# Source code for tvb.contrib.tests.cosimulation.parallel.update_function_test

# -*- coding: utf-8 -*-
#
#
#  TheVirtualBrain-Contributors Package. This package holds simulator extensions.
#
# (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
# either version 3 of the License, or (at your option) any later version.
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# 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/>.
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#
#   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 pytest

from tvb.tests.library.base_testcase import BaseTestCase
from tvb.contrib.tests.cosimulation.parallel.function_tvb import TvbSim

[docs]class TestUpdateModel(BaseTestCase):
"""
test the function of function_tvb
"""
[docs]    def test_update_model(self):
weight = np.array([[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
delay = np.array([[1.5, 1.5, 1.5, 1.5], [1.5, 1.5, 1.5, 1.5], [1.5, 1.5, 1.5, 1.5], [1.5, 1.5, 1.5, 1.5]])
resolution_simulation = 0.1
synchronization_time = 1.0
proxy_id = [0, 1]
firing_rate = np.array([[20.0, 10.0]]) * 10 ** -3  # units time in tvb is ms so the rate is in KHz

# Test the the update function
sim = TvbSim(weight, delay, proxy_id, resolution_simulation, synchronization_time)
time, result = sim(resolution_simulation,[np.array([resolution_simulation]), firing_rate])
for i in range(0, 100):
time, result = sim(synchronization_time,
[np.arange(i * synchronization_time, (i + 1) * synchronization_time,
resolution_simulation),
np.repeat(firing_rate.reshape(1, 2),
int(synchronization_time / resolution_simulation), axis=0)])
assert True

# Test a fail function due to the time of simulation too long
with pytest.raises(ValueError):
sim(synchronization_time,[np.arange(100 * synchronization_time, 102 * synchronization_time,
resolution_simulation),
np.repeat(firing_rate.reshape(1, 2),
int(synchronization_time / resolution_simulation)*2, axis=0)] )
# Test a fail function due to the resoulation time is not good
with pytest.raises(ValueError):
sim(synchronization_time,[np.arange(100 * synchronization_time, 101 * synchronization_time,
resolution_simulation*2),
np.repeat(firing_rate.reshape(1, 2),
int(synchronization_time / resolution_simulation)*2, axis=0)] )