def test_set_synaptic_parameters_fully_connected(sim):
sim.setup()
mpi_rank = sim.rank()
p1 = sim.Population(4, sim.IF_cond_exp())
p2 = sim.Population(2, sim.IF_cond_exp())
syn = sim.TsodyksMarkramSynapse(U=0.5, weight=0.123, delay=0.1)
prj = sim.Projection(p1, p2, sim.AllToAllConnector(), syn)
expected = numpy.array([
(0.0, 0.0, 0.123, 0.1, 0.5),
(0.0, 1.0, 0.123, 0.1, 0.5),
(1.0, 0.0, 0.123, 0.1, 0.5),
(1.0, 1.0, 0.123, 0.1, 0.5),
(2.0, 0.0, 0.123, 0.1, 0.5),
(2.0, 1.0, 0.123, 0.1, 0.5),
(3.0, 0.0, 0.123, 0.1, 0.5),
(3.0, 1.0, 0.123, 0.1, 0.5),
])
actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_almost_equal(actual[ind], expected, 1e-16)
positional_weights = numpy.array([[0, 1], [2, 3], [4, 5], [6, 7]],
dtype=float)
prj.set(weight=positional_weights)
expected = positional_weights
actual = prj.get('weight', format='array')
if mpi_rank == 0:
assert_arrays_equal(actual, expected)
u_list = [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2]
prj.set(U=u_list)
expected = numpy.array([[0.9, 0.8], [0.7, 0.6], [0.5, 0.4], [0.3, 0.2]])
actual = prj.get('U', format='array')
if mpi_rank == 0:
assert_arrays_equal(actual, expected)
f_delay = lambda d: 0.5 + d
prj.set(delay=f_delay)
expected = numpy.array([[0.5, 1.5], [1.5, 0.5], [2.5, 1.5], [3.5, 2.5]])
actual = prj.get('delay', format='array')
if mpi_rank == 0:
assert_arrays_equal(actual, expected)
# final sanity check
expected = numpy.array([
(0.0, 0.0, 0.0, 0.5, 0.9),
(0.0, 1.0, 1.0, 1.5, 0.8),
(1.0, 0.0, 2.0, 1.5, 0.7),
(1.0, 1.0, 3.0, 0.5, 0.6),
(2.0, 0.0, 4.0, 2.5, 0.5),
(2.0, 1.0, 5.0, 1.5, 0.4),
(3.0, 0.0, 6.0, 3.5, 0.3),
(3.0, 1.0, 7.0, 2.5, 0.2),
])
actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_equal(actual[ind], expected)
def test_set_synaptic_parameters_fully_connected(sim):
sim.setup()
mpi_rank = sim.rank()
p1 = sim.Population(4, sim.IF_cond_exp())
p2 = sim.Population(2, sim.IF_cond_exp())
syn = sim.TsodyksMarkramSynapse(U=0.5, weight=0.123, delay=0.1)
prj = sim.Projection(p1, p2, sim.AllToAllConnector(), syn)
expected = numpy.array([
(0.0, 0.0, 0.123, 0.1, 0.5),
(0.0, 1.0, 0.123, 0.1, 0.5),
(1.0, 0.0, 0.123, 0.1, 0.5),
(1.0, 1.0, 0.123, 0.1, 0.5),
(2.0, 0.0, 0.123, 0.1, 0.5),
(2.0, 1.0, 0.123, 0.1, 0.5),
(3.0, 0.0, 0.123, 0.1, 0.5),
(3.0, 1.0, 0.123, 0.1, 0.5),
])
actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_almost_equal(actual[ind], expected, 1e-16)
positional_weights = numpy.array([[0, 1], [2, 3], [4, 5], [6, 7]], dtype=float)
prj.set(weight=positional_weights)
expected = positional_weights
actual = prj.get('weight', format='array')
if mpi_rank == 0:
assert_arrays_equal(actual, expected)
u_list = [0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2]
prj.set(U=u_list)
expected = numpy.array([[0.9, 0.8], [0.7, 0.6], [0.5, 0.4], [0.3, 0.2]])
actual = prj.get('U', format='array')
if mpi_rank == 0:
assert_arrays_equal(actual, expected)
f_delay = lambda d: 0.5+d
prj.set(delay=f_delay)
expected = numpy.array([[0.5, 1.5], [1.5, 0.5], [2.5, 1.5], [3.5, 2.5]])
actual = prj.get('delay', format='array')
if mpi_rank == 0:
assert_arrays_equal(actual, expected)
# final sanity check
expected = numpy.array([
(0.0, 0.0, 0.0, 0.5, 0.9),
(0.0, 1.0, 1.0, 1.5, 0.8),
(1.0, 0.0, 2.0, 1.5, 0.7),
(1.0, 1.0, 3.0, 0.5, 0.6),
(2.0, 0.0, 4.0, 2.5, 0.5),
(2.0, 1.0, 5.0, 1.5, 0.4),
(3.0, 0.0, 6.0, 3.5, 0.3),
(3.0, 1.0, 7.0, 2.5, 0.2),
])
actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_equal(actual[ind], expected)
def test_columnwise_iteration_with_random_array_parallel_safe_with_mask():
random.mpi_rank=0; random.num_processes=2
input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
copy_input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4,3))
mask = numpy.array([False, False, True])
cols = [col for col in m.by_column(mask=mask)]
assert_equal(len(cols), 1)
assert_arrays_almost_equal(cols[0], copy_input.next(12, mask_local=False)[8:], 1e-15)
def all_to_all_static_no_self(sim):
sim.setup()
p = sim.Population(5, sim.IF_cond_exp())
synapse_type = sim.StaticSynapse(weight=RandomDistribution('gamma', k=2.0, theta=0.5), delay="0.2+0.3*d")
prj = sim.Projection(p, p, sim.AllToAllConnector(allow_self_connections=False), synapse_type)
weights = prj.get('weight', format='array', gather=False)
print(weights)
delays = prj.get('delay', format='list', gather=False)
i, j, d = numpy.array(delays).T
assert_arrays_almost_equal(d, 0.2 + 0.3 * abs(i - j), 1e-9)
assert_equal(d.size, p.size * (p.size - 1))
sim.end()
def all_to_all_static_no_self(sim):
sim.setup()
p = sim.Population(5, sim.IF_cond_exp())
synapse_type = sim.StaticSynapse(weight=RandomDistribution('gamma', k=2.0, theta=0.5), delay="0.2+0.3*d")
prj = sim.Projection(p, p, sim.AllToAllConnector(allow_self_connections=False), synapse_type)
weights = prj.get('weight', format='array', gather=False)
print(weights)
delays = prj.get('delay', format='list', gather=False)
i, j, d = numpy.array(delays).T
assert_arrays_almost_equal(d, 0.2 + 0.3 * abs(i - j), 1e-9)
assert_equal(d.size, p.size * (p.size - 1))
sim.end()
def test_columnwise_iteration_with_random_array_parallel_safe_with_mask():
random.mpi_rank = 0
random.num_processes = 2
input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
copy_input = random.RandomDistribution(rng=MockRNG(parallel_safe=True))
m = LazyArray(input, shape=(4, 3))
mask = numpy.array([False, False, True])
cols = [col for col in m.by_column(mask=mask)]
assert_equal(len(cols), 1)
assert_arrays_almost_equal(cols[0],
copy_input.next(12, mask_local=False)[8:],
1e-15)
def ticket195(sim):
"""
Check that the `connect()` function works correctly with single IDs (see
http://neuralensemble.org/trac/PyNN/ticket/195)
"""
sim.setup(timestep=0.01)
pre = sim.Population(10, sim.SpikeSourceArray, cellparams={'spike_times':range(1,10)})
post = sim.Population(10, sim.IF_cond_exp)
sim.connect(pre[0], post[0], weight=0.01, delay=0.1, p=1)
post.record()
sim.run(100.0)
assert_arrays_almost_equal(post.getSpikes(), numpy.array([[0.0, 13.4]]), 0.5)
def ticket195(sim):
"""
Check that the `connect()` function works correctly with single IDs (see
http://neuralensemble.org/trac/PyNN/ticket/195)
"""
init_logging(None, debug=True)
sim.setup(timestep=0.01)
pre = sim.Population(10, sim.SpikeSourceArray(spike_times=range(1,10)))
post = sim.Population(10, sim.IF_cond_exp())
#sim.connect(pre[0], post[0], weight=0.01, delay=0.1, p=1)
sim.connect(pre[0:1], post[0:1], weight=0.01, delay=0.1, p=1)
#prj = sim.Projection(pre, post, sim.FromListConnector([(0, 0, 0.01, 0.1)]))
post.record(['spikes', 'v'])
sim.run(100.0)
assert_arrays_almost_equal(post.get_data().segments[0].spiketrains[0], numpy.array([13.4])*pq.ms, 0.5)
def test_reset(sim):
"""
Run the same simulation n times without recreating the network,
and check the results are the same each time.
