Merge remote-tracking branch 'upstream/master' into refactor-plot-utils

* upstream/master:
  xfail cftimeindex multiindex test (pydata#2669)
  DOC: refresh "Why xarray" and shorten top-level description (pydata#2657)
  Remove broken Travis-CI builds (pydata#2661)
  Type checking with mypy (pydata#2655)
  Added Coarsen (pydata#2612)

.travis.yml

@@ -17,17 +17,9 @@ matrix:
   - env:
     - CONDA_ENV=py36
     - EXTRA_FLAGS="--run-flaky --run-network-tests"
-  - env: CONDA_ENV=py36-netcdf4-dev
-    addons:
-      apt_packages:
-        - libhdf5-serial-dev
-        - netcdf-bin
-        - libnetcdf-dev
   - env: CONDA_ENV=py36-dask-dev
   - env: CONDA_ENV=py36-pandas-dev
   - env: CONDA_ENV=py36-bottleneck-dev
-  - env: CONDA_ENV=py36-condaforge-rc
-  - env: CONDA_ENV=py36-pynio-dev
   - env: CONDA_ENV=py36-rasterio
   - env: CONDA_ENV=py36-zarr-dev
   - env: CONDA_ENV=docs
@@ -38,25 +30,16 @@ matrix:
   - env:
     - CONDA_ENV=py36
     - EXTRA_FLAGS="--run-flaky --run-network-tests"
-  - env: CONDA_ENV=py36-netcdf4-dev
-    addons:
-      apt_packages:
-        - libhdf5-serial-dev
-        - netcdf-bin
-        - libnetcdf-dev
   - env: CONDA_ENV=py36-pandas-dev
   - env: CONDA_ENV=py36-bottleneck-dev
-  - env: CONDA_ENV=py36-condaforge-rc
-  - env: CONDA_ENV=py36-pynio-dev
   - env: CONDA_ENV=py36-zarr-dev
 
 before_install:
-  - wget http://repo.continuum.io/miniconda/Miniconda3-3.16.0-Linux-x86_64.sh -O miniconda.sh;
+  - wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
   - bash miniconda.sh -b -p $HOME/miniconda
   - export PATH="$HOME/miniconda/bin:$PATH"
   - hash -r
   - conda config --set always_yes yes --set changeps1 no --set show_channel_urls true
-  - conda update -q conda
   - conda info -a
 
 install:

README.rst

@@ -9,49 +9,47 @@ xarray: N-D labeled arrays and datasets
    :target: https://coveralls.io/r/pydata/xarray
 .. image:: https://readthedocs.org/projects/xray/badge/?version=latest
    :target: http://xarray.pydata.org/
-.. image:: https://img.shields.io/pypi/v/xarray.svg
-   :target: https://pypi.python.org/pypi/xarray/
-.. image:: https://zenodo.org/badge/13221727.svg
-  :target: https://zenodo.org/badge/latestdoi/13221727
 .. image:: http://img.shields.io/badge/benchmarked%20by-asv-green.svg?style=flat
   :target: http://pandas.pydata.org/speed/xarray/
-.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
-  :target: http://numfocus.org
+.. image:: https://img.shields.io/pypi/v/xarray.svg
+   :target: https://pypi.python.org/pypi/xarray/
 
 **xarray** (formerly **xray**) is an open source project and Python package
 that makes working with labelled multi-dimensional arrays simple,
 efficient, and fun!
 
-Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
-"tensors") are an essential part of computational science.
-They are encountered in a wide range of fields, including physics, astronomy,
-geoscience, bioinformatics, engineering, finance, and deep learning.
-In Python, NumPy_ provides the fundamental data structure and API for
-working with raw ND arrays.
-However, real-world datasets are usually more than just raw numbers;
-they have labels which encode information about how the array values map
-to locations in space, time, etc.
+Xarray introduces labels in the form of dimensions, coordinates and
+attributes on top of raw NumPy_-like arrays, which allows for a more
+intuitive, more concise, and less error-prone developer experience.
+The package includes a large and growing library of domain-agnostic functions
+for advanced analytics and visualization with these data structures.
 
-By introducing *dimensions*, *coordinates*, and *attributes* on top of raw
-NumPy-like arrays, xarray is able to understand these labels and use them to
-provide a more intuitive, more concise, and less error-prone experience.
-Xarray also provides a large and growing library of functions for advanced
-analytics and visualization with these data structures.
 Xarray was inspired by and borrows heavily from pandas_, the popular data
 analysis package focused on labelled tabular data.
-Xarray can read and write data from most common labeled ND-array storage
-formats and is particularly tailored to working with netCDF_ files, which were
-the source of xarray's data model.
+It is particularly tailored to working with netCDF_ files, which were the
+source of xarray's data model, and integrates tightly with dask_ for parallel
+computing.
 
