default	kassia		16572	16597		16574	16599	2	0.999807	Neurolive	80863.5	80871.5	8

Data channel metadata

xdf.channel_metadata()     # Accepts optional stream IDs

Returns channel metadata as a pandas DataFrame, or a dictionary of such DataFrames. With no stream ID arguments returns channel metadata for all loaded streams.

Stream data as pandas data frames

# Get stream time-series data.
xdf.time_series()     # Accepts optional stream IDs

sample	(1, ’ch:0’)	(1, ’ch:1’)	(2, ’ch:0’)	(2, ’ch:1’)
0	2	2	2	2
1	3	3	3	3
2	4	4	4	4
3	5	5	5	5
4	6	6	6	6
5	7	7	7	7
6	8	8	8	8
7	9	9	9	9
8	5	5	5	5

# Get stream time-stamps.
xdf.time_stamps()     # Accepts optional stream IDs

sample	(1, ’time_stamp’)	(2, ’time_stamp’)
0	80863.5	80863.5
1	80864.5	80864.5
2	80865.5	80865.5
3	80866.5	80866.5
4	80867.5	80867.5
5	80868.5	80868.5
6	80869.5	80869.5
7	80870.5	80870.5
8	80871.5	80871.5