Zarr¶

Zarr is a format for the storage of chunked, compressed, N-dimensional arrays. These documents describe the Zarr format and its Python implementation.

Highlights¶

Create N-dimensional arrays with any NumPy dtype.
Chunk arrays along any dimension.
Compress and/or filter chunks using any NumCodecs codec.
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.

Status¶

Zarr is still a young project. Feedback and bug reports are very welcome, please get in touch via the GitHub issue tracker. See Contributing to Zarr for further information about contributing to Zarr.

Installation¶

Zarr depends on NumPy. It is generally best to install NumPy first using whatever method is most appropriate for you operating system and Python distribution. Other dependencies should be installed automatically if using one of the installation methods below.

Install Zarr from PyPI:

$ pip install zarr

Alternatively, install Zarr via conda:

$ conda install -c conda-forge zarr

To install the latest development version of Zarr, you can use pip with the latest GitHub master:

$ pip install git+https://github.com/zarr-developers/zarr-python.git

To work with Zarr source code in development, install from GitHub:

$ git clone --recursive https://github.com/zarr-developers/zarr-python.git
$ cd zarr-python
$ python setup.py install

To verify that Zarr has been fully installed, run the test suite:

$ pip install pytest
$ python -m pytest -v --pyargs zarr

Contents¶

Projects using Zarr¶

If you are using Zarr, we would love to hear about it.

Acknowledgments¶

The following people have contributed to the development of Zarr by contributing code, documentation, code reviews, comments and/or ideas:

Zarr is inspired by HDF5, h5py and bcolz.

Development of Zarr is supported by the MRC Centre for Genomics and Global Health.