Introduction
What
Niimpy is a Python package for analyzing and quantifying behavioral data. It uses pandas to read data from disk, perform basic manipulations, provides explorative data analysis functions, offers many high-level preprocessing functions for various types of data, and has functions for behavioral data analysis.
For Who
Niimpy is intended for researchers and data scientists analyzing digital behavioral data. Its purpose is to facilitate data analysis by providing a standardized replicable workflow.
Why
Digital behavioral studies using personal digital devices typically produce rich multi-sensor longitudinal datasets of mixed data types. Analyzing such data requires multidisciplinary expertise and software designed for the purpose. Currently, no standardized workflow or tools exist to analyze such data sets. The analysis requires domain knowledge in multiple fields and programming expertise. Niimpy package is specifically designed to analyze longitudinal, multimodal behavioral data. Niimpy is a user-friendly open-source package that can be easily expanded and adapted to specific research requirements. The toolbox facilitates the analysis phase by providing tools for data management, preprocessing, feature extraction, and visualization. The more advanced analysis methods will be incorporated into the toolbox in the future.
How
The toolbox is divided into four layers by functionality: 1) reading, 2) preprocessing, 3) exploration, and 4) analysis. For more information about the layers, refer the toolbox Architecture and workflow chapter. The quick start guide is be a good place to start. More detailed demo Jupyter notebooks are provided in the user guide chapter. Instructions for individual functions can be found under API chapter niimpy package.
This documentation has following chapters:
Basic information about the toolbox
Quickstart guide
API documentation
User guide
Community guide
Data documentation
Basic information contain this introduction, installation instructions, software architecture and workflow schematics, and information about compatible data and the required data schema.
The quickstart guide provides a minimal working analysis example to get you started.
The API documentation has all technical details, containing instruction about how to use the toolbox functions, classes, return types, arguments and such.
The user guide provide more thorough examples of each toolbox layer functionalities. The examples are in Jupyter notebook format.
The community guide has information in the contribution_guide.