ghostipy

ghostipy is an open-source Python toolbox offering a unique suite of signal processing and spectral analysis tools specifically designed for local field potential (LFP) recordings. It was developed by Joshua Chu and Caleb Kemere from the Realtime Neural Engineering Lab at Rice University (https://rnel.rice.edu/).

ghostipy includes a range of methods for digital filtering and time-frequency transformations, featuring:

• Multitaper analysis: Provides robust spectral estimates.
• Continuous Wavelet Transform (CWT): Enables precise time-frequency analysis.
• Synchrosqueezing Transform: Enhances the resolution of time-frequency representations.

The authors provide a comprehensive methods paper demonstrating ghostipy’s capabilities, including:

• Generating dynamic CWT spectrograms.
• Creating multitaper spectrograms that track changes in locomotion speed.
• Performing theta cycle clustering to analyze how power across different frequencies relates to the phase of theta rhythms.

The project’s GitHub page includes all necessary code for utilizing the toolbox and Jupyter notebooks that reproduce the figures from the methods paper.

In our own testing, we found ghostipy to be user-friendly, with straightforward installation on both Mac and Linux systems. The code itself is well-written, clearly documented, and easy to understand.

This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications. RRID:SCR_026231