upgrade black verison to 19.10b0

xarray/backends/api.py

@@ -677,7 +677,7 @@ def open_dataarray(
             "then select the variable you want."
         )
     else:
-        data_array, = dataset.data_vars.values()
+        (data_array,) = dataset.data_vars.values()
 
     data_array._file_obj = dataset._file_obj
 

xarray/core/alignment.py

@@ -252,7 +252,7 @@ def align(
 
     if not indexes and len(objects) == 1:
         # fast path for the trivial case
-        obj, = objects
+        (obj,) = objects
         return (obj.copy(deep=copy),)
 
     all_indexes = defaultdict(list)

xarray/core/combine.py

@@ -954,7 +954,7 @@ def _auto_concat(
                     "supply the ``concat_dim`` argument "
                     "explicitly"
                 )
-            dim, = concat_dims
+            (dim,) = concat_dims
         return concat(
             datasets,
             dim=dim,

xarray/core/computation.py

@@ -145,7 +145,7 @@ def result_name(objects: list) -> Any:
     names = {getattr(obj, "name", _DEFAULT_NAME) for obj in objects}
     names.discard(_DEFAULT_NAME)
     if len(names) == 1:
-        name, = names
+        (name,) = names
     else:
         name = None
     return name
@@ -187,7 +187,7 @@ def build_output_coords(
 
     if len(coords_list) == 1 and not exclude_dims:
         # we can skip the expensive merge
-        unpacked_coords, = coords_list
+        (unpacked_coords,) = coords_list
         merged_vars = dict(unpacked_coords.variables)
     else:
         # TODO: save these merged indexes, instead of re-computing them later
@@ -237,7 +237,7 @@ def apply_dataarray_vfunc(
             for variable, coords in zip(result_var, result_coords)
         )
     else:
-        coords, = result_coords
+        (coords,) = result_coords
         out = DataArray(result_var, coords, name=name, fastpath=True)
 
     return out
@@ -384,7 +384,7 @@ def apply_dataset_vfunc(
     if signature.num_outputs > 1:
         out = tuple(_fast_dataset(*args) for args in zip(result_vars, list_of_coords))
     else:
-        coord_vars, = list_of_coords
+        (coord_vars,) = list_of_coords
         out = _fast_dataset(result_vars, coord_vars)
 
     if keep_attrs and isinstance(first_obj, Dataset):

xarray/core/concat.py

@@ -148,10 +148,10 @@ def _calc_concat_dim_coord(dim):
         dim = dim_name
     elif not isinstance(dim, DataArray):
         coord = as_variable(dim).to_index_variable()
-        dim, = coord.dims
+        (dim,) = coord.dims
     else:
         coord = dim
-        dim, = coord.dims
+        (dim,) = coord.dims
     return dim, coord
 
 

xarray/core/dataarray.py

@@ -616,7 +616,7 @@ def _level_coords(self) -> Dict[Hashable, Hashable]:
             if var.ndim == 1 and isinstance(var, IndexVariable):
                 level_names = var.level_names
                 if level_names is not None:
-                    dim, = var.dims
+                    (dim,) = var.dims
                     level_coords.update({lname: dim for lname in level_names})
         return level_coords
 

xarray/core/dataset.py

@@ -4067,7 +4067,7 @@ def reduce(
                     if len(reduce_dims) == 1:
                         # unpack dimensions for the benefit of functions
                         # like np.argmin which can't handle tuple arguments
-                        reduce_dims, = reduce_dims
+                        (reduce_dims,) = reduce_dims
                     elif len(reduce_dims) == var.ndim:
                         # prefer to aggregate over axis=None rather than
                         # axis=(0, 1) if they will be equivalent, because

xarray/core/groupby.py

@@ -321,7 +321,7 @@ def __init__(
             raise ValueError("`group` must have a name")
 
         group, obj, stacked_dim, inserted_dims = _ensure_1d(group, obj)
-        group_dim, = group.dims
+        (group_dim,) = group.dims
 
         expected_size = obj.sizes[group_dim]
         if group.size != expected_size:
@@ -463,7 +463,7 @@ def _infer_concat_args(self, applied_example):
         else:
             coord = self._unique_coord
             positions = None
-        dim, = coord.dims
+        (dim,) = coord.dims
         if isinstance(coord, _DummyGroup):
             coord = None
         return coord, dim, positions
@@ -637,7 +637,7 @@ def _concat_shortcut(self, applied, dim, positions=None):
     def _restore_dim_order(self, stacked):
         def lookup_order(dimension):
             if dimension == self._group.name:
-                dimension, = self._group.dims
+                (dimension,) = self._group.dims
             if dimension in self._obj.dims:
                 axis = self._obj.get_axis_num(dimension)
             else:

xarray/core/indexing.py

@@ -212,7 +212,7 @@ def get_dim_indexers(data_obj, indexers):
     level_indexers = defaultdict(dict)
     dim_indexers = {}
     for key, label in indexers.items():
-        dim, = data_obj[key].dims
+        (dim,) = data_obj[key].dims
         if key != dim:
             # assume here multi-index level indexer
             level_indexers[dim][key] = label
@@ -1368,7 +1368,7 @@ def __getitem__(
         if isinstance(key, tuple) and len(key) == 1:
             # unpack key so it can index a pandas.Index object (pandas.Index
             # objects don't like tuples)
-            key, = key
+            (key,) = key
 
         if getattr(key, "ndim", 0) > 1:  # Return np-array if multidimensional
             return NumpyIndexingAdapter(self.array.values)[indexer]

xarray/core/merge.py

@@ -277,7 +277,7 @@ def append_all(variables, indexes):
 
 
 def collect_from_coordinates(
-    list_of_coords: "List[Coordinates]"
+    list_of_coords: "List[Coordinates]",
 ) -> Dict[Hashable, List[MergeElement]]:
     """Collect variables and indexes to be merged from Coordinate objects."""
     grouped: Dict[Hashable, List[Tuple[Variable, pd.Index]]] = {}
@@ -320,7 +320,7 @@ def merge_coordinates_without_align(
 
 
 def determine_coords(
-    list_of_mappings: Iterable["DatasetLike"]
+    list_of_mappings: Iterable["DatasetLike"],
 ) -> Tuple[Set[Hashable], Set[Hashable]]:
     """Given a list of dicts with xarray object values, identify coordinates.
 

