def simulate(self, Tend, nIntervals, gridWidth):
problem = Implicit_Problem(self.rhs, self.y0, self.yd0)
problem.name = 'IDA'
# solver.rhs = self.right_hand_side
problem.handle_result = self.handle_result
problem.state_events = self.state_events
problem.handle_event = self.handle_event
problem.time_events = self.time_events
problem.finalize = self.finalize
# Create IDA object and set additional parameters
simulation = IDA(problem)
simulation.atol = self.atol
simulation.rtol = self.rtol
simulation.verbosity = self.verbosity
if hasattr(simulation, 'continuous_output'):
simulation.continuous_output = False # default 0, if one step approach should be used
elif hasattr(simulation, 'report_continuously'):
simulation.report_continuously = False # default 0, if one step approach should be used
simulation.tout1 = self.tout1
simulation.lsoff = self.lsoff
# Calculate nOutputIntervals:
if gridWidth <> None:
nOutputIntervals = int((Tend - self.t0) / gridWidth)
else:
nOutputIntervals = nIntervals
# Check for feasible input parameters
if nOutputIntervals == 0:
print 'Error: gridWidth too high or nIntervals set to 0! Continue with nIntervals=1'
nOutputIntervals = 1
# Perform simulation
simulation.simulate(Tend, nOutputIntervals) # to get the values: t_new, y_new, yd_new = simulation.simulate
def simulate(self, Tend, nIntervals, gridWidth):
problem = Implicit_Problem(self.rhs, self.y0, self.yd0)
problem.name = 'IDA'
# solver.rhs = self.right_hand_side
problem.handle_result = self.handle_result
problem.state_events = self.state_events
problem.handle_event = self.handle_event
problem.time_events = self.time_events
problem.finalize = self.finalize
# Create IDA object and set additional parameters
simulation = IDA(problem)
simulation.atol = self.atol
simulation.rtol = self.rtol
simulation.verbosity = self.verbosity
if hasattr(simulation, 'continuous_output'):
simulation.continuous_output = False # default 0, if one step approach should be used
elif hasattr(simulation, 'report_continuously'):
simulation.report_continuously = False # default 0, if one step approach should be used
simulation.tout1 = self.tout1
simulation.lsoff = self.lsoff
# Calculate nOutputIntervals:
if gridWidth <> None:
nOutputIntervals = int((Tend - self.t0) / gridWidth)
else:
nOutputIntervals = nIntervals
# Check for feasible input parameters
if nOutputIntervals == 0:
print 'Error: gridWidth too high or nIntervals set to 0! Continue with nIntervals=1'
nOutputIntervals = 1
# Perform simulation
simulation.simulate(Tend, nOutputIntervals) # to get the values: t_new, y_new, yd_new = simulation.simulate
def solve(self):
# Setup IDA
assert self._initial_time is not None
problem = Implicit_Problem(self._residual_vector_eval,
self.solution.vector(),
self.solution_dot.vector(),
self._initial_time)
problem.jac = self._jacobian_matrix_eval
problem.handle_result = self._monitor
# Define an Assimulo IDA solver
solver = IDA(problem)
# Setup options
assert self._time_step_size is not None
solver.inith = self._time_step_size
if self._absolute_tolerance is not None:
solver.atol = self._absolute_tolerance
if self._max_time_steps is not None:
solver.maxsteps = self._max_time_steps
if self._relative_tolerance is not None:
solver.rtol = self._relative_tolerance
if self._report:
solver.verbosity = 10
solver.display_progress = True
solver.report_continuously = True
else:
solver.display_progress = False
solver.verbosity = 50
# Assert consistency of final time and time step size
assert self._final_time is not None
final_time_consistency = (
self._final_time - self._initial_time) / self._time_step_size
assert isclose(
round(final_time_consistency), final_time_consistency
), ("Final time should be occuring after an integer number of time steps"
)
# Prepare monitor computation if not provided by parameters
if self._monitor_initial_time is None:
self._monitor_initial_time = self._initial_time
assert isclose(
round(self._monitor_initial_time / self._time_step_size),
self._monitor_initial_time / self._time_step_size
), ("Monitor initial time should be a multiple of the time step size"
)
if self._monitor_time_step_size is None:
self._monitor_time_step_size = self._time_step_size
assert isclose(
round(self._monitor_time_step_size / self._time_step_size),
self._monitor_time_step_size / self._time_step_size
), ("Monitor time step size should be a multiple of the time step size"
)
monitor_t = arange(
self._monitor_initial_time,
self._final_time + self._monitor_time_step_size / 2.,
self._monitor_time_step_size)
# Solve
solver.simulate(self._final_time, ncp_list=monitor_t)
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_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 initialize_ode_solver(self, y_0, yd_0, t_0):
model = Implicit_Problem(self.residual, y_0, yd_0, t_0)
model.handle_result = self.handle_result
solver = IDA(model)
solver.rtol = self.solver_rtol
solver.atol = self.solver_atol # * np.array([100, 10, 1e-4, 1e-4])
solver.inith = 0.1 # self.wind.R_g / const.C
solver.maxh = self.dt * self.wind.R_g / const.C
solver.report_continuously = True
solver.display_progress = False
solver.verbosity = 50 # 50 = quiet
solver.num_threads = 3
# solver.display_progress = True
return solver
def buildsim(self, ):
"""
Setup the assimulo IDA simulator.
