def get_scan_parameters_table_from_meta_data(meta_data_array, scan_parameters=None):
if scan_parameters is None:<EOL><INDENT>try:<EOL><INDENT>last_not_parameter_column = meta_data_array.dtype.names.index('<STR_LIT>')  <EOL><DEDENT>except ValueError:<EOL><INDENT>return<EOL><DEDENT>if last_not_parameter_column == len(meta_data_array.dtype.names) - <NUM_LIT:1>:  <EOL><INDENT>return<EOL><DEDENT>scan_par_data = {name: meta_data_array.dtype.fields[name] for name in meta_data_array.dtype.names[last_not_parameter_column + <NUM_LIT:1>:]}<EOL><DEDENT>else:<EOL><INDENT>scan_par_data = collections.OrderedDict()<EOL>for name in scan_parameters:<EOL><INDENT>scan_par_data[name] = meta_data_array.dtype.fields[name]<EOL><DEDENT><DEDENT>return np.ndarray(meta_data_array.shape, np.dtype(scan_par_data), meta_data_array, <NUM_LIT:0>, meta_data_array.strides)<EOL>
Takes the meta data array and returns the scan parameter values as a view of a numpy array only containing the parameter data .
    Parameters
    ----------
    meta_data_array : numpy.ndarray
        The array with the scan parameters.
    scan_parameters : list of strings
        The name of the scan parameters to take. If none all are used.

    Returns
    -------
    numpy.Histogram