def _aligned_zeros(shape, dtype=float, order="C", align=None):
    """Allocate a new ndarray with aligned memory.
    Primary use case for this currently is working around a f2py issue
    in Numpy 1.9.1, where dtype.alignment is such that np.zeros() does
    not necessarily create arrays aligned up to it.
    """
    dtype = np.dtype(dtype)
    if align is None:
        align = dtype.alignment
    if not hasattr(shape, '__len__'):
        shape = (shape,)
    size = functools.reduce(operator.mul, shape) * dtype.itemsize
    buf = np.empty(size + align + 1, np.uint8)
    offset = buf.__array_interface__['data'][0] % align
    if offset != 0:
        offset = align - offset
    # Note: slices producing 0-size arrays do not necessarily change
    # data pointer --- so we use and allocate size+1
    buf = buf[offset:offset+size+1][:-1]
    data = np.ndarray(shape, dtype, buf, order=order)
    data.fill(0)
    return data
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/utils/arpack.py/_aligned_zeros/original