"""
# Create an Assimulo implicit solver (IDA)
imp_sim = IDA(self.imp_mod) # Create a IDA solver
# Sets the paramters
# 1e-4 #Default 1e-6
imp_sim.atol = self.p.RunInput['TIMESTEPPING']['SOLVER_TOL']
# 1e-4 #Default 1e-6
imp_sim.rtol = self.p.RunInput['TIMESTEPPING']['SOLVER_TOL']
# Suppres the algebraic variables on the error test
imp_sim.suppress_alg = True
imp_sim.display_progress = False
imp_sim.verbosity = 50
imp_sim.report_continuously = True
imp_sim.time_limit = 10.
self.imp_sim = imp_sim
def run_example(with_plots=True):
r"""
Example of the use of IDA for an implicit differential equation
with a discontinuity (state event) and the need for an event iteration.
on return:
- :dfn:`imp_mod` problem instance
- :dfn:`imp_sim` solver instance
"""
#Create an instance of the problem
imp_mod = Extended_Problem() #Create the problem
imp_sim = IDA(imp_mod) #Create the solver
imp_sim.verbosity = 0
imp_sim.report_continuously = True
#Simulate
t, y, yd = imp_sim.simulate(10.0,1000) #Simulate 10 seconds with 1000 communications points
#Plot
if with_plots:
import pylab as P
P.plot(t,y)
P.title(imp_mod.name)
P.ylabel('States')
P.xlabel('Time')
P.show()
#Basic test
nose.tools.assert_almost_equal(y[-1][0],8.0)
nose.tools.assert_almost_equal(y[-1][1],3.0)
nose.tools.assert_almost_equal(y[-1][2],2.0)
return imp_mod, imp_sim
def run_example(with_plots=True):
"""
This example show how to use Assimulo and IDA for simulating sensitivities
for initial conditions.::
0 = dy1/dt - -(k01+k21+k31)*y1 - k12*y2 - k13*y3 - b1
0 = dy2/dt - k21*y1 + (k02+k12)*y2
0 = dy3/dt - k31*y1 + k13*y3
y1(0) = p1, y2(0) = p2, y3(0) = p3
p1=p2=p3 = 0
See http://sundials.2283335.n4.nabble.com/Forward-sensitivities-for-initial-conditions-td3239724.html
on return:
- :dfn:`imp_mod` problem instance
- :dfn:`imp_sim` solver instance
"""
def f(t, y, yd, p):
y1, y2, y3 = y
yd1, yd2, yd3 = yd
k01 = 0.0211
k02 = 0.0162
k21 = 0.0111
k12 = 0.0124
k31 = 0.0039
k13 = 0.000035
b1 = 49.3
res_0 = -yd1 - (k01 + k21 + k31) * y1 + k12 * y2 + k13 * y3 + b1
res_1 = -yd2 + k21 * y1 - (k02 + k12) * y2
res_2 = -yd3 + k31 * y1 - k13 * y3
return N.array([res_0, res_1, res_2])
#The initial conditions
y0 = [0.0, 0.0, 0.0] #Initial conditions for y
yd0 = [49.3, 0., 0.]
p0 = [0.0, 0.0, 0.0] #Initial conditions for parameters
yS0 = N.array([[1, 0, 0], [0, 1, 0], [0, 0, 1.]])
