propagator
#
This module provides the functionality for creating propagator settings.
References#
 1(1,2,3)
Vittaldev, V., Mooij, E., & Naeije, M. C. (2012). Unified State Model theory and application in Astrodynamics. Celestial Mechanics and Dynamical Astronomy, 112(3), 253282.
 2
Wakker, K. F. (2015). Fundamentals of astrodynamics.
 3
Hintz, G. R. (2008). Survey of orbit element sets. Journal of guidance, control, and dynamics, 31(3), 785790.
 4
Vallado, D. A. (2001). Fundamentals of astrodynamics and applications (Vol. 12). Springer Science & Business Media.
Functions#

Factory function to create translational state propagator settings with stopping condition at given final time. 

Factory function to create rotational state propagator settings. 

Factory function to create mass propagator settings 

Factory function to create multitype propagator settings. 

Factory function to create multiarc propagator settings. 

Factory function to create hybridarc propagator settings. 

Factory function to create time termination settings for the propagation. 

Factory function to create CPU time termination settings for the propagation. 
Factory function to create termination settings for the propagation based on a dependent variable. 


Factory function to create custom termination settings for the propagation. 

Factory function to create hybrid termination settings for the propagation. 
Function to add dependent variables to existing propagator settings. 
 translational(central_bodies: List[str], acceleration_models: Dict[str, Dict[str, List[tudatpy.kernel.numerical_simulation.propagation.AccelerationModel]]], bodies_to_integrate: List[str], initial_states: numpy.ndarray[numpy.float64[m, 1]], termination_settings: tudat::propagators::PropagationTerminationSettings, propagator: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.TranslationalPropagatorType = <TranslationalPropagatorType.cowell: 0>, output_variables: List[tudat::propagators::SingleDependentVariableSaveSettings] = [], print_interval: float = nan) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.TranslationalStatePropagatorSettings #
Factory function to create translational state propagator settings with stopping condition at given final time.
Factory function to create translational state propagator settings for N bodies. The propagated state vector is defined by the combination of integrated bodies, and their central body, the combination of which define the relative translational states for which a differential equation is to be solved. The propagator input defines the formulation in which the differential equations are set up The dynamical models are defined by an
AccelerationMap
, as created bycreate_acceleration_models()
function. Parameters
central_bodies (list[str]) – List of central bodies with respect to which the bodies to be integrated are propagated.
acceleration_models (AccelerationMap) – Set of accelerations acting on the bodies to propagate, provided as acceleration models.
bodies_to_integrate (list[str]) – List of bodies to be numerically propagated, whose order reflects the order of the central bodies.
initial_states (numpy.ndarray) – Initial states of the bodies to integrate (one initial state for each body, concatenated into a single array), provided in the same order as the bodies to integrate. The initial states must be expressed in Cartesian elements, w.r.t. the central body of each integrated body. The states must be defined with the same frame orientation as the global frame orientation of the environment (specified when creating a system of bodies, see for instance
get_default_body_settings()
andcreate_system_of_bodies()
). Consequently, for N integrated bodies, this input is a vector with size size 6N.termination_settings (PropagationTerminationSettings) – Generic termination settings object to check whether the propagation should be ended.
propagator (TranslationalPropagatorType, default=cowell) – Type of translational propagator to be used (see TranslationalPropagatorType enum).
output_variables (list[SingleDependentVariableSaveSettings], default=[]) – List of dependent variables to be saved (by default, no dependent variables are saved).
print_interval (float, default=TUDAT_NAN) – Variable indicating how often (in seconds or in the unit of the independent variable) the current state and time are to be printed to the console (by default, they are never printed).
 Returns
Translational state propagator settings object.
 Return type
 rotational(torque_models: Dict[str, Dict[str, List[tudatpy.kernel.numerical_simulation.propagation.TorqueModel]]], bodies_to_integrate: List[str], initial_states: numpy.ndarray[numpy.float64[m, 1]], termination_settings: tudat::propagators::PropagationTerminationSettings, propagator: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.RotationalPropagatorType = <RotationalPropagatorType.quaternions: 0>, output_variables: List[tudat::propagators::SingleDependentVariableSaveSettings] = [], print_interval: float = nan) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.RotationalStatePropagatorSettings #
Factory function to create rotational state propagator settings.
Factory function to create rotational state propagator settings for N bodies. The propagated state vector is defined by the integrated bodies, which defines the bodies for which the differential equation defining the evolution of the rotational state between an inertial and bodyfixed frame are to be solved. The propagator input defines the formulation in which the differential equations are set up. The dynamical models are defined by an
TorqueModelMap
, as created bycreate_torque_models
function. Parameters
torque_models (TorqueModelMap) – Set of torques acting on the bodies to propagate, provided as torque models.
