from __future__ import division, absolute_import, print_function

import sys
import os
import re
import functools
import itertools
import warnings
import weakref
from operator import itemgetter, index as opindex

import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core import overrides
from numpy.core.multiarray import packbits, unpackbits
from numpy.core.overrides import set_module
from numpy.core._internal import recursive
from ._iotools import (
    LineSplitter, NameValidator, StringConverter, ConverterError,
    ConverterLockError, ConversionWarning, _is_string_like,
    has_nested_fields, flatten_dtype, easy_dtype, _decode_line
    )

from numpy.compat import (
    asbytes, asstr, asunicode, asbytes_nested, bytes, basestring, unicode,
    os_fspath, os_PathLike
    )
from numpy.core.numeric import pickle

if sys.version_info[0] >= 3:
    from collections.abc import Mapping
else:
    from future_builtins import map
    from collections import Mapping


@set_module('numpy')
def loads(*args, **kwargs):
    # NumPy 1.15.0, 2017-12-10
    warnings.warn(
        "np.loads is deprecated, use pickle.loads instead",
        DeprecationWarning, stacklevel=2)
    return pickle.loads(*args, **kwargs)


__all__ = [
    'savetxt', 'loadtxt', 'genfromtxt', 'ndfromtxt', 'mafromtxt',
    'recfromtxt', 'recfromcsv', 'load', 'loads', 'save', 'savez',
    'savez_compressed', 'packbits', 'unpackbits', 'fromregex', 'DataSource'
    ]


array_function_dispatch = functools.partial(
    overrides.array_function_dispatch, module='numpy')


class BagObj(object):
    """
    BagObj(obj)

    Convert attribute look-ups to getitems on the object passed in.

    Parameters
    ----------
    obj : class instance
        Object on which attribute look-up is performed.

    Examples
    --------
    >>> from numpy.lib.npyio import BagObj as BO
    >>> class BagDemo(object):
    ...     def __getitem__(self, key): # An instance of BagObj(BagDemo)
    ...                                 # will call this method when any
    ...                                 # attribute look-up is required
    ...         result = "Doesn't matter what you want, "
    ...         return result + "you're gonna get this"
    ...
    >>> demo_obj = BagDemo()
    >>> bagobj = BO(demo_obj)
    >>> bagobj.hello_there
    "Doesn't matter what you want, you're gonna get this"
    >>> bagobj.I_can_be_anything
    "Doesn't matter what you want, you're gonna get this"

    """

    def __init__(self, obj):
        # Use weakref to make NpzFile objects collectable by refcount
        self._obj = weakref.proxy(obj)

    def __getattribute__(self, key):
        try:
            return object.__getattribute__(self, '_obj')[key]
        except KeyError:
            raise AttributeError(key)

    def __dir__(self):
        """
        Enables dir(bagobj) to list the files in an NpzFile.

        This also enables tab-completion in an interpreter or IPython.
        """
        return list(object.__getattribute__(self, '_obj').keys())


def zipfile_factory(file, *args, **kwargs):
    """
    Create a ZipFile.

    Allows for Zip64, and the `file` argument can accept file, str, or
    pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile
    constructor.
    """
    if not hasattr(file, 'read'):
        file = os_fspath(file)
    import zipfile
    kwargs['allowZip64'] = True
    return zipfile.ZipFile(file, *args, **kwargs)


class NpzFile(Mapping):
    """
    NpzFile(fid)

    A dictionary-like object with lazy-loading of files in the zipped
    archive provided on construction.

    `NpzFile` is used to load files in the NumPy ``.npz`` data archive
    format. It assumes that files in the archive have a ``.npy`` extension,
    other files are ignored.

    The arrays and file strings are lazily loaded on either
    getitem access using ``obj['key']`` or attribute lookup using
    ``obj.f.key``. A list of all files (without ``.npy`` extensions) can
    be obtained with ``obj.files`` and the ZipFile object itself using
    ``obj.zip``.

    Attributes
    ----------
    files : list of str
        List of all files in the archive with a ``.npy`` extension.
    zip : ZipFile instance
        The ZipFile object initialized with the zipped archive.
    f : BagObj instance
        An object on which attribute can be performed as an alternative
        to getitem access on the `NpzFile` instance itself.
    allow_pickle : bool, optional
        Allow loading pickled data. Default: True
    pickle_kwargs : dict, optional
        Additional keyword arguments to pass on to pickle.load.
        These are only useful when loading object arrays saved on
        Python 2 when using Python 3.

    Parameters
    ----------
    fid : file or str
        The zipped archive to open. This is either a file-like object
        or a string containing the path to the archive.
    own_fid : bool, optional
        Whether NpzFile should close the file handle.
        Requires that `fid` is a file-like object.

    Examples
    --------
    >>> from tempfile import TemporaryFile
    >>> outfile = TemporaryFile()
    >>> x = np.arange(10)
    >>> y = np.sin(x)
    >>> np.savez(outfile, x=x, y=y)
    >>> outfile.seek(0)

    >>> npz = np.load(outfile)
    >>> isinstance(npz, np.lib.io.NpzFile)
    True
    >>> npz.files
    ['y', 'x']
    >>> npz['x']  # getitem access
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    >>> npz.f.x  # attribute lookup
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

    """

    def __init__(self, fid, own_fid=False, allow_pickle=True,
                 pickle_kwargs=None):
        # Import is postponed to here since zipfile depends on gzip, an
        # optional component of the so-called standard library.
        _zip = zipfile_factory(fid)
        self._files = _zip.namelist()
        self.files = []
        self.allow_pickle = allow_pickle
        self.pickle_kwargs = pickle_kwargs
        for x in self._files:
            if x.endswith('.npy'):
                self.files.append(x[:-4])
            else:
                self.files.append(x)
        self.zip = _zip
        self.f = BagObj(self)
        if own_fid:
            self.fid = fid
        else:
            self.fid = None

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        self.close()

    def close(self):
        """
        Close the file.

        """
        if self.zip is not None:
            self.zip.close()
            self.zip = None
        if self.fid is not None:
            self.fid.close()
            self.fid = None
        self.f = None  # break reference cycle

    def __del__(self):
        self.close()

    # Implement the Mapping ABC
    def __iter__(self):
        return iter(self.files)

    def __len__(self):
        return len(self.files)

    def __getitem__(self, key):
        # FIXME: This seems like it will copy strings around
        #   more than is strictly necessary.  The zipfile
        #   will read the string and then
        #   the format.read_array will copy the string
        #   to another place in memory.
        #   It would be better if the zipfile could read
        #   (or at least uncompress) the data
        #   directly into the array memory.
        member = False
        if key in self._files:
            member = True
        elif key in self.files:
            member = True
            key += '.npy'
        if member:
            bytes = self.zip.open(key)
            magic = bytes.read(len(format.MAGIC_PREFIX))
            bytes.close()
            if magic == format.MAGIC_PREFIX:
                bytes = self.zip.open(key)
                return format.read_array(bytes,
                                         allow_pickle=self.allow_pickle,
                                         pickle_kwargs=self.pickle_kwargs)
            else:
                return self.zip.read(key)
        else:
            raise KeyError("%s is not a file in the archive" % key)


    if sys.version_info.major == 3:
        # deprecate the python 2 dict apis that we supported by accident in
        # python 3. We forgot to implement itervalues() at all in earlier
        # versions of numpy, so no need to deprecated it here.

        def iteritems(self):
            # Numpy 1.15, 2018-02-20
            warnings.warn(
                "NpzFile.iteritems is deprecated in python 3, to match the "
                "removal of dict.itertems. Use .items() instead.",
                DeprecationWarning, stacklevel=2)
            return self.items()

        def iterkeys(self):
            # Numpy 1.15, 2018-02-20
            warnings.warn(
                "NpzFile.iterkeys is deprecated in python 3, to match the "
                "removal of dict.iterkeys. Use .keys() instead.",
                DeprecationWarning, stacklevel=2)
            return self.keys()


@set_module('numpy')
def load(file, mmap_mode=None, allow_pickle=True, fix_imports=True,
         encoding='ASCII'):
    """
    Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.

