[pre-commit.ci] pre-commit autoupdate (#4587)

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

updates:
- [github.com/psf/black: 21.12b0 → 22.1.0](psf/black@21.12b0...22.1.0)
- [github.com/asottile/blacken-docs: v1.12.0 → v1.12.1](adamchainz/blacken-docs@v1.12.0...v1.12.1)
- [github.com/aio-libs/sort-all: v1.1.0 → v1.2.0](aio-libs/sort-all@v1.1.0...v1.2.0)

* [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

@@ -29,7 +29,7 @@ repos:
     -   id: no-commit-to-branch
 
 -   repo: https://github.com/psf/black
-    rev: 21.12b0
+    rev: 22.1.0
     hooks:
     -   id: black
         pass_filenames: false
@@ -50,14 +50,14 @@ repos:
         args: [--filter-files]
 
 -   repo: https://github.com/asottile/blacken-docs
-    rev: v1.12.0
+    rev: v1.12.1
     hooks:
     -   id: blacken-docs
         types: [file, rst]
         additional_dependencies: [black==21.6b0]
 
 -   repo: https://github.com/aio-libs/sort-all
-    rev: v1.1.0
+    rev: v1.2.0
     hooks:
     -   id: sort-all
         types: [file, python]

benchmarks/benchmarks/generate_data/um_files.py

@@ -107,9 +107,9 @@ def add_field(level_: int, time_step_: int) -> None:
         six_rec = 20 / 3
         three_rec = six_rec / 2
 
-        new_field.blev = level_1 ** 2 * six_rec - six_rec
+        new_field.blev = level_1**2 * six_rec - six_rec
         new_field.brsvd1 = (
-            level_1 ** 2 * six_rec + (six_rec * level_1) - three_rec
+            level_1**2 * six_rec + (six_rec * level_1) - three_rec
         )
 
         brsvd2_simulated = np.linspace(0.995, 0, len_z)

benchmarks/benchmarks/plot.py

@@ -22,7 +22,7 @@ def setup(self):
         # Should generate 10 distinct contours, regardless of dim size.
         dim_size = int(ARTIFICIAL_DIM_SIZE / 5)
         repeat_number = int(dim_size / 10)
-        repeat_range = range(int((dim_size ** 2) / repeat_number))
+        repeat_range = range(int((dim_size**2) / repeat_number))
         data = np.repeat(repeat_range, repeat_number)
         data = data.reshape((dim_size,) * 2)
 

docs/gallery_code/meteorology/plot_wind_barbs.py

@@ -30,7 +30,7 @@ def main():
 
     # To illustrate the full range of barbs, scale the wind speed up to pretend
     # that a storm is passing over
-    magnitude = (uwind ** 2 + vwind ** 2) ** 0.5
+    magnitude = (uwind**2 + vwind**2) ** 0.5
     magnitude.convert_units("knot")
     max_speed = magnitude.collapsed(
         ("latitude", "longitude"), iris.analysis.MAX
@@ -41,7 +41,7 @@ def main():
     vwind = vwind / max_speed * max_desired
 
     # Create a cube containing the wind speed
-    windspeed = (uwind ** 2 + vwind ** 2) ** 0.5
+    windspeed = (uwind**2 + vwind**2) ** 0.5
     windspeed.rename("windspeed")
     windspeed.convert_units("knot")
 

docs/gallery_code/meteorology/plot_wind_speed.py

@@ -27,7 +27,7 @@ def main():
     vwind = iris.load_cube(infile, "y_wind")
 
     # Create a cube containing the wind speed.
-    windspeed = (uwind ** 2 + vwind ** 2) ** 0.5
+    windspeed = (uwind**2 + vwind**2) ** 0.5
     windspeed.rename("windspeed")
 
     # Plot the wind speed as a contour plot.

lib/iris/analysis/__init__.py

@@ -1394,7 +1394,7 @@ def _lazy_rms(array, axis, **kwargs):
     # all. Thus trying to use this aggregator with weights will currently
     # raise an error in dask due to the unexpected keyword `weights`,
     # rather than silently returning the wrong answer.
-    return da.sqrt(da.mean(array ** 2, axis=axis, **kwargs))
+    return da.sqrt(da.mean(array**2, axis=axis, **kwargs))
 
 
 @_build_dask_mdtol_function

lib/iris/analysis/_grid_angles.py

@@ -120,7 +120,7 @@ def _angle(p, q, r):
     mid_lons = np.deg2rad(q[0])
 
     pr = _3d_xyz_from_latlon(r[0], r[1]) - _3d_xyz_from_latlon(p[0], p[1])
-    pr_norm = np.sqrt(np.sum(pr ** 2, axis=0))
+    pr_norm = np.sqrt(np.sum(pr**2, axis=0))
     pr_top = pr[1] * np.cos(mid_lons) - pr[0] * np.sin(mid_lons)
 
     index = pr_norm == 0

lib/iris/analysis/_scipy_interpolate.py

@@ -229,7 +229,7 @@ def compute_interp_weights(self, xi, method=None):
             xi_shape, method, indices, norm_distances, out_of_bounds = prepared
 
             # Allocate arrays for describing the sparse matrix.
-            n_src_values_per_result_value = 2 ** ndim
+            n_src_values_per_result_value = 2**ndim
             n_result_values = len(indices[0])
             n_non_zero = n_result_values * n_src_values_per_result_value
             weights = np.ones(n_non_zero, dtype=norm_distances[0].dtype)

lib/iris/analysis/calculus.py

@@ -629,14 +629,10 @@ def curl(i_cube, j_cube, k_cube=None):
         #    (d/dtheta (i_cube * sin(lat)) - d_j_cube_dphi)
         # phi_cmpt = 1/r * ( d/dr (r * j_cube) - d_k_cube_dtheta)
         # theta_cmpt = 1/r * ( 1/cos(lat) * d_k_cube_dphi - d/dr (r * i_cube)
-        if (
-            y_coord.name()
-            not in [
-                "latitude",
-                "grid_latitude",
-            ]
-            or x_coord.name() not in ["longitude", "grid_longitude"]
-        ):
+        if y_coord.name() not in [
+            "latitude",
+            "grid_latitude",
+        ] or x_coord.name() not in ["longitude", "grid_longitude"]:
             raise ValueError(
                 "Expecting latitude as the y coord and "
                 "longitude as the x coord for spherical curl."

lib/iris/analysis/cartography.py

@@ -335,7 +335,7 @@ def _quadrant_area(radian_lat_bounds, radian_lon_bounds, radius_of_earth):
         raise ValueError("Bounds must be [n,2] array")
 
     # fill in a new array of areas
-    radius_sqr = radius_of_earth ** 2
+    radius_sqr = radius_of_earth**2
     radian_lat_64 = radian_lat_bounds.astype(np.float64)
     radian_lon_64 = radian_lon_bounds.astype(np.float64)
 
@@ -1010,8 +1010,8 @@ def _transform_distance_vectors_tolerance_mask(
     # Squared magnitudes should be equal to one within acceptable tolerance.
     # A value of atol=2e-3 is used, which corresponds to a change in magnitude
     # of approximately 0.1%.
-    sqmag_1_0 = u_one_t ** 2 + v_zero_t ** 2
-    sqmag_0_1 = u_zero_t ** 2 + v_one_t ** 2
+    sqmag_1_0 = u_one_t**2 + v_zero_t**2
+    sqmag_0_1 = u_zero_t**2 + v_one_t**2
     mask = np.logical_not(
         np.logical_and(
             np.isclose(sqmag_1_0, ones, atol=2e-3),

lib/iris/analysis/maths.py

@@ -540,7 +540,7 @@ def power(data, out=None):
     return _math_op_common(
         cube,
         power,
-        cube.units ** exponent,
+        cube.units**exponent,
         new_dtype=new_dtype,
         in_place=in_place,
     )

lib/iris/analysis/stats.py

@@ -168,10 +168,10 @@ def _ones_like(cube):
     covar = (s1 * s2).collapsed(
         corr_coords, iris.analysis.SUM, weights=weights_1, mdtol=mdtol
     )
-    var_1 = (s1 ** 2).collapsed(
+    var_1 = (s1**2).collapsed(
         corr_coords, iris.analysis.SUM, weights=weights_1
     )
-    var_2 = (s2 ** 2).collapsed(
+    var_2 = (s2**2).collapsed(
         corr_coords, iris.analysis.SUM, weights=weights_2
     )
 

lib/iris/fileformats/netcdf.py

@@ -2738,9 +2738,9 @@ def _create_cf_data_variable(
                     cmin, cmax = _co_realise_lazy_arrays([cmin, cmax])
                 n = dtype.itemsize * 8
                 if masked:
-                    scale_factor = (cmax - cmin) / (2 ** n - 2)
+                    scale_factor = (cmax - cmin) / (2**n - 2)
                 else:
-                    scale_factor = (cmax - cmin) / (2 ** n - 1)
+                    scale_factor = (cmax - cmin) / (2**n - 1)
                 if dtype.kind == "u":
                     add_offset = cmin
                 elif dtype.kind == "i":

lib/iris/fileformats/pp.py

@@ -403,7 +403,7 @@ def _calculate_str_value_from_value(self):
 
     def _calculate_value_from_str_value(self):
         self._value = np.sum(
-            [10 ** i * val for i, val in enumerate(self._strvalue)]
+            [10**i * val for i, val in enumerate(self._strvalue)]
         )
 
     def __len__(self):
@@ -418,7 +418,7 @@ def __getitem__(self, key):
         # if the key returns a list of values, then combine them together
         # to an integer
         if isinstance(val, list):
-            val = sum([10 ** i * val for i, val in enumerate(val)])
+            val = sum([10**i * val for i, val in enumerate(val)])
 
         return val
 

lib/iris/tests/integration/test_netcdf.py

@@ -416,7 +416,7 @@ def setUp(self):
         levels.units = "centimeters"
         levels.positive = "down"
         levels.axis = "Z"
-        levels[:] = np.linspace(0, 10 ** 5, 3)
+        levels[:] = np.linspace(0, 10**5, 3)
 
         volcello.id = "volcello"
         volcello.out_name = "volcello"
@@ -507,9 +507,9 @@ def _get_scale_factor_add_offset(cube, datatype):
     else:
         masked = False
     if masked:
-        scale_factor = (cmax - cmin) / (2 ** n - 2)
+        scale_factor = (cmax - cmin) / (2**n - 2)
     else:
-        scale_factor = (cmax - cmin) / (2 ** n - 1)
+        scale_factor = (cmax - cmin) / (2**n - 1)
     if dt.kind == "u":
         add_offset = cmin
     elif dt.kind == "i":

lib/iris/tests/test_basic_maths.py

@@ -249,7 +249,7 @@ def test_apply_ufunc(self):
             np.square,
             a,
             new_name="squared temperature",
-            new_unit=a.units ** 2,
+            new_unit=a.units**2,
             in_place=False,
         )
         self.assertCMLApproxData(a, ("analysis", "apply_ufunc_original.cml"))
@@ -259,14 +259,14 @@ def test_apply_ufunc(self):
             np.square,
             a,
             new_name="squared temperature",
-            new_unit=a.units ** 2,
+            new_unit=a.units**2,
             in_place=True,
         )
         self.assertCMLApproxData(b, ("analysis", "apply_ufunc.cml"))
         self.assertCMLApproxData(a, ("analysis", "apply_ufunc.cml"))
 
         def vec_mag(u, v):
-            return math.sqrt(u ** 2 + v ** 2)
+            return math.sqrt(u**2 + v**2)
 
         c = a.copy() + 2
 
@@ -295,7 +295,7 @@ def test_apply_ufunc_fail(self):
     def test_ifunc(self):
         a = self.cube
 
-        my_ifunc = iris.analysis.maths.IFunc(np.square, lambda a: a.units ** 2)
+        my_ifunc = iris.analysis.maths.IFunc(np.square, lambda a: a.units**2)
         b = my_ifunc(a, new_name="squared temperature", in_place=False)
 
         self.assertCMLApproxData(a, ("analysis", "apply_ifunc_original.cml"))
@@ -307,7 +307,7 @@ def test_ifunc(self):
         self.assertCMLApproxData(a, ("analysis", "apply_ifunc.cml"))
 
         def vec_mag(u, v):
-            return math.sqrt(u ** 2 + v ** 2)
+            return math.sqrt(u**2 + v**2)
 
         c = a.copy() + 2
 
@@ -347,7 +347,7 @@ def test_ifunc_init_fail(self):
     def test_ifunc_call_fail(self):
         a = self.cube
 
-        my_ifunc = iris.analysis.maths.IFunc(np.square, lambda a: a.units ** 2)
+        my_ifunc = iris.analysis.maths.IFunc(np.square, lambda a: a.units**2)
 
         # should now NOT fail because giving 2 arguments to an ifunc that
         # expects only one will now ignore the surplus argument and raise
@@ -367,7 +367,7 @@ def test_ifunc_call_fail(self):
             my_ifunc(a)
 
         my_ifunc = iris.analysis.maths.IFunc(
-            lambda a: (a, a ** 2.0), lambda cube: cf_units.Unit("1")
+            lambda a: (a, a**2.0), lambda cube: cf_units.Unit("1")
         )
 
         # should fail because data function returns a tuple
@@ -553,9 +553,9 @@ def test_square_root(self):
         a.data = abs(a.data)
         a.units **= 2
 
-        e = a ** 0.5
+        e = a**0.5
 
-        self.assertArrayAllClose(e.data, a.data ** 0.5)
+        self.assertArrayAllClose(e.data, a.data**0.5)
         self.assertCML(e, ("analysis", "sqrt.cml"), checksum=False)
         self.assertRaises(ValueError, iris.analysis.maths.exponentiate, a, 0.3)
 
@@ -585,26 +585,26 @@ def test_apply_ufunc(self):
             np.square,
             a,
             new_name="more_thingness",
-            new_unit=a.units ** 2,
+            new_unit=a.units**2,
             in_place=False,
         )
 
