def plot(self, x):
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pykep import epoch, AU, DAY2SEC
from pykep.sims_flanagan import sc_state
from pykep.orbit_plots import plot_planet, plot_sf_leg
start = epoch(x[0])
end = epoch(x[0] + x[1])
r, v = self.__earth.eph(start)
v = [a + b for a, b in zip(v, x[4:7])]
x0 = sc_state(r, v, self.__sc.mass)
r, v = self.__mars.eph(end)
xe = sc_state(r, v, x[3])
self.__leg.set(
start, x0, x[-3 * self.__nseg:], end, xe, x[2] * DAY2SEC)
fig = plt.figure()
axis = fig.gca(projection='3d')
# The Sun
axis.scatter([0], [0], [0], color='y')
# The leg
plot_sf_leg(self.__leg, units=AU, N=10, ax=axis)
# The planets
plot_planet(
self.__earth, start, units=AU, legend=True, color=(0.8, 0.8, 1), ax=axis)
plot_planet(
self.__mars, end, units=AU, legend=True, color=(0.8, 0.8, 1), ax=axis)
plt.show()
def run_example5():
import pygmo as pg
from pykep import epoch
from pykep.planet import jpl_lp
from pykep.trajopt import mga_1dsm
# We define an Earth-Venus-Earth problem (single-objective)
seq = [jpl_lp('earth'), jpl_lp('venus'), jpl_lp('earth')]
udp = mga_1dsm(
seq=seq,
t0=[epoch(5844), epoch(6209)],
tof=[0.7 * 365.25, 3 * 365.25],
vinf=[0.5, 2.5],
add_vinf_dep=False,
add_vinf_arr=True,
multi_objective=False
)
pg.problem(udp)
# We solve it!!
uda = pg.sade(gen=100)
archi = pg.archipelago(algo=uda, prob=udp, n=8, pop_size=20)
print(
"Running a Self-Adaptive Differential Evolution Algorithm .... on 8 parallel islands")
archi.evolve(10)
archi.wait()
sols = archi.get_champions_f()
idx = sols.index(min(sols))
print("Done!! Solutions found are: ", archi.get_champions_f())
udp.pretty(archi.get_champions_x()[idx])
udp.plot(archi.get_champions_x()[idx])
def _plot_traj(self, z, axes, units):
"""Plots spacecraft trajectory.
Args:
- z (``tuple``, ``list``, ``numpy.ndarray``): Decision chromosome.
- axes (``matplotlib.axes._subplots.Axes3DSubplot``): 3D axes to use for the plot
- units (``float``, ``int``): Length unit by which to normalise data.
Examples:
>>> prob.extract(pykep.trajopt.indirect_or2or).plot_traj(pop.champion_x)
"""
# times
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# Mean Anomalies
M0 = z[1] - self.elem0[1] * np.sin(z[1])
Mf = z[2] - self.elemf[1] * np.sin(z[2])
elem0 = np.hstack([self.elem0[:5], [M0]])
elemf = np.hstack([self.elemf[:5], [Mf]])
# Keplerian points
kep0 = pk.planet.keplerian(t0, elem0)
kepf = pk.planet.keplerian(tf, elemf)
# planets
pk.orbit_plots.plot_planet(
kep0, t0=t0, units=units, ax=axes, color=(0.8, 0.8, 0.8))
pk.orbit_plots.plot_planet(
kepf, t0=tf, units=units, ax=axes, color=(0.8, 0.8, 0.8))
def _plot_traj(self, z, axes, units=pk.AU):
"""Plots spacecraft trajectory.
Args:
- z (``tuple``, ``list``, ``numpy.ndarray``): Decision chromosome.
- axes (``matplotlib.axes._subplots.Axes3DSubplot``): 3D axes to use for the plot
- units (``float``, ``int``): Length unit by which to normalise data.
Examples:
>>> prob.extract(pykep.trajopt.indirect_pt2or).plot_traj(pop.champion_x)
"""
# times
t0 = pk.epoch(0)
tf = pk.epoch(z[1])
# Keplerian elements of the osculating orbit at start
elem0 = list(pk.ic2par(self.x0[0:3], self.x0[3:6], self.leg.mu))
# Eccentric to Mean Anomaly
elem0[5] = elem0[5] - elem0[1] * np.sin(elem0[5])
# Mean Anomaly at the target orbit
Mf = z[1] - self.elemf[1] * np.sin(z[1])
elemf = np.hstack([self.elemf[:5], [Mf]])
# Keplerian elements
kep0 = pk.planet.keplerian(t0, elem0)
kepf = pk.planet.keplerian(tf, self.elemf)
# plot departure and arrival
pk.orbit_plots.plot_planet(
kep0, t0, units=units, color=(0.8, 0.8, 0.8), ax=axes)
pk.orbit_plots.plot_planet(
kepf, tf, units=units, color=(0.8, 0.8, 0.8), ax=axes)
def _plot_traj(self, z, axes, units):
# states
x0 = self.leg.x0
xf = self.leg.xf
# times
t0 = pk.epoch(self.leg.t0)
tf = pk.epoch(self.leg.tf)
# Computes the osculating Keplerian elements at start and arrival
elem0 = list(pk.ic2par(x0[0:3], x0[3:6], self.leg.mu))
elemf = list(pk.ic2par(xf[0:3], xf[3:6], self.leg.mu))
# Converts the eccentric anomaly into eccentric anomaly
elem0[5] = elem0[5] - elem0[1] * np.sin(elem0[5])
elemf[5] = elemf[5] - elemf[1] * np.sin(elemf[5])
# Creates two virtual keplerian planets with the said elements
kep0 = pk.planet.keplerian(t0, elem0)
kepf = pk.planet.keplerian(tf, elemf)
# Plots the departure and arrival osculating orbits
pk.orbit_plots.plot_planet(
kep0, t0, units=units, color=(0.8, 0.8, 0.8), ax=axes)
pk.orbit_plots.plot_planet(
kepf, tf, units=units, color=(0.8, 0.8, 0.8), ax=axes)
def pretty(self, x):
"""
prob.pretty(x)
Args:
- x (``list``, ``tuple``, ``numpy.ndarray``): Decision chromosome, e.g. (``pygmo.population.champion_x``).
Prints human readable information on the trajectory represented by the decision vector x
"""
if not len(x) == len(self.get_bounds()[0]):
raise ValueError("Invalid length of the decision vector x")
n_seg = self.__n_seg
m_i = self.__sc.mass
t0 = x[0]
T = x[1]
m_f = x[2]
thrusts = [np.linalg.norm(x[3 + 3 * i: 6 + 3 * i])
for i in range(n_seg)]
tf = t0 + T
mP = m_i - m_f
deltaV = self.__sc.isp * pk.G0 * np.log(m_i / m_f)
dt = np.append(self.__fwd_dt, self.__bwd_dt) * T / pk.DAY2SEC
time_thrusts_on = sum(dt[i] for i in range(
len(thrusts)) if thrusts[i] > 0.1)
print("Departure:", pk.epoch(t0), "(", t0, "mjd2000 )")
print("Time of flight:", T, "days")
print("Arrival:", pk.epoch(tf), "(", tf, "mjd2000 )")
print("Delta-v:", deltaV, "m/s")
print("Propellant consumption:", mP, "kg")
print("Thrust-on time:", time_thrusts_on, "days")
def run_example11(n_seg=30):
"""
This example demonstrates the use of the class lt_margo developed for the internal ESA CDF study on the
interplanetary mission named MARGO. The class was used to produce the preliminary traget selection
for the mission resulting in 88 selected possible targets
(http://www.esa.int/spaceinimages/Images/2017/11/Deep-space_CubeSat)
(http://www.esa.int/gsp/ACT/mad/projects/lt_to_NEA.html)
"""
import pykep as pk
import pygmo as pg
import numpy as np
from matplotlib import pyplot as plt
from pykep.examples import add_gradient, algo_factory
# 0 - Target asteroid from MPCORB
mpcorbline = "K14Y00D 24.3 0.15 K1794 105.44160 34.12337 117.64264 1.73560 0.0865962 0.88781021 1.0721510 2 MPO369254 104 1 194 days 0.24 M-v 3Eh MPCALB 2803 2014 YD 20150618"
# 1 - Algorithm
algo = algo_factory("snopt7")
# 2 - Problem
udp = add_gradient(pk.trajopt.lt_margo(
target=pk.planet.mpcorb(mpcorbline),
n_seg=n_seg,
grid_type="uniform",
t0=[pk.epoch(8000), pk.epoch(9000)],
tof=[200, 365.25 * 3],
m0=20.0,
Tmax=0.0017,
Isp=3000.0,
earth_gravity=False,
sep=True,
start="earth"),
with_grad=False
)
prob = pg.problem(udp)
prob.c_tol = [1e-5] * prob.get_nc()
# 3 - Population
pop = pg.population(prob, 1)
# 4 - Solve the problem (evolve)
pop = algo.evolve(pop)
# 5 - Inspect the solution
print("Feasible?:", prob.feasibility_x(pop.champion_x))
# 6 - plot trajectory
axis = udp.udp_inner.plot_traj(pop.champion_x, plot_segments=True)
plt.title("The trajectory in the heliocentric frame")
# 7 - plot control
udp.udp_inner.plot_dists_thrust(pop.champion_x)
# 8 - pretty
udp.udp_inner.pretty(pop.champion_x)
plt.ion()
plt.show()
def fitness(self, x_full):
retval = []
# 1 - obj fun
if self.__objective == 'mass':
retval.append(x_full[-1 - self.__dim_leg + 3])
elif self.__objective == 'time':
retval.append(x_full[-1 - self.__dim_leg] - x_full[0])
sc_mass = self.__start_mass
eqs = []
ineqs = []
# 2 - constraints
for i in range(self.__num_legs):
x = x_full[i * self.__dim_leg:(i + 1) * self.__dim_leg]
start = pk.epoch(x[0])
end = pk.epoch(x[0] + x[1])
# Computing starting spaceraft state
r, v = self.__seq[i].eph(start)
x0 = pk.sims_flanagan.sc_state(r, v, sc_mass)
# Computing ending spaceraft state
r, v = self.__seq[i + 1].eph(end)
xe = pk.sims_flanagan.sc_state(r, v, x[3])
# Building the SF leg
self.__legs[i].set_spacecraft(
pk.sims_flanagan.spacecraft(sc_mass, .3, 3000.))
