Computational Physics and Python Applications

Getting over ANOVA: Estimation graphics for multi-group comparisons

The paper introduces DABEST 2.0, an estimation framework for multi-group comparisons in experimental science, focusing on effect-size quantification instead of null-hypothesis significance testing.

BrainUnit: Integrating Physical Units into High-Performance AI-Driven Scientific Computing

BrainUnit integrates physical units into AI libraries to improve scientific computing accuracy and reliability without performance loss.

Systems Neuroscience Computing in Python (SyNCoPy): A Python Package for Large-scale Analysis of Electrophysiological Data

SyNCoPy is a Python package for analyzing large-scale electrophysiological data, offering time, frequency, and connectivity analyses with parallel processing and interoperability.

Pynapple, a toolbox for data analysis in neuroscience.

Pynapple provides an open-source framework for analyzing time-resolved data in systems neuroscience, offering easy-to-use objects and methods for data manipulation and analysis.

GhostiPy: an efficient signal processing and spectral analysis toolbox for large data

GhostiPy provides efficient signal processing and spectral analysis tools for large-scale neural data, outperforming commercial software in speed and memory usage.