Pull upstream changes

.gitignore

@@ -35,6 +35,7 @@ pip-log.txt
 .tox
 nosetests.xml
 .cache
+.mypy_cache
 .ropeproject/
 .tags*
 .testmon*

README.rst

@@ -97,7 +97,7 @@ to supporting the open source scientific computing community. If you like
 Xarray and want to support our mission, please consider making a donation_
 to support our efforts.
 
-.. _donation: https://www.flipcause.com/secure/cause_pdetails/NDE2NTU=
+.. _donation: https://numfocus.salsalabs.org/donate-to-xarray/
 
 History
 -------

ci/requirements-py35-min.yml

@@ -1,6 +1,6 @@
 name: test_env
 dependencies:
-  - python=3.5
+  - python=3.5.0
   - pytest
   - flake8
   - mock

doc/data-structures.rst

@@ -353,13 +353,6 @@ setting) variables and attributes:
 This is particularly useful in an exploratory context, because you can
 tab-complete these variable names with tools like IPython.
 
-.. warning::
-
-  We are changing the behavior of iterating over a Dataset the next major
-  release of xarray, to only include data variables instead of both data
-  variables and coordinates. In the meantime, prefer iterating over
-  ``ds.data_vars`` or ``ds.coords``.
-
 Dictionary like methods
 ~~~~~~~~~~~~~~~~~~~~~~~
 

doc/index.rst

@@ -52,6 +52,7 @@ Documentation
 * :doc:`reshaping`
 * :doc:`combining`
 * :doc:`time-series`
+* :doc:`weather-climate`
 * :doc:`pandas`
 * :doc:`io`
 * :doc:`dask`
@@ -70,6 +71,7 @@ Documentation
    reshaping
    combining
    time-series
+   weather-climate
    pandas
    io
    dask
@@ -138,7 +140,7 @@ to supporting the open source scientific computing community. If you like
 Xarray and want to support our mission, please consider making a donation_
 to support our efforts.
 
-.. _donation: https://www.flipcause.com/secure/cause_pdetails/NDE2NTU=
+.. _donation: https://numfocus.salsalabs.org/donate-to-xarray/
 
 
 History

doc/io.rst

@@ -1,11 +1,11 @@
 .. _io:
 
-Serialization and IO
-====================
+Reading and writing files
+=========================
 
 xarray supports direct serialization and IO to several file formats, from
 simple :ref:`io.pickle` files to the more flexible :ref:`io.netcdf`
-format.
+format (recommended).
 
 .. ipython:: python
    :suppress:
@@ -739,11 +739,14 @@ options are listed on the PseudoNetCDF page.
 .. _PseudoNetCDF: http://github.com/barronh/PseudoNetCDF
 
 
-Formats supported by Pandas
----------------------------
+CSV and other formats supported by Pandas
+-----------------------------------------
 
 For more options (tabular formats and CSV files in particular), consider
 exporting your objects to pandas and using its broad range of `IO tools`_.
+For CSV files, one might also consider `xarray_extras`_.
+
+.. _xarray_extras: https://xarray-extras.readthedocs.io/en/latest/api/csv.html
 
 .. _IO tools: http://pandas.pydata.org/pandas-docs/stable/io.html
 

doc/plotting.rst

@@ -39,6 +39,10 @@ For more extensive plotting applications consider the following projects:
   data structures for building even complex visualizations easily." Includes
   native support for xarray objects.
 
+- `hvplot <https://hvplot.pyviz.org/>`_: ``hvplot`` makes it very easy to produce
+  dynamic plots (backed by ``Holoviews`` or ``Geoviews``) by adding a ``hvplot``
+  accessor to DataArrays.
+
 - `Cartopy <http://scitools.org.uk/cartopy/>`_: Provides cartographic
   tools.
 

doc/related-projects.rst

@@ -13,6 +13,7 @@ Geosciences
 - `aospy <https://aospy.readthedocs.io>`_: Automated analysis and management of gridded climate data.
 - `infinite-diff <https://github.com/spencerahill/infinite-diff>`_: xarray-based finite-differencing, focused on gridded climate/meterology data
 - `marc_analysis <https://github.com/darothen/marc_analysis>`_: Analysis package for CESM/MARC experiments and output.
+- `MetPy <https://unidata.github.io/MetPy/dev/index.html>`_: A collection of tools in Python for reading, visualizing, and performing calculations with weather data.
 - `MPAS-Analysis <http://mpas-analysis.readthedocs.io>`_: Analysis for simulations produced with Model for Prediction Across Scales (MPAS) components and the Accelerated Climate Model for Energy (ACME).
 - `OGGM <http://oggm.org/>`_: Open Global Glacier Model
 - `Oocgcm <https://oocgcm.readthedocs.io/>`_: Analysis of large gridded geophysical datasets

doc/time-series.rst

@@ -212,140 +212,3 @@ Data that has indices outside of the given ``tolerance`` are set to ``NaN``.
 
 For more examples of using grouped operations on a time dimension, see
 :ref:`toy weather data`.
-
-
-.. _CFTimeIndex:
-
-Non-standard calendars and dates outside the Timestamp-valid range
-------------------------------------------------------------------
