def test_hyperbolic_orbits_2(self):
# same orbit pulled from KSP at different times
# lots of jitter, so tolerance for radius at time
# is quite large (1%)
o1 = Orbit(
i = 0.14149227768205455,
Ω = 3.977254620789031,
ω = 0.22395653996553322,
e = 1.0779572208620696,
a = -8855744.039847286,
M0 = -6.0078569863130475,
t0 = 34056398.642449796,
body = CelestialBody(
name = 'Kerbin',
equatorial_radius = 600000.0,
gravitational_parameter = 3531600035840.0,
rotational_speed = 0.0002908894093707204,
),
)
o2 = Orbit(
i = 0.14311811324451928,
Ω = 3.9859850089566558,
ω = 0.21660875587091438,
e = 1.07736727693924,
a = -8880527.593801336,
M0 = -0.012499818155590287,
t0 = 34140525.998688884,
body = CelestialBody(
name = 'Kerbin',
equatorial_radius = 600000.0,
gravitational_parameter = 3531600035840.0,
rotational_speed = 0.0002908894093707204,
),
)
tpe1 = o1.time_to_periapsis_at_epoch
tpe2 = o2.time_to_periapsis_at_epoch
self.isclose(o1.epoch+tpe1, o2.epoch+tpe2)
χexpect1 = np.sqrt(-o1.a) \
* (o1.eccentric_anomaly_at_time(o1.epoch+tpe1) \
- o1.eccentric_anomaly_at_epoch)
χexpect2 = np.sqrt(-o2.a) \
* (o2.eccentric_anomaly_at_time(o2.epoch+tpe2) \
- o2.eccentric_anomaly_at_epoch)
tt = np.linspace(o1.epoch,o1.epoch+1*6*60*60,50)
χχexpect = np.sqrt(-o1.a) \
* (o1.eccentric_anomaly_at_time(tt) \
- o1.eccentric_anomaly_at_epoch)
χχ = o1.universal_anomaly_at_time(tt)
t0 = o1.epoch
r0 = o1.radius_at_epoch
vr0 = o1.radial_speed_at_epoch
a = o1.semi_major_axis
x0 = o1.position_at_epoch/km
v0 = o1.velocity_at_epoch
#print('t0',t0)
#print('r0',r0)
#print('vr0',vr0)
#print('a',a)
#print('x0',x0)
#print('v0',v0)
#from matplotlib import pyplot
#pyplot.plot(tt,χχexpect, color='blue')
#pyplot.plot(tt[[0,-1]],χχexpect[[0,-1]], color='lightblue')
#pyplot.plot(tt,χχ, color='red')
#pyplot.plot(tt[[0,-1]],χχ[[0,-1]], color='pink')
#pyplot.show()
#print('θpe1',o1.true_anomaly_at_time(o1.epoch+tpe1))
#print('θpe2',o2.true_anomaly_at_time(o2.epoch+tpe2))
#print('rpeθ1',o1.radius_at_true_anomaly(
#o1.true_anomaly_at_time(o1.epoch+tpe1)))
#print('rpeθ2',o2.radius_at_true_anomaly(
#o2.true_anomaly_at_time(o2.epoch+tpe2)))
f1,g1,df1,dg1 = o1.lagrange_coefficients_at_time(o1.epoch+tpe1)
f2,g2,df2,dg2 = o2.lagrange_coefficients_at_time(o2.epoch+tpe2)
x1 = o1.position_at_lagrange_coefficients(f1,g1)
x2 = o2.position_at_lagrange_coefficients(f2,g2)
#print('rpe1(χexp)',np.sqrt(sum(x1**2)))
#print('rpe2(χexp)',np.sqrt(sum(x2**2)))
self.isclose(o2.periapsis, o2.radius_at_time(o2.epoch+tpe2))
self.isclose(o1.periapsis, o1.radius_at_time(o1.epoch+tpe1))
self.isclose(o1.periapsis, o2.periapsis,rtol=1e-2)
self.isclose(o1.radius_at_time(o1.epoch+tpe1),
o2.radius_at_time(o2.epoch+tpe2),
rtol=0.01)
