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.