diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 708f68ad..e08d8ffe 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -19,8 +19,13 @@ repos:
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.15.4
   hooks:
-  - id: ruff
-    args: [--fix, --show-fixes]
+  - id: ruff-check
+    exclude: '(dev/.*|.*_)\.py$'
+    args:
+    - --line-length=120
+    - --fix
+    - --exit-non-zero-on-fix
+    - --preview
   - id: ruff-format
 - repo: https://github.com/executablebooks/mdformat
   rev: 1.0.0
diff --git a/docs/notebooks/spatial/02-era5-reduce-data-over-geometries.ipynb b/docs/notebooks/spatial/02-era5-reduce-data-over-geometries.ipynb
index 7c380fcd..651095f5 100644
--- a/docs/notebooks/spatial/02-era5-reduce-data-over-geometries.ipynb
+++ b/docs/notebooks/spatial/02-era5-reduce-data-over-geometries.ipynb
@@ -125,9 +125,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "reduced_data = ekt.spatial.reduce(\n",
-    "    era5_xr, nuts_data, mask_dim=\"FID\", extra_reduce_dims=\"valid_time\", all_touched=True\n",
-    ")\n",
+    "reduced_data = ekt.spatial.reduce(era5_xr, nuts_data, mask_dim=\"FID\", extra_reduce_dims=\"valid_time\", all_touched=True)\n",
     "reduced_data"
    ]
   },
diff --git a/docs/notebooks/temporal/02-era5-daily-monthly-statistics.ipynb b/docs/notebooks/temporal/02-era5-daily-monthly-statistics.ipynb
index 9b82ae0a..7bc60253 100644
--- a/docs/notebooks/temporal/02-era5-daily-monthly-statistics.ipynb
+++ b/docs/notebooks/temporal/02-era5-daily-monthly-statistics.ipynb
@@ -197,9 +197,7 @@
     "era5_monthly_mean.t2m.isel(**isel_kwargs).plot(label=\"Monthly mean\", ax=ax, color=\"black\")\n",
     "upper_m = era5_monthly_mean.t2m + era5_monthly_std.t2m\n",
     "lower_m = era5_monthly_mean.t2m - era5_monthly_std.t2m\n",
-    "upper_m.isel(**isel_kwargs).plot(\n",
-    "    ax=ax, label=\"Monthly standard deviation spread\", linestyle=\"--\", color=\"black\"\n",
-    ")\n",
+    "upper_m.isel(**isel_kwargs).plot(ax=ax, label=\"Monthly standard deviation spread\", linestyle=\"--\", color=\"black\")\n",
     "lower_m.isel(**isel_kwargs).plot(ax=ax, linestyle=\"--\", color=\"black\")\n",
     "\n",
     "\n",
diff --git a/docs/notebooks/temporal/03-seas5-daily-statistics.ipynb b/docs/notebooks/temporal/03-seas5-daily-statistics.ipynb
index fdb0cdb8..3f9cec4d 100644
--- a/docs/notebooks/temporal/03-seas5-daily-statistics.ipynb
+++ b/docs/notebooks/temporal/03-seas5-daily-statistics.ipynb
@@ -108,9 +108,7 @@
     "\n",
     "for itime in range(3):\n",
     "    for number in range(25):\n",
-    "        t_data = seas_daily_median_by_step.t2m.isel(\n",
-    "            **isel_kwargs, number=number, forecast_reference_time=itime\n",
-    "        )\n",
+    "        t_data = seas_daily_median_by_step.t2m.isel(**isel_kwargs, number=number, forecast_reference_time=itime)\n",
     "        if number == 0:\n",
     "            extra_kwargs = {\"label\": f\"FC ref time: {str(t_data.forecast_reference_time.values)[:10]}\"}\n",
     "        else:\n",
diff --git a/docs/notebooks/temporal/05-accumulation-to-rate-examples.ipynb b/docs/notebooks/temporal/05-accumulation-to-rate-examples.ipynb
index d961ce4d..24ac0c59 100644
--- a/docs/notebooks/temporal/05-accumulation-to-rate-examples.ipynb
+++ b/docs/notebooks/temporal/05-accumulation-to-rate-examples.ipynb
@@ -110,9 +110,7 @@
     "color = \"tab:blue\"\n",
     "ax1.set_xlabel(\"Time\")\n",
     "ax1.set_ylabel(f\"Accumulation ({ds_era5['tp'].units})\", color=color)\n",
-    "ax1.bar(\n",
-    "    ds_era5[\"valid_time\"], ds_era5[\"tp\"].isel(plot_point), color=color, label=\"Accumulation\", width=0.1, lw=0\n",
-    ")\n",
+    "ax1.bar(ds_era5[\"valid_time\"], ds_era5[\"tp\"].isel(plot_point), color=color, label=\"Accumulation\", width=0.1, lw=0)\n",
     "ax1.tick_params(axis=\"y\", labelcolor=color)\n",
     "\n",
     "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
@@ -319,9 +317,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_seas5_rate = ekt.temporal.accumulation_to_rate(\n",
-    "    ds_seas5, accumulation_type=\"start_of_forecast\", from_first_step=True\n",
-    ")\n",
+    "ds_seas5_rate = ekt.temporal.accumulation_to_rate(ds_seas5, accumulation_type=\"start_of_forecast\", from_first_step=True)\n",
     "ds_seas5_rate"
    ]
   },
@@ -409,9 +405,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds_seas5_deaccumulate = ekt.temporal.deaccumulate(\n",
-    "    ds_seas5, accumulation_type=\"start_of_forecast\", from_first_step=True\n",
-    ")\n",
+    "ds_seas5_deaccumulate = ekt.temporal.deaccumulate(ds_seas5, accumulation_type=\"start_of_forecast\", from_first_step=True)\n",
     "ds_seas5_deaccumulate"
    ]
   },
diff --git a/pyproject.toml b/pyproject.toml
index 67446cc6..3229f4fb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -62,35 +62,29 @@ exclude = '^tests/legacy-api/'
 ignore_missing_imports = true
 
