def test_orbit_inverse_error_kep(self):
mjd0 = dpt.jd_to_mjd(2457126.2729)
orb_init_list = self._gen_orbits(1000)
prop = PropagatorKepler(
in_frame='EME',
out_frame='EME',
)
t = n.array([0.0])
for kep in orb_init_list:
state_ref = dpt.kep2cart(kep, m=self.init_data['m'], M_cent=M_earth, radians=False)
state = prop.get_orbit(
t=t, mjd0=mjd0,
a=kep[0], e=kep[1], inc=kep[2],
raan=kep[4], aop=kep[3], mu0=dpt.true2mean(kep[5], kep[1], radians=False),
m=self.init_data['m'],
radians=False,
)
state_diff1 = n.abs(state_ref - state[:,0])
nt.assert_array_less(state_diff1[:3], n.full((3,), 1e-5, dtype=state_diff1.dtype))
nt.assert_array_less(state_diff1[3:], n.full((3,), 1e-7, dtype=state_diff1.dtype))
def test_orbit_inverse_error_cart(self):
mjd0 = dpt.jd_to_mjd(2457126.2729)
orb_init_list = self._gen_orbits(1000)
prop = PropagatorKepler(
in_frame='TEME',
out_frame='TEME',
)
t = n.array([0.0])
orbs_done = 0
for kep in orb_init_list:
state_ref = dpt.kep2cart(kep, m=self.init_data['m'], M_cent=M_earth, radians=False)
state = prop.get_orbit_cart(
t=t, mjd0=mjd0,
x=state_ref[0], y=state_ref[1], z=state_ref[2],
vx=state_ref[3], vy=state_ref[4], vz=state_ref[5],
m=self.init_data['m'],
)
state_diff1 = n.abs(state_ref - state[:,0])
if n.linalg.norm(state_diff1[:3]) > 1e-5:
print(kep)
print(orbs_done)
nt.assert_array_less(state_diff1[:3], n.full((3,), 1e-5, dtype=state_diff1.dtype))
nt.assert_array_less(state_diff1[3:], n.full((3,), 1e-7, dtype=state_diff1.dtype))
orbs_done += 1
def test_TEME_to_TLE(self):
import propagator_sgp4
mjd0 = dpt.jd_to_mjd(2457126.2729)
reci = n.array([
-5339.76186573000,
5721.43584226500,
921.276953805000,
], dtype=n.float)
veci = n.array([
-4.88969089550000,
-3.83304653050000,
3.18013811100000,
], dtype=n.float)
state_TEME = n.concatenate((reci, veci), axis=0)
mean_elements = tle.TEME_to_TLE(state_TEME, mjd0=mjd0, kepler=False)
state = propagator_sgp4.sgp4_propagation(mjd0, mean_elements, B=0, dt=0.0)
state_diff = n.abs(state - state_TEME)*1e3
nt.assert_array_less(state_diff[:3], n.full((3,), 1e-2, dtype=state_diff.dtype))
nt.assert_array_less(state_diff[3:], n.full((3,), 1e-5, dtype=state_diff.dtype))
def test_orbit_kep_cart_correspondance(self):
mjd0 = dpt.jd_to_mjd(2457126.2729)
orb_init_list = self._gen_orbits(100)
prop = PropagatorKepler(
in_frame='EME',
out_frame='EME',
)
t = n.linspace(0, 12*3600, num=100, dtype=n.float)
for kep in orb_init_list:
state_ref = dpt.kep2cart(kep, m=self.init_data['m'], M_cent=M_earth, radians=False)
state_kep = prop.get_orbit(
t=t, mjd0=mjd0,
a=kep[0], e=kep[1], inc=kep[2],
raan=kep[4], aop=kep[3], mu0=dpt.true2mean(kep[5], kep[1], radians=False),
m=self.init_data['m'],
radians=False,
)
state_cart = prop.get_orbit_cart(
t=t, mjd0=mjd0,
x=state_ref[0], y=state_ref[1], z=state_ref[2],
vx=state_ref[3], vy=state_ref[4], vz=state_ref[5],
m=self.init_data['m'],
)
state_diff1 = n.abs(state_kep - state_cart)
nt.assert_array_less(state_diff1[:3,:], n.full((3,t.size), 1e-5, dtype=state_diff1.dtype))
nt.assert_array_less(state_diff1[3:,:], n.full((3,t.size), 1e-7, dtype=state_diff1.dtype))
def test_TEME_to_TLE_cases(self):
import propagator_sgp4
R_E = 6353.0e3
mjd0 = dpt.jd_to_mjd(2457126.2729)
a = R_E*2.0
orb_init_list = []
orb_range = n.array([a, 0.9, 180, 360, 360, 360], dtype=n.float)
orb_offset = n.array([R_E*1.1, 0, 0.0, 0.0, 0.0, 0.0], dtype=n.float)
test_n = 5000
while len(orb_init_list) < test_n:
orb = n.random.rand(6)
orb = orb_offset + orb*orb_range
if orb[0]*(1.0 - orb[1]) > R_E+200e3:
orb_init_list.append(orb)
orb_init_list.append(n.array([R_E*1.2, 0, 0.0, 0.0, 0.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0, 0.0, 0.0, 0.0, 270], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 1e-9, 0.0, 0.0, 0.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 0.0, 0.0, 0.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 75.0, 0.0, 0.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 0.0, 120.0, 0.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 0.0, 0.0, 35.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 75.0, 120.0, 0.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 75.0, 0.0, 35.0, 0.0], dtype=n.float))
orb_init_list.append(n.array([R_E*1.2, 0.1, 75.0, 120.0, 35.0, 0.0], dtype=n.float))
fail_inds = []
errs = n.empty((len(orb_init_list),2), dtype=n.float)
for ind, kep in enumerate(orb_init_list):
M_earth = propagator_sgp4.SGP4.GM*1e9/consts.G
state_TEME = dpt.kep2cart(kep, m=0.0, M_cent=M_earth, radians=False)*1e-3
mean_elements = tle.TEME_to_TLE(state_TEME, mjd0=mjd0, kepler=False)
state = propagator_sgp4.sgp4_propagation(mjd0, mean_elements, B=0, dt=0.0)
state_diff = n.abs(state - state_TEME)*1e3
er_r = n.linalg.norm(state_diff[:3])
er_v = n.linalg.norm(state_diff[3:])
errs[ind,0] = er_r
errs[ind,1] = er_v
try:
self.assertLess(er_r, 10.0)
self.assertLess(er_v, 1.0)
except AssertionError as err:
fail_inds.append(ind)
if len(fail_inds) > 0:
print('FAIL / TOTAL: {} / {}'.format(len(fail_inds), len(orb_init_list)))
self.assertLess(n.median(errs[:,0]), 1e-2)
self.assertLess(n.median(errs[:,1]), 1e-4)
assert len(fail_inds) < float(test_n)/100.
def TLE_propagation_TEME(line1, line2, jd_ut1, wgs='72'):
'''Convert Two-line element to TEME coordinates at a specific Julian date.
:param str line1: TLE line 1
:param str line2: TLE line 2
:param float/numpy.ndarray jd_ut1: Julian Date UT1 to propagate TLE to.
:param str wgs: The used WGS standard, options are :code:`'72'` or :code:`'84'`.
