maedoc/autotvb/boilerplate

from tvb.simulator.lab import *
import numpy
import matplotlib.pyplot as plt

from tvb.datatypes.sensors import SensorsEEG, SensorsMEG, SensorsInternal
from tvb.datatypes.projections import ProjectionSurfaceEEG
from tvb.simulator import monitors
# EEG
sensors_eeg = SensorsEEG.from_file('eeg_unitvector_62.txt.bz2')
proj_eeg = ProjectionSurfaceEEG.from_file('projection_eeg_62_surface_16k.mat')
mon_eeg = monitors.EEG(
    sensors=sensors_eeg,
    projection=proj_eeg,
    region_mapping=rm,  # RegionMapping object from surface setup
    period=5.0
)
mon_eeg.configure()
# iEEG / sEEG
sensors_ieeg = SensorsInternal.from_file('seeg_588.txt')
proj_ieeg = ProjectionSurfaceEEG.from_file('projection_seeg_588_surface_16k.mat')
mon_ieeg = monitors.iEEG(
    sensors=sensors_ieeg,
    projection=proj_ieeg,
    region_mapping=rm,
    period=1.0
)
mon_ieeg.configure()
# MEG
from tvb.datatypes.sensors import SensorsMEG
from tvb.datatypes.projections import ProjectionSurfaceMEG
sensors_meg = SensorsMEG.from_file('meg_sensors_306.txt.bz2')
proj_meg = ProjectionSurfaceMEG.from_file('projection_meg_306_surface_16k.mat')
mon_meg = monitors.MEG(
    sensors=sensors_meg,
    projection=proj_meg,
    region_mapping=rm,
    period=5.0
)
mon_meg.configure()

# 1. Connectivity
conn = connectivity.Connectivity.from_file()  # or from_file("custom.zip")
conn.speed = numpy.array([4.0])
conn.configure()
# 2. Model
model = models.Generic2dOscillator(a=numpy.array([-0.5]), b=numpy.array([-15.0]))
# 3. Coupling
coup = coupling.Linear(a=numpy.array([0.0154]))
# 4. Integrator
heunint = integrators.HeunDeterministic(dt=2**-4)
# 5. Monitors
mon_raw = monitors.Raw(period=0.5)
mon_avg = monitors.TemporalAverage(period=5.0)
# 6. Simulator
sim = simulator.Simulator(
    model=model,
    connectivity=conn,
    coupling=coup,
    integrator=heunint,
    monitors=(mon_raw, mon_avg)
)
sim.configure()
# 7. Run
# Option A: sim.run() returns a LIST of monitor result tuples.
# Best for: standard post-hoc analysis after the full simulation.
(t_raw, y_raw), (t_avg, y_avg) = sim.run(simulation_length=1e3)
# Option B: sim() is a generator yielding per-step monitor tuples.
# Best for: custom per-step logic, streaming, or when monitors have different periods.
raw_data = []
avg_data = []
for monitor_tuple in sim(simulation_length=1e3):
    if monitor_tuple[0] is not None:
        raw_data.append(monitor_tuple[0])
    if monitor_tuple[1] is not None:
        avg_data.append(monitor_tuple[1])
# Convert collected tuples to arrays
if raw_data:
    t_raw = numpy.array([r[0] for r in raw_data])
    y_raw = numpy.array([r[1] for r in raw_data])
if avg_data:
    t_avg = numpy.array([a[0] for a in avg_data])
    y_avg = numpy.array([a[1] for a in avg_data])