 [tool.ruff]
-# Black line length is 88, but black does not format comments.
-line-length = 110
-select = [
-  # pyflakes
-  "F",
-  # pycodestyle
-  "E",
-  "W",
-  # isort
-  "I",
-  # pydocstyle
-  "D"
-]
+line-length = 120
 
 [tool.ruff.lint]
 ignore = [
-  # pydocstyle: Missing Docstrings
-  "D1"
+  "D1",  # pydocstyle: Missing Docstrings
+  "D107",  # pydocstyle: numpy convention
+  "D203",
+  "D205",
+  "D212",
+  "D213",
+  "D401",
+  "D402",
+  "D413",
+  "D415",
+  "D416",
+  "D417"
 ]
 select = [
-  # pyflakes
-  "F",
-  # pycodestyle
-  "E",
-  "W",
-  # isort
-  "I",
-  # pydocstyle
-  "D"
+  "F",  # pyflakes
+  "E",  # pycodestyle
+  "W",  # pycodestyle warnings
+  "I",  # isort
+  "D"  # pydocstyle
 ]
 
 [tool.setuptools.packages.find]
diff --git a/src/earthkit/transforms/_tools.py b/src/earthkit/transforms/_tools.py
index 5ae65440..8dd2f7af 100644
--- a/src/earthkit/transforms/_tools.py
+++ b/src/earthkit/transforms/_tools.py
@@ -248,9 +248,7 @@ def latitude_weights(dataarray: xr.DataArray, lat_key: str | None = None):
         xp = array_namespace_from_object(lat_array[lat_key])
         return xp.cos(xp.radians(lat_array[lat_key]))
 
-    raise KeyError(
-        "Latitude variable name not detected or found in the dataarray. Please provide the correct key."
-    )
+    raise KeyError("Latitude variable name not detected or found in the dataarray. Please provide the correct key.")
 
 
 HOW_METHODS = {
@@ -495,9 +493,7 @@ def get_dim_key(
 
     # We have not been able to detect, so raise an error
     if raise_error:
-        raise ValueError(
-            f"Unable to find dimension key for axis '{axis}' in dataarray with dims: {dataarray.dims}."
-        )
+        raise ValueError(f"Unable to find dimension key for axis '{axis}' in dataarray with dims: {dataarray.dims}.")
 
     return axis
 
@@ -570,8 +566,7 @@ def groupby_time(
             grouped_data = dataarray.groupby(f"{time_dim}.{frequency}")
         except AttributeError:
             raise ValueError(
-                f"Invalid frequency '{frequency}' - see xarray documentation for "
-                f"a full list of valid frequencies."
+                f"Invalid frequency '{frequency}' - see xarray documentation for a full list of valid frequencies."
             )
 
     return grouped_data
@@ -590,8 +585,7 @@ def groupby_bins(
         grouped_data = dataarray.groupby_bins(f"{time_dim}.{frequency}", bin_widths)
     except AttributeError:
         raise ValueError(
-            f"Invalid frequency '{frequency}' - see xarray documentation for "
-            f"a full list of valid frequencies."
+            f"Invalid frequency '{frequency}' - see xarray documentation for a full list of valid frequencies."
         )
     return grouped_data
 
