Estimate when a Slocum glider will need to be recovered by

The main method uses the estimated percentage of battery remaining to predict when the percentage left will be a given value.

The input is a time series NetCDF with at least two columns:

• time a CF compliant time of the observation
• m_lithium_battery_relative_charge as defined by Slocum masterdata file

A compatible input NetCDF file is generated from my Slocum_DataHarvester in the sensors files.

A linear regression is done to:

m_lithium_battery_relative_charge = a + b (time - min(time))/86400

so the slope is in days.

Installation

Install required dependencies:

pip install numpy xarray scipy matplotlib

Usage

Basic usage:

./SlocumBatteryPercentageDuration.py sensor_data.nc

With plotting enabled:

./SlocumBatteryPercentageDuration.py --plot sensor_data.nc

Using only the last N days of data:

./SlocumBatteryPercentageDuration.py --ndays 7 sensor_data.nc

Specifying a custom threshold:

./SlocumBatteryPercentageDuration.py --threshold 10 sensor_data.nc

Using a specific time range:

./SlocumBatteryPercentageDuration.py --start "2025-01-01" --stop "2025-01-15" sensor_data.nc

For verbose output:

./SlocumBatteryPercentageDuration.py --verbose sensor_data.nc