dolfen package

Submodules

dolfen module

class 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)

Bases: object

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.

approxInvAcfInnerProd(sig1, sig2)

flatPSD_osb(f)

getDeriv(times, i)

getFIMchunk(times, ids, q)

getFisherInfo(times)

getFisherInfoSub()

getInvACFfunc(t, PSDfunc)

getSampleWeightsApprox(Fisher, Pf_inv)

log_likelihood_marg()

log_likelihood_nomarg()

makeSubSet(numsamps, numblocks=0, blocksize=1, scheme='rand', resume_dir='', prsrv_FIM=True)

opt_stepsize(model, boundsfactor, diffparam, times, params, numtests=250)

resetsampletimesanddata(t, PSD)

subFIMInnerProd(vec1, vec2)

whitenVec(vec)

Module contents

class 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)

Bases: object

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.

approxInvAcfInnerProd(sig1, sig2)

flatPSD_osb(f)

getDeriv(times, i)

getFIMchunk(times, ids, q)

getFisherInfo(times)

getFisherInfoSub()

getInvACFfunc(t, PSDfunc)

getSampleWeightsApprox(Fisher, Pf_inv)

log_likelihood_marg()

log_likelihood_nomarg()

makeSubSet(numsamps, numblocks=0, blocksize=1, scheme='rand', resume_dir='', prsrv_FIM=True)

opt_stepsize(model, boundsfactor, diffparam, times, params, numtests=250)

resetsampletimesanddata(t, PSD)

subFIMInnerProd(vec1, vec2)

whitenVec(vec)