test_solver_ADY_paper¶
test.ASC_SOLVER_PAPER.test_solver_ADY_paper
This is a pytest module needing documentation
Functions
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This is a pytest needing documentation |
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This is a pytest needing documentation |
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Test that runs the plotting code for H2. |
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This is a pytest needing documentation |
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This is a pytest needing documentation |
Details
- T_calc_H2(folder)[source]¶
This is a pytest needing documentation
code
1@pytest.mark.parametrize( 2 'folder', 3 [ 4 'ExampleModels_paper' 5 ] 6) 7def T_calc_H2(folder): 8 sysB = get_systemB(folder = folder) 9 io_scales = file_io.load(fjoin(folder, 'io_scales.yml')) # Saved by the normalization in the make function 10 11 if '_F3' in folder: 12 FOM_out = ["F3.out", "F2.out"] 13 else: 14 FOM_out = ["FBNS.out", "F2.out"] 15 wn_in = ["S.in", "O.in"] 16 control_in = ["U.in"] 17 meas_out = ["T.out"] 18 19 # currently NO-OP and could be removed 20 sysB.sys = ssutil.scale_io(sysB.sys, FOM_out[0], FBNS_scale) 21 sysB.sys = ssutil.scale_io(sysB.sys, FOM_out[1], FFlat_scale) 22 23 # Scale the DARM output 24 if 'DARM.out' in sysB.sys.outputs.keys(): 25 print('scaling DARM') 26 DARM_scale_factor = strain2m * SNR_intg_factor 27 sysB.sys = ssutil.scale_io(sysB.sys, 'DARM.out', DARM_scale_factor) 28 29 params = {} 30 if folder == 'ExampleModels': 31 params["F1_gain"] = np.geomspace(1e-0, 1e6, 30) * 1/FBNS_scale 32 elif folder == 'ExampleModels_newFOM_F3': 33 params["F1_gain"] = np.geomspace(1e-0, 1e6, 30) * 1/FBNS_scale 34 elif folder == 'ExampleModels_paper': 35 params["F1_gain"] = np.geomspace(1e2, 1e4, 30) * 1/FBNS_scale 36 else: 37 params["F1_gain"] = np.geomspace(1e2, 1e6, 15) * 1/FBNS_scale 38 #params["F1_gain"] = np.geomspace(1e2, 5e7, 30) * 1/FBNS_scale 39 40 results_H2_bare = compute.calcOpt( 41 sysB.sys, 42 control_in, 43 wn_in, 44 meas_out, 45 FOM_out, 46 params, 47 solver="LQG", 48 plant_orig=sysB.orig_plant, 49 ) 50 51 print("Length of H2 results: ", len(results_H2_bare)) 52 53 54 plotting_omega = np.logspace(-3, 4, 1000) * 2 * np.pi 55 results_H2 = compute.calcFullResults( 56 results_H2_bare, 57 colormap="jet", 58 plot_omega=plotting_omega, 59 io_scales = io_scales, 60 ) 61 62 ofile = "results_H2_ADY.pkl" 63 tfile = tjoin(ofile) 64 buzzutil.save_results(results_H2, tfile) 65 66 ffile = fjoin(folder, ofile) 67 buzzutil.save_results(results_H2, ffile) 68 69 # copy to the data directory if it is missing 70 dfile = fjoin(folder, ofile) 71 72 # should not copy into rootdir, especially for parametrized test 73 # should this be for ffile? 74 # if not os.path.exists(ofile): 75 # import shutil 76 # shutil.copy(tfile, ofile) 77 78 print("Now running plot test") 79 T_plot_H2(folder=folder, dfile=tfile)
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:
test.ASC_SOLVER_PAPER.test_solver_ADY_paper.T_calc_H2The 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.T_calc_H2[ExampleModels_paper]
output
Length of H2 results: 30 WARNING: The model is not stable Eigen Values that are > 0: [(273.3322789036303+0j), (0.2337652996148356+16.119536363035493j), (0.2337652996148356-16.119536363035493j)] WARNING: The model is not stable Eigen Values that are > 0: [2.73332279e+02 2.33765300e-01 2.33765300e-01] WARNING: The model is not stable Eigen Values that are > 0: [2.73332279e+02 2.33765300e-01 2.33765300e-01] Range from PSD: 172.7459264624443 Range from PSD: 174.33221277986348 Range from PSD: 175.83806804955677 WARNING: The model is not stable Eigen Values that are > 0: [(4.704042797105922+44.071691461228816j), (4.704042797105922-44.071691461228816j), (19.07483732412282+0j)] WARNING: The model is not stable Eigen Values that are > 0: [ 4.7040428 4.7040428 19.07483732] WARNING: The model is not stable Eigen Values that are > 0: [ 4.7040428 4.7040428 19.07483732] Range from PSD: 177.25973573135568 Range from PSD: 178.58380008684935 Range from PSD: 179.81027723625488 WARNING: The model is not stable Eigen Values that are > 0: [(40.90016824356615+27.6052911396406j), (40.90016824356615-27.6052911396406j)] WARNING: The model is not stable Eigen Values that are > 0: [40.90016824 40.90016824] WARNING: The model is not stable Eigen Values that are > 0: [40.90016824 40.90016824] Range from PSD: 180.97122656618146 Range from PSD: 182.04036444331703 Range from PSD: 183.03323099542735 WARNING: The model is not stable Eigen Values that are > 0: [(53.22315813574319+0j), (10.103445253814158+0j)] WARNING: The model is not stable Eigen Values that are > 0: [53.22315814 10.10344525] WARNING: The model is not stable Eigen Values that are > 0: [53.22315814 10.10344525] Range from PSD: 183.96448049273005 Range from PSD: 184.81168794540844 WARNING: The model is not stable Eigen Values that are > 0: [(24.63184448273307+256.1051519523114j), (24.63184448273307-256.1051519523114j), (47.143796270967314+0j)] WARNING: The model is not stable Eigen Values that are > 0: [24.63184448 24.63184448 47.14379627] WARNING: The model is not stable Eigen Values that are > 0: [24.63184448 24.63184448 47.14379627] Range from PSD: 185.61147362289483 Range from PSD: 186.34172255362267 Range from PSD: 187.0117015001398 WARNING: The model is not stable Eigen Values that are > 0: [(238.2479506791484+0j)] WARNING: The model is not stable Eigen Values that are > 0: [238.24795068] WARNING: The model is not stable Eigen Values that are > 0: [238.24795068] Range from PSD: 187.62999176560808 WARNING: The model is not stable Eigen Values that are > 0: [(320.8355772480715+0j)] WARNING: The model is not stable Eigen Values that are > 0: [320.83557725] WARNING: The model is not stable Eigen Values that are > 0: [320.83557725] Range from PSD: 188.2031356665985 WARNING: The model is not stable Eigen Values that are > 0: [(27.37175828744471+0j)] WARNING: The model is not stable Eigen Values that are > 0: [27.37175829] WARNING: The model is not stable Eigen Values that are > 0: [27.37175829] Range from PSD: 188.71119755675215 WARNING: The model is not stable Eigen Values that are > 0: [(192.33706436269782+0j)] WARNING: The model is not stable Eigen Values that are > 0: [192.33706436] WARNING: The model is not stable Eigen Values that are > 0: [192.33706436] Range from PSD: 189.20725973162166 WARNING: The model is not stable Eigen Values that are > 0: [(9.757399233038846+0j)] WARNING: The model is not stable Eigen Values that are > 0: [9.75739923] WARNING: The model is not stable Eigen Values that are > 0: [9.75739923] Range from PSD: 189.6525149438547 WARNING: The model is not stable Eigen Values that are > 0: [(81.74058597857112+0j)] WARNING: The model is not stable Eigen Values that are > 0: [81.74058598] WARNING: The model is not stable Eigen Values that are > 0: [81.74058598] Range from PSD: 190.04520415873836 WARNING: The model is not stable Eigen Values that are > 0: [(67.20615194846428+0j)] WARNING: The model is not stable Eigen Values that are > 0: [67.20615195] WARNING: The model is not stable Eigen Values that are > 0: [67.20615195] Range from PSD: 190.42844427786883 WARNING: The model is not stable Eigen Values that are > 0: [(73.06424087079247+217.14892325859032j), (73.06424087079247-217.14892325859032j)] WARNING: The model is not stable Eigen Values that are > 0: [73.06424087 73.06424087] WARNING: The model is not stable Eigen Values that are > 0: [73.06424087 73.06424087] Range from PSD: 190.77041175376905 Range from PSD: 191.0870580919192 WARNING: The model is not stable Eigen Values that are > 0: [(134.74055711645588+0j), (19.36883618944001+20.791817680635436j), (19.36883618944001-20.791817680635436j)] WARNING: The model is not stable Eigen Values that are > 0: [134.74055712 19.36883619 19.36883619] WARNING: The model is not stable Eigen Values that are > 0: [134.74055712 19.36883619 19.36883619] Range from PSD: 191.37498192547966 WARNING: The model is not stable Eigen Values that are > 0: [(84.76963487686832+0j), (13.103948029427377+0j)] WARNING: The model is not stable Eigen Values that are > 0: [84.76963488 13.10394803] WARNING: The model is not stable Eigen Values that are > 0: [84.76963488 13.10394803] Range from PSD: 191.63371812567962 Range from PSD: 191.88462188635773 WARNING: The model is not stable Eigen Values that are > 0: [(24.215513515383794+0j)] WARNING: The model is not stable Eigen Values that are > 0: [24.21551352] WARNING: The model is not stable Eigen Values that are > 0: [24.21551352] Range from PSD: 192.10669795467336 WARNING: The model is not stable Eigen Values that are > 0: [(12.863087618214145+13.47167276143245j), (12.863087618214145-13.47167276143245j), (14.91801475409276+0j)] WARNING: The model is not stable Eigen Values that are > 0: [12.86308762 12.86308762 14.91801475] WARNING: The model is not stable Eigen Values that are > 0: [12.86308762 12.86308762 14.91801475] Range from PSD: 192.31883726580685 WARNING: The model is not stable Eigen Values that are > 0: [(51.746185716206156+0j)] WARNING: The model is not stable Eigen Values that are > 0: [51.74618572] WARNING: The model is not stable Eigen Values that are > 0: [51.74618572] Range from PSD: 192.50815300711156 WARNING: The model is not stable Eigen Values that are > 0: [(15.19246379391597+14.52518688630395j), (15.19246379391597-14.52518688630395j)] WARNING: The model is not stable Eigen Values that are > 0: [15.19246379 15.19246379] WARNING: The model is not stable Eigen Values that are > 0: [15.19246379 15.19246379] Range from PSD: 192.67691643377404 Now running plot test dfile: /builds/buzz/docs/maketests/test_results/test_solver_ADY_paper.py/T_calc_H2[ExampleModels_paper]/results_H2_ADY.pkl len H2_results: 30 <IPython.core.display.Markdown object> <IPython.core.display.Markdown object> Range from PSD: 192.26892101593648 Plotting RMS Plot with lost range Plotting RMS Plot with lost range (PSD computation) RMS: 389664.5123393369 2228.174832028176 RMS: 2.337510162690648e-34 5.427539476350936e-17 Captured stderr call 0%| | 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- T_calc_HiB(folder)[source]¶
This is a pytest needing documentation
code
1@pytest.mark.parametrize( 2 'folder', 3 [ 4 'ExampleModels_paper' 5 ] 6) 7def T_calc_HiB(folder): 8 sysB = get_systemB(folder = folder) 9 io_scales = file_io.load(fjoin(folder, 'io_scales.yml')) # Saved by the normalization in the make function 10 11 if '_F3' in folder: 12 FOM_out = ["F3.out", "F2.out"] 13 else: 14 FOM_out = ["FBNS.out", "F2.out"] 15 wn_in = ["S.in", "O.in"] 16 Zinf = ["Zinf.in"] 17 control_in = ["U.in"] 18 meas_out = ["T.out"] 19 20 sysB.sys = ssutil.scale_io(sysB.sys, FOM_out[0], FBNS_scale) 21 sysB.sys = ssutil.scale_io(sysB.sys, FOM_out[1], FFlat_scale) 22 23 # Scale the DARM output 24 if 'DARM.out' in sysB.sys.outputs.keys(): 25 print('scaling DARM') 26 DARM_scale_factor = strain2m * SNR_intg_factor 27 sysB.sys = ssutil.scale_io(sysB.sys, 'DARM.out', DARM_scale_factor) 28 29 params = {} 30 if folder == 'ExampleModels': 31 params['F1_gain'] = np.geomspace(1e1, 1e5, 5) * 1/FBNS_scale # was 1e1 3e3 32 # elif folder == 'ExampleModels_newFOM_F3': 33 # params['F1_gain'] = np.geomspace(1e-6, 1e-3, 5) * 1/FBNS_scale 34 else: 35 params["F1_gain"] = np.geomspace(4e1, 1e5, 6) * 1/FBNS_scale 36 #params["F1_gain"] = np.array([0.09146101038546522]) 37 38 params["igsq_capture"] = [ 39 1e-4, # gain-100 limit 40 1.8e-2, # 1 deg, around gain-10 41 1e-1, # 6 deg 42 0.20, # 11.5 deg 43 0.3, # 17 deg 44 0.5, # 30 deg 45 0.7653668647301797, # 45 deg 46 0.85, # 50.0 deg 47 0.90, # 53.5 deg 48 0.95, # 56.5 deg 49 # above this it gets very hairy 50 # these are 10 solutions 51 ] 52 53 # params["igsq_capture"] = np.geomspace(1e-6, 0.99, 40) 54 # params["igsq_capture"] = np.concatenate([np.geomspace(1e-6, 0.1, 6), np.geomspace(.1, 0.95, 6)]) 55 56 plotting_omega = np.logspace(-3, 4, 1000) * 2 * np.pi 57 results_Hib_bare = compute.calcOpt( 58 sysB.sys, 59 control_in, 60 wn_in, 61 meas_out, 62 FOM_out, 63 params, 64 Zinf=Zinf, 65 solver="HB", 66 plant_orig=sysB.orig_plant, 67 BH_solver = BH1solverset, 68 debug_mode=True, 69 ) 70 71 print("Calculating Full Results Now:") 72 results_Hib = compute.calcFullResults( 73 results_Hib_bare, 74 colormap="RdYlGn", 75 plot_omega=plotting_omega, 76 color_param="igsq", 77 label=False, 78 io_scales = io_scales, 79 ) 80 81 ofile = "results_Hib_ADY.pkl" 82 tfile = tjoin(ofile) 83 buzzutil.save_results(results_Hib, tfile) 84 85 ffile = fjoin(folder, ofile) 86 buzzutil.save_results(results_Hib, ffile) 87 88 # TODO, should also check if the data is identical and warn that it should be copied 89 # copy to the data directory if it is missing 90 dfile = fjoin(folder, ofile) 91 # should not save into the root folder for parametrized test - Lee 92 # if not os.path.exists(ofile): 93 # import shutil 94 # shutil.copy(tfile, ofile) 95 96 print("Now running plot test") 97 T_plot_HiB(folder=folder, dfile=tfile)
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:
test.ASC_SOLVER_PAPER.test_solver_ADY_paper.T_calc_HiBThe 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.T_calc_HiB[ExampleModels_paper]
output
