Add broadcast function to the API

This is a renaming and update of the existing `xray.broadcast_arrays` function,
which now works properly in the light of GH648.

Examples
--------

Broadcast two data arrays against one another to fill out their dimensions:

    >>> a = xray.DataArray([1, 2, 3], dims='x')
    >>> b = xray.DataArray([5, 6], dims='y')
    >>> a
    <xray.DataArray (x: 3)>
    array([1, 2, 3])
    Coordinates:
      * x        (x) int64 0 1 2
    >>> b
    <xray.DataArray (y: 2)>
    array([5, 6])
    Coordinates:
      * y        (y) int64 0 1
    >>> a2, b2 = xray.broadcast(a, b)
    >>> a2
    <xray.DataArray (x: 3, y: 2)>
    array([[1, 1],
           [2, 2],
           [3, 3]])
    Coordinates:
      * x        (x) int64 0 1 2
      * y        (y) int64 0 1
    >>> b2
    <xray.DataArray (x: 3, y: 2)>
    array([[5, 6],
           [5, 6],
           [5, 6]])
    Coordinates:
      * y        (y) int64 0 1
      * x        (x) int64 0 1 2

Fill out the dimensions of all data variables in a dataset:

    >>> ds = xray.Dataset({'a': a, 'b': b})
    >>> ds2, = xray.broadcast(ds)  # use tuple unpacking to extract one dataset
    >>> ds2
    <xray.Dataset>
    Dimensions:  (x: 3, y: 2)
    Coordinates:
      * x        (x) int64 0 1 2
      * y        (y) int64 0 1
    Data variables:
        a        (x, y) int64 1 1 2 2 3 3
        b        (x, y) int64 5 6 5 6 5 6

Author: shoyer

doc/api.rst

@@ -15,6 +15,7 @@ Top-level functions
    :toctree: generated/
 
    align
+   broadcast
    concat
    set_options
 

doc/whats-new.rst

@@ -67,7 +67,7 @@ Bug fixes
 - Fixes for several issues found on ``DataArray`` objects with the same name
   as one of their coordinates (see :ref:`v0.7.0.breaking` for more details).
 
-- ``DataArray.to_masked_array`` always returns masked array with mask being an array 
+- ``DataArray.to_masked_array`` always returns masked array with mask being an array
 (not a scalar value) (:issue:`684`)
 
 v0.6.2 (unreleased)
@@ -96,6 +96,18 @@ Enhancements
   moves both data and coordinates.
 - Assigning a ``pandas`` object to a ``Dataset`` directly is now permitted. Its
   index names correspond to the `dims`` of the ``Dataset``, and its data is aligned
+- New function :py:func:`~xray.broadcast` for explicitly broadcasting
+  ``DataArray`` and ``Dataset`` objects against each other. For example:
+
+  .. ipython:: python
+
+      a = xray.DataArray([1, 2, 3], dims='x')
+      b = xray.DataArray([5, 6], dims='y')
+      a
+      b
+      a2, b2 = xray.broadcast(a, b)
+      a2
+      b2
 