:return: (6,len(jd_ut1)) numpy.ndarray of Cartesian states [SI units]
'''
if wgs == '72':
from sgp4.earth_gravity import wgs72 as wgs_sys
elif wgs == '84':
from sgp4.earth_gravity import wgs84 as wgs_sys
else:
raise Exception('WGS standard "{}" not recognized.'.format(wgs))
satellite = twoline2rv(line1, line2, wgs_sys)
if isinstance(jd_ut1, np.ndarray):
if len(jd_ut1.shape) > 1:
raise Exception(
'Only 1-D date array allowed: jd_ut1.shape = {}'.format(
jd_ut1.shape))
else:
if isinstance(jd_ut1, float):
jd_ut1 = np.array([jd_ut1], dtype=np.float)
states = np.empty((6, jd_ut1.size), dtype=np.float)
for jdi in range(jd_ut1.size):
year, month, day, hour, minute, second, usec = dpt.npdt2date(
dpt.mjd2npdt(dpt.jd_to_mjd(jd_ut1[jdi])))
position, velocity = satellite.propagate(
year=year,
month=month,
day=day,
hour=hour,
minute=minute,
second=second + usec * 1e-6,
)
state = np.array(position + velocity, dtype=np.float)
state *= 1e3 #km to m
states[:, jdi] = state
return states
def gen_pop():
mass=0.8111E+04
diam=0.8960E+01
m_to_A=128.651
a=7159.5
e=0.0001
i=98.55
raan=248.99
aop=90.72
M=47.37
A=mass/m_to_A
# epoch in unix seconds
ut0=1241136000.0
# modified julian day epoch
mjd0 = dpt.jd_to_mjd(dpt.unix_to_jd(ut0))
print('\n' + 'mjd0: {}'.format(mjd0) + '\n')
_params = ['A', 'm', 'd', 'C_D', 'C_R']
pop = Population(
name='ENVISAT',
extra_columns = _params,
space_object_uses = [True]*len(_params),
#propagator = PropagatorNeptune,
propagator = PropagatorSGP4,
propagator_options = {
'out_frame': 'ITRF'
},
)
pop.allocate(1)
pop['oid'][0] = 1
pop['a'][0] = a
pop['e'][0] = e
pop['i'][0] = i
pop['raan'][0] = raan
pop['aop'][0] = aop
pop['mu0'][0] = M
pop['mjd0'][0] = mjd0
pop['A'][0] = A
pop['m'][0] = mass
pop['d'][0] = 1.0
pop['C_D'][0] = 2.3
pop['C_R'][0] = 1.0
return pop
def get_orbit(o):
#print('{:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(*o.tolist()))
ecef = prop.get_orbit(
t=0.0,
a=o[0],
e=o[1],
inc=o[2],
raan=o[4],
aop=o[3],
mu0=dpt.true2mean(o[5], o[1], radians=False),
mjd0=dpt.jd_to_mjd(2457126.2729),
C_D=2.3,
A=1.0,
m=1.0,
)
return ecef
def get_orbit_cart(o):
cart = dpt.kep2cart(o, m=1.0, M_cent=M_earth, radians=False)
#print('{:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}, {:.2f}'.format(*cart.tolist()))
ecef = prop.get_orbit_cart(
t=0.0,
x=cart[0],
y=cart[1],
z=cart[2],
vx=cart[3],
vy=cart[4],
vz=cart[5],
mjd0=dpt.jd_to_mjd(2457126.2729),
C_D=2.3,
A=1.0,
m=1.0,
)
return ecef
def _get_MicrosatR_state(mjd):
tle_raw = '''1 43947U 19006A 19085.80962006 .00050945 29637-5 93510-4 0 9994
2 43947 96.6379 359.2115 0016283 220.5574 212.4511 16.01198803 9765'''
tle_raw = [line.strip() for line in tle_raw.split('\n')]
if len(tle_raw) % 2 != 0:
raise Exception('Not even number of lines [not TLE compatible]')
line1, line2 = tle_raw[0], tle_raw[1]
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
sat_id = tle.tle_id(line1)
state_TEME = tle.TLE_propagation_TEME(line1, line2, dpt.mjd_to_jd(mjd))
return state_TEME, sat_id
def tryActivity():
line1 = '1 43947U 19006A 19069.62495353 .00222140 22344-4 39735-3 0 9995'
line2 = '2 43947 96.6263 343.1609 0026772 226.5664 224.4731 16.01032328 7176'
mjd0 = dpt.jd_to_mjd(tle.tle_jd(line1))
state, epoch = tle.TLE_to_TEME(line1, line2)
x, y, z, vx, vy, vz = state
p1 = PropagatorOrekit(solar_activity_strength='STRONG', )
p2 = PropagatorOrekit(solar_activity_strength='WEAK', )
kwargs = dict(m=800., C_R=1., C_D=2.3, A=4.0)
t = np.linspace(0, 3 * 24 * 3600.0, num=5000, dtype=np.float64)
pv1 = p1.get_orbit_cart(t, x, y, z, vx, vy, vz, mjd0, **kwargs)
pv2 = p2.get_orbit_cart(t, x, y, z, vx, vy, vz, mjd0, **kwargs)
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(211)
ax.plot(t / 3600., np.linalg.norm(pv1[:3, :] - pv2[:3, :], axis=0) * 1e-3)
ax.set(
xlabel='Time [h]',
ylabel='Position difference [km]',
title='Strong vs weak solar activity',
)
ax = fig.add_subplot(212)
ax.plot(t / 3600., np.linalg.norm(pv1[:3, :] * 1e-3, axis=0), label='Weak')
ax.plot(t / 3600.,
np.linalg.norm(pv2[:3, :] * 1e-3, axis=0),
label='Strong')
ax.set(
xlabel='Time [h]',
ylabel='Range [km]',
)
plt.legend()
plt.show()
def get_orbit_TLE(self, t, line1, line2):
'''Takes a TLE and propagates it forward in time directly using the SGP4 algorithm.
:param float/list/numpy.ndarray t: Time in seconds to propagate relative the initial state epoch.
:param str line1: TLE line 1
:param str line2: TLE line 2
'''
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
jddates = jd0 + t/(3600.0*24.0)
states_TEME = tle.TLE_propagation_TEME(line1, line2, jddates)
if self.out_frame == 'TEME':
return states_TEME
elif self.out_frame == 'ITRF':
ecefs = np.empty(states_TEME.shape, dtype=states_TEME.dtype)
if self.polar_motion:
PM_data = tle.get_Polar_Motion(jddates)
for jdi in range(jddates.size):
ecefs[:,jdi] = tle.TEME_to_ITRF(
states_TEME[:,jdi],
jddates[jdi],
PM_data[jdi,0],
PM_data[jdi,1],
)
else:
for jdi in range(jddates.size):
ecefs[:,jdi] = tle.TEME_to_ITRF(
states_TEME[:,jdi],
jddates[jdi],
0.0,
0.0,
)
return ecefs
else:
raise Exception('Output frame {} not found'.format(self.out_frame))
def trySteps():
p = PropagatorOrekit()
line1 = '1 43947U 19006A 19069.62495353 .00222140 22344-4 39735-3 0 9995'
line2 = '2 43947 96.6263 343.1609 0026772 226.5664 224.4731 16.01032328 7176'
mjd0 = dpt.jd_to_mjd(tle.tle_jd(line1))
state, epoch = tle.TLE_to_TEME(line1, line2)
x, y, z, vx, vy, vz = state
kwargs = dict(m=800., C_R=1., C_D=2.3, A=2.0)
t_end = 24 * 3600.0
t_vecs = [
np.linspace(0, t_end, num=ind) for ind in [100, 200, 500, 1000, 10000]
]
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(111)
for t in t_vecs:
pv = p.get_orbit_cart(t, x, y, z, vx, vy, vz, mjd0, **kwargs)
ax.plot(t / 3600.,
np.linalg.norm(pv[:3, :] * 1e-3, axis=0),
label='{} steps @ {} s step size'.format(
len(t), t_end / float(len(t))))
ax.set(
xlabel='Time [h]',
ylabel='Range [km]',
title='Step number difference',
)
plt.legend()
plt.show()
def simulate_Microsat_R_debris(num, max_dv, radii_range, mass_range, C_D_range, seed, propagator, propagator_options, mjd):
tle_raw = '''1 43947U 19006A 19069.62495353 .00222140 22344-4 39735-3 0 9995
2 43947 96.6263 343.1609 0026772 226.5664 224.4731 16.01032328 7176
1 43947U 19006A 19069.68634861 .00217235 21442-4 38851-3 0 9995
2 43947 96.6282 343.2231 0026267 227.5469 217.1086 16.01062873 7183
1 43947U 19006A 19069.81093322 .00066734 37128-5 12017-3 0 9992
2 43947 96.6282 343.3467 0026246 227.0726 215.1493 16.01048534 7204
1 43947U 19006A 19070.54563657 .00111882 69857-5 20248-3 0 9991
2 43947 96.6271 344.0664 0025783 228.7058 125.4384 16.00925959 7318
1 43947U 19006A 19070.80923755 .00013653 19858-5 25403-4 0 9991
2 43947 96.6268 344.3320 0024927 225.1848 207.3499 16.01142181 7364
1 43947U 19006A 19071.12831853 .00154965 11740-4 27719-3 0 9998
