def test_SetStatusVth_E_L(self):
"""SetStatus of reversal and threshold potential """
m = "sample_neuron"
cynest.ResetKernel()
neuron1 = cynest.Create(m)
neuron2 = cynest.Create(m)
# must not depend on the order.
new_EL = -90.
new_Vth = -10.
cynest.SetStatus(neuron1, {'E_L': new_EL})
cynest.SetStatus(neuron2, {'V_th': new_Vth})
cynest.SetStatus(neuron1, {'V_th': new_Vth})
cynest.SetStatus(neuron2, {'E_L': new_EL})
# next three lines for debugging
vth1, vth2 = cynest.GetStatus(neuron1, 'V_th'), cynest.GetStatus(
neuron2, 'V_th')
if vth1 != vth2:
print(m, vth1, vth2, cynest.GetStatus(neuron1, 'E_L'),
cynest.GetStatus(neuron2, 'E_L'))
assert (cynest.GetStatus(neuron1,
'V_th') == cynest.GetStatus(neuron2, 'V_th'))
def build(self, neuronName):
"""
Create all nodes, used in the model.
"""
if self.built == True: return
self.calibrate()
#self.nodes = cynest.Create("iaf_psc_delta",self.N_neurons)
#self.nodes = cynest.Create("cython_neuron",self.N_neurons)
#self.nodes = cynest.Create("brunell-sli_neuron",self.N_neurons)
print neuronName
self.nodes = cynest.Create(neuronName, self.N_neurons)
self.noise = cynest.Create("poisson_generator", 1,
{"rate": self.p_rate})
self.spikes = cynest.Create("spike_detector", 2,
[{
"label": "brunel-py-ex"
}, {
"label": "brunel-py-in"
}])
self.nodes_E = self.nodes[:self.N_E]
self.nodes_I = self.nodes[self.N_E:]
self.spikes_E = self.spikes[:1]
self.spikes_I = self.spikes[1:]
self.built = True
def build(self, neuronName, custom):
"""
Create all nodes, used in the model.
"""
if self.built == True: return
self.calibrate()
self.nodes = cynest.Create(neuronName, self.N_neurons)
self.noise = cynest.Create("poisson_generator", 1,
{"rate": self.p_rate})
self.spikes = cynest.Create("spike_detector", 2,
[{
"label": "brunel-py-ex"
}, {
"label": "brunel-py-in"
}])
if custom == True:
cynest.SetStatus(self.nodes, [{"optimized": True}])
self.nodes_E = self.nodes[:self.N_E]
self.nodes_I = self.nodes[self.N_E:]
self.spikes_E = self.spikes[:1]
self.spikes_I = self.spikes[1:]
self.built = True
def build(self):
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': self.n_threads})
if self.params:
nest.SetDefaults(self.model,self.params)
self.neuron=nest.Create(self.model,self.n_neurons)
self.noise=nest.Create('noise_generator')
self.spike=nest.Create('spike_detector')
nest.SetStatus(self.spike,[{'to_memory':True, 'to_file':False}])
def test_DivergentConnect(self):
"""DivergentConnect pre to post"""
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 1)
post = nest.Create("iaf_neuron", 3)
nest.DivergentConnect(pre, post)
connections = nest.FindConnections(pre)
targets = nest.GetStatus(connections, "target")
self.assertEqual(targets, post)
def test_ConvergentConnect(self):
"""ConvergentConnect pre to post"""
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 3)
post = nest.Create("iaf_neuron", 1)
nest.ConvergentConnect(pre, post)
expected_targets = [post[0] for x in range(len(pre))]
connections = nest.FindConnections(pre)
targets = nest.GetStatus(connections, "target")
self.assertEqual(expected_targets, targets)
def test_ConnectPrePost(self):
"""Connect pre to post"""
# Connect([pre], [post])
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post)
connections = nest.FindConnections(pre)
targets = nest.GetStatus(connections, "target")
self.assertEqual(targets, post)
def test_GetStatus(self):
"""GetStatus"""
m = "sample_neuron"
cynest.ResetKernel()
n = cynest.Create(m)
d = cynest.GetStatus(n)
v1 = cynest.GetStatus(n)[0]['param']
v2 = cynest.GetStatus(n, 'param')[0]
self.assertEqual(v1, v2)
n = cynest.Create(m, 10)
self.assertEqual(len(cynest.GetStatus(n, 'param')), 10)
def test_DivergentConnectWD(self):
"""DivergentConnect pre to post with weight and delay"""
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 1)
post = nest.Create("iaf_neuron", 3)
nest.DivergentConnect(pre,
post,
weight=[2.0, 2.0, 2.0],
delay=[1.0, 2.0, 3.0])
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
delays = nest.GetStatus(connections, "delay")
self.assertEqual(weights, [2.0, 2.0, 2.0])
self.assertEqual(delays, [1.0, 2.0, 3.0])
def test_Events_1(self):