LPL [] MPL [] eq37 Before S: 5459.059814426293 eq37 S: 8.856385434798898e-07 LINF: 2.4e+07 LINF full: 2.4e+07, vs: 1.0e+00 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.0001 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.0001 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.0001 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.0001 Reset #1, asq_eff=1.2e+00, igsq=0.0001 Reset #2, asq_eff=1.3e+00, igsq=0.0001 Reset #3, asq_eff=1.0e+00, igsq=0.0001 Reset #4, asq_eff=1.0e+00, igsq=0.0001 Reset #5, asq_eff=1.0e+00, igsq=0.0001 Reset #6, asq_eff=1.0e+00, igsq=0.0001 Reset #7, asq_eff=1.0e+00, igsq=0.0001 Reset #8, asq_eff=1.0e+00, igsq=0.0001 Reset #9, asq_eff=1.1e+00, igsq=0.0001 Reset #10, asq_eff=1.1e+00, igsq=0.0001 Reset #11, asq_eff=1.1e+00, igsq=0.0001 Reset #12, asq_eff=1.1e+00, igsq=0.0001 Reset #13, asq_eff=1.0e+00, igsq=0.0001 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.018 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.018 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.018 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.018 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.1 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.1 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.1 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.1 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.2 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.2 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.2 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.2 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.3 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.3 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.3 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.3 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.5 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.5 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.5 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.5 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.7653668647301797 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.7653668647301797 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.7653668647301797 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.7653668647301797 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.85 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.85 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.85 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.85 Reset #1, asq_eff=3.3e+00, igsq=0.85 Reset #2, asq_eff=2.5e+00, igsq=0.85 Reset #3, asq_eff=2.4e+00, igsq=0.85 Reset #4, asq_eff=2.3e+00, igsq=0.85 Reset #5, asq_eff=2.3e+00, igsq=0.85 Reset #6, asq_eff=2.2e+00, igsq=0.85 Reset #7, asq_eff=2.2e+00, igsq=0.85 Steps remaining: 38, asq_eff=2.1e+00, igsq=0.85 Reset #8, asq_eff=2.2e+00, igsq=0.85 Failed on igsq: 0.85 Failed to find a finite solution. Reset #9, asq_eff=2.2e+00, igsq=0.85 Reset #10, asq_eff=2.2e+00, igsq=0.85 Reset #11, asq_eff=2.2e+00, igsq=0.85 Reset #12, asq_eff=2.2e+00, igsq=0.85 Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=8.50e-01 and asq=2.20e+00, N_step=136 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.9 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.9 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.9 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.9 Reset #1, asq_eff=1.3e+01, igsq=0.9 Reset #2, asq_eff=9.9e+00, igsq=0.9 Failed on igsq: 0.9 Failed to find a finite solution. Reset #3, asq_eff=8.7e+00, igsq=0.9 Reset #4, asq_eff=8.2e+00, igsq=0.9 Reset #5, asq_eff=7.9e+00, igsq=0.9 Reset #6, asq_eff=7.9e+00, igsq=0.9 Reset #7, asq_eff=7.9e+00, igsq=0.9 Reset #8, asq_eff=7.9e+00, igsq=0.9 Reset #9, asq_eff=7.9e+00, igsq=0.9 Failed on igsq: 0.9 Failed to find a finite solution. Reset #10, asq_eff=7.9e+00, igsq=0.9 Reset #11, asq_eff=7.8e+00, igsq=0.9 Reset #12, asq_eff=7.9e+00, igsq=0.9 Reset #13, asq_eff=7.8e+00, igsq=0.9 Reset #14, asq_eff=7.9e+00, igsq=0.9 Reset #15, asq_eff=7.8e+00, igsq=0.9 Reset #16, asq_eff=7.9e+00, igsq=0.9 Reset #17, asq_eff=7.9e+00, igsq=0.9 Reset #18, asq_eff=7.7e+00, igsq=0.9 Reset #19, asq_eff=7.8e+00, igsq=0.9 Reset #20, asq_eff=7.7e+00, igsq=0.9 Steps remaining: 102, asq_eff=7.6e+00, igsq=0.9 Reset #21, asq_eff=7.8e+00, igsq=0.9 Reset #22, asq_eff=7.7e+00, igsq=0.9 Reset #23, asq_eff=7.6e+00, igsq=0.9 Reset #24, asq_eff=7.7e+00, igsq=0.9 Reset #25, asq_eff=7.6e+00, igsq=0.9 Reset #26, asq_eff=7.7e+00, igsq=0.9 Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.00e-01 and asq=7.71e+00, N_step=169 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.95 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.95 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.95 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.95 Reset #1, asq_eff=3.6e+01, igsq=0.95 Reset #2, asq_eff=2.8e+01, igsq=0.95 Reset #3, asq_eff=2.6e+01, igsq=0.95 Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.50e-01 and asq=2.64e+01, N_step=103 K_usable is not stable Results list length: 1 K_usable is not stable Results list length: 2 K_usable is not stable Results list length: 3 K_usable is not stable Results list length: 4 K_usable is not stable Results list length: 5 K_usable is not stable Results list length: 6 K_usable is not stable Results list length: 7 LPL [] MPL [] eq37 Before S: 26104.243165321852 eq37 S: 9.186606166466292e-07 LINF: 2.4e+07 LINF full: 2.4e+07, vs: 1.0e+00 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.0001 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.0001 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.0001 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.0001 Reset #1, asq_eff=9.1e+00, igsq=0.0001 Reset #2, asq_eff=8.4e+00, igsq=0.0001 Reset #3, asq_eff=8.0e+00, igsq=0.0001 Reset #4, asq_eff=7.2e+00, igsq=0.0001 Reset #5, asq_eff=7.3e+00, igsq=0.0001 Reset #6, asq_eff=7.4e+00, igsq=0.0001 Reset #7, asq_eff=7.0e+00, igsq=0.0001 Reset #8, asq_eff=7.1e+00, igsq=0.0001 Steps remaining: 96, asq_eff=6.7e+00, igsq=0.0001 Reset #9, asq_eff=6.2e+00, igsq=0.0001 Reset #10, asq_eff=5.5e+00, igsq=0.0001 Reset #11, asq_eff=4.8e+00, igsq=0.0001 Reset #12, asq_eff=4.9e+00, igsq=0.0001 Steps remaining: 77, asq_eff=4.6e+00, igsq=0.0001 Reset #13, asq_eff=4.5e+00, igsq=0.0001 Reset #14, asq_eff=4.6e+00, igsq=0.0001 Reset #15, asq_eff=4.2e+00, igsq=0.0001 Reset #16, asq_eff=4.2e+00, igsq=0.0001 Reset #17, asq_eff=4.3e+00, igsq=0.0001 Reset #18, asq_eff=4.2e+00, igsq=0.0001 Reset #19, asq_eff=4.3e+00, igsq=0.0001 Reset #20, asq_eff=4.3e+00, igsq=0.0001 Reset #21, asq_eff=4.4e+00, igsq=0.0001 Reset #22, asq_eff=4.3e+00, igsq=0.0001 Reset #23, asq_eff=4.4e+00, igsq=0.0001 Reset #24, asq_eff=4.4e+00, igsq=0.0001 Reset #25, asq_eff=4.4e+00, igsq=0.0001 Reset #26, asq_eff=4.3e+00, igsq=0.0001 Reset #27, asq_eff=4.4e+00, igsq=0.0001 Reset #28, asq_eff=4.2e+00, igsq=0.0001 Reset #29, asq_eff=4.2e+00, igsq=0.0001 Steps remaining: 69, asq_eff=4.0e+00, igsq=0.0001 Reset #30, asq_eff=4.1e+00, igsq=0.0001 Reset #31, asq_eff=4.0e+00, igsq=0.0001 Reset #32, asq_eff=4.0e+00, igsq=0.0001 Reset #33, asq_eff=4.0e+00, igsq=0.0001 Reset #34, asq_eff=4.0e+00, igsq=0.0001 Reset #35, asq_eff=4.0e+00, igsq=0.0001 Reset #36, asq_eff=4.0e+00, igsq=0.0001 Reset #37, asq_eff=4.0e+00, igsq=0.0001 Reset #38, asq_eff=4.1e+00, igsq=0.0001 Reset #39, asq_eff=4.1e+00, igsq=0.0001 Reset #40, asq_eff=4.2e+00, igsq=0.0001 Reset #41, asq_eff=4.2e+00, igsq=0.0001 Reset #42, asq_eff=4.1e+00, igsq=0.0001 Reset #43, asq_eff=4.1e+00, igsq=0.0001 Reset #44, asq_eff=4.2e+00, igsq=0.0001 Reset #45, asq_eff=4.2e+00, igsq=0.0001 Reset #46, asq_eff=4.2e+00, igsq=0.0001 Reset #47, asq_eff=3.8e+00, igsq=0.0001 Reset #48, asq_eff=3.8e+00, igsq=0.0001 Reset #49, asq_eff=3.8e+00, igsq=0.0001 Reset #50, asq_eff=3.7e+00, igsq=0.0001 Reset #51, asq_eff=3.6e+00, igsq=0.0001 Reset #52, asq_eff=3.7e+00, igsq=0.0001 Reset #53, asq_eff=3.6e+00, igsq=0.0001 Reset #54, asq_eff=3.6e+00, igsq=0.0001 Reset #55, asq_eff=3.6e+00, igsq=0.0001 Steps remaining: 64, asq_eff=3.6e+00, igsq=0.0001 Non-optimal solve at igsq=1.00e-04 and asq=3.51e+00, N_step=301 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.018 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.018 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.018 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.018 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.1 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.1 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.1 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.1 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.2 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.2 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.2 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.2 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.3 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.3 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.3 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.3 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.5 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.5 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.5 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.5 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.7653668647301797 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.7653668647301797 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.7653668647301797 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.7653668647301797 Reset #1, asq_eff=1.7e+00, igsq=0.7653668647301797 Reset #2, asq_eff=1.3e+00, igsq=0.7653668647301797 Reset #3, asq_eff=1.3e+00, igsq=0.7653668647301797 Reset #4, asq_eff=1.2e+00, igsq=0.7653668647301797 Reset #5, asq_eff=1.2e+00, igsq=0.7653668647301797 Reset #6, asq_eff=1.1e+00, igsq=0.7653668647301797 Steps remaining: 6, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #7, asq_eff=1.2e+00, igsq=0.7653668647301797 Reset #8, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #9, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #10, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #11, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #12, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #13, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #14, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #15, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #16, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #17, asq_eff=1.1e+00, igsq=0.7653668647301797 Steps remaining: 3, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #18, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #19, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #20, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #21, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #22, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #23, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #24, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #25, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #26, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #27, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #28, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #29, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #30, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #31, asq_eff=1.1e+00, igsq=0.7653668647301797 Reset #32, asq_eff=1.1e+00, igsq=0.7653668647301797 Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=7.65e-01 and asq=1.08e+00, N_step=192 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.85 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.85 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.85 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.85 Reset #1, asq_eff=9.1e+00, igsq=0.85 Reset #2, asq_eff=7.1e+00, igsq=0.85 Reset #3, asq_eff=6.2e+00, igsq=0.85 Reset #4, asq_eff=6.1e+00, igsq=0.85 Reset #5, asq_eff=5.9e+00, igsq=0.85 Reset #6, asq_eff=5.8e+00, igsq=0.85 Reset #7, asq_eff=5.9e+00, igsq=0.85 Reset #8, asq_eff=5.8e+00, igsq=0.85 Reset #9, asq_eff=5.9e+00, igsq=0.85 Reset #10, asq_eff=5.8e+00, igsq=0.85 Reset #11, asq_eff=5.9e+00, igsq=0.85 Reset #12, asq_eff=5.8e+00, igsq=0.85 Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=8.50e-01 and asq=5.84e+00, N_step=133 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.9 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.9 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.9 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.9 Reset #1, asq_eff=1.8e+01, igsq=0.9 Reset #2, asq_eff=1.7e+01, igsq=0.9 Reset #3, asq_eff=1.6e+01, igsq=0.9 Reset #4, asq_eff=1.5e+01, igsq=0.9 Reset #5, asq_eff=1.5e+01, igsq=0.9 Reset #6, asq_eff=1.5e+01, igsq=0.9 Reset #7, asq_eff=1.4e+01, igsq=0.9 Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Reset #8, asq_eff=1.4e+01, igsq=0.9 Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.00e-01 and asq=1.43e+01, N_step=120 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.95 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.95 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.95 Reset #1, asq_eff=5.0e+01, igsq=0.95 Reset #2, asq_eff=3.9e+01, igsq=0.95 Reset #3, asq_eff=3.4e+01, igsq=0.95 Reset #4, asq_eff=3.3e+01, igsq=0.95 Reset #5, asq_eff=3.3e+01, igsq=0.95 Reset #6, asq_eff=3.3e+01, igsq=0.95 Reset #7, asq_eff=3.3e+01, igsq=0.95 Reset #8, asq_eff=3.3e+01, igsq=0.95 Reset #9, asq_eff=3.3e+01, igsq=0.95 Reset #10, asq_eff=3.3e+01, igsq=0.95 Reset #11, asq_eff=3.3e+01, igsq=0.95 Steps remaining: 176, asq_eff=3.2e+01, igsq=0.95 Reset #12, asq_eff=3.3e+01, igsq=0.95 Reset #13, asq_eff=3.3e+01, igsq=0.95 Reset #14, asq_eff=3.3e+01, igsq=0.95 Reset #15, asq_eff=3.3e+01, igsq=0.95 Reset #16, asq_eff=3.3e+01, igsq=0.95 Reset #17, asq_eff=3.3e+01, igsq=0.95 Reset #18, asq_eff=3.3e+01, igsq=0.95 Reset #19, asq_eff=3.3e+01, igsq=0.95 Reset #20, asq_eff=3.3e+01, igsq=0.95 Reset #21, asq_eff=3.3e+01, igsq=0.95 Reset #22, asq_eff=3.3e+01, igsq=0.95 Reset #23, asq_eff=3.3e+01, igsq=0.95 Steps remaining: 176, asq_eff=3.2e+01, igsq=0.95 Reset #24, asq_eff=3.3e+01, igsq=0.95 Reset #25, asq_eff=3.3e+01, igsq=0.95 Reset #26, asq_eff=3.3e+01, igsq=0.95 Reset #27, asq_eff=3.3e+01, igsq=0.95 Reset #28, asq_eff=3.3e+01, igsq=0.95 Reset #29, asq_eff=3.3e+01, igsq=0.95 Reset #30, asq_eff=3.3e+01, igsq=0.95 Reset #31, asq_eff=3.3e+01, igsq=0.95 Reset #32, asq_eff=3.3e+01, igsq=0.95 Reset #33, asq_eff=3.3e+01, igsq=0.95 Reset #34, asq_eff=3.3e+01, igsq=0.95 Reset #35, asq_eff=3.3e+01, igsq=0.95 Steps remaining: 176, asq_eff=3.2e+01, igsq=0.95 Reset #36, asq_eff=3.3e+01, igsq=0.95 Reset #37, asq_eff=3.3e+01, igsq=0.95 Reset #38, asq_eff=3.3e+01, igsq=0.95 Reset #39, asq_eff=3.3e+01, igsq=0.95 Reset #40, asq_eff=3.3e+01, igsq=0.95 Reset #41, asq_eff=3.3e+01, igsq=0.95 Reset #42, asq_eff=3.3e+01, igsq=0.95 Reset #43, asq_eff=3.3e+01, igsq=0.95 Reset #44, asq_eff=3.3e+01, igsq=0.95 Reset #45, asq_eff=3.3e+01, igsq=0.95 Reset #46, asq_eff=3.3e+01, igsq=0.95 Reset #47, asq_eff=3.3e+01, igsq=0.95 Steps remaining: 176, asq_eff=3.2e+01, igsq=0.95 Reset #48, asq_eff=3.3e+01, igsq=0.95 Reset #49, asq_eff=3.3e+01, igsq=0.95 Reset #50, asq_eff=3.3e+01, igsq=0.95 Reset #51, asq_eff=3.3e+01, igsq=0.95 Reset #52, asq_eff=3.3e+01, igsq=0.95 Reset #53, asq_eff=3.3e+01, igsq=0.95 Reset #54, asq_eff=3.3e+01, igsq=0.95 Reset #55, asq_eff=3.3e+01, igsq=0.95 Reset #56, asq_eff=3.3e+01, igsq=0.95 Reset #57, asq_eff=3.3e+01, igsq=0.95 Reset #58, asq_eff=3.3e+01, igsq=0.95 Reset #59, asq_eff=3.3e+01, igsq=0.95 Steps remaining: 176, asq_eff=3.2e+01, igsq=0.95 Reset #60, asq_eff=3.3e+01, igsq=0.95 Reset #61, asq_eff=3.3e+01, igsq=0.95 Reset #62, asq_eff=3.3e+01, igsq=0.95 Reset #63, asq_eff=3.3e+01, igsq=0.95 Reset #64, asq_eff=3.3e+01, igsq=0.95 Reset #65, asq_eff=3.3e+01, igsq=0.95 Reset #66, asq_eff=3.3e+01, igsq=0.95 Reset #67, asq_eff=3.3e+01, igsq=0.95 Reset #68, asq_eff=3.3e+01, igsq=0.95 Reset #69, asq_eff=3.3e+01, igsq=0.95 Reset #70, asq_eff=3.3e+01, igsq=0.95 Reset #71, asq_eff=3.3e+01, igsq=0.95 Steps remaining: 176, asq_eff=3.2e+01, igsq=0.95 Reset #72, asq_eff=3.3e+01, igsq=0.95 Reset #73, asq_eff=3.3e+01, igsq=0.95 Reset #74, asq_eff=3.3e+01, igsq=0.95 Reset #75, asq_eff=3.2e+01, igsq=0.95 Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.50e-01 and asq=3.24e+01, N_step=285 K_usable is not stable Results list length: 8 K_usable is not stable Results list length: 9 K_usable is not stable Results list length: 10 K_usable is not stable Results list length: 11 K_usable is not stable Results list length: 12 LPL [] MPL [] eq37 Before S: 124826.14094123188 eq37 S: 2.37669993759539e-06 LINF: 2.4e+07 LINF full: 2.4e+07, vs: 1.0e+00 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.0001 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.0001 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.0001 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.0001 Reset #1, asq_eff=1.3e+01, igsq=0.0001 Reset #2, asq_eff=1.4e+01, igsq=0.0001 Reset #3, asq_eff=1.2e+01, igsq=0.0001 Reset #4, asq_eff=1.1e+01, igsq=0.0001 Reset #5, asq_eff=1.0e+01, igsq=0.0001 Reset #6, asq_eff=1.0e+01, igsq=0.0001 Reset #7, asq_eff=1.0e+01, igsq=0.0001 Steps remaining: 116, asq_eff=9.9e+00, igsq=0.0001 Reset #8, asq_eff=1.0e+01, igsq=0.0001 Reset #9, asq_eff=9.9e+00, igsq=0.0001 Reset #10, asq_eff=1.0e+01, igsq=0.0001 Reset #11, asq_eff=9.3e+00, igsq=0.0001 Reset #12, asq_eff=8.8e+00, igsq=0.0001 Reset #13, asq_eff=8.4e+00, igsq=0.0001 Reset #14, asq_eff=8.5e+00, igsq=0.0001 Reset #15, asq_eff=8.1e+00, igsq=0.0001 Reset #16, asq_eff=8.0e+00, igsq=0.0001 Reset #17, asq_eff=8.1e+00, igsq=0.0001 Reset #18, asq_eff=7.8e+00, igsq=0.0001 Reset #19, asq_eff=7.8e+00, igsq=0.0001 Reset #20, asq_eff=7.8e+00, igsq=0.0001 Reset #21, asq_eff=7.5e+00, igsq=0.0001 Reset #22, asq_eff=7.4e+00, igsq=0.0001 Steps remaining: 100, asq_eff=7.3e+00, igsq=0.0001 Reset #23, asq_eff=6.9e+00, igsq=0.0001 Reset #24, asq_eff=6.6e+00, igsq=0.0001 Reset #25, asq_eff=6.6e+00, igsq=0.0001 Reset #26, asq_eff=6.7e+00, igsq=0.0001 Reset #27, asq_eff=6.8e+00, igsq=0.0001 Reset #28, asq_eff=6.8e+00, igsq=0.0001 Reset #29, asq_eff=6.8e+00, igsq=0.0001 Steps remaining: 95, asq_eff=6.5e+00, igsq=0.0001 Reset #30, asq_eff=6.7e+00, igsq=0.0001 Reset #31, asq_eff=6.5e+00, igsq=0.0001 Reset #32, asq_eff=6.6e+00, igsq=0.0001 Reset #33, asq_eff=6.6e+00, igsq=0.0001 Reset #34, asq_eff=6.6e+00, igsq=0.0001 Reset #35, asq_eff=6.6e+00, igsq=0.0001 Non-optimal solve at igsq=1.00e-04 and asq=6.63e+00, N_step=241 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.018 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.018 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.018 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.018 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.1 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.1 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.1 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.1 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.2 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.2 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.2 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.2 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.3 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.3 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.3 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.3 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.5 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.5 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.5 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.5 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.7653668647301797 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.7653668647301797 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.7653668647301797 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.7653668647301797 Reset #1, asq_eff=4.6e+00, igsq=0.7653668647301797 Reset #2, asq_eff=3.6e+00, igsq=0.7653668647301797 Reset #3, asq_eff=3.2e+00, igsq=0.7653668647301797 Reset #4, asq_eff=3.0e+00, igsq=0.7653668647301797 Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=7.65e-01 and asq=2.96e+00, N_step=114 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.85 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.85 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.85 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.85 Reset #1, asq_eff=1.3e+01, igsq=0.85 Reset #2, asq_eff=1.2e+01, igsq=0.85 Reset #3, asq_eff=1.1e+01, igsq=0.85 Reset #4, asq_eff=1.1e+01, igsq=0.85 Reset #5, asq_eff=1.1e+01, igsq=0.85 Reset #6, asq_eff=1.1e+01, igsq=0.85 Reset #7, asq_eff=1.1e+01, igsq=0.85 Reset #8, asq_eff=1.1e+01, igsq=0.85 Reset #9, asq_eff=1.1e+01, igsq=0.85 Reset #10, asq_eff=1.1e+01, igsq=0.85 Steps remaining: 119, asq_eff=1.1e+01, igsq=0.85 Reset #11, asq_eff=1.1e+01, igsq=0.85 Reset #12, asq_eff=1.1e+01, igsq=0.85 Reset #13, asq_eff=1.1e+01, igsq=0.85 Reset #14, asq_eff=1.1e+01, igsq=0.85 Reset #15, asq_eff=1.1e+01, igsq=0.85 Reset #16, asq_eff=1.1e+01, igsq=0.85 Reset #17, asq_eff=1.1e+01, igsq=0.85 Reset #18, asq_eff=1.1e+01, igsq=0.85 Reset #19, asq_eff=1.1e+01, igsq=0.85 Reset #20, asq_eff=1.1e+01, igsq=0.85 Reset #21, asq_eff=1.1e+01, igsq=0.85 Reset #22, asq_eff=1.1e+01, igsq=0.85 Reset #23, asq_eff=1.1e+01, igsq=0.85 Reset #24, asq_eff=1.1e+01, igsq=0.85 Reset #25, asq_eff=1.1e+01, igsq=0.85 Reset #26, asq_eff=1.1e+01, igsq=0.85 Reset #27, asq_eff=1.1e+01, igsq=0.85 Reset #28, asq_eff=1.1e+01, igsq=0.85 Reset #29, asq_eff=1.1e+01, igsq=0.85 Reset #30, asq_eff=1.1e+01, igsq=0.85 Reset #31, asq_eff=1.1e+01, igsq=0.85 Reset #32, asq_eff=1.1e+01, igsq=0.85 Reset #33, asq_eff=1.1e+01, igsq=0.85 Reset #34, asq_eff=1.1e+01, igsq=0.85 Reset #35, asq_eff=1.1e+01, igsq=0.85 Reset #36, asq_eff=1.1e+01, igsq=0.85 Reset #37, asq_eff=1.1e+01, igsq=0.85 Reset #38, asq_eff=1.1e+01, igsq=0.85 Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=8.50e-01 and asq=1.06e+01, N_step=195 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.9 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.9 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.9 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.9 Reset #1, asq_eff=2.5e+01, igsq=0.9 Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.00e-01 and asq=2.53e+01, N_step=97 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.95 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.95 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.95 Reset #1, asq_eff=5.0e+01, igsq=0.95 Reset #2, asq_eff=4.6e+01, igsq=0.95 Reset #3, asq_eff=4.4e+01, igsq=0.95 Reset #4, asq_eff=4.1e+01, igsq=0.95 Reset #5, asq_eff=4.1e+01, igsq=0.95 Reset #6, asq_eff=4.0e+01, igsq=0.95 Reset #7, asq_eff=4.0e+01, igsq=0.95 Reset #8, asq_eff=4.0e+01, igsq=0.95 Reset #9, asq_eff=4.0e+01, igsq=0.95 Reset #10, asq_eff=4.0e+01, igsq=0.95 Reset #11, asq_eff=4.0e+01, igsq=0.95 Steps remaining: 185, asq_eff=3.9e+01, igsq=0.95 Reset #12, asq_eff=4.0e+01, igsq=0.95 Reset #13, asq_eff=4.0e+01, igsq=0.95 Reset #14, asq_eff=4.0e+01, igsq=0.95 Reset #15, asq_eff=4.0e+01, igsq=0.95 Reset #16, asq_eff=4.0e+01, igsq=0.95 Reset #17, asq_eff=4.0e+01, igsq=0.95 Reset #18, asq_eff=4.0e+01, igsq=0.95 Reset #19, asq_eff=4.0e+01, igsq=0.95 Reset #20, asq_eff=4.0e+01, igsq=0.95 Reset #21, asq_eff=4.0e+01, igsq=0.95 Reset #22, asq_eff=4.0e+01, igsq=0.95 Reset #23, asq_eff=4.0e+01, igsq=0.95 Steps remaining: 185, asq_eff=3.9e+01, igsq=0.95 Reset #24, asq_eff=4.0e+01, igsq=0.95 Reset #25, asq_eff=4.0e+01, igsq=0.95 Reset #26, asq_eff=4.0e+01, igsq=0.95 Reset #27, asq_eff=4.0e+01, igsq=0.95 Reset #28, asq_eff=4.0e+01, igsq=0.95 Reset #29, asq_eff=4.0e+01, igsq=0.95 Reset #30, asq_eff=4.0e+01, igsq=0.95 Reset #31, asq_eff=4.0e+01, igsq=0.95 Reset #32, asq_eff=4.0e+01, igsq=0.95 Reset #33, asq_eff=4.0e+01, igsq=0.95 Reset #34, asq_eff=4.0e+01, igsq=0.95 Reset #35, asq_eff=4.0e+01, igsq=0.95 Steps remaining: 185, asq_eff=3.9e+01, igsq=0.95 Reset #36, asq_eff=4.0e+01, igsq=0.95 Reset #37, asq_eff=4.0e+01, igsq=0.95 Reset #38, asq_eff=4.0e+01, igsq=0.95 Reset #39, asq_eff=4.0e+01, igsq=0.95 Reset #40, asq_eff=4.0e+01, igsq=0.95 Reset #41, asq_eff=4.0e+01, igsq=0.95 Reset #42, asq_eff=4.0e+01, igsq=0.95 Reset #43, asq_eff=4.0e+01, igsq=0.95 Reset #44, asq_eff=4.0e+01, igsq=0.95 Reset #45, asq_eff=4.0e+01, igsq=0.95 Reset #46, asq_eff=4.0e+01, igsq=0.95 Reset #47, asq_eff=4.0e+01, igsq=0.95 Steps remaining: 185, asq_eff=3.9e+01, igsq=0.95 Reset #48, asq_eff=4.0e+01, igsq=0.95 Reset #49, asq_eff=4.0e+01, igsq=0.95 Reset #50, asq_eff=4.0e+01, igsq=0.95 Reset #51, asq_eff=4.0e+01, igsq=0.95 Reset #52, asq_eff=4.0e+01, igsq=0.95 Reset #53, asq_eff=4.0e+01, igsq=0.95 Reset #54, asq_eff=4.0e+01, igsq=0.95 Reset #55, asq_eff=4.0e+01, igsq=0.95 Reset #56, asq_eff=4.0e+01, igsq=0.95 Reset #57, asq_eff=4.1e+01, igsq=0.95 Reset #58, asq_eff=4.0e+01, igsq=0.95 Reset #59, asq_eff=4.1e+01, igsq=0.95 Reset #60, asq_eff=4.0e+01, igsq=0.95 Reset #61, asq_eff=4.1e+01, igsq=0.95 Reset #62, asq_eff=4.0e+01, igsq=0.95 Reset #63, asq_eff=4.1e+01, igsq=0.95 Reset #64, asq_eff=4.0e+01, igsq=0.95 Reset #65, asq_eff=4.1e+01, igsq=0.95 Reset #66, asq_eff=4.0e+01, igsq=0.95 Reset #67, asq_eff=4.1e+01, igsq=0.95 Reset #68, asq_eff=4.0e+01, igsq=0.95 Reset #69, asq_eff=4.1e+01, igsq=0.95 Reset #70, asq_eff=4.0e+01, igsq=0.95 Reset #71, asq_eff=4.1e+01, igsq=0.95 Reset #72, asq_eff=4.0e+01, igsq=0.95 Reset #73, asq_eff=4.1e+01, igsq=0.95 Reset #74, asq_eff=4.0e+01, igsq=0.95 Reset #75, asq_eff=4.1e+01, igsq=0.95 Reset #76, asq_eff=4.0e+01, igsq=0.95 Reset #77, asq_eff=4.1e+01, igsq=0.95 Reset #78, asq_eff=4.0e+01, igsq=0.95 Reset #79, asq_eff=4.0e+01, igsq=0.95 Reset #80, asq_eff=4.0e+01, igsq=0.95 Reset #81, asq_eff=4.1e+01, igsq=0.95 Reset #82, asq_eff=4.0e+01, igsq=0.95 Reset #83, asq_eff=4.1e+01, igsq=0.95 Non-optimal solve at igsq=9.50e-01 and asq=4.00e+01, N_step=301 K_usable is not stable Results list length: 13 K_usable is not stable Results list length: 14 K_usable is not stable Results list length: 15 K_usable is not stable Results list length: 16 K_usable is not stable Results list length: 17 LPL [] MPL [] eq37 Before S: 596891.3086931325 eq37 S: 4.222101598825882e-06 LINF: 2.4e+07 LINF full: 2.4e+07, vs: 1.0e+00 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.0001 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.0001 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.0001 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.0001 Reset #1, asq_eff=2.5e+01, igsq=0.0001 Reset #2, asq_eff=1.4e+01, igsq=0.0001 Non-optimal solve at igsq=1.00e-04 and asq=1.40e+01, N_step=103 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.018 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.018 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.018 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.018 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.1 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.1 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.1 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.1 Failed on igsq: 0.1 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.1 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.1 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.1 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=1.00e-01 and asq=1.00e+00, N_step=106 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.2 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.2 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.2 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.2 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.3 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.3 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.3 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.3 Reset #1, asq_eff=1.2e+00, igsq=0.3 Non-optimal solve at igsq=3.00e-01 and asq=1.00e+00, N_step=109 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.5 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.5 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.5 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.5 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.7653668647301797 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.7653668647301797 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.7653668647301797 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.7653668647301797 Reset #1, asq_eff=3.6e+01, igsq=0.7653668647301797 Reset #2, asq_eff=3.3e+01, igsq=0.7653668647301797 Reset #3, asq_eff=2.0e+01, igsq=0.7653668647301797 Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=7.65e-01 and asq=2.05e+01, N_step=107 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.85 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.85 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.85 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.85 Reset #1, asq_eff=2.5e+01, igsq=0.85 Reset #2, asq_eff=2.3e+01, igsq=0.85 Reset #3, asq_eff=1.6e+01, igsq=0.85 Reset #4, asq_eff=1.5e+01, igsq=0.85 Reset #5, asq_eff=1.5e+01, igsq=0.85 Reset #6, asq_eff=1.5e+01, igsq=0.85 Reset #7, asq_eff=1.5e+01, igsq=0.85 Reset #8, asq_eff=1.5e+01, igsq=0.85 Reset #9, asq_eff=1.5e+01, igsq=0.85 Steps remaining: 135, asq_eff=1.4e+01, igsq=0.85 Reset #10, asq_eff=1.5e+01, igsq=0.85 Reset #11, asq_eff=1.5e+01, igsq=0.85 Reset #12, asq_eff=1.5e+01, igsq=0.85 Reset #13, asq_eff=1.5e+01, igsq=0.85 Reset #14, asq_eff=1.5e+01, igsq=0.85 Reset #15, asq_eff=1.5e+01, igsq=0.85 Reset #16, asq_eff=1.5e+01, igsq=0.85 Reset #17, asq_eff=1.5e+01, igsq=0.85 Reset #18, asq_eff=1.5e+01, igsq=0.85 Reset #19, asq_eff=1.5e+01, igsq=0.85 Reset #20, asq_eff=1.5e+01, igsq=0.85 Reset #21, asq_eff=1.5e+01, igsq=0.85 Steps remaining: 135, asq_eff=1.4e+01, igsq=0.85 Reset #22, asq_eff=1.5e+01, igsq=0.85 Reset #23, asq_eff=1.5e+01, igsq=0.85 Reset #24, asq_eff=1.5e+01, igsq=0.85 Reset #25, asq_eff=1.5e+01, igsq=0.85 Reset #26, asq_eff=1.5e+01, igsq=0.85 Reset #27, asq_eff=1.5e+01, igsq=0.85 Reset #28, asq_eff=1.5e+01, igsq=0.85 Reset #29, asq_eff=1.5e+01, igsq=0.85 Reset #30, asq_eff=1.5e+01, igsq=0.85 Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=8.50e-01 and asq=1.49e+01, N_step=176 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.9 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.9 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.9 Reset #1, asq_eff=5.0e+01, igsq=0.9 Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.00e-01 and asq=5.00e+01, N_step=95 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.95 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.95 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.95 Reset #1, asq_eff=7.0e+01, igsq=0.95 Steps remaining: 24, asq_eff=5.9e+01, igsq=0.95 Reset #2, asq_eff=5.4e+01, igsq=0.95 Reset #3, asq_eff=4.8e+01, igsq=0.95 Reset #4, asq_eff=4.5e+01, igsq=0.95 Reset #5, asq_eff=4.4e+01, igsq=0.95 Reset #6, asq_eff=4.3e+01, igsq=0.95 Reset #7, asq_eff=4.4e+01, igsq=0.95 Reset #8, asq_eff=4.3e+01, igsq=0.95 Reset #9, asq_eff=4.4e+01, igsq=0.95 Reset #10, asq_eff=4.3e+01, igsq=0.95 Reset #11, asq_eff=4.4e+01, igsq=0.95 Reset #12, asq_eff=4.3e+01, igsq=0.95 Reset #13, asq_eff=4.4e+01, igsq=0.95 Reset #14, asq_eff=4.3e+01, igsq=0.95 Reset #15, asq_eff=4.4e+01, igsq=0.95 Reset #16, asq_eff=4.3e+01, igsq=0.95 Reset #17, asq_eff=4.4e+01, igsq=0.95 Reset #18, asq_eff=4.4e+01, igsq=0.95 Reset #19, asq_eff=4.4e+01, igsq=0.95 Reset #20, asq_eff=4.3e+01, igsq=0.95 Reset #21, asq_eff=4.4e+01, igsq=0.95 Reset #22, asq_eff=4.3e+01, igsq=0.95 Reset #23, asq_eff=4.4e+01, igsq=0.95 Reset #24, asq_eff=4.3e+01, igsq=0.95 Reset #25, asq_eff=4.4e+01, igsq=0.95 Reset #26, asq_eff=4.3e+01, igsq=0.95 Reset #27, asq_eff=4.4e+01, igsq=0.95 Reset #28, asq_eff=4.3e+01, igsq=0.95 Reset #29, asq_eff=4.4e+01, igsq=0.95 Reset #30, asq_eff=4.3e+01, igsq=0.95 Reset #31, asq_eff=4.4e+01, igsq=0.95 Reset #32, asq_eff=4.3e+01, igsq=0.95 Reset #33, asq_eff=4.4e+01, igsq=0.95 Reset #34, asq_eff=4.3e+01, igsq=0.95 Reset #35, asq_eff=4.4e+01, igsq=0.95 Reset #36, asq_eff=4.3e+01, igsq=0.95 Reset #37, asq_eff=4.4e+01, igsq=0.95 Reset #38, asq_eff=4.3e+01, igsq=0.95 Reset #39, asq_eff=4.4e+01, igsq=0.95 Reset #40, asq_eff=4.3e+01, igsq=0.95 Reset #41, asq_eff=4.4e+01, igsq=0.95 Reset #42, asq_eff=4.3e+01, igsq=0.95 Reset #43, asq_eff=4.4e+01, igsq=0.95 Reset #44, asq_eff=4.3e+01, igsq=0.95 Reset #45, asq_eff=4.4e+01, igsq=0.95 Reset #46, asq_eff=4.3e+01, igsq=0.95 Reset #47, asq_eff=4.4e+01, igsq=0.95 Reset #48, asq_eff=4.3e+01, igsq=0.95 Reset #49, asq_eff=4.4e+01, igsq=0.95 Reset #50, asq_eff=4.3e+01, igsq=0.95 Reset #51, asq_eff=4.4e+01, igsq=0.95 Reset #52, asq_eff=4.3e+01, igsq=0.95 Reset #53, asq_eff=4.4e+01, igsq=0.95 Reset #54, asq_eff=4.3e+01, igsq=0.95 Reset #55, asq_eff=4.4e+01, igsq=0.95 Reset #56, asq_eff=4.3e+01, igsq=0.95 Reset #57, asq_eff=4.4e+01, igsq=0.95 Reset #58, asq_eff=4.3e+01, igsq=0.95 Reset #59, asq_eff=4.4e+01, igsq=0.95 Reset #60, asq_eff=4.3e+01, igsq=0.95 Reset #61, asq_eff=4.4e+01, igsq=0.95 Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.50e-01 and asq=4.35e+01, N_step=250 K_usable is not stable Results list length: 18 K_usable is not stable Results list length: 19 K_usable is not stable Results list length: 20 LPL [] MPL [] eq37 Before S: 2854196.476840715 eq37 S: 1.4484570058839469e-05 LINF: 2.4e+07 LINF full: 2.4e+07, vs: 1.0e+00 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.0001 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.0001 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.0001 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.0001 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.018 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.018 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.018 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.018 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.1 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.1 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.1 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.1 Reset #1, asq_eff=2.3e+00, igsq=0.1 Failed on igsq: 0.1 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.1 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=1.00e-01 and asq=1.00e+00, N_step=111 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.2 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.2 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.2 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.2 Reset #1, asq_eff=3.3e+00, igsq=0.2 Reset #2, asq_eff=3.6e+00, igsq=0.2 Reset #3, asq_eff=1.7e+00, igsq=0.2 Failed on igsq: 0.2 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.2 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.2 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.2 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=2.00e-01 and asq=1.74e+00, N_step=116 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.3 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.3 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.3 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.3 Non-optimal solve at igsq=3.00e-01 and asq=1.00e+00, N_step=106 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.5 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.5 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.5 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.5 Reset #1, asq_eff=1.2e+00, igsq=0.5 Reset #2, asq_eff=1.3e+00, igsq=0.5 Failed on igsq: 0.5 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.5 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=5.00e-01 and asq=1.00e+00, N_step=113 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.7653668647301797 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.7653668647301797 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.7653668647301797 Reset #1, asq_eff=5.0e+01, igsq=0.7653668647301797 Reset #2, asq_eff=2.3e+01, igsq=0.7653668647301797 Reset #3, asq_eff=1.2e+01, igsq=0.7653668647301797 Reset #4, asq_eff=1.2e+01, igsq=0.7653668647301797 Steps remaining: 107, asq_eff=9.7e+00, igsq=0.7653668647301797 Reset #5, asq_eff=9.4e+00, igsq=0.7653668647301797 Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.7653668647301797 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=7.65e-01 and asq=9.42e+00, N_step=129 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.85 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.85 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.85 Reset #1, asq_eff=5.0e+01, igsq=0.85 Reset #2, asq_eff=2.0e+01, igsq=0.85 Reset #3, asq_eff=1.6e+01, igsq=0.85 Reset #4, asq_eff=1.6e+01, igsq=0.85 Reset #5, asq_eff=1.5e+01, igsq=0.85 Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.85 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=8.50e-01 and asq=1.50e+01, N_step=115 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.9 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.9 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.9 Steps remaining: 9, asq_eff=2.1e+01, igsq=0.9 Reset #1, asq_eff=3.6e+01, igsq=0.9 Reset #2, asq_eff=3.3e+01, igsq=0.9 Reset #3, asq_eff=3.4e+01, igsq=0.9 Reset #4, asq_eff=3.3e+01, igsq=0.9 Reset #5, asq_eff=3.4e+01, igsq=0.9 Reset #6, asq_eff=3.3e+01, igsq=0.9 Reset #7, asq_eff=2.6e+01, igsq=0.9 Steps remaining: 164, asq_eff=2.6e+01, igsq=0.9 Reset #8, asq_eff=2.6e+01, igsq=0.9 Reset #9, asq_eff=2.7e+01, igsq=0.9 Reset #10, asq_eff=2.6e+01, igsq=0.9 Reset #11, asq_eff=2.6e+01, igsq=0.9 Reset #12, asq_eff=2.6e+01, igsq=0.9 Reset #13, asq_eff=2.6e+01, igsq=0.9 Reset #14, asq_eff=2.6e+01, igsq=0.9 Reset #15, asq_eff=2.7e+01, igsq=0.9 Reset #16, asq_eff=2.6e+01, igsq=0.9 Reset #17, asq_eff=2.6e+01, igsq=0.9 Reset #18, asq_eff=2.6e+01, igsq=0.9 Reset #19, asq_eff=2.6e+01, igsq=0.9 Steps remaining: 164, asq_eff=2.6e+01, igsq=0.9 Reset #20, asq_eff=2.6e+01, igsq=0.9 Reset #21, asq_eff=2.6e+01, igsq=0.9 Reset #22, asq_eff=2.6e+01, igsq=0.9 Reset #23, asq_eff=2.6e+01, igsq=0.9 Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.9 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.00e-01 and asq=2.61e+01, N_step=164 Steps remaining: 99, asq_eff=4.2e+14, igsq=0.95 Steps remaining: 69, asq_eff=1.6e+10, igsq=0.95 Steps remaining: 39, asq_eff=5.8e+05, igsq=0.95 Reset #1, asq_eff=1.4e+02, igsq=0.95 Reset #2, asq_eff=1.3e+02, igsq=0.95 Reset #3, asq_eff=1.3e+02, igsq=0.95 Reset #4, asq_eff=9.3e+01, igsq=0.95 Reset #5, asq_eff=8.6e+01, igsq=0.95 Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Failed on igsq: 0.95 Reordering of (A, B) failed because the transformed matrix pair (A, B) would be too far from generalized Schur form; the problem is very ill-conditioned. (A, B) may have been partially reordered. Non-optimal solve at igsq=9.50e-01 and asq=8.59e+01, N_step=114 K_usable is not stable Results list length: 21 K_usable is not stable Results list length: 22 LPL [] MPL [] eq37 Before S: 13648153.81279787 eq37 S: 116008.57188821709 LINF: 2.4e+07 LINF full: 2.4e+07, vs: 1.0e+00 Reset #1, asq_eff=7.0e+14, igsq=0.0001 Non-optimal solve at igsq=1.00e-04 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.018 Non-optimal solve at igsq=1.80e-02 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.1 Non-optimal solve at igsq=1.00e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.2 Non-optimal solve at igsq=2.00e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.3 Non-optimal solve at igsq=3.00e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.5 Non-optimal solve at igsq=5.00e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.7653668647301797 Non-optimal solve at igsq=7.65e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.85 Non-optimal solve at igsq=8.50e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.9 Non-optimal solve at igsq=9.00e-01 and asq=7.01e+14, N_step=5 Reset #1, asq_eff=7.0e+14, igsq=0.95 Non-optimal solve at igsq=9.50e-01 and asq=7.01e+14, N_step=5 Calculating Full Results Now: WARNING: The model is not stable Eigen Values that are > 0: [(418.1471627558395+0j)] WARNING: The model is not stable Eigen Values that are > 0: [418.14716276] WARNING: The model is not stable Eigen Values that are > 0: [418.14716276] Range from PSD: 162.19255282429583 Range from PSD: 153.16575054230935 WARNING: The model is not stable Eigen Values that are > 0: [(325.40307255852105+0j), (3.717706142981129+0j)] WARNING: The model is not stable Eigen Values that are > 0: [325.40307256 3.71770614] WARNING: The model is not stable Eigen Values that are > 0: [325.40307256 3.71770614] Range from PSD: 172.9465831069878 WARNING: The model is not stable Eigen Values that are > 0: [(74.27159745624778+0j), (10.804296460525245+9.748500237755163j), (10.804296460525245-9.748500237755163j), (0.5199091146204519+18.381811081167303j), (0.5199091146204519-18.381811081167303j), (0.1693904817840711+16.28968339517745j), (0.1693904817840711-16.28968339517745j), (3.3509369175335597+0j), (0.7325621878777517+8.10233349189706j), (0.7325621878777517-8.10233349189706j), (0.5246859658762605+0j)] WARNING: The model is not stable Eigen Values that are > 0: [74.27159746 10.80429646 10.80429646 0.51990911 0.51990911 0.16939048 0.16939048 3.35093692 0.73256219 0.73256219 0.52468597] WARNING: The model is not stable Eigen Values that are > 0: [74.27159746 10.80429646 10.80429646 0.51990911 0.51990911 0.16939048 0.16939048 3.35093692 0.73256219 0.73256219 0.52468597] Range from PSD: 179.31124456872516 WARNING: The model is not stable Eigen Values that are > 0: [(2018.692737017845+0j)] WARNING: The model is not stable Eigen Values that are > 0: [2018.69273702] WARNING: The model is not stable Eigen Values that are > 0: [2018.69273702] Range from PSD: 183.27039929149342 WARNING: The model is not stable Eigen Values that are > 0: [(0.017749566798610283+15.662354275641844j), (0.017749566798610283-15.662354275641844j)] WARNING: The model is not stable Eigen Values that are > 0: [0.01774957 0.01774957] WARNING: The model is not stable Eigen Values that are > 0: [0.01774957 0.01774957] Range from PSD: 188.35219137436985 WARNING: The model is not