gb

Galactic binary parameterization, priors and likelihood.

class m3c2.gb.likelihood.FlatLogLik(data, GB, noise, frange, tmax, N=1, fdot_log=True, phiLR=False)

Fit for N GB source parameters as the same time.

Noise is supposed to be known.

as_gb_param(pp)

switch from search parameterization to fastGB parameterization

as_search_param(pp)

Switch from fastGB parameterizatoin to search parameterization

get_log_prior()

Log prior is computed once for all.

in_prior(pars, prior, Npars=8)

Check that GB parameters are in prior

loglik(x, plotIt=False, full_output=False, **kwargs)

logprior(x, **kwargs)

make_prior()

Default GB parameters bounds

m3c2.gb.likelihood.is_in_prior(pp, prior)

prior is a npar x 2 matrix defining lower and upper bound for each param.

it could also be a npar x 3 matrix, where the last column defines if the absolute value of the corresponding parameter should be used.

m3c2.gb.likelihood.likelihood_flat(pars, as_gb_param, Npars=8, AET_data=None, GB=None, noise=None, plotIt=False, full_output=False, noise_ampl=1)

pars is Nsrc x Npars

m3c2.gb.likelihood.plotAE(A, E, At, Et, SA)

m3c2.gb.likelihood.random_point(prior)

Additional proposals which could be used for GB search.

class m3c2.gb.proposal.HarmonicShift(names, **kwargs)

Shift freq of +/- N/T

shift(x, chain=None, **kwargs)

m3c2.gb.proposal.harmonic_shift(x, names)

Shift freq of +/- N/T

class m3c2.gb.fstatistics.Fstat(data, GB, noise)

check_coeff(Ap, Ac, phi_, psi_)

Compute coeff for a given phi, psi variation.

compute(fr_i, bet_i, lam_i, option='X')

Compute F-statistics for a given freq and sky position.

f_stats(X1, X2, fr_i, E1=None, E2=None)

Compute F-statistics for a pair of TDI vectors.

fixangle(fr_i, bet_i, lam_i, phi0, psi, option='X')

Get angle which gives correct a1,a2,a3,a4.

Avoid extra degeneracies.

get_coeff(fr_i, bet_i, lam_i, option='X')