integrator

None

Functions

euler(initial_time, initial_time_step, …)

Creates the settings for the Euler integrator.

runge_kutta_4(initial_time, …)

Creates the settings for the Runge Kutta 4 integrator.

runge_kutta_variable_step_size(*args, **kwargs)

Overloaded function.

bulirsch_stoer(initial_time, …)

Creates the settings for the Bulirsch-Stoer integrator.

adams_bashforth_moulton(initial_time, …)

Creates the settings for the Bulirsch-Stoer integrator.
euler(initial_time: float, initial_time_step: float, save_frequency: int = 1, assess_termination_on_minor_steps: bool = False) → tudatpy.kernel.simulation.propagation_setup.integrator.IntegratorSettings

Creates the settings for the Euler integrator.

Factory function to create settings for the Euler integrator. For this integrator, the step size is kept constant.

Parameters

  • initial_time (float) – Start time (independent variable) of numerical integration.

  • initial_time_step (float) – Initial and constant value for the time step.

  • save_frequency (int, default=1) – Frequency at which to save the numerical integrated states (expressed per unit integration time step, with n = saveFrequency, so n = 1 means that the state is saved once per integration step).

  • assess_termination_on_minor_steps (bool, default=false) – Whether the propagation termination conditions should be evaluated during the intermediate sub-steps of the integrator (true) or only at the end of each integration step (false).

Returns

Integrator settings object.

Return type

IntegratorSettings

runge_kutta_4(initial_time: float, initial_time_step: float, save_frequency: int = 1, assess_termination_on_minor_steps: bool = False) → tudatpy.kernel.simulation.propagation_setup.integrator.IntegratorSettings

Creates the settings for the Runge Kutta 4 integrator.

Factory function to create settings for the Runge Kutta 4 integrator. For this integrator, the step size is kept constant.

Parameters

  • initial_time (float) – Start time (independent variable) of numerical integration.

  • initial_time_step (float) – Initial and constant value for the time step.

  • save_frequency (int, default=1) – Frequency at which to save the numerical integrated states (expressed per unit integration time step, with n = saveFrequency, so n = 1 means that the state is saved once per integration step).

  • assess_termination_on_minor_steps (bool, default=false) – Whether the propagation termination conditions should be evaluated during the intermediate sub-steps of the integrator (true) or only at the end of each integration step (false).

Returns

Integrator settings object.

Return type

IntegratorSettings

runge_kutta_variable_step_size(*args, **kwargs)

Overloaded function.

  1. runge_kutta_variable_step_size(initial_time: float, initial_time_step: float, coefficient_set: tudatpy.kernel.simulation.propagation_setup.integrator.RKCoefficientSets, minimum_step_size: float, maximum_step_size: float, relative_error_tolerance: float, absolute_error_tolerance: float, save_frequency: int = 1, assess_termination_on_minor_steps: bool = False, safety_factor: float = 0.8, maximum_factor_increase: float = 4.0, minimum_factor_increase: float = 0.1) -> tudatpy.kernel.simulation.propagation_setup.integrator.IntegratorSettings

    Creates the settings for the Runge-Kutta variable step size integrator.

    Factory function to create settings for the Runge-Kutta variable step size integrator with vector tolerances. # [py] For this integrator, the step size is varied based on the tolerances and safety factor provided. # [py] The tolerance can be either scalar or vector; it is composed of an absolute and a relative part. # [py] Different coefficient sets (Butcher’s tableau) can be used (see the RungeKuttaCoefficients::CoefficientSets enum). # [py]

    initial_timefloat

    Start time (independent variable) of numerical integration.

    initial_time_stepfloat

    Initial time step to be used.

    coefficient_setRungeKuttaCoefficients::CoefficientSets

    Coefficient set (Butcher’s tableau) to be used in the integration.

    minimum_step_sizefloat

    Minimum time step to be used during the integration.

    maximum_step_sizefloat

    Maximum time step to be used during the integration.

    relative_error_tolerancefloat or np.ndarray

    Relative vector tolerance to adjust the time step.

    absolute_error_tolerancefloat or np.ndarray

    Absolute vector tolerance to adjust the time step.

    save_frequencyint, default=1

    Frequency at which to save the numerical integrated states (expressed per unit integration time step, with n = saveFrequency, so n = 1 means that the state is saved once per integration step).

    assess_termination_on_minor_stepsbool, default=false

    Whether the propagation termination conditions should be evaluated during the intermediate sub-steps of the integrator (true) or only at the end of each integration step (false).

    safety_factorfloat, default=0.8

    Safety factor used in the step size control.

    maximum_factor_increasefloat, default=4.0

    Maximum increase between consecutive time steps, expressed as the factor between new and old step size.

    minimum_factor_increasefloat, default=0.1

    Minimum increase between consecutive time steps, expressed as the factor between new and old step size.

    RungeKuttaVariableStepSizeSettingsScalarTolerances or RungeKuttaVariableStepSizeSettingsVectorTolerances

    RungeKuttaVariableStepSizeSettingsScalarTolerances or RungeKuttaVariableStepSizeSettingsVectorTolerances object.