stable Eigen Values that are > 0: [(467.9287354055619+0j), (0.022972858788393613+15.706853285481019j), (0.022972858788393613-15.706853285481019j), (1.7113627965024167+0j), (0.37441435827739045+5.154084974650858j), (0.37441435827739045-5.154084974650858j), (0.09867830121161661+1.253521882873057j), (0.09867830121161661-1.253521882873057j)] WARNING: The model is not stable Eigen Values that are > 0: [4.67928735e+02 2.29728588e-02 2.29728588e-02 1.71136280e+00 3.74414358e-01 3.74414358e-01 9.86783012e-02 9.86783012e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.67928735e+02 2.29728588e-02 2.29728588e-02 1.71136280e+00 3.74414358e-01 3.74414358e-01 9.86783012e-02 9.86783012e-02] Range from PSD: 190.30199636407585 WARNING: The model is not stable Eigen Values that are > 0: [(422.1864032134236+0j), (11.061231737503501+11.055219218308698j), (11.061231737503501-11.055219218308698j), (0.7280079294404227+7.466862061221714j), (0.7280079294404227-7.466862061221714j)] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] Range from PSD: 187.72899538774075 WARNING: The model is not stable Eigen Values that are > 0: [(175.75251695333228+0j), (0.4863022253408999+1.246269453321472j), (0.4863022253408999-1.246269453321472j), (0.17166127537922649+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] Range from PSD: 192.09829680976983 WARNING: The model is not stable Eigen Values that are > 0: [(0.943733978399338+0j)] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] Range from PSD: 193.32544972201305 WARNING: The model is not stable Eigen Values that are > 0: [(49.19095945869148+170.0496622391314j), (49.19095945869148-170.0496622391314j), (0.6045347134659886+19.8823933430864j), (0.6045347134659886-19.8823933430864j), (0.5986864254096691+17.860463564543963j), (0.5986864254096691-17.860463564543963j), (0.06505180270668065+16.22580126192144j), (0.06505180270668065-16.22580126192144j), (0.02519934283797382+3.207329302609284j), (0.02519934283797382-3.207329302609284j)] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] Range from PSD: 193.81178748710568 WARNING: The model is not stable Eigen Values that are > 0: [(809.5422483992226+0j), (0.009285233405991189+16.429910863006555j), (0.009285233405991189-16.429910863006555j), (0.10806689456824148+15.901772190071355j), (0.10806689456824148-15.901772190071355j), (0.8975298072928334+0j)] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] Range from PSD: 194.08260712230265 WARNING: The model is not stable Eigen Values that are > 0: [(0.7175071904696981+12.684975076864854j), (0.7175071904696981-12.684975076864854j), (0.02908379747194867+8.530745212524888j), (0.02908379747194867-8.530745212524888j)] WARNING: The model is not stable Eigen Values that are > 0: [0.71750719 0.71750719 0.0290838 0.0290838 ] WARNING: The model is not stable Eigen Values that are > 0: [0.71750719 0.71750719 0.0290838 0.0290838 ] Range from PSD: 193.94508874092188 WARNING: The model is not stable Eigen Values that are > 0: [(17.480091286156263+0j), (0.08979343751551894+16.371082795196624j), (0.08979343751551894-16.371082795196624j), (0.2096812040337932+15.947659138865001j), (0.2096812040337932-15.947659138865001j), (0.1455459430707851+8.129571886682484j), (0.1455459430707851-8.129571886682484j), (0.10072896978299742+1.1668656915338689j), (0.10072896978299742-1.1668656915338689j), (0.024803847933613288+3.496256159772997j), (0.024803847933613288-3.496256159772997j), (0.10928452295212834+0j)] WARNING: The model is not stable Eigen Values that are > 0: [17.48009129 0.08979344 0.08979344 0.2096812 0.2096812 0.14554594 0.14554594 0.10072897 0.10072897 0.02480385 0.02480385 0.10928452] WARNING: The model is not stable Eigen Values that are > 0: [17.48009129 0.08979344 0.08979344 0.2096812 0.2096812 0.14554594 0.14554594 0.10072897 0.10072897 0.02480385 0.02480385 0.10928452] Range from PSD: 194.43700703610548 WARNING: The model is not stable Eigen Values that are > 0: [(232.41767384893978+0j), (70.21318232720527+0j), (0.2600432830369148+16.236159945072142j), (0.2600432830369148-16.236159945072142j), (0.9718329701892334+14.25244110820402j), (0.9718329701892334-14.25244110820402j), (4.777053875086522+0j), (0.6508016716754814+8.453692722062984j), (0.6508016716754814-8.453692722062984j), (0.022666827340204643+7.640752375999391j), (0.022666827340204643-7.640752375999391j)] WARNING: The model is not stable Eigen Values that are > 0: [2.32417674e+02 7.02131823e+01 2.60043283e-01 2.60043283e-01 9.71832970e-01 9.71832970e-01 4.77705388e+00 6.50801672e-01 6.50801672e-01 2.26668273e-02 2.26668273e-02] WARNING: The model is not stable Eigen Values that are > 0: [2.32417674e+02 7.02131823e+01 2.60043283e-01 2.60043283e-01 9.71832970e-01 9.71832970e-01 4.77705388e+00 6.50801672e-01 6.50801672e-01 2.26668273e-02 2.26668273e-02] Range from PSD: 194.52496075248436 WARNING: The model is not stable Eigen Values that are > 0: [(110.75760617273023+0j), (0.5135089322856994+16.048108577321706j), (0.5135089322856994-16.048108577321706j), (2.9989039843688685+0j), (1.8684294163475195+0j), (0.31378291139017245+7.116662322062661j), (0.31378291139017245-7.116662322062661j), (0.10263973076492514+0.6956943534839114j), (0.10263973076492514-0.6956943534839114j)] WARNING: The model is not stable Eigen Values that are > 0: [1.10757606e+02 5.13508932e-01 5.13508932e-01 2.99890398e+00 1.86842942e+00 3.13782911e-01 3.13782911e-01 1.02639731e-01 1.02639731e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.10757606e+02 5.13508932e-01 5.13508932e-01 2.99890398e+00 1.86842942e+00 3.13782911e-01 3.13782911e-01 1.02639731e-01 1.02639731e-01] Range from PSD: 194.5416618245685 WARNING: The model is not stable Eigen Values that are > 0: [(7.258229265651461+0j), (0.04717749793139337+1.200619045873482j), (0.04717749793139337-1.200619045873482j), (0.12895546577776773+0.4187903567514308j), (0.12895546577776773-0.4187903567514308j)] WARNING: The model is not stable Eigen Values that are > 0: [7.25822927 0.0471775 0.0471775 0.12895547 0.12895547] WARNING: The model is not stable Eigen Values that are > 0: [7.25822927 0.0471775 0.0471775 0.12895547 0.12895547] Range from PSD: 194.5616345952493 WARNING: The model is not stable Eigen Values that are > 0: [(16.38106812121105+0j), (0.4495428409111979+0.9951832598500499j), (0.4495428409111979-0.9951832598500499j)] WARNING: The model is not stable Eigen Values that are > 0: [16.38106812 0.44954284 0.44954284] WARNING: The model is not stable Eigen Values that are > 0: [16.38106812 0.44954284 0.44954284] Range from PSD: 194.55194022947276 WARNING: The model is not stable Eigen Values that are > 0: [(181.1837814716469+0j), (0.03684610821797827+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.81183781e+02 3.68461082e-02] WARNING: The model is not stable Eigen Values that are > 0: [1.81183781e+02 3.68461082e-02] Range from PSD: 194.58745327642487 WARNING: The model is not stable Eigen Values that are > 0: [(173.83867240214832+0j), (0.14030969967714743+1.1103854038570584j), (0.14030969967714743-1.1103854038570584j), (0.5282153122795941+0.3550762860092207j), (0.5282153122795941-0.3550762860092207j)] WARNING: The model is not stable Eigen Values that are > 0: [1.73838672e+02 1.40309700e-01 1.40309700e-01 5.28215312e-01 5.28215312e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.73838672e+02 1.40309700e-01 1.40309700e-01 5.28215312e-01 5.28215312e-01] Range from PSD: 194.58552176321837 WARNING: The model is not stable Eigen Values that are > 0: [(31.613508390595022+51.27050674003169j), (31.613508390595022-51.27050674003169j)] WARNING: The model is not stable Eigen Values that are > 0: [31.61350839 31.61350839] WARNING: The model is not stable Eigen Values that are > 0: [31.61350839 31.61350839] Range from PSD: 194.43131096878759 WARNING: The model is not stable Eigen Values that are > 0: [(37.29562705716835+45.75751102314879j), (37.29562705716835-45.75751102314879j), (0.08117147004595271+16.19727900357282j), (0.08117147004595271-16.19727900357282j), (0.5598788338473593+0j), (0.6494423549332886+6.193779828954125j), (0.6494423549332886-6.193779828954125j), (0.5141778458487678+7.7728868969887j), (0.5141778458487678-7.7728868969887j), (0.21949339072000945+1.2281748388005682j), (0.21949339072000945-1.2281748388005682j)] WARNING: The model is not stable Eigen Values that are > 0: [37.29562706 37.29562706 0.08117147 0.08117147 0.55987883 0.64944235 0.64944235 0.51417785 0.51417785 0.21949339 0.21949339] WARNING: The model is not stable Eigen Values that are > 0: [37.29562706 37.29562706 0.08117147 0.08117147 0.55987883 0.64944235 0.64944235 0.51417785 0.51417785 0.21949339 0.21949339] Range from PSD: 194.5966617523974 Now running plot test save fldr: /builds/buzz/test/ASC_SOLVER_PAPER/ExampleModels_paper/results_H2_ADY.pkl Range from PSD: 192.26892101593648 Plotting RMS Plot Plotting RMS Plot with lost range Plotting RMS Plot with lost range (PSD computation) WARNING: The model is not stable Eigen Values that are > 0: [(422.1864032134236+0j), (11.061231737503501+11.055219218308698j), (11.061231737503501-11.055219218308698j), (0.7280079294404227+7.466862061221714j), (0.7280079294404227-7.466862061221714j)] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] Range from PSD: 187.72899538774075 WARNING: The model is not stable Eigen Values that are > 0: [(175.75251695333228+0j), (0.4863022253408999+1.246269453321472j), (0.4863022253408999-1.246269453321472j), (0.17166127537922649+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] Range from PSD: 192.09829680976983 WARNING: The model is not stable Eigen Values that are > 0: [(0.943733978399338+0j)] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] Range from PSD: 193.32544972201305 WARNING: The model is not stable Eigen Values that are > 0: [(49.19095945869148+170.0496622391314j), (49.19095945869148-170.0496622391314j), (0.6045347134659886+19.8823933430864j), (0.6045347134659886-19.8823933430864j), (0.5986864254096691+17.860463564543963j), (0.5986864254096691-17.860463564543963j), (0.06505180270668065+16.22580126192144j), (0.06505180270668065-16.22580126192144j), (0.02519934283797382+3.207329302609284j), (0.02519934283797382-3.207329302609284j)] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] Range from PSD: 193.81178748710568 WARNING: The model is not stable Eigen Values that are > 0: [(809.5422483992226+0j), (0.009285233405991189+16.429910863006555j), (0.009285233405991189-16.429910863006555j), (0.10806689456824148+15.901772190071355j), (0.10806689456824148-15.901772190071355j), (0.8975298072928334+0j)] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] Range from PSD: 194.08260712230265 Plotting RMS Plot With Range Plotting RMS Plot With Range from PSD Plotting Heatmap Plotting RMS Plot with Heatmap Heatmap Failed:, Points cannot contain NaN Plotting RMS Range Plot single result Plotting Open Loop Plotting Closed Loop Plotting gm pm plot WARNING: The model is not stable Eigen Values that are > 0: [(418.1471627558395+0j)] WARNING: The model is not stable Eigen Values that are > 0: [418.14716276] WARNING: The model is not stable Eigen Values that are > 0: [418.14716276] Range from PSD: 162.19255282429583 Range from PSD: 153.16575054230935 WARNING: The model is not stable Eigen Values that are > 0: [(325.40307255852105+0j), (3.717706142981129+0j)] WARNING: The model is not stable Eigen Values that are > 0: [325.40307256 3.71770614] WARNING: The model is not stable Eigen Values that are > 0: [325.40307256 3.71770614] Range from PSD: 172.9465831069878 WARNING: The model is not stable Eigen Values that are > 0: [(74.27159745624778+0j), (10.804296460525245+9.748500237755163j), (10.804296460525245-9.748500237755163j), (0.5199091146204519+18.381811081167303j), (0.5199091146204519-18.381811081167303j), (0.1693904817840711+16.28968339517745j), (0.1693904817840711-16.28968339517745j), (3.3509369175335597+0j), (0.7325621878777517+8.10233349189706j), (0.7325621878777517-8.10233349189706j), (0.5246859658762605+0j)] WARNING: The model is not stable Eigen Values that are > 0: [74.27159746 10.80429646 10.80429646 0.51990911 0.51990911 0.16939048 0.16939048 3.35093692 0.73256219 0.73256219 0.52468597] WARNING: The model is not stable Eigen Values that are > 0: [74.27159746 10.80429646 10.80429646 0.51990911 0.51990911 0.16939048 0.16939048 3.35093692 0.73256219 0.73256219 0.52468597] Range from PSD: 179.31124456872516 WARNING: The model is not stable Eigen Values that are > 0: [(2018.692737017845+0j)] WARNING: The model is not stable Eigen Values that are > 0: [2018.69273702] WARNING: The model is not stable Eigen Values that are > 0: [2018.69273702] Range from PSD: 183.27039929149342 WARNING: The model is not stable Eigen Values that are > 0: [(0.017749566798610283+15.662354275641844j), (0.017749566798610283-15.662354275641844j)] WARNING: The model is not stable Eigen Values that are > 0: [0.01774957 0.01774957] WARNING: The model is not stable Eigen Values that are > 0: [0.01774957 0.01774957] Range from PSD: 188.35219137436985 WARNING: The model is not stable Eigen Values that are > 0: [(467.9287354055619+0j), (0.022972858788393613+15.706853285481019j), (0.022972858788393613-15.706853285481019j), (1.7113627965024167+0j), (0.37441435827739045+5.154084974650858j), (0.37441435827739045-5.154084974650858j), (0.09867830121161661+1.253521882873057j), (0.09867830121161661-1.253521882873057j)] WARNING: The model is not stable Eigen Values that are > 0: [4.67928735e+02 2.29728588e-02 2.29728588e-02 1.71136280e+00 3.74414358e-01 3.74414358e-01 9.86783012e-02 9.86783012e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.67928735e+02 2.29728588e-02 2.29728588e-02 1.71136280e+00 3.74414358e-01 3.74414358e-01 9.86783012e-02 9.86783012e-02] Range from PSD: 190.30199636407585 WARNING: The model is not stable Eigen Values that are > 0: [(422.1864032134236+0j), (11.061231737503501+11.055219218308698j), (11.061231737503501-11.055219218308698j), (0.7280079294404227+7.466862061221714j), (0.7280079294404227-7.466862061221714j)] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] Range from PSD: 187.72899538774075 WARNING: The model is not stable Eigen Values that are > 0: [(175.75251695333228+0j), (0.4863022253408999+1.246269453321472j), (0.4863022253408999-1.246269453321472j), (0.17166127537922649+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] Range from PSD: 192.09829680976983 WARNING: The model is not stable Eigen Values that are > 0: [(0.943733978399338+0j)] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] Range from PSD: 193.32544972201305 WARNING: The model is not stable Eigen Values that are > 0: [(49.19095945869148+170.0496622391314j), (49.19095945869148-170.0496622391314j), (0.6045347134659886+19.8823933430864j), (0.6045347134659886-19.8823933430864j), (0.5986864254096691+17.860463564543963j), (0.5986864254096691-17.860463564543963j), (0.06505180270668065+16.22580126192144j), (0.06505180270668065-16.22580126192144j), (0.02519934283797382+3.207329302609284j), (0.02519934283797382-3.207329302609284j)] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] Range from PSD: 193.81178748710568 WARNING: The model is not stable Eigen Values that are > 0: [(809.5422483992226+0j), (0.009285233405991189+16.429910863006555j), (0.009285233405991189-16.429910863006555j), (0.10806689456824148+15.901772190071355j), (0.10806689456824148-15.901772190071355j), (0.8975298072928334+0j)] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] Range from PSD: 194.08260712230265 WARNING: The model is not stable Eigen Values that are > 0: [(0.7175071904696981+12.684975076864854j), (0.7175071904696981-12.684975076864854j), (0.02908379747194867+8.530745212524888j), (0.02908379747194867-8.530745212524888j)] WARNING: The model is not stable Eigen Values that are > 0: [0.71750719 0.71750719 0.0290838 0.0290838 ] WARNING: The model is not stable Eigen Values that are > 0: [0.71750719 0.71750719 0.0290838 0.0290838 ] Range from PSD: 193.94508874092188 WARNING: The model is not stable Eigen Values that are > 0: [(17.480091286156263+0j), (0.08979343751551894+16.371082795196624j), (0.08979343751551894-16.371082795196624j), (0.2096812040337932+15.947659138865001j), (0.2096812040337932-15.947659138865001j), (0.1455459430707851+8.129571886682484j), (0.1455459430707851-8.129571886682484j), (0.10072896978299742+1.1668656915338689j), (0.10072896978299742-1.1668656915338689j), (0.024803847933613288+3.496256159772997j), (0.024803847933613288-3.496256159772997j), (0.10928452295212834+0j)] WARNING: The model is not stable Eigen Values that are > 0: [17.48009129 0.08979344 0.08979344 0.2096812 0.2096812 0.14554594 0.14554594 0.10072897 0.10072897 0.02480385 0.02480385 0.10928452] WARNING: The model is not stable Eigen Values that are > 0: [17.48009129 0.08979344 0.08979344 0.2096812 0.2096812 0.14554594 0.14554594 0.10072897 0.10072897 0.02480385 0.02480385 0.10928452] Range from PSD: 194.43700703610548 WARNING: The model is not stable Eigen Values that are > 0: [(232.41767384893978+0j), (70.21318232720527+0j), (0.2600432830369148+16.236159945072142j), (0.2600432830369148-16.236159945072142j), (0.9718329701892334+14.25244110820402j), (0.9718329701892334-14.25244110820402j), (4.777053875086522+0j), (0.6508016716754814+8.453692722062984j), (0.6508016716754814-8.453692722062984j), (0.022666827340204643+7.640752375999391j), (0.022666827340204643-7.640752375999391j)] WARNING: The model is not stable Eigen Values that are > 0: [2.32417674e+02 7.02131823e+01 2.60043283e-01 2.60043283e-01 9.71832970e-01 9.71832970e-01 4.77705388e+00 6.50801672e-01 6.50801672e-01 2.26668273e-02 2.26668273e-02] WARNING: The model is not stable Eigen Values that are > 0: [2.32417674e+02 7.02131823e+01 2.60043283e-01 2.60043283e-01 9.71832970e-01 9.71832970e-01 4.77705388e+00 6.50801672e-01 6.50801672e-01 2.26668273e-02 2.26668273e-02] Range from PSD: 194.52496075248436 WARNING: The model is not stable Eigen Values that are > 0: [(110.75760617273023+0j), (0.5135089322856994+16.048108577321706j), (0.5135089322856994-16.048108577321706j), (2.9989039843688685+0j), (1.8684294163475195+0j), (0.31378291139017245+7.116662322062661j), (0.31378291139017245-7.116662322062661j), (0.10263973076492514+0.6956943534839114j), (0.10263973076492514-0.6956943534839114j)] WARNING: The model is not stable Eigen Values that are > 0: [1.10757606e+02 5.13508932e-01 5.13508932e-01 2.99890398e+00 1.86842942e+00 3.13782911e-01 3.13782911e-01 1.02639731e-01 1.02639731e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.10757606e+02 5.13508932e-01 5.13508932e-01 2.99890398e+00 1.86842942e+00 3.13782911e-01 3.13782911e-01 1.02639731e-01 1.02639731e-01] Range from PSD: 194.5416618245685 WARNING: The model is not stable Eigen Values that are > 0: [(7.258229265651461+0j), (0.04717749793139337+1.200619045873482j), (0.04717749793139337-1.200619045873482j), (0.12895546577776773+0.4187903567514308j), (0.12895546577776773-0.4187903567514308j)] WARNING: The model is not stable Eigen Values that are > 0: [7.25822927 0.0471775 0.0471775 0.12895547 0.12895547] WARNING: The model is not stable Eigen Values that are > 0: [7.25822927 0.0471775 0.0471775 0.12895547 0.12895547] Range from PSD: 194.5616345952493 WARNING: The model is not stable Eigen Values that are > 0: [(16.38106812121105+0j), (0.4495428409111979+0.9951832598500499j), (0.4495428409111979-0.9951832598500499j)] WARNING: The model is not stable Eigen Values that are > 0: [16.38106812 0.44954284 0.44954284] WARNING: The model is not stable Eigen Values that are > 0: [16.38106812 0.44954284 0.44954284] Range from PSD: 194.55194022947276 WARNING: The model is not stable Eigen Values that are > 0: [(181.1837814716469+0j), (0.03684610821797827+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.81183781e+02 3.68461082e-02] WARNING: The model is not stable Eigen Values that are > 0: [1.81183781e+02 3.68461082e-02] Range from PSD: 194.58745327642487 WARNING: The model is not stable Eigen Values that are > 0: [(173.83867240214832+0j), (0.14030969967714743+1.1103854038570584j), (0.14030969967714743-1.1103854038570584j), (0.5282153122795941+0.3550762860092207j), (0.5282153122795941-0.3550762860092207j)] WARNING: The model is not stable Eigen Values that are > 0: [1.73838672e+02 1.40309700e-01 1.40309700e-01 5.28215312e-01 5.28215312e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.73838672e+02 1.40309700e-01 1.40309700e-01 5.28215312e-01 5.28215312e-01] Range from PSD: 194.58552176321837 WARNING: The model is not stable Eigen Values that are > 0: [(31.613508390595022+51.27050674003169j), (31.613508390595022-51.27050674003169j)] WARNING: The model is not stable Eigen Values that are > 0: [31.61350839 31.61350839] WARNING: The model is not stable Eigen Values that are > 0: [31.61350839 31.61350839] Range from PSD: 194.43131096878759 WARNING: The model is not stable Eigen Values that are > 0: [(37.29562705716835+45.75751102314879j), (37.29562705716835-45.75751102314879j), (0.08117147004595271+16.19727900357282j), (0.08117147004595271-16.19727900357282j), (0.5598788338473593+0j), (0.6494423549332886+6.193779828954125j), (0.6494423549332886-6.193779828954125j), (0.5141778458487678+7.7728868969887j), (0.5141778458487678-7.7728868969887j), (0.21949339072000945+1.2281748388005682j), (0.21949339072000945-1.2281748388005682j)] WARNING: The model is not stable Eigen Values that are > 0: [37.29562706 37.29562706 0.08117147 0.08117147 0.55987883 0.64944235 0.64944235 0.51417785 0.51417785 0.21949339 0.21949339] WARNING: The model is not stable Eigen Values that are > 0: [37.29562706 37.29562706 0.08117147 0.08117147 0.55987883 0.64944235 0.64944235 0.51417785 0.51417785 0.21949339 0.21949339] Range from PSD: 194.5966617523974 Range from PSD: 192.26892101593648 Plotting vegaplot Captured stderr call 0%| | 0/6 [00:00<?, ?it/s] 17%|█▋ | 1/6 [08:32<42:43, 512.74s/it] 33%|███▎ | 2/6 [19:10<39:04, 586.05s/it] 50%|█████ | 3/6 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- T_plot_H2(folder, dfile=None)[source]¶
Test that runs the plotting code for H2. This test can be run from another test
code
docstring
""" Test that runs the plotting code for H2. This test can be run from another test """
1@pytest.mark.parametrize( 2 'folder', [ 3 'ExampleModels_paper' 4 ] 5) 6def T_plot_H2(folder, dfile = None): 7 8 9 if folder is None: 10 folder = "ExampleModels" 11 12 io_scales = file_io.load(fjoin(folder, 'io_scales.yml')) # Saved by the normalization in the make function 13 sysB = get_systemB(folder=folder) 14 15 if dfile is None: 16 ofile = "results_H2_ADY.pkl" 17 dfile = fjoin(folder, ofile) 18 print('dfile: ', dfile) 19 H2_results = buzzutil.load_results(dfile) 20 21 print('len H2_results: ', len(H2_results)) 22 23 # add a gamma value to the results for plotting 24 for point in H2_results: 25 if point["gamma"] == None: 26 point["gamma"] = np.inf 27 28 # Remove the labels from the results 29 for datadict in H2_results: 30 datadict["label1"] = None 31 datadict["label2"] = None 32 33 # Adding the current controller to the plot 34 module_gain = -40 # this is the gain of the filter module 35 filter_list = np.array([1, 3, 4, 5, 6]) # this is a list of the filters in the module that are on 36 37 zpksys, filtinfo = readFilter.readFilterSys_Scipy( 38 fjoin("H1ASC.txt"), "ASC_DHARD_Y", np.array(filter_list) 39 ) 40 41 z, p, k = FilteringUtils.d2c( 42 (zpksys.zeros, zpksys.poles, zpksys.gain), fs=1 / zpksys.dt 43 ) 44 45 k = k.real * module_gain 46 K_mod = SISO.zpk(z, p, k, angular=True, fiducial_rtol=1e-5, fiducial_atol=1e-10).asSS 47 K_iod = {"C.in": 0, "C.out": 0} 48 K = wieldSS(K_mod, K_iod) 49 50 axB = ssutil.bode(ssutil.asSISO(K)*sysB.orig_plant.siso("P.out", "P.in"), label="K_hand * orig_plant Controller") 51 axC = ssutil.bode(K, label="K_hand") 52 53 K = (ssutil.asSISO(K) * sysB.sys["S.out", "SA.in"]).mimo( 54 "C.out", "C.in" 55 ) # Add in the part of P that was moved to env. noise block 56 K = ssutil.balance_sys_gain(K) 57 58 axB = ssutil.bode(-1*ssutil.asSISO(K)*H2_results[0]['plant'].siso("P.out", "P.in"), axB =axB, label="K_hand * S_anex * P") 59 axB = ssutil.bode(H2_results[1]['K'].siso("C.out", "C.in")*H2_results[1]['plant'].siso("P.out", "P.in"), axB =axB, label="K_H2[-15] * P") 60 axB.save(tjoin("bode_KP.pdf")) 61 axB.save(tjoin("bode_KP.png")) 62 63 axC = ssutil.bode(H2_results[1]['K_usable'], axB =axC, label="K_useable") 64 axB = ssutil.bode(ssutil.asSISO(H2_results[1]['K'])*sysB.sys.siso("S.out", "SA.in")**(-1), axB =axC, label="K / S_anex") 65 axC.save(tjoin("bode_K.pdf")) 66 axC.save(tjoin("bode_K.png")) 67 68 curdict = H2_results[0].copy() 69 curdict["Ac"] = K.A 70 curdict["Bc"] = K.B 71 curdict["Cc"] = K.C 72 curdict["Dc"] = K.D 73 curdict["K"] = K 74 curdict["label"] = "Hand Tuned Controller" 75 76 extraplots = compute.calcFullResults( 77 [curdict], 78 color="dimgrey", 79 label="Hand Tuned Controller", 80 plot_omega=curdict["plot_omega"], 81 io_scales = io_scales, 82 ) 83 extraplots[0]["F1_gain"] = sum(buzzutil.listparams(H2_results, "F1_gain")) 84 extraplots[0]["linecolor"] = "Black" #'DodgerBlue' 85 extraplots[0]["linestyle"] = "--" 86 extraplots[0]["solver"] = "Hand Tuned" 87 extraplots[0]["rms_marker"] = "^" 88 89 f_Hz_range = extraplots[0]['FOM1_wn_ASD_Hz'] 90 budget_range = gwinc.load_budget('aLIGO', freq=f_Hz_range) 91 trace_range = budget_range.run(freq=f_Hz_range) 92 93 plt.loglog(f_Hz_range, extraplots[0]['FOM1_wn_ASD']*4000, label='white noise to darm out with hand tuned controller') 94 plt.loglog(f_Hz_range, trace_range.psd**0.5*4000, label='aLIGO') 95 plt.legend() 96 plt.xlabel('Frequency [Hz]') 97 plt.ylabel('ASD [m/rtHz]') 98 plt.xlim([6, 6e2]) 99 plt.ylim([4e-21, 3e-17]) 100 plt.savefig(tjoin('DARM_spec.png'), bbox_inches='tight') 101 plt.savefig(tjoin('DARM_spec.pdf'), bbox_inches='tight') 102 plt.close() 103 104 H2_results_E = H2_results + extraplots 105 106 every_nth = 1 # removes every nth element 107 H2_results_E_short = H2_results[every_nth - 1:: every_nth] + extraplots 108 109 setname_H2 = "ADY_H2" 110 fig_rms_H2 = plotting.plotRMS( 111 H2_results_E_short, 112 setname=setname_H2, 113 outlineparam="pm", 114 text="pm", 115 f1line=False, 116 h2line=False, 117 plotrange=False, 118 test=True, 119 ) 120 fig_rms_H2[0].savefig(tjoin("rms_H2.pdf"), bbox_inches="tight") 121 fig_rms_H2[0].savefig(tjoin("rms_H2.png"), bbox_inches="tight") 122 123 fig_rms_H2_range_PSD = plotting.plotRMS( 124 H2_results_E_short, 125 setname=setname_H2, 126 outlineparam="pm", 127 text="pm", 128 f1line=False, 129 h2line=False, 130 plotrange='PSD', 131 test=True, 132 ) 133 fig_rms_H2_range_PSD[0].savefig(tjoin("rms_H2_range_PSD.pdf"), bbox_inches="tight") 134 fig_rms_H2_range_PSD[0].savefig(tjoin("rms_H2_range_PSD.png"), bbox_inches="tight") 135 136 137 print('Plotting RMS Plot with lost range') 138 fig_rms_lost_range_H2 = plotting.plotRMS( 139 H2_results_E, 140 setname=setname_H2, 141 outlineparam='pm', 142 text='pm', 143 f1line=True, 144 h2line=True, 145 plotrange='diff', 146 test=True, 147 #xlim=xlim, 148 #ylim=ylim 149 ) 150 fig_rms_lost_range_H2[0].savefig(tjoin("rms_H2_lost_range.pdf"), bbox_inches="tight") 151 fig_rms_lost_range_H2[0].savefig(tjoin("rms_H2_lost_range.png"), bbox_inches="tight") 152 153 print('Plotting RMS Plot with lost range (PSD computation)') 154 fig_rms_lost_range_H2_psd = plotting.plotRMS( 155 H2_results_E, 156 setname=setname_H2, 157 outlineparam='pm', 158 text='pm', 159 f1line=True, 160 h2line=True, 161 plotrange='diff_psd', 162 test=True, 163 #xlim=xlim, 164 #ylim=ylim 165 ) 166 fig_rms_lost_range_H2_psd[0].savefig(tjoin("rms_H2_lost_range_PSD.pdf"), bbox_inches="tight") 167 fig_rms_lost_range_H2_psd[0].savefig(tjoin("rms_H2_lost_range_PSD.png"), bbox_inches="tight") 168 169 170 ylim = [1e-4, 1e8] 171 fig_ol_H2, ax = plotting.plotLoop(H2_results_E_short, "OL", setname=setname_H2, ylim=ylim, UG_line=True, test=True) 172 fig_ol_H2.savefig(tjoin("ol_H2.pdf"), bbox_inches="tight") 173 fig_ol_H2.savefig(tjoin("ol_H2.png"), bbox_inches="tight") 174 175 fig_cl_H2, ax = plotting.plotLoop(H2_results_E_short, "CL", setname=setname_H2, UG_line=True, test=True) 176 fig_cl_H2.savefig(tjoin("cl_H2.pdf"), bbox_inches="tight") 177 fig_cl_H2.savefig(tjoin("cl_H2.png"), bbox_inches="tight") 178 179 noise_plot_fig, noise_plot_axs = plotting.plot_CLnoises(H2_results_E_short[0], test=True) 180 noise_plot_fig.savefig(tjoin("noise_H2.pdf"), bbox_inches="tight") 181 noise_plot_fig.savefig(tjoin("noise_H2.png"), bbox_inches="tight") 182 183 fig_darm_H2, ax = plotting.plotDARM(H2_results_E_short, setname=setname_H2, test=True) 184 fig_darm_H2.savefig(tjoin("All_DARM_Spec.pdf"), bbox_inches="tight") 185 fig_darm_H2.savefig(tjoin("All_DARM_Spec.png"), bbox_inches="tight") 186 187 setname_vega = 'ASC_vega' 188 vega_chart = plotting.vega_plot(H2_results_E, setname=setname_vega, h2line=False, size=500, plotrange=False, test=True) 189 vega_chart.save(tjoin("vega_results_plot.html")) 190 191 if 'FBNS.DARM.out' in extraplots[0]['CL_all_io'].iod: 192 DARM_out_name = 'FBNS.DARM.out' 193 darm_in_mod = True 194 elif 'F3.DARM.out' in extraplots[0]['CL_all_io'].iod: 195 DARM_out_name = 'F3.DARM.out' 196 darm_in_mod = True 197 else: 198 DARM_out_name = None 199 200 if DARM_out_name: 201 DARM_plot_omega = np.logspace(-2, 3, 1000) * 2 * np.pi 202 DARM_noise_plot_fig, DARM_noise_plot_axs = plotting.plot_CLnoises(extraplots[0], extra_fom=DARM_out_name, extra_only=True, plot_omega=DARM_plot_omega, test=True, scale=1/io_scales.w2_rescale) 203 DARM_noise_plot_fig.savefig(tjoin("DARM_noise_H2.pdf"), bbox_inches="tight") 204 DARM_noise_plot_fig.savefig(tjoin("DARM_noise_H2.png"), bbox_inches="tight") 205 else: 206 print('DARM not in outputs. Not plotting DARM noise') 207 print(extraplots[0]['CL_all_io'].outputs)
