A toolkit for processing Seaglider base station NetCDF files: despiking, smoothing, outlier detection, backscatter, fluorescence quenching, calibration, gridding, interpolation. Documentation at https://glidertools.readthedocs.io
Project description
Glider tools is a Python 3.6+ package designed to process data from the first level of processing to a science ready dataset. 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 (http://socco.org.za/).
Please cite the original publication of this package and the version that you’ve used: Reference here with https://doi.org/10.3389/fmars.2019.00738
Installation
PyPI
To install the core package run: pip install glidertools.
GitHub
Clone glidertools to your local machine: git clone https://github.com/GliderToolsCommunity/GliderTools
Change to the parent directory of GliderTools
Install glidertools with pip install -e ./GliderTools. This will allow changes you make locally, to be reflected when you import the package in Python
Recommended, but optional packages
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)
How you can contribute
Error reporting with using GitLab (https://github.com/GliderToolsCommunity/GliderTools/issues/new). Please copy the entire error message (even if it’s long).
Join our slack group: https://join.slack.com/t/glidertools/shared_invite/zt-dm30fsed-ik_NE_zbb8aEs_pnnxo3xQ
Detailed error reporting so users know where the fault lies.
Oxygen processing is rudimentary as we do not have the expertise in our group to address this
Acknowledgements
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 …
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