"""Recorder Events"""
nest.ResetKernel()
sd = nest.Create('spike_detector', 1, {'withtime': True})
d = nest.GetStatus(sd, 'events')[0]
senders = d['senders']
times = d['times']
vm = nest.Create('voltmeter', 1, {'withtime': True})
d = nest.GetStatus(vm, 'events')[0]
senders = d['V_m']
times = d['times']
def test_EventsVoltage(self):
"""Voltage Events"""
nest.ResetKernel()
nest.sr('20 setverbosity')
n = nest.Create('iaf_neuron')
vm = nest.Create('voltmeter', 1, {'withtime': True, 'interval': 1.})
nest.Connect(vm, n)
nest.SetKernelStatus({'print_time': False})
nest.Simulate(10)
d = nest.GetStatus(vm, 'events')[0]
self.assertEqual(len(d['V_m']), 9)
def test_CopyModel(self):
"""CopyModel"""
cynest.ResetKernel()
cynest.CopyModel("sample_neuron", 'new_neuron', {'V_m': 10.0})
vm = cynest.GetDefaults('new_neuron')['V_m']
self.assertEqual(vm, 10.0)
n = cynest.Create('new_neuron', 10)
vm = cynest.GetStatus([n[0]])[0]['V_m']
self.assertEqual(vm, 10.0)
cynest.CopyModel('static_synapse', 'new_synapse', {'weight': 10.})
cynest.Connect([n[0]], [n[1]], model='new_synapse')
w = cynest.GetDefaults('new_synapse')['weight']
self.assertEqual(w, 10.0)
try:
cynest.CopyModel(
"sample_neuron",
'new_neuron') # shouldn't be possible a second time
self.fail('an error should have risen!') # should not be reached
except:
info = sys.exc_info()[1]
if not "NewModelNameExists" in info.__str__():
self.fail('could not pass error message to cynest!')
def test_ParamTypes(self):
"""Test of all parameter types"""
cynest.ResetKernel()
node = cynest.Create("sample_neuron")
cynest.SetStatus(node, {"param1":2, "param2":6.5, "param3": True, "param4":"sd", "param5":'r', \
"param6":[1,2,3,4], "param7":{"c1":1, "c2":2}, "param8":{"e1":{"s1":1}}})
cynest.Simulate(1)
s = cynest.GetStatus(node)[0]
self.assertEqual(s["param1"], 2)
self.assertEqual(s["param2"], 6.5)
self.assertEqual(s["param3"], True)
self.assertEqual(s["param4"], "sd")
self.assertEqual(s["param5"], 'r')
self.assertEqual(s["param6"][0], 1)
self.assertEqual(s["param6"][1], 2)
self.assertEqual(s["param6"][2], 3)
self.assertEqual(s["param6"][3], 4)
self.assertEqual(s["param7"]["c1"], 1)
self.assertEqual(s["param7"]["c2"], 2)
self.assertEqual(s["param8"]["e1"]["s1"], 1)
def test_EventsSpikes(self):
"""Spike Events"""
nest.ResetKernel()
nest.sr('20 setverbosity')
n = nest.Create('iaf_neuron', 1, {'I_e': 1000.})
sd = nest.Create('spike_detector', 1, {'withtime': True})
nest.Connect(n, sd)
nest.SetKernelStatus({'print_time': False})
nest.Simulate(1000)
d = nest.GetStatus(sd, 'events')[0]
self.assert_(len(d['times']) > 0)
def build_network(dt):
nest.ResetKernel()
nest.SetKernelStatus({"local_num_threads": 1, "resolution": dt})
neuron = nest.Create('iaf_neuron')
nest.SetStatus(neuron, "I_e", 376.0)
vm = nest.Create('voltmeter')
nest.SetStatus(vm, "withtime", True)
sd = nest.Create('spike_detector')
nest.Connect(vm, neuron)
nest.Connect(neuron, sd)
return vm, sd
def test_UnexcpectedEvent(self):
"""Unexpected Event"""
nest.ResetKernel()
n = nest.Create('iaf_neuron')
vm = nest.Create('voltmeter')
sd = nest.Create('spike_detector')
try:
nest.Connect(sd, n)
self.fail() # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "UnexpectedEvent" in info.__str__():
self.fail()
# another error has been thrown, this is wrong
except:
self.fail()
def test_ModelDicts(self):
"""sample_neuron Creation with N and dicts"""
cynest.ResetKernel()
V_m = [0.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.]
n = cynest.Create("sample_neuron", 10, [{'V_m': v} for v in V_m])
self.assertEqual(cynest.GetStatus(n, 'V_m'), V_m)
self.assertEqual([key['V_m'] for key in cynest.GetStatus(n)], V_m)
def test_ThreadsFindConnections(self):
"""FindConnections with threads"""
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': 8})
pre = nest.Create("iaf_neuron")
post = nest.Create("iaf_neuron", 6)
nest.DivergentConnect(pre, post)
conn = nest.FindConnections(pre)
targets = nest.GetStatus(conn, "target")
self.assertEqual(targets, post)
def test_SetStatusParam(self):
"""SetStatus with parameter"""
m = "sample_neuron"
cynest.ResetKernel()
n = cynest.Create(m)
cynest.SetStatus(n, 'V_m', 3.)
