def setUp(self):
"""Generate the neuron model code"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
# generate the "jit" model (co-generated neuron and synapse), that does not rely on ArchivingNode
to_nest(input_path=["models/neurons/iaf_psc_exp.nestml", "models/synapses/stdp_nn_pre_centered.nestml"],
target_path="/tmp/nestml-jit",
logging_level="INFO",
module_name="nestml_jit_module",
suffix="_nestml",
codegen_opts={"neuron_parent_class": "StructuralPlasticityNode",
"neuron_parent_class_include": "structural_plasticity_node.h",
"neuron_synapse_pairs": [{"neuron": "iaf_psc_exp",
"synapse": "stdp_nn_pre_centered",
"post_ports": ["post_spikes"]}]})
install_nest("/tmp/nestml-jit", nest_path)
# generate the "non-jit" model, that relies on ArchivingNode
to_nest(input_path="models/neurons/iaf_psc_exp.nestml",
target_path="/tmp/nestml-non-jit",
logging_level="INFO",
module_name="nestml_non_jit_module",
suffix="_nestml_non_jit",
codegen_opts={"neuron_parent_class": "ArchivingNode",
"neuron_parent_class_include": "archiving_node.h"})
install_nest("/tmp/nestml-non-jit", nest_path)
def setUp(self):
"""Generate the model code"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
# generate the "jit" model (co-generated neuron and synapse), that does not rely on ArchivingNode
to_nest(input_path=[
"tests/nest_tests/resources/iaf_psc_exp_resolution_test.nestml",
os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "..", "valid",
"CoCoResolutionLegallyUsed.nestml")))
],
target_path="target",
logging_level="INFO",
module_name="nestmlmodule",
suffix="_nestml",
codegen_opts={
"neuron_parent_class":
"StructuralPlasticityNode",
"neuron_parent_class_include":
"structural_plasticity_node.h",
"neuron_synapse_pairs": [{
"neuron":
"iaf_psc_exp_resolution_test",
"synapse":
"CoCoResolutionLegallyUsed"
}]
})
install_nest("target", nest_path)
def compile_modules(modules, recompile=False, config=CONFIGURED, logger=LOG):
# ...unless we need to first compile it:
from pynestml.frontend.pynestml_frontend import install_nest
if not os.path.exists(config.MODULES_BLDS_DIR):
logger.info("Creating MODULES_BLDS_DIR: %s" % config.MODULES_BLDS_DIR)
os.makedirs(config.MODULES_BLDS_DIR)
for module in ensure_list(modules):
logger.info("Compiling %s..." % module)
module_bld_dir = os.path.join(config.MODULES_BLDS_DIR, module)
logger.info("from in build_interfaces directory %s..." %
module_bld_dir)
if not os.path.exists(module_bld_dir) or recompile:
source_path = os.path.join(config.MODULES_DIR, module)
logger.info("copying sources from %s\ninto %s..." %
(source_path, module_bld_dir))
shutil.copytree(source_path, module_bld_dir)
logger.info("Running compilation...")
install_nest(module_bld_dir, config.NEST_PATH)
if os.path.isfile(os.path.join(config.MODULES_BLDS_DIR, module + "module.so")) and \
os.path.isfile(os.path.join(config.MODULES_BLDS_DIR, "lib" + module + "module.so")):
logger.info("DONE compiling %s!" % module)
else:
logger.warn(
"Something seems to have gone wrong with compiling %s!" %
module)
def setUp(self):
"""Generate the neuron model code"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
to_nest(input_path="models/synapses/noisy_synapse.nestml",
target_path="/tmp/nestml-noisy-synapse",
logging_level="INFO",
module_name="nestml_noisy_synapse_module",
suffix="_nestml")
install_nest("/tmp/nestml-noisy-synapse", nest_path)
def install(data):
# save nestml models to files
filename = os.path.join(models_path, data['neuron'])
data_nestml_format = converter.json_to_nestml_format(data)
converter.write_file(filename, data_nestml_format)
to_nest(models_path, build_path, module_name='nestmlmodule')
install_nest(build_path, nest_install_dir)
return {'data': data}
def simulate_OU_noise_neuron(self, resolution):
'''
Simulates a single neuron with OU noise conductances.
Parameters
----------
resolution : float
Resolution of the NEST simulation
Returns
-------
dict
State of the multimeter, which is connected to the neuron.
