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Gauss-Radau Small-body Simulator. Maintainer: @rahil-makadia

Home Page: https://rahil-makadia.github.io/grss/

License: MIT License

CMake 0.08% Shell 0.05% Python 6.15% C++ 11.49% Jupyter Notebook 82.08% TeX 0.10% C 0.05%
asteroids astronomy comets orbit-determination orbit-propagation

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grss's Issues

Need to define Earth body-fixed frame for epochs before 1972

The high-precision Earth binary orientation files do not have data before Jan-01-1972, therefore I need to make SPICE aware of an IAU_EARTH frame that is valid for times before this. Currently, there is no workaround for this, and an error will be thrown if a user attempts to fit astrometry before 1972.

STM propagation

Need to add capability to propagate state transition matrices alongside states and additional parameters to have the option of using analytic partials for orbit fitting

get_mpc_optical_obs_array crashes when querying optical observations

By chance I was confronted with a new issue that get_mpc_optical_obs_array crashed when reading optical observations of 530871:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/fit/fit_optical.py", line 955, in get_mpc_optical_obs_array
    observer_program_codes) = get_optical_data(body_id, de_kernel_path, optical_obs_file,
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/fit/fit_optical.py", line 137, in get_optical_data
    full_obs_code = get_space_based_observer_code(obs_time_mjd_tdb, first_80, last_80)
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/fit/fit_optical.py", line 37, in get_space_based_observer_code
    if last_80[32] == '1':
IndexError: string index out of range

I suspect that this issue is caused by the following observation:

 r0871K10A85R* x2010 01 07.86121 21 48 04.53 +63 48 59.2                L~0I1lC51

which, unlike other C51 measurements, does not contain the state vector of the spacecraft.

Sun J2 acceleration

The Sun J2 acceleration calculation needs to account for Sun pole direction (or for that matter, the J2 calcs need to take body pole directions as input)

invalid value & more frequent outlier rejection failure

I did a git pull to update grss but then I started to have the following issues popping out frequently with exactly the same steps of downloading optical + radar observations.

Issue 1:

Software/anaconda3/lib/python3.9/site-packages/grss/fit/fit_simulation.py:47: RuntimeWarning: invalid value encountered in sqrt
  variance = np.sqrt(np.diag(self.covariance))

Issue 2:
The package seems to be more vulnerable to rejection of `bad' Gaia FPR observations compared to before. The following example is with 5786:

1			 0.501			 9.000			 825.862			 0.301
2			 0.861			 9.682			 2229.015			 0.811
3			 3.579			 29.089			 38730.936			 14.094
4			 23.208			 171.871			 1582192.194			 575.761
5			 141.711			 1038.769			 58744111.822			 21377.042
6			 857.477			 6260.708			 2145982043.198			 780925.052
7			 65441.396			 81808.656			 14619462321301.691			 5320037234.826
8			 111617.170			 244251.420			 35633434073642.562			 12967042967.119
9			 325809.396			 1169012.455			 447851888690552.000			 162973758621.016
Warning: Downleg light time did not converge for body integBody_pert_w_minus at time 58017.4, change from previous iteration was 1.43635e-15
10			 403163.630			 42274032.154			 1835539810212089.500			 667954807209.640
WARNING: Maximum number of iterations reached without converging.

After getting rid of the Gaia FPR observations, the convergence is quick:

Iteration		Unweighted RMS		Weighted RMS 		Chi-squared		Reduced Chi-squared
1			 0.530			 0.553			 733.074			 0.307
2			 0.527			 0.548			 719.551			 0.301
3			 0.527			 0.548			 719.551			 0.301
Converged without rejecting outliers. Starting outlier rejection now...
4			 0.466			 0.493			 582.173			 0.245
5			 0.466			 0.492			 580.154			 0.245
6			 0.466			 0.492			 580.154			 0.245
Converged after rejecting outliers. Rejected 9 out of 1198 optical observations.

The previous outlier rejection algorithm may have worked even better.

