def test_get_set(self):
n_neurons = 4
label = "pop_1"
sim.setup(timestep=1.0)
pop_1 = sim.Population(n_neurons, sim.IF_curr_exp(), label=label)
view = PopulationView(pop_1, [1, 3], label="Odds")
pop_1.set(tau_m=2)
self.assertEqual([2, 2, 2, 2], pop_1.get("tau_m"))
self.assertEqual([2, 2], view.get("tau_m", simplify=False))
view.set(tau_m=3)
self.assertEqual([2, 3, 2, 3], pop_1.get("tau_m"))
sim.end()
def create_population(self,
sim,
n_neurons,
label,
spike_rate=None,
spike_rec_indexes=None,
v_rate=None,
v_rec_indexes=None):
v_start = self.V_PATTERN * int(math.ceil(n_neurons / self.V_COUNT))
v_start = v_start[:n_neurons]
pop = sim.Population(n_neurons,
sim.IF_curr_exp(i_offset=1, tau_refrac=0),
label=label)
pop.initialize(v=v_start)
if spike_rec_indexes is None:
pop.record(['spikes'], sampling_interval=spike_rate)
else:
view = PopulationView(pop, spike_rec_indexes)
view.record(['spikes'], sampling_interval=spike_rate)
if v_rec_indexes is None:
pop.record(['v'], sampling_interval=v_rate)
else:
view = PopulationView(pop, v_rec_indexes)
view.record(['v'], sampling_interval=v_rate)
return pop
def __getitem__(self, index_or_slice):
if isinstance(index_or_slice, int):
return IDMixin(self, index_or_slice)
else:
return PopulationView(self,
index_or_slice,
label="view over {}".format(self.label))
def test_projection(self):
sim.setup(timestep=1.0)
pop = sim.Population(5, sim.IF_curr_exp(), label="pop_1")
view = PopulationView(pop, [1, 3], label="Odds")
try:
sim.Projection(pop, view, sim.OneToOneConnector())
except NotImplementedError:
pass # Exceptable but better if it worked
with pytest.raises(ConfigurationException):
sim.Projection(pop, "SOMETHING WIERD", sim.OneToOneConnector())
def test_simple(self):
n_neurons = 5
label = "pop_1"
sim.setup(timestep=1.0)
pop_1 = sim.Population(n_neurons, sim.IF_curr_exp(), label=label)
mask = [1, 3]
view = PopulationView(pop_1, mask, label=label)
self.assertEqual(2, view.size)
self.assertEqual(2, view.local_size)
self.assertEqual(label, view.label)
self.assertEqual(pop_1.celltype, view.celltype)
view_initial_values = view.initial_values
pop_initial_values = pop_1.initial_values
self.assertEqual(len(view_initial_values), len(pop_initial_values))
for key in pop_initial_values:
self.assertEqual(pop_initial_values[key][3],
view_initial_values[key][1])
self.assertEqual(pop_1, view.parent)
self.assertEqual(mask, view.mask)
cells = view.all_cells
self.assertEqual(2, len(cells))
self.assertEqual(1, cells[0].id)
self.assertEqual(3, cells[1].id)
self.assertEqual(cells, view.local_cells)
self.assertEqual(cells[0], view[1])
iterator = iter(view)
self.assertEqual(1, next(iterator).id)
self.assertEqual(3, next(iterator).id)
with pytest.raises(StopIteration):
next(iterator)
self.assertEqual(2, len(view))
iterator = view.all()
self.assertEqual(1, next(iterator).id)
self.assertEqual(3, next(iterator).id)
with pytest.raises(StopIteration):
next(iterator)
self.assertEqual(view.can_record("v"), pop_1.can_record("v"))
self.assertEqual(view.conductance_based, pop_1.conductance_based)
describe = view.describe()
self.assertIn('PopulationView "pop_1"', describe)
self.assertIn('parent : "pop_1"', describe)
self.assertIn('size : 2', describe)
self.assertIn('mask : [1, 3]', describe)
self.assertEqual(pop_1.find_units("v"), view.find_units("v"))
sim.end()
def test_initial_value(self):
sim.setup(timestep=1.0)
pop = sim.Population(5, sim.IF_curr_exp(), label="pop_1")
self.assertEqual([-65, -65, -65, -65, -65], pop.get_initial_value("v"))
view = PopulationView(pop, [1, 3], label="Odds")
view2 = PopulationView(pop, [1, 2], label="OneTwo")
view_iv = view.initial_values
self.assertEqual(1, len(view_iv))
self.assertEqual([-65, -65], view_iv["v"])
view.initialize(v=-60)
self.assertEqual([-65, -60, -65, -60, -65], pop.get_initial_value("v"))
self.assertEqual([-60, -60], view.initial_values["v"])
self.assertEqual([-60, -65], view2.initial_values["v"])
rand_distr = RandomDistribution("uniform",
parameters_pos=[-65.0, -55.0],
rng=NumpyRNG(seed=85524))
view.initialize(v=rand_distr)
self.assertEqual([-64.43349869042906, -63.663421790102184],