bodies_to_integrate (list[str]) – List of bodies to be numerically propagated, whose order reflects the order of the central bodies.
initial_states (numpy.ndarray) – Initial rotational states of the bodies to integrate (one initial state for each body), provided in the same order as the bodies to integrate. Regardless of the propagator that is selected, the initial rotational state is always defined as four quaternion entries, and the angular velocity of the body, as defined in more detail here.
termination_settings (PropagationTerminationSettings) – Generic termination settings object to check whether the propagation should be ended.
propagator (RotationalPropagatorType, default=quaternions) – Type of rotational propagator to be used (see RotationalPropagatorType enum).
output_variables (list[SingleDependentVariableSaveSettings], default=[]) – List of dependent variables to be saved (by default, no dependent variables are saved).
print_interval (float, default=TUDAT_NAN) – Variable indicating how often (in seconds or in the unit of the independent variable) the current state and time are to be printed to the console (by default, they are never printed).
 Returns
Rotational state propagator settings object.
 Return type
 mass(bodies_with_mass_to_propagate: List[str], mass_rate_models: Dict[str, List[tudatpy.kernel.numerical_simulation.propagation.MassRateModel]], initial_body_masses: numpy.ndarray[numpy.float64[m, 1]], termination_settings: tudat::propagators::PropagationTerminationSettings, output_variables: List[tudat::propagators::SingleDependentVariableSaveSettings] = [], print_interval: float = nan) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MassPropagatorSettings #
Factory function to create mass propagator settings
Factory function to create mass propagator settings It works by providing a keyvalue mass rate container, containing the list of mass rate settings objects associated to each body. In this function, the dependent variables to save are provided as a list of SingleDependentVariableSaveSettings objects. In this function, the termination conditions are set through the termination settings object provided.
 Parameters
bodies_with_mass_to_propagate (list[str]) – List of bodies whose mass should be numerically propagated.
mass_rate_settings (SelectedMassRateModelMap) – Mass rates associated to each body, provided as a mass rate settings object.
initial_body_masses (numpy.ndarray) – Initial masses of the bodies to integrate (one initial mass for each body), provided in the same order as the bodies to integrate.
termination_settings (PropagationTerminationSettings) – Generic termination settings object to check whether the propagation should be ended.
output_variables (list[SingleDependentVariableSaveSettings], default=[]) – List of dependent variables to be saved (by default, no dependent variables are saved).
print_interval (float, default=TUDAT_NAN) – Variable indicating how often (in seconds or in the unit of the independent variable) the current state and time are to be printed to the console (by default, they are never printed).
 Returns
Mass propagator settings object.
 Return type
MassPropagatorSettings
 multitype(propagator_settings_list: List[tudatpy.kernel.numerical_simulation.propagation_setup.propagator.SingleArcPropagatorSettings], termination_settings: tudat::propagators::PropagationTerminationSettings, output_variables: List[tudat::propagators::SingleDependentVariableSaveSettings] = [], print_interval: float = nan) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MultiTypePropagatorSettings #
Factory function to create multitype propagator settings.
Factory function to create multitype propagator settings. It works by providing a list of SingleArcPropagatorSettings objects. When using this function, only the termination and output settings provided here are used, any such settings in the constituent propagator settings are ignored
Note
The propagated state contains the state types in the following order: Translational ( C ), Rotational ( R ), Mass ( M ), and Custom ( C ). When propagating two bodies, an example of what the output state would look like is for instance: [ T Body 1, T Body 2, R Body 1, R Body 2, M Body 1, M Body 2 ]
 Parameters
propagator_settings_list (list[SingleArcPropagatorSettings]) – List of SingleArcPropagatorSettings objects to use.
termination_settings (PropagationTerminationSettings) – Generic termination settings object to check whether the propagation should be ended.
output_variables (list[SingleDependentVariableSaveSettings], default=[]) – List of dependent variables to be saved (by default, no dependent variables are saved).
print_interval (float, default=TUDAT_NAN) – Variable indicating how often (in seconds or in the unit of the independent variable) the current state and time are to be printed to the console (by default, they are never printed).
 Returns
Mass propagator settings object.
 Return type
MassPropagatorSettings
 multi_arc(single_arc_settings: List[tudatpy.kernel.numerical_simulation.propagation_setup.propagator.SingleArcPropagatorSettings], transfer_state_to_next_arc: bool = False) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MultiArcPropagatorSettings #
Factory function to create multiarc propagator settings.
Factory function to create multiarc propagator settings. It works by providing separate settings for each arc in a list.
 Parameters
single_arc_settings (list[SingleArcPropagatorSettings]) – List of SingleArcPropagatorSettings objects to use, one for each arc.
transfer_state_to_next_arc (bool, default=False) – It denotes whether whether the initial state of arc N+1 is to be taken from arc N (for N>0).
 Returns
