Source: xarray-eopf
Section: python
Maintainer: Debian GIS Project <pkg-grass-devel@lists.alioth.debian.org>
Uploaders: Antonio Valentino <antonio.valentino@tiscali.it>
Build-Depends: debhelper-compat (= 13),
               dh-sequence-python3,
               pybuild-plugin-pyproject,
               python3-all,
               python3-aiohttp,
               python3-dask,
               python3-dask-image,
               python3-fsspec,
               python3-numpy,
               python3-pyproj,
               python3-pytest <!nocheck>,
               python3-requests,
               python3-s3fs,
               python3-setuptools,
               python3-xarray,
               python3-xcube-resampling,
               python3-zarr
Standards-Version: 4.7.3
Testsuite: autopkgtest-pkg-pybuild
Homepage: https://github.com/EOPF-Sample-Service/xarray-eopf
Vcs-Browser: https://salsa.debian.org/debian-gis-team/xarray-eopf
Vcs-Git: https://salsa.debian.org/debian-gis-team/xarray-eopf.git
Description: EOPF Zarr backend for xarray
 An xarray backend implementation for ESA EOPF data products in
 Zarr format.
 .
 After installing this package, user can specify a new xarray backend
 named `"eopf-zarr"` to open EOPF sample products. The backend has
 two modes of operation, default analysis mode and the native mode.
 Both modes allow
 .
  * to open EOPF sample products from the local filesystem or from
    their original object storage using URLs with both `https` or `s3`
    protocols;
  * to open entire products as `xarray.DataTree` or `xarray.Dataset`;
  * to open a subgroup as `xarray.Dataset`. This works with
    local filesystem or `s3`-URLs.
 .
 The default analysis mode has the aim to represent the EOPF data
 products in an analysis-ready and convenient way. It provides the
 following features:
 .
  * Open the deeply nested EOPF products as flat `xarray.Dataset`
    objects.
  * All bands and quality images resampled to a single, user provided
    resolution, hence, spatial dimensions will be just `x` and `y`.
  * User-specified resampling by passing interpolation methods for
    up-scaling and aggregation methods for downscaling.
  * CF-compliant spatial referencing of datasets using a shared grid
    mapping variable `spatial_ref`.
  * Attach other CF-compliant metadata enhancements such as flag values
    and meanings for pixel quality information, such as the Sentinel-2
    scene classification (variable `scl`).
 .
 The native mode does not modify any contents or data, instead it
 basically delegates to the built-in `zarr` backend.


Package: python3-xarray-eopf
Architecture: all
Depends: ${python3:Depends},
         ${misc:Depends}
Description: ${source:Synopsis}
 ${source:Extended-Description}