def _adapt_input(self, input):
if input != None:
input_names = input[0]
self.input_len_names = len(input_names)
self.input_real_value_refs = []
self.input_real_mask = []
self.input_other = []
self.input_other_mask = []
if isinstance(input_names,str):
input_names = [input_names]
for i,name in enumerate(input_names):
if self._model.get_variable_causality(name) != fmi.FMI2_INPUT:
raise fmi.FMUException("Variable '%s' is not an input. Only variables specified to be inputs are allowed."%name)
if self._model.get_variable_data_type(name) == fmi.FMI2_REAL:
self.input_real_value_refs.append(self._model.get_variable_valueref(name))
self.input_real_mask.append(i)
else:
self.input_other.append(name)
self.input_other_mask.append(i)
self.input_real_mask = N.array(self.input_real_mask)
self.input_other_mask = N.array(self.input_other_mask)
self.input = input
def j(self, t, y, sw=None):
"""
The jacobian function for an ODE problem.
"""
if self._extra_f_nbr > 0:
y_extra = y[-self._extra_f_nbr:]
y = y[:-self._extra_f_nbr]
#Moving data to the model
self._model.time = t
#Check if there are any states
if self._f_nbr != 0:
self._model.continuous_states = y
#Sets the inputs, if any
self._set_input_values(t)
#Evaluating the jacobian
#If there are no states return a dummy jacobian.
if self._f_nbr == 0:
return N.array([[0.0]])
A = self._model._get_A(add_diag=True, output_matrix=self._A)
if self._A is None:
self._A = A
if self._extra_f_nbr > 0:
if hasattr(self._extra_equations, "jac"):
if self._sparse_representation:
Jac = A.tocoo() #Convert to COOrdinate
A2 = self._extra_equations.jac(y_extra).tocoo()
data = N.append(Jac.data, A2.data)
row = N.append(Jac.row, A2.row+self._f_nbr)
col = N.append(Jac.col, A2.col+self._f_nbr)
#Convert to compresssed sparse column
Jac = sp.coo_matrix((data, (row, col)))
Jac = Jac.tocsc()
else:
Jac = N.zeros((self._f_nbr+self._extra_f_nbr,self._f_nbr+self._extra_f_nbr))
Jac[:self._f_nbr,:self._f_nbr] = A if isinstance(A, N.ndarray) else A.toarray()
Jac[self._f_nbr:,self._f_nbr:] = self._extra_equations.jac(y_extra)
else:
raise fmi.FMUException("No Jacobian provided for the extra equations")
else:
Jac = A
return Jac
def __init__(self, model, input=None, result_file_name='',
with_jacobian=False, start_time=0.0, logging=False, result_handler=None):
"""
Initialize the problem.
"""
self._model = model
self._adapt_input(input)
self.timings = {"handle_result": 0.0}
#Set start time to the model
self._model.time = start_time
self.t0 = start_time
self.y0 = self._model.continuous_states
self.name = self._model.get_name()
[f_nbr, g_nbr] = self._model.get_ode_sizes()
self._f_nbr = f_nbr
self._g_nbr = g_nbr
if g_nbr > 0:
self.state_events = self.g
self.time_events = self.t
#If there is no state in the model, add a dummy
#state der(y)=0
if f_nbr == 0:
self.y0 = N.array([0.0])
#Determine the result file name
if result_file_name == '':
self.result_file_name = model.get_name()+'_result.txt'
else:
self.result_file_name = result_file_name
self.debug_file_name = model.get_name().replace(".","_")+'_debug.txt'
self.debug_file_object = None
#Default values
self.export = result_handler
#Internal values
self._sol_time = []
self._sol_real = []
self._sol_int = []
self._sol_bool = []
self._logg_step_event = []
self._write_header = True
self._logging = logging
#Stores the first time point
#[r,i,b] = self._model.save_time_point()
#self._sol_time += [self._model.t]
#self._sol_real += [r]
#self._sol_int += [i]
#self._sol_bool += b
self._jm_fmu = self._model.get_generation_tool() == "JModelica.org"
if with_jacobian:
raise fmi.FMUException("Jacobians are not supported using FMI 1.0, please use FMI 2.0")
def j(self, t, y, sw=None):
"""
The jacobian function for an ODE problem.
