class_docstr: Parameters
  ---------
  country_group : h5py.Group
      The group for a single country in an HDF5 daily pricing file.
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docstr: Parameters
  ----------
  columns : list of str
     'open', 'high', 'low', 'close', or 'volume'
  start_date: Timestamp
     Beginning of the window range.
  end_date: Timestamp
     End of the window range.
  assets : list of int
     The asset identifiers in the window.
  
  Returns
  -------
  list of np.ndarray
      A list with an entry per field of ndarrays with shape
      (minutes in range, sids) with a dtype of float64, containing the
      values for the respective field over start and end dt range.
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code: def load_raw_arrays(self, columns, start_date, end_date, assets):
      """
      Parameters
      ----------
      columns : list of str
         'open', 'high', 'low', 'close', or 'volume'
      start_date: Timestamp
         Beginning of the window range.
      end_date: Timestamp
         End of the window range.
      assets : list of int
         The asset identifiers in the window.
  
      Returns
      -------
      list of np.ndarray
          A list with an entry per field of ndarrays with shape
          (minutes in range, sids) with a dtype of float64, containing the
          values for the respective field over start and end dt range.
      """
      self._validate_timestamp(start_date)
      self._validate_timestamp(end_date)
  
      start = start_date.asm8
      end = end_date.asm8
      date_slice = self._compute_date_range_slice(start, end)
      n_dates = date_slice.stop - date_slice.start
  
      # Create a buffer into which we'll read data from the h5 file.
      # Allocate an extra row of space that will always contain null values.
      # We'll use that space to provide "data" for entries in ``assets`` that
      # are unknown to us.
      full_buf = np.zeros(
          (len(self.sids) + 1, n_dates), dtype=np.uint32
      )
      # We'll only read values into this portion of the read buf.
      mutable_buf = full_buf[:-1]
  
      # Indexer that converts an array aligned to self.sids (which is what we
      # pull from the h5 file) into an array aligned to ``assets``.
      #
      # Unknown assets will have an index of -1, which means they'll always
      # pull from the last row of the read buffer. We allocated an extra
      # empty row above so that these lookups will cause us to fill our
      # output buffer with "null" values.
      sid_selector = self._make_sid_selector(assets)
  
      out = []
      for column in columns:
          # Zero the buffer to prepare to receive new data.
          mutable_buf.fill(0)
  
          dataset = self._country_group[DATA][column]
  
          # Fill the mutable portion of our buffer with data from the file.
          dataset.read_direct(
              mutable_buf,
              np.s_[:, date_slice],
          )
  
          # Select data from the **full buffer**. Unknown assets will pull
          # from the last row, which is always empty.
          out.append(
              self._postprocessors[column](full_buf[sid_selector].T)
          )
  
      return out
  
imports: from functools import partial
  import h5py
  import logbook
  import numpy
  import pandas
  from six import iteritems, raise_from, viewkeys
  from six.moves import reduce
  from zipline.data.bar_reader import NoDataAfterDate, NoDataBeforeDate, NoDataForSid, NoDataOnDate
  from zipline.data.session_bars import CurrencyAwareSessionBarReader
  from zipline.utils.memoize import lazyval
  from zipline.utils.numpy_utils import bytes_array_to_native_str_object_array
  from zipline.utils.pandas_utils import check_indexes_all_same
label_desc: Use image node_id 5 for calling the HDF5DailyBarReader obj's underlying member method code with example usage: obj.load_raw_arrays(columns, start_date, end_date, assets) and returns: out