test_oplev¶
wield.control.fitting.SISO.test.v2.test_oplev
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Details
- test_oplev_iy()[source][github]¶
This is a pytest needing documentation
code
1@pytest.mark.xfail(True, reason="Triggering some overflow to look into") 2@skipdeco 3def test_oplev_iy(): 4 from wield.utilities.file_io import load_any 5 6 fname = matlab_test_data.matfiles["OplevPlant.mat"] 7 fdict = load_any(fname=fname, ftype="mat") 8 9 out = v2.data2filter( 10 F_Hz=fdict["ap"]["iy"]["ff"], 11 data=fdict["ap"]["iy"]["plant"], 12 F_nyquist_Hz=None, 13 delay_s=0, 14 hints=[sign_validate_and_plot_hint(tjoin('error'))], 15 ) 16 with plot_on_assert(tjoin('output'), out.fitter, plot_anyway=True): 17 pass 18 return
pytest information
This code is wrapped in a pytest function using conventions detailed in Pytest Conventions. The full name of this test, as known by the documentation, is:
wield.control.fitting.SISO.test.v2.test_oplev.test_oplev_iyThe full name is useful when building documentation, to link a reference to this page using
:func:`name`, or directly include it with an autofunction directive. The collapse nodes below show every instance of the test run. There may only be one, but if the test was run multiple times through pytest parametrizations, the list can be longer.test_oplev_iy
output
{'F_Hz': array([0.0000000e+00, 7.8125000e-03, 1.5625000e-02, ..., 4.9984375e+01, 4.9992188e+01, 5.0000000e+01]), 'data': array([-4.8860516e-03+0.0000000e+00j, -4.5855157e-03+8.9761830e-05j, -4.3419674e-03+2.4111182e-04j, ..., 1.5403209e-09+2.9354810e-08j, 1.2409613e-08+4.0623608e-08j, 2.2163795e-09+3.6025902e-08j]), 'F_nyquist_Hz': None, 'delay_s': 0, 'hints': [{'fitter_update_validate': <function sign_validate_and_plot_hint.<locals>.sign_validate_plot at 0x7eff83ff5ee0>, 'fitter_check_validate': <function sign_validate_and_plot_hint.<locals>.sign_validate_plot at 0x7eff83ff5ee0>, 'rational_fitter_validate': <function rational_fitter_validate at 0x7eff849a3240>}]} TEE_LOGFILE None A [] B [] ------------:SNR Fix Test: ------------:rational fitting: 4P 0.05 chebychev rational fit RELDEG: 0 0 0 3P 0.33 Initial Order: (Z=20, P=20, Z-P=0) 3P 0.40 Fastdrop Order: (Z=18, P=18, Z-P=0) 4P 0.42 mag fitting and phase patching --------------:rational fitting:sample variance (from magnitude): 3A 7.94 Weight Scaling determined: 0.9295889280291497 3A 7.94 Weight Scaling Used 0.9641519216540253 ------------:Q-ranked order reduction: 4P 36.79 order reduced annealing 5P 46.77 zero flipping, maxzp 40, residuals=7.98e+01, 2.56e+02, reldeg=-22 ------------:selective order reduction: 5P 114.27 order reduced to 39, residuals=6.25e+01, reldeg=-21 5P 163.01 order reduced to 39, residuals=5.97e+01, reldeg=-22 5P 171.70 order reduced to 37, residuals=5.94e+01, reldeg=-22 5P 203.35 order reduced to 37, residuals=5.90e+01, reldeg=-24 5P 220.82 order reduced to 36, residuals=5.91e+01, reldeg=-23 5P 252.71 order reduced to 36, residuals=5.85e+01, reldeg=-25 5P 287.30 order reduced to 34, residuals=5.83e+01, reldeg=-25 5P 319.62 order reduced to 34, residuals=5.10e+01, reldeg=-27 5P 369.46 order reduced to 34, residuals=4.99e+01, reldeg=-28 5P 419.82 order reduced to 32, residuals=4.94e+01, reldeg=-27 5P 463.91 order reduced to 32, residuals=4.94e+01, reldeg=-28 