Adopt ruff format (#2083)

### What kind of change does this PR introduce? * Enables the `ruff` auto-formatter: * The maximum line-length is now 120 characters. * Docstring formatting is now enabled. * Line endings in files now must be Unix-compatible (`LF`). * The `blackdoc` pre-commit hook now only examines `.rst` and `.md` files. ### Does this PR introduce a breaking change? Depends on your definition. The code styling now uses `ruff` exclusively and is no longer fully conformant with `black` formatting. ### Other information: `ruff` has a different opinion on [parentheses for long-nested expressions](https://docs.astral.sh/ruff/formatter/black/#parenthesizing-long-nested-expressions) compared to `black`, hence the significant changes.
Ouranosinc · Feb 21, 2025 · c0b7afc · c0b7afc
2 parents d806c7d + 31c7d4a
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -43,6 +43,8 @@ repos:
     hooks:
       - id: ruff
         args: [ '--fix', '--show-fixes' ]
+      - id: ruff-format
+        exclude: '(src/xclim/indices/__init__.py|docs/installation.rst)'
   - repo: https://github.com/pylint-dev/pylint
     rev: v3.3.4
     hooks:
@@ -85,8 +87,7 @@ repos:
     hooks:
       - id: blackdoc
         additional_dependencies: [ 'black==25.1.0' ]
-        exclude: '(src/xclim/indices/__init__.py|docs/installation.rst)'
-      # - id: blackdoc-autoupdate-black
+        exclude: '(.py|docs/installation.rst)'
   - repo: https://github.com/codespell-project/codespell
     rev: v2.4.1
     hooks:

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -14,6 +14,11 @@ Breaking changes
 Internal changes
 ^^^^^^^^^^^^^^^^
 * `black`, `isort`, and `nbqa` have all been dropped from the development dependencies. (:issue:`1805`, :pull:`2082`).
+* `ruff` has been configured to provide code formatting. (:pull:`2083`):
+    * The maximum line-length is now 120 characters.
+    * Docstring formatting is now enabled.
+    * Line endings in files now must be `Unix`-compatible (`LF`).
+* The `blackdoc` pre-commit hook now only examines `.rst` and `.md` files. (:pull:`2083`).
 
 v0.55.0 (2025-02-17)
 --------------------

diff --git a/Makefile b/Makefile
@@ -56,8 +56,7 @@ lint: ## check style with flake8 and black
 	python -m ruff check src/xclim tests
 	python -m flake8 --config=.flake8 src/xclim tests
 	python -m vulture src/xclim tests
-	python -m blackdoc --check --exclude=src/xclim/indices/__init__.py src/xclim
-	python -m blackdoc --check docs
+	python -m blackdoc --check README.rst CHANGELOG.rst CONTRIBUTING.rst docs --exclude=".py"
 	codespell src/xclim tests docs
 	python -m numpydoc lint src/xclim/*.py src/xclim/ensembles/*.py src/xclim/indices/*.py src/xclim/indicators/*.py src/xclim/testing/*.py
 	python -m deptry src

diff --git a/docs/conf.py b/docs/conf.py
@@ -240,9 +240,7 @@ class XCStyle(AlphaStyle):
 
 # General information about the project.
 project = "xclim"
-copyright = (
-    f"2018-{datetime.datetime.now().year}, Ouranos Inc., Travis Logan, and contributors"
-)
+copyright = f"2018-{datetime.datetime.now().year}, Ouranos Inc., Travis Logan, and contributors"
 author = "xclim Project Development Team"
 
 # The version info for the project you're documenting, acts as replacement

diff --git a/docs/notebooks/analogs.ipynb b/docs/notebooks/analogs.ipynb
@@ -133,9 +133,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = analog.spatial_analogs(\n",
-    "    sim[[\"tg_mean\"]], obs[[\"tg_mean\"]], method=\"seuclidean\"\n",
-    ")\n",
+    "results = analog.spatial_analogs(sim[[\"tg_mean\"]], obs[[\"tg_mean\"]], method=\"seuclidean\")\n",
     "\n",
     "results.plot()\n",
     "plt.plot(sim.lon, sim.lat, \"ro\", label=\"Target\")\n",
@@ -169,9 +167,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "results = analog.spatial_analogs(\n",
-    "    sim[[\"tg_mean\"]], obs[[\"tg_mean\"]], method=\"zech_aslan\"\n",
-    ")\n",
+    "results = analog.spatial_analogs(sim[[\"tg_mean\"]], obs[[\"tg_mean\"]], method=\"zech_aslan\")\n",
     "\n",
     "results.plot(center=False)\n",
     "plt.plot(sim.lon, sim.lat, \"ro\", label=\"Target\")\n",

