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
# 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
if self.options["result_handling"] == "file":
self.result_handler = ResultHandlerFile(self.model)
elif self.options["result_handling"] == "memory":
self.result_handler = ResultHandlerMemory(self.model)
elif self.options["result_handling"] == "custom":
self.result_handler = self.options["result_handler"]
if self.result_handler == None:
raise Exception(
"The result handler needs to be specified when using a custom result handling."
)
if not isinstance(self.result_handler, ResultHandler):
raise Exception(
"The result handler needs to be a subclass of ResultHandler."
)
else:
raise Exception("Unknown option to result_handling.")
self.result_handler.set_options(self.options)
# 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,
StopTimeDefined=True)
elif isinstance(self.model, fmi.FMUModelCS2):
self.model.setup_experiment(start_time=start_time,
stop_time_defined=True,
stop_time=final_time)
self.model.initialize()
else:
raise Exception("Unknown model.")
self.result_handler.initialize_complete()
elif self.model.time == None and isinstance(self.model,
fmi.FMUModelCS2):
raise Exception(
"Setup Experiment has not been called, this has to be called prior to the initialization call."
)
self.result_handler.simulation_start()
class FMICSAlg(AlgorithmBase):
"""
Simulation algortihm for FMUs (Co-simulation).
"""
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
# 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
if self.options["result_handling"] == "file":
self.result_handler = ResultHandlerFile(self.model)
elif self.options["result_handling"] == "memory":
self.result_handler = ResultHandlerMemory(self.model)
elif self.options["result_handling"] == "custom":
self.result_handler = self.options["result_handler"]
if self.result_handler == None:
raise Exception(
"The result handler needs to be specified when using a custom result handling."
)
if not isinstance(self.result_handler, ResultHandler):
raise Exception(
"The result handler needs to be a subclass of ResultHandler."
)
else:
raise Exception("Unknown option to result_handling.")
self.result_handler.set_options(self.options)
# 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,
StopTimeDefined=True)
elif isinstance(self.model, fmi.FMUModelCS2):
self.model.setup_experiment(start_time=start_time,
stop_time_defined=True,
stop_time=final_time)
self.model.initialize()
else:
raise Exception("Unknown model.")
self.result_handler.initialize_complete()
elif self.model.time == None and isinstance(self.model,
fmi.FMUModelCS2):
raise Exception(
"Setup Experiment has not been called, this has to be called prior to the initialization call."
)
self.result_handler.simulation_start()
def _set_options(self):
"""
Helper function that sets options for FMICS algorithm.
"""
# no of communication points
self.ncp = self.options['ncp']
self.write_scaled_result = self.options['write_scaled_result']
# result file name
if self.options['result_file_name'] == '':
self.result_file_name = self.model.get_identifier() + '_result.txt'
else:
self.result_file_name = self.options['result_file_name']
def _set_solver_options(self):
"""
Helper function that sets options for the solver.
"""
pass #No solver options
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 = 0.0
#For result writing
result_handler.integration_point()
#Start of simulation, start the clock
time_start = time.clock()
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 Exception(
"The simulation failed. See the log for more information. Return flag %d."
% status)
elif status == fmi.FMI_DISCARD and isinstance(
self.model, fmi.FMUModelCS1):
try:
last_time = self.model.get_real_status(
fmi.FMI1_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
result_handler.integration_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
result_handler.integration_point()
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 = time.clock()
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.')
def get_result(self):
"""
Write result to file, load result data and create an FMICSResult
object.
Returns::
The FMICSResult object.
"""
# Get the result
res = self.result_handler.get_result()
# create and return result object
return FMIResult(self.model,
self.result_file_name,
None,
res,
self.options,
status=self.status)
@classmethod
def get_default_options(cls):
"""
Get an instance of the options class for the FMICSAlg algorithm,
prefilled with default values. (Class method.)
"""
return FMICSAlgOptions()
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
if not assimulo_present:
raise Exception(
'Could not find Assimulo package. Check pyfmi.check_packages()'
)
# 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()
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, :])
if self.options["result_handling"] == "file":
self.result_handler = ResultHandlerFile(self.model)
elif self.options["result_handling"] == "memory":
self.result_handler = ResultHandlerMemory(self.model)
elif self.options["result_handling"] == "custom":
self.result_handler = self.options["result_handler"]
if self.result_handler == None:
raise Exception(
"The result handler needs to be specified when using a custom result handling."
)
if not isinstance(self.result_handler, ResultHandler):
raise Exception(
"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 Exception("Unknown option to result_handling.")
self.result_handler.set_options(self.options)
# 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(relativeTolerance=rtol)
elif isinstance(self.model, fmi.FMUModelME2):
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 Exception("Unknown model.")
self.result_handler.initialize_complete()
elif self.model.time == None and isinstance(self.model,
fmi.FMUModelME2):
raise Exception(
"Setup Experiment has not been called, this has to be called prior to the initialization call."