-.. _NumPy: http://www.numpy.org/
+.. _NumPy: http://www.numpy.org
 .. _pandas: http://pandas.pydata.org
+.. _dask: http://dask.org
 .. _netCDF: http://www.unidata.ucar.edu/software/netcdf
 
 Why xarray?
 -----------
 
-Adding dimensions names and coordinate indexes to numpy's ndarray_ makes many
-powerful array operations possible:
+Multi-dimensional (a.k.a. N-dimensional, ND) arrays (sometimes called
+"tensors") are an essential part of computational science.
+They are encountered in a wide range of fields, including physics, astronomy,
+geoscience, bioinformatics, engineering, finance, and deep learning.
+In Python, NumPy_ provides the fundamental data structure and API for
+working with raw ND arrays.
+However, real-world datasets are usually more than just raw numbers;
+they have labels which encode information about how the array values map
+to locations in space, time, etc.
+
+Xarray doesn't just keep track of labels on arrays -- it uses them to provide a
+powerful and concise interface. For example:
 
 -  Apply operations over dimensions by name: ``x.sum('time')``.
 -  Select values by label instead of integer location:
@@ -65,42 +63,10 @@ powerful array operations possible:
 -  Keep track of arbitrary metadata in the form of a Python dictionary:
    ``x.attrs``.
 
-pandas_ provides many of these features, but it does not make use of dimension
-names, and its core data structures are fixed dimensional arrays.
-
-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 xarray comes in.
-
-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 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/>`__.
-
-Our target audience is anyone who needs N-dimensional labeled arrays, but we
-are particularly focused on the data analysis needs of physical scientists --
-especially geoscientists who already know and love netCDF_.
-
-.. _ndarray: http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html
-.. _pandas: http://pandas.pydata.org
-.. _netCDF: http://www.unidata.ucar.edu/software/netcdf
-
 Documentation
 -------------
 
-The official documentation is hosted on ReadTheDocs at http://xarray.pydata.org/
+Learn more about xarray in its official documentation at http://xarray.pydata.org/
 
 Contributing
 ------------

ci/install_python.ps1

@@ -2,16 +2,16 @@
 # Authors: Olivier Grisel, Jonathan Helmus and Kyle Kastner
 # License: CC0 1.0 Universal: http://creativecommons.org/publicdomain/zero/1.0/
 
-$MINICONDA_URL = "http://repo.continuum.io/miniconda/"
+$MINICONDA_URL = "https://repo.anaconda.com/miniconda/"
 $BASE_URL = "https://www.python.org/ftp/python/"
 
 
 function DownloadMiniconda ($python_version, $platform_suffix) {
     $webclient = New-Object System.Net.WebClient
-    if ($python_version -match "3.6") {
-        $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe"
-    } else {
+    if ($python_version -match "2.7") {
         $filename = "Miniconda2-latest-Windows-" + $platform_suffix + ".exe"
+    } else {
+        $filename = "Miniconda3-latest-Windows-" + $platform_suffix + ".exe"
     }
     $url = $MINICONDA_URL + $filename
 

ci/requirements-py35.yml

@@ -10,8 +10,8 @@ dependencies:
   - matplotlib=1.5
   - netcdf4
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - flake8
   - numpy

ci/requirements-py36-bottleneck-dev.yml

@@ -11,8 +11,8 @@ dependencies:
   - matplotlib
   - netcdf4
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - flake8
   - numpy

ci/requirements-py36-dask-dev.yml

@@ -9,8 +9,8 @@ dependencies:
   - matplotlib
   - netcdf4
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - flake8
   - numpy

ci/requirements-py36-hypothesis.yml

@@ -10,8 +10,8 @@ dependencies:
   - matplotlib
   - netcdf4
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - hypothesis
   - flake8

ci/requirements-py36-pandas-dev.yml

@@ -12,8 +12,8 @@ dependencies:
   - matplotlib
   - netcdf4
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - flake8
   - numpy

ci/requirements-py36-rasterio.yml

@@ -11,8 +11,8 @@ dependencies:
   - matplotlib
   - netcdf4
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - numpy
   - pandas

ci/requirements-py36-zarr-dev.yml

@@ -8,8 +8,8 @@ dependencies:
   - distributed
   - matplotlib
   - pytest
-  - pytest-env
   - pytest-cov
+  - pytest-env
   - coveralls
   - flake8
   - numpy

ci/requirements-py36.yml

@@ -14,7 +14,7 @@ dependencies:
   - pytest-cov
   - pytest-env
   - coveralls
-  - flake8
+  - pycodestyle
   - numpy
   - pandas
   - scipy
@@ -32,3 +32,4 @@ dependencies:
   - lxml
   - pip:
     - cfgrib>=0.9.2
+    - mypy==0.650

ci/requirements-py37.yml

@@ -29,3 +29,4 @@ dependencies:
   - pydap
   - pip:
     - cfgrib>=0.9.2
+    - mypy==0.650

doc/api.rst

@@ -147,6 +147,7 @@ Computation
    Dataset.groupby
    Dataset.groupby_bins
    Dataset.rolling
+   Dataset.coarsen
    Dataset.resample
    Dataset.diff
    Dataset.quantile
@@ -312,6 +313,7 @@ Computation
    DataArray.groupby
    DataArray.groupby_bins
    DataArray.rolling
+   DataArray.coarsen
    DataArray.dt
    DataArray.resample
    DataArray.get_axis_num