def configure_simulation(stimulate): """ Set up a Simulator object (a brain network model and all its individual components + output modalities) """ # eeg projection matrix from regions to sensors LOG.info("Reading sensors info") pr = ProjectionSurfaceEEG(load_default=True) sensors = SensorsEEG.from_file(source_file="eeg_brainstorm_65.txt") rm = RegionMapping(load_default=True) #Initialise a Model, Connectivity, Coupling, set speed. oscilator = models.Generic2dOscillator(a=-0.5, b=-10., c=0.0, d=0.02) white_matter = connectivity.Connectivity(load_default=True) white_matter.speed = numpy.array([4.0]) white_matter_coupling = coupling.Linear(a=0.042) #Initialise an Integrator hiss = noise.Additive(nsig=numpy.array([0.00])) # nsigm 0.015 heunint = integrators.HeunStochastic(dt=2**-6, noise=hiss) # Recording techniques what_to_watch = (monitors.TemporalAverage(period=1e3 / 4096.), monitors.EEG(projection=pr, sensors=sensors, region_mapping=rm, period=1e3 / 4096.)) # Stimulation paradigm if stimulate: stimulus = build_stimulus(white_matter) else: stimulus = None #Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors. sim = simulator.Simulator(model=oscilator, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=what_to_watch, stimulus=stimulus) sim.configure() return sim
def configure_simulation(stimulate): """ Set up a Simulator object (a brain network model and all its individual components + output modalities) """ # eeg projection matrix from regions to sensors LOG.info("Reading sensors info") pr = ProjectionSurfaceEEG(load_default=True) sensors = SensorsEEG.from_file(source_file="eeg_brainstorm_65.txt") rm = RegionMapping(load_default=True) #Initialise a Model, Connectivity, Coupling, set speed. oscilator = models.Generic2dOscillator(a=-0.5, b=-10., c=0.0, d=0.02) white_matter = connectivity.Connectivity(load_default=True) white_matter.speed = numpy.array([4.0]) white_matter_coupling = coupling.Linear(a=0.042) #Initialise an Integrator hiss = noise.Additive(nsig=numpy.array([0.00])) # nsigm 0.015 heunint = integrators.HeunStochastic(dt=2 ** -6, noise=hiss) # Recording techniques what_to_watch = (monitors.TemporalAverage(period=1e3 / 4096.), monitors.EEG(projection=pr, sensors=sensors, region_mapping=rm, period=1e3 / 4096.)) # Stimulation paradigm if stimulate: stimulus = build_stimulus(white_matter) else: stimulus = None #Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors. sim = simulator.Simulator(model=oscilator, connectivity=white_matter, coupling=white_matter_coupling, integrator=heunint, monitors=what_to_watch, stimulus=stimulus) sim.configure() return sim
eeg_projection_file=eeg_fname) ctx.configure() print("cortex loaded") pyplot.figure() ax = pyplot.subplot(111, projection='3d') x, y, z = ctx.vertices.T ax.plot_trisurf(x, y, z, triangles=ctx.triangles, alpha=0.1, edgecolor='k') # pyplot.show() print("cortex plot ready") # unit vectors that describe the location of eeg sensors sensoreeg_fname = os.path.join(master_path, 'DH_20120806_EEGLocations.txt') rm = RegionMapping.from_file(region_fname) sensorsEEG = SensorsEEG.from_file(sensoreeg_fname) prEEG = ProjectionSurfaceEEG.from_file(eeg_fname) fsamp = 1e3 / 1024.0 # 1024 Hz mon = monitors.EEG(sensors=sensorsEEG, projection=prEEG, region_mapping=rm, period=fsamp) sim = simulator.Simulator( connectivity=conn, # conduction speed: 3 mm/ms # coupling: linear - rescales activity propagated # stimulus: None - can be a spatiotemporal function # model: Generic 2D Oscillator - neural mass has two state variables that
def __init__(self,Ps): """ Initialize simulation ---------------------- """ sim_length = Ps['sim_params']['length'] outdir = Ps['sim_params']['outdir'] if not os.path.isdir(outdir): os.mkdir(outdir) print '\nConfiguring sim...' sim = simulator.Simulator() _classes = [models, connectivity, coupling, integrators, monitors ] _names = ['model', 'connectivity', 'coupling', 'integrator', 'monitors'] for _class,_name in zip(_classes,_names): if _name is 'monitors': thisattr = tuple([getattr(_class,m['type'])(**m['params']) for m in Ps['monitors'] ]) else: if 'type' in Ps[_name]: thisattr = getattr(_class,Ps[_name]['type'])(**Ps[_name]['params']) setattr(sim,_name,thisattr) # Additionals - parameters that are functions of other classes # (example = larter_breakdspear demo) if 'additionals' in Ps: for a in Ps['additionals']: setattr(eval(a[0]), a[1],eval(a[2])) #sim,eval(a[0]),eval(a[1])) # Stochastic integrator if 