linting

Author: pdevis

obsarray/flag_accessor.py

@@ -194,12 +194,12 @@ def __setitem__(self, flag_meaning: str, flag_value: Union[bool, np.ndarray]):
         )
 
         if flag_meaning not in flag_meanings:
-            self._obj[
-                self._flag_var_name
-            ].attrs = DatasetUtil.add_flag_meaning_to_attrs(
-                self._obj[self._flag_var_name].attrs,
-                flag_meaning,
-                self._obj[self._flag_var_name].dtype,
+            self._obj[self._flag_var_name].attrs = (
+                DatasetUtil.add_flag_meaning_to_attrs(
+                    self._obj[self._flag_var_name].attrs,
+                    flag_meaning,
+                    self._obj[self._flag_var_name].dtype,
+                )
             )
 
         self[flag_meaning][:] = flag_value

obsarray/templater/dataset_util.py

@@ -259,8 +259,8 @@ def create_flags_variable(
             attributes=attributes,
         )
 
-        #initialise flags to zero (instead of fillvalue)
-        variable.values=0*variable.values
+        # initialise flags to zero (instead of fillvalue)
+        variable.values = 0 * variable.values
 
         # add flag attributes
         variable.attrs.update(DatasetUtil.pack_flag_attrs(meanings))
@@ -290,7 +290,7 @@ def pack_flag_attrs(
         )
 
         if flag_masks is None:
-            flag_masks = [2 ** i for i in range(0, n_masks)]
+            flag_masks = [2**i for i in range(0, n_masks)]
 
         flag_attrs["flag_masks"] = str(flag_masks)[1:-1]
 
@@ -333,7 +333,7 @@ def add_flag_meaning_to_attrs(
 
         # check if variable for available flags
         max_n_flags = numpy.iinfo(dtype).bits
-        all_flag_masks = [2 ** i for i in range(0, max_n_flags)]
+        all_flag_masks = [2**i for i in range(0, max_n_flags)]
         available_flag_masks = list(set(all_flag_masks) - set(flag_masks))
 
         if not available_flag_masks:
@@ -435,7 +435,9 @@ def add_encoding(
         if fill_value is not None:
             encoding_dict.update({"_FillValue": fill_value})
         else:
-            encoding_dict.update({"_FillValue": DatasetUtil.get_default_fill_value(dtype)})
+            encoding_dict.update(
+                {"_FillValue": DatasetUtil.get_default_fill_value(dtype)}
+            )
 
         variable.encoding = encoding_dict
 

obsarray/test/test_err_corr_forms.py

@@ -85,14 +85,18 @@ def test_get_sliced_dim_sizes_uncvar(self):
         basicerrcorr = self.BasicErrCorrForm(
             self.ds, "u_ran_temperature", ["x"], [], []
         )
-        dim_sizes=basicerrcorr.get_sliced_dim_sizes_uncvar((slice(None),0,slice(0,2,1)))
+        dim_sizes = basicerrcorr.get_sliced_dim_sizes_uncvar(
+            (slice(None), 0, slice(0, 2, 1))
+        )
         assert dim_sizes == {"x": 2, "time": 2}
 
     def test_get_sliced_dim_sizes_errcorr(self):
         basicerrcorr = self.BasicErrCorrForm(
             self.ds, "u_ran_temperature", ["x"], [], []
         )
-        dim_sizes = basicerrcorr.get_sliced_dim_sizes_errcorr((slice(None), 0, slice(0, 2, 1)))
+        dim_sizes = basicerrcorr.get_sliced_dim_sizes_errcorr(
+            (slice(None), 0, slice(0, 2, 1))
+        )
         assert dim_sizes == {"x": 2}
 
     def test_get_sliced_dims_errcorr(self):
@@ -112,7 +116,7 @@ def test_get_sliced_shape_errcorr(self):
             self.ds, "u_ran_temperature", ["x", "time"], [], []
         )
         shape = basicerrcorr.get_sliced_shape_errcorr((slice(None), 0, slice(0, 2, 1)))
-        assert shape == (2,2)
+        assert shape == (2, 2)
 
     def test_slice_flattened_matrix(self):
         basicerrcorr = self.BasicErrCorrForm(
@@ -121,7 +125,7 @@ def test_slice_flattened_matrix(self):
 
         full_matrix = np.arange(144).reshape((12, 12))
         slice_matrix = basicerrcorr.slice_flattened_matrix(
-            full_matrix, (2,2,3), (slice(None), slice(None), 0)
+            full_matrix, (2, 2, 3), (slice(None), slice(None), 0)
         )
 
         exp_slice_matrix = np.array(
@@ -146,6 +150,7 @@ def test_slice_full_cov_slice(self):
 
         np.testing.assert_equal(slice_matrix, exp_slice_matrix)
 