"""
repeats = 3
sim.setup()
p = sim.Population(1, sim.IF_cond_exp, {"i_offset": 0.1})
p.record_v()
data = []
for i in range(repeats):
sim.run(10.0)
data.append(p.get_v())
sim.reset()
sim.end()
for rec in data:
assert_arrays_almost_equal(rec, data[0], 1e-12)
def issue259(sim):
"""
A test that retrieving data with "clear=True" gives correct spike trains.
"""
sim.setup(timestep=0.05, spike_precision="off_grid")
p = sim.Population(1, sim.SpikeSourceArray(spike_times=[0.075, 10.025, 12.34, 1000.025]))
p.record('spikes')
sim.run(10.0)
spiketrains0 = p.get_data('spikes', clear=True).segments[0].spiketrains
print(spiketrains0[0])
sim.run(10.0)
spiketrains1 = p.get_data('spikes', clear=True).segments[0].spiketrains
print(spiketrains1[0])
sim.run(10.0)
spiketrains2 = p.get_data('spikes', clear=True).segments[0].spiketrains
print(spiketrains2[0])
sim.end()
assert_arrays_almost_equal(spiketrains0[0], numpy.array([0.075])*pq.ms, 1e-17)
assert_arrays_almost_equal(spiketrains1[0], numpy.array([10.025, 12.34])*pq.ms, 1e-14)
assert_equal(spiketrains2[0].size, 0)
def test_reset(sim):
"""
Run the same simulation n times without recreating the network,
and check the results are the same each time.
"""
repeats = 3
dt = 1
sim.setup(timestep=dt, min_delay=dt)
p = sim.Population(1, sim.IF_curr_exp(i_offset=0.1))
p.record('v')
for i in range(repeats):
sim.run(10.0)
sim.reset()
data = p.get_data(clear=False)
sim.end()
assert len(data.segments) == repeats
for segment in data.segments[1:]:
assert_arrays_almost_equal(segment.analogsignalarrays[0],
data.segments[0].analogsignalarrays[0], 1e-11)
def test_reset(sim):
"""
Run the same simulation n times without recreating the network,
and check the results are the same each time.
"""
repeats = 3
dt = 1
sim.setup(timestep=dt, min_delay=dt)
p = sim.Population(1, sim.IF_curr_exp, {"i_offset": 0.1})
p.record_v()
data = []
for i in range(repeats):
sim.run(10.0)
data.append(p.get_v())
sim.reset()
sim.end()
for rec in data:
assert_arrays_almost_equal(rec, data[0], 1e-11)
def test_reset_with_clear(sim):
"""
Run the same simulation n times without recreating the network,
and check the results are the same each time.
"""
repeats = 3
dt = 1
sim.setup(timestep=dt, min_delay=dt)
p = sim.Population(1, sim.IF_curr_exp(i_offset=0.1))
p.record('v')
data = []
for i in range(repeats):
sim.run(10.0)
data.append(p.get_data(clear=True))
sim.reset()
sim.end()
for rec in data:
assert len(rec.segments) == 1
assert_arrays_almost_equal(rec.segments[0].analogsignalarrays[0],
data[0].segments[0].analogsignalarrays[0], 1e-11)
def issue259(sim):
"""
A test that retrieving data with "clear=True" gives correct spike trains.
