def sim_uhf(N_samples=10):
outdir="./master_validation_data_uhf"
os.system("mkdir -p %s"%(outdir))
incs=n.arange(69,113,2)
apogs=n.arange(6971,8371,100)
print(len(apogs)*len(incs))
radar=rl.eiscat_uhf()
for inci,i in enumerate(incs):
for ai in range(comm.rank,len(apogs),comm.size):
a=apogs[ai]
print("inc %f apog %f"%(i,a))
sample_detections(radar,N=N_samples,a=a,inc=i,outdir=outdir)
def test_correlator(self):
radar = rlib.eiscat_uhf()
measurement_folder = './data/uhf_test_data/events'
tle_file = './data/uhf_test_data/tle-201801.txt'
measurement_file = measurement_folder + '/det-000000.h5'
with h5py.File(measurement_file, 'r') as h_det:
r = h_det['r'].value * 1e3
t = h_det['t'].value
v = -h_det['v'].value
dat = {
'r': r,
't': t,
'v': v,
}
pop = population_library.tle_snapshot(tle_file, sgp4_propagation=True)
pop.filter('oid', lambda oid: n.abs(oid - 43075) < 50)
cdat = correlator.correlate(
data=dat,
station=radar._rx[0],
population=pop,
metric=correlator.residual_distribution_metric,
n_closest=2,
out_file=None,
verbose=False,
MPI_on=False,
)
assert int(cdat[0]['sat_id']) == 43075
self.assertLess(n.abs(cdat[0]['stat'][0]), 1e3)
self.assertLess(n.abs(cdat[0]['stat'][1]), 500)
self.assertLess(n.abs(cdat[0]['stat'][2]), 10.0)
self.assertLess(n.abs(cdat[0]['stat'][3]), 5.0)
if len(d["range"]) > 0:
for di in range(len(d["range"])):
print("det oi %d fi %d di %d angle %1.3f" %
(oi, fi, di, d["on_axis_angle"][di]))
fname = "%s/det_%09d_%06d_%03d.h5" % (
outdir, m["id"][oi], fi, di)
print(fname)
ho = h5py.File(fname, "w")
ho["time"] = d["tm"][di]
ho["range"] = d["range"][di]
ho["range_rate"] = d["range_rate"][di]
ho["snr"] = d["snr"][di]
ho["oid"] = m["id"][oi]
ho["rx_gain"] = d["tx_gain"][di]
ho["tx_gain"] = d["tx_gain"][di]
ho["on_axis_angle"] = d["on_axis_angle"][di]
ho.close()
comm.barrier()
if __name__ == "__main__":
m = read_master()
uhf = True
esr = False
if uhf:
radar = rl.eiscat_uhf()
proof(radar, m, outdir="proof_uhf")
if esr:
radar = rl.eiscat_svalbard()
proof(radar, m, outdir="proof_esr")
import numpy as n from mpi4py import MPI import sys sys.path.insert(0, "/home/danielk/IRF/IRF_GITLAB/SORTSpp") # SORTS imports import population as p import radar_library as rl import radar_scan_library as rslib import simulation as s import scheduler_library as sch sim_root = '/home/danielk/IRF/E3D_PA/SORTSpp_sim/sim_rho' #initialize the radar setup e3d = rl.eiscat_uhf() e3d._min_on_axis=25.0 e3d._min_SNRdb=1.0 e3d._tx[0].enr_thresh = 1.0 #SNR? e3d._tx[0].el_thresh = 30.0 #deg for rx in e3d._rx: rx.el_thresh = 30.0 #deg e3d._tx[0].ipp = 10e-3 # pulse spacing e3d._tx[0].n_ipp = 5 # number of ipps to coherently integrate e3d._tx[0].pulse_length = 1e-3 #initialize the observing mode e3d_scan = rslib.beampark_model(az=0.0, el=90.0, alt = 150, dwell_time = 0.1) e3d_scan.set_radar_location(e3d)
_opts = {
'in_frame': 'TEME',
'out_frame': 'ITRF',