    Parameters
    ----------
    file : file-like object, string, or pathlib.Path
        The file to read. File-like objects must support the
        ``seek()`` and ``read()`` methods. Pickled files require that the
        file-like object support the ``readline()`` method as well.
    mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional
        If not None, then memory-map the file, using the given mode (see
        `numpy.memmap` for a detailed description of the modes).  A
        memory-mapped array is kept on disk. However, it can be accessed
        and sliced like any ndarray.  Memory mapping is especially useful
        for accessing small fragments of large files without reading the
        entire file into memory.
    allow_pickle : bool, optional
        Allow loading pickled object arrays stored in npy files. Reasons for
        disallowing pickles include security, as loading pickled data can
        execute arbitrary code. If pickles are disallowed, loading object
        arrays will fail.
        Default: True
    fix_imports : bool, optional
        Only useful when loading Python 2 generated pickled files on Python 3,
        which includes npy/npz files containing object arrays. If `fix_imports`
        is True, pickle will try to map the old Python 2 names to the new names
        used in Python 3.
    encoding : str, optional
        What encoding to use when reading Python 2 strings. Only useful when
        loading Python 2 generated pickled files in Python 3, which includes
        npy/npz files containing object arrays. Values other than 'latin1',
        'ASCII', and 'bytes' are not allowed, as they can corrupt numerical
        data. Default: 'ASCII'

    Returns
    -------
    result : array, tuple, dict, etc.
        Data stored in the file. For ``.npz`` files, the returned instance
        of NpzFile class must be closed to avoid leaking file descriptors.

    Raises
    ------
    IOError
        If the input file does not exist or cannot be read.
    ValueError
        The file contains an object array, but allow_pickle=False given.

    See Also
    --------
    save, savez, savez_compressed, loadtxt
    memmap : Create a memory-map to an array stored in a file on disk.
    lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.

    Notes
    -----
    - If the file contains pickle data, then whatever object is stored
      in the pickle is returned.
    - If the file is a ``.npy`` file, then a single array is returned.
    - If the file is a ``.npz`` file, then a dictionary-like object is
      returned, containing ``{filename: array}`` key-value pairs, one for
      each file in the archive.
    - If the file is a ``.npz`` file, the returned value supports the
      context manager protocol in a similar fashion to the open function::

        with load('foo.npz') as data:
            a = data['a']

      The underlying file descriptor is closed when exiting the 'with'
      block.

    Examples
    --------
    Store data to disk, and load it again:

    >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
    >>> np.load('/tmp/123.npy')
    array([[1, 2, 3],
           [4, 5, 6]])

    Store compressed data to disk, and load it again:

    >>> a=np.array([[1, 2, 3], [4, 5, 6]])
    >>> b=np.array([1, 2])
    >>> np.savez('/tmp/123.npz', a=a, b=b)
    >>> data = np.load('/tmp/123.npz')
    >>> data['a']
    array([[1, 2, 3],
           [4, 5, 6]])
    >>> data['b']
    array([1, 2])
    >>> data.close()

    Mem-map the stored array, and then access the second row
    directly from disk:

    >>> X = np.load('/tmp/123.npy', mmap_mode='r')
    >>> X[1, :]
    memmap([4, 5, 6])

    """
    if encoding not in ('ASCII', 'latin1', 'bytes'):
        # The 'encoding' value for pickle also affects what encoding
        # the serialized binary data of NumPy arrays is loaded
        # in. Pickle does not pass on the encoding information to
        # NumPy. The unpickling code in numpy.core.multiarray is
        # written to assume that unicode data appearing where binary
        # should be is in 'latin1'. 'bytes' is also safe, as is 'ASCII'.
        #
        # Other encoding values can corrupt binary data, and we
        # purposefully disallow them. For the same reason, the errors=
        # argument is not exposed, as values other than 'strict'
        # result can similarly silently corrupt numerical data.
        raise ValueError("encoding must be 'ASCII', 'latin1', or 'bytes'")

    if sys.version_info[0] >= 3:
        pickle_kwargs = dict(encoding=encoding, fix_imports=fix_imports)
    else:
        # Nothing to do on Python 2
        pickle_kwargs = {}

    # TODO: Use contextlib.ExitStack once we drop Python 2
    if hasattr(file, 'read'):
        fid = file
        own_fid = False
    else:
        fid = open(os_fspath(file), "rb")
        own_fid = True

    try:
        # Code to distinguish from NumPy binary files and pickles.
        _ZIP_PREFIX = b'PK\x03\x04'
        _ZIP_SUFFIX = b'PK\x05\x06' # empty zip files start with this
        N = len(format.MAGIC_PREFIX)
        magic = fid.read(N)
        # If the file size is less than N, we need to make sure not
        # to seek past the beginning of the file
        fid.seek(-min(N, len(magic)), 1)  # back-up
        if magic.startswith(_ZIP_PREFIX) or magic.startswith(_ZIP_SUFFIX):
            # zip-file (assume .npz)
            # Transfer file ownership to NpzFile
            ret = NpzFile(fid, own_fid=own_fid, allow_pickle=allow_pickle,
                          pickle_kwargs=pickle_kwargs)
            own_fid = False
            return ret
        elif magic == format.MAGIC_PREFIX:
            # .npy file
            if mmap_mode:
                return format.open_memmap(file, mode=mmap_mode)
            else:
                return format.read_array(fid, allow_pickle=allow_pickle,
                                         pickle_kwargs=pickle_kwargs)
        else:
            # Try a pickle
            if not allow_pickle:
                raise ValueError("Cannot load file containing pickled data "
                                 "when allow_pickle=False")
            try:
                return pickle.load(fid, **pickle_kwargs)
            except Exception:
                raise IOError(
                    "Failed to interpret file %s as a pickle" % repr(file))
    finally:
        if own_fid:
            fid.close()


def _save_dispatcher(file, arr, allow_pickle=None, fix_imports=None):
    return (arr,)


@array_function_dispatch(_save_dispatcher)
def save(file, arr, allow_pickle=True, fix_imports=True):
    """
    Save an array to a binary file in NumPy ``.npy`` format.

    Parameters
    ----------
    file : file, str, or pathlib.Path
        File or filename to which the data is saved.  If file is a file-object,
        then the filename is unchanged.  If file is a string or Path, a ``.npy``
        extension will be appended to the file name if it does not already
        have one.
    arr : array_like
        Array data to be saved.
    allow_pickle : bool, optional
        Allow saving object arrays using Python pickles. Reasons for disallowing
        pickles include security (loading pickled data can execute arbitrary
        code) and portability (pickled objects may not be loadable on different
        Python installations, for example if the stored objects require libraries
        that are not available, and not all pickled data is compatible between
        Python 2 and Python 3).
        Default: True
    fix_imports : bool, optional
        Only useful in forcing objects in object arrays on Python 3 to be
        pickled in a Python 2 compatible way. If `fix_imports` is True, pickle
        will try to map the new Python 3 names to the old module names used in
        Python 2, so that the pickle data stream is readable with Python 2.

    See Also
    --------
    savez : Save several arrays into a ``.npz`` archive
    savetxt, load

    Notes
    -----
    For a description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.

    Examples
    --------
    >>> from tempfile import TemporaryFile
    >>> outfile = TemporaryFile()

    >>> x = np.arange(10)
    >>> np.save(outfile, x)

    >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
    >>> np.load(outfile)
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

    """
    own_fid = False
    if hasattr(file, 'read'):
        fid = file
    else:
        file = os_fspath(file)
        if not file.endswith('.npy'):
            file = file + '.npy'
        fid = open(file, "wb")
        own_fid = True

    if sys.version_info[0] >= 3:
        pickle_kwargs = dict(fix_imports=fix_imports)
    else:
        # Nothing to do on Python 2
        pickle_kwargs = None

    try:
        arr = np.asanyarray(arr)
        format.write_array(fid, arr, allow_pickle=allow_pickle,
                           pickle_kwargs=pickle_kwargs)
    finally:
        if own_fid:
            fid.close()


def _savez_dispatcher(file, *args, **kwds):
    for a in args:
        yield a
    for v in kwds.values():
        yield v


@array_function_dispatch(_savez_dispatcher)
def savez(file, *args, **kwds):
    """
    Save several arrays into a single file in uncompressed ``.npz`` format.

    If arguments are passed in with no keywords, the corresponding variable
    names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword
    arguments are given, the corresponding variable names, in the ``.npz``
    file will match the keyword names.

    Parameters
    ----------
    file : str or file
        Either the file name (string) or an open file (file-like object)
        where the data will be saved. If file is a string or a Path, the
        ``.npz`` extension will be appended to the file name if it is not
        already there.
    args : Arguments, optional
        Arrays to save to the file. Since it is not possible for Python to
        know the names of the arrays outside `savez`, the arrays will be saved
        with names "arr_0", "arr_1", and so on. These arguments can be any
        expression.
    kwds : Keyword arguments, optional
        Arrays to save to the file. Arrays will be saved in the file with the
        keyword names.

    Returns
    -------
    None

    See Also
    --------
    save : Save a single array to a binary file in NumPy format.
    savetxt : Save an array to a file as plain text.
    savez_compressed : Save several arrays into a compressed ``.npz`` archive

    Notes
    -----
    The ``.npz`` file format is a zipped archive of files named after the
    variables they contain.  The archive is not compressed and each file
    in the archive contains one variable in ``.npy`` format. For a
    description of the ``.npy`` format, see :py:mod:`numpy.lib.format`.

