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docs/about.rst

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About
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=====
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Zarr is a format for the storage of chunked, compressed, N-dimensional arrays
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inspired by `HDF5 <https://www.hdfgroup.org/HDF5/>`_, `h5py
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<https://www.h5py.org/>`_ and `bcolz <https://bcolz.readthedocs.io/>`_.
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These documents describe the Zarr Python implementation. More information
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about the Zarr format can be found on the `main website <https://zarr.dev>`_.
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If you are using Zarr, we would `love to hear about it
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<https://github.com/zarr-developers/community/issues/19>`_.
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Funding
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-------
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The project is fiscally sponsored by `NumFOCUS <https://numfocus.org/>`_, a US
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501(c)(3) public charity, and development is supported by the
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`MRC Centre for Genomics and Global Health <https://www.cggh.org>`_
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and the `Chan Zuckerberg Initiative <https://chanzuckerberg.com/>`_.
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.. _NumCodecs: https://numcodecs.readthedocs.io/

docs/getting_started.rst

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Getting Started
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===============
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Zarr is a format for the storage of chunked, compressed, N-dimensional arrays
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inspired by `HDF5 <https://www.hdfgroup.org/HDF5/>`_, `h5py
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<https://www.h5py.org/>`_ and `bcolz <https://bcolz.readthedocs.io/>`_.
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The project is fiscally sponsored by `NumFOCUS <https://numfocus.org/>`_, a US
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501(c)(3) public charity, and development is supported by the
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`MRC Centre for Genomics and Global Health <https://www.cggh.org>`_
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and the `Chan Zuckerberg Initiative <https://chanzuckerberg.com/>`_.
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These documents describe the Zarr Python implementation. More information
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about the Zarr format can be found on the `main website <https://zarr.dev>`_.
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Highlights
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----------
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* Create N-dimensional arrays with any NumPy dtype.
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* Chunk arrays along any dimension.
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* Compress and/or filter chunks using any NumCodecs_ codec.
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* Store arrays in memory, on disk, inside a Zip file, on S3, ...
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* Read an array concurrently from multiple threads or processes.
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* Write to an array concurrently from multiple threads or processes.
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* Organize arrays into hierarchies via groups.
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Contributing
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Installation
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------------
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Feedback and bug reports are very welcome, please get in touch via
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the `GitHub issue tracker <https://github.com/zarr-developers/zarr-python/issues>`_. See
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:doc:`contributing` for further information about contributing to Zarr.
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pip
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~~~
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Projects using Zarr
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-------------------
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.. code-block:: console
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If you are using Zarr, we would `love to hear about it
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<https://github.com/zarr-developers/community/issues/19>`_.
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$ pip install zarr
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.. toctree::
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:caption: Getting Started
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:hidden:
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There are a number of optional dependency groups you can install for extra functionality.
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These can be installed using ``pip install "zarr[<extra>]"``, e.g. ``pip install "zarr[gpu]"``
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installation
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- ``gpu``: support for GPUs
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- ``fsspec``: support for reading/writing to remote data stores
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- ``tree``: support for pretty printing of directory trees
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.. _NumCodecs: https://numcodecs.readthedocs.io/
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conda
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~~~~~
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.. code-block:: console
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$ conda install -c conda-forge zarr
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Conda does not support optional dependencies, so you will have to manually install any packages
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needed to enable extra functionality.
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Dependency support
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~~~~~~~~~~~~~~~~~~
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Zarr has endorsed `Scientific-Python SPEC 0 <https://scientific-python.org/specs/spec-0000/>`_ and now follows the version support window as outlined below:
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- Python: 36 months after initial release
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- Core package dependencies (e.g. NumPy): 24 months after initial release
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Development
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~~~~~~~~~~~
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To install the latest development version of Zarr, see `the contributing guide <contributing.html>`_.

docs/index.rst

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license
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contributing
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roadmap
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about
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**Version**: |version|
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**Download documentation**: `PDF/Zipped HTML <https://readthedocs.org/projects/zarr/downloads/>`_
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**Useful links**:
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`Installation <installation.html>`_ |
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`Source Repository <https://github.com/zarr-developers/zarr-python>`_ |
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`Issue Tracker <https://github.com/zarr-developers/zarr-python/issues>`_ |
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`Zulip Chat <https://ossci.zulipchat.com/>`_
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Zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.
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Highlights
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----------
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* Create N-dimensional arrays with any NumPy dtype.
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* Chunk arrays along any dimension.
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* Compress and/or filter chunks using any NumCodecs_ codec.
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* Store arrays in memory, on disk, inside a Zip file, on S3, ...
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* Read an array concurrently from multiple threads or processes.
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* Write to an array concurrently from multiple threads or processes.
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* Organize arrays into hierarchies via groups.
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.. grid:: 2
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.. grid-item-card::

docs/installation.rst

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