示例#1
0
 def _create_sim(self, integrator=None, inhom_mmpr=False, delays=False,
         run_sim=True):
     mpr = MontbrioPazoRoxin()
     conn = Connectivity.from_file()
     if inhom_mmpr:
         dispersion = 1 + np.random.randn(conn.weights.shape[0])*0.1
         mpr = MontbrioPazoRoxin(eta=mpr.eta*dispersion)
     conn.speed = np.r_[3.0 if delays else np.inf]
     if integrator is None:
         dt = 0.01
         integrator = EulerDeterministic(dt=dt)
     else:
         dt = integrator.dt
     sim = Simulator(connectivity=conn, model=mpr, integrator=integrator, 
         monitors=[Raw()],
         simulation_length=0.1)  # 10 steps
     sim.configure()
     if not delays:
         self.assertTrue((conn.idelays == 0).all())
     buf = sim.history.buffer[...,0]
     # kernel has history in reverse order except 1st element 🤕
     rbuf = np.concatenate((buf[0:1], buf[1:][::-1]), axis=0)
     state = np.transpose(rbuf, (1, 0, 2)).astype('f')
     self.assertEqual(state.shape[0], 2)
     self.assertEqual(state.shape[2], conn.weights.shape[0])
     if isinstance(sim.integrator, IntegratorStochastic):
         sim.integrator.noise.reset_random_stream()
     if run_sim:
         (t,y), = sim.run()
         return sim, state, t, y
     else:
         return sim
示例#2
0
    def test_shape(self):

        # try to avoid introspector picking up this model
        Gen2D = copy.deepcopy(models.Generic2dOscillator)

        class CouplingShapeTestModel(Gen2D):
            def __init__(self, test_case=None, n_node=None, **kwds):
                super(CouplingShapeTestModel, self).__init__(**kwds)
                self.cvar = numpy.r_[0, 1]
                self.n_node = n_node
                self.test_case = test_case

            def dfun(self, state, coupling, local_coupling):
                if self.test_case is not None:
                    self.test_case.assert_equal((2, self.n_node, 1),
                                                coupling.shape)
                    return state

        conn = connectivity.Connectivity.from_file()
        surf = cortex.Cortex.from_file()
        surf.region_mapping_data.connectivity = conn
        sim = Simulator(model=CouplingShapeTestModel(self,
                                                     surf.vertices.shape[0]),
                        connectivity=conn,
                        surface=surf)

        sim.configure()

        for _ in sim(simulation_length=sim.integrator.dt * 2):
            pass
示例#3
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 def _prep_sim(self, coupling) -> Simulator:
     "Prepare simulator for testing a coupling function."
     con = Connectivity.from_file()
     con.weights[:] = 1.0
     # con = Connectivity(
     #     region_labels=np.array(['']),
     #     weights=con.weights[:5][:,:5],
     #     tract_lengths=con.tract_lengths[:5][:,:5],
     #     speed=np.array([10.0]),
     #     centres=np.array([0.0]))
     sim = Simulator(connectivity=con,
                     model=LinearModel(gamma=np.r_[0.0]),
                     coupling=coupling,
                     integrator=Identity(dt=1.0),
                     monitors=[Raw()],
                     simulation_length=0.5)
     sim.configure()
     return sim
示例#4
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class TestsExactPropagation(BaseTestCase):
    def build_simulator(self, n=4):

        self.conn = numpy.zeros((n, n))  # , numpy.int32)
        for i in range(self.conn.shape[0] - 1):
            self.conn[i, i + 1] = 1

        self.dist = numpy.r_[:n * n].reshape((n, n))
        self.dist = numpy.array(self.dist, dtype=float)

        self.sim = Simulator(
            conduction_speed=1.0,
            coupling=IdCoupling(),
            surface=None,
            stimulus=None,
            integrator=Identity(dt=1.0),
            initial_conditions=numpy.ones((n * n, 1, n, 1)),
            simulation_length=10.0,
            connectivity=Connectivity(region_labels=numpy.array(['']),
                                      weights=self.conn,
                                      tract_lengths=self.dist,
                                      speed=numpy.array([1.0]),
                                      centres=numpy.array([0.0])),
            model=Sum(),
            monitors=(Raw(), ),
        )

        self.sim.configure()

    def test_propagation(self):
        n = 4
        self.build_simulator(n=n)
        # x = numpy.zeros((n, ))
        xs = []
        for (t, raw), in self.sim(simulation_length=10):
            xs.append(raw.flat[:].copy())
        xs = numpy.array(xs)
        xs_ = numpy.array([[2., 2., 2., 1.], [3., 3., 3.,
                                              1.], [5., 4., 4., 1.],
                           [8., 5., 5., 1.], [12., 6., 6., 1.],
                           [17., 7., 7., 1.], [23., 8., 8., 1.],
                           [30., 10., 9., 1.], [38., 13., 10., 1.],
                           [48., 17., 11., 1.]])
        assert numpy.allclose(xs, xs_)
示例#5
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def main_example(tvb_sim_model,
                 connectivity_zip=CONFIGURED.DEFAULT_CONNECTIVITY_ZIP,
                 dt=0.1,
                 noise_strength=0.001,
                 simulation_length=100.0,
                 config=CONFIGURED):

    plotter = Plotter(config)

    # --------------------------------------1. Load TVB connectivity----------------------------------------------------
    connectivity = Connectivity.from_file(connectivity_zip)
    connectivity.configure()
    plotter.plot_tvb_connectivity(connectivity)

