silence sphinx warnings round 3

doc/api.rst

@@ -31,6 +31,8 @@ Top-level functions
    ones_like
    dot
    map_blocks
+   show_versions
+   set_options
 
 Dataset
 =======
@@ -74,7 +76,9 @@ and values given by ``DataArray`` objects.
    Dataset.__setitem__
    Dataset.__delitem__
    Dataset.update
+   Dataset.get
    Dataset.items
+   Dataset.keys
    Dataset.values
 
 Dataset contents
@@ -537,6 +541,15 @@ DataArray methods
    DataArray.unify_chunks
    DataArray.map_blocks
 
+Coordinates objects
+===================
+
+.. autosummary::
+   :toctree: generated/
+
+   core.coordinates.DataArrayCoordinates
+   core.coordinates.DatasetCoordinates
+
 GroupBy objects
 ===============
 
@@ -564,6 +577,16 @@ Rolling objects
    core.rolling.DatasetRolling.reduce
    core.rolling_exp.RollingExp
 
+Coarsen objects
+===============
+
+.. autosummary::
+   :toctree: generated/
+
+   core.rolling.DataArrayCoarsen
+   core.rolling.DatasetCoarsen
+
+
 Resample objects
 ================
 
@@ -625,6 +648,7 @@ Plotting
    plot.imshow
    plot.line
    plot.pcolormesh
+   plot.FacetGrid
 
 Faceting
 --------
@@ -644,6 +668,14 @@ Faceting
    plot.FacetGrid.set_xlabels
    plot.FacetGrid.set_ylabels
 
+Tutorial
+========
+
+.. autosummary::
+   :toctree: generated/
+
+   tutorial.open_dataset
+   tutorial.load_dataset
 
 Testing
 =======
@@ -681,7 +713,7 @@ Advanced API
 
 These backends provide a low-level interface for lazily loading data from
 external file-formats or protocols, and can be manually invoked to create
-arguments for the ``from_store`` and ``dump_to_store`` Dataset methods:
+arguments for the ``load_store`` and ``dump_to_store`` Dataset methods:
 
 .. autosummary::
    :toctree: generated/
@@ -697,6 +729,9 @@ arguments for the ``from_store`` and ``dump_to_store`` Dataset methods:
 Deprecated / Pending Deprecation
 ================================
 
+.. autosummary::
+   :toctree: generated/
+
    Dataset.drop
    DataArray.drop
    Dataset.apply

doc/groupby.rst

@@ -94,7 +94,7 @@ Apply
 ~~~~~
 
 To apply a function to each group, you can use the flexible
-:py:meth:`~xarray.DatasetGroupBy.map` method. The resulting objects are automatically
+:py:meth:`~xarray.core.groupby.DatasetGroupBy.map` method. The resulting objects are automatically
 concatenated back together along the group axis:
 
 .. ipython:: python
@@ -104,8 +104,8 @@ concatenated back together along the group axis:
 
     arr.groupby('letters').map(standardize)
 
-GroupBy objects also have a :py:meth:`~xarray.DatasetGroupBy.reduce` method and
-methods like :py:meth:`~xarray.DatasetGroupBy.mean` as shortcuts for applying an
+GroupBy objects also have a :py:meth:`~xarray.core.groupby.DatasetGroupBy.reduce` method and
+methods like :py:meth:`~xarray.core.groupby.DatasetGroupBy.mean` as shortcuts for applying an
 aggregation function:
 
 .. ipython:: python

doc/howdoi.rst

@@ -22,7 +22,7 @@ How do I ...
    * - change the order of dimensions
      - :py:meth:`DataArray.transpose`, :py:meth:`Dataset.transpose`
    * - remove a variable from my object
-     - :py:meth:`Dataset.drop`, :py:meth:`DataArray.drop`
+     - :py:meth:`Dataset.drop_vars`, :py:meth:`DataArray.drop_vars`
    * - remove dimensions of length 1 or 0
      - :py:meth:`DataArray.squeeze`, :py:meth:`Dataset.squeeze`
    * - remove all variables with a particular dimension
@@ -48,7 +48,7 @@ How do I ...
    * - write xarray objects with complex values to a netCDF file
      - :py:func:`Dataset.to_netcdf`, :py:func:`DataArray.to_netcdf` specifying ``engine="h5netcdf", invalid_netcdf=True``
    * - make xarray objects look like other xarray objects
-     - :py:func:`~xarray.ones_like`, :py:func:`~xarray.zeros_like`, :py:func:`~xarray.full_like`, :py:meth:`Dataset.reindex_like`, :py:meth:`Dataset.interpolate_like`, :py:meth:`Dataset.broadcast_like`, :py:meth:`DataArray.reindex_like`, :py:meth:`DataArray.interpolate_like`, :py:meth:`DataArray.broadcast_like`
+     - :py:func:`~xarray.ones_like`, :py:func:`~xarray.zeros_like`, :py:func:`~xarray.full_like`, :py:meth:`Dataset.reindex_like`, :py:meth:`Dataset.interp_like`, :py:meth:`Dataset.broadcast_like`, :py:meth:`DataArray.reindex_like`, :py:meth:`DataArray.interp_like`, :py:meth:`DataArray.broadcast_like`
    * - replace NaNs with other values
      - :py:meth:`Dataset.fillna`, :py:meth:`Dataset.ffill`, :py:meth:`Dataset.bfill`, :py:meth:`Dataset.interpolate_na`, :py:meth:`DataArray.fillna`, :py:meth:`DataArray.ffill`, :py:meth:`DataArray.bfill`, :py:meth:`DataArray.interpolate_na`
    * - extract the year, month, day or similar from a DataArray of time values

doc/indexing.rst

@@ -132,7 +132,7 @@ use them explicitly to slice data. There are two ways to do this:
 
 The arguments to these methods can be any objects that could index the array
 along the dimension given by the keyword, e.g., labels for an individual value,
-Python :py:func:`slice` objects or 1-dimensional arrays.
+Python :py:class:`slice` objects or 1-dimensional arrays.
 
 .. note::
 

doc/interpolation.rst

@@ -48,7 +48,7 @@ array-like, which gives the interpolated result as an array.
     # interpolation
     da.interp(time=[2.5, 3.5])
 
-To interpolate data with a :py:func:`numpy.datetime64` coordinate you can pass a string.
+To interpolate data with a :py:doc:`numpy.datetime64 <reference/arrays.datetime>` coordinate you can pass a string.
 
 .. ipython:: python
 
@@ -128,7 +128,7 @@ It is now possible to safely compute the difference ``other - interpolated``.
 Interpolation methods
 ---------------------
 
-We use :py:func:`scipy.interpolate.interp1d` for 1-dimensional interpolation and
+We use :py:class:`scipy.interpolate.interp1d` for 1-dimensional interpolation and
 :py:func:`scipy.interpolate.interpn` for multi-dimensional interpolation.
 
 The interpolation method can be specified by the optional ``method`` argument.