def write_array(fp, array, version=None):
    """
    Write an array to an NPY file, including a header.

    If the array is neither C-contiguous nor Fortran-contiguous AND the
    file_like object is not a real file object, this function will have to
    copy data in memory.

    Parameters
    ----------
    fp : file_like object
        An open, writable file object, or similar object with a ``.write()``
        method.
    array : ndarray
        The array to write to disk.
    version : (int, int) or None, optional
        The version number of the format. None means use the oldest supported
        version that is able to store the data.  Default: None

    Raises
    ------
    ValueError
        If the array cannot be persisted.
    Various other errors
        If the array contains Python objects as part of its dtype, the
        process of pickling them may raise various errors if the objects
        are not picklable.

    """
    _check_version(version)
    used_ver = _write_array_header(fp, header_data_from_array_1_0(array),
                                   version)
    # this warning can be removed when 1.9 has aged enough
    if version != (2, 0) and used_ver == (2, 0):
        warnings.warn("Stored array in format 2.0. It can only be"
                      "read by NumPy >= 1.9", UserWarning)

    # Set buffer size to 16 MiB to hide the Python loop overhead.
    buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)

    if array.dtype.hasobject:
        # We contain Python objects so we cannot write out the data directly.
        # Instead, we will pickle it out with version 2 of the pickle protocol.
        pickle.dump(array, fp, protocol=2)
    elif array.flags.f_contiguous and not array.flags.c_contiguous:
        if isfileobj(fp):
            array.T.tofile(fp)
        else:
            for chunk in numpy.nditer(
                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
                    buffersize=buffersize, order='F'):
                fp.write(chunk.tobytes('C'))
    else:
        if isfileobj(fp):
            array.tofile(fp)
        else:
            for chunk in numpy.nditer(
                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
                    buffersize=buffersize, order='C'):
                fp.write(chunk.tobytes('C'))

def write_array(fp, array, version=(1, 0)):
    """
    Write an array to an NPY file, including a header.

    If the array is neither C-contiguous nor Fortran-contiguous AND the
    file_like object is not a real file object, this function will have to
    copy data in memory.

    Parameters
    ----------
    fp : file_like object
        An open, writable file object, or similar object with a ``.write()``
        method.
    array : ndarray
        The array to write to disk.
    version : (int, int), optional
        The version number of the format.  Default: (1, 0)

    Raises
    ------
    ValueError
        If the array cannot be persisted.
    Various other errors
        If the array contains Python objects as part of its dtype, the
        process of pickling them may raise various errors if the objects
        are not picklable.

    """
    if version != (1, 0):
        msg = "we only support format version (1,0), not %s"
        raise ValueError(msg % (version,))
    fp.write(magic(*version))
    write_array_header_1_0(fp, header_data_from_array_1_0(array))

    # Set buffer size to 16 MiB to hide the Python loop overhead.
    buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)

    if array.dtype.hasobject:
        # We contain Python objects so we cannot write out the data directly.
        # Instead, we will pickle it out with version 2 of the pickle protocol.
        pickle.dump(array, fp, protocol=2)
    elif array.flags.f_contiguous and not array.flags.c_contiguous:
        if isfileobj(fp):
            array.T.tofile(fp)
        else:
            for chunk in numpy.nditer(
                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
                    buffersize=buffersize, order='F'):
                fp.write(chunk.tostring('C'))
    else:
        if isfileobj(fp):
            array.tofile(fp)
        else:
            for chunk in numpy.nditer(
                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
                    buffersize=buffersize, order='C'):
                fp.write(chunk.tostring('C'))

def test_isfileobj():
    with tempdir(prefix="numpy_test_compat_") as folder:
        filename = join(folder, 'a.bin')

        with open(filename, 'wb') as f:
            assert_(isfileobj(f))

        with open(filename, 'ab') as f:
            assert_(isfileobj(f))

        with open(filename, 'rb') as f:
            assert_(isfileobj(f))

def write_array(fp, array, version=(1,0)):
    """
    Write an array to an NPY file, including a header.

    If the array is neither C-contiguous or Fortran-contiguous AND if the
    filelike object is not a real file object, then this function will have
    to copy data in memory.

    Parameters
    ----------
    fp : filelike object
        An open, writable file object or similar object with a `.write()`
        method.
    array : numpy.ndarray
        The array to write to disk.
    version : (int, int), optional
        The version number of the format.

