In [1]:
%pylab inline
from tvb.simulator.lab import *
Populating the interactive namespace from numpy and matplotlib
   INFO  log level set to INFO
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.wilson_cowan.WilsonCowan.state_variable_range = Final(field_type=<class 'dict'>, default={'E': array([0., 1.]), 'I': array([0., 1.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.stefanescu_jirsa.ReducedSetFitzHughNagumo.state_variable_range = Final(field_type=<class 'dict'>, default={'xi': array([-4.,  4.]), 'eta': array([-3.,  3.]), 'alpha': array([-4.,  4.]), 'beta': array([-3.,  3.])}, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.stefanescu_jirsa.ReducedSetHindmarshRose.a = NArray(label=':math:`a`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 3.0 
   attribute  tvb.simulator.models.stefanescu_jirsa.ReducedSetHindmarshRose.b = NArray(label=':math:`b`', dtype=float64, default=array([3.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.stefanescu_jirsa.ReducedSetHindmarshRose.c = NArray(label=':math:`c`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 3.3 
   attribute  tvb.simulator.models.stefanescu_jirsa.ReducedSetHindmarshRose.mu = NArray(label=':math:`\\mu`', dtype=float64, default=array([3.3]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.stefanescu_jirsa.ReducedSetHindmarshRose.state_variable_range = Final(field_type=<class 'dict'>, default={'xi': array([-4.,  4.]), 'eta': array([-25.,  20.]), 'tau': array([ 2., 10.]), 'alpha': array([-4.,  4.]), 'beta': array([-20.,  20.]), 'gamma': array([ 2., 10.])}, required=True)
WARNING  default contains values out of the declared domain. Ex 0.12 
   attribute  tvb.simulator.models.jansen_rit.JansenRit.p_min = NArray(label=':math:`p_{min}`', dtype=float64, default=array([0.12]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 0.32 
   attribute  tvb.simulator.models.jansen_rit.JansenRit.p_max = NArray(label=':math:`p_{max}`', dtype=float64, default=array([0.32]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 0.22 
   attribute  tvb.simulator.models.jansen_rit.JansenRit.mu = NArray(label=':math:`\\mu_{max}`', dtype=float64, default=array([0.22]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.jansen_rit.JansenRit.state_variable_range = Final(field_type=<class 'dict'>, default={'y0': array([-1.,  1.]), 'y1': array([-500.,  500.]), 'y2': array([-50.,  50.]), 'y3': array([-6.,  6.]), 'y4': array([-20.,  20.]), 'y5': array([-500.,  500.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.jansen_rit.ZetterbergJansen.state_variable_range = Final(field_type=<class 'dict'>, default={'v1': array([-100.,  100.]), 'y1': array([-500.,  500.]), 'v2': array([-100.,   50.]), 'y2': array([-100.,    6.]), 'v3': array([-100.,    6.]), 'y3': array([-100.,    6.]), 'v4': array([-100.,   20.]), 'y4': array([-100.,   20.]), 'v5': array([-100.,   20.]), 'y5': array([-500.,  500.]), 'v6': array([-100.,   20.]), 'v7': array([-100.,   20.])}, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.oscillator.Generic2dOscillator.gamma = NArray(label=':math:`\\gamma`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.oscillator.Generic2dOscillator.state_variable_range = Final(field_type=<class 'dict'>, default={'V': array([-2.,  4.]), 'W': array([-6.,  6.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.oscillator.Kuramoto.state_variable_range = Final(field_type=<class 'dict'>, default={'theta': array([0.        , 6.28318531])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.oscillator.supHopf.state_variable_range = Final(field_type=<class 'dict'>, default={'x': array([-5.,  5.]), 'y': array([-5.,  5.])}, required=True)
WARNING  default contains values out of the declared domain. Ex -0.01 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.TCa = NArray(label=':math:`T_{Ca}`', dtype=float64, default=array([-0.01]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 0.3 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.TNa = NArray(label=':math:`T_{Na}`', dtype=float64, default=array([0.3]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 2.0 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.aei = NArray(label=':math:`a_{ei}`', dtype=float64, default=array([2.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 2.0 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.aie = NArray(label=':math:`a_{ie}`', dtype=float64, default=array([2.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.ane = NArray(label=':math:`a_{ne}`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 0.3 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.Iext = NArray(label=':math:`I_{ext}`', dtype=float64, default=array([0.3]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.QV_max = NArray(label=':math:`Q_{max}`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.QZ_max = NArray(label=':math:`Q_{max}`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.larter_breakspear.LarterBreakspear.t_scale = NArray(label=':math:`t_{scale}`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.larter_breakspear.LarterBreakspear.state_variable_range = Final(field_type=<class 'dict'>, default={'V': array([-1.5,  1.5]), 'W': array([-1.5,  1.5]), 'Z': array([-1.5,  1.5])}, required=True)
