new_contents: #Make a function for this since we will be doing this multiple times
  from utils.data_cube_utilities.dc_mosaic import (ls7_unpack_qa, ls8_unpack_qa, ls5_unpack_qa)
  import numpy as np
  
  
  def landsat_qa_clean_mask(dataset, platform):
      
      processing_options = {
          "LANDSAT_5": ls5_unpack_qa,
          "LANDSAT_7": ls7_unpack_qa,
          "LANDSAT_8": ls8_unpack_qa
      }
      
      #Clean mask creation to filter out pixels that are not suitable for analysis
      clear_xarray  = processing_options[platform](dataset.pixel_qa, "clear")  
      water_xarray  = processing_options[platform](dataset.pixel_qa, "water")
      
      #use logical or statement to elect viable pixels for analysis
      return np.logical_or(clear_xarray.values.astype(bool), water_xarray.values.astype(bool))
  
  
  # def landsat_cf_clean_mask(dataset, platform):
      
  #     processing_options = {
  #         "LANDSAT_8": l8_clean_mask,
  #         "LANDSAT_7": ls7_unpack_qa
  #     }
      
  #     #Clean mask creation to filter out pixels that are not suitable for analysis
  #     clear_xarray  = processing_options[platform](dataset.pixel_qa, "clear")  
  #     water_xarray  = processing_options[platform](dataset.pixel_qa, "water")
      
  #     #use logical or statement to elect viable pixels for analysis
  #     return np.logical_or(clear_xarray.values.astype(bool), water_xarray.values.astype(bool))
subject: Create clean_mask module in data_cube_utilities