    Raises
    ------
    ValueError
        If the array cannot be persisted.
    Various other errors
        If the array contains Python objects as part of its dtype, the
        process of pickling them may raise arbitrary errors if the objects
        are not picklable.

    """
    if version != (1, 0):
        msg = "we only support format version (1,0), not %s"
        raise ValueError(msg % (version,))
    fp.write(magic(*version))
    write_array_header_1_0(fp, header_data_from_array_1_0(array))
    if array.dtype.hasobject:
        # We contain Python objects so we cannot write out the data directly.
        # Instead, we will pickle it out with version 2 of the pickle protocol.
        cPickle.dump(array, fp, protocol=2)
    elif array.flags.f_contiguous and not array.flags.c_contiguous:
        if isfileobj(fp):
            array.T.tofile(fp)
        else:
            fp.write(array.T.tostring('C'))
    else:
        if isfileobj(fp):
            array.tofile(fp)
        else:
            # XXX: We could probably chunk this using something like
            # arrayterator.
            fp.write(array.tostring('C'))

def read_array(fp):
    """
    Read an array from an NPY file.

    Parameters
    ----------
    fp : filelike object
        If this is not a real file object, then this may take extra memory and
        time.

    Returns
    -------
    array : numpy.ndarray
        The array from the data on disk.

    Raises
    ------
    ValueError
        If the data is invalid.

    """
    version = read_magic(fp)
    if version != (1, 0):
        msg = "only support version (1,0) of file format, not %r"
        raise ValueError(msg % (version,))
    shape, fortran_order, dtype = read_array_header_1_0(fp)
    if len(shape) == 0:
        count = 1
    else:
        count = numpy.multiply.reduce(shape)

    # Now read the actual data.
    if dtype.hasobject:
        # The array contained Python objects. We need to unpickle the data.
        array = cPickle.load(fp)
    else:
        if isfileobj(fp):
            # We can use the fast fromfile() function.
            array = numpy.fromfile(fp, dtype=dtype, count=count)
        else:
            # This is not a real file. We have to read it the memory-intensive
            # way.
            # XXX: we can probably chunk this to avoid the memory hit.
            data = fp.read(int(count * dtype.itemsize))
            array = numpy.fromstring(data, dtype=dtype, count=count)

        if fortran_order:
            array.shape = shape[::-1]
            array = array.transpose()
        else:
            array.shape = shape

    return array

def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
          names=None, titles=None, aligned=False, byteorder=None, copy=True):
    """Construct a record array from a wide-variety of objects.
    """

    if (isinstance(obj, (type(None), str)) or isfileobj(obj)) \
           and (formats is None) \
           and (dtype is None):
        raise ValueError("Must define formats (or dtype) if object is "\
                         "None, string, or an open file")

    kwds = {}
    if dtype is not None:
        dtype = sb.dtype(dtype)
    elif formats is not None:
        dtype = format_parser(formats, names, titles,
                              aligned, byteorder)._descr
    else:
        kwds = {'formats': formats,
                'names' : names,
                'titles' : titles,
                'aligned' : aligned,
                'byteorder' : byteorder
                }

    if obj is None:
        if shape is None:
            raise ValueError("Must define a shape if obj is None")
        return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)

    elif isinstance(obj, bytes):
        return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)

    elif isinstance(obj, (list, tuple)):
        if isinstance(obj[0], (tuple, list)):
            return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
        else:
            return fromarrays(obj, dtype=dtype, shape=shape, **kwds)

    elif isinstance(obj, recarray):
        if dtype is not None and (obj.dtype != dtype):
            new = obj.view(dtype)
        else:
            new = obj
        if copy:
            new = new.copy()
        return new

    elif isfileobj(obj):
        return fromfile(obj, dtype=dtype, shape=shape, offset=offset)

    elif isinstance(obj, ndarray):
        if dtype is not None and (obj.dtype != dtype):
            new = obj.view(dtype)
        else:
            new = obj
        if copy:
            new = new.copy()
        res = new.view(recarray)
        if issubclass(res.dtype.type, nt.void):
            res.dtype = sb.dtype((record, res.dtype))
        return res

    else:
        interface = getattr(obj, "__array_interface__", None)
        if interface is None or not isinstance(interface, dict):
            raise ValueError("Unknown input type")
        obj = sb.array(obj)
        if dtype is not None and (obj.dtype != dtype):
            obj = obj.view(dtype)
        res = obj.view(recarray)
        if issubclass(res.dtype.type, nt.void):
            res.dtype = sb.dtype((record, res.dtype))
        return res

def open_memmap(filename, mode='r+', dtype=None, shape=None,
                fortran_order=False, version=None):
    """
    Open a .npy file as a memory-mapped array.

