def _constructNetwork(self):
'''Construct the E/I network'''
self.e_neuron_params = gc_neurons.getENeuronParams(self.no)
self.i_neuron_params = gc_neurons.getINeuronParams(self.no)
self.B_GABA = 1.0 # Must be here for compatibility with brian code
self.e_model_name = "iaf_gridcells"
self.i_model_name = "iaf_gridcells"
self.e_receptors = \
nest.GetDefaults(self.e_model_name)['receptor_types']
self.i_receptors = \
nest.GetDefaults(self.i_model_name)['receptor_types']
self.E_pop = nest.Create(self.e_model_name,
self.net_Ne,
params=self.e_neuron_params)
self.I_pop = nest.Create(self.i_model_name,
self.net_Ni,
params=self.i_neuron_params)
nest.CopyModel('static_synapse',
'I_AMPA_NMDA',
params={'receptor_type': self.i_receptors['AMPA_NMDA']})
nest.CopyModel('static_synapse',
'E_GABA_A',
params={'receptor_type': self.e_receptors['GABA_A']})
nest.CopyModel('static_synapse',
'PC_AMPA',
params={'receptor_type': self.e_receptors['AMPA']})
# Connect E-->I and I-->E
self._connect_network()
def run(self, simtime=300):
'''
Simulate the model for simtime milliseconds and print the firing
rates of the network during this period.
'''
if not self.connected:
self.connect()
nest.Simulate(simtime)
events_ex = nest.GetStatus(self.espikes, "n_events")[0]
events_in = nest.GetStatus(self.ispikes, "n_events")[0]
rate_ex = events_ex / simtime * 1000.0 / self.N_rec
rate_in = events_in / simtime * 1000.0 / self.N_rec
num_synapses = (nest.GetDefaults("excitatory")["num_connections"] +
nest.GetDefaults("inhibitory")["num_connections"])
print("Brunel network simulation (Python)")
print("Number of neurons : {0}".format(self.N_neurons))
print("Number of synapses: {0}".format(num_synapses))
print(" Exitatory : {0}".format(int(self.CE * self.N_neurons) + self.N_neurons))
print(" Inhibitory : {0}".format(int(self.CI * self.N_neurons)))
print("Excitatory rate : %.2f Hz" % rate_ex)
print("Inhibitory rate : %.2f Hz" % rate_in)
# print("Building time : %.2f s" % build_time)
# print("Simulation time : %.2f s" % sim_time)
nest.raster_plot.from_device(self.espikes, hist=True)
pl.show()
def test_CopyModel(self):
"""CopyModel"""
nest.ResetKernel()
nest.CopyModel('iaf_neuron', 'new_neuron', {'V_m': 10.0})
vm = nest.GetDefaults('new_neuron')['V_m']
self.assertEqual(vm, 10.0)
n = nest.Create('new_neuron', 10)
vm = nest.GetStatus([n[0]])[0]['V_m']
self.assertEqual(vm, 10.0)
nest.CopyModel('static_synapse', 'new_synapse', {'weight': 10.})
nest.Connect([n[0]], [n[1]], model='new_synapse')
w = nest.GetDefaults('new_synapse')['weight']
self.assertEqual(w, 10.0)
try:
nest.CopyModel('iaf_neuron',
'new_neuron') # shouldn't be possible a second time
self.fail('an error should have risen!') # should not be reached
except nest.NESTError:
info = sys.exc_info()[1]
if not "NewModelNameExists" in info.__str__():
self.fail('could not pass error message to NEST!')
# another error has been thrown, this is wrong
except:
self.fail('wrong error has been thrown')
def testAAAResetKernel(self):
"""Test reset of recording backend defaults.
As ResetKernel is used by many of the tests in this file to
ensure a consistent initial state, this test has to be run
before all others. This is ensured by prefix "AAA" in front of
the name. See https://docs.python.org/3/library/unittest.html
for details on test execution order.
