def spacex_ssoa_analysis():
# # Using Thomas excel file
# obj_id = 90000
# UTC = datetime(2018, 6, 23, 2, 13, 21)
#
# ecef_dict = {}
# ecef_dict[obj_id] = {}
#
# r_ITRF = np.array([-3651380.321, 1598487.431, -5610448.359]) * 0.001
# v_ITRF = np.array([5276.523548, -3242.081015, -4349.310553]) * 0.001
#
# ecef_dict[obj_id]['r_ITRF'] = r_ITRF.reshape(3,1)
# ecef_dict[obj_id]['v_ITRF'] = v_ITRF.reshape(3,1)
# ecef_dict[obj_id]['UTC'] = UTC
# Using Rasit TLE
# obj_id_list = [43690, 43691, 43692, 43693, 43694, 43695, 43696, 43697]
obj_id_list = [43758, 43763]
# line1 = '1 99999U 18999B 18315.16116898 +.00000500 +00000-0 +32002-2 0 9993'
# line2 = '2 99999 085.0168 090.4036 0012411 292.8392 108.1000 15.20833469601616'
#
# line1 = '1 99999U 18999B 18315.19693718 +.00000500 +00000-0 +60940-2 0 9999'
# line2 = '2 99999 085.0165 102.9279 0012642 291.6624 115.0006 15.20806704601617'
# line1 = '1 43690U 18088A 18315.20213355 .00000372 -11738-5 00000+0 0 9993'
# line2 = '2 43690 85.0339 102.9499 0224293 222.7416 214.1638 15.71130100 06'
#
# UTC = tletime2datetime(line1)
# tle_dict = {}
# tle_dict[obj_id] = {}
# tle_dict[obj_id]['line1_list'] = [line1]
# tle_dict[obj_id]['line2_list'] = [line2]
# tle_dict[obj_id]['UTC_list'] = [UTC]
UTC = datetime(2018, 12, 3, 0, 0, 0)
load = Loader(os.path.join(metis_dir, 'skyfield_data'))
ephemeris = load('de430t.bsp')
ts = load.timescale()
sensor_id_list = [
'RMIT ROO', 'UNSW Falcon', 'USAFA Falcon', 'Mamalluca Falcon'
]
# Times for visibility check
ndays = 5
dt = 10
UTC0 = ts.utc(UTC.replace(tzinfo=utc)).utc
sec_array = list(range(0, 86400 * ndays, dt))
skyfield_times = ts.utc(UTC0[0], UTC0[1], UTC0[2], UTC0[3], UTC0[4],
sec_array)
vis_dict = compute_visible_passes(skyfield_times, obj_id_list,
sensor_id_list, ephemeris)
print(vis_dict)
# Generate output file
vis_file_min_el = 0.
outdir = os.getcwd()
vis_file = os.path.join(outdir, 'SSOA_visible_passes_2018_12_04.csv')
generate_visibility_file(vis_dict, vis_file, vis_file_min_el)
return
# print(UTC0)
# sec_array = list(range(0,86400*ndays,dt))
# skyfield_times = ts.utc(UTC0[0], UTC0[1]+2, UTC0[2],
# UTC0[3], UTC0[4], sec_array)
#
# print(skyfield_times[0])
# mistake
# obj_id_list = [45727, 47967, 29648, 46113, 24846, 23528, 26624, 35491]
obj_id_list = [47967]
sensor_id_list = ['UNSW Falcon', 'CMU Falcon']
UTC0 = datetime(2021, 9, 13, 0, 0, 0)
delta_t = 7. * 86400.
dt = 10.
UTC_list = [
UTC0 + timedelta(seconds=ti) for ti in list(np.arange(0, delta_t, dt))
]
sensor_dict = define_sensors(sensor_id_list)
vis_dict = compute_visible_passes(UTC_list, obj_id_list, sensor_dict)
# print(vis_dict)
# Generate output file
vis_file_min_el = 10.
# outdir = os.path.join(metis_dir, 'skyfield_data')
# vis_file = os.path.join(outdir, 'test_visible_passes.csv')
vis_file = 'M2_visible_passes_2021_09_13.csv'
generate_visibility_file(vis_dict, vis_file, vis_file_min_el)
def pslv_analysis():
obj_id = 90000
# Using Lawrence orbit parameters
h = 505.
a = Re + h
e = 1e-4
LTAN = 10.
launch_dt = datetime(2018, 11, 19, 16, 30, 0)
RAAN = sunsynch_RAAN(launch_dt, LTAN)
i = sunsynch_inclination(a, e)
w = 0.
M = 180.
kep_dict = {}
kep_dict[obj_id] = {}
kep_dict[obj_id]['a'] = a
kep_dict[obj_id]['e'] = e
kep_dict[obj_id]['i'] = i
kep_dict[obj_id]['RAAN'] = RAAN
kep_dict[obj_id]['w'] = w
kep_dict[obj_id]['M'] = M
kep_dict[obj_id]['UTC'] = launch_dt
tle_dict, tle_df = kep2tle([obj_id], kep_dict)
print(tle_dict)
print(a, e, i, RAAN, w, M)
UTC = datetime(2018, 11, 19, 16, 30, 0)
load = Loader(os.path.join(metis_dir, 'skyfield_data'))
ephemeris = load('de430t.bsp')
ts = load.timescale()
sensor_id_list = [
'Stromlo Optical', 'RMIT ROO', 'UNSW Falcon', 'FLC Falcon',
'Mamalluca Falcon'
]
# Times for visibility check
ndays = 14
dt = 10
obj_id_list = [obj_id]
UTC0 = ts.utc(UTC.replace(tzinfo=utc)).utc
sec_array = list(range(0, 86400 * ndays, dt))
skyfield_times = ts.utc(UTC0[0], UTC0[1], UTC0[2], UTC0[3], UTC0[4],
sec_array)
vis_dict = compute_visible_passes(skyfield_times, obj_id_list,
sensor_id_list, ephemeris, tle_dict)
print(vis_dict)
# Generate output file
vis_file_min_el = 0.
outdir = os.getcwd()
vis_file = os.path.join(outdir, 'PSLV_visible_passes.csv')
generate_visibility_file(vis_dict, vis_file, vis_file_min_el)
return
#
# print(tle_dict)
# Generate pandas dataframe
# tle_df = tledict2dataframe(tle_dict)
sensor_id_list = [
'Stromlo Optical', 'Zimmerwald Optical', 'Arequipa Optical',
'Haleakala Optical', 'Yarragadee Optical', 'RMIT ROO', 'UNSW Falcon',
'FLC Falcon', 'Mamalluca Falcon'
]
# Times for visibility check
ndays = 3
dt = 10
UTC0 = ts.utc(UTC.replace(tzinfo=utc)).utc
sec_array = list(range(0, 86400 * ndays, dt))
skyfield_times = ts.utc(UTC0[0], UTC0[1], UTC0[2], UTC0[3], UTC0[4],
sec_array)
vis_dict = compute_visible_passes(skyfield_times, obj_id_list,
sensor_id_list, ephemeris, tle_dict)
print(vis_dict)
# Generate output file
vis_file_min_el = 10.
outdir = os.getcwd()
vis_file = os.path.join(outdir, 'rocketlab_visible_passes.csv')
generate_visibility_file(vis_dict, vis_file, vis_file_min_el)