Merge pull request #720 from pydata/rename-xarray

Rename 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/_static/dataset-diagram-logo.png

@@ -174,7 +174,7 @@
         emphstyle=ultra thick,
     }
     \tikzcuboid{%
-        shiftx=26.2cm,%
+        shiftx=26.8cm,%
         shifty=8cm,%
         scale=1.00,%
         rotation=0,%
@@ -194,7 +194,7 @@
         emphstyle=ultra thick,
     }
     \tikzcuboid{%
-        shiftx=27.6cm,%
+        shiftx=28.6cm,%
         shifty=8cm,%
         scale=1.00,%
         rotation=0,%
@@ -214,23 +214,36 @@
         emphstyle=ultra thick,
     }
     % \tikzcuboid{%
-    %     shiftx=25.8cm,%
-    %     shifty=12cm,%
+    %     shiftx=27.1cm,%
+    %     shifty=10.1cm,%
     %     scale=1.00,%
     %     rotation=0,%
-    %     densityx=2,%
-    %     densityx=2,%
-    %     densityy=100,%
+    %     densityx=100,%
+    %     densityy=2,%
     %     densityz=100,%
-    %     dimx=4,%
-    %     dimy=0,%
+    %     dimx=0,%
+    %     dimy=3,%
     %     dimz=0,%
     %     emphedge=Y,%
     %     emphstyle=ultra thick,
     % }
+    % \tikzcuboid{%
+    %     shiftx=27.1cm,%
+    %     shifty=10.1cm,%
+    %     scale=1.00,%
+    %     rotation=180,%
+    %     densityx=100,%
+    %     densityy=100,%
+    %     densityz=2,%
+    %     dimx=0,%
+    %     dimy=0,%
+    %     dimz=3,%
+    %     emphedge=Y,%
+    %     emphstyle=ultra thick,
+    % }
     \tikzcuboid{%
-        shiftx=25.8cm,%
-        shifty=11.6cm,%
+        shiftx=26.8cm,%
+        shifty=11.4cm,%
         scale=1.00,%
         rotation=0,%
         densityx=100,%
@@ -243,8 +256,8 @@
         emphstyle=ultra thick,
     }
     \tikzcuboid{%
-        shiftx=25.8cm,%
-        shifty=11.6cm,%
+        shiftx=25.3cm,%
+        shifty=12.9cm,%
         scale=1.00,%
         rotation=180,%
         densityx=100,%
@@ -256,8 +269,9 @@
         emphedge=Y,%
         emphstyle=ultra thick,
     }
-    \fill (27,11.6) circle[radius=2pt];
-    \node [font=\bfseries\fontsize{100}{100}\selectfont, anchor=west, text width=8cm] at (29,10.25) {xray};
+    % \fill (27.1,10.1) circle[radius=2pt];
+    \node [font=\fontsize{100}{100}\fontfamily{phv}\selectfont, anchor=west, text width=9cm, color=white!50!black] at (30,10.6) {\textbf{\emph{x}}};
+    \node [font=\fontsize{100}{100}\fontfamily{phv}\selectfont, anchor=west, text width=9cm] at (32,10.25) {{array}};
 \end{tikzpicture}
 
 \end{document}

doc/_static/dataset-diagram-logo.tex

@@ -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-hidden.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`