def setUp(self):
"""Test setUp. Load the test model."""
# Load models
self.model = JMUModel("CSTRLib_Components_Two_CSTRs_stat_init.jmu")
self.problem = JMUAlgebraic(self.model)
self.solver = KINSOL(self.problem)
"""
self.model_test_start = JMUModel(self.invalidStart)
self.problem_test_start = JMUAlgebraic(self.model_test_start)
"""
self.model_test_minmax = JMUModel("TestMinMax_TestGuess.jmu")
self.problem_test_minmax = JMUAlgebraic(self.model_test_minmax)
# Set inputs for Stationary point A
u1_0_A = 1
u2_0_A = 1
self.model.set('u1', u1_0_A)
self.model.set('u2', u2_0_A)
self.dx0 = N.zeros(6)
self.x0 = N.array([
200., 3.57359316e-02, 446.471014, 100., 1.79867213e-03, 453.258466
])
self.w0 = N.array([100., 100., -48.1909])
def test_scaling(self):
"""
This tests a simulation when scaling is ON and OFF.
"""
jmu_name = compile_jmu("Englezos652", os.path.join(get_files_path()
,"Modelica","Englezos652.mop"),
compiler_options={"enable_variable_scaling":False})
# Load a model instance into Python
model = JMUModel(jmu_name)
# Get and set the options
opts = model.simulate_options()
opts['IDA_options']['atol'] = 1.0e-9
opts['IDA_options']['rtol'] = 1.0e-9
opts['IDA_options']['sensitivity'] = True
opts['ncp'] = 400
res_no_scale = model.simulate(final_time=1697000, options=opts)
jmu_name = compile_jmu("Englezos652", os.path.join(get_files_path()
,"Modelica","Englezos652.mop"),
compiler_options={"enable_variable_scaling":True})
# Load a model instance into Python
model = JMUModel(jmu_name)
res_with_scale = model.simulate(final_time=1697000, options=opts)
nose.tools.assert_almost_equal(res_with_scale['x1'][-1],
res_no_scale['x1'][-1], 4)
nose.tools.assert_almost_equal(res_with_scale['dx1/dk1'][-1],
res_no_scale['dx1/dk1'][-1], 1)
def setUpClass(cls):
cls.curr_dir = os.path.dirname(os.path.abspath(__file__))
mofile = os.path.join(get_files_path(), 'Modelica',
'BlockingError.mop')
m = compile_jmu("BlockingInitPack.M_init", mofile)
cls.model = JMUModel(m)
m = compile_jmu("BlockingInitPack.M_Opt", mofile)
cls.opt_model = JMUModel(m)
def test_alias_variables(self):
"""
This tests a simulation when there are alias in the sensitivity parameters.
"""
#DAE with sens
file_name = os.path.join(get_files_path(), 'Modelica',
'SensitivityTests.mop')
model_name = 'SensitivityTests.SensTest1'
jmu_name = compile_jmu(model_name, file_name)
model = JMUModel(jmu_name)
opts = model.simulate_options()
opts['IDA_options']['sensitivity'] = True
res = model.simulate(options=opts)
x1 = res['dx1/da']
#x2 = res['dx2/da']
x3 = res['dx3/da']
x4 = res['dx4/da']
x5 = res['dx5/da']
#nose.tools.assert_almost_equal(x2[-1], 0.000000, 4)
nose.tools.assert_almost_equal(x3[-1], 1.000000, 4)
nose.tools.assert_almost_equal(x4[-1], -1.000000, 4)
nose.tools.assert_almost_equal(x1[-1], x5[-1], 4)
#The same test using continuous writing
jmu_name = 'SensitivityTests_SensTest1.jmu'
model = JMUModel(jmu_name)
opts = model.simulate_options()
opts['IDA_options']['sensitivity'] = True
opts['report_continuously'] = True
res = model.simulate(options=opts)
x1 = res['dx1/da']
#x2 = res['dx2/da']
x3 = res['dx3/da']
x4 = res['dx4/da']
x5 = res['dx5/da']
#nose.tools.assert_almost_equal(x2[-1], 0.000000, 4)
nose.tools.assert_almost_equal(x3[-1], 1.000000, 4)
nose.tools.assert_almost_equal(x4[-1], -1.000000, 4)
nose.tools.assert_almost_equal(x1[-1], x5[-1], 4)
def setUp(self):
"""Load the test model."""