2 43947 96.6279 344.6467 0025572 226.6832 243.5148 16.01113233 7412
1 43947U 19006A 19071.80921646 .00118874 76545-5 21379-3 0 9993
2 43947 96.6279 345.3224 0025491 224.0468 208.0149 16.01043615 7523
1 43947U 19006A 19072.44153689 .00037015 24665-5 66863-4 0 9999
2 43947 96.6279 345.9498 0025442 221.6016 252.7257 16.01159223 7620
1 43947U 19006A 19072.74405995 .00022228 21113-5 40609-4 0 9997
2 43947 96.6279 346.2500 0025432 220.3955 196.4626 16.01147542 7675
1 43947U 19006A 19073.12825718 .00008242 19370-5 15873-4 0 9996
2 43947 96.6304 346.6133 0023002 223.0617 246.6563 16.01114545 7738
1 43947U 19006A 19073.44154376 .00035052 24102-5 63784-4 0 9998
2 43947 96.6300 346.9244 0022588 225.2511 249.1037 16.01188227 7782
1 43947U 19006A 19073.80920446 -.00023017 21041-5 -40417-4 0 9994
2 43947 96.6337 347.2908 0022039 223.4278 208.5467 16.01076766 7841
1 43947U 19006A 19074.74408622 .00119327 76982-5 21614-3 0 9994
2 43947 96.6289 348.2321 0022856 222.6623 194.1149 16.01049775 7992
1 43947U 19006A 19075.01672454 .00167822 13470-4 30180-3 0 9994
2 43947 96.6290 348.5015 0022778 221.4659 325.7020 16.01154108 8039
1 43947U 19006A 19075.54702546 .00026769 22010-5 48973-4 0 9993
2 43947 96.6290 349.0278 0022754 219.3583 142.5100 16.01177853 8128
1 43947U 19006A 19075.74902949 .00160298 12418-4 29196-3 0 9995
2 43947 96.6301 349.2308 0021693 223.2409 221.9908 16.00986874 8150
1 43947U 19006A 19075.80608134 .00151204 11245-4 27527-3 0 9991
2 43947 96.6283 349.2862 0021642 222.7701 191.0541 16.01000711 8166
1 43947U 19006A 19075.80608134 .00152613 11422-4 27780-3 0 9994
2 43947 96.6283 349.2862 0021642 222.7701 191.0553 16.01002279 8160
1 43947U 19006A 19076.04950662 .00212767 20643-4 38419-3 0 9990
2 43947 96.6283 349.5277 0021615 221.8443 154.0766 16.01125308 8205
1 43947U 19006A 19076.38314227 .00228397 23614-4 40852-3 0 9999
2 43947 96.6283 349.8588 0021611 220.6464 277.1313 16.01285801 8254
1 43947U 19006A 19076.56965602 .00375665 62286-4 68387-3 0 9990
2 43947 96.6317 350.0396 0020897 222.9971 269.0493 16.01007118 8281
1 43947U 19006A 19076.62489356 .00301460 40170-4 54811-3 0 9991
2 43947 96.6317 350.0945 0020879 222.7479 227.4454 16.01029701 8290
1 43947U 19006A 19076.74888822 .00206456 19517-4 37455-3 0 9990
2 43947 96.6317 350.2206 0020779 222.3519 222.0257 16.01081824 8311
1 43947U 19006A 19076.80334391 .00189842 16753-4 34417-3 0 9997
2 43947 96.6317 350.2747 0020760 222.1378 175.8977 16.01098177 8326
1 43947U 19006A 19077.25548485 .00042586 26475-5 77957-4 0 9991
2 43947 96.6337 350.7139 0018680 228.2848 254.2224 16.01199217 8396
1 43947U 19006A 19077.49923692 .00048049 28483-5 87602-4 0 9993
2 43947 96.6337 350.9559 0018675 227.3154 219.3457 16.01241589 8433
1 43947U 19006A 19077.73867148 .00176479 14671-4 32436-3 0 9999
2 43947 96.6324 351.1992 0019599 225.2817 160.3011 16.00913438 8475
1 43947U 19006A 19078.62402633 .00193781 17362-4 35487-3 0 9996
2 43947 96.6337 352.0814 0019337 224.7610 220.1468 16.00981700 8611
1 43947U 19006A 19079.44446882 .00056887 32197-5 10424-3 0 9994
2 43947 96.6318 352.8859 0017932 222.6020 267.9762 16.01149860 8742
1 43947U 19006A 19079.56114259 .00331197 48376-4 60603-3 0 9996
2 43947 96.6357 353.0133 0018767 223.2624 219.3325 16.01004143 8769
1 43947U 19006A 19079.87121502 .00017670 20377-5 33169-4 0 9996
2 43947 96.6348 353.3097 0018626 221.5456 207.0221 16.01103300 8819
1 43947U 19006A 19080.12837572 .00018161 20446-5 34105-4 0 9991
2 43947 96.6342 353.5628 0017856 223.7454 246.0274 16.01109643 8852
1 43947U 19006A 19080.25184005 .00021666 21015-5 40443-4 0 9990
2 43947 96.6342 353.6854 0017855 223.2528 237.6932 16.01123784 8871
1 43947U 19006A 19080.56838169 .00007182 19289-5 14116-4 0 9996
2 43947 96.6323 353.9969 0018497 223.3308 260.9364 16.01125965 8927
1 43947U 19006A 19080.80934521 -.00010028 19397-5 -17132-4 0 9998
2 43947 96.6323 354.2362 0018496 222.3649 209.7823 16.01086764 8962
1 43947U 19006A 19081.01817130 -.00000602 19026-5 00000+0 0 9994
2 43947 96.6305 354.4520 0018602 217.7557 337.0831 16.00999357 8992
1 43947U 19006A 19081.19970958 .00192780 17239-4 35047-3 0 9995
2 43947 96.6332 354.6387 0017808 222.1497 298.3751 16.01166044 9020
1 43947U 19006A 19081.44288380 .00132357 90623-5 24030-3 0 9997
2 43947 96.6332 354.8802 0017799 221.2143 260.0465 16.01211967 9068
1 43947U 19006A 19081.68209472 .00185493 16056-4 34001-3 0 9993
2 43947 96.6354 355.1161 0017576 220.8387 198.1644 16.01033820 9107
1 43947U 19006A 19081.80977756 .00126608 84347-5 23219-3 0 9995
2 43947 96.6356 355.2444 0017575 222.5408 211.8678 16.01049036 9124
1 43947U 19006A 19082.19785193 .00009851 19489-5 19026-4 0 9997
2 43947 96.6356 355.6299 0017571 221.0666 288.6619 16.01097025 9186
1 43947U 19006A 19082.25710510 .00009887 19472-5 19026-4 0 9996
2 43947 96.6318 355.6880 0017118 220.4674 270.5748 16.01171238 9192
1 43947U 19006A 19082.38407701 .00138221 97199-5 25077-3 0 9994
2 43947 96.6325 355.8124 0017241 219.6702 282.7837 16.01240682 9219
1 43947U 19006A 19082.68646591 .00149753 11067-4 27555-3 0 9993
2 43947 96.6363 356.1139 0016876 222.4115 221.6813 16.01004284 9261
1 43947U 19006A 19082.81114447 .00088395 50732-5 16304-3 0 9995
2 43947 96.6363 356.2377 0016873 221.9425 220.2429 16.01009980 9289
1 43947U 19006A 19083.00642146 .00143972 10375-4 26369-3 0 9992
2 43947 96.6358 356.4314 0016878 221.0757 265.8708 16.01087252 9317
1 43947U 19006A 19083.49288397 -.03265362 32488-2 -61588-2 0 9990
2 43947 96.6406 356.9162 0017256 219.6745 189.3161 16.00630469 9392
1 43947U 19006A 19083.74911125 .00084825 48174-5 15749-3 0 9999
2 43947 96.6345 357.1709 0015989 224.4362 220.2191 16.00930159 9436
1 43947U 19006A 19083.81116759 .00103815 62739-5 19235-3 0 9995
2 43947 96.6324 357.2279 0015947 223.4735 218.5865 16.00945210 9446
1 43947U 19006A 19083.93495759 .00147725 10814-4 27243-3 0 9990
2 43947 96.6324 357.3507 0015945 222.9977 212.0347 16.00998010 9463
1 43947U 19006A 19084.04992207 .00154144 11623-4 28342-3 0 9996
2 43947 96.6354 357.4673 0016254 222.4775 154.7117 16.01036332 9486
1 43947U 19006A 19084.56994306 -.00389064 56746-4 -71643-3 0 9995
2 43947 96.6360 357.9786 0015819 222.1151 270.2904 16.00942425 9569
1 43947U 19006A 19084.68712708 .00107795 66250-5 19877-3 0 9992
2 43947 96.6366 358.0976 0015957 221.7662 225.5779 16.01024149 9583
1 43947U 19006A 19084.74556389 .00097765 57838-5 18034-3 0 9991
2 43947 96.6363 358.1566 0015910 221.6645 202.2537 16.01028963 9598
1 43947U 19006A 19084.98720666 .00152043 11367-4 27860-3 0 9992
2 43947 96.6362 358.3958 0015832 220.6056 155.1371 16.01110841 9636
1 43947U 19006A 19085.19976117 .00022830 21215-5 42802-4 0 9998
2 43947 96.6349 358.6073 0016094 221.4560 298.5857 16.01085713 9665
1 43947U 19006A 19085.49952644 .00052661 30351-5 96803-4 0 9996
2 43947 96.6377 358.9036 0016197 221.8414 224.9544 16.01169686 9718