"""
if self._extra_f_nbr > 0:
y_extra = y[-self._extra_f_nbr:]
y = y[:-self._extra_f_nbr]
#Moving data to the model
self._model.time = t
#Check if there are any states
if self._f_nbr != 0:
self._model.continuous_states = y
#Sets the inputs, if any
self._set_input_values(t)
#Evaluating the jacobian
#If there are no states return a dummy jacobian.
if self._f_nbr == 0:
return N.array([[0.0]])
#Mimic the epsilon computation from the respective solver
"""
if self._solver == "CVode":
h = t - self._model._last_accepted_time
w = 1.0/(self._model._relative_tolerance*abs(y)+self._model.nominal_continuous_states*self._model._relative_tolerance)
fnorm = (sum((y*w)**2)/self._f_nbr)**0.5
inc = (1000 * abs(h) * self._RUROUND * self._f_nbr * fnorm) if (fnorm != 0.0 and h != 0.0) else 1.0
for i in range(self._f_nbr):
w[i] = max(self._RUROUND*abs(y[i]), inc/w[i])
eps = w
elif self._solver == "Radau5ODE":
eps = (self._UROUND*N.maximum(1e-5,abs(y)))**0.5
else:
eps = None
"""
eps = None
A = self._model._get_A(add_diag=True,
output_matrix=self._A,
perturbation=eps)
if self._A is None:
self._A = A
if self._extra_f_nbr > 0:
if hasattr(self._extra_equations, "jac"):
if self._sparse_representation:
Jac = A.tocoo() #Convert to COOrdinate
A2 = self._extra_equations.jac(y_extra).tocoo()
data = N.append(Jac.data, A2.data)
row = N.append(Jac.row, A2.row + self._f_nbr)
col = N.append(Jac.col, A2.col + self._f_nbr)
#Convert to compresssed sparse column
Jac = sp.coo_matrix((data, (row, col)))
Jac = Jac.tocsc()
else:
Jac = N.zeros((self._f_nbr + self._extra_f_nbr,
self._f_nbr + self._extra_f_nbr))
Jac[:self._f_nbr, :self._f_nbr] = A if isinstance(
A, N.ndarray) else A.toarray()
Jac[self._f_nbr:,
self._f_nbr:] = self._extra_equations.jac(y_extra)
else:
raise fmi.FMUException(
"No Jacobian provided for the extra equations")
else:
Jac = A
return Jac
def solve(self):
"""
Runs the simulation.
"""
result_handler = self.result_handler
h = (self.final_time-self.start_time)/self.ncp
grid = N.linspace(self.start_time,self.final_time,self.ncp+1)[:-1]
status = 0
final_time = self.start_time
#For result writing
start_time_point = timer()
result_handler.integration_point()
self.timings["storing_result"] = timer() - start_time_point
#Start of simulation, start the clock
time_start = timer()
for t in grid:
status = self.model.do_step(t,h)
self.status = status
if status != 0:
if status == fmi.FMI_ERROR:
result_handler.simulation_end()
raise fmi.FMUException("The simulation failed. See the log for more information. Return flag %d."%status)
elif status == fmi.FMI_DISCARD and (isinstance(self.model, fmi.FMUModelCS1) or
isinstance(self.model, fmi.FMUModelCS2)):
try:
if isinstance(self.model, fmi.FMUModelCS1):
last_time = self.model.get_real_status(fmi.FMI1_LAST_SUCCESSFUL_TIME)
else:
last_time = self.model.get_real_status(fmi.FMI2_LAST_SUCCESSFUL_TIME)
if last_time > t: #Solver succeeded in taken a step a little further than the last time
self.model.time = last_time
final_time = last_time
start_time_point = timer()
result_handler.integration_point()
self.timings["storing_result"] += timer() - start_time_point
except fmi.FMUException:
pass
break
#result_handler.simulation_end()
#raise Exception("The simulation failed. See the log for more information. Return flag %d"%status)
final_time = t+h
start_time_point = timer()
result_handler.integration_point()
self.timings["storing_result"] += timer() - start_time_point
if self.options["time_limit"] and (timer() - time_start) > self.options["time_limit"]:
raise fmi.TimeLimitExceeded("The time limit was exceeded at integration time %.8E."%final_time)
if self.input_traj != None:
self.model.set(self.input_traj[0], self.input_traj[1].eval(t+h)[0,:])
#End of simulation, stop the clock
time_stop = timer()
result_handler.simulation_end()
if self.status != 0:
print('Simulation terminated prematurely. See the log for possibly more information. Return flag %d.'%status)
#Log elapsed time
print('Simulation interval : ' + str(self.start_time) + ' - ' + str(final_time) + ' seconds.')
print('Elapsed simulation time: ' + str(time_stop-time_start) + ' seconds.')
self.timings["computing_solution"] = time_stop - time_start - self.timings["storing_result"]
def __init__(self,
start_time,
final_time,
input,
model,
options):
"""
Simulation algortihm for FMUs (Co-simulation).
