[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: requirements-txt-fixer
       - id: trailing-whitespace
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
       - id: codespell
         args:
@@ -25,11 +25,11 @@ repos:
           ]
         exclude: "BIBLIOGRAPHY.bib|CONTRIBUTORS.rst"
   - repo: https://github.com/PyCQA/isort
-    rev: "5.13.2"
+    rev: "6.0.1"
     hooks:
       - id: isort
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.8.2"
+    rev: "v0.11.8"
     hooks:
       - id: ruff-format
       - id: ruff

colour/algebra/common.py

@@ -73,18 +73,16 @@
 """
 
 
-def get_sdiv_mode() -> (
-    Literal[
-        "Numpy",
-        "Ignore",
-        "Warning",
-        "Raise",
-        "Ignore Zero Conversion",
-        "Warning Zero Conversion",
-        "Ignore Limit Conversion",
-        "Warning Limit Conversion",
-    ]
-):
+def get_sdiv_mode() -> Literal[
+    "Numpy",
+    "Ignore",
+    "Warning",
+    "Raise",
+    "Ignore Zero Conversion",
+    "Warning Zero Conversion",
+    "Ignore Limit Conversion",
+    "Warning Limit Conversion",
+]:
     """
     Return *Colour* safe division mode.
 
@@ -144,16 +142,7 @@ def set_sdiv_mode(
     global _SDIV_MODE  # noqa: PLW0603
 
     _SDIV_MODE = cast(
-        Literal[
-            "Numpy",
-            "Ignore",
-            "Warning",
-            "Raise",
-            "Ignore Zero Conversion",
-            "Warning Zero Conversion",
-            "Ignore Limit Conversion",
-            "Warning Limit Conversion",
-        ],
+        "Literal['Numpy', 'Ignore', 'Warning', 'Raise', 'Ignore Zero Conversion', 'Warning Zero Conversion', 'Ignore Limit Conversion', 'Warning Limit Conversion']",
         validate_method(
             mode,
             (

colour/algebra/interpolation.py

@@ -758,8 +758,7 @@ class NearestNeighbourInterpolator(KernelInterpolator):
 
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         kwargs["kernel"] = kernel_nearest_neighbour
-        if "kernel_kwargs" in kwargs:
-            del kwargs["kernel_kwargs"]
+        kwargs.pop("kernel_kwargs", None)
 
         super().__init__(*args, **kwargs)
 
@@ -850,7 +849,7 @@ def x(self) -> NDArrayFloat:
     def x(self, value: ArrayLike) -> None:
         """Setter for the **self.x** property."""
 
-        value = cast(NDArrayFloat, np.atleast_1d(value).astype(self._dtype))
+        value = cast("NDArrayFloat", np.atleast_1d(value).astype(self._dtype))
 
         attest(
             value.ndim == 1,
@@ -883,7 +882,7 @@ def y(self) -> NDArrayFloat:
     def y(self, value: ArrayLike) -> None:
         """Setter for the **self.y** property."""
 
-        value = cast(NDArrayFloat, np.atleast_1d(value).astype(self._dtype))
+        value = cast("NDArrayFloat", np.atleast_1d(value).astype(self._dtype))
 
         attest(
             value.ndim == 1,
@@ -1136,8 +1135,7 @@ def y(self, value: ArrayLike) -> None:
 
         attest(
             len(value) >= 6,
-            '"y" dependent variable values count must be equal to or '
-            "greater than 6!",
+            '"y" dependent variable values count must be equal to or greater than 6!',
         )
 
         self._y = value
@@ -1423,7 +1421,7 @@ def x(self) -> NDArrayFloat:
     def x(self, value: ArrayLike) -> None:
         """Setter for the **self.x** property."""
 
