def get_before_and_after(self):
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons, neurons_per_core=neurons_per_core,
weight_to_spike=weight_to_spike, delay=delay,
placement_constraint=placement_constraint,
run_times=runtimes, get_weights=get_weights,
get_delays=get_delays)
weights = synfire_run.get_weights()
self.assertEqual(n_neurons, len(weights[0]))
self.assertEqual(n_neurons, len(weights[1]))
self.assertTrue(numpy.allclose(weights[0][0][2], weights[1][0][2]))
delays = synfire_run.get_delay()
self.assertEqual(n_neurons, len(delays[0]))
self.assertEqual(n_neurons, len(delays[1]))
self.assertTrue(numpy.allclose(delays[0][0][2], delays[1][0][2]))
self.assertTrue(numpy.allclose(delays[0][0][2], delays[1][0][2]))
def test_get_before_and_after(self):
self.runsafe(self.get_before_and_after)
if __name__ == '__main__':
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
weight_to_spike=weight_to_spike,
delay=delay,
placement_constraint=placement_constraint,
run_times=runtimes,
get_weights=get_weights,
get_delays=get_delays)
weights = synfire_run.get_weights()
delays = synfire_run.get_delay()
print("weights[0]")
print(weights[0])
print(weights[0].shape)
print("weights[1]")
print(weights[1])
print(weights[1].shape)
print("delays[0]")
print(delays[0])
print(delays[0].shape)
print("delays[1]")
print(delays[1])
print(delays[1].shape)
