docs: def _read_general_type(content, settings):
      """Read a type 11 (general type) RES2DINV data block
  
      Parameters
      ----------
      content : :py:class:`StringIO.StringIO`
          Content of data file
      settings : dict
          Settings for the importer. Not used at the moment
  
      Returns
      -------
  
      """
      header_raw = []
      index = 0
      while index < 9:
          line = content.readline()
          # filter comments
          if line.startswith('//'):
              continue
          else:
              header_raw.append(line.strip())
              index += 1
      # parse header
      header = {
          'name': header_raw[0],
          # unit is meters?
          'unit_spacing': float(header_raw[1]),
          'type': int(header_raw[2]),
          'type2': int(header_raw[3]),
          'type_of_measurements': int(header_raw[5]),
          'nr_measurements': int(header_raw[6]),
          'type_of_x_location': int(header_raw[7]),
          'ip_data': int(header_raw[8]),
      }
  
      if header['type_of_measurements'] == 0:
          raise Exception('Reading in app. resistivity not supported yet')
  
      df = pd.read_csv(
          content,
          delim_whitespace=True,
          header=None,
          names=(
              'nr_elecs',
              'x1',
              'z1',
              'x2',
              'z2',
              'x3',
              'z3',
              'x4',
              'z4',
              'value',
          ),
      )
  
      # print('xxx', df.ix[10, ['x1', 'x2', 'x3', 'x4']])
      # for now ignore the z coordinates and compute simple electrode denotations
      df['a'] = df['x1'] / header['unit_spacing'] + 1
      df['b'] = df['x2'] / header['unit_spacing'] + 1
      df['m'] = df['x3'] / header['unit_spacing'] + 1
      df['n'] = df['x4'] / header['unit_spacing'] + 1
      # print('abmn', df.ix[10, ['a', 'b', 'm', 'n']])
  
      # for now assume value in resistances
      df['r'] = df['value']
  
      # remove any nan values
      df.dropna(axis=0, subset=['a', 'b', 'm', 'n', 'r'], inplace=True)
  
      # ABMN are integers
      df['a'] = df['a'].astype(int)
      df['b'] = df['b'].astype(int)
      df['m'] = df['m'].astype(int)
      df['n'] = df['n'].astype(int)
  
      # drop unused columns
      df.drop(
          [
              'nr_elecs',
              'x1', 'z1',
              'x2', 'z2',
              'x3', 'z3',
              'x4', 'z4',
              'value',
          ], axis=1, inplace=True
      )
      return header, df