def get_states(self, index, item, **kw):
t = sorts.equidistant_sampling(
orbit=item.state,
start_t=self.scheduler.controllers[0].t.min(),
end_t=self.scheduler.controllers[0].t.max(),
max_dpos=1e3,
)
state = item.get_state(t)
return state, t
def get_states(self, index, item):
obj = self.population.get_object(item)
t = sorts.equidistant_sampling(
orbit=obj.orbit,
start_t=self.scheduler.controllers[0].t.min(),
end_t=self.scheduler.controllers[0].t.max(),
max_dpos=1e3,
)
state = obj.get_state(t)
return state, t
def run_scanning_simulation(radar_ctrl):
p = Profiler()
radar_ctrl.profiler = p
p.start('total')
scheduler = ObservedScanning(
radar=eiscat3d,
controllers=[radar_ctrl],
logger=logger,
profiler=p,
)
p.start('equidistant_sampling')
t = sorts.equidistant_sampling(
orbit=obj.state,
start_t=0,
end_t=end_t,
max_dpos=1e3,
)
p.stop('equidistant_sampling')
print(f'Temporal points obj: {len(t)}')
p.start('get_state')
states = obj.get_state(t)
p.stop('get_state')
p.start('find_passes')
#rename cache_data to something more descriptive
passes = eiscat3d.find_passes(t, states, cache_data=True)
p.stop('find_passes')
p.start('observe_passes')
data = scheduler.observe_passes(passes, space_object=obj, snr_limit=False)
p.stop('observe_passes')
for psi in data:
for txps in psi:
for rxtxps in txps:
print(f'Max SNR={10*np.log10(rxtxps["snr"].max())} dB')
p.stop('total')
print(f'\n {radar_ctrl.__class__}: len(t) = {len(radar_ctrl.t)} \n')
print(p.fmt(normalize='total'))
from sorts.propagator import SGP4
Prop_cls = SGP4
Prop_opts = dict(
settings = dict(
out_frame='ITRS',
),
)
prop = Prop_cls(**Prop_opts)
orb = pyorb.Orbit(M0 = pyorb.M_earth, direct_update=True, auto_update=True, degrees=True, a=7200e3, e=0.05, i=75, omega=0, Omega=79, anom=72, epoch=53005.0)
print(orb)
t = sorts.equidistant_sampling(
orbit = orb,
start_t = 0,
end_t = 3600*24*1,
max_dpos=1e3,
)
print(f'Temporal points: {len(t)}')
states = prop.propagate(t, orb.cartesian[:,0], orb.epoch, A=1.0, C_R = 1.0, C_D = 1.0)
passes = eiscat3d.find_passes(t, states)
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(111, projection='3d')
ax.plot(states[0,:], states[1,:], states[2,:])
fig = plt.figure(figsize=(15,15))
axes = [
[
e=0.1,
i=75,
raan=79,
aop=0,
mu0=mu0,
epoch=53005.0,
parameters=dict(d=1.0, ),
) for mu0 in [62.0, 61.9]
]
for obj in objs:
print(obj)
t = sorts.equidistant_sampling(
orbit=objs[0].state,
start_t=0,
end_t=3600 * 6,
max_dpos=1e3,
)
print(f'Temporal points: {len(t)}')
states0 = objs[0].get_state(t)
states1 = objs[1].get_state(t)
#set cache_data = True to save the data in local coordinates
#for each pass inside the Pass instance, setting to false saves RAM
passes0 = eiscat3d.find_passes(t, states0, cache_data=False)
passes1 = eiscat3d.find_passes(t, states1, cache_data=False)
#just create a controller for observing 10 points of the first pass
ps = passes0[0][0][0]
use_inds = np.arange(0, len(ps.inds), len(ps.inds) // 10)
p.start('total')
scheduler = ObservedScanning(
radar=eiscat3d,
controllers=[scanner_ctrl],
logger=logger,
profiler=p,
)
datas = []
passes = []
states = []
for ind in range(len(objs)):
p.start('equidistant_sampling')
t = sorts.equidistant_sampling(
orbit=objs[ind].state,
start_t=0,
end_t=end_t,
max_dpos=1e3,
)
p.stop('equidistant_sampling')
print(f'Temporal points obj {ind}: {len(t)}')
p.start('get_state')
states += [objs[ind].get_state(t)]
p.stop('get_state')
p.start('find_passes')
#rename cache_data to something more descriptive
passes += [eiscat3d.find_passes(t, states[ind], cache_data=True)]
p.stop('find_passes')