Behavioural Force Data (BeForData)

Data structures for handling behavioural force data

This package provides core classes and utilities for loading, processing, and analysing behavioural force data, such as those collected in experimental psychology or neuroscience. It offers a structured approach to manage epochs and records of force measurements, enabling efficient data manipulation and analysis.

BeForData is based on two Data Structures of force data: one for the representation of the raw time-based force measurements in the shape of a dataframe (BeForRecord) and one for epoch-based representations as matrices (BeForEpochs). See Usage for a detailed usage guide.

Features

• Flexible loading and saving of force data in common formats (e.g., CSV, XDF).

• Efficient slicing and indexing of epochs and records for batch analysis.

• Metadata management for experimental context, including event markers and annotations.

• Utilities for preprocessing, such as filtering and baseline correction.

• Integration with scientific Python libraries (NumPy, pandas) for advanced analysis.

Source code: lindemann09/befordata

(c) Oliver Lindemann

Install via pip

pip install befordata

Julia

A Julia implementation of BeForData is available as a beta release.

Contents

• Usage
  • Create BeForRecord from csv-file
  • Epochs-based representation
  • Pyarrow & Feather Format
  • Extracting data from XDF files (as used by LSL)
  • Example of data preprocessing with experimental design
  • Selecting subset of epochs
• API Reference
  • Data Structures
  • Tools
  • Filtering
  • File Formats
  • Visualizing
  • Indices and tables