def setup_method(self): oscillator = models.Generic2dOscillator() white_matter = connectivity.Connectivity.from_file( 'connectivity_%d.zip' % (self.n_regions, )) white_matter.speed = numpy.array([self.speed]) white_matter_coupling = coupling.Difference(a=self.coupling_a) heunint = integrators.HeunStochastic( dt=2**-4, noise=noise.Additive(nsig=numpy.array([ 2**-10, ]))) mons = ( monitors.EEG.from_file(period=self.period), monitors.MEG.from_file(period=self.period), monitors.iEEG.from_file(period=self.period), ) local_coupling_strength = numpy.array([2**-10]) region_mapping = RegionMapping.from_file('regionMapping_16k_%d.txt' % (self.n_regions, )) default_cortex = Cortex(region_mapping_data=region_mapping, load_default=True) default_cortex.coupling_strength = local_coupling_strength self.sim = simulator.Simulator(model=oscillator, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=mons, surface=default_cortex) self.sim.configure()
def configure(self, dt=2**-3, model=models.Generic2dOscillator, speed=4.0, coupling_strength=0.00042, method="HeunDeterministic", surface_sim=False, default_connectivity=True): """ Create an instance of the Simulator class, by default use the generic plane oscillator local dynamic model and the deterministic version of Heun's method for the numerical integration. """ self.method = method if default_connectivity: white_matter = connectivity.Connectivity(load_default=True) # NOTE: This is the default region mapping should consider changing the name. region_mapping = RegionMapping.from_file( source_file= "cortex_reg13/region_mapping/o52r00_irp2008_hemisphere_both_subcortical_false_regions_74.txt.bz2" ) else: white_matter = connectivity.Connectivity.from_file( source_file="connectivity_190.zip") region_mapping = RegionMapping.from_file( source_file= "cortex_reg13/region_mapping/o52r00_irp2008_hemisphere_both_subcortical_true_regions_190.txt.bz2" ) white_matter_coupling = coupling.Linear(a=coupling_strength) white_matter.speed = speed dynamics = model() if method[-10:] == "Stochastic": hisss = noise.Additive(nsig=numpy.array([2**-11])) integrator = eval("integrators." + method + "(dt=dt, noise=hisss)") else: integrator = eval("integrators." + method + "(dt=dt)") if surface_sim: local_coupling_strength = numpy.array([2**-10]) default_cortex = Cortex(load_default=True, region_mapping_data=region_mapping) default_cortex.coupling_strength = local_coupling_strength default_cortex.local_connectivity = LocalConnectivity( load_default=default_connectivity, surface=default_cortex) else: default_cortex = None # Order of monitors determines order of returned values. self.sim = simulator.Simulator(model=dynamics, connectivity=white_matter, coupling=white_matter_coupling, integrator=integrator, monitors=self.monitors, surface=default_cortex) self.sim.configure()
def make_cortex(self, local_connectivity=None, coupling_strength=None): self._cortex = Cortex() self._cortex.region_mapping_data = self.cortical_region_mapping if isinstance(local_connectivity, LocalConnectivity): self._cortex.local_connectivity = local_connectivity if coupling_strength is not None: self._cortex.coupling_strength = coupling_strength self._cortex.configure() return self._cortex
def test_assign_complex_attr(self): """ Test scientific methods are executed """ default_cortex = Cortex.from_file() default_cortex.coupling_strength = 0.0121 self.assertTrue(default_cortex.local_connectivity is None) #default_cortex.local_connectivity = surfaces.LocalConnectivity(cutoff=2, surface=default_cortex) #default_cortex.compute_local_connectivity() #self.assertTrue(default_cortex.local_connectivity is not None) default_lc = LocalConnectivity(load_default=True, cutoff=2) other_cortex = Cortex(local_connectivity=default_lc) self.assertTrue(other_cortex.local_connectivity is not None)
def test_assign_complex_attr(self): """ Test scientific methods are executed """ default_cortex = Cortex(load_file="cortex_16384.zip") default_cortex.coupling_strength = 0.0121 assert default_cortex.local_connectivity is None # default_cortex.local_connectivity = surfaces.LocalConnectivity(cutoff=2, surface=default_cortex) # default_cortex.compute_local_connectivity() # self.assertTrue(default_cortex.local_connectivity is not None) default_lc = LocalConnectivity( cutoff=2, load_file="local_connectivity_16384.mat") other_cortex = Cortex(local_connectivity=default_lc) assert other_cortex.local_connectivity is not None
def test_region_boundaries(self): cortex = Cortex.from_file() white_matter = connectivity.Connectivity(load_default=True) white_matter.configure() rb = region_boundaries.RegionBoundaries(cortex) assert len( rb.region_neighbours.keys()) == white_matter.number_of_regions
def configure(self, dt=2 ** -3, model=models.Generic2dOscillator, speed=4.0, coupling_strength=0.00042, method="HeunDeterministic", surface_sim=False, default_connectivity=True): """ Create an instance of the Simulator class, by default use the generic plane oscillator local dynamic model and the deterministic version of Heun's method for the numerical integration. """ self.method = method if default_connectivity: white_matter = Connectivity(load_file="connectivity_76.zip") region_mapping = RegionMapping(load_file="regionMapping_16k_76.txt") else: white_matter = Connectivity(load_file="connectivity_192.zip") region_mapping = RegionMapping(load_file="regionMapping_16k_192.txt") white_matter_coupling = coupling.Linear(a=coupling_strength) white_matter.speed = speed dynamics = model() if method[-10:] == "Stochastic": hisss = noise.Additive(nsig=numpy.array([2 ** -11])) integrator = eval("integrators." + method + "(dt=dt, noise=hisss)") else: integrator = eval("integrators." + method + "(dt=dt)") if surface_sim: local_coupling_strength = numpy.array([2 ** -10]) default_cortex = Cortex(region_mapping_data=region_mapping, load_file="cortex_16384.zip") default_cortex.coupling_strength = local_coupling_strength default_cortex.local_connectivity = LocalConnectivity(load_file="local_connectivity_16384.mat") else: default_cortex = None # Order of monitors determines order of returned values. self.sim = simulator.Simulator(model=dynamics, connectivity=white_matter, coupling=white_matter_coupling, integrator=integrator, monitors=self.monitors, surface=default_cortex) self.sim.configure()
def test_surface_sim_with_projections(self): # Setup Simulator obj oscillator = models.Generic2dOscillator() white_matter = connectivity.Connectivity.from_file('connectivity_%d.zip' % (self.n_regions,)) white_matter.speed = numpy.array([self.speed]) white_matter_coupling = coupling.Difference(a=self.coupling_a) heunint = integrators.HeunStochastic( dt=2 ** -4, noise=noise.Additive(nsig=numpy.array([2 ** -10, ])) ) mons = ( monitors.EEG.from_file(period=self.period), monitors.MEG.from_file(period=self.period), # monitors.iEEG.from_file(period=self.period), # SEEG projection data is not part of tvb-data on Pypi, thus this can not work generic ) local_coupling_strength = numpy.array([2 ** -10]) region_mapping = RegionMapping.from_file('regionMapping_16k_%d.txt' % (self.n_regions,)) region_mapping.surface = CorticalSurface.from_file() default_cortex = Cortex.from_file() default_cortex.region_mapping_data = region_mapping default_cortex.coupling_strength = local_coupling_strength sim = simulator.Simulator(model=oscillator, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=mons, surface=default_cortex) sim.configure() # check configured simulation connectivity attribute conn = sim.connectivity assert conn.number_of_regions == self.n_regions assert conn.speed == self.speed # test monitor properties lc_n_node = sim.surface.local_connectivity.matrix.shape[0] for mon in sim.monitors: assert mon.period == self.period n_sens, g_n_node = mon.gain.shape assert g_n_node == sim.number_of_nodes assert n_sens == mon.sensors.number_of_sensors assert lc_n_node == g_n_node # check output shape ys = {} mons = 'eeg meg seeg'.split() for key in mons: ys[key] = [] for data in sim(simulation_length=3.0): for key, dat in zip(mons, data): if dat: _, y = dat ys[key].append(y) for mon, key in zip(sim.monitors, mons): ys[key] = numpy.array(ys[key]) assert ys[key].shape[2] == mon.gain.shape[0]
def test_cortex_reg_map_without_subcorticals(self): dt = Cortex.from_file() dt.region_mapping_data.connectivity = Connectivity.from_file() self.add_subcorticals_to_conn(dt.region_mapping_data.connectivity) dt.region_mapping_data.connectivity.configure() assert isinstance(dt, Cortex) assert dt.region_mapping is not None assert numpy.unique( dt.region_mapping ).size == dt.region_mapping_data.connectivity.number_of_regions
def test_cortexdata(self): dt = Cortex.from_file( local_connectivity_file="local_connectivity_16384.mat") dt.region_mapping_data.connectivity = Connectivity.from_file() assert isinstance(dt, Cortex) assert dt.region_mapping is not None dt.configure() assert dt.vertices.shape == (16384, 3) assert dt.vertex_normals.shape == (16384, 3) assert dt.triangles.shape == (32760, 3)
def _prepare_simulator_from_view_model(self, view_model): simulator = Simulator() simulator.gid = view_model.gid conn = self.load_traited_by_gid(view_model.connectivity) simulator.connectivity = conn simulator.conduction_speed = view_model.conduction_speed simulator.coupling = view_model.coupling rm_surface = None if view_model.surface: simulator.surface = Cortex() rm_index = self.load_entity_by_gid( view_model.surface.region_mapping_data.hex) rm = h5.load_from_index(rm_index) rm_surface_index = self.load_entity_by_gid(rm_index.fk_surface_gid) rm_surface = h5.load_from_index(rm_surface_index, CorticalSurface) rm.surface = rm_surface rm.connectivity = conn simulator.surface.region_mapping_data = rm if simulator.surface.local_connectivity: lc = self.load_traited_by_gid( view_model.surface.local_connectivity) assert lc.surface.gid == rm_index.fk_surface_gid lc.surface = rm_surface simulator.surface.local_connectivity = lc if view_model.stimulus: stimulus_index = self.load_entity_by_gid(view_model.stimulus.hex) stimulus = h5.load_from_index(stimulus_index) simulator.stimulus = stimulus if isinstance(stimulus, StimuliSurface): simulator.stimulus.surface = rm_surface else: simulator.stimulus.connectivity = simulator.connectivity simulator.model = view_model.model simulator.integrator = view_model.integrator simulator.initial_conditions = view_model.initial_conditions simulator.monitors = view_model.monitors simulator.simulation_length = view_model.simulation_length # TODO: why not load history here? # if view_model.history: # history_index = dao.get_datatype_by_gid(view_model.history.hex) # history = h5.load_from_index(history_index) # assert isinstance(history, SimulationHistory) # history.fill_into(self.algorithm) return simulator
def test_cortexdata(self): dt = Cortex.from_file() dt.__setattr__('valid_for_simulations', True) assert isinstance(dt, Cortex) assert dt.region_mapping is not None ## Initialize Local Connectivity, to avoid long computation time. dt.local_connectivity = LocalConnectivity.from_file() dt.configure() assert dt.vertices.shape == (16384, 3) assert dt.vertex_normals.shape == (16384, 3) assert dt.triangles.shape == (32760, 3)
def setup_method(self): oscillator = models.Generic2dOscillator() white_matter = connectivity.Connectivity(load_file='connectivity_' + str(self.n_regions) + '.zip') white_matter.speed = numpy.array([self.speed]) white_matter_coupling = coupling.Difference(a=self.coupling_a) heunint = integrators.HeunStochastic( dt=2**-4, noise=noise.Additive(nsig=numpy.array([ 2**-10, ]))) mons = ( monitors.EEG(projection=ProjectionMatrix( load_file='projection_eeg_65_surface_16k.npy'), sensors=SensorsEEG(load_file="eeg_brainstorm_65.txt"), period=self.period), monitors.MEG( projection=ProjectionMatrix( load_file='projection_meg_276_surface_16k.npy'), sensors=SensorsMEG(load_file='meg_brainstorm_276.txt'), period=self.period), monitors.iEEG(projection=ProjectionMatrix( load_file='projection_seeg_588_surface_16k.npy'), sensors=SensorsInternal(load_file='seeg_588.txt'), period=self.period), ) local_coupling_strength = numpy.array([2**-10]) region_mapping = RegionMapping(load_file='regionMapping_16k_' + str(self.n_regions) + '.txt') default_cortex = Cortex( region_mapping_data=region_mapping, load_file="cortex_16384.zip" ) #region_mapping_file="regionMapping_16k_192.txt") default_cortex.coupling_strength = local_coupling_strength self.sim = simulator.Simulator(model=oscillator, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=mons, surface=default_cortex) self.sim.configure()
def test_cortexdata(self): dt = Cortex(load_default=True) assert isinstance(dt, Cortex) assert dt.region_mapping is not None ## Initialize Local Connectivity, to avoid long computation time. dt.local_connectivity = LocalConnectivity(load_default=True) dt.configure() summary_info = dt.summary_info assert abs(summary_info['Region area, maximum (mm:math:`^2`)'] - 9333.39) < 0.01 assert abs(summary_info['Region area, mean (mm:math:`^2`)'] - 3038.51) < 0.01 assert abs(summary_info['Region area, minimum (mm:math:`^2`)'] - 540.90) < 0.01 assert dt.get_data_shape('vertices') == (16384, 3) assert dt.get_data_shape('vertex_normals') == (16384, 3) assert dt.get_data_shape('triangles') == (32760, 3)
def cortex(self): cortex = Cortex() cortex.region_mapping_data = self.cortical_region_mapping cortex = cortex.populate_cortex(self.cortical_surface._tvb, {}) for s_type, sensors in self.sensors.items(): if isinstance(sensors, OrderedDict) and len(sensors) > 0: projection = sensors.values()[0] if projection is not None: setattr(cortex, s_type.lower(), projection.projection_data) cortex.configure() return cortex
def test_cortexdata(self): dt = Cortex(load_default=True) self.assertTrue(isinstance(dt, Cortex)) self.assertTrue(dt.region_mapping is not None) ## Initialize Local Connectivity, to avoid long computation time. dt.local_connectivity = LocalConnectivity(load_default=True) dt.configure() summary_info = dt.summary_info self.assertTrue( abs(summary_info['Region area, maximum (mm:math:`^2`)'] - 9119.4540365252615) < 0.00000001) self.assertTrue( abs(summary_info['Region area, mean (mm:math:`^2`)'] - 3366.2542250541251) < 0.00000001) self.assertTrue( abs(summary_info['Region area, minimum (mm:math:`^2`)'] - 366.48271886512993) < 0.00000001) self.assertEqual(dt.get_data_shape('vertices'), (16384, 3)) self.assertEqual(dt.get_data_shape('vertex_normals'), (16384, 3)) self.assertEqual(dt.get_data_shape('triangles'), (32760, 3))
def test_cortexdata(self): dt = Cortex(load_file="cortex_16384.zip", region_mapping_data=RegionMapping( load_file="regionMapping_16k_76.txt")) assert isinstance(dt, Cortex) assert dt.region_mapping_data is not None ## Initialize Local Connectivity, to avoid long computation time. dt.local_connectivity = LocalConnectivity( load_file="local_connectivity_16384.mat") dt.configure() summary_info = dt._find_summary_info() assert abs(summary_info['Region area, maximum (mm:math:`^2`)'] - 9333.39) < 0.01 assert abs(summary_info['Region area, mean (mm:math:`^2`)'] - 3038.51) < 0.01 assert abs(summary_info['Region area, minimum (mm:math:`^2`)'] - 540.90) < 0.01 assert dt.vertices.shape == (16384, 3) assert dt.vertex_normals.shape == (16384, 3) assert dt.triangles.shape == (32760, 3)
def configure(self, dt=2**-3, model=ModelsEnum.GENERIC_2D_OSCILLATOR.get_class(), speed=4.0, coupling_strength=0.00042, method=HeunDeterministic, surface_sim=False, default_connectivity=True, with_stimulus=False): """ Create an instance of the Simulator class, by default use the generic plane oscillator local dynamic model and the deterministic version of Heun's method for the numerical integration. """ self.method = method if default_connectivity: white_matter = Connectivity.from_file() region_mapping = RegionMapping.from_file( source_file="regionMapping_16k_76.txt") else: white_matter = Connectivity.from_file( source_file="connectivity_192.zip") region_mapping = RegionMapping.from_file( source_file="regionMapping_16k_192.txt") region_mapping.surface = CorticalSurface.from_file() white_matter_coupling = coupling.Linear( a=numpy.array([coupling_strength])) white_matter.speed = numpy.array( [speed]) # no longer allow scalars to numpy array promotion dynamics = model() if issubclass(method, IntegratorStochastic): hisss = noise.Additive(nsig=numpy.array([2**-11])) integrator = method(dt=dt, noise=hisss) else: integrator = method(dt=dt) if surface_sim: local_coupling_strength = numpy.array([2**-10]) default_cortex = Cortex.from_file() default_cortex.region_mapping_data = region_mapping default_cortex.coupling_strength = local_coupling_strength if default_connectivity: default_cortex.local_connectivity = LocalConnectivity.from_file( ) else: default_cortex.local_connectivity = LocalConnectivity() default_cortex.local_connectivity.surface = default_cortex.region_mapping_data.surface # TODO stimulus else: default_cortex = None if with_stimulus: weights = StimuliRegion.get_default_weights( white_matter.weights.shape[0]) weights[self.stim_nodes] = 1. stimulus = StimuliRegion(temporal=Linear(parameters={ "a": 0.0, "b": self.stim_value }), connectivity=white_matter, weight=weights) # Order of monitors determines order of returned values. self.sim = simulator.Simulator() self.sim.surface = default_cortex self.sim.model = dynamics self.sim.integrator = integrator self.sim.connectivity = white_matter self.sim.coupling = white_matter_coupling self.sim.monitors = self.monitors if with_stimulus: self.sim.stimulus = stimulus self.sim.configure()
oscillator = models.Generic2dOscillator() white_matter = connectivity.Connectivity.from_file('connectivity_192.zip') white_matter.speed = numpy.array([4.0]) white_matter_coupling = coupling.Difference(a=0.014) heunint = integrators.HeunStochastic( dt=2**-4, noise=noise.Additive(nsig=numpy.array([2 ** -10, ])) ) fsamp = 1e3/1024.0 # 1024 Hz monitors = ( monitors.EEG.from_file('eeg-brainstorm-65.txt', 'projection_EEG_surface.npy', period=fsamp), monitors.MEG.from_file('meg-brainstorm-276.txt', 'projection_MEG_surface.npy', period=fsamp), monitors.iEEG.from_file('SEEG_588.txt', 'projection_SEEG_surface.npy', period=fsamp), ) local_coupling_strength = numpy.array([2 ** -10]) default_cortex = Cortex(region_mapping_data=RegionMapping.from_file('regionMapping_16k_192.txt'), load_default=True) default_cortex.coupling_strength = local_coupling_strength sim = simulator.Simulator(model=oscillator, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=monitors, surface=default_cortex) sim.configure() ts, ys = {}, {} mons = 'eeg meg seeg'.split() for key in mons: ts[key] = [] ys[key] = [] for data in sim(simulation_length=2**2): for key, dat in zip(mons, data):
class Simulator(core.Type): """ The Simulator class coordinates classes from all other modules in the simulator package in order to perform simulations. In general, it is necessary to initialiaze a simulator with the desired components and then call the simulator in a loop to obtain simulation data: >>> sim = Simulator(...) >>> for output in sim(simulation_length=1000): ... Please refer to the user guide and the demos for more detail. .. #Currently there seems to be a clash betwen traits and autodoc, autodoc .. #can't find the methods of the class, the class specific names below get .. #us around this... .. automethod:: Simulator.__init__ .. automethod:: Simulator.configure .. automethod:: Simulator.