    When opening the saved ``.npz`` file with `load` a `NpzFile` object is
    returned. This is a dictionary-like object which can be queried for
    its list of arrays (with the ``.files`` attribute), and for the arrays
    themselves.

    Examples
    --------
    >>> from tempfile import TemporaryFile
    >>> outfile = TemporaryFile()
    >>> x = np.arange(10)
    >>> y = np.sin(x)

    Using `savez` with \\*args, the arrays are saved with default names.

    >>> np.savez(outfile, x, y)
    >>> outfile.seek(0) # Only needed here to simulate closing & reopening file
    >>> npzfile = np.load(outfile)
    >>> npzfile.files
    ['arr_1', 'arr_0']
    >>> npzfile['arr_0']
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

    Using `savez` with \\**kwds, the arrays are saved with the keyword names.

    >>> outfile = TemporaryFile()
    >>> np.savez(outfile, x=x, y=y)
    >>> outfile.seek(0)
    >>> npzfile = np.load(outfile)
    >>> npzfile.files
    ['y', 'x']
    >>> npzfile['x']
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

    """
    _savez(file, args, kwds, False)


def _savez_compressed_dispatcher(file, *args, **kwds):
    for a in args:
        yield a
    for v in kwds.values():
        yield v


@array_function_dispatch(_savez_compressed_dispatcher)
def savez_compressed(file, *args, **kwds):
    """
    Save several arrays into a single file in compressed ``.npz`` format.

    If keyword arguments are given, then filenames are taken from the keywords.
    If arguments are passed in with no keywords, then stored file names are
    arr_0, arr_1, etc.

    Parameters
    ----------
    file : str or file
        Either the file name (string) or an open file (file-like object)
        where the data will be saved. If file is a string or a Path, the
        ``.npz`` extension will be appended to the file name if it is not
        already there.
    args : Arguments, optional
        Arrays to save to the file. Since it is not possible for Python to
        know the names of the arrays outside `savez`, the arrays will be saved
        with names "arr_0", "arr_1", and so on. These arguments can be any
        expression.
    kwds : Keyword arguments, optional
        Arrays to save to the file. Arrays will be saved in the file with the
        keyword names.

    Returns
    -------
    None

    See Also
    --------
    numpy.save : Save a single array to a binary file in NumPy format.
    numpy.savetxt : Save an array to a file as plain text.
    numpy.savez : Save several arrays into an uncompressed ``.npz`` file format
    numpy.load : Load the files created by savez_compressed.

    Notes
    -----
    The ``.npz`` file format is a zipped archive of files named after the
    variables they contain.  The archive is compressed with
    ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable
    in ``.npy`` format. For a description of the ``.npy`` format, see 
    :py:mod:`numpy.lib.format`.


    When opening the saved ``.npz`` file with `load` a `NpzFile` object is
    returned. This is a dictionary-like object which can be queried for
    its list of arrays (with the ``.files`` attribute), and for the arrays
    themselves.

    Examples
    --------
    >>> test_array = np.random.rand(3, 2)
    >>> test_vector = np.random.rand(4)
    >>> np.savez_compressed('/tmp/123', a=test_array, b=test_vector)
    >>> loaded = np.load('/tmp/123.npz')
    >>> print(np.array_equal(test_array, loaded['a']))
    True
    >>> print(np.array_equal(test_vector, loaded['b']))
    True

    """
    _savez(file, args, kwds, True)


def _savez(file, args, kwds, compress, allow_pickle=True, pickle_kwargs=None):
    # Import is postponed to here since zipfile depends on gzip, an optional
    # component of the so-called standard library.
    import zipfile

    if not hasattr(file, 'read'):
        file = os_fspath(file)
        if not file.endswith('.npz'):
            file = file + '.npz'

    namedict = kwds
    for i, val in enumerate(args):
        key = 'arr_%d' % i
        if key in namedict.keys():
            raise ValueError(
                "Cannot use un-named variables and keyword %s" % key)
        namedict[key] = val

    if compress:
        compression = zipfile.ZIP_DEFLATED
    else:
        compression = zipfile.ZIP_STORED

    zipf = zipfile_factory(file, mode="w", compression=compression)

    if sys.version_info >= (3, 6):
        # Since Python 3.6 it is possible to write directly to a ZIP file.
        for key, val in namedict.items():
            fname = key + '.npy'
            val = np.asanyarray(val)
            force_zip64 = val.nbytes >= 2**30
            with zipf.open(fname, 'w', force_zip64=force_zip64) as fid:
                format.write_array(fid, val,
                                   allow_pickle=allow_pickle,
                                   pickle_kwargs=pickle_kwargs)
    else:
        # Stage arrays in a temporary file on disk, before writing to zip.

        # Import deferred for startup time improvement
        import tempfile
        # Since target file might be big enough to exceed capacity of a global
        # temporary directory, create temp file side-by-side with the target file.
        file_dir, file_prefix = os.path.split(file) if _is_string_like(file) else (None, 'tmp')
        fd, tmpfile = tempfile.mkstemp(prefix=file_prefix, dir=file_dir, suffix='-numpy.npy')
        os.close(fd)
        try:
            for key, val in namedict.items():
                fname = key + '.npy'
                fid = open(tmpfile, 'wb')
                try:
                    format.write_array(fid, np.asanyarray(val),
                                       allow_pickle=allow_pickle,
                                       pickle_kwargs=pickle_kwargs)
                    fid.close()
                    fid = None
                    zipf.write(tmpfile, arcname=fname)
                except IOError as exc:
                    raise IOError("Failed to write to %s: %s" % (tmpfile, exc))
                finally:
                    if fid:
                        fid.close()
        finally:
            os.remove(tmpfile)

    zipf.close()


def _getconv(dtype):
    """ Find the correct dtype converter. Adapted from matplotlib """

    def floatconv(x):
        x.lower()
        if '0x' in x:
            return float.fromhex(x)
        return float(x)

    typ = dtype.type
    if issubclass(typ, np.bool_):
        return lambda x: bool(int(x))
    if issubclass(typ, np.uint64):
        return np.uint64
    if issubclass(typ, np.int64):
        return np.int64
    if issubclass(typ, np.integer):
        return lambda x: int(float(x))
    elif issubclass(typ, np.longdouble):
        return np.longdouble
    elif issubclass(typ, np.floating):
        return floatconv
    elif issubclass(typ, complex):
        return lambda x: complex(asstr(x).replace('+-', '-'))
    elif issubclass(typ, np.bytes_):
        return asbytes
    elif issubclass(typ, np.unicode_):
        return asunicode
    else:
        return asstr

# amount of lines loadtxt reads in one chunk, can be overridden for testing
_loadtxt_chunksize = 50000


@set_module('numpy')
def loadtxt(fname, dtype=float, comments='#', delimiter=None,
            converters=None, skiprows=0, usecols=None, unpack=False,
            ndmin=0, encoding='bytes', max_rows=None):
    """
    Load data from a text file.

    Each row in the text file must have the same number of values.

    Parameters
    ----------
    fname : file, str, or pathlib.Path
        File, filename, or generator to read.  If the filename extension is
        ``.gz`` or ``.bz2``, the file is first decompressed. Note that
        generators should return byte strings for Python 3k.
    dtype : data-type, optional
        Data-type of the resulting array; default: float.  If this is a
        structured data-type, the resulting array will be 1-dimensional, and
        each row will be interpreted as an element of the array.  In this
        case, the number of columns used must match the number of fields in
        the data-type.
    comments : str or sequence of str, optional
        The characters or list of characters used to indicate the start of a
        comment. None implies no comments. For backwards compatibility, byte
        strings will be decoded as 'latin1'. The default is '#'.
    delimiter : str, optional
        The string used to separate values. For backwards compatibility, byte
        strings will be decoded as 'latin1'. The default is whitespace.
    converters : dict, optional
        A dictionary mapping column number to a function that will parse the
        column string into the desired value.  E.g., if column 0 is a date
        string: ``converters = {0: datestr2num}``.  Converters can also be
        used to provide a default value for missing data (but see also
        `genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``.
        Default: None.
    skiprows : int, optional
        Skip the first `skiprows` lines; 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.

        .. versionchanged:: 1.11.0
            When a single column has to be read it is possible to use
            an integer instead of a tuple. E.g ``usecols = 3`` reads the
            fourth column the same way as ``usecols = (3,)`` would.
    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.

        .. versionadded:: 1.6.0
    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'.

        .. versionadded:: 1.14.0
    max_rows : int, optional
        Read `max_rows` lines of content after `skiprows` lines. The default
        is to read all the lines.

        .. versionadded:: 1.16.0

    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.

