def test_connect_with_valid_list(self):
connection_list = [
(0, 0, 0.1, 0.1),
(3, 0, 0.2, 0.11),
(2, 3, 0.3, 0.12), # local
(2, 2, 0.4, 0.13),
(0, 1, 0.5, 0.14), # local
]
C = connectors.FromListConnector(connection_list)
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[(0, 1, 0.5, 0.14), (2, 3, 0.3, 0.12)])
def test_connect_with_random_weights_parallel_safe(self):
rd_w = random.RandomDistribution(rng=MockRNG(delta=1.0, parallel_safe=True))
rd_d = random.RandomDistribution(rng=MockRNG(start=1.0, delta=0.1, parallel_safe=True))
syn = sim.StaticSynapse(weight=rd_w, delay=rd_d)
connections = numpy.array([
[0, 1, 1, 0],
[1, 1, 0, 1],
[0, 0, 1, 0],
])
C = connectors.ArrayConnector(connections, safe=False)
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay"], format='list', gather=False), # use gather False because we are faking the MPI
[(1, 0, 0.0, 1.0),
(0, 2, 3.0, 1.3),
(2, 2, 4.0, 1.4000000000000001)]) # better to do an "almost-equal" check
def test_get_weights_as_list(self, sim=sim):
prj = sim.Projection(self.p1,
self.p2,
connector=self.all2all,
synapse_type=self.syn2)
weights = prj.get("weight", format="list")
weights = _sort_by_column(weights, 1)[:5]
target = np.array([
(0, 0, 0.007),
(1, 0, 0.007),
(2, 0, 0.007),
(3, 0, 0.007),
(4, 0, 0.007),
])
assert_array_equal(weights, target)
def test_with_n_smaller_than_population_size(self):
C = connectors.FixedNumberPreConnector(n=3, rng=MockRNG(delta=1))
syn = sim.StaticSynapse(weight="0.1*d")
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[
(3, 1, 0.2, 0.123),
(2, 1, 0.1, 0.123),
(1, 1, 0.0, 0.123),
(3, 3, 0.0, 0.123),
(2, 3, 0.1, 0.123),
(1, 3, 0.2, 0.123),
])
def test_get_weights_as_array_with_multapses_min(self, sim=sim):
C = sim.FixedNumberPreConnector(n=7, rng=MockRNG(delta=1))
prj = sim.Projection(self.p2, self.p3, C, synapse_type=self.syn1)
target = np.array([
[0.006, 0.006, 0.006, 0.006, 0.006],
[0.006, 0.006, 0.006, 0.006, 0.006],
[0.006, 0.006, 0.006, 0.006, 0.006],
[0.006, 0.006, 0.006, 0.006, 0.006],
])
# use gather False because we are faking the MPI
weights = prj.get("weight",
format="array",
gather=False,
multiple_synapses='min')
assert_array_equal(weights, target)
def test_connect_with_scalar_weights_and_delays(self, sim=sim):
C = connectors.AllToAllConnector(safe=False)
syn = sim.StaticSynapse(weight=5.0, delay=0.5)
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[(0, 1, 5.0, 0.5), (1, 1, 5.0, 0.5), (2, 1, 5.0, 0.5),
(3, 1, 5.0, 0.5), (0, 3, 5.0, 0.5), (1, 3, 5.0, 0.5),
(2, 3, 5.0, 0.5), (3, 3, 5.0, 0.5)])
nan = np.nan
assert_array_equal(
prj.get('weight', format='array', gather=False),
np.array([[nan, 5.0, nan, 5.0, nan], [nan, 5.0, nan, 5.0, nan],
[nan, 5.0, nan, 5.0, nan], [nan, 5.0, nan, 5.0, nan]]))
def test_with_replacement(self, sim=sim):
C = connectors.FixedNumberPostConnector(n=3,
with_replacement=True,
rng=MockRNG(delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
# 0 - 0 1 2
# 1 - 3 4 0
# 2 - 1 2 3
# 3 - 4 0 1
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[(0, 1, 0.0, 0.123), (2, 1, 0.0, 0.123), (3, 1, 0.0, 0.123),
(1, 3, 0.0, 0.123), (2, 3, 0.0, 0.123)])
def test_with_plastic_synapse(self, sim=sim):
connection_list = [
(0, 0, 0.1, 0.1, 100, 400),
(3, 0, 0.2, 0.11, 101, 500),
(2, 3, 0.3, 0.12, 102, 600), # local
(2, 2, 0.4, 0.13, 103, 700),
(0, 1, 0.5, 0.14, 104, 800), # local