__call__ .. automethod:: Simulator.configure_history .. automethod:: Simulator.configure_integrator_noise .. automethod:: Simulator.memory_requirement .. automethod:: Simulator.runtime .. automethod:: Simulator.storage_requirement """ connectivity = connectivity_dtype.Connectivity( label="Long-range connectivity", default=None, order=1, required=True, filters_ui=[ UIFilter(linked_elem_name="projection_matrix_data", linked_elem_field=FilterChain.datatype + "._sources", linked_elem_parent_name="monitors", linked_elem_parent_option="EEG"), UIFilter(linked_elem_name="region_mapping_data", linked_elem_field=FilterChain.datatype + "._connectivity", linked_elem_parent_name="surface", linked_elem_parent_option=None) ], doc="""A tvb.datatypes.Connectivity object which contains the structural long-range connectivity data (i.e., white-matter tracts). In combination with the ``Long-range coupling function`` it defines the inter-regional connections. These couplings undergo a time delay via signal propagation with a propagation speed of ``Conduction Speed``""") conduction_speed = basic.Float( label="Conduction Speed", default=3.0, order=2, required=False, range=basic.Range(lo=0.01, hi=100.0, step=1.0), doc="""Conduction speed for ``Long-range connectivity`` (mm/ms)""") coupling = coupling_module.Coupling( label="Long-range coupling function", default=coupling_module.Linear(), required=True, order=2, doc="""The coupling function is applied to the activity propagated between regions by the ``Long-range connectivity`` before it enters the local dynamic equations of the Model. Its primary purpose is to 'rescale' the incoming activity to a level appropriate to Model.""") surface = Cortex( label="Cortical surface", default=None, order=3, required=False, filters_backend=FilterChain( fields=[FilterChain.datatype + '._valid_for_simulations'], operations=["=="], values=[True]), filters_ui=[ UIFilter(linked_elem_name="projection_matrix_data", linked_elem_field=FilterChain.datatype + "._sources", linked_elem_parent_name="monitors", linked_elem_parent_option="EEG"), UIFilter(linked_elem_name="local_connectivity", linked_elem_field=FilterChain.datatype + "._surface", linked_elem_parent_name="surface", linked_elem_parent_option=None) ], doc="""By default, a tvb.datatypes.Cortex object which represents the cortical surface defined by points in the 3D physical space and their neighborhood relationship. In the current TVB version, when setting up a surface-based simulation, the option to configure the spatial spread of the ``Local Connectivity`` is available.""") stimulus = patterns_dtype.SpatioTemporalPattern( label="Spatiotemporal stimulus", default=None, order=4, required=False, doc= """A ``Spatiotemporal stimulus`` can be defined at the region or surface level. It's composed of spatial and temporal components. For region defined stimuli the spatial component is just the strength with which the temporal component is applied to each region. For surface defined stimuli, a (spatial) function, with finite-support, is used to define the strength of the stimuli on the surface centred around one or more focal points. In the current version of TVB, stimuli are applied to the first state variable of the ``Local dynamic model``.""") model = models_module.Model( label="Local dynamic model", default=models_module.Generic2dOscillator, required=True, order=5, doc="""A tvb.simulator.Model object which describe the local dynamic equations, their parameters, and, to some extent, where connectivity (local and long-range) enters and which state-variables the Monitors monitor. By default the 'Generic2dOscillator' model is used. Read the Scientific documentation to learn more about this model.""") integrator = integrators_module.Integrator( label="Integration scheme", default=integrators_module.HeunDeterministic, required=True, order=6, doc="""A tvb.simulator.Integrator object which is an integration scheme with supporting attributes such as integration step size and noise specification for stochastic methods. It is used to compute the time courses of the model state variables.""") initial_conditions = arrays_dtype.FloatArray( label="Initial Conditions", default=None, order=-1, required=False, doc="""Initial conditions from which the simulation will begin. By default, random initial conditions are provided. Needs to be the same shape as simulator 'history', ie, initial history function which defines the minimal initial state of the network with time delays before time t=0. If the number of time points in the provided array is insufficient the array will be padded with random values based on the 'state_variables_range' attribute.""") monitors = monitors_module.Monitor( label="Monitor(s)", default=monitors_module.TemporalAverage, required=True, order=8, select_multiple=True, doc="""A tvb.simulator.Monitor or a list of tvb.simulator.Monitor objects that 'know' how to record relevant data from the simulation. Two main types exist: 1) simple, spatial and temporal, reductions (subsets or averages); 2) physiological measurements, such as EEG, MEG and fMRI. By default the Model's specified variables_of_interest are returned, temporally downsampled from the raw integration rate to a sample rate of 1024Hz.""") simulation_length = basic.Float( label="Simulation Length (ms)", default=1000.0, # ie 1 second required=True, order=9, doc="""The length of a simulation in milliseconds (ms).""") def __init__(self, **kwargs): """ Use the base class' mechanisms to initialise the traited attributes declared above, overriding defaults with any provided keywords. Then declare any non-traited attributes. """ super(Simulator, self).__init__(**kwargs) LOG.debug(str(kwargs)) self.calls = 0 self.current_step = 0 self.number_of_nodes = None self.horizon = None self.good_history_shape = None self.history = None self._memory_requirement_guess = None self._memory_requirement_census = None self._storage_requirement = None self._runtime = None def __str__(self): return "Simulator(**kwargs)" def preconfigure(self): """ Configure just the basic fields, so that memory can be estimated """ self.connectivity.configure() if self.surface: self.surface.configure() if self.stimulus: self.stimulus.configure() self.coupling.configure() self.model.configure() self.integrator.configure() # monitors needs to be a list or tuple, even if there is only one... if not isinstance(self.monitors, (list, tuple)): self.monitors = [self.monitors] # Configure monitors for monitor in self.monitors: monitor.configure() ##------------- Now the the interdependant configuration -------------## #"Nodes" refers to either regions or vertices + non-cortical regions. if self.surface is None: self.number_of_nodes = self.connectivity.number_of_regions else: #try: self.number_of_nodes = self.surface.region_mapping.shape[0] #except AttributeError: # msg = "%s: Surface needs region mapping defined... " # LOG.error(msg % (repr(self))) # Estimate of memory usage self._guesstimate_memory_requirement() def configure(self, full_configure=True): """ The first step of configuration is to run the configure methods of all the Simulator's components, ie its traited attributes. Configuration of a Simulator primarily consists of calculating the attributes, etc, which depend on the combinations of the Simulator's traited attributes (keyword args). Converts delays from physical time units into integration steps and updates attributes that depend on combinations of the 6 inputs. """ if full_configure: # When run from GUI, preconfigure is run separately, and we want to avoid running that part twice self.preconfigure() #Make sure spatialised model parameters have the right shape (number_of_nodes, 1) excluded_params = ("state_variable_range", "variables_of_interest", "noise", "psi_table", "nerf_table") for param in self.model.trait.keys(): if param in excluded_params: continue #If it's a surface sim and model parameters were provided at the region level region_parameters = getattr(self.model, param) if self.surface is not None: if region_parameters.size == self.connectivity.number_of_regions: new_parameters = region_parameters[ self.surface.region_mapping].reshape((-1, 1)) setattr(self.model, param, new_parameters) region_parameters = getattr(self.model, param) if region_parameters.size == self.number_of_nodes: new_parameters = region_parameters.reshape((-1, 1)) setattr(self.model, param, new_parameters) #Configure spatial component of any stimuli self.configure_stimuli() #Set delays, provided in physical units, in integration steps. self.connectivity.set_idelays(self.integrator.dt) self.horizon = numpy.max(self.connectivity.idelays) + 1 LOG.info("horizon is %d steps" % self.horizon) # workspace -- minimal state of network with delays self.good_history_shape = (self.horizon, self.model.nvar, self.number_of_nodes, self.model.number_of_modes) msg = "%s: History shape will be: %s" LOG.debug(msg % (repr(self), str(self.good_history_shape))) #Reshape integrator.noise.nsig, if necessary. if isinstance(self.integrator, integrators_module.IntegratorStochastic): self.configure_integrator_noise() self.configure_history(self.initial_conditions) #Configure Monitors to work with selected Model, etc... self.configure_monitors() #Estimate of memory usage. self._census_memory_requirement() def __call__(self, simulation_length=None, random_state=None): """ When a Simulator is called it returns an iterator. kwargs: ``simulation_length``: total time of simulation ``random_state``: a state for the NumPy random number generator, saved from a previous call to permit consistent continuation of a simulation. """ #The number of times this Simulator has been called. self.calls += 1 #Update the simulator objects simulation_length attribute, if simulation_length is None: simulation_length = self.simulation_length else: self.simulation_length = simulation_length #Estimate run time and storage requirements, with logging. self._guesstimate_runtime() self._calculate_storage_requirement() if random_state is not None: if isinstance(self.integrator, integrators_module.IntegratorStochastic): self.integrator.noise.random_stream.set_state(random_state) msg = "%s: random_state supplied. Seed is: %s" LOG.info( msg % (str(self), str(self.integrator.noise.random_stream.get_state()[1][0]) )) else: msg = "%s: random_state supplied for non-stochastic integration" LOG.warn(msg % str(self)) #Determine the number of integration steps required to produce #data of simulation_length int_steps = int(simulation_length / self.integrator.dt) LOG.info("%s: gonna do %d integration steps" % (str(self), int_steps)) # locals for cleaner code. horizon = self.horizon history = self.history dfun = self.model.dfun coupling = self.coupling scheme = self.integrator.scheme npsum = numpy.sum npdot = numpy.dot ncvar = len(self.model.cvar) number_of_regions = self.connectivity.number_of_regions nsn = (number_of_regions, 1, number_of_regions) # Exact