oocgcm : out of core analysis of general circulation models in python.¶
This project provides tools for processing and analysing output of general circulation models and gridded satellite data in the field of Earth system science.
Our aim is to simplify the analysis of very large datasets of model output (~1-100Tb) like those produced by basin-to-global scale sub-mesoscale permitting ocean models and ensemble simulations of eddying ocean models by leveraging the potential of xarray and dask python packages.
The main ambition of this project is to provide simple tools for performing out-of-core computations with model output and gridded data, namely processing data that is too large to fit into memory at one time.
The project is so far mostly targeting NEMO ocean model and gridded ocean satellite data (AVISO, SST, ocean color...) but our aim is to build a framework that can be used for a variety of models based on the Arakawa C-grid. The framework can in principle also be used for atmospheric general circulation models.
We are trying to develop a framework flexible enough in order not to impose too strictly a specific workflow to the end user.
oocgcm is a pure Python package and we try to keep the list of dependencies as small as possible in order to simplify the deployment on a number of platforms.
oocgcm is not intended to provide advanced visualization functionalities for gridded geographical data as several powerful tools already exist in the python ecosystem (see in particular cartopy and basemap).
We rather focus on building a framework that simplifies the design and production of advanced statistical and dynamical analyses of large datasets of model output and gridded data.
oocgcm is at the pre-alpha stage. The API is therefore unstable and likely to change without notice.