    This may be used to read an existing file or create a new one.

    Parameters
    ----------
    filename : str or path-like
        The name of the file on disk.  This may *not* be a file-like
        object.
    mode : str, optional
        The mode in which to open the file; the default is 'r+'.  In
        addition to the standard file modes, 'c' is also accepted to mean
        "copy on write."  See `memmap` for the available mode strings.
    dtype : data-type, optional
        The data type of the array if we are creating a new file in "write"
        mode, if not, `dtype` is ignored.  The default value is None, which
        results in a data-type of `float64`.
    shape : tuple of int
        The shape of the array if we are creating a new file in "write"
        mode, in which case this parameter is required.  Otherwise, this
        parameter is ignored and is thus optional.
    fortran_order : bool, optional
        Whether the array should be Fortran-contiguous (True) or
        C-contiguous (False, the default) if we are creating a new file in
        "write" mode.
    version : tuple of int (major, minor) or None
        If the mode is a "write" mode, then this is the version of the file
        format used to create the file.  None means use the oldest
        supported version that is able to store the data.  Default: None

    Returns
    -------
    marray : memmap
        The memory-mapped array.

    Raises
    ------
    ValueError
        If the data or the mode is invalid.
    IOError
        If the file is not found or cannot be opened correctly.

    See Also
    --------
    memmap

    """
    if isfileobj(filename):
        raise ValueError("Filename must be a string or a path-like object."
                         "  Memmap cannot use existing file handles.")

    if 'w' in mode:
        # We are creating the file, not reading it.
        # Check if we ought to create the file.
        _check_version(version)
        # Ensure that the given dtype is an authentic dtype object rather
        # than just something that can be interpreted as a dtype object.
        dtype = numpy.dtype(dtype)
        if dtype.hasobject:
            msg = "Array can't be memory-mapped: Python objects in dtype."
            raise ValueError(msg)
        d = dict(
            descr=dtype_to_descr(dtype),
            fortran_order=fortran_order,
            shape=shape,
        )
        # If we got here, then it should be safe to create the file.
        fp = open(os_fspath(filename), mode+'b')
        try:
            used_ver = _write_array_header(fp, d, version)
            # this warning can be removed when 1.9 has aged enough
            if version != (2, 0) and used_ver == (2, 0):
                warnings.warn("Stored array in format 2.0. It can only be"
                              "read by NumPy >= 1.9", UserWarning, stacklevel=2)
            offset = fp.tell()
        finally:
            fp.close()
    else:
        # Read the header of the file first.
        fp = open(os_fspath(filename), 'rb')
        try:
            version = read_magic(fp)
            _check_version(version)

            shape, fortran_order, dtype = _read_array_header(fp, version)
            if dtype.hasobject:
                msg = "Array can't be memory-mapped: Python objects in dtype."
                raise ValueError(msg)
            offset = fp.tell()
        finally:
            fp.close()

    if fortran_order:
        order = 'F'
    else:
        order = 'C'

#.........这里部分代码省略.........

def read_array(fp, allow_pickle=True, pickle_kwargs=None):
    """
    Read an array from an NPY file.

    Parameters
    ----------
    fp : file_like object
        If this is not a real file object, then this may take extra memory
        and time.
    allow_pickle : bool, optional
        Whether to allow reading pickled data. Default: True
    pickle_kwargs : dict
        Additional keyword arguments to pass to pickle.load. These are only
        useful when loading object arrays saved on Python 2 when using
        Python 3.

    Returns
    -------
    array : ndarray
        The array from the data on disk.

    Raises
    ------
    ValueError
        If the data is invalid, or allow_pickle=False and the file contains
        an object array.