-
-Through the standalone ``cftime`` library and a custom subclass of
-:py:class:`pandas.Index`, xarray supports a subset of the indexing
-functionality enabled through the standard :py:class:`pandas.DatetimeIndex` for
-dates from non-standard calendars commonly used in climate science or dates
-using a standard calendar, but outside the `Timestamp-valid range`_
-(approximately between years 1678 and 2262).
-
-.. note::
-
-   As of xarray version 0.11, by default, :py:class:`cftime.datetime` objects
-   will be used to represent times (either in indexes, as a
-   :py:class:`~xarray.CFTimeIndex`, or in data arrays with dtype object) if
-   any of the following are true:
-
-   - The dates are from a non-standard calendar
-   - Any dates are outside the Timestamp-valid range.
-
-   Otherwise pandas-compatible dates from a standard calendar will be
-   represented with the ``np.datetime64[ns]`` data type, enabling the use of a
-   :py:class:`pandas.DatetimeIndex` or arrays with dtype ``np.datetime64[ns]``
-   and their full set of associated features.
-
-For example, you can create a DataArray indexed by a time
-coordinate with dates from a no-leap calendar and a
-:py:class:`~xarray.CFTimeIndex` will automatically be used:
-
-.. ipython:: python
-
-   from itertools import product
-   from cftime import DatetimeNoLeap
-   dates = [DatetimeNoLeap(year, month, 1) for year, month in
-            product(range(1, 3), range(1, 13))]
-   da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')
-
-xarray also includes a :py:func:`~xarray.cftime_range` function, which enables
-creating a :py:class:`~xarray.CFTimeIndex` with regularly-spaced dates.  For
-instance, we can create the same dates and DataArray we created above using:
-
-.. ipython:: python
-
-   dates = xr.cftime_range(start='0001', periods=24, freq='MS', calendar='noleap')
-   da = xr.DataArray(np.arange(24), coords=[dates], dims=['time'], name='foo')
-
-For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
-
-- `Partial datetime string indexing`_ using strictly `ISO 8601-format`_ partial
-  datetime strings:
-
-.. ipython:: python
-
-   da.sel(time='0001')
-   da.sel(time=slice('0001-05', '0002-02'))
-
-- Access of basic datetime components via the ``dt`` accessor (in this case
-  just "year", "month", "day", "hour", "minute", "second", "microsecond",
-  "season", "dayofyear", and "dayofweek"):
-
-.. ipython:: python
-
-   da.time.dt.year
-   da.time.dt.month
-   da.time.dt.season
-   da.time.dt.dayofyear
-   da.time.dt.dayofweek
-
-- Group-by operations based on datetime accessor attributes (e.g. by month of
-  the year):
-
-.. ipython:: python
-
-   da.groupby('time.month').sum()
-
-- Interpolation using :py:class:`cftime.datetime` objects:
-
-.. ipython:: python
-
-   da.interp(time=[DatetimeNoLeap(1, 1, 15), DatetimeNoLeap(1, 2, 15)])
-
-- Interpolation using datetime strings:
-
-.. ipython:: python
-
-   da.interp(time=['0001-01-15', '0001-02-15'])
-
-- Differentiation:
-
-.. ipython:: python
-
-   da.differentiate('time')
-
-- Serialization:
-
-.. ipython:: python
-
-   da.to_netcdf('example-no-leap.nc')
-   xr.open_dataset('example-no-leap.nc')
-
-- And resampling along the time dimension for data indexed by a :py:class:`~xarray.CFTimeIndex`:
-
-.. ipython:: python
-
-    da.resample(time='81T', closed='right', label='right', base=3).mean()
-
-.. note::
-
-
-   For some use-cases it may still be useful to convert from
-   a :py:class:`~xarray.CFTimeIndex` to a :py:class:`pandas.DatetimeIndex`,
-   despite the difference in calendar types. The recommended way of doing this
-   is to use the built-in :py:meth:`~xarray.CFTimeIndex.to_datetimeindex`
-   method:
-
-   .. ipython:: python
-      :okwarning:
-
-       modern_times = xr.cftime_range('2000', periods=24, freq='MS', calendar='noleap')
-       da = xr.DataArray(range(24), [('time', modern_times)])
-       da
-       datetimeindex = da.indexes['time'].to_datetimeindex()
-       da['time'] = datetimeindex
-
-   However in this case one should use caution to only perform operations which
-   do not depend on differences between dates (e.g. differentiation,
-   interpolation, or upsampling with resample), as these could introduce subtle
-   and silent errors due to the difference in calendar types between the dates
-   encoded in your data and the dates stored in memory.
-
-.. _Timestamp-valid range: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#timestamp-limitations
-.. _ISO 8601-format: https://en.wikipedia.org/wiki/ISO_8601
-.. _partial datetime string indexing: https://pandas.pydata.org/pandas-docs/stable/timeseries.html#partial-string-indexing