def __init__(self, lc=None, norm='frac', gti=None): """ Make a Periodogram (power spectrum) from a (binned) light curve. Periodograms can be Leahy normalized or fractional rms normalized. You can also make an empty Periodogram object to populate with your own fourier-transformed data (this can sometimes be useful when making binned periodograms). Parameters ---------- lc: lightcurve.Lightcurve object, optional, default None The light curve data to be Fourier-transformed. norm: {"leahy" | "frac" | "abs" | "none" }, optional, default "frac" The normaliation of the periodogram to be used. Options are "leahy", "frac", "abs" and "none", default is "frac". Other Parameters ---------------- gti: 2-d float array [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] -- Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care! Attributes ---------- norm: {"leahy" | "frac" | "abs" | "none"} the normalization of the periodogram 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 powerspectrum). 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 """ Crossspectrum.__init__(self, lc1=lc, lc2=lc, norm=norm, gti=gti) self.nphots = self.nphots1
def __init__(self, lc=None, norm='frac', gti=None): """ Make a Periodogram (power spectrum) from a (binned) light curve. Periodograms can be Leahy normalized or fractional rms normalized. You can also make an empty Periodogram object to populate with your own fourier-transformed data (this can sometimes be useful when making binned periodograms). Parameters ---------- lc: lightcurve.Lightcurve object, optional, default None The light curve data to be Fourier-transformed. norm: {"leahy" | "frac" | "abs" | "none" }, optional, default "frac" The normaliation of the periodogram to be used. Options are "leahy", "frac", "abs" and "none", default is "frac". Other Parameters ---------------- gti: 2-d float array [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] -- Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care! Attributes ---------- norm: {"leahy" | "frac" | "abs" | "none"} the normalization of the periodogram 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 powerspectrum). 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 """ Crossspectrum.__init__(self, lc1=lc, lc2=lc, norm=norm, gti=gti) self.nphots = self.nphots1
def __init__(self, data=None, norm="frac", gti=None, dt=None, lc=None): if lc is not None: warnings.warn("The lc keyword is now deprecated. Use data " "instead", DeprecationWarning) if data is None: data = lc Crossspectrum.__init__(self, data1=data, data2=data, norm=norm, gti=gti, dt=dt) self.nphots = self.nphots1 self.dt = dt
def __init__(self, lc=None, norm='frac', gti=None): """ Make a Periodogram (power spectrum) from a (binned) light curve. Periodograms can be Leahy normalized or fractional rms normalized. You can also make an empty Periodogram object to populate with your own fourier-transformed data (this can sometimes be useful when making binned periodograms). Parameters ---------- lc: lightcurve.Lightcurve object, optional, default None The light curve data to be Fourier-transformed. norm: {"leahy" | "rms"}, optional, default "rms" The normaliation of the periodogram to be used. Options are "leahy" or "rms", default is "rms". Other Parameters ---------------- gti: 2-d float array [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] -- Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care! Attributes ---------- norm: {"leahy" | "rms"} the normalization of the periodogram 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 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 """ Crossspectrum.__init__(self, lc1=lc, lc2=lc, norm=norm, gti=gti) self.nphots = self.nphots1
def __init__(self, lc=None, norm='frac', gti=None): """ Make a Periodogram (power spectrum) from a (binned) light curve. Periodograms can be Leahy normalized or fractional rms normalized. You can also make an empty Periodogram object to populate with your own fourier-transformed data (this can sometimes be useful when making binned periodograms). Parameters ---------- lc: lightcurve.Lightcurve object, optional, default None The light curve data to be Fourier-transformed. norm: {"leahy" | "rms"}, optional, default "rms" The normaliation of the periodogram to be used. Options are "leahy" or "rms", default is "rms". Other Parameters ---------------- gti: 2-d float array [[gti0_0, gti0_1], [gti1_0, gti1_1], ...] -- Good Time intervals. This choice overrides the GTIs in the single light curves. Use with care! Attributes ---------- norm: {"leahy" | "rms"} the normalization of the periodogram 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 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 """ Crossspectrum.__init__(self, lc1=lc, lc2=lc, norm=norm, gti=gti) self.nphots = self.nphots1
def __init__(self, data=None, norm="frac", gti=None, dt=None, lc=None, skip_checks=False, legacy=False): self._type = None if lc is not None: warnings.warn("The lc keyword is now deprecated. Use data " "instead", DeprecationWarning) if data is None: data = lc good_input = True if not skip_checks: good_input = self.initial_checks( data1=data, data2=data, norm=norm, gti=gti, lc1=lc, lc2=lc, dt=dt ) norm = norm.lower() self.norm = norm self.dt = dt if not good_input: return self._initialize_empty() if not legacy and data is not None: return self._initialize_from_any_input(data, dt=dt, norm=norm) Crossspectrum.__init__(self, data1=data, data2=data, norm=norm, gti=gti, dt=dt, skip_checks=True, legacy=legacy) self.nphots = self.nphots1 self.dt = dt
def __init__(self, lc=None, norm='frac', gti=None): Crossspectrum.__init__(self, lc1=lc, lc2=lc, norm=norm, gti=gti) self.nphots = self.nphots1
def __init__(self, lc=None, norm='frac', gti=None): Crossspectrum.__init__(self, lc1=lc, lc2=lc, norm=norm, gti=gti) self.nphots = self.nphots1