package_DAE = 'Pendulum_pack_Pendulum.jmu'
package_DISC = 'IfExpExamples_IfExpExample2.jmu'
package_INPUT = 'DoubleInput_Nominal.jmu'
# Load the dynamic library and XML data
self.m_DAE = JMUModel(package_DAE)
self.m_DISC = JMUModel(package_DISC)
self.m_INPUT = JMUModel(package_INPUT)
# Creates the solvers
self.DAE = JMIDAE(self.m_DAE)
self.DISC = JMIDAE(self.m_DISC)
def test_scaling_test_2(self):
"""
This tests a simulation when scaling is ON and there are input variables
that are not used.
"""
jmu_name = compile_jmu(
"Englezos652_with_input",
os.path.join(get_files_path(), "Modelica", "Englezos652.mop"),
compiler_options={"enable_variable_scaling": False})
# Load a model instance into Python
model = JMUModel(jmu_name)
# Get and set the options
opts = model.simulate_options()
opts['IDA_options']['atol'] = 1.0e-6
opts['IDA_options']['rtol'] = 1.0e-6
opts['IDA_options']['sensitivity'] = True
opts['ncp'] = 400
res = model.simulate(0, 1697000 / 3, options=opts)
x1 = res["x1"][-1]
r1 = res["r1"][-1]
u1 = res["u1"][-1]
nose.tools.assert_almost_equal(x1, 0.45537058, 3)
nose.tools.assert_almost_equal(r1, 5.3287e-8, 2)
nose.tools.assert_almost_equal(u1, 0.00000, 3)
def test_scaled_input_continuous(self):
"""
Tests that simulation of scaled input works for writing continuous.
"""
# Load the dynamic library and XML data
self.m_INPUT = JMUModel('DoubleInput_Nominal.jmu')
t = N.linspace(0., 10., 100)
u1 = N.cos(t)
u2 = N.sin(t)
u_traj = N.transpose(N.vstack((t, u1, u2)))
opts = self.m_INPUT.simulate_options()
opts['IDA_options']['atol'] = 1.0e-6
opts['IDA_options']['rtol'] = 1.0e-6
opts['IDA_options']['sensitivity'] = True
#opts['IDA_options']['write_cont'] = True
opts['ncp'] = 0
res = self.m_INPUT.simulate(final_time=10,
input=(['u1', 'u2'], u_traj),
options=opts)
nose.tools.assert_almost_equal(res.initial('u1'), 1.000000000, 3)
nose.tools.assert_almost_equal(res.initial('u2'), 0.000000000, 3)
nose.tools.assert_almost_equal(res.final('u1'), -0.839071529, 3)
nose.tools.assert_almost_equal(res.final('u2'), -0.544021110, 3)
def test_get_column(self):
"""
Test the get_column and get_data_matrix.
"""
model_file = os.path.join(get_files_path(), 'Modelica',
'RLC_Circuit.mo')
compile_jmu('RLC_Circuit', model_file)
model = JMUModel('RLC_Circuit.jmu')
res = model.simulate()
assert res.is_negated('resistor1.n.i')
assert res.is_negated('capacitor.n.i')
assert not res.is_variable('sine.freqHz')
dataMatrix = res.data_matrix
col = res.get_column('capacitor.v')
nose.tools.assert_almost_equal(dataMatrix[0, col],
res.initial('capacitor.v'), 5)
nose.tools.assert_almost_equal(dataMatrix[-1, col],
res.final('capacitor.v'), 5)
nose.tools.assert_raises(VariableNotTimeVarying, res.get_column,
'sine.freqHz')
def setUp(self):
"""
Setup test cases.
"""
# Load the dynamic library and XML data
self.fname = "CSTR_CSTR_Init_Optimization.jmu"
self.mod = JMUModel(self.fname)
def setUp(self):
"""
Setup test cases.
"""
# Load the dynamic library and XML data
self.fname = "VDP_pack_VDP_Opt_Min_Time.jmu"
self.vdp = JMUModel(self.fname)
def setUpClass(cls):
curr_dir = os.path.dirname(os.path.abspath(__file__));
mofile = os.path.join(get_files_path(), 'Modelica', 'StaticOptimizationTest.mop')
compile_jmu("StaticOptimizationTest.StaticOptimizationTest2", mofile)
cls.model = JMUModel("StaticOptimizationTest_StaticOptimizationTest2.jmu")
cls.nlp = NLPInitialization(cls.model,stat=1)
cls.ipopt_nlp = InitializationOptimizer(cls.nlp)
def test_order_input(self):
"""
This tests that the inputs are sorted in an correct value reference order
when being written to file.
"""
fpath = os.path.join(get_files_path(), 'Modelica', 'OrderInputs.mop')
cpath = 'OptimInputs'
unames = ['u1', 'u_e2', 'u_h3', 'u_c4', 'u_p5']
n = len(unames)
uvalues = [1., 2., 3., 4., 5.]
data = N.array([[0., 1., 2., 3., 4., 5.], [1., 1., 2., 3., 4., 5.],
[2., 1., 2., 3., 4., 5.]])
inputtuple = (unames, data)
jmu_name = compile_jmu(cpath, fpath)
model = JMUModel(jmu_name)
res = model.simulate(0, 2, input=inputtuple)
nose.tools.assert_almost_equal(res["u1"][-1], 1.000000000, 3)
nose.tools.assert_almost_equal(res["u_e2"][-1], 2.00000, 3)
nose.tools.assert_almost_equal(res["u_h3"][-1], 3.00000, 3)
nose.tools.assert_almost_equal(res["u_c4"][-1], 4.000000, 3)
nose.tools.assert_almost_equal(res["u_p5"][-1], 5.000000, 3)
nose.tools.assert_almost_equal(res["T.u1"][-1], 1.000000000, 3)
nose.tools.assert_almost_equal(res["T.u_e2"][-1], 2.00000, 3)
nose.tools.assert_almost_equal(res["T.u_h3"][-1], 3.00000, 3)
nose.tools.assert_almost_equal(res["T.u_c4"][-1], 4.000000, 3)
nose.tools.assert_almost_equal(res["T.u_p5"][-1], 5.000000, 3)
def test_no_events(self):
"""
Tests that models containing events cannot be simulated with JMIDAESens.
"""
self.m_DISC = JMUModel('IfExpExamples_IfExpExample2.jmu')
opts = self.m_DISC.simulate_options()
opts["IDA_options"]["sensitivity"] = True
nose.tools.assert_raises(JMIModel_Exception,self.m_DISC.simulate, 0, 1,None,"AssimuloAlg", opts)
def test_parameter_alias(self):
""" Test simulate and write to file when model has parameter alias.
(Test so that write to file does not crash.)
"""
model_file = os.path.join(get_files_path(), 'Modelica', 'ParameterAlias.mo')
compile_jmu('ParameterAlias', model_file)
model = JMUModel('ParameterAlias.jmu')
model.simulate()
def test_optimize(self):
""" Test the pyjmi.JMUModel.optimize function using all default parameters. """
fpath_pend = os.path.join(get_files_path(), 'Modelica', 'Pendulum_pack.mop')
cpath_pend = "Pendulum_pack.Pendulum_Opt"
jmu_pend = compile_jmu(cpath_pend, fpath_pend,compiler_options={'state_start_values_fixed':True})
pend = JMUModel(jmu_pend)
res = pend.optimize()
assert N.abs(res.final('cost') - 1.2921683e-01) < 1e-3
def test_simulate_initialize_arg(self):
""" Test pyjmi.JMUModel.simulate alg_arg 'initialize'. """
# This test is built on that simulation without initialization fails.
# Since simulation without initialization fails far down in Sundials
# no "proper" exception is thrown which means that I can only test that
# the general Exception is returned which means that the test is pretty
# unspecific (the test will pass for every type of error thrown). Therefore,
# I first test that running the simulation with default settings succeeds, so
# at least one knows that the error has with initialization to do.
name = compile_jmu(self.cpath_minit, self.fpath_minit)
model = JMUModel(name)
nose.tools.ok_(model.simulate())
model = JMUModel(name)
nose.tools.assert_raises(Exception,
model.simulate,
options={'initialize':False})
def test_dependent_parameters(self):
""" Test evaluation of dependent parameters. """
path = os.path.join(get_files_path(), 'Modelica', 'DepPar.mo')
jmu_name = compile_jmu('DepPar.DepPar1', path,
compiler_options={'state_start_values_fixed':True})
model = JMUModel(jmu_name)
assert (model.get('p1') == 1.0)
assert (model.get('p2') == 2.0)
assert (model.get('p3') == 6.0)
def test_double_input(self):
"""
This tests double input.
"""
fpath = os.path.join(get_files_path(), 'Modelica', 'DoubleInput.mo')
cpath = 'DoubleInput'
# compile VDP
fname = compile_jmu(
cpath, fpath, compiler_options={'state_start_values_fixed': True})
# Load the dynamic library and XML data
dInput = JMUModel(fname)
t = N.linspace(0., 10., 100)
u1 = N.cos(t)
u2 = N.sin(t)
u_traj = N.transpose(N.vstack((t, u1, u2)))
res = dInput.simulate(final_time=10, input=(['u1', 'u2'], u_traj))
nose.tools.assert_almost_equal(res.initial('u1'), 1.000000000, 3)
nose.tools.assert_almost_equal(res.initial('u2'), 0.000000000, 3)
nose.tools.assert_almost_equal(res.final('u1'), -0.839071529, 3)
nose.tools.assert_almost_equal(res.final('u2'), -0.544021110, 3)
#TEST REVERSE ORDER OF INPUT
# Load the dynamic library and XML data
dInput = JMUModel(fname)
t = N.linspace(0., 10., 100)
u1 = N.cos(t)
u2 = N.sin(t)
u_traj = N.transpose(N.vstack((t, u2, u1)))
res = dInput.simulate(final_time=10, input=(['u2', 'u1'], u_traj))
nose.tools.assert_almost_equal(res.initial('u1'), 1.000000000, 3)
nose.tools.assert_almost_equal(res.initial('u2'), 0.000000000, 3)
nose.tools.assert_almost_equal(res.final('u1'), -0.839071529, 3)
nose.tools.assert_almost_equal(res.final('u2'), -0.544021110, 3)
def setUp(self):
"""
Sets up the test case.
"""
self.cpath_vdp = "VDP_pack.VDP_Opt"
self.fpath_vdp = os.path.join(get_files_path(),'Modelica','VDP.mop')
self.cpath_minit = "must_initialize"
self.fpath_minit = os.path.join(get_files_path(), 'Modelica', 'must_initialize.mo')
self.fpath_rlc = os.path.join(get_files_path(),'Modelica','RLC_Circuit.mo')
self.cpath_rlc = "RLC_Circuit"
self.model_rlc = JMUModel(Test_init_assimulo.jmu_name)
def setUp(self):
"""Test setUp. Load the test model."""
self.cstr = JMUModel("CSTR_CSTR_Opt.jmu")
# Initialize the mesh
n_e = 150 # Number of elements
hs = N.ones(n_e)*1./n_e # Equidistant points
n_cp = 3; # Number of collocation points in each element
# Create an NLP object
self.nlp = ipopt.NLPCollocationLagrangePolynomials(
self.cstr,n_e,hs,n_cp)
def test_p0(self):
"""
This test that the correct number of parameters are found.
