code: def calculate_distance_profile(
      dot_prod, q_mean, q_std, t_mean, t_std, q_len, n_t_subs
  ):
      """Calculate the distance profile for the given query.
  
      Parameters
      ----------
          dot_prod: numpy.array
              Sliding dot products between the time series and the query.
          q_mean: float
              Mean of the elements of the query.
          q_std: float
              Standard deviation of elements of the query.
          t_mean: numpy.array
              Array with the mean of the elements from each subsequence of
              length(query) from the time series.
          t_std: numpy.array
              Array with the standard deviation of the elements from each
              subsequence of length(query) from the time series.
          q_len: int
              Length of the query.
          n_t_subs: int
              Number of subsequences in the time series.
  
      Output
      ------
          d: numpy.array
              Distance profile of query q.
      """
      d = [
          2
          * q_len
          * (
              1
              - (
                  (dot_prod[i] - q_len * q_mean * t_mean[i])
                  / (q_len * q_std * t_std[i])
              )
          )
          for i in range(0, n_t_subs)
      ]
      d = np.absolute(d)
      d = np.sqrt(d)
  
      return d
  
imports: import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_distance_profile(dot_prod, q_mean, q_std, t_mean, t_std, q_len, n_t_subs) and returns: d