]
C = connectors.FromListConnector(connection_list, column_names=[
"weight", "delay", "U", "tau_rec"])
syn = sim.TsodyksMarkramSynapse(U=99, tau_facil=88.8)
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay", "tau_facil", "tau_rec", "U"], format='list', gather=False), # use gather False because we are faking the MPI
[(0, 1, 0.5, 0.14, 88.8, 800.0, 104.0),
(2, 3, 0.3, 0.12, 88.8, 600.0, 102.0)])
def setUp(self, sim=sim, **extra):
self.MIN_DELAY = 0.123
sim.setup(num_processes=2, rank=1, min_delay=self.MIN_DELAY, **extra)
self.p1 = sim.Population(7, sim.IF_cond_exp())
self.p2 = sim.Population(4, sim.IF_cond_exp())
self.p3 = sim.Population(5, sim.IF_curr_alpha())
self.projections = {"cond": {}, "curr": {}}
for psr, post in (("cond", self.p2), ("curr", self.p3)):
for rt in ("excitatory", "inhibitory"):
self.projections[psr][rt] = sim.Projection(
self.p1,
post,
sim.AllToAllConnector(safe=True),
sim.StaticSynapse(),
receptor_type=rt)
def test_connect_with_default_args(self):
C = connectors.FixedProbabilityConnector(p_connect=0.85,
rng=MockRNG(
delta=0.1,
parallel_safe=True))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
# 20 possible connections. Due to the mock RNG, only the
# first 9 are created (0,0), (1,0), (2,0), (3,0), (0,1), (1,1), (2,1), (3,1), (0,2)
# of these, (0,1), (1,1), (2,1), (3,1) are created on this node
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[(0, 1, 0.0, 0.123), (1, 1, 0.0, 0.123), (2, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123)])
def test_connect_with_probability_1(self, sim=sim):
# see https://github.com/NeuralEnsemble/PyNN/issues/309
C = connectors.FixedProbabilityConnector(p_connect=1,
rng=MockRNG(
delta=0.01,
parallel_safe=True))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
# 20 connections, only some of which are created on this node
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[(0, 1, 0.0, 0.123), (1, 1, 0.0, 0.123), (2, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123), (0, 3, 0.0, 0.123), (1, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123), (3, 3, 0.0, 0.123)])
def test_with_plastic_synapses_not_distributed(self, sim=sim):
connection_list = [
(0, 0, 0.1, 0.1, 100, 100),
(3, 0, 0.2, 0.11, 110, 99),
(2, 3, 0.3, 0.12, 120, 98), # local
(2, 2, 0.4, 0.13, 130, 97),
(0, 1, 0.5, 0.14, 140, 96), # local
]
file = recording.files.StandardTextFile("test.connections.2", mode='wb')
file.write(connection_list, {"columns": ["i", "j", "weight", "delay", "U", "tau_rec"]})
C = connectors.FromFileConnector("test.connections.2", distributed=False)
syn = sim.TsodyksMarkramSynapse(tau_facil=88.8)
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay", "U", "tau_rec", "tau_facil"], format='list', gather=False), # use gather False because we are faking the MPI
[(0, 1, 0.5, 0.14, 140.0, 96.0, 88.8),
(2, 3, 0.3, 0.12, 120.0, 98.0, 88.8)])
def test_with_replacement_with_variable_n(self):
n = random.RandomDistribution('binomial', (5, 0.5),
rng=MockRNG(start=1, delta=2))
# should give (1, 3, 0, 2, 4)
C = connectors.FixedNumberPreConnector(n=n,
with_replacement=True,
rng=MockRNG(delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[ #(0, 0, 0.0, 0.123),
(1, 1, 0.0, 0.123), (2, 1, 0.0, 0.123), (3, 1, 0.0, 0.123),
(0, 3, 0.0, 0.123), (1, 3, 0.0, 0.123)
])
def test_with_replacement_no_self_connections(self, sim=sim):