"""
nest.ResetKernel()
mm_defaults = nest.GetDefaults("multimeter")
rb_properties = ["record_to", "time_in_steps"]
rb_defaults_initial = [mm_defaults[k] for k in rb_properties]
self.assertEqual(rb_defaults_initial, ["memory", False])
mm_defaults = {"record_to": "ascii", "time_in_steps": True}
nest.SetDefaults("multimeter", mm_defaults)
mm_defaults = nest.GetDefaults("multimeter")
rb_properties = ["record_to", "time_in_steps"]
rb_defaults = [mm_defaults[k] for k in rb_properties]
self.assertEqual(rb_defaults, ["ascii", True])
nest.ResetKernel()
mm_defaults = nest.GetDefaults("multimeter")
rb_properties = ["record_to", "time_in_steps"]
rb_defaults = [mm_defaults[k] for k in rb_properties]
self.assertEqual(rb_defaults_initial, rb_defaults)
def test_SetStatusVth_E_L(self):
"""SetStatus of reversal and threshold potential """
excluded = ['a2eif_cond_exp_HW', 'mat2_psc_exp', 'amat2_psc_exp']
models = [m for m in nest.Models() if m not in excluded]
for m in models:
if all(key in nest.GetDefaults(m) for key in ('V_th', 'E_L')):
nest.ResetKernel()
neuron1 = nest.Create(m)
neuron2 = nest.Create(m)
# must not depend on the order
new_EL = -90.
new_Vth = -10.
if 'V_reset' in nest.GetDefaults(m):
nest.SetStatus(neuron1 + neuron2, {'V_reset': new_EL})
nest.SetStatus(neuron1, {'E_L': new_EL})
nest.SetStatus(neuron2, {'V_th': new_Vth})
nest.SetStatus(neuron1, {'V_th': new_Vth})
nest.SetStatus(neuron2, {'E_L': new_EL})
vth1, vth2 = nest.GetStatus(neuron1 + neuron2, 'V_th')
self.assertEqual(vth1, vth2)
def test_SetDefaults(self):
"""SetDefaults"""
models = nest.Models()
# sli_neuron does not work under PyNEST
models.remove('sli_neuron')
for m in models:
if nest.GetDefaults(m).has_key('V_m'):
nest.ResetKernel()
v_m = nest.GetDefaults(m)['V_m']
nest.SetDefaults(m, {'V_m': -1.})
self.assertEqual(nest.GetDefaults(m)['V_m'], -1.)
nest.SetDefaults(m, {'V_m': v_m})
try:
nest.SetDefaults(m, {'DUMMY': 0})
except nest.NESTError:
info = sys.exc_info()[1]
if not "DictError" in info.__str__():
self.fail('wrong error message')
# any other error is wrongly thrown
except:
self.fail('wrong error has been thrown')
def test_GetDefaults_JSON(self):
"""JSON data of GetDefaults"""
for model in nest.node_models + nest.synapse_models:
d_json = nest.GetDefaults(model, output='json')
self.assertIsInstance(d_json, str)
d = nest.GetDefaults(model)
d_json = nest.to_json(d)
self.assertIsInstance(d_json, str)
def test_GetDefaults_JSON(self):
"""JSON data of GetDefaults"""
for m in nest.Models():
d_json = nest.GetDefaults(m, output='json')
self.assertIsInstance(d_json, str)
d = nest.GetDefaults(m)
d_json = nest.hl_api.to_json(d)
self.assertIsInstance(d_json, str)
def test_ConnectNeuronsWithUrbanczikSynapse(self):
"""Ensures that the restriction to supported neuron models works."""