    """
    version = read_magic(fp)
    _check_version(version)
    shape, fortran_order, dtype = _read_array_header(fp, version)
    if len(shape) == 0:
        count = 1
    else:
        count = numpy.multiply.reduce(shape, dtype=numpy.int64)

    # Now read the actual data.
    if dtype.hasobject:
        # The array contained Python objects. We need to unpickle the data.
        if not allow_pickle:
            raise ValueError("Object arrays cannot be loaded when "
                             "allow_pickle=False")
        if pickle_kwargs is None:
            pickle_kwargs = {}
        try:
            array = pickle.load(fp, **pickle_kwargs)
        except UnicodeError as err:
            if sys.version_info[0] >= 3:
                # Friendlier error message
                raise UnicodeError("Unpickling a python object failed: %r\n"
                                   "You may need to pass the encoding= option "
                                   "to numpy.load" % (err,))
            raise
    else:
        if isfileobj(fp):
            # We can use the fast fromfile() function.
            array = numpy.fromfile(fp, dtype=dtype, count=count)
        else:
            # This is not a real file. We have to read it the
            # memory-intensive way.
            # crc32 module fails on reads greater than 2 ** 32 bytes,
            # breaking large reads from gzip streams. Chunk reads to
            # BUFFER_SIZE bytes to avoid issue and reduce memory overhead
            # of the read. In non-chunked case count < max_read_count, so
            # only one read is performed.

            # Use np.ndarray instead of np.empty since the latter does
            # not correctly instantiate zero-width string dtypes; see
            # https://github.com/numpy/numpy/pull/6430
            array = numpy.ndarray(count, dtype=dtype)

            if dtype.itemsize > 0:
                # If dtype.itemsize == 0 then there's nothing more to read
                max_read_count = BUFFER_SIZE // min(BUFFER_SIZE, dtype.itemsize)

                for i in range(0, count, max_read_count):
                    read_count = min(max_read_count, count - i)
                    read_size = int(read_count * dtype.itemsize)
                    data = _read_bytes(fp, read_size, "array data")
                    array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
                                                             count=read_count)

        if fortran_order:
            array.shape = shape[::-1]
            array = array.transpose()
        else:
            array.shape = shape

    return array

def write_array(fp, array, version=None, allow_pickle=True, pickle_kwargs=None):
    """
    Write an array to an NPY file, including a header.

    If the array is neither C-contiguous nor Fortran-contiguous AND the
    file_like object is not a real file object, this function will have to
    copy data in memory.

    Parameters
    ----------
    fp : file_like object
        An open, writable file object, or similar object with a
        ``.write()`` method.
    array : ndarray
        The array to write to disk.
    version : (int, int) or None, optional
        The version number of the format. None means use the oldest
        supported version that is able to store the data.  Default: None
    allow_pickle : bool, optional
        Whether to allow writing pickled data. Default: True
    pickle_kwargs : dict, optional
        Additional keyword arguments to pass to pickle.dump, excluding
        'protocol'. These are only useful when pickling objects in object
        arrays on Python 3 to Python 2 compatible format.

    Raises
    ------
    ValueError
        If the array cannot be persisted. This includes the case of
        allow_pickle=False and array being an object array.
    Various other errors
        If the array contains Python objects as part of its dtype, the
        process of pickling them may raise various errors if the objects
        are not picklable.

    """
    _check_version(version)
    used_ver = _write_array_header(fp, header_data_from_array_1_0(array),
                                   version)
    # this warning can be removed when 1.9 has aged enough
    if version != (2, 0) and used_ver == (2, 0):
        warnings.warn("Stored array in format 2.0. It can only be"
                      "read by NumPy >= 1.9", UserWarning, stacklevel=2)

    if array.itemsize == 0:
        buffersize = 0
    else:
        # Set buffer size to 16 MiB to hide the Python loop overhead.
        buffersize = max(16 * 1024 ** 2 // array.itemsize, 1)

    if array.dtype.hasobject:
        # We contain Python objects so we cannot write out the data
        # directly.  Instead, we will pickle it out with version 2 of the
        # pickle protocol.
        if not allow_pickle:
            raise ValueError("Object arrays cannot be saved when "
                             "allow_pickle=False")
        if pickle_kwargs is None:
            pickle_kwargs = {}
        pickle.dump(array, fp, protocol=2, **pickle_kwargs)
    elif array.flags.f_contiguous and not array.flags.c_contiguous:
        if isfileobj(fp):
            array.T.tofile(fp)
        else:
            for chunk in numpy.nditer(
                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
                    buffersize=buffersize, order='F'):
                fp.write(chunk.tobytes('C'))
    else:
        if isfileobj(fp):
            array.tofile(fp)
        else:
            for chunk in numpy.nditer(
                    array, flags=['external_loop', 'buffered', 'zerosize_ok'],
                    buffersize=buffersize, order='C'):
                fp.write(chunk.tobytes('C'))

def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
             names=None, titles=None, aligned=False, byteorder=None):
    """Create an array from binary file data

