xarray-spatial grew out of the Datashader project, which provides fast rasterization of vector data (points, lines, polygons, meshes, and rasters) for use with xarray-spatial.
xarray-spatial does not depend on GDAL / GEOS, which makes it fully extensible in Python but does limit the breadth of operations that can be covered. xarray-spatial is meant to include the core raster-analysis functions needed for GIS developers / analysts, implemented independently of the non-Python geo stack.
Fast, Accurate Python library for Raster Operations
Extensible with Numba
Scalable with Dask
# via conda conda install -c makepath xarray-spatial # via pip pip install xarray-spatial
Rasters are regularly gridded datasets like GeoTIFFs, JPGs, and PNGs. In the GIS world, rasters are used for representing continuous phenomena (e.g. elevation, rainfall, distance), either directly as numerical values, or as RGB images created for humans to view. Rasters typically have two spatial dimensions, but may have any number of other dimensions (time, type of measurement, etc.)
import xarray as xr from xrspatial import hillshade my_dataarray = xr.DataArray(...) hillshaded_dataarray = hillshade(my_dataarray)
Check out the user guide here.
xarray-spatial currently depends on Datashader, but will soon be updated to depend only on xarray and numba, while still being able to make use of Datashader output when available.
Within the Python ecosystem, many geospatial libraries interface with the GDAL C++ library for raster and vector input, output, and analysis (e.g. rasterio, rasterstats, geopandas). GDAL is robust, performant, and has decades of great work behind it. For years, off-loading expensive computations to the C/C++ level in this way has been a key performance strategy for Python libraries (obviously…Python itself is implemented in C!). However, wrapping GDAL has a few drawbacks for Python developers and data scientists:
