Fixing xray -> xarray

Author: shoyer

.coveragerc

@@ -1,2 +1,2 @@
 [run]
-omit = xray/test/*
+omit = xarray/test/*

.gitignore

@@ -39,10 +39,10 @@ nosetests.xml
 .idea
 *.swp
 
-# xray specific
+# xarray specific
 doc/_build
 doc/generated
 doc/_static/*.png
-xray/version.py
+xarray/version.py
 
 .ipynb_checkpoints

.travis.yml

@@ -64,7 +64,7 @@ install:
   - python setup.py install
 
 script:
-  - py.test xray --cov=xray --cov-report term-missing
+  - py.test xarray --cov=xarray --cov-report term-missing
 
 after_success:
   - coveralls

README.rst

@@ -1,36 +1,36 @@
-xray: N-D labeled arrays and datasets
-=====================================
+xarray: N-D labeled arrays and datasets
+=======================================
 
-.. image:: https://travis-ci.org/xray/xray.svg?branch=master
-   :target: https://travis-ci.org/xray/xray
-.. image:: https://ci.appveyor.com/api/projects/status/github/xray/xray?svg=true&passingText=passing&failingText=failing&pendingText=pending
+.. image:: https://travis-ci.org/pydata/xarray.svg?branch=master
+   :target: https://travis-ci.org/pydata/xarray
+.. image:: https://ci.appveyor.com/api/projects/status/github/pydata/xarray?svg=true&passingText=passing&failingText=failing&pendingText=pending
    :target: https://ci.appveyor.com/project/shoyer/xray
-.. image:: https://coveralls.io/repos/xray/xray/badge.svg
-   :target: https://coveralls.io/r/xray/xray
-.. image:: https://landscape.io/github/xray/xray/master/landscape.svg?style=flat
-   :target: https://landscape.io/github/xray/xray/master
-.. image:: https://img.shields.io/pypi/v/xray.svg
-   :target: https://pypi.python.org/pypi/xray/
+.. image:: https://coveralls.io/repos/pydata/xarray/badge.svg
+   :target: https://coveralls.io/r/pydata/xarray
+.. image:: https://landscape.io/github/pydata/xarray/master/landscape.svg?style=flat
+   :target: https://landscape.io/github/pydata/xarray/master
+.. image:: https://img.shields.io/pypi/v/xarray.svg
+   :target: https://pypi.python.org/pypi/xarray/
 .. image:: https://badges.gitter.im/Join%20Chat.svg
-   :target: https://gitter.im/xray/xray
+   :target: https://gitter.im/pydata/xarray
 
-**xray** is an open source project and Python package that aims to bring the
+**xarray** (formerly "xray") is an open source project and Python package that aims to bring the
 labeled data power of pandas_ to the physical sciences, by providing
 N-dimensional variants of the core pandas data structures.
 
 Our goal is to provide a pandas-like and pandas-compatible toolkit for
 analytics on multi-dimensional arrays, rather than the tabular data for which
 pandas excels. Our approach adopts the `Common Data Model`_ for self-
 describing scientific data in widespread use in the Earth sciences:
-``xray.Dataset`` is an in-memory representation of a netCDF file.
+``xarray.Dataset`` is an in-memory representation of a netCDF file.
 
 .. _pandas: http://pandas.pydata.org
 .. _Common Data Model: http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/CDM
 .. _netCDF: http://www.unidata.ucar.edu/software/netcdf
 .. _OPeNDAP: http://www.opendap.org/
 
-Why xray?
----------
+Why xarray?
+-----------
 
 Adding dimensions names and coordinate indexes to numpy's ndarray_ makes many
 powerful array operations possible:
@@ -43,7 +43,7 @@ powerful array operations possible:
 -  Flexible split-apply-combine operations with groupby:
    ``x.groupby('time.dayofyear').mean()``.
 -  Database like alignment based on coordinate labels that smoothly
-   handles missing values: ``x, y = xray.align(x, y, join='outer')``.
+   handles missing values: ``x, y = xr.align(x, y, join='outer')``.
 -  Keep track of arbitrary metadata in the form of a Python dictionary:
    ``x.attrs``.
 
@@ -55,19 +55,19 @@ Why isn't pandas enough?
 
 pandas_ excels at working with tabular data. That suffices for many statistical
 analyses, but physical scientists rely on N-dimensional arrays -- which is
-where xray comes in.
+where xarray comes in.
 
-xray aims to provide a data analysis toolkit as powerful as pandas_ but
+xarray aims to provide a data analysis toolkit as powerful as pandas_ but
 designed for working with homogeneous N-dimensional arrays
 instead of tabular data. When possible, we copy the pandas API and rely on
 pandas's highly optimized internals (in particular, for fast indexing).
 
 Why netCDF?
 -----------
 
-Because xray implements the same data model as the netCDF_ file format,
-xray datasets have a natural and portable serialization format. But it is also
-easy to robustly convert an xray ``DataArray`` to and from a numpy ``ndarray``
+Because xarray implements the same data model as the netCDF_ file format,
+xarray datasets have a natural and portable serialization format. But it is also
+easy to robustly convert an xarray ``DataArray`` to and from a numpy ``ndarray``
 or a pandas ``DataFrame`` or ``Series``, providing compatibility with the full
 `PyData ecosystem <http://pydata.org/>`__.
 
@@ -82,28 +82,28 @@ especially geoscientists who already know and love netCDF_.
 Documentation
 -------------
 
-The official documentation is hosted on ReadTheDocs: http://xray.readthedocs.org/
+The official documentation is hosted on ReadTheDocs at http://xarray.pydata.org/
 
 Get in touch
 ------------
 
-- GitHub issue tracker: https://github.com/xray/xray/issues/
-- Mailing list: https://groups.google.com/forum/#!forum/xray-dev
-- Twitter: http://twitter.com/shoyer
+- GitHub issue tracker: https://github.com/pydata/xarray/issues/
+- Mailing list: https://groups.google.com/forum/#!forum/xarray
 
 History
 -------
 
-xray is an evolution of an internal tool developed at `The Climate
-Corporation`__, and was originally written by current and former Climate Corp
-researchers Stephan Hoyer, Alex Kleeman and Eugene Brevdo.
+xarray is an evolution of an internal tool developed at `The Climate
+Corporation`__. It was originally written by Climate Corp researchers Stephan
+Hoyer, Alex Kleeman and Eugene Brevdo and was released as open source in
+May 2014. The project was renamed from "xray" in January 2016.
 
 __ http://climate.com/
 
 License
 -------
 
-Copyright 2014, xray Developers
+Copyright 2014-2016, xarray Developers
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -117,5 +117,5 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 
-xray includes portions of pandas, NumPy and Seaborn. Their licenses are
+xarray includes portions of pandas, NumPy and Seaborn. Their licenses are
 included in the licenses directory.

appveyor.yml

@@ -25,11 +25,11 @@ install:
   - "python --version"
   - "python -c \"import struct; print(struct.calcsize('P') * 8)\""
 
-  # install xray and depenencies
+  # install xarray and depenencies
   - "conda install --yes --quiet pip pytest numpy pandas scipy netCDF4 matplotlib dask"
   - "python setup.py install"
 
 build: false
 
 test_script:
-  - "py.test xray"
+  - "py.test xarray"

doc/api-hidden.rst

@@ -2,7 +2,7 @@
 .. This extra page is a work around for sphinx not having any support for
 .. hiding an autosummary table.
 
-.. currentmodule:: xray
+.. currentmodule:: xarray
 
 .. autosummary::
    :toctree: generated/

doc/api.rst

@@ -1,10 +1,10 @@
-.. currentmodule:: xray
+.. currentmodule:: xarray
 
 #############
 API reference
 #############
 
-This page provides an auto-generated summary of xray's API. For more details
+This page provides an auto-generated summary of xarray's API. For more details
 and examples, refer to the relevant chapters in the main part of the
 documentation.
 
@@ -308,7 +308,7 @@ Universal functions
 ===================
 
 This functions are copied from NumPy, but extended to work on NumPy arrays,
-dask arrays and all xray objects. You can find them in the ``xray.ufuncs``
+dask arrays and all xarray objects. You can find them in the ``xarray.ufuncs``
 module:
 
 :py:attr:`~ufuncs.angle`

doc/combining.rst

@@ -8,7 +8,7 @@ Combining data
 
     import numpy as np
     import pandas as pd
-    import xray
+    import xarray as xr
     np.random.seed(123456)
 
 * For combining datasets or data arrays along a dimension, see concatenate_.
@@ -20,17 +20,17 @@ Concatenate
 ~~~~~~~~~~~
 
 To combine arrays along existing or new dimension into a larger array, you
-can use :py:func:`~xray.concat`. ``concat`` takes an iterable of ``DataArray``
+can use :py:func:`~xarray.concat`. ``concat`` takes an iterable of ``DataArray``
 or ``Dataset`` objects, as well as a dimension name, and concatenates along
 that dimension:
 
 .. ipython:: python
 
-    arr = xray.DataArray(np.random.randn(2, 3),
-                         [('x', ['a', 'b']), ('y', [10, 20, 30])])
+    arr = xr.DataArray(np.random.randn(2, 3),
+                       [('x', ['a', 'b']), ('y', [10, 20, 30])])
     arr[:, :1]
     # this resembles how you would use np.concatenate
-    xray.concat([arr[:, :1], arr[:, 1:]], dim='y')
+    xr.concat([arr[:, :1], arr[:, 1:]], dim='y')
 
 In addition to combining along an existing dimension, ``concat`` can create a
 new dimension by stacking lower dimensional arrays together:
@@ -39,34 +39,34 @@ new dimension by stacking lower dimensional arrays together:
 
     arr[0]
     # to combine these 1d arrays into a 2d array in numpy, you would use np.array
-    xray.concat([arr[0], arr[1]], 'x')
+    xr.concat([arr[0], arr[1]], 'x')
 
 If the second argument to ``concat`` is a new dimension name, the arrays will
 be concatenated along that new dimension, which is always inserted as the first
 dimension:
 
 .. ipython:: python
 
-    xray.concat([arr[0], arr[1]], 'new_dim')
+    xr.concat([arr[0], arr[1]], 'new_dim')
 
 The second argument to ``concat`` can also be an :py:class:`~pandas.Index` or
-:py:class:`~xray.DataArray` object as well as a string, in which case it is
+:py:class:`~xarray.DataArray` object as well as a string, in which case it is
 used to label the values along the new dimension:
 
 .. ipython:: python
 
-    xray.concat([arr[0], arr[1]], pd.Index([-90, -100], name='new_dim'))
+    xr.concat([arr[0], arr[1]], pd.Index([-90, -100], name='new_dim'))
 
 Of course, ``concat`` also works on ``Dataset`` objects:
 
 .. ipython:: python
 
     ds = arr.to_dataset(name='foo')
-    xray.concat([ds.sel(x='a'), ds.sel(x='b')], 'x')
+    xr.concat([ds.sel(x='a'), ds.sel(x='b')], 'x')
 
-:py:func:`~xray.concat` has a number of options which provide deeper control
+:py:func:`~xarray.concat` has a number of options which provide deeper control
 over which variables are concatenated and how it handles conflicting variables
-between datasets. With the default parameters, xray will load some coordinate
+between datasets. With the default parameters, xarray will load some coordinate
 variables into memory to compare them between datasets. This may be prohibitively
 expensive if you are manipulating your dataset lazily using :ref:`dask`.
 
@@ -76,7 +76,7 @@ Merge
 ~~~~~
 