1 43947U 19006A 19085.56842683 .00054526 31167-5 10000-3 0 9997
2 43947 96.6377 358.9720 0016198 221.5803 262.1287 16.01202917 9722
1 43947U 19006A 19085.80962006 .00050945 29637-5 93510-4 0 9994
2 43947 96.6379 359.2115 0016283 220.5574 212.4511 16.01198803 9765'''
mass = 740
tle_raw = [line.strip() for line in tle_raw.split('\n')]
if len(tle_raw) % 2 != 0:
raise Exception('Not even number of lines [not TLE compatible]')
TLEs = zip(tle_raw[0::2], tle_raw[1::2])
event_date = np.datetime64('2019-03-27T05:40')
event_mjd = dpt.npdt2mjd(event_date)
M_earth = propagator_sgp4.SGP4.GM*1e9/consts.G
pop = Population(
name='Simulated Microsat R debris',
extra_columns = ['A', 'm', 'd', 'C_D', 'C_R'],
space_object_uses = [True, True, True, True, True],
propagator = propagator,
propagator_options = propagator_options,
)
pop.allocate(num)
tle_mjds = np.empty((len(TLEs),), dtype=np.float64)
for line_id, lines in enumerate(TLEs):
line1, line2 = lines
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
tle_mjds[line_id] = mjd0
best_tle = np.argmin(np.abs(tle_mjds - event_mjd))
line1, line2 = TLEs[best_tle]
sat_id = tle.tle_id(line1)
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
state_TEME = tle.TLE_propagation_TEME(line1, line2, dpt.mjd_to_jd(event_mjd))
#first has no pert
states = np.repeat(state_TEME, num, axis=1)
np.random.seed(seed)
kep = dpt.cart2kep(state_TEME, m=mass, M_cent=M_earth, radians=False)
pop.objs[0][1] = kep[0]*1e-3
pop.objs[0][2] = kep[1]
pop.objs[0][3] = kep[2]
pop.objs[0][4] = kep[4]
pop.objs[0][5] = kep[3]
pop.objs[0][6] = dpt.true2mean(kep[5], kep[1], radians=False)
pop.objs[0][0] = float(sat_id)
pop.objs[0][7] = mjd0
pop.objs[0][8] = np.pi*1.0**2
pop.objs[0][9] = mass
pop.objs[0][10] = 2.0
pop.objs[0][11] = 2.3
pop.objs[0][12] = 1.0
ind = 1
while ind < num:
C_D = np.random.rand(1)*(C_D_range[1] - C_D_range[0]) + C_D_range[0]
rho = 11e3
while rho > 10e3:
r = np.random.rand(1)*(radii_range[1] - radii_range[0]) + radii_range[0]
A = np.pi*r**2
m = np.random.rand(1)*(mass_range[1] - mass_range[0]) + mass_range[0]
vol = 4.0/3.0*np.pi*r**3
rho = m/vol
pert_state = states[:,ind].copy()
dv = np.random.rand(1)*max_dv
ddir = _rand_sph(1).T
pert_state[:3] += ddir[:,0]*dv
kep = dpt.cart2kep(pert_state, m=mass, M_cent=M_earth, radians=False)
if kep[0]*(1.0 - kep[1]) > 6353.0e3+100e3:
pop.objs[ind][1] = kep[0]*1e-3
pop.objs[ind][2] = kep[1]
pop.objs[ind][3] = kep[2]
pop.objs[ind][4] = kep[4]
pop.objs[ind][5] = kep[3]
pop.objs[ind][6] = dpt.true2mean(kep[5], kep[1], radians=False)
pop.objs[ind][0] = float(sat_id) + num
pop.objs[ind][7] = mjd0
pop.objs[ind][8] = A
pop.objs[ind][9] = m
pop.objs[ind][10] = r*2.0
pop.objs[ind][11] = C_D
pop.objs[ind][12] = 1.0
ind += 1
if 'out_frame' in pop.propagator_options:
out_f = pop.propagator_options['out_frame']
else:
out_f = 'ITRF'
pop.propagator_options['out_frame'] = 'TEME'
pop = propagate_population(pop, mjd)
pop.propagator_options['out_frame'] = out_f
return pop
def test_envisat():
import space_object as so
mass = 0.8111E+04
diam = 0.8960E+01
m_to_A = 128.651
a = 7159.5
e = 0.0001
i = 98.55
raan = 248.99
aop = 90.72
M = 47.37
A = mass / m_to_A
# epoch in unix seconds
ut0 = 1241136000.0
# modified julian day epoch
mjd0 = dpt.jd_to_mjd(dpt.unix_to_jd(ut0))
print(mjd0)
o = so.SpaceObject(
a=a,
e=e,
i=i,
raan=raan,
aop=aop,
mu0=M,
C_D=2.3,
A=A,
m=mass,
diam=diam,
mjd0=mjd0,
#propagator = PropagatorNeptune,
propagator=PropagatorSGP4,
propagator_options={'out_frame': 'ITRF'},
)
e3d = rlib.eiscat_3d()
print("EISCAT Skibotn location x,y,z ECEF (meters)")
print(e3d._tx[0].ecef)
ski_ecef = e3d._tx[0].ecef
print("EISCAT Skibotn location %1.3f %1.3f %1.3f (lat,lon,alt)" %
(e3d._tx[0].lat, e3d._tx[0].lon, 0.0))
t_obs = np.linspace(4440, 5280, num=100) + 31.974890
t_obs2 = np.linspace(4440, 5280, num=100) + 31.974890 + 1.0
print("MJD %1.10f %sZ" % (mjd0 + t_obs[0] / 3600.0 / 24.0,
ccsds_write.unix2datestr(ut0 + t_obs[0])))
ecef = o.get_state(t_obs)
ecef2 = o.get_state(t_obs2)
print("ECEF state x,y,z,vx,vy,vz (km and km/s)")
print(ecef[:, 0] / 1e3)
print(
"Time (UTC) Range (km) Vel (km/s) ECEF X (km) ECEF Y (km) ECEF Z (km)"
)
for i in range(len(t_obs)):
dist = np.linalg.norm(ecef[0:3, i] - ski_ecef)
dist2 = np.linalg.norm(ecef2[0:3, i] - ski_ecef)
vel = (dist2 - dist) / 1.0
print("%s %1.3f %1.3f %1.3f %1.3f %1.3f" %
(ccsds_write.unix2datestr(ut0 + t_obs[i]), dist / 1e3, vel / 1e3,
ecef[0, i] / 1e3, ecef[1, i] / 1e3, ecef[2, i] / 1e3))
props = [
p_sgp4,
p_nept,
p_orekit,
]
prop_dat = [
('-b', 'SGP4'),
('-g', 'NEPTUNE'),
('-k', 'Orekit'),
]
ut0 = 1241136000.0
# modified Julian day epoch
mjd0 = dpt.jd_to_mjd(dpt.unix_to_jd(ut0))
R_e = 6371.0
m_to_A = 128.651
mass = 0.8111E+04
init_data = {
'a': 7159.5 * 1e3,
'e': 0.0001,
'inc': 98.55,
'raan': 248.99,
'aop': 90.72,
'mu0': 47.37,
'mjd0': mjd0,
'C_D': 2.3,
'C_R': 1.0,
'm': mass,
sim.run_scheduler()
sim.status(fout='{}h_to_{}h_scheduler'.format(0, int(SIM_TIME)))
if part == 3:
sim.set_version(branch_name)
sim.load()
data_file = './data/microsatR_uhf.h5'
with h5py.File(data_file, 'r') as hf:
t_obs = hf['t'].value
r_obs = hf['r'].value
v_obs = hf['v'].value
t_obs = (dpt.jd_to_mjd(dpt.unix_to_jd(t_obs)) - mjd)*3600.0*24.0 + 2.0*3600.0 #maybe not gmt
t_sort = np.argsort(t_obs)
t_obs = t_obs[t_sort]
r_obs = r_obs[t_sort]
v_obs = v_obs[t_sort]
t_select = t_obs < SIM_TIME*3600.0
t_obs = t_obs[t_select]
r_obs = r_obs[t_select]
v_obs = v_obs[t_select]
#create RV_T plots,
fig, axs = plt.subplots(2,1,figsize=(12,8),dpi=80)
axs[0].hist(sim.population['m'])
)
radar.set_scan(scan)
sims = []
data_file = './data/microsatR_uhf.h5'
with h5py.File(data_file, 'r') as hf:
t_obs = hf['t'].value
r_obs = hf['r'].value
v_obs = hf['v'].value
dt = np.datetime64('2019-04-02T12:01')
mjd = dpt.npdt2mjd(dt)
t_obs = (dpt.jd_to_mjd(dpt.unix_to_jd(t_obs)) -
mjd) * 3600.0 * 24.0 + 2.0 * 3600.0 #maybe not gmt
t_sort = np.argsort(t_obs)
t_obs = t_obs[t_sort]
r_obs = r_obs[t_sort]
v_obs = v_obs[t_sort]
t_select = t_obs < 24.0 * 3600.0
t_obs = t_obs[t_select]
r_obs = r_obs[t_select]
v_obs = v_obs[t_select]
for branch_name in branch_names:
fname = sim_root + '/{}_population.h5'.format(branch_name)
def tryTest1():
init_data = {
'a': 7500e3,
'e': 0,
'inc': 90.0,
'raan': 10,
'aop': 10,
'mu0': 40.0,
'mjd0': 57125.7729,
'C_D': 2.3,
'C_R': 1.0,
'm': 8000,
'A': 1.0,
}
mjd0 = dpt.jd_to_mjd(2457126.2729)
orb_init_list = _gen_orbits(100)
prop = PropagatorOrekit(
in_frame='EME',
out_frame='EME',
)
t = np.linspace(0, 12 * 3600, num=100, dtype=np.float)
for kep in orb_init_list:
state_ref = dpt.kep2cart(kep,
m=init_data['m'],
M_cent=prop.M_earth,
radians=False)
state_kep = prop.get_orbit(
t=t,
mjd0=mjd0,
a=kep[0],
e=kep[1],
inc=kep[2],
raan=kep[4],
aop=kep[3],
mu0=dpt.true2mean(kep[5], kep[1], radians=False),
