class TestNLPInitWrappers:
""" Tests for NLPInitialization wrapper methods.
"""
@classmethod
def setUpClass(cls):
"""Sets up the class."""
fpath = os.path.join(get_files_path(), 'Modelica', 'CSTR.mop')
cpath = "CSTR.CSTR_Init"
compile_jmu(cpath,
fpath,
compiler_options={'state_start_values_fixed': False})
def setUp(self):
"""Test setUp. Load the test model."""
cpath = "CSTR.CSTR_Init"
fname = cpath.replace('.', '_')
cstr = JMUModel(fname + '.jmu')
self.init_nlp = NLPInitialization(cstr)
@testattr(ipopt=True)
def test_init_opt_get_dimensions(self):
""" Test NLPInitialization.init_opt_get_dimensions"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
# @testattr(ipopt = True)
# def test_init_opt_get_x(self):
# """ Test NLPInitialization.init_opt_get_x"""
# (nx,n_h,dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
# x=self.init_nlp.init_opt_get_x()
# nose.tools.assert_equal(len(x),nx)
@testattr(ipopt=True)
def test_init_opt_getset_initial(self):
""" Test NLPInitialization.init_opt_get_initial"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
x_init = N.zeros(nx)
self.init_nlp.init_opt_get_initial(x_init)
self.init_nlp.init_opt_set_initial(x_init)
@testattr(ipopt=True)
def test_init_opt_getset_bounds(self):
""" Test NLPInitialization.init_opt_get_bounds and init_opt_set_bounds"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
x_lb = N.zeros(nx)
x_ub = N.zeros(nx)
self.init_nlp.init_opt_get_bounds(x_lb, x_ub)
self.init_nlp.init_opt_set_bounds(x_lb, x_ub)
@testattr(ipopt=True)
def test_init_opt_f(self):
""" Test NLPInitialization.init_opt_f"""
f = N.zeros(1)
self.init_nlp.init_opt_f(f)
@testattr(ipopt=True)
def test_init_opt_df(self):
""" Test NLPInitialization.init_opt_df"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
df = N.zeros(nx)
self.init_nlp.init_opt_df(df)
@testattr(ipopt=True)
def test_init_opt_h(self):
""" Test NLPInitialization.opt_sim_h"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
res = N.zeros(n_h)
self.init_nlp.init_opt_h(res)
@testattr(ipopt=True)
def test_init_opt_dh(self):
""" Test NLPInitialization.opt_sim_dh"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
jac = N.zeros(dh_n_nz)
self.init_nlp.init_opt_dh(jac)
@testattr(ipopt=True)
def test_opt_init_opt_dh_nz_indices(self):
""" Test NLPInitialization.opt_sim_dh_nz_indices"""
(nx, n_h, dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
irow = N.zeros(dh_n_nz, dtype=int)
icol = N.zeros(dh_n_nz, dtype=int)
self.init_nlp.init_opt_dh_nz_indices(irow, icol)
class TestNLPInitWrappers:
""" Tests for NLPInitialization wrapper methods.
"""
@classmethod
def setUpClass(cls):
"""Sets up the class."""
fpath = os.path.join(get_files_path(), 'Modelica', 'CSTR.mop')
cpath = "CSTR.CSTR_Init"
compile_jmu(cpath, fpath,
compiler_options={'state_start_values_fixed':False})
def setUp(self):
"""Test setUp. Load the test model."""
