Are you a scientist involved in movement ecology working with biologging data collected from central-place foraging seabirds? cpforager is a Python package designed to help you manipulate, process, analyse and visualise the biologging datasets with ease.

The main objectives of cpforager are :

1. Efficiently handle large-scale biologging datasets, including high-resolution sensor data (e.g. accelerometers).
2. Provide a modular and extensible architecture, allowing users to tailor the code to their specific research needs.
3. Facilitate a smooth transition to Python for movement ecology researchers familiar with other languages (e.g. R).

cpforager package supports various biologging sensor types commonly used in movement ecology and provides the following core classes:

• GPS : for handling position recordings.
• TDR : for handling pressure recordings.
• AXY : for handling tri-axial acceleration recordings at high resolution combined with lower resolution position and pressure recordings.
• GPS_TDR : for handling position and pressure recordings.

cpforager also allows to deal with a list of sensors using the following classes:

• GPS_Collection : for working with datasets composed of multiple GPS loggers.
• TDR_Collection : for working with datasets composed of multiple TDR loggers.
• AXY_Collection : for working with datasets composed of multiple AXY loggers.
• GPS_TDR_Collection : for working with datasets composed of multiple GPS_TDR loggers.

Each class automatically enhances raw data but also computes key features specific to each biologger (e.g. trip segmentation for GPS, dive segmentation for TDR, ODBA calculation for AXY). They are also accompanied with methods for data processing and visualisation.

Installation

The latest released version is available at the Python Package Index (PyPI).

pip install cpforager

Documentation

The documentation of cpforager is automatically generated using Sphinx and can be found here.

User guide

A detailed user guide can be found in the dedicated section in the documentation.

Also, the Python scripts in the /tests/ folder illustrate how the GPS, TDR, AXY, GPS_TDR, GPS_Collection, TDR_Collection, AXY_Collection and GPS_TDR_Collection classes can be used to fully and simply benefit the users. Results of the scripts are also found in the /tests/ folder.

Future developments

• make classes' methods available in documentation.
• hmmlearn for adding an estimation method of seabird's behaviour (foraging, searching, resting, traveling, etc.).
• draft for Applications in Methods in Ecology and Evolution.
• publish package to the conda-forge channel.

Infos

• Python version used is 3.13.3.
• OS used is Ubuntu 20.04.
• The graphic design of the logos was done by Lisa Brunel.