def __init__(self):
"""
Initialise the structural information, coupling function, and monitors.
"""
# Initialise some Monitors with period in physical time
raw = monitors.Raw()
gavg = monitors.GlobalAverage(period=2 ** -2)
subsamp = monitors.SubSample(period=2 ** -2)
tavg = monitors.TemporalAverage(period=2 ** -2)
# DON'T load a projection because it'll make this behave like it
# has a surface and do very, very bad things
eeg = monitors.EEG(sensors=SensorsEEG(load_file="eeg_brainstorm_65.txt"),
period=2 ** -2)
eeg2 = monitors.EEG(sensors=SensorsEEG(load_file="eeg_brainstorm_65.txt"),
period=2 ** -2,
reference='Fp2') # EEG with a reference electrode
meg = monitors.MEG(sensors=SensorsMEG(load_file='meg_brainstorm_276.txt'),
period=2 ** -2)
self.monitors = (raw, gavg, subsamp, tavg, eeg, eeg2, meg)
self.method = None
self.sim = None
def __init__(self):
"""
Initialise the structural information, coupling function, and monitors.
"""
# Initialise some Monitors with period in physical time
raw = monitors.Raw()
gavg = monitors.GlobalAverage(period=2 ** -2)
subsamp = monitors.SubSample(period=2 ** -2)
tavg = monitors.TemporalAverage(period=2 ** -2)
eeg = monitors.EEG(load_default=True, period=2 ** -2)
eeg2 = monitors.EEG(load_default=True, period=2 ** -2, reference='Fp2') # EEG with a reference electrode
meg = monitors.MEG(load_default=True, period=2 ** -2)
self.monitors = (raw, gavg, subsamp, tavg, eeg, eeg2, meg)
self.method = None
self.sim = None
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_monitor_eeg(self):
monitor = monitors.EEG()
assert monitor.period == self.default_period
LOG.info("Configuring...")
#Initialise a Model, Coupling, and Connectivity.
oscilator = models.Generic2dOscillator()
white_matter = connectivity.Connectivity()
white_matter.speed = 4.0
white_matter_coupling = coupling.Linear(a=-2**-9)
#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([0.0121])
grey_matter = surfaces.LocalConnectivity(equation=equations.Gaussian(),
cutoff=60.0)
grey_matter.equation.parameters['sigma'] = 10.0
grey_matter.equation.parameters['amp'] = 0.0
default_cortex = surfaces.Cortex(local_connectivity=grey_matter,
coupling_strength=local_coupling_strength)
def test_monitor_eeg(self):
monitor = monitors.EEG()
self.assertEqual(monitor.period, self.default_period)
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 = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/DH_20120806_EEGLocations.txt"
sensorsEEG = SensorsEEG(load_file=sensoreeg_fname)
prEEG = ProjectionSurfaceEEG(load_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
# represent a neuron's membrane potential and recovery; see the
# mathematics paper for default values and equations; runtime 8016 s
model=Linear(),
# model: Wilson & Cowan - two neural masses have an excitatory/inhibitory
# relationship; see paper for details; runtime 16031 s
# model: Wong & Wang - reduced system of two non-linear coupled differential
