ath instance
        Path to the mapped file.
    offset : int
        Offset position in the file.
    mode : str
        File mode.

    Methods
    -------
    flush
        Flush any changes in memory to file on disk.
        When you delete a memmap object, flush is called first to write
        changes to disk.


    See also
    --------
    lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.

    Notes
    -----
    The memmap object can be used anywhere an ndarray is accepted.
    Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
    ``True``.

    Memory-mapped files cannot be larger than 2GB on 32-bit systems.

    When a memmap causes a file to be created or extended beyond its
    current size in the filesystem, the contents of the new part are
    unspecified. On systems with POSIX filesystem semantics, the extended
    part will be filled with zero bytes.

    Examples
    --------
    >>> data = np.arange(12, dtype='float32')
    >>> data.resize((3,4))

    This example uses a temporary file so that doctest doesn't write
    files to your directory. You would use a 'normal' filename.

    >>> from tempfile import mkdtemp
    >>> import os.path as path
    >>> filename = path.join(mkdtemp(), 'newfile.dat')

    Create a memmap with dtype and shape that matches our data:

    >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
    >>> fp
    memmap([[0., 0., 0., 0.],
            [0., 0., 0., 0.],
            [0., 0., 0., 0.]], dtype=float32)

    Write data to memmap array:

    >>> fp[:] = data[:]
    >>> fp
    memmap([[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.],
            [  8.,   9.,  10.,  11.]], dtype=float32)

    >>> fp.filename == path.abspath(filename)
    True

    Flushes memory changes to disk in order to read them back

    >>> fp.flush()

    Load the memmap and verify data was stored:

    >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
    >>> newfp
    memmap([[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.],
            [  8.,   9.,  10.,  11.]], dtype=float32)

    Read-only memmap:

    >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
    >>> fpr.flags.writeable
    False

    Copy-on-write memmap:

    >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
    >>> fpc.flags.writeable
    True

    It's possible to assign to copy-on-write array, but values are only
    written into the memory copy of the array, and not written to disk:

    >>> fpc
    memmap([[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.],
            [  8.,   9.,  10.,  11.]], dtype=float32)
    >>> fpc[0,:] = 0
    >>> fpc
    memmap([[  0.,   0.,   0.,   0.],
            [  4.,   5.,   6.,   7.],
            [  8.,   9.,  10.,  11.]], dtype=float32)

    File on disk is unchanged:

    >>> fpr
    memmap([[  0.,   1.,   2.,   3.],
            [  4.,   5.,   6.,   7.],
            [  8.,   9.,  10.,  11.]], dtype=float32)

    Offset into a memmap:

    >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
    >>> fpo
    memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)

    g