Parameters::
model --
fmi.FMUModelCS1 object representation of the model.
options --
The options that should be used in the algorithm. For details on
the options, see:
* model.simulate_options('FMICSAlgOptions')
or look at the docstring with help:
* help(pyfmi.fmi_algorithm_drivers.FMICSAlgOptions)
Valid values are:
- A dict that overrides some or all of the default values
provided by FMICSAlgOptions. An empty dict will thus
give all options with default values.
- FMICSAlgOptions object.
"""
self.model = model
self.timings = {}
self.time_start_total = timer()
# set start time, final time and input trajectory
self.start_time = start_time
self.final_time = final_time
self.input = input
self.status = 0
# handle options argument
if isinstance(options, dict) and not \
isinstance(options, FMICSAlgOptions):
# user has passed dict with options or empty dict = default
self.options = FMICSAlgOptions(options)
elif isinstance(options, FMICSAlgOptions):
# user has passed FMICSAlgOptions instance
self.options = options
else:
raise InvalidAlgorithmOptionException(options)
# set options
self._set_options()
input_traj = None
if self.input:
if hasattr(self.input[1],"__call__"):
input_traj=(self.input[0],
TrajectoryUserFunction(self.input[1]))
else:
input_traj=(self.input[0],
TrajectoryLinearInterpolation(self.input[1][:,0],
self.input[1][:,1:]))
#Sets the inputs, if any
self.model.set(input_traj[0], input_traj[1].eval(self.start_time)[0,:])
self.input_traj = input_traj
#time_start = timer()
if self.options["result_handling"] == "file":
self.result_handler = ResultHandlerFile(self.model)
elif self.options["result_handling"] == "binary":
self.result_handler = ResultHandlerBinaryFile(self.model)
elif self.options["result_handling"] == "memory":
self.result_handler = ResultHandlerMemory(self.model)
elif self.options["result_handling"] == "csv":
self.result_handler = ResultHandlerCSV(self.model, delimiter=",")
elif self.options["result_handling"] == "custom":
self.result_handler = self.options["result_handler"]
if self.result_handler is None:
raise fmi.FMUException("The result handler needs to be specified when using a custom result handling.")
if not isinstance(self.result_handler, ResultHandler):
raise fmi.FMUException("The result handler needs to be a subclass of ResultHandler.")
elif self.options["result_handling"] == "none": #No result handling (for performance)
self.result_handler = ResultHandlerDummy(self.model)
else:
raise fmi.FMUException("Unknown option to result_handling.")
self.result_handler.set_options(self.options)
time_end = timer()
#self.timings["creating_result_object"] = time_end - time_start
time_start = time_end
time_res_init = 0.0
# Initialize?
if self.options['initialize']:
if isinstance(self.model, fmi.FMUModelCS1) or isinstance(self.model, fmi_extended.FMUModelME1Extended):
self.model.initialize(start_time, final_time, stop_time_defined=self.options["stop_time_defined"])
elif isinstance(self.model, fmi.FMUModelCS2):
self.model.setup_experiment(start_time=start_time, stop_time_defined=self.options["stop_time_defined"], stop_time=final_time)
self.model.initialize()
else:
raise fmi.FMUException("Unknown model.")
time_res_init = timer()
self.result_handler.initialize_complete()
time_res_init = timer() - time_res_init
elif self.model.time is None and isinstance(self.model, fmi.FMUModelCS2):
raise fmi.FMUException("Setup Experiment has not been called, this has to be called prior to the initialization call.")
elif self.model.time is None:
raise fmi.FMUException("The model need to be initialized prior to calling the simulate method if the option 'initialize' is set to False")
if abs(start_time - model.time) > 1e-14:
logging.warning('The simulation start time (%f) and the current time in the model (%f) is different. Is the simulation start time correctly set?'%(start_time, model.time))
time_end = timer()
self.timings["initializing_fmu"] = time_end - time_start - time_res_init
time_start = time_end
self.result_handler.simulation_start()
self.timings["initializing_result"] = timer() - time_start - time_res_init
def __init__(self,
start_time,
final_time,
input,
model,
options):
"""
Create a simulation algorithm using Assimulo.