view.initial_values["v"])
view.initialize(v=lambda i: -65 + i / 10.0)
self.assertEqual([-64.9, -64.7], view.initial_values["v"])
sim.end()
def test_view_of_view(self):
n_neurons = 10
sim.setup(timestep=1.0)
pop_1 = sim.Population(n_neurons, sim.IF_curr_exp(), label="pop_1")
view1 = PopulationView(pop_1, [1, 3, 5, 7, 9], label="Odds")
view2 = PopulationView(view1, [1, 3], label="AlternativeOdds")
# Not a normal way to access but good to test
self.assertEqual([3, 7], view2._indexes)
self.assertEqual(view2.parent, view1)
self.assertEqual(view1.grandparent, pop_1)
self.assertEqual(view2.grandparent, pop_1)
cells = view2.all_cells
self.assertEqual(3, cells[0].id)
self.assertEqual(7, cells[1].id)
self.assertEqual(3, view1.id_to_index(7))
self.assertEqual([3, 0], view1.id_to_index([7, 1]))
self.assertEqual(1, view2.id_to_index(7))
view3 = view1[1:3]
self.assertEqual([3, 5], view3._indexes)
view4 = view1.sample(2)
self.assertEqual(2, len(view4._indexes))
sim.end()
def run_script(simtime,
n_neurons,
run_split=1,
record_spikes=False,
spike_rate=None,
spike_rec_indexes=None,
spike_get_indexes=None,
record_v=False,
v_rate=None,
v_rec_indexes=None,
v_get_indexes=None,
record_exc=False,
exc_rate=None,
exc_rec_indexes=None,
exc_get_indexes=None,
record_inh=False,
inh_rate=None,
inh_rec_indexes=None,
inh_get_indexes=None,
file_prefix=""):
sim.setup(timestep=1)
pop_1 = sim.Population(n_neurons, sim.IF_curr_exp(), label="pop_1")
input1 = sim.Population(1,
sim.SpikeSourceArray(spike_times=[0]),
label="input")
sim.Projection(input1,
pop_1,
sim.AllToAllConnector(),
synapse_type=sim.StaticSynapse(weight=5, delay=1))
input2 = sim.Population(n_neurons,
sim.SpikeSourcePoisson(rate=100.0),
label="Stim_Exc",
additional_parameters={"seed": 1})
sim.Projection(input2,
pop_1,
sim.OneToOneConnector(),
synapse_type=sim.StaticSynapse(weight=5, delay=1))
if record_spikes:
if spike_rec_indexes is None:
pop_1.record(['spikes'], sampling_interval=spike_rate)
else:
view = PopulationView(pop_1, spike_rec_indexes)
view.record(['spikes'], sampling_interval=spike_rate)
if record_v:
if v_rec_indexes is None:
pop_1.record(['v'], sampling_interval=v_rate)
else:
view = PopulationView(pop_1, v_rec_indexes)
view.record(['v'], sampling_interval=v_rate)
if record_exc:
if exc_rec_indexes is None:
pop_1.record(['gsyn_exc'], sampling_interval=exc_rate)
else:
view = PopulationView(pop_1, exc_rec_indexes)
view.record(['gsyn_exc'], sampling_interval=exc_rate)
if record_inh:
if inh_rec_indexes is None:
pop_1.record(['gsyn_inh'], sampling_interval=inh_rate)
else:
view = PopulationView(pop_1, inh_rec_indexes)
view.record(['gsyn_inh'], sampling_interval=inh_rate)
for i in range(run_split):
sim.run(simtime / run_split)
if record_spikes:
if spike_get_indexes is None:
neo = pop_1.get_data("spikes")
else:
view = PopulationView(pop_1, spike_get_indexes)
neo = view.get_data("spikes")
spikes = neo.segments[0].spiketrains
spike_file = os.path.join(current_file_path,
file_prefix + "spikes.csv")
write_spikes(spikes, spike_file)
else:
spikes = None
if record_v:
if v_get_indexes is None:
neo = pop_1.get_data("v")
else:
view = PopulationView(pop_1, v_get_indexes)
neo = view.get_data("v")
v = neo.segments[0].filter(name='v')[0]
v_file = os.path.join(current_file_path, file_prefix + "v.csv")
numpy.savetxt(v_file, v, delimiter=',')
else:
v = None
if record_exc:
if exc_get_indexes is None:
neo = pop_1.get_data('gsyn_exc')
else:
view = PopulationView(pop_1, exc_get_indexes)
neo = view.get_data('gsyn_exc')
exc = neo.segments[0].filter(name='gsyn_exc')[0]
exc_file = os.path.join(current_file_path, file_prefix + "exc.csv")
numpy.savetxt(exc_file, exc, delimiter=',')
else:
exc = None
if record_inh:
if inh_get_indexes is None:
neo = pop_1.get_data('gsyn_inh')
else:
view = PopulationView(pop_1, inh_get_indexes)
neo = view.get_data('gsyn_inh')
inh = neo.segments[0].filter(name='gsyn_inh')[0]
inh_file = os.path.join(current_file_path, file_prefix + "inh.csv")
numpy.savetxt(inh_file, inh, delimiter=',')
else:
inh = None
sim.end()
return spikes, v, exc, inh