'solar_activity_strength': 'WEAK',
}
event_date = np.datetime64('2019-03-27T05:40')
event_mjd = dpt.npdt2mjd(event_date)
prop = _prop(**_opts)
#sim_root = '/ZFS_DATA/SORTSpp/FP_sims/T_UHF_MicrosatR_v2'
sim_root = '/home/danielk/IRF/E3D_PA/FP_sims/T_UHF_MicrosatR_v2'
#initialize the radar setup
radar = rlib.eiscat_uhf()
radar.set_FOV(max_on_axis=25.0, horizon_elevation=50.0)
radar.set_SNR_limits(min_total_SNRdb=14.0, min_pair_SNRdb=14.0)
radar.set_scan(scan)
fname = sim_root + '/population_{}.h5'.format(branch_name)
catname = sim_root + '/' + branch_name + '/catalogue_data.h5'
pop = Population.load(
fname,
propagator=_prop,
propagator_options=_opts,
)
#!/usr/bin/env python
import numpy as n
import matplotlib.pyplot as plt
import debris
import radar_library as rl
radars=[]
radars.append(rl.eiscat_uhf())
radars.append(rl.tromso_space_radar())
radars.append(rl.eiscat_svalbard())
radars.append(rl.eiscat_3d_module())
radars.append(rl.eiscat_3d())
range_m=10**n.linspace(5,8,num=100)
for r in radars:
min_diam=n.zeros(len(range_m))
gain_tx=r._tx[0].beam.I_0
gain_rx=r._rx[0].beam.I_0
rx_noise=r._tx[0].rx_noise
B=r._tx[0].coh_int_bandwidth
wavelength=r._tx[0].wavelength
tx_power=r._tx[0].tx_power
print(r._tx[0])
for ri,rng_m in enumerate(range_m):
min_diam[ri]=debris.target_diameter(gain_tx, gain_rx, wavelength, tx_power, rng_m, rng_m, enr=1.0, bandwidth=B, rx_noise_temp=rx_noise)
plt.loglog(range_m/1e3,min_diam,label=r.name)
plt.xlabel("Range (km)")
import numpy as np
import scipy.constants as c
import matplotlib.pyplot as plt
from radar_config import plot_radar
import radar_library as rl
radars = [
rl.eiscat_3d(beam='gauss'),
#rl.eiscat_3d(beam = 'array'),
rl.eiscat_3d_module(beam='gauss'),
#rl.eiscat_3d_module(beam = 'array'),
rl.eiscat_svalbard(),
rl.eiscat_uhf(),
]
for radar in radars:
plot_radar(radar)
space_o = space_object.SpaceObject(
a=7000,
e=0.0,
i=69,
raan=0,
aop=0,
mu0=0,
C_D=2.3,
A=1.0,
m=1.0,
C_R=1.0,
oid=42,
d=0.1,
mjd0=57125.7729,
)
print(space_o)
radars = [
radar_library.eiscat_uhf(),
radar_library.eiscat_3d(),
]
radars[0]._tx[0].scan.keyword_arguments(el=45.0)
for radar in radars:
plot_detections(radar, space_o, t_end=24.0 * 3600.0)
plt.show()
def test_envisat_detection():
from mpi4py import MPI
# SORTS imports CORE
import population_library as plib
from simulation import Simulation
#SORTS Libraries
import radar_library as rlib
import radar_scan_library as rslib
import scheduler_library as schlib
import antenna_library as alib
import rewardf_library as rflib
#SORTS functions
import ccsds_write
import dpt_tools as dpt
sim_root = './tests/tmp_test_data/envisat_sim_test'
radar = rlib.eiscat_uhf()
radar.set_FOV(max_on_axis=30.0, horizon_elevation=25.0)
scan = rslib.beampark_model(
lat=radar._tx[0].lat,
lon=radar._tx[0].lon,
alt=radar._tx[0].alt,
az=90.0,
el=75.0,
)
radar.set_scan(scan)
#tle files for envisat in 2016-09-05 to 2016-09-07 from space-track.
