docs: def dolnp3_0(Data):
      """
      DEPRECATED!!  USE dolnp()
      Desciption: takes a list of dicts with the controlled vocabulary of 3_0 and calls dolnp on them after reformating for compatibility.
      Parameters
      __________
      Data : nested list of dictionarys with keys
          dir_dec
          dir_inc
          dir_tilt_correction
          method_codes
  
      Returns
      -------
          ReturnData : dictionary with keys
              dec : fisher mean dec of data in Data
              inc : fisher mean inc of data in Data
              n_lines : number of directed lines [method_code = DE-BFL or DE-FM]
              n_planes : number of best fit planes [method_code = DE-BFP]
              alpha95  : fisher confidence circle from Data
              R : fisher R value of Data
              K : fisher k value of Data
      Effects
          prints to screen in case of no data
      """
      if len(Data) == 0:
          print("This function requires input Data have at least 1 entry")
          return {}
      if len(Data) == 1:
          ReturnData = {}
          ReturnData["dec"] = Data[0]['dir_dec']
          ReturnData["inc"] = Data[0]['dir_inc']
          ReturnData["n_total"] = '1'
          if "DE-BFP" in Data[0]['method_codes']:
              ReturnData["n_lines"] = '0'
              ReturnData["n_planes"] = '1'
          else:
              ReturnData["n_planes"] = '0'
              ReturnData["n_lines"] = '1'
          ReturnData["alpha95"] = ""
          ReturnData["R"] = ""
          ReturnData["K"] = ""
          return ReturnData
      else:
          LnpData = []
          for n, d in enumerate(Data):
              LnpData.append({})
              LnpData[n]['dec'] = d['dir_dec']
              LnpData[n]['inc'] = d['dir_inc']
              LnpData[n]['tilt_correction'] = d['dir_tilt_correction']
              if 'method_codes' in list(d.keys()):
                  if "DE-BFP" in d['method_codes']:
                      LnpData[n]['dir_type'] = 'p'
                  else:
                      LnpData[n]['dir_type'] = 'l'
          # get a sample average from all specimens
          ReturnData = dolnp(LnpData, 'dir_type')
          return ReturnData