  2. runge_kutta_variable_step_size(initial_time: float, initial_time_step: float, coefficient_set: tudatpy.kernel.simulation.propagation_setup.integrator.RKCoefficientSets, minimum_step_size: float, maximum_step_size: float, relative_error_tolerance: numpy.ndarray[numpy.float64[m, n]], absolute_error_tolerance: numpy.ndarray[numpy.float64[m, n]], save_frequency: int = 1, assess_termination_on_minor_steps: bool = False, safety_factor: float = 0.8, maximum_factor_increase: float = 4.0, minimum_factor_increase: float = 0.1) -> tudatpy.kernel.simulation.propagation_setup.integrator.IntegratorSettings

No documentation found.

bulirsch_stoer(initial_time: float, initial_time_step: float, extrapolation_sequence: tudatpy.kernel.simulation.propagation_setup.integrator.ExtrapolationMethodStepSequences, maximum_number_of_steps: int, minimum_step_size: float, maximum_step_size: float, relative_error_tolerance: float = 1e-12, absolute_error_tolerance: float = 1e-12, save_frequency: int = 1, assess_termination_on_minor_steps: bool = False, safety_factor: float = 0.7, maximum_factor_increase: float = 10.0, minimum_factor_increase: float = 0.1) → tudatpy.kernel.simulation.propagation_setup.integrator.IntegratorSettings

Creates the settings for the Bulirsch-Stoer integrator.

Factory function to create settings for the Bulirsch-Stoer integrator. For this integrator, the step size is varied based on the tolerances and safety factor provided. The tolerance is composed of an absolute and a relative part. Different extrapolation sequences can be used (see the ExtrapolationMethodStepSequences enum).

Parameters

  • initial_time (float) – Start time (independent variable) of numerical integration.

  • initial_time_step (float) – Initial time step to be used.

  • extrapolation_sequence (ExtrapolationMethodStepSequences) – Extrapolation sequence to be used in the integration.

  • maximum_number_of_steps (int) – Number of entries in the sequence (e.g., number of integrations used for a single extrapolation).

  • minimum_step_size (float) – Minimum time step to be used during the integration.

  • maximum_step_size (float) – Maximum time step to be used during the integration.

  • relative_error_tolerance (float, default=1.0E-12) – Relative tolerance to adjust the time step.

  • absolute_error_tolerance (float, default=1.0E-12) – Relative tolerance to adjust the time step.

  • save_frequency (int, default=1) – Frequency at which to save the numerical integrated states (expressed per unit integration time step, with n = saveFrequency, so n = 1 means that the state is saved once per integration step).

  • assess_termination_on_minor_steps (bool, default=false) – Whether the propagation termination conditions should be evaluated during the intermediate sub-steps of the integrator (true) or only at the end of each integration step (false).

  • safety_factor (float, default=0.7) – Safety factor used in the step size control.

  • maximum_factor_increase (float, default=10.0) – Maximum increase between consecutive time steps, expressed as the factor between new and old step size.

  • minimum_factor_increase (float, default=0.1) – Minimum increase between consecutive time steps, expressed as the factor between new and old step size.

Returns

BulirschStoerIntegratorSettings object.

Return type

BulirschStoerIntegratorSettings

adams_bashforth_moulton(initial_time: float, initial_time_step: float, minimum_step_size: float, maximum_step_size: float, relative_error_tolerance: float = 1e-12, absolute_error_tolerance: float = 1e-12, minimum_order: int = 6, maximum_order: int = 11, save_frequency: int = 1, assess_termination_on_minor_steps: bool = False, bandwidth: float = 200.0) → tudatpy.kernel.simulation.propagation_setup.integrator.IntegratorSettings

Creates the settings for the Bulirsch-Stoer integrator.

Factory function to create settings for the Adams-Bashorth-Moulton integrator. For this integrator, the step size is varied based on the tolerances and safety factor provided. The tolerance is composed of an absolute and a relative part. Different coefficient sets (Butcher’s tableau) can be used (see the RungeKuttaCoefficients::CoefficientSets enum).

Parameters

  • initial_time (float) – Start time (independent variable) of numerical integration.

  • initial_time_step (float) – Initial time step to be used.

  • minimum_step_size (float) – Minimum time step to be used during the integration.

  • maximum_step_size (float) – Maximum time step to be used during the integration.

  • relative_error_tolerance (float, default=1.0E-12) – Relative tolerance to adjust the time step.

  • absolute_error_tolerance (float, default=1.0E-12) – Relative tolerance to adjust the time step.

  • minimum_order – Minimum order of the integrator.

  • maximum_order – Maximum order of the integrator.

  • save_frequency (int, default=1) – Frequency at which to save the numerical integrated states (expressed per unit integration time step, with n = saveFrequency, so n = 1 means that the state is saved once per integration step).

  • assess_termination_on_minor_steps (bool, default=false) – Whether the propagation termination conditions should be evaluated during the intermediate sub-steps of the integrator (true) or only at the end of each integration step (false).

  • bandwidth (float, default=200.0) – Maximum error factor for doubling the stepsize.

Returns

AdamsBashforthMoultonSettings object.

Return type

AdamsBashforthMoultonSettings

Classes

class IntegratorSettings

Functional base class to define settings for integrators.

class RungeKuttaVariableStepSizeSettingsScalarTolerances

IntegratorSettings-derived class to define settings for Runge Kutta integrators with scalar tolerances.

class RungeKuttaVariableStepSizeSettingsVectorTolerances

IntegratorSettings-derived class to define settings for Runge Kutta integrators with vector tolerances.

class BulirschStoerIntegratorSettings

IntegratorSettings-derived class to define settings for Bulirsch-Stoer integrator settings.

class AdamsBashforthMoultonSettings

IntegratorSettings-derived class to define settings for Adams-Bashforth-Moulton integrator settings.