cpforager 1.2.0
Python package to handle data gathered from biologgers (GPS, TDR, AXY) attached to central-place foraging seabirds. The idea is to make movement ecology data a bit easier to process.
Requires Python: >=3.11
Keywords: accelerometer, biologging, central-place, ecology, foraging, gps, movement, movement-ecology, seabird, tdr, telemetry

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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.

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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).

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**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.

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