Python compat.isfileobj方法代碼示例

# 需要導入模塊: from numpy import compat [as 別名]
# 或者: from numpy.compat import isfileobj [as 別名]
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)) 

# 需要導入模塊: from numpy import compat [as 別名]
# 或者: from numpy.compat import isfileobj [as 別名]
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))
    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:
            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')) 

# 需要導入模塊: from numpy import compat [as 別名]
# 或者: from numpy.compat import isfileobj [as 別名]
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'))