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20 lines
840 B
20 lines
840 B
import numpy as np
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from hep_ml.reweight import BinsReweighter
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def select_feature(feature: np.ndarray, limits: tuple[float, float]) -> tuple[np.ndarray, list]:
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selection_indices = []
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for index, value in enumerate(feature):
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if value > limits[0] and value < limits[1]:
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selection_indices.append(index)
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return feature[selection_indices], selection_indices
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def reweight_feature(original_feature: list, target_feature: list, n_bins: int, n_neighs: int = 2):
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original_weights = np.ones(len(original_feature))
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bin_reweighter = BinsReweighter(n_bins = n_bins, n_neighs = n_neighs)
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bin_reweighter.fit(original = original_feature, target = target_feature, original_weight = original_weights)
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return bin_reweighter.predict_weights(original = original_feature, original_weight = original_weights)
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