def test_schedule_warning():
previous_device = get_device()
from uuid import uuid4
# TestDevice1 supports arbitrary schedules, TestDevice2 does not
class TestDevice1(Device):
# These functions are needed during the setup of the defaultclock
def get_value(self, var):
return np.array([0.0001])
def add_array(self, var):
pass
def init_with_zeros(self, var, dtype):
pass
def fill_with_array(self, var, arr):
pass
class TestDevice2(TestDevice1):
def __init__(self):
super(TestDevice2, self).__init__()
self.network_schedule = ['start', 'groups', 'synapses',
'thresholds', 'resets', 'end']
# Unique names are important for getting the warnings again for multiple
# runs of the test suite
name1 = 'testdevice_' + str(uuid4())
name2 = 'testdevice_' + str(uuid4())
all_devices[name1] = TestDevice1()
all_devices[name2] = TestDevice2()
set_device(name1)
assert schedule_propagation_offset() == 0*ms
net = Network()
assert schedule_propagation_offset(net) == 0*ms
# Any schedule should work
net.schedule = list(reversed(net.schedule))
with catch_logs() as l:
net.run(0*ms)
assert len(l) == 0, 'did not expect a warning'
assert schedule_propagation_offset(net) == defaultclock.dt
set_device(name2)
assert schedule_propagation_offset() == defaultclock.dt
# Using the correct schedule should work
net.schedule = ['start', 'groups', 'synapses', 'thresholds', 'resets', 'end']
with catch_logs() as l:
net.run(0*ms)
assert len(l) == 0, 'did not expect a warning'
assert schedule_propagation_offset(net) == defaultclock.dt
# Using another (e.g. the default) schedule should raise a warning
net.schedule = None
with catch_logs() as l:
net.run(0*ms)
assert len(l) == 1 and l[0][1].endswith('schedule_conflict')
reset_device(previous_device)
def test_schedule_warning():
previous_device = get_device()
from uuid import uuid4
# TestDevice1 supports arbitrary schedules, TestDevice2 does not
class TestDevice1(Device):
# These functions are needed during the setup of the defaultclock
def get_value(self, var):
return np.array([0.0001])
def add_array(self, var):
pass
def init_with_zeros(self, var, dtype):
pass
def fill_with_array(self, var, arr):
pass
class TestDevice2(TestDevice1):
def __init__(self):
super(TestDevice2, self).__init__()
self.network_schedule = ['start', 'groups', 'synapses',
'thresholds', 'resets', 'end']
# Unique names are important for getting the warnings again for multiple
# runs of the test suite
name1 = 'testdevice_' + str(uuid4())
name2 = 'testdevice_' + str(uuid4())
all_devices[name1] = TestDevice1()
all_devices[name2] = TestDevice2()
set_device(name1)
assert schedule_propagation_offset() == 0*ms
net = Network()
assert schedule_propagation_offset(net) == 0*ms
# Any schedule should work
net.schedule = list(reversed(net.schedule))
with catch_logs() as l:
net.run(0*ms)
assert len(l) == 0, 'did not expect a warning'
assert schedule_propagation_offset(net) == defaultclock.dt
set_device(name2)
assert schedule_propagation_offset() == defaultclock.dt
# Using the correct schedule should work
net.schedule = ['start', 'groups', 'synapses', 'thresholds', 'resets', 'end']
with catch_logs() as l:
net.run(0*ms)
assert len(l) == 0, 'did not expect a warning'
assert schedule_propagation_offset(net) == defaultclock.dt
# Using another (e.g. the default) schedule should raise a warning
net.schedule = None
with catch_logs() as l:
net.run(0*ms)
assert len(l) == 1 and l[0][1].endswith('schedule_conflict')
reset_device(previous_device)
def test_spikegenerator_connected():
'''
Test that `SpikeGeneratorGroup` connects properly.
'''
G = NeuronGroup(10, 'v:1')
mon = StateMonitor(G, 'v', record=True, when='end')
indices = np.array([3, 2, 1, 1, 4, 5])
times = np.array([6, 5, 4, 3, 3, 1]) * ms
SG = SpikeGeneratorGroup(10, indices, times)
S = Synapses(SG, G, on_pre='v+=1')
S.connect(j='i')
run(7 * ms)
# The following neurons should not receive any spikes
for idx in [0, 6, 7, 8, 9]:
assert all(mon[idx].v == 0)
offset = schedule_propagation_offset()
# The following neurons should receive a single spike
for idx, time in zip([2, 3, 4, 5], [5, 6, 3, 1] * ms):
assert all(mon[idx].v[mon.t < time + offset] == 0)
assert all(mon[idx].v[mon.t >= time + offset] == 1)
# This neuron receives two spikes
assert all(mon[1].v[mon.t < 3 * ms + offset] == 0)
assert all(mon[1].v[(mon.t >= 3 * ms + offset)
& (mon.t < 4 * ms + offset)] == 1)
assert all(mon[1].v[(mon.t >= 4 * ms + offset)] == 2)
def test_propagation():
# Using a PoissonGroup as a source for Synapses should work as expected
P = PoissonGroup(2, np.array([0, 1./defaultclock.dt])*Hz)
G = NeuronGroup(2, 'v:1')
S = Synapses(P, G, on_pre='v+=1')
S.connect(j='i')
run(2*defaultclock.dt + schedule_propagation_offset())
assert_equal(G.v[:], np.array([0., 2.]))
