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)