self.__legs[i].set(start, x0, x[-3 * self.__nseg:], end, xe)
# Setting all constraints
eqs.extend(self.__legs[i].mismatch_constraints())
ineqs.extend(self.__legs[i].throttles_constraints())
eqs[-7] /= pk.AU
eqs[-6] /= pk.AU
eqs[-5] /= pk.AU
eqs[-4] /= pk.EARTH_VELOCITY
eqs[-3] /= pk.EARTH_VELOCITY
eqs[-2] /= pk.EARTH_VELOCITY
eqs[-1] /= self.__start_mass
sc_mass = x[3] # update mass to final mass of leg
if i < self.__num_legs - 1:
x_next = x_full[
(i + 1) * self.__dim_leg:(i + 2) * self.__dim_leg]
time_ineq = x[0] + x[1] + x[2] - x_next[0]
ineqs.append(time_ineq / 365.25)
else:
final_time_ineq = x[0] + x[1] + x[2] - \
x_full[0] - x_full[-1] # <- total time
ineqs.append(final_time_ineq / 365.25)
retval = retval + eqs + ineqs
return retval
def fitness(self, z):
# departure and arrival times
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# departure and arrival eccentric anomolies
M0 = z[1]
Mf = z[2]
# departure costates
l0 = np.asarray(z[3:])
# set Keplerian elements
elem0 = np.hstack([self.elem0[:5], [M0]])
elemf = np.hstack([self.elemf[:5], [Mf]])
# compute Cartesian states
r0, v0 = pk.par2ic(elem0, self.leg.mu)
rf, vf = pk.par2ic(elemf, self.leg.mu)
# departure and arrival states (xf[6] is unused)
x0 = pk.sims_flanagan.sc_state(r0, v0, self.sc.mass)
xf = pk.sims_flanagan.sc_state(rf, vf, self.sc.mass / 10)
# set leg
self.leg.set(t0, x0, l0, tf, xf)
# equality constraints
ceq = self.leg.mismatch_constraints(atol=self.atol, rtol=self.rtol)
# final mass
obj = self.leg.trajectory[-1, -1]
# Transversality conditions
# At start
lambdas0 = np.array(self.leg.trajectory[0, 7:13])
r0norm = np.sqrt(r0[0] * r0[0] + r0[1] * r0[1] + r0[2] * r0[2])
tmp = - pk.MU_SUN / r0norm**3
tangent = np.array([v0[0], v0[1], v0[2], tmp *
r0[0], tmp * r0[1], tmp * r0[2]])
tangent_norm = np.linalg.norm(tangent)
tangent = tangent / tangent_norm
T0 = np.dot(lambdas0, tangent)
# At end
lambdasf = np.array(self.leg.trajectory[-1, 7:13])
rfnorm = np.sqrt(rf[0] * rf[0] + rf[1] * rf[1] + rf[2] * rf[2])
tmp = - pk.MU_SUN / rfnorm**3
tangent = np.array([vf[0], vf[1], vf[2], tmp *
rf[0], tmp * rf[1], tmp * rf[2]])
tangent_norm = np.linalg.norm(tangent)
tangent = tangent / tangent_norm
Tf = np.dot(lambdasf, tangent)
return np.hstack(([1], ceq, [T0, Tf]))
def _plot_traj(self, z, axis, units):
# times
t0 = pk.epoch(z[0])
tf = pk.epoch(z[0] + z[1])
# plot Keplerian
pk.orbit_plots.plot_planet(
self.p0, t0, units=units, color=(0.8, 0.8, 0.8), ax=axis)
pk.orbit_plots.plot_planet(
self.pf, tf, units=units, color=(0.8, 0.8, 0.8), ax=axis)
def _pretty(self, z):
print("\nLow-thrust NEP transfer from " +
self.p0.name + " to " + self.pf.name)
print("\nLaunch epoch: {!r} MJD2000, a.k.a. {!r}".format(
z[0], pk.epoch(z[0])))
print("Arrival epoch: {!r} MJD2000, a.k.a. {!r}".format(
z[0] + z[1], pk.epoch(z[0] + z[1])))
print("Time of flight (days): {!r} ".format(z[1]))
print("\nLaunch DV (km/s) {!r} - [{!r},{!r},{!r}]".format(np.sqrt(
z[3]**2 + z[4]**2 + z[5]**2) / 1000, z[3] / 1000, z[4] / 1000, z[5] / 1000))
print("Arrival DV (km/s) {!r} - [{!r},{!r},{!r}]".format(np.sqrt(
z[6]**2 + z[7]**2 + z[8]**2) / 1000, z[6] / 1000, z[7] / 1000, z[8] / 1000))
def _plot_traj(self, z, axis, units):
# times
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# Keplerian
kep0 = pk.planet.keplerian(t0, self.elem0)
kepf = pk.planet.keplerian(tf, self.elemf)
# plot Keplerian
pk.orbit_plots.plot_planet(
kep0, t0, units=units, color=(0.8, 0.8, 0.8), ax=axis)
pk.orbit_plots.plot_planet(
kepf, tf, units=units, color=(0.8, 0.8, 0.8), ax=axis)
def plot(self, x, ax=None):
"""
ax = prob.plot(x, ax=None)
- x: encoded trajectory
- ax: matplotlib axis where to plot. If None figure and axis will be created
- [out] ax: matplotlib axis where to plot
Plots the trajectory represented by a decision vector x on the 3d axis ax
Example::
ax = prob.plot(x)
"""
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pykep import epoch, AU
from pykep.sims_flanagan import sc_state
from pykep.orbit_plots import plot_planet, plot_sf_leg
# Creating the axis if necessary
if ax is None:
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
axis = fig.gca(projection='3d')
else:
axis = ax
# Plotting the Sun ........
axis.scatter([0], [0], [0], color='y')
# Plotting the pykep.planet both at departure and arrival dates
for i in range(self.__num_legs):
idx = i * self.__dim_leg
plot_planet(self.__seq[i], epoch(x[idx]), units=AU, legend=True, color=(
0.7, 0.7, 0.7), s=30, ax=axis)
plot_planet(self.__seq[i + 1], epoch(x[idx] + x[idx + 1]),
units=AU, legend=False, color=(0.7, 0.7, 0.7), s=30, ax=axis)
# Computing the legs
self.fitness(x)
# Plotting the legs
for leg in self.__legs:
plot_sf_leg(leg, units=AU, N=10, ax=axis, legend=False)
return axis
def fitness(self, z):
# times
t0 = self.t0
tf = pk.epoch(t0.mjd2000 + z[0])
# intial costates
l0 = np.asarray(z[1:])
# arrival conditions
rf, vf = self.pf.eph(tf)
# departure state
x0 = pk.sims_flanagan.sc_state(self.x0[0:3], self.x0[3:6], self.x0[6])
# arrival state (mass will be ignored)
xf = pk.sims_flanagan.sc_state(rf, vf, self.sc.mass / 10)
# set leg
self.leg.set(t0, x0, l0, tf, xf)
# equality constraints
ceq = self.leg.mismatch_constraints(atol=self.atol, rtol=self.rtol)
obj = self.leg.trajectory[-1, -1] * self.leg._dynamics.c2 * 1000
return np.hstack(([obj], ceq))
def _plot_traj(self, z, axes, units=pk.AU):
"""Plots spacecraft trajectory.
Args:
- z (``tuple``, ``list``, ``numpy.ndarray``): Decision chromosome.
- axes (``matplotlib.axes._subplots.Axes3DSubplot``): 3D axes to use for the plot
- units (``float``, ``int``): Length unit by which to normalise data.
Examples:
>>> prob.extract(pykep.trajopt.indirect_pt2or).plot_traj(pop.champion_x)
"""
# states
x0 = self.x0
# times
t0 = self.t0
tf = pk.epoch(t0.mjd2000 + z[0])
# Computes the osculating Keplerian elements at start
elem0 = list(pk.ic2par(x0[0:3], x0[3:6], self.leg.mu))
# Converts the eccentric anomaly into eccentric anomaly
elem0[5] = elem0[5] - elem0[1] * np.sin(elem0[5])
# Creates a virtual keplerian planet with the said elements
kep0 = pk.planet.keplerian(t0, elem0)
# Plots the departure and arrival osculating orbits
pk.orbit_plots.plot_planet(
kep0, t0, units=units, color=(0.8, 0.8, 0.8), ax=axes)
pk.orbit_plots.plot_planet(
self.pf, tf, units=units, color=(0.8, 0.8, 0.8), ax=axes)
def plot(self, x, axes=None, units=pk.AU, N=60):
"""plot(self, x, axes=None, units=pk.AU, N=60)
Plots the spacecraft trajectory.
Args:
- x (``tuple``, ``list``, ``numpy.ndarray``): Decision chromosome.
- axes (``matplotlib.axes._subplots.Axes3DSubplot``): 3D axes to use for the plot
- units (``float``, ``int``): Length unit by which to normalise data.
- N (``float``): Number of points to plot per leg
"""
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Creating the axes if necessary
if axes is None:
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
axes = fig.gca(projection='3d')
T = self._decode_tofs(x)
ep = np.insert(T, 0, x[0]) # [t0, T1, T2 ...]
ep = np.cumsum(ep) # [t0, t1, t2, ...]
DVlaunch, DVfb, DVarrival, l, _ = self._compute_dvs(x)
for pl, e in zip(self.seq, ep):
pk.orbit_plots.plot_planet(pl, pk.epoch(
e), units=units, legend=True, color=(0.7, 0.7, 1), ax=axes)
for lamb in l:
pk.orbit_plots.plot_lambert(
lamb, N=N, sol=0, units=units, color='k', legend=False, ax=axes, alpha=0.8)
return axes
def fitness(self, z):
# times
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# costates
l0 = np.asarray(z[1:])
# set leg
self.leg.set(t0, self.x0, l0, tf, self.xf)
# equality constraints
ceq = self.leg.mismatch_constraints(atol=self.atol, rtol=self.rtol)
return np.hstack(([1, ceq]))
def fitness(self, z):
# epochs (mjd2000)
t0 = pk.epoch(z[0])
tf = pk.epoch(z[0] + z[1])
# final mass
mf = z[2]
# controls
u = z[9:]
# compute Cartesian states of planets
r0, v0 = self.p0.eph(t0)
rf, vf = self.pf.eph(tf)
# add the vinfs from the chromosome
v0 = [a + b for a, b in zip(v0, z[3:6])]
vf = [a + b for a, b in zip(vf, z[6:9])]
# spacecraft states
x0 = pk.sims_flanagan.sc_state(r0, v0, self.sc.mass)
xf = pk.sims_flanagan.sc_state(rf, vf, mf)
# set leg
self.leg.set(t0, x0, u, tf, xf)
# compute equality constraints
ceq = np.asarray(self.leg.mismatch_constraints(), np.float64)
# nondimensionalise equality constraints
ceq[0:3] /= pk.AU
ceq[3:6] /= pk.EARTH_VELOCITY
ceq[6] /= self.sc.mass
# compute inequality constraints
cineq = np.asarray(self.leg.throttles_constraints(), np.float64)
# compute inequality constraints on departure and arrival velocities
v_dep_con = (z[3] ** 2 + z[4] ** 2 + z[5] ** 2 - self.vinf_dep ** 2)
v_arr_con = (z[6] ** 2 + z[7] ** 2 + z[8] ** 2 - self.vinf_arr ** 2)
# nondimensionalize inequality constraints
v_dep_con /= pk.EARTH_VELOCITY ** 2
v_arr_con /= pk.EARTH_VELOCITY ** 2
return np.hstack(([-mf], ceq, cineq, [v_dep_con, v_arr_con]))
def plot(self, x):
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pykep import epoch, AU
from pykep.sims_flanagan import sc_state
from pykep.orbit_plots import plot_planet, plot_sf_leg
t_E = epoch(x[0])
t_V = epoch(x[0] + x[1])
t_M = epoch(x[0] + x[1] + x[9])
rE, vE = self.__earth.eph(t_E)
rV, vV = self.__venus.eph(t_V)
rM, vM = self.__mercury.eph(t_M)
# First Leg
v = [a + b for a, b in zip(vE, x[3:6])]
x0 = sc_state(rE, v, self.__sc.mass)
v = [a + b for a, b in zip(vV, x[6:9])]
xe = sc_state(rV, v, x[2])
self.__leg1.set(
t_E, x0, x[-3 * (self.__nseg1 + self.__nseg2):-self.__nseg2 * 3], t_V, xe)
# Second leg
v = [a + b for a, b in zip(vV, x[11:14])]
x0 = sc_state(rV, v, x[2])
v = [a + b for a, b in zip(vM, x[14:17])]
xe = sc_state(rM, v, x[10])
self.__leg2.set(t_E, x0, x[(-3 * self.__nseg2):], t_V, xe)
fig = plt.figure()
axis = fig.gca(projection='3d')
# The Sun
axis.scatter([0], [0], [0], color='y')
# The legs
plot_sf_leg(self.__leg1, units=AU, N=10, ax=axis)
plot_sf_leg(self.__leg2, units=AU, N=10, ax=axis)
# The planets
plot_planet(
self.__earth, t_E, units=AU, legend=True, color=(0.7, 0.7, 1), ax=axis)
plot_planet(
self.__venus, t_V, units=AU, legend=True, color=(0.7, 0.7, 1), ax=axis)
plot_planet(
self.__mercury, t_M, units=AU, legend=True, color=(0.7, 0.7, 1), ax=axis)
plt.show()
def fitness(self, z):
# epochs (mjd2000)
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# final mass
mf = z[1]
# eccentric anomolies
E0 = z[2]
Ef = z[3]
# controls
u = z[4:]
# set Keplerian elements
self.elem0[5] = E0
self.elemf[5] = Ef
# compute Cartesian states
r0, v0 = pk.par2ic(self.elem0, self.mu)
rf, vf = pk.par2ic(self.elemf, self.mu)
# spacecraft states
x0 = pk.sims_flanagan.sc_state(r0, v0, self.sc.mass)
xf = pk.sims_flanagan.sc_state(rf, vf, mf)
# set leg
self.leg.set(t0, x0, u, tf, xf)
# compute equality constraints
ceq = np.asarray(self.leg.mismatch_constraints(), np.float64)
# nondimensionalise equality constraints
ceq[0:3] /= pk.AU
ceq[3:6] /= pk.EARTH_VELOCITY
ceq[6] /= self.sc.mass
# compute inequality constraints
cineq = np.asarray(self.leg.throttles_constraints(), np.float64)
return np.hstack(([-mf], ceq, cineq))
def fitness(self, z):
# times
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# final eccentric anomaly
Mf = z[1]
# intial costates
l0 = np.asarray(z[2:])
# set arrival Keplerian elements
self.elemf[5] = Mf
# departure state
x0 = pk.sims_flanagan.sc_state(self.x0[0:3], self.x0[3:6], self.x0[6])
# compute Cartesian arrival state
rf, vf = pk.par2ic(self.elemf, self.leg.mu)
xf = pk.sims_flanagan.sc_state(rf, vf, self.sc.mass / 10)
# set leg
self.leg.set(t0, x0, l0, tf, xf)
# equality constraints
ceq = self.leg.mismatch_constraints(atol=self.atol, rtol=self.rtol)
# final mass
# mf = self.leg.trajectory[-1, 6]
# Transversality condition at the end
lambdasf = np.array(self.leg.trajectory[-1, 7:13])
rfnorm = np.sqrt(rf[0] * rf[0] + rf[1] * rf[1] + rf[2] * rf[2])
tmp = - pk.MU_SUN / rfnorm**3
tangent = np.array([vf[0], vf[1], vf[2], tmp *
rf[0], tmp * rf[1], tmp * rf[2]])
tangent_norm = np.linalg.norm(tangent)
tangent = tangent / tangent_norm
Tf = np.dot(lambdasf, tangent)
return np.hstack(([1], ceq, [Tf]))
def plotContours(start_planet, end_planet, eph1, eph2, flt1, flt2,
sampling_size):
start_epochs = np.arange(eph1, eph2, sampling_size)
duration = np.arange(flt1, flt2, sampling_size)
data = list()
for start in start_epochs:
row = list()
for T in duration:
r1, v1 = start_planet.eph(pk.epoch(start))
r2, v2 = end_planet.eph(pk.epoch(start + T))
l = pk.lambert_problem(r1, r2, T * 60 * 60 * 24,
start_planet.mu_central_body)
DV1 = np.linalg.norm(array(v1) - array(l.get_v1()[0]))
DV2 = np.linalg.norm(array(v2) - array(l.get_v2()[0]))
DV1 = max([0, DV1 - eph1])
DV = DV1 + DV2
row.append(DV)
data.append(row)
minrows = [min(l) for l in data]
i_idx = np.argmin(minrows)
j_idx = np.argmin(data[i_idx])
best = data[i_idx][j_idx]
print('\nBest DV: ', best)
print('Launch epoch (MJD2000): ', start_epochs[i_idx])
print('Duration (days): ', duration[j_idx])
duration_pl2, start_epochs_pl2 = np.meshgrid(duration, start_epochs)
CP2 = plt.contourf(start_epochs_pl2,
duration_pl2,
array(data),
levels=list(np.linspace(best, 5000, 10)))
plt.colorbar(CP2).set_label('△V km/s')
plt.title(f'{start_planet.name} - {end_planet.name} Total △V Requirements'.
