MAINT: address Ruff issues

benchmarks/ampform.py

@@ -195,8 +195,8 @@ def test_fit(self, backend, benchmark, model, size):
 
 def print_data_sample(data: DataSample, sample_size: int) -> None:
     """Print a `.DataSample`, so it can be pasted into the expected sample."""
-    print()
-    pprint(
+    print()  # noqa: T201
+    pprint(  # noqa: T203
         {
             i: np.round(four_momenta[:sample_size], decimals=11).tolist()
             for i, four_momenta in data.items()

pyproject.toml

@@ -219,6 +219,7 @@ ignore = [
     "D407",
     "D416",
     "E501",
+    "RUF012",
     "S307",
     "SIM108",
 ]
@@ -231,6 +232,12 @@ target-version = "py37"
 task-tags = ["cspell"]
 
 [tool.ruff.per-file-ignores]
+"benchmarks/*" = [
+    "D",
+    "PLR0913",
+    "PLR2004",
+    "S101",
+]
 "docs/*" = [
     "E402",
     "INP001",

src/tensorwaves/estimator.py

@@ -43,6 +43,7 @@ def create_cached_function(
         backend: The computational backend to which in which to express the
             input :code:`expression`.
 
+        free_parameters: Symbols in the expression that change and should not be cached.
         use_cse: See :func:`.create_parametrized_function`.
 
     Returns:
@@ -116,7 +117,7 @@ class ChiSquared(Estimator):
     .. seealso:: :doc:`/usage/chi-squared`
     """
 
-    def __init__(
+    def __init__(  # noqa: PLR0913
         self,
         function: ParametrizedFunction,
         domain: DataSample,
@@ -183,7 +184,7 @@ class UnbinnedNLL(Estimator):
     .. seealso:: :doc:`/usage/unbinned-fit`
     """
 
-    def __init__(
+    def __init__(  # noqa: PLR0913
         self,
         function: ParametrizedFunction,
         data: DataSample,

src/tensorwaves/function/sympy/__init__.py

@@ -170,7 +170,7 @@ def _lambdify_normal_or_fast(
     )
 
 
-def lambdify(
+def lambdify(  # noqa: C901, PLR0911
     expression: sp.Expr,
     symbols: Sequence[sp.Symbol],
     backend: str,
@@ -278,7 +278,7 @@ def _sympy_lambdify(
     )
 
 
-def fast_lambdify(
+def fast_lambdify(  # noqa: PLR0913
     expression: sp.Expr,
     symbols: Sequence[sp.Symbol],
     backend: str,

src/tensorwaves/optimizer/callbacks.py

@@ -132,7 +132,7 @@ def on_optimize_start(self, logs: dict[str, Any] | None = None) -> None:
         if self.__iteration_step_size > 0:
             self.__latest_iteration = 0
         _close_stream(self.__stream)
-        self.__stream = open(self.__filename, "w", newline="")
+        self.__stream = open(self.__filename, "w", newline="")  # noqa: SIM115
         self.__writer = csv.DictWriter(
             self.__stream,
             fieldnames=list(self.__log_to_rowdict(logs)),
@@ -208,7 +208,7 @@ def cast_non_numeric(value: str) -> complex | float | int | str:
                     if float_value.is_integer():
                         return int(float_value)
                     return float_value
-                return complex_value
+                return complex_value  # noqa: TRY300
             except ValueError:
                 return value
 
@@ -296,7 +296,7 @@ def __del__(self) -> None:
 
     def on_optimize_start(self, logs: dict[str, Any] | None = None) -> None:
         _close_stream(self.__stream)
-        self.__stream = open(self.__filename, "w")
+        self.__stream = open(self.__filename, "w")  # noqa: SIM115
 
     def on_optimize_end(self, logs: dict[str, Any] | None = None) -> None:
         if logs is None:
@@ -336,7 +336,7 @@ def __dump_to_yaml(self, logs: dict[str, Any]) -> None:
     @staticmethod
     def load_latest_parameters(filename: Path | str) -> dict:
         with open(filename) as stream:
-            fit_stats = yaml.load(stream, Loader=yaml.Loader)
+            fit_stats = yaml.load(stream, Loader=yaml.Loader)  # noqa: S506
         return fit_stats["parameters"]
 
 

src/tensorwaves/optimizer/minuit.py

@@ -114,7 +114,7 @@ def wrapped_gradient(pars: list) -> Iterable[float]:
             name=tuple(flattened_parameters),
         )
         minuit.errors = tuple(
-            0.1 * abs(x) if abs(x) != 0.0 else 0.1
+            0.1 * abs(x) if abs(x) != 0.0 else 0.1  # noqa: PLR2004
             for x in flattened_parameters.values()
         )
         minuit.errordef = (
@@ -135,7 +135,7 @@ def wrapped_gradient(pars: list) -> Iterable[float]:
             parameter_values[name] = par_state.value
             parameter_errors[name] = par_state.error
 
-        assert minuit.fmin is not None
+        assert minuit.fmin is not None  # noqa: S101
         fit_result = FitResult(
             minimum_valid=minuit.valid,
             execution_time=end_time - start_time,

src/tensorwaves/optimizer/scipy.py

@@ -37,7 +37,7 @@ def __init__(
         self.__method = method
         self.__minimize_options = scipy_options
 
-    def optimize(
+    def optimize(  # noqa: C901
         self,
         estimator: Estimator,
         initial_parameters: Mapping[str, ParameterValue],