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:
test.ASC_SOLVER_PAPER.test_solver_ADY_paper.T_plot_H2The 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.T_plot_H2[ExampleModels_paper]
output
dfile: /builds/buzz/test/ASC_SOLVER_PAPER/ExampleModels_paper/results_H2_ADY.pkl len H2_results: 30 <IPython.core.display.Markdown object> <IPython.core.display.Markdown object> Range from PSD: 192.26892101593648 Plotting RMS Plot with lost range Plotting RMS Plot with lost range (PSD computation) RMS: 389664.5123393369 2228.174832028176 RMS: 2.337510162690648e-34 5.427539476350936e-17 Captured stderr call 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:01<00:00, 1.23s/it] 100%|██████████| 1/1 [00:01<00:00, 1.23s/it]
- T_plot_HiB(folder, dfile=None)[source]¶
This is a pytest needing documentation
code
docstring
"""HinfBounded_results_E = HinfBounded_results + extraplots H2_results_E = H2_results + extraplots every_nth = 2 # removes every nth element H2_results_E_short = H2_results[every_nth-1::every_nth]+ extraplots"""
1@pytest.mark.parametrize( 2 'folder', 3 [ 4 'ExampleModels_paper' 5 ] 6) 7def T_plot_HiB(folder, dfile = None): 8 if folder is None: 9 folder = "ExampleModels" 10 11 sysB = get_systemB(folder=folder) 12 io_scales = file_io.load(fjoin(folder, 'io_scales.yml')) # Saved by the normalization in the make function 13 14 15 16 H2_results = buzzutil.load_results(fjoin(folder, "results_H2_ADY.pkl")) 17 print('save fldr: ', fjoin(folder, "results_H2_ADY.pkl")) 18 19 if dfile is None: 20 ofile = "results_Hib_ADY.pkl" 21 dfile = fjoin(folder, ofile) 22 results_Hib = buzzutil.load_results(dfile) 23 24 25 # print('Gamma:', buzzutil.listparams(HinfBounded_results, 'gamma')) 26 # print('igsq:', np.unique(buzzutil.listparams(HinfBounded_results, 'igsq'))) 27 # print('F1_gain', np.unique(buzzutil.listparams(HinfBounded_results, 'F1_gain'))) 28 # print('F1_gain', buzzutil.listparams(HinfBounded_results, 'F1_gain')) 29 30 HinfBounded_results = results_Hib 31 # H2_results = results_H2 32 33 # f1_gains = np.unique(buzzutil.listparams(HinfBounded_results, 'F1_gain')) 34 35 # HinfBounded_results = buzzutil.filtparam(HinfBounded_results, "F1_gain", f1_gains[3], compare="eq") 36 37 for point in HinfBounded_results: 38 if point["gamma"] == None: 39 point["gamma"] = np.inf 40 41 # Remove labels 42 for datadict in HinfBounded_results: 43 datadict["label1"] = None 44 datadict["label2"] = None 45 46 # Adding the current controller to the plot 47 module_gain = -40 # this is the gain of the filter module 48 filter_list = np.array([1, 3, 4, 5, 6]) # this is a list of the filters in the module that are on 49 50 zpksys, filtinfo = readFilter.readFilterSys_Scipy( 51 fjoin("H1ASC.txt"), "ASC_DHARD_Y", np.array(filter_list) 52 ) 53 54 z, p, k = FilteringUtils.d2c( 55 (zpksys.zeros, zpksys.poles, zpksys.gain), fs=1 / zpksys.dt 56 ) 57 58 k = k.real * module_gain 59 K_mod = SISO.zpk(z, p, k, angular=True, fiducial_rtol=1e-5, fiducial_atol=1e-10).asSS 60 K_iod = {"C.in": 0, "C.out": 0} 61 K = wieldSS(K_mod, K_iod) 62 K = (K.siso("C.out", "C.in") * sysB.sys["S.out", "SA.in"]).mimo( 63 "C.out", "C.in" 64 ) # Add in the part of P that was moved to env. noise block 65 K = ssutil.balance_sys_gain(K) 66 67 curdict = HinfBounded_results[0].copy() 68 curdict["Ac"] = K.A 69 curdict["Bc"] = K.B 70 curdict["Cc"] = K.C 71 curdict["Dc"] = K.D 72 curdict["K"] = K 73 curdict["label"] = "Hand Tuned Controller" 74 75 extraplots = compute.calcFullResults( 76 [curdict], color="dimgrey", 77 label="Hand Tuned Controller", 78 plot_omega=curdict["plot_omega"], 79 io_scales=io_scales, 80 ) 81 extraplots[0]["F1_gain"] = sum(buzzutil.listparams(HinfBounded_results, "F1_gain")) 82 extraplots[0]["linecolor"] = "DodgerBlue" 83 extraplots[0]["linestyle"] = "--" 84 extraplots[0]["solver"] = "Hand Tuned" 85 extraplots[0]["rms_marker"] = "^" 86 87 HinfBounded_results_E = HinfBounded_results + extraplots 88 H2_results_E = H2_results + extraplots 89 90 every_nth = 2 # removes every nth element 91 H2_results_E_short = H2_results[every_nth - 1 :: every_nth] + extraplots 92 93 94 xlim= [0.2, 0.5] 95 ylim = [3e-14, 9e-14] 96 # Adding the current controller to the plot 97 setname_Hib = "ADY_Hib" 98 99 if len(HinfBounded_results_E + H2_results)>100: 100 plot_text=False 101 else: 102 plot_text='pm' 103 104 print('Plotting RMS Plot') 105 fig_rms_Hib = plotting.plotRMS( 106 HinfBounded_results_E + H2_results, 107 setname=setname_Hib, 108 outlineparam='pm', 109 text=plot_text, 110 f1line=True, 111 h2line=True, 112 plotrange=False, 113 #xlim=xlim, 114 #ylim=ylim 115 test=True, 116 ) 117 118 fig_rms_Hib[0].savefig(tjoin("rms_HiB.pdf"), bbox_inches="tight") 119 fig_rms_Hib[0].savefig(tjoin("rms_HiB.png"), bbox_inches="tight") 120 121 print('Plotting RMS Plot with lost range') 122 fig_rms_lost_range_Hib = plotting.plotRMS( 123 HinfBounded_results_E + H2_results, 124 setname=setname_Hib, 125 outlineparam='pm', 126 text=plot_text, 127 f1line=True, 128 h2line=True, 129 plotrange='diff', 130 test=True, 131 #xlim=xlim, 132 #ylim=ylim 133 ) 134 135 fig_rms_lost_range_Hib[0].savefig(tjoin("rms_HiB_lost_range.pdf"), bbox_inches="tight") 136 fig_rms_lost_range_Hib[0].savefig(tjoin("rms_HiB_lost_range.png"), bbox_inches="tight") 137 138 print('Plotting RMS Plot with lost range (PSD computation)') 139 fig_rms_lost_range_Hib_psd = plotting.plotRMS( 140 HinfBounded_results_E + H2_results, 141 setname=setname_Hib, 142 outlineparam='pm', 143 text=plot_text, 144 f1line=True, 145 h2line=True, 146 plotrange='diff_psd', 147 test=True, 148 #xlim=xlim, 149 #ylim=ylim 150 ) 151 152 fig_rms_lost_range_Hib_psd[0].savefig(tjoin("rms_HiB_lost_range_PSD.pdf"), bbox_inches="tight") 153 fig_rms_lost_range_Hib_psd[0].savefig(tjoin("rms_HiB_lost_range_PSD.png"), bbox_inches="tight") 154 155 156 f1gains_Hib = np.unique(buzzutil.listparams(HinfBounded_results, "F1_gain")) 157 try: 158 HinfBounded_results_single_f1gain = buzzutil.filtparam(HinfBounded_results, "F1_gain", f1gains_Hib[-4], compare="eq") 159 except: 160 HinfBounded_results_single_f1gain = buzzutil.filtparam(HinfBounded_results, "F1_gain", f1gains_Hib[0], compare="eq") 161 HinfBounded_results_single_f1gain = ( 162 compute.calcFullResults( 163 HinfBounded_results_single_f1gain, 164 colormap="RdYlGn", 165 plot_omega=HinfBounded_results[0]["plot_omega"], 166 color_param="igsq", 167 label=False, 168 io_scales=io_scales, 169 ) 170 + extraplots 171 ) 172 173 print('Plotting RMS Plot With Range') 174 fig_rms_range_Hib = plotting.plotRMS( 175 HinfBounded_results + H2_results, 176 setname=setname_Hib, 177 outlineparam='pm', 178 text=plot_text, 179 f1line=True, 180 h2line=True, 181 plotrange=True, 182 test=True, 183 #xlim=xlim, 184 #ylim=ylim 185 ) 186 187 fig_rms_range_Hib[0].savefig(tjoin("rms_HiB_range.pdf"), bbox_inches="tight") 188 fig_rms_range_Hib[0].savefig(tjoin("rms_HiB_range.png"), bbox_inches="tight") 189 190 xlim_PSD=[4.725, 4.805] 191 print('Plotting RMS Plot With Range from PSD') 192 fig_rms_range_PSD_Hib = plotting.plotRMS( 193 HinfBounded_results + H2_results, 194 setname=setname_Hib, 195 outlineparam='pm', 196 text=plot_text, 197 f1line=True, 198 h2line=True, 199 plotrange='PSD', 200 xlim=xlim_PSD, 201 test=True, 202 #ylim=ylim 203 ) 204 205 fig_rms_range_PSD_Hib[0].savefig(tjoin("rms_HiB_range_from_PSD.pdf"), bbox_inches="tight") 206 fig_rms_range_PSD_Hib[0].savefig(tjoin("rms_HiB_range_from_PSD.png"), bbox_inches="tight") 207 208 print('Plotting Heatmap') 209 fig_rmsheatmap = plotting.plotHeatmap(HinfBounded_results + H2_results, test=True) 210 fig_rmsheatmap[0].savefig(tjoin("heatmap.pdf"), bbox_inches="tight") 211 fig_rmsheatmap[0].savefig(tjoin("heatmap.png"), bbox_inches="tight") 212 213 try: 214 print('Plotting RMS Plot with Heatmap') 215 fig_rmsheatmap_Hib = plotting.plotRMS( 216 HinfBounded_results_E + H2_results, 217 setname=setname_Hib, 218 outlineparam='pm', 219 text=False, 220 f1line=True, 221 h2line=True, 222 plotrange=True, 223 heatmap=True, 224 test=True, 225 ) 226 227 fig_rmsheatmap_Hib[0].savefig(tjoin("rms_heatmap_HiB.pdf"), bbox_inches="tight") 228 fig_rmsheatmap_Hib[0].savefig(tjoin("rms_heatmap_HiB.png"), bbox_inches="tight") 229 except Exception as e: 230 print("Heatmap Failed:, ", e) 231 232 print('Plotting RMS Range Plot single result') 233 fig_single_Hib = plotting.plotRMS( 234 HinfBounded_results_single_f1gain + H2_results, 235 setname=setname_Hib, 236 outlineparam='pm', 237 text=False, 238 f1line=True, 239 h2line=True, 240 plotrange=True, 241 heatmap=False, 242 test=True, 243 ) 244 245 fig_single_Hib[0].savefig(tjoin("rms_HiB_singlef1gain_range.pdf"), bbox_inches="tight") 246 fig_single_Hib[0].savefig(tjoin("rms_HiB_singlef1gain_range.png"), bbox_inches="tight") 247 248 249 250 251 print('Plotting Open Loop') 252 ylim = [3e-4, 1e5] 253 fig_ol_Hib, ax_ol_Hib = plotting.plotLoop( 254 HinfBounded_results_single_f1gain, "OL", setname=setname_Hib, ylim=ylim, UG_line=True, test=True, 255 ) 256 fig_ol_Hib.savefig(tjoin("ol_HiB.pdf"), bbox_inches="tight") 257 fig_ol_Hib.savefig(tjoin("ol_HiB.png"), bbox_inches="tight") 258 259 print('Plotting Closed Loop') 260 xlim = [1e-1, 1e4] 261 fig_cl_Hib, ax_cl_Hib = plotting.plotLoop( 262 HinfBounded_results_single_f1gain, "CL", setname=setname_Hib, UG_line=True, xlim=xlim, test=True, 263 ) 264 fig_cl_Hib.savefig(tjoin("cl_HiB.pdf"), bbox_inches="tight") 265 fig_cl_Hib.savefig(tjoin("cl_HiB.png"), bbox_inches="tight") 266 267 print('Plotting gm pm plot') 268 ylim = None # [0, 12] 269 plt.clf() 270 fig_gm_pm, ax_gm_pm = plotting.plot_gm_pm( 271 HinfBounded_results_E, setname="", f1line=True, text=None, ylim=[0.5,2.5], xlim=[2.5,20], io_scales=io_scales, test=True, 272 ) 273 fig_gm_pm.savefig(tjoin("gm_HiB.pdf"), bbox_inches="tight") 274 fig_gm_pm.savefig(tjoin("gm_HiB.png"), bbox_inches="tight") 275 276 print('Plotting vegaplot') 277 setname_vega = 'ASC_vega' 278 vega_chart = plotting.vega_plot(HinfBounded_results_E + H2_results, setname=setname_vega, h2line=False, size=500, plotrange=False, test=True) 279 vega_chart.save(tjoin("vega_results_plot.html")) 280 281 DARM_out_name = 'DARM.out' 282 if DARM_out_name in extraplots[0]['CL_all_io'].outputs.keys(): 283 print('Plotting DARM noise') 284 DARM_plot_omega = np.logspace(-2, 3, 1000) * 2 * np.pi 285 DARM_noise_plot_fig, DARM_noise_plot_axs = plotting.plot_CLnoises(extraplots[0], extra_fom=DARM_out_name, extra_only=True, plot_omega=DARM_plot_omega, test=True, scale=1/io_scales.w2_rescale) 286 DARM_noise_plot_fig.savefig(tjoin("DARM_noise_H2.pdf"), bbox_inches="tight") 287 DARM_noise_plot_fig.savefig(tjoin("DARM_noise_H2.png"), bbox_inches="tight")
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:
test.ASC_SOLVER_PAPER.test_solver_ADY_paper.T_plot_HiBThe 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.T_plot_HiB[ExampleModels_paper]
output