nest.set_verbosity('M_WARNING')
# Multi-compartment models
mc_models = [
"iaf_cond_alpha_mc",
"pp_cond_exp_mc_urbanczik",
]
# Supported models
supported_models = [
"pp_cond_exp_mc_urbanczik",
]
# Compute not supported models
not_supported_models = [
n for n in nest.Models(mtype='nodes') if n not in supported_models
]
# Connect supported models with Urbanczik synapse
for nm in supported_models:
nest.ResetKernel()
r_type = 0
if nm in mc_models:
syns = nest.GetDefaults(nm)["receptor_types"]
r_type = syns["soma_exc"]
n = nest.Create(nm, 2)
nest.Connect(n, n, {"rule": "all_to_all"}, {
"synapse_model": "urbanczik_synapse",
"receptor_type": r_type
})
# Ensure that connecting not supported models fails
for nm in not_supported_models:
nest.ResetKernel()
r_type = 0
if nm in mc_models:
syns = nest.GetDefaults(nm)["receptor_types"]
r_type = syns["soma_exc"]
n = nest.Create(nm, 2)
# try to connect with urbanczik synapse
with self.assertRaises(nest.kernel.NESTError):
nest.Connect(n, n, {"rule": "all_to_all"}, {
"synapse_model": "urbanczik_synapse",
"receptor_type": r_type
})
def test_GetDefaults_JSON(self):
"""JSON data of GetDefaults"""
# sli_neuron does not work under PyNEST
models = (m for m in nest.Models() if m != 'sli_neuron')
for m in models:
d_json = nest.GetDefaults(m, output='json')
self.assertIsInstance(d_json, str)
d = nest.GetDefaults(m)
d_json = nest.hl_api.to_json(d)
self.assertIsInstance(d_json, str)
def test_models(self):
"""Time objects in models correctly updated"""
# Generate a dictionary of reference values for each model.
reference = {}
for model in nest.Models():
if model in self.ignored_models:
continue
try:
reference[model] = nest.GetDefaults(model)
except nest.kernel.NESTError:
# If we can't get the defaults, we ignore the model.
pass
# Change the tic-base.
nest.set(resolution=0.5, tics_per_ms=1500.0)
# At this point, Time objects in models should have been updated to
# account for the new tic-base. Values in model defaults should therefore
# be equal (within a tolerance) to the reference values.
failing_models = []
for model in reference.keys():
model_reference = reference[model]
model_defaults = nest.GetDefaults(model)
# Remove entries where the item contains more than one value, as this causes issues when comparing.
array_keys = [
key for key, value in model_defaults.items()
if isinstance(value, (list, tuple, dict, np.ndarray))
]
for key in array_keys:
del model_defaults[key]
del model_reference[key]
keydiff = []
for key, value in model_defaults.items():
# value may not be a number, so we test for equality first.
# If it's not equal to the reference value, we assume it is a number.
if value != model_reference[key] and abs(
value - model_reference[key]) > self.eps:
print(value - model_reference[key])
keydiff.append([key, model_reference[key], value])
# If any keys have values different from the reference, the model fails.
if len(keydiff) > 0:
print(model, keydiff)
failing_models.append(model)
# No models should fail for the test to pass.
self.assertEqual([], failing_models)
def test_GetLabeledSynapses(self):
"""Get labeled synapses with GetConnections."""
labeled_synapse_models = [s for s in nest.Models(
mtype='synapses') if s.endswith("_lbl")]
for syn in labeled_synapse_models:
a = self.default_network(syn)
# see if symmetric connections are required
symm = nest.GetDefaults(syn, 'requires_symmetric')
# some more connections
synapse_type = "static_synapse"
if syn in self.rate_model_connections:
synapse_type = "rate_connection_instantaneous"
if syn in self.siegert_connections:
synapse_type = "diffusion_connection"
nest.Connect(a, a, {"rule": "one_to_one"},
{"model": synapse_type})
# set a label during connection
nest.Connect(a, a, {"rule": "one_to_one", "make_symmetric": symm},
{"model": syn, "synapse_label": 123})
c = nest.GetConnections(a, a, synapse_label=123)
self.assertTrue(
all([
status['synapse_label'] == 123
for status in nest.GetStatus(c)
])
)
def test_SetLabelToSynapseSetStatus(self):
"""Set a label to a labeled synapse on SetStatus."""
labeled_synapse_models = [s for s in nest.Models(
mtype='synapses') if s.endswith("_lbl")]
for syn in labeled_synapse_models:
a = self.default_network(syn)
# see if symmetric connections are required
symm = nest.GetDefaults(syn, 'requires_symmetric')
# set no label during connection
nest.Connect(a, a, {"rule": "one_to_one", "make_symmetric": symm},
{"model": syn})
c = nest.GetConnections(a, a)
# still unlabeled
self.assertTrue(
all([
status['synapse_label'] == -1
for status in nest.GetStatus(c)
])
)
# set a label
nest.SetStatus(c, {'synapse_label': 123})
self.assertTrue(
all([
status['synapse_label'] == 123
for status in nest.GetStatus(c)
])
)
def extract_nestvalid_dict(d, param_type='neuron'):
"""
Verify whether the parameters dictionary are in the correct format, with adequate keys, in agreement with the nest
parameters dictionaries so that they can later be passed as direct input to nest.