def test_propagation():
# Using a PoissonGroup as a source for Synapses should work as expected
P = PoissonGroup(2, np.array([0, 1. / defaultclock.dt]) * Hz)
G = NeuronGroup(2, 'v:1')
S = Synapses(P, G, on_pre='v+=1')
S.connect(j='i')
run(2 * defaultclock.dt + schedule_propagation_offset())
assert_equal(G.v[:], np.array([0., 2.]))
def test_synaptic_propagation_2():
# This tests for the bug in github issue #461
source = NeuronGroup(100, '', threshold='True')
sub_source = source[99:]
target = NeuronGroup(1, 'v:1')
syn = Synapses(sub_source, target, on_pre='v+=1')
syn.connect()
run(defaultclock.dt + schedule_propagation_offset())
assert target.v[0] == 1.0
def test_synaptic_propagation_2():
# This tests for the bug in github issue #461
source = NeuronGroup(100, '', threshold='True')
sub_source = source[99:]
target = NeuronGroup(1, 'v:1')
syn = Synapses(sub_source, target, on_pre='v+=1')
syn.connect()
run(defaultclock.dt + schedule_propagation_offset())
assert target.v[0] == 1.0
def test_no_reference_3():
'''
Using subgroups without keeping an explicit reference. Monitors
'''
G = NeuronGroup(2, 'v:1', threshold='v>1', reset='v=0')
G.v = [1.1, 0]
S = Synapses(G[:1], G[1:], on_pre='v+=1')
S.connect()
run(defaultclock.dt + schedule_propagation_offset())
assert_equal(G.v[:], np.array([0, 1]))
def test_no_reference_3():
'''
Using subgroups without keeping an explicit reference. Monitors
'''
G = NeuronGroup(2, 'v:1', threshold='v>1', reset='v=0')
G.v = [1.1, 0]
S = Synapses(G[:1], G[1:], on_pre='v+=1')
S.connect()
run(defaultclock.dt + schedule_propagation_offset())
assert_equal(G.v[:], np.array([0, 1]))
def test_poissoninput_refractory():
eqs = '''
dv/dt = 0/second : 1 (unless refractory)
'''
G = NeuronGroup(10, eqs, reset='v=0', threshold='v>4.5', refractory=5*defaultclock.dt)
# Will increase the value by 1.0 at each time step
P = PoissonInput(G, 'v', 1, 1/defaultclock.dt, weight=1.0)
mon = StateMonitor(G, 'v', record=5)
run(10*defaultclock.dt)
expected = np.arange(10, dtype=np.float)
expected[6-int(schedule_propagation_offset()/defaultclock.dt):] = 0
assert_allclose(mon[5].v[:], expected)
def test_poissoninput_refractory():
eqs = '''
dv/dt = 0/second : 1 (unless refractory)
'''
G = NeuronGroup(10, eqs, reset='v=0', threshold='v>4.5', refractory=5*defaultclock.dt)
# Will increase the value by 1.0 at each time step
P = PoissonInput(G, 'v', 1, 1/defaultclock.dt, weight=1.0)
mon = StateMonitor(G, 'v', record=5)
run(10*defaultclock.dt)
expected = np.arange(10, dtype=np.float)
expected[6-int(schedule_propagation_offset()/defaultclock.dt):] = 0
assert_allclose(mon[5].v[:], expected)
def test_synaptic_propagation():
G1 = NeuronGroup(10, 'v:1', threshold='v>1', reset='v=0')
G1.v['i%2==1'] = 1.1 # odd numbers should spike
G2 = NeuronGroup(20, 'v:1')
SG1 = G1[1:6]
SG2 = G2[10:]
S = Synapses(SG1, SG2, on_pre='v+=1')
S.connect('i==j')
run(defaultclock.dt + schedule_propagation_offset())
expected = np.zeros(len(G2))
# Neurons 1, 3, 5 spiked and are connected to 10, 12, 14
expected[[10, 12, 14]] = 1
assert_equal(np.asarray(G2.v).flatten(), expected)
def test_synaptic_propagation():
G1 = NeuronGroup(10, 'v:1', threshold='v>1', reset='v=0')
