Xarray-styled package for reading and directly operating on unstructured grid datasets following UGRID conventions
Project description
Xarray extension for unstructured climate and global weather data
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What is it?
UXarray aims to address the geoscience community's need for tools that enable standard data analysis techniques to operate directly on unstructured grid data. UXarray provides Xarray-styled functionality to better read in and use unstructured grid datasets that follow standard conventions, including UGRID, MPAS, ICON, SCRIP, ESMF, and Exodus grid formats. This effort is a result of the collaboration between Project Raijin (NSF NCAR and Pennsylvania State University) and the SEATS Project (Argonne National Laboratory, UC Davis, and Lawrence Livermore National Laboratory). The UXarray team welcomes community members to become part of this collaboration at any level of contribution.
UXarray is implemented in pure Python and does not explicitly contain or require
any compiled code. This makes UXarray more accessible to the general Python
community. Any contributions to this repository in pure Python are welcome and
documentation for contribution guidelines can be found when clicking New Issue
under the Issues
tab in the UXarray repository.
Why is the name "UXarray"?
We have created UXarray based on Xarray (via inheritance of Xarray Dataset and DataArray classes), a Pangeo ecosystem package commonly-used for structured grids recognition, to support reading and recognizing unstructured grid model outputs. We picked the name "UXarray" (pronounced "you-ex-array"), with the "U" representing unstructured grids.
Features
Grid
class for storing grid information and providing grid-specific functionality- Support for reading UGRID, MPAS, ESMF, ICON, GEOS-CS, SCRIP, and EXODUS grid formats
- Extension of xarray's
DataArray
andDataset
classe to support unstructured grid operationsuxarray.UxDataArray
inheritsxarray.DataArray
and is attached to aGrid
instance through the.uxgrid
accessoruxarray.UxDataset
inheritsxarray.Dataset
and is attached to aGrid
instance through the.uxgrid
accessor
- Extension of xarray's
open_dataset
andopen_mfdataset
methods to support reading grid and data files - Plotting
- Native visualization of unstructured grids written using the
hvPlot
package - Support for grid topology visualization (i.e. exploring the geometry of a grid) and data visualization (i.e. data mapped to each face)
- Native visualization of unstructured grids written using the
- Subsetting
- Ability to select arbitrary regions of a grid using different selection methods (nearest neighbor, bounding circle, bounding box)
- Remapping
- Support for nearest neighbor and inverse distance weighted unstructured to unstructured remapping
- Topological Aggregations
- Perform aggregations within different topology elements using connectivity information
- Mathematical Operators
- Support for Integral, Difference, and Gradient calculations
Intended Features
The following intended features have been inspired by discussions with members of the scientific community, within the SEATS Project and Project Raijin, and on several community platforms such as Xarray GitHub Repository. The UXarray team is receptive to additional functionality requests.
- Support for arbitrary structured and unstructured grids on the sphere, including latitude-longitude grids, grids with only partial coverage of the sphere, and grids with concave faces.
- Support for finite volume and finite element outputs.
- Triangular decompositions.
- Calculation of supermeshes (consisting of grid lines from two input grids).
- Snapshots and composites following particular features.
Documentation
Contributors
Thank you to all of our contributors!
Citing UXarray
If you'd like to cite our work, please follow How to cite UXarray.
Support
EarthCube aims to transform the conduct of geosciences research by developing and maintaining a well-connected and facile environment that improves access, sharing, visualization, and analysis of data and related resources. |
Pangeo supports collaborative efforts to develop software and infrastructure to enable Big Data geoscience research. |
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