lightkurve.periodogram.
Periodogram
(frequency, power, nyquist=None, label=None, targetid=None, default_view='frequency', meta={})¶Bases: object
Generic class to represent a power spectrum (frequency vs power data).
The Periodogram class represents a power spectrum, with values of frequency on the xaxis (in any frequency units) and values of power on the yaxis (in units of ppm^2 / [frequency units]).
Attributes: 


Attributes Summary
frequency_at_max_power 
Returns the frequency corresponding to the highest peak in the periodogram. 
max_power 
Returns the power of the highest peak in the periodogram. 
period 
Returns the array of periods, i.e. 
period_at_max_power 
Returns the period corresponding to the highest peak in the periodogram. 
Methods Summary
bin ([binsize, method]) 
Bins the power spectrum. 
copy () 
Returns a copy of the Periodogram object. 
flatten ([method, filter_width, return_trend]) 
Estimates the SignalToNoise (SNR) spectrum by dividing out an estimate of the noise background. 
Periodogram.from_lightcurve 

plot ([scale, ax, xlabel, ylabel, title, …]) 
Plots the Periodogram. 
show_properties () 
Prints a summary of the noncallable attributes of the Periodogram object. 
smooth ([method, filter_width]) 
Smooths the power spectrum using the ‘boxkernel’ or ‘logmedian’ method. 
to_table () 
Exports the Periodogram as an Astropy Table. 
Attributes Documentation
frequency_at_max_power
¶Returns the frequency corresponding to the highest peak in the periodogram.
max_power
¶Returns the power of the highest peak in the periodogram.
period
¶Returns the array of periods, i.e. 1/frequency.
period_at_max_power
¶Returns the period corresponding to the highest peak in the periodogram.
Methods Documentation
bin
(binsize=10, method='mean')¶Bins the power spectrum.
Parameters: 


Returns: 

copy
()¶Returns a copy of the Periodogram object.
This method uses the copy.deepcopy
function to ensure that all
objects stored within the Periodogram are copied.
Returns: 


flatten
(method='logmedian', filter_width=0.01, return_trend=False)¶Estimates the SignalToNoise (SNR) spectrum by dividing out an estimate of the noise background.
This method divides the power spectrum by a background estimated
using a moving filter in log10 space by default. For details on the
method
and filter_width
parameters, see Periodogram.smooth()
Dividing the power through by the noise background produces a spectrum
with no units of power. Since the signal is divided through by a measure
of the noise, we refer to this as a SignalToNoise
spectrum.
Parameters: 


Returns: 

plot
(scale='linear', ax=None, xlabel=None, ylabel=None, title='', style='lightkurve', view=None, unit=None, **kwargs)¶Plots the Periodogram.
Parameters: 


Returns: 

show_properties
()¶Prints a summary of the noncallable attributes of the Periodogram object.
Prints in order of type (ints, strings, lists, arrays and others). Prints in alphabetical order.
smooth
(method='boxkernel', filter_width=0.1)¶Smooths the power spectrum using the ‘boxkernel’ or ‘logmedian’ method.
If method
is set to ‘boxkernel’, this method will smooth the power
spectrum by convolving with a numpy Box1DKernel with a width of
filter_width
, where filter width
is in units of frequency.
This is best for filtering out noise while maintaining seismic mode
peaks. This method requires the Periodogram to have an evenly spaced
grid of frequencies. A ValueError
exception will be raised if this is
not the case.
If method
is set to ‘logmedian’, it smooths the power spectrum using
a moving median which moves across the power spectrum in a steps of
log10(x0) + 0.5 * filter_width
where filter width
is in log10(frequency) space. This is best for
estimating the noise background, as it filters over the seismic peaks.
Periodograms that are unsmoothed have multiplicative noise that is distributed as chi squared 2 degrees of freedom. This noise distribution has a well defined mean and median but the two are not equivalent. The mean of a chi squared 2 dof distribution is 2, but the median is 2(8/9)**3. (see https://en.wikipedia.org/wiki/Chisquared_distribution) In order to maintain consistency between ‘boxkernel’ and ‘logmedian’ a correction factor of (8/9)**3 is applied to (i.e., the median is divided by the factor) to the median values.
In addition to consistency with the ‘boxkernel’ method, the correction of the median values is useful when applying the periodogram flatten method. The flatten method divides the periodgram by the smoothed periodogram using the ‘logmedian’ method. By appyling the correction factor we follow asteroseismic convention that the signaltonoise power has a mean value of unity. (note the signaltonoise power is really the signal plus noise divided by the noise and hence should be unity in the absence of any signal)
Parameters: 


Returns: 

to_table
()¶Exports the Periodogram as an Astropy Table.
Returns: 


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