xarray/core/variable.py

@@ -1524,7 +1524,7 @@ def concat(cls, variables, dim="concat_dim", positions=None, shortcut=False):
             along the given dimension.
         """
         if not isinstance(dim, str):
-            dim, = dim.dims
+            (dim,) = dim.dims
 
         # can't do this lazily: we need to loop through variables at least
         # twice
@@ -1994,7 +1994,7 @@ def concat(cls, variables, dim="concat_dim", positions=None, shortcut=False):
         arrays, if possible.
         """
         if not isinstance(dim, str):
-            dim, = dim.dims
+            (dim,) = dim.dims
 
         variables = list(variables)
         first_var = variables[0]

xarray/plot/plot.py

@@ -83,8 +83,8 @@ def _infer_line_data(darray, x, y, hue):
                     )
 
             else:
-                xdim, = darray[xname].dims
-                huedim, = darray[huename].dims
+                (xdim,) = darray[xname].dims
+                (huedim,) = darray[huename].dims
                 yplt = darray.transpose(xdim, huedim)
 
         else:
@@ -102,8 +102,8 @@ def _infer_line_data(darray, x, y, hue):
                     )
 
             else:
-                ydim, = darray[yname].dims
-                huedim, = darray[huename].dims
+                (ydim,) = darray[yname].dims
+                (huedim,) = darray[huename].dims
                 xplt = darray.transpose(ydim, huedim)
 
         huelabel = label_from_attrs(darray[huename])

xarray/tests/test_cftime_offsets.py

@@ -1187,5 +1187,5 @@ def test_dayofyear_after_cftime_range(freq):
 def test_cftime_range_standard_calendar_refers_to_gregorian():
     from cftime import DatetimeGregorian
 
-    result, = cftime_range("2000", periods=1)
+    (result,) = cftime_range("2000", periods=1)
     assert isinstance(result, DatetimeGregorian)

xarray/tests/test_dataarray.py

@@ -3121,11 +3121,11 @@ def test_align_copy(self):
 
         # Trivial align - 1 element
         x = DataArray([1, 2, 3], coords=[("a", [1, 2, 3])])
-        x2, = align(x, copy=False)
+        (x2,) = align(x, copy=False)
         assert_identical(x, x2)
         assert source_ndarray(x2.data) is source_ndarray(x.data)
 
-        x2, = align(x, copy=True)
+        (x2,) = align(x, copy=True)
         assert_identical(x, x2)
         assert source_ndarray(x2.data) is not source_ndarray(x.data)
 
@@ -3210,7 +3210,7 @@ def test_align_indexes(self):
         assert_identical(expected_x2, x2)
         assert_identical(expected_y2, y2)
 
-        x2, = align(x, join="outer", indexes={"a": [-2, 7, 10, -1]})
+        (x2,) = align(x, join="outer", indexes={"a": [-2, 7, 10, -1]})
         expected_x2 = DataArray([3, np.nan, 2, 1], coords=[("a", [-2, 7, 10, -1])])
         assert_identical(expected_x2, x2)
 
@@ -3289,7 +3289,7 @@ def test_broadcast_arrays_nocopy(self):
         assert source_ndarray(x2.data) is source_ndarray(x.data)
 
         # single-element broadcast (trivial case)
-        x2, = broadcast(x)
+        (x2,) = broadcast(x)
         assert_identical(x, x2)
         assert source_ndarray(x2.data) is source_ndarray(x.data)
 

xarray/tests/test_dataset.py

@@ -1945,7 +1945,7 @@ def test_align_nocopy(self):
 
     def test_align_indexes(self):
         x = Dataset({"foo": DataArray([1, 2, 3], dims="x", coords=[("x", [1, 2, 3])])})
-        x2, = align(x, indexes={"x": [2, 3, 1]})
+        (x2,) = align(x, indexes={"x": [2, 3, 1]})
         expected_x2 = Dataset(
             {"foo": DataArray([2, 3, 1], dims="x", coords={"x": [2, 3, 1]})}
         )
@@ -1973,7 +1973,7 @@ def test_broadcast(self):
             },
             {"c": ("x", [4])},
         )
-        actual, = broadcast(ds)
+        (actual,) = broadcast(ds)
         assert_identical(expected, actual)
 
         ds_x = Dataset({"foo": ("x", [1])})
@@ -1995,7 +1995,7 @@ def test_broadcast_nocopy(self):
         x = Dataset({"foo": (("x", "y"), [[1, 1]])})
         y = Dataset({"bar": ("y", [2, 3])})
 
-        actual_x, = broadcast(x)
+        (actual_x,) = broadcast(x)
         assert_identical(x, actual_x)
         assert source_ndarray(actual_x["foo"].data) is source_ndarray(x["foo"].data)