def do_all_constraint(self):
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
delay=delay,
run_times=[runtime],
placement_constraint=placement_constraint,
record=True,
record_7=True,
record_v=True,
record_v_7=True,
record_gsyn_exc=True,
record_gsyn_exc_7=True,
record_gsyn_inh=False)
gsyn_exc = synfire_run.get_output_pop_gsyn_exc_numpy()
v = synfire_run.get_output_pop_voltage_numpy()
spikes = synfire_run.get_output_pop_spikes_numpy()
self.assertEqual(n_neurons * runtime, len(gsyn_exc))
read_gsyn = numpy.loadtxt(gysn_file, delimiter=',')
self.assertTrue(numpy.allclose(read_gsyn, gsyn_exc, rtol=1e-04),
"gsyn neo method mismatch")
self.assertEqual(n_neurons * runtime, len(v))
read_v = numpy.loadtxt(v_file, delimiter=',')
self.assertTrue(numpy.allclose(read_v, v, rtol=1e-03),
"v neo method mismatch")
self.assertEqual(expected_spikes, len(spikes))
spike_checker.synfire_spike_checker(spikes, n_neurons)
read_spikes = numpy.loadtxt(spike_file, delimiter=',')
self.assertTrue(numpy.allclose(read_spikes, spikes),
"spikes neo method mismatch")
def do_v_no_constraint(self):
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
delay=delay,
run_times=[runtime],
record=False,
record_v=True,
record_gsyn_exc_7=False,
record_gsyn_inh=False)
v = synfire_run.get_output_pop_voltage_numpy()
self.assertEqual(n_neurons * runtime, len(v))
read_v = numpy.loadtxt(v_file, delimiter=',')
self.assertTrue(numpy.allclose(read_v, v, rtol=1e-03),
"v neo method mismatch")
def do_gsyn_no_constraint(self):
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
delay=delay,
run_times=[runtime],
record=False,
record_v=False,
record_gsyn_exc_7=True,
record_gsyn_inh=False)
gsyn_exc = synfire_run.get_output_pop_gsyn_exc_numpy()
self.assertEqual(n_neurons * runtime, len(gsyn_exc))
read_gsyn = numpy.loadtxt(gysn_file, delimiter=',')
self.assertTrue(numpy.allclose(read_gsyn, gsyn_exc, rtol=1e-04),
"gsyn neo method mismatch")
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]))
def do_all_no_constraint(self):
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
delay=delay,
run_times=[runtime],
record=True,
record_7=True,
record_v=True,
record_v_7=True,
record_gsyn_exc=True,
record_gsyn_exc_7=True,
record_gsyn_inh=False)
gsyn_exc_7 = synfire_run.get_output_pop_gsyn_exc_7()
v_7 = synfire_run.get_output_pop_voltage_7()
spikes_7 = synfire_run.get_output_pop_spikes_7()
gsyn_exc = synfire_run.get_output_pop_gsyn_exc_numpy()
v = synfire_run.get_output_pop_voltage_numpy()
spikes = synfire_run.get_output_pop_spikes_numpy()
self.assertEqual(n_neurons * runtime, len(gsyn_exc))
read_gsyn = numpy.loadtxt(gysn_file, delimiter=',')
if not numpy.allclose(read_gsyn, gsyn_exc_7):
for g1, g2 in zip(read_gsyn, gsyn_exc_7):
if not numpy.allclose(g1, g2, rtol=1e-04):
print(g1, g2, g1[2] - g2[2], (g1[2] - g2[2]) / g1[2])
self.assertTrue(numpy.allclose(read_gsyn, gsyn_exc_7, rtol=1e-04),
"gsyn synakker method mismatch")
self.assertTrue(numpy.allclose(read_gsyn, gsyn_exc, rtol=1e-04),
"gsyn neo method mismatch")
self.assertEqual(n_neurons * runtime, len(v))
read_v = numpy.loadtxt(v_file, delimiter=',')
self.assertTrue(numpy.allclose(read_v, v_7, rtol=1e-03),
"v synakker method mismatch")
self.assertTrue(numpy.allclose(read_v, v, rtol=1e-03),
"v neo method mismatch")
self.assertEqual(expected_spikes, len(spikes))
spike_checker.synfire_spike_checker(spikes, n_neurons)
read_spikes = numpy.loadtxt(spike_file, delimiter=',')
self.assertTrue(numpy.allclose(read_spikes, spikes_7),
"spikes synakker method mismatch")
self.assertTrue(numpy.allclose(read_spikes, spikes),
"spikes neo method mismatch")
def do_spikes_no_constraint(self):
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
delay=delay,
run_times=[runtime],
record=True,
record_v=False,
record_gsyn_exc_7=False,
record_gsyn_inh=False)
spikes = synfire_run.get_output_pop_spikes_numpy()
self.assertEqual(expected_spikes, len(spikes))
spike_checker.synfire_spike_checker(spikes, n_neurons)
read_spikes = numpy.loadtxt(spike_file, delimiter=',')
self.assertTrue(numpy.allclose(read_spikes, spikes),
"spikes neo method mismatch")
read_gsyn = numpy.loadtxt(gysn_file, delimiter=',')
self.assertTrue(numpy.allclose(read_gsyn, gsyn_exc, rtol=1e-04),
"gsyn neo method mismatch")
def test_gsyn_no_constraint(self):
self.runsafe(self.do_gsyn_no_constraint)
if __name__ == '__main__':
synfire_run = SynfireRunner()
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
delay=delay,
run_times=[runtime],
placement_constraint=placement_constraint,
record=True,
record_7=True,
record_v=True,
record_v_7=True,
record_gsyn_exc=True,
record_gsyn_exc_7=True,
record_gsyn_inh=False)
gsyn_exc = synfire_run.get_output_pop_gsyn_exc_numpy()
gsyn_exc_neo = synfire_run.get_output_pop_gsyn_exc_neo()
v = synfire_run.get_output_pop_voltage_numpy()
v_neo = synfire_run.get_output_pop_voltage_neo()
spikes = synfire_run.get_output_pop_spikes_numpy()
spikes_neo = synfire_run.get_output_pop_spikes_neo()
numpy.savetxt(spike_file, spikes, delimiter=',')
numpy.savetxt(v_file, v, delimiter=',')
neurons_per_core=neurons_per_core,
run_times=[runtime])
spikes = synfire_run.get_output_pop_spikes_numpy()
# CB Currently eight but could change
# Needs to be larger than 1000 timesteps version
self.assert_logs_messages(lc.records,
"*** Running simulation... ***", 'INFO',
3)
self.assertEqual(158, len(spikes))
spike_checker.synfire_spike_checker(spikes, n_neurons)
synfire_run.get_output_pop_gsyn_exc_numpy()
synfire_run.get_output_pop_voltage_numpy()
def more_runs(self):
self.runsafe(self.more_runs)
if __name__ == '__main__':
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
run_times=[runtime])
gsyn = synfire_run.get_output_pop_gsyn_exc_numpy()
v = synfire_run.get_output_pop_voltage_numpy()
spikes = synfire_run.get_output_pop_spikes_numpy()
print(len(spikes))
plot_utils.plot_spikes(spikes)
plot_utils.heat_plot(v)
plot_utils.heat_plot(gsyn)
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]))
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])
input_class=input_class,
start_time=start_time,
duration=duration,
rate=rate,
extract_between_runs=extract_between_runs,
set_between_runs=set_between_runs,
seed=12345)
spikes = synfire_run.get_output_pop_spikes_numpy()
# Check spikes increase in second half by at least a factor of ten
hist = numpy.histogram(spikes[:, 1], bins=[0, 5000, 10000])
self.assertLess(hist[0][0] * 10, hist[0][1])
def test_synfire_poisson_if_curr_exp_parameter(self):
self.runsafe(self.synfire_poisson_if_curr_exp_parameter)
if __name__ == '__main__':
synfire_run.do_run(n_neurons,
neurons_per_core=neurons_per_core,
run_times=run_times,
input_class=input_class,
start_time=start_time,
duration=duration,
rate=rate,
extract_between_runs=extract_between_runs,
set_between_runs=set_between_runs)
spikes = synfire_run.get_output_pop_spikes_numpy()
hist = numpy.histogram(spikes[:, 1], bins=[0, 5000, 10000])
print(hist[0][0] * 10, hist[0][1])
plot_utils.plot_spikes(spikes)