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)
e3d._tx[0].scan = e3d_scan
def pdf(a,e,i,omega,Omega,mu,s):
pass
#load the input population
pop = p.MC_sample(pdf,num=1e5)
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('BEAMPARK_RHO')
)
sim._verbose = True
sim.run_scan(48.0)
orbs = sim.detected_orbits(numpy=True,fname='orbs.txt')
#3 by 3 grid at 300km
az_points = n.arange(0,360,45).tolist() + [0.0];
el_points = [90.0-n.arctan(50.0/300.0)*180.0/n.pi, 90.0-n.arctan(n.sqrt(2)*50.0/300.0)*180.0/n.pi]*4+[90.0];
e3d_ionosphere = rslib.n_const_pointing_model(az_points,el_points,len(az_points), dwell_time = 7.5)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(SST=e3d_scan,secondary_list=[e3d_ionosphere])
#load the input population
pop = p.filtered_master_catalog_factor(e3d,treshhold=1e-2,seed=12345,filter_name='e3d_full_beam')
pop._objs = pop._objs[:2000,:]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('piggyback_test')
)
sim.calc_observation_params(\
duty_cycle=0.01, \
SST_fraction=0.1, \
tracking_fraction=1.0, \
SST_time_slice=0.2, \
interleaving_time_slice = 7.5, \
scan_during_interleaved = True)
sim._max_dpos = 50.0
sim._verbose = True
e3d.set_FOV(max_on_axis=25.0,horizon_elevation=30.0)
e3d.set_SNR_limits(min_total_SNRdb=10.0,min_pair_SNRdb=0.0)
e3d.set_TX_bandwith(bw = 1.0e6)
e3d.set_beam('TX', alib.planar_beam(az0 = 0.0,el0 = 90.0,lat = 60,lon = 19,f=233e6,I_0=10**4.2,a0=40.0,az1=0,el1=90.0) )
e3d.set_beam('RX', alib.planar_beam(az0 = 0.0,el0 = 90.0,lat = 60,lon = 19,f=233e6,I_0=10**4.5,a0=40.0,az1=0,el1=90.0) )
#initialize the observing mode
e3d_scan = rslib.ns_fence_rng_model(min_el = 30.0, angle_step = 2.0, dwell_time = 0.1)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(e3d_scan)
#load the input population
pop = p.filtered_master_catalog_factor(e3d,treshhold=1e-2,seed=12345,filter_name='e3d_planar_beam')
pop._objs = pop._objs[:2000,:]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('mov_test_v2')
)
sim.calc_observation_params(duty_cycle=0.25, SST_fraction=1.0, tracking_fraction=0.5, interleaving_time_slice = 0.4, SST_time_slice=0.2)
sim._max_dpos = 50.0
sim._verbose = True
for i in range(1000):
sim._catalogue._known[i] = True
#initialize the radar setup
e3d = rl.eiscat_3d()
e3d._max_on_axis=25.0
e3d._min_SNRdb=5.0
#initialize the observing mode
e3d_scan = rslib.ew_fence_model(min_el = 30, angle_step = 1, dwell_time = 0.1)
e3d_scan.set_radar_location(e3d)
e3d._tx[0].scan = e3d_scan
#rc.plot_radar_conf(e3d)
#load the input population
pop = p.master_catalog()
pop._objs = pop._objs[pop._objs[:,3] > 45.0,:]
pop._objs = pop._objs[pop._objs[:,8] > 1e-2,:]
pop._objs = pop._objs[pop._objs[:,2] < 1,:]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
scheduler = sch.isolated_static_sceduler,\
simulation_name = s.auto_sim_name('EW_FENCE')
)
sim._verbose = False
len(az_points),
dwell_time=0.4)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(SST=e3d_scan, secondary_list=[e3d_ionosphere])
#load the input population
pop = p.master_catalog_factor(treshhold=1e-2, seed=12345)
pop.filter('i', lambda x: x >= 45.0)
pop._objs = pop._objs[:50, :]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('FIN_ns_rng_fence_masterf_sst1')
)
# 25% duty, we get 10% for tracking, use all of it for tracking none for our own scan
#but we get data accsess to the interleaved experiment i.e. piggyback on ionospheric scan
sim.calc_observation_params(duty_cycle=0.25, \
SST_fraction=0.1, \
tracking_fraction=1.0, \
interleaving_time_slice = e3d_ionosphere.dwell_time(), \
scan_during_interleaved = True)
#coher_int_t=0.2
#sim._catalogue._known[:50] = True
sim._max_dpos = 50.0
sim._verbose = True
#initialize the observing mode
e3d_scan = rslib.ns_fence_rng_model(min_el=30.0,
angle_step=2.0,
dwell_time=0.2)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(e3d_scan)
#load the input population
pop = p.filtered_master_catalog_factor(e3d,
treshhold=1e-2,
seed=12345,
filter_name='e3d_full_beam')
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('coldstart_test')
)
sim.calc_observation_params(duty_cycle=0.25,
SST_fraction=1.0,
tracking_fraction=0.2,
interleaving_time_slice=0.4,
SST_time_slice=0.2)
sim._max_dpos = 50.0
sim._verbose = True
sim_root = '/ZFS_DATA/SORTSpp/sim_ew_fence_master_factor'
#initialize the radar setup
e3d = rl.eiscat_3d()
e3d._max_on_axis = 25.0
e3d._min_SNRdb = 5.0
#initialize the observing mode
e3d_scan = rslib.ew_fence_model(min_el=30, angle_step=1, dwell_time=0.1)
e3d_scan.set_radar_location(e3d)
e3d._tx[0].scan = e3d_scan
#rc.plot_radar_conf(e3d)
#load the input population
pop = p.master_catalog_factor()
pop._objs = pop._objs[pop._objs[:, 3] > 45.0, :]
pop._objs = pop._objs[pop._objs[:, 8] > 1e-2, :]
pop._objs = pop._objs[pop._objs[:, 2] < 1, :]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
scheduler = sch.isolated_static_sceduler,\
simulation_name = s.auto_sim_name('EW_FENCE_FACTOR')
)
sim._verbose = False
#initialize the observing mode
#lets say you measure ionospheric parameters in a 3-by-3 grid at 300km altitide separated by 50km directions, integration time 0.4s
#we piggyback a analysis on this, how good at discovery is it?