cpath = "CSTR.CSTR_Init"
fname = cpath.replace('.','_')
cstr = JMUModel(fname+'.jmu')
self.init_nlp = NLPInitialization(cstr)
@testattr(ipopt = True)
def test_init_opt_get_dimensions(self):
""" Test NLPInitialization.init_opt_get_dimensions"""
(nx,n_h,dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
# @testattr(ipopt = True)
# def test_init_opt_get_x(self):
# """ Test NLPInitialization.init_opt_get_x"""
# (nx,n_h,dh_n_nz) = self.init_nlp.init_opt_get_dimensions()
# x=self.init_nlp.init_opt_get_x()
# nose.tools.assert_equal(len(x),nx)
@testattr(ipopt = True)
def test_init_opt_getset_initial(self):
""" Test NLPInitialization.init_opt_get_initial"""
(nx,n_h,dh_n_nz)=self.init_nlp.init_opt_get_dimensions()
x_init=N.zeros(nx)
self.init_nlp.init_opt_get_initial(x_init)
self.init_nlp.init_opt_set_initial(x_init)
@testattr(ipopt = True)
def test_init_opt_getset_bounds(self):
""" Test NLPInitialization.init_opt_get_bounds and init_opt_set_bounds"""
(nx,n_h,dh_n_nz)=self.init_nlp.init_opt_get_dimensions()
x_lb=N.zeros(nx)
x_ub=N.zeros(nx)
self.init_nlp.init_opt_get_bounds(x_lb,x_ub)
self.init_nlp.init_opt_set_bounds(x_lb,x_ub)
@testattr(ipopt = True)
def test_init_opt_f(self):
""" Test NLPInitialization.init_opt_f"""
f=N.zeros(1)
self.init_nlp.init_opt_f(f)
@testattr(ipopt = True)
def test_init_opt_df(self):
""" Test NLPInitialization.init_opt_df"""
(nx,n_h,dh_n_nz)=self.init_nlp.init_opt_get_dimensions()
df=N.zeros(nx)
self.init_nlp.init_opt_df(df)
@testattr(ipopt = True)
def test_init_opt_h(self):
""" Test NLPInitialization.opt_sim_h"""
(nx,n_h,dh_n_nz)=self.init_nlp.init_opt_get_dimensions()
res=N.zeros(n_h)
self.init_nlp.init_opt_h(res)
@testattr(ipopt = True)
def test_init_opt_dh(self):
""" Test NLPInitialization.opt_sim_dh"""
(nx,n_h,dh_n_nz)=self.init_nlp.init_opt_get_dimensions()
jac=N.zeros(dh_n_nz)
self.init_nlp.init_opt_dh(jac)
@testattr(ipopt = True)
def test_opt_init_opt_dh_nz_indices(self):
""" Test NLPInitialization.opt_sim_dh_nz_indices"""
(nx,n_h,dh_n_nz)=self.init_nlp.init_opt_get_dimensions()
irow=N.zeros(dh_n_nz,dtype=int)
icol=N.zeros(dh_n_nz,dtype=int)
self.init_nlp.init_opt_dh_nz_indices(irow,icol)
class TestNLPInit:
""" Test evaluation of function in NLPInitialization and solution
of initialization problems.
"""
@classmethod
def setUpClass(cls):
"""Sets up the test class."""
fpath_daeinit = os.path.join(get_files_path(), 'Modelica',
'DAEInitTest.mo')
cpath_daeinit = "DAEInitTest"
compile_jmu(cpath_daeinit,
fpath_daeinit,
compiler_options={
'state_start_values_fixed': True,
'variability_propagation': False
})
def setUp(self):
"""Test setUp. Load the test model."""
# Load the dynamic library and XML data
cpath_daeinit = "DAEInitTest"
fname_daeinit = cpath_daeinit.replace('.', '_', 1)
self.dae_init_test = JMUModel(fname_daeinit + '.jmu')
