def init_sim(self):
comm = A.mpi_comm()
context = A.context(threads=1, gpu_id=None, mpi=A.mpi_comm())
self.rank = context.rank
self.ranks = context.ranks
recipe = lifN_recipe(context.ranks)
dd = A.partition_load_balance(recipe, context)
# Confirm decomposition has gid 0 on rank 0, ..., gid N-1 on rank N-1.
self.assertEqual(1, dd.num_local_cells)
local_groups = dd.groups
self.assertEqual(1, len(local_groups))
self.assertEqual([self.rank], local_groups[0].gids)
return A.simulation(recipe, dd, context)
def run(dT, n_pairs=1, do_plots=False):
recipe = single_recipe(dT, n_pairs)
context = arbor.context()
domains = arbor.partition_load_balance(recipe, context)
sim = arbor.simulation(recipe, domains, context)
sim.record(arbor.spike_recording.all)
reg_sched = arbor.regular_schedule(0.1)
handle_mem = sim.sample((0, 0), reg_sched)
handle_g = sim.sample((0, 1), reg_sched)
handle_apost = sim.sample((0, 2), reg_sched)
handle_apre = sim.sample((0, 3), reg_sched)
handle_weight_plastic = sim.sample((0, 4), reg_sched)
sim.run(tfinal=600)
if do_plots:
print("Plotting detailed results ...")
for (handle, var) in [(handle_mem, 'U'),
(handle_g, "g"),
(handle_apost, "apost"),
(handle_apre, "apre"),
(handle_weight_plastic, "weight_plastic")]:
data, meta = sim.samples(handle)[0]
df = pandas.DataFrame({'t/ms': data[:, 0], var: data[:, 1]})
seaborn.relplot(data=df, kind="line", x="t/ms", y=var,
ci=None).savefig('single_cell_stdp_result_{}.svg'.format(var))
weight_plastic, meta = sim.samples(handle_weight_plastic)[0]
return weight_plastic[:, 1][-1]
def test_simulation(self):
rcp = recipe()
ctx = arb.context()
dom = arb.partition_load_balance(rcp, ctx)
sim = arb.simulation(rcp, dom, ctx)
sim.run(tfinal=30)
def art_spiking_sim(context, art_spiker_recipe):
dd = arbor.partition_load_balance(art_spiker_recipe, context)
return arbor.simulation(art_spiker_recipe, dd, context)
return self.the_props recipe = single_recipe(cell, [probe]) # (4) Create an execution context context = arbor.context() # (5) Create a domain decomposition domains = arbor.partition_load_balance(recipe, context) # (6) Create a simulation sim = arbor.simulation(recipe, domains, context) # Instruct the simulation to record the spikes and sample the probe sim.record(arbor.spike_recording.all) probe_id = arbor.cell_member(0, 0) handle = sim.sample(probe_id, arbor.regular_schedule(0.02)) # (7) Run the simulation sim.run(tfinal=100, dt=0.025) # (8) Print or display the results spikes = sim.spikes()
decomp = arbor.partition_load_balance(recipe, context)
print(f'{decomp}')
hint = arbor.partition_hint()
hint.prefer_gpu = True
hint.gpu_group_size = 1000
print(f'{hint}')
hints = dict([(arbor.cell_kind.cable, hint)])
decomp = arbor.partition_load_balance(recipe, context, hints)
print(f'{decomp}')
meters.checkpoint('load-balance', context)
sim = arbor.simulation(recipe, decomp, context)
meters.checkpoint('simulation-init', context)
spike_recorder = arbor.attach_spike_recorder(sim)
# Attach a sampler to the voltage probe on cell 0.
# Sample rate of 10 sample every ms.
samplers = [arbor.attach_sampler(sim, 0.1, arbor.cell_member(gid,0)) for gid in range(ncells)]
tfinal=100
sim.run(tfinal)
print(f'{sim} finished')
meters.checkpoint('simulation-run', context)
self.props.catalogue = arb.load_catalogue(cat)
d = arb.decor()
d.paint('(all)', 'dummy')
d.set_property(Vm=0.0)
self.cell = arb.cable_cell(self.tree, arb.label_dict(), d)
def global_properties(self, _):
return self.props
def num_cells(self):
return 1
def cell_kind(self, gid):
return arb.cell_kind.cable
def cell_description(self, gid):
return self.cell
if not Path(cat).is_file():
print("""Catalogue not found in this directory.
Please ensure it has been compiled by calling")
<arbor>/scripts/build-catalogue cat <arbor>/python/examples/cat
where <arbor> is the location of the arbor source tree.""")
exit(1)
rcp = recipe()
ctx = arb.context()
dom = arb.partition_load_balance(rcp, ctx)
sim = arb.simulation(rcp, dom, ctx)
sim.run(tfinal=30)
def test_probe_addr_metadata(self):
recipe = cc_recipe()
context = A.context()
dd = A.partition_load_balance(recipe, context)
sim = A.simulation(recipe, dd, context)
all_cv_cables = [A.cable(0, 0, 1)]
m = sim.probe_metadata((0, 0))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.0), m[0])
m = sim.probe_metadata((0, 1))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 2))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.02), m[0])
m = sim.probe_metadata((0, 3))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.03), m[0])
m = sim.probe_metadata((0, 4))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 5))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 6))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.06), m[0])
m = sim.probe_metadata((0, 7))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 8))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.08), m[0].location)
self.assertEqual(1, m[0].multiplicity)
self.assertEqual(0, m[0].target)
m = sim.probe_metadata((0, 9))
self.assertEqual(1, len(m))
self.assertEqual(1, len(m[0]))
self.assertEqual(A.location(0, 0.09), m[0][0].location)
self.assertEqual(1, m[0][0].multiplicity)
self.assertEqual(1, m[0][0].target)
m = sim.probe_metadata((0, 10))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.10), m[0])
m = sim.probe_metadata((0, 11))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 12))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.12), m[0])
m = sim.probe_metadata((0, 13))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 14))
self.assertEqual(1, len(m))
self.assertEqual(A.location(0, 0.14), m[0])
m = sim.probe_metadata((0, 15))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
m = sim.probe_metadata((0, 16))
self.assertEqual(1, len(m))
self.assertEqual(all_cv_cables, m[0])
def init_sim(self, recipe):
context = A.context()
dd = A.partition_load_balance(recipe, context)
return A.simulation(recipe, dd, context)
