def test_probe_all_recursive(recursive):
model = nengo.Network(label='test_probing')
with model:
ens1 = nengo.Ensemble(n_neurons=1, dimensions=1)
node1 = nengo.Node(output=[0])
conn = nengo.Connection(node1, ens1)
subnet = nengo.Network(label='subnet')
with subnet:
ens2 = nengo.Ensemble(n_neurons=1, dimensions=1)
node2 = nengo.Node(output=[0])
probes = probe_all(model, recursive=recursive)
# test top level probing
total_number_probes1 = len(
ens1.probeable) + len(node1.probeable) + len(conn.probeable)
assert(len(model.probes) == total_number_probes1)
# test dictionary
assert(len(probes[ens1]) == len(ens1.probeable))
assert(len(probes[node1]) == len(node1.probeable))
assert(len(probes[conn]) == len(conn.probeable))
# test recursive probing
if recursive:
total_number_probes2 = len(ens2.probeable) + len(node2.probeable)
assert(len(subnet.probes) == total_number_probes2)
assert(len(probes[ens2]) == len(ens2.probeable))
assert(len(probes[node2]) == len(node2.probeable))
else:
assert(len(subnet.probes) == 0)
assert ens2 not in probes
def test_probe_all_kwargs():
model = nengo.Network(label='test_probing')
with model:
ens1 = nengo.Ensemble(n_neurons=1, dimensions=1)
node1 = nengo.Node(output=[0])
nengo.Connection(node1, ens1)
subnet = nengo.Network(label='subnet')
with subnet:
nengo.Ensemble(n_neurons=1, dimensions=1)
nengo.Node(output=[0])
probe_all(model, recursive=True, sample_every=0.1, seed=10)
for probe in model.probes + subnet.probes:
assert probe.sample_every == 0.1
assert probe.seed == 10
def test_probe_all_kwargs():
model = nengo.Network(label='test_probing')
with model:
ens1 = nengo.Ensemble(n_neurons=1, dimensions=1)
node1 = nengo.Node(output=[0])
nengo.Connection(node1, ens1)
subnet = nengo.Network(label='subnet')
with subnet:
nengo.Ensemble(n_neurons=1, dimensions=1)
nengo.Node(output=[0])
probe_all(model, recursive=True, sample_every=0.1, seed=10)
for probe in model.probes + subnet.probes:
assert probe.sample_every == 0.1
assert probe.seed == 10
def test_probe_all_recursive(recursive):
model = nengo.Network(label='test_probing')
with model:
ens1 = nengo.Ensemble(n_neurons=1, dimensions=1)
node1 = nengo.Node(output=[0])
conn = nengo.Connection(node1, ens1)
subnet = nengo.Network(label='subnet')
with subnet:
ens2 = nengo.Ensemble(n_neurons=1, dimensions=1)
node2 = nengo.Node(output=[0])
probes = probe_all(model, recursive=recursive)
# test top level probing
total_number_probes1 = len(ens1.probeable) + len(node1.probeable) + len(
conn.probeable)
assert (len(model.probes) == total_number_probes1)
# test dictionary
assert (len(probes[ens1]) == len(ens1.probeable))
assert (len(probes[node1]) == len(node1.probeable))
assert (len(probes[conn]) == len(conn.probeable))
# test recursive probing
if recursive:
total_number_probes2 = len(ens2.probeable) + len(node2.probeable)
assert (len(subnet.probes) == total_number_probes2)
assert (len(probes[ens2]) == len(ens2.probeable))
assert (len(probes[node2]) == len(node2.probeable))
else:
assert (len(subnet.probes) == 0)
assert ens2 not in probes
def test_probe_all_options():
model = nengo.Network(label='test_probing')
with model:
ens1 = nengo.Ensemble(n_neurons=1, dimensions=1)
node1 = nengo.Node(output=[0])
nengo.Connection(node1, ens1)
subnet = nengo.Network(label='subnet')
with subnet:
nengo.Ensemble(n_neurons=1, dimensions=1)
nengo.Node(output=[0])
probe_all(model, recursive=True, probe_options={
nengo.Ensemble: ['decoded_output']})
# only probes spikes and decoded output of the ensembles
assert(len(model.probes) == 1)
assert(len(subnet.probes) == 1)
def test_probe_all_options():
model = nengo.Network(label='test_probing')
with model:
ens1 = nengo.Ensemble(n_neurons=1, dimensions=1)
node1 = nengo.Node(output=[0])
nengo.Connection(node1, ens1)
subnet = nengo.Network(label='subnet')
with subnet:
nengo.Ensemble(n_neurons=1, dimensions=1)
nengo.Node(output=[0])
probe_all(model, recursive=True, probe_options={
nengo.Ensemble: ['decoded_output']})
# only probes spikes and decoded output of the ensembles
assert(len(model.probes) == 1)
assert(len(subnet.probes) == 1)