# This is to check that values set in the model prior to
# creation of the NLPInitialization object are used as an
# initial guess.
self.dae_init_test.set('y1', 0.3)
self.init_nlp = NLPInitialization(self.dae_init_test)
self.init_nlp_ipopt = InitializationOptimizer(self.init_nlp)
@testattr(ipopt=True)
def test_init_opt_get_dimensions(self):
""" Test NLPInitialization.init_opt_get_dimensions"""
res_n_x = 8
res_n_h = 8
res_dh_n_nz = 17
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
assert N.abs(res_n_x-n_x) + N.abs(res_n_h-n_h) + \
N.abs(res_dh_n_nz-dh_n_nz)==0
@testattr(ipopt=True)
def test_init_opt_get_set_x_init(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_get_x_init
res_x_init = N.array([0, 0, 3, 4, 1, 0, 0, 0])
x_init = N.zeros(n_x)
self.init_nlp.init_opt_get_initial(x_init)
#print x_init
assert N.sum(N.abs(res_x_init - x_init)) < 1e-3
# Test init_opt_set_x_init
res_x_init = N.ones(n_x)
x_init = N.ones(n_x)
self.init_nlp.init_opt_set_initial(x_init)
self.init_nlp.init_opt_get_initial(x_init)
assert N.sum(N.abs(res_x_init - x_init)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_get_set_bounds(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_get_bounds
res_x_lb = -1e20 * N.ones(n_x)
res_x_ub = 1e20 * N.ones(n_x)
x_lb = N.zeros(n_x)
x_ub = N.zeros(n_x)
self.init_nlp.init_opt_get_bounds(x_lb, x_ub)
assert N.sum(N.abs(res_x_lb - x_lb)) < 1e-3
assert N.sum(N.abs(res_x_lb - x_lb)) < 1e-3
# Test init_opt_set_bounds
res_x_lb = -5000 * N.ones(n_x)
res_x_ub = 5000 * N.ones(n_x)
x_lb = -5000 * N.ones(n_x)
x_ub = 5000 * N.ones(n_x)
self.init_nlp.init_opt_set_bounds(x_lb, x_ub)
self.init_nlp.init_opt_get_bounds(x_lb, x_ub)
assert N.sum(N.abs(res_x_lb - x_lb)) < 1e-3
assert N.sum(N.abs(res_x_lb - x_lb)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_f(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_f
res_f = N.array([0.0])
f = N.zeros(1)
self.init_nlp.init_opt_f(f)
#print f
assert N.sum(N.abs(res_f - f)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_df(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_df
res_df = N.zeros(n_x)
df = N.ones(n_x)
self.init_nlp.init_opt_df(df)
#print df
assert N.sum(N.abs(res_df - df)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_h(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_h
res_h = N.array([
-1.98158529e+02, -2.43197505e-01, 5.12000000e+02, 5.00000000e+00,
1.41120008e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00
])
h = N.zeros(n_h)
self.init_nlp.init_opt_h(h)
#print h
assert N.sum(N.abs(res_h - h)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_dh(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_dh
res_dh = N.array([
-1.,
-1.,
-135.,
192.,
-0.9899925,
-1.,
-48.,
0.65364362,
-1.,
0.54030231,
-2.,
-1.,
-1.,
0.9899925,
192.,
-1.,
-1.,
])
dh = N.ones(dh_n_nz)
self.init_nlp.init_opt_dh(dh)
#print dh
assert N.sum(N.abs(res_dh - dh)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_dh_nz_indices(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_dh_nz_indices
res_dh_irow = N.array(
[1, 2, 1, 3, 5, 7, 1, 2, 8, 1, 2, 6, 3, 5, 3, 4, 5])
res_dh_icol = N.array(
[1, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 8])
dh_irow = N.zeros(dh_n_nz, dtype=N.int32)
dh_icol = N.zeros(dh_n_nz, dtype=N.int32)
self.init_nlp.init_opt_dh_nz_indices(dh_irow, dh_icol)
assert N.sum(N.abs(res_dh_irow - dh_irow)) < 1e-3
assert N.sum(N.abs(res_dh_icol - dh_icol)) < 1e-3
@testattr(ipopt=True)
def test_init_opt_solve(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# self.init_nlp_ipopt.init_opt_ipopt_set_string_option("derivative_test","first-order")
self.init_nlp_ipopt.init_opt_ipopt_solve()
print self.dae_init_test.z
res_Z = N.array([
5., -198.1585290151921, -0.2431975046920718, 3.0, 4.0, 1.0, 2197.0,
5.0, -0.92009689684513785, 0., 0, 0, 0, 0, 0, 0, 0, 0
])
assert max(N.abs(res_Z - self.dae_init_test.z)) < 1e-3
@testattr(ipopt=True)
def test_statistics(self):
""" Test of 'jmi_init_opt_get_statistics'.
