Revert "Disallow function calls in default arguments"

This reverts commit 12a0a6a.

Author: yaugenst-flex

pyproject.toml

@@ -274,6 +274,7 @@ extend-ignore = [
   "RUF015", # next(iter(...)) instead of list(...)[0]
   "E501",   # line too long
   "B905",   # `zip()` without an explicit `strict=` parameter
+  "B008",   # TODO: Disallow function calls in default arguments
   "UP007",  # TODO: Remove once Python >= 3.10
   "NPY002", # TODO: Revisit RNG handling
 ]

tidy3d/components/grid/grid_spec.py

@@ -1073,9 +1073,9 @@ def from_layer_bounds(
         min_steps_along_axis: np.PositiveFloat = None,
         bounds_refinement: GridRefinement = None,
         bounds_snapping: Literal["bounds", "lower", "upper", "center"] = "lower",
-        corner_finder: CornerFinderSpec = None,
+        corner_finder: CornerFinderSpec = CornerFinderSpec(),
         corner_snapping: bool = True,
-        corner_refinement: GridRefinement = None,
+        corner_refinement: GridRefinement = GridRefinement(),
         refinement_inside_sim_only: bool = True,
         gap_meshing_iters: pd.NonNegativeInt = 1,
         dl_min_from_gap_width: bool = True,
@@ -1115,11 +1115,6 @@ def from_layer_bounds(
         >>> layer = LayerRefinementSpec.from_layer_bounds(axis=2, bounds=(0,1))
 
         """
-        if corner_finder is None:
-            corner_finder = CornerFinderSpec()
-        if corner_refinement is None:
-            corner_refinement = GridRefinement()
-
         center = Box.unpop_axis((bounds[0] + bounds[1]) / 2, (0, 0), axis)
         size = Box.unpop_axis((bounds[1] - bounds[0]), (inf, inf), axis)
 
@@ -1147,9 +1142,9 @@ def from_bounds(
         min_steps_along_axis: np.PositiveFloat = None,
         bounds_refinement: GridRefinement = None,
         bounds_snapping: Literal["bounds", "lower", "upper", "center"] = "lower",
-        corner_finder: CornerFinderSpec = None,
+        corner_finder: CornerFinderSpec = CornerFinderSpec(),
         corner_snapping: bool = True,
-        corner_refinement: GridRefinement = None,
+        corner_refinement: GridRefinement = GridRefinement(),
         refinement_inside_sim_only: bool = True,
         gap_meshing_iters: pd.NonNegativeInt = 1,
         dl_min_from_gap_width: bool = True,
@@ -1191,11 +1186,6 @@ def from_bounds(
         >>> layer = LayerRefinementSpec.from_bounds(axis=2, rmin=(0,0,0), rmax=(1,1,1))
 
         """
-        if corner_finder is None:
-            corner_finder = CornerFinderSpec()
-        if corner_refinement is None:
-            corner_refinement = GridRefinement()
-
         box = Box.from_bounds(rmin=rmin, rmax=rmax)
         if axis is None:
             axis = np.argmin(box.size)
@@ -1222,9 +1212,9 @@ def from_structures(
         min_steps_along_axis: np.PositiveFloat = None,
         bounds_refinement: GridRefinement = None,
         bounds_snapping: Literal["bounds", "lower", "upper", "center"] = "lower",
-        corner_finder: CornerFinderSpec = None,
+        corner_finder: CornerFinderSpec = CornerFinderSpec(),
         corner_snapping: bool = True,
-        corner_refinement: GridRefinement = None,
+        corner_refinement: GridRefinement = GridRefinement(),
         refinement_inside_sim_only: bool = True,
         gap_meshing_iters: pd.NonNegativeInt = 1,
         dl_min_from_gap_width: bool = True,
@@ -1259,10 +1249,6 @@ def from_structures(
             Take into account autodetected minimal PEC gap width when determining ``dl_min``.
 
         """
-        if corner_finder is None:
-            corner_finder = CornerFinderSpec()
-        if corner_refinement is None:
-            corner_refinement = GridRefinement()
 
         all_bounds = tuple(structure.geometry.bounds for structure in structures)
         rmin = tuple(min(b[i] for b, _ in all_bounds) for i in range(3))
@@ -2734,7 +2720,7 @@ def auto(
         layer_refinement_specs: list[LayerRefinementSpec] = (),
         dl_min: pd.NonNegativeFloat = 0.0,
         min_steps_per_sim_size: pd.PositiveFloat = 10.0,
-        mesher: MesherType = None,
+        mesher: MesherType = GradedMesher(),
     ) -> GridSpec:
         """Use the same :class:`AutoGrid` along each of the three directions.
 