#Create an Assimulo implicit problem
imp_mod = Implicit_Problem(f,
y0,
yd0,
p0=p0,
name='Example: Computing Sensitivities')
#Sets the options to the problem
imp_mod.yS0 = yS0
#Create an Assimulo explicit solver (IDA)
imp_sim = IDA(imp_mod)
#Sets the paramters
imp_sim.rtol = 1e-7
imp_sim.atol = 1e-6
imp_sim.pbar = [
1, 1, 1
] #pbar is used to estimate the tolerances for the parameters
imp_sim.report_continuously = True #Need to be able to store the result using the interpolate methods
imp_sim.sensmethod = 'SIMULTANEOUS' #Defines the sensitvity method used
imp_sim.suppress_sens = False #Dont suppress the sensitivity variables in the error test.
#Simulate
t, y, yd = imp_sim.simulate(400) #Simulate 400 seconds
#Basic test
nose.tools.assert_almost_equal(y[-1][0], 1577.6552477, 3)
nose.tools.assert_almost_equal(y[-1][1], 611.9574565, 3)
nose.tools.assert_almost_equal(y[-1][2], 2215.88563217, 3)
nose.tools.assert_almost_equal(imp_sim.p_sol[0][1][0], 1.0)
#Plot
if with_plots:
P.figure(1)
P.subplot(221)
P.plot(t,
N.array(imp_sim.p_sol[0])[:, 0], t,
N.array(imp_sim.p_sol[0])[:, 1], t,
N.array(imp_sim.p_sol[0])[:, 2])
P.title("Parameter p1")
P.legend(("p1/dy1", "p1/dy2", "p1/dy3"))
P.subplot(222)
P.plot(t,
N.array(imp_sim.p_sol[1])[:, 0], t,
N.array(imp_sim.p_sol[1])[:, 1], t,
N.array(imp_sim.p_sol[1])[:, 2])
P.title("Parameter p2")
P.legend(("p2/dy1", "p2/dy2", "p2/dy3"))
P.subplot(223)
P.plot(t,
N.array(imp_sim.p_sol[2])[:, 0], t,
N.array(imp_sim.p_sol[2])[:, 1], t,
N.array(imp_sim.p_sol[2])[:, 2])
P.title("Parameter p3")
P.legend(("p3/dy1", "p3/dy2", "p3/dy3"))
P.subplot(224)
P.title('ODE Solution')
P.plot(t, y)
P.suptitle(imp_mod.name)
P.show()
return imp_mod, imp_sim
def dae_solver(residual,
y0,
yd0,
t0,
p0=None,
jac=None,
name='DAE',
solver='IDA',
algvar=None,
atol=1e-6,
backward=False,
display_progress=True,
pbar=None,
report_continuously=False,
rtol=1e-6,
sensmethod='STAGGERED',
suppress_alg=False,
suppress_sens=False,
usejac=False,
usesens=False,
verbosity=30,
tfinal=10.,
ncp=500):
'''
DAE solver.
Parameters
----------
residual: function
Implicit DAE model.
y0: List[float]
Initial model state.
yd0: List[float]
Initial model state derivatives.
t0: float
Initial simulation time.
p0: List[float]
Parameters for which sensitivites are to be calculated.
jac: function
Model jacobian.
name: string
Model name.
solver: string
DAE solver.
algvar: List[bool]
A list for defining which variables are differential and which are algebraic.
The value True(1.0) indicates a differential variable and the value False(0.0) indicates an algebraic variable.
atol: float
Absolute tolerance.
backward: bool
Specifies if the simulation is done in reverse time.
display_progress: bool
Actives output during the integration in terms of that the current integration is periodically printed to the stdout.
Report_continuously needs to be activated.
pbar: List[float]
An array of positive floats equal to the number of parameters. Default absolute values of the parameters.
Specifies the order of magnitude for the parameters. Useful if IDAS is to estimate tolerances for the sensitivity solution vectors.
report_continuously: bool
Specifies if the solver should report the solution continuously after steps.
rtol: float
Relative tolerance.
sensmethod: string
Specifies the sensitivity solution method.
