Outputs rounded and formatted percentiles.

    Parameters
    ----------
    percentiles : list-like, containing floats from interval [0,1]

    Returns
    -------
    formatted : list of strings

    Notes
    -----
    Rounding precision is chosen so that: (1) if any two elements of
    ``percentiles`` differ, they remain different after rounding
    (2) no entry is *rounded* to 0% or 100%.
    Any non-integer is always rounded to at least 1 decimal place.

    Examples
    --------
    Keeps all entries different after rounding:

    >>> format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
    ['1.999%', '2.001%', '50%', '66.667%', '99.99%']

    No element is rounded to 0% or 100% (unless already equal to it).
    Duplicates are allowed:

    >>> format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
    ['0%', '50%', '2.0%', '50%', '66.67%', '99.99%']

def format_percentiles(percentiles):
    """
    Outputs rounded and formatted percentiles.

    Parameters
    ----------
    percentiles : list-like, containing floats from interval [0,1]

    Returns
    -------
    formatted : list of strings

    Notes
    -----
    Rounding precision is chosen so that: (1) if any two elements of
    ``percentiles`` differ, they remain different after rounding
    (2) no entry is *rounded* to 0% or 100%.
    Any non-integer is always rounded to at least 1 decimal place.

    Examples
    --------
    Keeps all entries different after rounding:

    >>> format_percentiles([0.01999, 0.02001, 0.5, 0.666666, 0.9999])
    ['1.999%', '2.001%', '50%', '66.667%', '99.99%']

    No element is rounded to 0% or 100% (unless already equal to it).
    Duplicates are allowed:

    >>> format_percentiles([0, 0.5, 0.02001, 0.5, 0.666666, 0.9999])
    ['0%', '50%', '2.0%', '50%', '66.67%', '99.99%']
    """

    percentiles = np.asarray(percentiles)

    # It checks for np.NaN as well
    with np.errstate(invalid='ignore'):
        if not is_numeric_dtype(percentiles) or not np.all(percentiles >= 0) \
                or not np.all(percentiles <= 1):
            raise ValueError("percentiles should all be in the interval [0,1]")

    percentiles = 100 * percentiles
    int_idx = (percentiles.astype(int) == percentiles)

    if np.all(int_idx):
        out = percentiles.astype(int).astype(str)
        return [i + '%' for i in out]

    unique_pcts = np.unique(percentiles)
    to_begin = unique_pcts[0] if unique_pcts[0] > 0 else None
    to_end = 100 - unique_pcts[-1] if unique_pcts[-1] < 100 else None

    # Least precision that keeps percentiles unique after rounding
    prec = -np.floor(np.log10(np.min(
        np.ediff1d(unique_pcts, to_begin=to_begin, to_end=to_end)
    ))).astype(int)
    prec = max(1, prec)
    out = np.empty_like(percentiles, dtype=object)
    out[int_idx] = percentiles[int_idx].astype(int).astype(str)
    out[~int_idx] = percentiles[~int_idx].round(prec).astype(str)
    return [i + '%' for i in out]