title())
plt.xlabel('Launch Date (MJD2000)')
plt.ylabel('Mission Duration (days)')
plt.show()
def double_segments(self, x):
"""
new_prob, new_x = prob.double_segments(self,x)
Args:
- x (``list``, ``tuple``, ``numpy.ndarray``): Decision chromosome, e.g. (``pygmo.population.champion_x``).
Returns:
- the new udp having twice the segments
- list: the new chromosome to be used as initial guess
Returns the decision vector encoding a low trust trajectory having double the number of segments with respect to x
and a 'similar' throttle history. In case high fidelity is True, and x is a feasible trajectory, the returned decision vector
also encodes a feasible trajectory that can be further optimized
Returns the problem and the decision vector encoding a low-thrust trajectory having double the number of
segments with respect to x and the same throttle history. If x is a feasible trajectory, the new chromosome is "slightly
unfeasible", due to the new refined Earth's gravity and/or SEP thrust that are now computed in the 2 halves of each segment.
"""
if not len(x) == len(self.get_bounds()[0]):
raise ValueError("Invalid length of the decision vector x")
new_x = np.append(x[:3], np.repeat(x[3:].reshape((-1, 3)), 2, axis=0))
new_prob = lt_margo(
target=self.target,
n_seg=2 * self.__n_seg,
grid_type=self.__grid_type,
t0=[pk.epoch(self.__lb[0]), pk.epoch(self.__ub[0])],
tof=[self.__lb[1], self.__ub[1]],
m0=self.__sc.mass,
Tmax=self.__sc.thrust,
Isp=self.__sc.isp,
earth_gravity=self.__earth_gravity,
sep=self.__sep,
start=self.__start
)
return new_prob, new_x
def __init__(
self,
t0_limits=[7000, 8000],
tof_limits=[[50, 420], [50, 400], [50, 400]],
max_revs: int = 2,
resonances=[[[1, 1], [5, 4], [4, 3]], [[1, 1], [5, 4], [4, 3]],
[[1, 1], [5, 4], [4, 3]], [[4, 3], [3, 2], [5, 3]],
[[4, 3], [3, 2], [5, 3]], [[4, 3], [3, 2], [5, 3]]],
safe_distance=350000,
max_mission_time=11.0 * 365.25,
max_dv0=5600,
):
"""
Args:
- t0_limits (``list`` of ``float``): start time bounds.
- tof_limits (``list`` of ``list`` of ``float``): time of flight bounds.
- max_revs (``int``): maximal number of revolutions for Lambert transfer.
- resonances (``list`` of ``list`` of ``int``): resonance options.
- safe_distance: (``float``): safe distance from planet at GA maneuver in m.
- max_mission_time: (``float``): max mission time in days.
- max_dv0: (``float``): max delta velocity at start.
"""
self._safe_distance = safe_distance
self._max_mission_time = max_mission_time
self._max_dv0 = max_dv0
self._min_beta = -math.pi
self._max_beta = math.pi
self._earth = jpl_lp("earth")
self._venus = jpl_lp("venus")
self._seq = [
self._earth, self._venus, self._venus, self._earth, self._venus,
self._venus, self._venus, self._venus, self._venus, self._venus
]
assert len(self._seq) - 4 == len(
resonances) # one resonance option selection for each VV sequence
self._resonances = resonances
self._t0 = t0_limits
self._tof = tof_limits
self._max_revs = max_revs
self._n_legs = len(self._seq) - 1
# initialize data to compute heliolatitude
t_plane_crossing = epoch(7645)
rotation_axis = self._seq[0].eph(t_plane_crossing)[0]
self._rotation_axis = rotation_axis / np.linalg.norm(rotation_axis)
self._theta = -7.25 * DEG2RAD # fixed direction of the rotation
def _plot_traj(self, z, axes, units):
"""Plots spacecraft trajectory.
Args:
- z (``tuple``, ``list``, ``numpy.ndarray``): Decision chromosome.
- axes (``matplotlib.axes._subplots.Axes3DSubplot``): 3D axes to use for the plot
- units (``float``, ``int``): Length unit by which to normalise data.
Examples:
>>> prob.extract(pykep.trajopt.indirect_or2or).plot_traj(pop.champion_x)
"""
# times
t0 = pk.epoch(0)
tf = pk.epoch(z[0])
# Mean Anomalies
M0 = z[1] - self.elem0[1] * np.sin(z[1])
Mf = z[2] - self.elemf[1] * np.sin(z[2])
elem0 = np.hstack([self.elem0[:5], [M0]])
elemf = np.hstack([self.elemf[:5], [Mf]])
# Keplerian points
kep0 = pk.planet.keplerian(t0, elem0)
kepf = pk.planet.keplerian(tf, elemf)
# planets
pk.orbit_plots.plot_planet(kep0,
t0=t0,
units=units,
axes=axes,
color=(0.8, 0.8, 0.8))
pk.orbit_plots.plot_planet(kepf,
t0=tf,
units=units,
axes=axes,
color=(0.8, 0.8, 0.8))
def fitness(self, x):
from pykep import epoch, AU, EARTH_VELOCITY, DAY2SEC
from pykep.sims_flanagan import sc_state
# This is the objective function
objfun = [-x[3]]
# And these are the constraints
start = epoch(x[0])
end = epoch(x[0] + x[1])
r, v = self.__earth.eph(start)
v = [a + b for a, b in zip(v, x[4:7])]
x0 = sc_state(r, v, self.__sc.mass)
r, v = self.__mars.eph(end)
xe = sc_state(r, v, x[3])
self.__leg.set(start, x0, x[-3 * self.__nseg:],
end, xe, x[2] * DAY2SEC)
v_inf_con = (x[4] * x[4] + x[5] * x[5] + x[6] * x[6] -
self.__Vinf * self.__Vinf) / (EARTH_VELOCITY * EARTH_VELOCITY)
try:
constraints = list(self.__leg.mismatch_constraints(
) + self.__leg.throttles_constraints()) + [v_inf_con]
except:
print(
"warning: CANNOT EVALUATE constraints .... possible problem in the Taylor integration in the Sundmann variable")
constraints = (1e14,) * (8 + 1 + self.__nseg + 2)
# We then scale all constraints to non-dimensional values
constraints[0] /= AU
constraints[1] /= AU
constraints[2] /= AU
constraints[3] /= EARTH_VELOCITY
constraints[4] /= EARTH_VELOCITY
constraints[5] /= EARTH_VELOCITY
constraints[6] /= self.__sc.mass
constraints[7] /= 365.25 * DAY2SEC
return objfun + constraints
def run_example1(impulses=4):
import pykep as pk
import pygmo as pg
import numpy as np
from matplotlib import pyplot as plt
from pykep.examples import add_gradient, algo_factory
# problem
udp = add_gradient(pk.trajopt.pl2pl_N_impulses(
start=pk.planet.jpl_lp('earth'),
target=pk.planet.jpl_lp('venus'),
N_max=impulses,
tof=[100., 1000.],
vinf=[0., 4],
phase_free=False,
multi_objective=False,
t0=[pk.epoch(0), pk.epoch(1000)]),
with_grad=False)
prob = pg.problem(udp)
# algorithm
uda = pg.cmaes(gen=1000, force_bounds=True)
algo = pg.algorithm(uda)
algo.set_verbosity(10)
# population
pop = pg.population(prob, 20)
# solve the problem
pop = algo.evolve(pop)
# inspect the solution
udp.udp_inner.plot(pop.champion_x)
plt.ion()
plt.show()
udp.udp_inner.pretty(pop.champion_x)
def fitness(self, x):
from pykep import epoch, AU, EARTH_VELOCITY, DAY2SEC
from pykep.sims_flanagan import sc_state
# This is the objective function
objfun = [-x[3]]
# And these are the constraints
start = epoch(x[0])
end = epoch(x[0] + x[1])
r, v = self.__earth.eph(start)
v = [a + b for a, b in zip(v, x[4:7])]
x0 = sc_state(r, v, self.__sc.mass)
r, v = self.__mars.eph(end)
xe = sc_state(r, v, x[3])
self.__leg.set(start, x0, x[-3 * self.__nseg:],
end, xe, x[2] * DAY2SEC)
v_inf_con = (x[4] * x[4] + x[5] * x[5] + x[6] * x[6] -
self.__Vinf * self.__Vinf) / (EARTH_VELOCITY * EARTH_VELOCITY)
try:
constraints = list(self.__leg.mismatch_constraints(
) + self.__leg.throttles_constraints()) + [v_inf_con]
except:
print(
"warning: CANNOT EVALUATE constraints .... possible problem in the Taylor integration in the Sundmann variable")
constraints = (1e14,) * (8 + 1 + self.__nseg + 2)
# We then scale all constraints to non-dimensional values
constraints[0] /= AU
constraints[1] /= AU
constraints[2] /= AU
constraints[3] /= EARTH_VELOCITY
constraints[4] /= EARTH_VELOCITY
constraints[5] /= EARTH_VELOCITY
constraints[6] /= self.__sc.mass
constraints[7] /= 365.25 * DAY2SEC
return objfun + constraints
def __init__(self,
x0=[
-24482087316.947845, -150000284705.77328,
-196089391.29376224, 31677.87649549203,
-5859.747563624047, -351.75278222719828, 1000
],
pf="mars",
mass=1000,
thrust=0.3,
isp=3000,
tof=[230, 280],
t0=1251.0286746844447,
mu=pk.MU_SUN,
alpha=0,
bound=False,
atol=1e-12,
rtol=1e-12):
"""Initialises ``pykep.trajopt.indirect_pt2or`` problem.
Args:
- x0 (``list``, ``tuple``, ``numpy.ndarray``): Departure state [m, m, m, m/s, m/s, m/s, kg].
- pf (``str``): Arrival planet name. (will be used to construct a planet.jpl_lp object) - mass (``float``, ``int``): Spacecraft wet mass [kg].
- thrust (``float``, ``int``): Spacecraft maximum thrust [N].
- isp (``float``, ``int``): Spacecraft specific impulse [s].
- atol (``float``, ``int``): Absolute integration solution tolerance.
- rtol (``float``, ``int``): Relative integration solution tolerance.
- tof (``list``): Transfer time bounds [days].
- t0 (``float``): launch epoch [MJD2000].
- freetime (``bool``): Activates final time transversality condition. Allows final time to vary.
- alpha (``float``, ``int``): Homotopy parameter, governing the degree to which the theoretical control law is intended to reduce propellant expenditure or energy.
- bound (``bool``): Activates bounded control, in which the control throttle is bounded between 0 and 1, otherwise the control throttle is allowed to unbounded.
- mu (``float``): Gravitational parameter of primary body [m^3/s^2].
"""
# initialise base
_indirect_base.__init__(self, mass, thrust, isp, mu, True, False,
alpha, bound, atol, rtol)
# departure epoch
self.t0 = pk.epoch(t0)
# departure state
self.x0 = np.asarray(x0, np.float64)
# arrival planet
self.pf = pk.planet.jpl_lp(pf)
# bounds on the time of flight
self.tof = tof
# store the alfa value (immutable)
self._alpha = alpha
def __init__(self,
x0=[-24482087316.947845, -150000284705.77328, -196089391.29376224,
31677.87649549203, -5859.747563624047, -351.75278222719828, 1000],
pf="mars",
mass=1000,
thrust=0.3,
isp=3000,
tof=[230, 280],
t0=1251.0286746844447,
mu=pk.MU_SUN,
alpha=0,
bound=False,
atol=1e-12,
rtol=1e-12
):
"""Initialises ``pykep.trajopt.indirect_pt2or`` problem.