    .. versionadded:: 1.10.0

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

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

    >>> d = StringIO(u"M 21 72\\nF 35 58")
    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),
    ...                      'formats': ('S1', 'i4', 'f4')})
    array([('M', 21, 72.0), ('F', 35, 58.0)],
          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])

    >>> c = StringIO(u"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.])

    """
    # Type conversions for Py3 convenience
    if comments is not None:
        if isinstance(comments, (basestring, bytes)):
            comments = [comments]
        comments = [_decode_line(x) for x in comments]
        # Compile regex for comments beforehand
        comments = (re.escape(comment) for comment in comments)
        regex_comments = re.compile('|'.join(comments))

    if delimiter is not None:
        delimiter = _decode_line(delimiter)

    user_converters = converters

    if encoding == 'bytes':
        encoding = None
        byte_converters = True
    else:
        byte_converters = False

    if usecols is not None:
        # Allow usecols to be a single int or a sequence of ints
        try:
            usecols_as_list = list(usecols)
        except TypeError:
            usecols_as_list = [usecols]
        for col_idx in usecols_as_list:
            try:
                opindex(col_idx)
            except TypeError as e:
                e.args = (
                    "usecols must be an int or a sequence of ints but "
                    "it contains at least one element of type %s" %
                    type(col_idx),
                    )
                raise
        # Fall back to existing code
        usecols = usecols_as_list

    fown = False
    try:
        if isinstance(fname, os_PathLike):
            fname = os_fspath(fname)
        if _is_string_like(fname):
            fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
            fencoding = getattr(fh, 'encoding', 'latin1')
            fh = iter(fh)
            fown = True
        else:
            fh = iter(fname)
            fencoding = getattr(fname, 'encoding', 'latin1')
    except TypeError:
        raise ValueError('fname must be a string, file handle, or generator')

    # input may be a python2 io stream
    if encoding is not None:
        fencoding = encoding
    # we must assume local encoding
    # TODO emit portability warning?
    elif fencoding is None:
        import locale
        fencoding = locale.getpreferredencoding()

    # not to be confused with the flatten_dtype we import...
    @recursive
    def flatten_dtype_internal(self, dt):
        """Unpack a structured data-type, and produce re-packing info."""
        if dt.names is None:
            # If the dtype is flattened, return.
            # If the dtype has a shape, the dtype occurs
            # in the list more than once.
            shape = dt.shape
            if len(shape) == 0:
                return ([dt.base], None)
            else:
                packing = [(shape[-1], list)]
                if len(shape) > 1:
                    for dim in dt.shape[-2::-1]:
                        packing = [(dim*packing[0][0], packing*dim)]
                return ([dt.base] * int(np.prod(dt.shape)), packing)
        else:
            types = []
            packing = []
            for field in dt.names:
                tp, bytes = dt.fields[field]
                flat_dt, flat_packing = self(tp)
                types.extend(flat_dt)
                # Avoid extra nesting for subarrays
                if tp.ndim > 0:
                    packing.extend(flat_packing)
                else:
                    packing.append((len(flat_dt), flat_packing))
            return (types, packing)

    @recursive
    def pack_items(self, items, packing):
        """Pack items into nested lists based on re-packing info."""
        if packing is None:
            return items[0]
        elif packing is tuple:
            return tuple(items)
        elif packing is list:
            return list(items)
        else:
            start = 0
            ret = []
            for length, subpacking in packing:
                ret.append(self(items[start:start+length], subpacking))
                start += length
            return tuple(ret)

    def split_line(line):
        """Chop off comments, strip, and split at delimiter. """
        line = _decode_line(line, encoding=encoding)

        if comments is not None:
            line = regex_comments.split(line, maxsplit=1)[0]
        line = line.strip('\r\n')
        if line:
            return line.split(delimiter)
        else:
            return []

    def read_data(chunk_size):
        """Parse each line, including the first.

        The file read, `fh`, is a global defined above.

        Parameters
        ----------
        chunk_size : int
            At most `chunk_size` lines are read at a time, with iteration
            until all lines are read.

        """
        X = []
        line_iter = itertools.chain([first_line], fh)
        line_iter = itertools.islice(line_iter, max_rows)
        for i, line in enumerate(line_iter):
            vals = split_line(line)
            if len(vals) == 0:
                continue
            if usecols:
                vals = [vals[j] for j in usecols]
            if len(vals) != N:
                line_num = i + skiprows + 1
                raise ValueError("Wrong number of columns at line %d"
                                 % line_num)

            # Convert each value according to its column and store
            items = [conv(val) for (conv, val) in zip(converters, vals)]

            # Then pack it according to the dtype's nesting
            items = pack_items(items, packing)
            X.append(items)
            if len(X) > chunk_size:
                yield X
                X = []
        if X:
            yield X

    try:
        # Make sure we're dealing with a proper dtype
        dtype = np.dtype(dtype)
        defconv = _getconv(dtype)

        # Skip the first `skiprows` lines
        for i in range(skiprows):
            next(fh)

        # Read until we find a line with some values, and use
        # it to estimate the number of columns, N.
        first_vals = None
        try:
            while not first_vals:
                first_line = next(fh)
                first_vals = split_line(first_line)
        except StopIteration:
            # End of lines reached
            first_line = ''
            first_vals = []
            warnings.warn('loadtxt: Empty input file: "%s"' % fname, stacklevel=2)
        N = len(usecols or first_vals)

        dtype_types, packing = flatten_dtype_internal(dtype)
        if len(dtype_types) > 1:
            # We're dealing with a structured array, each field of
            # the dtype matches a column
            converters = [_getconv(dt) for dt in dtype_types]
        else:
            # All fields have the same dtype
            converters = [defconv for i in range(N)]
            if N > 1:
                packing = [(N, tuple)]

        # By preference, use the converters specified by the user
        for i, conv in (user_converters or {}).items():
            if usecols:
                try:
                    i = usecols.index(i)
                except ValueError:
                    # Unused converter specified
                    continue
            if byte_converters:
                # converters may use decode to workaround numpy's old behaviour,
                # so encode the string again before passing to the user converter
                def tobytes_first(x, conv):
                    if type(x) is bytes:
                        return conv(x)
                    return conv(x.encode("latin1"))
                import functools
                converters[i] = functools.partial(tobytes_first, conv=conv)
            else:
                converters[i] = conv

        converters = [conv if conv is not bytes else
                      lambda x: x.encode(fencoding) for conv in converters]

        # read data in chunks and fill it into an array via resize
        # over-allocating and shrinking the array later may be faster but is
        # probably not relevant compared to the cost of actually reading and
        # converting the data
        X = None
        for x in read_data(_loadtxt_chunksize):
            if X is None:
                X = np.array(x, dtype)
            else:
                nshape = list(X.shape)
                pos = nshape[0]
                nshape[0] += len(x)
                X.resize(nshape, refcheck=False)
                X[pos:, ...] = x
    finally:
        if fown:
            fh.close()

    if X is None:
        X = np.array([], dtype)

    # Multicolumn data are returned with shape (1, N, M), i.e.
    # (1, 1, M) for a single row - remove the singleton dimension there
    if X.ndim == 3 and X.shape[:2] == (1, 1):
        X.shape = (1, -1)

    # Verify that the array has at least dimensions `ndmin`.
    # Check correctness of the values of `ndmin`
    if ndmin not in [0, 1, 2]:
        raise ValueError('Illegal value of ndmin keyword: %s' % ndmin)
    # Tweak the size and shape of the arrays - remove extraneous dimensions
    if X.ndim > ndmin:
        X = np.squeeze(X)
    # and ensure we have the minimum number of dimensions asked for
    # - has to be in this order for the odd case ndmin=1, X.squeeze().ndim=0
    if X.ndim < ndmin:
        if ndmin == 1:
            X = np.atleast_1d(X)
        elif ndmin == 2:
            X = np.atleast_2d(X).T

    if unpack:
        if len(dtype_types) > 1:
            # For structured arrays, return an array for each field.
            return [X[field] for field in dtype.names]
        else:
            return X.T
    else:
        return X


def _savetxt_dispatcher(fname, X, fmt=None, delimiter=None, newline=None,
                        header=None, footer=None, comments=None,
                        encoding=None):
    return (X,)


@array_function_dispatch(_savetxt_dispatcher)
def savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\n', header='',
            footer='', comments='# ', encoding=None):
    """
    Save an array to a text file.

    Parameters
    ----------
    fname : filename or file handle
        If the filename ends in ``.gz``, the file is automatically saved in
        compressed gzip format.  `loadtxt` understands gzipped files
        transparently.
    X : 1D or 2D array_like
        Data to be saved to a text file.
    fmt : str or sequence of strs, optional
        A single format (%10.5f), a sequence of formats, or a
        multi-format string, e.g. 'Iteration %d -- %10.5f', in which
        case `delimiter` is ignored. For complex `X`, the legal options
        for `fmt` are:

        * a single specifier, `fmt='%.4e'`, resulting in numbers formatted
          like `' (%s+%sj)' % (fmt, fmt)`
        * a full string specifying every real and imaginary part, e.g.
          `' %.4e %+.4ej %.4e %+.4ej %.4e %+.4ej'` for 3 columns
        * a list of specifiers, one per column - in this case, the real
          and imaginary part must have separate specifiers,
          e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns
    delimiter : str, optional
        String or character separating columns.
    newline : str, optional
        String or character separating lines.