Impact/Close approach handling

Add impact/close approach detection and calculations as well as B-plane calculation for future propagation (start with Öpik frame but need to have B.R & B.T framework as well)

Error in importing grss

While the installation went smoothly, I encountered the following error when I
import grss
in python3:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/__init__.py", line 2, in <module>
    from . import fit
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/fit/__init__.py", line 5, in <module>
    from .fit_simulation import *
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/fit/fit_simulation.py", line 7, in <module>
    from .. import prop
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/prop/__init__.py", line 2, in <module>
    from .prop_parallel import *
  File "/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/prop/prop_parallel.py", line 5, in <module>
    from . import prop_simulation as prop
ImportError: cannot import name 'prop_simulation' from partially initialized module 'grss.prop' (most likely due to a circular import) (/home/mthui/Software/anaconda3/lib/python3.9/site-packages/grss/prop/__init__.py)

Any idea as to how to fix this?

Oscillatory behavior of grss.fit.FitSimulation.filter_lsq()

By chance I encounter a situation where there is no convergence in the orbital solution with asteroid (1566) Icarus. Rather, the equilibrium turns out to be oscillatory between two RMS values, which should not happen. I've tried different initial condition values but it just didn't help. My gut feeling is that this is caused by some of the bad outliers. The following is just a copy of the outputs I had:

>>> fit_sim.filter_lsq()
Iteration		Unweighted RMS		Weighted RMS 		Chi-squared		Reduced Chi-squared
1			 31.836			 12.182			 490247.453			 126.352
2			 31.392			 11.885			 469864.100			 121.099
3			 31.392			 11.885			 469868.731			 121.100
Converged without rejecting outliers. Starting outlier rejection now...
4			 4.146			 5.193			 7842.118			 2.435
5			 5.734			 5.359			 15865.943			 4.822
6			 10.901			 9.744			 46691.113			 14.936
7			 4.526			 6.064			 7945.102			 2.337
8			 61.607			 45.862			 7057710.287			 2072.140
9			 4.559			 9.744			 7509.586			 2.266
10			 68.774			 74.668			 15706754.952			 4998.967
11			 48.223			 24.802			 1950870.070			 593.330
12			 60.913			 35.487			 4043622.686			 1207.052
13			 3.303			 28.167			 6108.411			 1.745
14			 3.302			 4.760			 3816.540			 1.079
15			 3.532			 4.779			 4640.068			 1.326
16			 5.273			 4.966			 11746.093			 3.366
17			 42.173			 26.511			 2364099.628			 677.781
18			 5.300			 7.304			 10401.501			 2.932
19			 3.192			 4.761			 3463.222			 0.976
20			 3.460			 4.779			 4502.867			 1.288
21			 4.067			 4.862			 6402.694			 1.824
22			 6.620			 6.091			 46566.127			 13.154
23			 3.387			 4.778			 4214.280			 1.206
24			 5.312			 5.054			 11505.927			 3.254
25			 3.410			 4.778			 4321.441			 1.237
26			 5.312			 5.054			 11506.174			 3.254
27			 3.410			 4.778			 4321.453			 1.237
28			 5.312			 5.054			 11506.107			 3.254
29			 3.410			 4.778			 4321.450			 1.237
30			 5.312			 5.054			 11506.274			 3.254
31			 3.410			 4.778			 4321.468			 1.237
32			 5.312			 5.054			 11505.915			 3.254
33			 3.410			 4.778			 4321.459			 1.237
34			 5.312			 5.054			 11506.042			 3.254
35			 3.410			 4.778			 4321.477			 1.237
36			 5.312			 5.054			 11505.956			 3.254
37			 3.410			 4.778			 4321.462			 1.237
38			 5.312			 5.054			 11506.182			 3.254
39			 3.410			 4.778			 4321.447			 1.237
40			 5.312			 5.054			 11506.135			 3.254
41			 3.410			 4.778			 4321.447			 1.237
42			 5.312			 5.054			 11505.979			 3.254
43			 3.410			 4.778			 4321.472			 1.237
44			 5.312			 5.054			 11505.972			 3.254
45			 3.410			 4.778			 4321.433			 1.237
46			 5.312			 5.054			 11505.866			 3.254
47			 3.410			 4.778			 4321.434			 1.237
48			 5.312			 5.054			 11505.838			 3.254
49			 3.410			 4.778			 4321.457			 1.237
50			 5.312			 5.054			 11505.929			 3.254
WARNING: Maximum number of iterations reached without converging.

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