+
 class TestRandomUnc(unittest.TestCase):
     def setUp(self) -> None:
         self.ds = create_ds()
@@ -197,8 +202,9 @@ def test_build_dot_matrix(self):
         x = self.build_matrix_1stdim()
         y = self.build_matrix_2nddim()
         time = self.build_matrix_3ddim()
-        print(x.dot(y),x,y)
+        print(x.dot(y), x, y)
         np.testing.assert_equal((x.dot(y)).dot(time), np.ones((12, 12)))
 
+
 if __name__ == "main":
     unittest.main()

obsarray/test/test_flag_accessor.py

@@ -92,7 +92,9 @@ def test___iter__(self):
             self.assertIsInstance(flag, obsarray.flag_accessor.FlagVariable)
             var_names.append(flag._flag_var_name)
 
-        self.assertCountEqual(var_names, ["temperature_flags", "general_flags", "time_flags"])
+        self.assertCountEqual(
+            var_names, ["temperature_flags", "general_flags", "time_flags"]
+        )
 
     def test_keys(self):
         self.assertCountEqual(

obsarray/test/test_unc_accessor.py

@@ -479,15 +479,17 @@ def test_systematic_unc(self, mock):
         self.ds.unc["temperature"][:, :, 0].systematic_unc()
         mock.assert_called_once_with(["u_sys_temperature"])
 
-    def test_total_err_corr_matrix(self, ):
+    def test_total_err_corr_matrix(
+        self,
+    ):
         tercm = self.ds.unc["temperature"].total_err_corr_matrix()
-        assert tercm.shape==(12,12)
-        tercm = self.ds.unc["temperature"][:,:,0].total_err_corr_matrix()
-        assert tercm.shape==(4,4)
-        tercm = self.ds.unc["temperature"][:,0,0:2].total_err_corr_matrix()
-        assert tercm.shape==(4,4)
-        tercm = self.ds.unc["temperature"][0,0,:].total_err_corr_matrix()
-        assert tercm.shape==(3,3)
+        assert tercm.shape == (12, 12)
+        tercm = self.ds.unc["temperature"][:, :, 0].total_err_corr_matrix()
+        assert tercm.shape == (4, 4)
+        tercm = self.ds.unc["temperature"][:, 0, 0:2].total_err_corr_matrix()
+        assert tercm.shape == (4, 4)
+        tercm = self.ds.unc["temperature"][0, 0, :].total_err_corr_matrix()
+        assert tercm.shape == (3, 3)
 
     def test_structured_err_corr_matrix(self):
         pass

obsarray/unc_accessor.py

@@ -336,8 +336,8 @@ def err_corr_matrix(self) -> xr.DataArray:
         # populate with error-correlation matrices built be each error-correlation
         # parameterisation object
         for dim_err_corr in self.err_corr:
-            sliced_dims=dim_err_corr[1].get_sliced_dims_errcorr(self._sli)
-            if len(dim_err_corr[1].get_sliced_dims_errcorr(self._sli))>0:
+            sliced_dims = dim_err_corr[1].get_sliced_dims_errcorr(self._sli)
+            if len(dim_err_corr[1].get_sliced_dims_errcorr(self._sli)) > 0:
                 err_corr_matrix.values = err_corr_matrix.values.dot(
                     dim_err_corr[1].build_dot_matrix(self._sli)
                 )
@@ -518,7 +518,7 @@ def _quadsum_unc(self, unc_var_names):
                 quadsum_unc += self[unc_var_name].abs_value ** 2.0
 
         if quadsum_unc is not None:
-            quadsum_unc = quadsum_unc ** 0.5
+            quadsum_unc = quadsum_unc**0.5
 
         return quadsum_unc
 

setup.py

@@ -9,7 +9,7 @@
 
 def read(filename):
     filename = os.path.join(os.path.dirname(__file__), filename)
-    text_type = type(u"")
+    text_type = type("")
     with io.open(filename, mode="r", encoding="utf-8") as fd:
         return re.sub(text_type(r":[a-z]+:`~?(.*?)`"), text_type(r"``\1``"), fd.read())