Source: zarr
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders: Antonio Valentino <antonio.valentino@tiscali.it>
Section: python
Testsuite: autopkgtest-pkg-pybuild
Priority: optional
Build-Depends: debhelper-compat (= 13),
               dh-python,
               dh-sequence-numpy3,
               dh-sequence-python3,
               dh-sequence-sphinxdoc,
               pybuild-plugin-pyproject,
               python3-all,
               python3-asciitree,
               python3-fasteners,
               python3-fsspec,
               python3-h5py,
               python3-msgpack,
               python3-numcodecs,
               python3-numpy,
               python3-numpydoc <!nodoc>,
               python3-pytest <!nocheck>,
               python3-pytest-doctestplus <!nocheck>,
               python3-pytest-timeout <!nocheck>,
               python3-setuptools,
               python3-setuptools-scm,
               python3-sphinx <!nodoc>,
               python3-sphinx-automodapi <!nodoc>,
               python3-sphinx-copybutton <!nodoc>,
               python3-sphinx-design <!nodoc>,
               python3-sphinx-issues <!nodoc>,
               python3-pydata-sphinx-theme <!nodoc>
Standards-Version: 4.6.2
Vcs-Browser: https://salsa.debian.org/science-team/zarr
Vcs-Git: https://salsa.debian.org/science-team/zarr.git
Homepage: https://github.com/zarr-developers/zarr-python
Rules-Requires-Root: no

Package: python3-zarr
Architecture: all
Depends: ${misc:Depends},
         ${python3:Depends},
         ${sphinxdoc:Depends},
         python3-sphinx-copybutton,
         python3-pydata-sphinx-theme
Recommends: python3-fsspec
Suggests: python3-h5py,
          jupyter-notebook
Description: chunked, compressed, N-dimensional arrays for Python
 Zarr is a Python package providing an implementation of compressed,
 chunked, N-dimensional arrays, designed for use in parallel
 computing. Some highlights:
 .
   - Create N-dimensional arrays with any NumPy dtype.
   - Chunk arrays along any dimension.
   - Compress chunks using the fast Blosc meta-compressor or
     alternatively using zlib, BZ2 or LZMA.
   - Store arrays in memory, on disk, inside a Zip file, on S3, ...
   - Read an array concurrently from multiple threads or processes.
   - Write to an array concurrently from multiple threads or processes.
   - Organize arrays into hierarchies via groups.
   - Use filters to preprocess data and improve compression.