dtypes and alignment are required by c speedups. Once we have history objects these will be encapsulated # cvar index array broadcastable to nodes, cvars, nodes cvar = numpy.array(self.model.cvar[numpy.newaxis, :, numpy.newaxis], dtype=numpy.intc) LOG.debug("%s: cvar is: %s" % (str(self), str(cvar))) # idelays array broadcastable to nodes, cvars, nodes idelays = numpy.array(self.connectivity.idelays[:, numpy.newaxis, :], dtype=numpy.intc, order='c') LOG.debug("%s: idelays shape is: %s" % (str(self), str(idelays.shape))) # weights array broadcastable to nodes, cva, nodes, modes weights = self.connectivity.weights[:, numpy.newaxis, :, numpy.newaxis] LOG.debug("%s: weights shape is: %s" % (str(self), str(weights.shape))) # node_ids broadcastable to nodes, cvars, nodes node_ids = numpy.array( numpy.arange(number_of_regions)[numpy.newaxis, numpy.newaxis, :], dtype=numpy.intc) LOG.debug("%s: node_ids shape is: %s" % (str(self), str(node_ids.shape))) if self.surface is None: local_coupling = 0.0 else: region_average = self.surface.region_average region_history = npdot( region_average, history ) # this may be very expensive ~60sec for epileptor (many states and modes ...) region_history = region_history.transpose((1, 2, 0, 3)) region_history = numpy.ascontiguousarray( region_history) # required by the c speedups if self.surface.coupling_strength.size == 1: local_coupling = (self.surface.coupling_strength[0] * self.surface.local_connectivity.matrix) elif self.surface.coupling_strength.size == self.surface.number_of_vertices: ind = numpy.arange(self.number_of_nodes, dtype=int) vec_cs = numpy.zeros((self.number_of_nodes, )) vec_cs[:self.surface. number_of_vertices] = self.surface.coupling_strength sp_cs = sparse.csc_matrix( (vec_cs, (ind, ind)), shape=(self.number_of_nodes, self.number_of_nodes)) local_coupling = sp_cs * self.surface.local_connectivity.matrix if self.stimulus is None: stimulus = 0.0 else: # TODO: Consider changing to simulator absolute time... This is an open discussion, a matter of interpretation of the stimuli time axis. time = numpy.arange(0, simulation_length, self.integrator.dt) time = time[numpy.newaxis, :] self.stimulus.configure_time(time) stimulus = numpy.zeros((self.model.nvar, self.number_of_nodes, 1)) LOG.debug("%s: stimulus shape is: %s" % (str(self), str(stimulus.shape))) # initial state, history[timepoint[0], state_variables, nodes, modes] state = history[self.current_step % horizon, :] LOG.debug("%s: state shape is: %s" % (str(self), str(state.shape))) if self.surface is not None: # the vertex mapping array is huge but sparse. # csr because I expect the row to have one value and I expect the dot to proceed row wise. vertex_mapping = sparse.csr_matrix(self.surface.vertex_mapping) # this is big a well. same shape as the vertex mapping. region_average = sparse.csr_matrix(region_average) node_coupling_shape = (vertex_mapping.shape[0], ncvar, self.model.number_of_modes) delayed_state = numpy.zeros( (number_of_regions, ncvar, number_of_regions, self.model.number_of_modes)) for step in xrange(self.current_step + 1, self.current_step + int_steps + 1): time_indices = (step - 1 - idelays) % horizon if self.surface is None: get_state(history, time_indices, cvar, node_ids, out=delayed_state) node_coupling = coupling(weights, state[self.model.cvar], delayed_state) else: get_state(region_history, time_indices, cvar, node_ids, out=delayed_state) region_coupling = coupling( weights, region_history[(step - 1) % horizon, self.model.cvar], delayed_state) node_coupling = numpy.empty(node_coupling_shape) # sparse matrices cannot multiply with 3d arrays so we use a loop over the modes for mi in xrange(self.model.number_of_modes): node_coupling[..., mi] = vertex_mapping * region_coupling[..., mi].T node_coupling = node_coupling.transpose((1, 0, 2)) if self.stimulus is not None: stimulus[self.model.cvar, :, :] = numpy.reshape( self.stimulus(step - (self.current_step + 1)), (1, -1, 1)) state = scheme(state, dfun, node_coupling, local_coupling, stimulus) history[step % horizon, :] = state if self.surface is not None: # this optimisation is similar to the one done for vertex_mapping above step_avg = numpy.empty((number_of_regions, state.shape[0], self.model.number_of_modes)) for mi in xrange(self.model.number_of_modes): step_avg[..., mi] = region_average.dot(state[..., mi].T) region_history[step % horizon, :] = step_avg.transpose( (1, 0, 2)) # monitor.things e.g. raw, average, eeg, meg, fmri... output = [monitor.record(step, state) for monitor in self.monitors] if any(outputi is not None for outputi in output): yield output # This -1 is here for not repeating the point on resume self.current_step = self.current_step + int_steps - 1 self.history = history def configure_history(self, initial_conditions=None): """ Set initial conditions for the simulation using either the provided initial_conditions or, if none are provided, the model's initial() method. This method is called durin the Simulator's __init__(). Any initial_conditions that are provided as an argument are expected to have dimensions 1, 2, and 3 with shapse corresponding to the number of state_variables, nodes and modes, respectively. If the provided inital_conditions are shorter in time (dim=0) than the required history the model's initial() method is called to make up the difference. """ history = self.history if initial_conditions is None: msg = "%s: Setting default history using model's initial() method." LOG.info(msg % str(self)) history = self.model.initial(self.integrator.dt, self.good_history_shape) else: # history should be [timepoints, state_variables, nodes, modes] LOG.info("%s: Received initial conditions as arg." % str(self)) ic_shape = initial_conditions.shape if ic_shape[1:] != self.good_history_shape[1:]: msg = "%s: bad initial_conditions[1:] shape %s, should be %s" msg %= self, ic_shape[1:], self.good_history_shape[1:] raise ValueError(msg) else: if ic_shape[0] >= self.horizon: msg = "%s: Using last %s time-steps for history." LOG.info(msg % (str(self), self.horizon)) history = initial_conditions[ -self.horizon:, :, :, :].copy() else: msg = "%s: initial_conditions shorter than required." LOG.info(msg % str(self)) msg = "%s: Using model's initial() method for difference." LOG.info(msg % str(self)) history = self.model.initial(self.integrator.dt, self.good_history_shape) csmh = self.current_step % self.horizon history = numpy.roll(history, -csmh, axis=0) history[:ic_shape[0], :, :, :] = initial_conditions history = numpy.roll(history, csmh, axis=0) self.current_step += ic_shape[0] - 1 msg = "%s: history shape is: %s" LOG.debug(msg % (str(self), str(history.shape))) self.history = history def configure_integrator_noise(self): """ This enables having noise to be state variable specific and/or to enter only via specific brain structures, for example it we only want to consider noise as an external input entering the brain via appropriate thalamic nuclei. Support 3 possible shapes: 1) number_of_nodes; 2) number_of_state_variables; and 3) (number_of_state_variables, number_of_nodes). """ noise = self.integrator.noise if self.integrator.noise.ntau > 0.0: self.integrator.noise.configure_coloured( self.integrator.dt, self.good_history_shape[1:]) else: self.integrator.noise.configure_white(self.integrator.dt, self.good_history_shape[1:]) if self.surface is not None: if self.integrator.noise.nsig.size == self.connectivity.number_of_regions: self.integrator.noise.nsig = self.integrator.noise.nsig[ self.surface.region_mapping] elif self.integrator.noise.nsig.size == self.model.nvar * self.connectivity.number_of_regions: self.integrator.noise.nsig = self.integrator.noise.nsig[:, self. surface . region_mapping] good_nsig_shape = (self.model.nvar, self.number_of_nodes, self.model.number_of_modes) nsig = self.integrator.noise.nsig LOG.debug("Simulator.integrator.noise.nsig shape: %s" % str(nsig.shape)) if nsig.shape in (good_nsig_shape, (1, )): return elif nsig.shape == (self.model.nvar, ): nsig = nsig.reshape((self.model.nvar, 1, 1)) elif nsig.shape == (self.number_of_nodes, ): nsig = nsig.reshape((1, self.number_of_nodes, 1)) elif nsig.shape == (self.model.nvar, self.number_of_nodes): nsig = nsig.reshape((self.model.nvar, self.number_of_nodes, 1)) else: msg = "Bad Simulator.integrator.noise.nsig shape: %s" LOG.error(msg % str(nsig.shape)) LOG.debug("Simulator.integrator.noise.nsig shape: %s" % str(nsig.shape)) self.integrator.noise.nsig = nsig def configure_monitors(self): """ Configure the requested Monitors for this Simulator """ if not isinstance(self.monitors, (list, tuple)): self.monitors = [self.monitors] # Configure monitors for monitor in self.monitors: monitor.config_for_sim(self) def configure_stimuli(self): """ Configure the defined Stimuli for this Simulator """ if self.stimulus is not None: if self.surface: self.stimulus.configure_space(self.surface.region_mapping) else: self.stimulus.configure_space() def memory_requirement(self): """ Return an estimated of the memory requirements (Bytes) for this simulator's current configuration. """ self._guesstimate_memory_requirement() return self._memory_requirement_guess def runtime(self, simulation_length): """ Return an estimated run time (seconds) for the simulator's current configuration and a specified simulation length. """ self.simulation_length = simulation_length self._guesstimate_runtime() return self._runtime def storage_requirement(self, simulation_length): """ Return an estimated storage requirement (Bytes) for the simulator's current configuration and a specified simulation length. """ self.simulation_length = simulation_length self._calculate_storage_requirement() return self._storage_requirement def _guesstimate_memory_requirement(self): """ guesstimate the memory required for this simulator. Guesstimate is based on the shape of the dominant arrays, and as such can operate before configuration. NOTE: Assumes returned/yeilded data is in some sense "taken care of" in the world outside the simulator, and so doesn't consider it, making the simulator's history, and surface if present, the dominant