Particles

class ssapy.particles.Particles(particles, rvprobability, lnpriors=None, ln_weights=None)[source][source]

Bases: object

A class for importance (re-)sampling orbit model parameter samples

Parameters:

particles ((num_particles, 6) array_like) – Positions and velocities of orbiting object at epoch in meters and meters / second. The ‘chain’ part of the output from rvsampler.XXSampler.sample
rvprobability (class instance) – An instance of an RVProbability for this epoch. The Particles class wraps the lnlike method of the supplied RVProbability object.
ln_weights ((num_particles,) array_like, optional) – Weights for each particle in the input particles

particles[source]

Positions and velocities of orbiting object at epoch in meters and meters / second. The ‘chain’ part of the output from RVSampler.sample

Type:: (num_particles, ) array_like

rvprobability[source]

An instance of an RVProbability for this epoch. The Particles class wraps the lnlike method of the supplied RVProbability object.

Type:: class instance

ln_wts[source]

Log weights for each particle.

Type:: (num_particles,) array_like

num_particles[source]

Number of particles

Type:: int

initial_particles[source]

Copy of input particles to enable reset_to_pseudo_prior()

Type:: (num_particles, 6) array_like

initial_ln_wts[source]

Copy of input log weights to enable reset_to_pseudo_prior()

Type:: (num_particles,) array_like

epoch[source]

The time at which the model parameters (position and velocity) are specified. If float, then should correspond to GPS seconds; i.e., seconds since 1980-01-06 00:00:00 UTC.

Type:: float or astropy.time.Time

orbits[source]

List of Orbit class instances derived from each particle

Type:: list

lnpriors[source]

Log prior probabilities for each particle. The priors are as set in the input RVProbability object.

Type:: (num_particles,) array_like

reset_to_pseudo_prior()[source][source]: Reset the particles and weights to their values at instantiation

move(epoch)[source][source]: Move particles to the specified epoch

lnlike(epoch_particles)[source][source]: Evaluate the log-likelihood of the input particles given internally stored measurements

reweight(epoch_particles)[source][source]: Reweight particles using cross-epoch likelihoods

resample(num_particles)[source][source]: Resample particles to achieve near-uniform weighting per particle

fuse(epoch_particles, verbose)[source][source]: Fuse particles from a different epoch and fuse internal particles and weights

draw_orbit()[source][source]: Draw a single orbit model from the internal list of particles

mean()[source][source]: Evaluate the weighted mean of the particle values

Attributes Summary

`epoch`	Get the epoch time at which the particles (i.e., position and velocity) are defined
`lnpriors`	Get an array of log prior probabilities for each particle
`orbits`	Get a list of orbits corresponding to each stored particle

Methods Summary

`draw_orbit`()	Draw a single orbit model from the internal particle collection with probability proportional to the internal weights.
`fuse`(epoch_particles[, verbose])	Add particles from a new epoch and fuse all epoch particles and weights accordingly
`lnlike`(orbits)	Evaluate this epoch likelihood given one or more particles
`mean`()	Evaluate the weighted mean of all particles
`move`(epoch)	Move particles to the given epoch
`resample`(num_particles)	Resample particles to achieve near-uniform weighting per particle
`reset_to_pseudo_prior`()
`reweight`(epoch_particles)	Reweight particles using cross-epoch likelihoods

Attributes Documentation

epoch[source]: Get the epoch time at which the particles (i.e., position and velocity) are defined

lnpriors[source]: Get an array of log prior probabilities for each particle

orbits[source]: Get a list of orbits corresponding to each stored particle

Methods Documentation

draw_orbit()[source][source]

Draw a single orbit model from the internal particle collection with probability proportional to the internal weights.

Returns:: particle – Position and velocity for one particle
Return type:: array (1, 6)

fuse(epoch_particles, verbose=False)[source][source]

Add particles from a new epoch and fuse all epoch particles and weights accordingly

Parameters:

epoch_particles (Particles) – The particles object from another observation
verbose (bool, optional) – Enable verbose output to stdout

Return type:

None. Side effect is to fuse the internal particles state.

lnlike(orbits)[source][source]

Evaluate this epoch likelihood given one or more particles

Parameters:: orbits (List of Orbit class instances) – A list of the Orbit models for each particle, e.g., as generated by Particles.orbits
Returns:: lnlike – Array of log-likelihood values for each particle
Return type:: array (num_particles, )

mean()[source][source]

Evaluate the weighted mean of all particles

Returns:: mean – Weighted mean of the particle values (3*m, 3*m/s)
Return type:: (6,) array_like

move(epoch)[source][source]

Move particles to the given epoch

Parameters:: epoch (astropy.time.Time) –
Returns:: particles – Position and velocity for each particle at the new epoch
Return type:: array (num_particles, 6)

resample(num_particles)[source][source]

Resample particles to achieve near-uniform weighting per particle

Parameters:: num_particles (int) – The number of particles to keep
Returns:: Side effect is to update the internal particles state.
Return type:: None

reset_to_pseudo_prior()[source][source]

reweight(epoch_particles)[source][source]

Reweight particles using cross-epoch likelihoods

The weights for the kth particle are:

w_k = log(Prod_i L_(d_i | theta_k) / L(d_j | theta_k))
    = Sum_{i /= j} log(L(d_i | theta_k))

where j is the current epoch, theta are the particle parameters, and d_i is the data from epoch i.

Parameters:: epoch_particles (Particles) – The particles object from another observation
Returns:: status – Status (True means success). Side effect is to update the internal particles state.
Return type:: bool