pylabianca

Python tools for spike analysis.

pylabianca:

• allows to read, analyse spike rate and statistically compare conditions in just a few steps
• follows the convenient API of mne-python
• provides two straightforward objects for storing spiking data: Spikes and SpikeEpochs
• allow storing trial-level metadata in these object (just like mne-python) and selecting trials based on these metadata
• returns xarrays (arrays with labeled dimensions and coordinates) as output from operations like cross-correlation, spiking rate calculation or decoding analysis
• these xarrays inherit all the trial-level metadata and can be visualised splitting by conditions using pylabianca.viz.plot_shaded or native xarray plotting
• the xarrays can be statistically tested with cluster based permutation test comparing condition metadata

installation

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

what's new?

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

docs

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.

sample data

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