def make_segments(date_start=None, date_end=None, date_freq=None, n_series=
    3, n_segments=2, seg_sep=None, means=None, stds=None, trends=None,
    amplitudes=None, phases=None):
    """ Simulate data from multiple temporal segments

    Parameters
    ----------
    date_start : str, datetime, and more, optional
        Starting date (in a format known to Pandas)
    date_end : str, datetime, and more, optional
        Ending date (in a format known to Pandas)
    date_freq : str, optional
        Date frequency
    n_series : int
        Number of series/spectral bands to simulate
    n_segments : int
        Number of segments to simulate
    seg_sep : Sequence[float]
        Separability of segments (i.e., the size of the disturbance)
    means : Sequence[float]
        The mean value for each series/spectral band (passed to
        :py:func:`make_time_series_mean`)
    stds : Sequence[float]
        The standard deviation for each series/spectral band (passed to
        :py:func:`make_time_series_mean`)
    trends : Sequence[float]
        The time trend for each series/spectral band (passed to
        :py:func:`make_time_series_trend`)
    amplitudes : Sequence[float]
        The harmonic amplitude value for each series/spectral band (passed to
        :py:func:`make_time_series_harmonic`)
    phases : Sequence[float]
        The harmonic phase value for each series/spectral band (passed to
        :py:func:`make_time_series_harmonic`)

    Returns
    -------
    xr.DataArray
        Simulated data for ``n_segments`` across ``n_series`` series/spectral
        bands
    np.ndarray
        Array of ``datetime64`` indicating the dates of change
        (size=``n_segments - 1``)
    """
    assert isinstance(n_series, int)
    assert isinstance(n_segments, int) and n_segments >= 1
    pass