tuple
Tuple with the NEST id of the simulated neuron
'''
seed = np.random.randint(0, 2**32 - 1)
print('seed: {}'.format(seed))
nest.SetKernelStatus({
'resolution': resolution,
'grng_seed': seed,
'rng_seeds': [seed + 1]
})
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "..", "..", "models",
"hh_cond_exp_destexhe.nestml")))
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log,
suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.Install('nestmlmodule')
neuron = nest.Create('hh_cond_exp_destexhe_nestml')
multi = nest.Create('multimeter',
params={
'record_from': self.record_from,
'interval': resolution
})
nest.Connect(multi, neuron)
nest.Simulate(500000)
return multi.get("events"), neuron
def nestml_to_nest_extension_module():
r"""Generate the neuron model code"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
# generate the "jit" model (co-generated neuron and synapse), that does not rely on ArchivingNode
to_nest(input_path=["models/neurons/iaf_psc_delta.nestml", "models/synapses/stdp_synapse.nestml"],
target_path="/tmp/nestml-jit",
logging_level="INFO",
module_name="nestml_jit_module",
suffix="_nestml",
codegen_opts={"neuron_parent_class": "StructuralPlasticityNode",
"neuron_parent_class_include": "structural_plasticity_node.h",
"neuron_synapse_pairs": [{"neuron": "iaf_psc_delta",
"synapse": "stdp",
"post_ports": ["post_spikes"]}]})
install_nest("/tmp/nestml-jit", nest_path)
nest.Install("nestml_jit_module")
def test_for_and_while_loop(self):
files = ["ForLoop.nestml", "WhileLoop.nestml"]
input_path = [
os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "resources", s)))
for s in files
]
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log,
suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install("nestmlmodule")
nrn = nest.Create("for_loop_nestml")
mm = nest.Create('multimeter')
mm.set({"record_from": ["V_m"]})
nest.Connect(mm, nrn)
nest.Simulate(5.0)
v_m = mm.get("events")["V_m"]
np.testing.assert_almost_equal(v_m[-1], 16.6)
nest.ResetKernel()
nrn = nest.Create("while_loop_nestml")
mm = nest.Create('multimeter')
mm.set({"record_from": ["y"]})
nest.Connect(mm, nrn)
nest.Simulate(5.0)
y = mm.get("events")["y"]
np.testing.assert_almost_equal(y[-1], 5.011)
def setUp(self):
"""Generate the neuron model code"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
to_nest(input_path=["models/neurons/iaf_psc_delta.nestml",
"tests/resources/synapse_event_priority_test.nestml",
"tests/resources/synapse_event_inv_priority_test.nestml"],
target_path="/tmp/nestml-synapse-event-priority-test",
logging_level="INFO",
module_name="nestml_module",
suffix="_nestml",
codegen_opts={"neuron_parent_class": "StructuralPlasticityNode",
"neuron_parent_class_include": "structural_plasticity_node.h",
"neuron_synapse_pairs": [{"neuron": "iaf_psc_delta",
"synapse": "synapse_event_priority_test",
"post_ports": ["post_spikes"]},
{"neuron": "iaf_psc_delta",
"synapse": "synapse_event_inv_priority_test",
"post_ports": ["post_spikes"]}]})
install_nest("/tmp/nestml-synapse-event-priority-test", nest_path)
def test_recordable_variables(self):
input_path = os.path.join(os.path.realpath(os.path.join(
os.path.dirname(__file__), "resources", "RecordableVariables.nestml")))
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = "target"
logging_level = "INFO"
module_name = "nestmlmodule"
store_log = False
suffix = "_nestml"
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log, suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install(module_name)
neuron = nest.Create("recordable_variables_nestml")
sg = nest.Create("spike_generator", params={"spike_times": [20., 80.]})
nest.Connect(sg, neuron)
mm = nest.Create('multimeter', params={'record_from': ['V_ex', 'V_m', 'V_abs', 'I_kernel__X__spikes'],
'interval': 0.1})
nest.Connect(mm, neuron)
nest.Simulate(100.)
# Get the recordable variables
events = nest.GetStatus(mm)[0]["events"]
V_reset = nest.GetStatus(neuron, "V_reset")
V_m = events["V_m"]
self.assertIsNotNone(V_m)
V_abs = events["V_abs"]
self.assertIsNotNone(V_abs)
np.testing.assert_allclose(V_m, V_abs + V_reset)
V_ex = events["V_ex"]
np.testing.assert_almost_equal(V_ex[-1], -10)
def generate_all_models(self):
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
all_synapse_models = [s[:-7] for s in list(os.walk("models/synapses"))[0][2] if s[-7:] == ".nestml"]
to_nest(input_path=["models"],
target_path="/tmp/nestml-allmodels",
logging_level="INFO",
module_name="nestml_allmodels_module",
suffix="_nestml",
codegen_opts={"neuron_parent_class": "StructuralPlasticityNode",
"neuron_parent_class_include": "structural_plasticity_node.h",
"neuron_synapse_pairs": [{"neuron": "iaf_psc_exp",
"synapse": "neuromodulated_stdp",
"post_ports": ["post_spikes"],
"vt_ports": ["mod_spikes"]},
{"neuron": "iaf_psc_exp",
"synapse": "stdp",
"post_ports": ["post_spikes"]},
{"neuron": "iaf_psc_delta",
"synapse": "stdp_triplet",
"post_ports": ["post_spikes"]},
{"neuron": "iaf_psc_delta",
"synapse": "stdp_triplet_nn",
"post_ports": ["post_spikes"]},
{"neuron": "iaf_psc_exp",
"synapse": "stdp_nn_symm",
"post_ports": ["post_spikes"]},
{"neuron": "iaf_psc_exp",
"synapse": "stdp_nn_restr_symm",
"post_ports": ["post_spikes"]},
{"neuron": "iaf_psc_exp_dend",
"synapse": "third_factor_stdp",
"post_ports": ["post_spikes",
["I_post_dend", "I_dend"]]},
{"neuron": "iaf_psc_exp",
"synapse": "stdp_nn_pre_centered",
"post_ports": ["post_spikes"]}]})
install_nest("/tmp/nestml-allmodels", nest_path)
def test_vectors(self):
input_path = os.path.join(os.path.realpath(os.path.join(os.path.dirname(__file__), "resources", "Vectors.nestml")))
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log, suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install("nestmlmodule")
neuron = nest.Create("vectors_nestml")
multimeter = nest.Create('multimeter')
recordables = list()
recordables.extend(["G_IN_" + str(i + 1) for i in range(0, 20)])
recordables.extend(["G_EX_" + str(i + 1) for i in range(0, 10)])
recordables.append("V_m")
multimeter.set({"record_from": recordables})
nest.Connect(multimeter, neuron)
nest.Simulate(2.0)
events = multimeter.get("events")
g_in = events["G_IN_1"]
g_ex = events["G_EX_2"]
print("g_in: {}, g_ex: {}".format(g_in, g_ex))
np.testing.assert_almost_equal(g_in[-1], 11.)