Args:
- x0 (``list``, ``tuple``, ``numpy.ndarray``): Departure state [m, m, m, m/s, m/s, m/s, kg].
- pf (``str``): Arrival planet name. (will be used to construct a planet.jpl_lp object) - mass (``float``, ``int``): Spacecraft wet mass [kg].
- thrust (``float``, ``int``): Spacecraft maximum thrust [N].
- isp (``float``, ``int``): Spacecraft specific impulse [s].
- atol (``float``, ``int``): Absolute integration solution tolerance.
- rtol (``float``, ``int``): Relative integration solution tolerance.
- tof (``list``): Transfer time bounds [days].
- t0 (``float``): launch epoch [MJD2000].
- freetime (``bool``): Activates final time transversality condition. Allows final time to vary.
- alpha (``float``, ``int``): Homotopy parametre, governing the degree to which the theoretical control law is intended to reduce propellant expenditure or energy.
- bound (``bool``): Activates bounded control, in which the control throttle is bounded between 0 and 1, otherwise the control throttle is allowed to unbounded.
- mu (``float``): Gravitational parametre of primary body [m^3/s^2].
"""
# initialise base
_indirect_base.__init__(
self, mass, thrust, isp, mu, True, False, alpha, bound,
atol, rtol
)
# departure epoch
self.t0 = pk.epoch(t0)
# departure state
self.x0 = np.asarray(x0, np.float64)
# arrival planet
self.pf = pk.planet.jpl_lp(pf)
# bounds on the time of flight
self.tof = tof
# store the alfa value (immutable)
self._alpha = alpha
def planet(self):
"""PyKep object (pykep.planet.keplerian) representation of Planet."""
if self.pykep_planet is None:
if self.planet_radius is None:
planet_radius_constant = Constant("Rpln")
self.planet_radius = planet_radius_constant.value
self.pykep_planet = keplerian(
epoch(0),
(self.semimajor_axis * AU, self.eccentricity, self.inclination,
self.longitude_of_ascending_node, self.argument_of_periapsis,
self.true_anomaly_at_epoch), self._star.gm, self.gm,
self.radius * self.planet_radius,
self.radius * self.planet_radius, self.name)
return self.pykep_planet
def rate__orbital(dep_ast, dep_t, leg_dT, **kwargs):
"""
Orbital Phasing Indicator.
See: http://arxiv.org/pdf/1511.00821.pdf#page=12
Estimates the cost of a `leg_dT` days transfer from departure asteroid
`dep_ast` at epoch `dep_t`, towards each of the available asteroids.
"""
dep_t = pk.epoch(dep_t, "mjd")
leg_dT *= DAY2SEC
orbi = [
orbital_indicator(ast, dep_t, leg_dT, **kwargs) for ast in asteroids
]
ast_orbi = np.array(orbi[dep_ast])
return np.linalg.norm(ast_orbi - orbi, axis=1)
def pretty(self, x):
"""
prob.pretty(x)
Args:
- x (``list``, ``tuple``, ``numpy.ndarray``): Decision chromosome, e.g. (``pygmo.population.champion_x``).
Prints human readable information on the trajectory represented by the decision vector x
"""
if not len(x) == len(self.get_bounds()[0]):
raise ValueError("Invalid length of the decision vector x")
n_seg = self.__n_seg
m_i = self.__sc.mass
t0 = x[0]
T = x[1]
m_f = x[2]
thrusts = [
np.linalg.norm(x[3 + 3 * i:6 + 3 * i]) for i in range(n_seg)
]
tf = t0 + T
mP = m_i - m_f
deltaV = self.__sc.isp * pk.G0 * np.log(m_i / m_f)
dt = np.append(self.__fwd_dt, self.__bwd_dt) * T / pk.DAY2SEC
time_thrusts_on = sum(dt[i] for i in range(len(thrusts))
if thrusts[i] > 0.1)
print("Departure:", pk.epoch(t0), "(", t0, "mjd2000 )")
print("Time of flight:", T, "days")
print("Arrival:", pk.epoch(tf), "(", tf, "mjd2000 )")
print("Delta-v:", deltaV, "m/s")
print("Propellant consumption:", mP, "kg")
print("Thrust-on time:", time_thrusts_on, "days")
def plot_planet_2d(planet, t0):
T = planet.compute_period(t0) * SEC2DAY
when = np.linspace(0, T, 60)
x = np.array([0.0] * 60)
y = np.array([0.0] * 60)
z = np.array([0.0] * 60)
for i, day in enumerate(when):
r, _ = planet.eph(epoch(t0.mjd2000 + day))
x[i] = r[0] / AU
y[i] = r[1] / AU
z[i] = r[2] / AU
return x, y, z
def plot(self, x, axes=None, units=AU, N=60):
import matplotlib as mpl
import matplotlib.pyplot as plt
from pykep.orbit_plots import plot_planet
rvt_outs, rvt_ins, rvt_pls, _, _ = self._compute_dvs(x)
rvt_outs = [
rvt.rotate(self._rotation_axis, self._theta) for rvt in rvt_outs
]
rvt_ins[1:] = [
rvt.rotate(self._rotation_axis, self._theta) for rvt in rvt_ins[1:]
]
rvt_pls = [
rvt.rotate(self._rotation_axis, self._theta) for rvt in rvt_pls
]
ep = [epoch(rvt_pl._t * SEC2DAY) for rvt_pl in rvt_pls]
# Creating the axes if necessary
if axes is None:
mpl.rcParams["legend.fontsize"] = 10
fig = plt.figure()
axes = fig.gca(projection="3d")
plt.xlim([-1, 1])
# planets
for pl, e in zip(self._seq, ep):
plot_planet(pl,
e,
units=units,
legend=True,
color=(0.7, 0.7, 1),
axes=axes)
# lamberts and resonances
for i in range(0, len(self._seq) - 1):
pl = self._seq[i]
# stay at planet: it is a resonance colored black
is_reso = pl == self._seq[i + 1]
rvt_out = rvt_outs[i]
tof = rvt_ins[i + 1]._t - rvt_out._t
rvt_out.plot(tof,
units=units,
N=4 * N,
color="k" if is_reso else "r",
axes=axes)
return axes
def eph_reference(ref_ast, t):
"""
Calculate reference r and v magnitudes for use in the normalization
of asteroid ephemeris.
Returns:
(|r|, |v|)
where (r,v) is the ephemeris of the given asteroid `ref_ast` (id, or object)
at epoch `t` (int/float, or epoch object).
"""
if type(ref_ast) is int:
ref_ast = asteroids[ref_ast]
if type(t) in [float, int]:
t = pk.epoch(t, "mjd")
r, v = ref_ast.eph(t)
return np.linalg.norm(r), np.linalg.norm(v)
def epoch_offset():
planet = Planet(1817514095, 1905216634, -455609026)
winter = np.arctan2(-1, 0)
a = -75.0
b = 75.0
for _ in range(1000):
c = (a + b) / 2
r_c, _ = planet.planet.eph(epoch(c))
angle = np.arctan2(r_c[1], r_c[0])
if angle - winter < 0:
a = c
else:
b = c
return jsonify(c) if has_app_context() else c
def _plot_traj(self, z, axes, units=pk.AU):
"""Plots spacecraft trajectory.
Args:
- z (``tuple``, ``list``, ``numpy.ndarray``): Decision chromosome.
- atol (``float``, ``int``): Absolute integration solution tolerance.
- rtol (``float``, ``int``): Relative integration solution tolerance.
- units (``float``, ``int``): Length unit by which to normalise data.
Examples:
>>> prob.extract(pykep.trajopt.indirect_pt2or).plot_traj(pop.champion_x)
"""
# states
x0 = self.x0
# times
t0 = self.t0
tf = pk.epoch(t0.mjd2000 + z[0])
# Computes the osculating Keplerian elements at start
elem0 = list(pk.ic2par(x0[0:3], x0[3:6], self.leg.mu))
# Converts the eccentric anomaly into eccentric anomaly
elem0[5] = elem0[5] - elem0[1] * np.sin(elem0[5])
# Creates a virtual keplerian planet with the said elements
kep0 = pk.planet.keplerian(t0, elem0)
# Plots the departure and arrival osculating orbits
pk.orbit_plots.plot_planet(kep0,
t0,
units=units,
color=(0.8, 0.8, 0.8),
ax=axes)
pk.orbit_plots.plot_planet(self.pf,
tf,
units=units,
color=(0.8, 0.8, 0.8),
ax=axes)
def ephemeris(planet, modifiedJulianDay, mode='jpl', ref='sun'):
'''
Query the ephemeris for the specified planet and epoch
Based on the Pykep package by ESA/ACT, developed with version 2.3
Returns the state vector (pos, vel) in Cartesian and Cylindrical coordinates
mode='jpl' uses low precision ephemerides of the planets ('earth', etc)
mode='spice' uses high precision spice kernel. These need to be loaded
into memory using loadSpiceKernels(). The kernel (430.bsp)
includes ephemerides for Earth, Moon and the planet system barycenters
('399', 301', '4', etc)
mode='gtoc2' uses the asteroids from the GTOC2 competition (Keplerian)
'''
epoch = pk.epoch(modifiedJulianDay, 'mjd2000')
if mode == 'jpl':
planet = pk.planet.jpl_lp(planet)
elif mode == 'spice':
planet = pk.planet.spice(planet, ref, 'eclipj2000')
elif mode == 'gtoc2':
planet = pk.planet.gtoc2(planet)
else:
print('ERROR: This is not a valid source of ephemerides.')
# retrieve Cartesian state vectors in SI units
rCart, vCart = planet.eph(epoch)
# conversion to cylindrical values to get boundary conditions
rCyl = Pcart2cyl(rCart)
vCyl = Vcart2cyl(vCart, rCart)
# combine into (6,) vectors (3xposition, 3xvelocity)
stateVectorCartesian = np.array((rCart, vCart)).reshape(6, )
stateVectorCylindrical = np.array((rCyl, vCyl)).reshape(6, )
return stateVectorCylindrical, stateVectorCartesian, planet
def plot_dists_thrust(self, x, axes=None):
"""
axes = prob.plot_dists_thrust(self, x, axes=None)
Args:
- x (``list``, ``tuple``, ``numpy.ndarray``): Decision chromosome, e.g. (``pygmo.population.champion_x``).
Returns:
matplotlib.axes: axes where to plot
Plots the distance of the spacecraft from the Earth/Sun and the thrust profile
"""
if not len(x) == len(self.get_bounds()[0]):
raise ValueError("Invalid length of the decision vector x")
import matplotlib as mpl
import matplotlib.pyplot as plt
# Creating the axes if necessary
if axes is None:
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
axes = fig.add_subplot(111)
n_seg = self.__n_seg
fwd_seg = self.__fwd_seg
bwd_seg = self.__bwd_seg
t0 = x[0]
T = x[1]
fwd_grid = t0 + T * self.__fwd_grid # days
bwd_grid = t0 + T * self.__bwd_grid # days
throttles = [np.linalg.norm(x[3 + 3 * i: 6 + 3 * i])
for i in range(n_seg)]
dist_earth = [0.0] * (n_seg + 2) # distances spacecraft - Earth
dist_sun = [0.0] * (n_seg + 2) # distances spacecraft - Sun
times = np.concatenate((fwd_grid, bwd_grid))
rfwd, rbwd, vfwd, vbwd, mfwd, mbwd, ufwd, ubwd, fwd_dt, bwd_dt, _, _ = self._propagate(
x)
# Forward propagation
xfwd = [0.0] * (fwd_seg + 1)
yfwd = [0.0] * (fwd_seg + 1)
zfwd = [0.0] * (fwd_seg + 1)
xfwd[0] = rfwd[0][0] / pk.AU
yfwd[0] = rfwd[0][1] / pk.AU
zfwd[0] = rfwd[0][2] / pk.AU
r_E = [ri / pk.AU for ri in self.__earth.eph(pk.epoch(fwd_grid[0]))[0]]
dist_earth[0] = np.linalg.norm(
[r_E[0] - xfwd[0], r_E[1] - yfwd[0], r_E[2] - zfwd[0]])
dist_sun[0] = np.linalg.norm([xfwd[0], yfwd[0], zfwd[0]])
for i in range(fwd_seg):
xfwd[i + 1] = rfwd[i + 1][0] / pk.AU
yfwd[i + 1] = rfwd[i + 1][1] / pk.AU
zfwd[i + 1] = rfwd[i + 1][2] / pk.AU
r_E = [
ri / pk.AU for ri in self.__earth.eph(pk.epoch(fwd_grid[i + 1]))[0]]
dist_earth[
i + 1] = np.linalg.norm([r_E[0] - xfwd[i + 1], r_E[1] - yfwd[i + 1], r_E[2] - zfwd[i + 1]])
dist_sun[
i + 1] = np.linalg.norm([xfwd[i + 1], yfwd[i + 1], zfwd[i + 1]])
# Backward propagation
xbwd = [0.0] * (bwd_seg + 1)
ybwd = [0.0] * (bwd_seg + 1)
zbwd = [0.0] * (bwd_seg + 1)
xbwd[-1] = rbwd[-1][0] / pk.AU
ybwd[-1] = rbwd[-1][1] / pk.AU
zbwd[-1] = rbwd[-1][2] / pk.AU
r_E = [
ri / pk.AU for ri in self.__earth.eph(pk.epoch(bwd_grid[-1]))[0]]
dist_earth[-1] = np.linalg.norm([r_E[0] - xbwd[-1],
r_E[1] - ybwd[-1], r_E[2] - zbwd[-1]])
dist_sun[-1] = np.linalg.norm([xbwd[-1], ybwd[-1], zbwd[-1]])
for i in range(bwd_seg):
xbwd[-i - 2] = rbwd[-i - 2][0] / pk.AU
ybwd[-i - 2] = rbwd[-i - 2][1] / pk.AU
zbwd[-i - 2] = rbwd[-i - 2][2] / pk.AU
r_E = [
ri / pk.AU for ri in self.__earth.eph(pk.epoch(bwd_grid[-i - 2]))[0]]
dist_earth[-i - 2] = np.linalg.norm(
[r_E[0] - xbwd[-i - 2], r_E[1] - ybwd[-i - 2], r_E[2] - zbwd[-i - 2]])
dist_sun[-i -
2] = np.linalg.norm([xbwd[-i - 2], ybwd[-i - 2], zbwd[-i - 2]])
axes.set_xlabel("t [mjd2000]")
# Plot Earth distance
axes.plot(times, dist_earth, c='b', label="sc-Earth")
# Plot Sun distance
axes.plot(times, dist_sun, c='y', label="sc-Sun")
axes.set_ylabel("distance [AU]", color='k')
axes.set_ylim(bottom=0.)
