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]))
delay = 17
run_times = [5000]
get_weights = True
synfire_run = SynfireRunner()
class SynfireIfCurr_exp(BaseTestCase):
def test_run(self):
synfire_run.do_run(nNeurons, neurons_per_core=neurons_per_core,
delay=delay, run_times=run_times,
get_weights=get_weights)
spikes = synfire_run.get_output_pop_spikes_numpy()
weights = synfire_run.get_weights()
self.assertEqual(263, len(spikes))
self.assertEqual(200, len(weights))
spike_checker.synfire_spike_checker(spikes, nNeurons)
if __name__ == '__main__':
synfire_run.do_run(nNeurons, neurons_per_core=neurons_per_core,
delay=delay, run_times=run_times,
get_weights=get_weights)
spikes = synfire_run.get_output_pop_spikes_numpy()
weights = synfire_run.get_weights()
print(len(spikes))
print(len(weights))
plot_utils.plot_spikes(spikes)
