Generate STAC Collections from xarray datasets.
$ xstac --help
usage:
Generate STAC Collections for Daymet from the Zarr Groups.
xstac template.json asset-key output.json
positional arguments:
template Template STAC Collection to merge with the result.
asset Asset key to use to load the data. Must be present in
the template file's 'assets'.
outfile Output file to write to. Defaults to stdout.
optional arguments:
-h, --help show this help message and exit
--reference-system REFERENCE_SYSTEM
--temporal_dimension TEMPORAL_DIMENSION
Coordinate name for the 'time' dimension
--x-dimension X_DIMENSION
Coordinate name for the 'x' dimension
--y-dimension Y_DIMENSION
Coordinate name for the 'y' dimension
--no-validate Whether to skip validation of the collection.
$ xstac examples/terraclimate/terraclimate-template.json \
zarr-https examples/terraclimate/terraclimate.json \
--x-dimension=lon --y-dimension=lat --reference-system=4326This generates the TerraClimate STAC Collection
Alternatively, you can generate STAC items:
$ xstac examples/terraclimate/item-template.json \
zarr-https examples/terraclimate/item.json \
--x-dimension=lon --y-dimension=lat --reference-system=4326
This generates the TerraClimate STAC item.
See examples/daymet/generate.py for an example using the Python API.
Kerchunk is a project and specification for representing chunked, compressed data where only the metadata and references to chunks of remote data are stored. You might want to include the Kerchunk metadata in a STAC item.
To do this, generate the Kerchunk indices and provide them as the
kerchunk_indices argument to xarray_to_stac.
>>> from stactools.noaa_nwm import stac
>>> import kerchunk.hdf
>>> href = "https://noaanwm.blob.core.windows.net/nwm/nwm.20231010/short_range/nwm.t00z.short_range.channel_rt.f001.conus.nc"
>>> indices = kerchunk.hdf.SingleHdf5ToZarr(href).translate()
>>> item = stac.create_item(href, kerchunk_indices=indices)