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xcube-resampling

xcube-resampling provides efficient algorithms for transforming datasets into different spatial grids and temporal scales. It is designed for geospatial workflows that need flexible resampling and reprojection. This library provides up and downsampling for both spatial and temporal domains.

✨ Features

  • Spatial Resampling

    • 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

  • Temporal Resampling

    • Time-based resampling – upsample or downsample data along the time dimension

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 spatial 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.

The temporal resampling method is built around the Resampler class, which provides methods to up or downsample a temporal dataset into the requested frequency.

resample_in_space — the gateway to Spatial Resampling of Gridded datasets

The central function for spatial up and down resampling 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.

resample_in_time — the gateway to Temporal Resampling of Gridded datasets

The central function for resampling in the temporal domain is resample_in_time which handles both up and downsampling of a dataset in the time dimension.

👉 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.

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