np.testing.assert_almost_equal(g_ex[-1], -2.)
v_m = multimeter.get("events")["V_m"]
print("V_m: {}".format(v_m))
np.testing.assert_almost_equal(v_m[-1], -0.3)
def setUp(self):
"""generate code for neuron and synapse and build NEST user module"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
to_nest(input_path=[
"models/neurons/iaf_psc_exp.nestml",
"models/synapses/neuromodulated_stdp.nestml"
],
target_path="/tmp/nestml-jit",
logging_level="INFO",
module_name="nestml_jit_module",
suffix="_nestml",
codegen_opts={
"neuron_parent_class":
"StructuralPlasticityNode",
"neuron_parent_class_include":
"structural_plasticity_node.h",
"neuron_synapse_pairs": [{
"neuron": "iaf_psc_exp",
"synapse": "neuromodulated_stdp",
"post_ports": ["post_spikes"],
"vt_ports": ["mod_spikes"]
}]
})
install_nest("/tmp/nestml-jit", nest_path)
to_nest(input_path="models/neurons/iaf_psc_exp.nestml",
target_path="/tmp/nestml-non-jit",
logging_level="INFO",
module_name="nestml_non_jit_module",
suffix="_nestml_non_jit",
codegen_opts={
"neuron_parent_class": "ArchivingNode",
"neuron_parent_class_include": "archiving_node.h"
})
install_nest("/tmp/nestml-non-jit", nest_path)
def nestml_to_nest_extension_module():
"""Generate the neuron model code"""
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
to_nest(input_path=[
"models/neurons/iaf_psc_delta.nestml",
"models/synapses/stdp_triplet_naive.nestml"
],
target_path="/tmp/nestml-triplet-stdp",
logging_level="INFO",
module_name="nestml_triplet_pair_module",
suffix="_nestml",
codegen_opts={
"neuron_parent_class":
"StructuralPlasticityNode",
"neuron_parent_class_include":
"structural_plasticity_node.h",
"neuron_synapse_pairs": [{
"neuron": "iaf_psc_delta",
"synapse": "stdp_triplet",
"post_ports": ["post_spikes"]
}]
})
install_nest("/tmp/nestml-triplet-stdp", nest_path)
import os
import nest
import shutil
from pynestml.frontend.pynestml_frontend import to_nest, install_nest
input_path = str(
os.path.realpath(
os.path.join(os.path.dirname(__file__), 'PrintVariables.nestml')))
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = '../target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log, suffix,
dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install(module_name)
neuron = nest.Create("print_variable_nestml")
nest.Simulate(0.1)
if os.path.exists(target_path):
shutil.rmtree(target_path)
def test_terub_gpe(self):
if not os.path.exists("target"):
os.makedirs("target")
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "../models",
"terub_gpe.nestml")))
target_path = "target"
module_name = 'terub_gpe_module'
nest_path = "/home/abolfazl/prog/nest-build/"
suffix = '_nestml'
if 1:
to_nest(input_path=input_path,
target_path=target_path,
logging_level="INFO",
suffix=suffix,
module_name=module_name)
install_nest(target_path, nest_path)
nest.Install(module_name)
model = "terub_gpe_nestml"
dt = 0.01
t_simulation = 2000.0
nest.SetKernelStatus({"resolution": dt})
neuron = nest.Create(model)
parameters = nest.GetDefaults(model)
if 0:
for i in parameters:
print(i, parameters[i])
nest.SetStatus(neuron, {'I_e': 0.0})
multimeter = nest.Create("multimeter")
nest.SetStatus(multimeter, {
"withtime": True,
"record_from": ["V_m"],
"interval": dt
})
spikedetector = nest.Create("spike_detector",
params={
"withgid": True,
"withtime": True
})
nest.Connect(multimeter, neuron)
nest.Connect(neuron, spikedetector)
nest.Simulate(t_simulation)
dmm = nest.GetStatus(multimeter)[0]
Voltages = dmm["events"]["V_m"]
tv = dmm["events"]["times"]
dSD = nest.GetStatus(spikedetector, keys='events')[0]
spikes = dSD['senders']
ts = dSD["times"]
firing_rate = len(spikes) / t_simulation * 1000
print("firing rate is ", firing_rate)
expected_value = np.abs(firing_rate - 14)
tolerance_value = 3 # Hz
# self.assertLessEqual(expected_value, tolerance_value)
if TEST_PLOTS:
fig, ax = plt.subplots(2, figsize=(8, 4), sharex=True)
ax[0].plot(tv, Voltages, lw=2, color="k")
ax[1].plot(ts, spikes, 'ko')
ax[1].set_xlabel("Time [ms]")
ax[1].set_xlim(0, t_simulation)
ax[1].set_ylabel("Spikes")
ax[0].set_ylabel("v [ms]")
# ax[0].set_ylim(-100, 50)
for i in ts:
ax[0].axvline(x=i, lw=1., ls="--", color="gray")
plt.savefig("resources/terub_gpe.png")
def test_fir_filter(self):
nestml_model_file = 'FIR_filter.nestml'
nestml_model_name = 'fir_filter_nestml'
target_path = '/tmp/fir-filter'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
# Generate the NEST code
input_path = os.path.join(os.path.realpath(os.path.join(os.path.dirname(__file__), 'resources', nestml_model_file)))
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
to_nest(input_path, target_path, logging_level, module_name, store_log, suffix, dev)
install_nest(target_path, nest_path)
t_sim = 101.
