Synthetic Image Results

Comparison plots for the synthetic and real images

from types import SimpleNamespace

import matplotlib.pyplot as plt
import numpy as np

import mirage as mr
import mirage.photo as mrp

np.random.seed(2)
info_path = '/Users/liamrobinson/Library/CloudStorage/OneDrive-purdue.edu/pogs/2022/2022-09-18/ObservationData.mat'
add_distortion = True
add_refraction = True
station = mr.Station()
station.telescope.fwhm = 3.5
station.telescope.gain = 5.1
mr.tic('Loading star catalog')
catalog = mr.GaiaSpectralStarCatalog(station, mr.now())
mr.toc()

res = mr.generate_matched_image(
    info_path,
    200,
    station,
    catalog,
    add_distortion,
    add_refraction,
    noise=True,
    obj_total_brightness=1.818e4,
)

print(res.keys())
print(res['fits_info'])

n = SimpleNamespace(**res)

plt.figure()
plt.plot(n.counts_syn, n.fit_counts_obs_of_syn(n.counts_syn), c='r', markersize=7)
plt.scatter(n.counts_syn, n.counts_obs, s=5)
plt.xlabel('Synthetic counts')
plt.ylabel('Observed counts')
plt.grid()
plt.xscale('log')
plt.yscale('log')
plt.legend(['Best linear fit', 'Data'])
plt.title('Observed vs Synthetic Counts (POGS)')


plt.figure()
plt.scatter(n.err_updated[:, 0], n.err_updated[:, 1], s=5)
plt.yscale('symlog')
plt.xscale('symlog')
t = np.linspace(0, 2 * np.pi + 0.1, 1000)
plt.plot(5 * np.cos(t), 5 * np.sin(t), c='k')
plt.plot(1 * np.cos(t), 1 * np.sin(t), c='r')
plt.legend(
    ['Centroid errors', '5-pixel boundary', '1-pixel boundary'], loc='upper right'
)
plt.ylim(-100, 100)
plt.xlim(-100, 100)
plt.xlabel('$x$ pixel error')
plt.ylabel('$y$ pixel error')
plt.title('Centroiding Errors After Image Alignment')
plt.grid()
plt.show()

# endd

• 
• 

Loading star catalog: 1.24e+00 seconds
Applying 1.14e-02 deg of refraction
fits_info["integration_time"]=4.0
sim counts are too high by a factor of 1.06
BEFORE QUEST: median error 84.77 pixels
Performing the QUEST fit with 158 stars
Applying a 70.47 arcsecond rotation to the telescope orientation
Look direction: [ 0.15285185 -0.88205489  0.44566297]
Up direction: [0.98453582 0.17497072 0.00862907]
AFTER QUEST: median error 1.14 pixels
Objects in frame 1:
NAVSTAR 80 (USA 309)
Synthesizing CCD Image: 1.70e+00 seconds
dict_keys(['img_syn', 'img', 'fits_info', 'stars_obs', 'expected_stars_corrected', 'counts_syn', 'counts_obs', 'fit_counts_obs_of_syn', 'data_mat', 'err_updated'])
{'ra_initial': array([4.88377607]), 'dec_initial': array([0.46459827]), 'station': <mirage.station.Station object at 0x162af6ed0>, 'dates': array([datetime.datetime(2022, 9, 18, 5, 13, 0, 879000, tzinfo=datetime.timezone.utc),
       datetime.datetime(2022, 9, 18, 5, 13, 4, 879000, tzinfo=datetime.timezone.utc)],
      dtype=object), 'date_mid': datetime.datetime(2022, 9, 18, 5, 13, 2, 879000, tzinfo=datetime.timezone.utc), 'look_dirs_eci': array([[ 0.15247134, -0.88090375,  0.44806369],
       [ 0.15279705, -0.88059979,  0.44854996]]), 'look_dir_eci_mid': array([[ 0.1526342 , -0.88075182,  0.44830685]]), 'ccd_adu': array([[1066, 1059, 1035, ..., 1060, 1050, 1041],
       [1055, 1051, 1049, ..., 1044, 1054, 1041],
       [1028, 1038, 1033, ..., 1040, 1047, 1056],
       ...,
       [1026, 1040, 1002, ..., 1056, 1019, 1038],
       [1024, 1033, 1003, ..., 1046, 1033, 1044],
       [1020, 1027, 1030, ..., 1034, 1040, 1043]], dtype=uint16), 'br_parabola': array([[1018.50497038, 1018.50502394, 1018.50504757, ..., 1018.4984989 ,
        1018.49847569, 1018.4984231 ],
       [1018.50502394, 1018.50507749, 1018.50510113, ..., 1018.49855191,
        1018.4985287 , 1018.4984761 ],
       [1018.50504757, 1018.50510113, 1018.50512477, ..., 1018.49857531,
        1018.49855209, 1018.4984995 ],
       ...,
       [1018.4984989 , 1018.49855191, 1018.49857531, ..., 1018.49209277,
        1018.49206979, 1018.49201775],
       [1018.49847569, 1018.4985287 , 1018.49855209, ..., 1018.49206979,
        1018.49204683, 1018.49199478],
       [1018.4984231 , 1018.4984761 , 1018.4984995 , ..., 1018.49201775,
        1018.49199478, 1018.49194274]]), 'airmass': 1.3733946736379, 'total_pix_tracked': np.float64(187.5251659334518), 'ccd_temp': -20.0, 'center_elevation_rad': np.float64(0.8155672038870313), 'integration_time': 4.0, 'ra_rate': 21.5079697, 'dec_rate': 28.0521493, 'ra_mid': array([4.88398459]), 'dec_mid': array([0.46487028])}

Overlaying the two images

# mrp.plot_fits(n.img_syn)
# plt.show()

# Background subtraction
n.img, br_map_real, br_mask_real, br_std_img = mrp.background_subtract(
    n.img,
    reduction_factor=1,
    sigma=3.0,
    box_size=100,
)
n.img_syn, br_map_synth, br_mask_synth, br_std_synth = mrp.background_subtract(
    n.img_syn,
    reduction_factor=1,
    sigma=3.0,
    box_size=100,
)

print(f'Standard deviation of the background (observed): {br_std_img:.2f}')
print(f'Standard deviation of the background (synthetic): {br_std_synth:.2f}')

np.savez(
    '/Users/liamrobinson/Documents/maintained-research/mirage/examples/14-digital-twin-2024/data/pogs.npz',
    img=n.img,
    img_syn=n.img_syn,
    br_std_img=br_std_img,
    br_std_synth=br_std_synth,
)

Standard deviation of the background (observed): 10.40
Standard deviation of the background (synthetic): 10.95