C = connectors.FixedNumberPostConnector(n=3, with_replacement=True,
allow_self_connections=False, rng=MockRNG(start=2, delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p2, self.p2, C, syn)
# 0 - 2 3 4
# 1 - 0 2 3
# 2 - 4 0 1
# 3 - 2 4 0
# 4 - 1 2 3
self.assertEqual(prj.get(["weight", "delay"], format='list', gather=False), # use gather False because we are faking the MPI
[(2, 1, 0.0, 0.123),
(4, 1, 0.0, 0.123),
(0, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),
(4, 3, 0.0, 0.123)])
def test_connect_with_scalar_weights_and_delays(self, sim=sim):
connections = np.array([
[0, 1, 1, 0],
[1, 1, 0, 1],
[0, 0, 1, 0],
], dtype=bool)
C = connectors.ArrayConnector(connections, safe=False)
syn = sim.StaticSynapse(weight=5.0, delay=0.5)
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay"], format='list'),
[(1, 0, 5.0, 0.5),
(0, 1, 5.0, 0.5),
(1, 1, 5.0, 0.5),
(0, 2, 5.0, 0.5),
(2, 2, 5.0, 0.5),
(1, 3, 5.0, 0.5)])
def test_connect_with_valid_list(self, sim=sim):
connection_list = [
(0, 0, 0.1, 0.18),
(3, 0, 0.2, 0.17),
(2, 3, 0.3, 0.16), # local
(2, 2, 0.4, 0.15),
(0, 1, 0.5, 0.14), # local
]
C = connectors.FromListConnector(connection_list)
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay"], format='list'),
[(0, 0, 0.1, 0.18),
(3, 0, 0.2, 0.17),
(0, 1, 0.5, 0.14),
(2, 2, 0.4, 0.15),
(2, 3, 0.3, 0.16)])
def test_connect_weight_function_and_one_post_synaptic_neuron_not_connected(self, sim=sim):
C = connectors.FixedProbabilityConnector(p_connect=0.8,
rng=MockRNG(delta=0.05))
syn = sim.StaticSynapse(weight=lambda d: 0.1 * d)
prj = sim.Projection(self.p1, self.p2, C, syn)
assert_array_almost_equal(prj.get(["weight", "delay"], format='array'),
np.array([
[[0., 0.1, 0.2, 0.3, nan],
[0.1, 0., 0.1, 0.2, nan],
[0.2, 0.1, 0.0, 0.1, nan],
[0.3, 0.2, 0.1, 0.0, nan]],
[[0.123, 0.123, 0.123, 0.123, nan],
[0.123, 0.123, 0.123, 0.123, nan],
[0.123, 0.123, 0.123, 0.123, nan],
[0.123, 0.123, 0.123, 0.123, nan]]
]),
9)
def test_with_replacement(self):
C = connectors.FixedNumberPreConnector(n=3, with_replacement=True, rng=MockRNG(delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay"], format='list', gather=False), # use gather False because we are faking the MPI
[#(0, 0, 0.0, 0.123),
#(1, 0, 0.0, 0.123),
#(2, 0, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(0, 1, 0.0, 0.123),
(1, 1, 0.0, 0.123),
#(2, 2, 0.0, 0.123),
#(3, 2, 0.0, 0.123),
#(0, 2, 0.0, 0.123),
(1, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(3, 3, 0.0, 0.123),])
def test_with_stdp_synapse(self, sim=sim):
connection_list = [
(0, 0, 0.1, 0.1, 10.0, 0.4),
(3, 0, 0.2, 0.11, 10.1, 0.5),
(2, 3, 0.3, 0.12, 10.2, 0.6), # local
(2, 2, 0.4, 0.13, 10.3, 0.7),
(0, 1, 0.5, 0.14, 10.4, 0.8), # local
]
C = connectors.FromListConnector(connection_list, column_names=[
"weight", "delay", "tau_plus", "w_max"])
syn = sim.STDPMechanism(timing_dependence=sim.SpikePairRule(tau_plus=12.3, tau_minus=33.3),
weight_dependence=sim.MultiplicativeWeightDependence(w_max=1.11),
weight=0.321, delay=0.2)
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay", "tau_plus", "tau_minus", "w_max"], format='list', gather=False), # use gather False because we are faking the MPI
[(0, 1, 0.5, 0.14, 10.4, 33.3, 0.8),
(2, 3, 0.3, 0.12, 10.2, 33.3, 0.6)])
def test_connect_with_array_weights(self, sim=sim):
C = connectors.AllToAllConnector(safe=False)