@@ -638,9 +632,7 @@ def _wrapper(_kwarg_types, _convert_types, *args, **kwargs):
                     _convert_types = {key: _convert_types for key in convert_kwargs}
 
                 convert_kwargs = [
-                    k
-                    for k in convert_kwargs
-                    if isinstance(kwargs[k], _ensure_tuple(_convert_types.get(k, ())))
+                    k for k in convert_kwargs if isinstance(kwargs[k], _ensure_tuple(_convert_types.get(k, ())))
                 ]
 
             # Transform args/kwargs
diff --git a/src/earthkit/transforms/climatology/_aggregate.py b/src/earthkit/transforms/climatology/_aggregate.py
index 72357997..a897298f 100644
--- a/src/earthkit/transforms/climatology/_aggregate.py
+++ b/src/earthkit/transforms/climatology/_aggregate.py
@@ -777,9 +777,7 @@ def _anomaly_dataarray(
     anomaly_array = groupby_time(dataarray, time_dim=time_dim, **groupby_kwargs) - climatology_da
 
     if relative:
-        anomaly_array = (
-            groupby_time(anomaly_array, time_dim=time_dim, **groupby_kwargs) / climatology_da
-        ) * 100.0
+        anomaly_array = (groupby_time(anomaly_array, time_dim=time_dim, **groupby_kwargs) / climatology_da) * 100.0
         name_tag = "relative anomaly"
         update_attrs = {"units": "%"}
     else:
@@ -788,9 +786,7 @@ def _anomaly_dataarray(
 
     anomaly_array = resample(anomaly_array, how="mean", **reduce_kwargs, **groupby_kwargs, dim=time_dim)
 
-    return _update_anomaly_array(
-        anomaly_array, dataarray, var_name, name_tag, update_attrs, how_label=how_label
-    )
+    return _update_anomaly_array(anomaly_array, dataarray, var_name, name_tag, update_attrs, how_label=how_label)
 
 
 def _update_anomaly_array(anomaly_array, original_array, var_name, name_tag, update_attrs, how_label=None):
diff --git a/src/earthkit/transforms/spatial/_aggregate.py b/src/earthkit/transforms/spatial/_aggregate.py
index ba8d7f77..d418a782 100644
--- a/src/earthkit/transforms/spatial/_aggregate.py
+++ b/src/earthkit/transforms/spatial/_aggregate.py
@@ -475,9 +475,7 @@ def reduce(
         else:
             raise TypeError("Return as type not recognised or incompatible with inputs")
     else:
-        return _reduce_dataarray_as_xarray(
-            dataarray, geodataframe=geodataframe, mask_arrays=_mask_arrays, **kwargs
-        )
+        return _reduce_dataarray_as_xarray(dataarray, geodataframe=geodataframe, mask_arrays=_mask_arrays, **kwargs)
 
 
 def _reduce_dataarray_as_xarray(
@@ -717,8 +715,7 @@ def _reduce_dataarray_as_pandas(
         out_dims = {dim: dataarray[dim].data for dim in _out_dims}
         reduce_attrs[f"{out_xr.name}"].update({"dims": out_dims})
         reduced_list = [
-            out_xr.sel(**{mask_dim_name: mask_dim_value}).data
-            for mask_dim_value in out_xr[mask_dim_name].data
+            out_xr.sel(**{mask_dim_name: mask_dim_value}).data for mask_dim_value in out_xr[mask_dim_name].data
         ]
         out = out.assign(**{f"{out_xr.name}": reduced_list})
 
diff --git a/src/earthkit/transforms/temporal/_aggregate.py b/src/earthkit/transforms/temporal/_aggregate.py
index a3461f93..dabdceb3 100644
--- a/src/earthkit/transforms/temporal/_aggregate.py
+++ b/src/earthkit/transforms/temporal/_aggregate.py
@@ -53,13 +53,9 @@ def standardise_time(
     try:
         source_times = [time_value.strftime(target_format) for time_value in dataarray[time_dim].data]
     except AttributeError:
-        source_times = [
-            pd.to_datetime(time_value).strftime(target_format) for time_value in dataarray[time_dim].data
-        ]
+        source_times = [pd.to_datetime(time_value).strftime(target_format) for time_value in dataarray[time_dim].data]
 
