esaenv_example1.py

import pykep as pk
import pygmo as pg
import numpy as np

# Plotting imports
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
udp = pk.trajopt.pl2pl_N_impulses(
    start=pk.planet.jpl_lp('earth'),
    target=pk.planet.jpl_lp('venus'),
    N_max=3,
    tof=[100., 1000.],
    vinf=[0., 4],
    phase_free=False,
    multi_objective=False,
    t0=[pk.epoch(0), pk.epoch(1000)])
# All pykep problems in the module trajopt are compatible with pygmo.
# So we create a pygmo problem from the pykep udp (User Defined Problem)
prob = pg.problem(udp)
print(prob)
uda = pg.cmaes(gen=1000, force_bounds = True)
algo = pg.algorithm(uda)
# Here we activate some degree of screen output (will only show in the terminal)
algo.set_verbosity(10)
# We construct a random population of 20 individuals (the initial guess)
pop = pg.population(prob, size = 20, seed = 123)
# We solve the problem
pop = algo.evolve(pop)
fig = plt.figure(figsize = (16,5))
ax1 = fig.add_subplot(1, 3, 1, projection='3d')
ax2 = fig.add_subplot(1, 3, 2, projection='3d')
ax3 = fig.add_subplot(1, 3, 3, projection='3d')
ax1 = udp.plot(pop.champion_x, axes = ax1)
ax2 = udp.plot(pop.champion_x, axes = ax2)
ax2.view_init(elev=90, azim=0)
ax3 = udp.plot(pop.champion_x, axes = ax3)
ax3.view_init(elev=0, azim=0)

# Inspect
udp.pretty(pop.champion_x)