"""
self.m_SENS = JMUModel('QuadTankSens.jmu')
self.SENS = JMIDAESens(self.m_SENS)
assert self.SENS._p_nbr == 4
assert self.SENS._parameter_names[0] == 'a1'
assert self.SENS._parameter_names[1] == 'a2'
assert self.SENS._parameter_names[2] == 'a3'
assert self.SENS._parameter_names[3] == 'a4'
def test_input_simulation(self):
"""
This tests that input simulation works.
"""
self.m_SENS = JMUModel('QuadTankSens.jmu')
self.SENS = JMIDAESens(self.m_SENS)
path_result = os.path.join(get_files_path(), 'Results',
'qt_par_est_data.mat')
data = loadmat(path_result,appendmat=False)
# Extract data series
t_meas = data['t'][6000::100,0]-60
u1 = data['u1_d'][6000::100,0]
u2 = data['u2_d'][6000::100,0]
# Build input trajectory matrix for use in simulation
u_data = N.transpose(N.vstack((t_meas,u1,u2)))
u_traj = TrajectoryLinearInterpolation(u_data[:,0],
u_data[:,1:])
input_object = (['u1','u2'], u_traj)
qt_mod = JMIDAESens(self.m_SENS, input_object)
qt_sim = IDA(qt_mod)
#Store data continuous during the simulation, important when solving a
#problem with sensitivites.
qt_sim.report_continuously = True
#Value used when IDA estimates the tolerances on the parameters
qt_sim.pbar = qt_mod.p0
#Let Sundials find consistent initial conditions by use of 'IDA_YA_YDP_INIT'
qt_sim.make_consistent('IDA_YA_YDP_INIT')
#Simulate
qt_sim.simulate(60) #Simulate 4 seconds with 400 communication points
#write_data(qt_sim)
res = ResultDymolaTextual('QuadTankSens_result.txt')
dx1da1 = res.get_variable_data('dx1/da1')
dx1da2 = res.get_variable_data('dx1/da2')
dx4da1 = res.get_variable_data('dx4/da1')
nose.tools.assert_almost_equal(dx1da2.x[0], 0.000000, 4)
nose.tools.assert_almost_equal(dx1da2.x[-1], 0.00000, 4)
def test_ordinary_dae(self):
"""
This tests a simulation using JMIDAESens without any parameters.
"""
self.m_DAE = JMUModel('Pendulum_pack_Pendulum.jmu')
self.DAE = JMIDAESens(self.m_DAE)
sim = IDA(self.DAE)
sim.simulate(1.0)
nose.tools.assert_almost_equal(sim.y_sol[-1][0], 0.15420124, 4)
nose.tools.assert_almost_equal(sim.y_sol[-1][1], 0.11721253, 4)
def test_init(self):
m = JMUModel(self.jn)
# Create a new collocation object
n_e = 30
coll = NLPCollocationLagrangePolynomials(m, n_e, N.ones(n_e) / n_e, 3)
# Initialize with optimization result
coll.set_initial_from_dymola(self.res.result_data, N.array([]), 0, 1)
# Write initial point to file
coll.export_result_dymola('Init_res.txt')
# Load result
res_init = ResultDymolaTextual('Init_res.txt')
# Load test fixture
res_init_fix = ResultDymolaTextual(
self.curr_dir + '/../files/Results/MinTimeInit_init_fix.txt')
# Extract trajectories
dx = res_init.get_variable_data('der(x)')
dv = res_init.get_variable_data('der(v)')
x = res_init.get_variable_data('x')
v = res_init.get_variable_data('v')
u = res_init.get_variable_data('u')
dx_fix = res_init_fix.get_variable_data('der(x)')
dv_fix = res_init_fix.get_variable_data('der(v)')
x_fix = res_init_fix.get_variable_data('x')
v_fix = res_init_fix.get_variable_data('v')
u_fix = res_init_fix.get_variable_data('u')
# Comparison tests
N.testing.assert_array_almost_equal(dx_fix.x, dx.x)
N.testing.assert_array_almost_equal(dv_fix.x, dv.x)
N.testing.assert_array_almost_equal(x_fix.x, x.x)
N.testing.assert_array_almost_equal(v_fix.x, v.x)
N.testing.assert_array_almost_equal(u_fix.x, u.x)
if False:
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(x.t, x.x, 'r')
plt.hold(True)
plt.plot(v.t, v.x, 'r')
plt.grid(True)
plt.subplot(2, 1, 2)
plt.plot(u.t, u.x, 'r')
plt.grid(True)
def test_double_input_with_function(self):
"""
This tests double input with function.