memory pigs... """ if self.surface: number_of_nodes = self.surface.number_of_vertices else: number_of_nodes = self.connectivity.number_of_regions number_of_regions = self.connectivity.number_of_regions magic_number = 2.42 # Current guesstimate is low by about a factor of 2, seems safer to over estimate... bits_64 = 8.0 # Bytes bits_32 = 4.0 # Bytes #NOTE: The speed hack for getting the first element of hist shape should # partially resolves calling of this method with a non-configured # connectivity, there remains the less common issue if no tract_lengths... hist_shape = ( self.connectivity.tract_lengths.max() / (self.conduction_speed or self.connectivity.speed or 3.0) / self.integrator.dt, self.model.nvar, number_of_nodes, self.model.number_of_modes) memreq = numpy.prod(hist_shape) * bits_64 if self.surface: memreq += self.surface.number_of_triangles * 3 * bits_32 * 2 # normals memreq += self.surface.number_of_vertices * 3 * bits_64 * 2 # normals memreq += number_of_nodes * number_of_regions * bits_64 * 4 # vertex_mapping, region_average, region_sum #???memreq += self.surface.local_connectivity.matrix.nnz * 8 if not isinstance(self.monitors, (list, tuple)): monitors = [self.monitors] else: monitors = self.monitors for monitor in monitors: if not isinstance(monitor, monitors_module.Bold): stock_shape = (monitor.period / self.integrator.dt, self.model.variables_of_interest.shape[0], number_of_nodes, self.model.number_of_modes) memreq += numpy.prod(stock_shape) * bits_64 if hasattr(monitor, "sensors"): try: memreq += number_of_nodes * monitor.sensors.number_of_sensors * bits_64 # projection_matrix except AttributeError: LOG.debug( "No sensors specified, guessing memory based on default EEG." ) memreq += number_of_nodes * 62.0 * bits_64 else: stock_shape = (monitor.hrf_length * monitor._stock_sample_rate, self.model.variables_of_interest.shape[0], number_of_nodes, self.model.number_of_modes) interim_stock_shape = ( 1.0 / (2.0**-2 * self.integrator.dt), self.model.variables_of_interest.shape[0], number_of_nodes, self.model.number_of_modes) memreq += numpy.prod(stock_shape) * bits_64 memreq += numpy.prod(interim_stock_shape) * bits_64 if psutil and memreq > psutil.virtual_memory().total: LOG.error("This is gonna get ugly...") self._memory_requirement_guess = magic_number * memreq msg = "Memory requirement guesstimate: simulation will need about %.1f MB" LOG.info(msg % (self._memory_requirement_guess / 1048576.0)) def _census_memory_requirement(self): """ Guesstimate the memory required for this simulator. Guesstimate is based on a census of the dominant arrays after the simulator has been configured. NOTE: Assumes returned/yeilded data is in some sense "taken care of" in the world outside the simulator, and so doesn't consider it, making the simulator's history, and surface if present, the dominant memory pigs... """ magic_number = 2.42 # Current guesstimate is low by about a factor of 2, seems safer to over estimate... memreq = self.history.nbytes try: memreq += self.surface.triangles.nbytes * 2 memreq += self.surface.vertices.nbytes * 2 memreq += self.surface.vertex_mapping.nbytes * 4 # vertex_mapping, region_average, region_sum memreq += self.surface.eeg_projection.nbytes memreq += self.surface.local_connectivity.matrix.nnz * 8 except AttributeError: pass for monitor in self.monitors: memreq += monitor._stock.nbytes if isinstance(monitor, monitors_module.Bold): memreq += monitor._interim_stock.nbytes if psutil and memreq > psutil.virtual_memory().total: LOG.error("This is gonna get ugly...") self._memory_requirement_census = magic_number * memreq #import pdb; pdb.set_trace() msg = "Memory requirement census: simulation will need about %.1f MB" LOG.info(msg % (self._memory_requirement_census / 1048576.0)) def _guesstimate_runtime(self): """ Estimate the runtime for this simulator. Spread in parallel executions of larger arrays means this will be an over-estimation, or rather a single threaded estimation... Different choice of integrators and monitors has an additional effect, on the magic number though relatively minor """ magic_number = 6.57e-06 # seconds self._runtime = (magic_number * self.number_of_nodes * self.model.nvar * self.model.number_of_modes * self.simulation_length / self.integrator.dt) msg = "Simulation single-threaded runtime should be about %s seconds!" LOG.info(msg % str(int(self._runtime))) def _calculate_storage_requirement(self): """ Calculate the storage requirement for the simulator, configured with models, monitors, etc being run for a particular simulation length. While this is only approximate, it is far more reliable/accurate than the memory and runtime guesstimates. """ LOG.info("Calculating storage requirement for ...") strgreq = 0 for monitor in self.monitors: # Avoid division by zero for monitor not yet configured # (in framework this is executed, when only preconfigure has been called): current_period = monitor.period or self.integrator.dt strgreq += (TvbProfile.current.MAGIC_NUMBER * self.simulation_length * self.number_of_nodes * self.model.nvar * self.model.number_of_modes / current_period) LOG.info("Calculated storage requirement for simulation: %d " % int(strgreq)) self._storage_requirement = int(strgreq)
sim = simulator.Simulator( model = models.Generic2dOscillator(), connectivity = connectivity.Connectivity(speed=4.0, load_default=True), coupling = coupling.Linear(a=-2 ** -9), integrator = integrators.HeunStochastic( dt=2 ** -4, noise=noise.Additive(nsig=ones((2,)) * 0.001) ), monitors = ( monitors.EEG(period=1e3/2 ** 10), # 1024 Hz monitors.Bold(period=500) # 0.5 Hz ), surface = Cortex( load_default=True, local_connectivity = lconn, coupling_strength = array([0.01]) ), ) sim.configure() # set delays to mean print sim.connectivity.idelays sim.connectivity.delays[:] = sim.connectivity.delays.mean() sim.connectivity.set_idelays(sim.integrator.dt) print sim.connectivity.idelays ts_eeg, ys_eeg = [], [] ts_bold, ys_bold = [], []
# # """ .. moduleauthor:: Stuart A. Knock <*****@*****.**> """ from tvb.datatypes.cortex import Cortex from tvb.simulator.lab import * from tvb.simulator.region_boundaries import RegionBoundaries from tvb.simulator.region_colours import RegionColours CORTEX = Cortex.from_file() CORTEX_BOUNDARIES = RegionBoundaries(CORTEX) region_colours = RegionColours(CORTEX_BOUNDARIES.region_neighbours) colouring = region_colours.back_track() #Make the hemispheres symmetric # TODO: should prob. et colouring for one hemisphere then just stack two copies... number_of_regions = len(CORTEX_BOUNDARIES.region_neighbours) for k in range(int(number_of_regions)): colouring[k + int(number_of_regions)] = colouring[k] mapping_colours = list("rgbcmyRGBCMY") colour_rgb = {"r": numpy.array([255, 0, 0], dtype=numpy.uint8), "g": numpy.array([ 0, 255, 0], dtype=numpy.uint8),
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)
white_matter_coupling = coupling.Linear(a=0.0043) # 0.0066 #Initialise an Integrator hiss = noise.Additive(nsig=numpy.array([2 ** -16, ])) heunint = integrators.HeunStochastic(dt=2 ** -4, noise=hiss) #Initialise some Monitors with period in physical time mon_tavg = monitors.TemporalAverage(period=2 ** -2) mon_savg = monitors.SpatialAverage(period=2 ** -2) mon_eeg = monitors.EEG(period=2 ** -2) #Bundle them what_to_watch = (mon_tavg, mon_savg, mon_eeg) #Initialise a surface fully loaded default_cortex = Cortex(load_default=True) #Initialise Simulator -- Model, Connectivity, Integrator, Monitors, and surface. sim = simulator.Simulator(model=rfhn, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=what_to_watch, surface=default_cortex) sim.configure() #Clear initial transient LOG.info("Initial run to clear transient...") for _, _, _ in sim(simulation_length=2 ** 6): pass LOG.info("Finished initial run to clear transient.")
mon_eeg = monitors.EEG(period=2**-2) #Bundle them what_to_watch = (mon_tavg, mon_savg, mon_eeg) #Initialise a surface: #First define the function describing the "local" connectivity. grey_matter = LocalConnectivity(cutoff=40.0) grey_matter.equation.parameters['sigma'] = 10.0 grey_matter.equation.parameters['amp'] = 1.0 #then a scaling factor, to adjust the strength of the local connectivity local_coupling_strength = numpy.array([-0.0115]) #finally, create a default cortex that includes the custom local connectivity. default_cortex = Cortex(load_default=True) default_cortex.local_connectivity = grey_matter default_cortex.coupling_strength = local_coupling_strength #Initialise Simulator -- Model, Connectivity, Integrator, Monitors, and surface. sim = simulator.Simulator(model=oscillator, connectivity=white_matter, integrator=heunint, monitors=what_to_watch, surface=default_cortex) sim.configure() LOG.info("Starting simulation...") #Perform the simulation tavg_data = [] tavg_time = []
white_matter_coupling = coupling.Linear(a=2 ** -7) #Initialise an Integrator heunint = integrators.HeunDeterministic(dt=2 ** -4) #Initialise some Monitors with period in physical time mon_tavg = monitors.TemporalAverage(period=2 ** -2) mon_savg = monitors.SpatialAverage(period=2 ** -2) mon_eeg = monitors.EEG(period=2 ** -2) #Bundle them what_to_watch = (mon_tavg, mon_savg, mon_eeg) #Initialise a surface local_coupling_strength = numpy.array([2 ** -6]) default_cortex = Cortex(load_default=True) default_cortex.coupling_strength = local_coupling_strength ##NOTE: THIS IS AN EXAMPLE OF DESCRIBING A SURFACE STIMULUS AT REGIONS LEVEL. # SURFACES ALSO SUPPORT STIMULUS SPECIFICATION BY A SPATIAL FUNCTION # CENTRED AT A VERTEX (OR VERTICES). #Define the stimulus #Specify a weighting for regions to receive stimuli... white_matter.configure() # Because we want access to number_of_regions nodes = [0, 7, 13, 33, 42] #NOTE: here, we specify space at region level simulator will map to surface #Specify a weighting for regions to receive stimuli... weighting = numpy.zeros((white_matter.number_of_regions, 1)) weighting[nodes] = numpy.array([2.0 ** -2, 2.0 ** -3, 2.0 ** -4, 2.0 ** -5, 2.0 ** -6])[:, numpy.newaxis] eqn_t = equations.Gaussian()