G1.v['i%2==1'] = 1.1 # odd numbers should spike
G2 = NeuronGroup(20, 'v:1')
SG1 = G1[1:6]
SG2 = G2[10:]
S = Synapses(SG1, SG2, on_pre='v+=1')
S.connect('i==j')
run(defaultclock.dt + schedule_propagation_offset())
expected = np.zeros(len(G2))
# Neurons 1, 3, 5 spiked and are connected to 10, 12, 14
expected[[10, 12, 14]] = 1
assert_equal(np.asarray(G2.v).flatten(), expected)
def test_no_reference_4():
'''
Using subgroups without keeping an explicit reference. Synapses
'''
G1 = NeuronGroup(10, 'v:1', threshold='v>1', reset='v=0')
G1.v['i%2==1'] = 1.1 # odd numbers should spike
G2 = NeuronGroup(20, 'v:1')
S = Synapses(G1[1:6], G2[10:], on_pre='v+=1')
S.connect('i==j')
run(defaultclock.dt + schedule_propagation_offset())
expected = np.zeros(len(G2))
# Neurons 1, 3, 5 spiked and are connected to 10, 12, 14
expected[[10, 12, 14]] = 1
assert_equal(np.asarray(G2.v).flatten(), expected)
def test_poissongroup_subgroup():
# It should be possible to take a subgroup of a PoissonGroup
P = PoissonGroup(4, [0, 0, 0, 0]*Hz)
P1 = P[:2]
P2 = P[2:]
P2.rates = 1./defaultclock.dt
G = NeuronGroup(4, 'v:1')
S1 = Synapses(P1, G[:2], on_pre='v+=1')
S1.connect(j='i')
S2 = Synapses(P2, G[2:], on_pre='v+=1')
S2.connect(j='i')
run(2*defaultclock.dt + schedule_propagation_offset())
assert_equal(G.v[:], np.array([0., 0., 2., 2.]))
def test_no_reference_4():
'''
Using subgroups without keeping an explicit reference. Synapses
'''
G1 = NeuronGroup(10, 'v:1', threshold='v>1', reset='v=0')
G1.v['i%2==1'] = 1.1 # odd numbers should spike
G2 = NeuronGroup(20, 'v:1')
S = Synapses(G1[1:6], G2[10:], on_pre='v+=1')
S.connect('i==j')
run(defaultclock.dt + schedule_propagation_offset())
expected = np.zeros(len(G2))
# Neurons 1, 3, 5 spiked and are connected to 10, 12, 14
expected[[10, 12, 14]] = 1
assert_equal(np.asarray(G2.v).flatten(), expected)
def test_poissongroup_subgroup():
# It should be possible to take a subgroup of a PoissonGroup
P = PoissonGroup(4, [0, 0, 0, 0] * Hz)
P1 = P[:2]
P2 = P[2:]
P2.rates = 1. / defaultclock.dt
G = NeuronGroup(4, 'v:1')
S1 = Synapses(P1, G[:2], on_pre='v+=1')
S1.connect(j='i')
S2 = Synapses(P2, G[2:], on_pre='v+=1')
S2.connect(j='i')
run(2 * defaultclock.dt + schedule_propagation_offset())
assert_equal(G.v[:], np.array([0., 0., 2., 2.]))
def test_spikegenerator_connected():
'''
Test that `SpikeGeneratorGroup` connects properly.
'''
G = NeuronGroup(10, 'v:1')
mon = StateMonitor(G, 'v', record=True, when='end')
indices = np.array([3, 2, 1, 1, 4, 5])
times = np.array([6, 5, 4, 3, 3, 1]) * ms
SG = SpikeGeneratorGroup(10, indices, times)
S = Synapses(SG, G, on_pre='v+=1')
S.connect(j='i')
run(7*ms)
# The following neurons should not receive any spikes
for idx in [0, 6, 7, 8, 9]:
assert all(mon[idx].v == 0)
offset = schedule_propagation_offset()
# The following neurons should receive a single spike
for idx, time in zip([2, 3, 4, 5], [5, 6, 3, 1]*ms):
assert all(mon[idx].v[mon.t<time+offset] == 0)
assert all(mon[idx].v[mon.t>=time+offset] == 1)
# This neuron receives two spikes
assert all(mon[1].v[mon.t<3*ms+offset] == 0)
assert all(mon[1].v[(mon.t>=3*ms+offset) & (mon.t<4*ms+offset)] == 1)
assert all(mon[1].v[(mon.t>=4*ms+offset)] == 2)