self.assertEqual(cynest.GetStatus(n, 'V_m')[0], 3.)
def test_SetStatusList(self):
"""SetStatus with list"""
m = "sample_neuron"
cynest.ResetKernel()
n = cynest.Create(m)
cynest.SetStatus(n, [{'V_m': 2.}])
self.assertEqual(cynest.GetStatus(n, 'V_m')[0], 2.)
def test_SetStatus(self):
"""SetStatus with dict"""
m = "sample_neuron"
cynest.ResetKernel()
n = cynest.Create(m)
cynest.SetStatus(n, {'V_m': 1.})
self.assertEqual(cynest.GetStatus(n, 'V_m')[0], 1.)
def test_WrongConnection(self):
"""Wrong Connections"""
nest.ResetKernel()
n = nest.Create('iaf_neuron')
vm = nest.Create('voltmeter')
sd = nest.Create('spike_detector')
try:
nest.Connect(n, vm)
self.fail() # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "IllegalConnection" in info.__str__():
self.fail()
# another error has been thrown, this is wrong
except:
self.fail()
def test_ModelDict(self):
"""sample_neuron Creation with N and dict"""
cynest.ResetKernel()
n = cynest.Create("sample_neuron", 10, [{'V_m': 12.0}])
V_m = [12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0]
self.assertEqual(cynest.GetStatus(n, 'V_m'), V_m)
self.assertEqual([key['V_m'] for key in cynest.GetStatus(n)], V_m)
def test_ModelCreateSimulate(self):
"""Model Creation and Simulation"""
cynest.ResetKernel()
cynest.SetDefaults("sample_neuron", {"param": 20})
node = cynest.Create("sample_neuron")
cynest.Simulate(1)
self.assertEqual(cynest.GetStatus(node)[0]["param"], 30)
def test_Function(self):
"""Test of function object"""
cynest.ResetKernel()
node = cynest.Create("sample_neuron")
try:
cynest.SetStatus(node, {"param2": testFct})
except:
info = sys.exc_info()[1]
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 test_ThreadsGetEvents(self):
""" Gathering events across threads """
# Test if we have a thread-enabled NEST
nest.sr("statusdict /have_pthreads get")
if not nest.spp(): return
threads = [1, 2, 4, 8]
n_events_sd = []
n_events_vm = []
N = 128
Simtime = 1000.
for t in threads:
nest.ResetKernel()
nest.SetKernelStatus({'local_num_threads': t})
n = nest.Create('iaf_psc_alpha', N,
{'I_e': 2000.}) # force a lot of spike events
sd = nest.Create('spike_detector')
vm = nest.Create('voltmeter')
nest.ConvergentConnect(n, sd)
nest.DivergentConnect(vm, n)
nest.Simulate(Simtime)
n_events_sd.append(nest.GetStatus(sd, 'n_events')[0])
n_events_vm.append(nest.GetStatus(vm, 'n_events')[0])
ref_vm = N * (Simtime - 1)
ref_sd = n_events_sd[0]
# could be done more elegantly with any(), ravel(),
# but we dont want to be dependent on numpy et al
[self.assertEqual(x, ref_vm) for x in n_events_vm]
[self.assertEqual(x, ref_sd) for x in n_events_sd]
def test_ConnectPrePostParams(self):
"""Connect pre to post with a params dict"""
# Connect([pre], [post], params)
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post, {"weight": 2.0})
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
self.assertEqual(weights, [2.0, 2.0])
# Connect([pre], [post], [params])
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post, [{"weight": 2.0}])
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
self.assertEqual(weights, [2.0, 2.0])
# Connect([pre], [post], [params, params])
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post, [{"weight": 2.0}, {"weight": 3.0}])
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
self.assertEqual(weights, [2.0, 3.0])
def test_ConnectPrePostWD(self):
"""Connect pre to post with a weight and delay"""
# Connect([pre], [post], w, d)
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post, 2.0, 2.0)
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
self.assertEqual(weights, [2.0, 2.0])
# Connect([pre], [post], [w], [d])
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post, [2.0], [2.0])
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
delays = nest.GetStatus(connections, "delay")
self.assertEqual(weights, [2.0, 2.0])
self.assertEqual(delays, [2.0, 2.0])
# Connect([pre], [post], [w, w], [d, d])
nest.ResetKernel()
pre = nest.Create("iaf_neuron", 2)
post = nest.Create("iaf_neuron", 2)
nest.Connect(pre, post, [2.0, 3.0], [2.0, 3.0])
connections = nest.FindConnections(pre)
weights = nest.GetStatus(connections, "weight")
delays = nest.GetStatus(connections, "delay")
self.assertEqual(weights, [2.0, 3.0])
self.assertEqual(delays, [2.0, 3.0])
