torqued: Refactor to share code with magd (#30238)

* refactor to extract common functions and classes out

* Add helpers.py to release files

* refactor
old-commit-hash: 9e24a11f17117caebc595165f59bfc0109af0287

release/files_common

@@ -246,6 +246,7 @@ selfdrive/locationd/models/gnss_helpers.py
 
 selfdrive/locationd/torqued.py
 selfdrive/locationd/calibrationd.py
+selfdrive/locationd/helpers.py
 
 system/logcatd/.gitignore
 system/logcatd/SConscript

selfdrive/locationd/helpers.py

@@ -0,0 +1,50 @@
+import numpy as np
+from typing import List, Optional, Tuple, Any
+
+
+class NPQueue:
+  def __init__(self, maxlen: int, rowsize: int) -> None:
+    self.maxlen = maxlen
+    self.arr = np.empty((0, rowsize))
+
+  def __len__(self) -> int:
+    return len(self.arr)
+
+  def append(self, pt: List[float]) -> None:
+    if len(self.arr) < self.maxlen:
+      self.arr = np.append(self.arr, [pt], axis=0)
+    else:
+      self.arr[:-1] = self.arr[1:]
+      self.arr[-1] = pt
+
+
+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:
+    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))
+    self.min_points_total = min_points_total
+
+  def bucket_lengths(self) -> List[int]:
+    return [len(v) for v in self.buckets.values()]
+
+  def __len__(self) -> int:
+    return sum(self.bucket_lengths())
+
+  def is_valid(self) -> bool:
+    individual_buckets_valid = all(len(v) >= min_pts for v, min_pts in zip(self.buckets.values(), self.buckets_min_points.values(), strict=True))
+    total_points_valid = self.__len__() >= self.min_points_total
+    return individual_buckets_valid and total_points_valid
+
+  def add_point(self, x: float, y: float, bucket_val: float) -> None:
+    raise NotImplementedError
+
+  def get_points(self, num_points: Optional[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:
+    for point in points:
+      self.add_point(*point)

selfdrive/locationd/torqued.py

@@ -12,6 +12,7 @@ from openpilot.common.realtime import config_realtime_process, DT_MDL
 from openpilot.common.filter_simple import FirstOrderFilter
 from openpilot.system.swaglog import cloudlog
 from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
+from openpilot.selfdrive.locationd.helpers import PointBuckets
 
 HISTORY = 5  # secs
 POINTS_PER_BUCKET = 1500
@@ -43,55 +44,13 @@ def slope2rot(slope):
   return np.array([[cos, -sin], [sin, cos]])
 
 
-class NPQueue:
-  def __init__(self, maxlen, rowsize):
-    self.maxlen = maxlen
-    self.arr = np.empty((0, rowsize))
-
-  def __len__(self):
-    return len(self.arr)
-
-  def append(self, pt):
-    if len(self.arr) < self.maxlen:
-      self.arr = np.append(self.arr, [pt], axis=0)
-    else:
-      self.arr[:-1] = self.arr[1:]
-      self.arr[-1] = pt
-
-
-class PointBuckets:
-  def __init__(self, x_bounds, min_points, min_points_total):
-    self.x_bounds = x_bounds
-    self.buckets = {bounds: NPQueue(maxlen=POINTS_PER_BUCKET, rowsize=3) for bounds in x_bounds}
-    self.buckets_min_points = dict(zip(x_bounds, min_points, strict=True))
-    self.min_points_total = min_points_total
-
-  def bucket_lengths(self):
-    return [len(v) for v in self.buckets.values()]
-
-  def __len__(self):
-    return sum(self.bucket_lengths())
-
-  def is_valid(self):
-    return all(len(v) >= min_pts for v, min_pts in zip(self.buckets.values(), self.buckets_min_points.values(), strict=True)) \
-                                                                                and (self.__len__() >= self.min_points_total)
-
+class TorqueBuckets(PointBuckets):
   def add_point(self, x, y):
     for bound_min, bound_max in self.x_bounds:
       if (x >= bound_min) and (x < bound_max):
         self.buckets[(bound_min, bound_max)].append([x, 1.0, y])
         break
 
-  def get_points(self, num_points=None):
-    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):
-    for x, y in points:
-      self.add_point(x, y)
-
 
 class TorqueEstimator:
   def __init__(self, CP, decimated=False):
@@ -175,7 +134,11 @@ class TorqueEstimator:
     self.resets += 1.0
     self.decay = MIN_FILTER_DECAY
     self.raw_points = defaultdict(lambda: deque(maxlen=self.hist_len))
-    self.filtered_points = PointBuckets(x_bounds=STEER_BUCKET_BOUNDS, min_points=self.min_bucket_points, min_points_total=self.min_points_total)
+    self.filtered_points = TorqueBuckets(x_bounds=STEER_BUCKET_BOUNDS,
+                                         min_points=self.min_bucket_points,
+                                         min_points_total=self.min_points_total,
+                                         points_per_bucket=POINTS_PER_BUCKET,
+                                         rowsize=3)
 
   def estimate_params(self):
     points = self.filtered_points.get_points(self.fit_points)