diff --git a/docs/notebooks/benchmarks/sdba_quantile.ipynb b/docs/notebooks/benchmarks/sdba_quantile.ipynb
@@ -100,19 +100,15 @@
     "for use_fnq in [True, False]:\n",
     "    sdba.nbutils.USE_FASTNANQUANTILE = use_fnq\n",
     "    # heat-up the jit\n",
-    "    sdba.nbutils.quantile(\n",
-    "        xr.DataArray(np.array([0, 1.5])), dim=\"dim_0\", q=np.array([0.5])\n",
-    "    )\n",
+    "    sdba.nbutils.quantile(xr.DataArray(np.array([0, 1.5])), dim=\"dim_0\", q=np.array([0.5]))\n",
     "    for size in np.arange(250, 2000 + 250, 250):\n",
     "        da = tx.isel(time=slice(0, size))\n",
     "        t0 = time.time()\n",
     "        for _i in range(num_tests):\n",
     "            sdba.nbutils.quantile(da, **kws).compute()\n",
     "        timed[use_fnq].append([size, time.time() - t0])\n",
     "\n",
-    "for k, lab in zip(\n",
-    "    [True, False], [\"xclim.core.utils.nan_quantile\", \"fastnanquantile\"], strict=False\n",
-    "):\n",
+    "for k, lab in zip([True, False], [\"xclim.core.utils.nan_quantile\", \"fastnanquantile\"], strict=False):\n",
     "    arr = np.array(timed[k])\n",
     "    plt.plot(arr[:, 0], arr[:, 1] / num_tests, label=lab)\n",
     "plt.legend()\n",

diff --git a/docs/notebooks/customize.ipynb b/docs/notebooks/customize.ipynb
@@ -35,11 +35,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tasmax = (\n",
-    "    xr.tutorial.load_dataset(\"air_temperature\")\n",
-    "    .air.resample(time=\"D\")\n",
-    "    .max(keep_attrs=True)\n",
-    ")\n",
+    "tasmax = xr.tutorial.load_dataset(\"air_temperature\").air.resample(time=\"D\").max(keep_attrs=True)\n",
     "tasmax = tasmax.where(tasmax.time.dt.day % 10 != 0)"
    ]
   },
@@ -137,9 +133,7 @@
    "outputs": [],
    "source": [
     "with xclim.set_options(check_missing=\"wmo\"):\n",
-    "    tx_mean = xclim.atmos.tx_mean(\n",
-    "        tasmax=tasmax, freq=\"MS\"\n",
-    "    )  # compute monthly max tasmax\n",
+    "    tx_mean = xclim.atmos.tx_mean(tasmax=tasmax, freq=\"MS\")  # compute monthly max tasmax\n",
     "tx_mean.sel(time=\"2013\", lat=75, lon=200)"
    ]
   },
@@ -165,7 +159,7 @@
     "\n",
     "To add additional arguments, one should override the `__init__` (receiving those arguments) and the `validate` static method, which validates them. The options are then stored in the `options` property of the instance. See example below and the docstrings in the module.\n",
     "\n",
-    "When registering the class with the `xclim.core.checks.register_missing_method` decorator, the keyword arguments will be registered as options for the missing method. "
+    "When registering the class with the `xclim.core.checks.register_missing_method` decorator, the keyword arguments will be registered as options for the missing method."
    ]
   },
   {
@@ -186,12 +180,9 @@
     "        super().__init__(max_n=max_n)\n",
     "\n",
     "    def is_missing(self, valid, count, freq):\n",
-    "        \"\"\"Return a boolean mask where True values are for elements that are considered missing and masked on the output.\"\"\"\n",
+    "        \"\"\"Return a boolean mask for elements that are considered missing and masked on the output.\"\"\"\n",
     "        null = ~valid\n",
-    "        return (\n",
-    "            null.resample(time=freq).map(longest_run, dim=\"time\")\n",
-    "            >= self.options[\"max_n\"]\n",
-    "        )\n",
+    "        return null.resample(time=freq).map(longest_run, dim=\"time\") >= self.options[\"max_n\"]\n",
     "\n",
     "    @staticmethod\n",
     "    def validate(max_n):\n",
@@ -212,12 +203,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with xclim.set_options(\n",
-    "    check_missing=\"consecutive\", missing_options={\"consecutive\": {\"max_n\": 2}}\n",
-    "):\n",
-    "    tx_mean = xclim.atmos.tx_mean(\n",
-    "        tasmax=tasmax, freq=\"MS\"\n",
-    "    )  # compute monthly max tasmax\n",
+    "with xclim.set_options(check_missing=\"consecutive\", missing_options={\"consecutive\": {\"max_n\": 2}}):\n",
+    "    tx_mean = xclim.atmos.tx_mean(tasmax=tasmax, freq=\"MS\")  # compute monthly max tasmax\n",
     "tx_mean.sel(time=\"2013\", lat=75, lon=200)"
    ]
   },