-    standardised_times = np.array(
-        [pd.to_datetime(time_string).to_datetime64() for time_string in source_times]
-    )
+    standardised_times = np.array([pd.to_datetime(time_string).to_datetime64() for time_string in source_times])
 
     dataarray = dataarray.assign_coords({time_dim: standardised_times})
 
diff --git a/src/earthkit/transforms/temporal/_rates.py b/src/earthkit/transforms/temporal/_rates.py
index 260ddd80..197c3d4d 100644
--- a/src/earthkit/transforms/temporal/_rates.py
+++ b/src/earthkit/transforms/temporal/_rates.py
@@ -372,19 +372,14 @@ def _accumulation_to_rate_dataarray(
     if "units" in dataarray.attrs:
         output.attrs.update({"units": dataarray.attrs["units"] + rate_units_str})
     if "long_name" in dataarray.attrs:
-        output.attrs["long_name"] = " ".join(
-            filter(None, [dataarray.attrs["long_name"], rate_label.replace("_", " ")])
-        )
+        output.attrs["long_name"] = " ".join(filter(None, [dataarray.attrs["long_name"], rate_label.replace("_", " ")]))
     if provenance or "history" in dataarray.attrs:
         output.attrs["history"] = "\n".join(
             filter(
                 None,
                 [
                     dataarray.attrs.get("history", ""),
-                    (
-                        "Converted from accumulation to rate using "
-                        "earthkit.transforms.temporal.accumulation_to_rate."
-                    ),
+                    ("Converted from accumulation to rate using earthkit.transforms.temporal.accumulation_to_rate."),
                 ],
             )
         )
diff --git a/tests/legacy-api/test_legacy_10_tools.py b/tests/legacy-api/test_legacy_10_tools.py
index 7d6d7e3a..df104662 100644
--- a/tests/legacy-api/test_legacy_10_tools.py
+++ b/tests/legacy-api/test_legacy_10_tools.py
@@ -35,9 +35,7 @@ def dummy_func2(dataarray, *args, time_dim=None, **kwargs):
 # Test case for the decorator when time_shift is provided
 def test_time_dim_decorator_time_shift_provided():
     # Prepare test data
-    dataarray = xr.DataArray(
-        [1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)}
-    )
+    dataarray = xr.DataArray([1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)})
 
     # Call the decorated function with time_shift provided
     result, result_time_dim = time_dim_decorator(dummy_func)(dataarray, time_shift={"days": 1})
@@ -52,14 +50,10 @@ def test_time_dim_decorator_time_shift_provided():
 # Test case for the decorator when both time_dim and time_shift are provided
 def test_time_dim_decorator_time_dim_and_time_shift_provided():
     # Prepare test data
-    dataarray = xr.DataArray(
-        [1, 2, 3], dims=["dummy"], coords={"dummy": pd.date_range("2000-01-01", periods=3)}
-    )
+    dataarray = xr.DataArray([1, 2, 3], dims=["dummy"], coords={"dummy": pd.date_range("2000-01-01", periods=3)})
 
     # Call the decorated function with both time_dim and time_shift provided
-    result, result_time_dim = time_dim_decorator(dummy_func)(
-        dataarray, time_dim="dummy", time_shift={"days": 1}
-    )
+    result, result_time_dim = time_dim_decorator(dummy_func)(dataarray, time_dim="dummy", time_shift={"days": 1})
 
     # Check if the time dimension remains unchanged
     assert result_time_dim == "dummy"
@@ -81,9 +75,7 @@ def test_time_dim_decorator_not_found_error():
 # Test case for the decorator when time_shift is provided and remove_partial_periods=True
 def test_time_dim_decorator_time_shift_provided_trim_shifted():
     # Prepare test data
-    dataarray = xr.DataArray(
-        [1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)}
-    )
+    dataarray = xr.DataArray([1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)})
 
     # Call the decorated function with time_shift provided
     result = time_dim_decorator(dummy_func2)(dataarray, time_shift={"days": 1}, remove_partial_periods=True)
@@ -114,9 +106,7 @@ def test_groupby_kwargs_decorator_provided():
     other_kwargs = {"method": "linear", "fill_value": 0}
 
