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revise top-level package description (#2430)
* revise main package description * Update doc/index.rst Co-Authored-By: rabernat <[email protected]> * Update doc/index.rst Co-Authored-By: rabernat <[email protected]> * Update doc/index.rst Co-Authored-By: rabernat <[email protected]> * next draft * add mention of netCDF * eliminate CDM reference * update README and setup.py * Split long paragraph, minor rewordings
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‎README.rst

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.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
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:target: http://numfocus.org
2020

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**xarray** (formerly **xray**) is an open source project and Python package that aims to bring the
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labeled data power of pandas_ to the physical sciences, by providing
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N-dimensional variants of the core pandas data structures.
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Our goal is to provide a pandas-like and pandas-compatible toolkit for
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analytics on multi-dimensional arrays, rather than the tabular data for which
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pandas excels. Our approach adopts the `Common Data Model`_ for self-
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describing scientific data in widespread use in the Earth sciences:
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``xarray.Dataset`` is an in-memory representation of a netCDF file.
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**xarray** (formerly **xray**) is an open source project and Python package
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that makes working with labelled multi-dimensional arrays simple,
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efficient, and fun!
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Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
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"tensors") are an essential part of computational science.
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They are encountered in a wide range of fields, including physics, astronomy,
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geoscience, bioinformatics, engineering, finance, and deep learning.
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In Python, NumPy_ provides the fundamental data structure and API for
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working with raw ND arrays.
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However, real-world datasets are usually more than just raw numbers;
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they have labels which encode information about how the array values map
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to locations in space, time, etc.
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By introducing *dimensions*, *coordinates*, and *attributes* on top of raw
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NumPy-like arrays, xarray is able to understand these labels and use them to
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provide a more intuitive, more concise, and less error-prone experience.
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Xarray also provides a large and growing library of functions for advanced
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analytics and visualization with these data structures.
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Xarray was inspired by and borrows heavily from pandas_, the popular data
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analysis package focused on labelled tabular data.
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Xarray can read and write data from most common labeled ND-array storage
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formats and is particularly tailored to working with netCDF_ files, which were
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the source of xarray's data model.
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.. _NumPy: http://www.numpy.org/
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.. _pandas: http://pandas.pydata.org
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.. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM
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.. _netCDF: http://www.unidata.ucar.edu/software/netcdf
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.. _OPeNDAP: http://www.opendap.org/
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Why xarray?
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-----------

‎doc/index.rst

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=================================================
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**xarray** (formerly **xray**) is an open source project and Python package
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that aims to bring the labeled data power of pandas_ to the physical sciences,
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by providing N-dimensional variants of the core pandas data structures.
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Our goal is to provide a pandas-like and pandas-compatible toolkit for
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analytics on multi-dimensional arrays, rather than the tabular data for which
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pandas excels. Our approach adopts the `Common Data Model`_ for self-
11-
describing scientific data in widespread use in the Earth sciences:
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``xarray.Dataset`` is an in-memory representation of a netCDF file.
13-
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that makes working with labelled multi-dimensional arrays simple,
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efficient, and fun!
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Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
9+
"tensors") are an essential part of computational science.
10+
They are encountered in a wide range of fields, including physics, astronomy,
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geoscience, bioinformatics, engineering, finance, and deep learning.
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In Python, NumPy_ provides the fundamental data structure and API for
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working with raw ND arrays.
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However, real-world datasets are usually more than just raw numbers;
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they have labels which encode information about how the array values map
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to locations in space, time, etc.
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By introducing *dimensions*, *coordinates*, and *attributes* on top of raw
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NumPy-like arrays, xarray is able to understand these labels and use them to
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provide a more intuitive, more concise, and less error-prone experience.
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Xarray also provides a large and growing library of functions for advanced
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analytics and visualization with these data structures.
23+
Xarray was inspired by and borrows heavily from pandas_, the popular data
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analysis package focused on labelled tabular data.
25+
Xarray can read and write data from most common labeled ND-array storage
26+
formats and is particularly tailored to working with netCDF_ files, which were
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the source of xarray's data model.
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.. _NumPy: http://www.numpy.org/
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.. _pandas: http://pandas.pydata.org
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.. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM
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.. _netCDF: http://www.unidata.ucar.edu/software/netcdf
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.. _OPeNDAP: http://www.opendap.org/
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Documentation
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-------------
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.. _2015 Unidata Users Workshop talk: https://www.youtube.com/watch?v=J9ypQOnt5l8
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.. _tutorial: https://github.com/Unidata/unidata-users-workshop/blob/master/notebooks/xray-tutorial.ipynb
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.. _with answers: https://github.com/Unidata/unidata-users-workshop/blob/master/notebooks/xray-tutorial-with-answers.ipynb
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.. _Nicolas Fauchereau's tutorial: http://nbviewer.ipython.org/github/nicolasfauchereau/metocean/blob/master/notebooks/xray.ipynb
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.. _Nicolas Fauchereau's tutorial: http://nbviewer.iPython.org/github/nicolasfauchereau/metocean/blob/master/notebooks/xray.ipynb
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Get in touch
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------------

‎setup.py

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DESCRIPTION = "N-D labeled arrays and datasets in Python"
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LONG_DESCRIPTION = """
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**xarray** (formerly **xray**) is an open source project and Python package
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that aims to bring the labeled data power of pandas_ to the physical sciences,
38-
by providing N-dimensional variants of the core pandas data structures.
37+
that makes working with labelled multi-dimensional arrays simple,
38+
efficient, and fun!
3939
40-
Our goal is to provide a pandas-like and pandas-compatible toolkit for
41-
analytics on multi-dimensional arrays, rather than the tabular data for which
42-
pandas excels. Our approach adopts the `Common Data Model`_ for self-
43-
describing scientific data in widespread use in the Earth sciences:
44-
``xarray.Dataset`` is an in-memory representation of a netCDF file.
40+
Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
41+
"tensors") are an essential part of computational science.
42+
They are encountered in a wide range of fields, including physics, astronomy,
43+
geoscience, bioinformatics, engineering, finance, and deep learning.
44+
In Python, NumPy_ provides the fundamental data structure and API for
45+
working with raw ND arrays.
46+
However, real-world datasets are usually more than just raw numbers;
47+
they have labels which encode information about how the array values map
48+
to locations in space, time, etc.
4549
50+
By introducing *dimensions*, *coordinates*, and *attributes* on top of raw
51+
NumPy-like arrays, xarray is able to understand these labels and use them to
52+
provide a more intuitive, more concise, and less error-prone experience.
53+
Xarray also provides a large and growing library of functions for advanced
54+
analytics and visualization with these data structures.
55+
Xarray was inspired by and borrows heavily from pandas_, the popular data
56+
analysis package focused on labelled tabular data.
57+
Xarray can read and write data from most common labeled ND-array storage
58+
formats and is particularly tailored to working with netCDF_ files, which were
59+
the source of xarray's data model.
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61+
.. _NumPy: http://www.numpy.org/
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.. _pandas: http://pandas.pydata.org
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.. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM
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.. _netCDF: http://www.unidata.ucar.edu/software/netcdf
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.. _OPeNDAP: http://www.opendap.org/
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Important links
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---------------

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