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