`.  When used with a structured
        data-type, arrays are returned for each field.  Default is False.
    dtype : numpy data type
        A NumPy dtype instance, can be a structured dtype to map to the
        columns of the file.
    encoding : str, optional
        Encoding used to decode the inputfile. The special value 'bytes'
        (the default) enables backwards-compatible behavior for `converters`,
        ensuring that inputs to the converter functions are encoded
        bytes objects. The special value 'bytes' has no additional effect if
        ``converters=None``. If encoding is ``'bytes'`` or ``None``, the
        default system encoding is used.

    Returns
    -------
    ndarray
        NumPy array.

    Examples
    --------
    First we create a file for the example.

    >>> s1 = '1.0,2.0,3.0\n4.0,5.0,6.0\n'
    >>> with open('example1.csv', 'w') as f:
    ...     f.write(s1)
    >>> a1 = read_from_filename('example1.csv')
    >>> a1
    array([[1., 2., 3.],
           [4., 5., 6.]])

    The second example has columns with different data types, so a
    one-dimensional array with a structured data type is returned.
    The tab character is used as the field delimiter.

    >>> s2 = '1.0\t10\talpha\n2.3\t25\tbeta\n4.5\t16\tgamma\n'
    >>> with open('example2.tsv', 'w') as f:
    ...     f.write(s2)
    >>> a2 = read_from_filename('example2.tsv', delimiter='\t')
    >>> a2
    array([(1. , 10, b'alpha'), (2.3, 25, b'beta'), (4.5, 16, b'gamma')],
          dtype=[('f0', '<f8'), ('f1', 'u1'), ('f2', 'S5')])
    Fr‚