 To combine variables and coordinates between multiple Datasets, you can use the
-:py:meth:`~xray.Dataset.merge` and :py:meth:`~xray.Dataset.update` methods.
+:py:meth:`~xarray.Dataset.merge` and :py:meth:`~xarray.Dataset.update` methods.
 Merge checks for conflicting variables before merging and by default it returns
 a new Dataset:
 
@@ -90,17 +90,17 @@ coordinates:
 
 .. ipython:: python
 
-    other = xray.Dataset({'bar': ('x', [1, 2, 3, 4]), 'x': list('abcd')})
+    other = xr.Dataset({'bar': ('x', [1, 2, 3, 4]), 'x': list('abcd')})
     ds.merge(other)
 
 This ensures that the ``merge`` is non-destructive.
 
 The same non-destructive merging between ``DataArray`` index coordinates is
-used in the :py:class:`~xray.Dataset` constructor:
+used in the :py:class:`~xarray.Dataset` constructor:
 
 .. ipython:: python
 
-    xray.Dataset({'a': arr[:-1], 'b': arr[1:]})
+    xr.Dataset({'a': arr[:-1], 'b': arr[1:]})
 
 .. _update:
 
@@ -131,42 +131,42 @@ syntax:
 
 .. ipython:: python
 
-    ds['baz'] = xray.DataArray([9, 9, 9, 9, 9], coords=[('x', list('abcde'))])
+    ds['baz'] = xr.DataArray([9, 9, 9, 9, 9], coords=[('x', list('abcde'))])
     ds.baz
 
 Equals and identical
 ~~~~~~~~~~~~~~~~~~~~
 
-xray objects can be compared by using the :py:meth:`~xray.Dataset.equals`,
-:py:meth:`~xray.Dataset.identical` and
-:py:meth:`~xray.Dataset.broadcast_equals` methods. These methods are used by
+xarray objects can be compared by using the :py:meth:`~xarray.Dataset.equals`,
+:py:meth:`~xarray.Dataset.identical` and
+:py:meth:`~xarray.Dataset.broadcast_equals` methods. These methods are used by
 the optional ``compat`` argument on ``concat`` and ``merge``.
 
-:py:attr:`~xray.Dataset.equals` checks dimension names, indexes and array
+:py:attr:`~xarray.Dataset.equals` checks dimension names, indexes and array
 values:
 
 .. ipython:: python
 
     arr.equals(arr.copy())
 
-:py:attr:`~xray.Dataset.identical` also checks attributes, and the name of each
+:py:attr:`~xarray.Dataset.identical` also checks attributes, and the name of each
 object:
 
 .. ipython:: python
 
     arr.identical(arr.rename('bar'))
 
-:py:attr:`~xray.Dataset.broadcast_equals` does a more relaxed form of equality
+:py:attr:`~xarray.Dataset.broadcast_equals` does a more relaxed form of equality
 check that allows variables to have different dimensions, as long as values
 are constant along those new dimensions:
 
 .. ipython:: python
 
-    left = xray.Dataset(coords={'x': 0})
-    right = xray.Dataset({'x': [0, 0, 0]})
+    left = xr.Dataset(coords={'x': 0})
+    right = xr.Dataset({'x': [0, 0, 0]})
     left.broadcast_equals(right)
 
-Like pandas objects, two xray objects are still equal or identical if they have
+Like pandas objects, two xarray objects are still equal or identical if they have
 missing values marked by ``NaN`` in the same locations.
 
 In contrast, the ``==`` operation performs element-wise comparison (like