Multiarc propagator settings object.
 Return type
 hybrid_arc(single_arc_settings: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.SingleArcPropagatorSettings, multi_arc_settings: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MultiArcPropagatorSettings) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.HybridArcPropagatorSettings #
Factory function to create hybridarc propagator settings.
Factory function to create hybridarc propagator settings (i.e., a combination of single and multiarc dynamics).
 Parameters
single_arc_settings (SingleArcPropagatorSettings) – SingleArcPropagatorSettings object to use for the propagation.
multi_arc_settings (MultiArcPropagatorSettings) – MultiArcPropagatorSettings object to use for the propagation.
 Returns
Hybridarc propagator settings object.
 Return type
 time_termination(termination_time: float, terminate_exactly_on_final_condition: bool = False) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.PropagationTerminationSettings #
Factory function to create time termination settings for the propagation.
Factory function to create time termination settings for the propagation. The propagation is stopped when the final time provided is reached. Note that the termination time is set as the absolute time (in seconds since J2000), not the time since the start of the propagation. Depending on the sign of the time step of the numerical integrator, the termination time will be treated as an upper bound (for positive time step) or lower bound (for negative time step). The simulator will normally finish the final timestep, which may cause the termination time to be slightly exceeded. This behaviour can be suppressed by providing the optional input argument
terminate_exactly_on_final_condition=True
, in which case the final propagation step will be exactly on the specified time. Parameters
 Returns
Time termination settings object.
 Return type
Notes
To reach exactly the final time, state derivative function evaluations beyond the final time may be required by the propagator. Reaching the final condition exactly is an iterative process and very minor deviations from the specified final condition can occur.
Examples
In this example, we set the termination time of the propagation equal to one day (86400 $s$).
# Set termination time (in seconds since J2000) termination_time = simulation_start_epoch + 86400.0 # Create time termination settings termination_settings = propagation_setup.propagator.time_termination( termination_time )
 cpu_time_termination(cpu_termination_time: float) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.PropagationTerminationSettings #
Factory function to create CPU time termination settings for the propagation.
Factory function to create CPU time termination settings for the propagation. The propagation is stopped when the final CPU time provided is reached.
 Parameters
cpu_termination_time (float) – Maximum CPU time for the propagation.
 Returns
CPU time termination settings object.
 Return type
Examples
In this case, we set a CPU time termination setting so that the propagation stops once your computer has run it for 120 seconds.
# Set CPU time to 120 seconds cpu_termination_time = 120.0 # Create termination settings termination_settings = propagation_setup.propagator.cpu_time_termination( cpu_termination_time )
 dependent_variable_termination(dependent_variable_settings: tudat::propagators::SingleDependentVariableSaveSettings, limit_value: float, use_as_lower_limit: bool, terminate_exactly_on_final_condition: bool = False, termination_root_finder_settings: tudatpy.kernel.math.root_finders.RootFinderSettings = None) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.PropagationTerminationSettings #
Factory function to create termination settings for the propagation based on a dependent variable.
Factory function to create termination settings for the propagation based on the value of a dependent variable. The propagation is stopped when a provided upper or lower limit value is reached. The simulator will normally finish the final timestep, which may cause the dependent variable to be slightly exceeded. This behaviour can be suppressed by providing the optional input argument
terminate_exactly_on_final_condition=True
, in which case the final propagation step will be exactly on the specified dependent variable value. Parameters
dependent_variable_settings (SingleDependentVariableSaveSettings) – Dependent variable object to be used as termination setting.
limit_value (float) – Limit value of the dependent variable; if reached, the propagation is stopped.
use_as_lower_limit (bool, default=False) – Denotes whether the limit value should be used as lower or upper limit.
terminate_exactly_on_final_condition (bool, default=False) – Denotes whether the propagation is to terminate exactly on the final condition, or whether it is to terminate on the first step where it is violated.
termination_root_finder_settings (bool, default=None) – Settings object to create root finder used to converge on exact final condition.
 Returns
Dependent variable termination settings object.
 Return type
Notes
To reach exactly the final dependent variable value, state derivative function evaluations beyond the final time may be required by the propagator. Reaching the final condition exactly is an iterative process and very minor deviations from the specified final condition can occur.
Examples
Below, an example is shown for termination on a given vehicle altitude. The exact termination condition is defined in the
termination_settings
. The propagation is terminated once the lower limit of 25 km in altitude is reached (as theuse_as_lower_limit
is set toTrue
). To use the above settings to terminate when an upper limit of 25 km is reached, set this boolean toFalse