     # Call the decorated function with groupby_kwargs provided
-    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(
-        **groupby_kwargs, **other_kwargs
-    )
+    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(**groupby_kwargs, **other_kwargs)
 
     assert result_groupby_kwargs == groupby_kwargs
     assert result_kwargs == other_kwargs
@@ -129,9 +119,7 @@ def test_groupby_kwargs_decorator_partial_provided():
     other_kwargs = {"method": "linear"}
 
     # Call the decorated function with some groupby_kwargs provided as keyword arguments
-    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(
-        **groupby_kwargs, **other_kwargs
-    )
+    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(**groupby_kwargs, **other_kwargs)
 
     assert result_groupby_kwargs == groupby_kwargs
     assert result_kwargs == other_kwargs
@@ -209,9 +197,7 @@ def test_get_how_xp_np():
     "data_object",
     (
         xr.DataArray(np.random.rand(10, 10), dims=["x", "y"]),
-        xr.Dataset(
-            {"var": (["x", "y"], np.random.rand(10, 10))}, coords={"x": np.arange(10), "y": np.arange(10)}
-        ),
+        xr.Dataset({"var": (["x", "y"], np.random.rand(10, 10))}, coords={"x": np.arange(10), "y": np.arange(10)}),
         np.random.rand(10, 10),
     ),
 )
diff --git a/tests/legacy-api/test_legacy_30_temporal.py b/tests/legacy-api/test_legacy_30_temporal.py
index e18d8991..c75f8a73 100644
--- a/tests/legacy-api/test_legacy_30_temporal.py
+++ b/tests/legacy-api/test_legacy_30_temporal.py
@@ -53,10 +53,7 @@ def test_standardise_time_basic():
 def test_standardise_time_monthly():
     data = get_data().to_xarray()
     data_standardised = temporal.standardise_time(data, target_format="%Y-%m-15")
-    assert all(
-        pd.to_datetime(time_value).day == 15
-        for time_value in data_standardised.forecast_reference_time.values
-    )
+    assert all(pd.to_datetime(time_value).day == 15 for time_value in data_standardised.forecast_reference_time.values)
 
 
 @pytest.mark.parametrize(
diff --git a/tests/test_10_tools.py b/tests/test_10_tools.py
index 7d6d7e3a..df104662 100644
--- a/tests/test_10_tools.py
+++ b/tests/test_10_tools.py
@@ -35,9 +35,7 @@ def dummy_func2(dataarray, *args, time_dim=None, **kwargs):
 # Test case for the decorator when time_shift is provided
 def test_time_dim_decorator_time_shift_provided():
     # Prepare test data
-    dataarray = xr.DataArray(
-        [1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)}
-    )
+    dataarray = xr.DataArray([1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)})
 
     # Call the decorated function with time_shift provided
     result, result_time_dim = time_dim_decorator(dummy_func)(dataarray, time_shift={"days": 1})
@@ -52,14 +50,10 @@ def test_time_dim_decorator_time_shift_provided():
 # Test case for the decorator when both time_dim and time_shift are provided
 def test_time_dim_decorator_time_dim_and_time_shift_provided():
     # Prepare test data
-    dataarray = xr.DataArray(
-        [1, 2, 3], dims=["dummy"], coords={"dummy": pd.date_range("2000-01-01", periods=3)}
-    )
+    dataarray = xr.DataArray([1, 2, 3], dims=["dummy"], coords={"dummy": pd.date_range("2000-01-01", periods=3)})
 
     # Call the decorated function with both time_dim and time_shift provided
-    result, result_time_dim = time_dim_decorator(dummy_func)(
-        dataarray, time_dim="dummy", time_shift={"days": 1}
-    )
+    result, result_time_dim = time_dim_decorator(dummy_func)(dataarray, time_dim="dummy", time_shift={"days": 1})
 
     # Check if the time dimension remains unchanged
     assert result_time_dim == "dummy"
@@ -81,9 +75,7 @@ def test_time_dim_decorator_not_found_error():
 # Test case for the decorator when time_shift is provided and remove_partial_periods=True
 def test_time_dim_decorator_time_shift_provided_trim_shifted():
     # Prepare test data
-    dataarray = xr.DataArray(
-        [1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)}
-    )
+    dataarray = xr.DataArray([1, 2, 3], dims=["time"], coords={"time": pd.date_range("2000-01-01", periods=3)})
 
     # Call the decorated function with time_shift provided
     result = time_dim_decorator(dummy_func2)(dataarray, time_shift={"days": 1}, remove_partial_periods=True)
@@ -114,9 +106,7 @@ def test_groupby_kwargs_decorator_provided():
     other_kwargs = {"method": "linear", "fill_value": 0}
 