        .. versionadded:: 1.5.0
    header : str, optional
        String that will be written at the beginning of the file.

        .. versionadded:: 1.7.0
    footer : str, optional
        String that will be written at the end of the file.

        .. versionadded:: 1.7.0
    comments : str, optional
        String that will be prepended to the ``header`` and ``footer`` strings,
        to mark them as comments. Default: '# ',  as expected by e.g.
        ``numpy.loadtxt``.

        .. versionadded:: 1.7.0
    encoding : {None, str}, optional
        Encoding used to encode the outputfile. Does not apply to output
        streams. If the encoding is something other than 'bytes' or 'latin1'
        you will not be able to load the file in NumPy versions < 1.14. Default
        is 'latin1'.

        .. versionadded:: 1.14.0


    See Also
    --------
    save : Save an array to a binary file in NumPy ``.npy`` format
    savez : Save several arrays into an uncompressed ``.npz`` archive
    savez_compressed : Save several arrays into a compressed ``.npz`` archive

    Notes
    -----
    Further explanation of the `fmt` parameter
    (``%[flag]width[.precision]specifier``):

    flags:
        ``-`` : left justify

        ``+`` : Forces to precede result with + or -.

        ``0`` : Left pad the number with zeros instead of space (see width).

    width:
        Minimum number of characters to be printed. The value is not truncated
        if it has more characters.

    precision:
        - For integer specifiers (eg. ``d,i,o,x``), the minimum number of
          digits.
        - For ``e, E`` and ``f`` specifiers, the number of digits to print
          after the decimal point.
        - For ``g`` and ``G``, the maximum number of significant digits.
        - For ``s``, the maximum number of characters.

    specifiers:
        ``c`` : character

        ``d`` or ``i`` : signed decimal integer

        ``e`` or ``E`` : scientific notation with ``e`` or ``E``.

        ``f`` : decimal floating point

        ``g,G`` : use the shorter of ``e,E`` or ``f``

        ``o`` : signed octal

        ``s`` : string of characters

        ``u`` : unsigned decimal integer

        ``x,X`` : unsigned hexadecimal integer

    This explanation of ``fmt`` is not complete, for an exhaustive
    specification see [1]_.

    References
    ----------
    .. [1] `Format Specification Mini-Language
           <https://docs.python.org/library/string.html#format-specification-mini-language>`_,
           Python Documentation.

    Examples
    --------
    >>> x = y = z = np.arange(0.0,5.0,1.0)
    >>> np.savetxt('test.out', x, delimiter=',')   # X is an array
    >>> np.savetxt('test.out', (x,y,z))   # x,y,z equal sized 1D arrays
    >>> np.savetxt('test.out', x, fmt='%1.4e')   # use exponential notation

    """

    # Py3 conversions first
    if isinstance(fmt, bytes):
        fmt = asstr(fmt)
    delimiter = asstr(delimiter)

    class WriteWrap(object):
        """Convert to unicode in py2 or to bytes on bytestream inputs.

        """
        def __init__(self, fh, encoding):
            self.fh = fh
            self.encoding = encoding
            self.do_write = self.first_write

        def close(self):
            self.fh.close()

        def write(self, v):
            self.do_write(v)

        def write_bytes(self, v):
            if isinstance(v, bytes):
                self.fh.write(v)
            else:
                self.fh.write(v.encode(self.encoding))

        def write_normal(self, v):
            self.fh.write(asunicode(v))

        def first_write(self, v):
            try:
                self.write_normal(v)
                self.write = self.write_normal
            except TypeError:
                # input is probably a bytestream
                self.write_bytes(v)
                self.write = self.write_bytes

    own_fh = False
    if isinstance(fname, os_PathLike):
        fname = os_fspath(fname)
    if _is_string_like(fname):
        # datasource doesn't support creating a new file ...
        open(fname, 'wt').close()
        fh = np.lib._datasource.open(fname, 'wt', encoding=encoding)
        own_fh = True
        # need to convert str to unicode for text io output
        if sys.version_info[0] == 2:
            fh = WriteWrap(fh, encoding or 'latin1')
    elif hasattr(fname, 'write'):
        # wrap to handle byte output streams
        fh = WriteWrap(fname, encoding or 'latin1')
    else:
        raise ValueError('fname must be a string or file handle')

    try:
        X = np.asarray(X)

        # Handle 1-dimensional arrays
        if X.ndim == 0 or X.ndim > 2:
            raise ValueError(
                "Expected 1D or 2D array, got %dD array instead" % X.ndim)
        elif X.ndim == 1:
            # Common case -- 1d array of numbers
            if X.dtype.names is None:
                X = np.atleast_2d(X).T
                ncol = 1

            # Complex dtype -- each field indicates a separate column
            else:
                ncol = len(X.dtype.descr)
        else:
            ncol = X.shape[1]

        iscomplex_X = np.iscomplexobj(X)
        # `fmt` can be a string with multiple insertion points or a
        # list of formats.  E.g. '%10.5f\t%10d' or ('%10.5f', '$10d')
        if type(fmt) in (list, tuple):
            if len(fmt) != ncol:
                raise AttributeError('fmt has wrong shape.  %s' % str(fmt))
            format = asstr(delimiter).join(map(asstr, fmt))
        elif isinstance(fmt, str):
            n_fmt_chars = fmt.count('%')
            error = ValueError('fmt has wrong number of %% formats:  %s' % fmt)
            if n_fmt_chars == 1:
                if iscomplex_X:
                    fmt = [' (%s+%sj)' % (fmt, fmt), ] * ncol
                else:
                    fmt = [fmt, ] * ncol
                format = delimiter.join(fmt)
            elif iscomplex_X and n_fmt_chars != (2 * ncol):
                raise error
            elif ((not iscomplex_X) and n_fmt_chars != ncol):
                raise error
            else:
                format = fmt
        else:
            raise ValueError('invalid fmt: %r' % (fmt,))

        if len(header) > 0:
            header = header.replace('\n', '\n' + comments)
            fh.write(comments + header + newline)
        if iscomplex_X:
            for row in X:
                row2 = []
                for number in row:
                    row2.append(number.real)
                    row2.append(number.imag)
                s = format % tuple(row2) + newline
                fh.write(s.replace('+-', '-'))
        else:
            for row in X:
                try:
                    v = format % tuple(row) + newline
                except TypeError:
                    raise TypeError("Mismatch between array dtype ('%s') and "
                                    "format specifier ('%s')"
                                    % (str(X.dtype), format))
                fh.write(v)

        if len(footer) > 0:
            footer = footer.replace('\n', '\n' + comments)
            fh.write(comments + footer + newline)
    finally:
        if own_fh:
            fh.close()


@set_module('numpy')
def fromregex(file, regexp, dtype, encoding=None):
    """
    Construct an array from a text file, using regular expression parsing.

    The returned array is always a structured array, and is constructed from
    all matches of the regular expression in the file. Groups in the regular
    expression are converted to fields of the structured array.

    Parameters
    ----------
    file : str or file
        File name or file object to read.
    regexp : str or regexp
        Regular expression used to parse the file.
        Groups in the regular expression correspond to fields in the dtype.
    dtype : dtype or list of dtypes
        Dtype for the structured array.
    encoding : str, optional
        Encoding used to decode the inputfile. Does not apply to input streams.

        .. versionadded:: 1.14.0

    Returns
    -------
    output : ndarray
        The output array, containing the part of the content of `file` that
        was matched by `regexp`. `output` is always a structured array.

    Raises
    ------
    TypeError
        When `dtype` is not a valid dtype for a structured array.

    See Also
    --------
    fromstring, loadtxt

    Notes
    -----
    Dtypes for structured arrays can be specified in several forms, but all
    forms specify at least the data type and field name. For details see
    `doc.structured_arrays`.

