doc fixes.

Fixes #2610

Author: dcherian

doc/api.rst

@@ -343,7 +343,6 @@ Computation
 :py:attr:`~DataArray.searchsorted`
 :py:attr:`~DataArray.round`
 :py:attr:`~DataArray.real`
-:py:attr:`~DataArray.T`
 :py:attr:`~DataArray.cumsum`
 :py:attr:`~DataArray.cumprod`
 :py:attr:`~DataArray.rank`

doc/dask.rst

@@ -179,7 +179,7 @@ Explicit conversion by wrapping a DataArray with ``np.asarray`` also works:
 Alternatively you can load the data into memory but keep the arrays as
 Dask arrays using the :py:meth:`~xarray.Dataset.persist` method:
 
-.. ipython::
+.. ipython:: python
 
    ds = ds.persist()
 

doc/examples/multidimensional-coords.rst

@@ -25,7 +25,7 @@ As an example, consider this dataset from the
 
 .. ipython:: python
 
-    ds = xr.tutorial.load_dataset('rasm')
+    ds = xr.tutorial.open_dataset('rasm').load()
     ds
 
 In this example, the *logical coordinates* are ``x`` and ``y``, while
@@ -107,7 +107,8 @@ function to specify the output coordinates of the group.
     # define a label for each bin corresponding to the central latitude
     lat_center = np.arange(1, 90, 2)
     # group according to those bins and take the mean
-    Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean()
+    Tair_lat_mean = (ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center)
+	             .mean(xr.ALL_DIMS))
     # plot the result
     @savefig xarray_multidimensional_coords_14_1.png width=5in
     Tair_lat_mean.plot();

doc/examples/weather-data.rst

@@ -123,6 +123,7 @@ The :py:func:`~xarray.Dataset.fillna` method on grouped objects lets you easily
 fill missing values by group:
 
 .. ipython:: python
+   :okwarning:
 
     # throw away the first half of every month
     some_missing = ds.tmin.sel(time=ds['time.day'] > 15).reindex_like(ds)

doc/groupby.rst

@@ -118,7 +118,7 @@ dimensions *other than* the provided one:
 
 .. ipython:: python
 
-    ds.groupby('x').std()
+    ds.groupby('x').std(xr.ALL_DIMS)
 
 First and last
 ~~~~~~~~~~~~~~
@@ -129,7 +129,7 @@ values for group along the grouped dimension:
 
 .. ipython:: python
 
-    ds.groupby('letters').first()
+    ds.groupby('letters').first(xr.ALL_DIMS)
 
 By default, they skip missing values (control this with ``skipna``).
 
@@ -144,7 +144,7 @@ coordinates. For example:
 
 .. ipython:: python
 
-    alt = arr.groupby('letters').mean()
+    alt = arr.groupby('letters').mean(xr.ALL_DIMS)
     alt
     ds.groupby('letters') - alt
 
@@ -197,7 +197,7 @@ __ http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#_two_dimen
                 'lat': (['ny','nx'], [[10,10],[20,20]] ),},
         dims=['ny','nx'])
     da
-    da.groupby('lon').sum()
+    da.groupby('lon').sum(xr.ALL_DIMS)
     da.groupby('lon').apply(lambda x: x - x.mean(), shortcut=False)
 
 Because multidimensional groups have the ability to generate a very large

doc/pandas.rst

@@ -173,6 +173,7 @@ So you can represent a Panel, in two ways:
 Let's take a look:
 
 .. ipython:: python
+   :okwarning:
 
     panel = pd.Panel(np.random.rand(2, 3, 4), items=list('ab'), major_axis=list('mno'),
                      minor_axis=pd.date_range(start='2000', periods=4, name='date'))

doc/plotting.rst

@@ -144,7 +144,7 @@ axes created by ``plt.subplots``.
     plt.tight_layout()
 
     @savefig plotting_example_existing_axes.png width=6in
-    plt.show()
+    plt.draw()
 
 On the right is a histogram created by :py:func:`xarray.plot.hist`.
 
@@ -343,7 +343,7 @@ matplotlib is available.
     plt.tight_layout()
 
     @savefig plotting_2d_call_matplotlib.png width=4in
-    plt.show()
+    plt.draw()
 
 .. note::
 
@@ -359,7 +359,7 @@ matplotlib is available.
         air2d.plot()
 
         @savefig plotting_2d_call_matplotlib2.png width=4in
-        plt.show()
+        plt.draw()
 
 Colormaps
 ~~~~~~~~~
@@ -444,9 +444,11 @@ if using ``imshow`` or ``pcolormesh`` (but not with ``contour`` or ``contourf``,
 since levels are chosen automatically).
 
 .. ipython:: python
+   :okwarning:
 
     @savefig plotting_seaborn_palette.png width=4in
     air2d.plot(levels=10, cmap='husl')
+    plt.draw()
 
 .. _plotting.faceting:
 
@@ -519,6 +521,11 @@ Other features
 
 Faceted plotting supports other arguments common to xarray 2d plots.
 