    # ----------------------2. Define a TVB simulator (model, integrator, monitors...)----------------------------------

    # Create a TVB simulator and set all desired inputs
    # (connectivity, model, surface, stimuli etc)
    # We choose all defaults in this example
    simulator = Simulator()
    simulator.integrator = HeunStochastic(dt=dt)
    simulator.integrator.noise.nsig = np.array(ensure_list(noise_strength))
    simulator.model = tvb_sim_model
    simulator.connectivity = connectivity
    mon_raw = Raw(period=simulator.integrator.dt)
    simulator.monitors = (mon_raw, )

    # -----------------------------------3. Simulate and gather results-------------------------------------------------

    # Configure the simulator with the TVB-NEST interface...
    # simulator.configure(tvb_nest_interface=tvb_nest_model)
    simulator.configure()
    # ...and simulate!
    t_start = time.time()
    results = simulator.run(simulation_length=simulation_length)
    print("\nSimulated in %f secs!" % (time.time() - t_start))

    # -------------------------------------------6. Plot results--------------------------------------------------------

    plot_results(results, simulator, None, "State Variables",
                 simulator.model.variables_of_interest, plotter)

    return connectivity, results
class SimulatorAdapter(ABCAsynchronous):
    """
    Interface between the Simulator and the Framework.
    """
    _ui_name = "Simulation Core"

    algorithm = None

    available_models = get_traited_subclasses(Model)
    available_monitors = get_traited_subclasses(Monitor)
    available_integrators = get_traited_subclasses(Integrator)
    available_couplings = get_traited_subclasses(Coupling)

    # This is a list with the monitors that actually return multi dimensions for the state variable dimension.
    # We exclude from this for example EEG, MEG or Bold which return 
    HAVE_STATE_VARIABLES = ["GlobalAverage", "SpatialAverage", "Raw", "SubSample", "TemporalAverage"]


    def __init__(self):
        super(SimulatorAdapter, self).__init__()
        self.log.debug("%s: Initialized..." % str(self))

    def get_input_tree2(self):
        sim = Simulator()
        sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
        result = sim.interface_experimental
        return result

    def get_input_tree(self):
        """
        Return a list of lists describing the interface to the simulator. This
        is used by the GUI to generate the menus and fields necessary for
        defining a simulation.
        """
        sim = Simulator()
        sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
        result = sim.interface[self.INTERFACE_ATTRIBUTES]
        # We should add as hidden the Simulator State attribute.
        result.append({self.KEY_NAME: 'simulation_state',
                       self.KEY_TYPE: 'tvb.datatypes.simulation_state.SimulationState',
                       self.KEY_LABEL: "Continuation of", self.KEY_REQUIRED: False, self.KEY_UI_HIDE: True})
        return result


    def get_output(self):
        """
        :returns: list of classes for possible results of the Simulator.
        """
        return [time_series.TimeSeries]


    def configure(self, model, model_parameters, integrator, integrator_parameters, connectivity,
                  monitors, monitors_parameters=None, surface=None, surface_parameters=None, stimulus=None,
                  coupling=None, coupling_parameters=None, initial_conditions=None,
                  conduction_speed=None, simulation_length=0, simulation_state=None):
        """
        Make preparations for the adapter launch.
        """
        self.log.debug("available_couplings: %s..." % str(self.available_couplings))
        self.log.debug("coupling: %s..." % str(coupling))
        self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))

        self.log.debug("%s: Initializing Model..." % str(self))
        noise_framework.build_noise(model_parameters)
        model_instance = self.available_models[str(model)](**model_parameters)
        self._validate_model_parameters(model_instance, connectivity, surface)

        self.log.debug("%s: Initializing Integration scheme..." % str(self))
        noise_framework.build_noise(integrator_parameters)
        integr = self.available_integrators[integrator](**integrator_parameters)

        self.log.debug("%s: Instantiating Monitors..." % str(self))
        monitors_list = []
        for monitor_name in monitors:
            if (monitors_parameters is not None) and (str(monitor_name) in monitors_parameters):
                current_monitor_parameters = monitors_parameters[str(monitor_name)]
                HRFKernelEquation.build_equation_from_dict('hrf_kernel', current_monitor_parameters, True)
                monitors_list.append(self.available_monitors[str(monitor_name)](**current_monitor_parameters))
            else:
                ### We have monitors without any UI settable parameter.
                monitors_list.append(self.available_monitors[str(monitor_name)]())

        if len(monitors) < 1:
            raise LaunchException("Can not launch operation without monitors selected !!!")

        self.log.debug("%s: Initializing Coupling..." % str(self))
        coupling_inst = self.available_couplings[str(coupling)](**coupling_parameters)

        self.log.debug("Initializing Cortex...")
        if self._is_surface_simulation(surface, surface_parameters):
            cortex_entity = Cortex(use_storage=False).populate_cortex(surface, surface_parameters)
            if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
                raise LaunchException("Incompatible RegionMapping -- Connectivity !!")
            if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException("Incompatible RegionMapping -- Surface !!")
            select_loc_conn = cortex_entity.local_connectivity
            if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException("Incompatible LocalConnectivity -- Surface !!")
        else:
            cortex_entity = None

        self.log.debug("%s: Instantiating requested simulator..." % str(self))

        if conduction_speed not in (0.0, None):
            connectivity.speed = numpy.array([conduction_speed])
        else:
            raise LaunchException("conduction speed cannot be 0 or missing")

        self.algorithm = Simulator(connectivity=connectivity, coupling=coupling_inst, surface=cortex_entity,
                                   stimulus=stimulus, model=model_instance, integrator=integr,
                                   monitors=monitors_list, initial_conditions=initial_conditions,
                                   conduction_speed=conduction_speed)
        self.simulation_length = simulation_length
        self.log.debug("%s: Initializing storage..." % str(self))
        try:
            self.algorithm.preconfigure()
        except ValueError as err:
            raise LaunchException("Failed to configure simulator due to invalid Input Values. It could be because "
                                  "of an incompatibility between different version of TVB code.", err)