. In this example, we also want to stop exactly at 25 km, so we setterminate_exactly_on_final_condition
toTrue
, and we specifytermination_root_finder_settings
.# Set dependent variable to be checked as termination setting termination_variable = propagation_setup.dependent_variable.altitude( "Spacecraft", "Earth" ) # Create termination settings termination_settings = propagation_setup.propagator.dependent_variable_termination( dependent_variable_settings = termination_variable, limit_value = 25.0E3, use_as_lower_limit = True, terminate_exactly_on_final_condition=True, termination_root_finder_settings=root_finders.secant( maximum_iteration=5, maximum_iteration_handling=root_finders.MaximumIterationHandling.accept_result) ) )
 custom_termination(custom_condition: Callable[[float], bool]) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.PropagationTerminationSettings #
Factory function to create custom termination settings for the propagation.
Factory function to create custom termination settings for the propagation. The propagation is stopped when the condition provided is verified. This custom function should take the current time as input and output a Boolean. It can use internal variables and calculations, for example retrieved from the environment.
 Parameters
custom_condition (callable[[float], bool]) – Function of time (independent variable) which is called during the propagation and returns a boolean value denoting whether the termination condition is verified.
 Returns
Custom termination settings object.
 Return type
Examples
# Create custom function returning a bool def custom_termination_function(time: float): # Do something set_condition = ... # Return bool return set_condition # Create termination settings termination_settings = propagation_setup.propagator.custom_termination( custom_termination_function)
 hybrid_termination(termination_settings: List[tudatpy.kernel.numerical_simulation.propagation_setup.propagator.PropagationTerminationSettings], fulfill_single_condition: bool) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.PropagationTerminationSettings #
Factory function to create hybrid termination settings for the propagation.
Factory function to create hybrid termination settings for the propagation. This function can be used to define that all conditions or a single condition of the conditions provided must be met to stop the propagation. Each termination condition should be created according to each individual factory function and then added to a list of termination conditions.
 Parameters
termination_settings (list[PropagationTerminationSettings]) – List of single PropagationTerminationSettings objects to be checked during the propagation.
fulfill_single_condition (bool, default=False) – Whether only a single condition of those provided must be met to stop the propagation (true) or all of them simultaneously (false).
 Returns
Hybrid termination settings object.
 Return type
Examples
In the following example, the propagation will terminate once one of the three termination settings (simulated time, cpu time, altitude) has reached the imposed limit value. The
fulfill_single_condition
variable determines whether the propagation terminates once a single condition is met (if True, as above) or once all conditions must be met (False).# Set simulation termination time termination_time = simulation_start_epoch + 86400.0 # Create simulation time termination setting time_termination_settings = propagation_setup.propagator.time_termination( termination_time ) # Set dependent variable termination setting termination_variable = propagation_setup.dependent_variable.altitude( "Spacecraft", "Earth" ) # Create altitudebased termination setting altitude_termination_settings = propagation_setup.propagator.dependent_variable_termination( dependent_variable_settings = termination_variable, limit_value = 25.0E3, use_as_lower_limit = True) # Set cpu termination time cpu_termination_time = 120.0 # Create cpu time termination setting cpu_termination_settings = propagation_setup.propagator.cpu_time_termination( cpu_termination_time ) # Store termination setting objects in a list termination_settings_list = [time_termination_settings, altitude_termination_settings, cpu_termination_settings] # Create hybrid termination settings termination_settings = propagation_setup.propagator.hybrid_termination( termination_settings_list, fulfill_single_condition = True )
 add_dependent_variable_settings(dependent_variable_settings: List[tudat::propagators::SingleDependentVariableSaveSettings], propagator_settings: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.SingleArcPropagatorSettings) None #
Function to add dependent variables to existing propagator settings.
Function to add dependent variables to existing
SingleArcPropagatorSettings
object. This function is added as an alternative to teh regular manner in which to defined dependent variables (use of input to factory functions for singlearc propagator settingstranslational()
,rotational()
,mass()
,multitype()
). Typically, this function is used to modify existing propagator settings in a loop when running multiple simulations Parameters
dependent_variable_settings (List[ SingleDependentVariableSaveSettings ]) – List of dependent variable settings that are to be added to propagator settings. Note that this function adds settings, and does not replace any existing settings (nor does it check for duplicate settings).
propagator_settings (SingleArcPropagatorSettings) – Propagator settings to which the additional dependent variables settings are to be added.
 Returns
None
 Return type
None
Enumerations#
Enumeration of available translational propagator types. 

