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