"""
sim.setup(timestep=0.05, spike_precision="off_grid")
p = sim.Population(
1, sim.SpikeSourceArray(spike_times=[0.075, 10.025, 12.34, 1000.025]))
p.record('spikes')
sim.run(10.0)
spiketrains0 = p.get_data('spikes', clear=True).segments[0].spiketrains
print(spiketrains0[0])
sim.run(10.0)
spiketrains1 = p.get_data('spikes', clear=True).segments[0].spiketrains
print(spiketrains1[0])
sim.run(10.0)
spiketrains2 = p.get_data('spikes', clear=True).segments[0].spiketrains
print(spiketrains2[0])
sim.end()
assert_arrays_almost_equal(spiketrains0[0],
numpy.array([0.075]) * pq.ms, 1e-17)
assert_arrays_almost_equal(spiketrains1[0],
numpy.array([10.025, 12.34]) * pq.ms, 1e-14)
assert_equal(spiketrains2[0].size, 0)
def test_set_synaptic_parameters_multiply_connected(sim):
sim.setup()
mpi_rank = sim.rank()
p1 = sim.Population(4, sim.IF_cond_exp())
p2 = sim.Population(2, sim.IF_cond_exp())
syn = sim.TsodyksMarkramSynapse(U=0.5, weight=0.123, delay=0.1)
prj = sim.Projection(p1, p2, sim.FromListConnector([(0, 0), (1, 0), (3, 0), (1, 1), (1, 0), (2, 1)]), syn)
expected = numpy.array([
(0.0, 0.0, 0.123, 0.1, 0.5),
(1.0, 0.0, 0.123, 0.1, 0.5),
(1.0, 0.0, 0.123, 0.1, 0.5),
(1.0, 1.0, 0.123, 0.1, 0.5),
(2.0, 1.0, 0.123, 0.1, 0.5),
(3.0, 0.0, 0.123, 0.1, 0.5),
])
actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_almost_equal(actual[ind], expected, 1e-16)
positional_weights = numpy.array([[0, nan], [2, 3], [nan, 5], [6, nan]], dtype=float)
prj.set(weight=positional_weights)
expected = numpy.array([
(0.0, 0.0, 0.0),
(1.0, 0.0, 2.0),
(1.0, 0.0, 2.0),
(1.0, 1.0, 3.0),
(2.0, 1.0, 5.0),
(3.0, 0.0, 6.0),
])
actual = numpy.array(prj.get('weight', format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_almost_equal(actual[ind], expected, 1e-16)
# postponing implementation of this functionality until after 0.8.0
# u_list = [0.9, 0.8, 0.7, 0.6, 0.5, 0.4]
# prj.set(U=u_list)
# expected = numpy.array([
# (0.0, 0.0, 0.9),
# (1.0, 0.0, 0.8),
# (1.0, 0.0, 0.7),
# (1.0, 1.0, 0.6),
# (2.0, 1.0, 0.5),
# (3.0, 0.0, 0.4),
# ])
# actual = numpy.array(prj.get('U', format='list'))
# if mpi_rank == 0:
# ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
# assert_arrays_almost_equal(actual[ind], expected, 1e-16)
f_delay = lambda d: 0.5+d
prj.set(delay=f_delay)
expected = numpy.array([
(0.0, 0.0, 0.5),
(1.0, 0.0, 1.5),
(1.0, 0.0, 1.5),
(1.0, 1.0, 0.5),
(2.0, 1.0, 1.5),
(3.0, 0.0, 3.5),
])
actual = numpy.array(prj.get('delay', format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_arrays_almost_equal(actual[ind], expected, 1e-16)
# final sanity check
expected = numpy.array([
(0.0, 0.0, 0.0, 0.5, 0.5),
(1.0, 0.0, 2.0, 1.5, 0.5),
(1.0, 0.0, 2.0, 1.5, 0.5),
(1.0, 1.0, 3.0, 0.5, 0.5),
(2.0, 1.0, 5.0, 1.5, 0.5),
(3.0, 0.0, 6.0, 3.5, 0.5),
])
actual = numpy.array(prj.get(['weight', 'delay', 'U'], format='list'))
if mpi_rank == 0:
ind = numpy.lexsort((actual[:, 1], actual[:, 0]))
assert_array_equal(actual[ind], expected)
