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data/expected_example_output/example_16/ob03235_1/ob03235_1_model_prior.png
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data/expected_example_output/example_16/ob03235_1/output.txt
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Mean acceptance fraction: 0.110 | ||
Mean autocorrelation time [steps]: nan | ||
Fitted parameters: | ||
t_0 : 2452848.07842 +0.09056 -0.11779 | ||
u_0 : 0.13006 +0.00292 -0.00434 | ||
t_E : 62.02397 +1.79534 -0.79956 | ||
rho : 0.00090 +0.00015 -0.00067 | ||
s : 1.12204 +0.28565 -0.00849 | ||
q : 0.0042899 +0.0015261 -0.0007855 | ||
alpha : 223.83464 +2.47217 -0.80972 | ||
Fitted fluxes (source and blending): | ||
flux_s_1 : 8.89612 +0.21550 -0.17854 | ||
flux_b_1 : 3.01760 +0.28343 -0.20127 | ||
flux_s_2 : 607.65455 +16.23915 -23.18048 | ||
flux_b_2 : -600.97205 +25.34592 -16.12195 | ||
Best model: | ||
chi2: 1644.3221 | ||
t_0 u_0 t_E rho s q alpha | ||
2452848.1246413593 0.13077424756331346 62.1115925977469 0.0009026783479118898 1.118492277496811 0.003861855664894637 223.299712544845 | ||
Fluxes: | ||
8.916830439840542 3.016002940234999 611.937887681789 -605.1043288009473 |
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data/expected_example_output/example_16/ob03235_1/standard_error.txt
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/usr/local/lib/python3.8/site-packages/emcee/autocorr.py:38: RuntimeWarning: invalid value encountered in true_divide | ||
acf /= acf[0] | ||
The chain is shorter than 50 times the integrated autocorrelation time for 6 parameter(s). Use this estimate with caution and run a longer chain! | ||
N/50 = 2; | ||
tau: [11.36266927 11.79981449 10.97669426 12.13697319 11.39767236 12.87928857 | ||
nan] | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/utils.py:147: UserWarning: Flux to magnitude conversion approached negative flux | ||
warnings.warn( | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/utils.py:150: RuntimeWarning: invalid value encountered in log10 | ||
mag = zeropoint - 2.5 * np.log10(flux) | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/utils.py:147: UserWarning: Flux to magnitude conversion approached negative flux | ||
warnings.warn( | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/utils.py:150: RuntimeWarning: invalid value encountered in log10 | ||
mag = zeropoint - 2.5 * np.log10(flux) | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/mulensdata.py:556: UserWarning: Some good data points have scaled errorbars with negative values. Setting them to zero for plotting. | ||
Dataset: OB03235_MOA.txt | ||
Epochs: [2451724.984743 2451735.999816 2451816.931292 2451817.868943 | ||
2452068.114436 2452176.880222 2452185.913052 2452420.970783 | ||
2452438.97773 2452458.914235 2452533.873316 2452547.926567 | ||
2452551.909314 2452551.924018 2452754.082603 2452759.235935] | ||
warnings.warn(msg.format(kind, self._get_name(), self.time[indexes])) | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/utils.py:147: UserWarning: Flux to magnitude conversion approached negative flux | ||
warnings.warn( | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/utils.py:150: RuntimeWarning: invalid value encountered in log10 | ||
mag = zeropoint - 2.5 * np.log10(flux) | ||
/home/data/caustic/rpoleski/MulensModel/source/MulensModel/mulensdata.py:556: UserWarning: Some good data points have scaled errorbars with negative values. Setting them to zero for plotting. | ||
Dataset: OB03235_MOA.txt | ||
Epochs: [2451724.984743 2451735.999816 2451816.931292 2451817.868943 | ||
2452068.114436 2452176.880222 2452185.913052 2452420.970783 | ||
2452438.97773 2452458.914235 2452533.873316 2452547.926567 | ||
2452551.909314 2452551.924018 2452754.082603 2452759.235935] | ||
warnings.warn(msg.format(kind, self._get_name(), self.time[indexes])) | ||
|
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data/expected_example_output/example_16/ob03235_2_full/ob03235_2_all_models.txt
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data/expected_example_output/example_16/ob03235_2_full/ob03235_2_all_results.yaml
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MulensModel and script versions: ['2.24.0', '0.36.2'] | ||
Mean acceptance fraction: 0.450 | ||
Mean autocorrelation time [steps]: nan | ||
Fixed parameters: | ||
t_0_par : 2452848.0 | ||
Fitted parameters: | ||
# [median, sigma+, sigma-] | ||
t_0 : [2452847.99392, +0.20297, -0.10625] | ||
u_0 : [0.13133, +0.01190, -0.00989] | ||
t_E : [61.53299, +0.84212, -0.61895] | ||
rho : [0.00106, +0.00013, -0.00030] | ||
pi_E_N : [0.00273, +0.00775, -0.01699] | ||
pi_E_E : [-0.00115, +0.01221, -0.02237] | ||
s : [1.10024, +0.00699, -0.00610] | ||
q : [0.0029446, +0.0044348, -0.0015029] | ||
alpha : [224.65427, +0.81415, -1.28011] | ||
Fitted fluxes: # (source and blending) | ||
# [median, sigma+, sigma-] | ||
flux_s_1 : [8.98438, +0.23701, -0.58582] | ||
flux_b_1 : [2.96644, +0.70747, -0.29924] | ||
flux_s_2 : [591.38326, +34.62791, -33.72225] | ||
flux_b_2 : [-582.47485, +34.28827, -35.26746] | ||
Best model: | ||
chi2: 1607.1935 | ||
Parameters: | ||
t_0: 2452847.9813169586 | ||
u_0: 0.1244083012837355 | ||
t_E: 61.801994310224785 | ||
rho: 0.0009857729823665759 | ||
pi_E_N: -0.00290808473075553 | ||
pi_E_E: 3.233308746249976e-05 | ||
s: 1.1012189522525833 | ||
q: 0.00219719097185293 | ||
alpha: 223.18272670658607 | ||
Fluxes: | ||
flux_s_1: 8.570631573704093 | ||
flux_b_1: 3.4774355109846873 | ||
flux_s_2: 577.1216293142585 | ||
flux_b_2: -569.0241772101244 |
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data/expected_example_output/example_16/ob03235_2_full/ob03235_2_models.npy
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