    def get_required_memory_size(self, **kwargs):
        """
        Return the required memory to run this algorithm.
        """
        return self.algorithm.memory_requirement()


    def get_required_disk_size(self, **kwargs):
        """
        Return the required disk size this algorithm estimates it will take. (in kB)
        """
        return self.algorithm.storage_requirement(self.simulation_length) / 2 ** 10
    
    
    def get_execution_time_approximation(self, **kwargs):
        """
        Method should approximate based on input arguments, the time it will take for the operation 
        to finish (in seconds).
        """
        # This is just a brute approx so cluster nodes won't kill operation before
        # it's finished. This should be done with a higher grade of sensitivity
        # Magic number connecting simulation length to simulation computation time
        # This number should as big as possible, as long as it is still realistic, to
        magic_number = 6.57e-06     # seconds
        approx_number_of_nodes = 500
        approx_nvar = 15
        approx_modes = 15

        simulation_length = int(float(kwargs['simulation_length']))
        approx_integrator_dt = float(kwargs['integrator_parameters']['dt'])

        if approx_integrator_dt == 0.0:
            approx_integrator_dt = 1.0

        if 'surface' in kwargs and kwargs['surface'] is not None and kwargs['surface'] != '':
            approx_number_of_nodes *= approx_number_of_nodes

        estimation = magic_number * approx_number_of_nodes * approx_nvar * approx_modes * simulation_length \
            / approx_integrator_dt

        return max(int(estimation), 1)


    def _try_find_mapping(self, mapping_class, connectivity_gid):
        """
        Try to find a DataType instance of class "mapping_class", linked to the given Connectivity.
        Entities in the current project will have priority.

        :param mapping_class: DT class, with field "_connectivity" on it
        :param connectivity_gid: GUID
        :return: None or instance of "mapping_class"
        """

        dts_list = dao.get_generic_entity(mapping_class, connectivity_gid, '_connectivity')
        if len(dts_list) < 1:
            return None

        for dt in dts_list:
            dt_operation = dao.get_operation_by_id(dt.fk_from_operation)
            if dt_operation.fk_launched_in == self.current_project_id:
                return dt
        return dts_list[0]



    def launch(self, model, model_parameters, integrator, integrator_parameters, connectivity,
               monitors, monitors_parameters=None, surface=None, surface_parameters=None, stimulus=None,
               coupling=None, coupling_parameters=None, initial_conditions=None,
               conduction_speed=None, simulation_length=0, simulation_state=None):
        """
        Called from the GUI to launch a simulation.
          *: string class name of chosen model, etc...
          *_parameters: dictionary of parameters for chosen model, etc...
          connectivity: tvb.datatypes.connectivity.Connectivity object.
          surface: tvb.datatypes.surfaces.CorticalSurface: or None.
          stimulus: tvb.datatypes.patters.* object
        """
        result_datatypes = dict()
        start_time = self.algorithm.current_step * self.algorithm.integrator.dt

        self.algorithm.configure(full_configure=False)
        if simulation_state is not None:
            simulation_state.fill_into(self.algorithm)

        region_map = self._try_find_mapping(region_mapping.RegionMapping, connectivity.gid)
        region_volume_map = self._try_find_mapping(region_mapping.RegionVolumeMapping, connectivity.gid)

        for monitor in self.algorithm.monitors:
            m_name = monitor.__class__.__name__
            ts = monitor.create_time_series(self.storage_path, connectivity, surface, region_map, region_volume_map)
            self.log.debug("Monitor %s created the TS %s" % (m_name, ts))
            # Now check if the monitor will return results for each state variable, in which case store
            # the labels for these state variables.
            # todo move these into monitors as well
            #   and replace check if ts.user_tag_1 with something better (e.g. pre_ex & post)
            state_variable_dimension_name = ts.labels_ordering[1]
            if ts.user_tag_1:
                ts.labels_dimensions[state_variable_dimension_name] = ts.user_tag_1.split(';')

            elif m_name in self.HAVE_STATE_VARIABLES:
                selected_vois = [self.algorithm.model.variables_of_interest[idx] for idx in monitor.voi]
                ts.labels_dimensions[state_variable_dimension_name] = selected_vois

            ts.start_time = start_time
            result_datatypes[m_name] = ts

        #### Create Simulator State entity and persist it in DB. H5 file will be empty now.
        if not self._is_group_launch():
            simulation_state = SimulationState(storage_path=self.storage_path)
            self._capture_operation_results([simulation_state])

        ### Run simulation
        self.log.debug("%s: Starting simulation..." % str(self))
        for result in self.algorithm(simulation_length=simulation_length):
            for j, monitor in enumerate(monitors):
                if result[j] is not None:
                    result_datatypes[monitor].write_time_slice([result[j][0]])
                    result_datatypes[monitor].write_data_slice([result[j][1]])

        self.log.debug("%s: Completed simulation, starting to store simulation state " % str(self))
        ### Populate H5 file for simulator state. This step could also be done while running sim, in background.
        if not self._is_group_launch():
            simulation_state.populate_from(self.algorithm)
            self._capture_operation_results([simulation_state])

        self.log.debug("%s: Simulation state persisted, returning results " % str(self))
        final_results = []
        for result in result_datatypes.values():
            result.close_file()
            final_results.append(result)
        self.log.info("%s: Adapter simulation finished!!" % str(self))
        return final_results


    def _validate_model_parameters(self, model_instance, connectivity, surface):
        """
        Checks if the size of the model parameters is set correctly.
        """
        ui_configurable_params = model_instance.ui_configurable_parameters
        for param in ui_configurable_params:
            param_value = eval('model_instance.' + param)
            if isinstance(param_value, numpy.ndarray):
                if len(param_value) == 1 or connectivity is None:
                    continue
                if surface is not None:
                    if (len(param_value) != surface.number_of_vertices
                            and len(param_value) != connectivity.number_of_regions):
                        msg = str(surface.number_of_vertices) + ' or ' + str(connectivity.number_of_regions)
                        msg = self._get_exception_message(param, msg, len(param_value))
                        self.log.error(msg)
                        raise LaunchException(msg)
                elif len(param_value) != connectivity.number_of_regions:
                    msg = self._get_exception_message(param, connectivity.number_of_regions, len(param_value))
                    self.log.error(msg)
                    raise LaunchException(msg)


