Glider tools is a Python 3.8+ package designed to process data from the first level of processing to a science ready dataset (delayed mode quality control). The package is designed to easily import data to a standard column format (numpy.ndarray or pandas.DataFrame). Cleaning and smoothing functions are flexible and can be applied as required by the user. We provide examples and demonstrate best practices as developed by the SOCCO Group, with early contributions from Polar Gliders at the University of Gothenburg. GliderTools includes contributions from VOTO. We aim to implement Best Practices developed by OceanGliders in the ongoing discussions.

Please cite the original publication of this package and the package itself.

To install the core package from conda-forge run: conda install -c conda-forge glidertools

To install the core package run: pip install glidertools.

1. Clone glidertools to your local machine: git clone https://github.com/GliderToolsCommunity/GliderTools
2. Change to the parent directory of GliderTools
3. Install glidertools with pip install -e ./GliderTools. This will allow changes you make locally, to be reflected when you import the package in Python

There are some packages that are not installed by default, as these are large packages or can result in installation errors, resulting in failure to install GliderTools. These should install automatically with pip install package_name:

• gsw: accurate density calculation (may fail in some cases)
• pykrige: variogram plotting (installation generally works, except when bundled)
• plotly: interactive 3D plots (large package)

• Join the community by introducing yourself (no need to be a Python or Git guru! Just say what you are working with and join the discussion)
• If you find an error, please report it on as a Github issue. Please copy the entire error message (even if it's long).
• Oxygen processing is rudimentary so far but we are on it and happy to get your support in this discussion

For contributing follow the instructions

• We rely heavily on ion_functions.data.flo_functions which was written by Christopher Wingard, Craig Risien, Russell Desiderio
• This work was initially funded by Pedro M Scheel Monteiro at the Council for Scientific and Industrial Research (where Luke was working at the time of writing the code).
• Testers for their feedback: SOCCO team at the CSIR and ...