Features

• Burstiness Parameter (BP) and Memory Coefficient (MC) Calculation: Calculate BP and MC to quantify the irregularity and memory effects of event timing within longitudinal data.

• Event Train Detection: Detect and label event trains based on user-defined criteria such as maximum inter-event time and minimum burst size.

• Train-Level Analysis: Analyse BP and MC for detected event trains, providing insights into temporal patterns within trains of events.

• Visualisation Tools: Visualise temporal patterns with scatter plots, histograms, kernel density estimates (KDE), and more, facilitating interpretation of analysis results.

• User-Friendly Interface: Designed for ease of use, with clear function parameters and output formats, making it accessible to both novice and experienced users.

Installation

You can install bursty_dynamics via pip:

pip install bursty_dynamics

Usage

Here’s a quick overview of how to use the main functionalities of the package

from bursty_dynamics.scores import calculate_scores
from bursty_dynamics.trains import train_detection, train_info, train_scores

# Load your longitudinal data into a DataFrame
# df = load_data()

# calculate BP and MC
score_df = calculate_scores(df, subject_id = 'eid', time_col = 'event_dt')

For more example of usage, please take a look at examples.ipynb in the example folder.

Getting Help

For more information about bursty_dynamics, please check out:

• bursty_dynamics documentation

License

bursty_dynamics is licensed under the MIT License. See the LICENSE file for more details.

Contributing

Contributions are welcome! If you encounter any issues or have suggestions for improvements, feel free to open an issue or for questions, suggestions, or general discussion related to our project, please visit our GitHub Discussions page.