code: def _linear_normalization_from_0to1(
          data,
          mask,
          theoretical_max,
          theoretical_min=0,
          message="normalizing equalized data to fit in 0 to 1 range"):
      
  
      LOG.debug(message)
      if theoretical_min != 0:
          data[mask] = data[mask] - theoretical_min
          theoretical_max = theoretical_max - theoretical_min
      data[mask] = data[mask] / theoretical_max
docs: Do a linear normalization so all data is in the 0 to 1 range.
  
      This is a sloppy but fast calculation that relies on parameters giving it
      the correct theoretical current max and min so it can scale the data
      accordingly.