diff --git a/docs/notebooks/ensembles-advanced.ipynb b/docs/notebooks/ensembles-advanced.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "nbsphinx": "hidden"
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This cell is not visible when the documentation is built.\n",
@@ -19,44 +17,40 @@
     "np.random.normal(loc=3.5, scale=1.5, size=50)\n",
     "# crit['delta_annual_tavg']\n",
     "np.random.seed(0)\n",
-    "test = xr.DataArray(\n",
-    "    np.random.normal(loc=3, scale=1.5, size=50), dims=[\"realization\"]\n",
-    ").assign_coords(horizon=\"2041-2070\")\n",
+    "test = xr.DataArray(np.random.normal(loc=3, scale=1.5, size=50), dims=[\"realization\"]).assign_coords(\n",
+    "    horizon=\"2041-2070\"\n",
+    ")\n",
     "test = xr.concat(\n",
     "    (\n",
     "        test,\n",
-    "        xr.DataArray(\n",
-    "            np.random.normal(loc=5.34, scale=2, size=50), dims=[\"realization\"]\n",
-    "        ).assign_coords(horizon=\"2071-2100\"),\n",
+    "        xr.DataArray(np.random.normal(loc=5.34, scale=2, size=50), dims=[\"realization\"]).assign_coords(\n",
+    "            horizon=\"2071-2100\"\n",
+    "        ),\n",
     "    ),\n",
     "    dim=\"horizon\",\n",
     ")\n",
     "\n",
     "ds_crit = xr.Dataset()\n",
     "\n",
     "ds_crit[\"delta_annual_tavg\"] = test\n",
-    "test = xr.DataArray(\n",
-    "    np.random.normal(loc=5, scale=5, size=50), dims=[\"realization\"]\n",
-    ").assign_coords(horizon=\"2041-2070\")\n",
+    "test = xr.DataArray(np.random.normal(loc=5, scale=5, size=50), dims=[\"realization\"]).assign_coords(horizon=\"2041-2070\")\n",
     "test = xr.concat(\n",
     "    (\n",
     "        test,\n",
-    "        xr.DataArray(\n",
-    "            np.random.normal(loc=10, scale=8, size=50), dims=[\"realization\"]\n",
-    "        ).assign_coords(horizon=\"2071-2100\"),\n",
+    "        xr.DataArray(np.random.normal(loc=10, scale=8, size=50), dims=[\"realization\"]).assign_coords(\n",
+    "            horizon=\"2071-2100\"\n",
+    "        ),\n",
     "    ),\n",
     "    dim=\"horizon\",\n",
     ")\n",
     "ds_crit[\"delta_annual_prtot\"] = test\n",
-    "test = xr.DataArray(\n",
-    "    np.random.normal(loc=0, scale=3, size=50), dims=[\"realization\"]\n",
-    ").assign_coords(horizon=\"2041-2070\")\n",
+    "test = xr.DataArray(np.random.normal(loc=0, scale=3, size=50), dims=[\"realization\"]).assign_coords(horizon=\"2041-2070\")\n",
     "test = xr.concat(\n",
     "    (\n",
     "        test,\n",
-    "        xr.DataArray(\n",
-    "            np.random.normal(loc=2, scale=4, size=50), dims=[\"realization\"]\n",
-    "        ).assign_coords(horizon=\"2071-2100\"),\n",
+    "        xr.DataArray(np.random.normal(loc=2, scale=4, size=50), dims=[\"realization\"]).assign_coords(\n",
+    "            horizon=\"2071-2100\"\n",
+    "        ),\n",
     "    ),\n",
     "    dim=\"horizon\",\n",
     ")\n",
@@ -242,7 +236,7 @@
     "\n",
     "`xclim` also makes available a similar ensemble reduction algorithm, `ensembles.kkz_reduce_ensemble`. See: https://doi.org/10.1175/JCLI-D-14-00636.1\n",
     "\n",
-    "The advantage of this algorithm is largely that fewer realizations are needed in order to reach the same level of representative members than the K-means clustering algorithm, as the KKZ methods tends towards identifying members that fall towards the extremes of criteria values.  \n",
+    "The advantage of this algorithm is largely that fewer realizations are needed in order to reach the same level of representative members than the K-means clustering algorithm, as the KKZ methods tends towards identifying members that fall towards the extremes of criteria values.\n",
     "\n",
     "This technique also produces nested selection results, where an additional increase in desired selection size does not alter the previous choices, which is not the case for the K-means algorithm."
    ]
@@ -306,11 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# UNNESTED results using k-means\n",
@@ -331,7 +321,7 @@
     "\n",
     "The **KKZ** algorithm iteratively maximizes distance from previous selected candidates - potentially resulting in 'off-center' results versus the original ensemble\n",
     "\n",
-    "The **K-means** algorithm will redivide the data space with each iteration, producing results that are consistently centered on the original ensemble but lacking coverage in the extremes "
+    "The **K-means** algorithm will redivide the data space with each iteration, producing results that are consistently centered on the original ensemble but lacking coverage in the extremes"
    ]
   },
   {