def __init__(self,
parameters,
measurements,
input,
model,
options):
"""
Estimation algortihm for FMUs .
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('SciEstAlgOptions')
or look at the docstring with help:
* help(pyfmi.fmi_algorithm_drivers.SciEstAlgAlgOptions)
Valid values are:
- A dict that overrides some or all of the default values
provided by SciEstAlgOptions. An empty dict will thus
give all options with default values.
- SciEstAlgOptions object.
"""
self.model = model
# set start time, final time and input trajectory
self.parameters = parameters
self.measurements = measurements
self.input = input
# handle options argument
if isinstance(options, dict) and not \
isinstance(options, SciEstAlgOptions):
# user has passed dict with options or empty dict = default
self.options = SciEstAlgOptions(options)
elif isinstance(options, SciEstAlgOptions):
# user has passed FMICSAlgOptions instance
self.options = options
else:
raise InvalidAlgorithmOptionException(options)
# set options
self._set_options()
self.result_handler = ResultHandlerCSV(self.model)
self.result_handler.set_options(self.options)
self.result_handler.initialize_complete()
def test_variable_alias_custom_handler(self):
simple_alias = Dummy_FMUModelME1([40],
"NegatedAlias.fmu",
os.path.join(
file_path, "files", "FMUs",
"XML", "ME1.0"),
_connect_dll=False)
opts = simple_alias.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(simple_alias)
res = simple_alias.simulate(options=opts)
# test that res['y'] returns a vector of the same length as the time
# vector
nose.tools.assert_equal(len(res['y']), len(res['time']),
"Wrong size of result vector.")
x = res["x"]
y = res["y"]
for i in range(len(x)):
nose.tools.assert_equal(x[i], -y[i])
def test_only_parameters(self):
model = Dummy_FMUModelME2([], "ParameterAlias.fmu", os.path.join(file_path, "files", "FMUs", "XML", "ME2.0"), _connect_dll=False)
opts = model.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(model)
opts["filter"] = "p2"
res = model.simulate(options=opts)
nose.tools.assert_almost_equal(3.0, res["p2"][0])
def test_only_parameters(self):
model = load_fmu("ParameterAlias.fmu")
opts = model.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(model)
opts["filter"] = "p2"
res = model.simulate(options=opts)
nose.tools.assert_almost_equal(model.get("p2"), res["p2"][0])
def __init__(self,
parameters,
measurements,
input,
model,
options):
"""
Estimation algortihm for FMUs .
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('SciEstAlgOptions')
or look at the docstring with help:
* help(pyfmi.fmi_algorithm_drivers.SciEstAlgAlgOptions)
Valid values are:
- A dict that overrides some or all of the default values
provided by SciEstAlgOptions. An empty dict will thus
give all options with default values.
- SciEstAlgOptions object.
"""
self.model = model
# set start time, final time and input trajectory
self.parameters = parameters
self.measurements = measurements
self.input = input
# handle options argument
if isinstance(options, dict) and not \
isinstance(options, SciEstAlgOptions):
# user has passed dict with options or empty dict = default
self.options = SciEstAlgOptions(options)
elif isinstance(options, SciEstAlgOptions):
# user has passed FMICSAlgOptions instance
self.options = options
else:
raise InvalidAlgorithmOptionException(options)
# set options
self._set_options()
self.result_handler = ResultHandlerCSV(self.model)
self.result_handler.set_options(self.options)
self.result_handler.initialize_complete()
def test_enumeration_csv(self):
model = Dummy_FMUModelME2([], "Friction2.fmu", os.path.join(file_path, "files", "FMUs", "XML", "ME2.0"), _connect_dll=False)
data_type = model.get_variable_data_type("mode")
assert data_type == fmi.FMI2_ENUMERATION
opts = model.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(model)
res = model.simulate(options=opts)
res["mode"] #Check that the enumeration variable is in the dict, otherwise exception
def test_enumeration_csv(self):
model = load_fmu(self.enum_name)
data_type = model.get_variable_data_type("mode")
assert data_type == fmi.FMI2_ENUMERATION
from pyfmi.common.io import ResultHandlerCSV
opts = model.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(model)
res = model.simulate(options=opts)
res["mode"] #Check that the enumeration variable is in the dict, otherwise exception
def test_no_variables(self):
model = Dummy_FMUModelME2([],
"ParameterAlias.fmu",
os.path.join(file_path, "files", "FMUs",
"XML", "ME2.0"),
_connect_dll=False)
opts = model.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(model)
opts["filter"] = "NoMatchingVariables"
opts["result_file_name"] = "NoMatchingTest.csv"
res = model.simulate(options=opts)
nose.tools.assert_almost_equal(1.0, res["time"][-1])
def test_variable_alias(self):
simple_alias = load_fmu("NegatedAlias.fmu")
opts = simple_alias.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(simple_alias)
res = simple_alias.simulate(options=opts)
# test that res['y'] returns a vector of the same length as the time
# vector
nose.tools.assert_equal(len(res['y']), len(res['time']),
"Wrong size of result vector.")