resolution = 0.1
nest.set_verbosity("M_ALL")
nest.Install(module_name)
nest.ResetKernel()
# Create a fir_filter node
neuron = nest.Create(nestml_model_name, {"N": 256})
# Create a spike generator
spikes = [1.0, 1.0, 1.5, 1.5, 1.5, 6.7, 10.0, 10.5, 10.5, 10.5, 10.5, 11.3, 11.3, 11.4, 11.4, 20., 22.5, 30.,
40., 42., 42., 42., 50.5, 50.5, 75., 88., 93., 93.]
sg = nest.Create("spike_generator", params={"spike_times": spikes})
nest.Connect(sg, neuron, syn_spec=dict(delay=resolution))
# Get N (order of the filter)
n = nest.GetStatus(neuron, "N")[0]
print("N: {}".format(n))
# Set filter coefficients
h = self.generate_filter_coefficients(n)
nest.SetStatus(neuron, {"h": h})
print("h: ", h)
# Multimeter
multimeter = nest.Create('multimeter')
nest.SetStatus(multimeter, {'interval': resolution})
multimeter.set({"record_from": ["y"]}) # output of the filter
nest.Connect(multimeter, neuron)
# Spike recorder
sr = nest.Create("spike_recorder")
nest.Connect(sg, sr)
nest.Connect(neuron, sr)
# Simulate
nest.Simulate(t_sim)
# Record from multimeter
events = multimeter.get("events")
y = events["y"]
times = events["times"]
spike_times = nest.GetStatus(sr, keys='events')[0]['times']
# Scipy filtering
spikes, bin_edges = np.histogram(spike_times, np.arange(0, t_sim, resolution))
output = scipy.signal.lfilter(h, 1, spikes)
# Plots
if TEST_PLOTS:
self.plot_output(spike_times, times, y, title='FIR FILTER (NESTML)',
filename='fir_filter_output_nestml.png')
self.plot_output(spike_times, bin_edges[1:], output, title='FIR FILTER (scipy)',
filename='fir_filter_output_scipy.png')
np.testing.assert_allclose(y, output)
def test_multisynapse(self):
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "resources",
"iaf_psc_exp_multisynapse.nestml")))
nest_path = "/home/travis/nest_install"
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log,
suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install(module_name)
# network construction
neuron = nest.Create("iaf_psc_exp_multisynapse_neuron_nestml")
sg = nest.Create("spike_generator", params={"spike_times": [20., 80.]})
nest.Connect(sg,
neuron,
syn_spec={
"receptor_type": 1,
"weight": 1000.,
"delay": 0.1
})
sg2 = nest.Create("spike_generator",
params={"spike_times": [40., 60.]})
nest.Connect(sg2,
neuron,
syn_spec={
"receptor_type": 2,
"weight": 1000.,
"delay": 0.1
})
sg3 = nest.Create("spike_generator",
params={"spike_times": [30., 70.]})
nest.Connect(sg3,
neuron,
syn_spec={
"receptor_type": 3,
"weight": 500.,
"delay": 0.1
})
mm = nest.Create('multimeter',
params={
'record_from': [
'I_kernel1__X__spikes1',
'I_kernel2__X__spikes2',
'I_kernel3__X__spikes3'
],
'interval':
0.1
})
nest.Connect(mm, neuron)
vm_1 = nest.Create('voltmeter')
nest.Connect(vm_1, neuron)
# simulate
nest.Simulate(125.)
# analysis
V_m_timevec = nest.GetStatus(vm_1)[0]["events"]["times"]
V_m = nest.GetStatus(vm_1)[0]["events"]["V_m"]
mm = nest.GetStatus(mm)[0]["events"]
MAX_ABS_ERROR = 1E-6
print("Final V_m = " + str(V_m[-1]))
assert abs(V_m[-1] - -72.89041451202348) < MAX_ABS_ERROR
if TEST_PLOTS:
fig, ax = plt.subplots(nrows=4)
ax[0].plot(V_m_timevec, V_m, label="V_m")
ax[0].set_ylabel("voltage")
ax[1].plot(mm["times"],
mm["I_kernel1__X__spikes1"],
label="I_kernel1")
ax[1].set_ylabel("current")
ax[2].plot(mm["times"],
mm["I_kernel2__X__spikes2"],
label="I_kernel2")
ax[2].set_ylabel("current")
ax[3].plot(mm["times"],
mm["I_kernel3__X__spikes3"],
label="I_kernel3")
ax[3].set_ylabel("current")
for _ax in ax:
_ax.legend(loc="upper right")
_ax.set_xlim(0., 125.)