 Bug fixes
 ~~~~~~~~~

xray/__init__.py

@@ -1,4 +1,4 @@
-from .core.alignment import align, broadcast_arrays
+from .core.alignment import align, broadcast, broadcast_arrays
 from .core.combine import concat, auto_combine
 from .core.variable import Variable, Coordinate
 from .core.dataset import Dataset

xray/core/alignment.py

@@ -253,46 +253,126 @@ def var_indexers(var, indexers):
     return reindexed
 
 
-def broadcast_arrays(*args):
-    """Explicitly broadcast any number of DataArrays against one another.
+def broadcast(*args):
+    """Explicitly broadcast any number of DataArray or Dataset objects against
+    one another.
 
     xray objects automatically broadcast against each other in arithmetic
     operations, so this function should not be necessary for normal use.
 
     Parameters
     ----------
-    *args: DataArray
+    *args: DataArray or Dataset objects
         Arrays to broadcast against each other.
 
     Returns
     -------
-    broadcast: tuple of DataArray
+    broadcast: tuple of xray objects
         The same data as the input arrays, but with additional dimensions
-        inserted so that all arrays have the same dimensions and shape.
+        inserted so that all data arrays have the same dimensions and shape.
 
     Raises
     ------
     ValueError
-        If indexes on the different arrays are not aligned.
+        If indexes on the different objects are not aligned.
+
+    Examples
+    --------
+
+    Broadcast two data arrays against one another to fill out their dimensions:
+
+    >>> a = xray.DataArray([1, 2, 3], dims='x')
+    >>> b = xray.DataArray([5, 6], dims='y')
+    >>> a
+    <xray.DataArray (x: 3)>
+    array([1, 2, 3])
+    Coordinates:
+      * x        (x) int64 0 1 2
+    >>> b
+    <xray.DataArray (y: 2)>
+    array([5, 6])
+    Coordinates:
+      * y        (y) int64 0 1
+    >>> a2, b2 = xray.broadcast(a, b)
+    >>> a2
+    <xray.DataArray (x: 3, y: 2)>
+    array([[1, 1],
+           [2, 2],
+           [3, 3]])
+    Coordinates:
+      * x        (x) int64 0 1 2
+      * y        (y) int64 0 1
+    >>> b2
+    <xray.DataArray (x: 3, y: 2)>
+    array([[5, 6],
+           [5, 6],
+           [5, 6]])
+    Coordinates:
+      * y        (y) int64 0 1
+      * x        (x) int64 0 1 2
+
+    Fill out the dimensions of all data variables in a dataset:
+
+    >>> ds = xray.Dataset({'a': a, 'b': b})
+    >>> ds2, = xray.broadcast(ds)  # use tuple unpacking to extract one dataset
+    >>> ds2
+    <xray.Dataset>
+    Dimensions:  (x: 3, y: 2)
+    Coordinates:
+      * x        (x) int64 0 1 2
+      * y        (y) int64 0 1
+    Data variables:
+        a        (x, y) int64 1 1 2 2 3 3
+        b        (x, y) int64 5 6 5 6 5 6
     """
-    # TODO: fixme for coordinate arrays
-
     from .dataarray import DataArray
+    from .dataset import Dataset
 
     all_indexes = _get_all_indexes(args)
     for k, v in all_indexes.items():
         if not all(v[0].equals(vi) for vi in v[1:]):
             raise ValueError('cannot broadcast arrays: the %s index is not '
                              'aligned (use xray.align first)' % k)
 
-    vars = broadcast_variables(*[a.variable for a in args])
-    indexes = dict((k, all_indexes[k][0]) for k in vars[0].dims)
+    common_coords = OrderedDict()
+    dims_map = OrderedDict()
+    for arg in args:
+        for dim in arg.dims:
+            if dim not in common_coords:
+                common_coords[dim] = arg.coords[dim].variable
+                dims_map[dim] = common_coords[dim].size
+
+    def _broadcast_array(array):
+        data = array.variable.expand_dims(dims_map)
+        coords = OrderedDict(array.coords)
+        coords.update(common_coords)
+        dims = tuple(common_coords)
+        return DataArray(data, coords, dims, name=array.name,
+                         attrs=array.attrs, encoding=array.encoding)
+
+    def _broadcast_dataset(ds):
+        data_vars = OrderedDict()
+        for k in ds.data_vars:
+            data_vars[k] = ds.variables[k].expand_dims(dims_map)
+
+        coords = OrderedDict(ds.coords)
+        coords.update(common_coords)
+
+        return Dataset(data_vars, coords, ds.attrs)
+
+    result = []
+    for arg in args:
+        if isinstance(arg, DataArray):
+            result.append(_broadcast_array(arg))
+        elif isinstance(arg, Dataset):
+            result.append(_broadcast_dataset(arg))
+        else:
+            raise ValueError('all input must be Dataset or DataArray objects')
 
