gcm_filters
Diffusion-based Spatial Filtering of Gridded Data
Diffusion-based Spatial Filtering of Gridded Data
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GCM-Filters is a python package that performs spatial filtering analysis in a flexible and efficient way. The GCM-Filters algorithm applies a discrete Laplacian to smooth a field through an iterative process that resembles diffusion (Grooms et al., 2021). The package can be used for either gridded observational data or gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Such GCM data come on complex curvilinear grids, whose geometry is respected by the GCM-Filters Laplacians. Through integration with dask, GCM-Filters enables parallel, out-of-core filter analysis on both CPUs and GPUs.
Summary
Diffusion-based Spatial Filtering of Gridded Data
Last Updated
Nov 13, 2024 at 23:55
License
LGPL-3.0-only
Total Downloads
28.9K
Supported Platforms
GitHub Repository
https://github.com/ocean-eddy-cpt/gcm-filtersDocumentation
https://gcm-filters.readthedocs.io/en/latest/