def n_beampark_plot_mov():
az_points = np.arange(0., 360., 60.).tolist()
el_points = np.linspace(80., 90., num=len(az_points)).tolist()
scan = rslib.n_const_pointing_model(az=az_points,
el=el_points,
lat=69.,
lon=19.,
alt=150.,
dwell_time=0.1)
plot_radar_scan_movie(scan, earth=True, rotate=False)
plt.show()
#initialize the radar setup
e3d = rl.eiscat_3d()
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.e3d_array_beam_stage1(opt='dense') )
e3d.set_beam('RX', alib.e3d_array_beam() )
#initialize the observing mode
e3d_scan = rslib.ns_fence_rng_model(min_el = 30.0, angle_step = 2.0, dwell_time = 0.2)
#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')
)
#initialize the radar setup
e3d = rl.eiscat_3d()
e3d._max_on_axis = 25.0
e3d._min_SNRdb = 5.0
#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,\
'64.48487395668005', '62.93289943117691', '61.33878768753357',
'59.69647098103616', '57.99766296011879', '56.23923388331565',
'54.40755219009088', '52.44010673902446', '50.4247836215483',
'48.29222295539205', '46.01278403125778', '43.55617730018973',
'40.8717857555977', '37.886094818016815', '34.478961143266076', '30.0'
]
els = [float(el) for el in els]
azs = [-180.0] * (len(els) - 1) + [0.0] * len(els)
els = els[1:][::-1] + els
dwell_time = sat_traverse_time / float(len(azs))
scan = rslib.n_const_pointing_model(
az=azs,
el=els,
lat=radar._tx[0].lat,
lon=radar._tx[0].lon,
alt=radar._tx[0].alt,
dwell_time=dwell_time,
)
sim.radar.set_scan(scan)
sim.observation_parameters(
duty_cycle=0.25,
SST_fraction=1.0,
tracking_fraction=0.25,
SST_time_slice=dwell_time,
)
sim.run_observation(SIM_TIME)
#Project 1: Create a funky spiral scan!
import numpy as np
import matplotlib.pyplot as plt
#SORTS++
import radar_scans
import radar_scan_library
#step sizes
d_az = 3.0
d_el = 0.1
az = [0.0]
el = [90.0]
while el[-1] > 50.0:
az += [np.mod(az[-1] + d_az, 360.0)]
el += [el[-1] - d_el]
SC = radar_scan_library.n_const_pointing_model(az,
el,
69,
31,
150,
dwell_time=0.1)
#Now we dont want to plt
#radar_scans.plot_radar_scan(SC, earth=True)
#plt.show()