)
#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()
self.result_handler.simulation_start()
# Sensitivities?
if self.options["sensitivities"]:
if self.model.get_generation_tool() != "JModelica.org":
raise Exception(
"Sensitivity calculations only possible with JModelica.org generated FMUs"
)
if self.options["solver"] != "CVode":
raise Exception(
"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):
if self.options["sensitivities"]:
self.probl = FMIODESENS2(
self.model,
result_file_name=self.result_file_name,
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,
start_time=self.start_time,
logging=self.options["logging"],
result_handler=self.result_handler)
elif isinstance(self.model, fmi.FMUModelME2):
if self.options["sensitivities"]:
self.probl = FMIODESENS2(
self.model,
input_traj,
result_file_name=self.result_file_name,
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,
start_time=self.start_time,
logging=self.options["logging"],
result_handler=self.result_handler)
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()
class AssimuloFMIAlg(AlgorithmBase):
"""
Simulation algortihm for FMUs using the Assimulo package.
"""
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
if not assimulo_present:
raise Exception(
'Could not find Assimulo package. Check pyfmi.check_packages()'
)
# set start time, final time and input trajectory
self.start_time = start_time
self.final_time = final_time
self.input = input
self.model.time = start_time #Also set start time into the model
# 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()
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, :])
if self.options["result_handling"] == "file":
self.result_handler = ResultHandlerFile(self.model)
elif self.options["result_handling"] == "memory":
self.result_handler = ResultHandlerMemory(self.model)
elif self.options["result_handling"] == "custom":
self.result_handler = self.options["result_handler"]
if self.result_handler == None:
raise Exception(
"The result handler needs to be specified when using a custom result handling."
)
if not isinstance(self.result_handler, ResultHandler):
raise Exception(
"The result handler needs to be a subclass of ResultHandler."
)
else:
raise Exception("Unknown option to result_handling.")
self.result_handler.set_options(self.options)
# Initialize?
if self.options['initialize']:
try:
self.model.initialize(
relativeTolerance=self.solver_options['rtol'])
except KeyError:
rtol, atol = self.model.get_tolerances()
self.model.initialize(relativeTolerance=rtol)
self.result_handler.initialize_complete()
self.result_handler.simulation_start()
# Sensitivities?
if self.options["sensitivities"]:
if self.model.get_generation_tool() != "JModelica.org":
raise Exception(
"Sensitivity calculations only possible with JModelica.org generated FMUs"
)
if self.options["solver"] != "CVode":
raise Exception(
"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):
if self.options["sensitivities"]:
self.probl = FMIODESENS2(
self.model,
result_file_name=self.result_file_name,
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,
start_time=self.start_time,
logging=self.options["logging"],
result_handler=self.result_handler)
elif isinstance(self.model, fmi.FMUModelME2):
if self.options["sensitivities"]:
self.probl = FMIODESENS2(
self.model,
input_traj,
result_file_name=self.result_file_name,
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,
start_time=self.start_time,
logging=self.options["logging"],
result_handler=self.result_handler)
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()
def _set_options(self):
"""
Helper function that sets options for AssimuloFMI algorithm.
"""
# no of communication points
self.ncp = self.options['ncp']
self.write_scaled_result = self.options['write_scaled_result']
self.with_jacobian = self.options['with_jacobian']
# result file name
if self.options['result_file_name'] == '':
self.result_file_name = self.model.get_identifier() + '_result.txt'
else:
self.result_file_name = self.options['result_file_name']
# solver
solver = self.options['solver']
if hasattr(solvers, solver):
self.solver = getattr(solvers, solver)
else:
raise InvalidAlgorithmOptionException("The solver: " + solver +
" is unknown.")
# solver options
try:
self.solver_options = self.options[solver + '_options']
except KeyError: #Default solver options not found
self.solver_options = {} #Empty dict
try:
self.solver.atol
self.solver_options["atol"] = "Default"
except AttributeError:
pass
try:
self.solver.rtol
self.solver_options["rtol"] = "Default"
except AttributeError:
pass
#Check relative tolerance
#If the tolerances are not set specifically, they are set
#according to the 'DefaultExperiment' from the XML file.
try:
if self.solver_options["rtol"] == "Default":
rtol, atol = self.model.get_tolerances()
self.solver_options['rtol'] = rtol
except KeyError:
pass
#Check absolute tolerance
try:
if self.solver_options["atol"] == "Default":
rtol, atol = self.model.get_tolerances()
fnbr, gnbr = self.model.get_ode_sizes()
if fnbr == 0:
self.solver_options['atol'] = 0.01 * rtol
else:
self.solver_options['atol'] = atol
except KeyError:
pass
def _set_solver_options(self):
"""
Helper function that sets options for the solver.
"""
solver_options = self.solver_options.copy()
#Set solver option continuous_output
self.simulator.report_continuously = True
#loop solver_args and set properties of solver
for k, v in solver_options.iteritems():
try:
getattr(self.simulator, k)
except AttributeError:
try:
getattr(self.probl, k)
except AttributeError:
raise InvalidSolverArgumentException(k)
setattr(self.probl, k, v)
continue
setattr(self.simulator, k, v)
def solve(self):
"""
Runs the simulation.
"""
self.simulator.simulate(self.final_time, self.ncp)
def get_result(self):
"""
Write result to file, load result data and create an AssimuloSimResult
object.
Returns::
The AssimuloSimResult object.
"""
# load result file
res = self.result_handler.get_result()
# create and return result object
return FMIResult(self.model, self.result_file_name, self.simulator,
res, self.options)
@classmethod
def get_default_options(cls):
"""
Get an instance of the options class for the AssimuloFMIAlg algorithm,
prefilled with default values. (Class method.)
"""
return AssimuloFMIAlgOptions()
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()