[pre-commit.ci] pre-commit autoupdate (pydata#6221)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/psf/black: 21.12b0 → 22.1.0](psf/black@21.12b0...22.1.0)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -26,7 +26,7 @@ repos:
           - "--py37-plus"
   # https://github.com/python/black#version-control-integration
   - repo: https://github.com/psf/black
-    rev: 21.12b0
+    rev: 22.1.0
     hooks:
       - id: black
       - id: black-jupyter

xarray/backends/common.py

@@ -65,7 +65,7 @@ def robust_getitem(array, key, catch=Exception, max_retries=6, initial_delay=500
         except catch:
             if n == max_retries:
                 raise
-            base_delay = initial_delay * 2 ** n
+            base_delay = initial_delay * 2**n
             next_delay = base_delay + np.random.randint(base_delay)
             msg = (
                 f"getitem failed, waiting {next_delay} ms before trying again "

xarray/coding/cftime_offsets.py

@@ -671,7 +671,7 @@ def __apply__(self, other):
 
 
 _FREQUENCY_CONDITION = "|".join(_FREQUENCIES.keys())
-_PATTERN = fr"^((?P<multiple>\d+)|())(?P<freq>({_FREQUENCY_CONDITION}))$"
+_PATTERN = rf"^((?P<multiple>\d+)|())(?P<freq>({_FREQUENCY_CONDITION}))$"
 
 
 # pandas defines these offsets as "Tick" objects, which for instance have

xarray/core/weighted.py

@@ -189,7 +189,7 @@ def _sum_of_squares(
 
         demeaned = da - da.weighted(self.weights).mean(dim=dim)
 
-        return self._reduce((demeaned ** 2), self.weights, dim=dim, skipna=skipna)
+        return self._reduce((demeaned**2), self.weights, dim=dim, skipna=skipna)
 
     def _weighted_sum(
         self,

xarray/tests/test_backends.py

@@ -1444,7 +1444,7 @@ def test_encoding_chunksizes_unlimited(self):
             "complevel": 0,
             "fletcher32": False,
             "contiguous": False,
-            "chunksizes": (2 ** 20,),
+            "chunksizes": (2**20,),
             "original_shape": (3,),
         }
         with self.roundtrip(ds) as actual:

xarray/tests/test_computation.py

@@ -475,13 +475,10 @@ def test_unified_dim_sizes() -> None:
         "x": 1,
         "y": 2,
     }
-    assert (
-        unified_dim_sizes(
-            [xr.Variable(("x", "z"), [[1]]), xr.Variable(("y", "z"), [[1, 2], [3, 4]])],
-            exclude_dims={"z"},
-        )
-        == {"x": 1, "y": 2}
-    )
+    assert unified_dim_sizes(
+        [xr.Variable(("x", "z"), [[1]]), xr.Variable(("y", "z"), [[1, 2], [3, 4]])],
+        exclude_dims={"z"},
+    ) == {"x": 1, "y": 2}
 
     # duplicate dimensions
     with pytest.raises(ValueError):
@@ -1947,7 +1944,7 @@ def test_polyval(use_dask, use_datetime) -> None:
         xcoord = xr.DataArray(x, dims=("x",), name="x")
 
     da = xr.DataArray(
-        np.stack((1.0 + x + 2.0 * x ** 2, 1.0 + 2.0 * x + 3.0 * x ** 2)),
+        np.stack((1.0 + x + 2.0 * x**2, 1.0 + 2.0 * x + 3.0 * x**2)),
         dims=("d", "x"),
         coords={"x": xcoord, "d": [0, 1]},
     )

xarray/tests/test_dataarray.py

@@ -3727,7 +3727,7 @@ def test_polyfit(self, use_dask, use_datetime):
 
         da_raw = DataArray(
             np.stack(
-                (10 + 1e-15 * x + 2e-28 * x ** 2, 30 + 2e-14 * x + 1e-29 * x ** 2)
+                (10 + 1e-15 * x + 2e-28 * x**2, 30 + 2e-14 * x + 1e-29 * x**2)
             ),
             dims=("d", "x"),
             coords={"x": xcoord, "d": [0, 1]},

xarray/tests/test_dataset.py

@@ -3644,7 +3644,7 @@ def test_assign(self):
         assert list(actual.variables) == ["x", "y"]
         assert_identical(ds, Dataset())
 
-        actual = actual.assign(y=lambda ds: ds.x ** 2)
+        actual = actual.assign(y=lambda ds: ds.x**2)
         expected = Dataset({"y": ("x", [0, 1, 4]), "x": [0, 1, 2]})
         assert_identical(actual, expected)
 

xarray/tests/test_interp.py

@@ -30,7 +30,7 @@ def get_example_data(case):
     data = xr.DataArray(
         np.sin(x[:, np.newaxis]) * np.cos(y),
         dims=["x", "y"],
-        coords={"x": x, "y": y, "x2": ("x", x ** 2)},
+        coords={"x": x, "y": y, "x2": ("x", x**2)},
     )
 