"""
# Solve the optimization problem
self.init_nlp_ipopt.init_opt_ipopt_solve()
(return_status, iters, cost,
time) = self.init_nlp_ipopt.init_opt_ipopt_get_statistics()
assert return_status == 0
assert abs(cost - 2.4134174e+06) < 1
@testattr(ipopt=True)
def test_init_opt_write_result(self):
cpath_daeinit = "DAEInitTest"
fname_daeinit = cpath_daeinit.replace('.', '_', 1)
# self.init_nlp_ipopt.init_opt_ipopt_set_string_option("derivative_test","first-order")
self.init_nlp_ipopt.init_opt_ipopt_solve()
self.init_nlp.export_result_dymola()
res = ResultDymolaTextual(fname_daeinit + "_result.txt")
res_Z = N.array([
5., -198.1585290151921, -0.2431975046920718, 3.0, 4.0, 1.0, 2197.0,
5.0, -0.92009689684513785, 0.
])
assert N.abs(res_Z[0] - res.get_variable_data("p").x[0]) < 1e-3
assert N.abs(res_Z[1] - res.get_variable_data("der(x1)").x[0]) < 1e-3
assert N.abs(res_Z[2] - res.get_variable_data("der(x2)").x[0]) < 1e-3
assert N.abs(res_Z[3] - res.get_variable_data("x1").x[0]) < 1e-3
assert N.abs(res_Z[4] - res.get_variable_data("x2").x[0]) < 1e-3
assert N.abs(res_Z[5] - res.get_variable_data("u").x[0]) < 1e-3
assert N.abs(res_Z[6] - res.get_variable_data("y1").x[0]) < 1e-3
assert N.abs(res_Z[7] - res.get_variable_data("y2").x[0]) < 1e-3
assert N.abs(res_Z[8] - res.get_variable_data("y3").x[0]) < 1e-3
@testattr(ipopt=True)
def test_invalid_string_option(self):
"""Test that exceptions are thrown when invalid IPOPT options are set."""
nose.tools.assert_raises(
Exception, self.init_nlp_ipopt.init_opt_ipopt_set_string_option,
'invalid_option', 'val')
@testattr(ipopt=True)
def test_invalid_int_option(self):
"""Test that exceptions are thrown when invalid IPOPT options are set."""
nose.tools.assert_raises(
Exception, self.init_nlp_ipopt.init_opt_ipopt_set_int_option,
'invalid_option', 1)
@testattr(ipopt=True)
def test_invalid_num_option(self):
"""Test that exceptions are thrown when invalid IPOPT options are set."""
nose.tools.assert_raises(
Exception, self.init_nlp_ipopt.init_opt_ipopt_set_num_option,
'invalid_option', 1.0)
class TestNLPInit:
""" Test evaluation of function in NLPInitialization and solution
of initialization problems.
"""
@classmethod
def setUpClass(cls):
"""Sets up the test class."""
fpath_daeinit = os.path.join(get_files_path(), 'Modelica',
'DAEInitTest.mo')
cpath_daeinit = "DAEInitTest"
compile_jmu(cpath_daeinit, fpath_daeinit,
compiler_options={'state_start_values_fixed':True, 'variability_propagation':False})
def setUp(self):
"""Test setUp. Load the test model."""
# Load the dynamic library and XML data
cpath_daeinit = "DAEInitTest"
fname_daeinit = cpath_daeinit.replace('.','_',1)
self.dae_init_test = JMUModel(fname_daeinit+'.jmu')
# This is to check that values set in the model prior to
# creation of the NLPInitialization object are used as an
# initial guess.
self.dae_init_test.set('y1',0.3)
self.init_nlp = NLPInitialization(self.dae_init_test)
self.init_nlp_ipopt = InitializationOptimizer(self.init_nlp)
@testattr(ipopt = True)
def test_init_opt_get_dimensions(self):
""" Test NLPInitialization.init_opt_get_dimensions"""
res_n_x = 8
res_n_h = 8
res_dh_n_nz = 17
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
assert N.abs(res_n_x-n_x) + N.abs(res_n_h-n_h) + \
N.abs(res_dh_n_nz-dh_n_nz)==0
@testattr(ipopt = True)
def test_init_opt_get_set_x_init(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_get_x_init
res_x_init = N.array([0,0,3,4,1,0,0,0])
x_init = N.zeros(n_x)
self.init_nlp.init_opt_get_initial(x_init)
#print x_init
assert N.sum(N.abs(res_x_init-x_init))<1e-3
# Test init_opt_set_x_init
res_x_init = N.ones(n_x)
x_init = N.ones(n_x)
self.init_nlp.init_opt_set_initial(x_init)
self.init_nlp.init_opt_get_initial(x_init)
assert N.sum(N.abs(res_x_init-x_init))<1e-3
@testattr(ipopt = True)
def test_init_opt_get_set_bounds(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_get_bounds
res_x_lb = -1e20*N.ones(n_x)
res_x_ub = 1e20*N.ones(n_x)
x_lb = N.zeros(n_x)
x_ub = N.zeros(n_x)
self.init_nlp.init_opt_get_bounds(x_lb,x_ub)
assert N.sum(N.abs(res_x_lb-x_lb))<1e-3
assert N.sum(N.abs(res_x_lb-x_lb))<1e-3
# Test init_opt_set_bounds
res_x_lb = -5000*N.ones(n_x)
res_x_ub = 5000*N.ones(n_x)
x_lb = -5000*N.ones(n_x)
x_ub = 5000*N.ones(n_x)
self.init_nlp.init_opt_set_bounds(x_lb,x_ub)
self.init_nlp.init_opt_get_bounds(x_lb,x_ub)
assert N.sum(N.abs(res_x_lb-x_lb))<1e-3
assert N.sum(N.abs(res_x_lb-x_lb))<1e-3
@testattr(ipopt = True)
def test_init_opt_f(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_f
res_f = N.array([0.0])
f = N.zeros(1)
self.init_nlp.init_opt_f(f)
#print f
assert N.sum(N.abs(res_f-f))<1e-3
@testattr(ipopt = True)
def test_init_opt_df(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_df
res_df = N.zeros(n_x)
df = N.ones(n_x)
self.init_nlp.init_opt_df(df)
#print df
assert N.sum(N.abs(res_df-df))<1e-3
@testattr(ipopt = True)
def test_init_opt_h(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_h
res_h = N.array([ -1.98158529e+02, -2.43197505e-01, 5.12000000e+02, 5.00000000e+00,
1.41120008e-01, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00])
h = N.zeros(n_h)
self.init_nlp.init_opt_h(h)
#print h
assert N.sum(N.abs(res_h-h))<1e-3
@testattr(ipopt = True)
def test_init_opt_dh(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_dh
res_dh = N.array([ -1., -1., -135., 192., -0.9899925, -1.,
-48., 0.65364362, -1., 0.54030231, -2., -1.,
-1., 0.9899925, 192., -1., -1., ])
dh = N.ones(dh_n_nz)
self.init_nlp.init_opt_dh(dh)
#print dh
assert N.sum(N.abs(res_dh-dh))<1e-3
@testattr(ipopt = True)
