lv_sens.main

Main: run a sensitivity analysis of the Lotka-Volterra equations.

Functions

compare_gradient_fd(theta, x_data, dt, N, x0)	Compare the adjoint-based gradient with a finite difference approximation.
generate_data(theta, dt, N, x0, noise_std[, ...])	Generate measurement data from a ground truth forward solution.
get_parser()	Return arguments parser.
main()	Main function.

lv_sens.main.compare_gradient_fd(theta: ndarray[Any, dtype[float64]], x_data: ndarray[Any, dtype[float64]], dt: float, N: float, x0: ndarray[Any, dtype[float64]]) → None

Compare the adjoint-based gradient with a finite difference approximation.

Parameters:

• theta – parameters at which to evaluate gradient

• x_data – data array

• dt – time step

• N – number of time steps

• x0 – initial guess

lv_sens.main.generate_data(theta: ndarray[Any, dtype[float64]], dt: float, N: int, x0: ndarray[Any, dtype[float64]], noise_std: float, plot: bool = False) → ndarray[Any, dtype[float64]]

Generate measurement data from a ground truth forward solution.

Parameters:

• theta – parameter for reference solution

• dt – time step

• N – number of time steps

• x0 – initial guess

• noise_std – noise standard deviation

• plot – toggle to plot solution and data points

Returns:

data array

lv_sens.main.get_parser() → ArgumentParser

Return arguments parser.

lv_sens.main.main() → None

Main function.

Parse command line arguments and run the chosen simulation.