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Merge pull request zarr-developers#495 from mzjp2/readme-update
Update README file to be more detailed
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‎README.md

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<div align="center">
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<img src="https://raw.githubusercontent.com/zarr-developers/community/master/logos/logo2.png"><br>
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</div>
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# Zarr
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<table>
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<tr>
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<td>Latest Release</td>
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<td>
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<a href="https://pypi.org/project/zarr/">
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<img src="https://badge.fury.io/py/zarr.svg" alt="latest release" />
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</a>
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</td>
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</tr>
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<td></td>
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<td>
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<a href="https://anaconda.org/anaconda/zarr/">
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<img src="https://anaconda.org/conda-forge/zarr/badges/version.svg" alt="latest release" />
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</a>
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</td>
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</tr>
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<tr>
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<td>Package Status</td>
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<td>
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<a href="https://pypi.org/project/zarr/">
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<img src="https://img.shields.io/pypi/status/zarr.svg" alt="status" />
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</a>
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</td>
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</tr>
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<tr>
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<td>License</td>
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<td>
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<a href="https://github.com/zarr-developers/zarr-python/blob/master/LICENSE">
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<img src="https://img.shields.io/pypi/l/zarr.svg" alt="license" />
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</a>
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</td>
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</tr>
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<tr>
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<td>Build Status</td>
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<a href="https://travis-ci.org/zarr-developers/zarr-python">
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<img src="https://travis-ci.org/zarr-developers/zarr-python.svg?branch=master" alt="travis build status" />
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</a>
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</td>
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</tr>
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<td>Coverage</td>
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<td>
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<a href="https://coveralls.io/github/zarr-developers/zarr-python?branch=master">
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<img src="https://coveralls.io/repos/github/zarr-developers/zarr-python/badge.svg?branch=master" alt="coverage" />
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</a>
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</td>
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</tr>
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<td>Downloads</td>
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<td>
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<a href="https://pandas.pydata.org">
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<img src="https://pepy.tech/badge/zarr" alt="pypi downloads" />
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</a>
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</td>
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</tr>
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<td>Gitter</td>
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<td>
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<a href="https://gitter.im/zarr-developers/community">
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<img src="https://badges.gitter.im/zarr-developers/community.svg" />
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</a>
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</td>
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</tr>
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</table>
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## What is it?
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Zarr is a Python package providing an implementation of compressed, chunked, N-dimensional arrays, designed for use in parallel computing. See the [documentation](https://zarr.readthedocs.io) for more information.
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## Main Features
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- [**Create**](https://zarr.readthedocs.io/en/stable/tutorial.html#creating-an-array) N-dimensional arrays with any NumPy `dtype`.
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- [**Chunk arrays**](https://zarr.readthedocs.io/en/stable/tutorial.html#chunk-optimizations) along any dimension.
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- [**Compress**](https://zarr.readthedocs.io/en/stable/tutorial.html#compressors) and/or filter chunks using any NumCodecs codec.
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- [**Store arrays**](https://zarr.readthedocs.io/en/stable/tutorial.html#tutorial-storage) in memory, on disk, inside a zip file, on S3, etc...
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- [**Read**](https://zarr.readthedocs.io/en/stable/tutorial.html#reading-and-writing-data) an array [**concurrently**](https://zarr.readthedocs.io/en/stable/tutorial.html#parallel-computing-and-synchronization) 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**](https://zarr.readthedocs.io/en/stable/tutorial.html#groups).
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## Where to get it
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Zarr can be installed from PyPI using `pip`:
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```bash
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pip install zarr
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```
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or via `conda`:
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```bash
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conda install -c conda-forge zarr
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```
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For more details, including how to install from source, see the [installation documentation](https://zarr.readthedocs.io/en/stable/#installation).

‎README.rst

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‎setup.py

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DESCRIPTION = 'An implementation of chunked, compressed, ' \
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'N-dimensional arrays for Python.'
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with open('README.rst') as f:
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with open('README.md') as f:
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LONG_DESCRIPTION = f.read()
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dependencies = [
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name='zarr',
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description=DESCRIPTION,
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long_description=LONG_DESCRIPTION,
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long_description_content_type='text/markdown',
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use_scm_version={
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'version_scheme': 'guess-next-dev',
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'local_scheme': 'dirty-tag',
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'write_to': 'zarr/version.py'
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},
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setup_requires=[
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'setuptools>18.0',
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'setuptools>=38.6.0',
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'setuptools-scm>1.5.4'
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],
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python_requires='>=3.5',

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