Merge branch 'master' into yohai-ds_scatter

* master:
  Remove broken Travis-CI builds (pydata#2661)
  Type checking with mypy (pydata#2655)
  Added Coarsen (pydata#2612)
  Improve test for GH 2649 (pydata#2654)
  revise top-level package description (pydata#2430)
  Convert ref_date to UTC in encode_cf_datetime (pydata#2651)
  Change an `==` to an `is`. Fix tests so that this won't happen again. (pydata#2648)
  ENH: switch Dataset and DataArray to use explicit indexes (pydata#2639)
  Use pycodestyle for lint checks. (pydata#2642)
  Switch whats-new for 0.11.2 -> 0.11.3
  DOC: document v0.11.2 release
  Use built-in interp for interpolation with resample (pydata#2640)
  BUG: pytest-runner no required for setup.py (pydata#2643)

Author: dcherian

.pep8speaks.yml

@@ -1,12 +1,16 @@
 # File : .pep8speaks.yml
 
+# This should be kept in sync with the duplicate config in the [pycodestyle]
+# block of setup.cfg.
+
 scanner:
-    diff_only: True  # If True, errors caused by only the patch are shown
+    diff_only: False  # If True, errors caused by only the patch are shown
 
 pycodestyle:
     max-line-length: 79
     ignore:  # Errors and warnings to ignore
-        - E402,  # module level import not at top of file
-        - E731,  # do not assign a lambda expression, use a def
-        - W503   # line break before binary operator
-        - W504   # line break after binary operator
+        - E402  # module level import not at top of file
+        - E731  # do not assign a lambda expression, use a def
+        - E741  # ambiguous variable name
+        - W503  # line break before binary operator
+        - W504  # line break after binary operator

.travis.yml

@@ -17,52 +17,35 @@ 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
-  - env: CONDA_ENV=flake8
+  - env: CONDA_ENV=lint
   - env: CONDA_ENV=py36-hypothesis
 
   allow_failures:
   - 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:
   - if [[ "$CONDA_ENV" == "docs" ]]; then
       conda env create -n test_env --file doc/environment.yml;
-    elif [[ "$CONDA_ENV" == "flake8" ]]; then
+    elif [[ "$CONDA_ENV" == "lint" ]]; then
       conda env create -n test_env --file ci/requirements-py37.yml;
     else
       conda env create -n test_env --file ci/requirements-$CONDA_ENV.yml;
@@ -79,8 +62,8 @@ script:
   - if [[ "$CONDA_ENV" == "docs" ]]; then
       conda install -c conda-forge sphinx sphinx_rtd_theme sphinx-gallery numpydoc;
       sphinx-build -n -j auto -b html -d _build/doctrees doc _build/html;
-    elif [[ "$CONDA_ENV" == "flake8" ]]; then
-      flake8 xarray ;
+    elif [[ "$CONDA_ENV" == "lint" ]]; then
+      pycodestyle xarray ;
     elif [[ "$CONDA_ENV" == "py36-hypothesis" ]]; then
       pytest properties ;
     else

README.rst

@@ -18,20 +18,34 @@ xarray: N-D labeled arrays and datasets
 .. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
   :target: http://numfocus.org
 
-**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:
-``xarray.Dataset`` is an in-memory representation of a netCDF file.
-
+**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.
+
+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.
+
+.. _NumPy: http://www.numpy.org/
 .. _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 xarray?
 -----------

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-condaforge-rc.yml

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

ci/requirements-py36-dask-dev.yml

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

ci/requirements-py36-hypothesis.yml

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

ci/requirements-py36-netcdf4-dev.yml

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

ci/requirements-py36-pandas-dev.yml

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

ci/requirements-py36-pynio-dev.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-py36-rasterio.yml

@@ -14,7 +14,7 @@ dependencies:
   - pytest-cov
   - pytest-env
   - coveralls
-  - flake8
+  - pycodestyle
   - numpy
   - pandas
   - scipy
@@ -28,4 +28,5 @@ dependencies:
   - eccodes
   - pydap
   - pip:
-    - cfgrib>=0.9.2
+    - cfgrib>=0.9.2
+    - mypy==0.650

ci/requirements-py36-zarr-dev.yml

@@ -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

ci/requirements-py36.yml

@@ -199,6 +199,47 @@ You can also use ``construct`` to compute a weighted rolling sum:
   To avoid this, use ``skipna=False`` as the above example.
 
 
+.. _comput.coarsen:
+
+Coarsen large arrays
+====================
+
+``DataArray`` and ``Dataset`` objects include a
+:py:meth:`~xarray.DataArray.coarsen` and :py:meth:`~xarray.Dataset.coarsen`
+methods. This supports the block aggregation along multiple dimensions,
+
+.. ipython:: python
+
+  x = np.linspace(0, 10, 300)
+  t = pd.date_range('15/12/1999', periods=364)
+  da = xr.DataArray(np.sin(x) * np.cos(np.linspace(0, 1, 364)[:, np.newaxis]),
+                    dims=['time', 'x'], coords={'time': t, 'x': x})
+  da
+
+In order to take a block mean for every 7 days along ``time`` dimension and
+every 2 points along ``x`` dimension,
+
+.. ipython:: python
+
+  da.coarsen(time=7, x=2).mean()
+
+:py:meth:`~xarray.DataArray.coarsen` raises an ``ValueError`` if the data
+length is not a multiple of the corresponding window size.
+You can choose ``boundary='trim'`` or ``boundary='pad'`` options for trimming
+the excess entries or padding ``nan`` to insufficient entries,
+
+.. ipython:: python
+
+  da.coarsen(time=30, x=2, boundary='trim').mean()
+
+If you want to apply a specific function to coordinate, you can pass the
+function or method name to ``coord_func`` option,
+
+.. ipython:: python
+
+  da.coarsen(time=7, x=2, coord_func={'time': 'min'}).mean()
+
+
 Computation using Coordinates
 =============================