Script started on 2024-06-17 17:01:53-07:00 [TERM="xterm-256color" TTY="/dev/pts/8" COLUMNS="106" LINES="44"] [?2004h]0;louis.dartez@cdsws22: ~/scratch/tia_meas_fit_20240617louis.dartez@cdsws22: exitpython /ligo/groups/cal/common/scripts/electronics/omctransimpedanceamplifier/fits/fit_H1_OMC_TIA_20240617.pyconda activate /ligo/home/louis.dartez/.conda/envs/iirrational  [?2004l [?2004h(iirrational) ]0;louis.dartez@cdsws22: ~/scratch/tia_meas_fit_20240617louis.dartez@cdsws22: conda activate /ligo/home/louis.dartez/.conda/envs/iirrational exitpython /ligo/groups/cal/common/scripts/electronics/omctransimpedanceamplifier/fits/fit_H1_OMC_TIA_20240617.py [?2004l TEE_LOGFILE None 3W 0.00 Estimating SNR from sample variance with nearby points (SNR_est_width=10 > 0). This technique works semi-OK, but could probably be much better.. use the resulting fit to estimate the sample variance and generate improved SNR estimates, iterate. 3W 0.00 21 SNR<1 element(s) dropped (of 199). Too many low SNR elements confuses the rational nonparametric fitter. A [] B [] ------------:SNR Fix Test: 3W 0.00 The number of effective data points N=(ΣW^2)^2/(ΣW^4)=8.85e-02*len(W) [where W=SNR] is below the configured 'SNR_regularize_scale'=10, given the maximum SNR=1167.0816512251956. Now Finding an SNR ceiling that balances the ratio with max SNR. 3W 0.00 Using SNR<50.55696406553934 ceiling. ------------:rational fitting: 4P 0.00 chebychev rational fit RELDEG: 0 0 0 3P 0.01 Initial Order: (Z=4, P=4, Z-P=0) 3W 0.01 Fitter_checkpoint improvement succeed, None 3P 0.01 Fastdrop Order: (Z=4, P=4, Z-P=0) 4P 0.01 mag fitting and phase patching --------------:rational fitting:sample variance (from magnitude): 3A 0.06 Weight Scaling determined: 3.3289817769754335 3A 0.06 Weight Scaling Used 1.824549746369069 ------------:Q-ranked order reduction: 4P 0.15 order reduced annealing 5P 0.17 zero flipping, maxzp 5, residuals=1.67e+01, 2.04e+01, reldeg=-1 ------------:selective order reduction: 3W 0.76 Fitter_checkpoint improvement succeed, None 5P 0.76 order reduced to 5, residuals=9.56e-01, reldeg=-2 4P 0.98 activating delay fitting 3W 0.99 Fitter_checkup improvement succeed 3W 0.99 Fitter_checkpoint improvement succeed, None 2A 0.99 Baseline fit delay: 1.1357092559527437e-06 2A 0.99 Baseline fit residuals: 2.29e-03, at order 5 ------------:successive order reduction: 5P 1.10 order reduced to 4, residuals=3.84e+00 5P 1.65 order reduced to 3, residuals=1.36e+02 5P 2.28 order reduced to 3, residuals=7.68e+01 5P 2.36 order reduced to 2, residuals=1.41e+02 BASELINE: 5 ------------:investigations: 2I 2.36 max(z, p) ChiSq. order avg. res. med. res. max. res. ----------- ------------ ------------ ----------- 2 140.692 90.2274 6837.47 3 76.7977 36.4898 1639.75 4 3.84265 0.121325 261.41 5 0.00229394 0.000447742 0.167107 DCPD:DCPDA Fit Zeros: [6.606 2.306 2.482] Hz Fit Poles: [1.117e+04 -0.j 3.286e+01 -0.j 1.014e+04 -0.j 5.764e+00-22.229j 5.764e+00+22.229j] Hz Fit Gain: 1.132e+13 TEE_LOGFILE None 3W 0.00 Estimating SNR from sample variance with nearby points (SNR_est_width=10 > 0). This technique works semi-OK, but could probably be much better.. use the resulting fit to estimate the sample variance and generate improved SNR estimates, iterate. 3W 0.00 21 SNR<1 element(s) dropped (of 199). Too many low SNR elements confuses the rational nonparametric fitter. A [] B [] ------------:SNR Fix Test: 3W 0.00 The number of effective data points N=(ΣW^2)^2/(ΣW^4)=8.94e-02*len(W) [where W=SNR] is below the configured 'SNR_regularize_scale'=10, given the maximum SNR=1193.4528024862911. Now Finding an SNR ceiling that balances the ratio with max SNR. 3W 0.00 Using SNR<51.66249995025931 ceiling. ------------:rational fitting: 4P 0.00 chebychev rational fit RELDEG: 0 0 0 3P 0.01 Initial Order: (Z=4, P=4, Z-P=0) 3W 0.01 Fitter_checkpoint improvement succeed, None 3P 0.01 Fastdrop Order: (Z=4, P=4, Z-P=0) 4P 0.01 mag fitting and phase patching --------------:rational fitting:sample variance (from magnitude): 3A 0.05 Weight Scaling determined: 5.72044213936272 3A 0.05 Weight Scaling Used 2.391744580711477 3W 0.09 Fitter_checkpoint improvement succeed, None ------------:Q-ranked order reduction: 3W 0.11 Fitter_checkpoint improvement succeed, None 4P 0.11 order reduced annealing 3W 0.12 Fitter_checkpoint improvement succeed, None 3W 0.13 Fitter_checkpoint improvement succeed, None 5P 0.13 zero flipping, maxzp 5, residuals=6.14e+01, 6.14e+01, reldeg=-1 ------------:selective order reduction: 3W 0.39 Fitter_checkpoint improvement succeed, None 5P 0.39 order reduced to 5, residuals=1.01e+00, reldeg=-2 4P 0.77 activating delay fitting 3W 0.78 Fitter_checkup improvement succeed 3W 0.78 Fitter_checkpoint improvement succeed, None 2A 0.78 Baseline fit delay: 1.14213656115425e-06 2A 0.78 Baseline fit residuals: 6.22e-04, at order 5 ------------:successive order reduction: 5P 0.90 order reduced to 4, residuals=1.68e+01 5P 0.98 order reduced to 3, residuals=2.13e+01 5P 1.11 order reduced to 2, residuals=1.48e+02 BASELINE: 5 ------------:investigations: 2I 1.12 max(z, p) ChiSq. order avg. res. med. res. max. res. ----------- ------------ ------------- ------------ 2 147.649 102.182 7130.57 3 21.3059 13.3411 403.064 4 16.8322 9.39906 395.916 5 0.00062212 0.000191724 0.0200045 DCPD:DCPDB Fit Zeros: [1.774 6.534 2.519] Hz Fit Poles: [1.120e+04 -0.j 3.264e+01 -0.j 1.013e+04 -0.j 4.807e+00-19.822j 4.807e+00+19.822j] Hz Fit Gain: 1.135e+13 [?2004h(iirrational) ]0;louis.dartez@cdsws22: ~/scratch/tia_meas_fit_20240617louis.dartez@cdsws22:  (iirrational) ]0;louis.dartez@cdsws22: ~/scratch/tia_meas_fit_20240617louis.dartez@cdsws22: exit [?2004l exit Script done on 2024-06-17 17:02:38-07:00 [COMMAND_EXIT_CODE="0"]