Make a cross spectrum from an unevenly sampled light curve.
You can also make an empty :class:`Crossspectrum` object to populate with your
own Fourier-transformed data (this can sometimes be useful when making
binned power spectra).

Parameters
----------
data1: :class:`stingray.Lightcurve` or :class:`stingray.events.EventList`, optional, default ``None``
    The dataset for the first channel/band of interest.

data2: :class:`stingray.Lightcurve` or :class:`stingray.events.EventList`, optional, default ``None``
    The dataset for the second, or "reference", band.

norm: {``frac``, ``abs``, ``leahy``, ``none``}, default ``none``
    The normalization of the (real part of the) cross spectrum.

power_type: string, optional, default ``real``
    Parameter to choose among complete, real part and magnitude of the cross spectrum.

fullspec: boolean, optional, default ``False``
    If False, keep only the positive frequencies, or if True, keep all of them .

Other Parameters
----------------
dt: float
    The time resolution of the light curve. Only needed when constructing
    light curves in the case where ``data1``, ``data2`` are
    :class:`EventList` objects

skip_checks: bool
    Skip initial checks, for speed or other reasons (you need to trust your
    inputs!)

min_freq : float
    Minimum frequency to take the Lomb-Scargle Fourier Transform

max_freq: float
    Maximum frequency to take the Lomb-Scargle Fourier Transform

df : float
    The time resolution of the light curve. Only needed where ``data1``, ``data2`` are

method : str
    The method to be used by the Lomb-Scargle Fourier Transformation function. `fast`
    and `slow` are the alloowed values. Default is `fast`. fast uses the optimized Press
    and Rybicki O(n*log(n))

Attributes
----------
freq: numpy.ndarray
    The array of mid-bin frequencies that the Fourier transform samples

power: numpy.ndarray
    The array of cross spectra (complex numbers)

power_err: numpy.ndarray
    The uncertainties of ``power``.
    An approximation for each bin given by ``power_err= power/sqrt(m)``.
    Where ``m`` is the number of power averaged in each bin (by frequency
    binning, or averaging more than one spectra). Note that for a single
    realization (``m=1``) the error is equal to the power.

df: float
    The frequency resolution

m: int
    The number of averaged cross-spectra amplitudes in each bin.

n: int
    The number of data points/time bins in one segment of the light
    curves.

k: array of int
    The rebinning scheme if the object has been rebinned otherwise is set to 1.

nphots1: float
    The total number of photons in light curve 1

nphots2: float
    The total number of photons in light curve 2

Make a :class:`LombScarglePowerspectrum` (also called periodogram) from a unevenly sampled (binned)
light curve. Periodograms can be normalized by either Leahy normalization,
fractional rms normalization, absolute rms normalization, or not at all.

You can also make an empty :class:`LombScarglePowerspectrum` object to populate with
your own fourier-transformed data (this can sometimes be useful when making
binned power spectra).

Parameters
----------
data: :class:`stingray.lightcurve.Lightcurve` or :class:`stingray.events.EventList` object, optional, default ``None``
    The light curve data to be Fourier-transformed.

norm: {"leahy" | "frac" | "abs" | "none" }, optional, default "frac"
    The normaliation of the power spectrum to be used. Options are
    "leahy", "frac", "abs" and "none", default is "frac".

Other Parameters
----------------
dt: float
    The time resolution of the light curve. Only needed when constructing
    light curves in the case where ``data`` is a
    :class:`EventList` object

skip_checks: bool
    Skip initial checks, for speed or other reasons (you need to trust your
    inputs!).

min_freq : float
    Minimum frequency to take the Lomb-Scargle Fourier Transform

max_freq: float
    Maximum frequency to take the Lomb-Scargle Fourier Transform

df : float
    The time resolution of the light curve. Only needed where ``data`` is a :class`stingray.Eventlist` object

method : str
    The method to be used by the Lomb-Scargle Fourier Transformation function. `fast`
    and `slow` are the alloowed values. Default is `fast`. fast uses the optimized Press
    and Rybicki O(n*log(n))

Attributes
----------
norm: {"leahy" | "frac" | "abs" | "none" }
    The normalization of the power spectrum.

freq: numpy.ndarray
    The array of mid-bin frequencies that the Fourier transform samples.

power: numpy.ndarray
    The array of normalized squared absolute values of Fourier
    amplitudes.

power_err: numpy.ndarray
    The uncertainties of ``power``.
    An approximation for each bin given by ``power_err= power/sqrt(m)``.
    Where ``m`` is the number of power averaged in each bin (by frequency
    binning, or averaging power spectra of segments of a light curve).
    Note that for a single realization (``m=1``) the error is equal to the
    power.

df: float
    The frequency resolution.

m: int
    The number of averaged powers in each bin.

n: int
    The number of data points in the light curve.

nphots: float
    The total number of photons in the light curve.