Installation#

zarrio can be installed in several ways depending on your needs.

Using pip#

The easiest way to install zarrio is using pip:

pip install zarrio

This will install the latest stable version from PyPI.

Using conda#

If you’re using conda, you can install zarrio from conda-forge:

conda install -c conda-forge zarrio

Installing from source#

To install the latest development version from source:

git clone https://github.com/oceanum/zarrio.git
cd zarrio
pip install -e .

Dependencies#

zarrio requires the following dependencies:

Core dependencies:#

  • Python >= 3.8

  • xarray >= 0.18.0

  • zarr >= 2.10.0

  • numpy >= 1.20.0

  • pandas >= 1.3.0

  • click >= 8.0.0

  • pyyaml >= 5.4.0

  • dask >= 2021.0.0

  • netCDF4 >= 1.5.0

Optional dependencies:#

  • blosc: For Blosc compression support

  • numcodecs: For additional compression codecs

  • intake: For intake catalog support

  • fsspec: For cloud storage support

  • gcsfs: For Google Cloud Storage support

Development dependencies:#

  • pytest >= 6.2.0

  • pytest-cov >= 2.12.0

  • black >= 21.0.0

  • flake8 >= 3.9.0

  • mypy >= 0.910

  • sphinx >= 4.0.0

  • sphinx-rtd-theme >= 1.0.0

  • pre-commit >= 2.13.0

To install development dependencies:

pip install -e ".[dev]"

Verification#

To verify that zarrio is installed correctly, you can run:

python -c "import zarrio; print(f'zarrio version: {zarrio.__version__}')"

Or using the CLI:

zarrio --version

Docker Installation#

zarrio can also be used via Docker containers. See the Docker Support documentation for details on building and running Docker images.

System requirements#

zarrio is designed to work on Linux, macOS, and Windows systems with Python 3.8 or higher.

For large datasets, ensure you have sufficient disk space and memory. The library can handle datasets larger than available RAM through dask’s chunked array operations.