C_D=init_data['C_D'],
m=init_data['m'],
A=init_data['A'],
C_R=init_data['C_R'],
radians=False,
)
state_cart = prop.get_orbit_cart(
t=t,
mjd0=mjd0,
x=state_ref[0],
y=state_ref[1],
z=state_ref[2],
vx=state_ref[3],
vy=state_ref[4],
vz=state_ref[5],
C_D=init_data['C_D'],
m=init_data['m'],
A=init_data['A'],
C_R=init_data['C_R'],
)
state_diff1 = np.abs(state_kep - state_cart)
try:
nt.assert_array_less(
state_diff1[:3, :],
np.full((3, t.size), 1e-5, dtype=state_diff1.dtype))
nt.assert_array_less(
state_diff1[3:, :],
np.full((3, t.size), 1e-7, dtype=state_diff1.dtype))
except:
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(211)
ax.plot(t / 3600.0,
np.sqrt(np.sum(state_diff1[:3, :], axis=0)) * 1e-3)
ax.set_xlabel('Time [h]')
ax.set_ylabel('Position difference [km]')
ax.set_title(
'Propagation difference diameter simple vs advanced models')
ax = fig.add_subplot(212)
ax.plot(t / 3600.0,
np.sqrt(np.sum(state_diff1[3:, :], axis=0)) * 1e-3)
ax.set_xlabel('Time [h]')
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(111, projection='3d')
plothelp.draw_earth_grid(ax)
ax.plot(state_kep[0, :],
state_kep[1, :],
state_kep[2, :],
"-",
alpha=0.5,
color="green",
label='KEP input')
ax.plot(state_cart[0, :],
state_cart[1, :],
state_cart[2, :],
"-",
alpha=0.5,
color="red",
label='CART input')
plt.legend()
plt.show()
def setUp(self):
self.mjd0 = dpt.jd_to_mjd(2457126.2729) # 2015-04-13T18:32:58.560019
self.C_D = 2.3
self.m = 8000
self.A = 1.0
self.prop = propagator_sgp4.PropagatorSGP4()
def test_PropagatorSGP4_cart_kep_cases(self):
R_E = 6353.0e3
mjd0 = dpt.jd_to_mjd(2457126.2729)
a = R_E*2.0
orb_init_list = []
orb_range = np.array([a, 0.9, 180, 360, 360, 360], dtype=np.float)
orb_offset = np.array([R_E*1.1, 0, 0.0, 0.0, 0.0, 0.0], dtype=np.float)
test_n = 100
while len(orb_init_list) < test_n:
orb = np.random.rand(6)
orb = orb_offset + orb*orb_range
if orb[0]*(1.0 - orb[1]) > R_E+200e3:
orb_init_list.append(orb)
orb_init_list.append(np.array([R_E*1.2, 0, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0, 0.0, 0.0, 0.0, 270], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 1e-9, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 0.0, 120.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 0.0, 0.0, 35.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 120.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 0.0, 35.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 120.0, 35.0, 0.0], dtype=np.float))
prop = propagator_sgp4.PropagatorSGP4(polar_motion=False)
t = np.linspace(0, 12*3600, num=100, dtype=np.float)
M_earth = propagator_sgp4.SGP4.GM*1e9/consts.G
for kep in orb_init_list:
state_TEME = dpt.kep2cart(kep, m=self.m, M_cent=M_earth, radians=False)
ecefs_kep = prop.get_orbit(
t=t, mjd0=mjd0,
a=kep[0], e=kep[1], inc=kep[2],
raan=kep[4], aop=kep[3], mu0=dpt.true2mean(kep[5], kep[1], radians=False),
C_D=self.C_D, m=self.m, A=self.A,
radians=False,
)
ecefs_cart = prop.get_orbit_cart(
t=t, mjd0=mjd0,
x=state_TEME[0], y=state_TEME[1], z=state_TEME[2],
vx=state_TEME[3], vy=state_TEME[4], vz=state_TEME[5],
C_D=self.C_D, m=self.m, A=self.A,
)
p = ecefs_kep[:3,:]*1e-3
v = ecefs_kep[3:,:]*1e-3
kep_TEME = propagator_sgp4.ecef2teme(t, p, v, mjd0=mjd0)*1e3
p = ecefs_kep[:3,:]*1e-3
v = ecefs_kep[3:,:]*1e-3
cart_TEME = propagator_sgp4.ecef2teme(t, p, v, mjd0=mjd0)*1e3
state_diff1 = np.abs(kep_TEME - cart_TEME)
nt.assert_array_less(state_diff1[:3,:], np.full((3,t.size), 1e-5, dtype=state_diff1.dtype))
nt.assert_array_less(state_diff1[3:,:], np.full((3,t.size), 1e-7, dtype=state_diff1.dtype))
def test_PropagatorSGP4_cart_kep_inverse_cases(self):
R_E = 6353.0e3
mjd0 = dpt.jd_to_mjd(2457126.2729)
a = R_E*2.0
orb_init_list = []
orb_range = np.array([a, 0.9, 180, 360, 360, 360], dtype=np.float)
orb_offset = np.array([R_E*1.1, 0, 0.0, 0.0, 0.0, 0.0], dtype=np.float)
test_n = 5000
while len(orb_init_list) < test_n:
orb = np.random.rand(6)
orb = orb_offset + orb*orb_range
if orb[0]*(1.0 - orb[1]) > R_E+200e3:
orb_init_list.append(orb)
orb_init_list.append(np.array([R_E*1.2, 0, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0, 0.0, 0.0, 0.0, 270], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 1e-9, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 0.0, 120.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 0.0, 0.0, 35.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 120.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 0.0, 35.0, 0.0], dtype=np.float))
orb_init_list.append(np.array([R_E*1.2, 0.1, 75.0, 120.0, 35.0, 0.0], dtype=np.float))
prop = propagator_sgp4.PropagatorSGP4(polar_motion=False)
t = np.array([0], dtype=np.float)
M_earth = propagator_sgp4.SGP4.GM*1e9/consts.G
fail_inds = []
errs = np.empty((len(orb_init_list),2), dtype=np.float)
for ind, kep in enumerate(orb_init_list):
state_TEME = dpt.kep2cart(kep, m=self.m, M_cent=M_earth, radians=False)
ecefs_kep = self.prop.get_orbit(
t=t, mjd0=mjd0,
a=kep[0], e=kep[1], inc=kep[2],
raan=kep[4], aop=kep[3], mu0=dpt.true2mean(kep[5],kep[1],radians=False),
C_D=self.C_D, m=self.m, A=self.A,
)
p = ecefs_kep[:3]*1e-3
p.shape=(3,1)
v = ecefs_kep[3:]*1e-3
v.shape=(3,1)
kep_TEME = propagator_sgp4.ecef2teme(np.array([0.0]), p, v, mjd0=mjd0)*1e3
kep_TEME.shape = (6,)
ecefs_cart = self.prop.get_orbit_cart(
t=t, mjd0=mjd0,
x=kep_TEME[0], y=kep_TEME[1], z=kep_TEME[2],
vx=kep_TEME[3], vy=kep_TEME[4], vz=kep_TEME[5],
C_D=self.C_D, m=self.m, A=self.A,
)
p = ecefs_cart[:3]*1e-3
p.shape=(3,1)
v = ecefs_cart[3:]*1e-3
v.shape=(3,1)
cart_TEME = propagator_sgp4.ecef2teme(np.array([0.0]), p, v, mjd0=mjd0)*1e3
cart_TEME.shape = (6,)
state_diff1 = np.abs(kep_TEME - state_TEME)
try:
nt.assert_array_less(state_diff1[:3], np.full((3,), 1e-2, dtype=state_diff1.dtype))
nt.assert_array_less(state_diff1[3:], np.full((3,), 1e-5, dtype=state_diff1.dtype))
except AssertionError as err:
if ind not in fail_inds:
fail_inds.append(ind)
state_diff2 = np.abs(cart_TEME - state_TEME)
try:
nt.assert_array_less(state_diff2[:3], np.full((3,), 1e-2, dtype=state_diff2.dtype))
nt.assert_array_less(state_diff2[3:], np.full((3,), 1e-5, dtype=state_diff2.dtype))
except AssertionError as err:
if ind not in fail_inds:
fail_inds.append(ind)
state_diff = np.abs(cart_TEME - kep_TEME)
try:
nt.assert_array_less(state_diff[:3], np.full((3,), 1e-2, dtype=state_diff.dtype))
nt.assert_array_less(state_diff[3:], np.full((3,), 1e-5, dtype=state_diff.dtype))
except AssertionError as err:
if ind not in fail_inds:
fail_inds.append(ind)
er_r = np.linalg.norm(state_diff1[:3])
er_v = np.linalg.norm(state_diff1[3:])
errs[ind,0] = er_r
errs[ind,1] = er_v
if len(fail_inds) > 0:
print('FAIL / TOTAL: {} / {}'.format(len(fail_inds), len(orb_init_list)))
assert np.median(errs[:,0]) < 1e-2
assert np.median(errs[:,1]) < 1e-4
assert len(fail_inds) < float(test_n)/100.
