def testStatistics(self):
conn_params = self.conn_dict.copy()
conn_params['allow_autapses'] = True
conn_params['allow_multapses'] = True
conn_params['N'] = self.N
for fan in ['in', 'out']:
expected = connect_test_base.get_expected_degrees_totalNumber(
self.N, fan, self.N_s, self.N_t)
pvalues = []
for i in range(self.stat_dict['n_runs']):
connect_test_base.reset_seed(i + 1, self.nr_threads)
self.setUpNetwork(conn_dict=conn_params,
N1=self.N_s,
N2=self.N_t)
degrees = connect_test_base.get_degrees(
fan, self.pop1, self.pop2)
degrees = connect_test_base.gather_data(degrees)
if degrees is not None:
chi, p = connect_test_base.chi_squared_check(
degrees, expected)
pvalues.append(p)
connect_test_base.mpi_barrier()
p = None
if degrees is not None:
ks, p = scipy.stats.kstest(pvalues, 'uniform')
p = connect_test_base.bcast_data(p)
self.assertGreater(p, self.stat_dict['alpha2'])
def testSymmetricFlag(self):
conn_dict_symmetric = self.conn_dict.copy()
conn_dict_symmetric['make_symmetric'] = True
self.setUpNetwork(conn_dict_symmetric)
M1 = connect_test_base.get_connectivity_matrix(self.pop1, self.pop2)
M2 = connect_test_base.get_connectivity_matrix(self.pop2, self.pop1)
# test that connections were created in both directions
connect_test_base.mpi_assert(
M1, np.transpose(connect_test_base.gather_data(M2)), self)
# test that no other connections were created
connect_test_base.mpi_assert(M1,
np.zeros_like(M1) + np.identity(self.N),
self)
def testMultapsesFalse(self):
conn_params = self.conn_dict.copy()
N = 3
conn_params['allow_autapses'] = True
# test that no multapses exist
conn_params['indegree'] = N
conn_params['allow_multapses'] = False
pop = nest.Create('iaf_psc_alpha', N)
nest.Connect(pop, pop, conn_params)
M = connect_test_base.get_connectivity_matrix(pop, pop)
M = connect_test_base.gather_data(M)
if M is not None:
self.assertTrue(M.flatten, np.ones(N * N))
def testAutapsesTrue(self):
conn_params = self.conn_dict.copy()
N = 3
# test that autapses exist
conn_params['N'] = N * N * N
conn_params['allow_autapses'] = True
pop = nest.Create('iaf_psc_alpha', N)
nest.Connect(pop, pop, conn_params)
# make sure all connections do exist
M = connect_test_base.get_connectivity_matrix(pop, pop)
M = connect_test_base.gather_data(M)
if M is not None:
self.assertTrue(np.sum(np.diag(M)) > N)
def testMakeSymmetric(self):
conn_params = self.conn_dict.copy()
N = 100
# test that all connections are symmetric
conn_params['make_symmetric'] = True
nest.ResetKernel()
pop = nest.Create('iaf_psc_alpha', N)
nest.Connect(pop, pop, conn_params)
M = connect_test_base.get_connectivity_matrix(pop, pop)
print(M)
M_all = connect_test_base.gather_data(M)
print(M_all)
if M_all is not None:
self.assertTrue(np.array_equal(M_all, M_all.T))
def testRPortDistribution(self):
n_rport = 10
nr_neurons = 100
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 = nest.Create(neuron_model, nr_neurons, neuron_dict)
self.pop2 = nest.Create(neuron_model, nr_neurons, neuron_dict)
syn_params = {'synapse_model': 'static_synapse'}
syn_params['receptor_type'] = 1 + nest.random.uniform_int(n_rport)
nest.Connect(self.pop1, self.pop2, self.conn_dict, syn_params)
M = connect_test_base.get_weighted_connectivity_matrix(
self.pop1, self.pop2, 'receptor')
M = connect_test_base.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)
def testStatistics(self):
for fan in ['in', 'out']:
expected = connect_test_base.get_expected_degrees_bernoulli(
self.p, fan, self.N_s, self.N_t)
pvalues = []
for i in range(self.stat_dict['n_runs']):
connect_test_base.reset_seed(i + 1, self.nr_threads)
self.setUpNetwork(conn_dict=self.conn_dict,
N1=self.N_s,
N2=self.N_t)
degrees = connect_test_base.get_degrees(
fan, self.pop1, self.pop2)
degrees = connect_test_base.gather_data(degrees)
# degrees = self.comm.gather(degrees, root=0)
# if self.rank == 0:
if degrees is not None:
chi, p = connect_test_base.chi_squared_check(
degrees, expected, self.rule)
pvalues.append(p)
connect_test_base.mpi_barrier()
if degrees is not None:
ks, p = scipy.stats.kstest(pvalues, 'uniform')
self.assertTrue(p > self.stat_dict['alpha2'])