axes.tick_params('y', colors='k')
axes.legend(loc=2)
# draw threshold where Earth gravity equals 0.1*Tmax
if self.__earth_gravity:
axes.axhline(y=np.sqrt(pk.MU_EARTH * self.__sc.mass / (self.__sc.thrust * 0.1)
) / pk.AU, c='g', ls=":", lw=1, label="earth_g = 0.1*Tmax")
# Plot thrust profile
axes = axes.twinx()
if self.__sep:
max_thrust = self.__sc.thrust
thrusts = np.linalg.norm(
np.array(ufwd + ubwd), axis=1) / max_thrust
# plot maximum thrust achievable at that distance from the Sun
distsSun = dist_sun[:fwd_seg] + \
dist_sun[-bwd_seg:] + [dist_sun[-1]]
Tmaxs = [self._sep_model(d)[0] / max_thrust for d in distsSun]
axes.step(np.concatenate(
(fwd_grid, bwd_grid[1:])), Tmaxs, where="post", c='lightgray', linestyle=':')
else:
thrusts = throttles.copy()
# duplicate the last for plotting
thrusts = np.append(thrusts, thrusts[-1])
axes.step(np.concatenate(
(fwd_grid, bwd_grid[1:])), thrusts, where="post", c='r', linestyle='--')
axes.set_ylabel("T/Tmax$_{1AU}$", color='r')
axes.tick_params('y', colors='r')
axes.set_xlim([times[0], times[-1]])
axes.set_ylim([0, max(thrusts) + 0.2])
return axes
def rate__edelbaum(dep_ast, dep_t, **kwargs):
dep_ast = asteroids[dep_ast]
dep_t = pk.epoch(dep_t, "mjd")
return [edelbaum_dv(dep_ast, arr_ast, dep_t) for arr_ast in asteroids]
intervals = 1.0
start_epochs = np.arange(start_date, end_date, intervals)
max_tof = 400.0
duration = np.arange(120.0, max_tof, 1.0)
origin = 'earth'
destination = 'mars'
earth = pk.planet.jpl_lp(origin)
mars = pk.planet.jpl_lp(destination)
data = list()
for start in start_epochs:
row = list()
for tof in duration:
pos_1, v1 = earth.eph(pk.epoch(start, 'mjd2000'))
pos_2, v2 = mars.eph(pk.epoch(start + tof, 'mjd2000'))
seconds_elapsed = tof * 60 * 60 * 24 if tof > 0 else 1 * 60 * 60 * 24
l = pk.lambert_problem(pos_1, pos_2, seconds_elapsed, pk.MU_SUN)
DV1 = np.linalg.norm(np.array(v1) - np.array(l.get_v1()[0]))
DV2 = np.linalg.norm(np.array(v2) - np.array(l.get_v2()[0]))
DV1 = max([0, DV1 - 4000])
DV = DV1 + DV2
row.append(DV)
data.append(row)
from pykep.trajopt import mga_1dsm
from pykep.planet import jpl_lp, keplerian
from pykep import AU, DEG2RAD, MU_SUN, epoch
# ROSETTA (we need to modify the safe radius of the planets to match the wanted problem)
_churyumov = keplerian(epoch(52504.23754000012, "mjd"), [
3.50294972836275 * AU, 0.6319356, 7.12723 * DEG2RAD, 50.92302 * DEG2RAD,
11.36788 * DEG2RAD, 0. * DEG2RAD
], MU_SUN, 0., 0., 0., "Churyumov-Gerasimenko")
_mars_rosetta = jpl_lp('mars')
_mars_rosetta.safe_radius = 1.05
_seq_rosetta = [
jpl_lp('earth'),
jpl_lp('earth'), _mars_rosetta,
jpl_lp('earth'),
jpl_lp('earth'), _churyumov
]
class _rosetta_udp(mga_1dsm):
"""
This class represents a rendezvous mission to the comet 67P/Churyumov-Gerasimenko modelled as an MGA-1DSM transfer.
The fly-by sequence selected (i.e. E-EMEE-C) is similar to the one planned for the spacecraft Rosetta.
The objective function considered is the total mission delta V. No launcher model is employed and a final randezvous
with the comet is included in the delta V computations.
.. note::
A significantly similar version of this problem was part of the no longer maintained GTOP database,
https://www.esa.int/gsp/ACT/projects/gtop/gtop.html. The exact definition is, though, different and results
def plot_planet(plnt,
t0=0,
tf=None,
N=60,
units=1.0,
color='k',
alpha=1.0,
s=40,
legend=(False, False),
axes=None):
"""
ax = plot_planet(plnt, t0=0, tf=None, N=60, units=1.0, color='k', alpha=1.0, s=40, legend=(False, False), axes=None):
- axes: 3D axis object created using fig.gca(projection='3d')
- plnt: pykep.planet object we want to plot
- t0: a pykep.epoch or float (mjd2000) indicating the first date we want to plot the planet position
- tf: a pykep.epoch or float (mjd2000) indicating the final date we want to plot the planet position.
if None this is computed automatically from the orbital period (prone to error for non periodic orbits)
- units: the length unit to be used in the plot
- color: color to use to plot the orbit (passed to matplotlib)
- s: planet size (passed to matplotlib)
- legend 2-D tuple of bool or string: The first element activates the planet scatter plot,
the second to the actual orbit. If a bool value is used, then an automated legend label is generated (if True), if a string is used, the string is the legend. Its also possible but deprecated to use a single bool value. In which case that value is used for both the tuple components.
Plots the planet position and its orbit.
Example::
import pykep as pk
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.gca(projection = '3d')
pl = pk.planet.jpl_lp('earth')
t_plot = pk.epoch(219)
ax = pk.orbit_plots.plot_planet(pl, ax = ax, color='b')
"""
from pykep import MU_SUN, SEC2DAY, epoch, AU, RAD2DEG
from pykep.planet import keplerian
from math import pi, sqrt
import numpy as np
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
if axes is None:
fig = plt.figure()
ax = fig.gca(projection='3d')
else:
ax = axes
if type(t0) is not epoch:
t0 = epoch(t0)
# This is to make the tuple API compatible with the old API
if type(legend) is bool:
legend = (legend, legend)
if tf is None:
# orbit period at epoch
T = plnt.compute_period(t0) * SEC2DAY
else:
if type(tf) is not epoch:
tf = epoch(tf)
T = (tf.mjd2000 - t0.mjd2000)
if T < 0:
raise ValueError("tf should be after t0 when plotting an orbit")
# points where the orbit will be plotted
when = np.linspace(0, T, N)
# Ephemerides Calculation for the given planet
x = np.array([0.0] * N)
y = np.array([0.0] * N)
z = np.array([0.0] * N)
for i, day in enumerate(when):
r, v = plnt.eph(epoch(t0.mjd2000 + day))
x[i] = r[0] / units
y[i] = r[1] / units
z[i] = r[2] / units
# Actual plot commands
if (legend[0] is True):
label1 = plnt.name + " " + t0.__repr__()[0:11]
elif (legend[0] is False):
label1 = None
elif (legend[0] is None):
label1 = None
else:
label1 = legend[0]
if (legend[1] is True):
label2 = plnt.name + " orbit"
elif (legend[1] is False):
label2 = None
elif (legend[1] is None):
label2 = None
else:
label2 = legend[1]
ax.plot(x, y, z, label=label2, c=color, alpha=alpha)
ax.scatter([x[0]], [y[0]], [z[0]],
s=s,
marker='o',
alpha=0.8,
c=[color],
label=label1)
if legend[0] or legend[1]:
ax.legend()
return ax
def fineCorrectionBurn(self):
#Propagate orbit to correction burn 1, then apply it to orbit, then propagage further and check.
#If okay, then add in the manual inclination correction, and then do a final Lambert.