_ax.grid(True)
for _ax in ax[:-1]:
_ax.set_xticklabels([])
ax[-1].set_xlabel("time")
plt.show()
"""Install adaptive LIF neuron using nestml"""
import nest
from pynestml.frontend.pynestml_frontend import to_nest, install_nest
NEST_SIMULATOR_INSTALL_LOCATION = nest.ll_api.sli_func("statusdict/prefix ::")
to_nest(input_path="adapt_lif.nestml",
target_path="/tmp/nestml-component",
logging_level="ERROR")
install_nest("/tmp/nestml-component", NEST_SIMULATOR_INSTALL_LOCATION)
from os import system
from pynestml.frontend.pynestml_frontend import to_nest, install_nest
to_nest(input_path=".",
target_path="../tmp/module",
logging_level="INFO",
module_name="tmp_nestml_module",
suffix="_nestml")
install_nest("../tmp/module",
"/home/abolfazl/prog/install-dir/nest-simulator-2.18.0_build")
def test_terub_stn_multisyn(self):
model_name = "terub_stn_multisyn"
if not os.path.exists("target"):
os.makedirs("target")
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "../models",
"{}.nestml".format(model_name))))
target_path = "target"
module_name = '{}_module'.format(model_name)
nest_path = "/home/abolfazl/prog/nest-build/"
suffix = '_nestml'
if 1: #! compile
to_nest(input_path=input_path,
target_path=target_path,
logging_level="INFO",
suffix=suffix,
module_name=module_name)
install_nest(target_path, nest_path)
nest.Install(module_name)
model = "{}_nestml".format(model_name)
dt = 0.01
t_simulation = 1500.0
nest.SetKernelStatus({"resolution": dt})
neuron1 = nest.Create(model, 1)
neuron2 = nest.Create(model, 1)
# parameters = nest.GetDefaults(model)
# if 0:
# for i in parameters:
# print(i, parameters[i])
for neuron in neuron1 + neuron2:
nest.SetStatus([neuron], {'I_e': 0.0 + rand() * 20.0 - 20})
nest.SetStatus([neuron], {'V_m': -65.0 + rand() * 10. - 5.})
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 1}) # AMPA
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 2}) # NMDA
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 3}) # GABAA
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 4}) # GABAB
record_from = [
"V_m", "I_syn_ampa", "I_syn_nmda", "I_syn_gaba_a", "I_syn_gaba_b"
]
multimeter = nest.Create("multimeter", 2)
nest.SetStatus(multimeter, {
"withtime": True,
"record_from": record_from,
"interval": dt
})
spikedetector = nest.Create("spike_detector",
params={
"withgid": True,
"withtime": True
})
nest.Connect(multimeter, neuron1 + neuron2, "one_to_one")
nest.Connect(neuron1 + neuron2, spikedetector)
nest.Simulate(t_simulation)
dSD = nest.GetStatus(spikedetector, keys='events')[0]
spikes = dSD['senders']
firing_rate = len(spikes) / t_simulation * 1000
print("firing rate is ", firing_rate / 2)
def plot_data(index=[0]):
fig, ax = plt.subplots(3, figsize=(10, 6), sharex=True)
for i in index:
dmm = nest.GetStatus(multimeter, keys='events')[i]
Voltages = dmm["V_m"]
tv = dmm["times"]
ax[0].plot(tv, Voltages, lw=1, label=str(i + 1))
labels = ["ampa", "nmda", "gaba_a", "gaba_b"]
j = 0
dmm = nest.GetStatus(multimeter)[1]
tv = dmm['events']["times"]
for i in record_from[1:]:
g = dmm["events"][i]
ax[1].plot(tv, g, lw=2, label=labels[j])
j += 1
dSD = nest.GetStatus(spikedetector, keys='events')[0]
spikes = dSD['senders']
ts = dSD["times"]
ax[2].plot(ts, spikes, 'ko', ms=3)
ax[2].set_xlabel("Time [ms]")
ax[2].set_xlim(0, t_simulation)
ax[2].set_ylabel("Spikes")
ax[0].set_title("recording from PSP")
ax[0].set_ylabel("v [ms]")
ax[1].set_ylabel("I_syn")
ax[1].legend(frameon=False, loc="upper right")
ax[0].legend()
plt.savefig(join("resources", "terub_stn_multisyn.png"), dpi=150)
# plt.show()
plot_data(index=[0, 1])
def test_logarithmic_function(self):
MAX_SSE = 1E-12
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "resources")))
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log,
suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install("nestmlmodule")
nrn = nest.Create("logarithm_function_test_nestml")
mm = nest.Create('multimeter')
ln_state_specifier = 'ln_state'
log10_state_specifier = 'log10_state'
mm.set(
{"record_from": [ln_state_specifier, log10_state_specifier, "x"]})
nest.Connect(mm, nrn)
nest.Simulate(100.0)
timevec = mm.get("events")["x"]
ln_state_ts = mm.get("events")[ln_state_specifier]
log10_state_ts = mm.get("events")[log10_state_specifier]
ref_ln_state_ts = np.log(timevec - 1)
ref_log10_state_ts = np.log10(timevec - 1)
assert np.all((ln_state_ts - ref_ln_state_ts)**2 < MAX_SSE)
assert np.all((log10_state_ts - ref_log10_state_ts)**2 < MAX_SSE)
# test that expected failure occurs
nest.ResetKernel()
nrn = nest.Create("logarithm_function_test_invalid_nestml")
mm = nest.Create('multimeter')
ln_state_specifier = 'ln_state'
log10_state_specifier = 'log10_state'
mm.set(
{"record_from": [ln_state_specifier, log10_state_specifier, "x"]})
nest.Connect(mm, nrn)
nest.Simulate(100.0)
timevec = mm.get("events")["x"]
ln_state_ts = mm.get("events")[ln_state_specifier]
log10_state_ts = mm.get("events")[log10_state_specifier]
ref_ln_state_ts = np.log(timevec - 1)
ref_log10_state_ts = np.log10(timevec - 1)
assert not np.all((ln_state_ts - ref_ln_state_ts)**2 < MAX_SSE)
assert not np.all((log10_state_ts - ref_log10_state_ts)**2 < MAX_SSE)
def test_biexp_synapse(self):
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "resources",
"BiexponentialPostSynapticResponse.nestml")))
nest_path = "/home/travis/nest_install"
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log,
suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install(module_name)
# network construction
neuron = nest.Create("biexp_postsynaptic_response_nestml")
sg = nest.Create("spike_generator", params={"spike_times": [10., 30.]})
nest.Connect(sg,
neuron,
syn_spec={
"receptor_type": 1,
"weight": 1000.,
"delay": 0.1
})
sg2 = nest.Create("spike_generator",
params={"spike_times": [20., 40.]})
nest.Connect(sg2,
neuron,
syn_spec={
"receptor_type": 2,
"weight": 1000.,
"delay": 0.1
})
sg3 = nest.Create("spike_generator",
params={"spike_times": [25., 45.]})
nest.Connect(sg3,
neuron,
syn_spec={
"receptor_type": 3,
"weight": 1000.,
"delay": 0.1
})
sg4 = nest.Create("spike_generator",
params={"spike_times": [35., 55.]})
nest.Connect(sg3,
neuron,
syn_spec={
"receptor_type": 4,
"weight": 1000.,
"delay": 0.1
})
i_1 = nest.Create('multimeter',
params={
'record_from': [
'g_gap__X__spikeGap', 'g_ex__X__spikeExc',
'g_in__X__spikeInh', 'g_GABA__X__spikeGABA'
],
'interval':
.1
})
nest.Connect(i_1, neuron)
vm_1 = nest.Create('voltmeter')
nest.Connect(vm_1, neuron)
# simulate
nest.Simulate(125.)
