code: def _get_critical_values(
      dist_matrix, alpha, p_ix, r_max, r_min=0
  ):  # pragma: no cover
      """Computes the critical values of a given distance matrix.
  
      Parameters
      ----------
      dist_matrix : array-like, shape (n_samples, n_features)
          The distance matrix w.r.t. to the training samples.
  
      p_ix : int
          Subsetting index
  
      alpha : int, default = 0.5
          The neighbourhood parameter measures how large of a neighbourhood
          should be considered "local".
  
      r_max : int
          Maximum neighbourhood radius
  
      r_min : int, default = 0
          Minimum neighbourhood radius
  
      Returns
      -------
      cv : array, shape (n_critical_val, )
          Returns a list of critical values.
      """
  
      distances = dist_matrix[p_ix, :]
      mask = (r_min < distances) & (distances <= r_max)
      cv = np.sort(
          np.concatenate((distances[mask], distances[mask] / alpha))
      )
      return cv
  
file_path: pyod/pyod/models/loci.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from numba import njit
  from scipy.spatial.distance import pdist, squareform
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 1 for calling a global function with example usage: _get_critical_values(dist_matrix, alpha, p_ix, r_max, r_min) and returns: cv