old_contents: import warnings
  import numpy as np
  import scipy.io.wavfile
  
  def peak(data, target):
      """ Peak normalize a signal.
      
      Normalize an input signal to a user specifed peak amplitude.   
  
      Params
      -------
      data : ndarray
          Input multichannel audio data.
      target : float
          Desired peak amplitude in dB.
  
      Returns
      -------
      output : ndarray
          Peak normalized output data.
      """
      # find the amplitude of the largest peak
      current_peak = np.max(np.abs(data))
  
      # calculate the gain needed to scale to the desired peak level
      gain = np.power(10.0, target/20.0) / current_peak
      output = gain * data
      
      # check for potentially clipped samples
      if np.max(np.abs(output)) >= 1.0:
          warnings.warn("Possible clipped samples in output.")
  
      return output
  
  def loudness(data, input_loudness, target_loudness):
      """ Loudness normalize a signal.
      
      Normalize an input signal to a user loudness in dB LKFS.   
  
      Params
      -------
      data : ndarray
          Input multichannel audio data.
      input_loudness : float
          Loudness of the input in dB LUFS. 
      target_loudness : float
          Target loudness of the output in dB LUFS.
          
      Returns
      -------
      output : ndarray
          Loudness normalized output data.
      """    
      # calculate the gain needed to scale to the desired loudness level
      delta_loudness = target_loudness - input_loudness
      gain = np.power(10.0, delta_loudness/20.0)
  
      output = gain * data
  
      # check for potentially clipped samples
      if np.max(np.abs(output)) >= 1.0:
          warnings.warn("Possible clipped samples in output.")
  
      return output
new_contents: import warnings
  import numpy as np
  
  
  def peak(data, target):
      """ Peak normalize a signal.
      
      Normalize an input signal to a user specifed peak amplitude.   
  
      Params
      -------
      data : ndarray
          Input multichannel audio data.
      target : float
          Desired peak amplitude in dB.
  
      Returns
      -------
      output : ndarray
          Peak normalized output data.
      """
      # find the amplitude of the largest peak
      current_peak = np.max(np.abs(data))
  
      # calculate the gain needed to scale to the desired peak level
      gain = np.power(10.0, target/20.0) / current_peak
      output = gain * data
      
      # check for potentially clipped samples
      if np.max(np.abs(output)) >= 1.0:
          warnings.warn("Possible clipped samples in output.")
  
      return output
  
  def loudness(data, input_loudness, target_loudness):
      """ Loudness normalize a signal.
      
      Normalize an input signal to a user loudness in dB LKFS.   
  
      Params
      -------
      data : ndarray
          Input multichannel audio data.
      input_loudness : float
          Loudness of the input in dB LUFS. 
      target_loudness : float
          Target loudness of the output in dB LUFS.
          
      Returns
      -------
      output : ndarray
          Loudness normalized output data.
      """    
      # calculate the gain needed to scale to the desired loudness level
      delta_loudness = target_loudness - input_loudness
      gain = np.power(10.0, delta_loudness/20.0)
  
      output = gain * data
  
      # check for potentially clipped samples
      if np.max(np.abs(output)) >= 1.0:
          warnings.warn("Possible clipped samples in output.")
  
      return output
  
subject: Update normalize.py