pyActigraphy tutorials

This series of notebooks are meant to illustrate the different features of the pyActigraphy package.

Introduction

• A gentle introduction to the basic functionalities: intro

• How to read files by batch: batch

Feature examples

• How to discard invalid sequences in actigraphy recordings before analysis:

  • Invalid sequences during the recordings

  • Invalid sequences at the beginning and/or the end of the recordings

• How to calculate the usual rest-activity rhythm variables

• How to visualise sleep diaries and compute summary statistics

• How to detect rest periods automatically

• How to quantify sleep fragmentation using state transition probabilities

Analysis examples

• How to perform a Cosinor analysis with pyActigraphy: cosinor

• How to perform a Functional linear modelling with pyActigraphy: flm

• How to perform a (Multifractal) Detrended fluctuation analysis (MF-DFA) with pyActigraphy: mfdfa

• How to perform a Singular spectrum analysis (SSA) with pyActigraphy: ssa

pyLight examples

The pyActigraphy package contains a module dedicated to the analysis of light exposure data, named pyLight. The following tutorials specifically illustrate its functionalities:

• A gentle introduction to the basics of pyLight: pylight_intro

• How to process light exposure data with pyLight: pylight_manip

• How to calculate various light exposure metrics with pyLight: pylight_metrics

Work-in-progress

The features illustrated in this section have to be considered as work-in-progress; most likely, they will be turned into full-fledged features in a next release. However, for impatient and brave users, it provides a starting point for testing:

• How to read actigraphy data stored in a pandas.DataFrame with pyActigraphy: pandas

Suggestions & Requests

If a feature of the pyActigraphy package is not illustrated here, do not hesitate to suggest it by fill an issue.

Or, even better, contribute to this section by providing us with your favourite notebook where you illustrate how this feature is relevant for your analysis.