     # Call the decorated function with groupby_kwargs provided
-    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(
-        **groupby_kwargs, **other_kwargs
-    )
+    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(**groupby_kwargs, **other_kwargs)
 
     assert result_groupby_kwargs == groupby_kwargs
     assert result_kwargs == other_kwargs
@@ -129,9 +119,7 @@ def test_groupby_kwargs_decorator_partial_provided():
     other_kwargs = {"method": "linear"}
 
     # Call the decorated function with some groupby_kwargs provided as keyword arguments
-    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(
-        **groupby_kwargs, **other_kwargs
-    )
+    result_groupby_kwargs, result_kwargs = groupby_kwargs_decorator(gb_dummy_func)(**groupby_kwargs, **other_kwargs)
 
     assert result_groupby_kwargs == groupby_kwargs
     assert result_kwargs == other_kwargs
@@ -209,9 +197,7 @@ def test_get_how_xp_np():
     "data_object",
     (
         xr.DataArray(np.random.rand(10, 10), dims=["x", "y"]),
-        xr.Dataset(
-            {"var": (["x", "y"], np.random.rand(10, 10))}, coords={"x": np.arange(10), "y": np.arange(10)}
-        ),
+        xr.Dataset({"var": (["x", "y"], np.random.rand(10, 10))}, coords={"x": np.arange(10), "y": np.arange(10)}),
         np.random.rand(10, 10),
     ),
 )
diff --git a/tests/test_20_aggregate.py b/tests/test_20_aggregate.py
index aea9231c..67c88610 100644
--- a/tests/test_20_aggregate.py
+++ b/tests/test_20_aggregate.py
@@ -141,9 +141,7 @@ def create_dataarray_rolling(weights=None):
 # Test case for reducing without weights using built-in method
 def test_rolling_reduce_dataarray(how, expected_result):
     dataarray = create_dataarray_rolling()
-    result = _rolling_reduce_dataarray(
-        dataarray, how_reduce=how, x=2, center=True, chunk=False, how_dropna="any"
-    )
+    result = _rolling_reduce_dataarray(dataarray, how_reduce=how, x=2, center=True, chunk=False, how_dropna="any")
     assert isinstance(result, xr.DataArray)
     assert result.shape == (4, 5)
     assert result.values[0, 0] == expected_result
diff --git a/tests/test_30_spatial.py b/tests/test_30_spatial.py
index 1c3c2040..1e04f99a 100644
--- a/tests/test_30_spatial.py
+++ b/tests/test_30_spatial.py
@@ -250,9 +250,7 @@ def test_reduce_invalid_how():
 
 def test_reduce_with_mask():
     dataarray = create_test_dataarray()
-    mask = xr.DataArray(
-        np.random.randint(0, 2, size=dataarray.shape), coords=dataarray.coords, dims=dataarray.dims
-    )
+    mask = xr.DataArray(np.random.randint(0, 2, size=dataarray.shape), coords=dataarray.coords, dims=dataarray.dims)
     result = _spatial._reduce_dataarray_as_xarray(dataarray, mask_arrays=[mask], how="sum")
     assert isinstance(result, xr.DataArray)
 
@@ -260,9 +258,7 @@ def test_reduce_with_mask():
 def test_return_geometry_as_coord():
     dataarray = create_test_dataarray()
     geodataframe = create_test_geodataframe()
-    result = _spatial._reduce_dataarray_as_xarray(
-        dataarray, geodataframe=geodataframe, return_geometry_as_coord=True
-    )
+    result = _spatial._reduce_dataarray_as_xarray(dataarray, geodataframe=geodataframe, return_geometry_as_coord=True)
     assert "geometry" in result.coords
     assert len(result.coords["geometry"].values) == len(geodataframe)
 
@@ -284,9 +280,7 @@ def test_reduce_as_pandas_with_geodataframe():
 def test_reduce_as_pandas_compact():
     dataarray = create_test_dataarray()
     geodataframe = create_test_geodataframe()
-    result = _spatial._reduce_dataarray_as_pandas(
-        dataarray, geodataframe=geodataframe, compact=True, how="mean"
-    )
+    result = _spatial._reduce_dataarray_as_pandas(dataarray, geodataframe=geodataframe, compact=True, how="mean")
     assert isinstance(result, pd.DataFrame)
     assert f"{dataarray.name}" in result.columns
 
diff --git a/tests/test_30_temporal_aggregate.py b/tests/test_30_temporal_aggregate.py
index 4ee8937b..9c1aac12 100644
--- a/tests/test_30_temporal_aggregate.py
+++ b/tests/test_30_temporal_aggregate.py
@@ -53,10 +53,7 @@ def test_standardise_time_basic():
 def test_standardise_time_monthly():
     data = get_data().to_xarray()
     data_standardised = temporal.standardise_time(data, target_format="%Y-%m-15")
-    assert all(
-        pd.to_datetime(time_value).day == 15
-        for time_value in data_standardised.forecast_reference_time.values
-    )
+    assert all(pd.to_datetime(time_value).day == 15 for time_value in data_standardised.forecast_reference_time.values)
 
