find_elevation

pycraf.atm.find_elevation(obs_alt, target_alt, arc_length, atm_layers_cache, niter=50, interval=10, stepsize=0.05, seed=None)

Finds the optimal path elevation angle from an observer to reach target.

Based on basinhopping; see their manual for an explanation of the hyper-parameters (niter, interval, stepsize, and seed).

Parameters:

obs_altQuantity, scalar

Height of observer above sea-level [km]

obs_altQuantity, scalar

Height of target above sea-level [km]

arc_lengthQuantity, scalar

Arc-length (true angular distance) between observer and target [deg]

atm_layers_cachedict

Pre-computed physical parameters for each atmopheric layer as returned by the atm_layers function.

niterinteger, optional

The number of basin hopping iterations (default: 50)

intervalinteger, optional

Interval for how often to update the stepsize (default: 10)

stepsizefloat, optional

Initial step size for use in the random displacement. (default: 0.05)

seedint or np.random.RandomState, optional

Seed to use for internal random number generation. (default: None)

Notes

Because of the approximation of Earth’s atmosphere with layers of discrete refractive indices, caustics are generated (see pycraf manual), i.e., there are certains target points that cannot be reached, regardless of the elevation angle at the observer. This in turns means that there is only the possibility of using stochastic optimization algorithms to get an approximate solution to the problem.