    @staticmethod
    def _get_exception_message(param_name, expected_size, actual_size):
        """
        Creates the message that will be displayed to the user when the size of a model parameter is incorrect.
        """
        msg = "The length of the parameter '" + param_name + "' is not correct."
        msg += " It is expected to be an array of length " + str(expected_size) + "."
        msg += " It is an array of length " + str(actual_size) + "."
        return msg

    @staticmethod
    def _is_surface_simulation(surface, surface_parameters):
        """
        Is this a surface simulation?
        """
        return surface is not None and surface_parameters is not None
示例#7
0
class SimulatorAdapter(ABCAsynchronous):
    """
    Interface between the Simulator and the Framework.
    """
    _ui_name = "Simulation Core"

    algorithm = None

    available_models = get_traited_subclasses(Model)
    available_monitors = get_traited_subclasses(Monitor)
    available_integrators = get_traited_subclasses(Integrator)
    available_couplings = get_traited_subclasses(Coupling)

    # This is a list with the monitors that actually return multi dimensions for the state variable dimension.
    # We exclude from this for example EEG, MEG or Bold which return
    HAVE_STATE_VARIABLES = [
        "GlobalAverage", "SpatialAverage", "Raw", "SubSample",
        "TemporalAverage"
    ]

    def __init__(self):
        super(SimulatorAdapter, self).__init__()
        self.log.debug("%s: Initialized..." % str(self))

    def get_input_tree2(self):
        sim = Simulator()
        sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
        result = sim.interface_experimental
        return result

    def get_input_tree(self):
        """
        Return a list of lists describing the interface to the simulator. This
        is used by the GUI to generate the menus and fields necessary for
        defining a simulation.
        """
        sim = Simulator()
        sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
        result = sim.interface[self.INTERFACE_ATTRIBUTES]
        # We should add as hidden the Simulator State attribute.
        result.append({
            self.KEY_NAME: 'simulation_state',
            self.KEY_TYPE: 'tvb.datatypes.simulation_state.SimulationState',
            self.KEY_LABEL: "Continuation of",
            self.KEY_REQUIRED: False,
            self.KEY_UI_HIDE: True
        })
        return result

    def get_output(self):
        """
        :returns: list of classes for possible results of the Simulator.
        """
        return [time_series.TimeSeries]

    def configure(self,
                  model,
                  model_parameters,
                  integrator,
                  integrator_parameters,
                  connectivity,
                  monitors,
                  monitors_parameters=None,
                  surface=None,
                  surface_parameters=None,
                  stimulus=None,
                  coupling=None,
                  coupling_parameters=None,
                  initial_conditions=None,
                  conduction_speed=None,
                  simulation_length=0,
                  simulation_state=None):
        """
        Make preparations for the adapter launch.
        """
        self.log.debug("available_couplings: %s..." %
                       str(self.available_couplings))
        self.log.debug("coupling: %s..." % str(coupling))
        self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))

        self.log.debug("%s: Initializing Model..." % str(self))
        noise_framework.build_noise(model_parameters)
        model_instance = self.available_models[str(model)](**model_parameters)
        self._validate_model_parameters(model_instance, connectivity, surface)

        self.log.debug("%s: Initializing Integration scheme..." % str(self))
        noise_framework.build_noise(integrator_parameters)
        integr = self.available_integrators[integrator](
            **integrator_parameters)

        self.log.debug("%s: Instantiating Monitors..." % str(self))
        monitors_list = []
        for monitor_name in monitors:
            if (monitors_parameters is not None) and (str(monitor_name)
                                                      in monitors_parameters):
                current_monitor_parameters = monitors_parameters[str(
                    monitor_name)]
                HRFKernelEquation.build_equation_from_dict(
                    'hrf_kernel', current_monitor_parameters, True)
                monitors_list.append(
                    self.available_monitors[str(monitor_name)](
                        **current_monitor_parameters))
            else:
                ### We have monitors without any UI settable parameter.
                monitors_list.append(
                    self.available_monitors[str(monitor_name)]())

        if len(monitors) < 1:
            raise LaunchException(
                "Can not launch operation without monitors selected !!!")

