TY - JOUR T1 - GhostiPy: an efficient signal processing and spectral analysis toolbox for large data JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0202-21.2021 SP - ENEURO.0202-21.2021 AU - Joshua P. Chu AU - Caleb T. Kemere Y1 - 2021/09/23 UR - http://www.eneuro.org/content/early/2021/09/22/ENEURO.0202-21.2021.abstract N2 - Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large scale modalities. Here we introduce ghostipy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. ghostipy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.Significance StatementDue to technological innovation the size of neural recordings has increased dramatically, but downstream analysis code is often not optimized to handle such large scales of data efficiently. Here we have developed GhostiPy, an open source Python package prioritizing performance and efficiency for large data in the context of typical spectral analysis and signal processing algorithms. Users can control hardware resource consumption (such as system memory) by setting the level of parallelization and enabling out-of-core processing. Thus algorithms can be run on a variety of hardware, from laptops to dedicated compute servers. Overall, GhostiPy improves experimental throughput by increasing the portability of analyses. ER -