def test_init_opt_dh_nz_indices(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# Test init_opt_dh_nz_indices
res_dh_irow = N.array([1, 2, 1, 3, 5, 7, 1, 2, 8, 1, 2, 6, 3, 5, 3, 4, 5])
res_dh_icol = N.array([1, 2, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 7, 8])
dh_irow = N.zeros(dh_n_nz,dtype=N.int32)
dh_icol = N.zeros(dh_n_nz,dtype=N.int32)
self.init_nlp.init_opt_dh_nz_indices(dh_irow,dh_icol)
assert N.sum(N.abs(res_dh_irow-dh_irow))<1e-3
assert N.sum(N.abs(res_dh_icol-dh_icol))<1e-3
@testattr(ipopt = True)
def test_init_opt_solve(self):
n_x, n_h, dh_n_nz = self.init_nlp.init_opt_get_dimensions()
# self.init_nlp_ipopt.init_opt_ipopt_set_string_option("derivative_test","first-order")
self.init_nlp_ipopt.init_opt_ipopt_solve()
print self.dae_init_test.z
res_Z = N.array([5.,
-198.1585290151921,
-0.2431975046920718,
3.0,
4.0,
1.0,
2197.0,
5.0,
-0.92009689684513785,
0.,0,0,0,0,0,0,0,0])
assert max(N.abs(res_Z-self.dae_init_test.z))<1e-3
@testattr(ipopt = True)
def test_statistics(self):
""" Test of 'jmi_init_opt_get_statistics'.
"""
# Solve the optimization problem
self.init_nlp_ipopt.init_opt_ipopt_solve()
(return_status,iters,cost,time) = self.init_nlp_ipopt.init_opt_ipopt_get_statistics()
assert return_status==0
assert abs(cost-2.4134174e+06)<1
@testattr(ipopt = True)
def test_init_opt_write_result(self):
cpath_daeinit = "DAEInitTest"
fname_daeinit = cpath_daeinit.replace('.','_',1)
# self.init_nlp_ipopt.init_opt_ipopt_set_string_option("derivative_test","first-order")
self.init_nlp_ipopt.init_opt_ipopt_solve()
self.init_nlp.export_result_dymola()
res = ResultDymolaTextual(fname_daeinit + "_result.txt")
res_Z = N.array([5.,
-198.1585290151921,
-0.2431975046920718,
3.0,
4.0,
1.0,
2197.0,
5.0,
-0.92009689684513785,
0.])
assert N.abs(res_Z[0] - res.get_variable_data("p").x[0])<1e-3
assert N.abs(res_Z[1] - res.get_variable_data("der(x1)").x[0])<1e-3
assert N.abs(res_Z[2] - res.get_variable_data("der(x2)").x[0])<1e-3
assert N.abs(res_Z[3] - res.get_variable_data("x1").x[0])<1e-3
assert N.abs(res_Z[4] - res.get_variable_data("x2").x[0])<1e-3
assert N.abs(res_Z[5] - res.get_variable_data("u").x[0])<1e-3
assert N.abs(res_Z[6] - res.get_variable_data("y1").x[0])<1e-3
assert N.abs(res_Z[7] - res.get_variable_data("y2").x[0])<1e-3
assert N.abs(res_Z[8] - res.get_variable_data("y3").x[0])<1e-3
@testattr(ipopt = True)
def test_invalid_string_option(self):
"""Test that exceptions are thrown when invalid IPOPT options are set."""
nose.tools.assert_raises(Exception, self.init_nlp_ipopt.init_opt_ipopt_set_string_option, 'invalid_option','val')
@testattr(ipopt = True)
def test_invalid_int_option(self):
"""Test that exceptions are thrown when invalid IPOPT options are set."""
nose.tools.assert_raises(Exception, self.init_nlp_ipopt.init_opt_ipopt_set_int_option, 'invalid_option',1)
@testattr(ipopt = True)
def test_invalid_num_option(self):
"""Test that exceptions are thrown when invalid IPOPT options are set."""
nose.tools.assert_raises(Exception, self.init_nlp_ipopt.init_opt_ipopt_set_num_option, 'invalid_option',1.0)
