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