class ProjectionMatrix(core.Type): """ Provides the mechanisms necessary to access OpenMEEG for the calculation of EEG and MEG projection matrices, ie matrices that map source activity to sensor activity. It is initialised with datatypes of TVB and ultimately returns the projection matrix as a Numpy ndarray. """ brain_skull = surfaces_module.BrainSkull( label = "Boundary between skull and skin domains", default = None, required = True, doc = """A ... surface on which ... including ...""") skull_skin = surfaces_module.SkullSkin( label = "surface and auxillary for surface sim", default = None, required = True, doc = """A ... surface on which ... including ...""") skin_air = surfaces_module.SkinAir( label = "surface and auxillary for surface sim", default = None, required = True, doc = """A ... surface on which ... including ...""") conductances = basic.Dict( label = "Domain conductances", default = {'air': 0.0, 'skin': 1.0, 'skull': 0.01, 'brain': 1.0}, required = True, doc = """A dictionary representing the conductances of ...""") sources = Cortex( label = "surface and auxillary for surface sim", default = None, required = True, doc = """A cortical surface on which ... including ...""") sensors = sensors_module.Sensors( label = "surface and auxillary for surface sim", default = None, required = False, doc = """A cortical surface on which ... including ... If left as None then EEG is assumed and skin_air is expected to already has sensors associated""") def __init__(self, **kwargs): """ Initialse traited attributes and attributes that will hold OpenMEEG objects. """ super(ProjectionMatrix, self).__init__(**kwargs) LOG.debug(str(kwargs)) #OpenMEEG attributes self.om_head = None self.om_sources = None self.om_sensors = None self.om_head2sensor = None self.om_inverse_head = None self.om_source_matrix = None self.om_source2sensor = None #For MEG, not used for EEG def configure(self): """ Converts TVB objects into a for accessible to OpenMEEG, then uses the OpenMEEG library to calculate the intermediate matrices needed in obtaining the final projection matrix. """ super(ProjectionMatrix, self).configure() if self.sensors is None: self.sensors = self.skin_air.sensors if isinstance(self.sensors, sensors_module.SensorsEEG): self.skin_air.sensors = self.sensors self.skin_air.sensor_locations = self.sensors.sensors_to_surface(self.skin_air) # Create OpenMEEG objects from TVB objects. self.om_head = self.create_om_head() self.om_sources = self.create_om_sources() self.om_sensors = self.create_om_sensors() # Calculate based on type of sources if isinstance(self.sources, Cortex): self.om_source_matrix = self.surface_source() #NOTE: ~1 hr elif isinstance(self.sources, connectivity_module.Connectivity): self.om_source_matrix = self.dipole_source() # Calculate based on type of sensors if isinstance(self.sensors, sensors_module.SensorsEEG): self.om_head2sensor = self.head2eeg() elif isinstance(self.sensors, sensors_module.SensorsMEG): self.om_head2sensor = self.head2meg() if isinstance(self.sources, Cortex): self.om_source2sensor = self.surf2meg() elif isinstance(self.sources, connectivity_module.Connectivity): self.om_source2sensor = self.dip2meg() #NOTE: ~1 hr self.om_inverse_head = self.inverse_head(inv_head_mat_file = "hminv_uid") def __call__(self): """ Having configured the ProjectionMatrix instance, that is having run the configure() method or otherwise provided the intermedite OpenMEEG (om_*) attributes, the oblect can be called as a function -- returning a projection matrix as a Numpy array. """ #Check source type and sensor type, then call appripriate methods to #generate intermediate data, cascading all the way back to geometry #calculation if it wasn't already done. #Then return a projection matrix... # NOTE: returned projection_matrix is a numpy.ndarray if isinstance(self.sensors, sensors_module.SensorsEEG): projection_matrix = self.eeg_gain() elif isinstance(self.sensors, sensors_module.SensorsMEG): projection_matrix = self.meg_gain() return projection_matrix ##------------------------------------------------------------------------## ##--------------- Methods for creating openmeeg objects ------------------## ##------------------------------------------------------------------------## def create_om_head(self): #TODO: Prob. need to make file names specifiable """ Generates 5 files:: skull_skin.tri skin_air.tri brain_skull.tri head_model.geom head_model.cond Containing the specification of a head in a form that can be read by OpenMEEG, then creates and returns an OpenMEEG Geometry object containing this information. """ surface_files = [] surface_files.append(self._tvb_surface_to_tri("skull_skin.tri")) surface_files.append(self._tvb_surface_to_tri("brain_skull.tri")) surface_files.append(self._tvb_surface_to_tri("skin_air.tri")) geometry_file = self._write_head_geometry(surface_files, "head_model.geom") conductances_file = self._write_conductances("head_model.cond") LOG.info("Creating OpenMEEG Geometry object for the head...") om_head = om.Geometry() om_head.read(geometry_file, conductances_file) #om_head.selfCheck() #Didn't catch bad order... LOG.info("OpenMEEG Geometry object for the head successfully created.") return om_head def create_om_sources(self): #TODO: Prob. should make file names specifiable """ Take a TVB Connectivity or Cortex object and return an OpenMEEG object that specifies sources, a Matrix object for region level sources or a Mesh object for a cortical surface source. """ if isinstance(self.sources, connectivity_module.Connectivity): sources_file = self._tvb_connectivity_to_txt("sources.txt") om_sources = om.Matrix() elif isinstance(self.sources, Cortex): sources_file = self._tvb_surface_to_tri("sources.tri") om_sources = om.Mesh() else: LOG.error("sources must be either a Connectivity or Cortex.") om_sources.load(sources_file) return om_sources def create_om_sensors(self, file_name=None): """ Take a TVB Sensors object and return an OpenMEEG Sensors object. """ if isinstance(self.sensors, sensors_module.SensorsEEG): file_name = file_name or "eeg_sensors.txt" sensors_file = self._tvb_eeg_sensors_to_txt(file_name) elif isinstance(self.sensors, sensors_module.SensorsMEG): file_name = file_name or "meg_sensors.squid" sensors_file = self._tvb_meg_sensors_to_squid(file_name) else: LOG.error("sensors should be either SensorsEEG or SensorsMEG") LOG.info("Wrote sensors to temporary file: %s" % str(file_name)) om_sensors = om.Sensors() om_sensors.load(sensors_file) return om_sensors ##------------------------------------------------------------------------## ##--------- Methods for calling openmeeg methods, with logging. ----------## ##------------------------------------------------------------------------## def surf2meg(self): """ Create a matrix that can be used to map an OpenMEEG surface source to an OpenMEEG MEG Sensors object. NOTE: This source to sensor mapping is not required for EEG. """ LOG.info("Computing DipSource2MEGMat...") surf2meg_mat = om.SurfSource2MEGMat(self.om_sources, self.om_sensors) LOG.info("surf2meg: %d x %d" % (surf2meg_mat.nlin(), surf2meg_mat.ncol())) return surf2meg_mat def dip2meg(self): """ Create an OpenMEEG Matrix that can be used to map OpenMEEG dipole sources to an OpenMEEG MEG Sensors object. NOTE: This source to sensor mapping is not required for EEG. """ LOG.info("Computing DipSource2MEGMat...") dip2meg_mat = om.DipSource2MEGMat(self.om_sources, self.om_sensors) LOG.info("dip2meg: %d x %d" % (dip2meg_mat.nlin(), dip2meg_mat.ncol())) return dip2meg_mat def head2eeg(self): """ Call OpenMEEG's Head2EEGMat method to calculate the head to EEG sensor matrix. """ LOG.info("Computing Head2EEGMat...") h2s_mat = om.Head2EEGMat(self.om_head, self.om_sensors) LOG.info("head2eeg: %d x %d" % (h2s_mat.nlin(), h2s_mat.ncol())) return h2s_mat def head2meg(self): """ Call OpenMEEG's Head2MEGMat method to calculate the head to MEG sensor matrix. """ LOG.info("Computing Head2MEGMat...") h2s_mat = om.Head2MEGMat(self.om_head, self.om_sensors) LOG.info("head2meg: %d x %d" % (h2s_mat.nlin(), h2s_mat.ncol())) return h2s_mat def surface_source(self, gauss_order = 3, surf_source_file=None): """ Call OpenMEEG's SurfSourceMat method to calculate a surface source matrix. Optionaly saving the matrix for later use. """ LOG.info("Computing SurfSourceMat...") ssm = om.SurfSourceMat(self.om_head, self.om_sources, gauss_order) LOG.info("surface_source_mat: %d x %d" % (ssm.nlin(), ssm.ncol())) if surf_source_file is not None: LOG.info("Saving surface_source matrix as %s..." % surf_source_file) ssm.save(os.path.join(OM_STORAGE_DIR, surf_source_file + OM_SAVE_SUFFIX)) #~3GB return ssm def dipole_source(self, gauss_order = 3, use_adaptive_integration = True, dip_source_file=None): """ Call OpenMEEG's DipSourceMat method to calculate a dipole source matrix. Optionaly saving the matrix for later use. """ LOG.info("Computing DipSourceMat...") dsm = om.DipSourceMat(self.om_head, self.om_sources, gauss_order, use_adaptive_integration) LOG.info("dipole_source_mat: %d x %d" % (dsm.nlin(), dsm.ncol())) if dip_source_file is not None: LOG.info("Saving dipole_source matrix as %s..." % dip_source_file) dsm.save(os.path.join(OM_STORAGE_DIR, dip_source_file + OM_SAVE_SUFFIX)) return dsm def inverse_head(self, gauss_order = 3, inv_head_mat_file = None): """ Call OpenMEEG's HeadMat method to calculate a head matrix. The inverse method of the head matrix is subsequently called to invert the matrix. Optionaly saving the inverted matrix for later use. Runtime ~8 hours, mostly in martix inverse as I just use a stock ATLAS install which doesn't appear to be multithreaded (custom building ATLAS should sort this)... Under Windows it should use MKL, not sure for Mac For reg13+potato surfaces, saved file size: hminv ~ 5GB, ssm ~ 3GB. """ LOG.info("Computing HeadMat...") head_matrix = om.HeadMat(self.om_head, gauss_order) LOG.info("head_matrix: %d x %d" % (head_matrix.nlin(), head_matrix.ncol())) LOG.info("Inverting HeadMat...") hminv = head_matrix.inverse() LOG.info("inverse head_matrix: %d x %d" % (hminv.nlin(), hminv.ncol())) if inv_head_mat_file is not None: LOG.info("Saving inverse_head matrix as %s..." % inv_head_mat_file) hminv.save(os.path.join(OM_STORAGE_DIR, inv_head_mat_file + OM_SAVE_SUFFIX)) #~5GB return hminv def eeg_gain(self, eeg_file=None): """ Call OpenMEEG's GainEEG method to calculate the final projection matrix. Optionaly saving the matrix for later use. The OpenMEEG matrix is converted to a Numpy array before return. """ LOG.info("Computing GainEEG...") eeg_gain = om.GainEEG(self.om_inverse_head, self.om_source_matrix, self.om_head2sensor) LOG.info("eeg_gain: %d x %d" % (eeg_gain.nlin(), eeg_gain.ncol())) if eeg_file is not None: LOG.info("Saving eeg_gain as %s..." % eeg_file) eeg_gain.save(os.path.join(OM_STORAGE_DIR, eeg_file + OM_SAVE_SUFFIX)) return om.asarray(eeg_gain) def meg_gain(self, meg_file=None): """ Call OpenMEEG's GainMEG method to calculate the final projection matrix. Optionaly saving the matrix for later use. The OpenMEEG matrix is converted to a Numpy array before return. """ LOG.info("Computing GainMEG...") meg_gain = om.GainMEG(self.om_inverse_head, self.om_source_matrix, self.om_head2sensor, self.om_source2sensor) LOG.info("meg_gain: %d x %d" % (meg_gain.nlin(), meg_gain.ncol())) if meg_file is not None: LOG.info("Saving meg_gain as %s..." % meg_file) meg_gain.save(os.path.join(OM_STORAGE_DIR, meg_file + OM_SAVE_SUFFIX)) return om.asarray(meg_gain) ##------------------------------------------------------------------------## ##------- Methods for writting temporary files loaded by openmeeg --------## ##------------------------------------------------------------------------## def _tvb_meg_sensors_to_squid(self, sensors_file_name): """ Write a tvb meg_sensor datatype to a .squid file, so that OpenMEEG can read it and compute the projection matrix for MEG... """ sensors_file_path = os.path.join(OM_STORAGE_DIR, sensors_file_name) meg_sensors = numpy.hstack((self.sensors.locations, self.sensors.orientations)) numpy.savetxt(sensors_file_path, meg_sensors) return sensors_file_path def _tvb_connectivity_to_txt(self, dipoles_file_name): """ Write position and orientation information from a TVB connectivity object to a text file that can be read as source dipoles by OpenMEEG. NOTE: Region level simulations lack sufficient detail of source orientation, etc, to provide anything but superficial relevance. It's probably better to do a mapping of region level simulations to a surface and then perform the EEG projection from the mapped data... """ NotImplementedError def _tvb_surface_to_tri(self, surface_file_name): """ Write a tvb surface datatype to .tri format, so that OpenMEEG can read it and compute projection matrices for EEG/MEG/... """ surface_file_path = os.path.join(OM_STORAGE_DIR, surface_file_name) #TODO: check file doesn't already exist LOG.info("Writing TVB surface to .tri file: %s" % surface_file_path) file_handle = file(surface_file_path, "a") file_handle.write("- %d \n" % self.sources.number_of_vertices) verts_norms = numpy.hstack((self.sources.vertices, self.sources.vertex_normals)) numpy.savetxt(file_handle, verts_norms) tri_str = "- " + (3 * (str(self.sources.number_of_triangles) + " ")) + "\n" file_handle.write(tri_str) numpy.savetxt(file_handle, self.sources.triangles, fmt="%d") file_handle.close() LOG.info("%s written successfully." % surface_file_name) return surface_file_path def _tvb_eeg_sensors_to_txt(self, sensors_file_name): """ Write a tvb eeg_sensor datatype (after mapping to the head surface to be used) to a .txt file, so that OpenMEEG can read it and compute leadfield/projection/forward_solution matrices for EEG... """ sensors_file_path = os.path.join(OM_STORAGE_DIR, sensors_file_name) LOG.info("Writing TVB sensors to .txt file: %s" % sensors_file_path) numpy.savetxt(sensors_file_path, self.skin_air.sensor_locations) LOG.info("%s written successfully." % sensors_file_name) return sensors_file_path #TODO: enable specifying ?or determining? domain surface relationships... def _write_head_geometry(self, boundary_file_names, geom_file_name): """ Write a geometry file that is read in by OpenMEEG, this file specifies the files containng the boundary surfaces and there relationship to the domains that comprise the head. NOTE: Currently the list of files is expected to be in a specific order, namely:: skull_skin brain_skull skin_air which is reflected in the static setting of domains. Should be generalised. """ geom_file_path = os.path.join(OM_STORAGE_DIR, geom_file_name) #TODO: Check that the file doesn't already exist. LOG.info("Writing head geometry file: %s" % geom_file_path) file_handle = file(geom_file_path, "a") file_handle.write("# Domain Description 1.0\n\n") file_handle.write("Interfaces %d Mesh\n\n" % len(boundary_file_names)) for file_name in boundary_file_names: file_handle.write("%s\n" % file_name) file_handle.write("\nDomains %d\n\n" % (len(boundary_file_names) + 1)) file_handle.write("Domain Scalp %s %s\n" % (1, -3)) file_handle.write("Domain Brain %s %s\n" % ("-2", "shared")) file_handle.write("Domain Air %s\n" % 3) file_handle.write("Domain Skull %s %s\n" % (2, -1)) file_handle.close() LOG.info("%s written successfully." % geom_file_path) return geom_file_path def _write_conductances(self, cond_file_name): """ Write a conductance file that is read in by OpenMEEG, this file specifies the conductance of each of the domains making up the head. NOTE: Vaules are restricted to have 2 decimal places, ie #.##, setting values of the form 0.00# will result in 0.01 or 0.00, for numbers greater or less than ~0.00499999999999999967, respecitvely... """ cond_file_path = os.path.join(OM_STORAGE_DIR, cond_file_name) #TODO: Check that the file doesn't already exist. LOG.info("Writing head conductance file: %s" % cond_file_path) file_handle = file(cond_file_path, "a") file_handle.write("# Properties Description 1.0 (Conductivities)\n\n") file_handle.write("Air %4.2f\n" % self.conductances["air"]) file_handle.write("Scalp %4.2f\n" % self.conductances["skin"]) file_handle.write("Brain %4.2f\n" % self.conductances["brain"]) file_handle.write("Skull %4.2f\n" % self.conductances["skull"]) file_handle.close() LOG.info("%s written successfully." % cond_file_path) return cond_file_path #TODO: Either make these utility functions or have them load directly into # the appropriate attribute... ##------------------------------------------------------------------------## ##---- Methods for loading precomputed matrices into openmeeg objects ----## ##------------------------------------------------------------------------## def _load_om_inverse_head_mat(self, file_name): """ Load a previously stored inverse head matrix into an OpenMEEG SymMatrix object. """ inverse_head_martix = om.SymMatrix() inverse_head_martix.load(file_name) return inverse_head_martix def _load_om_source_mat(self, file_name): """ Load a previously stored source matrix into an OpenMEEG Matrix object. """ source_matrix = om.Matrix() source_matrix.load(file_name) return source_matrix
def configure(self, dt=2**-3, model=models.Generic2dOscillator, speed=4.0, coupling_strength=0.00042, method=HeunDeterministic, surface_sim=False, default_connectivity=True): """ Create an instance of the Simulator class, by default use the generic plane oscillator local dynamic model and the deterministic version of Heun's method for the numerical integration. """ self.method = method if default_connectivity: white_matter = Connectivity.from_file() region_mapping = RegionMapping.from_file( source_file="regionMapping_16k_76.txt") else: white_matter = Connectivity.from_file( source_file="connectivity_192.zip") region_mapping = RegionMapping.from_file( source_file="regionMapping_16k_192.txt") region_mapping.surface = CorticalSurface.from_file() white_matter_coupling = coupling.Linear( a=numpy.array([coupling_strength])) white_matter.speed = numpy.array( [speed]) # no longer allow scalars to numpy array promotion dynamics = model() if issubclass(method, IntegratorStochastic): hisss = noise.Additive(nsig=numpy.array([2**-11])) integrator = method(dt=dt, noise=hisss) else: integrator = method(dt=dt) if surface_sim: local_coupling_strength = numpy.array([2**-10]) default_cortex = Cortex.from_file() default_cortex.region_mapping_data = region_mapping default_cortex.coupling_strength = local_coupling_strength if default_connectivity: default_cortex.local_connectivity = LocalConnectivity.from_file( ) else: default_cortex.local_connectivity = LocalConnectivity() default_cortex.local_connectivity.surface = default_cortex.region_mapping_data.surface else: default_cortex = None # Order of monitors determines order of returned values. self.sim = simulator.Simulator() self.sim.surface = default_cortex self.sim.model = dynamics self.sim.integrator = integrator self.sim.connectivity = white_matter self.sim.coupling = white_matter_coupling self.sim.monitors = self.monitors self.sim.configure()
