In [1]:
%pylab inline
In [3]:
import numpy
import matplotlib as mpl
mpl.rc('savefig', dpi=300)
from tvb.tests.library import setup_test_console_env
setup_test_console_env()
from tvb.simulator.monitors import MonitorTransforms
from tvb.simulator import models, coupling, integrators, noise, simulator
from tvb.datatypes import connectivity
from tvb.simulator.monitors import Raw
Very often it's useful to apply specific transformations to the state variables before applying the observation model of a monitor. Additionally, it can be useful to apply other transformations on the monitor's output.
The pre_expr and post_expr attributes of the Monitor classes allow for this.
In [35]:
sim = simulator.Simulator(
model=models.Generic2dOscillator(),
connectivity=connectivity.Connectivity(load_default=True),
coupling=coupling.Linear(),
integrator=integrators.EulerDeterministic(),
monitors=Raw(pre_expr='V;W;V**2;W-V', post_expr=';;sin(mon);exp(mon)'))
sim.configure()
ts, ys = [], []
for (t, y), in sim(simulation_length=250):
ts.append(t)
ys.append(y)
t = numpy.array(ts)
v, w, sv2, ewmv = numpy.array(ys).transpose((1, 0, 2, 3))
Plotting the results demonstrates the effect of the transformations of the state variables through the monitor. Here, a Raw monitor was used to make the effects clear, but the pre- and post-expressions can be provided to any of the Monitors.
In [36]:
figure(figsize=(7, 5), dpi=600)
subplot(311)
plot(t, v[:, 0, 0], 'k')
plot(t, w[:, 0, 0], 'k')
ylabel('$V(t), W(t)$')
grid(True, axis='x')
xticks(xticks()[0], [])
subplot(312)
plot(t, sv2[:, 0, 0], 'k')
ylabel('$\\sin(G(V^2(t)))$')
grid(True, axis='x')
xticks(xticks()[0], [])
subplot(313)
plot(t, ewmv[:, 0, 0], 'k')
ylabel('$\\exp(G(W(t)-V(t)))$')
grid(True, axis='x')
xlabel('Time (ms)')
tight_layout()
In this case, the chosen transformations are primarily visible during the transient.