41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
from __future__ import annotations
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import math
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from typing import TYPE_CHECKING
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import numpy as np
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if TYPE_CHECKING:
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import numpy.typing as npt
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TWO_SIGMA = 2
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def bootstrap_minima(series: list[int], bootstrap_size: int) -> npt.NDArray[np.int64]:
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rng = np.random.default_rng()
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return np.array([np.min(rng.choice(series, len(series), replace=True)) for _ in range(bootstrap_size)])
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def bootstrap_noise_floor(series: list[int], bootstrap_size: int) -> np.float64:
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return np.std(bootstrap_minima(series, bootstrap_size))
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def combined_series_noise_floor(series1: list[int], series2: list[int], bootstrap_size: int) -> float:
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noise_floor1 = bootstrap_noise_floor(series1, bootstrap_size)
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noise_floor2 = bootstrap_noise_floor(series2, bootstrap_size)
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return math.sqrt(noise_floor1 * noise_floor1 + noise_floor2 * noise_floor2)
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def series2_faster_95_confidence(
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series1: list[int], series2: list[int], bootstrap_size: int
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) -> tuple[float, float] | None:
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min1 = min(series1)
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min_diff = min1 - min(series2)
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if min_diff <= 0:
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return None
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combined_noise_floor = combined_series_noise_floor(series1, series2, bootstrap_size)
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percent_diff = 100 * min_diff / min1
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uncertainty = TWO_SIGMA * combined_noise_floor / min1
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if combined_noise_floor == 0 or min_diff / combined_noise_floor > TWO_SIGMA:
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return percent_diff, uncertainty
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return None
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