src.dolfen
Module Contents
Classes
The 'downsampled' approximation of the Bayesian likelihood function defined by the signal and noise models, and injection parameters. |
- class src.dolfen.likelihood(t, model, inj_params, PSD, priors, numsamps=400, scheme='rand', prsrv_FIM=None, MCS_override=0, addnoise=False, f_low_cut=0.0, f_high_cut=0.0, GWmodel_margphase=None, blocksize=1, param_diffs=None, save_load_FIM=True, overwritesavedFIM=False, phase_int_points=2357, resume_dir='', numprocs=0, forcerun=False)
The ‘downsampled’ approximation of the Bayesian likelihood function defined by the signal and noise models, and injection parameters.
Parameters
- t1D numpy array
The set of time-stamps of the ‘original’ dataset, sampled at least at the Nyquist frequency of all waveforms with non-negligible probability (just above the Nyquist frequency of the injected signal should do).
- modelcallable
The user-defined waveform model, the first argument must be the times-stamps, t.
- inj_paramsdict
The signal’s injection parameters for each parameter of the signal model: key=param name, value=param value.
- PSDcallable
The ‘Power Spectral Density’, a function of frequency, f. This encodes the noise model. Must return 1D array when passed a 1D array.
- priorsdict
The parameters (dict keys) and the priors (values). If the parameter is known the prior should be a float, otherwise it should be an object with a “.minimum” and a “.maximum” property.
- numsampsint
The number of datapoints to use for downsampling.
- schemestring
The ‘downsampling scheme’ that should be employed by dolfen. Currently accepted schemes are
“rand”: random downsampling;
“unif”: uniform downsampling (a.k.a. decimation);
“hybrid”: a 50/50 split of uniform and random downsampling;
“cluster,n”: random samples are taken from ‘n’ uniformly spaced blocks of size ‘blocksize’. if n is not specified, n is set to the number of model parameters;
“prand”: ‘pseudo-random’ downsampling. Mainly used for testing dolfen, for any given system, a seed is fixed for random sample selection.
- prsrv_FIMBool
True to use the (approximate) FIM preservation method of downsampling. This is the most accurate method. False to use the minimisation of Jeffreys’ divergence of the FIMs of fully- and down-sampled datasets. If not supplied, dolfen will try to find FIM preservation solutions a number of times, then revert to FIM Jeffreys’ divergence minimisation if no solution can be found.
- MCS_overrideint
The number of maximum correlated samples of any given sample as determined by the inverse autocorrelation function. Minimum 1; a value <1 means dolfen will automatically compute this.
- addnoiseBool
True to add a random noise realisation to the data from the PSD.
- f_low_cut, f_high_cutfloats
Lowest and highest frequencies at which the PSD is irrelevant to the likelihood; the PSD below f_low_cut is set equal to the PSD at f_low_cut, the PSD above f_high_cut is set equal to the PSD at f_high_cut.
- GWmodel_margphasecallable
Dolfen was created for studying likelihoods of gravitational waves of low-mass CBCs in LISA. Numerically marginalising the phase of the time-domain CBC GW waveform is possible as per the example included with dolfen; the model is required to separately return the plus and cross GW polarisations so that the phase marginalised likelihood can be computed quickly.
- blocksizeint
For use with “cluster” downsampling; size of blocks from which to sample
- param_diffslist
The step-sizes for numerical derivatives of signal model with respect to its parameters, in case dolfen has difficulty in automatically computing them.
- save_load_FIMBool
True if one wishes to write to disk the computed Fisher matrix and derivative step-sizes.
- overwritesavedFIMBool
If FIM data has already been saved to disk, overwrite it.
- phase_int_pointsint
The number of discrete integration points to use for marginalising GW phase.
- resume_dirstring
Directory for writing/reading downsampling solution information for given dolfen inputs.
- numprocsint
Number of processors to use to compute the Fisher information matrix. Uses all available if not set.
- forcerunBool
True to force run even if the MCS is large and downsampling would not be expected to yield a significant reduction in likelihood evaluation time.