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docs/index.rst

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@@ -20,29 +20,34 @@ Highlights
2020
Status
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------
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Zarr is still in an early, experimental phase of development. Feedback and bug
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Zarr is still in an early phase of development. Feedback and bug
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reports are very welcome, please get in touch via the `GitHub issue
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tracker <https://github.com/alimanfoo/zarr/issues>`_.
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Installation
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------------
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Install from PyPI::
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Zarr depends on NumPy. It is generally best to `install NumPy
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<http://docs.scipy.org/doc/numpy/user/install.html>`_ first using
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whatever method is most appropriate for you operating system and
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Python distribution.
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Install Zarr from PyPI::
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$ pip install zarr
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Alternatively, install via conda::
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Alternatively, install Zarr via conda::
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$ conda install -c conda-forge zarr
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Please note that Zarr includes a C extension providing integration
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with the Blosc library. Installing via conda or installing on Windows
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via pip will install a pre-compiled binary distribution. However, if
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you have a newer CPU that supports the AVX2 instruction set (e.g.,
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Intel Haswell, Broadwell or Skylake) then compiling from source is
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preferable, as the Blosc library includes some optimisations for those
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architectures::
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Zarr includes a C extension providing integration with the Blosc_
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library. Installing on any operating system via conda or installing on
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Windows via pip will install a pre-compiled binary
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distribution. However, if you have a newer CPU that supports the AVX2
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instruction set (e.g., Intel Haswell, Broadwell or Skylake) then
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compiling from source is preferable as the Blosc library includes some
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optimisations for AVX2::
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$ pip install --no-binary=:all: zarr
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To work with Zarr source code in development, install from GitHub::
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$ cd zarr
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$ python setup.py install
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To verify that Zarr has been fully installed (including the Blosc
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extension) run the test suite::
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$ pip install nose zict heapdict
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$ python -m nose -v zarr
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Contents
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--------
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docs/release.rst

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still recommended, however, as the automatic installation of NumPy may
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fail or be sub-optimal on some platforms.
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The :mod:`zarr.blosc` Cython extension is now optional and compilation
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will only be attempted on posix systems; other systems will fall back
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to a pure-Python installation (`#25
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<https://github.com/alimanfoo/zarr/issues/25>`_). On these systems
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only 'zlib', 'bz2' and 'lzma' compression will be available.
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Some optimizations have been made within the :mod:`zarr.blosc`
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extension to avoid unnecessary memory copies, giving a ~10-20%
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performance improvement for multi-threaded compression operations.
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machinery for slicing arrays, to properly handle getting and setting
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partial slices.
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Acknowledgments
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~~~~~~~~~~~~~~~
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Thanks to Matthew Rocklin (mrocklin_), Stephan Hoyer (shoyer_),
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Francesc Alted (FrancescAlted_), Anthony Scopatz (scopatz_) and Martin
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Durant (martindurant_) for contributions and comments.
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.. _release_0.4.0:
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0.4.0
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See `v0.3.0 release notes on GitHub
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<https://github.com/alimanfoo/zarr/releases/tag/v0.3.0>`_.
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.. _mrocklin: https://github.com/mrocklin
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.. _shoyer: https://github.com/shoyer
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.. _scopatz: https://github.com/scopatz
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.. _martindurant: https://github.com/martindurant
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.. _FrancescAlted: https://github.com/FrancescAlted

docs/tutorial.rst

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that data in the array should only be written once and write
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operations should be aligned with chunk boundaries.
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The Dask project has implementations of the ``MutableMapping``
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interface for distributed storage systems, see the `S3Map
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<http://s3fs.readthedocs.io/en/latest/api.html#s3fs.mapping.S3Map>`_
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and `HDFSMap
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<https://github.com/dask/hdfs3/blob/master/hdfs3/mapping.py>`_
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classes.
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.. _tutorial_tips_chunks:
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Chunk size and shape

requirements_test.txt

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flake8
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heapdict
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zict
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twine

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