-        value = cast(NDArrayFloat, np.atleast_1d(value).astype(self._dtype))
+        value = cast("NDArrayFloat", np.atleast_1d(value).astype(self._dtype))
 
         attest(
             value.ndim == 1,
@@ -1456,7 +1454,7 @@ def y(self) -> NDArrayFloat:
     def y(self, value: ArrayLike) -> None:
         """Setter for the **self.y** property."""
 
-        value = cast(NDArrayFloat, np.atleast_1d(value).astype(self._dtype))
+        value = cast("NDArrayFloat", np.atleast_1d(value).astype(self._dtype))
 
         attest(
             value.ndim == 1,

colour/algebra/tests/test_interpolation.py

@@ -483,7 +483,7 @@
 )
 
 LUT_TABLE: NDArrayFloat = cast(
-    LUT3D,
+    "LUT3D",
     read_LUT(
         os.path.join(
             os.path.dirname(__file__),

colour/appearance/ciecam02.py

@@ -777,7 +777,7 @@ def full_chromatic_adaptation_forward(
     with sdiv_mode():
         RGB_c = (Y_w[..., None] * sdiv(D[..., None], RGB_w) + 1 - D[..., None]) * RGB
 
-    return cast(NDArrayFloat, RGB_c)
+    return cast("NDArrayFloat", RGB_c)
 
 
 def full_chromatic_adaptation_inverse(
@@ -825,7 +825,7 @@ def full_chromatic_adaptation_inverse(
     with sdiv_mode():
         RGB_c = RGB / (Y_w[..., None] * sdiv(D[..., None], RGB_w) + 1 - D[..., None])
 
-    return cast(NDArrayFloat, RGB_c)
+    return cast("NDArrayFloat", RGB_c)
 
 
 def RGB_to_rgb(RGB: ArrayLike) -> NDArrayFloat:

colour/appearance/hunt.py

@@ -506,8 +506,8 @@ def XYZ_to_Hunt(
     # Computing the correlate of *brightness* :math:`Q`.
     # -------------------------------------------------------------------------
     # Computing achromatic signal :math:`A`.
-    A = achromatic_signal(cast(NDArrayFloat, L_AS), S_p, S_w_p, N_bb, A_a)
-    A_w = achromatic_signal(cast(NDArrayFloat, L_AS), S_w_p, S_w_p, N_bb, A_aw)
+    A = achromatic_signal(cast("NDArrayFloat", L_AS), S_p, S_w_p, N_bb, A_a)
+    A_w = achromatic_signal(cast("NDArrayFloat", L_AS), S_w_p, S_w_p, N_bb, A_aw)
 
     Q = brightness_correlate(A, A_w, M, surround.N_b)
     brightness_w = brightness_correlate(A_w, A_w, M_w, surround.N_b)
@@ -742,9 +742,9 @@ def chromatic_adaptation(
     # Computing chromatic adaptation factors.
     if not discount_illuminant:
         L_A_p = spow(L_A, 1 / 3)
-        F_rgb = cast(NDArrayFloat, (1 + L_A_p + h_rgb) / (1 + L_A_p + (1 / h_rgb)))
+        F_rgb = cast("NDArrayFloat", (1 + L_A_p + h_rgb) / (1 + L_A_p + (1 / h_rgb)))
     else:
-        F_rgb = ones(cast(NDArrayFloat, h_rgb).shape)
+        F_rgb = ones(cast("NDArrayFloat", h_rgb).shape)
 
     # Computing Helson-Judd effect parameters.
     if helson_judd_effect:

colour/appearance/nayatani95.py

@@ -787,7 +787,7 @@ def chromatic_strength_function(
 
     theta = np.radians(theta)
 
-    E_s = cast(NDArrayFloat, 0.9394)
+    E_s = cast("NDArrayFloat", 0.9394)
     E_s += -0.2478 * np.sin(1 * theta)
     E_s += -0.0743 * np.sin(2 * theta)
     E_s += +0.0666 * np.sin(3 * theta)

colour/characterisation/aces_it.py

@@ -390,7 +390,7 @@ def generate_illuminants_rawtoaces_v1() -> CanonicalMapping:
 
