astrodynx.prop.cowell_method

Contents

astrodynx.prop.cowell_method#

astrodynx.prop.cowell_method(orbdyn, x0, t1, saveat, dt0=None, max_steps=4096, solver=Tsit5(), stepsize_controller=PIDController(rtol=1e-08, atol=1e-08))[source]#

General Cowell’s method orbital propagation function.

This function provides a flexible interface for propagating orbital states using Cowell’s method with customizable step size control and output times.

Parameters:

  • orbdyn (OrbDynx) – Orbital dynamics configuration containing the differential equation terms, static arguments, and optional events.

  • x0 (ArrayLike) – (6,)Initial state vector [x, y, z, vx, vy, vz] in canonical units. Position components are in distance units, velocity components are in distance/time units.

  • t1 (DTypeLike) – Final integration time in canonical time units. Must be positive for forward propagation.

  • saveat (SaveAt) – Configuration specifying when to save the solution during integration.

  • dt0 (DTypeLike) – Initial time step size for integration in canonical time units. If None, the solver will choose an appropriate initial step size.

  • max_steps (int) – Maximum number of integration steps before terminating unconditionally. Prevents infinite loops in case of integration issues. Defaults to 4096.

  • solver (AbstractSolver) – Numerical integration method. Defaults to diffeq.Tsit5() (5th-order Runge-Kutta method).

  • stepsize_controller (AbstractStepSizeController) – Step size controller for adaptive or fixed stepping.

Return type:

Solution

Returns:

Integration solution containing

  • ts: Array of time points where solution was saved

  • ys: Array of state vectors at each time point

  • stats: Integration statistics and diagnostics

  • result: Integration termination status

Notes

This function serves as a general interface for Cowell’s method orbital propagation. Users can specify custom step size controllers and output times to suit their specific needs. It is recommended to use the specialized functions (fixed_steps(), adaptive_steps(), custom_steps(), to_final()) for common use cases for better clarity and ease of use.

Examples

Basic orbital propagation with dense output: >>> import jax.numpy as jnp >>> from astrodynx import diffeq >>> import astrodynx as adx >>> def vector_field(t, x, args): … acc = adx.gravity.point_mass_grav(t, x, args) … return jnp.concatenate([x[3:], acc]) >>> orbdyn = adx.prop.OrbDynx( … terms=diffeq.ODETerm(vector_field), … args={“mu”: 1.0} … ) >>> x0 = jnp.array([1.0, 0.0, 0.0, 0.0, 1.0, 0.0]) >>> t1 = jnp.pi*2 # One orbital period >>> saveat=diffeq.SaveAt(dense=True,subs=diffeq.SubSaveAt(t1=True)) >>> sol = adx.prop.cowell_method( … orbdyn, x0, t1, … saveat=saveat … ) >>> tf = sol.ts[-1] >>> yf = sol.ys[-1] >>> yf_dense = sol.evaluate(tf) >>> assert jnp.allclose(yf, yf_dense, atol=1e-7)

Contents