:param d: parameter dictionary
:param param_type: type of parameters - kernel, neuron, population, network, connections, topology
:return: valid dictionary
"""
if param_type == 'neuron' or param_type == 'synapse' or param_type == 'device':
assert d['model'] in nest.Models(
), "Model %s not currently implemented in %s" % (d['model'],
nest.version())
accepted_keys = nest.GetDefaults(d['model']).keys()
accepted_keys.remove('model')
nest_dict = {k: v for k, v in d.iteritems() if k in accepted_keys}
elif param_type == 'kernel':
accepted_keys = nest.GetKernelStatus().keys()
nest_dict = {k: v for k, v in d.iteritems() if k in accepted_keys}
else:
# TODO
logger.error("{!s} not implemented yet".format(param_type))
exit(-1)
return nest_dict
def test_GetStatus(self):
"""GetStatus"""
for m in nest.Models():
if 'V_m' in nest.GetDefaults(m):
nest.ResetKernel()
n = nest.Create(m)
d = nest.GetStatus(n)
self.assertIsInstance(d, tuple)
self.assertIsInstance(d[0], dict)
self.assertGreater(len(d[0]), 1)
v1 = nest.GetStatus(n)[0]['V_m']
v2 = nest.GetStatus(n, 'V_m')[0]
self.assertEqual(v1, v2)
n = nest.Create(m, 10)
d = nest.GetStatus(n, 'V_m')
self.assertEqual(len(d), len(n))
self.assertIsInstance(d[0], float)
test_keys = ("V_m", ) * 3
d = nest.GetStatus(n, test_keys)
self.assertEqual(len(d), len(n))
self.assertEqual(len(d[0]), len(test_keys))
def create(self):
from nest import topology as tp
import nest
# Create sublayers
self._layer_call('create')
# Set the GID
self._gid = flatten(self._layer_get('gid'))
# Connect stimulators to parrots, one-to-one
# IMPORTANT: This assumes that all layers are the same size!
radius = self.extent_units(0.1)
one_to_one_connection = {
'sources': {
'model': self.stimulator_model
},
'targets': {
'model': self.PARROT_MODEL
},
'connection_type': 'convergent',
'synapse_model': 'static_synapse',
'mask': {
'circular': {
'radius': radius
}
}
}
tp.ConnectLayers(self._gid, self._gid, one_to_one_connection)
# Get stimulator type
self.stimulator_type = nest.GetDefaults(self.stimulator_model,
'type_id')
def test_w_decay(self):
"""Decay of w_jk, proportional to alpha"""
self.setUp_decay(
params={
'p_fail': 1.,
'n_pot_conns': 1,
'A2_corr': 0.,
'A4_corr': 0.,
'A4_post': 0.
})
nest.SetStatus(self.syn, {'w_jk': [1.]})
syn_defaults = nest.GetDefaults('testsyn')
alpha = syn_defaults['alpha']
nest.Simulate(101.)
syn_status = nest.GetStatus(self.syn)
nest.Simulate(100.)
syn_status = nest.GetStatus(self.syn)
dt = 0.001
prop = np.exp(-dt * alpha)
val = syn_status[0]['w_jk'][0]
val_exp = 1. * prop**200.
self.assertAlmostEqualDetailed(val_exp, val,
"Weight decay (alpha) not correct.")
