def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm_params = {
"record_to": ["memory"],
"withgid": True,
"withtime": True,
"interval": 0.1
}
vm = nest.Create("voltmeter", params=vm_params)
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
from nest import visualization
allnodes = pg + nest.GetLeaves(pop1)[0] + nest.GetLeaves(pop2)[0] + vm
visualization.plot_network(allnodes, "test_csa.png")
if havePIL:
im = Image.open("test_csa.png")
im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
def test_CSA_OneToOne_params(self):
"""One-to-one connectivity"""
nest.ResetKernel()
n = 4 # number of neurons
pop0 = nest.LayoutNetwork("iaf_neuron", [n])
pop1 = nest.LayoutNetwork("iaf_neuron", [n])
cs = csa.cset(csa.oneToOne, 10000.0, 1.0)
nest.CGConnect(pop0, pop1, cs, {"weight": 0, "delay": 1})
sources = nest.GetLeaves(pop0)[0]
targets = nest.GetLeaves(pop1)[0]
for i in xrange(n):
conns = nest.GetStatus(nest.FindConnections([sources[i]]),
'target')
self.assertEqual(len(conns), 1)
self.assertEqual(conns[0], targets[i])
conns = nest.GetStatus(nest.FindConnections([targets[i]]),
'target')
self.assertEqual(len(conns), 0)
def test_CSA_OneToOne_subnet_1d(self):
"""One-to-one connectivity with 1-dim subnets"""
nest.ResetKernel()
n = 4 # number of neurons
pop0 = nest.LayoutNetwork("iaf_neuron", [n])
pop1 = nest.LayoutNetwork("iaf_neuron", [n])
cg = csa.cset(csa.oneToOne)
nest.CGConnect(pop0, pop1, cg)
sources = nest.GetLeaves(pop0)[0]
targets = nest.GetLeaves(pop1)[0]
for i in range(n):
conns = nest.GetStatus(nest.FindConnections([sources[i]]),
'target')
self.assertEqual(len(conns), 1)
self.assertEqual(conns[0], targets[i])
conns = nest.GetStatus(nest.FindConnections([targets[i]]),
'target')
self.assertEqual(len(conns), 0)
def test_libcsa_OneToOne_subnet_nd(self):
"""One-to-one connectivity with n-dim subnets"""
nest.ResetKernel()
n = 2 # number of neurons per dimension
pop0 = nest.LayoutNetwork("iaf_neuron", [n, n])
pop1 = nest.LayoutNetwork("iaf_neuron", [n, n])
cg = libcsa.oneToOne
self.assertRaisesRegex(nest.NESTError, "BadProperty", nest.CGConnect,
pop0, pop1, cg)
def test_GetNodes(self):
"""GetNodes"""
nest.ResetKernel()
model = 'iaf_psc_alpha'
l = nest.LayoutNetwork(model, (2, 3))
allNodes = tuple(range(2, 10))
allSubnets = (2, 6)
allLeaves = tuple(n for n in allNodes if n not in allSubnets)
# test all
self.assertEqual(nest.GetNodes(l), (allNodes, ))
# test all with empty dict
self.assertEqual(nest.GetNodes(l, properties={}), (allNodes, ))
# test iteration over subnets
self.assertEqual(nest.GetNodes(l + l), (allNodes, allNodes))
# children of l are nodes
self.assertEqual(nest.GetNodes(
l, properties={'parent': l[0]}), (allSubnets, ))
# local id of second intermediate subnet and middle nodes
self.assertEqual(nest.GetNodes(
l, properties={'local_id': 2}), ((4, 6, 8), ))
# selection by model type
self.assertEqual(nest.GetNodes(
l, properties={'model': 'subnet'}), (allSubnets, ))
self.assertEqual(nest.GetNodes(
l, properties={'model': model}), (allLeaves, ))
def test_GetLeaves(self):
"""GetLeaves"""
nest.ResetKernel()
model = 'iaf_psc_alpha'
l = nest.LayoutNetwork(model, (2, 3))
allLeaves = (3, 4, 5, 7, 8, 9)
# test all
self.assertEqual(nest.GetLeaves(l), (allLeaves, ))
# test all with empty dict
self.assertEqual(nest.GetLeaves(l, properties={}), (allLeaves, ))
# test iteration over subnets
self.assertEqual(nest.GetLeaves(l + l), (allLeaves, allLeaves))
# children of l are not leaves, should yield empty
self.assertEqual(nest.GetLeaves(
l, properties={'parent': l[0]}), (tuple(), ))
# local id of middle nodes
self.assertEqual(nest.GetLeaves(
l, properties={'local_id': 2}), ((4, 8), ))
# selection by model type
self.assertEqual(nest.GetLeaves(
l, properties={'model': model}), (allLeaves, ))
def test_GetChildren(self):
"""GetChildren"""
nest.ResetKernel()
model = 'iaf_psc_alpha'
l = nest.LayoutNetwork(model, (2, 3))
