def relative(dataset, ori=0, column=1, fail_silently=True):
    """
    Convert dataset to relative value from the value of :attr:`ori`

    Parameters
    ----------
    dataset : list of numpy array list
        A list of numpy array list
    ori : integer or numpy array, optional
        A relative original data index or numpy array
    column : integer, optional
        An index of base column to calculate the relative value
    fail_silently : boolean
        If `True`, do not raise exception if no data exists

    Returns
    -------
    ndarray
        A list of numpy array list

    Examples
    --------
    >>> import numpy as np
    >>> from maidenhair.filters.relative import relative
    >>> dataset = []
    >>> dataset.append([np.array([0, 1, 2]), np.array([3, 4, 5])])
    >>> dataset.append([np.array([0, 1, 2]), np.array([3, 5, 7])])
    >>> dataset.append([np.array([0, 1, 2]), np.array([100, 103, 106])])
    >>> expected = [
    ...     [np.array([0, 1, 2]), np.array([0, 50, 100])],
    ...     [np.array([0, 1, 2]), np.array([0, 100, 200])],
    ...     [np.array([0, 1, 2]), np.array([4850, 5000, 5150])],
    ... ]
    >>> proceed = relative(dataset)
    >>> np.array_equal(proceed, expected)
    True

    """
    try:
        if isinstance(ori, int):
            # relative from the [ori]th array
            ori = dataset[ori][column]
        if isinstance(ori[0], (list, tuple, np.ndarray)):
            # calculate min/max difference
            for i in range(len(ori[0])):
                orimin = np.min(ori[:,i])
                orimax = np.max(ori[:,i])
                oridiff = orimax - orimin
                # baseline
                for data in dataset:
                    data[column][:,i] -= orimin
                # convert
                for data in dataset:
                    data[column][:,i] /= oridiff / 100.0
        else:
            orimin = np.min(ori)
            orimax = np.max(ori)
            oridiff = orimax - orimin
            # baseline
            for data in dataset:
                data[column] -= orimin
            # convert
            for data in dataset:
                data[column] /= oridiff / 100.0
        return dataset
    except IndexError, e:
        if fail_silently:
            # fail silently
            return dataset
        raise e
Convert dataset to relative value from the value of :attr:`ori`

    Parameters
    ----------
    dataset : list of numpy array list
        A list of numpy array list
    ori : integer or numpy array, optional
        A relative original data index or numpy array
    column : integer, optional
        An index of base column to calculate the relative value
    fail_silently : boolean
        If `True`, do not raise exception if no data exists

    Returns
    -------
    ndarray
        A list of numpy array list

    Examples
    --------
    >>> import numpy as np
    >>> from maidenhair.filters.relative import relative
    >>> dataset = []
    >>> dataset.append([np.array([0, 1, 2]), np.array([3, 4, 5])])
    >>> dataset.append([np.array([0, 1, 2]), np.array([3, 5, 7])])
    >>> dataset.append([np.array([0, 1, 2]), np.array([100, 103, 106])])
    >>> expected = [
    ...     [np.array([0, 1, 2]), np.array([0, 50, 100])],
    ...     [np.array([0, 1, 2]), np.array([0, 100, 200])],
    ...     [np.array([0, 1, 2]), np.array([4850, 5000, 5150])],
    ... ]
    >>> proceed = relative(dataset)
    >>> np.array_equal(proceed, expected)
    True