 
 @pytest.mark.parametrize(
diff --git a/tests/test_30_temporal_rates.py b/tests/test_30_temporal_rates.py
index 6e91b282..6492b0db 100644
--- a/tests/test_30_temporal_rates.py
+++ b/tests/test_30_temporal_rates.py
@@ -136,9 +136,7 @@ def test_accumulation_to_rate_start_of_step_rate_units(rate_units, expected_unit
     numeric_test_sample = rate_data.isel(latitude=5, longitude=5, **{accum_time_dim: slice(0, 5)})
     assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
     assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
-    expected_sample = (data.isel(latitude=5, longitude=5, **{accum_time_dim: slice(0, 5)}).values) / (
-        sample_sf
-    )
+    expected_sample = (data.isel(latitude=5, longitude=5, **{accum_time_dim: slice(0, 5)}).values) / (sample_sf)
     np.testing.assert_allclose(numeric_test_sample.values, expected_sample)
     assert not np.all(np.isnan(numeric_test_sample.values)), "Array contains only NaN values"
 
@@ -170,9 +168,7 @@ def test_accumulation_to_rate_start_of_forecast(time_dim_mode):
     np.testing.assert_allclose(numeric_test_sample.values, expected_sample)
 
     # Check the from_first_step=True option, should preserve first timestep
-    rate_data = temporal.accumulation_to_rate(
-        data, accumulation_type="start_of_forecast", from_first_step=True
-    )
+    rate_data = temporal.accumulation_to_rate(data, accumulation_type="start_of_forecast", from_first_step=True)
     assert "tp_rate" == rate_data.name
     assert original_units + " s^-1" == rate_data.attrs["units"]
     assert "standard_name" not in rate_data.attrs
@@ -202,18 +198,14 @@ def test_accumulation_to_rate_start_of_forecast(time_dim_mode):
         ("step_length", "", 1.0),
     ],
 )
-def test_accumulation_to_rate_start_of_forecast_rate_units(
-    rate_units, expected_units, sample_sf, time_dim_mode
-):
+def test_accumulation_to_rate_start_of_forecast_rate_units(rate_units, expected_units, sample_sf, time_dim_mode):
     test_file = "seas5-precip-3deg-202401.grib"
     accumulation_type = "start_of_forecast"
     accum_time_dim = ACCUM_TIME_DIM.get(time_dim_mode, time_dim_mode)
     data = get_data(test_file).to_xarray(**TO_XARRAY_KWARGS[time_dim_mode])["tp"]
     original_units = data.attrs["units"]
 
-    rate_data = temporal.accumulation_to_rate(
-        data, accumulation_type=accumulation_type, rate_units=rate_units
-    )
+    rate_data = temporal.accumulation_to_rate(data, accumulation_type=accumulation_type, rate_units=rate_units)
     assert f"tp_{RATE_SUFFIX.get(rate_units, 'rate')}" == rate_data.name
     assert original_units + expected_units == rate_data.attrs["units"]
     assert rate_data.attrs["long_name"].endswith(f" {RATE_SUFFIX.get(rate_units, 'rate').replace('_', ' ')}")
@@ -248,9 +240,7 @@ def test_accumulation_to_rate_start_of_day(time_dim_mode):
     assert rate_data[accum_time_dim][0].values == data[accum_time_dim][1].values
 
