TheVirtualBrain:
TheDocumentationwebsite.
# -*- coding: utf-8 -*-
#
#
# TheVirtualBrain-Contributors Package. This package holds simulator extensions.
# See also http://www.thevirtualbrain.org
#
# (c) 2012-2022, Baycrest Centre for Geriatric Care ("Baycrest") and others
#
# This program is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software Foundation,
# either version 3 of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
# PARTICULAR PURPOSE. See the GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along with this
# program. If not, see <http://www.gnu.org/licenses/>.
#
#
# CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
#
# Paula Sanz Leon, Stuart A. Knock, M. Marmaduke Woodman, Lia Domide,
# Jochen Mersmann, Anthony R. McIntosh, Viktor Jirsa (2013)
# The Virtual Brain: a simulator of primate brain network dynamics.
# Frontiers in Neuroinformatics (7:10. doi: 10.3389/fninf.2013.00010)
"""
.. moduleauthor:: Lionel Kusch <lkusch@thevirtualbrain.org>
.. moduleauthor:: Dionysios Perdikis <dionperd@gmail.com>
"""
import numpy as np
import tvb.simulator.lab as lab
from tvb.tests.library.base_testcase import BaseTestCase
from tvb.contrib.tests.cosimulation.parallel.ReducedWongWang import ReducedWongWangProxy
from tvb.contrib.cosimulation.cosim_monitors import RawCosim
from tvb.contrib.cosimulation.cosimulator import CoSimulator
[docs]class TestModifyWongWang(BaseTestCase):
"""
test for compare the version in tvb and the modified version in different condition
"""
_simulation_length = 3.0
@staticmethod
def _reference_simulation(simulation_length, model_class=lab.models.ReducedWongWang, simulator=lab.simulator.Simulator, init=None):
# reference simulation
np.random.seed(42)
if init is None:
init = np.random.random_sample((385, 1, 76, 1))
np.random.seed(42)
model = model_class(tau_s=np.random.rand(76))
connectivity = lab.connectivity.Connectivity().from_file()
connectivity.speed = np.array([4.0])
coupling = lab.coupling.Linear(a=np.array(0.0154))
integrator = lab.integrators.HeunDeterministic(dt=0.1, bounded_state_variable_indices=np.array([0]),
state_variable_boundaries=np.array([[0.0, 1.0]]))
monitors = lab.monitors.Raw(period=0.1, variables_of_interest=np.array(0, dtype=np.int))
# Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors.
sim = simulator(model=model,
connectivity=connectivity,
coupling=coupling,
integrator=integrator,
monitors=(monitors,),
initial_conditions=init,
simulation_length=simulation_length
)
sim.configure()
result_all = sim.run()
result = result_all[0][1][0][0]
return connectivity, coupling, integrator, monitors, sim, result, result_all
[docs] def test_with_no_cosimulation(self):
connectivity, coupling, integrator, monitors, sim, result, result_all = self._reference_simulation(self._simulation_length)
np.random.seed(42)
init = np.concatenate((np.random.random_sample((385, 1, 76, 1)),
np.random.random_sample((385, 1, 76, 1))), axis=1)
np.random.seed(42)
result_2 = self._reference_simulation(self._simulation_length,model_class=ReducedWongWangProxy, simulator=CoSimulator, init=init)[5]
np.testing.assert_array_equal(result, result_2)
[docs] def test_precision_with_proxy(self):
connectivity, coupling, integrator, monitors, sim, result, result_all = self._reference_simulation(self._simulation_length)
# New simulator with proxy
np.random.seed(42)
init = np.concatenate((np.random.random_sample((385, 1, 76, 1)),
np.random.random_sample((385, 1, 76, 1))), axis=1)
np.random.seed(42)
model_1 = ReducedWongWangProxy(tau_s=np.random.rand(76))
synchronization_time = 1.
# Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors.
sim_1 = CoSimulator(
voi=np.array([0]),
synchronization_time=synchronization_time,
cosim_monitors=(RawCosim(),),
proxy_inds=np.asarray([0], dtype=np.int),
model=model_1,
connectivity=connectivity,
coupling=coupling,
integrator=integrator,
monitors=(monitors,),
initial_conditions=init,
)
sim_1.configure()
sim_to_sync_time = int(self._simulation_length / synchronization_time)
sync_steps = int(synchronization_time / integrator.dt)
result_1_all = [np.empty((0,)), np.empty((sync_steps, 2, 76, 1))]
sim_1.run() # run the first steps because the history is delayed
for j in range(0, sim_to_sync_time):
result_1_all_step = sim_1.run(
cosim_updates=[np.array([result_all[0][0][(sync_steps * j) + i] for i in range(sync_steps)]),
np.array([result_all[0][1][(sync_steps * j) + i][0][0]
for i in range(sync_steps)]).reshape((sync_steps, 1, 1, 1))])
result_1_all[0] = np.concatenate((result_1_all[0], result_1_all_step[0][0]))
result_1_all[1] = np.concatenate((result_1_all[1], result_1_all_step[0][1]))
for i in range(int(self._simulation_length/integrator.dt)):
np.testing.assert_array_equal(result_all[0][1][i][0][1:],result_1_all[1][i+sync_steps, 0, 1:])
np.testing.assert_array_equal(result_all[0][1][i][0][:1], result_1_all[1][i+sync_steps, 0, :1])