ostk.astrodynamics.solver.LeastSquaresSolver¶

class LeastSquaresSolver( self: ostk.astrodynamics.solver.LeastSquaresSolver, maximum_iteration_count: int, rms_update_threshold: ostk.core.type.Real, finite_difference_solver: ostk.astrodynamics.solver.FiniteDifferenceSolver = FiniteDifferenceSolver.default(), )¶

Bases: pybind11_object

Class to solve non-linear least squares problems.

Constructor.

Parameters:

maximum_iteration_count (int) -- Maximum number of iterations.
rms_update_threshold (float) -- Minimum RMS threshold.
finite_difference_solver (FiniteDifferenceSolver) -- Finite difference solver. Defaults to FiniteDifferenceSolver.Default().

Methods

`calculate_empirical_covariance`	Calculate the empirical covariance matrix from an array of state residuals.
`default`	Create a default instance of LeastSquaresSolver.
`get_finite_difference_solver`	Get the finite difference solver.
`get_max_iteration_count`	Get the maximum iteration count.
`get_rms_update_threshold`	Get the RMS update threshold.
`solve`	Solve the non-linear least squares problem.

class Analysis(self: ostk.astrodynamics.solver.LeastSquaresSolver.Analysis, termination_criteria: ostk.core.type.String, estimated_state: ostk::astrodynamics::trajectory::State, estimated_covariance: numpy.ndarray[numpy.float64[m, n]], estimated_frisbee_covariance: numpy.ndarray[numpy.float64[m, n]], computed_observations: list[ostk::astrodynamics::trajectory::State], steps: list[ostk.astrodynamics.solver.LeastSquaresSolver.Step])¶

Bases: pybind11_object

Class representing the analysis of the least squares solver.

Constructor.

Parameters:

termination_criteria (str) -- The termination criteria.
estimated_state (State) -- The estimated state.
estimated_covariance (np.ndarray) -- The estimated covariance matrix.
estimated_frisbee_covariance (np.ndarray) -- The estimated Frisbee covariance matrix.
computed_observations (list[State]) -- The computed observations of the final iteration.
steps (list[LeastSquaresSolver.Step]) -- The steps.

compute_residual_states(self: ostk.astrodynamics.solver.LeastSquaresSolver.Analysis, observations: list[ostk::astrodynamics::trajectory::State]) → list[ostk::astrodynamics::trajectory::State]¶

Compute the residual states.

Parameters:: observations (list[State]) -- The observations.
Returns:: The residuals.
Return type:: list[State]

property computed_observations¶

The computed observations of the final iteration.

Type:: np.ndarray

property estimated_covariance¶

The estimated covariance matrix.

Type:: np.ndarray

property estimated_frisbee_covariance¶

The estimated Frisbee covariance matrix.

Type:: np.ndarray

property estimated_state¶

The estimated state.

Type:: State

property iteration_count¶

The iteration count.

Type:: int

property observation_count¶

The observation count.

Type:: int

property rms_error¶

The RMS error.

Type:: float

property steps¶

The steps.

Type:: list[LeastSquaresSolver.Step]

property termination_criteria¶

The termination criteria.

Type:: str

class Step( self: ostk.astrodynamics.solver.LeastSquaresSolver.Step, rms_error: ostk.core.type.Real, x_hat: numpy.ndarray[numpy.float64[m, 1]], )¶

Bases: pybind11_object

Class representing a step in the least squares solver.

Constructor.

Parameters:

rms_error (float) -- The RMS error.
x_hat (np.ndarray) -- The X hat vector.

property rms_error¶

The RMS error.

Type:: float

property x_hat¶

The X hat vector.

Type:: np.ndarray

static calculate_empirical_covariance(residuals: list[ostk::astrodynamics::trajectory::State]) → numpy.ndarray[numpy.float64[m, n]]¶

Calculate the empirical covariance matrix from an array of state residuals.

Parameters:: residuals (list[State]) -- A list of state residuals.
Returns:: The empirical covariance matrix.
Return type:: np.ndarray

Throws:: ostk::core::error::runtime::Undefined: If the residual array is empty.

static default() → ostk.astrodynamics.solver.LeastSquaresSolver¶

Create a default instance of LeastSquaresSolver.

Returns:: A default instance of LeastSquaresSolver.
Return type:: LeastSquaresSolver

get_finite_difference_solver( self: ostk.astrodynamics.solver.LeastSquaresSolver, ) → ostk.astrodynamics.solver.FiniteDifferenceSolver¶

Get the finite difference solver.

Returns:: The finite difference solver.
Return type:: FiniteDifferenceSolver

get_max_iteration_count( self: ostk.astrodynamics.solver.LeastSquaresSolver, ) → int¶

Get the maximum iteration count.

Returns:: The maximum iteration count.
Return type:: int

get_rms_update_threshold( self: ostk.astrodynamics.solver.LeastSquaresSolver, ) → ostk.core.type.Real¶

Get the RMS update threshold.

Returns:: The RMS update threshold.
Return type:: float

solve(self: ostk.astrodynamics.solver.LeastSquaresSolver, initial_guess: ostk::astrodynamics::trajectory::State, observations: list[ostk::astrodynamics::trajectory::State], state_generator: Callable[[ostk::astrodynamics::trajectory::State, list[ostk.physics.time.Instant]], list[ostk::astrodynamics::trajectory::State]], initial_guess_sigmas: dict[ostk::astrodynamics::trajectory::state::CoordinateSubset, numpy.ndarray[numpy.float64[m, 1]]] = {}, observation_sigmas: dict[ostk::astrodynamics::trajectory::state::CoordinateSubset, numpy.ndarray[numpy.float64[m, 1]]] = {}) → ostk.astrodynamics.solver.LeastSquaresSolver.Analysis¶

Solve the non-linear least squares problem.

Parameters:

initial_guess (State) -- Initial guess state (the Estimated State is of the same domain as this state).
observations (list[State]) -- List of observations.
state_generator (callable[list[State],[State, list[Instant]]]) -- Function to generate states.
initial_guess_sigmas (dict[CoordinateSubset, np.ndarray], optional) -- Dictionary of sigmas for initial guess.
observation_sigmas (dict[CoordinateSubset, np.ndarray], optional) -- Dictionary of sigmas for observations.

Returns:

The analysis of the estimate.

Return type:

LeastSquaresSolver::Analysis