cogwheel.likelihood.likelihood.CBCLikelihood

class cogwheel.likelihood.likelihood.CBCLikelihood(event_data, waveform_generator)

Bases: JSONMixin

Class that accesses the event data and waveform generator.

Provides methods for computing likelihood as a function of compact binary coalescence parameters without using relative binning, and also the asd-drift correction.

Subclassed by RelativeBinningLikelihood.

Parameters:

event_datadata.EventData

waveform_generatorwaveform.WaveformGenerator

Methods

compute_asd_drift	Estimate local standard deviation of the matched-filter output at the time of the event for each detector.
get_average_frequency	Return average frequency in Hz.
get_init_dict	Return dictionary with keyword arguments to __init__.
get_module_name	Name of the module that defines the instance's class.
lnlike_fft	Return log likelihood computed on the FFT grid, without using relative binning.
plot_whitened_wf	Plot the whitened strain and waveform model in the time domain in all detectors.
postprocess_samples	Placeholder method that will be called after sampling and may be overriden by subclasses.
reinstantiate	Return a new instance of the class, possibly updating init_kwargs.
to_json	Write class instance to json file.

Attributes

asd_drift	Array of length ndetectors with ASD drift-correction.
params	Parameters expected in par_dic for likelihood evaluation.
subclass_registry

property asd_drift

Array of length ndetectors with ASD drift-correction.

Values > 1 mean local noise variance is higher than average.

compute_asd_drift(par_dic, tol=0.02, max_tcorr_contiguous_low=16.0, **kwargs)

Estimate local standard deviation of the matched-filter output at the time of the event for each detector.

Note: all harmonic_modes of the approximant are used even if waveform_generator.harmonic_modes is set differently. This is so that asd_drift does not change and one can make apples-to- apples comparisons of the likelihood toggling harmonic modes on and off.

Parameters:

par_dicdict

Waveform parameters, keys should match self.waveform_generator.params.

tolfloat

Stochastic measurement error tolerance, used to decide the number of samples.

max_tcorr_contiguous_lowfloat

Maximum number of contiguous correlation times with values below the average noise level to allow (these are classified as a hole and disregarded in the average).

**kwargs

Passed to safe_std, keys include: expected_high, reject_nearby.

get_average_frequency(par_dic, ref_det_name=None, moment=1.0)

Return average frequency in Hz.

The average frequency is defined as (avg(f^moment))^(1/moment) where avg is the frequency-domain average with weight ~ |h(f)|^2 / PSD(f).

The answer is rounded to nearest Hz to ease reporting.

Parameters:

par_dicdict

Waveform parameters, keys should match self.waveform_generator.params.

ref_det_namestr or None

Name of the detector from which to get the PSD, e.g. ‘H’ for Hanford, or None (default) to combine the PSDs of all detectors.

momentnonzero float

Controls the frequency weights in the average.

get_init_dict(**kwargs)

Return dictionary with keyword arguments to __init__.

Only works if the class stores its init parameters as attributes with the same names. Otherwise, the subclass should override this method.

Parameters:

**kwargs

Allows to manually override some keys. The remaining ones will be read from the instance’s attributes. All keywords must be in the __init__ signature. It’s mostly here to facilitate overriding by subclasses.

get_module_name()

Name of the module that defines the instance’s class.

lnlike_fft(par_dic)

Return log likelihood computed on the FFT grid, without using relative binning.

Parameters:

par_dicdict

Waveform parameters, keys should match self.waveform_generator.params.

property params

Parameters expected in par_dic for likelihood evaluation.

plot_whitened_wf(par_dic, trng=(-0.7, 0.1), plot_data=True, fig=None, figsize=None, by_m=False, **wf_plot_kwargs)

Plot the whitened strain and waveform model in the time domain in all detectors.

Parameters:

par_dicdict

Waveform parameters, keys should match self.waveform_generator.params.

trng(float, float)

Range of time to plot relative to self.tgps (s).

plot_databool

Whether to include detector data in plot.

figmatplotlib.figure.Figure, optional

None (default) creates a new figure.

figsize(float, float)

Figure width and height in inches, used if fig=None.

**wf_plot_kwargs

Keyword arguments passed to plt.plot() for waveform. Additionally, keyword arguments for the data plot can be passed as a dict data_plot_kwargs.

Returns:

matplotlib.figure.Figure

Figure with plots.

postprocess_samples(samples)

Placeholder method that will be called after sampling and may be overriden by subclasses. (E.g. marginalized likelihoods demarginalize the distribution in postprocessing.)

Parameters:

samplespandas.DataFrame

Rows are samples, columns must contain .params.

reinstantiate(**new_init_kwargs)

Return a new instance of the class, possibly updating init_kwargs.

Values not passed will be taken from the current instance.

to_json(dirname, basename=None, *, dir_permissions=493, file_permissions=420, overwrite=False)

Write class instance to json file.

It can then be loaded with read_json.