class_docstr: Distort signal by clipping a random percentage of points
  
  The percentage of points that will be clipped is drawn from a uniform distribution between
  the two input parameters min_percentile_threshold and max_percentile_threshold. If for instance
  30% is drawn, the samples are clipped if they're below the 15th or above the 85th percentile.
docstr: :param min_percentile_threshold: int, A lower bound on the total percent of samples that
      will be clipped
  :param max_percentile_threshold: int, An upper bound on the total percent of samples that
      will be clipped
  :param p: The probability of applying this transform
code: def __init__(
      self,
      min_percentile_threshold: int = 0,
      max_percentile_threshold: int = 40,
      p: float = 0.5,
  ):
      """
      :param min_percentile_threshold: int, A lower bound on the total percent of samples that
          will be clipped
      :param max_percentile_threshold: int, An upper bound on the total percent of samples that
          will be clipped
      :param p: The probability of applying this transform
      """
      super().__init__(p)
      assert min_percentile_threshold <= max_percentile_threshold
      assert 0 <= min_percentile_threshold <= 100
      assert 0 <= max_percentile_threshold <= 100
      self.min_percentile_threshold = min_percentile_threshold
      self.max_percentile_threshold = max_percentile_threshold
  
file_path: audiomentations/audiomentations/augmentations/clipping_distortion.py
imports: import random
  import numpy
  from numpy.typing import NDArray
  from audiomentations.core.transforms_interface import BaseWaveformTransform
label_desc: Use image node_id 1 to create a new ClippingDistortion object from inherited base classes: BaseWaveformTransform with example: obj = ClippingDistortion(min_percentile_threshold, max_percentile_threshold, p)