Ejemplo n.º 1
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()
Ejemplo n.º 2
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        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])
Ejemplo n.º 3
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 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])
Ejemplo n.º 4
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    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])
Ejemplo n.º 5
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    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()
Ejemplo n.º 6
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 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
Ejemplo n.º 7
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 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
Ejemplo n.º 8
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        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])
Ejemplo n.º 9
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    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])
Ejemplo n.º 10
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 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])
Ejemplo n.º 11
0
    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()
Ejemplo n.º 12
0
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()
Ejemplo n.º 13
0
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()
Ejemplo n.º 14
0
    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()
Ejemplo n.º 15
0
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()
Ejemplo n.º 16
0
    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
Ejemplo n.º 17
0
    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()
Ejemplo n.º 18
0
    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)
Ejemplo n.º 19
0
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()
Ejemplo n.º 20
0
    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()