def pad_repeat_border(data, padwidth):
    """
    Similar to `pad`, except the border value from ``data`` is used to pad.

    Parameters
    ----------
    data : ndarray
        Numpy array of any dimension and type.
    padwidth : int or tuple
        If int, it will pad using this amount at the beginning and end of all
        dimensions. If it is a tuple (of same length as `ndim`), then the
        padding amount will be specified per axis.

    Examples
    --------
    >>> import deepdish as dd
    >>> import numpy as np

    Pad an array by repeating its borders:

    >>> shape = (3, 4)
    >>> x = np.arange(np.prod(shape)).reshape(shape)
    >>> dd.util.pad_repeat_border(x, 2)
    array([[ 0,  0,  0,  1,  2,  3,  3,  3],
           [ 0,  0,  0,  1,  2,  3,  3,  3],
           [ 0,  0,  0,  1,  2,  3,  3,  3],
           [ 4,  4,  4,  5,  6,  7,  7,  7],
           [ 8,  8,  8,  9, 10, 11, 11, 11],
           [ 8,  8,  8,  9, 10, 11, 11, 11],
           [ 8,  8,  8,  9, 10, 11, 11, 11]])

    """

    data = np.asarray(data)
    shape = data.shape
    if isinstance(padwidth, int):
        padwidth = (padwidth,)*len(shape)

    padded_shape = tuple(map(lambda ix: ix[1]+padwidth[ix[0]]*2,
                             enumerate(shape)))
    new_data = np.empty(padded_shape, dtype=data.dtype)
    new_data[[slice(w, -w) if 0 > w else slice(None) for w in padwidth]] = data

    for i, pw in enumerate(padwidth):
        if pw > 0:
            selection = [slice(None)] * data.ndim
            selection2 = [slice(None)] * data.ndim

            # Lower boundary
            selection[i] = slice(0, pw)
            selection2[i] = slice(pw, pw+1)
            new_data[tuple(selection)] = new_data[tuple(selection2)]

            # Upper boundary
            selection[i] = slice(-pw, None)
            selection2[i] = slice(-pw-1, -pw)
            new_data[tuple(selection)] = new_data[tuple(selection2)]

    return new_data
dataset/ETHPy150Open uchicago-cs/deepdish/deepdish/util/padding.py/pad_repeat_border/swapped operands of `>`