TLEs = [
('1 27386U 02009A 16249.14961597 .00000004 00000-0 15306-4 0 9994',
'2 27386 98.2759 299.6736 0001263 83.7600 276.3746 14.37874511760117'
),
('1 27386U 02009A 16249.42796553 .00000002 00000-0 14411-4 0 9997',
'2 27386 98.2759 299.9417 0001256 82.8173 277.3156 14.37874515760157'
),
('1 27386U 02009A 16249.77590267 .00000010 00000-0 17337-4 0 9998',
'2 27386 98.2757 300.2769 0001253 82.2763 277.8558 14.37874611760201'
),
('1 27386U 02009A 16250.12384028 .00000006 00000-0 15974-4 0 9995',
'2 27386 98.2755 300.6121 0001252 82.5872 277.5467 14.37874615760253'
),
('1 27386U 02009A 16250.75012691 .00000017 00000-0 19645-4 0 9999',
'2 27386 98.2753 301.2152 0001254 82.1013 278.0311 14.37874790760345'
),
]
pop = plib.tle_snapshot(TLEs, sgp4_propagation=True)
pop['d'] = n.sqrt(4 * 2.3 * 4 / n.pi)
pop['m'] = 2300.
pop['C_R'] = 1.0
pop['C_D'] = 2.3
pop['A'] = 4 * 2.3
ccsds_file = './data/uhf_test_data/events/2002-009A-2016-09-06_08:27:08.tdm'
obs_data = ccsds_write.read_ccsds(ccsds_file)
jd_obs = dpt.mjd_to_jd(dpt.npdt2mjd(obs_data['date']))
jd_sort = jd_obs.argsort()
jd_obs = jd_obs[jd_sort]
jd_det = jd_obs[0]
pop.delete([0, 1, 2, 4]) #now just best ID left
jd_pop = dpt.mjd_to_jd(pop['mjd0'][0])
tt_obs = (jd_obs - jd_pop) * 3600.0 * 24.0
sim = Simulation(
radar=radar,
population=pop,
root=sim_root,
scheduler=schlib.dynamic_scheduler,
)
sim.observation_parameters(
duty_cycle=0.125,
SST_fraction=1.0,
tracking_fraction=0.0,
SST_time_slice=0.2,
)
sim.run_observation(jd_obs[-1] - jd_pop + 1.0)
sim.print_maintenance()
sim.print_detections()
sim.set_scheduler_args(logger=sim.logger, )
sim.run_scheduler()
sim.print_tracks()
print(sim.catalogue.tracklets[0]['t'])
print(jd_obs)
shutil.rmtree(sim_root)
assert False
def test_create_tracklet(self):
radar = rlib.eiscat_uhf()
radar.set_FOV(30.0, 25.0)
#tle files for envisat in 2016-09-05 to 2016-09-07 from space-track.
