def test_RandomDivergentConnect(self):
"""RandomDivergentConnect"""
cynest.ResetKernel()
a=cynest.Create("iaf_neuron", 1000)
source=[1]
targets=range(2,1000)
cynest.RandomDivergentConnect(source,targets, 500, [1.0], [1.0])
conn1=cynest.GetConnections(source)
self.assertEqual(len(conn1), 500)
def test_RandomConvergentConnect(self):
"""RandomConvergentConnect"""
cynest.ResetKernel()
a=cynest.Create("iaf_neuron", 1000)
sources=list(range(2,1000))
target=[1]
cynest.RandomConvergentConnect(sources,target, 500, [1.0], [1.0])
conn1=cynest.GetConnections(sources)
self.assertEqual(len(conn1), 500)
def connPlot(spop, smod, tmod, syn, titl):
'''Plot connections.
spop, smod source population and model
tmod target model
syn synapse type
titl figure title'''
# center location, cast to int since ``pyreport`` makes all div double
ctr = [int(Params["N"] / 2), int(Params["N"] / 2)]
# get element at center
src = topo.GetElement(spop, ctr)
# if center element is not individual neuron, find one
# neuron of desired model in center element
if nest.GetStatus(src, 'model')[0] == 'subnet':
src = [
n for n in nest.GetLeaves(src)[0]
if nest.GetStatus([n], 'model')[0] == smod
][:1]
# get position (in degrees) of chosen source neuron
srcpos = topo.GetPosition(src)
# get all targets
tgts = nest.GetConnections(src, syn)[0]['targets']
# pick targets of correct model type, get their positions,
# convert list of (x,y) pairs to pair of x- and y-lists
pos = zip(*[
topo.GetPosition([n]) for n in tgts
if nest.GetStatus([n], 'model')[0] == tmod
])
# plot source neuron in red, slightly larger, targets on blue
pylab.clf()
pylab.plot(pos[0], pos[1], 'bo', markersize=5, zorder=99, label='Targets')
pylab.plot(srcpos[:1],
srcpos[1:],
'ro',
markersize=10,
markeredgecolor='r',
zorder=1,
label='Source')
axsz = Params['visSize'] / 2 + 0.2
pylab.axis([-axsz, axsz, -axsz, axsz])
pylab.title(titl)
pylab.legend()
# pylab.show() required for `pyreport`
pylab.show()
def test_DivergentConnect(self):
"""DivergentConnect"""
cynest.ResetKernel()
a=cynest.Create("iaf_neuron", 10)
source=[1]
targets=[1 * x for x in list(range(2,11))]
cynest.DivergentConnect(source, targets, [1.0], [1.0])
conn1=cynest.GetConnections(source)
stat1=cynest.GetStatus(conn1)
target1=[ d['target'] for d in stat1]
self.assertEqual(targets, target1)
def test_ConvergentConnect(self):
"""ConvergentConnect"""
cynest.ResetKernel()
a=cynest.Create("iaf_neuron", 10)
target=[1]
sources=[1 * x for x in list(range(2,11))]
cynest.ConvergentConnect(sources, target, [1.0], [1.0])
conn1=cynest.GetConnections(sources)
stat1=cynest.GetStatus(conn1)
target1=[ d['source'] for d in stat1]
self.assertEqual(sources, target1)
def test_DataConnect2(self):
"""DataConnect"""
cynest.ResetKernel()
a=cynest.Create("iaf_neuron", 100000)
sources=[1]
target=[x for x in list(range(2,100000))]
weight=[2.0 * x for x in target]
delay=[1.0 * x for x in target]
connections=[{'target':target, 'weight':weight, 'delay':delay} for t in target ]
cynest.DataConnect(sources,connections, "static_synapse", 2)
conn1=cynest.GetConnections(sources)
stat1=cynest.GetStatus(conn1)
target1=[ d['target'] for d in stat1]
self.assertEqual(target, target1)