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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