status: skipped
duration: 306.070s
Captured stdout call
{'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 0x7f92427ea020>, 'fitter_check_validate': <function sign_validate_and_plot_hint.<locals>.sign_validate_plot at 0x7f92427ea020>, 'rational_fitter_validate': <function rational_fitter_validate at 0x7f924357b380>}]}
TEE_LOGFILE None
A []
B []
------------:SNR Fix Test:
------------:rational fitting:
4P   0.02    chebychev rational fit
RELDEG:  0 0 0
3P   0.30    Initial Order: (Z=20, P=20, Z-P=0)
3P   0.36    Fastdrop Order: (Z=16, P=16, Z-P=0)
4P   0.38    mag fitting and phase patching
--------------:rational fitting:sample variance (from magnitude):
3A   1.03      Weight Scaling determined:  28.727737811076437
3A   1.03      Weight Scaling Used  5.359826285531691
--------------:rational fitting:Phase patching:
------------:Q-ranked order reduction:
4P  23.61    order reduced annealing
5P  59.22  zero flipping, maxzp 27, residuals=5.29e+01, 5.29e+01, reldeg=-9
5P  94.11  zero flipped, maxzp 27, residuals=5.12e+01, reldeg=-9
5P 159.44  zero flipped, maxzp 27, residuals=4.97e+01, reldeg=-9
5P 207.18  zero flipped, maxzp 27, residuals=4.73e+01, reldeg=-9
5P 249.45  zero flipped, maxzp 27, residuals=4.72e+01, reldeg=-9
5P 295.37  zero flipped, maxzp 27, residuals=4.56e+01, reldeg=-9
<wield.control.fitting.SISO.fitters_ZPK.MRF.MultiReprFilterS object at 0x7f92426731a0>
None
rB(-80.16738089288114, -79.88051482031545, -2.3743776420202884, -0.008390248896272275, 0.3445053197470788±0.10916184388653634j, 0.6334971855642022±4.0714123810219975j, 81.17427052834026±45.60794196443892j, -79.9228464310736±46.62591954008791j, -86.90969757132956±51.37351811766385j, 91.49593794826549±56.45600619571937j, 108.72073928686964±73.17543680576108j)
rB()
<wield.control.fitting.SISO.representations.zpk_with_data.ZPKwData object at 0x7f923854af90>
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   0.00  6015 SNR<1 element(s) dropped (of 6401).
           Too many low SNR elements confuses the rational nonparametric fitter.
3W   0.01    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   0.01    Using SNR<20.64128132953338 ceiling.
3W   0.35    Fitter_checkpoint improvement succeed, None
1W   1.10      Phase patching did not remove all unstable roots in
               a single pass. It is not programmed to accommodate this
               failure mode. Unstable zeros (or poles) may be wrong.
1W   1.15      Phase patching did not remove all unstable roots in
               a single pass. It is not programmed to accommodate this
               failure mode. Unstable zeros (or poles) may be wrong.
1W   1.24      Phase patching did not remove all unstable roots in
               a single pass. It is not programmed to accommodate this
               failure mode. Unstable zeros (or poles) may be wrong.
1W   1.33      Phase patching did not remove all unstable roots in
               a single pass. It is not programmed to accommodate this
               failure mode. Unstable zeros (or poles) may be wrong.
1W   1.61      Phase patching did not remove all unstable roots in
               a single pass. It is not programmed to accommodate this
               failure mode. Unstable zeros (or poles) may be wrong.
3W   1.61    High Confidence that an unstable pole exists at 2.6073791152266104+2.6754669663107586i Hz!
             Adding it to the filter. Prevent this with 'never_unstable_poles'
3W   1.61    High Confidence that an unstable pole exists at 0.7041661326225369+0.7565356015498867i Hz!
             Adding it to the filter. Prevent this with 'never_unstable_poles'
5W   1.61    High Confidence that an unstable zero exists at 6.060215386981508+7.539410694180567e+18i Hz
             Adding it to the filter. Prevent this with 'never_unstable_zeros'
5W   1.61    High Confidence that an unstable zero exists at 2.49289506692862+0.25338418881597985i Hz
             Adding it to the filter. Prevent this with 'never_unstable_zeros'
3W   1.61    High Confidence that an unstable pole exists at 0.0004425258451575154+1.062577937635966i Hz!
             Adding it to the filter. Prevent this with 'never_unstable_poles'
3W   1.62    High Confidence that an unstable pole exists at 2.5777117713492295e-06+0.1962881126089361i Hz!
             Adding it to the filter. Prevent this with 'never_unstable_poles'
5W   1.62    High Confidence that an unstable zero exists at 1.2443441055813007+0.07798366116554925i Hz
             Adding it to the filter. Prevent this with 'never_unstable_zeros'
3W  22.92  Fitter_checkpoint improvement succeed, None
3W  23.59    Fitter_checkpoint improvement succeed, None
3W  59.20  Fitter_checkpoint improvement succeed, None
captured errors:
@pytest.mark.xfail(True, reason="Triggering some overflow to look into")
    @skipdeco
    def test_oplev_iy():
        from wield.utilities.file_io import load_any
    
