fit_arc_with_gaussian_prior

ssapy.correlate_tracks.fit_arc_with_gaussian_prior(arc, mu, cinvcholfac, verbose=False, propagator=KeplerianPropagator(), mode='rv', lsq=True, optimizerkw={}, orbitattr=None, **kw)[source][source]

Fit an orbit to an arc.

See documentation for fit_arc_blind. fit_arc_with_gaussian_prior implements the same interface, but takes an additional mu, cinvcholfac, and epoch arguments. These specify a gaussian prior on the parameters. The mean and covariance of the Gaussian are given by mu and cinvcholfac, the inverse Cholesky matrix corresponding to the covariance matrix. The initial guess is taken to be the mean of the Gaussian prior. Presently, any other priors are assumed to have been folded into the Gaussian. epoch specifies the time at which Gaussian prior parameters are applicable. For greatest speed, the arc should contain a single observation and the epoch should be equal to the time of that observation; then no propagations are needed in this function.

Parameters:

  • arc (array_like (n)) – numpy ndarray containing several fields necessary for describing observations. rStation_GCRF (m), vStation_GCRF (m), time (astropy.time.Time or gps seconds), ra, dec, pmra, pmdec are all required fields.

  • mu (array_like (n_par)) – parameters for initial guess

  • cinvcholfac (array_like (n_par, n_par)) – inverse cholesky matrix for covariance matrix

  • verbose (bool) – True for more verbose output

  • propagator (ssapy.propagator.Propagator instance) – propagator to use in fitting

  • mode (str) – Mode for fitting, must be one of ‘rv’, ‘equinoctial’, or ‘angle’. In the first case, the parameters defining the orbit are taken to be the position and velocity of the object. In the second case, the parameters are taken to be the equinoctial elements. In the third case, the parameters are taken to be the angular position and proper motion of the object, together with its line-of-sight range and velocity.

  • orbitattr (list(str)) – list of strings of names of additional propagation attributes to fit (mass, area, cr, cd, …)

  • optimizerkw (dict) – any extra keywords to pass to the optimizer

  • **kw (dict) – any extra keywords to pass to optimizer.optimize()