class Head(HasTraits): """ One patient virtualization. Fully configured for defining hypothesis on it. """ # TODO: find a solution with cross-references between tvb-scripts and TVB datatypes title = Attr(str, default="Head", required=False) path = Attr(str, default="path", required=False) connectivity = Attr(field_type=TVBConnectivity) cortical_surface = Attr(field_type=TVBSurface, required=False) subcortical_surface = Attr(field_type=TVBSurface, required=False) cortical_region_mapping = Attr(field_type=TVBRegionMapping, required=False) subcortical_region_mapping = Attr(field_type=TVBRegionMapping, required=False) region_volume_mapping = Attr(field_type=TVBRegionVolumeMapping, required=False) local_connectivity = Attr(field_type=TVBLocalConnectivity, required=False) t1 = Attr(field_type=TVBStructuralMRI, required=False) t2 = Attr(field_type=TVBStructuralMRI, required=False) flair = Attr(field_type=TVBStructuralMRI, required=False) b0 = Attr(field_type=TVBStructuralMRI, required=False) eeg_sensors = Attr(field_type=TVBSensors, required=False) seeg_sensors = Attr(field_type=TVBSensors, required=False) meg_sensors = Attr(field_type=TVBSensors, required=False) eeg_projection = Attr(field_type=TVBProjectionMatrix, required=False) seeg_projection = Attr(field_type=TVBProjectionMatrix, required=False) meg_projection = Attr(field_type=TVBProjectionMatrix, required=False) _cortex = None def __init__(self, **kwargs): super(Head, self).__init__(**kwargs) def configure(self): if isinstance(self.connectivity, TVBConnectivity): self.connectivity.configure() if isinstance(self.connectivity, TVBLocalConnectivity): self.local_connectivity.configure() if isinstance(self.cortical_surface, TVBSurface): self.cortical_surface.configure() if not isinstance(self.cortical_surface, TVBCorticalSurface): self.log.warning("cortical_surface is not an instance of TVB CorticalSurface!") if isinstance(self.cortical_region_mapping, TVBRegionMapping): self.cortical_region_mapping.connectivity = self.connectivity self.cortical_region_mapping.surface = self.cortical_surface self.cortical_region_mapping.configure() if isinstance(self.subcortical_surface, TVBSurface): self.subcortical_surface.configure() if not isinstance(self.subcortical_surface, CorticalSurface): self.log.warning("cortical_surface is not an instance of SubcorticalSurface!") if isinstance(self.subcortical_region_mapping, TVBRegionMapping): self.subcortical_region_mapping.connectivity = self.connectivity self.subcortical_region_mapping.surface = self.subcortical_surface self.subcortical_region_mapping.configure() structural = None for s_type in ["b0", "flair", "t2", "t1"]: instance = getattr(self, s_type) if isinstance(instance, TVBStructuralMRI): instance.configure() structural = instance if structural is not None: if isinstance(self.region_volume_mapping, TVBRegionVolumeMapping): self.region_volume_mapping.connectivity = self.connectivity self.region_volume_mapping.volume = structural.volume self.region_volume_mapping.configure() for s_type, p_type, s_datatype, p_datatype \ in zip(["eeg", "seeg", "meg"], [ProjectionsType.EEG.value, ProjectionsType.SEEG.value, ProjectionsType.MEG.value], [TVBSensorsEEG, TVBSensorsInternal, TVBSensorsMEG], [TVBProjectionSurfaceEEG, TVBProjectionSurfaceSEEG, TVBProjectionSurfaceMEG]): sensor_name = "%s_sensors" % s_type sensors = getattr(self, sensor_name) if isinstance(sensors, TVBSensors): sensors.configure() if not isinstance(sensors, s_datatype): self.log.warning("%s is not an instance of TVB %s!" % (sensor_name, s_datatype.__name__)) projection_name = "%s_projection" % s_type projection = getattr(self, projection_name) if isinstance(projection, TVBProjectionMatrix): projection.sensors = sensors if not isinstance(projection, p_datatype): self.log.warning("%s is not an instance of TVB %s!" % (projection_name, p_datatype.__name__)) if isinstance(self.surface, Surface): projection.sources = self.surface projection.projection_type = p_type projection.configure() def filter_regions(self, filter_arr): return self.connectivity.region_labels[filter_arr] def _get_filepath(self, filename, patterns, used_filepaths): # Search for default names if there is no filename provided if filename is None: for pattern in patterns: filepaths = insensitive_glob(os.path.join(self.path, "*%s*" % pattern)) if len(filepaths) > 0: for filepath in filepaths: if filepath not in used_filepaths and os.path.isfile(filepath): return filepath return None else: try: return insensitive_glob(os.path.join(self.path, "*%s*" % filename))[0] except: self.log.warning("No *%s* file found in %s path!" % (filename, self.path)) def _load_reference(self, datatype, arg_name, patterns, used_filepaths, **kwargs): # Load from file filepath = self._get_filepath(kwargs.pop(arg_name, None), patterns, used_filepaths) if filepath is not None: used_filepaths.append(filepath) if issubclass(datatype, BaseModel): if filepath.endswith("h5"): return datatype.from_h5_file(filepath), kwargs else: return datatype.from_tvb_file(filepath), kwargs else: return datatype.from_file(filepath), kwargs else: return None, kwargs @classmethod def from_folder(cls, path=None, head=None, **kwargs): # TODO confirm the filetypes and add (h5 and other) readers to all TVB classes .from_file methods # Default patterns: # *conn* for zip/h5 files # (*cort/subcort*)surf*(*cort/subcort*) / (*cort/subcort*)srf*(*cort/subcort*) for zip/h5 files # (*cort/subcort*)reg*map(*cort/subcort*) for txt files # *map*vol* / *vol*map* for txt files # *t1/t2/flair/b0 for ??? files # *eeg/seeg/meg*sensors/locations* / *sensors/locations*eeg/seeg/meg for txt files # # *eeg/seeg/meg*proj/gain* / *proj/gain*eeg/seeg/meg for npy/mat used_filepaths = [] if head is None: head = Head() head.path = path title = os.path.basename(path) if len(title) > 0: head.title = title # We need to read local_connectivity first to avoid confusing it with connectivity: head.local_connectivity, kwargs = \ head._load_reference(LocalConnectivity, 'local_connectivity', ["loc*conn", "conn*loc"], used_filepaths, **kwargs) # Connectivity is required # conn_instances connectivity, kwargs = \ head._load_reference(Connectivity, "connectivity", ["conn"], used_filepaths, **kwargs) if connectivity is None: raise_value_error("A Connectivity instance is minimally required for a Head instance!", cls.log) head.connectivity = connectivity # TVB only volume datatypes: do before region_mappings to avoid confusing them with volume_mapping structural = None for datatype, arg_name, patterns in zip([B0, Flair, T2, T1], ["b0", "flair", "t2", "t1", ], [["b0"], ["flair"], ["t2"], ["t1"]]): try: datatype.from_file instance, kwargs = head._load_reference(datatype, arg_name, patterns, used_filepaths, **kwargs) except: cls.log.warning("No 'from_file' method yet for %s!" % datatype.__class__.__name__) instance = None if instance is not None: setattr(head, arg_name, instance) volume_instance = instance if structural is not None: head.region_volume_mapping, kwargs = \ head._load_reference(RegionVolumeMapping, "region_volume_mapping", ["vol*map", "map*vol"], used_filepaths, **kwargs) # Surfaces and mappings # (read subcortical ones first to avoid confusion): head.subcortical_surface, kwargs = \ head._load_reference(SubcorticalSurface, "subcortical_surface", ["subcort*surf", "surf*subcort", "subcort*srf", "srf*subcort"], used_filepaths, **kwargs) if head.subcortical_surface is not None: # Region Mapping requires Connectivity and Surface head.subcortical_region_mapping, kwargs = \ head._load_reference(SubcorticalRegionMapping, "subcortical_region_mapping", ["subcort*reg*map", "reg*map*subcort"], used_filepaths, **kwargs) head.cortical_surface, kwargs = \ head._load_reference(CorticalSurface, "cortical_surface", ["cort*surf", "surf*cort", "cort*srf", "srf*cort", "surf", "srf"], used_filepaths, **kwargs) if head.cortical_surface is not None: # Region Mapping requires Connectivity and Surface head.cortical_region_mapping, kwargs = \ head._load_reference(CorticalRegionMapping, "cortical_region_mapping", ["cort*reg*map", "reg*map*cort", "reg*map"], used_filepaths, **kwargs) # Sensors and projections # (read seeg before eeg to avoid confusion!) for s_datatype, p_datatype, s_type in zip([SensorsSEEG, SensorsEEG, SensorsMEG], [ProjectionSurfaceSEEG, ProjectionSurfaceEEG, ProjectionSurfaceMEG], ["seeg", "eeg", "meg"]): arg_name = "%s_sensors" % s_type patterns = ["%s*sensors" % s_type, "sensors*%s" % s_type, "%s*locations" % s_type, "locations*%s" % s_type] sensors, kwargs = head._load_reference(s_datatype, arg_name, patterns, used_filepaths, **kwargs) if sensors is not None: setattr(head, arg_name, sensors) arg_name = "%s_projection" % s_type patterns = ["%s*proj" % s_type, "proj*%s" % s_type, "%s*gain" % s_type, "gain*%s" % s_type] projection, kwargs = head._load_reference(p_datatype, arg_name, patterns, used_filepaths, **kwargs) setattr(head, arg_name, projection) return head @classmethod def from_file(cls, path, **kwargs): filename = os.path.basename(path) dirname = os.path.dirname(path) if "head" in filename.lower(): import h5py head = Head() head.path = path h5file = h5py.File(path, 'r', libver='latest') for field in []: try: setattr(head, field, h5file['/' + field][()]) except: cls.log.warning("Failed to read Head field %s from file %s!" % (field, path)) for attr in ["title"]: try: setattr(head, attr, h5file.attrs.get(attr, h5file.attrs.get("TVB_%s" % attr))) except: cls.log.warning("Failed to read Head attribute %s from file %s!" % (attr, path)) head.path = dirname else: kwargs["connectivity"] = filename head = None return cls.from_folder(dirname, head, **kwargs) @classmethod def from_tvb_file(cls, path, **kwargs): return cls.from_file(path, **kwargs) def make_cortex(self, local_connectivity=None, coupling_strength=None): self._cortex = Cortex() self._cortex.region_mapping_data = self.cortical_region_mapping if isinstance(local_connectivity, LocalConnectivity): self._cortex.local_connectivity = local_connectivity if coupling_strength is not None: self._cortex.coupling_strength = coupling_strength self._cortex.configure() return self._cortex def cortex(self, local_connectivity=None, coupling_strength=None): if not isinstance(self._cortex, Cortex): self.make_cortex(local_connectivity, coupling_strength) return self._cortex @property def surface(self): return self.cortical_surface @property def number_of_regions(self): return self.connectivity.number_of_regions
#Initialise some Monitors with period in physical time mon_tavg = monitors.TemporalAverage(period=2**-2) mon_savg = monitors.SpatialAverage(period=2**-2) mon_eeg = monitors.EEG(period=2**-2) #Bundle them what_to_watch = (mon_tavg, mon_savg, mon_eeg) #Initialise a surface local_coupling_strength = numpy.array([0.0121]) grey_matter = LocalConnectivity(equation=equations.Gaussian(), cutoff=60.0) grey_matter.equation.parameters['sigma'] = 10.0 grey_matter.equation.parameters['amp'] = 1.0 default_cortex = Cortex.from_file( eeg_projection_file="surface_reg_13_eeg_62.mat") default_cortex.local_connectivity = grey_matter default_cortex.coupling_strength = local_coupling_strength #Define the stimulus eqn_t = equations.Gaussian() eqn_t.parameters["amp"] = 1.0 eqn_t.parameters["midpoint"] = 8.0 eqn_x = equations.Gaussian() eqn_x.parameters["amp"] = -0.0625 eqn_x.parameters["sigma"] = 28.0 stimulus = patterns.StimuliSurface(surface=default_cortex, temporal=eqn_t, spatial=eqn_x,