tle_raw = [line.strip() for line in tle_raw.split('\n')]
if len(tle_raw) % 2 != 0:
raise Exception('Not even number of lines [not TLE compatible]')
TLEs = zip(tle_raw[0::2], tle_raw[1::2])
event_date = np.datetime64('2019-03-27T05:40')
event_mjd = dpt.npdt2mjd(event_date)
M_earth = propagator_sgp4.SGP4.GM * 1e9 / consts.G
tle_mjds = np.empty((len(TLEs), ), dtype=np.float64)
for line_id, lines in enumerate(TLEs):
line1, line2 = lines
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
tle_mjds[line_id] = mjd0
best_tle = np.argmin(np.abs(tle_mjds - event_mjd))
line1, line2 = TLEs[best_tle]
sat_id = tle.tle_id(line1)
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
state_TEME = tle.TLE_propagation_TEME(line1, line2, dpt.mjd_to_jd(event_mjd))
kep = dpt.cart2kep(state_TEME, m=mass, M_cent=M_earth, radians=False)
print('{}'.format(kep[0, 0]))
print('{}'.format(kep[1, 0]))
def tle_snapshot(tle_file, sgp4_propagation=True, propagator = default_propagator, propagator_options = {}):
'''Reads a TLE-snapshot file and converts the TLE's to orbits in a TEME frame and creates a population file. The BSTAR parameter is saved in column BSTAR (or :code:`_objs[:,12`). A snapshot generally contains several TLE's for the same object thus will this population also contain duplicate objects.
*Numerical propagator assumptions:*
To propagate with a numerical propagator one needs to make assumptions.
* Density is :math:`5\cdot 10^3 \;\frac{kg}{m^3}`.
* Object is a sphere
* Drag coefficient is 2.3.
:param str/list tle_file: Path to the input TLE snapshot file. Or the TLE-set can be given directly as a list of two lines that can be unpacked in a loop, e.g. :code:`[(tle1_l1, tle1_l2), (tle2_l1, tle2_l2)]`.
:param bool sgp4_propagation: If :code:`False` then the population is specifically constructed to be propagated with :class:`propagator_orekit.PropagatorOrekit` and assumptions are made on mass, density and shape of objects. Otherwise the :class:`space_object.SpaceObject` is configured to use SGP4 propagation.
:return: TLE snapshot as a population with numerical propagator
:rtype: population.Population
'''
if isinstance(tle_file, str):
tle_raw = [line.rstrip('\n') for line in open(tle_file)]
if len(tle_raw) % 2 != 0:
raise Exception('Not even number of lines [not TLE compatible]')
TLEs = zip(tle_raw[0::2], tle_raw[1::2])
pop_name = tle_file.split('/')[-1]
else:
TLEs = tle_file
pop_name = 'TLE database'
M_earth = propagator_sgp4.SGP4.GM*1e9/consts.G
if sgp4_propagation:
pop = Population(
name=pop_name,
extra_columns = ['A', 'm', 'd', 'C_D', 'C_R'] + ['line1', 'line2'],
dtypes = ['float64']*5 + ['U70', 'U70'],
space_object_uses = [True, True, True, True, True] + [True, True],
propagator = propagator_sgp4.PropagatorTLE,
propagator_options = {
'out_frame': 'ITRF',
'polar_motion': False,
},
)
else:
pop = Population(
name=pop_name,
extra_columns = ['A', 'm', 'd', 'C_D', 'C_R'],
space_object_uses = [True, True, True, True, True],
propagator = propagator,
propagator_options = propagator_options,
)
pop.allocate(len(TLEs))
for line_id, lines in enumerate(TLEs):
line1, line2 = lines
sat_id = tle.tle_id(line1)
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
state_TEME, epoch = tle.TLE_to_TEME(line1,line2)
kep = dpt.cart2kep(state_TEME, m=0.0, M_cent=M_earth, radians=False)
pop.objs[line_id][1] = kep[0]*1e-3
pop.objs[line_id][2] = kep[1]
pop.objs[line_id][3] = kep[2]
pop.objs[line_id][4] = kep[4]
pop.objs[line_id][5] = kep[3]
pop.objs[line_id][6] = dpt.true2mean(kep[5], kep[1], radians=False)
pop.objs[line_id][0] = float(sat_id)
pop.objs[line_id][7] = mjd0
if sgp4_propagation:
for line_id, lines in enumerate(TLEs):
line1, line2 = lines
pop.objs[line_id][13] = line1[:-1]
pop.objs[line_id][14] = line2[:-1]
for line_id, lines in enumerate(TLEs):
line1, line2 = lines
bstar = tle.tle_bstar(line1)/(propagator_sgp4.SGP4.R_EARTH*1000.0)
B = bstar*2.0/propagator_sgp4.SGP4.RHO0
if B < 1e-9:
rho = 500.0
C_D = 0.0
r = 0.1
A = np.pi*r**2
m = rho*4.0/3.0*np.pi*r**3
else:
C_D = 2.3
rho = 5.0
r = (3.0*C_D)/(B*rho)
A = np.pi*r**2
m = rho*4.0/3.0*np.pi*r**3
pop.objs[line_id][8] = A
pop.objs[line_id][9] = m
pop.objs[line_id][10] = r*2.0
pop.objs[line_id][11] = C_D
pop.objs[line_id][12] = 1.0
return pop
def Microsat_R_debris(mjd, num, radii_range, mass_range, propagator, propagator_options):
tle_raw = Microsat_R_debris_raw_tle
tle_raw = [line.strip() for line in tle_raw.split('\n')]
if len(tle_raw) % 2 != 0:
raise Exception('Not even number of lines [not TLE compatible]')
TLEs = zip(tle_raw[0::2], tle_raw[1::2])
M_earth = propagator_sgp4.SGP4.GM*1e9/consts.G
pop = Population(
name='Microsat R debris',
extra_columns = ['A', 'm', 'd', 'C_D', 'C_R'],
space_object_uses = [True, True, True, True, True],
propagator = propagator,
propagator_options = propagator_options,
)
pop.allocate(len(TLEs)*num)
delete_inds = []
cnt = 0
for ind in range(num):
for line_id, lines in enumerate(TLEs):
line1, line2 = lines
sat_id = tle.tle_id(line1)
jd0 = tle.tle_jd(line1)
mjd0 = dpt.jd_to_mjd(jd0)
state_TEME = tle.TLE_propagation_TEME(line1, line2, dpt.mjd_to_jd(mjd))
kep = dpt.cart2kep(state_TEME, m=0.0, M_cent=M_earth, radians=False)
if np.any(np.isnan(kep)):
delete_inds.append(cnt)
continue
pop.objs[cnt][1] = kep[0]*1e-3
pop.objs[cnt][2] = kep[1]
pop.objs[cnt][3] = kep[2]
pop.objs[cnt][4] = kep[4]
pop.objs[cnt][5] = kep[3]
pop.objs[cnt][6] = dpt.true2mean(kep[5], kep[1], radians=False)
pop.objs[cnt][0] = float(sat_id)
pop.objs[cnt][7] = mjd
rho = 1.1e4
while rho > 1e4 or rho < 1e2:
r = np.random.rand(1)*(radii_range[1] - radii_range[0]) + radii_range[0]
A = np.pi*r**2
m = np.random.rand(1)*(mass_range[1] - mass_range[0]) + mass_range[0]
vol = 4.0/3.0*np.pi*r**3
rho = m/vol
pop.objs[cnt][8] = A
pop.objs[cnt][9] = m
pop.objs[cnt][10] = r*2.0
pop.objs[cnt][11] = 2.3
pop.objs[cnt][12] = 1.0
cnt += 1
pop.delete(delete_inds)
return pop
import numpy as np
import scipy.constants as consts