def _monitor(gids, nest_recorder, params):
new_record = []
recorders = []
for i, rec in enumerate(nest_recorder):
# multi/volt/conductancemeter
if "meter" in rec:
device = None
di_spec = {"rule": "all_to_all"}
if not params[i].get("to_accumulator", True):
device = nest.Create(rec, len(gids))
di_spec["rule"] = "one_to_one"
else:
device = nest.Create(rec)
recorders.append(device)
device_params = nest.GetDefaults(rec)
device_params.update(params[i])
new_record.append(device_params["record_from"])
nest.SetStatus(device, params[i])
nest.Connect(device, gids, conn_spec=di_spec)
# event detectors
elif "detector" in rec:
device = nest.Create(rec)
recorders.append(device)
new_record.append("spikes")
nest.SetStatus(device, params[i])
nest.Connect(gids, device)
else:
raise InvalidArgument('Invalid recorder item in `nest_recorder`: '
'{} is unknown.'.format(nest_recorder))
return tuple(recorders), new_record
def test_GetStatus(self):
"""GetStatus"""
# sli_neuron does not work under PyNEST
models = (m for m in nest.Models() if m != 'sli_neuron')
for m in models:
if 'V_m' in nest.GetDefaults(m):
nest.ResetKernel()
n = nest.Create(m)
d = nest.GetStatus(n)
self.assertIsInstance(d, tuple)
self.assertIsInstance(d[0], dict)
self.assertGreater(len(d[0]), 1)
v1 = nest.GetStatus(n)[0]['V_m']
v2 = nest.GetStatus(n, 'V_m')[0]
self.assertEqual(v1, v2)
n = nest.Create(m, 10)
d = nest.GetStatus(n, 'V_m')
self.assertEqual(len(d), len(n))
self.assertIsInstance(d[0], float)
test_keys = ("V_m", ) * 3
d = nest.GetStatus(n, test_keys)
self.assertEqual(len(d), len(n))
self.assertEqual(len(d[0]), len(test_keys))
def test_refractory_time(self):
'''
Check that refractory time implementation is correct.
'''
for model in tested_models:
self.reset()
if "t_ref" not in nest.GetDefaults(model):
continue
# Randomly set a refractory period
t_ref = 1.7
# Create the neuron and devices
nparams = {"t_ref": t_ref}
neuron = nest.Create(model, params=nparams)
name_Vm = "V_m.s" if model in mc_models else "V_m"
vm_params = {"interval": resolution, "record_from": [name_Vm]}
vm = nest.Create("voltmeter", params=vm_params)
sr = nest.Create("spike_recorder")
cg = nest.Create("dc_generator", params={"amplitude": 1200.})
# For models that do not clamp V_m, use very large current to
# trigger almost immediate spiking => t_ref almost equals
# interspike
if model in neurons_interspike_ps:
nest.SetStatus(cg, "amplitude", 10000000.)
elif model == 'ht_neuron':
# ht_neuron use too long time with a very large amplitude
nest.SetStatus(cg, "amplitude", 2000.)
elif model in neurons_interspike:
nest.SetStatus(cg, "amplitude", 15000.)
# Connect them and simulate
nest.Connect(vm, neuron)
nest.Connect(cg, neuron, syn_spec=add_connect_param.get(model, {}))
nest.Connect(neuron, sr)
nest.Simulate(simtime)
# Get and compare t_ref
t_ref_sim = self.compute_reftime(model, sr, vm, neuron)
if model in neurons_with_clamping:
t_ref_sim = t_ref_sim - nest.GetStatus(neuron, "t_clamp")[0]
# Approximate result for precise spikes (interpolation error)
if model in neurons_interspike_ps:
self.assertAlmostEqual(t_ref,
t_ref_sim,
places=3,
msg='''Error in model {}:
{} != {}'''.format(
model, t_ref, t_ref_sim))
else:
self.assertAlmostEqual(t_ref,
t_ref_sim,
msg='''Error in model {}:
{} != {}'''.format(
model, t_ref, t_ref_sim))
def testGlobalRecordingBackendProperties(self):
"""Test setting of global backend properties.
This sets and gets global backend properties and only runs if
compiled with SIONlib, as the corresponding backend is the
only backend with global parameters.