"""
fpath = os.path.join(get_files_path(), 'Modelica', 'DoubleInput.mo')
cpath = 'DoubleInput'
# compile VDP
fname = compile_jmu(
cpath, fpath, compiler_options={'state_start_values_fixed': True})
# Load the dynamic library and XML data
dInput = JMUModel(fname)
def func(t):
return N.array([N.cos(t), N.sin(t)])
res = dInput.simulate(final_time=10, input=(['u1', 'u2'], func))
nose.tools.assert_almost_equal(res.initial('u1'), 1.000000000, 3)
nose.tools.assert_almost_equal(res.initial('u2'), 0.000000000, 3)
nose.tools.assert_almost_equal(res.final('u1'), -0.839071529, 3)
nose.tools.assert_almost_equal(res.final('u2'), -0.544021110, 3)
#TEST REVERSE ORDER OF INPUT
# Load the dynamic library and XML data
dInput = JMUModel(fname)
def func(t):
return [N.sin(t), N.cos(t)]
res = dInput.simulate(final_time=10, input=(['u2', 'u1'], func))
nose.tools.assert_almost_equal(res.initial('u1'), 1.000000000, 3)
nose.tools.assert_almost_equal(res.initial('u2'), 0.000000000, 3)
nose.tools.assert_almost_equal(res.final('u1'), -0.839071529, 3)
nose.tools.assert_almost_equal(res.final('u2'), -0.544021110, 3)
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)
def setUp(self):
"""Test setUp. Load the test model."""
cpath_vdp = "VDP_pack.VDP_Opt_Min_Time"
fname_vdp = cpath_vdp.replace('.','_',1)
self.fname_vdp = fname_vdp
self.vdp = JMUModel(fname_vdp+'.jmu')
# Initialize the mesh
n_e = 100 # Number of elements
hs = N.ones(n_e)*1./n_e # Equidistant points
self.hs = hs
n_cp = 3; # Number of collocation points in each element
# Create an NLP object
self.nlp = ipopt.NLPCollocationLagrangePolynomials(
self.vdp,n_e,hs,n_cp)
self.nlp_ipopt = ipopt.CollocationOptimizer(self.nlp)
def test_time_shift(self):
"""
Test the time shift feature
"""
model_file = os.path.join(get_files_path(), 'Modelica', 'RLC_Circuit.mo')
compile_jmu('RLC_Circuit', model_file)
model = JMUModel('RLC_Circuit.jmu')
res = model.simulate()
time_shifted_fix = res['time'] + 11.
res.result_data.shift_time(11.)
time_shifted = res['time']
assert max(N.abs(time_shifted_fix - time_shifted)) < 1e-6
def setup_base(self, rel_tol, abs_tol):
"""
Set up a new test case. Configures test and creates model.
Call this with proper args from setUp().
rel_tol - the relative error tolerance when comparing values
abs_tol - the absolute error tolerance when comparing values
Any other named args are passed to the NLP constructor.
"""
global _model_name
self.rel_tol = rel_tol
self.abs_tol = abs_tol
self.model_name = _model_name
parts = _model_name.split('.')
self.format = parts[len(parts) - 1]
if self.format == 'jmu':
self.model = JMUModel(self.model_name)
else:
self.model = load_fmu(self.model_name)
def test_jac(self):
"""
This tests the jacobian for simulation of sensitivites.
"""
# Load the dynamic library and XML data
self.m_SENS = JMUModel('QuadTankSens.jmu')
model = self.m_SENS
opts = model.simulate_options()
opts['IDA_options']['sensitivity'] = True
res = model.simulate(final_time=10, options=opts)
prob = res.solver.problem
assert res.solver.usejac == True
assert prob.j == prob.jac
