Use ruff for formatting instead of black

.devcontainer/devcontainer.json

@@ -20,7 +20,7 @@
                       "source.organizeImports": true
                     }
                   },
-                  "python.formatting.provider": "black"
+                  "python.formatting.provider": "charliermarsh.ruff",
             }
         }
     }

.github/workflows/full_test.yml

@@ -21,6 +21,7 @@ jobs:
     - uses: actions/checkout@v3
     - uses: chartboost/ruff-action@v1 # Fail fast if there are any linting errors
       with:
+        version: 0.6.2
         src: "modelskill"
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v4

.vscode/settings.json

@@ -1,6 +1,6 @@
 {
     "[python]": {
-        "editor.defaultFormatter": "ms-python.black-formatter",
+        "editor.defaultFormatter": "charliermarsh.ruff",
         "editor.formatOnSave": true
     },
     "python.testing.pytestArgs": [

CONTRIBUTING.md

@@ -4,5 +4,5 @@
 2. Install package in editable mode `pip install -e .[dev]`, including development dependencies
 3. Make changes
 4. Verify that all tests passes by running `pytest` from the package root directory
-5. Format the code by running [black](https://black.readthedocs.io/en/stable/) e.g. `black nameofthefiletoformat.py`
+5. Format the code by running [`ruff format`](https://docs.astral.sh/ruff/formatter/)
 6. Make a pull request with a summary of the changes

Makefile

@@ -8,6 +8,9 @@ build: typecheck test
 lint:
 	ruff check $(LIB)
 
+format:
+	ruff format $(LIB)
+
 test:
 	pytest --disable-warnings
 

modelskill/comparison/__init__.py

@@ -1,6 +1,7 @@
-"""The `comparison` module contains different types of classes for single 
+"""The `comparison` module contains different types of classes for single
 observation comparison (Comparer), and collections of Comparers (ComparerCollection).
 """
+
 from ._comparison import Comparer
 from ._collection import ComparerCollection
 

modelskill/comparison/_comparison.py

@@ -233,6 +233,7 @@ def _validate_metrics(metrics: Iterable[Any]) -> None:
 @dataclass
 class ItemSelection:
     "Utility class to keep track of observation, model and auxiliary items"
+
     obs: str
     model: Sequence[str]
     aux: Sequence[str]
@@ -793,7 +794,8 @@ def __iadd__(self, other: Comparer):  # type: ignore
         else:
             self.raw_mod_data.update(other.raw_mod_data)
             matched = match_space_time(
-                observation=self._to_observation(), raw_mod_data=self.raw_mod_data  # type: ignore
+                observation=self._to_observation(),
+                raw_mod_data=self.raw_mod_data,  # type: ignore
             )
             self.data = matched
 
@@ -821,7 +823,8 @@ def __add__(
                 raw_mod_data = self.raw_mod_data.copy()
                 raw_mod_data.update(other.raw_mod_data)  # TODO!
                 matched = match_space_time(
-                    observation=self._to_observation(), raw_mod_data=raw_mod_data  # type: ignore
+                    observation=self._to_observation(),
+                    raw_mod_data=raw_mod_data,  # type: ignore
                 )
                 cmp = Comparer(matched_data=matched, raw_mod_data=raw_mod_data)
 
@@ -879,7 +882,9 @@ def sel(
             d = d.sel(time=d.time.to_index().to_frame().loc[start:end].index)  # type: ignore
 
             # Note: if user asks for a specific time, we also filter raw
-            raw_mod_data = {k: v.sel(time=slice(start, end)) for k, v in raw_mod_data.items()}  # type: ignore
+            raw_mod_data = {
+                k: v.sel(time=slice(start, end)) for k, v in raw_mod_data.items()
+            }  # type: ignore
         if time is not None:
             d = d.sel(time=time)
 

modelskill/matching.py

@@ -258,7 +258,9 @@ def match(
     if isinstance(obs, Collection):
         assert all(isinstance(o, get_args(ObsInputType)) for o in obs)
     else:
-        raise TypeError(f"Obs is not the correct type: it is {type(obs)}. Check the order of the arguments (obs, mod).")
+        raise TypeError(
+            f"Obs is not the correct type: it is {type(obs)}. Check the order of the arguments (obs, mod)."
+        )
 
     if len(obs) > 1 and isinstance(mod, Collection) and len(mod) > 1:
         if not all(isinstance(m, (DfsuModelResult, GridModelResult)) for m in mod):

modelskill/metrics.py

@@ -1,9 +1,9 @@
-"""The `metrics` module contains different skill metrics for evaluating the 
-difference between a model and an observation. 
+"""The `metrics` module contains different skill metrics for evaluating the
+difference between a model and an observation.
 