self.AddOrbitPoint("1:319:0:0:0", 14646793325, 9353.0)
rMan1, vMan1 = propagate_lagrangian(self.OrbitPoints[-2].r,
self.OrbitPoints[-2].v,
self.OrbitPoints[-1].t, self.mu_c)
print(str(vMan1))
vMan1 = np.asarray(vMan1)
vMan1 += [-34.8, 35.9,
0] #?????? WTF norm ok but direction is wrong (apprently)
#vMan1 += [-25.87664536,+42.44449723,0]
print(str(vMan1))
self.OrbitPoints[-1].r = rMan1
self.OrbitPoints[-1].r_n = norm(rMan1)
self.OrbitPoints[-1].v = vMan1
self.OrbitPoints[-1].v_n = norm(vMan1)
#Propagate to a new point
self.AddOrbitPoint("1:399:3:33:30", 17739170771, 7823.5)
rCheck, vCheck = propagate_lagrangian(
self.OrbitPoints[-2].r, self.OrbitPoints[-2].v,
self.OrbitPoints[-1].t - self.OrbitPoints[-2].t, self.mu_c)
self.printVect(rCheck, "r2 pred")
self.printVect(vCheck, "v2 pred")
self.deltasAtPoint(norm(rCheck), norm(vCheck),
(self.OrbitPoints[-1].r_n),
(self.OrbitPoints[-1].v_n), "r check fromZero",
"v check fromZero")
self.AddOrbitPoint("1:417:2:58:04", 18284938767, 7574.6)
rCheck, vCheck = propagate_lagrangian(
self.OrbitPoints[-3].r, self.OrbitPoints[-3].v,
self.OrbitPoints[-1].t - self.OrbitPoints[-3].t, self.mu_c)
self.printVect(rCheck, "r3 pred")
self.printVect(vCheck, "v3 pred")
self.deltasAtPoint(norm(rCheck), norm(vCheck),
(self.OrbitPoints[-1].r_n),
(self.OrbitPoints[-1].v_n), "r check2 fromZero",
"v check2 fromZero")
#self.OrbitPoints[-3].r, self.OrbitPoints[-3].v = propagate_lagrangian(self.OrbitPoints[-3].r,self.OrbitPoints[-3].v,0,self.mu_c)
plt.rcParams['savefig.dpi'] = 100
ManT = 76 # manoevre time
ManT_W = 5 # manoevre window
Edy2s = 24 * 3600
dy2s = 6 * 3600
start_epochs = np.arange(ManT, (ManT + ManT_W), 0.25)
ETA = 20
ETA_W = 200
duration = np.arange(ETA, (ETA + ETA_W), 0.25)
data = list()
v_data = list()
r_data = list()
v1_data = list()
for start in start_epochs:
row = list()
v_row = list()
r_row = list()
v1_row = list()
for T in duration:
#Need to replace the kerbin start point by the ship at time t using
r1, v1 = propagate_lagrangian(
self.OrbitPoints[-3].r, self.OrbitPoints[-3].v,
(start) * Edy2s - self.OrbitPoints[-3].t, self.mu_c)
#r1,v1 = Kerbin.eph(epoch(start))
r2, v2 = Duna.eph(epoch(start + T))
l = lambert_problem(r1, r2, T * Edy2s,
Kerbin.mu_central_body) #K day = 6h
DV1 = np.linalg.norm(array(v1) - array(l.get_v1()[0]))
v_DV1 = array(v1) - array(l.get_v1()[0])
#DV2 = np.linalg.norm( array(v2)-array(l.get_v2()[0]) )
#DV1 = max([0,DV1-4000])
#DV = DV1+DV2
DV = DV1
#DV = sqrt(dot(DV1, DV1) + 2 * Kerbin.mu_self / Kerbin.safe_radius) - sqrt(Kerbin.mu_self / Kerbin.safe_radius )
r_row.append(r1)
v1_row.append(v1)
v_row.append(v_DV1)
row.append(DV)
data.append(row)
v_data.append(v_row)
r_data.append(r_row)
v1_data.append(v1_row)
minrows = [min(l) for l in data]
i_idx = np.argmin(minrows)
j_idx = np.argmin(data[i_idx])
best = data[i_idx][j_idx]
v_best = v_data[i_idx][j_idx]
r1 = r_data[i_idx][j_idx]
v1 = v1_data[i_idx][j_idx]
progrd_uv = -array(v1) / linalg.norm(v1)
plane_uv = cross(v1, r1)
plane_uv = plane_uv / linalg.norm(plane_uv)
radial_uv = cross(plane_uv, progrd_uv)
EJBK = sqrt(
dot(v_best, v_best) + 2 * Kerbin.mu_central_body /
norm(r1)) - sqrt(Kerbin.mu_central_body / norm(r1))
progrd_v = dot(progrd_uv, v_best)
radial_v = dot(radial_uv, v_best)
#print(rad2deg(atan(radial_v/progrd_v)))
print("TransX escape plan - Kerbin escape")
print("--------------------------------------")
print("Best DV: " + str(best))
print("Best DV heliocentric components:" + str(v_best))
print("Launch epoch (K-days): " + str((start_epochs[i_idx]) * 4))
print("Duration (K-days): " + str(duration[j_idx] * 4))
print("Prograde: %10.3f m/s" %
np.round(dot(progrd_uv, v_best), 3))
print("Radial: %10.3f m/s" %
np.round(dot(radial_uv, v_best), 3))
print("Planar: %10.3f m/s" %
np.round(dot(plane_uv, v_best), 3))
print("Hyp. excess velocity:%10.3f m/s" %
np.round(sqrt(dot(v_best, v_best)), 3))
#print("Earth escape burn: %10.3f m/s" % np.round(EJBK, 3))
duration_pl, start_epochs_pl = np.meshgrid(duration, start_epochs)
plt.contour(start_epochs_pl * 4,
duration_pl * 4,
array(data),
levels=list(np.linspace(best, 3000, 12)))
#plt.imshow(array(data).T, cmap=plt.cm.rainbow, origin = "lower", vmin = best, vmax = 5000, extent=[0.0, 850, 10, 470.0], interpolation='bilinear')
#plt.colorbar(im);
plt.colorbar()
plt.show()
def fitness(self, x):
from pykep import epoch, AU, EARTH_VELOCITY, fb_con
from pykep.sims_flanagan import leg, sc_state
from numpy.linalg import norm
from math import sqrt, asin, acos
retval = [-x[10]]
# Ephemerides
t_E = epoch(x[0])
t_V = epoch(x[0] + x[1])
t_M = epoch(x[0] + x[1] + x[9])
rE, vE = self.__earth.eph(t_E)
rV, vV = self.__venus.eph(t_V)
rM, vM = self.__mercury.eph(t_M)
# First Leg
v = [a + b for a, b in zip(vE, x[3:6])]
x0 = sc_state(rE, v, self.__sc.mass)
v = [a + b for a, b in zip(vV, x[6:9])]
xe = sc_state(rV, v, x[2])
self.__leg1.set(
t_E, x0, x[-3 * (self.__nseg1 + self.__nseg2):-self.__nseg2 * 3], t_V, xe)
# Second leg
v = [a + b for a, b in zip(vV, x[11:14])]
x0 = sc_state(rV, v, x[2])
v = [a + b for a, b in zip(vM, x[14:17])]
xe = sc_state(rM, v, x[10])
self.__leg2.set(t_E, x0, x[(-3 * self.__nseg2):], t_V, xe)
# Defining the constraints
# departure
v_dep_con = (x[3] * x[3] + x[4] * x[4] + x[5] * x[5] -
self.__Vinf_dep * self.__Vinf_dep) / (EARTH_VELOCITY * EARTH_VELOCITY)
# arrival
v_arr_con = (x[14] * x[14] + x[15] * x[15] + x[16] * x[16] -
self.__Vinf_arr * self.__Vinf_arr) / (EARTH_VELOCITY * EARTH_VELOCITY)
# fly-by at Venus
DV_eq, alpha_ineq = fb_con(x[6:9], x[11:14], self.__venus)
# Assembling the constraints
constraints = list(self.__leg1.mismatch_constraints() + self.__leg2.mismatch_constraints()) + [DV_eq] + list(
self.__leg1.throttles_constraints() + self.__leg2.throttles_constraints()) + [v_dep_con] + [v_arr_con] + [alpha_ineq]
# We then scale all constraints to non-dimensional values
# leg 1
constraints[0] /= AU
constraints[1] /= AU
constraints[2] /= AU
constraints[3] /= EARTH_VELOCITY
constraints[4] /= EARTH_VELOCITY
constraints[5] /= EARTH_VELOCITY
constraints[6] /= self.__sc.mass
# leg 2
constraints[7] /= AU
constraints[8] /= AU
constraints[9] /= AU
constraints[10] /= EARTH_VELOCITY
constraints[11] /= EARTH_VELOCITY
constraints[12] /= EARTH_VELOCITY
constraints[13] /= self.__sc.mass
# fly-by at Venus
constraints[14] /= (EARTH_VELOCITY * EARTH_VELOCITY)
return retval + constraints
def fitness(self, x):
from pykep import epoch, AU, EARTH_VELOCITY, fb_con
from pykep.sims_flanagan import leg, sc_state
from numpy.linalg import norm
from math import sqrt, asin, acos
retval = [-x[10]]
# Ephemerides
t_E = epoch(x[0])
t_V = epoch(x[0] + x[1])
t_M = epoch(x[0] + x[1] + x[9])
rE, vE = self.__earth.eph(t_E)
rV, vV = self.__venus.eph(t_V)
rM, vM = self.__mercury.eph(t_M)
# First Leg
v = [a + b for a, b in zip(vE, x[3:6])]
x0 = sc_state(rE, v, self.__sc.mass)
v = [a + b for a, b in zip(vV, x[6:9])]
xe = sc_state(rV, v, x[2])
self.__leg1.set(
t_E, x0, x[-3 * (self.__nseg1 + self.__nseg2):-self.__nseg2 * 3], t_V, xe)
# Second leg
v = [a + b for a, b in zip(vV, x[11:14])]
x0 = sc_state(rV, v, x[2])
v = [a + b for a, b in zip(vM, x[14:17])]
xe = sc_state(rM, v, x[10])
self.__leg2.set(t_E, x0, x[(-3 * self.__nseg2):], t_V, xe)
# Defining the constraints
# departure
v_dep_con = (x[3] * x[3] + x[4] * x[4] + x[5] * x[5] -
self.__Vinf_dep * self.__Vinf_dep) / (EARTH_VELOCITY * EARTH_VELOCITY)
# arrival
v_arr_con = (x[14] * x[14] + x[15] * x[15] + x[16] * x[16] -
self.__Vinf_arr * self.__Vinf_arr) / (EARTH_VELOCITY * EARTH_VELOCITY)
# fly-by at Venus
DV_eq, alpha_ineq = fb_con(x[6:9], x[11:14], self.__venus)
# Assembling the constraints
constraints = list(self.__leg1.mismatch_constraints() + self.__leg2.mismatch_constraints()) + [DV_eq] + list(
self.__leg1.throttles_constraints() + self.__leg2.throttles_constraints()) + [v_dep_con] + [v_arr_con] + [alpha_ineq]
# We then scale all constraints to non-dimensional values
# leg 1
constraints[0] /= AU
constraints[1] /= AU
constraints[2] /= AU
constraints[3] /= EARTH_VELOCITY
constraints[4] /= EARTH_VELOCITY
constraints[5] /= EARTH_VELOCITY
constraints[6] /= self.__sc.mass
# leg 2
constraints[7] /= AU
constraints[8] /= AU
constraints[9] /= AU
constraints[10] /= EARTH_VELOCITY
constraints[11] /= EARTH_VELOCITY
constraints[12] /= EARTH_VELOCITY
constraints[13] /= self.__sc.mass
# fly-by at Venus
constraints[14] /= (EARTH_VELOCITY * EARTH_VELOCITY)
return retval + constraints
def correctionBurn(self):
plt.rcParams['savefig.dpi'] = 100
ManT = 76 # manoevre time
ManT_W = 200 # manoevre window
Edy2s = 24 * 3600
dy2s = 6 * 3600
start_epochs = np.arange(ManT, (ManT + ManT_W), 0.25)
ETA = 250
ETA_W = 250
duration = np.arange(ETA, (ETA + ETA_W), 0.25)
'''
#these are Earth days, to *4 to Kdays (for eph function).
#Sanity checks.
r2,v2 = Duna.eph(epoch(312.8*0.25)) #check jool position
print(norm(r2)-self.r_c)
print(norm(v2))
r1,v1 = propagate_lagrangian(self.OrbitPoints[-1].r,self.OrbitPoints[-1].v,312.8*dy2s,self.mu_c)
print(norm(r1)-self.r_c)
print(norm(v1))
'''
'''
Solving the lambert problem. the function need times in Edays, so convert later to Kdays.
