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 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 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()
