xcube-resampling
xcube-resampling provides efficient algorithms for transforming datasets into different spatial grid mappings. It is designed for geospatial workflows that need flexible resampling and reprojection.
✨ Features
- Affine resampling – simple resampling using affine transformations
- Reprojection – convert datasets between different coordinate reference systems (CRS)
- Rectification – transform irregular grids into regular, well-structured grids
All methods work seamlessly with chunked (lazily loaded) xarray.Datasets and are powered by Dask for scalable, out-of-core computation.
⚡ Lightweight & Independent
The package is independent of the core xcube framework and has minimal dependencies:
affine, dask, dask-image, numba, numpy, pyproj, xarray, zarr.
Overview
The resampling methods are built around the
GridMapping
class, which represents a spatial grid mapping and contains all necessary information
for resampling.
A GridMapping is required for affine transformation and reprojection, and optional
for rectification. If omitted for rectification, a simple rectification will be
performed while staying in the same CRS.
resample_in_space — the gateway to xcube-resampling
The central function in this package is
resample_in_space,
which integrates all three algorithms and automatically selects the appropriate one
based on the criteria below.
| Algorithm | Function | Selection Criteria |
|---|---|---|
| Affine Transformation | affine_transform_dataset |
Source and target grids are both regular and share the same CRS. |
| Reprojection | reproject_dataset |
Source and target grids are both regular but have different CRS. |
| Rectification | rectify_dataset |
Source grid is irregular and contains 2D coordinates. |
With resample_in_space, users do not need to worry about selecting the right
algorithm—the function determines and applies it automatically.
👉 For usage examples and details, see the User Guide.
License
xcube-resampling is open source made available under the terms and conditions of the
MIT license.