        self.log.debug("%s: Initializing Coupling..." % str(self))
        coupling_inst = self.available_couplings[str(coupling)](
            **coupling_parameters)

        self.log.debug("Initializing Cortex...")
        if self._is_surface_simulation(surface, surface_parameters):
            cortex_entity = Cortex(use_storage=False).populate_cortex(
                surface, surface_parameters)
            if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
                raise LaunchException(
                    "Incompatible RegionMapping -- Connectivity !!")
            if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException(
                    "Incompatible RegionMapping -- Surface !!")
            select_loc_conn = cortex_entity.local_connectivity
            if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException(
                    "Incompatible LocalConnectivity -- Surface !!")
        else:
            cortex_entity = None

        self.log.debug("%s: Instantiating requested simulator..." % str(self))

        if conduction_speed not in (0.0, None):
            connectivity.speed = numpy.array([conduction_speed])
        else:
            raise LaunchException("conduction speed cannot be 0 or missing")

        self.algorithm = Simulator(connectivity=connectivity,
                                   coupling=coupling_inst,
                                   surface=cortex_entity,
                                   stimulus=stimulus,
                                   model=model_instance,
                                   integrator=integr,
                                   monitors=monitors_list,
                                   initial_conditions=initial_conditions,
                                   conduction_speed=conduction_speed)
        self.simulation_length = simulation_length
        self.log.debug("%s: Initializing storage..." % str(self))
        try:
            self.algorithm.preconfigure()
        except ValueError as err:
            raise LaunchException(
                "Failed to configure simulator due to invalid Input Values. It could be because "
                "of an incompatibility between different version of TVB code.",
                err)

    def get_required_memory_size(self, **kwargs):
        """
        Return the required memory to run this algorithm.
        """
        return self.algorithm.memory_requirement()

    def get_required_disk_size(self, **kwargs):
        """
        Return the required disk size this algorithm estimates it will take. (in kB)
        """
        return self.algorithm.storage_requirement(
            self.simulation_length) / 2**10

    def get_execution_time_approximation(self, **kwargs):
        """
        Method should approximate based on input arguments, the time it will take for the operation 
        to finish (in seconds).
        """
        # This is just a brute approx so cluster nodes won't kill operation before
        # it's finished. This should be done with a higher grade of sensitivity
        # Magic number connecting simulation length to simulation computation time
        # This number should as big as possible, as long as it is still realistic, to
        magic_number = 6.57e-06  # seconds
        approx_number_of_nodes = 500
        approx_nvar = 15
        approx_modes = 15

        simulation_length = int(float(kwargs['simulation_length']))
        approx_integrator_dt = float(kwargs['integrator_parameters']['dt'])

        if approx_integrator_dt == 0.0:
            approx_integrator_dt = 1.0

        if 'surface' in kwargs and kwargs[
                'surface'] is not None and kwargs['surface'] != '':
            approx_number_of_nodes *= approx_number_of_nodes

        estimation = magic_number * approx_number_of_nodes * approx_nvar * approx_modes * simulation_length \
            / approx_integrator_dt

        return max(int(estimation), 1)

    def _try_find_mapping(self, mapping_class, connectivity_gid):
        """
        Try to find a DataType instance of class "mapping_class", linked to the given Connectivity.
        Entities in the current project will have priority.

        :param mapping_class: DT class, with field "_connectivity" on it
        :param connectivity_gid: GUID
        :return: None or instance of "mapping_class"
        """

        dts_list = dao.get_generic_entity(mapping_class, connectivity_gid,
                                          '_connectivity')
        if len(dts_list) < 1:
            return None

        for dt in dts_list:
            dt_operation = dao.get_operation_by_id(dt.fk_from_operation)
            if dt_operation.fk_launched_in == self.current_project_id:
                return dt
        return dts_list[0]

    def launch(self,
               model,
               model_parameters,
               integrator,
               integrator_parameters,
               connectivity,
               monitors,
               monitors_parameters=None,
               surface=None,
               surface_parameters=None,
               stimulus=None,
               coupling=None,
               coupling_parameters=None,
               initial_conditions=None,
               conduction_speed=None,
               simulation_length=0,
               simulation_state=None):
        """
        Called from the GUI to launch a simulation.
          *: string class name of chosen model, etc...
          *_parameters: dictionary of parameters for chosen model, etc...
          connectivity: tvb.datatypes.connectivity.Connectivity object.
          surface: tvb.datatypes.surfaces.CorticalSurface: or None.
          stimulus: tvb.datatypes.patters.* object
        """
        result_datatypes = dict()
        start_time = self.algorithm.current_step * self.algorithm.integrator.dt

        self.algorithm.configure(full_configure=False)
        if simulation_state is not None:
            simulation_state.fill_into(self.algorithm)

        region_map = self._try_find_mapping(region_mapping.RegionMapping,
                                            connectivity.gid)
        region_volume_map = self._try_find_mapping(
            region_mapping.RegionVolumeMapping, connectivity.gid)

        for monitor in self.algorithm.monitors:
            m_name = monitor.__class__.__name__
            ts = monitor.create_time_series(self.storage_path, connectivity,
                                            surface, region_map,
                                            region_volume_map)
            self.log.debug("Monitor %s created the TS %s" % (m_name, ts))
            # Now check if the monitor will return results for each state variable, in which case store
            # the labels for these state variables.
            # todo move these into monitors as well
            #   and replace check if ts.user_tag_1 with something better (e.g. pre_ex & post)
            state_variable_dimension_name = ts.labels_ordering[1]
            if ts.user_tag_1:
                ts.labels_dimensions[
                    state_variable_dimension_name] = ts.user_tag_1.split(';')

            elif m_name in self.HAVE_STATE_VARIABLES:
                selected_vois = [
                    self.algorithm.model.variables_of_interest[idx]
                    for idx in monitor.voi
                ]
                ts.labels_dimensions[
                    state_variable_dimension_name] = selected_vois

            ts.start_time = start_time
            result_datatypes[m_name] = ts

        #### Create Simulator State entity and persist it in DB. H5 file will be empty now.
        if not self._is_group_launch():
            simulation_state = SimulationState(storage_path=self.storage_path)
            self._capture_operation_results([simulation_state])

