Source code for nest.tests.test_connect_all_to_all

# -*- coding: utf-8 -*-
#
# test_connect_all_to_all.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the License, or
# (at your option) any later version.
#
# NEST 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 NEST.  If not, see <http://www.gnu.org/licenses/>.


import unittest
import numpy as np
import scipy.stats
from . import test_connect_helpers as hf
from .test_connect_parameters import TestParams


[docs]@hf.nest.check_stack class TestAllToAll(TestParams): # specify connection pattern rule = 'all_to_all' conn_dict = {'rule': rule} # sizes of populations N1 = 6 N2 = 7 N1_array = 500 N2_array = 10
[docs] def testConnectivity(self): self.setUpNetwork(self.conn_dict) # make sure all connections do exist M = hf.get_connectivity_matrix(self.pop1, self.pop2) M_all = np.ones((len(self.pop2), len(self.pop1))) hf.mpi_assert(M, M_all, self) # make sure no connections were drawn from the target to the source # population M = hf.get_connectivity_matrix(self.pop2, self.pop1) M_none = np.zeros((len(self.pop1), len(self.pop2))) hf.mpi_assert(M, M_none, self)
[docs] def testInputArray(self): for label in ['weight', 'delay']: syn_params = {} if label == 'weight': self.param_array = np.arange( self.N1_array * self.N2_array, dtype=float ).reshape(self.N2_array, self.N1_array) elif label == 'delay': self.param_array = np.arange( 1, self.N1_array * self.N2_array + 1 ).reshape(self.N2_array, self.N1_array) * 0.1 syn_params[label] = self.param_array hf.nest.ResetKernel() self.setUpNetwork(self.conn_dict, syn_params, N1=self.N1_array, N2=self.N2_array) M_nest = hf.get_weighted_connectivity_matrix( self.pop1, self.pop2, label) hf.mpi_assert(M_nest, self.param_array, self)
[docs] def testInputArrayWithoutAutapses(self): self.conn_dict['autapses'] = False for label in ['weight', 'delay']: syn_params = {} if label == 'weight': self.param_array = np.arange( self.N1 * self.N1, dtype=float).reshape(self.N1, self.N1) elif label == 'delay': self.param_array = np.arange( 1, self.N1 * self.N1 + 1).reshape(self.N1, self.N1) * 0.1 syn_params[label] = self.param_array self.setUpNetworkOnePop(self.conn_dict, syn_params) M_nest = hf.get_weighted_connectivity_matrix( self.pop, self.pop, label) np.fill_diagonal(self.param_array, 0) hf.mpi_assert(M_nest, self.param_array, self)
[docs] def testInputArrayRPort(self): syn_params = {} neuron_model = 'iaf_psc_exp_multisynapse' neuron_dict = {'tau_syn': [0.1 + i for i in range(self.N2)]} self.pop1 = hf.nest.Create(neuron_model, self.N1) self.pop2 = hf.nest.Create(neuron_model, self.N2, neuron_dict) self.param_array = np.transpose(np.asarray( [np.arange(1, self.N2 + 1) for i in range(self.N1)])) syn_params['receptor_type'] = self.param_array hf.nest.Connect(self.pop1, self.pop2, self.conn_dict, syn_params) M = hf.get_weighted_connectivity_matrix( self.pop1, self.pop2, 'receptor') hf.mpi_assert(M, self.param_array, self)
[docs] def testInputArrayToStdpSynapse(self): params = ['Wmax', 'alpha', 'lambda', 'mu_minus', 'mu_plus', 'tau_plus'] syn_params = {'model': 'stdp_synapse'} values = [ np.arange(self.N1 * self.N2, dtype=float).reshape(self.N2, self.N1) for i in range(6) ] for i, param in enumerate(params): syn_params[param] = values[i] self.setUpNetwork(self.conn_dict, syn_params) for i, param in enumerate(params): a = hf.get_weighted_connectivity_matrix( self.pop1, self.pop2, param) hf.mpi_assert(a, values[i], self)
# test single threaded for now
[docs] def testRPortDistribution(self): n_rport = 10 nr_neurons = 20 hf.nest.ResetKernel() # To reset local_num_threads neuron_model = 'iaf_psc_exp_multisynapse' neuron_dict = {'tau_syn': [0.1 + i for i in range(n_rport)]} self.pop1 = hf.nest.Create(neuron_model, nr_neurons, neuron_dict) self.pop2 = hf.nest.Create(neuron_model, nr_neurons, neuron_dict) syn_params = {'model': 'static_synapse'} syn_params['receptor_type'] = { 'distribution': 'uniform_int', 'low': 1, 'high': n_rport} hf.nest.Connect(self.pop1, self.pop2, self.conn_dict, syn_params) M = hf.get_weighted_connectivity_matrix( self.pop1, self.pop2, 'receptor') M = hf.gather_data(M) if M is not None: M = M.flatten() frequencies = scipy.stats.itemfreq(M) self.assertTrue(np.array_equal(frequencies[:, 0], np.arange( 1, n_rport + 1)), 'Missing or invalid rports') chi, p = scipy.stats.chisquare(frequencies[:, 1]) self.assertGreater(p, self.pval)
[docs]def suite(): suite = unittest.TestLoader().loadTestsFromTestCase(TestAllToAll) return suite
[docs]def run(): runner = unittest.TextTestRunner(verbosity=2) runner.run(suite())
if __name__ == '__main__': run()