+.. ipython:: python
+   :suppress:
+
+      plt.close('all')
+
 .. ipython:: python
 
     hasoutliers = t.isel(time=slice(0, 5)).copy()
@@ -528,7 +535,7 @@ Faceted plotting supports other arguments common to xarray 2d plots.
     @savefig plot_facet_robust.png
     g = hasoutliers.plot.pcolormesh('lon', 'lat', col='time', col_wrap=3,
                                     robust=True, cmap='viridis',
-				     cbar_kwargs={'label': 'this has outliers'})
+				    cbar_kwargs={'label': 'this has outliers'})
 
 FacetGrid Objects
 ~~~~~~~~~~~~~~~~~
@@ -568,7 +575,7 @@ they have been plotted.
     bottomright.annotate('bottom right', (240, 40))
 
     @savefig plot_facet_iterator.png
-    plt.show()
+    plt.draw()
 
 TODO: add an example of using the ``map`` method to plot dataset variables
 (e.g., with ``plt.quiver``).
@@ -603,7 +610,7 @@ by faceting are accessible in the object returned by ``plot``:
         ax.coastlines()
         ax.gridlines()
     @savefig plotting_maps_cartopy_facetting.png width=100%
-    plt.show();
+    plt.draw();
 
 
 Details
@@ -634,7 +641,7 @@ These are provided for user convenience; they all call the same code.
     xplt.line(da, ax=axes[1, 1])
     plt.tight_layout()
     @savefig plotting_ways_to_use.png width=6in
-    plt.show()
+    plt.draw()
 
 Here the output is the same. Since the data is 1 dimensional the line plot
 was used.

doc/reshaping.rst

@@ -186,8 +186,8 @@ labels for one or several dimensions:
     array
     array['c'] = ('x', ['a', 'b', 'c'])
     array.set_index(x='c')
-    array.set_index(x='c', inplace=True)
-    array.reset_index('x', drop=True)
+    array = array.set_index(x='c')
+    array = array.reset_index('x', drop=True)
 
 .. _reshape.shift_and_roll:
 
@@ -201,7 +201,7 @@ To adjust coordinate labels, you can use the :py:meth:`~xarray.Dataset.shift` an
 
 	array = xr.DataArray([1, 2, 3, 4], dims='x')
 	array.shift(x=2)
-	array.roll(x=2)
+	array.roll(x=2, roll_coords=True)
 
 .. _reshape.sort:
 

doc/time-series.rst

@@ -163,6 +163,7 @@ Datetime components couple particularly well with grouped operations (see
 calculate the mean by time of day:
 
 .. ipython:: python
+   :okwarning:
 
     ds.groupby('time.hour').mean()
 
@@ -176,6 +177,7 @@ same api as ``resample`` `in pandas`_.
 For example, we can downsample our dataset from hourly to 6-hourly:
 
 .. ipython:: python
+   :okwarning:
 
     ds.resample(time='6H')
 
@@ -184,6 +186,7 @@ necessary for resampling. All of the reduction methods which work with
 ``Resample`` objects can also be used for resampling:
 
 .. ipython:: python
+   :okwarning:
 
    ds.resample(time='6H').mean()
 
@@ -326,6 +329,7 @@ For data indexed by a :py:class:`~xarray.CFTimeIndex` xarray currently supports:
    :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)])

doc/whats-new.rst

@@ -174,7 +174,7 @@ Bug fixes
   By `Spencer Clark <https://github.com/spencerkclark>`_.
 - We now properly handle arrays of ``datetime.datetime`` and ``datetime.timedelta``
   provided as coordinates. (:issue:`2512`)
-  By `Deepak Cherian <https://github.com/dcherian`_.
+  By `Deepak Cherian <https://github.com/dcherian>`_.
 - ``xarray.DataArray.roll`` correctly handles multidimensional arrays.
   (:issue:`2445`)
   By `Keisuke Fujii <https://github.com/fujiisoup>`_.
@@ -2216,6 +2216,7 @@ Enhancements
   for shifting/rotating datasets or arrays along a dimension:
 
   .. ipython:: python
+     :okwarning:
 
       array = xray.DataArray([5, 6, 7, 8], dims='x')
       array.shift(x=2)
@@ -2723,6 +2724,7 @@ Enhancements
   need to supply the time dimension explicitly:
 
   .. ipython:: python
+     :verbatim:
 
       time = pd.date_range('2000-01-01', freq='6H', periods=10)
       array = xray.DataArray(np.arange(10), [('time', time)])
@@ -2732,26 +2734,30 @@ Enhancements
   options such as ``closed`` and ``label`` let you control labeling:
 
   .. ipython:: python
+     :verbatim:
 
       array.resample('1D', dim='time', how='sum', label='right')
 
   If the desired temporal resolution is higher than the original data
   (upsampling), xray will insert missing values:
 
   .. ipython:: python
+     :verbatim:
 
       array.resample('3H', 'time')
 
 - ``first`` and ``last`` methods on groupby objects let you take the first or
   last examples from each group along the grouped axis:
 
   .. ipython:: python
+     :verbatim:
 
       array.groupby('time.day').first()
 
   These methods combine well with ``resample``:
 
   .. ipython:: python
+     :verbatim:
 
       array.resample('1D', dim='time', how='first')