Enumeration of available rotational propagator types. 

Enumeration of available integrated state types. 

Enumeration of possible propagation termination types 
 class TranslationalPropagatorType#
Enumeration of available translational propagator types.
Members:
undefined_translational_propagator :
cowell :
Propagation of Cartesian elements (state vector size 6), without any transformations
encke :
Propagation of the difference in Cartesian elements of the orbit w.r.t. an unperturbed reference orbit. The reference orbit is generated from the initial state/central body, and not updated during the propagation (see Wakker, 2015 2)
gauss_keplerian :
Propagation of Keplerian elements (state vector size 6), with true anomaly as the ‘fast’ element (see Vallado, 2001 4)
gauss_modified_equinoctial :
Propagation of Modified equinoctial elements (state vector size 6), with the element \(I\) defining the location of the singularity based on the initial condition (see Hintz, 2008 3)
unified_state_model_quaternions :
Propagation of Unified state model using quaternions (state vector size 7, see Vittaldev et al., 2012 1)
unified_state_model_modified_rodrigues_parameters :
Propagation of Unified state model using modified Rodrigues parameters (state vector size 7, last element represents shadow parameter, see Vittaldev et al., 2012 1)
unified_state_model_exponential_map :
Propagation of Unified state model using exponential map (state vector size 7, last element represents shadow parameter, see Vittaldev et al., 2012 1)
 property name#
 class RotationalPropagatorType#
Enumeration of available rotational propagator types.
Members:
undefined_rotational_propagator :
quaternions :
Entries 14: The quaternion defining the rotation from inertial to bodyfixed frame (see here) Entries 57: The body’s angular velocity vector, expressed in its bodyfixed frame.
modified_rodrigues_parameters :
Entries 14: The modified Rodrigues parameters defining the rotation from inertial to bodyfixed frame (with entry four the shadow parameter) Entries 57: The body’s angular velocity vector, expressed in its bodyfixed frame.
exponential_map :
Entries 14: The exponential map defining the rotation from inertial to bodyfixed frame (with entry four the shadow parameter) Entries 57: The body’s angular velocity vector, expressed in its bodyfixed frame.
 property name#
 class StateType#
Enumeration of available integrated state types.
Members:
hybrid_type :
translational_type :
rotational_type :
mass_type : No documentation found.
custom_type :
 property name#
 class PropagationTerminationTypes#
Enumeration of possible propagation termination types
Members:
time_stopping_condition_type : No documentation found.
cpu_time_stopping_condition_type : No documentation found.
dependent_variable_stopping_condition_type : No documentation found.
hybrid_stopping_condition_type : No documentation found.
custom_stopping_condition_type : No documentation found.
 property name#
Classes#
Functional class to define settings for dependent variable to save. 

