Merge pull request #173 from shoyer/edge-cases

Edge cases

Author: shoyer

test/test_data_array.py

@@ -56,6 +56,16 @@ def test_properties(self):
         with self.assertRaisesRegexp(ValueError, 'must be 1-dimensional'):
             self.ds['foo'].as_index
 
+    def test_encoding(self):
+        expected = {'foo': 'bar'}
+        self.dv.encoding['foo'] = 'bar'
+        self.assertEquals(expected, self.dv.encoding)
+
+        expected = {'baz': 0}
+        self.dv.encoding = expected
+        self.assertEquals(expected, self.dv.encoding)
+        self.assertIsNot(expected, self.dv.encoding)
+
     def test_constructor(self):
         data = np.random.random((2, 3))
 
@@ -93,6 +103,10 @@ def test_constructor(self):
         actual = DataArray(data, indexes)
         self.assertDataArrayIdentical(expected, actual)
 
+        indexes = pd.Series([['a', 'b'], [-1, -2, -3]], ['x', 'y'])
+        actual = DataArray(data, indexes)
+        self.assertDataArrayIdentical(expected, actual)
+
         expected = Dataset({None: (['x', 'y'], data),
                             'x': ('x', ['a', 'b'])})[None]
         actual = DataArray(data, {'x': ['a', 'b']}, ['x', 'y'])

test/test_variable.py

@@ -294,11 +294,14 @@ def test_data(self):
         v.values = d2
         self.assertIs(source_ndarray(v.values), d2)
 
-    def test_item(self):
+    def test_numpy_same_methods(self):
         v = Variable([], np.float32(0.0))
         self.assertEqual(v.item(), 0)
         self.assertIs(type(v.item()), float)
 
+        v = Index('x', np.arange(5))
+        self.assertEqual(2, v.searchsorted(2))
+
     def test_datetime64_conversion(self):
         # verify that datetime64 is always converted to ns precision with
         # sources preserved

xray/common.py

@@ -57,10 +57,6 @@ def __long__(self):
     def __array__(self, dtype=None):
         return np.asarray(self.values, dtype=dtype)
 
-    def item(self):
-        """Calls numpy.ndarray.item on this array's values"""
-        return self.values.item()
-
     def __repr__(self):
         return array_repr(self)
 

xray/data_array.py

@@ -29,7 +29,7 @@ def _infer_indexes_and_dimensions(shape, indexes, dimensions):
 
     if _is_dict_like(indexes):
         if dimensions is None:
-            dimensions = list(indexes)
+            dimensions = list(indexes.keys())
         else:
             bad_indexes = [dim for dim in indexes if dim not in dimensions]
             if bad_indexes:
@@ -331,6 +331,10 @@ def encoding(self):
         serialized."""
         return self.variable.encoding
 
+    @encoding.setter
+    def encoding(self, value):
+        self.variable.encoding = value
+
     @property
     def coordinates(self):
         utils.alias_warning('coordinates', 'indexes', 3)

xray/ops.py

@@ -12,14 +12,24 @@
 if not PY3:
     NUM_BINARY_OPS.append('div')
 
+# methods which pass on the numpy return value unchanged
+# be careful not to list methods that we would want to wrap later
+NUMPY_SAME_METHODS = ['item', 'searchsorted']
 # methods which don't modify the data shape, so the result should still be
 # wrapped in an Variable/DataArray
 NUMPY_UNARY_METHODS = ['astype', 'argsort', 'clip', 'conj', 'conjugate',
                        'round']
 # methods which remove an axis
 NUMPY_REDUCE_METHODS = ['all', 'any', 'argmax', 'argmin', 'max', 'mean', 'min',
                         'prod', 'ptp', 'std', 'sum', 'var']
-# TODO: wrap cumprod, cumsum, take, dot, searchsorted
+# TODO: wrap cumprod/cumsum, take, dot, argsort/sort
+
+
+def _values_method_wrapper(f):
+    def func(self, *args, **kwargs):
+        return getattr(self.values, f)(*args, **kwargs)
+    func.__name__ = f
+    return func
 
 
 def _method_wrapper(f):
@@ -81,6 +91,8 @@ def inject_special_operations(cls, priority=50):
         setattr(cls, op_str('i' + name),
                 cls._inplace_binary_op(op('i' + name)))
     # patch in numpy methods
+    for name in NUMPY_SAME_METHODS:
+        setattr(cls, name, _values_method_wrapper(name))
     for name in NUMPY_UNARY_METHODS:
         setattr(cls, name, cls._unary_op(_method_wrapper(name)))
     inject_reduce_methods(cls)

xray/variable.py

@@ -217,7 +217,7 @@ def __init__(self, dims, data, attributes=None, encoding=None):
         if attributes is None:
             attributes = {}
         self._attributes = OrderedDict(attributes)
-        self.encoding = dict({} if encoding is None else encoding)
+        self._encoding = dict({} if encoding is None else encoding)
 
     @property
     def dtype(self):
@@ -360,6 +360,16 @@ def attrs(self):
     def attrs(self, value):
         self._attributes = OrderedDict(value)
 
+    @property
+    def encoding(self):
+        """Dictionary of encodings on this variable.
+        """
+        return self._encoding
+
+    @encoding.setter
+    def encoding(self, value):
+        self._encoding = dict(value)
+
     def copy(self, deep=True):
         """Returns a copy of this object.