It is with great pleasure that we announce the release of ClimateTools.jl, a climate science package. We felt that this was a good time to announce it and hopefully get some feedback from users for improvements and bugs.
The aims of this package is to ease the extraction, manipulation, computation and visualization of climate models data that are stored in CF-compliant netCDF files. It basically creates an in-memory representation of physical variables. As of now, we are able to do a lot of things and the features, while not complete and still being a WIP, cover typical workflows for the creation of climate scenarios and the analysis of climate simulations.
Climate analysis stands on a wide number of fields. As such, ClimateTools leverages awesome packages and hopefully combines them into tools used by the Climate community. To name a few: AxisArrays.jl (used to store the data), Interpolations.jl, NCDatasets.jl, NetCDF.jl, PyCall.jl, PyPlot.jl. Without these packages, ClimateTools.jl wouldn’t exist. I’d like to also highlight that some of the features of ClimateTools uses Python libraries under the hood: Scipy, Numpy and Matplotlib. These dependencies are mostly for mapping purposes, as the Julia framework is not enough advanced to use Julian-based solutions… yet. Hopefully, as time goes by, ClimateTools will rely less and less on these Python packages. The number of dependencies means that loading the package takes longer than most packages (~5-10sec once all the packages are precompiled).
More information on Github and in the documentation.
ClimateTools Github repo: https://github.com/Balinus/ClimateTools.jl
Documentation (stable): https://balinus.github.io/ClimateTools.jl/stable/
Documentation (latest): https://balinus.github.io/ClimateTools.jl/latest/
Thanks for your comments!