def test_probe_vector_recorders(self):
sim = self.init_sim(cc2_recipe())
ts = [0, 0.1, 0.3, 0.7]
h0 = sim.sample((1, 0), A.explicit_schedule(ts),
A.sampling_policy.exact)
h1 = sim.sample((1, 1), A.explicit_schedule(ts),
A.sampling_policy.exact)
sim.run(10., 0.01)
# probe (1, 0) is the whole cell voltage; expect time + 6 sample values per row in returned data (see test_probe_meta above).
s0, meta0 = sim.samples(h0)[0]
self.assertEqual(6, len(meta0))
self.assertEqual((len(ts), 7), s0.shape)
for i, t in enumerate(s0[:, 0]):
self.assertEqual(t, ts[i])
# probe (1, 1) is the 'g' state for all expsyn synapses.
# With the default descretization, expect two synapses with multiplicity 2 and two with multiplicity 1.
s1, meta1 = sim.samples(h1)[0]
self.assertEqual(4, len(meta1))
self.assertEqual((len(ts), 5), s1.shape)
for i, t in enumerate(s1[:, 0]):
self.assertEqual(t, ts[i])
meta1_mult = {(m.location.branch, m.location.pos): m.multiplicity
for m in meta1}
self.assertEqual(2, meta1_mult[(0, 0.3)])
self.assertEqual(2, meta1_mult[(0, 0.6)])
self.assertEqual(1, meta1_mult[(1, 0.3)])
self.assertEqual(1, meta1_mult[(2, 0.3)])
def test_probe_multi_scalar_recorders(self):
sim = self.init_sim(cc2_recipe())
ts = [0, 0.1, 0.3, 0.7]
h0 = sim.sample((0, 0), A.explicit_schedule(ts))
h1 = sim.sample((0, 1), A.explicit_schedule(ts))
h2 = sim.sample((0, 2), A.explicit_schedule(ts))
dt = 0.01
sim.run(10., dt)
r0 = sim.samples(h0)
self.assertEqual(1, len(r0))
s0, meta0 = r0[0]
r1 = sim.samples(h1)
self.assertEqual(1, len(r1))
s1, meta1 = r1[0]
r2 = sim.samples(h2)
self.assertEqual(2, len(r2))
s20, meta20 = r2[0]
s21, meta21 = r2[1]
# Probe id (0, 2) has probes over the two locations that correspond to probes (0, 0) and (0, 1).
# (order is not guaranteed to line up though)
if meta20 == meta0:
self.assertEqual(meta1, meta21)
self.assertTrue((s0[:, 1] == s20[:, 1]).all())
self.assertTrue((s1[:, 1] == s21[:, 1]).all())
else:
self.assertEqual(meta1, meta20)
self.assertTrue((s1[:, 1] == s20[:, 1]).all())
self.assertEqual(meta0, meta21)
self.assertTrue((s0[:, 1] == s21[:, 1]).all())
def event_generators(self, gid):
"""two stimuli: one that makes the cell spike, the other to monitor STDP
"""
stimulus_times = numpy.linspace(50, 500, self.n_pairs)
# strong enough stimulus
spike = arbor.event_generator("synapse", 1., arbor.explicit_schedule(stimulus_times))
# zero weight -> just modify synaptic weight via stdp
stdp = arbor.event_generator("stpd_synapse", 0., arbor.explicit_schedule(stimulus_times - self.dT))
return [spike, stdp]
def test_event_generator_explicit_schedule(self):
cm = arb.cell_member(0, 42)
es = arb.explicit_schedule([0, 1, 2, 3, 4.4])
eg = arb.event_generator(cm, -0.01, es)
self.assertEqual(eg.target.gid, 0)
self.assertEqual(eg.target.index, 42)
self.assertAlmostEqual(eg.weight, -0.01)
def test_event_generator_explicit_schedule(self):
cm = arb.cell_local_label("tgt1", arb.selection_policy.round_robin)
es = arb.explicit_schedule([0,1,2,3,4.4])
eg = arb.event_generator(cm, -0.01, es)
self.assertEqual(eg.target.label, "tgt1")
self.assertEqual(eg.target.policy, arb.selection_policy.round_robin)
self.assertAlmostEqual(eg.weight, -0.01)
def test_events_explicit_schedule(self):