     if case == 0:
@@ -46,7 +46,7 @@ def get_example_data(case):
         return xr.DataArray(
             np.sin(x[:, np.newaxis, np.newaxis]) * np.cos(y[:, np.newaxis]) * z,
             dims=["x", "y", "z"],
-            coords={"x": x, "y": y, "x2": ("x", x ** 2), "z": z},
+            coords={"x": x, "y": y, "x2": ("x", x**2), "z": z},
         )
     elif case == 4:
         return get_example_data(3).chunk({"z": 5})
@@ -440,7 +440,7 @@ def test_sorted():
     da = xr.DataArray(
         np.cos(x[:, np.newaxis, np.newaxis]) * np.cos(y[:, np.newaxis]) * z,
         dims=["x", "y", "z"],
-        coords={"x": x, "y": y, "x2": ("x", x ** 2), "z": z},
+        coords={"x": x, "y": y, "x2": ("x", x**2), "z": z},
     )
 
     x_new = np.linspace(0, 1, 30)

xarray/tests/test_plot.py

@@ -550,7 +550,7 @@ def test_geo_data(self):
                 [-137.85, -120.99, -103.28, -85.28, -67.62],
             ]
         )
-        data = np.sqrt(lon ** 2 + lat ** 2)
+        data = np.sqrt(lon**2 + lat**2)
         da = DataArray(
             data,
             dims=("y", "x"),
@@ -2886,8 +2886,8 @@ def test_plot_transposes_properly(plotfunc):
 def test_facetgrid_single_contour():
     # regression test for GH3569
     x, y = np.meshgrid(np.arange(12), np.arange(12))
-    z = xr.DataArray(np.sqrt(x ** 2 + y ** 2))
-    z2 = xr.DataArray(np.sqrt(x ** 2 + y ** 2) + 1)
+    z = xr.DataArray(np.sqrt(x**2 + y**2))
+    z2 = xr.DataArray(np.sqrt(x**2 + y**2) + 1)
     ds = xr.concat([z, z2], dim="time")
     ds["time"] = [0, 1]
 

xarray/tests/test_units.py

@@ -5459,7 +5459,7 @@ def test_grouped_operations(self, func, variant, dtype):
     def test_content_manipulation(self, func, variant, dtype):
         variants = {
             "data": (
-                (unit_registry.m ** 3, unit_registry.Pa, unit_registry.degK),
+                (unit_registry.m**3, unit_registry.Pa, unit_registry.degK),
                 1,
                 1,
             ),

xarray/tests/test_variable.py

@@ -343,8 +343,8 @@ def test_1d_math(self):
         assert_identical(base_v, 0 + v)
         assert_identical(base_v, v * 1)
         # binary ops with numpy arrays
-        assert_array_equal((v * x).values, x ** 2)
-        assert_array_equal((x * v).values, x ** 2)
+        assert_array_equal((v * x).values, x**2)
+        assert_array_equal((x * v).values, x**2)
         assert_array_equal(v - y, v - 1)
         assert_array_equal(y - v, 1 - v)
         # verify attributes are dropped
@@ -358,7 +358,7 @@ def test_1d_math(self):
         assert_array_equal((v * w).values, x * y)
 
         # something complicated
-        assert_array_equal((v ** 2 * w - 1 + x).values, x ** 2 * y - 1 + x)
+        assert_array_equal((v**2 * w - 1 + x).values, x**2 * y - 1 + x)
         # make sure dtype is preserved (for Index objects)
         assert float == (+v).dtype
         assert float == (+v).values.dtype
@@ -1019,7 +1019,7 @@ def test_datetime64_conversion_scalar(self):
             assert v.values.dtype == np.dtype("datetime64[ns]")
 
     def test_timedelta64_conversion_scalar(self):
-        expected = np.timedelta64(24 * 60 * 60 * 10 ** 9, "ns")
+        expected = np.timedelta64(24 * 60 * 60 * 10**9, "ns")
         for values in [
             np.timedelta64(1, "D"),
             pd.Timedelta("1 day"),
@@ -1048,7 +1048,7 @@ def test_0d_timedelta(self):
         for td in [pd.to_timedelta("1s"), np.timedelta64(1, "s")]:
             v = Variable([], td)
             assert v.dtype == np.dtype("timedelta64[ns]")
-            assert v.values == np.timedelta64(10 ** 9, "ns")
+            assert v.values == np.timedelta64(10**9, "ns")
 
     def test_equals_and_identical(self):
         d = np.random.rand(10, 3)

xarray/tests/test_weighted.py

@@ -393,7 +393,7 @@ def expected_weighted(da, weights, dim, skipna, operation):
         return weighted_mean
 
     demeaned = da - weighted_mean
-    sum_of_squares = ((demeaned ** 2) * weights).sum(dim=dim, skipna=skipna)
+    sum_of_squares = ((demeaned**2) * weights).sum(dim=dim, skipna=skipna)
 
     if operation == "sum_of_squares":
         return sum_of_squares

xarray/tutorial.py

@@ -246,7 +246,7 @@ def scatter_example_dataset(*, seed=None) -> Dataset:
             "w": 0.1 * rng.standard_normal(4),
         },
     )
-    B = 0.1 * A.x ** 2 + A.y ** 2.5 + 0.1 * A.z * A.w
+    B = 0.1 * A.x**2 + A.y**2.5 + 0.1 * A.z * A.w
     A = -0.1 * A.x + A.y / (5 + A.z) + A.w
     ds = Dataset({"A": A, "B": B})
     ds["w"] = ["one", "two", "three", "five"]