 * [bias][modelskill.metrics.bias]
 * [max_error][modelskill.metrics.max_error]
-* [root_mean_squared_error (rmse)][modelskill.metrics.root_mean_squared_error]    
+* [root_mean_squared_error (rmse)][modelskill.metrics.root_mean_squared_error]
 * [urmse][modelskill.metrics.urmse]
 * [mean_absolute_error (mae)][modelskill.metrics.mean_absolute_error]
 * [mean_absolute_percentage_error (mape)][modelskill.metrics.mean_absolute_percentage_error]

modelskill/model/_base.py

@@ -89,7 +89,6 @@ def _extract_track(
 
 
 class Alignable(Protocol):
-
     @property
     def time(self) -> pd.DatetimeIndex: ...
 

modelskill/model/dfsu.py

@@ -44,7 +44,6 @@ def __init__(
         quantity: Optional[Quantity] = None,
         aux_items: Optional[list[int | str]] = None,
     ) -> None:
-
         filename = None
 
         assert isinstance(

modelskill/model/grid.py

@@ -170,7 +170,8 @@ def _extract_point(
         # TODO: avoid runtrip to pandas if possible (potential loss of metadata)
         if "z" in self.data.dims and z is not None:
             ds = self.data.interp(
-                coords=dict(x=float(x), y=float(y), z=float(z)), method=method  # type: ignore
+                coords=dict(x=float(x), y=float(y), z=float(z)),
+                method=method,  # type: ignore
             )
         else:
             ds = self.data.interp(coords=dict(x=float(x), y=float(y)), method=method)  # type: ignore

modelskill/model/point.py

@@ -93,7 +93,7 @@ def interp_time(self, observation: Observation, **kwargs: Any) -> PointModelResu
         **kwargs
 
             Additional keyword arguments passed to xarray.interp
-            
+
         Returns
         -------
         PointModelResult
@@ -158,5 +158,3 @@ def _get_valid_times(
         # valid query times where time delta is less than max_gap
         valid_idx = df.dt <= max_gap
         return df[valid_idx].index
-
-

modelskill/settings.py

@@ -40,7 +40,7 @@
 plot.scatter.oneone_line.color : blue
 plot.scatter.oneone_line.label : 1:1
 plot.scatter.points.alpha : 0.5
-plot.scatter.points.label : 
+plot.scatter.points.label :
 plot.scatter.points.size : 20
 plot.scatter.quantiles.color : darkturquoise
 plot.scatter.quantiles.kwargs : {}
@@ -50,7 +50,7 @@
 plot.scatter.quantiles.markeredgewidth : 0.5
 plot.scatter.quantiles.markersize : 3.5
 plot.scatter.reg_line.kwargs : {'color': 'r'}
->>> ms.set_option("plot.scatter.points.size", 4)  
+>>> ms.set_option("plot.scatter.points.size", 4)
 >>> plot.scatter.points.size
 4
 >>> ms.get_option("plot.scatter.points.size")
@@ -414,7 +414,10 @@ def register_option(
 
     # save the option metadata
     _registered_options[key] = RegisteredOption(
-        key=key, defval=defval, doc=doc, validator=validator  # , cb=cb
+        key=key,
+        defval=defval,
+        doc=doc,
+        validator=validator,  # , cb=cb
     )
 
 

modelskill/skill_grid.py

@@ -148,12 +148,10 @@ def __repr__(self) -> str:
         return "\n".join(out)
 
     @overload
-    def __getitem__(self, key: Hashable) -> SkillGridArray:
-        ...
+    def __getitem__(self, key: Hashable) -> SkillGridArray: ...
 
     @overload
-    def __getitem__(self, key: Iterable[Hashable]) -> SkillGrid:
-        ...
+    def __getitem__(self, key: Iterable[Hashable]) -> SkillGrid: ...
 
     def __getitem__(
         self, key: Hashable | Iterable[Hashable]

modelskill/timeseries/_timeseries.py

@@ -128,9 +128,7 @@ class TimeSeries:
     """Time series data"""
 
     data: xr.Dataset
-    plotter: ClassVar = (
-        MatplotlibTimeSeriesPlotter  # TODO is this the best option to choose a plotter? Can we use the settings module?
-    )
+    plotter: ClassVar = MatplotlibTimeSeriesPlotter  # TODO is this the best option to choose a plotter? Can we use the settings module?
 
     def __init__(self, data: xr.Dataset) -> None:
         self.data = data if self._is_input_validated(data) else _validate_dataset(data)

pyproject.toml

@@ -46,12 +46,12 @@ dev = ["pytest",
         "mkdocs-material==9.5.32",
         "mkdocstrings==0.25.2",
         "mkdocstrings-python==1.10.8",
-        "black==22.3.0",
         "plotly >= 4.5",
-        "ruff",]
+        "ruff==0.6.2",]
 
 test =  [
             "pytest",
+            "pytest-cov", 
             "netCDF4",
             "openpyxl",
             "dask",