5P 509.02 order reduced to 31, residuals=4.92e+01, reldeg=-27 5P 553.14 order reduced to 31, residuals=4.69e+01, reldeg=-28 5P 581.54 order reduced to 30, residuals=4.66e+01, reldeg=-27 5P 610.67 order reduced to 30, residuals=4.64e+01, reldeg=-29 5P 636.87 order reduced to 29, residuals=4.63e+01, reldeg=-28 2A 673.75 Baseline fit residuals: 6.43e+02, at order 29 ------------:successive order reduction: 5P 701.77 order reduced to 28, residuals=2.75e+02 5P 722.09 order reduced to 27, residuals=6.02e+01 5P 746.68 order reduced to 26, residuals=6.37e+01 5P 767.34 order reduced to 25, residuals=6.85e+01 5P 787.05 order reduced to 24, residuals=6.84e+01 5P 807.18 order reduced to 23, residuals=6.85e+01 5P 824.49 order reduced to 21, residuals=6.90e+01 5P 836.65 order reduced to 20, residuals=6.87e+01 5P 849.65 order reduced to 18, residuals=7.54e+01 5P 858.41 order reduced to 16, residuals=4.57e+01 5P 865.88 order reduced to 15, residuals=4.14e+01 5P 869.51 order reduced to 14, residuals=4.29e+01 5P 873.27 order reduced to 13, residuals=4.48e+01 5P 878.62 order reduced to 12, residuals=4.37e+01 5P 881.02 order reduced to 11, residuals=4.53e+01 5P 884.27 order reduced to 10, residuals=1.16e+01 5P 887.81 order reduced to 9, residuals=1.12e+01 5P 888.54 order reduced to 8, residuals=1.16e+01 5P 890.80 order reduced to 7, residuals=1.16e+01 5P 891.25 order reduced to 6, residuals=1.17e+01 5P 891.54 order reduced to 4, residuals=2.46e+01 5P 891.74 order reduced to 2, residuals=5.92e+01 BASELINE: 29 ------------:investigations: 2I 891.96 max(z, p) ChiSq. order avg. res. med. res. max. res. ----------- ----------- ----------- ----------- 2 59.2377 75.0691 1673.08 4 24.6445 22.8512 405.758 6 11.7043 11.6484 141.975 7 11.5822 11.6931 148.44 9 11.2175 12.1042 159.094 Captured stderr call 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 0.02 6015 SNR<1 element(s) dropped (of 6401). Too many low SNR elements confuses the rational nonparametric fitter. 3W 0.04 The number of effective data points N=(ΣW^2)^2/(ΣW^4)=3.79e-01*len(W) [where W=SNR] is below the configured 'SNR_regularize_scale'=10, given the maximum SNR=49.74241939769682. Now Finding an SNR ceiling that balances the ratio with max SNR. 3W 0.04 Using SNR<20.64128132953338 ceiling. 3W 0.38 Fitter_checkpoint improvement succeed, None 3W 34.02 Fitter_checkpoint improvement succeed, None 3W 36.78 Fitter_checkpoint improvement succeed, None 3W 114.26 Fitter_checkpoint improvement succeed, None 3W 162.99 Fitter_checkpoint improvement succeed, None 3W 171.69 Fitter_checkpoint improvement succeed, None 3W 203.34 Fitter_checkpoint improvement succeed, None 3W 220.81 Fitter_checkpoint improvement succeed, None 3W 252.70 Fitter_checkpoint improvement succeed, None 3W 287.29 Fitter_checkpoint improvement succeed, None 3W 319.61 Fitter_checkpoint improvement succeed, None 3W 369.45 Fitter_checkpoint improvement succeed, None 3W 419.81 Fitter_checkpoint improvement succeed, None 3W 463.90 Fitter_checkpoint improvement succeed, None 3W 509.01 Fitter_checkpoint improvement succeed, None 3W 553.13 Fitter_checkpoint improvement succeed, None 3W 581.53 Fitter_checkpoint improvement succeed, None 3W 610.66 Fitter_checkpoint improvement succeed, None 3W 636.86 Fitter_checkpoint improvement succeed, None