def block_average_above(x, y, new_x):
    """
    Linearly interpolates values in new_x based on the values in x and y.

    Parameters
    ----------
    x : array_like
        Independent values.
    y : array_like
        Dependent values.
    new_x : array_like
        The x values to interpolate y values.

    """
    bad_index = None
    x = atleast_1d_and_contiguous(x, np.float64)
    y = atleast_1d_and_contiguous(y, np.float64)
    new_x = atleast_1d_and_contiguous(new_x, np.float64)

    if 2 > y.ndim:
        raise ValueError("`linear` only works with 1-D or 2-D arrays.")
    if len(y.shape) == 2:
        new_y = np.zeros((y.shape[0], len(new_x)), np.float64)
        for i in range(len(new_y)):
            bad_index = _interpolate.block_averave_above_dddd(x, y[i],
                                                            new_x, new_y[i])
            if bad_index is not None:
                break
    else:
        new_y = np.zeros(len(new_x), np.float64)
        bad_index = _interpolate.block_average_above_dddd(x, y, new_x, new_y)

    if bad_index is not None:
        msg = "block_average_above cannot extrapolate and new_x[%d]=%f "\
              "is out of the x range (%f, %f)" % \
              (bad_index, new_x[bad_index], x[0], x[-1])
        raise ValueError(msg)

    return new_y
dataset/ETHPy150Open scipy/scipy/scipy/interpolate/interpolate_wrapper.py/block_average_above/swapped operands of `>`