|  |  |  | import numpy as np
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							|  |  |  | from typing import Any
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							|  |  |  | from cereal import log
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							|  |  |  | class NPQueue:
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							|  |  |  |   def __init__(self, maxlen: int, rowsize: int) -> None:
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							|  |  |  |     self.maxlen = maxlen
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							|  |  |  |     self.arr = np.empty((0, rowsize))
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							|  |  |  |   def __len__(self) -> int:
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							|  |  |  |     return len(self.arr)
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							|  |  |  |   def append(self, pt: list[float]) -> None:
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							|  |  |  |     if len(self.arr) < self.maxlen:
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							|  |  |  |       self.arr = np.append(self.arr, [pt], axis=0)
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							|  |  |  |     else:
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							|  |  |  |       self.arr[:-1] = self.arr[1:]
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							|  |  |  |       self.arr[-1] = pt
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							|  |  |  | class PointBuckets:
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							|  |  |  |   def __init__(self, x_bounds: list[tuple[float, float]], min_points: list[float], min_points_total: int, points_per_bucket: int, rowsize: int) -> None:
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							|  |  |  |     self.x_bounds = x_bounds
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							|  |  |  |     self.buckets = {bounds: NPQueue(maxlen=points_per_bucket, rowsize=rowsize) for bounds in x_bounds}
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							|  |  |  |     self.buckets_min_points = dict(zip(x_bounds, min_points, strict=True))
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							|  |  |  |     self.min_points_total = min_points_total
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							|  |  |  |   def __len__(self) -> int:
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							|  |  |  |     return sum([len(v) for v in self.buckets.values()])
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							|  |  |  |   def is_valid(self) -> bool:
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							|  |  |  |     individual_buckets_valid = all(len(v) >= min_pts for v, min_pts in zip(self.buckets.values(), self.buckets_min_points.values(), strict=True))
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							|  |  |  |     total_points_valid = self.__len__() >= self.min_points_total
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							|  |  |  |     return individual_buckets_valid and total_points_valid
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							|  |  |  |   def is_calculable(self) -> bool:
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							|  |  |  |     return all(len(v) > 0 for v in self.buckets.values())
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							|  |  |  |   def add_point(self, x: float, y: float, bucket_val: float) -> None:
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							|  |  |  |     raise NotImplementedError
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							|  |  |  |   def get_points(self, num_points: int = None) -> Any:
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							|  |  |  |     points = np.vstack([x.arr for x in self.buckets.values()])
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							|  |  |  |     if num_points is None:
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							|  |  |  |       return points
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							|  |  |  |     return points[np.random.choice(np.arange(len(points)), min(len(points), num_points), replace=False)]
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							|  |  |  |   def load_points(self, points: list[list[float]]) -> None:
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							|  |  |  |     for point in points:
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							|  |  |  |       self.add_point(*point)
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							|  |  |  | class ParameterEstimator:
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							|  |  |  |   """ Base class for parameter estimators """
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							|  |  |  |   def reset(self) -> None:
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							|  |  |  |     raise NotImplementedError
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							|  |  |  |   def handle_log(self, t: int, which: str, msg: log.Event) -> None:
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							|  |  |  |     raise NotImplementedError
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							|  |  |  |   def get_msg(self, valid: bool, with_points: bool) -> log.Event:
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							|  |  |  |     raise NotImplementedError
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