Functional base class to define settings for propagators. 

PropagatorSettingsderived class to define settings for multiarc dynamics. 

PropagatorSettingsderived class to define settings for hybridarc dynamics. 

PropagatorSettingsderived class to define settings for singlearc dynamics. 

SingleArcPropagatorSettingsderived class to define settings for singlearc translational dynamics. 

SingleArcPropagatorSettingsderived class to define settings for singlearc rotational state propagation. 

SingleArcPropagatorSettingsderived class to define settings for propagation of multiple quantities. 

Functional base class to define termination settings for the propagation. 

PropagationTerminationSettingsderived class to define termination settings for the propagation from dependent variables. 

PropagationTerminationSettingsderived class to define termination settings for the propagation from propagation time. 

PropagationTerminationSettingsderived class to define termination settings for the propagation from CPU time. 

PropagationTerminationSettingsderived class to define custom termination settings for the propagation. 

PropagationTerminationSettingsderived class to define hybrid termination settings for the propagation. 
 class DependentVariableSaveSettings#
Functional class to define settings for dependent variable to save.
 class PropagatorSettings#
Functional base class to define settings for propagators.
Base class to define settings for propagators. Derived classes are split into settings for single and multiarc dynamics.
 property initial_states#
No documentation found.
 class MultiArcPropagatorSettings#
PropagatorSettingsderived class to define settings for multiarc dynamics.
 class HybridArcPropagatorSettings#
PropagatorSettingsderived class to define settings for hybridarc dynamics.
 class SingleArcPropagatorSettings#
PropagatorSettingsderived class to define settings for singlearc dynamics.
 termination_settings#
Settings for creating the object that checks whether the propagation is finished.
 property termination_settings#
No documentation found.
 class TranslationalStatePropagatorSettings#
SingleArcPropagatorSettingsderived class to define settings for singlearc translational dynamics.
 acceleration_settings#
Settings for retrieving the accelerations acting on the body during propagation.
 Type
SelectedAccelerationMap
 get_propagated_state_size(self: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.TranslationalStatePropagatorSettings) int #
 reset_and_recreate_acceleration_models(self: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.TranslationalStatePropagatorSettings, new_acceleration_settings: Dict[str, Dict[str, List[tudatpy.kernel.numerical_simulation.propagation_setup.acceleration.AccelerationSettings]]], bodies: tudatpy.kernel.numerical_simulation.environment.SystemOfBodies) None #
 class RotationalStatePropagatorSettings#
SingleArcPropagatorSettingsderived class to define settings for singlearc rotational state propagation.
 class MultiTypePropagatorSettings#
SingleArcPropagatorSettingsderived class to define settings for propagation of multiple quantities.
 recreate_state_derivative_models(self: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MultiTypePropagatorSettings, bodies: tudatpy.kernel.numerical_simulation.environment.SystemOfBodies) None #
 reset_initial_states(self: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MultiTypePropagatorSettings, initial_states: numpy.ndarray[numpy.float64[m, 1]]) None #
 single_type_settings(self: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.MultiTypePropagatorSettings, state_type: tudatpy.kernel.numerical_simulation.propagation_setup.propagator.StateType) tudatpy.kernel.numerical_simulation.propagation_setup.propagator.SingleArcPropagatorSettings #
 property propagator_settings_per_type#
None
 Type
dict[IntegratedStateType, list[SingleArcPropagatorSettings]]
 class PropagationTerminationSettings#
Functional base class to define termination settings for the propagation.
 class PropagationDependentVariableTerminationSettings#
PropagationTerminationSettingsderived class to define termination settings for the propagation from dependent variables.
 class PropagationTimeTerminationSettings#
PropagationTerminationSettingsderived class to define termination settings for the propagation from propagation time.
 class PropagationCPUTimeTerminationSettings#
PropagationTerminationSettingsderived class to define termination settings for the propagation from CPU time.
 class PropagationCustomTerminationSettings#
PropagationTerminationSettingsderived class to define custom termination settings for the propagation.
 class PropagationHybridTerminationSettings#
PropagationTerminationSettingsderived class to define hybrid termination settings for the propagation.