"""
nest.ResetKernel()
if HAVE_SIONLIB:
chunksize_before = nest.GetDefaults("sionlib", "sion_chunksize")
sionlib_options = {"sion_chunksize": chunksize_before + 1}
nest.SetDefaults("sionlib", sionlib_options)
chunksize_after = nest.GetDefaults("sionlib", "sion_chunksize")
self.assertEqual(chunksize_after, chunksize_before + 1)
def get_defaults(model_name):
valid_types = (int, float, Sequence, np.ndarray)
defaults = nest.GetDefaults(model_name)
variables = defaults.get('recordables', [])
ignore = [
'archiver_length', 'available', 'Ca', 'capacity', 'elementsize',
'frozen', 'instantiations', 'local', 'model', 'needs_prelim_update',
'recordables', 'state', 't_spike', 'tau_minus', 'tau_minus_triplet',
'thread', 'vp', 'receptor_types', 'events', 'global_id',
'element_type', 'type', 'type_id', 'has_connections', 'n_synapses',
'thread_local_id', 'node_uses_wfr', 'supports_precise_spikes',
'synaptic_elements', 'y_0', 'y_1', 'allow_offgrid_spikes',
'shift_now_spikes', 'post_trace'
]
default_params = {}
default_initial_values = {}
for name, value in defaults.items():
if name in variables:
default_initial_values[name] = value
elif name not in ignore:
if isinstance(value, valid_types):
default_params[name] = conversion.make_pynn_compatible(value)
else:
warnings.warn(
"Ignoring parameter '%s' since PyNN does not support %s" %
(name, type(value)))
return default_params, default_initial_values
def testSetDefaultRecordingBackend(self):
"""Test setting the default recording backend.
Test if setting another default recording backend for a
recording device works and does not influence the default
backend of all other recording devices.
"""
nest.ResetKernel()
nest.SetDefaults("multimeter", {"record_to": "ascii"})
rb_defaults_mm = nest.GetDefaults("multimeter")["record_to"]
rb_defaults_sr = nest.GetDefaults("spike_recorder")["record_to"]
self.assertEqual(rb_defaults_mm, "ascii")
self.assertEqual(rb_defaults_sr, "memory")
def test_GetLabeledSynapses(self):
"""Get labeled synapses with GetConnections."""
for syn in [s for s in nest.synapse_models if s.endswith("_lbl")]:
a, r_type = self.default_network(syn)
# see if symmetric connections are required
symm = nest.GetDefaults(syn, 'requires_symmetric')
# some more connections
synapse_type = "static_synapse"
if syn in self.rate_model_connections:
synapse_type = "rate_connection_instantaneous"
if syn in self.siegert_connections:
synapse_type = "diffusion_connection"
nest.Connect(a, a, {"rule": "one_to_one"}, {
"synapse_model": synapse_type,
"receptor_type": r_type
})
# set a label during connection
nest.Connect(a, a, {
"rule": "one_to_one",
"make_symmetric": symm
}, {
"synapse_model": syn,
"synapse_label": 123,
"receptor_type": r_type
})
c = nest.GetConnections(a, a, synapse_label=123)
self.assertTrue(all([x == 123 for x in c.get('synapse_label')]))
def test_SetLabelToNotLabeledSynapse(self):
"""Try set a label to an 'un-label-able' synapse."""
labeled_synapse_models = [s for s in nest.Models(
mtype='synapses') if not s.endswith("_lbl")]
for syn in labeled_synapse_models:
a = self.default_network(syn)
# see if symmetric connections are required
symm = nest.GetDefaults(syn, 'requires_symmetric')
# try set a label during SetDefaults
with self.assertRaises(nest.kernel.NESTError):
nest.SetDefaults(syn, {'synapse_label': 123})
# try set on connect
with self.assertRaises(nest.kernel.NESTError):
nest.Connect(a, a, {"rule": "one_to_one",
"make_symmetric": symm},
{"model": syn, "synapse_label": 123})
# plain connection
nest.Connect(a, a, {"rule": "one_to_one", "make_symmetric": symm},
{"model": syn})
# try set on SetStatus
c = nest.GetConnections(a, a)
with self.assertRaises(nest.kernel.NESTError):
nest.SetStatus(c, {'synapse_label': 123})
def test_decay_Rpre_half_fail(self):
"""Stochastic and selective increase of r_jk, plus decay"""
self.setUp_decay(params={'p_fail': .2, 'n_pot_conns': 2})
# this seed lets the first spike on syn 0 and the second spike on syn 1
# pass
msd = 4
N_vp = nest.GetKernelStatus(['total_num_virtual_procs'])[0]
nest.SetKernelStatus({'grng_seed': msd + N_vp})
nest.SetKernelStatus(
{'rng_seeds': range(msd + N_vp + 1, msd + 2 * N_vp + 1)})
nest.Simulate(210.)
