# spinn_front_end_common.utilities.exceptions.ConfigurationException:
# The number of params does not equal with
# the number of atoms in the vertex
def test_run(self):
(pre_spikes, post_spikes, weights) = do_run(seed=self._test_seed)
if self._test_seed == 1:
self.assertEquals(183, len(pre_spikes))
self.assertEquals(91, len(post_spikes))
self.assertEquals(787, len(weights))
else:
try:
self.assertLess(130, len(pre_spikes))
self.assertGreater(220, len(pre_spikes))
self.assertLess(70, len(post_spikes))
self.assertGreater(110, len(post_spikes))
self.assertLess(750, len(weights))
self.assertGreater(900, len(weights))
except Exception as ex:
# Just in case the range failed
raise SkipTest(ex)
if __name__ == '__main__':
(pre_spikes, post_spikes, weights) = do_run()
print(len(pre_spikes))
print(len(post_spikes))
plot_utils.plot_spikes([pre_spikes, post_spikes])
print(len(weights))
print("Weights:", weights)
populations[0].record("spikes")
p.run(500)
neo = populations[0].get_data(["v", "spikes", "gsyn_exc"])
v = neo_convertor.convert_data(neo, name="v")
gsyn = neo_convertor.convert_data(neo, name="gsyn_exc")
spikes = neo_convertor.convert_spikes(neo)
p.end()
return (v, gsyn, spikes)
class SynfireIzhikevich(BaseTestCase):
def test_run(self):
nNeurons = 200 # number of neurons in each population
(v, gsyn, spikes) = do_run(nNeurons)
spike_checker.synfire_spike_checker(spikes, nNeurons)
self.assertEquals(215, len(spikes))
if __name__ == '__main__':
nNeurons = 200 # number of neurons in each population
(v, gsyn, spikes) = do_run(nNeurons)
print(len(spikes))
plot_utils.plot_spikes(spikes)
plot_utils.heat_plot(v, title="v")
plot_utils.heat_plot(gsyn, title="gsyn")
spike_checker.synfire_multiple_lines_spike_checker(spikes,
n_neurons,
len(input),
wrap_around=False)
def test_second_none(self):
self.runsafe(self.second_none)
if __name__ == '__main__':
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)
gsyn = synfire_run.get_output_pop_gsyn_exc_numpy()
v = synfire_run.get_output_pop_voltage_numpy()
input = synfire_run.get_spike_source_spikes_numpy()
hist = numpy.histogram(input[:, 1], bins=[0, 5000, 10000])
print(hist[0][0], hist[0][1])
spikes = synfire_run.get_output_pop_spikes_numpy()
plot_utils.plot_spikes(input, spikes2=spikes)
plot_utils.heat_plot(v)
plot_utils.heat_plot(gsyn)
synfire_run = TestRun()
class Synfire2RunExtractionIfCurrExpHigher(BaseTestCase):
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)
if __name__ == '__main__':
synfire_run.do_run(nNeurons, spike_times=spike_times, run_times=run_times,
reset=reset)
gsyn = synfire_run.get_output_pop_gsyn()
v = synfire_run.get_output_pop_voltage()
spikes = synfire_run.get_output_pop_spikes()
print len(spikes[0])
print len(spikes[1])
plot_utils.plot_spikes(spikes[0], spikes[1])
plot_utils.heat_plot(v[0], title="v1")
plot_utils.heat_plot(gsyn[0], title="gysn1")
plot_utils.heat_plot(v[1], title="v2")
plot_utils.heat_plot(gsyn[1], title="gysn2")
pg_pop1.record("spikes")
pg_pop2.record("spikes")
sim.run(simtime)
neo = pg_pop1.get_data("spikes")
spikes1 = neo_convertor.convert_spikes(neo)
neo = pg_pop2.get_data("spikes")
spikes2 = neo_convertor.convert_spikes(neo)
sim.end()
return (spikes1, spikes2)
class TestPoisson(BaseTestCase):
def test_run(self):
(spikes1, spikes2) = do_run(self._test_seed)
self.assertEquals(19, len(spikes1))
self.assertEquals(24, len(spikes2))
if __name__ == '__main__':
(spikes1, spikes2) = do_run()
print(len(spikes1))
print(spikes1)
print(len(spikes2))
print(spikes2)
plot_utils.plot_spikes([spikes1, spikes2])
post_spikes = post_pop.getSpikes(compatible_output=True)
p.end()
return (weights, pre_spikes, post_spikes)
class stdp_example(BaseTestCase):
def test_run(self):
(weights, pre_spikes, post_spikes) = do_run()
try:
self.assertLess(140, len(pre_spikes))
self.assertGreater(170, len(post_spikes))
self.assertLess(80, len(post_spikes))
self.assertGreater(100, len(post_spikes))
self.assertLess(750, len(weights))
self.assertGreater(850, len(weights))
except Exception as ex:
# Just in case the range failed
raise SkipTest(ex)
if __name__ == '__main__':
(weights, pre_spikes, post_spikes) = do_run()
print len(pre_spikes)
print len(post_spikes)
plot_utils.plot_spikes(pre_spikes, title="pre-synaptic")
plot_utils.plot_spikes(post_spikes, title="post-synaptic")
print len(weights)
print("Weights:", weights)
return noise_spike_times, s_pop_spikes, s_pop_voltages
class TestIfCurExpSingleNeuron(BaseTestCase):
"""
tests the get spikes given a simulation at 0.1 ms time steps
"""
def test_single_neuron(self):
results = do_run()
(noise_spike_times, s_pop_spikes, s_pop_voltages) = results
try:
self.assertLess(800, len(noise_spike_times))
self.assertGreater(900, len(noise_spike_times))
self.assertLess(2, len(s_pop_spikes))
self.assertGreater(25, len(s_pop_spikes))
except Exception as ex:
# Just in case the range failed
raise SkipTest(ex)
if __name__ == '__main__':
results = do_run()
(noise_spike_times, s_pop_spikes, s_pop_voltages) = results
print noise_spike_times
print len(noise_spike_times)
print s_pop_spikes
print len(s_pop_spikes)
print s_pop_voltages
plot_utils.plot_spikes([noise_spike_times, s_pop_spikes])
plot_utils.line_plot(s_pop_voltages, title="s_pop_voltages")