az_points = n.arange(0, 360, 45).tolist() + [0.0]
el_points = [
90.0 - n.arctan(50.0 / 300.0) * 180.0 / n.pi,
90.0 - n.arctan(n.sqrt(2) * 50.0 / 300.0) * 180.0 / n.pi
] * 4 + [90.0]
e3d_scan = rslib.n_const_pointing_model(az_points,
el_points,
len(az_points),
dwell_time=0.4)
e3d_scan.set_radar_location(e3d)
e3d._tx[0].scan = e3d_scan
#load the input population
pop = p.master_catalog()
pop._objs = pop._objs[pop._objs[:, 3] > 45.0, :]
pop._objs = pop._objs[pop._objs[:, 8] > 1e-2, :]
pop._objs = pop._objs[pop._objs[:, 2] < 1, :]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
scheduler = sch.isolated_static_sceduler,\
simulation_name = s.auto_sim_name('PIGGYBACK_IONSPH_SCAN')
)
sim._verbose = False
az_points = []
el_points = []
for ind in range(len(el_points_fence)):
dwells.append(0.4)
az_points.append(0.0)
el_points.append(-90.0)
dwells.append(0.1)
az_points.append(az_points_fence[ind])
el_points.append(el_points_fence[ind])
e3d_scan = rslib.n_dyn_dwell_pointing_model(az_points, el_points, len(dwells),
dwells)
e3d_scan.set_radar_location(e3d)
e3d._tx[0].scan = e3d_scan
#load the input population
pop = p.master_catalog()
pop._objs = pop._objs[pop._objs[:, 3] > 45.0, :]
pop._objs = pop._objs[pop._objs[:, 8] > 1e-2, :]
pop._objs = pop._objs[pop._objs[:, 2] < 1, :]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
scheduler = sch.isolated_static_sceduler,\
simulation_name = s.auto_sim_name('SHARED_EW_FENCE')
)
sim._verbose = False
#initialize the observing mode
e3d_scan = rslib.ns_fence_rng_model(min_el=30.0,
angle_step=2.0,
dwell_time=0.1)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(e3d_scan)
#load the input population
pop = p.filtered_master_catalog_factor(e3d,
treshhold=1e-2,
seed=12345,
filter_name='e3d_planar_beam')
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('ns_fence_full_sst_maint')
)
sim.calc_observation_params(duty_cycle=0.25,
SST_fraction=1.0,
tracking_fraction=1.0,
interleaving_time_slice=0.4,
SST_time_slice=0.2)
sim._max_dpos = 50.0
sim._verbose = True
#initialize the observing mode
e3d_scan = rslib.ns_fence_rng_model(min_el=30.0,
angle_step=2.0,
dwell_time=0.1)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(e3d_scan)
#load the input population
pop = p.filtered_master_catalog_factor(e3d,
treshhold=1e-2,
seed=12345,
filter_name='e3d_planar_beam')
pop._objs = pop._objs[:100, :]
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('maint_test')
)
sim.calc_observation_params(duty_cycle=0.25,
SST_fraction=1.0,
tracking_fraction=1.0,
interleaving_time_slice=0.4,
SST_time_slice=0.2)
sim._max_dpos = 50.0
sim._verbose = True
#initialize the observing mode
e3d_scan = rslib.ns_fence_rng_model(min_el=30.0,
angle_step=2.0,
dwell_time=0.2)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(e3d_scan)
#load the input population
pop = p.filtered_master_catalog_factor(e3d,
treshhold=1e-2,
seed=12345,
filter_name='e3d_full_beam')
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('ns_fence_full_sst_cold_start')
)
sim.calc_observation_params(duty_cycle=0.25,
SST_fraction=1.0,
tracking_fraction=0.2,
interleaving_time_slice=0.4,
SST_time_slice=0.2)
sim._max_dpos = 50.0
sim._verbose = True
el_points,
len(az_points),
dwell_time=7.5)
e3d_scan.set_radar_location(e3d)
e3d.set_scan(SST=e3d_scan, secondary_list=[e3d_ionosphere])
#load the input population
pop = p.filtered_master_catalog_factor(e3d,
treshhold=1e-2,
seed=12345,
filter_name='e3d_full_beam')
sim = s.simulation( \
radar = e3d,\
population = pop,\
sim_root = sim_root,\
simulation_name = s.auto_sim_name('grid_piggyback_cold_start')
)
sim.calc_observation_params(\
duty_cycle=0.01, \
SST_fraction=0.1, \
tracking_fraction=1.0, \
SST_time_slice=0.2, \
interleaving_time_slice = 7.5, \
scan_during_interleaved = True)
sim._max_dpos = 50.0
sim._verbose = True