Chore/update linting tools

.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

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"
    ]
   },

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",

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",

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"
    ]
   },

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]

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

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):

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})
 

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})
 

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."),
                 ],
             )
         )

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),
     ),
 )

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(