def __init__(self,
n_neurons,
spike_time_file,
machine_time_step,
timescale_factor,
port=None,
tag=None,
ip_address=None,
board_address=None,
min_atom=None,
max_atom=None,
min_time=None,
max_time=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
constraints=None,
split_value="\t",
label="SpikeSourceArray"):
# pylint: disable=too-many-arguments, too-many-locals
spike_times = utility_calls.read_spikes_from_file(
spike_time_file, min_atom, max_atom, min_time, max_time,
split_value)
super(SpikeSourceFromFile,
self).__init__(n_neurons,
spike_times,
machine_time_step,
timescale_factor,
port=port,
tag=tag,
ip_address=ip_address,
board_address=board_address,
max_on_chip_memory_usage_for_spikes_in_bytes=(
max_on_chip_memory_usage_for_spikes_in_bytes),
constraints=constraints,
label=label)
def __init__(
self, spike_time_file, min_atom=None, max_atom=None, min_time=None,
max_time=None, split_value="\t"):
# pylint: disable=too-many-arguments, too-many-locals
spike_times = utility_calls.read_spikes_from_file(
spike_time_file, min_atom, max_atom, min_time, max_time,
split_value)
super(SpikeSourceFromFile, self).__init__(spike_times)
def __init__(
self, spike_time_file, min_atom=None, max_atom=None, min_time=None,
max_time=None, split_value="\t"):
# pylint: disable=too-many-arguments, too-many-locals
spike_times = utility_calls.read_spikes_from_file(
spike_time_file, min_atom, max_atom, min_time, max_time,
split_value)
super(SpikeSourceFromFile, self).__init__(spike_times)
def test_script(self):
"""
test that tests the printing of v from a pre determined recording
:return:
"""
try:
n_neurons = 20 # number of neurons in each population
current_file_path = os.path.dirname(os.path.abspath(__file__))
current_spike_file_path = os.path.join(current_file_path,
"spikes.data")
current_v_file_path = os.path.join(current_file_path, "v.data")
current_gsyn_file_path = os.path.join(current_file_path,
"gsyn.data")
synfire_run.do_run(n_neurons,
max_delay=14,
time_step=0.1,
neurons_per_core=1,
delay=1.7,
run_times=[50],
spike_path=current_spike_file_path,
gsyn_path=current_gsyn_file_path,
v_path=current_v_file_path)
spikes = synfire_run.get_output_pop_spikes()
v = synfire_run.get_output_pop_voltage()
gsyn = synfire_run.get_output_pop_gsyn()
read_in_spikes = utility_calls.read_spikes_from_file(
current_spike_file_path, 0, n_neurons, 0, 5000)
read_in_v = utility_calls.read_in_data_from_file(
current_v_file_path, 0, n_neurons, 0, 5000)
read_in_gsyn = utility_calls.read_in_data_from_file(
current_gsyn_file_path, 0, n_neurons, 0, 5000)
for spike_element, read_element in zip(spikes, read_in_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
for v_element, read_element in zip(v, read_in_v):
self.assertEqual(round(v_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(v_element[1], 1),
round(read_element[1], 1))
for gsyn_element, read_element in zip(gsyn, read_in_gsyn):
self.assertEqual(round(gsyn_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(gsyn_element[1], 1),
round(read_element[1], 1))
except SpinnmanTimeoutException as ex:
# System sometimes times outs
raise SkipTest(ex)
def test_script(self):
"""
test that tests the printing of v from a pre determined recording
:return:
"""
synfire_run.do_run(n_neurons,
max_delay=14,
time_step=1,
neurons_per_core=1,
delay=1.7,
run_times=[50],
spike_path=current_spike_file_path,
gsyn_path=current_gsyn_file_path,
v_path=current_v_file_path,
end_before_print=False)
gsyn = synfire_run.get_output_pop_gsyn()
v = synfire_run.get_output_pop_voltage()
spikes = synfire_run.get_output_pop_spikes()
read_in_spikes = utility_calls.read_spikes_from_file(
current_spike_file_path, 0, n_neurons, 0, 5000)
read_in_v = utility_calls.read_in_data_from_file(
current_v_file_path, 0, n_neurons, 0, 5000)
read_in_gsyn = utility_calls.read_in_data_from_file(
current_gsyn_file_path, 0, n_neurons, 0, 5000)
for spike_element, read_element in zip(spikes, read_in_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
for v_element, read_element in zip(v, read_in_v):
self.assertEqual(round(v_element[0], 1), round(read_element[0], 1))
self.assertEqual(round(v_element[1], 1), round(read_element[1], 1))
for gsyn_element, read_element in zip(gsyn, read_in_gsyn):
self.assertEqual(round(gsyn_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(gsyn_element[1], 1),
round(read_element[1], 1))
def test_print_spikes(self):
try:
synfire_run.do_run(n_neurons, time_step=timestep,
max_delay=max_delay, delay=delay,
neurons_per_core=neurons_per_core,
run_times=[runtime],
spike_path=current_file_path)
spikes = synfire_run.get_output_pop_spikes()
read_in_spikes = utility_calls.read_spikes_from_file(
current_file_path, min_atom=0, max_atom=n_neurons,
min_time=0, max_time=500)
for spike_element, read_element in zip(spikes, read_in_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
except SpinnmanTimeoutException as ex:
# System intentional overload so may error
raise SkipTest(ex)
def test_print_spikes(self):
synfire_run.do_run(n_neurons,
time_step=timestep,
max_delay=max_delay,
delay=delay,
neurons_per_core=neurons_per_core,
run_times=[runtime],
spike_path=current_file_path)
spikes = synfire_run.get_output_pop_spikes()
read_in_spikes = utility_calls.read_spikes_from_file(
current_file_path,
min_atom=0,
max_atom=n_neurons,
min_time=0,
max_time=500)
for spike_element, read_element in zip(spikes, read_in_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
def __init__(self,
n_neurons,
spike_time_file,
machine_time_step,
timescale_factor,
port=None,
tag=None,
ip_address=None,
board_address=None,
min_atom=None,
max_atom=None,
min_time=None,
max_time=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
constraints=None,
split_value="\t",
label="SpikeSourceArray"):
spike_times = utility_calls.read_spikes_from_file(
spike_time_file, min_atom, max_atom, min_time, max_time,
split_value)
SpikeSourceArray.__init__(
self,
n_neurons,
spike_times,
machine_time_step,
timescale_factor,
port=port,
tag=tag,
ip_address=ip_address,
board_address=board_address,
max_on_chip_memory_usage_for_spikes_in_bytes=(
max_on_chip_memory_usage_for_spikes_in_bytes),
constraints=constraints,
label=label)