topKids = (2, 6)
kids2 = (3, 4, 5)
kids6 = (7, 8, 9)
# test top level
self.assertEqual(nest.GetChildren(l), (topKids, ))
# test underlying level
self.assertEqual(nest.GetChildren((2, 6)), (kids2, kids6))
# test with empty dict
self.assertEqual(nest.GetChildren(l, properties={}), (topKids, ))
# local id of middle nodes
self.assertEqual(nest.GetChildren(
(2, 6), properties={'local_id': 2}), ((4, ), (8, )))
# selection by model type
self.assertEqual(nest.GetChildren(
l, properties={'model': 'subnet'}), (topKids, ))
self.assertEqual(nest.GetChildren(
(2, ), properties={'model': 'subnet'}), (tuple(), ))
self.assertEqual(nest.GetChildren(
(2, ), properties={'model': model}), (kids2, ))
def test_GetNodes(self):
"""GetNodes"""
nest.ResetKernel()
model = 'iaf_neuron'
l = nest.LayoutNetwork(model, [2, 3])
allNodes = range(2, 10)
allSubnets = [2, 6]
allLeaves = [n for n in allNodes if n not in allSubnets]
# test all
self.assertEqual(nest.GetNodes(l), [allNodes])
# test all with empty dict
self.assertEqual(nest.GetNodes(l, properties={}), [allNodes])
# test iteration over subnets
self.assertEqual(nest.GetNodes(l + l), [allNodes, allNodes])
# children of l are nodes
self.assertEqual(nest.GetNodes(l, properties={'parent': l[0]}),
[allSubnets])
# local id of second intermediate subnet and middle nodes
self.assertEqual(nest.GetNodes(l, properties={'local_id': 2}),
[[4, 6, 8]])
# selection by model type
self.assertEqual(nest.GetNodes(l, properties={'model': 'subnet'}),
[allSubnets])
self.assertEqual(nest.GetNodes(l, properties={'model': model}),
[allLeaves])
def test_GetLeaves(self):
"""GetLeaves"""
nest.ResetKernel()
model = 'iaf_neuron'
l = nest.LayoutNetwork(model, [2, 3])
allLeaves = [3, 4, 5, 7, 8, 9]
# test all
self.assertEqual(nest.GetLeaves(l), [allLeaves])
# test all with empty dict
self.assertEqual(nest.GetLeaves(l, properties={}), [allLeaves])
# test iteration over subnets
self.assertEqual(nest.GetLeaves(l + l), [allLeaves, allLeaves])
# children of l are not leaves, should yield empty
self.assertEqual(nest.GetLeaves(l, properties={'parent': l[0]}), [[]])
# local id of middle nodes
self.assertEqual(nest.GetLeaves(l, properties={'local_id': 2}),
[[4, 8]])
# selection by model type
self.assertEqual(nest.GetLeaves(l, properties={'model': model}),
[allLeaves])
def test_GetChildren(self):
"""GetChildren"""
nest.ResetKernel()
model = 'iaf_neuron'
l = nest.LayoutNetwork(model, [2, 3])
topKids = [2, 6]
kids2 = [3, 4, 5]
kids6 = [7, 8, 9]
# test top level
self.assertEqual(nest.GetChildren(l), [topKids])
# test underlying level
self.assertEqual(nest.GetChildren([2, 6]), [kids2, kids6])
# test with empty dict
self.assertEqual(nest.GetChildren(l, properties={}), [topKids])
# local id of middle nodes
self.assertEqual(nest.GetChildren([2, 6], properties={'local_id': 2}),
[[4], [8]])
# selection by model type
self.assertEqual(nest.GetChildren(l, properties={'model': 'subnet'}),
[topKids])
self.assertEqual(nest.GetChildren([2], properties={'model': 'subnet'}),
[[]])
self.assertEqual(nest.GetChildren([2], properties={'model': model}),
[kids2])
def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
# This does not work yet, as the implementation does not yet
# support nested subnets.
#
#pop1 = nest.LayoutNetwork("iaf_neuron", [4,4])
#pop2 = nest.LayoutNetwork("iaf_neuron", [4,4])
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm = nest.Create("voltmeter",
params={
"record_to": ["memory"],
"withgid": True,
"withtime": True,
"interval": 0.1
})
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
# Not yet supported in NEST
#from nest import visualization
#allnodes = [pg, nest.GetLeaves(pop1)[0], nest.GetLeaves(pop2)[0], vm]
#visualization.plot_network(allnodes, "test_csa.png", "png", plot_modelnames=True
#if havePIL:
# im = Image.open("test_csa.png")
# im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)