Can be either ‘SIMULTANEOUS’ or ‘STAGGERED’. Default is 'STAGGERED'.
suppress_alg: bool
Indicates that the error-tests are suppressed on algebraic variables.
suppress_sens: bool
Indicates that the error-tests are suppressed on the sensitivity variables.
usejac: bool
Sets the option to use the user defined jacobian.
usesens: bool
Aactivates or deactivates the sensitivity calculations.
verbosity: int
Determines the level of the output.
QUIET = 50 WHISPER = 40 NORMAL = 30 LOUD = 20 SCREAM = 10
tfinal: float
Simulation final time.
ncp: int
Number of communication points (number of return points).
Returns
-------
sol: solution [time, model states], List[float]
'''
if usesens is True: # parameter sensitivity
model = Implicit_Problem(residual, y0, yd0, t0, p0=p0)
else:
model = Implicit_Problem(residual, y0, yd0, t0)
model.name = name
if usejac is True: # jacobian
model.jac = jac
if algvar is not None: # differential or algebraic variables
model.algvar = algvar
if solver == 'IDA': # solver
from assimulo.solvers import IDA
sim = IDA(model)
sim.atol = atol
sim.rtol = rtol
sim.backward = backward # backward in time
sim.report_continuously = report_continuously
sim.display_progress = display_progress
sim.suppress_alg = suppress_alg
sim.verbosity = verbosity
if usesens is True: # sensitivity
sim.sensmethod = sensmethod
sim.pbar = np.abs(p0)
sim.suppress_sens = suppress_sens
# Simulation
# t, y, yd = sim.simulate(tfinal, ncp=(ncp - 1))
ncp_list = np.linspace(t0, tfinal, num=ncp, endpoint=True)
t, y, yd = sim.simulate(tfinal, ncp=0, ncp_list=ncp_list)
# Plot
# sim.plot()
# plt.figure()
# plt.subplot(221)
# plt.plot(t, y[:, 0], 'b.-')
# plt.legend([r'$\lambda$'])
# plt.subplot(222)
# plt.plot(t, y[:, 1], 'r.-')
# plt.legend([r'$\dot{\lambda}$'])
# plt.subplot(223)
# plt.plot(t, y[:, 2], 'k.-')
# plt.legend([r'$\eta$'])
# plt.subplot(224)
# plt.plot(t, y[:, 3], 'm.-')
# plt.legend([r'$\dot{\eta}$'])
# plt.figure()
# plt.subplot(221)
# plt.plot(t, yd[:, 0], 'b.-')
# plt.legend([r'$\dot{\lambda}$'])
# plt.subplot(222)
# plt.plot(t, yd[:, 1], 'r.-')
# plt.legend([r'$\ddot{\lambda}$'])
# plt.subplot(223)
# plt.plot(t, yd[:, 2], 'k.-')
# plt.legend([r'$\dot{\eta}$'])
# plt.subplot(224)
# plt.plot(t, yd[:, 3], 'm.-')
# plt.legend([r'$\ddot{\eta}$'])
# plt.figure()
# plt.subplot(121)
# plt.plot(y[:, 0], y[:, 1])
# plt.xlabel(r'$\lambda$')
# plt.ylabel(r'$\dot{\lambda}$')
# plt.subplot(122)
# plt.plot(y[:, 2], y[:, 3])
# plt.xlabel(r'$\eta$')
# plt.ylabel(r'$\dot{\eta}$')
# plt.figure()
# plt.subplot(121)
# plt.plot(yd[:, 0], yd[:, 1])
# plt.xlabel(r'$\dot{\lambda}$')
# plt.ylabel(r'$\ddot{\lambda}$')
# plt.subplot(122)
# plt.plot(yd[:, 2], yd[:, 3])
# plt.xlabel(r'$\dot{\eta}$')
# plt.ylabel(r'$\ddot{\eta}$')
# plt.figure()
# plt.subplot(121)
# plt.plot(y[:, 0], y[:, 2])
# plt.xlabel(r'$\lambda$')
# plt.ylabel(r'$\eta$')
# plt.subplot(122)
# plt.plot(y[:, 1], y[:, 3])
# plt.xlabel(r'$\dot{\lambda}$')
# plt.ylabel(r'$\dot{\eta}$')
# plt.figure()
# plt.subplot(121)
# plt.plot(yd[:, 0], yd[:, 2])
# plt.xlabel(r'$\dot{\lambda}$')
# plt.ylabel(r'$\dot{\eta}$')
# plt.subplot(122)
# plt.plot(yd[:, 1], yd[:, 3])
# plt.xlabel(r'$\ddot{\lambda}$')
# plt.ylabel(r'$\ddot{\eta}$')
# plt.show()
sol = [t, y, yd]
return sol
def run_example(with_plots=True):
r"""
This is the same example from the Sundials package (cvsRoberts_FSA_dns.c)
Its purpose is to demonstrate the use of parameters in the differential equation.
