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

Having multiple stimuli with different time courses for different nodes currently requires an extra class, see usage below

from tvb.basic.neotraits.api import NArray, Attr

class MultiStimuliRegion(patterns.StimuliRegion):
    connectivity = Attr(connectivity.Connectivity, required=False)
    temporal = Attr(field_type=equations.TemporalApplicableEquation, required=False)
    weight = NArray(required=False)
    
    def __init__(self, *stimuli):
        super(MultiStimuliRegion, self).__init__()
        self.stimuli = stimuli
    def configure_space(self, *args, **kwds):
        [stim.configure_space(*args, **kwds) for stim in self.stimuli]
    def configure_time(self, *args, **kwds):
        [stim.configure_time(*args, **kwds) for stim in self.stimuli]
    def __call__(self, *args, **kwds):
        return np.array([stim(*args, **kwds) for stim in self.stimuli]).sum(axis=0)

Now we can make several pulse trains with different temporal configurations and node weights, and combine them with above class.

conn = connectivity.Connectivity.from_file()
nnode = conn.weights.shape[0]

def make_train(node_idx, node_weights, **params):
    weighting = np.zeros(nnode)
    weighting[node_idx] = node_weights
    eqn_t = equations.PulseTrain()
    eqn_t.parameters.update(params)
    stimulus = patterns.StimuliRegion(
        temporal=eqn_t,
        connectivity=conn,
        weight=weighting)
    return stimulus

train1 = make_train([10, 20], 1.0, onset=1.5e3, T=100.0, tau=50.0)
train2 = make_train([30, 40], 2.0, onset=1.5e3, T=200.0, tau=100.0)
train3 = make_train(np.r_[7:74:5], 0.2, onset=5e2, T=50.0, tau=20.0)
stimulus = MultiStimuliRegion(train1, train2, train3)
stimulus.configure_space()
time = np.r_[1e3:2e3:10.0]
stimulus.configure_time(time)
pattern = stimulus()
plt.imshow(pattern, interpolation='none')
plt.xlabel('Time')
plt.ylabel('Space')
plt.colorbar()

WARNING  File 'hemispheres' not found in ZIP.

<matplotlib.colorbar.Colorbar at 0x19f49950748>

Here we just visualize the output of stimulus, but it could also be passed to a simulator object for simulation.