x = res["x"]
y = res["y"]
for i in range(len(x)):
nose.tools.assert_equal(x[i], -y[i])
def test_variable_alias(self):
model_file = os.path.join(get_files_path(), 'Modelica', 'NegatedAlias.mo')
name = compile_fmu("NegatedAlias", model_file)
simple_alias = load_fmu(name)
opts = simple_alias.simulate_options()
opts["result_handling"] = "custom"
opts["result_handler"] = ResultHandlerCSV(simple_alias)
res = simple_alias.simulate(options=opts)
# test that res['y'] returns a vector of the same length as the time
# vector
nose.tools.assert_equal(len(res['y']),len(res['time']),
"Wrong size of result vector.")
x = res["x"]
y = res["y"]
for i in range(len(x)):
nose.tools.assert_equal(x[i], -y[i])
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"] == "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 == 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 SciEstAlg(AlgorithmBase):
"""
Estimation algortihm for FMUs.
"""
def __init__(self,
parameters,
measurements,
input,
model,
options):
"""
Estimation algortihm for FMUs .
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('SciEstAlgOptions')
or look at the docstring with help:
* help(pyfmi.fmi_algorithm_drivers.SciEstAlgAlgOptions)
Valid values are:
- A dict that overrides some or all of the default values
provided by SciEstAlgOptions. An empty dict will thus
give all options with default values.
- SciEstAlgOptions object.
"""
self.model = model
# set start time, final time and input trajectory
self.parameters = parameters
self.measurements = measurements
self.input = input
# handle options argument
if isinstance(options, dict) and not \
isinstance(options, SciEstAlgOptions):
# user has passed dict with options or empty dict = default
self.options = SciEstAlgOptions(options)
elif isinstance(options, SciEstAlgOptions):
# user has passed FMICSAlgOptions instance
self.options = options
else:
raise InvalidAlgorithmOptionException(options)
# set options
self._set_options()
self.result_handler = ResultHandlerCSV(self.model)
self.result_handler.set_options(self.options)
self.result_handler.initialize_complete()
def _set_options(self):
"""
Helper function that sets options for FMICS algorithm.
"""
self.options["filter"] = self.parameters
if isinstance(self.options["scaling"], str) and self.options["scaling"] == "Default":
scale = []
for i,parameter in enumerate(self.parameters):
scale.append(self.model.get_variable_nominal(parameter))
self.options["scaling"] = N.array(scale)
if self.options["simulate_options"] == "Default":
self.options["simulate_options"] = self.model.simulate_options()
#Modifiy necessary options:
self.options["simulate_options"]['ncp'] = self.measurements[1].shape[0] - 1 #Store at the same points as measurment data
self.options["simulate_options"]['filter'] = self.measurements[0] #Only store the measurement variables (efficiency)
if "solver" in self.options["simulate_options"]:
solver = self.options["simulate_options"]["solver"]
self.options["simulate_options"][solver+"_options"]["verbosity"] = 50 #Disable printout (efficiency)
self.options["simulate_options"][solver+"_options"]["store_event_points"] = False #Disable extra store points
def _set_solver_options(self):
"""
Helper function that sets options for the solver.