        fname = matlab_test_data.matfiles["OplevPlant.mat"]
        fdict = load_any(fname=fname, ftype="mat")
    
>       out = v2.data2filter(
            F_Hz=fdict["ap"]["iy"]["ff"],
            data=fdict["ap"]["iy"]["plant"],
            F_nyquist_Hz=None,
            delay_s=0,
            hints=[sign_validate_and_plot_hint(tjoin('error'))],
        )

../../src/wield/control/fitting/SISO/test/v2/test_oplev.py:38: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../src/wield/control/fitting/SISO/v2/data2filter.py:423: in data2filter
    baseline_order = fit_full(aid, emphasis)
../../src/wield/control/fitting/SISO/v2/data2filter.py:489: in fit_full
    return _reduce(aid)
../../src/wield/control/fitting/SISO/v2/data2filter.py:1135: in _reduce
    order_reduce_flip.order_reduce_flip(
../../src/wield/control/fitting/SISO/v2/algorithms/order_reduce_flip.py:136: in order_reduce_flip
    ret = ranking_delay_flip(
../../src/wield/control/fitting/SISO/v2/algorithms/order_reduce_flip.py:94: in ranking_delay_flip
    did_reduce = aid.fitter_check(
../../src/wield/control/fitting/SISO/v2/fit_aid.py:371: in fitter_check
    val_func(self, fitter_new)
../../src/wield/control/fitting/SISO/testing/utilities.py:219: in sign_validate_plot
    sign_validate(aid, fitter)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

aid = <wield.control.fitting.SISO.v2.fit_aid.FitAid object at 0x7f924270dc40>
fitter = <wield.control.fitting.SISO.fitters_ZPK.MRF.MultiReprFilterS object at 0x7f92426731a0>

    def sign_validate(aid, fitter):
        """
        To be added as a hint to data2filter
        """
        rep = fitter.ZPKrep
        xfer = rep.xfer_fit
        data = rep.data
        rat = data / xfer
        rat_ang = np.exp(1j * np.angle(rat))
        ang_avg_rep = np.sum(rat_ang * rep.W ** 2) / np.sum(rep.W ** 2)
    
        xfer = fitter.xfer_fit
        data = fitter.data
        rat = data / xfer
        rat_ang = np.exp(1j * np.angle(rat))
        ang_avg_fit = np.sum(rat_ang * fitter.W ** 2) / np.sum(fitter.W ** 2)
        # print("SGN: ", ang_avg_rep, ang_avg_fit)
        # axB = plots.plots.plot_fit(
        #    fitter,
        #    fname = 'test1.png',
        # )
        # axB = plots.plots.plot_fitter_flag(
        #    fitter,
        #    fname = 'test3.png',
        # )
        # axB = plots.plots.plot_fit(
        #    fitter.ZPKrep,
        #    fname = 'test2.png',
        # )
    
        if isinstance(fitter, fitters_ZPK.MultiReprFilterBase):
            for coding in list(fitter.num_codings) + list(fitter.den_codings):
                rB = representations.RootBunch(
                    u=coding.roots(),
                    constraint=representations.root_constraints.no_constraint,
                )
                h1 = coding.transfer()
                h, lnG = rB.val_lnG(fitter.Xex_grid)
                h = h * np.exp(lnG)
                assert_almost_equal(h / h1, 1, 4)
    
>       assert ang_avg_fit.real > 0 and ang_avg_rep.real > 0
E       AssertionError

../../src/wield/control/fitting/SISO/testing/utilities.py:191: AssertionError