     isel_kwargs = {k: 1 for k in data.dims if k not in (accum_time_dim, "latitude", "longitude")}
-    numeric_test_sample = rate_data.isel(
-        **ERA5_LAND_TEST_POINT, **{accum_time_dim: slice(2, 22)}, **isel_kwargs
-    )
+    numeric_test_sample = rate_data.isel(**ERA5_LAND_TEST_POINT, **{accum_time_dim: slice(2, 22)}, **isel_kwargs)
     assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
     assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
     expected_sample = (
@@ -275,15 +265,11 @@ def test_accumulation_to_rate_start_of_day(time_dim_mode):
         numeric_test_sample = rate_data.isel(**ERA5_LAND_TEST_POINT, **{accum_time_dim: 25}, **isel_kwargs)
         assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
         assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
-        expected_sample = (
-            data.isel(**ERA5_LAND_TEST_POINT, **{accum_time_dim: 25}, **isel_kwargs).values
-        ) / 3600.0
+        expected_sample = (data.isel(**ERA5_LAND_TEST_POINT, **{accum_time_dim: 25}, **isel_kwargs).values) / 3600.0
         np.testing.assert_allclose(numeric_test_sample.values, expected_sample)
 
         # Check a period that is not the first day
-        numeric_test_sample = rate_data.isel(
-            **ERA5_LAND_TEST_POINT, **{accum_time_dim: slice(26, 31)}, **isel_kwargs
-        )
+        numeric_test_sample = rate_data.isel(**ERA5_LAND_TEST_POINT, **{accum_time_dim: slice(26, 31)}, **isel_kwargs)
         assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
         assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
         expected_sample = (
@@ -314,18 +300,14 @@ def test_accumulation_to_rate_start_of_day_rate_units(time_dim_mode, rate_units,
     data = get_data(test_file).to_xarray(**TO_XARRAY_KWARGS[time_dim_mode])["tp"]
 
     original_units = data.attrs["units"]
-    rate_data = temporal.accumulation_to_rate(
-        data, accumulation_type=accumulation_type, rate_units=rate_units
-    )
+    rate_data = temporal.accumulation_to_rate(data, accumulation_type=accumulation_type, rate_units=rate_units)
     assert f"tp_{RATE_SUFFIX.get(rate_units, 'rate')}" == rate_data.name
     assert original_units + expected_units == rate_data.attrs["units"]
     assert rate_data.attrs["long_name"].endswith(f" {RATE_SUFFIX.get(rate_units, 'rate').replace('_', ' ')}")
     assert "standard_name" not in rate_data.attrs
     assert rate_data[accum_time_dim][0].values == data[accum_time_dim][1].values
     isel_kwargs = {k: 1 for k in data.dims if k not in (accum_time_dim, "latitude", "longitude")}
-    numeric_test_sample = rate_data.isel(
-        **ERA5_LAND_TEST_POINT, **{accum_time_dim: slice(6, 11)}, **isel_kwargs
-    )
+    numeric_test_sample = rate_data.isel(**ERA5_LAND_TEST_POINT, **{accum_time_dim: slice(6, 11)}, **isel_kwargs)
     assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
     assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
     expected_sample = (
@@ -378,15 +360,11 @@ def test_deaccumulate(time_dim_mode):
     assert "standard_name" not in deaccum_data.attrs
 
     # Check first timestep is unchanged
-    np.testing.assert_allclose(
-        deaccum_data.isel(**{accum_time_dim: 0}).values, data.isel(**{accum_time_dim: 0}).values
-    )
+    np.testing.assert_allclose(deaccum_data.isel(**{accum_time_dim: 0}).values, data.isel(**{accum_time_dim: 0}).values)
 
     isel_kwargs = {k: 0 for k in data.dims if k not in (accum_time_dim, "latitude", "longitude")}
 
-    numeric_test_sample = deaccum_data.isel(
-        **{accum_time_dim: slice(1, 5)}, **SEAS5_TEST_POINT, **isel_kwargs
-    )
+    numeric_test_sample = deaccum_data.isel(**{accum_time_dim: slice(1, 5)}, **SEAS5_TEST_POINT, **isel_kwargs)
     assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
     assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
     expected_sample = (
@@ -403,9 +381,7 @@ def test_deaccumulate(time_dim_mode):
 
     isel_kwargs = {k: 0 for k in data.dims if k not in (accum_time_dim, "latitude", "longitude")}
 
-    numeric_test_sample = deaccum_data.isel(
-        **{accum_time_dim: slice(1, 5)}, **SEAS5_TEST_POINT, **isel_kwargs
-    )
+    numeric_test_sample = deaccum_data.isel(**{accum_time_dim: slice(1, 5)}, **SEAS5_TEST_POINT, **isel_kwargs)
     assert not np.all(np.isnan(numeric_test_sample.values)), "Sample array contains only NaN values"
     assert not np.all(numeric_test_sample.values == 0), "Sample array contains only zero values"
     expected_sample = (