syn = sim.StaticSynapse(weight=np.arange(0.0, 2.0, 0.1).reshape(4, 5),
delay=0.5)
prj = sim.Projection(self.p1, self.p2, C, syn)
assert_array_almost_equal(
# use gather False because we are faking the MPI
np.array(prj.get(["weight", "delay"], format='list',
gather=False)),
np.array([(0, 1, 0.1, 0.5), (1, 1, 0.6, 0.5), (2, 1, 1.1, 0.5),
(3, 1, 1.6, 0.5), (0, 3, 0.3, 0.5), (1, 3, 0.8, 0.5),
(2, 3, 1.3, 0.5), (3, 3, 1.8, 0.5)]))
nan = np.nan
assert_array_almost_equal(
prj.get('weight', format='array', gather=False),
np.array([[nan, 0.1, nan, 0.3, nan], [nan, 0.6, nan, 0.8, nan],
[nan, 1.1, nan, 1.3, nan], [nan, 1.6, nan, 1.8, nan]]),
9)
def test_connect_with_random_delays_parallel_safe(self):
rd = random.RandomDistribution('uniform', low=0.1, high=1.1, rng=MockRNG(start=1.0, delta=0.2, parallel_safe=True))
syn = sim.StaticSynapse(delay=rd)
C = connectors.FixedProbabilityConnector(p_connect=0.5,
rng=MockRNG2(1 - numpy.array([1, 0, 0, 1,
0, 0, 0, 1,
1, 1, 0, 0,
1, 0, 1, 0,
1, 1, 0, 1]),
parallel_safe=True))
prj = sim.Projection(self.p1, self.p2, C, syn)
nan = numpy.nan
assert_array_almost_equal(prj.get('delay', format='array', gather=False),
numpy.array([[nan, nan, nan, 2.0, nan],
[nan, nan, nan, nan, nan],
[nan, nan, nan, 2.2, nan],
[nan, 1.4, nan, nan, nan]]),
9)
def test_with_n_larger_than_population_size(self, sim=sim):
C = connectors.FixedNumberPostConnector(n=7, rng=MockRNG(delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
# each pre neuron will connect to all post neurons (population size 5 is less than n), then to 4, 3 (MockRNG.permutation)
self.assertEqual(prj.get(["weight", "delay"], format='list', gather=False), # use gather False because we are faking the MPI
[(0, 1, 0.0, 0.123),
(1, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(0, 3, 0.0, 0.123),
(0, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(3, 3, 0.0, 0.123),
(3, 3, 0.0, 0.123)])
def test_connect_with_default_args(self, sim=sim):
C = connectors.DistanceDependentProbabilityConnector(d_expression="d<1.5",
rng=MockRNG(delta=0.01))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
# 20 possible connections. Only those with a sufficiently small distance
# are created
self.assertEqual(prj.get(["weight", "delay"], format='list'),
[(0, 0, 0.0, 0.123),
(1, 0, 0.0, 0.123),
(0, 1, 0.0, 0.123),
(1, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(1, 2, 0.0, 0.123),
(2, 2, 0.0, 0.123),
(3, 2, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(3, 3, 0.0, 0.123),
(3, 4, 0.0, 0.123)])
def test_no_replacement_no_self_connections(self):
C = connectors.FixedNumberPreConnector(n=3,
with_replacement=False,
allow_self_connections=False,
rng=MockRNG(start=2, delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p2, self.p2, C, syn)
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[
(4, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(4, 3, 0.0, 0.123),
#(3, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),
])
def test_with_n_larger_than_population_size_no_self_connections(self):
C = connectors.FixedNumberPreConnector(n=7, allow_self_connections=False, rng=MockRNG(delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p2, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay"], format='list', gather=False), # use gather False because we are faking the MPI