'HeunStochastic' in Ps['integrator']: from tvb.simulator.lab import noise hiss = noise.Additive(nsig=np.array(Ps['integrator']['stochastic_nsig'])) # nsigm 0.015 sim.integrator.noise = hiss # Non-default connectivity # (to add here: # - load from other data structures, e.g. .cff file # - load weights, lengths, etc. directly from data matrices etc if 'connectivity' in Ps: if 'folder_path' in Ps['connectivity']: # (this is from the deterministic_stimulus demo) sim.connectivity.default.reload(sim.connectivity, Ps['connectivity']['folder_path']) sim.connectivity.configure() # EEG projections # (need to do this separately because don't seem to be able to do EEG(projection_matrix='<file>') for m_it, m in enumerate(Ps['monitors']): # (yes I know enumerate isn't necessary here; but it's more transparent imho) # assumption here is that the sim object doesn't re-order the list of monitors for any bizarre reason... # (which would almost certainly cause an error anyway...) #if m['type'] is 'EEG' and 'proj_mat_path' in m: # proj_mat = loadmat(m['proj_mat_path'])['ProjectionMatrix'] # pr = projections.ProjectionRegionEEG(projection_data=proj_mat) # sim.monitors[m_it].projection_matrix_data=pr if m['type'] is 'EEG': if m['proj_surf'] is 'default': pr = ProjectionSurfaceEEG(load_default=True) else: pr = ProjectionSurfaceEEG.from_file(m['proj_surf']) eeg_sens = SensorsEEG.from_file(source_file=m['source_file']) if m['reg_map'] is 'default': rm = RegionMapping(load_default=True) else: rm = RegionMapping.from_file(m['reg_map']) sim.monitors[m_it].projection = pr sim.monitors[m_it].sensors = eeg_sens sim.monitors[m_it].region_mapping = rm # Surface if 'surface' in Ps: surf = getattr(surfaces,Ps['surface']['surface_type']).default() if 'local_connectivity_params' in Ps['surface']: localsurfconn = getattr(surfaces,'LocalConnectivity')(**Ps['surface']['local_connectivity_params']) for ep in Ps['surface']['local_connectivity_equation_params'].items(): localsurfconn.equation.parameters[ep[0]] = ep[1] surf.local_connectivity = localsurfconn localcoupling = np.array( Ps['surface']['local_coupling_strength'] ) surf.coupling_strength = localcoupling sim.surface = surf # Stimulus if 'stimulus' in Ps: stim = getattr(patterns,Ps['stimulus']['type'])() if 'equation' in Ps['stimulus']: # looks like need to do this to keep the other params as default; slightly different to above stim_eqn_params = Ps['stimulus']['equation']['params'] # use this if need to evaluate text # (stim_eqn_params = {p[0]: eval(p[1]) for p in Ps['stimulus']['equation']['params'].items() } ( stim_eqn_t = getattr(equations,Ps['stimulus']['equation']['type'])() stim_eqn_t.parameters.update(**stim_eqn_params) stim.temporal = stim_eqn_t elif 'equation' not in Ps['stimulus']: # (still need to do this...) print 'something to do here' sim.connectivity.configure() stim_weighting = np.zeros((sim.connectivity.number_of_regions,)) stim_weighting[Ps['stimulus']['nodes']] = np.array(Ps['stimulus']['node_weightings']) stim.connectivity = sim.connectivity stim.weight = stim_weighting sim.stimulus = stim # Configure sim sim.configure() # Configure smooth parameter variation (if used) spv = {} if 'smooth_pvar' in Ps: par_length = eval(Ps['smooth_pvar']['par_length_str']) spv['mon_type'] = Ps['smooth_pvar']['monitor_type'] spv['mon_num'] = [m_it for m_it, m in enumerate(Ps['monitors']) if m == spv['mon_type'] ] # (yes, a bit clumsy..) # a) as an equally spaced range if 'equation' not in Ps['smooth_pvar']: spv['a'] = eval(Ps['smooth_pvar']['spv_a_str']) # b) using an Equation datadtype else: spv['params'] = {} for p in Ps['smooth_pvar']['equation']['params'].items(): spv['params'][p[0]] = eval(p[1]) #sim_length = Ps['sim_params']['length'] # temporary fix] #spv_a_params = {p[0]: eval(p[1]) for p in Ps['smooth_pvar']['equation']['params'].items() } spv['eqn_t'] = getattr(equations,Ps['smooth_pvar']['equation']['type'])() spv['eqn_t'].parameters.update(**spv['params']) spv['pattern'] = eval(Ps['smooth_pvar']['equation']['pattern_str']) spv['a'] = spv['pattern'] # omit above line? At moment this follows tutorial code # recent additions.... self.sim = sim self.Ps = Ps self.sim_length = sim_length self.spv = spv
def from_tvb_file(self, filepath, remove_leading_zeros_from_labels=False): self._tvb = TVBSensorsEEG.from_file(filepath, self._tvb) if len(self._tvb.labels) > 0: if remove_leading_zeros_from_labels: self.remove_leading_zeros_from_labels() return self