Parameters::
model --
fmi.FMUModel object representation of the model.
options --
The options that should be used in the algorithm. For details on
the options, see:
* model.simulate_options('AssimuloFMIAlgOptions')
or look at the docstring with help:
* help(pyfmi.fmi_algorithm_drivers.AssimuloFMIAlgOptions)
Valid values are:
- A dict that overrides some or all of the default values
provided by AssimuloFMIAlgOptions. An empty dict will thus
give all options with default values.
- AssimuloFMIAlgOptions object.
"""
self.model = model
self.timings = {}
self.time_start_total = timer()
try:
import assimulo
except:
raise fmi.FMUException(
'Could not find Assimulo package. Check pyfmi.check_packages()')
# import Assimulo dependent classes
from pyfmi.simulation.assimulo_interface import FMIODE, FMIODESENS, FMIODE2, FMIODESENS2
# set start time, final time and input trajectory
self.start_time = start_time
self.final_time = final_time
self.input = input
# handle options argument
if isinstance(options, dict) and not \
isinstance(options, AssimuloFMIAlgOptions):
# user has passed dict with options or empty dict = default
self.options = AssimuloFMIAlgOptions(options)
elif isinstance(options, AssimuloFMIAlgOptions):
# user has passed AssimuloFMIAlgOptions instance
self.options = options
else:
raise InvalidAlgorithmOptionException(options)
# set options
self._set_options()
#time_start = timer()
input_traj = None
if self.input:
if hasattr(self.input[1],"__call__"):
input_traj=(self.input[0],
TrajectoryUserFunction(self.input[1]))
else:
input_traj=(self.input[0],
TrajectoryLinearInterpolation(self.input[1][:,0],
self.input[1][:,1:]))
#Sets the inputs, if any
input_names = [input_traj[0]] if isinstance(input_traj[0],str) else input_traj[0]
input_values = input_traj[1].eval(self.start_time)[0,:]
if len(input_names) != len(input_values):
raise fmi.FMUException("The number of input variables is not equal to the number of input values, please verify the input object.")
self.model.set(input_names, input_values)
if self.options["result_handling"] == "file":
self.result_handler = ResultHandlerFile(self.model)
elif self.options["result_handling"] == "binary":
if self.options["sensitivities"]:
logging.warning('The binary result file do not currently support storing of sensitivity results. Switching to textual result format.')
self.result_handler = ResultHandlerFile(self.model)
else:
self.result_handler = ResultHandlerBinaryFile(self.model)
elif self.options["result_handling"] == "memory":
self.result_handler = ResultHandlerMemory(self.model)
elif self.options["result_handling"] == "csv":
self.result_handler = ResultHandlerCSV(self.model, delimiter=",")
elif self.options["result_handling"] == "custom":
self.result_handler = self.options["result_handler"]
if self.result_handler is None:
raise fmi.FMUException("The result handler needs to be specified when using a custom result handling.")
if not isinstance(self.result_handler, ResultHandler):
raise fmi.FMUException("The result handler needs to be a subclass of ResultHandler.")
elif self.options["result_handling"] == "none": #No result handling (for performance)
self.result_handler = ResultHandlerDummy(self.model)
else:
raise fmi.FMUException("Unknown option to result_handling.")
self.result_handler.set_options(self.options)
time_end = timer()
#self.timings["creating_result_object"] = time_end - time_start
time_start = time_end
time_res_init = 0.0
# Initialize?
if self.options['initialize']:
try:
rtol = self.solver_options['rtol']
except KeyError:
rtol, atol = self.model.get_tolerances()
if isinstance(self.model, fmi.FMUModelME1):
self.model.time = start_time #Set start time before initialization
self.model.initialize(tolerance=rtol)
elif isinstance(self.model, fmi.FMUModelME2) or isinstance(self.model, fmi_coupled.CoupledFMUModelME2):
self.model.setup_experiment(tolerance=rtol, start_time=self.start_time, stop_time=self.final_time)
self.model.initialize()
self.model.event_update()
self.model.enter_continuous_time_mode()
else:
raise fmi.FMUException("Unknown model.")