# analysis
vm_1 = nest.GetStatus(vm_1)[0]["events"]
i_1 = nest.GetStatus(i_1)[0]["events"]
if TEST_PLOTS:
self.plot(vm_1, i_1)
# verification
final_v_m = vm_1["V_m"][-1]
print("final V_m = " + str(final_v_m))
MAX_ABS_ERROR = 1E-6
assert abs(final_v_m - -64.2913308548727) < MAX_ABS_ERROR
def compile_modules(modules, recompile=False, config=CONFIGURED, logger=LOG):
"""Function to compile NEST modules.
Arguments:
modules: a sequence (list, tuple) of NEST modules' names (strings).
recompile: (bool) flag to recompile a module that is already compiled. Default = False.
config: configuration class instance. Default: imported default CONFIGURED object.
logger: logger object. Default: local LOG object.
"""
# ...unless we need to first compile it:
logger.info("Preparing MYMODULES_BLD_DIR: %s" % config.MYMODULES_BLD_DIR)
safe_makedirs(config.MYMODULES_BLD_DIR)
lib_path = os.path.join(os.environ["NEST_INSTALL_DIR"], "lib", "nest")
include_path = os.path.join(os.environ["NEST_INSTALL_DIR"], "include")
for module in ensure_list(modules):
modulemodule = module + "module"
module_bld_dir = os.path.join(config.MYMODULES_BLD_DIR, module)
solib_file = os.path.join(module_bld_dir, modulemodule + ".so")
dylib_file = os.path.join(module_bld_dir, "lib" + modulemodule + ".dylib")
include_file = os.path.join(module_bld_dir, modulemodule + ".h")
installed_solib_file = os.path.join(lib_path, os.path.basename(solib_file))
installed_dylib_file = os.path.join(lib_path, os.path.basename(dylib_file))
module_include_path = os.path.join(include_path, modulemodule)
installed_h_file = os.path.join(module_include_path, modulemodule + ".h")
if not os.path.isfile(solib_file) \
or not os.path.isfile(dylib_file) \
or not os.path.isfile(include_file) \
or recompile:
# If any of the .so, .dylib or .h files don't exist,
# or if the user requires recompilation,
# proceed with recompilation:
if os.path.exists(module_bld_dir):
# Delete any pre-compiled built files:
shutil.rmtree(module_bld_dir)
# Create a module build directory and copy there the source files:
source_path = os.path.join(config.MYMODULES_DIR, module)
logger.info("Copying module sources from %s\ninto %s..." % (source_path, module_bld_dir))
shutil.copytree(source_path, module_bld_dir)
# Now compile:
logger.info("Compiling %s..." % module)
logger.info("in build directory %s..." % module_bld_dir)
success_message = "DONE compiling and installing %s!" % module
from pynestml.frontend.pynestml_frontend import install_nest
install_nest(module_bld_dir, config.NEST_PATH)
logger.info("Compiling finished without errors...")
else:
logger.info("Installing precompiled module %s..." % module)
success_message = "DONE installing precompiled module %s!" % module
# Just copy the .h, .so, and .dylib files to the appropriate NEST build paths:
shutil.copyfile(solib_file, installed_solib_file)
shutil.copyfile(solib_file, installed_dylib_file)
safe_makedirs(include_path)
shutil.copyfile(os.path.join(module_bld_dir, modulemodule + ".h"), installed_h_file)
installed_files = {}
for file in [installed_solib_file, installed_dylib_file, installed_h_file]:
installed_files[file] = os.path.isfile(file)
if all(installed_files.values()):
logger.info(success_message)
else:
logger.warn("Something seems to have gone wrong with compiling and/or installing %s!"
"\n Installed files (not) found (True (False) respectively)!:\n%s"
% (module, str(installed_files)))
def test_non_linear_dendrite(self):
MAX_SSE = 1E-12
I_dend_alias_name = 'I_dend' # synaptic current
I_dend_internal_name = 'I_kernel2__X__I_2' # alias for the synaptic current
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "resources")),
"iaf_psc_exp_nonlineardendrite.nestml")
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log,
suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install("nestmlmodule")
nrn = nest.Create("iaf_psc_exp_nonlineardendrite_nestml")
sg = nest.Create("spike_generator",
params={"spike_times": [10., 20., 30.]})
nest.Connect(sg,
nrn,
syn_spec={
"receptor_type": 2,
"weight": 30.,
"delay": 1.