This simple example problem for IDA, due to Robertson
see http://www.dm.uniba.it/~testset/problems/rober.php,
is from chemical kinetics, and consists of the system:
.. math::
\dot y_1 -( -p_1 y_1 + p_2 y_2 y_3)&=0 \\
\dot y_2 -(p_1 y_1 - p_2 y_2 y_3 - p_3 y_2^2)&=0 \\
\dot y_3 -( p_3 y_ 2^2)&=0
on return:
- :dfn:`imp_mod` problem instance
- :dfn:`imp_sim` solver instance
"""
def f(t, y, yd, p):
res1 = -p[0] * y[0] + p[1] * y[1] * y[2] - yd[0]
res2 = p[0] * y[0] - p[1] * y[1] * y[2] - p[2] * y[1] ** 2 - yd[1]
res3 = y[0] + y[1] + y[2] - 1
return N.array([res1, res2, res3])
# The initial conditons
y0 = N.array([1.0, 0.0, 0.0]) # Initial conditions for y
yd0 = N.array([0.1, 0.0, 0.0]) # Initial conditions for dy/dt
p0 = [0.040, 1.0e4, 3.0e7] # Initial conditions for parameters
# Create an Assimulo implicit problem
imp_mod = Implicit_Problem(f, y0, yd0, p0=p0)
# Create an Assimulo implicit solver (IDA)
imp_sim = IDA(imp_mod) # Create a IDA solver
# Sets the paramters
imp_sim.atol = N.array([1.0e-8, 1.0e-14, 1.0e-6])
imp_sim.algvar = [1.0, 1.0, 0.0]
imp_sim.suppress_alg = False # Suppres the algebraic variables on the error test
imp_sim.report_continuously = True # Store data continuous during the simulation
imp_sim.pbar = p0
imp_sim.suppress_sens = False # Dont suppress the sensitivity variables in the error test.
# Let Sundials find consistent initial conditions by use of 'IDA_YA_YDP_INIT'
imp_sim.make_consistent('IDA_YA_YDP_INIT')
# Simulate
t, y, yd = imp_sim.simulate(4, 400) # Simulate 4 seconds with 400 communication points
print(imp_sim.p_sol[0][-1], imp_sim.p_sol[1][-1], imp_sim.p_sol[0][-1])
# Basic test
nose.tools.assert_almost_equal(y[-1][0], 9.05518032e-01, 4)
nose.tools.assert_almost_equal(y[-1][1], 2.24046805e-05, 4)
nose.tools.assert_almost_equal(y[-1][2], 9.44595637e-02, 4)
nose.tools.assert_almost_equal(imp_sim.p_sol[0][-1][0], -1.8761, 2) # Values taken from the example in Sundials
nose.tools.assert_almost_equal(imp_sim.p_sol[1][-1][0], 2.9614e-06, 8)
nose.tools.assert_almost_equal(imp_sim.p_sol[2][-1][0], -4.9334e-10, 12)
# Plot
if with_plots:
P.plot(t, y)
P.title(imp_mod.name)
P.xlabel('Time')
P.ylabel('State')
P.show()
return imp_mod, imp_sim
def DAE_integration_assimulo(self, **kwargs):
"""
Perform time integration for DAEs with the assimulo package
"""
assert self.set_time_setting == 1, 'Time discretization must be specified first'
if self.tclose > 0:
close = True
else:
close = False
# Control vector
self.U = interpolate(self.boundary_cntrl_space, self.Vb).vector()[self.bndr_i_b]
if self.discontinous_boundary_values == 1:
self.U[self.Corner_indices] = self.U[self.Corner_indices]/2
# Definition of the sparse solver for the DAE res function to
# be defined next M should be invertible !!
my_solver = factorized(csc_matrix(self.M))
rhs = self.my_mult(self.J, self.my_mult(self.Q,self.A0)) + self.my_mult(self.Bext,self.U* self.boundary_cntrl_time(0.,self.tclose))
self.AD0 = my_solver(rhs)
# Definition of the rhs function required in assimulo
def res(t,y,yd):
"""
Definition of the residual function required in the DAE part of assimulo
"""
if close:
if t < self.tclose:
z = self.my_mult(self.M,yd) - self.my_mult(self.J, self.my_mult(self.Q,y)) - self.my_mult(self.Bext,self.U* self.boundary_cntrl_time(t,self.tclose))
else:
z = self.my_mult(self.M,yd) - self.my_mult((self.J - self.R), self.my_mult(self.Q,y))
else:
z = self.my_mult(self.M,yd) - self.my_mult(self.J, self.my_mult(self.Q,y)) - self.my_mult(self.Bext,self.U* self.boundary_cntrl_time(t,self.tclose))
return z
# Definition of the jacobian function required in assimulo
def jac(c,t,y,yd):
"""
Definition of the Jacobian matrix required in the DAE part of assimulo
"""
Matrix = csr_matrix(self.my_mult(self.J,self.Q))
if close and t > self.tclose:
Matrix = csr_matrix(self.my_mult(self.J - self.R, self.Q))
return c*csr_matrix(self.M) - Matrix
# Definition of the jacobian matrix vector function required in assimulo
def jacv(t,y,yd,res,v,c):
"""
Jacobian matrix-vector product required in the DAE part of assimulo
"""
w = self.my_mult(self.Q, v)
z = self.my_mult(self.J, w)
if close and t > self.tclose:
z -= self.my_mult(self.R, w)
return c*self.my_mult(self.M,v) - z
print('DAE Integration using assimulo built-in functions:')
model = Implicit_Problem(res,self.A0,self.AD0,self.tinit)
model.jacv = jacv
#sim = Radau5DAE(model,**kwargs)
#
# IDA method from Assimulo
#
sim = IDA(model,**kwargs)
sim.algvar = [1 for i in range(self.M.shape[0])]
sim.atol = 1.e-6
sim.rtol = 1.e-6
sim.report_continuously = True
ncp = self.Nt
sim.usejac = True
sim.suppress_alg = True
sim.inith = self.dt
sim.maxord = 5
#sim.linear_solver = 'SPGMR'
time_span, DAE_y, DAE_yd = sim.simulate(self.tfinal,ncp)
#print(sim.get_options())
print(sim.print_statistics())
A_dae = DAE_y.transpose()
# Hamiltonian
self.Nt = A_dae.shape[1]
self.tspan = np.array(time_span)
Ham_dae = np.zeros(self.Nt)
for k in range(self.Nt):
#Ham_dae[k] = 1/2 * A_dae[:,k] @ self.M @ self.Q @ A_dae[:,k]
Ham_dae[k] = 1/2 * self.my_mult(A_dae[:,k].T, \
self.my_mult(self.M, self.my_mult(self.Q, A_dae[:,k])))
# Get q variables
Aq_dae = A_dae[:self.Nq,:]
# Get p variables
Ap_dae = A_dae[self.Nq:,:]
# Get Deformation
Rho = np.zeros(self.Np)
for i in range(self.Np):
Rho[i] = self.rho(self.coord_p[i])
W_dae = np.zeros((self.Np,self.Nt))
theta = .5
for k in range(self.Nt-1):
W_dae[:,k+1] = W_dae[:,k] + self.dt * 1/Rho[:] * ( theta * Ap_dae[:,k+1] + (1-theta) * Ap_dae[:,k] )
self.Ham_dae = Ham_dae
return Aq_dae, Ap_dae, Ham_dae, W_dae, np.array(time_span)
def run_example(with_plots=True):
r"""
Example for demonstrating the use of a user supplied Jacobian
ODE:
.. math::
\dot y_1-y_3 &= 0 \\
\dot y_2-y_4 &= 0 \\
\dot y_3+y_5 y_1 &= 0 \\
\dot y_4+y_5 y_2+9.82&= 0 \\
y_3^2+y_4^2-y_5(y_1^2+y_2^2)-9.82 y_2&= 0
on return:
- :dfn:`imp_mod` problem instance
- :dfn:`imp_sim` solver instance
"""
global order
order = []
#Defines the residual
def f(t, y, yd):
res_0 = yd[0] - y[2]
res_1 = yd[1] - y[3]
res_2 = yd[2] + y[4] * y[0]
res_3 = yd[3] + y[4] * y[1] + 9.82
res_4 = y[2]**2 + y[3]**2 - y[4] * (y[0]**2 + y[1]**2) - y[1] * 9.82
return N.array([res_0, res_1, res_2, res_3, res_4])
def handle_result(solver, t, y, yd):
global order
order.append(solver.get_last_order())
solver.t_sol.extend([t])
solver.y_sol.extend([y])
solver.yd_sol.extend([yd])
#The initial conditons
y0 = [1.0, 0.0, 0.0, 0.0, 5] #Initial conditions
yd0 = [0.0, 0.0, 0.0, -9.82, 0.0] #Initial conditions
#Create an Assimulo implicit problem
imp_mod = Implicit_Problem(f,
y0,
yd0,
name='Example for plotting used order')
imp_mod.handle_result = handle_result
#Sets the options to the problem
imp_mod.algvar = [1.0, 1.0, 1.0, 1.0, 0.0] #Set the algebraic components
#Create an Assimulo implicit solver (IDA)
imp_sim = IDA(imp_mod) #Create a IDA solver
#Sets the paramters
imp_sim.atol = 1e-6 #Default 1e-6
imp_sim.rtol = 1e-6 #Default 1e-6
imp_sim.suppress_alg = True #Suppres the algebraic variables on the error test
imp_sim.report_continuously = True
#Let Sundials find consistent initial conditions by use of 'IDA_YA_YDP_INIT'
imp_sim.make_consistent('IDA_YA_YDP_INIT')
#Simulate
t, y, yd = imp_sim.simulate(5) #Simulate 5 seconds
#Basic tests
nose.tools.assert_almost_equal(y[-1][0], 0.9401995, places=4)
nose.tools.assert_almost_equal(y[-1][1], -0.34095124, places=4)
nose.tools.assert_almost_equal(yd[-1][0], -0.88198927, places=4)
nose.tools.assert_almost_equal(yd[-1][1], -2.43227069, places=4)
nose.tools.assert_almost_equal(order[-1], 5, places=4)
#Plot
if with_plots:
P.figure(1)
P.plot(t, y, linestyle="dashed", marker="o") #Plot the solution
P.xlabel('Time')
P.ylabel('State')
P.title(imp_mod.name)
P.figure(2)
P.plot([0] + N.add.accumulate(N.diff(t)).tolist(), order)
P.title("Used order during the integration")
P.xlabel("Time")
P.ylabel("Order")
P.show()
return imp_mod, imp_sim
#imp_mod.set_iapp( I_app/10. )
#imp_sim.make_consistent('IDA_YA_YDP_INIT')
#ta, ya, yda = imp_sim.simulate(0.1,5)
##
#imp_mod.set_iapp( I_app/2. )
#imp_sim.make_consistent('IDA_YA_YDP_INIT')
#tb, yb, ydb = imp_sim.simulate(0.2,5)
#imp_mod.set_iapp( I_app )
#imp_sim.make_consistent('IDA_YA_YDP_INIT')
## Sim step 1
#t1, y1, yd1 = imp_sim.simulate(1./Crate*3600.*0.2,100)
imp_sim.display_progress = False
imp_sim.verbosity = 50
imp_sim.report_continuously = True
imp_sim.time_limit = 10.