WARNING  default contains values out of the declared domain. Ex 0.27 
   attribute  tvb.simulator.models.wong_wang.ReducedWongWang.a = NArray(label=':math:`a`', dtype=float64, default=array([0.27]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.wong_wang.ReducedWongWang.state_variable_range = Final(field_type=<class 'dict'>, default={'S': array([0., 1.])}, required=True)
WARNING  default contains values out of the declared domain. Ex 10.0 
   attribute  tvb.simulator.models.wong_wang_exc_io_inh_i.ReducedWongWangExcIOInhI.tau_i = NArray(label=':math:`\\tau_i`', dtype=float64, default=array([10.]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.wong_wang_exc_io_inh_i.ReducedWongWangExcIOInhI.state_variable_range = Final(field_type=<class 'dict'>, default={'S_e': array([0., 1.]), 'S_i': array([0., 1.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.linear.Linear.state_variable_range = Final(field_type=<class 'dict'>, default={'x': array([-1,  1])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.hopfield.Hopfield.state_variable_range = Final(field_type=<class 'dict'>, default={'x': array([-1.,  2.]), 'theta': array([0., 1.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.epileptor.Epileptor.state_variable_range = Final(field_type=<class 'dict'>, default={'x1': array([-2.,  1.]), 'y1': array([-20.,   2.]), 'z': array([2., 5.]), 'x2': array([-2.,  0.]), 'y2': array([0., 2.]), 'g': array([-1.,  1.])}, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.epileptor.Epileptor2D.tt = NArray(label='tt', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.epileptor.Epileptor2D.state_variable_range = Final(field_type=<class 'dict'>, default={'x1': array([-2.,  1.]), 'z': array([2., 5.])}, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.models.JCepileptor.JC_Epileptor.gamma_rs = NArray(label=":math:'\\gamma_rs'", dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.JCepileptor.JC_Epileptor.state_variable_range = Final(field_type=<class 'dict'>, default={'x1': array([-1.8, -1.4]), 'y1': array([-15, -10]), 'z': array([3.6, 4. ]), 'x2': array([-1.1, -0.9]), 'y2': array([0.001, 0.01 ]), 'g': array([-1.,  1.]), 'x_rs': array([-2.,  4.]), 'y_rs': array([-6.,  6.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.epileptorcodim3.EpileptorCodim3.state_variable_range = Final(field_type=<class 'dict'>, default={'x': array([0.4, 0.6]), 'y': array([-0.1,  0.1]), 'z': array([0.  , 0.15])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.epileptorcodim3.EpileptorCodim3SlowMod.state_variable_range = Final(field_type=<class 'dict'>, default={'x': array([0.4, 0.6]), 'y': array([-0.1,  0.1]), 'z': array([0. , 0.1]), 'uA': array([0., 0.]), 'uB': array([0., 0.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.Zerlaut.Zerlaut_adaptation_first_order.state_variable_range = Final(field_type=<class 'dict'>, default={'E': array([0. , 0.1]), 'I': array([0. , 0.1]), 'W': array([  0., 100.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.simulator.models.Zerlaut.Zerlaut_adaptation_second_order.state_variable_range = Final(field_type=<class 'dict'>, default={'E': array([0. , 0.1]), 'I': array([0. , 0.1]), 'C_ee': array([0., 0.]), 'C_ei': array([0., 0.]), 'C_ii': array([0., 0.]), 'W': array([  0., 100.])}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class 'dict'>, default={}, required=True)
WARNING  Field seems mutable and has a default value. Consider using a lambda as a value factory 
   attribute tvb.datatypes.projections.ProjectionMatrix.conductances = Attr(field_type=<class 'dict'>, default={'air': 0.0, 'skin': 1.0, 'skull': 0.01, 'brain': 1.0}, required=False)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.coupling.HyperbolicTangent.b = NArray(label=':math:`b`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)
WARNING  default contains values out of the declared domain. Ex 1.0 
   attribute  tvb.simulator.coupling.Kuramoto.a = NArray(label=':math:`a`', dtype=float64, default=array([1.]), dim_names=(), ndim=None, required=True)

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

In [2]:
class MultiStimuliRegion(patterns.StimuliRegion):
    def __init__(self, *stimuli):
        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.

In [3]:
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(r_[7:74:5], 0.2, onset=5e2, T=50.0, tau=20.0)
stimulus = MultiStimuliRegion(train1, train2, train3)
stimulus.configure_space()
time = r_[1e3:2e3:10.0]
stimulus.configure_time(time)
pattern = stimulus()
imshow(pattern, interpolation='none')
xlabel('Time')
ylabel('Space')
colorbar()
WARNING  File 'hemispheres' not found in ZIP.
Out[3]:
<matplotlib.colorbar.Colorbar at 0x21829e3bc50>

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

In [ ]: