updated docs and example

Author: rabernat

doc/examples.rst

@@ -7,3 +7,4 @@ Examples
     examples/quick-overview
     examples/weather-data
     examples/monthly-means
+    examples/multidimensional-coords

doc/examples/multidimensional-coords.rst

@@ -0,0 +1,200 @@
+
+Working with Multidimensional Coordinates
+=========================================
+
+Author: `Ryan Abernathey <http://github.org/rabernat>`__
+
+Many datasets have *physical coordinates* which differ from their
+*logical coordinates*. Xarray provides several ways to plot and analyze
+such datasets.
+
+.. code:: python
+
+    %matplotlib inline
+    import numpy as np
+    import pandas as pd
+    import xarray as xr
+    import cartopy.crs as ccrs
+    from matplotlib import pyplot as plt
+    
+    print("numpy version  : ", np.__version__)
+    print("pandas version : ", pd.__version__)
+    print("xarray version   : ", xr.version.version)
+
+
+.. parsed-literal::
+
+    ('numpy version  : ', '1.11.0')
+    ('pandas version : ', u'0.18.0')
+    ('xarray version   : ', '0.7.2-32-gf957eb8')
+
+
+As an example, consider this dataset from the
+`xarray-data <https://github.com/pydata/xarray-data>`__ repository.
+
+.. code:: python
+
+    ! curl -L -O https://github.com/pydata/xarray-data/raw/master/RASM_example_data.nc
+
+.. code:: python
+
+    ds = xr.open_dataset('RASM_example_data.nc')
+    ds
+
+
+
+
+.. parsed-literal::
+
+    <xarray.Dataset>
+    Dimensions:  (time: 36, x: 275, y: 205)
+    Coordinates:
+      * time     (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ...
+        yc       (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ...
+        xc       (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ...
+      * x        (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
+      * y        (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ...
+    Data variables:
+        Tair     (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ...
+    Attributes:
+        title: /workspace/jhamman/processed/R1002RBRxaaa01a/lnd/temp/R1002RBRxaaa01a.vic.ha.1979-09-01.nc
+        institution: U.W.
+        source: RACM R1002RBRxaaa01a
+        output_frequency: daily
+        output_mode: averaged
+        convention: CF-1.4
+        references: Based on the initial model of Liang et al., 1994, JGR, 99, 14,415- 14,429.
+        comment: Output from the Variable Infiltration Capacity (VIC) model.
+        nco_openmp_thread_number: 1
+        NCO: 4.3.7
+        history: history deleted for brevity
+
+
+
+In this example, the *logical coordinates* are ``x`` and ``y``, while
+the *physical coordinates* are ``xc`` and ``yc``, which represent the
+latitudes and longitude of the data.
+
+.. code:: python
+
+    print(ds.xc.attrs)
+    print(ds.yc.attrs)
+
+
+.. parsed-literal::
+
+    OrderedDict([(u'long_name', u'longitude of grid cell center'), (u'units', u'degrees_east'), (u'bounds', u'xv')])
+    OrderedDict([(u'long_name', u'latitude of grid cell center'), (u'units', u'degrees_north'), (u'bounds', u'yv')])
+
+
+Plotting
+--------
+
+Let's examine these coordinate variables by plotting them.
+
+.. code:: python
+
+    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4))
+    ds.xc.plot(ax=ax1)
+    ds.yc.plot(ax=ax2)
+
+
+
+
+.. parsed-literal::
+
+    <matplotlib.collections.QuadMesh at 0x118688fd0>
+
+
+
+.. parsed-literal::
+
+    /Users/rpa/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
+      if self._edgecolors == str('face'):
+
+
+
+.. image:: multidimensional_coords_files/xarray_multidimensional_coords_8_2.png
+
+
+Note that the variables ``xc`` (longitude) and ``yc`` (latitude) are
+two-dimensional scalar fields.
+
+If we try to plot the data variable ``Tair``, by default we get the
+logical coordinates.
+
+.. code:: python
+
+    ds.Tair[0].plot()
+
+
+
+
+.. parsed-literal::
+
+    <matplotlib.collections.QuadMesh at 0x11b6da890>
+
+
+
+
+.. image:: multidimensional_coords_files/xarray_multidimensional_coords_10_1.png
+
+
+In order to visualize the data on a conventional latitude-longitude
+grid, we can take advantage of xarray's ability to apply
+`cartopy <http://scitools.org.uk/cartopy/index.html>`__ map projections.
+
+.. code:: python
+
+    plt.figure(figsize=(14,6))
+    ax = plt.axes(projection=ccrs.PlateCarree())
+    ax.set_global()
+    ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False)
+    ax.coastlines()
+    ax.set_ylim([0,90]);
+
+
+
+.. image:: multidimensional_coords_files/xarray_multidimensional_coords_12_0.png
+
+
+Multidimensional Groupby
+------------------------
+
+The above example allowed us to visualize the data on a regular
+latitude-longitude grid. But what if we want to do a calculation that
+involves grouping over one of these physical coordinates (rather than
+the logical coordinates), for example, calculating the mean temperature
+at each latitude. This can be achieved using xarray's ``groupby``
+function, which accepts multidimensional variables. By default,
+``groupby`` will use every unique value in the variable, which is
+probably not what we want. Instead, we can use the ``groupby_bins``
+function to specify the output coordinates of the group.
+
+.. code:: python
+
+    # define two-degree wide latitude bins
+    lat_bins = np.arange(0,91,2)
+    # 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()
+    # plot the result
+    Tair_lat_mean.plot()
+
+
+
+
+.. parsed-literal::
+
+    [<matplotlib.lines.Line2D at 0x11cb92e90>]
+
+
+
+
+.. image:: multidimensional_coords_files/xarray_multidimensional_coords_14_1.png
+
+
+Note that the resulting coordinate for the ``groupby_bins`` operation
+got the ``_bins`` suffix appended: ``xc_bins``. This help us distinguish
+it from the original multidimensional variable ``xc``.

doc/examples/multidimensional_coords_files/xarray_multidimensional_coords_10_1.png

@@ -383,6 +383,8 @@ def groupby_bins(self, group, bins, right=True, labels=None, precision=3,
         grouped : GroupBy
             A `GroupBy` object patterned after `pandas.GroupBy` that can be
             iterated over in the form of `(unique_value, grouped_array)` pairs.
+            The name of the group has the added suffix `_bins` in order to
+            distinguish it from the original variable.
 
         References
         ----------