save fldr: /builds/buzz/test/ASC_SOLVER_PAPER/ExampleModels_paper/results_H2_ADY.pkl Range from PSD: 192.26892101593648 Plotting RMS Plot Plotting RMS Plot with lost range Plotting RMS Plot with lost range (PSD computation) WARNING: The model is not stable Eigen Values that are > 0: [(422.1864032134236+0j), (11.061231737503501+11.055219218308698j), (11.061231737503501-11.055219218308698j), (0.7280079294404227+7.466862061221714j), (0.7280079294404227-7.466862061221714j)] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] Range from PSD: 187.72899538774075 WARNING: The model is not stable Eigen Values that are > 0: [(175.75251695333228+0j), (0.4863022253408999+1.246269453321472j), (0.4863022253408999-1.246269453321472j), (0.17166127537922649+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] Range from PSD: 192.09829680976983 WARNING: The model is not stable Eigen Values that are > 0: [(0.943733978399338+0j)] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] Range from PSD: 193.32544972201305 WARNING: The model is not stable Eigen Values that are > 0: [(49.19095945869148+170.0496622391314j), (49.19095945869148-170.0496622391314j), (0.6045347134659886+19.8823933430864j), (0.6045347134659886-19.8823933430864j), (0.5986864254096691+17.860463564543963j), (0.5986864254096691-17.860463564543963j), (0.06505180270668065+16.22580126192144j), (0.06505180270668065-16.22580126192144j), (0.02519934283797382+3.207329302609284j), (0.02519934283797382-3.207329302609284j)] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] Range from PSD: 193.81178748710568 WARNING: The model is not stable Eigen Values that are > 0: [(809.5422483992226+0j), (0.009285233405991189+16.429910863006555j), (0.009285233405991189-16.429910863006555j), (0.10806689456824148+15.901772190071355j), (0.10806689456824148-15.901772190071355j), (0.8975298072928334+0j)] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] Range from PSD: 194.08260712230265 Plotting RMS Plot With Range Plotting RMS Plot With Range from PSD Plotting Heatmap Plotting RMS Plot with Heatmap Heatmap Failed:, Points cannot contain NaN Plotting RMS Range Plot single result Plotting Open Loop Plotting Closed Loop Plotting gm pm plot WARNING: The model is not stable Eigen Values that are > 0: [(418.1471627558395+0j)] WARNING: The model is not stable Eigen Values that are > 0: [418.14716276] WARNING: The model is not stable Eigen Values that are > 0: [418.14716276] Range from PSD: 162.19255282429583 Range from PSD: 153.16575054230935 WARNING: The model is not stable Eigen Values that are > 0: [(325.40307255852105+0j), (3.717706142981129+0j)] WARNING: The model is not stable Eigen Values that are > 0: [325.40307256 3.71770614] WARNING: The model is not stable Eigen Values that are > 0: [325.40307256 3.71770614] Range from PSD: 172.9465831069878 WARNING: The model is not stable Eigen Values that are > 0: [(74.27159745624778+0j), (10.804296460525245+9.748500237755163j), (10.804296460525245-9.748500237755163j), (0.5199091146204519+18.381811081167303j), (0.5199091146204519-18.381811081167303j), (0.1693904817840711+16.28968339517745j), (0.1693904817840711-16.28968339517745j), (3.3509369175335597+0j), (0.7325621878777517+8.10233349189706j), (0.7325621878777517-8.10233349189706j), (0.5246859658762605+0j)] WARNING: The model is not stable Eigen Values that are > 0: [74.27159746 10.80429646 10.80429646 0.51990911 0.51990911 0.16939048 0.16939048 3.35093692 0.73256219 0.73256219 0.52468597] WARNING: The model is not stable Eigen Values that are > 0: [74.27159746 10.80429646 10.80429646 0.51990911 0.51990911 0.16939048 0.16939048 3.35093692 0.73256219 0.73256219 0.52468597] Range from PSD: 179.31124456872516 WARNING: The model is not stable Eigen Values that are > 0: [(2018.692737017845+0j)] WARNING: The model is not stable Eigen Values that are > 0: [2018.69273702] WARNING: The model is not stable Eigen Values that are > 0: [2018.69273702] Range from PSD: 183.27039929149342 WARNING: The model is not stable Eigen Values that are > 0: [(0.017749566798610283+15.662354275641844j), (0.017749566798610283-15.662354275641844j)] WARNING: The model is not stable Eigen Values that are > 0: [0.01774957 0.01774957] WARNING: The model is not stable Eigen Values that are > 0: [0.01774957 0.01774957] Range from PSD: 188.35219137436985 WARNING: The model is not stable Eigen Values that are > 0: [(467.9287354055619+0j), (0.022972858788393613+15.706853285481019j), (0.022972858788393613-15.706853285481019j), (1.7113627965024167+0j), (0.37441435827739045+5.154084974650858j), (0.37441435827739045-5.154084974650858j), (0.09867830121161661+1.253521882873057j), (0.09867830121161661-1.253521882873057j)] WARNING: The model is not stable Eigen Values that are > 0: [4.67928735e+02 2.29728588e-02 2.29728588e-02 1.71136280e+00 3.74414358e-01 3.74414358e-01 9.86783012e-02 9.86783012e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.67928735e+02 2.29728588e-02 2.29728588e-02 1.71136280e+00 3.74414358e-01 3.74414358e-01 9.86783012e-02 9.86783012e-02] Range from PSD: 190.30199636407585 WARNING: The model is not stable Eigen Values that are > 0: [(422.1864032134236+0j), (11.061231737503501+11.055219218308698j), (11.061231737503501-11.055219218308698j), (0.7280079294404227+7.466862061221714j), (0.7280079294404227-7.466862061221714j)] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] WARNING: The model is not stable Eigen Values that are > 0: [422.18640321 11.06123174 11.06123174 0.72800793 0.72800793] Range from PSD: 187.72899538774075 WARNING: The model is not stable Eigen Values that are > 0: [(175.75251695333228+0j), (0.4863022253408999+1.246269453321472j), (0.4863022253408999-1.246269453321472j), (0.17166127537922649+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.75752517e+02 4.86302225e-01 4.86302225e-01 1.71661275e-01] Range from PSD: 192.09829680976983 WARNING: The model is not stable Eigen Values that are > 0: [(0.943733978399338+0j)] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] WARNING: The model is not stable Eigen Values that are > 0: [0.94373398] Range from PSD: 193.32544972201305 WARNING: The model is not stable Eigen Values that are > 0: [(49.19095945869148+170.0496622391314j), (49.19095945869148-170.0496622391314j), (0.6045347134659886+19.8823933430864j), (0.6045347134659886-19.8823933430864j), (0.5986864254096691+17.860463564543963j), (0.5986864254096691-17.860463564543963j), (0.06505180270668065+16.22580126192144j), (0.06505180270668065-16.22580126192144j), (0.02519934283797382+3.207329302609284j), (0.02519934283797382-3.207329302609284j)] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] WARNING: The model is not stable Eigen Values that are > 0: [4.91909595e+01 4.91909595e+01 6.04534713e-01 6.04534713e-01 5.98686425e-01 5.98686425e-01 6.50518027e-02 6.50518027e-02 2.51993428e-02 2.51993428e-02] Range from PSD: 193.81178748710568 WARNING: The model is not stable Eigen Values that are > 0: [(809.5422483992226+0j), (0.009285233405991189+16.429910863006555j), (0.009285233405991189-16.429910863006555j), (0.10806689456824148+15.901772190071355j), (0.10806689456824148-15.901772190071355j), (0.8975298072928334+0j)] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] WARNING: The model is not stable Eigen Values that are > 0: [8.09542248e+02 9.28523341e-03 9.28523341e-03 1.08066895e-01 1.08066895e-01 8.97529807e-01] Range from PSD: 194.08260712230265 WARNING: The model is not stable Eigen Values that are > 0: [(0.7175071904696981+12.684975076864854j), (0.7175071904696981-12.684975076864854j), (0.02908379747194867+8.530745212524888j), (0.02908379747194867-8.530745212524888j)] WARNING: The model is not stable Eigen Values that are > 0: [0.71750719 0.71750719 0.0290838 0.0290838 ] WARNING: The model is not stable Eigen Values that are > 0: [0.71750719 0.71750719 0.0290838 0.0290838 ] Range from PSD: 193.94508874092188 WARNING: The model is not stable Eigen Values that are > 0: [(17.480091286156263+0j), (0.08979343751551894+16.371082795196624j), (0.08979343751551894-16.371082795196624j), (0.2096812040337932+15.947659138865001j), (0.2096812040337932-15.947659138865001j), (0.1455459430707851+8.129571886682484j), (0.1455459430707851-8.129571886682484j), (0.10072896978299742+1.1668656915338689j), (0.10072896978299742-1.1668656915338689j), (0.024803847933613288+3.496256159772997j), (0.024803847933613288-3.496256159772997j), (0.10928452295212834+0j)] WARNING: The model is not stable Eigen Values that are > 0: [17.48009129 0.08979344 0.08979344 0.2096812 0.2096812 0.14554594 0.14554594 0.10072897 0.10072897 0.02480385 0.02480385 0.10928452] WARNING: The model is not stable Eigen Values that are > 0: [17.48009129 0.08979344 0.08979344 0.2096812 0.2096812 0.14554594 0.14554594 0.10072897 0.10072897 0.02480385 0.02480385 0.10928452] Range from PSD: 194.43700703610548 WARNING: The model is not stable Eigen Values that are > 0: [(232.41767384893978+0j), (70.21318232720527+0j), (0.2600432830369148+16.236159945072142j), (0.2600432830369148-16.236159945072142j), (0.9718329701892334+14.25244110820402j), (0.9718329701892334-14.25244110820402j), (4.777053875086522+0j), (0.6508016716754814+8.453692722062984j), (0.6508016716754814-8.453692722062984j), (0.022666827340204643+7.640752375999391j), (0.022666827340204643-7.640752375999391j)] WARNING: The model is not stable Eigen Values that are > 0: [2.32417674e+02 7.02131823e+01 2.60043283e-01 2.60043283e-01 9.71832970e-01 9.71832970e-01 4.77705388e+00 6.50801672e-01 6.50801672e-01 2.26668273e-02 2.26668273e-02] WARNING: The model is not stable Eigen Values that are > 0: [2.32417674e+02 7.02131823e+01 2.60043283e-01 2.60043283e-01 9.71832970e-01 9.71832970e-01 4.77705388e+00 6.50801672e-01 6.50801672e-01 2.26668273e-02 2.26668273e-02] Range from PSD: 194.52496075248436 WARNING: The model is not stable Eigen Values that are > 0: [(110.75760617273023+0j), (0.5135089322856994+16.048108577321706j), (0.5135089322856994-16.048108577321706j), (2.9989039843688685+0j), (1.8684294163475195+0j), (0.31378291139017245+7.116662322062661j), (0.31378291139017245-7.116662322062661j), (0.10263973076492514+0.6956943534839114j), (0.10263973076492514-0.6956943534839114j)] WARNING: The model is not stable Eigen Values that are > 0: [1.10757606e+02 5.13508932e-01 5.13508932e-01 2.99890398e+00 1.86842942e+00 3.13782911e-01 3.13782911e-01 1.02639731e-01 1.02639731e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.10757606e+02 5.13508932e-01 5.13508932e-01 2.99890398e+00 1.86842942e+00 3.13782911e-01 3.13782911e-01 1.02639731e-01 1.02639731e-01] Range from PSD: 194.5416618245685 WARNING: The model is not stable Eigen Values that are > 0: [(7.258229265651461+0j), (0.04717749793139337+1.200619045873482j), (0.04717749793139337-1.200619045873482j), (0.12895546577776773+0.4187903567514308j), (0.12895546577776773-0.4187903567514308j)] WARNING: The model is not stable Eigen Values that are > 0: [7.25822927 0.0471775 0.0471775 0.12895547 0.12895547] WARNING: The model is not stable Eigen Values that are > 0: [7.25822927 0.0471775 0.0471775 0.12895547 0.12895547] Range from PSD: 194.5616345952493 WARNING: The model is not stable Eigen Values that are > 0: [(16.38106812121105+0j), (0.4495428409111979+0.9951832598500499j), (0.4495428409111979-0.9951832598500499j)] WARNING: The model is not stable Eigen Values that are > 0: [16.38106812 0.44954284 0.44954284] WARNING: The model is not stable Eigen Values that are > 0: [16.38106812 0.44954284 0.44954284] Range from PSD: 194.55194022947276 WARNING: The model is not stable Eigen Values that are > 0: [(181.1837814716469+0j), (0.03684610821797827+0j)] WARNING: The model is not stable Eigen Values that are > 0: [1.81183781e+02 3.68461082e-02] WARNING: The model is not stable Eigen Values that are > 0: [1.81183781e+02 3.68461082e-02] Range from PSD: 194.58745327642487 WARNING: The model is not stable Eigen Values that are > 0: [(173.83867240214832+0j), (0.14030969967714743+1.1103854038570584j), (0.14030969967714743-1.1103854038570584j), (0.5282153122795941+0.3550762860092207j), (0.5282153122795941-0.3550762860092207j)] WARNING: The model is not stable Eigen Values that are > 0: [1.73838672e+02 1.40309700e-01 1.40309700e-01 5.28215312e-01 5.28215312e-01] WARNING: The model is not stable Eigen Values that are > 0: [1.73838672e+02 1.40309700e-01 1.40309700e-01 5.28215312e-01 5.28215312e-01] Range from PSD: 194.58552176321837 WARNING: The model is not stable Eigen Values that are > 0: [(31.613508390595022+51.27050674003169j), (31.613508390595022-51.27050674003169j)] WARNING: The model is not stable Eigen Values that are > 0: [31.61350839 31.61350839] WARNING: The model is not stable Eigen Values that are > 0: [31.61350839 31.61350839] Range from PSD: 194.43131096878759 WARNING: The model is not stable Eigen Values that are > 0: [(37.29562705716835+45.75751102314879j), (37.29562705716835-45.75751102314879j), (0.08117147004595271+16.19727900357282j), (0.08117147004595271-16.19727900357282j), (0.5598788338473593+0j), (0.6494423549332886+6.193779828954125j), (0.6494423549332886-6.193779828954125j), (0.5141778458487678+7.7728868969887j), (0.5141778458487678-7.7728868969887j), (0.21949339072000945+1.2281748388005682j), (0.21949339072000945-1.2281748388005682j)] WARNING: The model is not stable Eigen Values that are > 0: [37.29562706 37.29562706 0.08117147 0.08117147 0.55987883 0.64944235 0.64944235 0.51417785 0.51417785 0.21949339 0.21949339] WARNING: The model is not stable Eigen Values that are > 0: [37.29562706 37.29562706 0.08117147 0.08117147 0.55987883 0.64944235 0.64944235 0.51417785 0.51417785 0.21949339 0.21949339] Range from PSD: 194.5966617523974 Range from PSD: 192.26892101593648 Plotting vegaplot Captured stderr call 0%| | 0/1 [00:00<?, ?it/s] 100%|██████████| 1/1 [00:03<00:00, 3.57s/it] 100%|██████████| 1/1 [00:03<00:00, 3.57s/it] 0%| | 0/5 [00:00<?, ?it/s] 20%|██ | 1/5 [00:01<00:06, 1.69s/it] 40%|████ | 2/5 [00:04<00:06, 2.28s/it] 60%|██████ | 3/5 [00:06<00:04, 2.12s/it] 80%|████████ | 4/5 [00:10<00:02, 2.76s/it] 100%|██████████| 5/5 [00:11<00:00, 2.46s/it] 100%|██████████| 5/5 [00:11<00:00, 2.40s/it] 0%| | 0/23 [00:00<?, ?it/s] 4%|▍ | 1/23 [00:02<00:51, 2.33s/it] 9%|▊ | 2/23 [00:05<00:56, 2.70s/it] 13%|█▎ | 3/23 [00:07<00:49, 2.47s/it] 17%|█▋ | 4/23 [00:09<00:44, 2.34s/it] 22%|██▏ | 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- get_systemB(folder='ExampleModels')[source]¶
This is a pytest needing documentation
code
1def get_systemB(folder = 'ExampleModels'): 2 fname = fjoin(folder, "ADY_Sanex.mat") 3 sys = ssutil.loadSys(fname) 4 5 orig_plant_fname = fjoin(folder, "ADY_plant.mat") 6 orig_plant = ssutil.loadSys(orig_plant_fname) 7 8 return Bunch(locals())
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:
test.ASC_SOLVER_PAPER.test_solver_ADY_paper.get_systemBThe 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.