Carefull with the Jool ephemeris, since kerbol year starts at y1 d1, substract 1Kday = 0.25Eday
'''
data = list()
v_data = list()
r_data = list()
v1_data = list()
for start in start_epochs:
row = list()
v_row = list()
r_row = list()
v1_row = list()
for T in duration:
#Need to replace the kerbin start point by the ship at time t using
r1, v1 = propagate_lagrangian(self.OrbitPoints[-1].r,
self.OrbitPoints[-1].v,
(start) * Edy2s, self.mu_c)
#r1,v1 = Kerbin.eph(epoch(start))
r2, v2 = Jool.eph(epoch(start + T))
l = lambert_problem(r1, r2, T * Edy2s,
Kerbin.mu_central_body) #K day = 6h
DV1 = np.linalg.norm(array(v1) - array(l.get_v1()[0]))
v_DV1 = array(v1) - array(l.get_v1()[0])
#DV2 = np.linalg.norm( array(v2)-array(l.get_v2()[0]) )
#DV1 = max([0,DV1-4000])
#DV = DV1+DV2
DV = DV1
#DV = sqrt(dot(DV1, DV1) + 2 * Kerbin.mu_self / Kerbin.safe_radius) - sqrt(Kerbin.mu_self / Kerbin.safe_radius )
r_row.append(r1)
v1_row.append(v1)
v_row.append(v_DV1)
row.append(DV)
data.append(row)
v_data.append(v_row)
r_data.append(r_row)
v1_data.append(v1_row)
minrows = [min(l) for l in data]
i_idx = np.argmin(minrows)
j_idx = np.argmin(data[i_idx])
best = data[i_idx][j_idx]
v_best = v_data[i_idx][j_idx]
r1 = r_data[i_idx][j_idx]
v1 = v1_data[i_idx][j_idx]
progrd_uv = -array(v1) / linalg.norm(v1)
plane_uv = cross(v1, r1)
plane_uv = plane_uv / linalg.norm(plane_uv)
radial_uv = cross(plane_uv, progrd_uv)
EJBK = sqrt(
dot(v_best, v_best) + 2 * Kerbin.mu_central_body /
norm(r1)) - sqrt(Kerbin.mu_central_body / norm(r1))
progrd_v = dot(progrd_uv, v_best)
radial_v = dot(radial_uv, v_best)
#print(rad2deg(atan(radial_v/progrd_v)))
print("TransX escape plan - Kerbin escape")
print("--------------------------------------")
print("Best DV: " + str(best))
print("Best DV heliocentric components:" + str(v_best))
print("Launch epoch (K-days): " + str((start_epochs[i_idx]) * 4))
print("Duration (K-days): " + str(duration[j_idx] * 4))
print("Prograde: %10.3f m/s" %
np.round(dot(progrd_uv, v_best), 3))
print("Radial: %10.3f m/s" %
np.round(dot(radial_uv, v_best), 3))
print("Planar: %10.3f m/s" %
np.round(dot(plane_uv, v_best), 3))
print("Hyp. excess velocity:%10.3f m/s" %
np.round(sqrt(dot(v_best, v_best)), 3))
#print("Earth escape burn: %10.3f m/s" % np.round(EJBK, 3))
duration_pl, start_epochs_pl = np.meshgrid(duration, start_epochs)
plt.contour(start_epochs_pl * 4,
duration_pl * 4,
array(data),
levels=list(np.linspace(best, 3000, 12)))
#plt.imshow(array(data).T, cmap=plt.cm.rainbow, origin = "lower", vmin = best, vmax = 5000, extent=[0.0, 850, 10, 470.0], interpolation='bilinear')
#plt.colorbar(im);
plt.colorbar()
plt.show()
tof=tof,
vinf=[0.5, 2.5],
add_vinf_dep=False,
add_vinf_arr=True,
tof_encoding=tof_encoding,
multi_objective=False)
def get_name(self):
return "Earth-Venus-Earth mga-1dsm (Trajectory Optimisation Gym P7-9)"
def get_extra_info(self):
retval = "\ttof_encoding: " + self._tof_encoding
return retval
def __repr__(self):
return self.get_name()
# Problem P7: Earth-Venus-Earth MGA1DSM, single objective , direct encoding
eve_mga1dsm = _eve_mga1dsm_udp(tof_encoding='direct',
t0=[epoch(0), epoch(3000)],
tof=[[10, 500], [10, 500]])
# Problem P8: Earth-Venus-Earth MGA1DSM, single objective , alpha encoding
eve_mga1dsm_a = _eve_mga1dsm_udp(tof_encoding='alpha',
t0=[epoch(0), epoch(3000)],
tof=[300, 700])
# Problem P9: Earth-Venus-Earth MGA1DSM, single objective , eta encoding
eve_mga1dsm_n = _eve_mga1dsm_udp(tof_encoding='eta',
t0=[epoch(0), epoch(3000)],
tof=700)
def __init__(self,
seq=[pk.planet.jpl_lp('earth'), pk.planet.jpl_lp(
'venus'), pk.planet.jpl_lp('earth')],
t0=[0, 1000],
tof=[100, 500],
vinf=2.5,
multi_objective=False,
tof_encoding='direct',
orbit_insertion=False,
e_target=None,
rp_target=None
):
"""mga(seq=[pk.planet.jpl_lp('earth'), pk.planet.jpl_lp('venus'), pk.planet.jpl_lp('earth')], t0=[0, 1000], tof=[100, 500], vinf=2.5, multi_objective=False, alpha_encoding=False, orbit_insertion=False, e_target=None, rp_target=None)
Args:
- seq (``list of pk.planet``): sequence of body encounters including the starting object
- t0 (``list of pk.epoch``): the launch window
- tof (``list`` or ``float``): bounds on the time of flight. If *tof_encoding* is 'direct', this contains a list
of 2D lists defining the upper and lower bounds on each leg. If *tof_encoding* is 'alpha',
this contains a 2D list with the lower and upper bounds on the time-of-flight. If *tof_encoding*
is 'eta' tof is a float defining the upper bound on the time-of-flight
- vinf (``float``): the vinf provided at launch for free
- multi_objective (``bool``): when True constructs a multiobjective problem (dv, T). In this case, 'alpha' or `eta` encodings are reccomended
- tof_encoding (``str``): one of 'direct', 'alpha' or 'eta'. Selects the encoding for the time of flights
- orbit_insertion (``bool``): when True the arrival dv is computed as that required to acquire a target orbit defined by e_target and rp_target
- e_target (``float``): if orbit_insertion is True this defines the target orbit eccentricity around the final planet
- rp_target (``float``): if orbit_insertion is True this defines the target orbit pericenter around the final planet
Raises:
- ValueError: if *planets* do not share the same central body (checked on the mu_central_body attribute)
- ValueError: if *t0* does not contain objects able to construct a pk.epoch (e.g. pk. epoch or floats)
- ValueError: if *tof* is badly defined
- ValueError: it the target orbit is not defined and *orbit_insertion* is True
"""
# Sanity checks
# 1 - All planets need to have the same mu_central_body
if ([r.mu_central_body for r in seq].count(seq[0].mu_central_body) != len(seq)):
raise ValueError(
'All planets in the sequence need to have exactly the same mu_central_body')
# 2 - We try to build epochs out of the t0 list (mjd2000 by default)
for i in range(len(t0)):
if (type(t0[i]) != type(pk.epoch(0))):
t0[i] = pk.epoch(t0[i])
# 3 - Check the tof bounds
if tof_encoding is 'alpha':
if len(tof) != 2:
raise ValueError(
'When tof_encoding is alpha, the tof must have a length of 2 (lower and upper bounds on the tof)')
elif tof_encoding is 'direct':
if len(tof) != (len(seq) - 1):
raise ValueError(
'When tof_encoding is direct, the tof must be a float (upper bound on the time of flight)' + str(len(seq) - 1))
elif tof_encoding is 'eta':
if type(tof) != type(0.1):
raise ValueError(
'The tof needs to be have len equal to ' + str(len(seq) - 1))
if not tof_encoding in ['alpha', 'eta', 'direct']:
raise ValueError(
"tof_encoding must be one of 'alpha', 'eta', 'direct'")
# 4 - Check that if orbit insertion is selected e_target and r_p are
# defined
if orbit_insertion:
if rp_target is None:
raise ValueError(
'The rp_target needs to be specified when orbit insertion is selected')
if e_target is None:
raise ValueError(
'The e_target needs to be specified when orbit insertion is selected')
# Public data members
self.seq = seq
self.t0 = t0
self.tof = tof
self.vinf = vinf * 1000
self.multi_objective = multi_objective
self.tof_encoding = tof_encoding
self.orbit_insertion = orbit_insertion
self.e_target = e_target
self.rp_target = rp_target
# Private data members
self._n_legs = len(seq) - 1
self._common_mu = seq[0].mu_central_body
def __init__(self,
target=pk.planet.mpcorb(mpcorbline),
n_seg=30,
grid_type="uniform",
t0=[pk.epoch(8000), pk.epoch(9000)],
tof=[200, 365.25 * 3],
m0=20.0,
Tmax=0.0017,
Isp=3000.0,
earth_gravity=False,
sep=False,
start="earth"):
"""
prob = lt_margo(target = pk.planet.mpcorb(mpcorbline), n_seg = 30, grid_type = "uniform", t0 = [epoch(8000), epoch(9000)], tof = [200, 365.25*3], m0 = 20.0, Tmax = 0.0017, Isp = 3000.0, earth_gravity = False, sep = False, start = "earth")
Args:
- target (``pykep.planet``): target planet
- n_seg (``int``): number of segments to use in the problem transcription (time grid)
- grid_type (``string``): "uniform" for uniform segments, "nonuniform" toi use a denser grid in the first part of the trajectory
- t0 (``list``): list of two pykep.epoch defining the bounds on the launch epoch
- tof (``list``): list of two floats defining the bounds on the time of flight (days)
- m0 (``float``): initial mass of the spacecraft (kg)
- Tmax (``float``): maximum thrust at 1 AU (N)
- Isp (``float``): engine specific impulse (s)
- earth_gravity (``bool``): activates the Earth gravity effect in the dynamics
- sep (``bool``): Activates a Solar Electric Propulsion model for the thrust - distance dependency.
- start(``string``): Starting point ("earth", "l1", or "l2").
.. note::
L1 and L2 are approximated as the points on the line connecting the Sun and the Earth at a distance of, respectively, 0.99 and 1.01 AU from the Sun.
.. note::
If the Earth's gravity is enabled, the starting point cannot be the Earth
"""
# 1) Various checks
if start not in ["earth", "l1", "l2"]:
raise ValueError("start must be either 'earth', 'l1' or 'l2'")
if grid_type not in ["uniform", "nonuniform"]:
raise ValueError(
"grid_type must be either 'uniform' or 'nonuniform'")
if earth_gravity and start == "earth":
raise ValueError(
"If Earth gravity is enabled the starting point cannot be the Earth")
# 2) Class data members
# public:
self.target = target
# private:
self.__n_seg = n_seg
self.__grid_type = grid_type
self.__sc = pk.sims_flanagan._sims_flanagan.spacecraft(m0, Tmax, Isp)
self.__earth = pk.planet.jpl_lp('earth')
self.__earth_gravity = earth_gravity
self.__sep = sep
self.__start = start
# grid construction
if grid_type == "uniform":
grid = np.array([i / n_seg for i in range(n_seg + 1)])
elif grid_type == "nonuniform":
grid_f = lambda x: x**2 if x < 0.5 else 0.25 + 1.5 * \
(x - 0.5) # quadratic in [0,0.5], linear in [0.5,1]
grid = np.array([grid_f(i / n_seg) for i in range(n_seg + 1)])
# index corresponding to the middle of the transfer
fwd_seg = int(np.searchsorted(grid, 0.5, side='right'))
bwd_seg = n_seg - fwd_seg
fwd_grid = grid[:fwd_seg + 1]
bwd_grid = grid[fwd_seg:]
self.__fwd_seg = fwd_seg
self.__fwd_grid = fwd_grid
self.__fwd_dt = np.array([(fwd_grid[i + 1] - fwd_grid[i])
for i in range(fwd_seg)]) * pk.DAY2SEC
self.__bwd_seg = bwd_seg
self.__bwd_grid = bwd_grid
self.__bwd_dt = np.array([(bwd_grid[i + 1] - bwd_grid[i])
for i in range(bwd_seg)]) * pk.DAY2SEC
# 3) Bounds
lb = [t0[0].mjd2000] + [tof[0]] + [0] + [-1, -1, -1] * n_seg
ub = [t0[1].mjd2000] + [tof[1]] + [m0] + [1, 1, 1] * n_seg
self.__lb = lb
self.__ub = ub
def plot_traj(self, x, units=pk.AU, plot_segments=True, plot_thrusts=False, axes=None):
"""
ax = prob.plot_traj(self, x, units=AU, plot_segments=True, plot_thrusts=False, axes=None)
Args:
- x (``list``, ``tuple``, ``numpy.ndarray``): Decision chromosome, e.g. (``pygmo.population.champion_x``).