[(0, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(4, 1, 0.0, 0.123),
(4, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(0, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(4, 3, 0.0, 0.123),
(4, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),])
def test_connect_with_default_args(self, sim=sim):
C = connectors.FixedProbabilityConnector(p_connect=0.85,
rng=MockRNG(delta=0.1, parallel_safe=True))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
# 20 possible connections. Due to the mock RNG, only the
# first 9 are created (0,0), (1,0), (2,0), (3,0), (0,1), (1,1), (2,1), (3,1), (0,2)
self.assertEqual(prj.get(["weight", "delay"], format='list'),
[(0, 0, 0.0, 0.123),
(1, 0, 0.0, 0.123),
(2, 0, 0.0, 0.123),
(3, 0, 0.0, 0.123),
(0, 1, 0.0, 0.123),
(1, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(0, 2, 0.0, 0.123)
])
def test_no_replacement_parallel_unsafe(self, sim=sim):
C = connectors.FixedNumberPreConnector(n=3,
with_replacement=False,
rng=MockRNG(
delta=1,
parallel_safe=False))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[
(3, 1, 0.0, 0.123),
(2, 1, 0.0, 0.123),
(1, 1, 0.0, 0.123),
(3, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(1, 3, 0.0, 0.123),
])
def test_connect_with_random_weights_parallel_safe(self, sim=sim):
rd = random.RandomDistribution('uniform', (0, 1),
rng=MockRNG(delta=1.0,
parallel_safe=True))
syn = sim.StaticSynapse(weight=rd, delay=0.5)
C = connectors.AllToAllConnector(safe=False)
prj = sim.Projection(self.p1, self.p2, C, syn)
# note that the outer loop is over the post-synaptic cells, the inner loop over the pre-synaptic
self.assertEqual(
prj.get(["weight", "delay"], format='list', gather=False
), # use gather False because we are faking the MPI
[(0, 1, 4.0, 0.5), (1, 1, 5.0, 0.5), (2, 1, 6.0, 0.5),
(3, 1, 7.0, 0.5), (0, 3, 12.0, 0.5), (1, 3, 13.0, 0.5),
(2, 3, 14.0, 0.5), (3, 3, 15.0, 0.5)])
nan = np.nan
assert_array_almost_equal(
prj.get('weight', format='array', gather=False),
np.array([[nan, 4.0, nan, 12.0, nan], [nan, 5.0, nan, 13.0, nan],
[nan, 6.0, nan, 14.0, nan], [nan, 7.0, nan, 15.0, nan]]),
9)
def test_connect_with_weight_function(self, sim=sim):
C = connectors.FixedProbabilityConnector(p_connect=0.85,
rng=MockRNG(delta=0.1))
syn = sim.StaticSynapse(weight=lambda d: 0.1 * d)
prj = sim.Projection(self.p1, self.p2, C, syn)
# 20 possible connections. Due to the mock RNG, only the
# first 9 are created (0,0), (1,0), (2,0), (3,0), (0,1), (1,1), (2,1), (3,1), (0,2)
assert_array_almost_equal(prj.get(["weight", "delay"], format='array'),
np.array([
[[0., 0.1, 0.2, nan, nan],
[0.1, 0., nan, nan, nan],
[0.2, 0.1, nan, nan, nan],
[0.3, 0.2, nan, nan, nan]],
[[0.123, 0.123, 0.123, nan, nan],
[0.123, 0.123, nan, nan, nan],
[0.123, 0.123, nan, nan, nan],
[0.123, 0.123, nan, nan, nan]]
]),
9)
def test_with_replacement(self, sim=sim):
C = connectors.FixedNumberPreConnector(n=3, with_replacement=True, rng=MockRNG(delta=1))
syn = sim.StaticSynapse()
prj = sim.Projection(self.p1, self.p2, C, syn)
self.assertEqual(prj.get(["weight", "delay"], format='list'),
[(0, 0, 0.0, 0.123),
(1, 0, 0.0, 0.123),
(2, 0, 0.0, 0.123),
(3, 1, 0.0, 0.123),
(0, 1, 0.0, 0.123),
(1, 1, 0.0, 0.123),
(2, 2, 0.0, 0.123),
(3, 2, 0.0, 0.123),
(0, 2, 0.0, 0.123),
(1, 3, 0.0, 0.123),
(2, 3, 0.0, 0.123),
(3, 3, 0.0, 0.123),
(0, 4, 0.0, 0.123),
(1, 4, 0.0, 0.123),
(2, 4, 0.0, 0.123), ])
