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Source code for tvb.contrib.tests.cosimulation.parallel.double_proxy_precision_complex_test

# -*- coding: utf-8 -*-
#
#
#  TheVirtualBrain-Contributors Package. This package holds simulator extensions.
#  See also http://www.thevirtualbrain.org
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# (c) 2012-2022, Baycrest Centre for Geriatric Care ("Baycrest") and others
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#   CITATION:
# When using The Virtual Brain for scientific publications, please cite it as follows:
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#   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

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


[docs]class TestDoubleProxyPrecisionComplex(BaseTestCase): """ test the transmission of information between two model with proxy in most complex case """
[docs] def test_double_precision_complex(self): weight = np.array([[5, 2, 4, 0], [8, 5, 4, 1], [6, 1, 7, 9], [10, 0, 5, 6]]) delay = np.array([[7, 8, 5, 1], [10, 3, 7, 9], [4, 3, 2, 8], [9, 10, 11, 5]]) max = np.int(np.max(delay)*10+1) init_value = np.array([[[0.1,0.0], [0.1,0.0], [0.2,0.0], [0.9,0.0]]] * max) initial_condition = init_value.reshape((max, 2, weight.shape[0], 1)) resolution_simulation = 0.1 synchronization_time = 0.1 * 10 proxy_id_1 = [1] proxy_id_2 = [0, 2] # simulation with one proxy np.random.seed(42) sim_1 = TvbSim(weight, delay, proxy_id_1, resolution_simulation, synchronization_time, initial_condition=initial_condition) time, result_1 = sim_1(synchronization_time) # simulation_2 with one proxy np.random.seed(42) sim_2 = TvbSim(weight, delay, proxy_id_2, resolution_simulation, synchronization_time, initial_condition=initial_condition) time, result_2 = sim_2(synchronization_time) # full simulation np.random.seed(42) sim_ref = TvbSim(weight, delay, [], resolution_simulation, synchronization_time, initial_condition=initial_condition) time_ref, result_ref = sim_ref(synchronization_time) # COMPARE PROXY 1 np.testing.assert_array_equal(np.squeeze(result_ref[:, proxy_id_2, :], axis=2)[0], np.squeeze(result_1[0][:, proxy_id_2, :], axis=2)[0]) # COMPARE PROXY 2 np.testing.assert_array_equal(np.squeeze(result_ref[:, proxy_id_1, :], axis=2)[0], np.squeeze(result_2[0][:, proxy_id_1, :], axis=2)[0]) for i in range(0, 1000): time, result_2 = sim_2(synchronization_time, [time, result_1[0][:, proxy_id_2][:, :, 0]]) # compare with raw monitor delayed of synchronization_time np.testing.assert_array_equal(result_ref, result_2[1]) time, result_1 = sim_1(synchronization_time, [time_ref, result_ref[:, proxy_id_1][:, :, 0]]) # compare with raw monitor delayed of synchronization_time np.testing.assert_array_equal(result_ref, result_1[1]) time_ref, result_ref = sim_ref(synchronization_time) # COMPARE PROXY 1 np.testing.assert_array_equal(np.squeeze(result_ref[:, proxy_id_2, :], axis=2)[0], np.squeeze(result_1[0][:, proxy_id_2, :], axis=2)[0]) # COMPARE PROXY 2 np.testing.assert_array_equal(np.squeeze(result_ref[:, proxy_id_1, :], axis=2)[0], np.squeeze(result_2[0][:, proxy_id_1, :], axis=2)[0])