- units (``float``): the length unit to be used in the plot
- plot_segments (``bool``): when True plots also the segments boundaries
- plot_thrusts (``bool``): when True plots also the thrust vectors
Returns:
matplotlib.axes: axes where to plot
Visualizes the the trajectory in a 3D plot
"""
if not len(x) == len(self.get_bounds()[0]):
raise ValueError("Invalid length of the decision vector x")
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Creating the axes if necessary
if axes is None:
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
axes = fig.gca(projection='3d')
n_seg = self.__n_seg
fwd_seg = self.__fwd_seg
bwd_seg = self.__bwd_seg
t0 = x[0]
T = x[1]
isp = self.__sc.isp
veff = isp * pk.G0
fwd_grid = t0 + T * self.__fwd_grid # days
bwd_grid = t0 + T * self.__bwd_grid # days
throttles = [x[3 + 3 * i: 6 + 3 * i] for i in range(n_seg)]
alphas = [min(1., np.linalg.norm(t)) for t in throttles]
times = np.concatenate((fwd_grid, bwd_grid))
rfwd, rbwd, vfwd, vbwd, mfwd, mbwd, ufwd, ubwd, fwd_dt, bwd_dt, dfwd, dbwd = self._propagate(
x)
# Plotting the Sun, the Earth and the target
axes.scatter([0], [0], [0], color='y')
pk.orbit_plots.plot_planet(self.__earth, pk.epoch(
t0), units=units, legend=True, color=(0.7, 0.7, 1), ax=axes)
pk.orbit_plots.plot_planet(self.target, pk.epoch(
t0 + T), units=units, legend=True, color=(0.7, 0.7, 1), ax=axes)
# Forward propagation
xfwd = [0.0] * (fwd_seg + 1)
yfwd = [0.0] * (fwd_seg + 1)
zfwd = [0.0] * (fwd_seg + 1)
xfwd[0] = rfwd[0][0] / units
yfwd[0] = rfwd[0][1] / units
zfwd[0] = rfwd[0][2] / units
for i in range(fwd_seg):
if self.__sep:
r = math.sqrt(rfwd[i][0]**2 + rfwd[i][1]
** 2 + rfwd[i][2]**2) / pk.AU
_, isp = self._sep_model(r)
veff = isp * pk.G0
if self.__earth_gravity:
r3 = sum([r**2 for r in dfwd[i]])**(3 / 2)
disturbance = [mfwd[i] * pk.MU_EARTH /
r3 * ri for ri in dfwd[i]]
pk.orbit_plots.plot_taylor_disturbance(rfwd[i], vfwd[i], mfwd[i], ufwd[i], disturbance, fwd_dt[
i], pk.MU_SUN, veff, N=10, units=units, color=(alphas[i], 0, 1 - alphas[i]), ax=axes)
else:
pk.orbit_plots.plot_taylor(rfwd[i], vfwd[i], mfwd[i], ufwd[i], fwd_dt[
i], pk.MU_SUN, veff, N=10, units=units, color=(alphas[i], 0, 1 - alphas[i]), ax=axes)
xfwd[i + 1] = rfwd[i + 1][0] / units
yfwd[i + 1] = rfwd[i + 1][1] / units
zfwd[i + 1] = rfwd[i + 1][2] / units
if plot_segments:
axes.scatter(xfwd[:-1], yfwd[:-1], zfwd[:-1],
label='nodes', marker='o', s=5, c='k')
# Backward propagation
xbwd = [0.0] * (bwd_seg + 1)
ybwd = [0.0] * (bwd_seg + 1)
zbwd = [0.0] * (bwd_seg + 1)
xbwd[-1] = rbwd[-1][0] / units
ybwd[-1] = rbwd[-1][1] / units
zbwd[-1] = rbwd[-1][2] / units
for i in range(bwd_seg):
if self.__sep:
r = math.sqrt(rbwd[-i - 1][0]**2 + rbwd[-i - 1]
[1]**2 + rbwd[-i - 1][2]**2) / pk.AU
_, isp = self._sep_model(r)
veff = isp * pk.G0
if self.__earth_gravity:
r3 = sum([r**2 for r in dbwd[-i - 1]])**(3 / 2)
disturbance = [mfwd[i] * pk.MU_EARTH /
r3 * ri for ri in dbwd[-i - 1]]
pk.orbit_plots.plot_taylor_disturbance(rbwd[-i - 1], vbwd[-i - 1], mbwd[-i - 1], ubwd[-i - 1], disturbance, -bwd_dt[
-i - 1], pk.MU_SUN, veff, N=10, units=units, color=(alphas[-i - 1], 0, 1 - alphas[-i - 1]), ax=axes)
else:
pk.orbit_plots.plot_taylor(rbwd[-i - 1], vbwd[-i - 1], mbwd[-i - 1], ubwd[-i - 1], -bwd_dt[-i - 1],
pk.MU_SUN, veff, N=10, units=units, color=(alphas[-i - 1], 0, 1 - alphas[-i - 1]), ax=axes)
xbwd[-i - 2] = rbwd[-i - 2][0] / units
ybwd[-i - 2] = rbwd[-i - 2][1] / units
zbwd[-i - 2] = rbwd[-i - 2][2] / units
if plot_segments:
axes.scatter(xbwd[1:], ybwd[1:], zbwd[1:], marker='o', s=5, c='k')
# Plotting the thrust vectors
if plot_thrusts:
throttles = np.array(throttles)
xlim = axes.get_xlim()
xrange = xlim[1] - xlim[0]
ylim = axes.get_ylim()
yrange = ylim[1] - ylim[0]
zlim = axes.get_zlim()
zrange = zlim[1] - zlim[0]
scale = 0.1
throttles[:, 0] *= xrange
throttles[:, 1] *= yrange
throttles[:, 2] *= zrange
throttles *= scale
for (x, y, z, t) in zip(xfwd[:-1] + xbwd[:-1], yfwd[:-1] + ybwd[:-1], zfwd[:-1] + zbwd[:-1], throttles):
axes.plot([x, x + t[0]], [y, y + t[1]], [z, z + t[2]], c='g')
return axes
# http://en.wikipedia.org/wiki/Orbital_period#Calculation
# Instantiating PyKEP planet objects for each of the bodies (and making them indexable by body name)
# http://keptoolbox.sourceforge.net/documentation.html#PyKEP.planet
_planet = pk.planet.keplerian
### Problem is with the planet consutrctor or smth.
body_obj = OrderedDict([
(
_b.body,
_planet(
# when
# a PyKEP.epoch indicating the orbital elements epoch
pk.epoch(_b.Epoch), #, pk.epoch.epoch_type.MJD ),
# orbital_elements
# a sequence of six containing a,e,i,W,w,M (SI units, i.e. meters and radiants)
(
_b.a * 1000.,
_b.e,
radians(_b.i),
radians(_b.Node),
radians(_b.w),
_b.M,
),
# mu_central_body
# gravity parameter of the central body (SI units, i.e. m^2/s^3)
_b.mu_c * 1000.**3,
# asteroid. The second asteroid encounter (fly-by) corresponds to the delivery # of a 1 kg penetrator." MASS_EQUIPMENT = 40.0 MASS_PENETRATOR = 1.0 # constraint for the flyby to deliver the penetrator: # "flyby asteroid with a velocity not less than dV_min = 0.4 km/s." dV_fb_min = 400.0 # m/s # "The flight time, measured from start to the end must not exceed 15 years" TIME_MAX = 15 * YEAR2DAY # 5478.75 days # "The year of launch must lie in the range 2015 to 2025, inclusive: # 57023 MJD <= t_s <= 61041 MJD." TRAJ_START_MIN = pk.epoch(57023, "mjd") TRAJ_START_MAX = pk.epoch(61041, "mjd") # >>> TRAJ_START_MIN, TRAJ_START_MAX # (2015-Jan-01 00:00:00, 2026-Jan-01 00:00:00) TRAJ_END_MIN = pk.epoch(TRAJ_START_MIN.mjd + TIME_MAX, "mjd") TRAJ_END_MAX = pk.epoch(TRAJ_START_MAX.mjd + TIME_MAX, "mjd") # >>> TRAJ_END_MIN, TRAJ_END_MAX # (2029-Dec-31 18:00:00, 2040-Dec-31 18:00:00) # ==================================== # GTOC5 asteroids (body objects) # Earth's Keplerian orbital parameters # Source: http://dx.doi.org/10.2420/AF08.2014.9 (Table 1) _earth_op = (
def lpop_single(target_mjd):
return pykep_satellite.eph(pykep.epoch(target_mjd, 'mjd'))
for qq in range(1, numOfExperiments + 1):
satcat = loadData_satcat()
debris = loadData_tle()
# list of all EU states and ESA and its bodies
EU_list = [
"EU", "ASRA", "BEL", "CZCH", "DEN", "ESA", "ESRO", "EST", "EUME",
"EUTE", "FGER", "FR", "FRIT", "GER", "GREC", "HUN", "IT", "LTU",
"LUXE", "NETH", "NOR", "POL", "POR", "SPN", "SWED", "SWTZ", "UK"
]
agentList = ['CIS', 'US', 'PRC', 'EU']
agentListWithEU = ['CIS', 'US', 'PRC', EU_list]
# filtering only objects in LEO
debris_LEO = []
ep = kep.epoch(16. * 365.25)
for p in debris:
try:
oe = p.osculating_elements(ep)
if oe[0] * (1 - oe[1]) < 6378000.0 + 2000000:
debris_LEO.append(p)
except:
pass
debris = debris_LEO
logger.info(len(debris))
# osculating elements distributions
oe_histograms = []
oe_histograms = findHistograms(agentListWithEU)
yearLaunchesDistributions = oe_histograms[10]
def _propagate(self, x):
# 1 - We decode the chromosome
t0 = x[0]
T = x[1]
m_f = x[2]
# We extract the number of segments for forward and backward
# propagation
n_seg = self.__n_seg
fwd_seg = self.__fwd_seg
bwd_seg = self.__bwd_seg
# We extract information on the spacecraft
m_i = self.__sc.mass
max_thrust = self.__sc.thrust
isp = self.__sc.isp
veff = isp * pk.G0
# And on the leg
throttles = [x[3 + 3 * i: 6 + 3 * i] for i in range(n_seg)]
# Return lists
n_points_fwd = fwd_seg + 1
n_points_bwd = bwd_seg + 1
rfwd = [[0.0] * 3] * (n_points_fwd)
vfwd = [[0.0] * 3] * (n_points_fwd)
mfwd = [0.0] * (n_points_fwd)
ufwd = [[0.0] * 3] * (fwd_seg)
dfwd = [[0.0] * 3] * (fwd_seg) # distances E/S
rbwd = [[0.0] * 3] * (n_points_bwd)
vbwd = [[0.0] * 3] * (n_points_bwd)
mbwd = [0.0] * (n_points_bwd)
ubwd = [[0.0] * 3] * (bwd_seg)
dbwd = [[0.0] * 3] * (bwd_seg)
# 2 - We compute the initial and final epochs and ephemerides
t_i = pk.epoch(t0)
r_i, v_i = self.__earth.eph(t_i)
if self.__start == 'l1':
r_i = [r * 0.99 for r in r_i]
v_i = [v * 0.99 for v in v_i]
elif self.__start == 'l2':
r_i = [r * 1.01 for r in r_i]
v_i = [v * 1.01 for v in v_i]
t_f = pk.epoch(t0 + T)
r_f, v_f = self.target.eph(t_f)
# 3 - Forward propagation
fwd_grid = t0 + T * self.__fwd_grid # days
fwd_dt = T * self.__fwd_dt # seconds
# Initial conditions
rfwd[0] = r_i
vfwd[0] = v_i
mfwd[0] = m_i
# Propagate
for i, t in enumerate(throttles[:fwd_seg]):
if self.__sep:
r = math.sqrt(rfwd[i][0]**2 + rfwd[i][1]
** 2 + rfwd[i][2]**2) / pk.AU
max_thrust, isp = self._sep_model(r)
veff = isp * pk.G0
ufwd[i] = [max_thrust * thr for thr in t]
if self.__earth_gravity:
r_E, v_E = self.__earth.eph(pk.epoch(fwd_grid[i]))
dfwd[i] = [a - b for a, b in zip(r_E, rfwd[i])]
r3 = sum([r**2 for r in dfwd[i]])**(3 / 2)
disturbance = [mfwd[i] * pk.MU_EARTH /
r3 * ri for ri in dfwd[i]]
rfwd[i + 1], vfwd[i + 1], mfwd[i + 1] = pk.propagate_taylor_disturbance(
rfwd[i], vfwd[i], mfwd[i], ufwd[i], disturbance, fwd_dt[i], pk.MU_SUN, veff, -10, -10)
else:
rfwd[i + 1], vfwd[i + 1], mfwd[i + 1] = pk.propagate_taylor(
rfwd[i], vfwd[i], mfwd[i], ufwd[i], fwd_dt[i], pk.MU_SUN, veff, -10, -10)
# 4 - Backward propagation
bwd_grid = t0 + T * self.__bwd_grid # days
bwd_dt = T * self.__bwd_dt # seconds
# Final conditions
rbwd[-1] = r_f
vbwd[-1] = v_f
mbwd[-1] = m_f
# Propagate
for i, t in enumerate(throttles[-1:-bwd_seg - 1:-1]):
if self.__sep:
r = math.sqrt(rbwd[-i - 1][0]**2 + rbwd[-i - 1]
[1]**2 + rbwd[-i - 1][2]**2) / pk.AU
max_thrust, isp = self._sep_model(r)
veff = isp * pk.G0
ubwd[-i - 1] = [max_thrust * thr for thr in t]
if self.__earth_gravity:
r_E, v_E = self.__earth.eph(pk.epoch(bwd_grid[-i - 1]))
dbwd[-i - 1] = [a - b for a, b in zip(r_E, rbwd[-i - 1])]
r3 = sum([r**2 for r in dbwd[-i - 1]])**(3 / 2)
disturbance = [mfwd[i] * pk.MU_EARTH /
r3 * ri for ri in dbwd[-i - 1]]
rbwd[-i - 2], vbwd[-i - 2], mbwd[-i - 2] = pk.propagate_taylor_disturbance(
rbwd[-i - 1], vbwd[-i - 1], mbwd[-i - 1], ubwd[-i - 1], disturbance, -bwd_dt[-i - 1], pk.MU_SUN, veff, -10, -10)
else:
rbwd[-i - 2], vbwd[-i - 2], mbwd[-i - 2] = pk.propagate_taylor(
rbwd[-i - 1], vbwd[-i - 1], mbwd[-i - 1], ubwd[-i - 1], -bwd_dt[-i - 1], pk.MU_SUN, veff, -10, -10)
return rfwd, rbwd, vfwd, vbwd, mfwd, mbwd, ufwd, ubwd, fwd_dt, bwd_dt, dfwd, dbwd
def from_mjd_to_datetime(mjd_date):
e = pykep.epoch(mjd_date, 'mjd')
return datetime.datetime.strptime(str(e), '%Y-%b-%d %H:%M:%S.%f')
self.SMA = (self.Pe + self.Ap) / 2
self.e = 1 - 2 * self.Pe / (self.Pe + self.Ap)
self.t0 = 0
self.t1 = 0
self.r0 = 0
self.r1 = 0
self.v0 = 0
self.v1 = 0
self.N = sqrt(Kerbin.mu_c / (self.SMA ^ 3))
def setT0(self, year, day, hour, minute, second):
h2s = 3600
d2s = 6 * h2s
y2s = 2556.5 * h2s
self.t0 = year * y2s + day * d2s + hour * h2s + minute * 60 + second
def setT1(self, year, day, hour, minute, second):
h2s = 3600
d2s = 6 * h2s
y2s = 2556.5 * h2s
self.t0 = year * y2s + day * d2s + hour * h2s + minute * 60 + second
def EccAnom(self):
K = PI / 180
max_i = 50
i = 0
delta = 0.0001
plot_innerKerbol(epoch(100))