syn_status = nest.GetStatus(self.syn)
syn_defaults = nest.GetDefaults('testsyn')
val = syn_status[0]['r_jk']
dt = 0.001
tau = syn_defaults['tau']
prop = np.exp(-dt / tau)
spike0 = 1. / tau * prop**100. # propagate spike 1 by 100ms
# no spike increase on 1, only second spike increase on 2
val_exp = [spike0, 1. / tau]
for k in range(len(val)):
self.assertAlmostEqualDetailed(
val_exp[k], val[k],
"Decay of r_jk[%i] not as expected (stochastic case)" % k)
def _apply_generators_to_neurons(self, uuid, parameters, gids, multiple):
"""
Creates the generator(s), sets status and does the mapping
between gids and nest neurons.
"""
generator_name = str(parameters['model'][0])
del parameters['model'] # Removed to avoid a spurious exception
generators = _nest.Create(generator_name, len(gids) if multiple else 1)
try:
# Coercing numeric parameter types to avoid errors
defaults = _nest.GetDefaults(generator_name)
for key, value in parameters.items():
value_type = type(defaults[key])
type_name = value_type.__name__
if type_name == 'int' or type_name == 'float':
parameters[key] = value_type(value)
_nest.SetStatus(generators, parameters)
except _nest.pynestkernel.NESTError as e:
if 'DictError' not in str(e):
print(e)
self._generator_dict[uuid] = generators
neurons = []
for gid in gids:
neurons.append(self._gid_to_neuron_map[gid])
self._apply_generators_to_neurons_function(generators, neurons)
def syn(specs):
if isinstance(specs, str):
return specs
if len(specs) == 0:
return 'static_synapse'
spec_defaults = nest.GetDefaults(specs.get('model', 'static_synapse'))
return _paramify(specs, spec_defaults)
def setUpNodes(self):
self.n = nest.Create('iaf_cond_alpha_mc')
rqs = nest.GetDefaults('iaf_cond_alpha_mc')['recordables']
self.mm = nest.Create('multimeter',
params={
'record_from': rqs,
'interval': 0.1
})
self.cgs = nest.Create('dc_generator', 3)
nest.SetStatus(self.cgs, [
self.rec_dic_dc_soma, self.rec_dic_dc_proximal,
self.rec_dic_dc_distal
])
self.sgs = nest.Create('spike_generator', 6)
nest.SetStatus(self.sgs, [{
'spike_times': self.rec_sp_soma_ex
}, {
'spike_times': self.rec_sp_soma_in
}, {
'spike_times': self.rec_sp_prox_ex
}, {
'spike_times': self.rec_sp_prox_in
}, {
'spike_times': self.rec_sp_dist_ex
}, {
'spike_times': self.rec_sp_dist_in
}])
def test_nest_instantiability(self):
# N.B. all models are assumed to have been already built (see .travis.yml)
nest.ResetKernel()
nest.set_verbosity("M_ALL")
nest.Install("nestml_allmodels_module")
models = nest.Models(mtype="nodes")
neuron_models = [
m for m in models
if str(nest.GetDefaults(m, "element_type")) == "neuron"
]
_neuron_models = strip_suffix(neuron_models, "_neuron")
nestml_unit_test_models = [
neuron_model_name for neuron_model_name in _neuron_models
if neuron_model_name.endswith("_nestml")
]
nest.ResetKernel()
for neuron_model in nestml_unit_test_models:
print("Instantiating neuron model: " + str(neuron_model))
nest.Create(neuron_model)
nest.Simulate(100.)
