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Failed
run_tests.TestSimulation_mosek_conif.test_table_diff (from run_tests.TestSimulation_mosek_conif-20200304035102)
Failing for the past 1 build
(Since Failed
)
Error Message
Lists differ: ['L/D[73 chars]& 7.67e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n'] != ['L/D[73 chars]& 7.66e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n'] First differing element 1: 'A & 8.91e+00 & 7.54e+00 & 7.67e+00 & 8.27e+00\n' 'A & 8.91e+00 & 7.54e+00 & 7.66e+00 & 8.27e+00\n' Diff is 941 characters long. Set self.maxDiff to None to see it.
Stacktrace
Traceback (most recent call last):
File "/jenkins/workspace/CE_gplibrary_Push_research_models/mosek/robust/robust/testing/t_simulation.py", line 101, in test_table_diff
self.assertEqual(open(origfilename, 'r').readlines(), open(filename, 'r').readlines())
AssertionError: Lists differ: ['L/D[73 chars]& 7.67e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n'] != ['L/D[73 chars]& 7.66e+00 & 8.27e+00\n', 'Re & 5.01e+06 & 5.7[644 chars]3\n']
First differing element 1:
'A & 8.91e+00 & 7.54e+00 & 7.67e+00 & 8.27e+00\n'
'A & 8.91e+00 & 7.54e+00 & 7.66e+00 & 8.27e+00\n'
Diff is 941 characters long. Set self.maxDiff to None to see it.
Standard Output
SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.11% on GP solve 5. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.016% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.12% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.035% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.22% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.0029% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.027% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.024% on GP solve 6. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.065% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.076% on GP solve 3. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9.4% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 0.33% on GP solve 4. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities. SGP not convergent: Cost rose by 9% on GP solve 2. Details can be found in `m.program.results` or by solving at a higher verbosity. Note that convergence is not guaranteed for models with SignomialEqualities.