TLEs = [
('1 27386U 02009A 16249.14961597 .00000004 00000-0 15306-4 0 9994',
'2 27386 98.2759 299.6736 0001263 83.7600 276.3746 14.37874511760117'
),
('1 27386U 02009A 16249.42796553 .00000002 00000-0 14411-4 0 9997',
'2 27386 98.2759 299.9417 0001256 82.8173 277.3156 14.37874515760157'
),
('1 27386U 02009A 16249.77590267 .00000010 00000-0 17337-4 0 9998',
'2 27386 98.2757 300.2769 0001253 82.2763 277.8558 14.37874611760201'
),
('1 27386U 02009A 16250.12384028 .00000006 00000-0 15974-4 0 9995',
'2 27386 98.2755 300.6121 0001252 82.5872 277.5467 14.37874615760253'
),
('1 27386U 02009A 16250.75012691 .00000017 00000-0 19645-4 0 9999',
'2 27386 98.2753 301.2152 0001254 82.1013 278.0311 14.37874790760345'
),
]
pop = population_library.tle_snapshot(TLEs, sgp4_propagation=True)
#it seems to around 25m^2 area
d = n.sqrt(25.0 * 4 / n.pi)
pop.add_column('d', space_object_uses=True)
pop['d'] = d
ccsds_file = './data/uhf_test_data/events/2002-009A-1473150428.tdm'
obs_data = ccsds_write.read_ccsds(ccsds_file)
jd_obs = dpt.mjd_to_jd(dpt.npdt2mjd(obs_data['date']))
date_obs = obs_data['date']
sort_obs = n.argsort(date_obs)
date_obs = date_obs[sort_obs]
r_obs = obs_data['range'][sort_obs]
jd_sort = jd_obs.argsort()
jd_obs = jd_obs[jd_sort]
jd_det = jd_obs[0]
jd_pop = dpt.mjd_to_jd(pop['mjd0'])
pop_id = n.argmin(n.abs(jd_pop - jd_det))
obj = pop.get_object(pop_id)
print(obj)
jd_obj = dpt.mjd_to_jd(obj.mjd0)
print('Day difference detection - TLE: {}'.format(jd_det - jd_obj))
t_obs = (jd_obs - jd_obj) * (3600.0 * 24.0)
meas, fnames, ecef_stdevs = simulate_tracklet.create_tracklet(
obj,
radar,
t_obs,
hdf5_out=True,
ccsds_out=True,
dname="./tests/tmp_test_data",
noise=False,
)
out_h5 = fnames[0] + '.h5'
out_ccsds = fnames[0] + '.tdm'
print('FILES: ', fnames)
with h5py.File(out_h5, 'r') as h_det:
assert 'm_range' in h_det
assert 'm_range_rate' in h_det
assert 'm_time' in h_det
sim_data = ccsds_write.read_ccsds(out_ccsds)
date_sim = sim_data['date']
sort_sim = n.argsort(date_sim)
date_sim = date_sim[sort_sim]
r_sim = sim_data['range'][sort_sim]
v_sim = sim_data['doppler_instantaneous'][sort_sim]
lt_correction = n.round(r_sim / scipy.constants.c * 1e6).astype(
n.int64).astype('timedelta64[us]')
date_sim_cor = date_sim + lt_correction
t_sim = dpt.jd_to_unix(dpt.mjd_to_jd(dpt.npdt2mjd(date_sim_cor)))
for ind in range(len(date_sim)):
time_df = (dpt.npdt2mjd(date_sim_cor[ind]) -
dpt.npdt2mjd(date_obs[ind])) * 3600.0 * 24.0
assert time_df < 0.01
assert len(r_obs) == len(r_sim)
dat = {
't': t_sim,
'r': r_sim * 1e3,
'v': v_sim * 1e3,
}
cdat = correlator.correlate(
data=dat,
station=radar._rx[0],
population=pop,
metric=correlator.residual_distribution_metric,
n_closest=1,
out_file=None,
verbose=False,
MPI_on=False,
)
self.assertLess(n.abs(cdat[0]['stat'][0]), 5.0)
self.assertLess(n.abs(cdat[0]['stat'][1]), 50.0)
self.assertLess(n.abs(cdat[0]['stat'][2]), 5.0)
self.assertLess(n.abs(cdat[0]['stat'][3]), 50.0)
nt.assert_array_less(n.abs(r_sim - r_obs), 1.0)
os.remove(out_h5)
print('removed "{}"'.format(out_h5))
os.remove(out_ccsds)
print('removed "{}"'.format(out_ccsds))
sat_folder = os.sep.join(fnames[0].split(os.sep)[:-1])
os.rmdir(sat_folder)
print('removed "{}"'.format(sat_folder))