"""
pass
def solve(self):
"""
Runs the estimation.
"""
import scipy as sci
import scipy.optimize as sciopt
from pyfmi.fmi_util import parameter_estimation_f
#Define callback
global niter
niter = 0
def parameter_estimation_callback(y):
global niter
if niter % 10 == 0:
print(" iter parameters ")
#print '{:>5d} {:>15e}'.format(niter+1, parameter_estimation_f(y, self.parameters, self.measurements, self.model, self.input, self.options))
print('{:>5d} '.format(niter+1) + str(y))
niter += 1
#End of simulation, stop the clock
time_start = timer()
p0 = []
for i,parameter in enumerate(self.parameters):
p0.append(self.model.get(parameter)/self.options["scaling"][i])
print('\nRunning solver: ' + self.options["method"])
print(' Initial parameters (scaled): ' + str(N.array(p0).flatten()))
print(' ')
res = sciopt.minimize(parameter_estimation_f, p0,
args=(self.parameters, self.measurements, self.model, self.input, self.options),
method=self.options["method"],
bounds=None,
constraints=(),
tol=self.options["tolerance"],
callback=parameter_estimation_callback)
for i in range(len(self.parameters)):
res["x"][i] = res["x"][i]*self.options["scaling"][i]
self.res = res
self.status = res["success"]
#End of simulation, stop the clock
time_stop = timer()
if not res["success"]:
print('Estimation failed: ' + res["message"])
else:
print('\nEstimation terminated successfully!')
print(' Found parameters: ' + str(res["x"]))
print('Elapsed estimation time: ' + str(time_stop-time_start) + ' seconds.\n')
def get_result(self):
"""
Write result to file, load result data and create an SciEstResult
object.
Returns::
The SciEstResult object.
"""
for i,parameter in enumerate(self.parameters):
self.model.set(parameter, self.res["x"][i])
self.result_handler.simulation_start()
self.model.time = self.measurements[1][0,0]
self.result_handler.integration_point()
self.result_handler.simulation_end()
self.model.reset()
for i,parameter in enumerate(self.parameters):
self.model.set(parameter, self.res["x"][i])
return FMIResult(self.model, self.options["result_file_name"], None,
self.result_handler.get_result(), self.options, status=self.status)
@classmethod
def get_default_options(cls):
"""
Get an instance of the options class for the SciEstAlg algorithm,
prefilled with default values. (Class method.)
"""
return SciEstAlgOptions()
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"] == "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 == 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):
"""
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"] == "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 == 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 SciEstAlg(AlgorithmBase):
"""
Estimation algortihm for FMUs.
"""
def __init__(self,
parameters,
measurements,
input,
model,
options):
"""
Estimation algortihm for FMUs .
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('SciEstAlgOptions')
or look at the docstring with help:
* help(pyfmi.fmi_algorithm_drivers.SciEstAlgAlgOptions)
Valid values are:
- A dict that overrides some or all of the default values
provided by SciEstAlgOptions. An empty dict will thus
give all options with default values.
- SciEstAlgOptions object.
"""
self.model = model
# set start time, final time and input trajectory
self.parameters = parameters
self.measurements = measurements
self.input = input
# handle options argument
if isinstance(options, dict) and not \
isinstance(options, SciEstAlgOptions):
# user has passed dict with options or empty dict = default
self.options = SciEstAlgOptions(options)
elif isinstance(options, SciEstAlgOptions):
# user has passed FMICSAlgOptions instance
self.options = options
else:
raise InvalidAlgorithmOptionException(options)
# set options
self._set_options()
self.result_handler = ResultHandlerCSV(self.model)
self.result_handler.set_options(self.options)
self.result_handler.initialize_complete()
def _set_options(self):
"""
Helper function that sets options for FMICS algorithm.
"""
self.options["filter"] = self.parameters
if isinstance(self.options["scaling"], str) and self.options["scaling"] == "Default":
scale = []
for i,parameter in enumerate(self.parameters):
scale.append(self.model.get_variable_nominal(parameter))
self.options["scaling"] = N.array(scale)
if self.options["simulate_options"] == "Default":
self.options["simulate_options"] = self.model.simulate_options()
#Modifiy necessary options:
self.options["simulate_options"]['ncp'] = self.measurements[1].shape[0] - 1 #Store at the same points as measurment data
self.options["simulate_options"]['filter'] = self.measurements[0] #Only store the measurement variables (efficiency)
if "solver" in self.options["simulate_options"]:
solver = self.options["simulate_options"]["solver"]
self.options["simulate_options"][solver+"_options"]["verbosity"] = 50 #Disable printout (efficiency)
self.options["simulate_options"][solver+"_options"]["store_event_points"] = False #Disable extra store points
def _set_solver_options(self):
"""
Helper function that sets options for the solver.
"""
pass
def solve(self):
"""
Runs the estimation.
"""
import scipy as sci
import scipy.optimize as sciopt
from pyfmi.fmi_util import parameter_estimation_f
#Define callback
global niter
niter = 0
def parameter_estimation_callback(y):
global niter
if niter % 10 == 0:
print(" iter parameters ")
#print '{:>5d} {:>15e}'.format(niter+1, parameter_estimation_f(y, self.parameters, self.measurements, self.model, self.input, self.options))
print('{:>5d} '.format(niter+1) + str(y))
niter += 1
#End of simulation, stop the clock
time_start = timer()
p0 = []
for i,parameter in enumerate(self.parameters):
p0.append(self.model.get(parameter)/self.options["scaling"][i])
print('\nRunning solver: ' + self.options["method"])
print(' Initial parameters (scaled): ' + str(N.array(p0).flatten()))
print(' ')
res = sciopt.minimize(parameter_estimation_f, p0,
args=(self.parameters, self.measurements, self.model, self.input, self.options),
method=self.options["method"],
bounds=None,
constraints=(),
tol=self.options["tolerance"],
callback=parameter_estimation_callback)
for i in range(len(self.parameters)):
res["x"][i] = res["x"][i]*self.options["scaling"][i]
self.res = res
self.status = res["success"]
#End of simulation, stop the clock
time_stop = timer()
if not res["success"]:
print('Estimation failed: ' + res["message"])
else:
print('\nEstimation terminated successfully!')
print(' Found parameters: ' + str(res["x"]))
print('Elapsed estimation time: ' + str(time_stop-time_start) + ' seconds.\n')
def get_result(self):
"""
Write result to file, load result data and create an SciEstResult
object.
Returns::
The SciEstResult object.
"""
for i,parameter in enumerate(self.parameters):
self.model.set(parameter, self.res["x"][i])
self.result_handler.simulation_start()
self.model.time = self.measurements[1][0,0]
self.result_handler.integration_point()
self.result_handler.simulation_end()
self.model.reset()
for i,parameter in enumerate(self.parameters):
self.model.set(parameter, self.res["x"][i])
return FMIResult(self.model, self.options["result_file_name"], None,
self.result_handler.get_result(), self.options, status=self.status)
@classmethod
def get_default_options(cls):
"""
Get an instance of the options class for the SciEstAlg algorithm,
prefilled with default values. (Class method.)
"""
return SciEstAlgOptions()
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()
def test_work_flow_me2(self):
model = Dummy_FMUModelME2([], "bouncingBall.fmu", os.path.join(file_path, "files", "FMUs", "XML", "ME2.0"), _connect_dll=False)
model.setup_experiment()
model.initialize()
bouncingBall = ResultHandlerCSV(model)
bouncingBall.set_options(model.simulate_options())
bouncingBall.simulation_start()
bouncingBall.initialize_complete()
bouncingBall.integration_point()
bouncingBall.simulation_end()
res = ResultCSVTextual('bouncingBall_result.csv')
h = res.get_variable_data('h')
derh = res.get_variable_data('der(h)')
g = res.get_variable_data('g')
nose.tools.assert_almost_equal(h.x, 1.000000, 5)
nose.tools.assert_almost_equal(derh.x, 0.000000, 5)
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"] == "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 == 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"] == "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 == 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":
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 FMUException(
"The sensitivity parameter is not specified as an input which is required."
)
else:
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,
extra_equations=self.options["extra_equations"])
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,
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()