    Examples
    --------
    >>> f = open('test.dat', 'w')
    >>> f.write("1312 foo\\n1534  bar\\n444   qux")
    >>> f.close()

    >>> regexp = r"(\\d+)\\s+(...)"  # match [digits, whitespace, anything]
    >>> output = np.fromregex('test.dat', regexp,
    ...                       [('num', np.int64), ('key', 'S3')])
    >>> output
    array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],
          dtype=[('num', '<i8'), ('key', '|S3')])
    >>> output['num']
    array([1312, 1534,  444], dtype=int64)

    """
    own_fh = False
    if not hasattr(file, "read"):
        file = np.lib._datasource.open(file, 'rt', encoding=encoding)
        own_fh = True

    try:
        if not isinstance(dtype, np.dtype):
            dtype = np.dtype(dtype)

        content = file.read()
        if isinstance(content, bytes) and isinstance(regexp, np.unicode):
            regexp = asbytes(regexp)
        elif isinstance(content, np.unicode) and isinstance(regexp, bytes):
            regexp = asstr(regexp)

        if not hasattr(regexp, 'match'):
            regexp = re.compile(regexp)
        seq = regexp.findall(content)
        if seq and not isinstance(seq[0], tuple):
            # Only one group is in the regexp.
            # Create the new array as a single data-type and then
            #   re-interpret as a single-field structured array.
            newdtype = np.dtype(dtype[dtype.names[0]])
            output = np.array(seq, dtype=newdtype)
            output.dtype = dtype
        else:
            output = np.array(seq, dtype=dtype)

        return output
    finally:
        if own_fh:
            file.close()


#####--------------------------------------------------------------------------
#---- --- ASCII functions ---
#####--------------------------------------------------------------------------


@set_module('numpy')
def genfromtxt(fname, dtype=float, comments='#', delimiter=None,
               skip_header=0, skip_footer=0, converters=None,
               missing_values=None, filling_values=None, usecols=None,
               names=None, excludelist=None, deletechars=None,
               replace_space='_', autostrip=False, case_sensitive=True,
               defaultfmt="f%i", unpack=None, usemask=False, loose=True,
               invalid_raise=True, max_rows=None, encoding='bytes'):
    """
    Load data from a text file, with missing values handled as specified.

    Each line past the first `skip_header` lines is split at the `delimiter`
    character, and characters following the `comments` character are discarded.

    Parameters
    ----------
    fname : file, str, pathlib.Path, list of str, generator
        File, filename, list, or generator to read.  If the filename
        extension is `.gz` or `.bz2`, the file is first decompressed. Note
        that generators must return byte strings in Python 3k.  The strings
        in a list or produced by a generator are treated as lines.
    dtype : dtype, optional
        Data type of the resulting array.
        If None, the dtypes will be determined by the contents of each
        column, individually.
    comments : str, optional
        The character used to indicate the start of a comment.
        All the characters occurring on a line after a comment are discarded
    delimiter : str, int, or sequence, optional
        The string used to separate values.  By default, any consecutive
        whitespaces act as delimiter.  An integer or sequence of integers
        can also be provided as width(s) of each field.
    skiprows : int, optional
        `skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
    skip_header : int, optional
        The number of lines to skip at the beginning of the file.
    skip_footer : int, optional
        The number of lines to skip at the end of the file.
    converters : variable, optional
        The set of functions that convert the data of a column to a value.
        The converters can also be used to provide a default value
        for missing data: ``converters = {3: lambda s: float(s or 0)}``.
    missing : variable, optional
        `missing` was removed in numpy 1.10. Please use `missing_values`
        instead.
    missing_values : variable, optional
        The set of strings corresponding to missing data.
    filling_values : variable, optional
        The set of values to be used as default when the data are missing.
    usecols : 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.
    names : {None, True, str, sequence}, optional
        If `names` is True, the field names are read from the first line after
        the first `skip_header` lines.  This line can optionally be proceeded
        by a comment delimiter. If `names` is a sequence or a single-string of
        comma-separated names, the names will be used to define the field names
        in a structured dtype. If `names` is None, the names of the dtype
        fields will be used, if any.
    excludelist : sequence, optional
        A list of names to exclude. This list is appended to the default list
        ['return','file','print']. Excluded names are appended an underscore:
        for example, `file` would become `file_`.
    deletechars : str, optional
        A string combining invalid characters that must be deleted from the
        names.
    defaultfmt : str, optional
        A format used to define default field names, such as "f%i" or "f_%02i".
    autostrip : bool, optional
        Whether to automatically strip white spaces from the variables.
    replace_space : char, optional
        Character(s) used in replacement of white spaces in the variables
        names. By default, use a '_'.
    case_sensitive : {True, False, 'upper', 'lower'}, optional
        If True, field names are case sensitive.
        If False or 'upper', field names are converted to upper case.
        If 'lower', field names are converted to lower case.
    unpack : bool, optional
        If True, the returned array is transposed, so that arguments may be
        unpacked using ``x, y, z = loadtxt(...)``
    usemask : bool, optional
        If True, return a masked array.
        If False, return a regular array.
    loose : bool, optional
        If True, do not raise errors for invalid values.
    invalid_raise : bool, optional
        If True, an exception is raised if an inconsistency is detected in the
        number of columns.
        If False, a warning is emitted and the offending lines are skipped.
    max_rows : int,  optional
        The maximum number of rows to read. Must not be used with skip_footer
        at the same time.  If given, the value must be at least 1. Default is
        to read the entire file.

        .. versionadded:: 1.10.0
    encoding : str, optional
        Encoding used to decode the inputfile. Does not apply when `fname` is
        a file object.  The special value 'bytes' enables backward compatibility
        workarounds that ensure that you receive byte arrays when 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'.

        .. versionadded:: 1.14.0

    Returns
    -------
    out : ndarray
        Data read from the text file. If `usemask` is True, this is a
        masked array.

    See Also
    --------
    numpy.loadtxt : equivalent function when no data is missing.

    Notes
    -----
    * When spaces are used as delimiters, or when no delimiter has been given
      as input, there should not be any missing data between two fields.
    * When the variables are named (either by a flexible dtype or with `names`,
      there must not be any header in the file (else a ValueError
      exception is raised).
    * Individual values are not stripped of spaces by default.
      When using a custom converter, make sure the function does remove spaces.

    References
    ----------
    .. [1] NumPy User Guide, section `I/O with NumPy
           <https://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.

    Examples
    ---------
    >>> from io import StringIO
    >>> import numpy as np

    Comma delimited file with mixed dtype

    >>> s = StringIO(u"1,1.3,abcde")
    >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
    ... ('mystring','S5')], delimiter=",")
    >>> data
    array((1, 1.3, 'abcde'),
          dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])

    Using dtype = None

    >>> s.seek(0) # needed for StringIO example only
    >>> data = np.genfromtxt(s, dtype=None,
    ... names = ['myint','myfloat','mystring'], delimiter=",")
    >>> data
    array((1, 1.3, 'abcde'),
          dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])

    Specifying dtype and names

    >>> s.seek(0)
    >>> data = np.genfromtxt(s, dtype="i8,f8,S5",
    ... names=['myint','myfloat','mystring'], delimiter=",")
    >>> data
    array((1, 1.3, 'abcde'),
          dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])

    An example with fixed-width columns

    >>> s = StringIO(u"11.3abcde")
    >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
    ...     delimiter=[1,3,5])
    >>> data
    array((1, 1.3, 'abcde'),
          dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])

    """
    if max_rows is not None:
        if skip_footer:
            raise ValueError(
                    "The keywords 'skip_footer' and 'max_rows' can not be "
                    "specified at the same time.")
        if max_rows < 1:
            raise ValueError("'max_rows' must be at least 1.")

    if usemask:
        from numpy.ma import MaskedArray, make_mask_descr
    # Check the input dictionary of converters
    user_converters = converters or {}
    if not isinstance(user_converters, dict):
        raise TypeError(
            "The input argument 'converter' should be a valid dictionary "
            "(got '%s' instead)" % type(user_converters))

    if encoding == 'bytes':
        encoding = None
        byte_converters = True
    else:
        byte_converters = False

    # Initialize the filehandle, the LineSplitter and the NameValidator
    own_fhd = False
    try:
        if isinstance(fname, os_PathLike):
            fname = os_fspath(fname)
        if isinstance(fname, basestring):
            fhd = iter(np.lib._datasource.open(fname, 'rt', encoding=encoding))
            own_fhd = True
        else:
            fhd = iter(fname)
    except TypeError:
        raise TypeError(
            "fname must be a string, filehandle, list of strings, "
            "or generator. Got %s instead." % type(fname))

    split_line = LineSplitter(delimiter=delimiter, comments=comments,
                              autostrip=autostrip, encoding=encoding)
    validate_names = NameValidator(excludelist=excludelist,
                                   deletechars=deletechars,
                                   case_sensitive=case_sensitive,
                                   replace_space=replace_space)

    # Skip the first `skip_header` rows
    for i in range(skip_header):
        next(fhd)

    # Keep on until we find the first valid values
    first_values = None
    try:
        while not first_values:
            first_line = _decode_line(next(fhd), encoding)
            if (names is True) and (comments is not None):
                if comments in first_line:
                    first_line = (
                        ''.join(first_line.split(comments)[1:]))
            first_values = split_line(first_line)
    except StopIteration:
        # return an empty array if the datafile is empty
        first_line = ''
        first_values = []
        warnings.warn('genfromtxt: Empty input file: "%s"' % fname, stacklevel=2)