-    arrays = []
-    for a, v in zip(args, vars):
-        arr = DataArray(v.values, indexes, v.dims, a.name, a.attrs, a.encoding)
-        for k, v in a.coords.items():
-            arr.coords[k] = v
-        arrays.append(arr)
+    return tuple(result)
 
-    return tuple(arrays)
+
+def broadcast_arrays(*args):
+    warnings.warn('xray.broadcast_arrays is deprecated: use xray.broadcast '
+                  'instead', DeprecationWarning, stacklevel=2)
+    return broadcast(*args)

xray/test/test_dataarray.py

@@ -4,7 +4,7 @@
 from copy import deepcopy
 from textwrap import dedent
 
-from xray import (align, broadcast_arrays, Dataset, DataArray,
+from xray import (align, broadcast, Dataset, DataArray,
                   Coordinate, Variable)
 from xray.core.pycompat import iteritems, OrderedDict
 from . import TestCase, ReturnItem, source_ndarray, unittest, requires_dask
@@ -1267,7 +1267,7 @@ def test_align_dtype(self):
     def test_broadcast_arrays(self):
         x = DataArray([1, 2], coords=[('a', [-1, -2])], name='x')
         y = DataArray([1, 2], coords=[('b', [3, 4])], name='y')
-        x2, y2 = broadcast_arrays(x, y)
+        x2, y2 = broadcast(x, y)
         expected_coords = [('a', [-1, -2]), ('b', [3, 4])]
         expected_x2 = DataArray([[1, 1], [2, 2]], expected_coords, name='x')
         expected_y2 = DataArray([[1, 2], [1, 2]], expected_coords, name='y')
@@ -1276,15 +1276,27 @@ def test_broadcast_arrays(self):
 
         x = DataArray(np.random.randn(2, 3), dims=['a', 'b'])
         y = DataArray(np.random.randn(3, 2), dims=['b', 'a'])
-        x2, y2 = broadcast_arrays(x, y)
+        x2, y2 = broadcast(x, y)
         expected_x2 = x
         expected_y2 = y.T
         self.assertDataArrayIdentical(expected_x2, x2)
         self.assertDataArrayIdentical(expected_y2, y2)
 
+        z = DataArray([1, 2], coords=[('a', [-10, 20])])
         with self.assertRaisesRegexp(ValueError, 'cannot broadcast'):
-            z = DataArray([1, 2], coords=[('a', [-10, 20])])
-            broadcast_arrays(x, z)
+            broadcast(x, z)
+
+    def test_broadcast_coordinates(self):
+        # regression test for GH649
+        ds = Dataset({'a': (['x', 'y'], np.ones((5, 6)))})
+        x_bc, y_bc, a_bc = broadcast(ds.x, ds.y, ds.a)
+        self.assertDataArrayIdentical(ds.a, a_bc)
+
+        X, Y = np.meshgrid(np.arange(5), np.arange(6), indexing='ij')
+        exp_x = DataArray(X, dims=['x', 'y'], name='x')
+        exp_y = DataArray(Y, dims=['x', 'y'], name='y')
+        self.assertDataArrayIdentical(exp_x, x_bc)
+        self.assertDataArrayIdentical(exp_y, y_bc)
 
     def test_to_pandas(self):
         # 0d

xray/test/test_dataset.py

@@ -12,8 +12,8 @@
 import numpy as np
 import pandas as pd
 
-from xray import (align, concat, conventions, backends, Dataset, DataArray,
-                  Variable, Coordinate, auto_combine, open_dataset,
+from xray import (align, broadcast, concat, conventions, backends, Dataset,
+                  DataArray, Variable, Coordinate, auto_combine, open_dataset,
                   set_options)
 from xray.core import indexing, utils
 from xray.core.pycompat import iteritems, OrderedDict
@@ -953,6 +953,30 @@ def test_align(self):
         with self.assertRaises(TypeError):
             align(left, right, foo='bar')
 
+    def test_broadcast(self):
+        ds = Dataset({'foo': 0, 'bar': ('x', [1]), 'baz': ('y', [2, 3])},
+                     {'c': ('x', [4])})
+        expected = Dataset({'foo': (('x', 'y'), [[0, 0]]),
+                            'bar': (('x', 'y'), [[1, 1]]),
+                            'baz': (('x', 'y'), [[2, 3]])},
+                            {'c': ('x', [4])})
+        actual, = broadcast(ds)
+        self.assertDatasetIdentical(expected, actual)
+
+        ds_x = Dataset({'foo': ('x', [1])})
+        ds_y = Dataset({'bar': ('y', [2, 3])})
+        expected_x = Dataset({'foo': (('x', 'y'), [[1, 1]])})
+        expected_y = Dataset({'bar': (('x', 'y'), [[2, 3]])})
+        actual_x, actual_y = broadcast(ds_x, ds_y)
+        self.assertDatasetIdentical(expected_x, actual_x)
+        self.assertDatasetIdentical(expected_y, actual_y)
+
+        array_y = ds_y['bar']
+        expected_y = expected_y['bar']
+        actual_x, actual_y = broadcast(ds_x, array_y)
+        self.assertDatasetIdentical(expected_x, actual_x)
+        self.assertDataArrayIdentical(expected_y, actual_y)
+
     def test_variable_indexing(self):
         data = create_test_data()
         v = data['var1']