pylabianca offers a simple and efficient way to read, analyze, and statistically compare spike data in just a few steps. Key features include:

• A familiar API inspired by mne-python, ensuring ease of use for experienced users.
• Two intuitive data structures - Spikes and SpikeEpochs - for organizing and storing spike data.
• Integrated support for storing trial-level metadata, enabling easy trial selection based on conditions, similar to mne-python.
• Outputs in the form of xarray DataArrays, which come with labeled dimensions and coordinates.
• Seamless metadata inheritance in xarrays, allowing for visualizations by condition using pylabianca.viz.plot_shaded or native xarray plotting functions.
• Built-in support for statistical testing via cluster-based permutation tests, facilitating comparisons between different conditions based on trial metadata.

pylabianca can be installed using pip:

pip install pylabianca

To get most up-to-date version you can also install directly from github:

pip install git+https://github.com/labianca/pylabianca

See whats_new.md for documentation of recent changes in pylabianca.

Online docs are currently under construction.

Below you can find jupyter notebook examples showcasing pylabianca features.

• introductory notebook - a general overview using human intracranial spike data (sorted with Osort).
• FiedTrip data example notebook - another broad overview using fieldtrip sample spike data from non-human primates.
• decoding example - overview of decoding with pylabianca
• spike-triggered LFP analysis - use pylabianca and MNE-Python to perform spike-triggered analysis of LFP
• working with spiketools - example of how spiketools and pylabianca can be used together

To better understand the data formats read natively by pylabianca (and how to read other formats) see data formats page.

You can get example human data that are used in the examples here.
The preprocessed FieldTrip data used in the examples are available here.