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import dsgp4 | ||
import numpy as np | ||
import random | ||
import torch | ||
import unittest | ||
|
||
error_string="Error: deep space propagation not supported (yet). The provided satellite has \ | ||
an orbital period above 225 minutes. If you want to let us know you need it or you want to \ | ||
contribute to implement it, open a PR or raise an issue at: https://github.com/esa/dSGP4." | ||
|
||
class UtilTestCase(unittest.TestCase): | ||
def test_mldsgp4_single_tles(self): | ||
lines=file.splitlines() | ||
#I randomly select 50 indexes out of 500 satellites | ||
indexes=list(range(1,len(lines),3)) | ||
tles=[] | ||
for i in indexes: | ||
data=[] | ||
data.append(lines[i]) | ||
data.append(lines[i+1]) | ||
data.append(lines[i+2]) | ||
tles.append(dsgp4.tle.TLE(data)) | ||
|
||
#let's load the ML-dSGP4 model without input/output correction | ||
#this should correspond to just SGP4 | ||
ml_dsgp4=dsgp4.mldsgp4(hidden_size=35, | ||
input_correction=0., | ||
output_correction=0., | ||
normalization_R=6958.137, | ||
normalization_V=7.947155867983262) | ||
#random times: | ||
tsinces=torch.rand(len(tles),)*10000 | ||
for tle in tles: | ||
#we first propagate with the ML-dSGP4: | ||
try: | ||
#we use torch.no_grad() to avoid keeping track of the derivatives: | ||
with torch.no_grad(): | ||
states_mldsgp4_out=ml_dsgp4(tle,tsinces) | ||
#let's unnormalize the output: | ||
states_mldsgp4_out[:,:3]*=ml_dsgp4.normalization_R | ||
states_mldsgp4_out[:,3:]*=ml_dsgp4.normalization_V | ||
except Exception as e: | ||
self.assertTrue((str(e).split()==error_string.split())) | ||
#now with the SGP4: | ||
try: | ||
states_dsgp4_out=dsgp4.propagate(tle, tsinces, initialized=False) | ||
except Exception as e: | ||
self.assertTrue((str(e).split()==error_string.split())) | ||
#testing the results: | ||
self.assertTrue(np.allclose(states_mldsgp4_out.detach().numpy().reshape(-1,2,3),states_dsgp4_out.detach().numpy(),atol=1e-13)) | ||
|
||
def test_mldsgp4_batch_tles(self): | ||
lines=file.splitlines() | ||
#I randomly select 50 indexes out of 500 satellites | ||
indexes=list(range(1,len(lines),3)) | ||
tles=[] | ||
for i in indexes: | ||
data=[] | ||
data.append(lines[i]) | ||
data.append(lines[i+1]) | ||
data.append(lines[i+2]) | ||
tles.append(dsgp4.tle.TLE(data)) | ||
tles=tles[:20] | ||
#let's load the ML-dSGP4 model without input/output correction | ||
#this should correspond to just SGP4 | ||
ml_dsgp4=dsgp4.mldsgp4(hidden_size=35, | ||
input_correction=0., | ||
output_correction=0., | ||
normalization_R=6958.137, | ||
normalization_V=7.947155867983262) | ||
#we now create a batch of TLE to later propagate it | ||
tles_=[] | ||
for tle in tles: | ||
tles_+=[tle]*100 | ||
tsinces = torch.cat([torch.linspace(0,24*60,100)]*len(tles)) | ||
|
||
#we first propagate with the ML-dSGP4 and we use torch.no_grad() to avoid keeping track of the derivatives: | ||
with torch.no_grad(): | ||
states_mldsgp4_out=ml_dsgp4(tles_,tsinces) | ||
#let's unnormalize the output: | ||
states_mldsgp4_out[:,:3]*=ml_dsgp4.normalization_R | ||
states_mldsgp4_out[:,3:]*=ml_dsgp4.normalization_V | ||
states_dsgp4_out=dsgp4.propagate_batch(tles_, tsinces, initialized=False) | ||
#testing the results: | ||
self.assertTrue(np.allclose(states_mldsgp4_out.detach().numpy().reshape(-1,2,3),states_dsgp4_out.detach().numpy(),atol=1e-12)) | ||
|
||
|
||
file=""" | ||
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2 34779 8.2706 50.6554 0319604 249.7419 106.8765 0.94516854 47397 | ||
0 CZ-3C R/B | ||
1 34780U 09018B 22069.16415195 .00005617 00000-0 24655-2 0 9994 | ||
2 34780 20.7006 321.7320 7131875 39.9300 354.9073 2.45578427112046 | ||
0 COSMOS 2251 DEB | ||
1 34784U 93036AAM 22068.36638343 .00000887 00000-0 22329-3 0 9995 | ||
2 34784 74.0090 52.3058 0060054 197.0015 162.9136 14.51648502685406 | ||
0 COSMOS 2251 DEB | ||
1 34787U 93036AAQ 22069.09891571 .00006428 00000-0 10002-2 0 9994 | ||
2 34787 74.0161 309.5714 0013326 73.4927 286.7708 14.73138766692625 | ||
0 COSMOS 2251 DEB | ||
1 34788U 93036AAR 22068.94122777 .00003389 00000-0 29271-3 0 9995 | ||
2 34788 73.9903 280.8094 0134553 31.4587 33.2058 14.90884651707015 | ||
0 COSMOS 2251 DEB | ||
1 34789U 93036AAS 22068.79813514 .00004225 00000-0 14561-2 0 9990 | ||
2 34789 74.2048 192.1639 0103795 18.1297 106.5324 14.33192302674468 | ||
0 COSMOS 2251 DEB | ||
1 34790U 93036AAT 22068.77443678 .00000752 00000-0 35584-3 0 9990 | ||
2 34790 74.0573 182.7815 0109231 218.6289 269.6003 14.18323488670567 | ||
0 COSMOS 2251 DEB | ||
1 34791U 93036AAU 22068.74233478 .00008295 00000-0 85591-3 0 9996 | ||
2 34791 74.0186 83.2071 0046207 213.6720 146.1524 14.89529078701119 | ||
0 COSMOS 2251 DEB | ||
1 34792U 93036AAV 22068.52062462 .00001556 00000-0 48324-3 0 9992 | ||
2 34792 74.0244 222.0367 0016818 284.6326 138.2886 14.41827284677664 | ||
0 COSMOS 2251 DEB | ||
1 34793U 93036AAW 22068.80776917 .00000342 00000-0 12642-3 0 9997 | ||
2 34793 74.0020 260.9066 0053385 18.5431 353.3504 14.35371548675985 | ||
0 COSMOS 2251 DEB | ||
1 34796U 93036AAZ 22068.93184426 .00000768 00000-0 23839-3 0 9994 | ||
2 34796 74.0333 248.2516 0116421 327.7024 155.9459 14.39028736682118 | ||
0 COSMOS 2251 DEB | ||
1 34797U 93036ABA 22068.80523530 .00001512 00000-0 62383-3 0 9999 | ||
2 34797 73.9824 95.8766 0092337 161.8617 198.5862 14.25350974677447 | ||
0 RISAT 2 | ||
1 34807U 09019A 22069.20015293 .00039560 00000-0 69476-3 0 9990 | ||
2 34807 41.2102 146.3902 0010166 13.1639 10.3869 15.49708764724344 | ||
0 BLOCK DM-SL R/B | ||
1 34811U 09020B 22068.85335389 -.00000141 00000-0 00000-0 0 9997 | ||
2 34811 2.0707 53.4388 4737112 332.3433 217.9075 1.81487904 85390 | ||
""" |