    # Should we take the first values as names ?
    if names is True:
        fval = first_values[0].strip()
        if comments is not None:
            if fval in comments:
                del first_values[0]

    # Check the columns to use: make sure `usecols` is a list
    if usecols is not None:
        try:
            usecols = [_.strip() for _ in usecols.split(",")]
        except AttributeError:
            try:
                usecols = list(usecols)
            except TypeError:
                usecols = [usecols, ]
    nbcols = len(usecols or first_values)

    # Check the names and overwrite the dtype.names if needed
    if names is True:
        names = validate_names([str(_.strip()) for _ in first_values])
        first_line = ''
    elif _is_string_like(names):
        names = validate_names([_.strip() for _ in names.split(',')])
    elif names:
        names = validate_names(names)
    # Get the dtype
    if dtype is not None:
        dtype = easy_dtype(dtype, defaultfmt=defaultfmt, names=names,
                           excludelist=excludelist,
                           deletechars=deletechars,
                           case_sensitive=case_sensitive,
                           replace_space=replace_space)
    # Make sure the names is a list (for 2.5)
    if names is not None:
        names = list(names)

    if usecols:
        for (i, current) in enumerate(usecols):
            # if usecols is a list of names, convert to a list of indices
            if _is_string_like(current):
                usecols[i] = names.index(current)
            elif current < 0:
                usecols[i] = current + len(first_values)
        # If the dtype is not None, make sure we update it
        if (dtype is not None) and (len(dtype) > nbcols):
            descr = dtype.descr
            dtype = np.dtype([descr[_] for _ in usecols])
            names = list(dtype.names)
        # If `names` is not None, update the names
        elif (names is not None) and (len(names) > nbcols):
            names = [names[_] for _ in usecols]
    elif (names is not None) and (dtype is not None):
        names = list(dtype.names)

    # Process the missing values ...............................
    # Rename missing_values for convenience
    user_missing_values = missing_values or ()
    if isinstance(user_missing_values, bytes):
        user_missing_values = user_missing_values.decode('latin1')

    # Define the list of missing_values (one column: one list)
    missing_values = [list(['']) for _ in range(nbcols)]

    # We have a dictionary: process it field by field
    if isinstance(user_missing_values, dict):
        # Loop on the items
        for (key, val) in user_missing_values.items():
            # Is the key a string ?
            if _is_string_like(key):
                try:
                    # Transform it into an integer
                    key = names.index(key)
                except ValueError:
                    # We couldn't find it: the name must have been dropped
                    continue
            # Redefine the key as needed if it's a column number
            if usecols:
                try:
                    key = usecols.index(key)
                except ValueError:
                    pass
            # Transform the value as a list of string
            if isinstance(val, (list, tuple)):
                val = [str(_) for _ in val]
            else:
                val = [str(val), ]
            # Add the value(s) to the current list of missing
            if key is None:
                # None acts as default
                for miss in missing_values:
                    miss.extend(val)
            else:
                missing_values[key].extend(val)
    # We have a sequence : each item matches a column
    elif isinstance(user_missing_values, (list, tuple)):
        for (value, entry) in zip(user_missing_values, missing_values):
            value = str(value)
            if value not in entry:
                entry.append(value)
    # We have a string : apply it to all entries
    elif isinstance(user_missing_values, basestring):
        user_value = user_missing_values.split(",")
        for entry in missing_values:
            entry.extend(user_value)
    # We have something else: apply it to all entries
    else:
        for entry in missing_values:
            entry.extend([str(user_missing_values)])

    # Process the filling_values ...............................
    # Rename the input for convenience
    user_filling_values = filling_values
    if user_filling_values is None:
        user_filling_values = []
    # Define the default
    filling_values = [None] * nbcols
    # We have a dictionary : update each entry individually
    if isinstance(user_filling_values, dict):
        for (key, val) in user_filling_values.items():
            if _is_string_like(key):
                try:
                    # Transform it into an integer
                    key = names.index(key)
                except ValueError:
                    # We couldn't find it: the name must have been dropped,
                    continue
            # Redefine the key if it's a column number and usecols is defined
            if usecols:
                try:
                    key = usecols.index(key)
                except ValueError:
                    pass
            # Add the value to the list
            filling_values[key] = val
    # We have a sequence : update on a one-to-one basis
    elif isinstance(user_filling_values, (list, tuple)):
        n = len(user_filling_values)
        if (n <= nbcols):
            filling_values[:n] = user_filling_values
        else:
            filling_values = user_filling_values[:nbcols]
    # We have something else : use it for all entries
    else:
        filling_values = [user_filling_values] * nbcols

    # Initialize the converters ................................
    if dtype is None:
        # Note: we can't use a [...]*nbcols, as we would have 3 times the same
        # ... converter, instead of 3 different converters.
        converters = [StringConverter(None, missing_values=miss, default=fill)
                      for (miss, fill) in zip(missing_values, filling_values)]
    else:
        dtype_flat = flatten_dtype(dtype, flatten_base=True)
        # Initialize the converters
        if len(dtype_flat) > 1:
            # Flexible type : get a converter from each dtype
            zipit = zip(dtype_flat, missing_values, filling_values)
            converters = [StringConverter(dt, locked=True,
                                          missing_values=miss, default=fill)
                          for (dt, miss, fill) in zipit]
        else:
            # Set to a default converter (but w/ different missing values)
            zipit = zip(missing_values, filling_values)
            converters = [StringConverter(dtype, locked=True,
                                          missing_values=miss, default=fill)
                          for (miss, fill) in zipit]
    # Update the converters to use the user-defined ones
    uc_update = []
    for (j, conv) in user_converters.items():
        # If the converter is specified by column names, use the index instead
        if _is_string_like(j):
            try:
                j = names.index(j)
                i = j
            except ValueError:
                continue
        elif usecols:
            try:
                i = usecols.index(j)
            except ValueError:
                # Unused converter specified
                continue
        else:
            i = j
        # Find the value to test - first_line is not filtered by usecols:
        if len(first_line):
            testing_value = first_values[j]
        else:
            testing_value = None
        if conv is bytes:
            user_conv = asbytes
        elif byte_converters:
            # converters may use decode to workaround numpy's old behaviour,
            # so encode the string again before passing to the user converter
            def tobytes_first(x, conv):
                if type(x) is bytes:
                    return conv(x)
                return conv(x.encode("latin1"))
            import functools
            user_conv = functools.partial(tobytes_first, conv=conv)
        else:
            user_conv = conv
        converters[i].update(user_conv, locked=True,
                             testing_value=testing_value,
                             default=filling_values[i],
                             missing_values=missing_values[i],)
        uc_update.append((i, user_conv))
    # Make sure we have the corrected keys in user_converters...
    user_converters.update(uc_update)

    # Fixme: possible error as following variable never used.
    # miss_chars = [_.missing_values for _ in converters]

    # Initialize the output lists ...
    # ... rows
    rows = []
    append_to_rows = rows.append
    # ... masks
    if usemask:
        masks = []
        append_to_masks = masks.append
    # ... invalid
    invalid = []
    append_to_invalid = invalid.append

    # Parse each line
    for (i, line) in enumerate(itertools.chain([first_line, ], fhd)):
        values = split_line(line)
        nbvalues = len(values)
        # Skip an empty line
        if nbvalues == 0:
            continue
        if usecols:
            # Select only the columns we need
            try:
                values = [values[_] for _ in usecols]
            except IndexError:
                append_to_invalid((i + skip_header + 1, nbvalues))
                continue
        elif nbvalues != nbcols:
            append_to_invalid((i + skip_header + 1, nbvalues))
            continue
        # Store the values
        append_to_rows(tuple(values))
        if usemask:
            append_to_masks(tuple([v.strip() in m
                                   for (v, m) in zip(values,
                                                     missing_values)]))
        if len(rows) == max_rows:
            break

    if own_fhd:
        fhd.close()

    # Upgrade the converters (if needed)
    if dtype is None:
        for (i, converter) in enumerate(converters):
            current_column = [itemgetter(i)(_m) for _m in rows]
            try:
                converter.iterupgrade(current_column)
            except ConverterLockError:
                errmsg = "Converter #%i is locked and cannot be upgraded: " % i
                current_column = map(itemgetter(i), rows)
                for (j, value) in enumerate(current_column):
                    try:
                        converter.upgrade(value)
                    except (ConverterError, ValueError):
                        errmsg += "(occurred line #%i for value '%s')"
                        errmsg %= (j + 1 + skip_header, value)
                        raise ConverterError(errmsg)