    If file is a string or a path-like object then that file is opened,
    else it is assumed to be a file object. The file object must
    support random access (i.e. it must have tell and seek methods).

    >>> from tempfile import TemporaryFile
    >>> a = np.empty(10,dtype='f8,i4,a5')
    >>> a[5] = (0.5,10,'abcde')
    >>>
    >>> fd=TemporaryFile()
    >>> a = a.newbyteorder('<')
    >>> a.tofile(fd)
    >>>
    >>> _ = fd.seek(0)
    >>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
    ... byteorder='<')
    >>> print(r[5])
    (0.5, 10, 'abcde')
    >>> r.shape
    (10,)
    """
    
    if dtype is None and formats is None:
        raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")

    if (shape is None or shape == 0):
        shape = (-1,)
    elif isinstance(shape, (int, long)):
        shape = (shape,)

    if isfileobj(fd):
        # file already opened
        name = 0
    else:
        # open file
        fd = open(os_fspath(fd), 'rb')
        name = 1

    if (offset > 0):
        fd.seek(offset, 1)
    size = get_remaining_size(fd)

    if dtype is not None:
        descr = sb.dtype(dtype)
    else:
        descr = format_parser(formats, names, titles, aligned, byteorder)._descr

    itemsize = descr.itemsize

    shapeprod = sb.array(shape).prod(dtype=nt.intp)
    shapesize = shapeprod * itemsize
    if shapesize < 0:
        shape = list(shape)
        shape[shape.index(-1)] = size // -shapesize
        shape = tuple(shape)
        shapeprod = sb.array(shape).prod(dtype=nt.intp)

    nbytes = shapeprod * itemsize

    if nbytes > size:
        raise ValueError(
                "Not enough bytes left in file for specified shape and type")

    # create the array
    _array = recarray(shape, descr)
    nbytesread = fd.readinto(_array.data)
    if nbytesread != nbytes:
        raise IOError("Didn't read as many bytes as expected")
    if name:
        fd.close()

    return _array

def read_array(fp):
    """
    Read an array from an NPY file.

    Parameters
    ----------
    fp : file_like object
        If this is not a real file object, then this may take extra memory
        and time.

    Returns
    -------
    array : ndarray
        The array from the data on disk.

    Raises
    ------
    ValueError
        If the data is invalid.

    """
    version = read_magic(fp)
    if version != (1, 0):
        msg = "only support version (1,0) of file format, not %r"
        raise ValueError(msg % (version,))
    shape, fortran_order, dtype = read_array_header_1_0(fp)
    if len(shape) == 0:
        count = 1
    else:
        count = numpy.multiply.reduce(shape)

    # Now read the actual data.
    if dtype.hasobject:
        # The array contained Python objects. We need to unpickle the data.
        array = pickle.load(fp)
    else:
        if isfileobj(fp):
            # We can use the fast fromfile() function.
            array = numpy.fromfile(fp, dtype=dtype, count=count)
        else:
            # This is not a real file. We have to read it the memory-intensive
            # way.
            # crc32 module fails on reads greater than 2 ** 32 bytes, breaking
            # large reads from gzip streams. Chunk reads to BUFFER_SIZE bytes to
            # avoid issue and reduce memory overhead of the read. In
            # non-chunked case count < max_read_count, so only one read is
            # performed.

            max_read_count = BUFFER_SIZE // dtype.itemsize

            array = numpy.empty(count, dtype=dtype)

            for i in range(0, count, max_read_count):
                read_count = min(max_read_count, count - i)

                data = fp.read(int(read_count * dtype.itemsize))
                array[i:i+read_count] = numpy.frombuffer(data, dtype=dtype,
                                                         count=read_count)

        if fortran_order:
            array.shape = shape[::-1]
            array = array.transpose()
        else:
            array.shape = shape

    return array