@@ -2768,8 +2754,6 @@ def auto(
         GridSpec
             :class:`GridSpec` with the same automatic nonuniform grid settings in each direction.
         """
-        if mesher is None:
-            mesher = GradedMesher()
 
         grid_1d = AutoGrid(
             min_steps_per_wvl=min_steps_per_wvl,
@@ -2813,7 +2797,7 @@ def quasiuniform(
         max_scale: pd.PositiveFloat = 1.4,
         override_structures: list[StructureType] = (),
         snapping_points: tuple[CoordinateOptional, ...] = (),
-        mesher: MesherType = None,
+        mesher: MesherType = GradedMesher(),
     ) -> GridSpec:
         """Use the same :class:`QuasiUniformGrid` along each of the three directions.
 
@@ -2837,8 +2821,6 @@ def quasiuniform(
         GridSpec
             :class:`GridSpec` with the same uniform grid size in each direction.
         """
-        if mesher is None:
-            mesher = GradedMesher()
 
         grid_1d = QuasiUniformGrid(dl=dl, max_scale=max_scale, mesher=mesher)
         return cls(

tidy3d/components/mode/solver.py

@@ -472,9 +472,8 @@ def solver_diagonal(
         # code associated with these options is included below in case it's useful in the future
         enable_preconditioner = False
         analyze_conditioning = False
-        _threshold = 0.9 * np.abs(pec_val)
 
-        def incidence_matrix_for_pec(eps_vec, threshold=_threshold):
+        def incidence_matrix_for_pec(eps_vec, threshold=0.9 * np.abs(pec_val)):
             """Incidence matrix indicating non-PEC entries associated with 'eps_vec'."""
             nnz = eps_vec[np.abs(eps_vec) < threshold]
             eps_nz = eps_vec.copy()

tidy3d/plugins/dispersion/web.py

@@ -314,7 +314,7 @@ def run(
     num_poles: PositiveInt = 1,
     num_tries: PositiveInt = 50,
     tolerance_rms: NonNegativeFloat = 1e-2,
-    advanced_param: AdvancedFitterParam = None,
+    advanced_param: AdvancedFitterParam = AdvancedFitterParam(),
 ) -> tuple[PoleResidue, float]:
     """Execute the data fit using the stable fitter in the server.
 
@@ -336,8 +336,6 @@ def run(
     Tuple[:class:`.PoleResidue`, float]
         Best results of multiple fits: (dispersive medium, RMS error).
     """
-    if advanced_param is None:
-        advanced_param = AdvancedFitterParam()
     task = FitterData.create(fitter, num_poles, num_tries, tolerance_rms, advanced_param)
     return task.run()
 
@@ -359,9 +357,7 @@ def fit(
         num_tries: PositiveInt = 50,
         tolerance_rms: NonNegativeFloat = 1e-2,
         guess: PoleResidue = None,
-        advanced_param: AdvancedFitterParam = None,
+        advanced_param: AdvancedFitterParam = AdvancedFitterParam(),
     ) -> tuple[PoleResidue, float]:
         """Deprecated."""
-        if advanced_param is None:
-            advanced_param = AdvancedFitterParam()
         return run(self, num_poles, num_tries, tolerance_rms, advanced_param)

tidy3d/plugins/microwave/array_factor.py

@@ -712,7 +712,7 @@ def array_factor(
         theta: Union[float, ArrayLike],
         phi: Union[float, ArrayLike],
         frequency: Union[NonNegativeFloat, ArrayLike],
-        medium: MediumType3D = None,
+        medium: MediumType3D = Medium(),
     ) -> ArrayLike:
         """
         Compute the array factor for a 3D antenna array.
@@ -731,8 +731,6 @@ def array_factor(
         ArrayLike
             Array factor values for each combination of theta and phi.
         """
-        if medium is None:
-            medium = Medium()
 
         # Convert all inputs to numpy arrays
         theta_array = np.atleast_1d(theta)