Tweak type hints for bezier.py

manimlib/utils/bezier.py

@@ -12,22 +12,23 @@ from manimlib.utils.space_ops import midpoint
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from typing import Callable, Iterable, Sequence, TypeVar
-
-    import numpy.typing as npt
+    from typing import Callable, Sequence, TypeVar
+    from manimlib.typing import VectN, FloatArray
 
     T = TypeVar("T")
 
+    Scalable = float | VectN
+
 
 CLOSED_THRESHOLD = 0.001
 
 
 def bezier(
-    points: Iterable[float | np.ndarray]
-) -> Callable[[float], float | np.ndarray]:
+    points: Sequence[Scalable]
+) -> Callable[[float], Scalable]:
     n = len(points) - 1
 
-    def result(t):
+    def result(t: Scalable) -> Scalable:
         return sum(
             ((1 - t)**(n - k)) * (t**k) * choose(n, k) * point
             for k, point in enumerate(points)
@@ -37,10 +38,10 @@ def bezier(
 
 
 def partial_bezier_points(
-    points: Sequence[np.ndarray],
+    points: Sequence[Scalable],
     a: float,
     b: float
-) -> list[float]:
+) -> list[Scalable]:
     """
     Given an list of points which define
     a bezier curve, and two numbers 0<=a<b<=1,
@@ -67,10 +68,10 @@ def partial_bezier_points(
 # Shortened version of partial_bezier_points just for quadratics,
 # since this is called a fair amount
 def partial_quadratic_bezier_points(
-    points: Sequence[np.ndarray],
+    points: Sequence[Scalable],
     a: float,
     b: float
-) -> list[float]:
+) -> list[Scalable]:
     if a == 1:
         return 3 * [points[-1]]
 
@@ -88,7 +89,7 @@ def partial_quadratic_bezier_points(
 # Linear interpolation variants
 
 
-def interpolate(start: T, end: T, alpha: np.ndarray | float) -> T:
+def interpolate(start: Scalable, end: Scalable, alpha: Scalable) -> Scalable:
     try:
         return (1 - alpha) * start + alpha * end
     except TypeError:
@@ -100,10 +101,10 @@ def interpolate(start: T, end: T, alpha: np.ndarray | float) -> T:
 
 
 def outer_interpolate(
-    start: np.ndarray | float,
-    end: np.ndarray | float,
-    alpha: np.ndarray | float,
-) -> T:
+    start: Scalable,
+    end: Scalable,
+    alpha: Scalable,
+) -> np.ndarray:
     result = np.outer(1 - alpha, start) + np.outer(alpha, end)
     return result.reshape((*np.shape(alpha), *np.shape(start)))
 
@@ -120,8 +121,8 @@ def set_array_by_interpolation(
 
 
 def integer_interpolate(
-    start: T,
-    end: T,
+    start: int,
+    end: int,
     alpha: float
 ) -> tuple[int, float]:
     """
@@ -144,21 +145,21 @@ def integer_interpolate(
     return (value, residue)
 
 
-def mid(start: T, end: T) -> T:
+def mid(start: Scalable, end: Scalable) -> Scalable:
     return (start + end) / 2.0
 
 
-def inverse_interpolate(start: T, end: T, value: T) -> float:
+def inverse_interpolate(start: Scalable, end: Scalable, value: Scalable) -> Scalable:
     return np.true_divide(value - start, end - start)
 
 
 def match_interpolate(
-    new_start: T, 
-    new_end: T, 
-    old_start: T, 
-    old_end: T, 
-    old_value: T
-) -> T:
+    new_start: Scalable,
+    new_end: Scalable,
+    old_start: Scalable,
+    old_end: Scalable,
+    old_value: Scalable
+) -> Scalable:
     return interpolate(
         new_start, new_end,
         inverse_interpolate(old_start, old_end, old_value)
@@ -166,8 +167,8 @@ def match_interpolate(
 
 
 def get_smooth_quadratic_bezier_handle_points(
-    points: Sequence[np.ndarray]
-) -> np.ndarray | list[np.ndarray]:
+    points: FloatArray
+) -> FloatArray:
     """
     Figuring out which bezier curves most smoothly connect a sequence of points.
 
@@ -200,8 +201,8 @@ def get_smooth_quadratic_bezier_handle_points(
 
 
 def get_smooth_cubic_bezier_handle_points(
-    points: npt.ArrayLike
-) -> tuple[np.ndarray, np.ndarray]:
+    points: Sequence[VectN]
+) -> tuple[FloatArray, FloatArray]:
     points = np.array(points)
     num_handles = len(points) - 1
     dim = points.shape[1]
@@ -279,17 +280,17 @@ def diag_to_matrix(
     return matrix
 
 
-def is_closed(points: Sequence[np.ndarray]) -> bool:
+def is_closed(points: FloatArray) -> bool:
     return np.allclose(points[0], points[-1])
 
 
 # Given 4 control points for a cubic bezier curve (or arrays of such)
 # return control points for 2 quadratics (or 2n quadratics) approximating them.
 def get_quadratic_approximation_of_cubic(
-    a0: npt.ArrayLike,
-    h0: npt.ArrayLike,
-    h1: npt.ArrayLike,
-    a1: npt.ArrayLike
+    a0: FloatArray,
+    h0: FloatArray,
+    h1: FloatArray,
+    a1: FloatArray
 ) -> np.ndarray:
     a0 = np.array(a0, ndmin=2)
     h0 = np.array(h0, ndmin=2)
@@ -359,7 +360,7 @@ def get_quadratic_approximation_of_cubic(
 
 
 def get_smooth_quadratic_bezier_path_through(
-    points: list[np.ndarray]
+    points: Sequence[VectN]
 ) -> np.ndarray:
     # TODO
     h0, h1 = get_smooth_cubic_bezier_handle_points(points)