wip

selfdrive/locationd/helpers.py

@@ -1,5 +1,5 @@
 import numpy as np
-from typing import List, Optional, Tuple, Any
+from typing import Any
 
 from cereal import log
 
@@ -12,7 +12,7 @@ class NPQueue:
   def __len__(self) -> int:
     return len(self.arr)
 
-  def append(self, pt: List[float]) -> None:
+  def append(self, pt: list[float]) -> None:
     if len(self.arr) < self.maxlen:
       self.arr = np.append(self.arr, [pt], axis=0)
     else:
@@ -21,7 +21,7 @@ class NPQueue:
 
 
 class PointBuckets:
-  def __init__(self, x_bounds: List[Tuple[float, float]], min_points: List[float], min_points_total: int, points_per_bucket: int, rowsize: int) -> None:
+  def __init__(self, x_bounds: list[tuple[float, float]], min_points: list[float], min_points_total: int, points_per_bucket: int, rowsize: int) -> None:
     self.x_bounds = x_bounds
     self.buckets = {bounds: NPQueue(maxlen=points_per_bucket, rowsize=rowsize) for bounds in x_bounds}
     self.buckets_min_points = dict(zip(x_bounds, min_points, strict=True))
@@ -41,13 +41,13 @@ class PointBuckets:
   def add_point(self, x: float, y: float, bucket_val: float) -> None:
     raise NotImplementedError
 
-  def get_points(self, num_points: Optional[int] = None) -> Any:
+  def get_points(self, num_points: int = None) -> Any:
     points = np.vstack([x.arr for x in self.buckets.values()])
     if num_points is None:
       return points
     return points[np.random.choice(np.arange(len(points)), min(len(points), num_points), replace=False)]
 
-  def load_points(self, points: List[List[float]]) -> None:
+  def load_points(self, points: list[list[float]]) -> None:
     for point in points:
       self.add_point(*point)