         # Blackbody from 1000K to 4000K.
         for i in np.arange(1000, 4000, 500):
-            sd = sd_blackbody(cast(float, i), SPECTRAL_SHAPE_RAWTOACES)
+            sd = sd_blackbody(cast("float", i), SPECTRAL_SHAPE_RAWTOACES)
             illuminants[sd.name] = sd
 
         # A.M.P.A.S. variant of ISO 7589 Studio Tungsten.
@@ -498,7 +498,7 @@ def best_illuminant(
             sse = sse_c
             illuminant_b = illuminant
 
-    return cast(SpectralDistribution, illuminant_b)
+    return cast("SpectralDistribution", illuminant_b)
 
 
 def normalise_illuminant(
@@ -734,9 +734,9 @@ def whitepoint_preserving_matrix(
     return M
 
 
-def optimisation_factory_rawtoaces_v1() -> (
-    Tuple[NDArrayFloat, Callable, Callable, Callable]
-):
+def optimisation_factory_rawtoaces_v1() -> Tuple[
+    NDArrayFloat, Callable, Callable, Callable
+]:
     """
     Produce the objective function and *CIE XYZ* colourspace to optimisation
     colourspace/colour model function according to *RAW to ACES* v1.
@@ -861,9 +861,9 @@ def finaliser_function(M: ArrayLike) -> NDArrayFloat:
     )
 
 
-def optimisation_factory_Oklab_15() -> (
-    Tuple[NDArrayFloat, Callable, Callable, Callable]
-):
+def optimisation_factory_Oklab_15() -> Tuple[
+    NDArrayFloat, Callable, Callable, Callable
+]:
     """
     Produce the objective function and *CIE XYZ* colourspace to optimisation
     colourspace/colour model function based on the *Oklab* colourspace.

colour/colorimetry/spectrum.py

@@ -394,7 +394,7 @@ def __contains__(self, wavelength: ArrayLike) -> bool:
         False
         """
 
-        decimals = np.finfo(cast(Any, DTYPE_FLOAT_DEFAULT)).precision
+        decimals = np.finfo(cast("Any", DTYPE_FLOAT_DEFAULT)).precision
 
         return bool(
             np.all(
@@ -531,7 +531,7 @@ def range(self, dtype: Type[DTypeFloat] | None = None) -> NDArrayFloat:
         _CACHE_SHAPE_RANGE[hash_key] = range_
 
         if interval_effective != self._interval:
-            self._interval = cast(float, interval_effective)
+            self._interval = cast("float", interval_effective)
             runtime_warning(
                 f'"{(start, end, interval)}" shape could not be honoured, '
                 f'using "{self}"!'
@@ -717,7 +717,7 @@ def _on_domain_changed(
         """Invalidate *sd._shape* when *sd._domain* is changed."""
 
         if name == "_domain":
-            sd._shape = None  # noqa: SLF001
+            sd._shape = None
 
         return value
 
@@ -1883,8 +1883,7 @@ def display_labels(self, value: Sequence) -> None:
 
         attest(
             is_iterable(value),
-            f'"display_labels" property: "{value}" is not an "iterable" like '
-            f"object!",
+            f'"display_labels" property: "{value}" is not an "iterable" like object!',
         )
 
         attest(
@@ -1899,7 +1898,7 @@ def display_labels(self, value: Sequence) -> None:
 
         self._display_labels = [str(label) for label in value]
         for i, signal in enumerate(self.signals.values()):
-            cast(SpectralDistribution, signal).display_name = self._display_labels[i]
+            cast("SpectralDistribution", signal).display_name = self._display_labels[i]
 