})
mm = nest.Create('multimeter')
mm.set({
"record_from": [
I_dend_alias_name, I_dend_internal_name, 'V_m',
'dend_curr_enabled', 'I_dend_ap'
]
})
nest.Connect(mm, nrn)
nest.Simulate(100.0)
timevec = mm.get("events")["times"]
I_dend_alias_ts = mm.get("events")[I_dend_alias_name]
I_dend_internal_ts = mm.get("events")[I_dend_internal_name]
if TEST_PLOTS:
fig, ax = plt.subplots(3, 1)
ax[0].plot(timevec, I_dend_alias_ts, label="aliased I_dend_syn")
ax[0].plot(timevec,
I_dend_internal_ts,
label="internal I_dend_syn")
ax[0].legend()
ax_ = ax[0].twinx()
ax_.plot(timevec, mm.get("events")["dend_curr_enabled"])
ax_.set_ylabel("dend_curr_enabled")
ax[1].plot(timevec, mm.get("events")["I_dend_ap"])
ax[1].set_ylabel("I_dend_AP")
ax[2].plot(timevec, mm.get("events")["V_m"], label="V_m")
for _ax in ax:
_ax.legend()
_ax.grid()
plt.ylabel("Dendritic current $I_{dend}$")
plt.suptitle("Reset of synaptic integration after dendritic spike")
plt.savefig("/tmp/nestml_triplet_stdp_test.png")
assert np.all(
I_dend_alias_ts == I_dend_internal_ts
), "Variable " + str(
I_dend_alias_name
) + " and (internal) variable " + str(
I_dend_internal_name
) + " should measure the same thing, but discrepancy in values occurred."
tidx = np.argmin((timevec - 40)**2)
assert mm.get("events")["I_dend_ap"][
tidx] > 0., "Expected a dendritic action potential around t = 40 ms, but dendritic action potential current is zero"
assert mm.get("events")["dend_curr_enabled"][
tidx] == 0., "Dendritic synaptic current should be disabled during dendritic action potential"
tidx_ap_end = tidx + np.where(
mm.get("events")["dend_curr_enabled"][tidx:] == 1.)[0][0]
assert np.all(
I_dend_alias_ts[tidx_ap_end:] == 0.
), "After dendritic spike, dendritic current should be reset to 0 and stay at 0."
def test_wb_cond_exp(self):
if not os.path.exists("target"):
os.makedirs("target")
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "../../models",
"wb_cond_exp.nestml")))
target_path = "target"
module_name = 'nestmlmodule'
nest_path = "/home/travis/nest_install"
suffix = '_nestml'
to_nest(input_path=input_path,
target_path=target_path,
logging_level="INFO",
suffix=suffix,
module_name=module_name)
install_nest(target_path, nest_path)
nest.Install(module_name)
model = "wb_cond_exp_nestml"
dt = 0.01
t_simulation = 1000.0
nest.SetKernelStatus({"resolution": dt})
neuron = nest.Create(model)
parameters = nest.GetDefaults(model)
neuron.set({'I_e': 75.0})
multimeter = nest.Create("multimeter")
multimeter.set({"record_from": ["V_m"], "interval": dt})
spikedetector = nest.Create("spike_detector")
nest.Connect(multimeter, neuron)
nest.Connect(neuron, spikedetector)
nest.Simulate(t_simulation)
dmm = nest.GetStatus(multimeter)[0]
Voltages = dmm["events"]["V_m"]
tv = dmm["events"]["times"]
dSD = nest.GetStatus(spikedetector, keys='events')[0]
spikes = dSD['senders']
ts = dSD["times"]
firing_rate = len(spikes) / t_simulation * 1000
print("firing rate is ", firing_rate)
expected_value = np.abs(firing_rate - 50)
tolerance_value = 5 # Hz
if TEST_PLOTS:
fig, ax = plt.subplots(2, figsize=(8, 6), sharex=True)
ax[0].plot(tv, Voltages, lw=2, color="k")
ax[1].plot(ts, spikes, 'ko')
ax[1].set_xlabel("Time [ms]")
ax[1].set_xlim(0, t_simulation)
ax[1].set_ylabel("Spikes")
ax[0].set_ylabel("v [ms]")
ax[0].set_ylim(-100, 50)
for i in ts:
ax[0].axvline(x=i, lw=1., ls="--", color="gray")
plt.savefig("resources/wb_cond_exp.png")
# plt.show()
self.assertLessEqual(expected_value, tolerance_value)
import os
from pynestml.frontend.pynestml_frontend import to_nest, install_nest
# folder
home = os.path.expanduser("~")
folder = os.path.dirname(__file__)
root = os.path.abspath(folder if folder else ".")
