(28751) Eggl orbit determination test

import grss
prop = grss.prop
fit = grss.fit

import numpy as np
np.set_printoptions(precision=40, linewidth=np.inf)

body_id = '28751'
init_sol, init_cov, nongrav_info = fit.get_sbdb_info(body_id)
de_kernel = 440

add_gaia_obs = True
optical_obs_file = None
t_min_tdb = None
t_max_tdb = None
debias_lowres = True
deweight = True
eliminate = False
num_obs_per_night = 4
verbose = True
obs_df = fit.get_optical_obs(body_id, optical_obs_file, t_min_tdb, t_max_tdb, debias_lowres, deweight, eliminate, num_obs_per_night, verbose)
obs_df = fit.add_radar_obs(obs_df, t_min_tdb, t_max_tdb, verbose)
if add_gaia_obs:
    gaia_dr = 'gaiafpr'
    obs_df = fit.add_gaia_obs(obs_df, t_min_tdb, t_max_tdb, gaia_dr, verbose)

Read in 1510 observations from the MPC.
        WARNING: At least one deprecated star catalog in the data.
        Filtered to 1510 observations that satisfy the time range and accepted observatory constraints.
Applying Eggl et al. (2020) debiasing scheme to the observations.
        Unknown star catalog: GSC
        Unknown star catalog: UNK
        No debiasing needed for 604 observations.
        Debiased 891 observations.
        No bias information for 15 observations.
Applying Vereš et al. (2017) weighting scheme to the observations.
        Using 1412 CCD observations with station-specific weight rules.
Applying sqrt(N/4) deweighting scheme.
        Deweighted 297 observations.
Read in 273 Gaia observations from gaiafpr
        Filtered to 273 observations that satisfy the time range constraints.

n_iter_max = 10
fit_sim = fit.FitSimulation(init_sol, obs_df, init_cov, n_iter_max=n_iter_max, de_kernel=de_kernel, nongrav_info=nongrav_info)

fit_sim.filter_lsq()

Iteration               Unweighted RMS          Weighted RMS            Chi-squared             Reduced Chi-squared
1                        0.381                   0.550                   1078.819                        0.303
2                        0.381                   0.550                   1078.397                        0.303
Converged without rejecting outliers. Starting outlier rejection now...
3                        0.371                   0.533                   1011.850                        0.285
4                        0.371                   0.533                   1011.492                        0.285
Converged after rejecting outliers. Rejected 5 out of 1783 optical observations.

fit_sim.print_summary()

Summary of the orbit fit calculations after postfit pass:
==============================================================
RMS unweighted: 0.37124363565131496
RMS weighted: 0.5325868969205222
chi-squared: 1011.4916306829228
reduced chi-squared: 0.2849272199106825
square root of reduced chi-squared: 0.5337857434501998
--------------------------------------------------------------
Solution Time: MJD 57925.000 TDB = 2017-06-21 00:00:00.000 TDB
Solution Observation Arc: 10751.92 days (29.44 years)
--------------------------------------------------------------
Fitted Variable         Initial Value                   Uncertainty                     Fitted Value                    Uncertainty                     Change                          Change (sigma)
e                       1.13513023195e-01               2.67007990945e-09               1.13513023699e-01               2.67513861027e-09               +5.03566716104e-10              +0.188
q                       2.23466959797e+00               5.18141566983e-09               2.23466959743e+00               5.17869282268e-09               -5.40652855818e-10              -0.104
tp                      5.79392660677e+04               3.10261576800e-06               5.79392660677e+04               3.10175869749e-06               +2.32321326621e-08              +0.007
om                      2.76276478601e+02               2.72877315675e-06               2.76276478735e+02               2.72847257859e-06               +1.33750461373e-07              +0.049
w                       4.93445457313e+01               2.91298357846e-06               4.93445455952e+01               2.91279717229e-06               -1.36120377192e-07              -0.047
i                       1.74605654303e+00               7.18318012437e-08               1.74605654397e+00               7.17370736695e-08               +9.40268973793e-10              +0.013

fit_sim.plot_summary(auto_close=True)

fit_sim.iters[-1].plot_iteration_summary(title='Postfit Residuals', auto_close=True)

mean_0 = np.array(list(init_sol.values())[1:])
cov_0 = init_cov
mean_f = np.array(list(fit_sim.x_nom.values()))
cov_f = fit_sim.covariance

maha_dist_f, maha_dist_0, bhattacharya, bhatt_coeff = fit.get_similarity_stats(mean_0, cov_0, mean_f, cov_f)
print(f'Mahalonobis distance between JPL and GRSS solution: {maha_dist_f:0.2f}')
print(f'Mahalonobis distance between GRSS and JPL solution: {maha_dist_0:0.2f}')
print(f'Bhattacharya distance between JPL and GRSS solution: {bhattacharya:0.4f}')
print(f'Bhattacharya coefficient between JPL and GRSS solution: {bhatt_coeff:0.4f}')

Mahalonobis distance between JPL and GRSS solution: 0.24
Mahalonobis distance between GRSS and JPL solution: 0.24
Bhattacharya distance between JPL and GRSS solution: 0.0000
Bhattacharya coefficient between JPL and GRSS solution: 1.0000

assert maha_dist_f < 5.0
assert maha_dist_0 < 5.0
assert bhattacharya < 0.10
assert bhatt_coeff > 0.90