        ### Run simulation
        self.log.debug("%s: Starting simulation..." % str(self))
        for result in self.algorithm(simulation_length=simulation_length):
            for j, monitor in enumerate(monitors):
                if result[j] is not None:
                    result_datatypes[monitor].write_time_slice([result[j][0]])
                    result_datatypes[monitor].write_data_slice([result[j][1]])

        self.log.debug(
            "%s: Completed simulation, starting to store simulation state " %
            str(self))
        ### Populate H5 file for simulator state. This step could also be done while running sim, in background.
        if not self._is_group_launch():
            simulation_state.populate_from(self.algorithm)
            self._capture_operation_results([simulation_state])

        self.log.debug("%s: Simulation state persisted, returning results " %
                       str(self))
        final_results = []
        for result in result_datatypes.values():
            result.close_file()
            final_results.append(result)
        self.log.info("%s: Adapter simulation finished!!" % str(self))
        return final_results

    def _validate_model_parameters(self, model_instance, connectivity,
                                   surface):
        """
        Checks if the size of the model parameters is set correctly.
        """
        ui_configurable_params = model_instance.ui_configurable_parameters
        for param in ui_configurable_params:
            param_value = eval('model_instance.' + param)
            if isinstance(param_value, numpy.ndarray):
                if len(param_value) == 1 or connectivity is None:
                    continue
                if surface is not None:
                    if (len(param_value) != surface.number_of_vertices
                            and len(param_value) !=
                            connectivity.number_of_regions):
                        msg = str(surface.number_of_vertices) + ' or ' + str(
                            connectivity.number_of_regions)
                        msg = self._get_exception_message(
                            param, msg, len(param_value))
                        self.log.error(msg)
                        raise LaunchException(msg)
                elif len(param_value) != connectivity.number_of_regions:
                    msg = self._get_exception_message(
                        param, connectivity.number_of_regions,
                        len(param_value))
                    self.log.error(msg)
                    raise LaunchException(msg)

    @staticmethod
    def _get_exception_message(param_name, expected_size, actual_size):
        """
        Creates the message that will be displayed to the user when the size of a model parameter is incorrect.
        """
        msg = "The length of the parameter '" + param_name + "' is not correct."
        msg += " It is expected to be an array of length " + str(
            expected_size) + "."
        msg += " It is an array of length " + str(actual_size) + "."
        return msg

    @staticmethod
    def _is_surface_simulation(surface, surface_parameters):
        """
        Is this a surface simulation?
        """
        return surface is not None and surface_parameters is not None
class SimulatorAdapter(ABCAsynchronous):
    """
    Interface between the Simulator and the Framework.
    """
    _ui_name = "Simulation Core"

    algorithm = None

    available_models = get_traited_subclasses(Model)
    available_monitors = get_traited_subclasses(Monitor)
    available_integrators = get_traited_subclasses(Integrator)
    available_couplings = get_traited_subclasses(Coupling)


### Info: This are the possible results returned with this adapter from different Monitors.
###       When a list appears(surface & region), we actually return only one based on param surface being None or not.

#    MONITOR_RESULTS = {"Raw": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
#                       "SubSample": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
#                       "SpatialAverage": time_series.TimeSeries,
#                       "GlobalAverage": time_series.TimeSeries,
#                       "TemporalAverage": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
#                       "EEG": time_series.TimeSeriesEEG,
#                       "SphericalEEG": time_series.TimeSeriesEEG,
#                       "SphericalMEG": time_series.TimeSeriesMEG,
#                       "Bold": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface]}

    RESULTS_MAP = {time_series.TimeSeriesEEG: ["SphericalEEG", "EEG"],
                   time_series.TimeSeriesMEG: ["SphericalMEG"],  # Add here also "MEG" monitor reference
                   time_series.TimeSeries: ["GlobalAverage", "SpatialAverage"],
                   time_series.TimeSeriesSEEG: ["SEEG"]}

                   # time_series.TimeSeriesVolume: ["Bold"],
                   #SK:   For a number of reasons, it's probably best to avoid returning TimeSeriesVolume ,
                   #      from a simulation directly, instead just stick with the source, i.e. Region and Surface,
                   #      then later we can add a voxelisation "analyser" to produce TimeSeriesVolume on which Volume
                   #      based analysers and visualisers (which don't exist yet) can operate.

    # This is a list with the monitors that actually return multi dimensions for the state variable dimension.
    # We exclude from this for example EEG, MEG or Bold which return 
    HAVE_STATE_VARIABLES = ["GlobalAverage", "SpatialAverage", "Raw", "SubSample", "TemporalAverage"]


    def __init__(self):
        super(SimulatorAdapter, self).__init__()
        self.log.debug("%s: Initialized..." % str(self))


    def get_input_tree(self):
        """
        Return a list of lists describing the interface to the simulator. This
        is used by the GUI to generate the menus and fields necessary for
        defining a simulation.
        """
        sim = Simulator()
        sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
        result = sim.interface[self.INTERFACE_ATTRIBUTES]
        # We should add as hidden the Simulator State attribute.
        result.append({self.KEY_NAME: 'simulation_state', self.KEY_TYPE: SimulationState,
                       self.KEY_LABEL: "Continuation of", self.KEY_REQUIRED: False, self.KEY_UI_HIDE: True})
        return result


    def get_output(self):
        """
        :returns: list of classes for possible results of the Simulator.
        """
        return [time_series.TimeSeries]


    def configure(self, model, model_parameters, integrator, integrator_parameters, connectivity,
                  monitors, monitors_parameters=None, surface=None, surface_parameters=None, stimulus=None,
                  coupling=None, coupling_parameters=None, initial_conditions=None,
                  conduction_speed=None, simulation_length=0, simulation_state=None):
        """
        Make preparations for the adapter launch.
        """
        self.log.debug("available_couplings: %s..." % str(self.available_couplings))
        self.log.debug("coupling: %s..." % str(coupling))
        self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))