target = root + "/build"
# build
to_nest(logging_level='ERROR',
input_path=root,
target_path=target,
module_name="energy_module")
# install
import nest
nest_file = nest.__file__
pos = nest_file.find("/lib")
install_nest(target, nest_file[:pos])
def test_traub_cond_multisyn(self):
if not os.path.exists("target"):
os.makedirs("target")
input_path = os.path.join(
os.path.realpath(
os.path.join(os.path.dirname(__file__), "../../models/neurons",
"traub_cond_multisyn.nestml")))
target_path = "target"
module_name = 'nestmlmodule'
nest_path = nest.ll_api.sli_func("statusdict/prefix ::")
suffix = '_nestml'
to_nest(input_path=input_path,
target_path=target_path,
logging_level="INFO",
suffix=suffix,
module_name=module_name)
install_nest(target_path, nest_path)
nest.Install("nestmlmodule")
model = "traub_cond_multisyn_nestml"
dt = 0.01
t_simulation = 1000.0
nest.SetKernelStatus({"resolution": dt})
neuron1 = nest.Create(model, 1)
neuron1.set({'I_e': 100.0})
neuron2 = nest.Create(model)
neuron2.set({"tau_AMPA_1": 0.1, "tau_AMPA_2": 2.4, "AMPA_g_peak": 0.1})
multimeter = nest.Create("multimeter", 2)
multimeter[0].set({"record_from": ["V_m"], "interval": dt})
record_from = [
"V_m", "I_syn_ampa", "I_syn_nmda", "I_syn_gaba_a", "I_syn_gaba_b"
]
multimeter[1].set({"record_from": record_from, "interval": dt})
# {'AMPA': 1, 'NMDA': 2, 'GABA_A': 3, 'GABA_B': 4}
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 1}) # AMPA
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 2}) # NMDA
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 3}) # GABAA
nest.Connect(neuron1, neuron2, syn_spec={"receptor_type": 4}) # GABAB
nest.Connect(multimeter[0], neuron1, "one_to_one")
nest.Connect(multimeter[1], neuron2)
spike_recorder = nest.Create("spike_recorder")
nest.Connect(neuron1, spike_recorder)
nest.Simulate(t_simulation)
dmm = nest.GetStatus(multimeter)[1]
Voltages = dmm["events"]["V_m"]
tv = dmm["events"]["times"]
dSD = nest.GetStatus(spike_recorder, keys='events')[0]
spikes = dSD['senders']
ts = dSD["times"]
firing_rate = len(spikes) / t_simulation * 1000
print("firing rate is ", firing_rate)
expected_value = np.abs(firing_rate - 40)
tolerance_value = 5 # Hz
self.assertLessEqual(expected_value, tolerance_value)
if TEST_PLOTS:
fig, ax = plt.subplots(3, figsize=(8, 6), sharex=True)
ax[0].plot(tv, Voltages, lw=2, label=str(2))
labels = ["ampa", "nmda", "gaba_a", "gaba_b"]
j = 0
for i in record_from[1:]:
g = dmm["events"][i]
ax[1].plot(tv, g, lw=2, label=labels[j])
j += 1
ax[2].plot(ts, spikes, 'k.')
ax[2].set_xlabel("Time [ms]")
ax[2].set_xlim(0, t_simulation)
ax[2].set_ylabel("Spikes")
ax[0].set_title("recording from PSP")
ax[0].set_ylabel("v [ms]")
ax[1].set_ylabel("I_syn")
ax[1].legend(frameon=False, loc="upper right")
plt.savefig("traub_cond_multisyn.png")
def test_multisynapse(self):
input_path = os.path.join(os.path.realpath(os.path.join(os.path.dirname(__file__), "..", "resources", "iaf_psc_exp_multisynapse.nestml")))
nest_path = "/home/travis/nest_install"
target_path = 'target'
logging_level = 'INFO'
module_name = 'nestmlmodule'
store_log = False
suffix = '_nestml'
dev = True
to_nest(input_path, target_path, logging_level, module_name, store_log, suffix, dev)
install_nest(target_path, nest_path)
nest.set_verbosity("M_ALL")
nest.ResetKernel()
nest.Install("nestmlmodule")
# network construction
neuron = nest.Create("iaf_psc_exp_multisynapse_neuron_nestml")
sg = nest.Create("spike_generator", params={"spike_times": [20., 80.]})
nest.Connect(sg, neuron, syn_spec={"receptor_type" : 1, "weight": 1000., "delay": 0.1})
sg2 = nest.Create("spike_generator", params={"spike_times": [40., 60.]})
nest.Connect(sg2, neuron, syn_spec={"receptor_type" : 2, "weight": 1000., "delay": 0.1})
sg3 = nest.Create("spike_generator", params={"spike_times": [30., 70.]})
nest.Connect(sg3, neuron, syn_spec={"receptor_type" : 3, "weight": 500., "delay": 0.1})
i_1 = nest.Create('multimeter', params={'record_from': ['I_shape', 'I_shape2', 'I_shape3'], 'interval': 0.1})
nest.Connect(i_1, neuron)
vm_1 = nest.Create('voltmeter')
nest.Connect(vm_1, neuron)
# simulate
nest.Simulate(125.)
# analysis
if TEST_PLOTS:
vm_1 = vm_1.get("events")
i_1 = i_1.get("events")
fig, ax = plt.subplots(nrows=4)
ax[0].plot(vm_1["times"], vm_1["V_m"], label="V_m")
ax[0].set_ylabel("voltage")
ax[1].plot(i_1["times"], i_1["I_shape"], label="I_shape")
ax[1].set_ylabel("current")
ax[2].plot(i_1["times"], i_1["I_shape2"], label="I_shape2")
ax[2].set_ylabel("current")
ax[3].plot(i_1["times"], i_1["I_shape3"], label="I_shape3")
ax[3].set_ylabel("current")
for _ax in ax:
_ax.legend(loc="upper right")
_ax.set_xlim(0., 125.)
_ax.grid(True)
for _ax in ax[:-1]:
_ax.set_xticklabels([])
ax[-1].set_xlabel("time")
plt.show()
