ards compatibility, byte
    strings will be decoded as 'latin1'. The default is '#'.
delimiter : str, optional
    The character used to separate the values. For backwards compatibility,
    byte strings will be decoded as 'latin1'. The default is whitespace.

    .. versionchanged:: 1.23.0
       Only single character delimiters are supported. Newline characters
       cannot be used as the delimiter.

converters : dict or callable, optional
    Converter functions to customize value parsing. If `converters` is
    callable, the function is applied to all columns, else it must be a
    dict that maps column number to a parser function.
    See examples for further details.
    Default: None.

    .. versionchanged:: 1.23.0
       The ability to pass a single callable to be applied to all columns
       was added.

skiprows : int, optional
    Skip the first `skiprows` lines, including comments; default: 0.
usecols : int or sequence, optional
    Which columns to read, with 0 being the first. For example,
    ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.
    The default, None, results in all columns being read.
unpack : bool, optional
    If True, the returned array is transposed, so that arguments may be
    unpacked using ``x, y, z = loadtxt(...)``.  When used with a
    structured data-type, arrays are returned for each field.
    Default is False.
ndmin : int, optional
    The returned array will have at least `ndmin` dimensions.
    Otherwise mono-dimensional axes will be squeezed.
    Legal values: 0 (default), 1 or 2.
encoding : str, optional
    Encoding used to decode the inputfile. Does not apply to input streams.
    The special value 'bytes' enables backward compatibility workarounds
    that ensures you receive byte arrays as results if possible and passes
    'latin1' encoded strings to converters. Override this value to receive
    unicode arrays and pass strings as input to converters.  If set to None
    the system default is used. The default value is 'bytes'.

    .. versionchanged:: 2.0
        Before NumPy 2, the default was ``'bytes'`` for Python 2
        compatibility. The default is now ``None``.

max_rows : int, optional
    Read `max_rows` rows of content after `skiprows` lines. The default is
    to read all the rows. Note that empty rows containing no data such as
    empty lines and comment lines are not counted towards `max_rows`,
    while such lines are counted in `skiprows`.

    .. versionchanged:: 1.23.0
        Lines containing no data, including comment lines (e.g., lines
        starting with '#' or as specified via `comments`) are not counted
        towards `max_rows`.
quotechar : unicode character or None, optional
    The character used to denote the start and end of a quoted item.
    Occurrences of the delimiter or comment characters are ignored within
    a quoted item. The default value is ``quotechar=None``, which means
    quoting support is disabled.

    If two consecutive instances of `quotechar` are found within a quoted
    field, the first is treated as an escape character. See examples.

    .. versionadded:: 1.23.0
${ARRAY_FUNCTION_LIKE}

    .. versionadded:: 1.20.0

Returns
-------
out : ndarray
    Data read from the text file.

See Also
--------
load, fromstring, fromregex
genfromtxt : Load data with missing values handled as specified.
scipy.io.loadmat : reads MATLAB data files

Notes
-----
This function aims to be a fast reader for simply formatted files.  The
`genfromtxt` function provides more sophisticated handling of, e.g.,
lines with missing values.

Each row in the input text file must have the same number of values to be
able to read all values. If all rows do not have same number of values, a
subset of up to n columns (where n is the least number of values present
in all rows) can be read by specifying the columns via `usecols`.

The strings produced by the Python float.hex method can be used as
input for floats.

Examples
--------
>>> import numpy as np
>>> from io import StringIO   # StringIO behaves like a file object
>>> c = StringIO("0 1\n2 3")
>>> np.loadtxt(c)
array([[0., 1.],
       [2., 3.]])

>>> d = StringIO("M 21 72\nF 35 58")
>>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
...                      'formats': ('S1', 'i4', 'f4')})
array([(b'M', 21, 72.), (b'F', 35, 58.)],
      dtype=[('gender', 'S1'), ('age', '<i4'), ('weight', '<f4')])

>>> c = StringIO("1,0,2\n3,0,4")
>>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)
>>> x
array([1., 3.])
>>> y
array([2., 4.])

The `converters` argument is used to specify functions to preprocess the
text prior to parsing. `converters` can be a dictionary that maps
preprocessing functions to each column:

>>> s = StringIO("1.618, 2.296\n3.141, 4.669\n")
>>> conv = {
...     0: lambda x: np.floor(float(x)),  # conversion fn for column 0
...     1: lambda x: np.ceil(float(x)),  # conversion fn for column 1
... }
>>> np.loadtxt(s, delimiter=",", converters=conv)
array([[1., 3.],
       [3., 5.]])

`converters` can be a callable instead of a dictionary, in which case it
is applied to all columns:

>>> s = StringIO("0xDE 0xAD\n0xC0 0xDE")
>>> import functools
>>> conv = functools.partial(int, base=16)
>>> np.loadtxt(s, converters=conv)
array([[222., 173.],
       [192., 222.]])

This example shows how `converters` can be used to convert a field
with a trailing minus sign into a negative number.

>>> s = StringIO("10.01 31.25-\n19.22 64.31\n17.57- 63.94")
>>> def conv(fld):
...     return -float(fld[:-1]) if fld.endswith("-") else float(fld)
...
>>> np.loadtxt(s, converters=conv)
array([[ 10.01, -31.25],
       [ 19.22,  64.31],
       [-17.57,  63.94]])

Using a callable as the converter can be particularly useful for handling
values with different formatting, e.g. floats with underscores:

>>> s = StringIO("1 2.7 100_000")
>>> np.loadtxt(s, converters=float)
array([1.e+00, 2.7e+00, 1.e+05])

This idea can be extended to automatically handle values specified in
many different formats, such as hex values:

>>> def conv(val):
...     try:
...         return float(val)
...     except ValueError:
...         return float.fromhex(val)
>>> s = StringIO("1, 2.5, 3_000, 0b4, 0x1.4000000000000p+2")
>>> np.loadtxt(s, delimiter=",", converters=conv)
array([1.0e+00, 2.5e+00, 3.0e+03, 1.8e+02, 5.0e+00])

Or a format where the ``-`` sign comes after the number:

>>> s = StringIO("10.01 31.25-\n19.22 64.31\n17.57- 63.94")
>>> conv = lambda x: -float(x[:-1]) if x.endswith("-") else float(x)
>>> np.loadtxt(s, converters=conv)
array([[ 10.01, -31.25],
       [ 19.22,  64.31],
       [-17.57,  63.94]])

Support for quoted fields is enabled with the `quotechar` parameter.
Comment and delimiter characters are ignored when they appear within a
quoted item delineated by `quotechar`:

>>> s = StringIO('"alpha, #42", 10.0\n"beta, #64", 2.0\n')
>>> dtype = np.dtype([("label", "U12"), ("value", float)])
>>> np.loadtxt(s, dtype=dtype, delimiter=",", quotechar='"')
array([('alpha, #42', 10.), ('beta, #64',  2.)],
      dtype=[('label', '<U12'), ('value', '<f8')])

Quoted fields can be separated by multiple whitespace characters:

>>> s = StringIO('"alpha, #42"       10.0\n"beta, #64" 2.0\n')
>>> dtype = np.dtype([("label", "U12"), ("value", float)])
>>> np.loadtxt(s, dtype=dtype, delimiter=None, quotechar='"')
array([('alpha, #42', 10.), ('beta, #64',  2.)],
      dtype=[('label', '<U12'), ('value', '<f8')])

Two consecutive quote characters within a quoted field are treated as a
single escaped character:

>>> s = StringIO('"Hello, my name is ""Monty""!"')
>>> np.loadtxt(s, dtype="U", delimiter=",", quotechar='"')
array('Hello, my name is "Monty"!', dtype='<U26')

Read subset of columns when all rows do not contain equal number of values:

>>> d = StringIO("1 2\n2 4\n3 9 12\n4 16 20")
>>> np.loadtxt(d, usecols=(0, 1))
array([[ 1.,  2.],
       [ 2.,  4.],
       [ 3.,  9.],
       [ 4., 16.]])

)
r