        self.log.debug("%s: Initializing Model..." % str(self))
        noise_framework.build_noise(model_parameters)
        model_instance = self.available_models[str(model)](**model_parameters)
        self._validate_model_parameters(model_instance, connectivity, surface)

        self.log.debug("%s: Initializing Integration scheme..." % str(self))
        noise_framework.build_noise(integrator_parameters)
        integr = self.available_integrators[integrator](**integrator_parameters)

        self.log.debug("%s: Instantiating Monitors..." % str(self))
        monitors_list = []
        for monitor_name in monitors:
            if (monitors_parameters is not None) and (str(monitor_name) in monitors_parameters):
                current_monitor_parameters = monitors_parameters[str(monitor_name)]
                HRFKernelEquation.build_equation_from_dict('hrf_kernel', current_monitor_parameters, True)
                monitors_list.append(self.available_monitors[str(monitor_name)](**current_monitor_parameters))
            else:
                ### We have monitors without any UI settable parameter.
                monitors_list.append(self.available_monitors[str(monitor_name)]())

        if len(monitors) < 1:
            raise LaunchException("Can not launch operation without monitors selected !!!")

        self.log.debug("%s: Initializing Coupling..." % str(self))
        coupling_inst = self.available_couplings[str(coupling)](**coupling_parameters)

        self.log.debug("Initializing Cortex...")
        if self._is_surface_simulation(surface, surface_parameters):
            cortex_entity = Cortex(use_storage=False).populate_cortex(surface, surface_parameters)
            if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
                raise LaunchException("Incompatible RegionMapping -- Connectivity !!")
            if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException("Incompatible RegionMapping -- Surface !!")
            select_loc_conn = cortex_entity.local_connectivity
            if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException("Incompatible LocalConnectivity -- Surface !!")
        else:
            cortex_entity = None

        self.log.debug("%s: Instantiating requested simulator..." % str(self))
        connectivity.configure()
        self.algorithm = Simulator(connectivity=connectivity, coupling=coupling_inst, surface=cortex_entity,
                                   stimulus=stimulus, model=model_instance, integrator=integr,
                                   monitors=monitors_list, initial_conditions=initial_conditions,
                                   conduction_speed=conduction_speed)
        self.simulation_length = simulation_length
        self.log.debug("%s: Initializing storage..." % str(self))
        try:
            self.algorithm.configure()
            if simulation_state is not None:
                simulation_state.fill_into(self.algorithm)
        except ValueError, err:
            raise LaunchException("Failed to configure simulator due to invalid Input Values. It could be because "
                                  "of an incompatibility between different version of TVB code.", err)
示例#9
0
class SimulatorAdapter(ABCAsynchronous):
    """
    Interface between the Simulator and the Framework.
    """
    _ui_name = "Simulation Core"

    algorithm = None

    available_models = get_traited_subclasses(Model)
    available_monitors = get_traited_subclasses(Monitor)
    available_integrators = get_traited_subclasses(Integrator)
    available_couplings = get_traited_subclasses(Coupling)
    available_noise = get_traited_subclasses(Noise)


### Info: This are the possible results returned with this adapter from different Monitors.
###       When a list appears(surface & region), we actually return only one based on param surface being None or not.

#    MONITOR_RESULTS = {"Raw": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
#                       "SubSample": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
#                       "SpatialAverage": time_series.TimeSeries,
#                       "GlobalAverage": time_series.TimeSeries,
#                       "TemporalAverage": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface],
#                       "EEG": time_series.TimeSeriesEEG,
#                       "SphericalEEG": time_series.TimeSeriesEEG,
#                       "SphericalMEG": time_series.TimeSeriesMEG,
#                       "Bold": [time_series.TimeSeriesRegion, time_series.TimeSeriesSurface]}

    RESULTS_MAP = {time_series.TimeSeriesEEG: ["SphericalEEG", "EEG"],
                   time_series.TimeSeriesMEG: ["SphericalMEG"],  # Add here also "MEG" monitor reference
                   time_series.TimeSeries: ["GlobalAverage", "SpatialAverage"]}

                   # time_series.TimeSeriesVolume: ["Bold"],
                   #SK:   For a number of reasons, it's probably best to avoid returning TimeSeriesVolume ,
                   #      from a simulation directly, instead just stick with the source, i.e. Region and Surface,
                   #      then later we can add a voxelisation "analyser" to produce TimeSeriesVolume on which Volume
                   #      based analysers and visualisers (which don't exist yet) can operate.

    # This is a list with the monitors that actually return multi dimensions for the state variable dimension.
    # We exclude from this for example EEG, MEG or Bold which return 
    HAVE_STATE_VARIABLES = ["GlobalAverage", "SpatialAverage", "Raw", "SubSample", "TemporalAverage"]


    def __init__(self):
        super(SimulatorAdapter, self).__init__()
        self.log.debug("%s: Initialized..." % str(self))


    def get_input_tree(self):
        """
        Return a list of lists describing the interface to the simulator. This
        is used by the GUI to generate the menus and fields necessary for
        defining a simulation.
        """
        sim = Simulator()
        sim.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY
        result = sim.interface[self.INTERFACE_ATTRIBUTES]
        # We should add as hidden the Simulator State attribute.
        result.append({'name': 'simulation_state', 'type': SimulationState, 'required': False, 'ui_hidden': True})
        return result


    def get_output(self):
        """
        :returns: list of classes for possible results of the Simulator.
        """
        return [time_series.TimeSeries]


    def configure(self, model, model_parameters, integrator, integrator_parameters, connectivity,
                  monitors, monitors_parameters=None, surface=None, surface_parameters=None, stimulus=None,
                  coupling=None, coupling_parameters=None, initial_conditions=None,
                  conduction_speed=None, simulation_length=0, simulation_state=None):
        """
        Make preparations for the adapter launch.
        """
        self.log.debug("available_couplings: %s..." % str(self.available_couplings))
        self.log.debug("coupling: %s..." % str(coupling))
        self.log.debug("coupling_parameters: %s..." % str(coupling_parameters))

