def test_run(self):
synfire_run.do_run(nNeurons, spike_times=SPIKE_TIMES,
run_times=run_times, reset=reset,
extract_between_runs=extract_between_runs)
spikes = synfire_run.get_output_pop_spikes_numpy()
self.assertEquals(454, len(spikes))
spike_checker.synfire_multiple_lines_spike_checker(spikes, nNeurons, 6)
def test_run(self):
synfire_run.do_run(nNeurons, spike_times=spike_times,
run_times=run_times, reset=reset)
spikes = synfire_run.get_output_pop_spikes()
self.assertEquals(53, len(spikes[0]))
self.assertEquals(261, len(spikes[1]))
spike_checker.synfire_spike_checker(spikes[0], nNeurons)
spike_checker.synfire_multiple_lines_spike_checker(spikes[1], nNeurons,
2)
def test_run(self):
synfire_run.do_run(n_neurons, neurons_per_core=neurons_per_core,
spike_times=spike_times, run_times=runtimes,
reset=reset,
extract_between_runs=extract_between_runs)
spikes = synfire_run.get_output_pop_spikes()
self.assertEquals(454, len(spikes))
spike_checker.synfire_multiple_lines_spike_checker(spikes, n_neurons,
6)
def test_run(self):
synfire_run.do_run(n_neurons, neurons_per_core=neurons_per_core,
spike_times=spike_times, run_times=runtimes,
reset=reset)
spikes = synfire_run.get_output_pop_spikes_list_numpy()
self.assertEquals(53, len(spikes[0]))
self.assertEquals(103, len(spikes[1]))
spike_checker.synfire_spike_checker(spikes[0], n_neurons)
spike_checker.synfire_multiple_lines_spike_checker(spikes[1],
n_neurons, 2)
def test_run(self):
synfire_run.do_run(nNeurons,
spike_times_list=spike_times_list,
run_times=run_times)
spikes = synfire_run.get_output_pop_spikes_list_numpy()
self.assertEquals(53, len(spikes[0]))
self.assertEquals(156, len(spikes[1]))
spike_checker.synfire_spike_checker(spikes[0], nNeurons)
spike_checker.synfire_multiple_lines_spike_checker(
spikes[1], nNeurons, 2)
def test_run(self):
synfire_run.do_run(nNeurons,
spike_times=spike_times,
reset=reset,
run_times=runtimes,
neurons_per_core=neurons_per_core,
get_all=True)
neos = synfire_run.get_output_pop_all_list()
spikes_0_0 = neo_convertor.convert_spikes(neos[0], 0)
spikes_1_1 = neo_convertor.convert_spikes(neos[1], 1)
self.assertEquals(53, len(spikes_0_0))
self.assertEquals(156, len(spikes_1_1))
spike_checker.synfire_spike_checker(spikes_0_0, nNeurons)
spike_checker.synfire_multiple_lines_spike_checker(
spikes_1_1, nNeurons, 2)
# v + gsyn_exc + gsyn_ihn = 3 (spikes not in analogsignalarrays
if pynn8_syntax:
self.assertEquals(3, len(neos[0].segments[0].analogsignalarrays))
self.assertEquals(3, len(neos[1].segments[0].analogsignalarrays))
self.assertEquals(3, len(neos[1].segments[1].analogsignalarrays))
else:
self.assertEquals(3, len(neos[0].segments[0].analogsignals))
self.assertEquals(3, len(neos[1].segments[0].analogsignals))
self.assertEquals(3, len(neos[1].segments[1].analogsignals))
neo_compare.compare_segments(neos[0].segments[0], neos[1].segments[0])
# neo compare does all the compares so just some safety come once
spikes_1_0 = neo_convertor.convert_spikes(neos[1], 0)
for s1, s2 in zip(spikes_0_0, spikes_1_0):
for (a1, a2) in zip(s1, s2):
self.assertEquals(a1, a2)
v_0_0 = neo_convertor.convert_data(neos[0], "v", 0)
v_1_0 = neo_convertor.convert_data(neos[1], "v", 0)
v_1_1 = neo_convertor.convert_data(neos[1], "v", 1)
self.assertEquals(nNeurons * runtimes[0], len(v_0_0))
self.assertEquals(nNeurons * runtimes[0], len(v_1_0))
self.assertEquals(nNeurons * runtimes[1], len(v_1_1))
gsyn_exc_0_0 = neo_convertor.convert_data(neos[0], "gsyn_exc", 0)
gsyn_exc_1_0 = neo_convertor.convert_data(neos[1], "gsyn_exc", 0)
gsyn_exc_1_1 = neo_convertor.convert_data(neos[1], "gsyn_exc", 1)
self.assertEquals(nNeurons * runtimes[0], len(gsyn_exc_0_0))
self.assertEquals(nNeurons * runtimes[0], len(gsyn_exc_1_0))
self.assertEquals(nNeurons * runtimes[1], len(gsyn_exc_1_1))
gsyn_inh_0_0 = neo_convertor.convert_data(neos[0], "gsyn_inh", 0)
gsyn_inh_1_0 = neo_convertor.convert_data(neos[1], "gsyn_inh", 0)
gsyn_inh_1_1 = neo_convertor.convert_data(neos[1], "gsyn_inh", 1)
self.assertEquals(nNeurons * runtimes[0], len(gsyn_inh_0_0))
self.assertEquals(nNeurons * runtimes[0], len(gsyn_inh_1_0))
self.assertEquals(nNeurons * runtimes[1], len(gsyn_inh_1_1))
def test_synfire_posson_if_curr_exp_parameter_test_second_none(self):
synfire_run.do_run(n_neurons, neurons_per_core=neurons_per_core,
run_times=run_times,
use_wrap_around_connections=wrap_around,
input_class=input_class,
start_time=start_time, duration=duration, rate=rate,
extract_between_runs=extract_between_runs,
set_between_runs=set_between_runs,
record_input_spikes=record_input_spikes)
input = synfire_run.get_spike_source_spikes()
spikes = synfire_run.get_output_pop_spikes()
# Check input spikes stop
hist = numpy.histogram(input[:, 1], bins=[0, 5000, 10000])
self.assertEqual(0, hist[0][1])
spike_checker.synfire_multiple_lines_spike_checker(spikes, n_neurons,
len(input),
wrap_around=False)