### Simulate
t01, t02 = 0.1, 0.2
imp_mod.set_iapp(I_app / 10.)
imp_sim.make_consistent('IDA_YA_YDP_INIT')
ta, ya, yda = imp_sim.simulate(t01, 2)
imp_mod.set_iapp(I_app / 2.)
imp_sim.make_consistent('IDA_YA_YDP_INIT')
tb, yb, ydb = imp_sim.simulate(t02, 2)
print 'yb shape', yb.shape
def DAE_integration_assimulo_lagrangian(self, **kwargs):
"""
Perform time integration for DAEs with the assimulo package
Lagrangian variant
"""
assert self.set_time_setting == 1, 'Time discretization must be specified first'
if self.tclose > 0:
close = True
else:
close = False
# Control vector
self.U = interpolate(self.boundary_cntrl_space, self.Vl).vector()[self.bndr_i_l]
if self.discontinous_boundary_values == 1:
self.U[self.Corner_indices] = self.U[self.Corner_indices]/2
self.UU = np.zeros(self.Nb)
for i in range(self.Nb):
self.UU[i] = self.boundary_cntrl_space(self.coord_b[i])
# Definition of the sparse solver for the DAE res function to
# be defined next M should be invertible !!
#my_solver = factorized(csc_matrix(self.M))
#rhs = self.my_mult(self.J, self.my_mult(self.Q,self.A0)) + self.my_mult(self.Bext,self.U* self.boundary_cntrl_time(0.,self.tclose))
#self.AD0 = my_solver(rhs)
# Definition of the rhs function required in assimulo
def res(t,y,yd):
"""
Definition of the residual function required in the DAE part of assimulo
"""
z = np.zeros(self.Np+self.Nl)
z[0:self.Np] = self.my_mult(self.M_class, yd[0:self.Np]) + self.my_mult(self.D_class, y[0:self.Np]) - self.my_mult(self.C_class, y[self.Np:])
z[self.Np:self.Np+self.Nl] = self.my_mult(self.C_class.T, y[0:self.Np]) - self.L_class * self.boundary_cntrl_time(t,self.tclose)
return z
# Definition of the jacobian function required in assimulo
def jac(c,t,y,yd):
"""
Definition of the Jacobian matrix required in the DAE part of assimulo
"""
#Matrix = csr_matrix(self.my_mult(self.J,self.Q))
#if close and t > self.tclose:
# Matrix = csr_matrix(self.my_mult(self.J - self.R, self.Q))
#return c*csr_matrix(self.M) - Matrix
return None
# Definition of the jacobian matrix vector function required in assimulo
def jacv(t,y,yd,res,v,c):
"""
Jacobian matrix-vector product required in the DAE part of assimulo
"""
#w = self.my_mult(self.Q, v)
#z = self.my_mult(self.J, w)
#if close and t > self.tclose:
# z -= self.my_mult(self.R, w)
#return c*self.my_mult(self.M,v) - z
return None
print('DAE Integration using assimulo built-in functions:')
#def handle_result(solver, t ,y, yd):
# global order
# order.append(solver.get_last_order())
#
# solver.t_sol.extend([t])
# solver.y_sol.extend([y])
# solver.yd_sol.extend([yd])
# The initial conditons
y0 = np.concatenate(( self.Tp0, np.zeros(self.Nl) ))
yd0 = np.zeros(self.Np + self.Nl)
model = Implicit_Problem(res,y0,yd0,self.tinit)
#model.handle_result = handle_result
#model.jacv = jacv
#sim = Radau5DAE(model,**kwargs)
#
# IDA method from Assimulo
#
sim = IDA(model,**kwargs)
sim.algvar = list(np.concatenate((np.ones(self.Np), np.zeros(self.Nl) )) )
sim.atol = 1.e-6
sim.rtol = 1.e-6
sim.report_continuously = True
ncp = self.Nt
#sim.usejac = True
sim.suppress_alg = True
sim.inith = self.dt
sim.maxord = 5
#sim.linear_solver = 'SPGMR'
sim.make_consistent('IDA_YA_YDP_INIT')
#time_span, DAE_y, DAE_yd = sim.simulate(self.tfinal,ncp, self.tspan)
time_span, DAE_y, DAE_yd = sim.simulate(self.tfinal, 0, self.tspan)
#print(sim.get_options())
print(sim.print_statistics())
A_dae = DAE_y[:,0:self.Np].transpose()
# Hamiltonian
self.Nt = A_dae.shape[1]
self.tspan = np.array(time_span)
Ham_dae = np.zeros(self.Nt)
for k in range(self.Nt):
Ham_dae[k] = 1/2 * self.my_mult(A_dae[:,k].T, \
self.my_mult(self.Mp_rho_Cv, A_dae[:,k]))
self.Ham_dae = Ham_dae
return Ham_dae, np.array(time_span)