     @property
     def wavelengths(self) -> NDArrayFloat:
@@ -2217,7 +2216,7 @@ def interpolate(
         """
 
         for signal in self.signals.values():
-            cast(SpectralDistribution, signal).interpolate(
+            cast("SpectralDistribution", signal).interpolate(
                 shape, interpolator, interpolator_kwargs
             )
 
@@ -2303,7 +2302,7 @@ def extrapolate(
         """
 
         for signal in self.signals.values():
-            cast(SpectralDistribution, signal).extrapolate(
+            cast("SpectralDistribution", signal).extrapolate(
                 shape, extrapolator, extrapolator_kwargs
             )
 
@@ -2451,7 +2450,7 @@ def align(
         """
 
         for signal in self.signals.values():
-            cast(SpectralDistribution, signal).align(
+            cast("SpectralDistribution", signal).align(
                 shape,
                 interpolator,
                 interpolator_kwargs,
@@ -2536,7 +2535,7 @@ def trim(self, shape: SpectralShape) -> Self:
         """
 
         for signal in self.signals.values():
-            cast(SpectralDistribution, signal).trim(shape)
+            cast("SpectralDistribution", signal).trim(shape)
 
         return self
 
@@ -2585,7 +2584,7 @@ def normalise(self, factor: Real = 1) -> Self:
         """
 
         for signal in self.signals.values():
-            cast(SpectralDistribution, signal).normalise(factor)
+            cast("SpectralDistribution", signal).normalise(factor)
 
         return self
 
@@ -2639,7 +2638,7 @@ def to_sds(self) -> List[SpectralDistribution]:
         """
 
         return [
-            cast(SpectralDistribution, signal.copy())
+            cast("SpectralDistribution", signal.copy())
             for signal in self.signals.values()
         ]
 

colour/colorimetry/tristimulus_values.py

@@ -691,7 +691,7 @@ def sd_to_XYZ_integration(
             "A spectral shape must be explicitly passed with a spectral data array!",
         )
 
-        shape = cast(SpectralShape, shape)
+        shape = cast("SpectralShape", shape)
 
         R = as_float_array(sd)
         shape_R = R.shape
@@ -719,7 +719,7 @@ def sd_to_XYZ_integration(
     d_w = cmfs.shape.interval
 
     with sdiv_mode():
-        k = cast(Real, optional(k, sdiv(100, (np.sum(XYZ_b[..., 1] * S) * d_w))))
+        k = cast("Real", optional(k, sdiv(100, (np.sum(XYZ_b[..., 1] * S) * d_w))))
 
     XYZ = k * np.dot(R * S, XYZ_b) * d_w
 
@@ -1274,8 +1274,7 @@ def sd_to_XYZ(
 
     if isinstance(sd, MultiSpectralDistributions):
         runtime_warning(
-            "A multi-spectral distributions was passed, enforcing integration "
-            "method!"
+            "A multi-spectral distributions was passed, enforcing integration method!"
         )
         function = sd_to_XYZ_integration
     else:

colour/constants/common.py

@@ -45,14 +45,14 @@
 int.
 """
 
-EPSILON: float = cast(float, np.finfo(np.double).eps)
+EPSILON: float = cast("float", np.finfo(np.double).eps)
 """
 Default epsilon value for tolerance and singularities avoidance in various
 computations.
 """
 
 DTYPE_INT_DEFAULT: Type[np.int32 | np.int64] = cast(
-    Type[np.int32 | np.int64],
+    "Type[np.int32 | np.int64]",
     np.sctypeDict.get(
         os.environ.get("COLOUR_SCIENCE__DEFAULT_INT_DTYPE", "int64"), np.int64
     ),
@@ -61,7 +61,7 @@
 
 
 DTYPE_FLOAT_DEFAULT: Type[DTypeFloat] = cast(
-    Type[DTypeFloat],
+    "Type[DTypeFloat]",
     np.sctypeDict.get(
         os.environ.get("COLOUR_SCIENCE__DEFAULT_FLOAT_DTYPE", "float64"),
         np.float64,