import numpy.testing as nt
import TLE_tools as tle
import dpt_tools as dpt
from propagator_orekit import PropagatorOrekit
import matplotlib.pyplot as plt
mjd0 = dpt.jd_to_mjd(2457126.2729)
R_E = 6353.0e3
a = R_E * 2.0
orb_init_list = []
orb_init_list.append(
np.array([R_E * 1.2, 0, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 0, 0.0, 0.0, 0.0, 270], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 1e-9, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 0.1, 0.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 0.1, 75.0, 0.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 0.1, 0.0, 120.0, 0.0, 0.0], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 0.1, 0.0, 0.0, 35.0, 0.0], dtype=np.float))
orb_init_list.append(
np.array([R_E * 1.2, 0.1, 75.0, 120.0, 0.0, 0.0], dtype=np.float))
def test_OD(root, sub_path):
import orbit_determination
import TLE_tools as tle
import dpt_tools as dpt
import radar_library as rlib
import propagator_sgp4
#import propagator_orekit
#import propagator_neptune
import ccsds_write
radar = rlib.eiscat_3d(beam='interp', stage=1)
radar.set_FOV(max_on_axis=90.0, horizon_elevation=10.0)
radar.set_SNR_limits(min_total_SNRdb=10.0, min_pair_SNRdb=1.0)
radar.set_TX_bandwith(bw = 1.0e6)
#prop = propagator_neptune.PropagatorNeptune()
prop = propagator_sgp4.PropagatorSGP4()
mass=0.8111E+04
diam=0.8960E+01
m_to_A=128.651
params = dict(
A = {
'dist': None,
'val': mass/m_to_A,
},
d = {
'dist': None,
'val': diam,
},
m = {
'dist': None,
'val': mass,
},
C_D = {
'dist': None,
'val': 2.3,
},
)
fname = glob.glob(root + sub_path + '*.oem')[0]
prior_data, prior_meta = ccsds_write.read_oem(fname)
prior_sort = np.argsort(prior_data['date'])
prior_data = prior_data[prior_sort][0]
prior_mjd = dpt.npdt2mjd(prior_data['date'])
prior_jd = dpt.mjd_to_jd(prior_mjd)
state0 = np.empty((6,), dtype=np.float64)
state0[0] = prior_data['x']
state0[1] = prior_data['y']
state0[2] = prior_data['z']
state0[3] = prior_data['vx']
state0[4] = prior_data['vy']
state0[5] = prior_data['vz']
#state0_ITRF = state0.copy()
state0 = tle.ITRF_to_TEME(state0, prior_jd, 0.0, 0.0)
#state0_TEME = state0.copy()
#state0_ITRF_ref = tle.TEME_to_ITRF(state0_TEME, prior_jd, 0.0, 0.0)
#print(state0_ITRF_ref - state0_ITRF)
#exit()
data_folder = root + sub_path
data_h5 = glob.glob(data_folder + '*.h5')
data_h5_sort = np.argsort(np.array([int(_h.split('/')[-1].split('-')[1]) for _h in data_h5])).tolist()
true_prior_h5 = data_h5[data_h5_sort[0]]
true_obs_h5 = data_h5[data_h5_sort[1]]
print(true_prior_h5)
print(true_obs_h5)
with h5py.File(true_prior_h5, 'r') as hf:
true_prior = hf['true_state'].value.T*1e3
true_prior_jd = dpt.unix_to_jd(hf['true_time'].value)
print('-- True time diff prior [s] --')
prior_match_ind = np.argmin(np.abs(true_prior_jd-prior_jd))
jd_diff = prior_jd - true_prior_jd[prior_match_ind]
state0_true = true_prior[:,prior_match_ind]
state0_true = tle.ITRF_to_TEME(state0_true, true_prior_jd[prior_match_ind], 0.0, 0.0)
print(prior_match_ind)
print(jd_diff*3600.0*24.0)
with h5py.File(true_obs_h5, 'r') as hf:
true_obs = hf['true_state'].value.T*1e3
true_obs_jd = dpt.unix_to_jd(hf['true_time'].value)
data_tdm = glob.glob(data_folder + '*.tdm')
#this next line i wtf, maybe clean up
data_tdm_sort = np.argsort(np.array([int(_h.split('/')[-1].split('-')[-1][2]) for _h in data_tdm])).tolist()
ccsds_files = [data_tdm[_tdm] for _tdm in data_tdm_sort]
print('prior true vs prior mean')
print(state0_true - state0)
for _fh in ccsds_files:
print(_fh)
r_obs_v = []
r_sig_v = []
v_obs_v = []
v_sig_v = []
t_obs_v = []
for ccsds_file in ccsds_files:
obs_data = ccsds_write.read_ccsds(ccsds_file)
sort_obs = np.argsort(obs_data['date'])
obs_data = obs_data[sort_obs]
jd_obs = dpt.mjd_to_jd(dpt.npdt2mjd(obs_data['date']))
date_obs = obs_data['date']
sort_obs = np.argsort(date_obs)
date_obs = date_obs[sort_obs]
r_obs = obs_data['range'][sort_obs]*1e3 #to m
v_obs = -obs_data['doppler_instantaneous'][sort_obs]*1e3 #to m/s
#v_obs = obs_data['doppler_instantaneous'][sort_obs]*1e3 #to m/s
r_sig = 2.0*obs_data['range_err'][sort_obs]*1e3 #to m
v_sig = 2.0*obs_data['doppler_instantaneous_err'][sort_obs]*1e3 #to m/s
#TRUNCATE FOR DEBUG
inds = np.linspace(0,len(jd_obs)-1,num=10,dtype=np.int64)
jd_obs = jd_obs[inds]
r_obs = r_obs[inds]
v_obs = v_obs[inds]
r_sig = r_sig[inds]
v_sig = v_sig[inds]
if ccsds_file.split('/')[-1].split('.')[0] == true_obs_h5.split('/')[-1].split('.')[0]:
print('-- True time diff obs [s] --')
jd_diff = jd_obs - true_obs_jd[inds]
print(jd_diff*3600.0*24.0)
#r_sig = np.full(r_obs.shape, 100.0, dtype=r_obs.dtype)
#v_sig = np.full(v_obs.shape, 10.0, dtype=v_obs.dtype)
r_obs_v.append(r_obs)
r_sig_v.append(r_sig)
v_obs_v.append(v_obs)
v_sig_v.append(v_sig)
t_obs = (jd_obs - prior_jd)*(3600.0*24.0)
#correct for light time approximently
lt_correction = r_obs*0.5/scipy.constants.c
t_obs -= lt_correction
t_obs_v.append(t_obs)
print('='*10 + 'Dates' + '='*10)
print('{:<8}: {} JD'.format('Prior', prior_jd))
for ind, _jd in enumerate(jd_obs):
print('Obs {:<4}: {} JD'.format(ind, _jd))
print('='*10 + 'Observations' + '='*10)
print(len(jd_obs))
prior = {}
prior['cov'] = np.diag([1e3, 1e3, 1e3, 1e1, 1e1, 1e1])*1.0
prior['mu'] = state0
print('='*10 + 'Prior Mean' + '='*10)
print(prior['mu'])
print('='*10 + 'Prior Covariance' + '='*10)
print(prior['cov'])
rx_ecef = []
for rx in radar._rx:
rx_ecef.append(rx.ecef)
tx_ecef = radar._tx[0].ecef
tune = 0
trace = orbit_determination.determine_orbit(
num = 2000,
r = r_obs_v,
sd_r = r_sig_v,
v = v_obs_v,
sd_v = v_sig_v,
grad_dx = [10.0]*3 + [1.0]*3,
rx_ecef = rx_ecef,
tx_ecef = tx_ecef,
t = t_obs_v,
mjd0 = prior_mjd,
params = params,
prior = prior,
propagator = prop,
step = 'Metropolis',
step_opts = {
'scaling': 0.75,
},
pymc_opts = {
'tune': tune,
'discard_tuned_samples': True,
'cores': 1,
'chains': 1,
'parallelize': True,
},
)
#if comm.rank != 0:
# exit()
var = ['$X$ [km]', '$Y$ [km]', '$Z$ [km]', '$V_X$ [km/s]', '$V_Y$ [km/s]', '$V_Z$ [km/s]']
fig = plt.figure(figsize=(15,15))
for ind in range(6):
ax = fig.add_subplot(231+ind)
ax.plot(trace['state'][:,ind]*1e-3)
ax.set(
xlabel='Iteration',
ylabel='{}'.format(var[ind]),
)
state1 = np.mean(trace['state'], axis=0)
print('='*10 + 'Trace summary' + '='*10)
print(pm.summary(trace))
_form = '{:<10}: {}'
print('='*10 + 'Prior Mean' + '='*10)
for ind in range(6):
print(_form.format(var[ind], state0[ind]*1e-3))
print('='*10 + 'Posterior state mean' + '='*10)
for ind in range(6):
print(_form.format(var[ind], state1[ind]*1e-3))
stated = state1 - state0
print('='*10 + 'State shift' + '='*10)
for ind in range(6):
print(_form.format(var[ind], stated[ind]*1e-3))
print('='*10 + 'True posterior' + '='*10)
for ind in range(6):
print(_form.format(var[ind], state0_true[ind]*1e-3))
print('='*10 + 'Posterior error' + '='*10)
for ind in range(6):
print(_form.format(var[ind],(state1[ind] - state0_true[ind])*1e-3))
print('='*10 + 'Parameter shift' + '='*10)
theta0 = {}
theta1 = {}
for key, val in params.items():
if val['dist'] is not None:
theta0[key] = val['mu']
theta1[key] = np.mean(trace[key], axis=0)[0]
print('{}: {}'.format(key, theta1[key] - theta0[key]))
else:
theta0[key] = val['val']
theta1[key] = val['val']
range_v_prior = []
vel_v_prior = []
range_v = []
vel_v = []
range_v_true = []
vel_v_true = []
for rxi in range(len(rx_ecef)):
t_obs = t_obs_v[rxi]
print('Generating tracklet simulated data RX {}: {} points'.format(rxi, len(t_obs)))
states0 = orbit_determination.propagate_state(state0, t_obs, dpt.jd_to_mjd(prior_jd), prop, theta0)
states1 = orbit_determination.propagate_state(state1, t_obs, dpt.jd_to_mjd(prior_jd), prop, theta1)
states0_true = orbit_determination.propagate_state(state0_true, t_obs, dpt.jd_to_mjd(prior_jd), prop, theta1)
range_v_prior += [np.empty((len(t_obs), ), dtype=np.float64)]
vel_v_prior += [np.empty((len(t_obs), ), dtype=np.float64)]
range_v += [np.empty((len(t_obs), ), dtype=np.float64)]
vel_v += [np.empty((len(t_obs), ), dtype=np.float64)]
range_v_true += [np.empty((len(t_obs), ), dtype=np.float64)]
vel_v_true += [np.empty((len(t_obs), ), dtype=np.float64)]
for ind in range(len(t_obs)):
range_v_prior[rxi][ind], vel_v_prior[rxi][ind] = orbit_determination.generate_measurements(states0[:,ind], rx_ecef[rxi], tx_ecef)
range_v[rxi][ind], vel_v[rxi][ind] = orbit_determination.generate_measurements(states1[:,ind], rx_ecef[rxi], tx_ecef)
range_v_true[rxi][ind], vel_v_true[rxi][ind] = orbit_determination.generate_measurements(states0_true[:, ind], rx_ecef[rxi], tx_ecef)
prop_states = orbit_determination.propagate_state(
state0,
np.linspace(0, (np.max(jd_obs) - prior_jd)*(3600.0*24.0), num=1000),
dpt.jd_to_mjd(prior_jd),
prop,
theta1,
)
'''
pop = gen_pop()
obj = pop.get_object(0)
t_obs_pop = t_obs + (dpt.jd_to_mjd(prior_jd) - obj.mjd0)*3600.0*24.0
states0_true2 = obj.get_state(t_obs_pop)
print(states0_true2)
print(states0_true2 - states0_true)
'''
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(111, projection='3d')
plothelp.draw_earth_grid(ax)
for ind, ecef in enumerate(rx_ecef):
if ind == 0:
ax.plot([ecef[0]], [ecef[1]], [ecef[2]], 'or', label='EISCAT 3D RX')
else:
ax.plot([ecef[0]], [ecef[1]], [ecef[2]], 'or')
ax.plot(states0[0,:], states0[1,:], states0[2,:], 'xb', label = 'Prior', alpha = 0.75)
ax.plot(states1[0,:], states1[1,:], states1[2,:], 'xr', label = 'Posterior', alpha = 0.75)
ax.plot(prop_states[0,:], prop_states[1,:], prop_states[2,:], '-k', label = 'Prior-propagation', alpha = 0.5)
ax.plot(true_prior[0,:], true_prior[1,:], true_prior[2,:], '-b', label = 'Prior-True', alpha = 0.75)
ax.plot(true_obs[0,:], true_obs[1,:], true_obs[2,:], '-r', label = 'Posterior-True', alpha = 0.75)
ax.legend()
for rxi in range(len(rx_ecef)):
fig = plt.figure(figsize=(15,15))
t_obs_h = t_obs_v[rxi]/3600.0
ax = fig.add_subplot(221)
lns = []
line1 = ax.plot(t_obs_h, (r_obs_v[rxi] - range_v[rxi])*1e-3, '-b', label='Maximum a posteriori: RX{}'.format(rxi))
line0 = ax.plot(t_obs_h, (r_obs_v[rxi] - range_v_true[rxi])*1e-3, '.b', label='True prior: RX{}'.format(rxi))
ax.set(
xlabel='Time [h]',
ylabel='2-way-Range residuals [km]',
)
ax2 = ax.twinx()
line2 = ax2.plot(t_obs_h, (r_obs_v[rxi] - range_v_prior[rxi])*1e-3, '-k', label='Maximum a priori: RX{}'.format(rxi))
ax.tick_params(axis='y', labelcolor='b')
ax2.tick_params(axis='y', labelcolor='k')
lns += line0+line1+line2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=0)
ax = fig.add_subplot(222)
lns = []
line1 = ax.plot(t_obs_h, (v_obs_v[rxi] - vel_v[rxi])*1e-3, '-b', label='Maximum a posteriori: RX{}'.format(rxi))
line0 = ax.plot(t_obs_h, (v_obs_v[rxi] - vel_v_true[rxi])*1e-3, '.b', label='True prior: RX{}'.format(rxi))
ax.set(
xlabel='Time [h]',
ylabel='2-way-Velocity residuals [km/s]',
)
ax2 = ax.twinx()
line2 = ax2.plot(t_obs_h, (v_obs_v[rxi] - vel_v_prior[rxi])*1e-3, '-k', label='Maximum a priori: RX{}'.format(rxi))
ax.tick_params(axis='y', labelcolor='b')
ax2.tick_params(axis='y', labelcolor='k')
lns += line0+line1+line2
labs = [l.get_label() for l in lns]
ax.legend(lns, labs, loc=0)
ax = fig.add_subplot(223)
ax.errorbar(t_obs_h, r_obs_v[rxi]*1e-3, yerr=r_sig_v[rxi]*1e-3, label='Measurements: RX{}'.format(rxi))
ax.plot(t_obs_h, range_v[rxi]*1e-3, label='Maximum a posteriori: RX{}'.format(rxi))
ax.plot(t_obs_h, range_v_prior[rxi]*1e-3, label='Maximum a priori: RX{}'.format(rxi))
ax.set(
xlabel='Time [h]',
ylabel='2-way-Range [km]',
)
ax.legend()
ax = fig.add_subplot(224)
ax.errorbar(t_obs_h, v_obs_v[rxi]*1e-3, yerr=v_sig_v[rxi]*1e-3, label='Measurements: RX{}'.format(rxi))
ax.plot(t_obs_h, vel_v[rxi]*1e-3, label='Maximum a posteriori: RX{}'.format(rxi))
ax.plot(t_obs_h, vel_v_prior[rxi]*1e-3, label='Maximum a priori: RX{}'.format(rxi))
ax.set(
xlabel='Time [h]',
ylabel='2-way-Velocity [km/s]',
)
ax.legend()
#dpt.posterior(trace['state']*1e-3, var, show=False)
plt.show()