-        ans = a.data ** 2
+        ans = a.data**2
 
         self.assertArrayEqual(b.data, ans)
         self.assertEqual(b.name(), "more_thingness")
         self.assertEqual(b.units, cf_units.Unit("m^2"))
 
         def vec_mag(u, v):
-            return math.sqrt(u ** 2 + v ** 2)
+            return math.sqrt(u**2 + v**2)
 
         c = a.copy() + 2
 
         vec_mag_ufunc = np.frompyfunc(vec_mag, 2, 1)
         b = iris.analysis.maths.apply_ufunc(vec_mag_ufunc, a, c)
 
-        ans = a.data ** 2 + c.data ** 2
-        b2 = b ** 2
+        ans = a.data**2 + c.data**2
+        b2 = b**2
 
         self.assertArrayAlmostEqual(b2.data, ans)
 
@@ -617,17 +617,17 @@ def test_ifunc(self):
         a = self.cube
         a.units = cf_units.Unit("meters")
 
-        my_ifunc = iris.analysis.maths.IFunc(np.square, lambda x: x.units ** 2)
+        my_ifunc = iris.analysis.maths.IFunc(np.square, lambda x: x.units**2)
         b = my_ifunc(a, new_name="more_thingness", in_place=False)
 
-        ans = a.data ** 2
+        ans = a.data**2
 
         self.assertArrayEqual(b.data, ans)
         self.assertEqual(b.name(), "more_thingness")
         self.assertEqual(b.units, cf_units.Unit("m^2"))
 
         def vec_mag(u, v):
-            return math.sqrt(u ** 2 + v ** 2)
+            return math.sqrt(u**2 + v**2)
 
         c = a.copy() + 2
 
@@ -637,12 +637,12 @@ def vec_mag(u, v):
         )
         b = my_ifunc(a, c)
 
-        ans = (a.data ** 2 + c.data ** 2) ** 0.5
+        ans = (a.data**2 + c.data**2) ** 0.5
 
         self.assertArrayAlmostEqual(b.data, ans)
 
         def vec_mag_data_func(u_data, v_data):
-            return np.sqrt(u_data ** 2 + v_data ** 2)
+            return np.sqrt(u_data**2 + v_data**2)
 
         vec_mag_ifunc = iris.analysis.maths.IFunc(
             vec_mag_data_func, lambda a, b: (a + b).units

lib/iris/tests/unit/analysis/cartography/test_rotate_winds.py

@@ -343,8 +343,8 @@ def test_orig_coords(self):
     def test_magnitude_preservation(self):
         u, v = self._uv_cubes_limited_extent()
         ut, vt = rotate_winds(u, v, iris.coord_systems.OSGB())
-        orig_sq_mag = u.data ** 2 + v.data ** 2
-        res_sq_mag = ut.data ** 2 + vt.data ** 2
+        orig_sq_mag = u.data**2 + v.data**2
+        res_sq_mag = ut.data**2 + vt.data**2
         self.assertArrayAllClose(orig_sq_mag, res_sq_mag, rtol=5e-4)
 
     def test_data_values(self):
@@ -437,9 +437,9 @@ def test_rotated_to_osgb(self):
         self.assertArrayEqual(expected_mask, vt.data.mask)
 
         # Check unmasked values have sufficiently small error in mag.
-        expected_mag = np.sqrt(u.data ** 2 + v.data ** 2)
+        expected_mag = np.sqrt(u.data**2 + v.data**2)
         # Use underlying data to ignore mask in calculation.
-        res_mag = np.sqrt(ut.data.data ** 2 + vt.data.data ** 2)
+        res_mag = np.sqrt(ut.data.data**2 + vt.data.data**2)
         # Calculate percentage error (note there are no zero magnitudes
         # so we can divide safely).
         anom = 100.0 * np.abs(res_mag - expected_mag) / expected_mag

lib/iris/tests/unit/analysis/regrid/test_RectilinearRegridder.py

@@ -33,7 +33,7 @@ def setUp(self):
         self.xs, self.ys = np.meshgrid(self.x.points, self.y.points)
 
         def transformation(x, y):
-            return x + y ** 2
+            return x + y**2
 
         # Construct a function which adds dimensions to the 2D data array
         # so that we can test higher dimensional functionality.

lib/iris/tests/unit/coord_categorisation/test_add_categorised_coord.py

@@ -36,7 +36,7 @@ def test_vectorise_call(self):
         # The reason we use numpy.vectorize is to support multi-dimensional
         # coordinate points.
         def fn(coord, v):
-            return v ** 2
+            return v**2
 
         with mock.patch(
             "numpy.vectorize", return_value=self.vectorised