    # Check that we don't have invalid values
    nbinvalid = len(invalid)
    if nbinvalid > 0:
        nbrows = len(rows) + nbinvalid - skip_footer
        # Construct the error message
        template = "    Line #%%i (got %%i columns instead of %i)" % nbcols
        if skip_footer > 0:
            nbinvalid_skipped = len([_ for _ in invalid
                                     if _[0] > nbrows + skip_header])
            invalid = invalid[:nbinvalid - nbinvalid_skipped]
            skip_footer -= nbinvalid_skipped
#
#            nbrows -= skip_footer
#            errmsg = [template % (i, nb)
#                      for (i, nb) in invalid if i < nbrows]
#        else:
        errmsg = [template % (i, nb)
                  for (i, nb) in invalid]
        if len(errmsg):
            errmsg.insert(0, "Some errors were detected !")
            errmsg = "\n".join(errmsg)
            # Raise an exception ?
            if invalid_raise:
                raise ValueError(errmsg)
            # Issue a warning ?
            else:
                warnings.warn(errmsg, ConversionWarning, stacklevel=2)

    # Strip the last skip_footer data
    if skip_footer > 0:
        rows = rows[:-skip_footer]
        if usemask:
            masks = masks[:-skip_footer]

    # Convert each value according to the converter:
    # We want to modify the list in place to avoid creating a new one...
    if loose:
        rows = list(
            zip(*[[conv._loose_call(_r) for _r in map(itemgetter(i), rows)]
                  for (i, conv) in enumerate(converters)]))
    else:
        rows = list(
            zip(*[[conv._strict_call(_r) for _r in map(itemgetter(i), rows)]
                  for (i, conv) in enumerate(converters)]))

    # Reset the dtype
    data = rows
    if dtype is None:
        # Get the dtypes from the types of the converters
        column_types = [conv.type for conv in converters]
        # Find the columns with strings...
        strcolidx = [i for (i, v) in enumerate(column_types)
                     if v == np.unicode_]

        if byte_converters and strcolidx:
            # convert strings back to bytes for backward compatibility
            warnings.warn(
                "Reading unicode strings without specifying the encoding "
                "argument is deprecated. Set the encoding, use None for the "
                "system default.",
                np.VisibleDeprecationWarning, stacklevel=2)
            def encode_unicode_cols(row_tup):
                row = list(row_tup)
                for i in strcolidx:
                    row[i] = row[i].encode('latin1')
                return tuple(row)

            try:
                data = [encode_unicode_cols(r) for r in data]
            except UnicodeEncodeError:
                pass
            else:
                for i in strcolidx:
                    column_types[i] = np.bytes_

        # Update string types to be the right length
        sized_column_types = column_types[:]
        for i, col_type in enumerate(column_types):
            if np.issubdtype(col_type, np.character):
                n_chars = max(len(row[i]) for row in data)
                sized_column_types[i] = (col_type, n_chars)

        if names is None:
            # If the dtype is uniform (before sizing strings)
            base = {
                c_type
                for c, c_type in zip(converters, column_types)
                if c._checked}
            if len(base) == 1:
                uniform_type, = base
                (ddtype, mdtype) = (uniform_type, bool)
            else:
                ddtype = [(defaultfmt % i, dt)
                          for (i, dt) in enumerate(sized_column_types)]
                if usemask:
                    mdtype = [(defaultfmt % i, bool)
                              for (i, dt) in enumerate(sized_column_types)]
        else:
            ddtype = list(zip(names, sized_column_types))
            mdtype = list(zip(names, [bool] * len(sized_column_types)))
        output = np.array(data, dtype=ddtype)
        if usemask:
            outputmask = np.array(masks, dtype=mdtype)
    else:
        # Overwrite the initial dtype names if needed
        if names and dtype.names:
            dtype.names = names
        # Case 1. We have a structured type
        if len(dtype_flat) > 1:
            # Nested dtype, eg [('a', int), ('b', [('b0', int), ('b1', 'f4')])]
            # First, create the array using a flattened dtype:
            # [('a', int), ('b1', int), ('b2', float)]
            # Then, view the array using the specified dtype.
            if 'O' in (_.char for _ in dtype_flat):
                if has_nested_fields(dtype):
                    raise NotImplementedError(
                        "Nested fields involving objects are not supported...")
                else:
                    output = np.array(data, dtype=dtype)
            else:
                rows = np.array(data, dtype=[('', _) for _ in dtype_flat])
                output = rows.view(dtype)
            # Now, process the rowmasks the same way
            if usemask:
                rowmasks = np.array(
                    masks, dtype=np.dtype([('', bool) for t in dtype_flat]))
                # Construct the new dtype
                mdtype = make_mask_descr(dtype)
                outputmask = rowmasks.view(mdtype)
        # Case #2. We have a basic dtype
        else:
            # We used some user-defined converters
            if user_converters:
                ishomogeneous = True
                descr = []
                for i, ttype in enumerate([conv.type for conv in converters]):
                    # Keep the dtype of the current converter
                    if i in user_converters:
                        ishomogeneous &= (ttype == dtype.type)
                        if np.issubdtype(ttype, np.character):
                            ttype = (ttype, max(len(row[i]) for row in data))
                        descr.append(('', ttype))
                    else:
                        descr.append(('', dtype))
                # So we changed the dtype ?
                if not ishomogeneous:
                    # We have more than one field
                    if len(descr) > 1:
                        dtype = np.dtype(descr)
                    # We have only one field: drop the name if not needed.
                    else:
                        dtype = np.dtype(ttype)
            #
            output = np.array(data, dtype)
            if usemask:
                if dtype.names:
                    mdtype = [(_, bool) for _ in dtype.names]
                else:
                    mdtype = bool
                outputmask = np.array(masks, dtype=mdtype)
    # Try to take care of the missing data we missed
    names = output.dtype.names
    if usemask and names:
        for (name, conv) in zip(names, converters):
            missing_values = [conv(_) for _ in conv.missing_values
                              if _ != '']
            for mval in missing_values:
                outputmask[name] |= (output[name] == mval)
    # Construct the final array
    if usemask:
        output = output.view(MaskedArray)
        output._mask = outputmask
    if unpack:
        return output.squeeze().T
    return output.squeeze()


def ndfromtxt(fname, **kwargs):
    """
    Load ASCII data stored in a file and return it as a single array.

    Parameters
    ----------
    fname, kwargs : For a description of input parameters, see `genfromtxt`.

    See Also
    --------
    numpy.genfromtxt : generic function.

    """
    kwargs['usemask'] = False
    return genfromtxt(fname, **kwargs)


def mafromtxt(fname, **kwargs):
    """
    Load ASCII data stored in a text file and return a masked array.

    Parameters
    ----------
    fname, kwargs : For a description of input parameters, see `genfromtxt`.

    See Also
    --------
    numpy.genfromtxt : generic function to load ASCII data.

    """
    kwargs['usemask'] = True
    return genfromtxt(fname, **kwargs)


def recfromtxt(fname, **kwargs):
    """
    Load ASCII data from a file and return it in a record array.

    If ``usemask=False`` a standard `recarray` is returned,
    if ``usemask=True`` a MaskedRecords array is returned.

    Parameters
    ----------
    fname, kwargs : For a description of input parameters, see `genfromtxt`.

    See Also
    --------
    numpy.genfromtxt : generic function

    Notes
    -----
    By default, `dtype` is None, which means that the data-type of the output
    array will be determined from the data.

    """
    kwargs.setdefault("dtype", None)
    usemask = kwargs.get('usemask', False)
    output = genfromtxt(fname, **kwargs)
    if usemask:
        from numpy.ma.mrecords import MaskedRecords
        output = output.view(MaskedRecords)
    else:
        output = output.view(np.recarray)
    return output


def recfromcsv(fname, **kwargs):
    """
    Load ASCII data stored in a comma-separated file.

    The returned array is a record array (if ``usemask=False``, see
    `recarray`) or a masked record array (if ``usemask=True``,
    see `ma.mrecords.MaskedRecords`).

    Parameters
    ----------
    fname, kwargs : For a description of input parameters, see `genfromtxt`.

    See Also
    --------
    numpy.genfromtxt : generic function to load ASCII data.

    Notes
    -----
    By default, `dtype` is None, which means that the data-type of the output
    array will be determined from the data.

    """
    # Set default kwargs for genfromtxt as relevant to csv import.
    kwargs.setdefault("case_sensitive", "lower")
    kwargs.setdefault("names", True)
    kwargs.setdefault("delimiter", ",")
    kwargs.setdefault("dtype", None)
    output = genfromtxt(fname, **kwargs)

    usemask = kwargs.get("usemask", False)
    if usemask:
        from numpy.ma.mrecords import MaskedRecords
        output = output.view(MaskedRecords)
    else:
        output = output.view(np.recarray)
    return output