time_res_init = timer()
self.result_handler.initialize_complete()
time_res_init = timer() - time_res_init
elif self.model.time is None and isinstance(self.model, fmi.FMUModelME2):
raise fmi.FMUException("Setup Experiment has not been called, this has to be called prior to the initialization call.")
elif self.model.time is None:
raise fmi.FMUException("The model need to be initialized prior to calling the simulate method if the option 'initialize' is set to False")
#See if there is an time event at start time
if isinstance(self.model, fmi.FMUModelME1):
event_info = self.model.get_event_info()
if event_info.upcomingTimeEvent and event_info.nextEventTime == model.time:
self.model.event_update()
if abs(start_time - model.time) > 1e-14:
logging.warning('The simulation start time (%f) and the current time in the model (%f) is different. Is the simulation start time correctly set?'%(start_time, model.time))
time_end = timer()
self.timings["initializing_fmu"] = time_end - time_start - time_res_init
time_start = time_end
self.result_handler.simulation_start()
self.timings["initializing_result"] = timer() - time_start + time_res_init
# Sensitivities?
if self.options["sensitivities"]:
if self.model.get_generation_tool() != "JModelica.org" and \
self.model.get_generation_tool() != "Optimica Compiler Toolkit":
if isinstance(self.model, fmi.FMUModelME2):
for var in self.options["sensitivities"]:
causality = self.model.get_variable_causality(var)
if causality != fmi.FMI2_INPUT:
raise fmi.FMUException("The sensitivity parameter is not specified as an input which is required.")
else:
raise fmi.FMUException("Sensitivity calculations only possible with JModelica.org generated FMUs")
if self.options["solver"] != "CVode":
raise fmi.FMUException("Sensitivity simulations currently only supported using the solver CVode.")
#Checks to see if all the sensitivities are inside the model
#else there will be an exception
self.model.get(self.options["sensitivities"])
if not self.input and (isinstance(self.model, fmi.FMUModelME2) or isinstance(self.model, fmi_coupled.CoupledFMUModelME2)):
if self.options["sensitivities"]:
self.probl = FMIODESENS2(self.model, result_file_name=self.result_file_name, with_jacobian=self.with_jacobian, start_time=self.start_time, parameters=self.options["sensitivities"],logging=self.options["logging"], result_handler=self.result_handler)
else:
self.probl = FMIODE2(self.model, result_file_name=self.result_file_name, with_jacobian=self.with_jacobian, start_time=self.start_time,logging=self.options["logging"], result_handler=self.result_handler,extra_equations=self.options["extra_equations"])
elif isinstance(self.model, fmi.FMUModelME2) or isinstance(self.model, fmi_coupled.CoupledFMUModelME2):
if self.options["sensitivities"]:
self.probl = FMIODESENS2(
self.model, input_traj, result_file_name=self.result_file_name, with_jacobian=self.with_jacobian, start_time=self.start_time,parameters=self.options["sensitivities"],logging=self.options["logging"], result_handler=self.result_handler)
else:
self.probl = FMIODE2(
self.model, input_traj, result_file_name=self.result_file_name, with_jacobian=self.with_jacobian, start_time=self.start_time,logging=self.options["logging"], result_handler=self.result_handler, extra_equations=self.options["extra_equations"])
elif not self.input:
if self.options["sensitivities"]:
self.probl = FMIODESENS(self.model, result_file_name=self.result_file_name,with_jacobian=self.with_jacobian,start_time=self.start_time,parameters=self.options["sensitivities"],logging=self.options["logging"], result_handler=self.result_handler)
else:
self.probl = FMIODE(self.model, result_file_name=self.result_file_name,with_jacobian=self.with_jacobian,start_time=self.start_time,logging=self.options["logging"], result_handler=self.result_handler)
else:
if self.options["sensitivities"]:
self.probl = FMIODESENS(
self.model, input_traj, result_file_name=self.result_file_name,with_jacobian=self.with_jacobian,start_time=self.start_time,parameters=self.options["sensitivities"],logging=self.options["logging"], result_handler=self.result_handler)
else:
self.probl = FMIODE(
self.model, input_traj, result_file_name=self.result_file_name,with_jacobian=self.with_jacobian,start_time=self.start_time,logging=self.options["logging"], result_handler=self.result_handler)
# instantiate solver and set options
self.simulator = self.solver(self.probl)
self._set_solver_options()