Failed

run_tests.TestSimulation_mosek_cli.test_simulate (from run_tests.TestSimulation_mosek_cli-20200302201219)

Error Message

flat() got an unexpected keyword argument 'constraintsets'

Stacktrace

Traceback (most recent call last):
  File "/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/reynolds/optimizer/mosek/robust/robust/testing/t_simulation.py", line 30, in test_simulate
    simulate.generate_model_properties(model, number_of_time_average_solves, number_of_iterations)
  File "/jenkins/workspace/CE_gpkit_PR_research_models/buildnode/reynolds/optimizer/mosek/robust/robust/simulations/simulate.py", line 381, in generate_model_properties
    nominal_number_of_constraints = len([cs for cs in model.flat(constraintsets=False)])
TypeError: flat() got an unexpected keyword argument 'constraintsets'
		

Standard Output

SGP not convergent: Cost rose by 9% on iteration 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 iteration 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 iteration 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 iteration 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 12% on iteration 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 iteration 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 iteration 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 iteration 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 iteration 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 iteration 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 12% on iteration 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 iteration 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.

	

Standard Error