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_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_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)