        self.log.debug("%s: Initializing Model..." % str(self))
        noise_framework.build_noise(model_parameters)
        model_instance = self.available_models[str(model)](**model_parameters)
        self._validate_model_parameters(model_instance, connectivity, surface)

        self.log.debug("%s: Initializing Integration scheme..." % str(self))
        noise_framework.build_noise(integrator_parameters)
        integr = self.available_integrators[integrator](**integrator_parameters)

        self.log.debug("%s: Instantiating Monitors..." % str(self))
        monitors_list = []
        for monitor_name in monitors:
            if (monitors_parameters is not None) and (str(monitor_name) in monitors_parameters):
                monitors_list.append(self.available_monitors[str(monitor_name)
                                                             ](**monitors_parameters[str(monitor_name)]))
            else:
                ### We have monitors without any UI settable parameter.
                monitors_list.append(self.available_monitors[str(monitor_name)]())

        if len(monitors) < 1:
            raise LaunchException("Can not launch operation without monitors selected !!!")

        self.log.debug("%s: Initializing Coupling..." % str(self))
        coupling_inst = self.available_couplings[str(coupling)](**coupling_parameters)

        self.log.debug("Initializing Cortex...")
        if surface is not None and surface_parameters is not None:
            cortex_entity = Cortex(use_storage=False).populate_cortex(surface, surface_parameters)
            if cortex_entity.region_mapping_data.connectivity.number_of_regions != connectivity.number_of_regions:
                raise LaunchException("Incompatible RegionMapping -- Connectivity !!")
            if cortex_entity.region_mapping_data.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException("Incompatible RegionMapping -- Surface !!")
            select_loc_conn = cortex_entity.local_connectivity
            if select_loc_conn is not None and select_loc_conn.surface.number_of_vertices != surface.number_of_vertices:
                raise LaunchException("Incompatible LocalConnectivity -- Surface !!")
        else:
            cortex_entity = None

        self.log.debug("%s: Instantiating requested simulator..." % str(self))
        connectivity.configure()
        self.algorithm = Simulator(connectivity=connectivity, coupling=coupling_inst, surface=cortex_entity,
                                   stimulus=stimulus, model=model_instance, integrator=integr,
                                   monitors=monitors_list, initial_conditions=initial_conditions,
                                   conduction_speed=conduction_speed)
        self.simulation_length = simulation_length
        self.log.debug("%s: Initializing storage..." % str(self))
        try:
            self.algorithm.configure()
            if simulation_state is not None:
                simulation_state.fill_into(self.algorithm)
        except ValueError, err:
            raise LaunchException("Failed to configure simulator due to invalid Input Values. It could be because "
                                  "of an incompatibility between different version of TVB code.", err)
示例#10
0
文件: utils.py 项目: bvalean/tvb-root
def generate_region_demo_data(file_path=os.path.join(os.getcwd(), "demo_data_region_16s_2048Hz.npy")):
    """
    Generate 16 seconds of 2048Hz data at the region level, stochastic integration.

    ``Run time``: approximately 4 minutes (workstation circa 2010)

    ``Memory requirement``: < 1GB
    ``Storage requirement``: ~ 19MB

    .. moduleauthor:: Stuart A. Knock <*****@*****.**>

    """

    ##----------------------------------------------------------------------------##
    ##-                      Perform the simulation                              -##
    ##----------------------------------------------------------------------------##

    LOG.info("Configuring...")

    # Initialise a Model, Coupling, and Connectivity.
    pars = {'a': np.array([1.05]),
            'b': np.array([-1]),
            'c': np.array([0.0]),
            'd': np.array([0.1]),
            'e': np.array([0.0]),
            'f': np.array([1 / 3.]),
            'g': np.array([1.0]),
            'alpha': np.array([1.0]),
            'beta': np.array([0.2]),
            'tau': np.array([1.25]),
            'gamma': np.array([-1.0])}

    oscillator = Generic2dOscillator(**pars)

    white_matter = Connectivity.from_file()
    white_matter.speed = np.array([4.0])
    white_matter_coupling = Linear(a=np.array([0.033]))

    # Initialise an Integrator
    hiss = Additive(nsig=np.array([2 ** -10, ]))
    heunint = HeunStochastic(dt=0.06103515625, noise=hiss)

    # Initialise a Monitor with period in physical time
    what_to_watch = TemporalAverage(period=0.48828125)  # 2048Hz => period=1000.0/2048.0

    # Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors.
    sim = Simulator(model=oscillator, connectivity=white_matter,
                    coupling=white_matter_coupling,
                    integrator=heunint, monitors=[what_to_watch])

    sim.configure()

    # Perform the simulation
    tavg_data = []
    tavg_time = []
    LOG.info("Starting simulation...")
    for tavg in sim(simulation_length=16000):
        if tavg is not None:
            tavg_time.append(tavg[0][0])  # TODO:The first [0] is a hack for single monitor
            tavg_data.append(tavg[0][1])  # TODO:The first [0] is a hack for single monitor

    LOG.info("Finished simulation.")

    ##----------------------------------------------------------------------------##
    ##-                     Save the data to a file                              -##
    ##----------------------------------------------------------------------------##

    # Make the list a numpy.array.
    LOG.info("Converting result to array...")
    TAVG = np.array(tavg_data)

    # Save it
    LOG.info("Saving array to %s..." % file_path)
    np.save(file_path, TAVG)

    LOG.info("Done.")