times = [0.1, 0.3, 1.0, 2.2, 1.25, 1.7]
expected = [0.1, 0.3, 1.0]
es = arb.explicit_schedule(times)
for i in range(len(expected)):
self.assertAlmostEqual(expected[i], es.events(0., 1.25)[i], places = 2)
expected = [0.3, 1.0, 1.25, 1.7]
for i in range(len(expected)):
self.assertAlmostEqual(expected[i], es.events(0.3, 1.71)[i], places = 2)
def test_exceptions_explicit_schedule(self):
with self.assertRaisesRegex(
RuntimeError,
"explicit time schedule can not contain negative values"):
arb.explicit_schedule([-1])
with self.assertRaises(TypeError):
arb.explicit_schedule(['times'])
with self.assertRaises(TypeError):
arb.explicit_schedule([None])
with self.assertRaises(TypeError):
arb.explicit_schedule([[1, 2, 3]])
def test_probe_scalar_recorders(self):
sim = self.init_sim(cc2_recipe())
ts = [0, 0.1, 0.3, 0.7]
h = sim.sample((0, 0), A.explicit_schedule(ts))
dt = 0.01
sim.run(10., dt)
s, meta = sim.samples(h)[0]
self.assertEqual(A.location(1, 1), meta)
for i, t in enumerate(s[:, 0]):
self.assertLess(abs(t - ts[i]), dt)
sim.remove_sampler(h)
sim.reset()
h = sim.sample(A.cell_member(0, 0), A.explicit_schedule(ts),
A.sampling_policy.exact)
sim.run(10., dt)
s, meta = sim.samples(h)[0]
for i, t in enumerate(s[:, 0]):
self.assertEqual(t, ts[i])
def test_exceptions_explicit_schedule(self):
with self.assertRaisesRegex(RuntimeError,
"explicit time schedule cannot contain negative values"):
arb.explicit_schedule([-1])
with self.assertRaises(TypeError):
arb.explicit_schedule(['times'])
with self.assertRaises(TypeError):
arb.explicit_schedule([None])
with self.assertRaises(TypeError):
arb.explicit_schedule([[1,2,3]])
with self.assertRaisesRegex(RuntimeError,
"t1 must be a non-negative number"):
rs = arb.regular_schedule(0.1)
rs.events(1., -1.)
def event_generators(self, gid):
if gid==0:
sched = arbor.explicit_schedule([1])
return [arbor.event_generator(arbor.cell_member(0,0), 0.1, sched)]
return []
def cell_description(self, gid):
if gid < 3:
return arbor.spike_source_cell(
"src", arbor.explicit_schedule(self.trains[gid]))
else:
return self._cable_cell()
def test_event_generator_explicit_schedule(self):
es = arb.explicit_schedule([0,1,2,3,4.4])
eg = arb.event_generator(42, -0.01, es)
self.assertEqual(eg.target, 42)
self.assertAlmostEqual(eg.weight, -0.01)
def event_generators(self, gid):
if gid==0:
sched = arbor.explicit_schedule([1]) # one event at 1 ms
weight = 0.1 # 0.1 μS on expsyn
return [arbor.event_generator('syn', weight, sched)]
return []
def test_set_times_explicit_schedule(self):
es = arb.explicit_schedule()
es.times = [42, 43, 44, 55.5, 100]
self.assertEqual(es.times, [42, 43, 44, 55.5, 100])
def test_times_contor_explicit_schedule(self):
es = arb.explicit_schedule([1, 2, 3, 4.5])
self.assertEqual(es.times, [1, 2, 3, 4.5])
def event_generators(self, gid):
if gid==0:
sched = arbor.explicit_schedule([1])
weight = 0.1
return [arbor.event_generator('syn', weight, sched)]
return []
def event_generators(self, gid):
print_v('Getting event_generators for: %s'%(gid))
if gid==0:
sched = arbor.explicit_schedule([1])
return [arbor.event_generator((0,0), 0.1, sched)]
return []
def cell_description(self, gid):
return A.spike_source_cell("src",
A.explicit_schedule(self.trains[gid]))
